diff --git a/.gitignore b/.gitignore index 9757054a50f9e..d54d21b802be8 100644 --- a/.gitignore +++ b/.gitignore @@ -6,6 +6,7 @@ *.iml *.iws *.pyc +*.pyo .idea/ .idea_modules/ build/*.jar @@ -62,6 +63,8 @@ ec2/lib/ rat-results.txt scalastyle.txt scalastyle-output.xml +R-unit-tests.log +R/unit-tests.out # For Hive metastore_db/ diff --git a/.rat-excludes b/.rat-excludes index 769defbac11b7..8aca5a7f7a967 100644 --- a/.rat-excludes +++ b/.rat-excludes @@ -1,4 +1,5 @@ target +cache .gitignore .gitattributes .project @@ -18,6 +19,7 @@ fairscheduler.xml.template spark-defaults.conf.template log4j.properties log4j.properties.template +metrics.properties metrics.properties.template slaves slaves.template @@ -65,3 +67,5 @@ logs .*scalastyle-output.xml .*dependency-reduced-pom.xml known_translations +DESCRIPTION +NAMESPACE diff --git a/LICENSE b/LICENSE index d0bb2ae15bc1a..defdb17029a5f 100644 --- a/LICENSE +++ b/LICENSE @@ -771,6 +771,22 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. +======================================================================== +For TestTimSort (core/src/test/java/org/apache/spark/util/collection/TestTimSort.java): +======================================================================== +Copyright (C) 2015 Stijn de Gouw + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. ======================================================================== For LimitedInputStream diff --git a/R/.gitignore b/R/.gitignore new file mode 100644 index 0000000000000..9a5889ba28b2a --- /dev/null +++ b/R/.gitignore @@ -0,0 +1,6 @@ +*.o +*.so +*.Rd +lib +pkg/man +pkg/html diff --git a/R/DOCUMENTATION.md b/R/DOCUMENTATION.md new file mode 100644 index 0000000000000..931d01549b265 --- /dev/null +++ b/R/DOCUMENTATION.md @@ -0,0 +1,12 @@ +# SparkR Documentation + +SparkR documentation is generated using in-source comments annotated using using +`roxygen2`. After making changes to the documentation, to generate man pages, +you can run the following from an R console in the SparkR home directory + + library(devtools) + devtools::document(pkg="./pkg", roclets=c("rd")) + +You can verify if your changes are good by running + + R CMD check pkg/ diff --git a/R/README.md b/R/README.md new file mode 100644 index 0000000000000..a6970e39b55f3 --- /dev/null +++ b/R/README.md @@ -0,0 +1,67 @@ +# R on Spark + +SparkR is an R package that provides a light-weight frontend to use Spark from R. + +### SparkR development + +#### Build Spark + +Build Spark with [Maven](http://spark.apache.org/docs/latest/building-spark.html#building-with-buildmvn) and include the `-PsparkR` profile to build the R package. For example to use the default Hadoop versions you can run +``` + build/mvn -DskipTests -Psparkr package +``` + +#### Running sparkR + +You can start using SparkR by launching the SparkR shell with + + ./bin/sparkR + +The `sparkR` script automatically creates a SparkContext with Spark by default in +local mode. To specify the Spark master of a cluster for the automatically created +SparkContext, you can run + + ./bin/sparkR --master "local[2]" + +To set other options like driver memory, executor memory etc. you can pass in the [spark-submit](http://spark.apache.org/docs/latest/submitting-applications.html) arguments to `./bin/sparkR` + +#### Using SparkR from RStudio + +If you wish to use SparkR from RStudio or other R frontends you will need to set some environment variables which point SparkR to your Spark installation. For example +``` +# Set this to where Spark is installed +Sys.setenv(SPARK_HOME="/Users/shivaram/spark") +# This line loads SparkR from the installed directory +.libPaths(c(file.path(Sys.getenv("SPARK_HOME"), "R", "lib"), .libPaths())) +library(SparkR) +sc <- sparkR.init(master="local") +``` + +#### Making changes to SparkR + +The [instructions](https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark) for making contributions to Spark also apply to SparkR. +If you only make R file changes (i.e. no Scala changes) then you can just re-install the R package using `R/install-dev.sh` and test your changes. +Once you have made your changes, please include unit tests for them and run existing unit tests using the `run-tests.sh` script as described below. + +#### Generating documentation + +The SparkR documentation (Rd files and HTML files) are not a part of the source repository. To generate them you can run the script `R/create-docs.sh`. This script uses `devtools` and `knitr` to generate the docs and these packages need to be installed on the machine before using the script. + +### Examples, Unit tests + +SparkR comes with several sample programs in the `examples/src/main/r` directory. +To run one of them, use `./bin/sparkR `. For example: + + ./bin/sparkR examples/src/main/r/pi.R local[2] + +You can also run the unit-tests for SparkR by running (you need to install the [testthat](http://cran.r-project.org/web/packages/testthat/index.html) package first): + + R -e 'install.packages("testthat", repos="http://cran.us.r-project.org")' + ./R/run-tests.sh + +### Running on YARN +The `./bin/spark-submit` and `./bin/sparkR` can also be used to submit jobs to YARN clusters. You will need to set YARN conf dir before doing so. For example on CDH you can run +``` +export YARN_CONF_DIR=/etc/hadoop/conf +./bin/spark-submit --master yarn examples/src/main/r/pi.R 4 +``` diff --git a/R/WINDOWS.md b/R/WINDOWS.md new file mode 100644 index 0000000000000..3f889c0ca3d1e --- /dev/null +++ b/R/WINDOWS.md @@ -0,0 +1,13 @@ +## Building SparkR on Windows + +To build SparkR on Windows, the following steps are required + +1. Install R (>= 3.1) and [Rtools](http://cran.r-project.org/bin/windows/Rtools/). Make sure to +include Rtools and R in `PATH`. +2. Install +[JDK7](http://www.oracle.com/technetwork/java/javase/downloads/jdk7-downloads-1880260.html) and set +`JAVA_HOME` in the system environment variables. +3. Download and install [Maven](http://maven.apache.org/download.html). Also include the `bin` +directory in Maven in `PATH`. +4. Set `MAVEN_OPTS` as described in [Building Spark](http://spark.apache.org/docs/latest/building-spark.html). +5. Open a command shell (`cmd`) in the Spark directory and run `mvn -DskipTests -Psparkr package` diff --git a/R/create-docs.sh b/R/create-docs.sh new file mode 100755 index 0000000000000..4194172a2e115 --- /dev/null +++ b/R/create-docs.sh @@ -0,0 +1,46 @@ +#!/bin/bash + +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# Script to create API docs for SparkR +# This requires `devtools` and `knitr` to be installed on the machine. + +# After running this script the html docs can be found in +# $SPARK_HOME/R/pkg/html + +# Figure out where the script is +export FWDIR="$(cd "`dirname "$0"`"; pwd)" +pushd $FWDIR + +# Generate Rd file +Rscript -e 'library(devtools); devtools::document(pkg="./pkg", roclets=c("rd"))' + +# Install the package +./install-dev.sh + +# Now create HTML files + +# knit_rd puts html in current working directory +mkdir -p pkg/html +pushd pkg/html + +Rscript -e 'library(SparkR, lib.loc="../../lib"); library(knitr); knit_rd("SparkR")' + +popd + +popd diff --git a/R/install-dev.bat b/R/install-dev.bat new file mode 100644 index 0000000000000..008a5c668bc45 --- /dev/null +++ b/R/install-dev.bat @@ -0,0 +1,27 @@ +@echo off + +rem +rem Licensed to the Apache Software Foundation (ASF) under one or more +rem contributor license agreements. See the NOTICE file distributed with +rem this work for additional information regarding copyright ownership. +rem The ASF licenses this file to You under the Apache License, Version 2.0 +rem (the "License"); you may not use this file except in compliance with +rem the License. You may obtain a copy of the License at +rem +rem http://www.apache.org/licenses/LICENSE-2.0 +rem +rem Unless required by applicable law or agreed to in writing, software +rem distributed under the License is distributed on an "AS IS" BASIS, +rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +rem See the License for the specific language governing permissions and +rem limitations under the License. +rem + +rem Install development version of SparkR +rem + +set SPARK_HOME=%~dp0.. + +MKDIR %SPARK_HOME%\R\lib + +R.exe CMD INSTALL --library="%SPARK_HOME%\R\lib" %SPARK_HOME%\R\pkg\ diff --git a/R/install-dev.sh b/R/install-dev.sh new file mode 100755 index 0000000000000..55ed6f4be1a4a --- /dev/null +++ b/R/install-dev.sh @@ -0,0 +1,36 @@ +#!/bin/bash + +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# This scripts packages the SparkR source files (R and C files) and +# creates a package that can be loaded in R. The package is by default installed to +# $FWDIR/lib and the package can be loaded by using the following command in R: +# +# library(SparkR, lib.loc="$FWDIR/lib") +# +# NOTE(shivaram): Right now we use $SPARK_HOME/R/lib to be the installation directory +# to load the SparkR package on the worker nodes. + + +FWDIR="$(cd `dirname $0`; pwd)" +LIB_DIR="$FWDIR/lib" + +mkdir -p $LIB_DIR + +# Install R +R CMD INSTALL --library=$LIB_DIR $FWDIR/pkg/ diff --git a/R/log4j.properties b/R/log4j.properties new file mode 100644 index 0000000000000..701adb2a3da1d --- /dev/null +++ b/R/log4j.properties @@ -0,0 +1,28 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# Set everything to be logged to the file target/unit-tests.log +log4j.rootCategory=INFO, file +log4j.appender.file=org.apache.log4j.FileAppender +log4j.appender.file.append=true +log4j.appender.file.file=R-unit-tests.log +log4j.appender.file.layout=org.apache.log4j.PatternLayout +log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n + +# Ignore messages below warning level from Jetty, because it's a bit verbose +log4j.logger.org.eclipse.jetty=WARN +org.eclipse.jetty.LEVEL=WARN diff --git a/R/pkg/DESCRIPTION b/R/pkg/DESCRIPTION new file mode 100644 index 0000000000000..1c1779a763c7e --- /dev/null +++ b/R/pkg/DESCRIPTION @@ -0,0 +1,35 @@ +Package: SparkR +Type: Package +Title: R frontend for Spark +Version: 1.4.0 +Date: 2013-09-09 +Author: The Apache Software Foundation +Maintainer: Shivaram Venkataraman +Imports: + methods +Depends: + R (>= 3.0), + methods, +Suggests: + testthat +Description: R frontend for Spark +License: Apache License (== 2.0) +Collate: + 'generics.R' + 'jobj.R' + 'RDD.R' + 'pairRDD.R' + 'schema.R' + 'column.R' + 'group.R' + 'DataFrame.R' + 'SQLContext.R' + 'backend.R' + 'broadcast.R' + 'client.R' + 'context.R' + 'deserialize.R' + 'serialize.R' + 'sparkR.R' + 'utils.R' + 'zzz.R' diff --git a/R/pkg/NAMESPACE b/R/pkg/NAMESPACE new file mode 100644 index 0000000000000..80283643861ac --- /dev/null +++ b/R/pkg/NAMESPACE @@ -0,0 +1,196 @@ +#exportPattern("^[[:alpha:]]+") +exportClasses("RDD") +exportClasses("Broadcast") +exportMethods( + "aggregateByKey", + "aggregateRDD", + "cache", + "cartesian", + "checkpoint", + "coalesce", + "cogroup", + "collect", + "collectAsMap", + "collectPartition", + "combineByKey", + "count", + "countByKey", + "countByValue", + "distinct", + "Filter", + "filterRDD", + "first", + "flatMap", + "flatMapValues", + "fold", + "foldByKey", + "foreach", + "foreachPartition", + "fullOuterJoin", + "glom", + "groupByKey", + "intersection", + "join", + "keyBy", + "keys", + "length", + "lapply", + "lapplyPartition", + "lapplyPartitionsWithIndex", + "leftOuterJoin", + "lookup", + "map", + "mapPartitions", + "mapPartitionsWithIndex", + "mapValues", + "maximum", + "minimum", + "numPartitions", + "partitionBy", + "persist", + "pipeRDD", + "reduce", + "reduceByKey", + "reduceByKeyLocally", + "repartition", + "rightOuterJoin", + "sampleByKey", + "sampleRDD", + "saveAsTextFile", + "saveAsObjectFile", + "sortBy", + "sortByKey", + "subtract", + "subtractByKey", + "sumRDD", + "take", + "takeOrdered", + "takeSample", + "top", + "unionRDD", + "unpersist", + "value", + "values", + "zipRDD", + "zipWithIndex", + "zipWithUniqueId" + ) + +# S3 methods exported +export( + "textFile", + "objectFile", + "parallelize", + "hashCode", + "includePackage", + "broadcast", + "setBroadcastValue", + "setCheckpointDir" + ) +export("sparkR.init") +export("sparkR.stop") +export("print.jobj") +useDynLib(SparkR, stringHashCode) +importFrom(methods, setGeneric, setMethod, setOldClass) + +# SparkRSQL + +exportClasses("DataFrame") + +exportMethods("columns", + "distinct", + "dtypes", + "except", + "explain", + "filter", + "groupBy", + "head", + "insertInto", + "intersect", + "isLocal", + "limit", + "orderBy", + "names", + "printSchema", + "registerTempTable", + "repartition", + "sampleDF", + "saveAsParquetFile", + "saveAsTable", + "saveDF", + "schema", + "select", + "selectExpr", + "show", + "showDF", + "sortDF", + "toJSON", + "toRDD", + "unionAll", + "where", + "withColumn", + "withColumnRenamed") + +exportClasses("Column") + +exportMethods("abs", + "alias", + "approxCountDistinct", + "asc", + "avg", + "cast", + "contains", + "countDistinct", + "desc", + "endsWith", + "getField", + "getItem", + "isNotNull", + "isNull", + "last", + "like", + "lower", + "max", + "mean", + "min", + "rlike", + "sqrt", + "startsWith", + "substr", + "sum", + "sumDistinct", + "upper") + +exportClasses("GroupedData") +exportMethods("agg") + +export("sparkRSQL.init", + "sparkRHive.init") + +export("cacheTable", + "clearCache", + "createDataFrame", + "createExternalTable", + "dropTempTable", + "jsonFile", + "jsonRDD", + "loadDF", + "parquetFile", + "sql", + "table", + "tableNames", + "tables", + "toDF", + "uncacheTable") + +export("sparkRSQL.init", + "sparkRHive.init") + +export("structField", + "structField.jobj", + "structField.character", + "print.structField", + "structType", + "structType.jobj", + "structType.structField", + "print.structType") diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R new file mode 100644 index 0000000000000..861fe1c78b0db --- /dev/null +++ b/R/pkg/R/DataFrame.R @@ -0,0 +1,1272 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# DataFrame.R - DataFrame class and methods implemented in S4 OO classes + +#' @include generics.R jobj.R schema.R RDD.R pairRDD.R column.R group.R +NULL + +setOldClass("jobj") + +#' @title S4 class that represents a DataFrame +#' @description DataFrames can be created using functions like +#' \code{jsonFile}, \code{table} etc. +#' @rdname DataFrame +#' @seealso jsonFile, table +#' +#' @param env An R environment that stores bookkeeping states of the DataFrame +#' @param sdf A Java object reference to the backing Scala DataFrame +#' @export +setClass("DataFrame", + slots = list(env = "environment", + sdf = "jobj")) + +setMethod("initialize", "DataFrame", function(.Object, sdf, isCached) { + .Object@env <- new.env() + .Object@env$isCached <- isCached + + .Object@sdf <- sdf + .Object +}) + +#' @rdname DataFrame +#' @export +dataFrame <- function(sdf, isCached = FALSE) { + new("DataFrame", sdf, isCached) +} + +############################ DataFrame Methods ############################################## + +#' Print Schema of a DataFrame +#' +#' Prints out the schema in tree format +#' +#' @param x A SparkSQL DataFrame +#' +#' @rdname printSchema +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' printSchema(df) +#'} +setMethod("printSchema", + signature(x = "DataFrame"), + function(x) { + schemaString <- callJMethod(schema(x)$jobj, "treeString") + cat(schemaString) + }) + +#' Get schema object +#' +#' Returns the schema of this DataFrame as a structType object. +#' +#' @param x A SparkSQL DataFrame +#' +#' @rdname schema +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' dfSchema <- schema(df) +#'} +setMethod("schema", + signature(x = "DataFrame"), + function(x) { + structType(callJMethod(x@sdf, "schema")) + }) + +#' Explain +#' +#' Print the logical and physical Catalyst plans to the console for debugging. +#' +#' @param x A SparkSQL DataFrame +#' @param extended Logical. If extended is False, explain() only prints the physical plan. +#' @rdname explain +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' explain(df, TRUE) +#'} +setMethod("explain", + signature(x = "DataFrame"), + function(x, extended = FALSE) { + queryExec <- callJMethod(x@sdf, "queryExecution") + if (extended) { + cat(callJMethod(queryExec, "toString")) + } else { + execPlan <- callJMethod(queryExec, "executedPlan") + cat(callJMethod(execPlan, "toString")) + } + }) + +#' isLocal +#' +#' Returns True if the `collect` and `take` methods can be run locally +#' (without any Spark executors). +#' +#' @param x A SparkSQL DataFrame +#' +#' @rdname isLocal +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' isLocal(df) +#'} +setMethod("isLocal", + signature(x = "DataFrame"), + function(x) { + callJMethod(x@sdf, "isLocal") + }) + +#' ShowDF +#' +#' Print the first numRows rows of a DataFrame +#' +#' @param x A SparkSQL DataFrame +#' @param numRows The number of rows to print. Defaults to 20. +#' +#' @rdname showDF +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' showDF(df) +#'} +setMethod("showDF", + signature(x = "DataFrame"), + function(x, numRows = 20) { + cat(callJMethod(x@sdf, "showString", numToInt(numRows)), "\n") + }) + +#' show +#' +#' Print the DataFrame column names and types +#' +#' @param x A SparkSQL DataFrame +#' +#' @rdname show +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' show(df) +#'} +setMethod("show", "DataFrame", + function(object) { + cols <- lapply(dtypes(object), function(l) { + paste(l, collapse = ":") + }) + s <- paste(cols, collapse = ", ") + cat(paste("DataFrame[", s, "]\n", sep = "")) + }) + +#' DataTypes +#' +#' Return all column names and their data types as a list +#' +#' @param x A SparkSQL DataFrame +#' +#' @rdname dtypes +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' dtypes(df) +#'} +setMethod("dtypes", + signature(x = "DataFrame"), + function(x) { + lapply(schema(x)$fields(), function(f) { + c(f$name(), f$dataType.simpleString()) + }) + }) + +#' Column names +#' +#' Return all column names as a list +#' +#' @param x A SparkSQL DataFrame +#' +#' @rdname columns +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' columns(df) +#'} +setMethod("columns", + signature(x = "DataFrame"), + function(x) { + sapply(schema(x)$fields(), function(f) { + f$name() + }) + }) + +#' @rdname columns +#' @export +setMethod("names", + signature(x = "DataFrame"), + function(x) { + columns(x) + }) + +#' Register Temporary Table +#' +#' Registers a DataFrame as a Temporary Table in the SQLContext +#' +#' @param x A SparkSQL DataFrame +#' @param tableName A character vector containing the name of the table +#' +#' @rdname registerTempTable +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' registerTempTable(df, "json_df") +#' new_df <- sql(sqlCtx, "SELECT * FROM json_df") +#'} +setMethod("registerTempTable", + signature(x = "DataFrame", tableName = "character"), + function(x, tableName) { + callJMethod(x@sdf, "registerTempTable", tableName) + }) + +#' insertInto +#' +#' Insert the contents of a DataFrame into a table registered in the current SQL Context. +#' +#' @param x A SparkSQL DataFrame +#' @param tableName A character vector containing the name of the table +#' @param overwrite A logical argument indicating whether or not to overwrite +#' the existing rows in the table. +#' +#' @rdname insertInto +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' df <- loadDF(sqlCtx, path, "parquet") +#' df2 <- loadDF(sqlCtx, path2, "parquet") +#' registerTempTable(df, "table1") +#' insertInto(df2, "table1", overwrite = TRUE) +#'} +setMethod("insertInto", + signature(x = "DataFrame", tableName = "character"), + function(x, tableName, overwrite = FALSE) { + callJMethod(x@sdf, "insertInto", tableName, overwrite) + }) + +#' Cache +#' +#' Persist with the default storage level (MEMORY_ONLY). +#' +#' @param x A SparkSQL DataFrame +#' +#' @rdname cache-methods +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' cache(df) +#'} +setMethod("cache", + signature(x = "DataFrame"), + function(x) { + cached <- callJMethod(x@sdf, "cache") + x@env$isCached <- TRUE + x + }) + +#' Persist +#' +#' Persist this DataFrame with the specified storage level. For details of the +#' supported storage levels, refer to +#' http://spark.apache.org/docs/latest/programming-guide.html#rdd-persistence. +#' +#' @param x The DataFrame to persist +#' @rdname persist +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' persist(df, "MEMORY_AND_DISK") +#'} +setMethod("persist", + signature(x = "DataFrame", newLevel = "character"), + function(x, newLevel) { + callJMethod(x@sdf, "persist", getStorageLevel(newLevel)) + x@env$isCached <- TRUE + x + }) + +#' Unpersist +#' +#' Mark this DataFrame as non-persistent, and remove all blocks for it from memory and +#' disk. +#' +#' @param x The DataFrame to unpersist +#' @param blocking Whether to block until all blocks are deleted +#' @rdname unpersist-methods +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' persist(df, "MEMORY_AND_DISK") +#' unpersist(df) +#'} +setMethod("unpersist", + signature(x = "DataFrame"), + function(x, blocking = TRUE) { + callJMethod(x@sdf, "unpersist", blocking) + x@env$isCached <- FALSE + x + }) + +#' Repartition +#' +#' Return a new DataFrame that has exactly numPartitions partitions. +#' +#' @param x A SparkSQL DataFrame +#' @param numPartitions The number of partitions to use. +#' @rdname repartition +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' newDF <- repartition(df, 2L) +#'} +setMethod("repartition", + signature(x = "DataFrame", numPartitions = "numeric"), + function(x, numPartitions) { + sdf <- callJMethod(x@sdf, "repartition", numToInt(numPartitions)) + dataFrame(sdf) + }) + +#' toJSON +#' +#' Convert the rows of a DataFrame into JSON objects and return an RDD where +#' each element contains a JSON string. +#' +#' @param x A SparkSQL DataFrame +#' @return A StringRRDD of JSON objects +#' @rdname tojson +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' newRDD <- toJSON(df) +#'} +setMethod("toJSON", + signature(x = "DataFrame"), + function(x) { + rdd <- callJMethod(x@sdf, "toJSON") + jrdd <- callJMethod(rdd, "toJavaRDD") + RDD(jrdd, serializedMode = "string") + }) + +#' saveAsParquetFile +#' +#' Save the contents of a DataFrame as a Parquet file, preserving the schema. Files written out +#' with this method can be read back in as a DataFrame using parquetFile(). +#' +#' @param x A SparkSQL DataFrame +#' @param path The directory where the file is saved +#' @rdname saveAsParquetFile +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' saveAsParquetFile(df, "/tmp/sparkr-tmp/") +#'} +setMethod("saveAsParquetFile", + signature(x = "DataFrame", path = "character"), + function(x, path) { + invisible(callJMethod(x@sdf, "saveAsParquetFile", path)) + }) + +#' Distinct +#' +#' Return a new DataFrame containing the distinct rows in this DataFrame. +#' +#' @param x A SparkSQL DataFrame +#' @rdname distinct +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' distinctDF <- distinct(df) +#'} +setMethod("distinct", + signature(x = "DataFrame"), + function(x) { + sdf <- callJMethod(x@sdf, "distinct") + dataFrame(sdf) + }) + +#' SampleDF +#' +#' Return a sampled subset of this DataFrame using a random seed. +#' +#' @param x A SparkSQL DataFrame +#' @param withReplacement Sampling with replacement or not +#' @param fraction The (rough) sample target fraction +#' @rdname sampleDF +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' collect(sampleDF(df, FALSE, 0.5)) +#' collect(sampleDF(df, TRUE, 0.5)) +#'} +setMethod("sampleDF", + # TODO : Figure out how to send integer as java.lang.Long to JVM so + # we can send seed as an argument through callJMethod + signature(x = "DataFrame", withReplacement = "logical", + fraction = "numeric"), + function(x, withReplacement, fraction) { + if (fraction < 0.0) stop(cat("Negative fraction value:", fraction)) + sdf <- callJMethod(x@sdf, "sample", withReplacement, fraction) + dataFrame(sdf) + }) + +#' Count +#' +#' Returns the number of rows in a DataFrame +#' +#' @param x A SparkSQL DataFrame +#' +#' @rdname count +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' count(df) +#' } +setMethod("count", + signature(x = "DataFrame"), + function(x) { + callJMethod(x@sdf, "count") + }) + +#' Collects all the elements of a Spark DataFrame and coerces them into an R data.frame. +#' +#' @param x A SparkSQL DataFrame +#' @param stringsAsFactors (Optional) A logical indicating whether or not string columns +#' should be converted to factors. FALSE by default. + +#' @rdname collect-methods +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' collected <- collect(df) +#' firstName <- collected[[1]]$name +#' } +setMethod("collect", + signature(x = "DataFrame"), + function(x, stringsAsFactors = FALSE) { + # listCols is a list of raw vectors, one per column + listCols <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", "dfToCols", x@sdf) + cols <- lapply(listCols, function(col) { + objRaw <- rawConnection(col) + numRows <- readInt(objRaw) + col <- readCol(objRaw, numRows) + close(objRaw) + col + }) + names(cols) <- columns(x) + do.call(cbind.data.frame, list(cols, stringsAsFactors = stringsAsFactors)) + }) + +#' Limit +#' +#' Limit the resulting DataFrame to the number of rows specified. +#' +#' @param x A SparkSQL DataFrame +#' @param num The number of rows to return +#' @return A new DataFrame containing the number of rows specified. +#' +#' @rdname limit +#' @export +#' @examples +#' \dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' limitedDF <- limit(df, 10) +#' } +setMethod("limit", + signature(x = "DataFrame", num = "numeric"), + function(x, num) { + res <- callJMethod(x@sdf, "limit", as.integer(num)) + dataFrame(res) + }) + +# Take the first NUM rows of a DataFrame and return a the results as a data.frame + +#' @rdname take +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' take(df, 2) +#' } +setMethod("take", + signature(x = "DataFrame", num = "numeric"), + function(x, num) { + limited <- limit(x, num) + collect(limited) + }) + +#' Head +#' +#' Return the first NUM rows of a DataFrame as a data.frame. If NUM is NULL, +#' then head() returns the first 6 rows in keeping with the current data.frame +#' convention in R. +#' +#' @param x A SparkSQL DataFrame +#' @param num The number of rows to return. Default is 6. +#' @return A data.frame +#' +#' @rdname head +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' head(df) +#' } +setMethod("head", + signature(x = "DataFrame"), + function(x, num = 6L) { + # Default num is 6L in keeping with R's data.frame convention + take(x, num) + }) + +#' Return the first row of a DataFrame +#' +#' @param x A SparkSQL DataFrame +#' +#' @rdname first +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' first(df) +#' } +setMethod("first", + signature(x = "DataFrame"), + function(x) { + take(x, 1) + }) + +#' toRDD() +#' +#' Converts a Spark DataFrame to an RDD while preserving column names. +#' +#' @param x A Spark DataFrame +#' +#' @rdname DataFrame +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' rdd <- toRDD(df) +#' } +setMethod("toRDD", + signature(x = "DataFrame"), + function(x) { + jrdd <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", "dfToRowRDD", x@sdf) + colNames <- callJMethod(x@sdf, "columns") + rdd <- RDD(jrdd, serializedMode = "row") + lapply(rdd, function(row) { + names(row) <- colNames + row + }) + }) + +#' GroupBy +#' +#' Groups the DataFrame using the specified columns, so we can run aggregation on them. +#' +#' @param x a DataFrame +#' @return a GroupedData +#' @seealso GroupedData +#' @rdname DataFrame +#' @export +#' @examples +#' \dontrun{ +#' # Compute the average for all numeric columns grouped by department. +#' avg(groupBy(df, "department")) +#' +#' # Compute the max age and average salary, grouped by department and gender. +#' agg(groupBy(df, "department", "gender"), salary="avg", "age" -> "max") +#' } +setMethod("groupBy", + signature(x = "DataFrame"), + function(x, ...) { + cols <- list(...) + if (length(cols) >= 1 && class(cols[[1]]) == "character") { + sgd <- callJMethod(x@sdf, "groupBy", cols[[1]], listToSeq(cols[-1])) + } else { + jcol <- lapply(cols, function(c) { c@jc }) + sgd <- callJMethod(x@sdf, "groupBy", listToSeq(jcol)) + } + groupedData(sgd) + }) + +#' Agg +#' +#' Compute aggregates by specifying a list of columns +#' +#' @rdname DataFrame +#' @export +setMethod("agg", + signature(x = "DataFrame"), + function(x, ...) { + agg(groupBy(x), ...) + }) + + +############################## RDD Map Functions ################################## +# All of the following functions mirror the existing RDD map functions, # +# but allow for use with DataFrames by first converting to an RRDD before calling # +# the requested map function. # +################################################################################### + +#' @rdname lapply +setMethod("lapply", + signature(X = "DataFrame", FUN = "function"), + function(X, FUN) { + rdd <- toRDD(X) + lapply(rdd, FUN) + }) + +#' @rdname lapply +setMethod("map", + signature(X = "DataFrame", FUN = "function"), + function(X, FUN) { + lapply(X, FUN) + }) + +#' @rdname flatMap +setMethod("flatMap", + signature(X = "DataFrame", FUN = "function"), + function(X, FUN) { + rdd <- toRDD(X) + flatMap(rdd, FUN) + }) + +#' @rdname lapplyPartition +setMethod("lapplyPartition", + signature(X = "DataFrame", FUN = "function"), + function(X, FUN) { + rdd <- toRDD(X) + lapplyPartition(rdd, FUN) + }) + +#' @rdname lapplyPartition +setMethod("mapPartitions", + signature(X = "DataFrame", FUN = "function"), + function(X, FUN) { + lapplyPartition(X, FUN) + }) + +#' @rdname foreach +setMethod("foreach", + signature(x = "DataFrame", func = "function"), + function(x, func) { + rdd <- toRDD(x) + foreach(rdd, func) + }) + +#' @rdname foreach +setMethod("foreachPartition", + signature(x = "DataFrame", func = "function"), + function(x, func) { + rdd <- toRDD(x) + foreachPartition(rdd, func) + }) + + +############################## SELECT ################################## + +getColumn <- function(x, c) { + column(callJMethod(x@sdf, "col", c)) +} + +#' @rdname select +setMethod("$", signature(x = "DataFrame"), + function(x, name) { + getColumn(x, name) + }) + +setMethod("$<-", signature(x = "DataFrame"), + function(x, name, value) { + stopifnot(class(value) == "Column") + cols <- columns(x) + if (name %in% cols) { + cols <- lapply(cols, function(c) { + if (c == name) { + alias(value, name) + } else { + col(c) + } + }) + nx <- select(x, cols) + } else { + nx <- withColumn(x, name, value) + } + x@sdf <- nx@sdf + x + }) + +#' @rdname select +setMethod("[[", signature(x = "DataFrame"), + function(x, i) { + if (is.numeric(i)) { + cols <- columns(x) + i <- cols[[i]] + } + getColumn(x, i) + }) + +#' @rdname select +setMethod("[", signature(x = "DataFrame", i = "missing"), + function(x, i, j, ...) { + if (is.numeric(j)) { + cols <- columns(x) + j <- cols[j] + } + if (length(j) > 1) { + j <- as.list(j) + } + select(x, j) + }) + +#' Select +#' +#' Selects a set of columns with names or Column expressions. +#' @param x A DataFrame +#' @param col A list of columns or single Column or name +#' @return A new DataFrame with selected columns +#' @export +#' @rdname select +#' @examples +#' \dontrun{ +#' select(df, "*") +#' select(df, "col1", "col2") +#' select(df, df$name, df$age + 1) +#' select(df, c("col1", "col2")) +#' select(df, list(df$name, df$age + 1)) +#' # Columns can also be selected using `[[` and `[` +#' df[[2]] == df[["age"]] +#' df[,2] == df[,"age"] +#' # Similar to R data frames columns can also be selected using `$` +#' df$age +#' } +setMethod("select", signature(x = "DataFrame", col = "character"), + function(x, col, ...) { + sdf <- callJMethod(x@sdf, "select", col, toSeq(...)) + dataFrame(sdf) + }) + +#' @rdname select +#' @export +setMethod("select", signature(x = "DataFrame", col = "Column"), + function(x, col, ...) { + jcols <- lapply(list(col, ...), function(c) { + c@jc + }) + sdf <- callJMethod(x@sdf, "select", listToSeq(jcols)) + dataFrame(sdf) + }) + +#' @rdname select +#' @export +setMethod("select", + signature(x = "DataFrame", col = "list"), + function(x, col) { + cols <- lapply(col, function(c) { + if (class(c)== "Column") { + c@jc + } else { + col(c)@jc + } + }) + sdf <- callJMethod(x@sdf, "select", listToSeq(cols)) + dataFrame(sdf) + }) + +#' SelectExpr +#' +#' Select from a DataFrame using a set of SQL expressions. +#' +#' @param x A DataFrame to be selected from. +#' @param expr A string containing a SQL expression +#' @param ... Additional expressions +#' @return A DataFrame +#' @rdname selectExpr +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' selectExpr(df, "col1", "(col2 * 5) as newCol") +#' } +setMethod("selectExpr", + signature(x = "DataFrame", expr = "character"), + function(x, expr, ...) { + exprList <- list(expr, ...) + sdf <- callJMethod(x@sdf, "selectExpr", listToSeq(exprList)) + dataFrame(sdf) + }) + +#' WithColumn +#' +#' Return a new DataFrame with the specified column added. +#' +#' @param x A DataFrame +#' @param colName A string containing the name of the new column. +#' @param col A Column expression. +#' @return A DataFrame with the new column added. +#' @rdname withColumn +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' newDF <- withColumn(df, "newCol", df$col1 * 5) +#' } +setMethod("withColumn", + signature(x = "DataFrame", colName = "character", col = "Column"), + function(x, colName, col) { + select(x, x$"*", alias(col, colName)) + }) + +#' WithColumnRenamed +#' +#' Rename an existing column in a DataFrame. +#' +#' @param x A DataFrame +#' @param existingCol The name of the column you want to change. +#' @param newCol The new column name. +#' @return A DataFrame with the column name changed. +#' @rdname withColumnRenamed +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' newDF <- withColumnRenamed(df, "col1", "newCol1") +#' } +setMethod("withColumnRenamed", + signature(x = "DataFrame", existingCol = "character", newCol = "character"), + function(x, existingCol, newCol) { + cols <- lapply(columns(x), function(c) { + if (c == existingCol) { + alias(col(c), newCol) + } else { + col(c) + } + }) + select(x, cols) + }) + +setClassUnion("characterOrColumn", c("character", "Column")) + +#' SortDF +#' +#' Sort a DataFrame by the specified column(s). +#' +#' @param x A DataFrame to be sorted. +#' @param col Either a Column object or character vector indicating the field to sort on +#' @param ... Additional sorting fields +#' @return A DataFrame where all elements are sorted. +#' @rdname sortDF +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' sortDF(df, df$col1) +#' sortDF(df, "col1") +#' sortDF(df, asc(df$col1), desc(abs(df$col2))) +#' } +setMethod("sortDF", + signature(x = "DataFrame", col = "characterOrColumn"), + function(x, col, ...) { + if (class(col) == "character") { + sdf <- callJMethod(x@sdf, "sort", col, toSeq(...)) + } else if (class(col) == "Column") { + jcols <- lapply(list(col, ...), function(c) { + c@jc + }) + sdf <- callJMethod(x@sdf, "sort", listToSeq(jcols)) + } + dataFrame(sdf) + }) + +#' @rdname sortDF +#' @export +setMethod("orderBy", + signature(x = "DataFrame", col = "characterOrColumn"), + function(x, col) { + sortDF(x, col) + }) + +#' Filter +#' +#' Filter the rows of a DataFrame according to a given condition. +#' +#' @param x A DataFrame to be sorted. +#' @param condition The condition to sort on. This may either be a Column expression +#' or a string containing a SQL statement +#' @return A DataFrame containing only the rows that meet the condition. +#' @rdname filter +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' filter(df, "col1 > 0") +#' filter(df, df$col2 != "abcdefg") +#' } +setMethod("filter", + signature(x = "DataFrame", condition = "characterOrColumn"), + function(x, condition) { + if (class(condition) == "Column") { + condition <- condition@jc + } + sdf <- callJMethod(x@sdf, "filter", condition) + dataFrame(sdf) + }) + +#' @rdname filter +#' @export +setMethod("where", + signature(x = "DataFrame", condition = "characterOrColumn"), + function(x, condition) { + filter(x, condition) + }) + +#' Join +#' +#' Join two DataFrames based on the given join expression. +#' +#' @param x A Spark DataFrame +#' @param y A Spark DataFrame +#' @param joinExpr (Optional) The expression used to perform the join. joinExpr must be a +#' Column expression. If joinExpr is omitted, join() wil perform a Cartesian join +#' @param joinType The type of join to perform. The following join types are available: +#' 'inner', 'outer', 'left_outer', 'right_outer', 'semijoin'. The default joinType is "inner". +#' @return A DataFrame containing the result of the join operation. +#' @rdname join +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' df1 <- jsonFile(sqlCtx, path) +#' df2 <- jsonFile(sqlCtx, path2) +#' join(df1, df2) # Performs a Cartesian +#' join(df1, df2, df1$col1 == df2$col2) # Performs an inner join based on expression +#' join(df1, df2, df1$col1 == df2$col2, "right_outer") +#' } +setMethod("join", + signature(x = "DataFrame", y = "DataFrame"), + function(x, y, joinExpr = NULL, joinType = NULL) { + if (is.null(joinExpr)) { + sdf <- callJMethod(x@sdf, "join", y@sdf) + } else { + if (class(joinExpr) != "Column") stop("joinExpr must be a Column") + if (is.null(joinType)) { + sdf <- callJMethod(x@sdf, "join", y@sdf, joinExpr@jc) + } else { + if (joinType %in% c("inner", "outer", "left_outer", "right_outer", "semijoin")) { + sdf <- callJMethod(x@sdf, "join", y@sdf, joinExpr@jc, joinType) + } else { + stop("joinType must be one of the following types: ", + "'inner', 'outer', 'left_outer', 'right_outer', 'semijoin'") + } + } + } + dataFrame(sdf) + }) + +#' UnionAll +#' +#' Return a new DataFrame containing the union of rows in this DataFrame +#' and another DataFrame. This is equivalent to `UNION ALL` in SQL. +#' +#' @param x A Spark DataFrame +#' @param y A Spark DataFrame +#' @return A DataFrame containing the result of the union. +#' @rdname unionAll +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' df1 <- jsonFile(sqlCtx, path) +#' df2 <- jsonFile(sqlCtx, path2) +#' unioned <- unionAll(df, df2) +#' } +setMethod("unionAll", + signature(x = "DataFrame", y = "DataFrame"), + function(x, y) { + unioned <- callJMethod(x@sdf, "unionAll", y@sdf) + dataFrame(unioned) + }) + +#' Intersect +#' +#' Return a new DataFrame containing rows only in both this DataFrame +#' and another DataFrame. This is equivalent to `INTERSECT` in SQL. +#' +#' @param x A Spark DataFrame +#' @param y A Spark DataFrame +#' @return A DataFrame containing the result of the intersect. +#' @rdname intersect +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' df1 <- jsonFile(sqlCtx, path) +#' df2 <- jsonFile(sqlCtx, path2) +#' intersectDF <- intersect(df, df2) +#' } +setMethod("intersect", + signature(x = "DataFrame", y = "DataFrame"), + function(x, y) { + intersected <- callJMethod(x@sdf, "intersect", y@sdf) + dataFrame(intersected) + }) + +#' except +#' +#' Return a new DataFrame containing rows in this DataFrame +#' but not in another DataFrame. This is equivalent to `EXCEPT` in SQL. +#' +#' @param x A Spark DataFrame +#' @param y A Spark DataFrame +#' @return A DataFrame containing the result of the except operation. +#' @rdname except +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' df1 <- jsonFile(sqlCtx, path) +#' df2 <- jsonFile(sqlCtx, path2) +#' exceptDF <- except(df, df2) +#' } +#' @rdname except +#' @export +setMethod("except", + signature(x = "DataFrame", y = "DataFrame"), + function(x, y) { + excepted <- callJMethod(x@sdf, "except", y@sdf) + dataFrame(excepted) + }) + +#' Save the contents of the DataFrame to a data source +#' +#' The data source is specified by the `source` and a set of options (...). +#' If `source` is not specified, the default data source configured by +#' spark.sql.sources.default will be used. +#' +#' Additionally, mode is used to specify the behavior of the save operation when +#' data already exists in the data source. There are four modes: +#' append: Contents of this DataFrame are expected to be appended to existing data. +#' overwrite: Existing data is expected to be overwritten by the contents of +# this DataFrame. +#' error: An exception is expected to be thrown. +#' ignore: The save operation is expected to not save the contents of the DataFrame +# and to not change the existing data. +#' +#' @param df A SparkSQL DataFrame +#' @param path A name for the table +#' @param source A name for external data source +#' @param mode One of 'append', 'overwrite', 'error', 'ignore' +#' +#' @rdname saveAsTable +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' saveAsTable(df, "myfile") +#' } +setMethod("saveDF", + signature(df = "DataFrame", path = 'character', source = 'character', + mode = 'character'), + function(df, path = NULL, source = NULL, mode = "append", ...){ + if (is.null(source)) { + sqlCtx <- get(".sparkRSQLsc", envir = .sparkREnv) + source <- callJMethod(sqlCtx, "getConf", "spark.sql.sources.default", + "org.apache.spark.sql.parquet") + } + allModes <- c("append", "overwrite", "error", "ignore") + if (!(mode %in% allModes)) { + stop('mode should be one of "append", "overwrite", "error", "ignore"') + } + jmode <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", "saveMode", mode) + options <- varargsToEnv(...) + if (!is.null(path)) { + options[['path']] = path + } + callJMethod(df@sdf, "save", source, jmode, options) + }) + + +#' saveAsTable +#' +#' Save the contents of the DataFrame to a data source as a table +#' +#' The data source is specified by the `source` and a set of options (...). +#' If `source` is not specified, the default data source configured by +#' spark.sql.sources.default will be used. +#' +#' Additionally, mode is used to specify the behavior of the save operation when +#' data already exists in the data source. There are four modes: +#' append: Contents of this DataFrame are expected to be appended to existing data. +#' overwrite: Existing data is expected to be overwritten by the contents of +# this DataFrame. +#' error: An exception is expected to be thrown. +#' ignore: The save operation is expected to not save the contents of the DataFrame +# and to not change the existing data. +#' +#' @param df A SparkSQL DataFrame +#' @param tableName A name for the table +#' @param source A name for external data source +#' @param mode One of 'append', 'overwrite', 'error', 'ignore' +#' +#' @rdname saveAsTable +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' saveAsTable(df, "myfile") +#' } +setMethod("saveAsTable", + signature(df = "DataFrame", tableName = 'character', source = 'character', + mode = 'character'), + function(df, tableName, source = NULL, mode="append", ...){ + if (is.null(source)) { + sqlCtx <- get(".sparkRSQLsc", envir = .sparkREnv) + source <- callJMethod(sqlCtx, "getConf", "spark.sql.sources.default", + "org.apache.spark.sql.parquet") + } + allModes <- c("append", "overwrite", "error", "ignore") + if (!(mode %in% allModes)) { + stop('mode should be one of "append", "overwrite", "error", "ignore"') + } + jmode <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", "saveMode", mode) + options <- varargsToEnv(...) + callJMethod(df@sdf, "saveAsTable", tableName, source, jmode, options) + }) + diff --git a/R/pkg/R/RDD.R b/R/pkg/R/RDD.R new file mode 100644 index 0000000000000..128431334ca52 --- /dev/null +++ b/R/pkg/R/RDD.R @@ -0,0 +1,1592 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# RDD in R implemented in S4 OO system. + +setOldClass("jobj") + +#' @title S4 class that represents an RDD +#' @description RDD can be created using functions like +#' \code{parallelize}, \code{textFile} etc. +#' @rdname RDD +#' @seealso parallelize, textFile +#' +#' @slot env An R environment that stores bookkeeping states of the RDD +#' @slot jrdd Java object reference to the backing JavaRDD +#' to an RDD +#' @export +setClass("RDD", + slots = list(env = "environment", + jrdd = "jobj")) + +setClass("PipelinedRDD", + slots = list(prev = "RDD", + func = "function", + prev_jrdd = "jobj"), + contains = "RDD") + +setMethod("initialize", "RDD", function(.Object, jrdd, serializedMode, + isCached, isCheckpointed) { + # Check that RDD constructor is using the correct version of serializedMode + stopifnot(class(serializedMode) == "character") + stopifnot(serializedMode %in% c("byte", "string", "row")) + # RDD has three serialization types: + # byte: The RDD stores data serialized in R. + # string: The RDD stores data as strings. + # row: The RDD stores the serialized rows of a DataFrame. + + # We use an environment to store mutable states inside an RDD object. + # Note that R's call-by-value semantics makes modifying slots inside an + # object (passed as an argument into a function, such as cache()) difficult: + # i.e. one needs to make a copy of the RDD object and sets the new slot value + # there. + + # The slots are inheritable from superclass. Here, both `env' and `jrdd' are + # inherited from RDD, but only the former is used. + .Object@env <- new.env() + .Object@env$isCached <- isCached + .Object@env$isCheckpointed <- isCheckpointed + .Object@env$serializedMode <- serializedMode + + .Object@jrdd <- jrdd + .Object +}) + +setMethod("initialize", "PipelinedRDD", function(.Object, prev, func, jrdd_val) { + .Object@env <- new.env() + .Object@env$isCached <- FALSE + .Object@env$isCheckpointed <- FALSE + .Object@env$jrdd_val <- jrdd_val + if (!is.null(jrdd_val)) { + # This tracks the serialization mode for jrdd_val + .Object@env$serializedMode <- prev@env$serializedMode + } + + .Object@prev <- prev + + isPipelinable <- function(rdd) { + e <- rdd@env + !(e$isCached || e$isCheckpointed) + } + + if (!inherits(prev, "PipelinedRDD") || !isPipelinable(prev)) { + # This transformation is the first in its stage: + .Object@func <- cleanClosure(func) + .Object@prev_jrdd <- getJRDD(prev) + .Object@env$prev_serializedMode <- prev@env$serializedMode + # NOTE: We use prev_serializedMode to track the serialization mode of prev_JRDD + # prev_serializedMode is used during the delayed computation of JRDD in getJRDD + } else { + pipelinedFunc <- function(split, iterator) { + func(split, prev@func(split, iterator)) + } + .Object@func <- cleanClosure(pipelinedFunc) + .Object@prev_jrdd <- prev@prev_jrdd # maintain the pipeline + # Get the serialization mode of the parent RDD + .Object@env$prev_serializedMode <- prev@env$prev_serializedMode + } + + .Object +}) + +#' @rdname RDD +#' @export +#' +#' @param jrdd Java object reference to the backing JavaRDD +#' @param serializedMode Use "byte" if the RDD stores data serialized in R, "string" if the RDD +#' stores strings, and "row" if the RDD stores the rows of a DataFrame +#' @param isCached TRUE if the RDD is cached +#' @param isCheckpointed TRUE if the RDD has been checkpointed +RDD <- function(jrdd, serializedMode = "byte", isCached = FALSE, + isCheckpointed = FALSE) { + new("RDD", jrdd, serializedMode, isCached, isCheckpointed) +} + +PipelinedRDD <- function(prev, func) { + new("PipelinedRDD", prev, func, NULL) +} + +# Return the serialization mode for an RDD. +setGeneric("getSerializedMode", function(rdd, ...) { standardGeneric("getSerializedMode") }) +# For normal RDDs we can directly read the serializedMode +setMethod("getSerializedMode", signature(rdd = "RDD"), function(rdd) rdd@env$serializedMode ) +# For pipelined RDDs if jrdd_val is set then serializedMode should exist +# if not we return the defaultSerialization mode of "byte" as we don't know the serialization +# mode at this point in time. +setMethod("getSerializedMode", signature(rdd = "PipelinedRDD"), + function(rdd) { + if (!is.null(rdd@env$jrdd_val)) { + return(rdd@env$serializedMode) + } else { + return("byte") + } + }) + +# The jrdd accessor function. +setMethod("getJRDD", signature(rdd = "RDD"), function(rdd) rdd@jrdd ) +setMethod("getJRDD", signature(rdd = "PipelinedRDD"), + function(rdd, serializedMode = "byte") { + if (!is.null(rdd@env$jrdd_val)) { + return(rdd@env$jrdd_val) + } + + packageNamesArr <- serialize(.sparkREnv[[".packages"]], + connection = NULL) + + broadcastArr <- lapply(ls(.broadcastNames), + function(name) { get(name, .broadcastNames) }) + + serializedFuncArr <- serialize(rdd@func, connection = NULL) + + prev_jrdd <- rdd@prev_jrdd + + if (serializedMode == "string") { + rddRef <- newJObject("org.apache.spark.api.r.StringRRDD", + callJMethod(prev_jrdd, "rdd"), + serializedFuncArr, + rdd@env$prev_serializedMode, + packageNamesArr, + as.character(.sparkREnv[["libname"]]), + broadcastArr, + callJMethod(prev_jrdd, "classTag")) + } else { + rddRef <- newJObject("org.apache.spark.api.r.RRDD", + callJMethod(prev_jrdd, "rdd"), + serializedFuncArr, + rdd@env$prev_serializedMode, + serializedMode, + packageNamesArr, + as.character(.sparkREnv[["libname"]]), + broadcastArr, + callJMethod(prev_jrdd, "classTag")) + } + # Save the serialization flag after we create a RRDD + rdd@env$serializedMode <- serializedMode + rdd@env$jrdd_val <- callJMethod(rddRef, "asJavaRDD") # rddRef$asJavaRDD() + rdd@env$jrdd_val + }) + +setValidity("RDD", + function(object) { + jrdd <- getJRDD(object) + cls <- callJMethod(jrdd, "getClass") + className <- callJMethod(cls, "getName") + if (grep("spark.api.java.*RDD*", className) == 1) { + TRUE + } else { + paste("Invalid RDD class ", className) + } + }) + + +############ Actions and Transformations ############ + +#' Persist an RDD +#' +#' Persist this RDD with the default storage level (MEMORY_ONLY). +#' +#' @param x The RDD to cache +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10, 2L) +#' cache(rdd) +#'} +#' @rdname cache-methods +#' @aliases cache,RDD-method +setMethod("cache", + signature(x = "RDD"), + function(x) { + callJMethod(getJRDD(x), "cache") + x@env$isCached <- TRUE + x + }) + +#' Persist an RDD +#' +#' Persist this RDD with the specified storage level. For details of the +#' supported storage levels, refer to +#' http://spark.apache.org/docs/latest/programming-guide.html#rdd-persistence. +#' +#' @param x The RDD to persist +#' @param newLevel The new storage level to be assigned +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10, 2L) +#' persist(rdd, "MEMORY_AND_DISK") +#'} +#' @rdname persist +#' @aliases persist,RDD-method +setMethod("persist", + signature(x = "RDD", newLevel = "character"), + function(x, newLevel) { + callJMethod(getJRDD(x), "persist", getStorageLevel(newLevel)) + x@env$isCached <- TRUE + x + }) + +#' Unpersist an RDD +#' +#' Mark the RDD as non-persistent, and remove all blocks for it from memory and +#' disk. +#' +#' @param x The RDD to unpersist +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10, 2L) +#' cache(rdd) # rdd@@env$isCached == TRUE +#' unpersist(rdd) # rdd@@env$isCached == FALSE +#'} +#' @rdname unpersist-methods +#' @aliases unpersist,RDD-method +setMethod("unpersist", + signature(x = "RDD"), + function(x) { + callJMethod(getJRDD(x), "unpersist") + x@env$isCached <- FALSE + x + }) + +#' Checkpoint an RDD +#' +#' Mark this RDD for checkpointing. It will be saved to a file inside the +#' checkpoint directory set with setCheckpointDir() and all references to its +#' parent RDDs will be removed. This function must be called before any job has +#' been executed on this RDD. It is strongly recommended that this RDD is +#' persisted in memory, otherwise saving it on a file will require recomputation. +#' +#' @param x The RDD to checkpoint +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' setCheckpointDir(sc, "checkpoint") +#' rdd <- parallelize(sc, 1:10, 2L) +#' checkpoint(rdd) +#'} +#' @rdname checkpoint-methods +#' @aliases checkpoint,RDD-method +setMethod("checkpoint", + signature(x = "RDD"), + function(x) { + jrdd <- getJRDD(x) + callJMethod(jrdd, "checkpoint") + x@env$isCheckpointed <- TRUE + x + }) + +#' Gets the number of partitions of an RDD +#' +#' @param x A RDD. +#' @return the number of partitions of rdd as an integer. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10, 2L) +#' numPartitions(rdd) # 2L +#'} +#' @rdname numPartitions +#' @aliases numPartitions,RDD-method +setMethod("numPartitions", + signature(x = "RDD"), + function(x) { + jrdd <- getJRDD(x) + partitions <- callJMethod(jrdd, "splits") + callJMethod(partitions, "size") + }) + +#' Collect elements of an RDD +#' +#' @description +#' \code{collect} returns a list that contains all of the elements in this RDD. +#' +#' @param x The RDD to collect +#' @param ... Other optional arguments to collect +#' @param flatten FALSE if the list should not flattened +#' @return a list containing elements in the RDD +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10, 2L) +#' collect(rdd) # list from 1 to 10 +#' collectPartition(rdd, 0L) # list from 1 to 5 +#'} +#' @rdname collect-methods +#' @aliases collect,RDD-method +setMethod("collect", + signature(x = "RDD"), + function(x, flatten = TRUE) { + # Assumes a pairwise RDD is backed by a JavaPairRDD. + collected <- callJMethod(getJRDD(x), "collect") + convertJListToRList(collected, flatten, + serializedMode = getSerializedMode(x)) + }) + + +#' @description +#' \code{collectPartition} returns a list that contains all of the elements +#' in the specified partition of the RDD. +#' @param partitionId the partition to collect (starts from 0) +#' @rdname collect-methods +#' @aliases collectPartition,integer,RDD-method +setMethod("collectPartition", + signature(x = "RDD", partitionId = "integer"), + function(x, partitionId) { + jPartitionsList <- callJMethod(getJRDD(x), + "collectPartitions", + as.list(as.integer(partitionId))) + + jList <- jPartitionsList[[1]] + convertJListToRList(jList, flatten = TRUE, + serializedMode = getSerializedMode(x)) + }) + +#' @description +#' \code{collectAsMap} returns a named list as a map that contains all of the elements +#' in a key-value pair RDD. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(list(1, 2), list(3, 4)), 2L) +#' collectAsMap(rdd) # list(`1` = 2, `3` = 4) +#'} +#' @rdname collect-methods +#' @aliases collectAsMap,RDD-method +setMethod("collectAsMap", + signature(x = "RDD"), + function(x) { + pairList <- collect(x) + map <- new.env() + lapply(pairList, function(i) { assign(as.character(i[[1]]), i[[2]], envir = map) }) + as.list(map) + }) + +#' Return the number of elements in the RDD. +#' +#' @param x The RDD to count +#' @return number of elements in the RDD. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' count(rdd) # 10 +#' length(rdd) # Same as count +#'} +#' @rdname count +#' @aliases count,RDD-method +setMethod("count", + signature(x = "RDD"), + function(x) { + countPartition <- function(part) { + as.integer(length(part)) + } + valsRDD <- lapplyPartition(x, countPartition) + vals <- collect(valsRDD) + sum(as.integer(vals)) + }) + +#' Return the number of elements in the RDD +#' @export +#' @rdname count +setMethod("length", + signature(x = "RDD"), + function(x) { + count(x) + }) + +#' Return the count of each unique value in this RDD as a list of +#' (value, count) pairs. +#' +#' Same as countByValue in Spark. +#' +#' @param x The RDD to count +#' @return list of (value, count) pairs, where count is number of each unique +#' value in rdd. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, c(1,2,3,2,1)) +#' countByValue(rdd) # (1,2L), (2,2L), (3,1L) +#'} +#' @rdname countByValue +#' @aliases countByValue,RDD-method +setMethod("countByValue", + signature(x = "RDD"), + function(x) { + ones <- lapply(x, function(item) { list(item, 1L) }) + collect(reduceByKey(ones, `+`, numPartitions(x))) + }) + +#' Apply a function to all elements +#' +#' This function creates a new RDD by applying the given transformation to all +#' elements of the given RDD +#' +#' @param X The RDD to apply the transformation. +#' @param FUN the transformation to apply on each element +#' @return a new RDD created by the transformation. +#' @rdname lapply +#' @aliases lapply +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' multiplyByTwo <- lapply(rdd, function(x) { x * 2 }) +#' collect(multiplyByTwo) # 2,4,6... +#'} +setMethod("lapply", + signature(X = "RDD", FUN = "function"), + function(X, FUN) { + func <- function(split, iterator) { + lapply(iterator, FUN) + } + lapplyPartitionsWithIndex(X, func) + }) + +#' @rdname lapply +#' @aliases map,RDD,function-method +setMethod("map", + signature(X = "RDD", FUN = "function"), + function(X, FUN) { + lapply(X, FUN) + }) + +#' Flatten results after apply a function to all elements +#' +#' This function return a new RDD by first applying a function to all +#' elements of this RDD, and then flattening the results. +#' +#' @param X The RDD to apply the transformation. +#' @param FUN the transformation to apply on each element +#' @return a new RDD created by the transformation. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' multiplyByTwo <- flatMap(rdd, function(x) { list(x*2, x*10) }) +#' collect(multiplyByTwo) # 2,20,4,40,6,60... +#'} +#' @rdname flatMap +#' @aliases flatMap,RDD,function-method +setMethod("flatMap", + signature(X = "RDD", FUN = "function"), + function(X, FUN) { + partitionFunc <- function(part) { + unlist( + lapply(part, FUN), + recursive = F + ) + } + lapplyPartition(X, partitionFunc) + }) + +#' Apply a function to each partition of an RDD +#' +#' Return a new RDD by applying a function to each partition of this RDD. +#' +#' @param X The RDD to apply the transformation. +#' @param FUN the transformation to apply on each partition. +#' @return a new RDD created by the transformation. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' partitionSum <- lapplyPartition(rdd, function(part) { Reduce("+", part) }) +#' collect(partitionSum) # 15, 40 +#'} +#' @rdname lapplyPartition +#' @aliases lapplyPartition,RDD,function-method +setMethod("lapplyPartition", + signature(X = "RDD", FUN = "function"), + function(X, FUN) { + lapplyPartitionsWithIndex(X, function(s, part) { FUN(part) }) + }) + +#' mapPartitions is the same as lapplyPartition. +#' +#' @rdname lapplyPartition +#' @aliases mapPartitions,RDD,function-method +setMethod("mapPartitions", + signature(X = "RDD", FUN = "function"), + function(X, FUN) { + lapplyPartition(X, FUN) + }) + +#' Return a new RDD by applying a function to each partition of this RDD, while +#' tracking the index of the original partition. +#' +#' @param X The RDD to apply the transformation. +#' @param FUN the transformation to apply on each partition; takes the partition +#' index and a list of elements in the particular partition. +#' @return a new RDD created by the transformation. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10, 5L) +#' prod <- lapplyPartitionsWithIndex(rdd, function(split, part) { +#' split * Reduce("+", part) }) +#' collect(prod, flatten = FALSE) # 0, 7, 22, 45, 76 +#'} +#' @rdname lapplyPartitionsWithIndex +#' @aliases lapplyPartitionsWithIndex,RDD,function-method +setMethod("lapplyPartitionsWithIndex", + signature(X = "RDD", FUN = "function"), + function(X, FUN) { + PipelinedRDD(X, FUN) + }) + +#' @rdname lapplyPartitionsWithIndex +#' @aliases mapPartitionsWithIndex,RDD,function-method +setMethod("mapPartitionsWithIndex", + signature(X = "RDD", FUN = "function"), + function(X, FUN) { + lapplyPartitionsWithIndex(X, FUN) + }) + +#' This function returns a new RDD containing only the elements that satisfy +#' a predicate (i.e. returning TRUE in a given logical function). +#' The same as `filter()' in Spark. +#' +#' @param x The RDD to be filtered. +#' @param f A unary predicate function. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' unlist(collect(filterRDD(rdd, function (x) { x < 3 }))) # c(1, 2) +#'} +#' @rdname filterRDD +#' @aliases filterRDD,RDD,function-method +setMethod("filterRDD", + signature(x = "RDD", f = "function"), + function(x, f) { + filter.func <- function(part) { + Filter(f, part) + } + lapplyPartition(x, filter.func) + }) + +#' @rdname filterRDD +#' @aliases Filter +setMethod("Filter", + signature(f = "function", x = "RDD"), + function(f, x) { + filterRDD(x, f) + }) + +#' Reduce across elements of an RDD. +#' +#' This function reduces the elements of this RDD using the +#' specified commutative and associative binary operator. +#' +#' @param x The RDD to reduce +#' @param func Commutative and associative function to apply on elements +#' of the RDD. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' reduce(rdd, "+") # 55 +#'} +#' @rdname reduce +#' @aliases reduce,RDD,ANY-method +setMethod("reduce", + signature(x = "RDD", func = "ANY"), + function(x, func) { + + reducePartition <- function(part) { + Reduce(func, part) + } + + partitionList <- collect(lapplyPartition(x, reducePartition), + flatten = FALSE) + Reduce(func, partitionList) + }) + +#' Get the maximum element of an RDD. +#' +#' @param x The RDD to get the maximum element from +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' maximum(rdd) # 10 +#'} +#' @rdname maximum +#' @aliases maximum,RDD +setMethod("maximum", + signature(x = "RDD"), + function(x) { + reduce(x, max) + }) + +#' Get the minimum element of an RDD. +#' +#' @param x The RDD to get the minimum element from +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' minimum(rdd) # 1 +#'} +#' @rdname minimum +#' @aliases minimum,RDD +setMethod("minimum", + signature(x = "RDD"), + function(x) { + reduce(x, min) + }) + +#' Add up the elements in an RDD. +#' +#' @param x The RDD to add up the elements in +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' sumRDD(rdd) # 55 +#'} +#' @rdname sumRDD +#' @aliases sumRDD,RDD +setMethod("sumRDD", + signature(x = "RDD"), + function(x) { + reduce(x, "+") + }) + +#' Applies a function to all elements in an RDD, and force evaluation. +#' +#' @param x The RDD to apply the function +#' @param func The function to be applied. +#' @return invisible NULL. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' foreach(rdd, function(x) { save(x, file=...) }) +#'} +#' @rdname foreach +#' @aliases foreach,RDD,function-method +setMethod("foreach", + signature(x = "RDD", func = "function"), + function(x, func) { + partition.func <- function(x) { + lapply(x, func) + NULL + } + invisible(collect(mapPartitions(x, partition.func))) + }) + +#' Applies a function to each partition in an RDD, and force evaluation. +#' +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' foreachPartition(rdd, function(part) { save(part, file=...); NULL }) +#'} +#' @rdname foreach +#' @aliases foreachPartition,RDD,function-method +setMethod("foreachPartition", + signature(x = "RDD", func = "function"), + function(x, func) { + invisible(collect(mapPartitions(x, func))) + }) + +#' Take elements from an RDD. +#' +#' This function takes the first NUM elements in the RDD and +#' returns them in a list. +#' +#' @param x The RDD to take elements from +#' @param num Number of elements to take +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' take(rdd, 2L) # list(1, 2) +#'} +#' @rdname take +#' @aliases take,RDD,numeric-method +setMethod("take", + signature(x = "RDD", num = "numeric"), + function(x, num) { + resList <- list() + index <- -1 + jrdd <- getJRDD(x) + numPartitions <- numPartitions(x) + serializedModeRDD <- getSerializedMode(x) + + # TODO(shivaram): Collect more than one partition based on size + # estimates similar to the scala version of `take`. + while (TRUE) { + index <- index + 1 + + if (length(resList) >= num || index >= numPartitions) + break + + # a JList of byte arrays + partitionArr <- callJMethod(jrdd, "collectPartitions", as.list(as.integer(index))) + partition <- partitionArr[[1]] + + size <- num - length(resList) + # elems is capped to have at most `size` elements + elems <- convertJListToRList(partition, + flatten = TRUE, + logicalUpperBound = size, + serializedMode = serializedModeRDD) + + resList <- append(resList, elems) + } + resList + }) + + +#' First +#' +#' Return the first element of an RDD +#' +#' @rdname first +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' first(rdd) +#' } +setMethod("first", + signature(x = "RDD"), + function(x) { + take(x, 1)[[1]] + }) + +#' Removes the duplicates from RDD. +#' +#' This function returns a new RDD containing the distinct elements in the +#' given RDD. The same as `distinct()' in Spark. +#' +#' @param x The RDD to remove duplicates from. +#' @param numPartitions Number of partitions to create. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, c(1,2,2,3,3,3)) +#' sort(unlist(collect(distinct(rdd)))) # c(1, 2, 3) +#'} +#' @rdname distinct +#' @aliases distinct,RDD-method +setMethod("distinct", + signature(x = "RDD"), + function(x, numPartitions = SparkR::numPartitions(x)) { + identical.mapped <- lapply(x, function(x) { list(x, NULL) }) + reduced <- reduceByKey(identical.mapped, + function(x, y) { x }, + numPartitions) + resRDD <- lapply(reduced, function(x) { x[[1]] }) + resRDD + }) + +#' Return an RDD that is a sampled subset of the given RDD. +#' +#' The same as `sample()' in Spark. (We rename it due to signature +#' inconsistencies with the `sample()' function in R's base package.) +#' +#' @param x The RDD to sample elements from +#' @param withReplacement Sampling with replacement or not +#' @param fraction The (rough) sample target fraction +#' @param seed Randomness seed value +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) # ensure each num is in its own split +#' collect(sampleRDD(rdd, FALSE, 0.5, 1618L)) # ~5 distinct elements +#' collect(sampleRDD(rdd, TRUE, 0.5, 9L)) # ~5 elements possibly with duplicates +#'} +#' @rdname sampleRDD +#' @aliases sampleRDD,RDD +setMethod("sampleRDD", + signature(x = "RDD", withReplacement = "logical", + fraction = "numeric", seed = "integer"), + function(x, withReplacement, fraction, seed) { + + # The sampler: takes a partition and returns its sampled version. + samplingFunc <- function(split, part) { + set.seed(seed) + res <- vector("list", length(part)) + len <- 0 + + # Discards some random values to ensure each partition has a + # different random seed. + runif(split) + + for (elem in part) { + if (withReplacement) { + count <- rpois(1, fraction) + if (count > 0) { + res[(len + 1):(len + count)] <- rep(list(elem), count) + len <- len + count + } + } else { + if (runif(1) < fraction) { + len <- len + 1 + res[[len]] <- elem + } + } + } + + # TODO(zongheng): look into the performance of the current + # implementation. Look into some iterator package? Note that + # Scala avoids many calls to creating an empty list and PySpark + # similarly achieves this using `yield'. + if (len > 0) + res[1:len] + else + list() + } + + lapplyPartitionsWithIndex(x, samplingFunc) + }) + +#' Return a list of the elements that are a sampled subset of the given RDD. +#' +#' @param x The RDD to sample elements from +#' @param withReplacement Sampling with replacement or not +#' @param num Number of elements to return +#' @param seed Randomness seed value +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:100) +#' # exactly 5 elements sampled, which may not be distinct +#' takeSample(rdd, TRUE, 5L, 1618L) +#' # exactly 5 distinct elements sampled +#' takeSample(rdd, FALSE, 5L, 16181618L) +#'} +#' @rdname takeSample +#' @aliases takeSample,RDD +setMethod("takeSample", signature(x = "RDD", withReplacement = "logical", + num = "integer", seed = "integer"), + function(x, withReplacement, num, seed) { + # This function is ported from RDD.scala. + fraction <- 0.0 + total <- 0 + multiplier <- 3.0 + initialCount <- count(x) + maxSelected <- 0 + MAXINT <- .Machine$integer.max + + if (num < 0) + stop(paste("Negative number of elements requested")) + + if (initialCount > MAXINT - 1) { + maxSelected <- MAXINT - 1 + } else { + maxSelected <- initialCount + } + + if (num > initialCount && !withReplacement) { + total <- maxSelected + fraction <- multiplier * (maxSelected + 1) / initialCount + } else { + total <- num + fraction <- multiplier * (num + 1) / initialCount + } + + set.seed(seed) + samples <- collect(sampleRDD(x, withReplacement, fraction, + as.integer(ceiling(runif(1, + -MAXINT, + MAXINT))))) + # If the first sample didn't turn out large enough, keep trying to + # take samples; this shouldn't happen often because we use a big + # multiplier for thei initial size + while (length(samples) < total) + samples <- collect(sampleRDD(x, withReplacement, fraction, + as.integer(ceiling(runif(1, + -MAXINT, + MAXINT))))) + + # TODO(zongheng): investigate if this call is an in-place shuffle? + sample(samples)[1:total] + }) + +#' Creates tuples of the elements in this RDD by applying a function. +#' +#' @param x The RDD. +#' @param func The function to be applied. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(1, 2, 3)) +#' collect(keyBy(rdd, function(x) { x*x })) # list(list(1, 1), list(4, 2), list(9, 3)) +#'} +#' @rdname keyBy +#' @aliases keyBy,RDD +setMethod("keyBy", + signature(x = "RDD", func = "function"), + function(x, func) { + apply.func <- function(x) { + list(func(x), x) + } + lapply(x, apply.func) + }) + +#' Return a new RDD that has exactly numPartitions partitions. +#' Can increase or decrease the level of parallelism in this RDD. Internally, +#' this uses a shuffle to redistribute data. +#' If you are decreasing the number of partitions in this RDD, consider using +#' coalesce, which can avoid performing a shuffle. +#' +#' @param x The RDD. +#' @param numPartitions Number of partitions to create. +#' @seealso coalesce +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(1, 2, 3, 4, 5, 6, 7), 4L) +#' numPartitions(rdd) # 4 +#' numPartitions(repartition(rdd, 2L)) # 2 +#'} +#' @rdname repartition +#' @aliases repartition,RDD +setMethod("repartition", + signature(x = "RDD", numPartitions = "numeric"), + function(x, numPartitions) { + coalesce(x, numToInt(numPartitions), TRUE) + }) + +#' Return a new RDD that is reduced into numPartitions partitions. +#' +#' @param x The RDD. +#' @param numPartitions Number of partitions to create. +#' @seealso repartition +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(1, 2, 3, 4, 5), 3L) +#' numPartitions(rdd) # 3 +#' numPartitions(coalesce(rdd, 1L)) # 1 +#'} +#' @rdname coalesce +#' @aliases coalesce,RDD +setMethod("coalesce", + signature(x = "RDD", numPartitions = "numeric"), + function(x, numPartitions, shuffle = FALSE) { + numPartitions <- numToInt(numPartitions) + if (shuffle || numPartitions > SparkR::numPartitions(x)) { + func <- function(s, part) { + set.seed(s) # split as seed + start <- as.integer(sample(numPartitions, 1) - 1) + lapply(seq_along(part), + function(i) { + pos <- (start + i) %% numPartitions + list(pos, part[[i]]) + }) + } + shuffled <- lapplyPartitionsWithIndex(x, func) + repartitioned <- partitionBy(shuffled, numPartitions) + values(repartitioned) + } else { + jrdd <- callJMethod(getJRDD(x), "coalesce", numPartitions, shuffle) + RDD(jrdd) + } + }) + +#' Save this RDD as a SequenceFile of serialized objects. +#' +#' @param x The RDD to save +#' @param path The directory where the file is saved +#' @seealso objectFile +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:3) +#' saveAsObjectFile(rdd, "/tmp/sparkR-tmp") +#'} +#' @rdname saveAsObjectFile +#' @aliases saveAsObjectFile,RDD +setMethod("saveAsObjectFile", + signature(x = "RDD", path = "character"), + function(x, path) { + # If serializedMode == "string" we need to serialize the data before saving it since + # objectFile() assumes serializedMode == "byte". + if (getSerializedMode(x) != "byte") { + x <- serializeToBytes(x) + } + # Return nothing + invisible(callJMethod(getJRDD(x), "saveAsObjectFile", path)) + }) + +#' Save this RDD as a text file, using string representations of elements. +#' +#' @param x The RDD to save +#' @param path The directory where the splits of the text file are saved +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:3) +#' saveAsTextFile(rdd, "/tmp/sparkR-tmp") +#'} +#' @rdname saveAsTextFile +#' @aliases saveAsTextFile,RDD +setMethod("saveAsTextFile", + signature(x = "RDD", path = "character"), + function(x, path) { + func <- function(str) { + toString(str) + } + stringRdd <- lapply(x, func) + # Return nothing + invisible( + callJMethod(getJRDD(stringRdd, serializedMode = "string"), "saveAsTextFile", path)) + }) + +#' Sort an RDD by the given key function. +#' +#' @param x An RDD to be sorted. +#' @param func A function used to compute the sort key for each element. +#' @param ascending A flag to indicate whether the sorting is ascending or descending. +#' @param numPartitions Number of partitions to create. +#' @return An RDD where all elements are sorted. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(3, 2, 1)) +#' collect(sortBy(rdd, function(x) { x })) # list (1, 2, 3) +#'} +#' @rdname sortBy +#' @aliases sortBy,RDD,RDD-method +setMethod("sortBy", + signature(x = "RDD", func = "function"), + function(x, func, ascending = TRUE, numPartitions = SparkR::numPartitions(x)) { + values(sortByKey(keyBy(x, func), ascending, numPartitions)) + }) + +# Helper function to get first N elements from an RDD in the specified order. +# Param: +# x An RDD. +# num Number of elements to return. +# ascending A flag to indicate whether the sorting is ascending or descending. +# Return: +# A list of the first N elements from the RDD in the specified order. +# +takeOrderedElem <- function(x, num, ascending = TRUE) { + if (num <= 0L) { + return(list()) + } + + partitionFunc <- function(part) { + if (num < length(part)) { + # R limitation: order works only on primitive types! + ord <- order(unlist(part, recursive = FALSE), decreasing = !ascending) + part[ord[1:num]] + } else { + part + } + } + + newRdd <- mapPartitions(x, partitionFunc) + + resList <- list() + index <- -1 + jrdd <- getJRDD(newRdd) + numPartitions <- numPartitions(newRdd) + serializedModeRDD <- getSerializedMode(newRdd) + + while (TRUE) { + index <- index + 1 + + if (index >= numPartitions) { + ord <- order(unlist(resList, recursive = FALSE), decreasing = !ascending) + resList <- resList[ord[1:num]] + break + } + + # a JList of byte arrays + partitionArr <- callJMethod(jrdd, "collectPartitions", as.list(as.integer(index))) + partition <- partitionArr[[1]] + + # elems is capped to have at most `num` elements + elems <- convertJListToRList(partition, + flatten = TRUE, + logicalUpperBound = num, + serializedMode = serializedModeRDD) + + resList <- append(resList, elems) + } + resList +} + +#' Returns the first N elements from an RDD in ascending order. +#' +#' @param x An RDD. +#' @param num Number of elements to return. +#' @return The first N elements from the RDD in ascending order. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(10, 1, 2, 9, 3, 4, 5, 6, 7)) +#' takeOrdered(rdd, 6L) # list(1, 2, 3, 4, 5, 6) +#'} +#' @rdname takeOrdered +#' @aliases takeOrdered,RDD,RDD-method +setMethod("takeOrdered", + signature(x = "RDD", num = "integer"), + function(x, num) { + takeOrderedElem(x, num) + }) + +#' Returns the top N elements from an RDD. +#' +#' @param x An RDD. +#' @param num Number of elements to return. +#' @return The top N elements from the RDD. +#' @rdname top +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(10, 1, 2, 9, 3, 4, 5, 6, 7)) +#' top(rdd, 6L) # list(10, 9, 7, 6, 5, 4) +#'} +#' @rdname top +#' @aliases top,RDD,RDD-method +setMethod("top", + signature(x = "RDD", num = "integer"), + function(x, num) { + takeOrderedElem(x, num, FALSE) + }) + +#' Fold an RDD using a given associative function and a neutral "zero value". +#' +#' Aggregate the elements of each partition, and then the results for all the +#' partitions, using a given associative function and a neutral "zero value". +#' +#' @param x An RDD. +#' @param zeroValue A neutral "zero value". +#' @param op An associative function for the folding operation. +#' @return The folding result. +#' @rdname fold +#' @seealso reduce +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(1, 2, 3, 4, 5)) +#' fold(rdd, 0, "+") # 15 +#'} +#' @rdname fold +#' @aliases fold,RDD,RDD-method +setMethod("fold", + signature(x = "RDD", zeroValue = "ANY", op = "ANY"), + function(x, zeroValue, op) { + aggregateRDD(x, zeroValue, op, op) + }) + +#' Aggregate an RDD using the given combine functions and a neutral "zero value". +#' +#' Aggregate the elements of each partition, and then the results for all the +#' partitions, using given combine functions and a neutral "zero value". +#' +#' @param x An RDD. +#' @param zeroValue A neutral "zero value". +#' @param seqOp A function to aggregate the RDD elements. It may return a different +#' result type from the type of the RDD elements. +#' @param combOp A function to aggregate results of seqOp. +#' @return The aggregation result. +#' @rdname aggregateRDD +#' @seealso reduce +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(1, 2, 3, 4)) +#' zeroValue <- list(0, 0) +#' seqOp <- function(x, y) { list(x[[1]] + y, x[[2]] + 1) } +#' combOp <- function(x, y) { list(x[[1]] + y[[1]], x[[2]] + y[[2]]) } +#' aggregateRDD(rdd, zeroValue, seqOp, combOp) # list(10, 4) +#'} +#' @rdname aggregateRDD +#' @aliases aggregateRDD,RDD,RDD-method +setMethod("aggregateRDD", + signature(x = "RDD", zeroValue = "ANY", seqOp = "ANY", combOp = "ANY"), + function(x, zeroValue, seqOp, combOp) { + partitionFunc <- function(part) { + Reduce(seqOp, part, zeroValue) + } + + partitionList <- collect(lapplyPartition(x, partitionFunc), + flatten = FALSE) + Reduce(combOp, partitionList, zeroValue) + }) + +#' Pipes elements to a forked external process. +#' +#' The same as 'pipe()' in Spark. +#' +#' @param x The RDD whose elements are piped to the forked external process. +#' @param command The command to fork an external process. +#' @param env A named list to set environment variables of the external process. +#' @return A new RDD created by piping all elements to a forked external process. +#' @rdname pipeRDD +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' collect(pipeRDD(rdd, "more") +#' Output: c("1", "2", ..., "10") +#'} +#' @rdname pipeRDD +#' @aliases pipeRDD,RDD,character-method +setMethod("pipeRDD", + signature(x = "RDD", command = "character"), + function(x, command, env = list()) { + func <- function(part) { + trim.trailing.func <- function(x) { + sub("[\r\n]*$", "", toString(x)) + } + input <- unlist(lapply(part, trim.trailing.func)) + res <- system2(command, stdout = TRUE, input = input, env = env) + lapply(res, trim.trailing.func) + } + lapplyPartition(x, func) + }) + +# TODO: Consider caching the name in the RDD's environment +#' Return an RDD's name. +#' +#' @param x The RDD whose name is returned. +#' @rdname name +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(1,2,3)) +#' name(rdd) # NULL (if not set before) +#'} +#' @rdname name +#' @aliases name,RDD +setMethod("name", + signature(x = "RDD"), + function(x) { + callJMethod(getJRDD(x), "name") + }) + +#' Set an RDD's name. +#' +#' @param x The RDD whose name is to be set. +#' @param name The RDD name to be set. +#' @return a new RDD renamed. +#' @rdname setName +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(1,2,3)) +#' setName(rdd, "myRDD") +#' name(rdd) # "myRDD" +#'} +#' @rdname setName +#' @aliases setName,RDD +setMethod("setName", + signature(x = "RDD", name = "character"), + function(x, name) { + callJMethod(getJRDD(x), "setName", name) + x + }) + +#' Zip an RDD with generated unique Long IDs. +#' +#' Items in the kth partition will get ids k, n+k, 2*n+k, ..., where +#' n is the number of partitions. So there may exist gaps, but this +#' method won't trigger a spark job, which is different from +#' zipWithIndex. +#' +#' @param x An RDD to be zipped. +#' @return An RDD with zipped items. +#' @seealso zipWithIndex +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list("a", "b", "c", "d", "e"), 3L) +#' collect(zipWithUniqueId(rdd)) +#' # list(list("a", 0), list("b", 3), list("c", 1), list("d", 4), list("e", 2)) +#'} +#' @rdname zipWithUniqueId +#' @aliases zipWithUniqueId,RDD +setMethod("zipWithUniqueId", + signature(x = "RDD"), + function(x) { + n <- numPartitions(x) + + partitionFunc <- function(split, part) { + mapply( + function(item, index) { + list(item, (index - 1) * n + split) + }, + part, + seq_along(part), + SIMPLIFY = FALSE) + } + + lapplyPartitionsWithIndex(x, partitionFunc) + }) + +#' Zip an RDD with its element indices. +#' +#' The ordering is first based on the partition index and then the +#' ordering of items within each partition. So the first item in +#' the first partition gets index 0, and the last item in the last +#' partition receives the largest index. +#' +#' This method needs to trigger a Spark job when this RDD contains +#' more than one partition. +#' +#' @param x An RDD to be zipped. +#' @return An RDD with zipped items. +#' @seealso zipWithUniqueId +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list("a", "b", "c", "d", "e"), 3L) +#' collect(zipWithIndex(rdd)) +#' # list(list("a", 0), list("b", 1), list("c", 2), list("d", 3), list("e", 4)) +#'} +#' @rdname zipWithIndex +#' @aliases zipWithIndex,RDD +setMethod("zipWithIndex", + signature(x = "RDD"), + function(x) { + n <- numPartitions(x) + if (n > 1) { + nums <- collect(lapplyPartition(x, + function(part) { + list(length(part)) + })) + startIndices <- Reduce("+", nums, accumulate = TRUE) + } + + partitionFunc <- function(split, part) { + if (split == 0) { + startIndex <- 0 + } else { + startIndex <- startIndices[[split]] + } + + mapply( + function(item, index) { + list(item, index - 1 + startIndex) + }, + part, + seq_along(part), + SIMPLIFY = FALSE) + } + + lapplyPartitionsWithIndex(x, partitionFunc) + }) + +#' Coalesce all elements within each partition of an RDD into a list. +#' +#' @param x An RDD. +#' @return An RDD created by coalescing all elements within +#' each partition into a list. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, as.list(1:4), 2L) +#' collect(glom(rdd)) +#' # list(list(1, 2), list(3, 4)) +#'} +#' @rdname glom +#' @aliases glom,RDD +setMethod("glom", + signature(x = "RDD"), + function(x) { + partitionFunc <- function(part) { + list(part) + } + + lapplyPartition(x, partitionFunc) + }) + +############ Binary Functions ############# + +#' Return the union RDD of two RDDs. +#' The same as union() in Spark. +#' +#' @param x An RDD. +#' @param y An RDD. +#' @return a new RDD created by performing the simple union (witout removing +#' duplicates) of two input RDDs. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:3) +#' unionRDD(rdd, rdd) # 1, 2, 3, 1, 2, 3 +#'} +#' @rdname unionRDD +#' @aliases unionRDD,RDD,RDD-method +setMethod("unionRDD", + signature(x = "RDD", y = "RDD"), + function(x, y) { + if (getSerializedMode(x) == getSerializedMode(y)) { + jrdd <- callJMethod(getJRDD(x), "union", getJRDD(y)) + union.rdd <- RDD(jrdd, getSerializedMode(x)) + } else { + # One of the RDDs is not serialized, we need to serialize it first. + if (getSerializedMode(x) != "byte") x <- serializeToBytes(x) + if (getSerializedMode(y) != "byte") y <- serializeToBytes(y) + jrdd <- callJMethod(getJRDD(x), "union", getJRDD(y)) + union.rdd <- RDD(jrdd, "byte") + } + union.rdd + }) + +#' Zip an RDD with another RDD. +#' +#' Zips this RDD with another one, returning key-value pairs with the +#' first element in each RDD second element in each RDD, etc. Assumes +#' that the two RDDs have the same number of partitions and the same +#' number of elements in each partition (e.g. one was made through +#' a map on the other). +#' +#' @param x An RDD to be zipped. +#' @param other Another RDD to be zipped. +#' @return An RDD zipped from the two RDDs. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd1 <- parallelize(sc, 0:4) +#' rdd2 <- parallelize(sc, 1000:1004) +#' collect(zipRDD(rdd1, rdd2)) +#' # list(list(0, 1000), list(1, 1001), list(2, 1002), list(3, 1003), list(4, 1004)) +#'} +#' @rdname zipRDD +#' @aliases zipRDD,RDD +setMethod("zipRDD", + signature(x = "RDD", other = "RDD"), + function(x, other) { + n1 <- numPartitions(x) + n2 <- numPartitions(other) + if (n1 != n2) { + stop("Can only zip RDDs which have the same number of partitions.") + } + + rdds <- appendPartitionLengths(x, other) + jrdd <- callJMethod(getJRDD(rdds[[1]]), "zip", getJRDD(rdds[[2]])) + # The jrdd's elements are of scala Tuple2 type. The serialized + # flag here is used for the elements inside the tuples. + rdd <- RDD(jrdd, getSerializedMode(rdds[[1]])) + + mergePartitions(rdd, TRUE) + }) + +#' Cartesian product of this RDD and another one. +#' +#' Return the Cartesian product of this RDD and another one, +#' that is, the RDD of all pairs of elements (a, b) where a +#' is in this and b is in other. +#' +#' @param x An RDD. +#' @param other An RDD. +#' @return A new RDD which is the Cartesian product of these two RDDs. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:2) +#' sortByKey(cartesian(rdd, rdd)) +#' # list(list(1, 1), list(1, 2), list(2, 1), list(2, 2)) +#'} +#' @rdname cartesian +#' @aliases cartesian,RDD,RDD-method +setMethod("cartesian", + signature(x = "RDD", other = "RDD"), + function(x, other) { + rdds <- appendPartitionLengths(x, other) + jrdd <- callJMethod(getJRDD(rdds[[1]]), "cartesian", getJRDD(rdds[[2]])) + # The jrdd's elements are of scala Tuple2 type. The serialized + # flag here is used for the elements inside the tuples. + rdd <- RDD(jrdd, getSerializedMode(rdds[[1]])) + + mergePartitions(rdd, FALSE) + }) + +#' Subtract an RDD with another RDD. +#' +#' Return an RDD with the elements from this that are not in other. +#' +#' @param x An RDD. +#' @param other An RDD. +#' @param numPartitions Number of the partitions in the result RDD. +#' @return An RDD with the elements from this that are not in other. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd1 <- parallelize(sc, list(1, 1, 2, 2, 3, 4)) +#' rdd2 <- parallelize(sc, list(2, 4)) +#' collect(subtract(rdd1, rdd2)) +#' # list(1, 1, 3) +#'} +#' @rdname subtract +#' @aliases subtract,RDD +setMethod("subtract", + signature(x = "RDD", other = "RDD"), + function(x, other, numPartitions = SparkR::numPartitions(x)) { + mapFunction <- function(e) { list(e, NA) } + rdd1 <- map(x, mapFunction) + rdd2 <- map(other, mapFunction) + keys(subtractByKey(rdd1, rdd2, numPartitions)) + }) + +#' Intersection of this RDD and another one. +#' +#' Return the intersection of this RDD and another one. +#' The output will not contain any duplicate elements, +#' even if the input RDDs did. Performs a hash partition +#' across the cluster. +#' Note that this method performs a shuffle internally. +#' +#' @param x An RDD. +#' @param other An RDD. +#' @param numPartitions The number of partitions in the result RDD. +#' @return An RDD which is the intersection of these two RDDs. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd1 <- parallelize(sc, list(1, 10, 2, 3, 4, 5)) +#' rdd2 <- parallelize(sc, list(1, 6, 2, 3, 7, 8)) +#' collect(sortBy(intersection(rdd1, rdd2), function(x) { x })) +#' # list(1, 2, 3) +#'} +#' @rdname intersection +#' @aliases intersection,RDD +setMethod("intersection", + signature(x = "RDD", other = "RDD"), + function(x, other, numPartitions = SparkR::numPartitions(x)) { + rdd1 <- map(x, function(v) { list(v, NA) }) + rdd2 <- map(other, function(v) { list(v, NA) }) + + filterFunction <- function(elem) { + iters <- elem[[2]] + all(as.vector( + lapply(iters, function(iter) { length(iter) > 0 }), mode = "logical")) + } + + keys(filterRDD(cogroup(rdd1, rdd2, numPartitions = numPartitions), filterFunction)) + }) diff --git a/R/pkg/R/SQLContext.R b/R/pkg/R/SQLContext.R new file mode 100644 index 0000000000000..4f05ba524a01a --- /dev/null +++ b/R/pkg/R/SQLContext.R @@ -0,0 +1,494 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# SQLcontext.R: SQLContext-driven functions + +#' infer the SQL type +infer_type <- function(x) { + if (is.null(x)) { + stop("can not infer type from NULL") + } + + # class of POSIXlt is c("POSIXlt" "POSIXt") + type <- switch(class(x)[[1]], + integer = "integer", + character = "string", + logical = "boolean", + double = "double", + numeric = "double", + raw = "binary", + list = "array", + environment = "map", + Date = "date", + POSIXlt = "timestamp", + POSIXct = "timestamp", + stop(paste("Unsupported type for DataFrame:", class(x)))) + + if (type == "map") { + stopifnot(length(x) > 0) + key <- ls(x)[[1]] + list(type = "map", + keyType = "string", + valueType = infer_type(get(key, x)), + valueContainsNull = TRUE) + } else if (type == "array") { + stopifnot(length(x) > 0) + names <- names(x) + if (is.null(names)) { + list(type = "array", elementType = infer_type(x[[1]]), containsNull = TRUE) + } else { + # StructType + types <- lapply(x, infer_type) + fields <- lapply(1:length(x), function(i) { + structField(names[[i]], types[[i]], TRUE) + }) + do.call(structType, fields) + } + } else if (length(x) > 1) { + list(type = "array", elementType = type, containsNull = TRUE) + } else { + type + } +} + +#' Create a DataFrame from an RDD +#' +#' Converts an RDD to a DataFrame by infer the types. +#' +#' @param sqlCtx A SQLContext +#' @param data An RDD or list or data.frame +#' @param schema a list of column names or named list (StructType), optional +#' @return an DataFrame +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' rdd <- lapply(parallelize(sc, 1:10), function(x) list(a=x, b=as.character(x))) +#' df <- createDataFrame(sqlCtx, rdd) +#' } + +# TODO(davies): support sampling and infer type from NA +createDataFrame <- function(sqlCtx, data, schema = NULL, samplingRatio = 1.0) { + if (is.data.frame(data)) { + # get the names of columns, they will be put into RDD + schema <- names(data) + n <- nrow(data) + m <- ncol(data) + # get rid of factor type + dropFactor <- function(x) { + if (is.factor(x)) { + as.character(x) + } else { + x + } + } + data <- lapply(1:n, function(i) { + lapply(1:m, function(j) { dropFactor(data[i,j]) }) + }) + } + if (is.list(data)) { + sc <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", "getJavaSparkContext", sqlCtx) + rdd <- parallelize(sc, data) + } else if (inherits(data, "RDD")) { + rdd <- data + } else { + stop(paste("unexpected type:", class(data))) + } + + if (is.null(schema) || (!inherits(schema, "structType") && is.null(names(schema)))) { + row <- first(rdd) + names <- if (is.null(schema)) { + names(row) + } else { + as.list(schema) + } + if (is.null(names)) { + names <- lapply(1:length(row), function(x) { + paste("_", as.character(x), sep = "") + }) + } + + # SPAKR-SQL does not support '.' in column name, so replace it with '_' + # TODO(davies): remove this once SPARK-2775 is fixed + names <- lapply(names, function(n) { + nn <- gsub("[.]", "_", n) + if (nn != n) { + warning(paste("Use", nn, "instead of", n, " as column name")) + } + nn + }) + + types <- lapply(row, infer_type) + fields <- lapply(1:length(row), function(i) { + structField(names[[i]], types[[i]], TRUE) + }) + schema <- do.call(structType, fields) + } + + stopifnot(class(schema) == "structType") + # schemaString <- tojson(schema) + + jrdd <- getJRDD(lapply(rdd, function(x) x), "row") + srdd <- callJMethod(jrdd, "rdd") + sdf <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", "createDF", + srdd, schema$jobj, sqlCtx) + dataFrame(sdf) +} + +#' toDF +#' +#' Converts an RDD to a DataFrame by infer the types. +#' +#' @param x An RDD +#' +#' @rdname DataFrame +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' rdd <- lapply(parallelize(sc, 1:10), function(x) list(a=x, b=as.character(x))) +#' df <- toDF(rdd) +#' } + +setGeneric("toDF", function(x, ...) { standardGeneric("toDF") }) + +setMethod("toDF", signature(x = "RDD"), + function(x, ...) { + sqlCtx <- if (exists(".sparkRHivesc", envir = .sparkREnv)) { + get(".sparkRHivesc", envir = .sparkREnv) + } else if (exists(".sparkRSQLsc", envir = .sparkREnv)) { + get(".sparkRSQLsc", envir = .sparkREnv) + } else { + stop("no SQL context available") + } + createDataFrame(sqlCtx, x, ...) + }) + +#' Create a DataFrame from a JSON file. +#' +#' Loads a JSON file (one object per line), returning the result as a DataFrame +#' It goes through the entire dataset once to determine the schema. +#' +#' @param sqlCtx SQLContext to use +#' @param path Path of file to read. A vector of multiple paths is allowed. +#' @return DataFrame +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' } + +jsonFile <- function(sqlCtx, path) { + # Allow the user to have a more flexible definiton of the text file path + path <- normalizePath(path) + # Convert a string vector of paths to a string containing comma separated paths + path <- paste(path, collapse = ",") + sdf <- callJMethod(sqlCtx, "jsonFile", path) + dataFrame(sdf) +} + + +#' JSON RDD +#' +#' Loads an RDD storing one JSON object per string as a DataFrame. +#' +#' @param sqlCtx SQLContext to use +#' @param rdd An RDD of JSON string +#' @param schema A StructType object to use as schema +#' @param samplingRatio The ratio of simpling used to infer the schema +#' @return A DataFrame +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' rdd <- texFile(sc, "path/to/json") +#' df <- jsonRDD(sqlCtx, rdd) +#' } + +# TODO: support schema +jsonRDD <- function(sqlCtx, rdd, schema = NULL, samplingRatio = 1.0) { + rdd <- serializeToString(rdd) + if (is.null(schema)) { + sdf <- callJMethod(sqlCtx, "jsonRDD", callJMethod(getJRDD(rdd), "rdd"), samplingRatio) + dataFrame(sdf) + } else { + stop("not implemented") + } +} + + +#' Create a DataFrame from a Parquet file. +#' +#' Loads a Parquet file, returning the result as a DataFrame. +#' +#' @param sqlCtx SQLContext to use +#' @param ... Path(s) of parquet file(s) to read. +#' @return DataFrame +#' @export + +# TODO: Implement saveasParquetFile and write examples for both +parquetFile <- function(sqlCtx, ...) { + # Allow the user to have a more flexible definiton of the text file path + paths <- lapply(list(...), normalizePath) + sdf <- callJMethod(sqlCtx, "parquetFile", paths) + dataFrame(sdf) +} + +#' SQL Query +#' +#' Executes a SQL query using Spark, returning the result as a DataFrame. +#' +#' @param sqlCtx SQLContext to use +#' @param sqlQuery A character vector containing the SQL query +#' @return DataFrame +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' registerTempTable(df, "table") +#' new_df <- sql(sqlCtx, "SELECT * FROM table") +#' } + +sql <- function(sqlCtx, sqlQuery) { + sdf <- callJMethod(sqlCtx, "sql", sqlQuery) + dataFrame(sdf) +} + + +#' Create a DataFrame from a SparkSQL Table +#' +#' Returns the specified Table as a DataFrame. The Table must have already been registered +#' in the SQLContext. +#' +#' @param sqlCtx SQLContext to use +#' @param tableName The SparkSQL Table to convert to a DataFrame. +#' @return DataFrame +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' registerTempTable(df, "table") +#' new_df <- table(sqlCtx, "table") +#' } + +table <- function(sqlCtx, tableName) { + sdf <- callJMethod(sqlCtx, "table", tableName) + dataFrame(sdf) +} + + +#' Tables +#' +#' Returns a DataFrame containing names of tables in the given database. +#' +#' @param sqlCtx SQLContext to use +#' @param databaseName name of the database +#' @return a DataFrame +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' tables(sqlCtx, "hive") +#' } + +tables <- function(sqlCtx, databaseName = NULL) { + jdf <- if (is.null(databaseName)) { + callJMethod(sqlCtx, "tables") + } else { + callJMethod(sqlCtx, "tables", databaseName) + } + dataFrame(jdf) +} + + +#' Table Names +#' +#' Returns the names of tables in the given database as an array. +#' +#' @param sqlCtx SQLContext to use +#' @param databaseName name of the database +#' @return a list of table names +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' tableNames(sqlCtx, "hive") +#' } + +tableNames <- function(sqlCtx, databaseName = NULL) { + if (is.null(databaseName)) { + callJMethod(sqlCtx, "tableNames") + } else { + callJMethod(sqlCtx, "tableNames", databaseName) + } +} + + +#' Cache Table +#' +#' Caches the specified table in-memory. +#' +#' @param sqlCtx SQLContext to use +#' @param tableName The name of the table being cached +#' @return DataFrame +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' registerTempTable(df, "table") +#' cacheTable(sqlCtx, "table") +#' } + +cacheTable <- function(sqlCtx, tableName) { + callJMethod(sqlCtx, "cacheTable", tableName) +} + +#' Uncache Table +#' +#' Removes the specified table from the in-memory cache. +#' +#' @param sqlCtx SQLContext to use +#' @param tableName The name of the table being uncached +#' @return DataFrame +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' registerTempTable(df, "table") +#' uncacheTable(sqlCtx, "table") +#' } + +uncacheTable <- function(sqlCtx, tableName) { + callJMethod(sqlCtx, "uncacheTable", tableName) +} + +#' Clear Cache +#' +#' Removes all cached tables from the in-memory cache. +#' +#' @param sqlCtx SQLContext to use +#' @examples +#' \dontrun{ +#' clearCache(sqlCtx) +#' } + +clearCache <- function(sqlCtx) { + callJMethod(sqlCtx, "clearCache") +} + +#' Drop Temporary Table +#' +#' Drops the temporary table with the given table name in the catalog. +#' If the table has been cached/persisted before, it's also unpersisted. +#' +#' @param sqlCtx SQLContext to use +#' @param tableName The name of the SparkSQL table to be dropped. +#' @examples +#' \dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' df <- loadDF(sqlCtx, path, "parquet") +#' registerTempTable(df, "table") +#' dropTempTable(sqlCtx, "table") +#' } + +dropTempTable <- function(sqlCtx, tableName) { + if (class(tableName) != "character") { + stop("tableName must be a string.") + } + callJMethod(sqlCtx, "dropTempTable", tableName) +} + +#' Load an DataFrame +#' +#' Returns the dataset in a data source as a DataFrame +#' +#' The data source is specified by the `source` and a set of options(...). +#' If `source` is not specified, the default data source configured by +#' "spark.sql.sources.default" will be used. +#' +#' @param sqlCtx SQLContext to use +#' @param path The path of files to load +#' @param source the name of external data source +#' @return DataFrame +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' df <- load(sqlCtx, "path/to/file.json", source = "json") +#' } + +loadDF <- function(sqlCtx, path = NULL, source = NULL, ...) { + options <- varargsToEnv(...) + if (!is.null(path)) { + options[['path']] <- path + } + sdf <- callJMethod(sqlCtx, "load", source, options) + dataFrame(sdf) +} + +#' Create an external table +#' +#' Creates an external table based on the dataset in a data source, +#' Returns the DataFrame associated with the external table. +#' +#' The data source is specified by the `source` and a set of options(...). +#' If `source` is not specified, the default data source configured by +#' "spark.sql.sources.default" will be used. +#' +#' @param sqlCtx SQLContext to use +#' @param tableName A name of the table +#' @param path The path of files to load +#' @param source the name of external data source +#' @return DataFrame +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' df <- sparkRSQL.createExternalTable(sqlCtx, "myjson", path="path/to/json", source="json") +#' } + +createExternalTable <- function(sqlCtx, tableName, path = NULL, source = NULL, ...) { + options <- varargsToEnv(...) + if (!is.null(path)) { + options[['path']] <- path + } + sdf <- callJMethod(sqlCtx, "createExternalTable", tableName, source, options) + dataFrame(sdf) +} diff --git a/R/pkg/R/backend.R b/R/pkg/R/backend.R new file mode 100644 index 0000000000000..2fb6fae55f28c --- /dev/null +++ b/R/pkg/R/backend.R @@ -0,0 +1,115 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# Methods to call into SparkRBackend. + + +# Returns TRUE if object is an instance of given class +isInstanceOf <- function(jobj, className) { + stopifnot(class(jobj) == "jobj") + cls <- callJStatic("java.lang.Class", "forName", className) + callJMethod(cls, "isInstance", jobj) +} + +# Call a Java method named methodName on the object +# specified by objId. objId should be a "jobj" returned +# from the SparkRBackend. +callJMethod <- function(objId, methodName, ...) { + stopifnot(class(objId) == "jobj") + if (!isValidJobj(objId)) { + stop("Invalid jobj ", objId$id, + ". If SparkR was restarted, Spark operations need to be re-executed.") + } + invokeJava(isStatic = FALSE, objId$id, methodName, ...) +} + +# Call a static method on a specified className +callJStatic <- function(className, methodName, ...) { + invokeJava(isStatic = TRUE, className, methodName, ...) +} + +# Create a new object of the specified class name +newJObject <- function(className, ...) { + invokeJava(isStatic = TRUE, className, methodName = "", ...) +} + +# Remove an object from the SparkR backend. This is done +# automatically when a jobj is garbage collected. +removeJObject <- function(objId) { + invokeJava(isStatic = TRUE, "SparkRHandler", "rm", objId) +} + +isRemoveMethod <- function(isStatic, objId, methodName) { + isStatic == TRUE && objId == "SparkRHandler" && methodName == "rm" +} + +# Invoke a Java method on the SparkR backend. Users +# should typically use one of the higher level methods like +# callJMethod, callJStatic etc. instead of using this. +# +# isStatic - TRUE if the method to be called is static +# objId - String that refers to the object on which method is invoked +# Should be a jobj id for non-static methods and the classname +# for static methods +# methodName - name of method to be invoked +invokeJava <- function(isStatic, objId, methodName, ...) { + if (!exists(".sparkRCon", .sparkREnv)) { + stop("No connection to backend found. Please re-run sparkR.init") + } + + # If this isn't a removeJObject call + if (!isRemoveMethod(isStatic, objId, methodName)) { + objsToRemove <- ls(.toRemoveJobjs) + if (length(objsToRemove) > 0) { + sapply(objsToRemove, + function(e) { + removeJObject(e) + }) + rm(list = objsToRemove, envir = .toRemoveJobjs) + } + } + + + rc <- rawConnection(raw(0), "r+") + + writeBoolean(rc, isStatic) + writeString(rc, objId) + writeString(rc, methodName) + + args <- list(...) + writeInt(rc, length(args)) + writeArgs(rc, args) + + # Construct the whole request message to send it once, + # avoiding write-write-read pattern in case of Nagle's algorithm. + # Refer to http://en.wikipedia.org/wiki/Nagle%27s_algorithm for the details. + bytesToSend <- rawConnectionValue(rc) + close(rc) + rc <- rawConnection(raw(0), "r+") + writeInt(rc, length(bytesToSend)) + writeBin(bytesToSend, rc) + requestMessage <- rawConnectionValue(rc) + close(rc) + + conn <- get(".sparkRCon", .sparkREnv) + writeBin(requestMessage, conn) + + # TODO: check the status code to output error information + returnStatus <- readInt(conn) + stopifnot(returnStatus == 0) + readObject(conn) +} diff --git a/R/pkg/R/broadcast.R b/R/pkg/R/broadcast.R new file mode 100644 index 0000000000000..583fa2e7fdcfd --- /dev/null +++ b/R/pkg/R/broadcast.R @@ -0,0 +1,86 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# S4 class representing Broadcast variables + +# Hidden environment that holds values for broadcast variables +# This will not be serialized / shipped by default +.broadcastNames <- new.env() +.broadcastValues <- new.env() +.broadcastIdToName <- new.env() + +#' @title S4 class that represents a Broadcast variable +#' @description Broadcast variables can be created using the broadcast +#' function from a \code{SparkContext}. +#' @rdname broadcast-class +#' @seealso broadcast +#' +#' @param id Id of the backing Spark broadcast variable +#' @export +setClass("Broadcast", slots = list(id = "character")) + +#' @rdname broadcast-class +#' @param value Value of the broadcast variable +#' @param jBroadcastRef reference to the backing Java broadcast object +#' @param objName name of broadcasted object +#' @export +Broadcast <- function(id, value, jBroadcastRef, objName) { + .broadcastValues[[id]] <- value + .broadcastNames[[as.character(objName)]] <- jBroadcastRef + .broadcastIdToName[[id]] <- as.character(objName) + new("Broadcast", id = id) +} + +#' @description +#' \code{value} can be used to get the value of a broadcast variable inside +#' a distributed function. +#' +#' @param bcast The broadcast variable to get +#' @rdname broadcast +#' @aliases value,Broadcast-method +setMethod("value", + signature(bcast = "Broadcast"), + function(bcast) { + if (exists(bcast@id, envir = .broadcastValues)) { + get(bcast@id, envir = .broadcastValues) + } else { + NULL + } + }) + +#' Internal function to set values of a broadcast variable. +#' +#' This function is used internally by Spark to set the value of a broadcast +#' variable on workers. Not intended for use outside the package. +#' +#' @rdname broadcast-internal +#' @seealso broadcast, value + +#' @param bcastId The id of broadcast variable to set +#' @param value The value to be set +#' @export +setBroadcastValue <- function(bcastId, value) { + bcastIdStr <- as.character(bcastId) + .broadcastValues[[bcastIdStr]] <- value +} + +#' Helper function to clear the list of broadcast variables we know about +#' Should be called when the SparkR JVM backend is shutdown +clearBroadcastVariables <- function() { + bcasts <- ls(.broadcastNames) + rm(list = bcasts, envir = .broadcastNames) +} diff --git a/R/pkg/R/client.R b/R/pkg/R/client.R new file mode 100644 index 0000000000000..1281c41213e32 --- /dev/null +++ b/R/pkg/R/client.R @@ -0,0 +1,57 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# Client code to connect to SparkRBackend + +# Creates a SparkR client connection object +# if one doesn't already exist +connectBackend <- function(hostname, port, timeout = 6000) { + if (exists(".sparkRcon", envir = .sparkREnv)) { + if (isOpen(.sparkREnv[[".sparkRCon"]])) { + cat("SparkRBackend client connection already exists\n") + return(get(".sparkRcon", envir = .sparkREnv)) + } + } + + con <- socketConnection(host = hostname, port = port, server = FALSE, + blocking = TRUE, open = "wb", timeout = timeout) + + assign(".sparkRCon", con, envir = .sparkREnv) + con +} + +launchBackend <- function(args, sparkHome, jars, sparkSubmitOpts) { + if (.Platform$OS.type == "unix") { + sparkSubmitBinName = "spark-submit" + } else { + sparkSubmitBinName = "spark-submit.cmd" + } + + if (sparkHome != "") { + sparkSubmitBin <- file.path(sparkHome, "bin", sparkSubmitBinName) + } else { + sparkSubmitBin <- sparkSubmitBinName + } + + if (jars != "") { + jars <- paste("--jars", jars) + } + + combinedArgs <- paste(jars, sparkSubmitOpts, args, sep = " ") + cat("Launching java with spark-submit command", sparkSubmitBin, combinedArgs, "\n") + invisible(system2(sparkSubmitBin, combinedArgs, wait = F)) +} diff --git a/R/pkg/R/column.R b/R/pkg/R/column.R new file mode 100644 index 0000000000000..95fb9ff0887b6 --- /dev/null +++ b/R/pkg/R/column.R @@ -0,0 +1,199 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# Column Class + +#' @include generics.R jobj.R schema.R +NULL + +setOldClass("jobj") + +#' @title S4 class that represents a DataFrame column +#' @description The column class supports unary, binary operations on DataFrame columns + +#' @rdname column +#' +#' @param jc reference to JVM DataFrame column +#' @export +setClass("Column", + slots = list(jc = "jobj")) + +setMethod("initialize", "Column", function(.Object, jc) { + .Object@jc <- jc + .Object +}) + +column <- function(jc) { + new("Column", jc) +} + +col <- function(x) { + column(callJStatic("org.apache.spark.sql.functions", "col", x)) +} + +#' @rdname show +setMethod("show", "Column", + function(object) { + cat("Column", callJMethod(object@jc, "toString"), "\n") + }) + +operators <- list( + "+" = "plus", "-" = "minus", "*" = "multiply", "/" = "divide", "%%" = "mod", + "==" = "equalTo", ">" = "gt", "<" = "lt", "!=" = "notEqual", "<=" = "leq", ">=" = "geq", + # we can not override `&&` and `||`, so use `&` and `|` instead + "&" = "and", "|" = "or" #, "!" = "unary_$bang" +) +column_functions1 <- c("asc", "desc", "isNull", "isNotNull") +column_functions2 <- c("like", "rlike", "startsWith", "endsWith", "getField", "getItem", "contains") +functions <- c("min", "max", "sum", "avg", "mean", "count", "abs", "sqrt", + "first", "last", "lower", "upper", "sumDistinct") + +createOperator <- function(op) { + setMethod(op, + signature(e1 = "Column"), + function(e1, e2) { + jc <- if (missing(e2)) { + if (op == "-") { + callJMethod(e1@jc, "unary_$minus") + } else { + callJMethod(e1@jc, operators[[op]]) + } + } else { + if (class(e2) == "Column") { + e2 <- e2@jc + } + callJMethod(e1@jc, operators[[op]], e2) + } + column(jc) + }) +} + +createColumnFunction1 <- function(name) { + setMethod(name, + signature(x = "Column"), + function(x) { + column(callJMethod(x@jc, name)) + }) +} + +createColumnFunction2 <- function(name) { + setMethod(name, + signature(x = "Column"), + function(x, data) { + if (class(data) == "Column") { + data <- data@jc + } + jc <- callJMethod(x@jc, name, data) + column(jc) + }) +} + +createStaticFunction <- function(name) { + setMethod(name, + signature(x = "Column"), + function(x) { + jc <- callJStatic("org.apache.spark.sql.functions", name, x@jc) + column(jc) + }) +} + +createMethods <- function() { + for (op in names(operators)) { + createOperator(op) + } + for (name in column_functions1) { + createColumnFunction1(name) + } + for (name in column_functions2) { + createColumnFunction2(name) + } + for (x in functions) { + createStaticFunction(x) + } +} + +createMethods() + +#' alias +#' +#' Set a new name for a column +setMethod("alias", + signature(object = "Column"), + function(object, data) { + if (is.character(data)) { + column(callJMethod(object@jc, "as", data)) + } else { + stop("data should be character") + } + }) + +#' An expression that returns a substring. +#' +#' @param start starting position +#' @param stop ending position +setMethod("substr", signature(x = "Column"), + function(x, start, stop) { + jc <- callJMethod(x@jc, "substr", as.integer(start - 1), as.integer(stop - start + 1)) + column(jc) + }) + +#' Casts the column to a different data type. +#' @examples +#' \dontrun{ +#' cast(df$age, "string") +#' cast(df$name, list(type="array", elementType="byte", containsNull = TRUE)) +#' } +setMethod("cast", + signature(x = "Column"), + function(x, dataType) { + if (is.character(dataType)) { + column(callJMethod(x@jc, "cast", dataType)) + } else if (is.list(dataType)) { + json <- tojson(dataType) + jdataType <- callJStatic("org.apache.spark.sql.types.DataType", "fromJson", json) + column(callJMethod(x@jc, "cast", jdataType)) + } else { + stop("dataType should be character or list") + } + }) + +#' Approx Count Distinct +#' +#' Returns the approximate number of distinct items in a group. +#' +setMethod("approxCountDistinct", + signature(x = "Column"), + function(x, rsd = 0.95) { + jc <- callJStatic("org.apache.spark.sql.functions", "approxCountDistinct", x@jc, rsd) + column(jc) + }) + +#' Count Distinct +#' +#' returns the number of distinct items in a group. +#' +setMethod("countDistinct", + signature(x = "Column"), + function(x, ...) { + jcol <- lapply(list(...), function (x) { + x@jc + }) + jc <- callJStatic("org.apache.spark.sql.functions", "countDistinct", x@jc, + listToSeq(jcol)) + column(jc) + }) + diff --git a/R/pkg/R/context.R b/R/pkg/R/context.R new file mode 100644 index 0000000000000..ebbb8fba1052d --- /dev/null +++ b/R/pkg/R/context.R @@ -0,0 +1,225 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# context.R: SparkContext driven functions + +getMinSplits <- function(sc, minSplits) { + if (is.null(minSplits)) { + defaultParallelism <- callJMethod(sc, "defaultParallelism") + minSplits <- min(defaultParallelism, 2) + } + as.integer(minSplits) +} + +#' Create an RDD from a text file. +#' +#' This function reads a text file from HDFS, a local file system (available on all +#' nodes), or any Hadoop-supported file system URI, and creates an +#' RDD of strings from it. +#' +#' @param sc SparkContext to use +#' @param path Path of file to read. A vector of multiple paths is allowed. +#' @param minSplits Minimum number of splits to be created. If NULL, the default +#' value is chosen based on available parallelism. +#' @return RDD where each item is of type \code{character} +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' lines <- textFile(sc, "myfile.txt") +#'} +textFile <- function(sc, path, minSplits = NULL) { + # Allow the user to have a more flexible definiton of the text file path + path <- suppressWarnings(normalizePath(path)) + #' Convert a string vector of paths to a string containing comma separated paths + path <- paste(path, collapse = ",") + + jrdd <- callJMethod(sc, "textFile", path, getMinSplits(sc, minSplits)) + # jrdd is of type JavaRDD[String] + RDD(jrdd, "string") +} + +#' Load an RDD saved as a SequenceFile containing serialized objects. +#' +#' The file to be loaded should be one that was previously generated by calling +#' saveAsObjectFile() of the RDD class. +#' +#' @param sc SparkContext to use +#' @param path Path of file to read. A vector of multiple paths is allowed. +#' @param minSplits Minimum number of splits to be created. If NULL, the default +#' value is chosen based on available parallelism. +#' @return RDD containing serialized R objects. +#' @seealso saveAsObjectFile +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- objectFile(sc, "myfile") +#'} +objectFile <- function(sc, path, minSplits = NULL) { + # Allow the user to have a more flexible definiton of the text file path + path <- suppressWarnings(normalizePath(path)) + #' Convert a string vector of paths to a string containing comma separated paths + path <- paste(path, collapse = ",") + + jrdd <- callJMethod(sc, "objectFile", path, getMinSplits(sc, minSplits)) + # Assume the RDD contains serialized R objects. + RDD(jrdd, "byte") +} + +#' Create an RDD from a homogeneous list or vector. +#' +#' This function creates an RDD from a local homogeneous list in R. The elements +#' in the list are split into \code{numSlices} slices and distributed to nodes +#' in the cluster. +#' +#' @param sc SparkContext to use +#' @param coll collection to parallelize +#' @param numSlices number of partitions to create in the RDD +#' @return an RDD created from this collection +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10, 2) +#' # The RDD should contain 10 elements +#' length(rdd) +#'} +parallelize <- function(sc, coll, numSlices = 1) { + # TODO: bound/safeguard numSlices + # TODO: unit tests for if the split works for all primitives + # TODO: support matrix, data frame, etc + if ((!is.list(coll) && !is.vector(coll)) || is.data.frame(coll)) { + if (is.data.frame(coll)) { + message(paste("context.R: A data frame is parallelized by columns.")) + } else { + if (is.matrix(coll)) { + message(paste("context.R: A matrix is parallelized by elements.")) + } else { + message(paste("context.R: parallelize() currently only supports lists and vectors.", + "Calling as.list() to coerce coll into a list.")) + } + } + coll <- as.list(coll) + } + + if (numSlices > length(coll)) + numSlices <- length(coll) + + sliceLen <- ceiling(length(coll) / numSlices) + slices <- split(coll, rep(1:(numSlices + 1), each = sliceLen)[1:length(coll)]) + + # Serialize each slice: obtain a list of raws, or a list of lists (slices) of + # 2-tuples of raws + serializedSlices <- lapply(slices, serialize, connection = NULL) + + jrdd <- callJStatic("org.apache.spark.api.r.RRDD", + "createRDDFromArray", sc, serializedSlices) + + RDD(jrdd, "byte") +} + +#' Include this specified package on all workers +#' +#' This function can be used to include a package on all workers before the +#' user's code is executed. This is useful in scenarios where other R package +#' functions are used in a function passed to functions like \code{lapply}. +#' NOTE: The package is assumed to be installed on every node in the Spark +#' cluster. +#' +#' @param sc SparkContext to use +#' @param pkg Package name +#' +#' @export +#' @examples +#'\dontrun{ +#' library(Matrix) +#' +#' sc <- sparkR.init() +#' # Include the matrix library we will be using +#' includePackage(sc, Matrix) +#' +#' generateSparse <- function(x) { +#' sparseMatrix(i=c(1, 2, 3), j=c(1, 2, 3), x=c(1, 2, 3)) +#' } +#' +#' rdd <- lapplyPartition(parallelize(sc, 1:2, 2L), generateSparse) +#' collect(rdd) +#'} +includePackage <- function(sc, pkg) { + pkg <- as.character(substitute(pkg)) + if (exists(".packages", .sparkREnv)) { + packages <- .sparkREnv$.packages + } else { + packages <- list() + } + packages <- c(packages, pkg) + .sparkREnv$.packages <- packages +} + +#' @title Broadcast a variable to all workers +#' +#' @description +#' Broadcast a read-only variable to the cluster, returning a \code{Broadcast} +#' object for reading it in distributed functions. +#' +#' @param sc Spark Context to use +#' @param object Object to be broadcast +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:2, 2L) +#' +#' # Large Matrix object that we want to broadcast +#' randomMat <- matrix(nrow=100, ncol=10, data=rnorm(1000)) +#' randomMatBr <- broadcast(sc, randomMat) +#' +#' # Use the broadcast variable inside the function +#' useBroadcast <- function(x) { +#' sum(value(randomMatBr) * x) +#' } +#' sumRDD <- lapply(rdd, useBroadcast) +#'} +broadcast <- function(sc, object) { + objName <- as.character(substitute(object)) + serializedObj <- serialize(object, connection = NULL) + + jBroadcast <- callJMethod(sc, "broadcast", serializedObj) + id <- as.character(callJMethod(jBroadcast, "id")) + + Broadcast(id, object, jBroadcast, objName) +} + +#' @title Set the checkpoint directory +#' +#' Set the directory under which RDDs are going to be checkpointed. The +#' directory must be a HDFS path if running on a cluster. +#' +#' @param sc Spark Context to use +#' @param dirName Directory path +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' setCheckpointDir(sc, "~/checkpoint") +#' rdd <- parallelize(sc, 1:2, 2L) +#' checkpoint(rdd) +#'} +setCheckpointDir <- function(sc, dirName) { + invisible(callJMethod(sc, "setCheckpointDir", suppressWarnings(normalizePath(dirName)))) +} diff --git a/R/pkg/R/deserialize.R b/R/pkg/R/deserialize.R new file mode 100644 index 0000000000000..257b435607ce8 --- /dev/null +++ b/R/pkg/R/deserialize.R @@ -0,0 +1,184 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# Utility functions to deserialize objects from Java. + +# Type mapping from Java to R +# +# void -> NULL +# Int -> integer +# String -> character +# Boolean -> logical +# Double -> double +# Long -> double +# Array[Byte] -> raw +# Date -> Date +# Time -> POSIXct +# +# Array[T] -> list() +# Object -> jobj + +readObject <- function(con) { + # Read type first + type <- readType(con) + readTypedObject(con, type) +} + +readTypedObject <- function(con, type) { + switch (type, + "i" = readInt(con), + "c" = readString(con), + "b" = readBoolean(con), + "d" = readDouble(con), + "r" = readRaw(con), + "D" = readDate(con), + "t" = readTime(con), + "l" = readList(con), + "n" = NULL, + "j" = getJobj(readString(con)), + stop(paste("Unsupported type for deserialization", type))) +} + +readString <- function(con) { + stringLen <- readInt(con) + string <- readBin(con, raw(), stringLen, endian = "big") + rawToChar(string) +} + +readInt <- function(con) { + readBin(con, integer(), n = 1, endian = "big") +} + +readDouble <- function(con) { + readBin(con, double(), n = 1, endian = "big") +} + +readBoolean <- function(con) { + as.logical(readInt(con)) +} + +readType <- function(con) { + rawToChar(readBin(con, "raw", n = 1L)) +} + +readDate <- function(con) { + as.Date(readString(con)) +} + +readTime <- function(con) { + t <- readDouble(con) + as.POSIXct(t, origin = "1970-01-01") +} + +# We only support lists where all elements are of same type +readList <- function(con) { + type <- readType(con) + len <- readInt(con) + if (len > 0) { + l <- vector("list", len) + for (i in 1:len) { + l[[i]] <- readTypedObject(con, type) + } + l + } else { + list() + } +} + +readRaw <- function(con) { + dataLen <- readInt(con) + data <- readBin(con, raw(), as.integer(dataLen), endian = "big") +} + +readRawLen <- function(con, dataLen) { + data <- readBin(con, raw(), as.integer(dataLen), endian = "big") +} + +readDeserialize <- function(con) { + # We have two cases that are possible - In one, the entire partition is + # encoded as a byte array, so we have only one value to read. If so just + # return firstData + dataLen <- readInt(con) + firstData <- unserialize( + readBin(con, raw(), as.integer(dataLen), endian = "big")) + + # Else, read things into a list + dataLen <- readInt(con) + if (length(dataLen) > 0 && dataLen > 0) { + data <- list(firstData) + while (length(dataLen) > 0 && dataLen > 0) { + data[[length(data) + 1L]] <- unserialize( + readBin(con, raw(), as.integer(dataLen), endian = "big")) + dataLen <- readInt(con) + } + unlist(data, recursive = FALSE) + } else { + firstData + } +} + +readDeserializeRows <- function(inputCon) { + # readDeserializeRows will deserialize a DataOutputStream composed of + # a list of lists. Since the DOS is one continuous stream and + # the number of rows varies, we put the readRow function in a while loop + # that termintates when the next row is empty. + data <- list() + while(TRUE) { + row <- readRow(inputCon) + if (length(row) == 0) { + break + } + data[[length(data) + 1L]] <- row + } + data # this is a list of named lists now +} + +readRowList <- function(obj) { + # readRowList is meant for use inside an lapply. As a result, it is + # necessary to open a standalone connection for the row and consume + # the numCols bytes inside the read function in order to correctly + # deserialize the row. + rawObj <- rawConnection(obj, "r+") + on.exit(close(rawObj)) + readRow(rawObj) +} + +readRow <- function(inputCon) { + numCols <- readInt(inputCon) + if (length(numCols) > 0 && numCols > 0) { + lapply(1:numCols, function(x) { + obj <- readObject(inputCon) + if (is.null(obj)) { + NA + } else { + obj + } + }) # each row is a list now + } else { + list() + } +} + +# Take a single column as Array[Byte] and deserialize it into an atomic vector +readCol <- function(inputCon, numRows) { + # sapply can not work with POSIXlt + do.call(c, lapply(1:numRows, function(x) { + value <- readObject(inputCon) + # Replace NULL with NA so we can coerce to vectors + if (is.null(value)) NA else value + })) +} diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R new file mode 100644 index 0000000000000..6c6233390134c --- /dev/null +++ b/R/pkg/R/generics.R @@ -0,0 +1,573 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +############ RDD Actions and Transformations ############ + +#' @rdname aggregateRDD +#' @seealso reduce +#' @export +setGeneric("aggregateRDD", function(x, zeroValue, seqOp, combOp) { standardGeneric("aggregateRDD") }) + +#' @rdname cache-methods +#' @export +setGeneric("cache", function(x) { standardGeneric("cache") }) + +#' @rdname coalesce +#' @seealso repartition +#' @export +setGeneric("coalesce", function(x, numPartitions, ...) { standardGeneric("coalesce") }) + +#' @rdname checkpoint-methods +#' @export +setGeneric("checkpoint", function(x) { standardGeneric("checkpoint") }) + +#' @rdname collect-methods +#' @export +setGeneric("collect", function(x, ...) { standardGeneric("collect") }) + +#' @rdname collect-methods +#' @export +setGeneric("collectAsMap", function(x) { standardGeneric("collectAsMap") }) + +#' @rdname collect-methods +#' @export +setGeneric("collectPartition", + function(x, partitionId) { + standardGeneric("collectPartition") + }) + +#' @rdname count +#' @export +setGeneric("count", function(x) { standardGeneric("count") }) + +#' @rdname countByValue +#' @export +setGeneric("countByValue", function(x) { standardGeneric("countByValue") }) + +#' @rdname distinct +#' @export +setGeneric("distinct", function(x, numPartitions = 1L) { standardGeneric("distinct") }) + +#' @rdname filterRDD +#' @export +setGeneric("filterRDD", function(x, f) { standardGeneric("filterRDD") }) + +#' @rdname first +#' @export +setGeneric("first", function(x) { standardGeneric("first") }) + +#' @rdname flatMap +#' @export +setGeneric("flatMap", function(X, FUN) { standardGeneric("flatMap") }) + +#' @rdname fold +#' @seealso reduce +#' @export +setGeneric("fold", function(x, zeroValue, op) { standardGeneric("fold") }) + +#' @rdname foreach +#' @export +setGeneric("foreach", function(x, func) { standardGeneric("foreach") }) + +#' @rdname foreach +#' @export +setGeneric("foreachPartition", function(x, func) { standardGeneric("foreachPartition") }) + +# The jrdd accessor function. +setGeneric("getJRDD", function(rdd, ...) { standardGeneric("getJRDD") }) + +#' @rdname glom +#' @export +setGeneric("glom", function(x) { standardGeneric("glom") }) + +#' @rdname keyBy +#' @export +setGeneric("keyBy", function(x, func) { standardGeneric("keyBy") }) + +#' @rdname lapplyPartition +#' @export +setGeneric("lapplyPartition", function(X, FUN) { standardGeneric("lapplyPartition") }) + +#' @rdname lapplyPartitionsWithIndex +#' @export +setGeneric("lapplyPartitionsWithIndex", + function(X, FUN) { + standardGeneric("lapplyPartitionsWithIndex") + }) + +#' @rdname lapply +#' @export +setGeneric("map", function(X, FUN) { standardGeneric("map") }) + +#' @rdname lapplyPartition +#' @export +setGeneric("mapPartitions", function(X, FUN) { standardGeneric("mapPartitions") }) + +#' @rdname lapplyPartitionsWithIndex +#' @export +setGeneric("mapPartitionsWithIndex", + function(X, FUN) { standardGeneric("mapPartitionsWithIndex") }) + +#' @rdname maximum +#' @export +setGeneric("maximum", function(x) { standardGeneric("maximum") }) + +#' @rdname minimum +#' @export +setGeneric("minimum", function(x) { standardGeneric("minimum") }) + +#' @rdname sumRDD +#' @export +setGeneric("sumRDD", function(x) { standardGeneric("sumRDD") }) + +#' @rdname name +#' @export +setGeneric("name", function(x) { standardGeneric("name") }) + +#' @rdname numPartitions +#' @export +setGeneric("numPartitions", function(x) { standardGeneric("numPartitions") }) + +#' @rdname persist +#' @export +setGeneric("persist", function(x, newLevel) { standardGeneric("persist") }) + +#' @rdname pipeRDD +#' @export +setGeneric("pipeRDD", function(x, command, env = list()) { standardGeneric("pipeRDD")}) + +#' @rdname reduce +#' @export +setGeneric("reduce", function(x, func) { standardGeneric("reduce") }) + +#' @rdname repartition +#' @seealso coalesce +#' @export +setGeneric("repartition", function(x, numPartitions) { standardGeneric("repartition") }) + +#' @rdname sampleRDD +#' @export +setGeneric("sampleRDD", + function(x, withReplacement, fraction, seed) { + standardGeneric("sampleRDD") + }) + +#' @rdname saveAsObjectFile +#' @seealso objectFile +#' @export +setGeneric("saveAsObjectFile", function(x, path) { standardGeneric("saveAsObjectFile") }) + +#' @rdname saveAsTextFile +#' @export +setGeneric("saveAsTextFile", function(x, path) { standardGeneric("saveAsTextFile") }) + +#' @rdname setName +#' @export +setGeneric("setName", function(x, name) { standardGeneric("setName") }) + +#' @rdname sortBy +#' @export +setGeneric("sortBy", + function(x, func, ascending = TRUE, numPartitions = 1L) { + standardGeneric("sortBy") + }) + +#' @rdname take +#' @export +setGeneric("take", function(x, num) { standardGeneric("take") }) + +#' @rdname takeOrdered +#' @export +setGeneric("takeOrdered", function(x, num) { standardGeneric("takeOrdered") }) + +#' @rdname takeSample +#' @export +setGeneric("takeSample", + function(x, withReplacement, num, seed) { + standardGeneric("takeSample") + }) + +#' @rdname top +#' @export +setGeneric("top", function(x, num) { standardGeneric("top") }) + +#' @rdname unionRDD +#' @export +setGeneric("unionRDD", function(x, y) { standardGeneric("unionRDD") }) + +#' @rdname unpersist-methods +#' @export +setGeneric("unpersist", function(x, ...) { standardGeneric("unpersist") }) + +#' @rdname zipRDD +#' @export +setGeneric("zipRDD", function(x, other) { standardGeneric("zipRDD") }) + +#' @rdname zipWithIndex +#' @seealso zipWithUniqueId +#' @export +setGeneric("zipWithIndex", function(x) { standardGeneric("zipWithIndex") }) + +#' @rdname zipWithUniqueId +#' @seealso zipWithIndex +#' @export +setGeneric("zipWithUniqueId", function(x) { standardGeneric("zipWithUniqueId") }) + + +############ Binary Functions ############# + +#' @rdname cartesian +#' @export +setGeneric("cartesian", function(x, other) { standardGeneric("cartesian") }) + +#' @rdname countByKey +#' @export +setGeneric("countByKey", function(x) { standardGeneric("countByKey") }) + +#' @rdname flatMapValues +#' @export +setGeneric("flatMapValues", function(X, FUN) { standardGeneric("flatMapValues") }) + +#' @rdname intersection +#' @export +setGeneric("intersection", function(x, other, numPartitions = 1L) { + standardGeneric("intersection") }) + +#' @rdname keys +#' @export +setGeneric("keys", function(x) { standardGeneric("keys") }) + +#' @rdname lookup +#' @export +setGeneric("lookup", function(x, key) { standardGeneric("lookup") }) + +#' @rdname mapValues +#' @export +setGeneric("mapValues", function(X, FUN) { standardGeneric("mapValues") }) + +#' @rdname sampleByKey +#' @export +setGeneric("sampleByKey", + function(x, withReplacement, fractions, seed) { + standardGeneric("sampleByKey") + }) + +#' @rdname values +#' @export +setGeneric("values", function(x) { standardGeneric("values") }) + + +############ Shuffle Functions ############ + +#' @rdname aggregateByKey +#' @seealso foldByKey, combineByKey +#' @export +setGeneric("aggregateByKey", + function(x, zeroValue, seqOp, combOp, numPartitions) { + standardGeneric("aggregateByKey") + }) + +#' @rdname cogroup +#' @export +setGeneric("cogroup", + function(..., numPartitions) { + standardGeneric("cogroup") + }, + signature = "...") + +#' @rdname combineByKey +#' @seealso groupByKey, reduceByKey +#' @export +setGeneric("combineByKey", + function(x, createCombiner, mergeValue, mergeCombiners, numPartitions) { + standardGeneric("combineByKey") + }) + +#' @rdname foldByKey +#' @seealso aggregateByKey, combineByKey +#' @export +setGeneric("foldByKey", + function(x, zeroValue, func, numPartitions) { + standardGeneric("foldByKey") + }) + +#' @rdname join-methods +#' @export +setGeneric("fullOuterJoin", function(x, y, numPartitions) { standardGeneric("fullOuterJoin") }) + +#' @rdname groupByKey +#' @seealso reduceByKey +#' @export +setGeneric("groupByKey", function(x, numPartitions) { standardGeneric("groupByKey") }) + +#' @rdname join-methods +#' @export +setGeneric("join", function(x, y, ...) { standardGeneric("join") }) + +#' @rdname join-methods +#' @export +setGeneric("leftOuterJoin", function(x, y, numPartitions) { standardGeneric("leftOuterJoin") }) + +#' @rdname partitionBy +#' @export +setGeneric("partitionBy", function(x, numPartitions, ...) { standardGeneric("partitionBy") }) + +#' @rdname reduceByKey +#' @seealso groupByKey +#' @export +setGeneric("reduceByKey", function(x, combineFunc, numPartitions) { standardGeneric("reduceByKey")}) + +#' @rdname reduceByKeyLocally +#' @seealso reduceByKey +#' @export +setGeneric("reduceByKeyLocally", + function(x, combineFunc) { + standardGeneric("reduceByKeyLocally") + }) + +#' @rdname join-methods +#' @export +setGeneric("rightOuterJoin", function(x, y, numPartitions) { standardGeneric("rightOuterJoin") }) + +#' @rdname sortByKey +#' @export +setGeneric("sortByKey", + function(x, ascending = TRUE, numPartitions = 1L) { + standardGeneric("sortByKey") + }) + +#' @rdname subtract +#' @export +setGeneric("subtract", + function(x, other, numPartitions = 1L) { + standardGeneric("subtract") + }) + +#' @rdname subtractByKey +#' @export +setGeneric("subtractByKey", + function(x, other, numPartitions = 1L) { + standardGeneric("subtractByKey") + }) + + +################### Broadcast Variable Methods ################# + +#' @rdname broadcast +#' @export +setGeneric("value", function(bcast) { standardGeneric("value") }) + + + +#################### DataFrame Methods ######################## + +#' @rdname schema +#' @export +setGeneric("columns", function(x) {standardGeneric("columns") }) + +#' @rdname schema +#' @export +setGeneric("dtypes", function(x) { standardGeneric("dtypes") }) + +#' @rdname explain +#' @export +setGeneric("explain", function(x, ...) { standardGeneric("explain") }) + +#' @rdname except +#' @export +setGeneric("except", function(x, y) { standardGeneric("except") }) + +#' @rdname filter +#' @export +setGeneric("filter", function(x, condition) { standardGeneric("filter") }) + +#' @rdname DataFrame +#' @export +setGeneric("groupBy", function(x, ...) { standardGeneric("groupBy") }) + +#' @rdname insertInto +#' @export +setGeneric("insertInto", function(x, tableName, ...) { standardGeneric("insertInto") }) + +#' @rdname intersect +#' @export +setGeneric("intersect", function(x, y) { standardGeneric("intersect") }) + +#' @rdname isLocal +#' @export +setGeneric("isLocal", function(x) { standardGeneric("isLocal") }) + +#' @rdname limit +#' @export +setGeneric("limit", function(x, num) {standardGeneric("limit") }) + +#' @rdname sortDF +#' @export +setGeneric("orderBy", function(x, col) { standardGeneric("orderBy") }) + +#' @rdname schema +#' @export +setGeneric("printSchema", function(x) { standardGeneric("printSchema") }) + +#' @rdname registerTempTable +#' @export +setGeneric("registerTempTable", function(x, tableName) { standardGeneric("registerTempTable") }) + +#' @rdname sampleDF +#' @export +setGeneric("sampleDF", + function(x, withReplacement, fraction, seed) { + standardGeneric("sampleDF") + }) + +#' @rdname saveAsParquetFile +#' @export +setGeneric("saveAsParquetFile", function(x, path) { standardGeneric("saveAsParquetFile") }) + +#' @rdname saveAsTable +#' @export +setGeneric("saveAsTable", function(df, tableName, source, mode, ...) { + standardGeneric("saveAsTable") +}) + +#' @rdname saveAsTable +#' @export +setGeneric("saveDF", function(df, path, source, mode, ...) { standardGeneric("saveDF") }) + +#' @rdname schema +#' @export +setGeneric("schema", function(x) { standardGeneric("schema") }) + +#' @rdname select +#' @export +setGeneric("select", function(x, col, ...) { standardGeneric("select") } ) + +#' @rdname select +#' @export +setGeneric("selectExpr", function(x, expr, ...) { standardGeneric("selectExpr") }) + +#' @rdname showDF +#' @export +setGeneric("showDF", function(x,...) { standardGeneric("showDF") }) + +#' @rdname sortDF +#' @export +setGeneric("sortDF", function(x, col, ...) { standardGeneric("sortDF") }) + +#' @rdname tojson +#' @export +setGeneric("toJSON", function(x) { standardGeneric("toJSON") }) + +#' @rdname DataFrame +#' @export +setGeneric("toRDD", function(x) { standardGeneric("toRDD") }) + +#' @rdname unionAll +#' @export +setGeneric("unionAll", function(x, y) { standardGeneric("unionAll") }) + +#' @rdname filter +#' @export +setGeneric("where", function(x, condition) { standardGeneric("where") }) + +#' @rdname withColumn +#' @export +setGeneric("withColumn", function(x, colName, col) { standardGeneric("withColumn") }) + +#' @rdname withColumnRenamed +#' @export +setGeneric("withColumnRenamed", function(x, existingCol, newCol) { + standardGeneric("withColumnRenamed") }) + + +###################### Column Methods ########################## + +#' @rdname column +#' @export +setGeneric("approxCountDistinct", function(x, ...) { standardGeneric("approxCountDistinct") }) + +#' @rdname column +#' @export +setGeneric("asc", function(x) { standardGeneric("asc") }) + +#' @rdname column +#' @export +setGeneric("avg", function(x, ...) { standardGeneric("avg") }) + +#' @rdname column +#' @export +setGeneric("cast", function(x, dataType) { standardGeneric("cast") }) + +#' @rdname column +#' @export +setGeneric("contains", function(x, ...) { standardGeneric("contains") }) +#' @rdname column +#' @export +setGeneric("countDistinct", function(x, ...) { standardGeneric("countDistinct") }) + +#' @rdname column +#' @export +setGeneric("desc", function(x) { standardGeneric("desc") }) + +#' @rdname column +#' @export +setGeneric("endsWith", function(x, ...) { standardGeneric("endsWith") }) + +#' @rdname column +#' @export +setGeneric("getField", function(x, ...) { standardGeneric("getField") }) + +#' @rdname column +#' @export +setGeneric("getItem", function(x, ...) { standardGeneric("getItem") }) + +#' @rdname column +#' @export +setGeneric("isNull", function(x) { standardGeneric("isNull") }) + +#' @rdname column +#' @export +setGeneric("isNotNull", function(x) { standardGeneric("isNotNull") }) + +#' @rdname column +#' @export +setGeneric("last", function(x) { standardGeneric("last") }) + +#' @rdname column +#' @export +setGeneric("like", function(x, ...) { standardGeneric("like") }) + +#' @rdname column +#' @export +setGeneric("lower", function(x) { standardGeneric("lower") }) + +#' @rdname column +#' @export +setGeneric("rlike", function(x, ...) { standardGeneric("rlike") }) + +#' @rdname column +#' @export +setGeneric("startsWith", function(x, ...) { standardGeneric("startsWith") }) + +#' @rdname column +#' @export +setGeneric("sumDistinct", function(x) { standardGeneric("sumDistinct") }) + +#' @rdname column +#' @export +setGeneric("upper", function(x) { standardGeneric("upper") }) + diff --git a/R/pkg/R/group.R b/R/pkg/R/group.R new file mode 100644 index 0000000000000..02237b3672d6b --- /dev/null +++ b/R/pkg/R/group.R @@ -0,0 +1,135 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# group.R - GroupedData class and methods implemented in S4 OO classes + +#' @include generics.R jobj.R schema.R column.R +NULL + +setOldClass("jobj") + +#' @title S4 class that represents a GroupedData +#' @description GroupedDatas can be created using groupBy() on a DataFrame +#' @rdname GroupedData +#' @seealso groupBy +#' +#' @param sgd A Java object reference to the backing Scala GroupedData +#' @export +setClass("GroupedData", + slots = list(sgd = "jobj")) + +setMethod("initialize", "GroupedData", function(.Object, sgd) { + .Object@sgd <- sgd + .Object +}) + +#' @rdname DataFrame +groupedData <- function(sgd) { + new("GroupedData", sgd) +} + + +#' @rdname show +setMethod("show", "GroupedData", + function(object) { + cat("GroupedData\n") + }) + +#' Count +#' +#' Count the number of rows for each group. +#' The resulting DataFrame will also contain the grouping columns. +#' +#' @param x a GroupedData +#' @return a DataFrame +#' @export +#' @examples +#' \dontrun{ +#' count(groupBy(df, "name")) +#' } +setMethod("count", + signature(x = "GroupedData"), + function(x) { + dataFrame(callJMethod(x@sgd, "count")) + }) + +#' Agg +#' +#' Aggregates on the entire DataFrame without groups. +#' The resulting DataFrame will also contain the grouping columns. +#' +#' df2 <- agg(df, = ) +#' df2 <- agg(df, newColName = aggFunction(column)) +#' +#' @param x a GroupedData +#' @return a DataFrame +#' @rdname agg +#' @examples +#' \dontrun{ +#' df2 <- agg(df, age = "sum") # new column name will be created as 'SUM(age#0)' +#' df2 <- agg(df, ageSum = sum(df$age)) # Creates a new column named ageSum +#' } +setGeneric("agg", function (x, ...) { standardGeneric("agg") }) + +setMethod("agg", + signature(x = "GroupedData"), + function(x, ...) { + cols = list(...) + stopifnot(length(cols) > 0) + if (is.character(cols[[1]])) { + cols <- varargsToEnv(...) + sdf <- callJMethod(x@sgd, "agg", cols) + } else if (class(cols[[1]]) == "Column") { + ns <- names(cols) + if (!is.null(ns)) { + for (n in ns) { + if (n != "") { + cols[[n]] = alias(cols[[n]], n) + } + } + } + jcols <- lapply(cols, function(c) { c@jc }) + # the GroupedData.agg(col, cols*) API does not contain grouping Column + sdf <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", "aggWithGrouping", + x@sgd, listToSeq(jcols)) + } else { + stop("agg can only support Column or character") + } + dataFrame(sdf) + }) + + +# sum/mean/avg/min/max +methods <- c("sum", "mean", "avg", "min", "max") + +createMethod <- function(name) { + setMethod(name, + signature(x = "GroupedData"), + function(x, ...) { + sdf <- callJMethod(x@sgd, name, toSeq(...)) + dataFrame(sdf) + }) +} + +createMethods <- function() { + for (name in methods) { + createMethod(name) + } +} + +createMethods() + diff --git a/R/pkg/R/jobj.R b/R/pkg/R/jobj.R new file mode 100644 index 0000000000000..a8a25230b636d --- /dev/null +++ b/R/pkg/R/jobj.R @@ -0,0 +1,104 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# References to objects that exist on the JVM backend +# are maintained using the jobj. + +#' @include generics.R +NULL + +# Maintain a reference count of Java object references +# This allows us to GC the java object when it is safe +.validJobjs <- new.env(parent = emptyenv()) + +# List of object ids to be removed +.toRemoveJobjs <- new.env(parent = emptyenv()) + +# Check if jobj was created with the current SparkContext +isValidJobj <- function(jobj) { + if (exists(".scStartTime", envir = .sparkREnv)) { + jobj$appId == get(".scStartTime", envir = .sparkREnv) + } else { + FALSE + } +} + +getJobj <- function(objId) { + newObj <- jobj(objId) + if (exists(objId, .validJobjs)) { + .validJobjs[[objId]] <- .validJobjs[[objId]] + 1 + } else { + .validJobjs[[objId]] <- 1 + } + newObj +} + +# Handler for a java object that exists on the backend. +jobj <- function(objId) { + if (!is.character(objId)) { + stop("object id must be a character") + } + # NOTE: We need a new env for a jobj as we can only register + # finalizers for environments or external references pointers. + obj <- structure(new.env(parent = emptyenv()), class = "jobj") + obj$id <- objId + obj$appId <- get(".scStartTime", envir = .sparkREnv) + + # Register a finalizer to remove the Java object when this reference + # is garbage collected in R + reg.finalizer(obj, cleanup.jobj) + obj +} + +#' Print a JVM object reference. +#' +#' This function prints the type and id for an object stored +#' in the SparkR JVM backend. +#' +#' @param x The JVM object reference +#' @param ... further arguments passed to or from other methods +print.jobj <- function(x, ...) { + cls <- callJMethod(x, "getClass") + name <- callJMethod(cls, "getName") + cat("Java ref type", name, "id", x$id, "\n", sep = " ") +} + +cleanup.jobj <- function(jobj) { + if (isValidJobj(jobj)) { + objId <- jobj$id + # If we don't know anything about this jobj, ignore it + if (exists(objId, envir = .validJobjs)) { + .validJobjs[[objId]] <- .validJobjs[[objId]] - 1 + + if (.validJobjs[[objId]] == 0) { + rm(list = objId, envir = .validJobjs) + # NOTE: We cannot call removeJObject here as the finalizer may be run + # in the middle of another RPC. Thus we queue up this object Id to be removed + # and then run all the removeJObject when the next RPC is called. + .toRemoveJobjs[[objId]] <- 1 + } + } + } +} + +clearJobjs <- function() { + valid <- ls(.validJobjs) + rm(list = valid, envir = .validJobjs) + + removeList <- ls(.toRemoveJobjs) + rm(list = removeList, envir = .toRemoveJobjs) +} diff --git a/R/pkg/R/pairRDD.R b/R/pkg/R/pairRDD.R new file mode 100644 index 0000000000000..13efebc11c46e --- /dev/null +++ b/R/pkg/R/pairRDD.R @@ -0,0 +1,909 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# Operations supported on RDDs contains pairs (i.e key, value) +#' @include generics.R jobj.R RDD.R +NULL + +############ Actions and Transformations ############ + +#' Look up elements of a key in an RDD +#' +#' @description +#' \code{lookup} returns a list of values in this RDD for key key. +#' +#' @param x The RDD to collect +#' @param key The key to look up for +#' @return a list of values in this RDD for key key +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' pairs <- list(c(1, 1), c(2, 2), c(1, 3)) +#' rdd <- parallelize(sc, pairs) +#' lookup(rdd, 1) # list(1, 3) +#'} +#' @rdname lookup +#' @aliases lookup,RDD-method +setMethod("lookup", + signature(x = "RDD", key = "ANY"), + function(x, key) { + partitionFunc <- function(part) { + filtered <- part[unlist(lapply(part, function(i) { identical(key, i[[1]]) }))] + lapply(filtered, function(i) { i[[2]] }) + } + valsRDD <- lapplyPartition(x, partitionFunc) + collect(valsRDD) + }) + +#' Count the number of elements for each key, and return the result to the +#' master as lists of (key, count) pairs. +#' +#' Same as countByKey in Spark. +#' +#' @param x The RDD to count keys. +#' @return list of (key, count) pairs, where count is number of each key in rdd. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(c("a", 1), c("b", 1), c("a", 1))) +#' countByKey(rdd) # ("a", 2L), ("b", 1L) +#'} +#' @rdname countByKey +#' @aliases countByKey,RDD-method +setMethod("countByKey", + signature(x = "RDD"), + function(x) { + keys <- lapply(x, function(item) { item[[1]] }) + countByValue(keys) + }) + +#' Return an RDD with the keys of each tuple. +#' +#' @param x The RDD from which the keys of each tuple is returned. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(list(1, 2), list(3, 4))) +#' collect(keys(rdd)) # list(1, 3) +#'} +#' @rdname keys +#' @aliases keys,RDD +setMethod("keys", + signature(x = "RDD"), + function(x) { + func <- function(k) { + k[[1]] + } + lapply(x, func) + }) + +#' Return an RDD with the values of each tuple. +#' +#' @param x The RDD from which the values of each tuple is returned. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(list(1, 2), list(3, 4))) +#' collect(values(rdd)) # list(2, 4) +#'} +#' @rdname values +#' @aliases values,RDD +setMethod("values", + signature(x = "RDD"), + function(x) { + func <- function(v) { + v[[2]] + } + lapply(x, func) + }) + +#' Applies a function to all values of the elements, without modifying the keys. +#' +#' The same as `mapValues()' in Spark. +#' +#' @param X The RDD to apply the transformation. +#' @param FUN the transformation to apply on the value of each element. +#' @return a new RDD created by the transformation. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' makePairs <- lapply(rdd, function(x) { list(x, x) }) +#' collect(mapValues(makePairs, function(x) { x * 2) }) +#' Output: list(list(1,2), list(2,4), list(3,6), ...) +#'} +#' @rdname mapValues +#' @aliases mapValues,RDD,function-method +setMethod("mapValues", + signature(X = "RDD", FUN = "function"), + function(X, FUN) { + func <- function(x) { + list(x[[1]], FUN(x[[2]])) + } + lapply(X, func) + }) + +#' Pass each value in the key-value pair RDD through a flatMap function without +#' changing the keys; this also retains the original RDD's partitioning. +#' +#' The same as 'flatMapValues()' in Spark. +#' +#' @param X The RDD to apply the transformation. +#' @param FUN the transformation to apply on the value of each element. +#' @return a new RDD created by the transformation. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(list(1, c(1,2)), list(2, c(3,4)))) +#' collect(flatMapValues(rdd, function(x) { x })) +#' Output: list(list(1,1), list(1,2), list(2,3), list(2,4)) +#'} +#' @rdname flatMapValues +#' @aliases flatMapValues,RDD,function-method +setMethod("flatMapValues", + signature(X = "RDD", FUN = "function"), + function(X, FUN) { + flatMapFunc <- function(x) { + lapply(FUN(x[[2]]), function(v) { list(x[[1]], v) }) + } + flatMap(X, flatMapFunc) + }) + +############ Shuffle Functions ############ + +#' Partition an RDD by key +#' +#' This function operates on RDDs where every element is of the form list(K, V) or c(K, V). +#' For each element of this RDD, the partitioner is used to compute a hash +#' function and the RDD is partitioned using this hash value. +#' +#' @param x The RDD to partition. Should be an RDD where each element is +#' list(K, V) or c(K, V). +#' @param numPartitions Number of partitions to create. +#' @param ... Other optional arguments to partitionBy. +#' +#' @param partitionFunc The partition function to use. Uses a default hashCode +#' function if not provided +#' @return An RDD partitioned using the specified partitioner. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' pairs <- list(list(1, 2), list(1.1, 3), list(1, 4)) +#' rdd <- parallelize(sc, pairs) +#' parts <- partitionBy(rdd, 2L) +#' collectPartition(parts, 0L) # First partition should contain list(1, 2) and list(1, 4) +#'} +#' @rdname partitionBy +#' @aliases partitionBy,RDD,integer-method +setMethod("partitionBy", + signature(x = "RDD", numPartitions = "integer"), + function(x, numPartitions, partitionFunc = hashCode) { + + #if (missing(partitionFunc)) { + # partitionFunc <- hashCode + #} + + partitionFunc <- cleanClosure(partitionFunc) + serializedHashFuncBytes <- serialize(partitionFunc, connection = NULL) + + packageNamesArr <- serialize(.sparkREnv$.packages, + connection = NULL) + broadcastArr <- lapply(ls(.broadcastNames), function(name) { + get(name, .broadcastNames) }) + jrdd <- getJRDD(x) + + # We create a PairwiseRRDD that extends RDD[(Array[Byte], + # Array[Byte])], where the key is the hashed split, the value is + # the content (key-val pairs). + pairwiseRRDD <- newJObject("org.apache.spark.api.r.PairwiseRRDD", + callJMethod(jrdd, "rdd"), + as.integer(numPartitions), + serializedHashFuncBytes, + getSerializedMode(x), + packageNamesArr, + as.character(.sparkREnv$libname), + broadcastArr, + callJMethod(jrdd, "classTag")) + + # Create a corresponding partitioner. + rPartitioner <- newJObject("org.apache.spark.HashPartitioner", + as.integer(numPartitions)) + + # Call partitionBy on the obtained PairwiseRDD. + javaPairRDD <- callJMethod(pairwiseRRDD, "asJavaPairRDD") + javaPairRDD <- callJMethod(javaPairRDD, "partitionBy", rPartitioner) + + # Call .values() on the result to get back the final result, the + # shuffled acutal content key-val pairs. + r <- callJMethod(javaPairRDD, "values") + + RDD(r, serializedMode = "byte") + }) + +#' Group values by key +#' +#' This function operates on RDDs where every element is of the form list(K, V) or c(K, V). +#' and group values for each key in the RDD into a single sequence. +#' +#' @param x The RDD to group. Should be an RDD where each element is +#' list(K, V) or c(K, V). +#' @param numPartitions Number of partitions to create. +#' @return An RDD where each element is list(K, list(V)) +#' @seealso reduceByKey +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' pairs <- list(list(1, 2), list(1.1, 3), list(1, 4)) +#' rdd <- parallelize(sc, pairs) +#' parts <- groupByKey(rdd, 2L) +#' grouped <- collect(parts) +#' grouped[[1]] # Should be a list(1, list(2, 4)) +#'} +#' @rdname groupByKey +#' @aliases groupByKey,RDD,integer-method +setMethod("groupByKey", + signature(x = "RDD", numPartitions = "integer"), + function(x, numPartitions) { + shuffled <- partitionBy(x, numPartitions) + groupVals <- function(part) { + vals <- new.env() + keys <- new.env() + pred <- function(item) exists(item$hash, keys) + appendList <- function(acc, i) { + addItemToAccumulator(acc, i) + acc + } + makeList <- function(i) { + acc <- initAccumulator() + addItemToAccumulator(acc, i) + acc + } + # Each item in the partition is list of (K, V) + lapply(part, + function(item) { + item$hash <- as.character(hashCode(item[[1]])) + updateOrCreatePair(item, keys, vals, pred, + appendList, makeList) + }) + # extract out data field + vals <- eapply(vals, + function(i) { + length(i$data) <- i$counter + i$data + }) + # Every key in the environment contains a list + # Convert that to list(K, Seq[V]) + convertEnvsToList(keys, vals) + } + lapplyPartition(shuffled, groupVals) + }) + +#' Merge values by key +#' +#' This function operates on RDDs where every element is of the form list(K, V) or c(K, V). +#' and merges the values for each key using an associative reduce function. +#' +#' @param x The RDD to reduce by key. Should be an RDD where each element is +#' list(K, V) or c(K, V). +#' @param combineFunc The associative reduce function to use. +#' @param numPartitions Number of partitions to create. +#' @return An RDD where each element is list(K, V') where V' is the merged +#' value +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' pairs <- list(list(1, 2), list(1.1, 3), list(1, 4)) +#' rdd <- parallelize(sc, pairs) +#' parts <- reduceByKey(rdd, "+", 2L) +#' reduced <- collect(parts) +#' reduced[[1]] # Should be a list(1, 6) +#'} +#' @rdname reduceByKey +#' @aliases reduceByKey,RDD,integer-method +setMethod("reduceByKey", + signature(x = "RDD", combineFunc = "ANY", numPartitions = "integer"), + function(x, combineFunc, numPartitions) { + reduceVals <- function(part) { + vals <- new.env() + keys <- new.env() + pred <- function(item) exists(item$hash, keys) + lapply(part, + function(item) { + item$hash <- as.character(hashCode(item[[1]])) + updateOrCreatePair(item, keys, vals, pred, combineFunc, identity) + }) + convertEnvsToList(keys, vals) + } + locallyReduced <- lapplyPartition(x, reduceVals) + shuffled <- partitionBy(locallyReduced, numPartitions) + lapplyPartition(shuffled, reduceVals) + }) + +#' Merge values by key locally +#' +#' This function operates on RDDs where every element is of the form list(K, V) or c(K, V). +#' and merges the values for each key using an associative reduce function, but return the +#' results immediately to the driver as an R list. +#' +#' @param x The RDD to reduce by key. Should be an RDD where each element is +#' list(K, V) or c(K, V). +#' @param combineFunc The associative reduce function to use. +#' @return A list of elements of type list(K, V') where V' is the merged value for each key +#' @seealso reduceByKey +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' pairs <- list(list(1, 2), list(1.1, 3), list(1, 4)) +#' rdd <- parallelize(sc, pairs) +#' reduced <- reduceByKeyLocally(rdd, "+") +#' reduced # list(list(1, 6), list(1.1, 3)) +#'} +#' @rdname reduceByKeyLocally +#' @aliases reduceByKeyLocally,RDD,integer-method +setMethod("reduceByKeyLocally", + signature(x = "RDD", combineFunc = "ANY"), + function(x, combineFunc) { + reducePart <- function(part) { + vals <- new.env() + keys <- new.env() + pred <- function(item) exists(item$hash, keys) + lapply(part, + function(item) { + item$hash <- as.character(hashCode(item[[1]])) + updateOrCreatePair(item, keys, vals, pred, combineFunc, identity) + }) + list(list(keys, vals)) # return hash to avoid re-compute in merge + } + mergeParts <- function(accum, x) { + pred <- function(item) { + exists(item$hash, accum[[1]]) + } + lapply(ls(x[[1]]), + function(name) { + item <- list(x[[1]][[name]], x[[2]][[name]]) + item$hash <- name + updateOrCreatePair(item, accum[[1]], accum[[2]], pred, combineFunc, identity) + }) + accum + } + reduced <- mapPartitions(x, reducePart) + merged <- reduce(reduced, mergeParts) + convertEnvsToList(merged[[1]], merged[[2]]) + }) + +#' Combine values by key +#' +#' Generic function to combine the elements for each key using a custom set of +#' aggregation functions. Turns an RDD[(K, V)] into a result of type RDD[(K, C)], +#' for a "combined type" C. Note that V and C can be different -- for example, one +#' might group an RDD of type (Int, Int) into an RDD of type (Int, Seq[Int]). + +#' Users provide three functions: +#' \itemize{ +#' \item createCombiner, which turns a V into a C (e.g., creates a one-element list) +#' \item mergeValue, to merge a V into a C (e.g., adds it to the end of a list) - +#' \item mergeCombiners, to combine two C's into a single one (e.g., concatentates +#' two lists). +#' } +#' +#' @param x The RDD to combine. Should be an RDD where each element is +#' list(K, V) or c(K, V). +#' @param createCombiner Create a combiner (C) given a value (V) +#' @param mergeValue Merge the given value (V) with an existing combiner (C) +#' @param mergeCombiners Merge two combiners and return a new combiner +#' @param numPartitions Number of partitions to create. +#' @return An RDD where each element is list(K, C) where C is the combined type +#' +#' @seealso groupByKey, reduceByKey +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' pairs <- list(list(1, 2), list(1.1, 3), list(1, 4)) +#' rdd <- parallelize(sc, pairs) +#' parts <- combineByKey(rdd, function(x) { x }, "+", "+", 2L) +#' combined <- collect(parts) +#' combined[[1]] # Should be a list(1, 6) +#'} +#' @rdname combineByKey +#' @aliases combineByKey,RDD,ANY,ANY,ANY,integer-method +setMethod("combineByKey", + signature(x = "RDD", createCombiner = "ANY", mergeValue = "ANY", + mergeCombiners = "ANY", numPartitions = "integer"), + function(x, createCombiner, mergeValue, mergeCombiners, numPartitions) { + combineLocally <- function(part) { + combiners <- new.env() + keys <- new.env() + pred <- function(item) exists(item$hash, keys) + lapply(part, + function(item) { + item$hash <- as.character(hashCode(item[[1]])) + updateOrCreatePair(item, keys, combiners, pred, mergeValue, createCombiner) + }) + convertEnvsToList(keys, combiners) + } + locallyCombined <- lapplyPartition(x, combineLocally) + shuffled <- partitionBy(locallyCombined, numPartitions) + mergeAfterShuffle <- function(part) { + combiners <- new.env() + keys <- new.env() + pred <- function(item) exists(item$hash, keys) + lapply(part, + function(item) { + item$hash <- as.character(hashCode(item[[1]])) + updateOrCreatePair(item, keys, combiners, pred, mergeCombiners, identity) + }) + convertEnvsToList(keys, combiners) + } + lapplyPartition(shuffled, mergeAfterShuffle) + }) + +#' Aggregate a pair RDD by each key. +#' +#' Aggregate the values of each key in an RDD, using given combine functions +#' and a neutral "zero value". This function can return a different result type, +#' U, than the type of the values in this RDD, V. Thus, we need one operation +#' for merging a V into a U and one operation for merging two U's, The former +#' operation is used for merging values within a partition, and the latter is +#' used for merging values between partitions. To avoid memory allocation, both +#' of these functions are allowed to modify and return their first argument +#' instead of creating a new U. +#' +#' @param x An RDD. +#' @param zeroValue A neutral "zero value". +#' @param seqOp A function to aggregate the values of each key. It may return +#' a different result type from the type of the values. +#' @param combOp A function to aggregate results of seqOp. +#' @return An RDD containing the aggregation result. +#' @seealso foldByKey, combineByKey +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(list(1, 1), list(1, 2), list(2, 3), list(2, 4))) +#' zeroValue <- list(0, 0) +#' seqOp <- function(x, y) { list(x[[1]] + y, x[[2]] + 1) } +#' combOp <- function(x, y) { list(x[[1]] + y[[1]], x[[2]] + y[[2]]) } +#' aggregateByKey(rdd, zeroValue, seqOp, combOp, 2L) +#' # list(list(1, list(3, 2)), list(2, list(7, 2))) +#'} +#' @rdname aggregateByKey +#' @aliases aggregateByKey,RDD,ANY,ANY,ANY,integer-method +setMethod("aggregateByKey", + signature(x = "RDD", zeroValue = "ANY", seqOp = "ANY", + combOp = "ANY", numPartitions = "integer"), + function(x, zeroValue, seqOp, combOp, numPartitions) { + createCombiner <- function(v) { + do.call(seqOp, list(zeroValue, v)) + } + + combineByKey(x, createCombiner, seqOp, combOp, numPartitions) + }) + +#' Fold a pair RDD by each key. +#' +#' Aggregate the values of each key in an RDD, using an associative function "func" +#' and a neutral "zero value" which may be added to the result an arbitrary +#' number of times, and must not change the result (e.g., 0 for addition, or +#' 1 for multiplication.). +#' +#' @param x An RDD. +#' @param zeroValue A neutral "zero value". +#' @param func An associative function for folding values of each key. +#' @return An RDD containing the aggregation result. +#' @seealso aggregateByKey, combineByKey +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(list(1, 1), list(1, 2), list(2, 3), list(2, 4))) +#' foldByKey(rdd, 0, "+", 2L) # list(list(1, 3), list(2, 7)) +#'} +#' @rdname foldByKey +#' @aliases foldByKey,RDD,ANY,ANY,integer-method +setMethod("foldByKey", + signature(x = "RDD", zeroValue = "ANY", + func = "ANY", numPartitions = "integer"), + function(x, zeroValue, func, numPartitions) { + aggregateByKey(x, zeroValue, func, func, numPartitions) + }) + +############ Binary Functions ############# + +#' Join two RDDs +#' +#' @description +#' \code{join} This function joins two RDDs where every element is of the form list(K, V). +#' The key types of the two RDDs should be the same. +#' +#' @param x An RDD to be joined. Should be an RDD where each element is +#' list(K, V). +#' @param y An RDD to be joined. Should be an RDD where each element is +#' list(K, V). +#' @param numPartitions Number of partitions to create. +#' @return a new RDD containing all pairs of elements with matching keys in +#' two input RDDs. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd1 <- parallelize(sc, list(list(1, 1), list(2, 4))) +#' rdd2 <- parallelize(sc, list(list(1, 2), list(1, 3))) +#' join(rdd1, rdd2, 2L) # list(list(1, list(1, 2)), list(1, list(1, 3)) +#'} +#' @rdname join-methods +#' @aliases join,RDD,RDD-method +setMethod("join", + signature(x = "RDD", y = "RDD"), + function(x, y, numPartitions) { + xTagged <- lapply(x, function(i) { list(i[[1]], list(1L, i[[2]])) }) + yTagged <- lapply(y, function(i) { list(i[[1]], list(2L, i[[2]])) }) + + doJoin <- function(v) { + joinTaggedList(v, list(FALSE, FALSE)) + } + + joined <- flatMapValues(groupByKey(unionRDD(xTagged, yTagged), numToInt(numPartitions)), + doJoin) + }) + +#' Left outer join two RDDs +#' +#' @description +#' \code{leftouterjoin} This function left-outer-joins two RDDs where every element is of the form list(K, V). +#' The key types of the two RDDs should be the same. +#' +#' @param x An RDD to be joined. Should be an RDD where each element is +#' list(K, V). +#' @param y An RDD to be joined. Should be an RDD where each element is +#' list(K, V). +#' @param numPartitions Number of partitions to create. +#' @return For each element (k, v) in x, the resulting RDD will either contain +#' all pairs (k, (v, w)) for (k, w) in rdd2, or the pair (k, (v, NULL)) +#' if no elements in rdd2 have key k. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd1 <- parallelize(sc, list(list(1, 1), list(2, 4))) +#' rdd2 <- parallelize(sc, list(list(1, 2), list(1, 3))) +#' leftOuterJoin(rdd1, rdd2, 2L) +#' # list(list(1, list(1, 2)), list(1, list(1, 3)), list(2, list(4, NULL))) +#'} +#' @rdname join-methods +#' @aliases leftOuterJoin,RDD,RDD-method +setMethod("leftOuterJoin", + signature(x = "RDD", y = "RDD", numPartitions = "integer"), + function(x, y, numPartitions) { + xTagged <- lapply(x, function(i) { list(i[[1]], list(1L, i[[2]])) }) + yTagged <- lapply(y, function(i) { list(i[[1]], list(2L, i[[2]])) }) + + doJoin <- function(v) { + joinTaggedList(v, list(FALSE, TRUE)) + } + + joined <- flatMapValues(groupByKey(unionRDD(xTagged, yTagged), numPartitions), doJoin) + }) + +#' Right outer join two RDDs +#' +#' @description +#' \code{rightouterjoin} This function right-outer-joins two RDDs where every element is of the form list(K, V). +#' The key types of the two RDDs should be the same. +#' +#' @param x An RDD to be joined. Should be an RDD where each element is +#' list(K, V). +#' @param y An RDD to be joined. Should be an RDD where each element is +#' list(K, V). +#' @param numPartitions Number of partitions to create. +#' @return For each element (k, w) in y, the resulting RDD will either contain +#' all pairs (k, (v, w)) for (k, v) in x, or the pair (k, (NULL, w)) +#' if no elements in x have key k. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd1 <- parallelize(sc, list(list(1, 2), list(1, 3))) +#' rdd2 <- parallelize(sc, list(list(1, 1), list(2, 4))) +#' rightOuterJoin(rdd1, rdd2, 2L) +#' # list(list(1, list(2, 1)), list(1, list(3, 1)), list(2, list(NULL, 4))) +#'} +#' @rdname join-methods +#' @aliases rightOuterJoin,RDD,RDD-method +setMethod("rightOuterJoin", + signature(x = "RDD", y = "RDD", numPartitions = "integer"), + function(x, y, numPartitions) { + xTagged <- lapply(x, function(i) { list(i[[1]], list(1L, i[[2]])) }) + yTagged <- lapply(y, function(i) { list(i[[1]], list(2L, i[[2]])) }) + + doJoin <- function(v) { + joinTaggedList(v, list(TRUE, FALSE)) + } + + joined <- flatMapValues(groupByKey(unionRDD(xTagged, yTagged), numPartitions), doJoin) + }) + +#' Full outer join two RDDs +#' +#' @description +#' \code{fullouterjoin} This function full-outer-joins two RDDs where every element is of the form list(K, V). +#' The key types of the two RDDs should be the same. +#' +#' @param x An RDD to be joined. Should be an RDD where each element is +#' list(K, V). +#' @param y An RDD to be joined. Should be an RDD where each element is +#' list(K, V). +#' @param numPartitions Number of partitions to create. +#' @return For each element (k, v) in x and (k, w) in y, the resulting RDD +#' will contain all pairs (k, (v, w)) for both (k, v) in x and +#' (k, w) in y, or the pair (k, (NULL, w))/(k, (v, NULL)) if no elements +#' in x/y have key k. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd1 <- parallelize(sc, list(list(1, 2), list(1, 3), list(3, 3))) +#' rdd2 <- parallelize(sc, list(list(1, 1), list(2, 4))) +#' fullOuterJoin(rdd1, rdd2, 2L) # list(list(1, list(2, 1)), +#' # list(1, list(3, 1)), +#' # list(2, list(NULL, 4))) +#' # list(3, list(3, NULL)), +#'} +#' @rdname join-methods +#' @aliases fullOuterJoin,RDD,RDD-method +setMethod("fullOuterJoin", + signature(x = "RDD", y = "RDD", numPartitions = "integer"), + function(x, y, numPartitions) { + xTagged <- lapply(x, function(i) { list(i[[1]], list(1L, i[[2]])) }) + yTagged <- lapply(y, function(i) { list(i[[1]], list(2L, i[[2]])) }) + + doJoin <- function(v) { + joinTaggedList(v, list(TRUE, TRUE)) + } + + joined <- flatMapValues(groupByKey(unionRDD(xTagged, yTagged), numPartitions), doJoin) + }) + +#' For each key k in several RDDs, return a resulting RDD that +#' whose values are a list of values for the key in all RDDs. +#' +#' @param ... Several RDDs. +#' @param numPartitions Number of partitions to create. +#' @return a new RDD containing all pairs of elements with values in a list +#' in all RDDs. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd1 <- parallelize(sc, list(list(1, 1), list(2, 4))) +#' rdd2 <- parallelize(sc, list(list(1, 2), list(1, 3))) +#' cogroup(rdd1, rdd2, numPartitions = 2L) +#' # list(list(1, list(1, list(2, 3))), list(2, list(list(4), list())) +#'} +#' @rdname cogroup +#' @aliases cogroup,RDD-method +setMethod("cogroup", + "RDD", + function(..., numPartitions) { + rdds <- list(...) + rddsLen <- length(rdds) + for (i in 1:rddsLen) { + rdds[[i]] <- lapply(rdds[[i]], + function(x) { list(x[[1]], list(i, x[[2]])) }) + } + union.rdd <- Reduce(unionRDD, rdds) + group.func <- function(vlist) { + res <- list() + length(res) <- rddsLen + for (x in vlist) { + i <- x[[1]] + acc <- res[[i]] + # Create an accumulator. + if (is.null(acc)) { + acc <- initAccumulator() + } + addItemToAccumulator(acc, x[[2]]) + res[[i]] <- acc + } + lapply(res, function(acc) { + if (is.null(acc)) { + list() + } else { + acc$data + } + }) + } + cogroup.rdd <- mapValues(groupByKey(union.rdd, numPartitions), + group.func) + }) + +#' Sort a (k, v) pair RDD by k. +#' +#' @param x A (k, v) pair RDD to be sorted. +#' @param ascending A flag to indicate whether the sorting is ascending or descending. +#' @param numPartitions Number of partitions to create. +#' @return An RDD where all (k, v) pair elements are sorted. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(list(3, 1), list(2, 2), list(1, 3))) +#' collect(sortByKey(rdd)) # list (list(1, 3), list(2, 2), list(3, 1)) +#'} +#' @rdname sortByKey +#' @aliases sortByKey,RDD,RDD-method +setMethod("sortByKey", + signature(x = "RDD"), + function(x, ascending = TRUE, numPartitions = SparkR::numPartitions(x)) { + rangeBounds <- list() + + if (numPartitions > 1) { + rddSize <- count(x) + # constant from Spark's RangePartitioner + maxSampleSize <- numPartitions * 20 + fraction <- min(maxSampleSize / max(rddSize, 1), 1.0) + + samples <- collect(keys(sampleRDD(x, FALSE, fraction, 1L))) + + # Note: the built-in R sort() function only works on atomic vectors + samples <- sort(unlist(samples, recursive = FALSE), decreasing = !ascending) + + if (length(samples) > 0) { + rangeBounds <- lapply(seq_len(numPartitions - 1), + function(i) { + j <- ceiling(length(samples) * i / numPartitions) + samples[j] + }) + } + } + + rangePartitionFunc <- function(key) { + partition <- 0 + + # TODO: Use binary search instead of linear search, similar with Spark + while (partition < length(rangeBounds) && key > rangeBounds[[partition + 1]]) { + partition <- partition + 1 + } + + if (ascending) { + partition + } else { + numPartitions - partition - 1 + } + } + + partitionFunc <- function(part) { + sortKeyValueList(part, decreasing = !ascending) + } + + newRDD <- partitionBy(x, numPartitions, rangePartitionFunc) + lapplyPartition(newRDD, partitionFunc) + }) + +#' Subtract a pair RDD with another pair RDD. +#' +#' Return an RDD with the pairs from x whose keys are not in other. +#' +#' @param x An RDD. +#' @param other An RDD. +#' @param numPartitions Number of the partitions in the result RDD. +#' @return An RDD with the pairs from x whose keys are not in other. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd1 <- parallelize(sc, list(list("a", 1), list("b", 4), +#' list("b", 5), list("a", 2))) +#' rdd2 <- parallelize(sc, list(list("a", 3), list("c", 1))) +#' collect(subtractByKey(rdd1, rdd2)) +#' # list(list("b", 4), list("b", 5)) +#'} +#' @rdname subtractByKey +#' @aliases subtractByKey,RDD +setMethod("subtractByKey", + signature(x = "RDD", other = "RDD"), + function(x, other, numPartitions = SparkR::numPartitions(x)) { + filterFunction <- function(elem) { + iters <- elem[[2]] + (length(iters[[1]]) > 0) && (length(iters[[2]]) == 0) + } + + flatMapValues(filterRDD(cogroup(x, + other, + numPartitions = numPartitions), + filterFunction), + function (v) { v[[1]] }) + }) + +#' Return a subset of this RDD sampled by key. +#' +#' @description +#' \code{sampleByKey} Create a sample of this RDD using variable sampling rates +#' for different keys as specified by fractions, a key to sampling rate map. +#' +#' @param x The RDD to sample elements by key, where each element is +#' list(K, V) or c(K, V). +#' @param withReplacement Sampling with replacement or not +#' @param fraction The (rough) sample target fraction +#' @param seed Randomness seed value +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:3000) +#' pairs <- lapply(rdd, function(x) { if (x %% 3 == 0) list("a", x) +#' else { if (x %% 3 == 1) list("b", x) else list("c", x) }}) +#' fractions <- list(a = 0.2, b = 0.1, c = 0.3) +#' sample <- sampleByKey(pairs, FALSE, fractions, 1618L) +#' 100 < length(lookup(sample, "a")) && 300 > length(lookup(sample, "a")) # TRUE +#' 50 < length(lookup(sample, "b")) && 150 > length(lookup(sample, "b")) # TRUE +#' 200 < length(lookup(sample, "c")) && 400 > length(lookup(sample, "c")) # TRUE +#' lookup(sample, "a")[which.min(lookup(sample, "a"))] >= 0 # TRUE +#' lookup(sample, "a")[which.max(lookup(sample, "a"))] <= 2000 # TRUE +#' lookup(sample, "b")[which.min(lookup(sample, "b"))] >= 0 # TRUE +#' lookup(sample, "b")[which.max(lookup(sample, "b"))] <= 2000 # TRUE +#' lookup(sample, "c")[which.min(lookup(sample, "c"))] >= 0 # TRUE +#' lookup(sample, "c")[which.max(lookup(sample, "c"))] <= 2000 # TRUE +#' fractions <- list(a = 0.2, b = 0.1, c = 0.3, d = 0.4) +#' sample <- sampleByKey(pairs, FALSE, fractions, 1618L) # Key "d" will be ignored +#' fractions <- list(a = 0.2, b = 0.1) +#' sample <- sampleByKey(pairs, FALSE, fractions, 1618L) # KeyError: "c" +#'} +#' @rdname sampleByKey +#' @aliases sampleByKey,RDD-method +setMethod("sampleByKey", + signature(x = "RDD", withReplacement = "logical", + fractions = "vector", seed = "integer"), + function(x, withReplacement, fractions, seed) { + + for (elem in fractions) { + if (elem < 0.0) { + stop(paste("Negative fraction value ", fractions[which(fractions == elem)])) + } + } + + # The sampler: takes a partition and returns its sampled version. + samplingFunc <- function(split, part) { + set.seed(bitwXor(seed, split)) + res <- vector("list", length(part)) + len <- 0 + + # mixing because the initial seeds are close to each other + runif(10) + + for (elem in part) { + if (elem[[1]] %in% names(fractions)) { + frac <- as.numeric(fractions[which(elem[[1]] == names(fractions))]) + if (withReplacement) { + count <- rpois(1, frac) + if (count > 0) { + res[(len + 1):(len + count)] <- rep(list(elem), count) + len <- len + count + } + } else { + if (runif(1) < frac) { + len <- len + 1 + res[[len]] <- elem + } + } + } else { + stop("KeyError: \"", elem[[1]], "\"") + } + } + + # TODO(zongheng): look into the performance of the current + # implementation. Look into some iterator package? Note that + # Scala avoids many calls to creating an empty list and PySpark + # similarly achieves this using `yield'. (duplicated from sampleRDD) + if (len > 0) { + res[1:len] + } else { + list() + } + } + + lapplyPartitionsWithIndex(x, samplingFunc) + }) diff --git a/R/pkg/R/schema.R b/R/pkg/R/schema.R new file mode 100644 index 0000000000000..e442119086b17 --- /dev/null +++ b/R/pkg/R/schema.R @@ -0,0 +1,162 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# A set of S3 classes and methods that support the SparkSQL `StructType` and `StructField +# datatypes. These are used to create and interact with DataFrame schemas. + +#' structType +#' +#' Create a structType object that contains the metadata for a DataFrame. Intended for +#' use with createDataFrame and toDF. +#' +#' @param x a structField object (created with the field() function) +#' @param ... additional structField objects +#' @return a structType object +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' rdd <- lapply(parallelize(sc, 1:10), function(x) { list(x, as.character(x)) }) +#' schema <- structType(structField("a", "integer"), structField("b", "string")) +#' df <- createDataFrame(sqlCtx, rdd, schema) +#' } +structType <- function(x, ...) { + UseMethod("structType", x) +} + +structType.jobj <- function(x) { + obj <- structure(list(), class = "structType") + obj$jobj <- x + obj$fields <- function() { lapply(callJMethod(obj$jobj, "fields"), structField) } + obj +} + +structType.structField <- function(x, ...) { + fields <- list(x, ...) + if (!all(sapply(fields, inherits, "structField"))) { + stop("All arguments must be structField objects.") + } + sfObjList <- lapply(fields, function(field) { + field$jobj + }) + stObj <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", + "createStructType", + listToSeq(sfObjList)) + structType(stObj) +} + +#' Print a Spark StructType. +#' +#' This function prints the contents of a StructType returned from the +#' SparkR JVM backend. +#' +#' @param x A StructType object +#' @param ... further arguments passed to or from other methods +print.structType <- function(x, ...) { + cat("StructType\n", + sapply(x$fields(), function(field) { paste("|-", "name = \"", field$name(), + "\", type = \"", field$dataType.toString(), + "\", nullable = ", field$nullable(), "\n", + sep = "") }) + , sep = "") +} + +#' structField +#' +#' Create a structField object that contains the metadata for a single field in a schema. +#' +#' @param x The name of the field +#' @param type The data type of the field +#' @param nullable A logical vector indicating whether or not the field is nullable +#' @return a structField object +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' rdd <- lapply(parallelize(sc, 1:10), function(x) { list(x, as.character(x)) }) +#' field1 <- structField("a", "integer", TRUE) +#' field2 <- structField("b", "string", TRUE) +#' schema <- structType(field1, field2) +#' df <- createDataFrame(sqlCtx, rdd, schema) +#' } + +structField <- function(x, ...) { + UseMethod("structField", x) +} + +structField.jobj <- function(x) { + obj <- structure(list(), class = "structField") + obj$jobj <- x + obj$name <- function() { callJMethod(x, "name") } + obj$dataType <- function() { callJMethod(x, "dataType") } + obj$dataType.toString <- function() { callJMethod(obj$dataType(), "toString") } + obj$dataType.simpleString <- function() { callJMethod(obj$dataType(), "simpleString") } + obj$nullable <- function() { callJMethod(x, "nullable") } + obj +} + +structField.character <- function(x, type, nullable = TRUE) { + if (class(x) != "character") { + stop("Field name must be a string.") + } + if (class(type) != "character") { + stop("Field type must be a string.") + } + if (class(nullable) != "logical") { + stop("nullable must be either TRUE or FALSE") + } + options <- c("byte", + "integer", + "double", + "numeric", + "character", + "string", + "binary", + "raw", + "logical", + "boolean", + "timestamp", + "date") + dataType <- if (type %in% options) { + type + } else { + stop(paste("Unsupported type for Dataframe:", type)) + } + sfObj <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", + "createStructField", + x, + dataType, + nullable) + structField(sfObj) +} + +#' Print a Spark StructField. +#' +#' This function prints the contents of a StructField returned from the +#' SparkR JVM backend. +#' +#' @param x A StructField object +#' @param ... further arguments passed to or from other methods +print.structField <- function(x, ...) { + cat("StructField(name = \"", x$name(), + "\", type = \"", x$dataType.toString(), + "\", nullable = ", x$nullable(), + ")", + sep = "") +} diff --git a/R/pkg/R/serialize.R b/R/pkg/R/serialize.R new file mode 100644 index 0000000000000..c53d0a961016f --- /dev/null +++ b/R/pkg/R/serialize.R @@ -0,0 +1,192 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# Utility functions to serialize R objects so they can be read in Java. + +# Type mapping from R to Java +# +# NULL -> Void +# integer -> Int +# character -> String +# logical -> Boolean +# double, numeric -> Double +# raw -> Array[Byte] +# Date -> Date +# POSIXct,POSIXlt -> Time +# +# list[T] -> Array[T], where T is one of above mentioned types +# environment -> Map[String, T], where T is a native type +# jobj -> Object, where jobj is an object created in the backend + +writeObject <- function(con, object, writeType = TRUE) { + # NOTE: In R vectors have same type as objects. So we don't support + # passing in vectors as arrays and instead require arrays to be passed + # as lists. + type <- class(object)[[1]] # class of POSIXlt is c("POSIXlt", "POSIXt") + if (writeType) { + writeType(con, type) + } + switch(type, + NULL = writeVoid(con), + integer = writeInt(con, object), + character = writeString(con, object), + logical = writeBoolean(con, object), + double = writeDouble(con, object), + numeric = writeDouble(con, object), + raw = writeRaw(con, object), + list = writeList(con, object), + jobj = writeJobj(con, object), + environment = writeEnv(con, object), + Date = writeDate(con, object), + POSIXlt = writeTime(con, object), + POSIXct = writeTime(con, object), + stop(paste("Unsupported type for serialization", type))) +} + +writeVoid <- function(con) { + # no value for NULL +} + +writeJobj <- function(con, value) { + if (!isValidJobj(value)) { + stop("invalid jobj ", value$id) + } + writeString(con, value$id) +} + +writeString <- function(con, value) { + utfVal <- enc2utf8(value) + writeInt(con, as.integer(nchar(utfVal, type = "bytes") + 1)) + writeBin(utfVal, con, endian = "big") +} + +writeInt <- function(con, value) { + writeBin(as.integer(value), con, endian = "big") +} + +writeDouble <- function(con, value) { + writeBin(value, con, endian = "big") +} + +writeBoolean <- function(con, value) { + # TRUE becomes 1, FALSE becomes 0 + writeInt(con, as.integer(value)) +} + +writeRawSerialize <- function(outputCon, batch) { + outputSer <- serialize(batch, ascii = FALSE, connection = NULL) + writeRaw(outputCon, outputSer) +} + +writeRowSerialize <- function(outputCon, rows) { + invisible(lapply(rows, function(r) { + bytes <- serializeRow(r) + writeRaw(outputCon, bytes) + })) +} + +serializeRow <- function(row) { + rawObj <- rawConnection(raw(0), "wb") + on.exit(close(rawObj)) + writeRow(rawObj, row) + rawConnectionValue(rawObj) +} + +writeRow <- function(con, row) { + numCols <- length(row) + writeInt(con, numCols) + for (i in 1:numCols) { + writeObject(con, row[[i]]) + } +} + +writeRaw <- function(con, batch) { + writeInt(con, length(batch)) + writeBin(batch, con, endian = "big") +} + +writeType <- function(con, class) { + type <- switch(class, + NULL = "n", + integer = "i", + character = "c", + logical = "b", + double = "d", + numeric = "d", + raw = "r", + list = "l", + jobj = "j", + environment = "e", + Date = "D", + POSIXlt = 't', + POSIXct = 't', + stop(paste("Unsupported type for serialization", class))) + writeBin(charToRaw(type), con) +} + +# Used to pass arrays where all the elements are of the same type +writeList <- function(con, arr) { + # All elements should be of same type + elemType <- unique(sapply(arr, function(elem) { class(elem) })) + stopifnot(length(elemType) <= 1) + + # TODO: Empty lists are given type "character" right now. + # This may not work if the Java side expects array of any other type. + if (length(elemType) == 0) { + elemType <- class("somestring") + } + + writeType(con, elemType) + writeInt(con, length(arr)) + + if (length(arr) > 0) { + for (a in arr) { + writeObject(con, a, FALSE) + } + } +} + +# Used to pass in hash maps required on Java side. +writeEnv <- function(con, env) { + len <- length(env) + + writeInt(con, len) + if (len > 0) { + writeList(con, as.list(ls(env))) + vals <- lapply(ls(env), function(x) { env[[x]] }) + writeList(con, as.list(vals)) + } +} + +writeDate <- function(con, date) { + writeString(con, as.character(date)) +} + +writeTime <- function(con, time) { + writeDouble(con, as.double(time)) +} + +# Used to serialize in a list of objects where each +# object can be of a different type. Serialization format is +# for each object +writeArgs <- function(con, args) { + if (length(args) > 0) { + for (a in args) { + writeObject(con, a) + } + } +} diff --git a/R/pkg/R/sparkR.R b/R/pkg/R/sparkR.R new file mode 100644 index 0000000000000..bc82df01f0fff --- /dev/null +++ b/R/pkg/R/sparkR.R @@ -0,0 +1,266 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +.sparkREnv <- new.env() + +sparkR.onLoad <- function(libname, pkgname) { + .sparkREnv$libname <- libname +} + +# Utility function that returns TRUE if we have an active connection to the +# backend and FALSE otherwise +connExists <- function(env) { + tryCatch({ + exists(".sparkRCon", envir = env) && isOpen(env[[".sparkRCon"]]) + }, error = function(err) { + return(FALSE) + }) +} + +#' Stop the Spark context. +#' +#' Also terminates the backend this R session is connected to +sparkR.stop <- function() { + env <- .sparkREnv + if (exists(".sparkRCon", envir = env)) { + # cat("Stopping SparkR\n") + if (exists(".sparkRjsc", envir = env)) { + sc <- get(".sparkRjsc", envir = env) + callJMethod(sc, "stop") + rm(".sparkRjsc", envir = env) + } + + if (exists(".backendLaunched", envir = env)) { + callJStatic("SparkRHandler", "stopBackend") + } + + # Also close the connection and remove it from our env + conn <- get(".sparkRCon", envir = env) + close(conn) + + rm(".sparkRCon", envir = env) + rm(".scStartTime", envir = env) + } + + if (exists(".monitorConn", envir = env)) { + conn <- get(".monitorConn", envir = env) + close(conn) + rm(".monitorConn", envir = env) + } + + # Clear all broadcast variables we have + # as the jobj will not be valid if we restart the JVM + clearBroadcastVariables() + + # Clear jobj maps + clearJobjs() +} + +#' Initialize a new Spark Context. +#' +#' This function initializes a new SparkContext. +#' +#' @param master The Spark master URL. +#' @param appName Application name to register with cluster manager +#' @param sparkHome Spark Home directory +#' @param sparkEnvir Named list of environment variables to set on worker nodes. +#' @param sparkExecutorEnv Named list of environment variables to be used when launching executors. +#' @param sparkJars Character string vector of jar files to pass to the worker nodes. +#' @param sparkRLibDir The path where R is installed on the worker nodes. +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init("local[2]", "SparkR", "/home/spark") +#' sc <- sparkR.init("local[2]", "SparkR", "/home/spark", +#' list(spark.executor.memory="1g")) +#' sc <- sparkR.init("yarn-client", "SparkR", "/home/spark", +#' list(spark.executor.memory="1g"), +#' list(LD_LIBRARY_PATH="/directory of JVM libraries (libjvm.so) on workers/"), +#' c("jarfile1.jar","jarfile2.jar")) +#'} + +sparkR.init <- function( + master = "", + appName = "SparkR", + sparkHome = Sys.getenv("SPARK_HOME"), + sparkEnvir = list(), + sparkExecutorEnv = list(), + sparkJars = "", + sparkRLibDir = "") { + + if (exists(".sparkRjsc", envir = .sparkREnv)) { + cat("Re-using existing Spark Context. Please stop SparkR with sparkR.stop() or restart R to create a new Spark Context\n") + return(get(".sparkRjsc", envir = .sparkREnv)) + } + + sparkMem <- Sys.getenv("SPARK_MEM", "512m") + jars <- suppressWarnings(normalizePath(as.character(sparkJars))) + + # Classpath separator is ";" on Windows + # URI needs four /// as from http://stackoverflow.com/a/18522792 + if (.Platform$OS.type == "unix") { + collapseChar <- ":" + uriSep <- "//" + } else { + collapseChar <- ";" + uriSep <- "////" + } + + existingPort <- Sys.getenv("EXISTING_SPARKR_BACKEND_PORT", "") + if (existingPort != "") { + backendPort <- existingPort + } else { + path <- tempfile(pattern = "backend_port") + launchBackend( + args = path, + sparkHome = sparkHome, + jars = jars, + sparkSubmitOpts = Sys.getenv("SPARKR_SUBMIT_ARGS", "sparkr-shell")) + # wait atmost 100 seconds for JVM to launch + wait <- 0.1 + for (i in 1:25) { + Sys.sleep(wait) + if (file.exists(path)) { + break + } + wait <- wait * 1.25 + } + if (!file.exists(path)) { + stop("JVM is not ready after 10 seconds") + } + f <- file(path, open='rb') + backendPort <- readInt(f) + monitorPort <- readInt(f) + close(f) + file.remove(path) + if (length(backendPort) == 0 || backendPort == 0 || + length(monitorPort) == 0 || monitorPort == 0) { + stop("JVM failed to launch") + } + assign(".monitorConn", socketConnection(port = monitorPort), envir = .sparkREnv) + assign(".backendLaunched", 1, envir = .sparkREnv) + } + + .sparkREnv$backendPort <- backendPort + tryCatch({ + connectBackend("localhost", backendPort) + }, error = function(err) { + stop("Failed to connect JVM\n") + }) + + if (nchar(sparkHome) != 0) { + sparkHome <- normalizePath(sparkHome) + } + + if (nchar(sparkRLibDir) != 0) { + .sparkREnv$libname <- sparkRLibDir + } + + sparkEnvirMap <- new.env() + for (varname in names(sparkEnvir)) { + sparkEnvirMap[[varname]] <- sparkEnvir[[varname]] + } + + sparkExecutorEnvMap <- new.env() + if (!any(names(sparkExecutorEnv) == "LD_LIBRARY_PATH")) { + sparkExecutorEnvMap[["LD_LIBRARY_PATH"]] <- paste0("$LD_LIBRARY_PATH:",Sys.getenv("LD_LIBRARY_PATH")) + } + for (varname in names(sparkExecutorEnv)) { + sparkExecutorEnvMap[[varname]] <- sparkExecutorEnv[[varname]] + } + + nonEmptyJars <- Filter(function(x) { x != "" }, jars) + localJarPaths <- sapply(nonEmptyJars, function(j) { utils::URLencode(paste("file:", uriSep, j, sep = "")) }) + + # Set the start time to identify jobjs + # Seconds resolution is good enough for this purpose, so use ints + assign(".scStartTime", as.integer(Sys.time()), envir = .sparkREnv) + + assign( + ".sparkRjsc", + callJStatic( + "org.apache.spark.api.r.RRDD", + "createSparkContext", + master, + appName, + as.character(sparkHome), + as.list(localJarPaths), + sparkEnvirMap, + sparkExecutorEnvMap), + envir = .sparkREnv + ) + + sc <- get(".sparkRjsc", envir = .sparkREnv) + + # Register a finalizer to sleep 1 seconds on R exit to make RStudio happy + reg.finalizer(.sparkREnv, function(x) { Sys.sleep(1) }, onexit = TRUE) + + sc +} + +#' Initialize a new SQLContext. +#' +#' This function creates a SparkContext from an existing JavaSparkContext and +#' then uses it to initialize a new SQLContext +#' +#' @param jsc The existing JavaSparkContext created with SparkR.init() +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#'} + +sparkRSQL.init <- function(jsc) { + if (exists(".sparkRSQLsc", envir = .sparkREnv)) { + return(get(".sparkRSQLsc", envir = .sparkREnv)) + } + + sqlCtx <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", + "createSQLContext", + jsc) + assign(".sparkRSQLsc", sqlCtx, envir = .sparkREnv) + sqlCtx +} + +#' Initialize a new HiveContext. +#' +#' This function creates a HiveContext from an existing JavaSparkContext +#' +#' @param jsc The existing JavaSparkContext created with SparkR.init() +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRHive.init(sc) +#'} + +sparkRHive.init <- function(jsc) { + if (exists(".sparkRHivesc", envir = .sparkREnv)) { + return(get(".sparkRHivesc", envir = .sparkREnv)) + } + + ssc <- callJMethod(jsc, "sc") + hiveCtx <- tryCatch({ + newJObject("org.apache.spark.sql.hive.HiveContext", ssc) + }, error = function(err) { + stop("Spark SQL is not built with Hive support") + }) + + assign(".sparkRHivesc", hiveCtx, envir = .sparkREnv) + hiveCtx +} diff --git a/R/pkg/R/utils.R b/R/pkg/R/utils.R new file mode 100644 index 0000000000000..23305d3c67074 --- /dev/null +++ b/R/pkg/R/utils.R @@ -0,0 +1,547 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# Utilities and Helpers + +# Given a JList, returns an R list containing the same elements, the number +# of which is optionally upper bounded by `logicalUpperBound` (by default, +# return all elements). Takes care of deserializations and type conversions. +convertJListToRList <- function(jList, flatten, logicalUpperBound = NULL, + serializedMode = "byte") { + arrSize <- callJMethod(jList, "size") + + # Datasets with serializedMode == "string" (such as an RDD directly generated by textFile()): + # each partition is not dense-packed into one Array[Byte], and `arrSize` + # here corresponds to number of logical elements. Thus we can prune here. + if (serializedMode == "string" && !is.null(logicalUpperBound)) { + arrSize <- min(arrSize, logicalUpperBound) + } + + results <- if (arrSize > 0) { + lapply(0:(arrSize - 1), + function(index) { + obj <- callJMethod(jList, "get", as.integer(index)) + + # Assume it is either an R object or a Java obj ref. + if (inherits(obj, "jobj")) { + if (isInstanceOf(obj, "scala.Tuple2")) { + # JavaPairRDD[Array[Byte], Array[Byte]]. + + keyBytes = callJMethod(obj, "_1") + valBytes = callJMethod(obj, "_2") + res <- list(unserialize(keyBytes), + unserialize(valBytes)) + } else { + stop(paste("utils.R: convertJListToRList only supports", + "RDD[Array[Byte]] and", + "JavaPairRDD[Array[Byte], Array[Byte]] for now")) + } + } else { + if (inherits(obj, "raw")) { + if (serializedMode == "byte") { + # RDD[Array[Byte]]. `obj` is a whole partition. + res <- unserialize(obj) + # For serialized datasets, `obj` (and `rRaw`) here corresponds to + # one whole partition dense-packed together. We deserialize the + # whole partition first, then cap the number of elements to be returned. + } else if (serializedMode == "row") { + res <- readRowList(obj) + # For DataFrames that have been converted to RRDDs, we call readRowList + # which will read in each row of the RRDD as a list and deserialize + # each element. + flatten <<- FALSE + # Use global assignment to change the flatten flag. This means + # we don't have to worry about the default argument in other functions + # e.g. collect + } + # TODO: is it possible to distinguish element boundary so that we can + # unserialize only what we need? + if (!is.null(logicalUpperBound)) { + res <- head(res, n = logicalUpperBound) + } + } else { + # obj is of a primitive Java type, is simplified to R's + # corresponding type. + res <- list(obj) + } + } + res + }) + } else { + list() + } + + if (flatten) { + as.list(unlist(results, recursive = FALSE)) + } else { + as.list(results) + } +} + +# Returns TRUE if `name` refers to an RDD in the given environment `env` +isRDD <- function(name, env) { + obj <- get(name, envir = env) + inherits(obj, "RDD") +} + +#' Compute the hashCode of an object +#' +#' Java-style function to compute the hashCode for the given object. Returns +#' an integer value. +#' +#' @details +#' This only works for integer, numeric and character types right now. +#' +#' @param key the object to be hashed +#' @return the hash code as an integer +#' @export +#' @examples +#' hashCode(1L) # 1 +#' hashCode(1.0) # 1072693248 +#' hashCode("1") # 49 +hashCode <- function(key) { + if (class(key) == "integer") { + as.integer(key[[1]]) + } else if (class(key) == "numeric") { + # Convert the double to long and then calculate the hash code + rawVec <- writeBin(key[[1]], con = raw()) + intBits <- packBits(rawToBits(rawVec), "integer") + as.integer(bitwXor(intBits[2], intBits[1])) + } else if (class(key) == "character") { + .Call("stringHashCode", key) + } else { + warning(paste("Could not hash object, returning 0", sep = "")) + as.integer(0) + } +} + +# Create a new RDD with serializedMode == "byte". +# Return itself if already in "byte" format. +serializeToBytes <- function(rdd) { + if (!inherits(rdd, "RDD")) { + stop("Argument 'rdd' is not an RDD type.") + } + if (getSerializedMode(rdd) != "byte") { + ser.rdd <- lapply(rdd, function(x) { x }) + return(ser.rdd) + } else { + return(rdd) + } +} + +# Create a new RDD with serializedMode == "string". +# Return itself if already in "string" format. +serializeToString <- function(rdd) { + if (!inherits(rdd, "RDD")) { + stop("Argument 'rdd' is not an RDD type.") + } + if (getSerializedMode(rdd) != "string") { + ser.rdd <- lapply(rdd, function(x) { toString(x) }) + # force it to create jrdd using "string" + getJRDD(ser.rdd, serializedMode = "string") + return(ser.rdd) + } else { + return(rdd) + } +} + +# Fast append to list by using an accumulator. +# http://stackoverflow.com/questions/17046336/here-we-go-again-append-an-element-to-a-list-in-r +# +# The accumulator should has three fields size, counter and data. +# This function amortizes the allocation cost by doubling +# the size of the list every time it fills up. +addItemToAccumulator <- function(acc, item) { + if(acc$counter == acc$size) { + acc$size <- acc$size * 2 + length(acc$data) <- acc$size + } + acc$counter <- acc$counter + 1 + acc$data[[acc$counter]] <- item +} + +initAccumulator <- function() { + acc <- new.env() + acc$counter <- 0 + acc$data <- list(NULL) + acc$size <- 1 + acc +} + +# Utility function to sort a list of key value pairs +# Used in unit tests +sortKeyValueList <- function(kv_list, decreasing = FALSE) { + keys <- sapply(kv_list, function(x) x[[1]]) + kv_list[order(keys, decreasing = decreasing)] +} + +# Utility function to generate compact R lists from grouped rdd +# Used in Join-family functions +# param: +# tagged_list R list generated via groupByKey with tags(1L, 2L, ...) +# cnull Boolean list where each element determines whether the corresponding list should +# be converted to list(NULL) +genCompactLists <- function(tagged_list, cnull) { + len <- length(tagged_list) + lists <- list(vector("list", len), vector("list", len)) + index <- list(1, 1) + + for (x in tagged_list) { + tag <- x[[1]] + idx <- index[[tag]] + lists[[tag]][[idx]] <- x[[2]] + index[[tag]] <- idx + 1 + } + + len <- lapply(index, function(x) x - 1) + for (i in (1:2)) { + if (cnull[[i]] && len[[i]] == 0) { + lists[[i]] <- list(NULL) + } else { + length(lists[[i]]) <- len[[i]] + } + } + + lists +} + +# Utility function to merge compact R lists +# Used in Join-family functions +# param: +# left/right Two compact lists ready for Cartesian product +mergeCompactLists <- function(left, right) { + result <- list() + length(result) <- length(left) * length(right) + index <- 1 + for (i in left) { + for (j in right) { + result[[index]] <- list(i, j) + index <- index + 1 + } + } + result +} + +# Utility function to wrapper above two operations +# Used in Join-family functions +# param (same as genCompactLists): +# tagged_list R list generated via groupByKey with tags(1L, 2L, ...) +# cnull Boolean list where each element determines whether the corresponding list should +# be converted to list(NULL) +joinTaggedList <- function(tagged_list, cnull) { + lists <- genCompactLists(tagged_list, cnull) + mergeCompactLists(lists[[1]], lists[[2]]) +} + +# Utility function to reduce a key-value list with predicate +# Used in *ByKey functions +# param +# pair key-value pair +# keys/vals env of key/value with hashes +# updateOrCreatePred predicate function +# updateFn update or merge function for existing pair, similar with `mergeVal` @combineByKey +# createFn create function for new pair, similar with `createCombiner` @combinebykey +updateOrCreatePair <- function(pair, keys, vals, updateOrCreatePred, updateFn, createFn) { + # assume hashVal bind to `$hash`, key/val with index 1/2 + hashVal <- pair$hash + key <- pair[[1]] + val <- pair[[2]] + if (updateOrCreatePred(pair)) { + assign(hashVal, do.call(updateFn, list(get(hashVal, envir = vals), val)), envir = vals) + } else { + assign(hashVal, do.call(createFn, list(val)), envir = vals) + assign(hashVal, key, envir = keys) + } +} + +# Utility function to convert key&values envs into key-val list +convertEnvsToList <- function(keys, vals) { + lapply(ls(keys), + function(name) { + list(keys[[name]], vals[[name]]) + }) +} + +# Utility function to capture the varargs into environment object +varargsToEnv <- function(...) { + pairs <- as.list(substitute(list(...)))[-1L] + env <- new.env() + for (name in names(pairs)) { + env[[name]] <- pairs[[name]] + } + env +} + +getStorageLevel <- function(newLevel = c("DISK_ONLY", + "DISK_ONLY_2", + "MEMORY_AND_DISK", + "MEMORY_AND_DISK_2", + "MEMORY_AND_DISK_SER", + "MEMORY_AND_DISK_SER_2", + "MEMORY_ONLY", + "MEMORY_ONLY_2", + "MEMORY_ONLY_SER", + "MEMORY_ONLY_SER_2", + "OFF_HEAP")) { + match.arg(newLevel) + storageLevel <- switch(newLevel, + "DISK_ONLY" = callJStatic("org.apache.spark.storage.StorageLevel", "DISK_ONLY"), + "DISK_ONLY_2" = callJStatic("org.apache.spark.storage.StorageLevel", "DISK_ONLY_2"), + "MEMORY_AND_DISK" = callJStatic("org.apache.spark.storage.StorageLevel", "MEMORY_AND_DISK"), + "MEMORY_AND_DISK_2" = callJStatic("org.apache.spark.storage.StorageLevel", "MEMORY_AND_DISK_2"), + "MEMORY_AND_DISK_SER" = callJStatic("org.apache.spark.storage.StorageLevel", "MEMORY_AND_DISK_SER"), + "MEMORY_AND_DISK_SER_2" = callJStatic("org.apache.spark.storage.StorageLevel", "MEMORY_AND_DISK_SER_2"), + "MEMORY_ONLY" = callJStatic("org.apache.spark.storage.StorageLevel", "MEMORY_ONLY"), + "MEMORY_ONLY_2" = callJStatic("org.apache.spark.storage.StorageLevel", "MEMORY_ONLY_2"), + "MEMORY_ONLY_SER" = callJStatic("org.apache.spark.storage.StorageLevel", "MEMORY_ONLY_SER"), + "MEMORY_ONLY_SER_2" = callJStatic("org.apache.spark.storage.StorageLevel", "MEMORY_ONLY_SER_2"), + "OFF_HEAP" = callJStatic("org.apache.spark.storage.StorageLevel", "OFF_HEAP")) +} + +# Utility function for functions where an argument needs to be integer but we want to allow +# the user to type (for example) `5` instead of `5L` to avoid a confusing error message. +numToInt <- function(num) { + if (as.integer(num) != num) { + warning(paste("Coercing", as.list(sys.call())[[2]], "to integer.")) + } + as.integer(num) +} + +# create a Seq in JVM +toSeq <- function(...) { + callJStatic("org.apache.spark.sql.api.r.SQLUtils", "toSeq", list(...)) +} + +# create a Seq in JVM from a list +listToSeq <- function(l) { + callJStatic("org.apache.spark.sql.api.r.SQLUtils", "toSeq", l) +} + +# Utility function to recursively traverse the Abstract Syntax Tree (AST) of a +# user defined function (UDF), and to examine variables in the UDF to decide +# if their values should be included in the new function environment. +# param +# node The current AST node in the traversal. +# oldEnv The original function environment. +# defVars An Accumulator of variables names defined in the function's calling environment, +# including function argument and local variable names. +# checkedFunc An environment of function objects examined during cleanClosure. It can +# be considered as a "name"-to-"list of functions" mapping. +# newEnv A new function environment to store necessary function dependencies, an output argument. +processClosure <- function(node, oldEnv, defVars, checkedFuncs, newEnv) { + nodeLen <- length(node) + + if (nodeLen > 1 && typeof(node) == "language") { + # Recursive case: current AST node is an internal node, check for its children. + if (length(node[[1]]) > 1) { + for (i in 1:nodeLen) { + processClosure(node[[i]], oldEnv, defVars, checkedFuncs, newEnv) + } + } else { # if node[[1]] is length of 1, check for some R special functions. + nodeChar <- as.character(node[[1]]) + if (nodeChar == "{" || nodeChar == "(") { # Skip start symbol. + for (i in 2:nodeLen) { + processClosure(node[[i]], oldEnv, defVars, checkedFuncs, newEnv) + } + } else if (nodeChar == "<-" || nodeChar == "=" || + nodeChar == "<<-") { # Assignment Ops. + defVar <- node[[2]] + if (length(defVar) == 1 && typeof(defVar) == "symbol") { + # Add the defined variable name into defVars. + addItemToAccumulator(defVars, as.character(defVar)) + } else { + processClosure(node[[2]], oldEnv, defVars, checkedFuncs, newEnv) + } + for (i in 3:nodeLen) { + processClosure(node[[i]], oldEnv, defVars, checkedFuncs, newEnv) + } + } else if (nodeChar == "function") { # Function definition. + # Add parameter names. + newArgs <- names(node[[2]]) + lapply(newArgs, function(arg) { addItemToAccumulator(defVars, arg) }) + for (i in 3:nodeLen) { + processClosure(node[[i]], oldEnv, defVars, checkedFuncs, newEnv) + } + } else if (nodeChar == "$") { # Skip the field. + processClosure(node[[2]], oldEnv, defVars, checkedFuncs, newEnv) + } else if (nodeChar == "::" || nodeChar == ":::") { + processClosure(node[[3]], oldEnv, defVars, checkedFuncs, newEnv) + } else { + for (i in 1:nodeLen) { + processClosure(node[[i]], oldEnv, defVars, checkedFuncs, newEnv) + } + } + } + } else if (nodeLen == 1 && + (typeof(node) == "symbol" || typeof(node) == "language")) { + # Base case: current AST node is a leaf node and a symbol or a function call. + nodeChar <- as.character(node) + if (!nodeChar %in% defVars$data) { # Not a function parameter or local variable. + func.env <- oldEnv + topEnv <- parent.env(.GlobalEnv) + # Search in function environment, and function's enclosing environments + # up to global environment. There is no need to look into package environments + # above the global or namespace environment that is not SparkR below the global, + # as they are assumed to be loaded on workers. + while (!identical(func.env, topEnv)) { + # Namespaces other than "SparkR" will not be searched. + if (!isNamespace(func.env) || + (getNamespaceName(func.env) == "SparkR" && + !(nodeChar %in% getNamespaceExports("SparkR")))) { # Only include SparkR internals. + # Set parameter 'inherits' to FALSE since we do not need to search in + # attached package environments. + if (tryCatch(exists(nodeChar, envir = func.env, inherits = FALSE), + error = function(e) { FALSE })) { + obj <- get(nodeChar, envir = func.env, inherits = FALSE) + if (is.function(obj)) { # If the node is a function call. + funcList <- mget(nodeChar, envir = checkedFuncs, inherits = F, + ifnotfound = list(list(NULL)))[[1]] + found <- sapply(funcList, function(func) { + ifelse(identical(func, obj), TRUE, FALSE) + }) + if (sum(found) > 0) { # If function has been examined, ignore. + break + } + # Function has not been examined, record it and recursively clean its closure. + assign(nodeChar, + if (is.null(funcList[[1]])) { + list(obj) + } else { + append(funcList, obj) + }, + envir = checkedFuncs) + obj <- cleanClosure(obj, checkedFuncs) + } + assign(nodeChar, obj, envir = newEnv) + break + } + } + + # Continue to search in enclosure. + func.env <- parent.env(func.env) + } + } + } +} + +# Utility function to get user defined function (UDF) dependencies (closure). +# More specifically, this function captures the values of free variables defined +# outside a UDF, and stores them in the function's environment. +# param +# func A function whose closure needs to be captured. +# checkedFunc An environment of function objects examined during cleanClosure. It can be +# considered as a "name"-to-"list of functions" mapping. +# return value +# a new version of func that has an correct environment (closure). +cleanClosure <- function(func, checkedFuncs = new.env()) { + if (is.function(func)) { + newEnv <- new.env(parent = .GlobalEnv) + func.body <- body(func) + oldEnv <- environment(func) + # defVars is an Accumulator of variables names defined in the function's calling + # environment. First, function's arguments are added to defVars. + defVars <- initAccumulator() + argNames <- names(as.list(args(func))) + for (i in 1:(length(argNames) - 1)) { # Remove the ending NULL in pairlist. + addItemToAccumulator(defVars, argNames[i]) + } + # Recursively examine variables in the function body. + processClosure(func.body, oldEnv, defVars, checkedFuncs, newEnv) + environment(func) <- newEnv + } + func +} + +# Append partition lengths to each partition in two input RDDs if needed. +# param +# x An RDD. +# Other An RDD. +# return value +# A list of two result RDDs. +appendPartitionLengths <- function(x, other) { + if (getSerializedMode(x) != getSerializedMode(other) || + getSerializedMode(x) == "byte") { + # Append the number of elements in each partition to that partition so that we can later + # know the boundary of elements from x and other. + # + # Note that this appending also serves the purpose of reserialization, because even if + # any RDD is serialized, we need to reserialize it to make sure its partitions are encoded + # as a single byte array. For example, partitions of an RDD generated from partitionBy() + # may be encoded as multiple byte arrays. + appendLength <- function(part) { + len <- length(part) + part[[len + 1]] <- len + 1 + part + } + x <- lapplyPartition(x, appendLength) + other <- lapplyPartition(other, appendLength) + } + list (x, other) +} + +# Perform zip or cartesian between elements from two RDDs in each partition +# param +# rdd An RDD. +# zip A boolean flag indicating this call is for zip operation or not. +# return value +# A result RDD. +mergePartitions <- function(rdd, zip) { + serializerMode <- getSerializedMode(rdd) + partitionFunc <- function(split, part) { + len <- length(part) + if (len > 0) { + if (serializerMode == "byte") { + lengthOfValues <- part[[len]] + lengthOfKeys <- part[[len - lengthOfValues]] + stopifnot(len == lengthOfKeys + lengthOfValues) + + # For zip operation, check if corresponding partitions of both RDDs have the same number of elements. + if (zip && lengthOfKeys != lengthOfValues) { + stop("Can only zip RDDs with same number of elements in each pair of corresponding partitions.") + } + + if (lengthOfKeys > 1) { + keys <- part[1 : (lengthOfKeys - 1)] + } else { + keys <- list() + } + if (lengthOfValues > 1) { + values <- part[(lengthOfKeys + 1) : (len - 1)] + } else { + values <- list() + } + + if (!zip) { + return(mergeCompactLists(keys, values)) + } + } else { + keys <- part[c(TRUE, FALSE)] + values <- part[c(FALSE, TRUE)] + } + mapply( + function(k, v) { list(k, v) }, + keys, + values, + SIMPLIFY = FALSE, + USE.NAMES = FALSE) + } else { + part + } + } + + PipelinedRDD(rdd, partitionFunc) +} diff --git a/R/pkg/R/zzz.R b/R/pkg/R/zzz.R new file mode 100644 index 0000000000000..80d796d467943 --- /dev/null +++ b/R/pkg/R/zzz.R @@ -0,0 +1,21 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +.onLoad <- function(libname, pkgname) { + sparkR.onLoad(libname, pkgname) +} + diff --git a/R/pkg/inst/profile/general.R b/R/pkg/inst/profile/general.R new file mode 100644 index 0000000000000..8fe711b622086 --- /dev/null +++ b/R/pkg/inst/profile/general.R @@ -0,0 +1,22 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +.First <- function() { + home <- Sys.getenv("SPARK_HOME") + .libPaths(c(file.path(home, "R", "lib"), .libPaths())) + Sys.setenv(NOAWT=1) +} diff --git a/R/pkg/inst/profile/shell.R b/R/pkg/inst/profile/shell.R new file mode 100644 index 0000000000000..7a7f2031152a0 --- /dev/null +++ b/R/pkg/inst/profile/shell.R @@ -0,0 +1,31 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +.First <- function() { + home <- Sys.getenv("SPARK_HOME") + .libPaths(c(file.path(home, "R", "lib"), .libPaths())) + Sys.setenv(NOAWT=1) + + library(utils) + library(SparkR) + sc <- sparkR.init(Sys.getenv("MASTER", unset = "")) + assign("sc", sc, envir=.GlobalEnv) + sqlCtx <- sparkRSQL.init(sc) + assign("sqlCtx", sqlCtx, envir=.GlobalEnv) + cat("\n Welcome to SparkR!") + cat("\n Spark context is available as sc, SQL context is available as sqlCtx\n") +} diff --git a/R/pkg/inst/tests/test_binaryFile.R b/R/pkg/inst/tests/test_binaryFile.R new file mode 100644 index 0000000000000..ca4218f3819f8 --- /dev/null +++ b/R/pkg/inst/tests/test_binaryFile.R @@ -0,0 +1,90 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +context("functions on binary files") + +# JavaSparkContext handle +sc <- sparkR.init() + +mockFile = c("Spark is pretty.", "Spark is awesome.") + +test_that("saveAsObjectFile()/objectFile() following textFile() works", { + fileName1 <- tempfile(pattern="spark-test", fileext=".tmp") + fileName2 <- tempfile(pattern="spark-test", fileext=".tmp") + writeLines(mockFile, fileName1) + + rdd <- textFile(sc, fileName1, 1) + saveAsObjectFile(rdd, fileName2) + rdd <- objectFile(sc, fileName2) + expect_equal(collect(rdd), as.list(mockFile)) + + unlink(fileName1) + unlink(fileName2, recursive = TRUE) +}) + +test_that("saveAsObjectFile()/objectFile() works on a parallelized list", { + fileName <- tempfile(pattern="spark-test", fileext=".tmp") + + l <- list(1, 2, 3) + rdd <- parallelize(sc, l, 1) + saveAsObjectFile(rdd, fileName) + rdd <- objectFile(sc, fileName) + expect_equal(collect(rdd), l) + + unlink(fileName, recursive = TRUE) +}) + +test_that("saveAsObjectFile()/objectFile() following RDD transformations works", { + fileName1 <- tempfile(pattern="spark-test", fileext=".tmp") + fileName2 <- tempfile(pattern="spark-test", fileext=".tmp") + writeLines(mockFile, fileName1) + + rdd <- textFile(sc, fileName1) + + words <- flatMap(rdd, function(line) { strsplit(line, " ")[[1]] }) + wordCount <- lapply(words, function(word) { list(word, 1L) }) + + counts <- reduceByKey(wordCount, "+", 2L) + + saveAsObjectFile(counts, fileName2) + counts <- objectFile(sc, fileName2) + + output <- collect(counts) + expected <- list(list("awesome.", 1), list("Spark", 2), list("pretty.", 1), + list("is", 2)) + expect_equal(sortKeyValueList(output), sortKeyValueList(expected)) + + unlink(fileName1) + unlink(fileName2, recursive = TRUE) +}) + +test_that("saveAsObjectFile()/objectFile() works with multiple paths", { + fileName1 <- tempfile(pattern="spark-test", fileext=".tmp") + fileName2 <- tempfile(pattern="spark-test", fileext=".tmp") + + rdd1 <- parallelize(sc, "Spark is pretty.") + saveAsObjectFile(rdd1, fileName1) + rdd2 <- parallelize(sc, "Spark is awesome.") + saveAsObjectFile(rdd2, fileName2) + + rdd <- objectFile(sc, c(fileName1, fileName2)) + expect_true(count(rdd) == 2) + + unlink(fileName1, recursive = TRUE) + unlink(fileName2, recursive = TRUE) +}) + diff --git a/R/pkg/inst/tests/test_binary_function.R b/R/pkg/inst/tests/test_binary_function.R new file mode 100644 index 0000000000000..c15553ba28517 --- /dev/null +++ b/R/pkg/inst/tests/test_binary_function.R @@ -0,0 +1,68 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +context("binary functions") + +# JavaSparkContext handle +sc <- sparkR.init() + +# Data +nums <- 1:10 +rdd <- parallelize(sc, nums, 2L) + +# File content +mockFile <- c("Spark is pretty.", "Spark is awesome.") + +test_that("union on two RDDs", { + actual <- collect(unionRDD(rdd, rdd)) + expect_equal(actual, as.list(rep(nums, 2))) + + fileName <- tempfile(pattern="spark-test", fileext=".tmp") + writeLines(mockFile, fileName) + + text.rdd <- textFile(sc, fileName) + union.rdd <- unionRDD(rdd, text.rdd) + actual <- collect(union.rdd) + expect_equal(actual, c(as.list(nums), mockFile)) + expect_true(getSerializedMode(union.rdd) == "byte") + + rdd<- map(text.rdd, function(x) {x}) + union.rdd <- unionRDD(rdd, text.rdd) + actual <- collect(union.rdd) + expect_equal(actual, as.list(c(mockFile, mockFile))) + expect_true(getSerializedMode(union.rdd) == "byte") + + unlink(fileName) +}) + +test_that("cogroup on two RDDs", { + rdd1 <- parallelize(sc, list(list(1, 1), list(2, 4))) + rdd2 <- parallelize(sc, list(list(1, 2), list(1, 3))) + cogroup.rdd <- cogroup(rdd1, rdd2, numPartitions = 2L) + actual <- collect(cogroup.rdd) + expect_equal(actual, + list(list(1, list(list(1), list(2, 3))), list(2, list(list(4), list())))) + + rdd1 <- parallelize(sc, list(list("a", 1), list("a", 4))) + rdd2 <- parallelize(sc, list(list("b", 2), list("a", 3))) + cogroup.rdd <- cogroup(rdd1, rdd2, numPartitions = 2L) + actual <- collect(cogroup.rdd) + + expected <- list(list("b", list(list(), list(2))), list("a", list(list(1, 4), list(3)))) + expect_equal(sortKeyValueList(actual), + sortKeyValueList(expected)) +}) diff --git a/R/pkg/inst/tests/test_broadcast.R b/R/pkg/inst/tests/test_broadcast.R new file mode 100644 index 0000000000000..fee91a427d6d5 --- /dev/null +++ b/R/pkg/inst/tests/test_broadcast.R @@ -0,0 +1,48 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +context("broadcast variables") + +# JavaSparkContext handle +sc <- sparkR.init() + +# Partitioned data +nums <- 1:2 +rrdd <- parallelize(sc, nums, 2L) + +test_that("using broadcast variable", { + randomMat <- matrix(nrow=10, ncol=10, data=rnorm(100)) + randomMatBr <- broadcast(sc, randomMat) + + useBroadcast <- function(x) { + sum(value(randomMatBr) * x) + } + actual <- collect(lapply(rrdd, useBroadcast)) + expected <- list(sum(randomMat) * 1, sum(randomMat) * 2) + expect_equal(actual, expected) +}) + +test_that("without using broadcast variable", { + randomMat <- matrix(nrow=10, ncol=10, data=rnorm(100)) + + useBroadcast <- function(x) { + sum(randomMat * x) + } + actual <- collect(lapply(rrdd, useBroadcast)) + expected <- list(sum(randomMat) * 1, sum(randomMat) * 2) + expect_equal(actual, expected) +}) diff --git a/R/pkg/inst/tests/test_context.R b/R/pkg/inst/tests/test_context.R new file mode 100644 index 0000000000000..e4aab37436a74 --- /dev/null +++ b/R/pkg/inst/tests/test_context.R @@ -0,0 +1,50 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +context("test functions in sparkR.R") + +test_that("repeatedly starting and stopping SparkR", { + for (i in 1:4) { + sc <- sparkR.init() + rdd <- parallelize(sc, 1:20, 2L) + expect_equal(count(rdd), 20) + sparkR.stop() + } +}) + +test_that("rdd GC across sparkR.stop", { + sparkR.stop() + sc <- sparkR.init() # sc should get id 0 + rdd1 <- parallelize(sc, 1:20, 2L) # rdd1 should get id 1 + rdd2 <- parallelize(sc, 1:10, 2L) # rdd2 should get id 2 + sparkR.stop() + + sc <- sparkR.init() # sc should get id 0 again + + # GC rdd1 before creating rdd3 and rdd2 after + rm(rdd1) + gc() + + rdd3 <- parallelize(sc, 1:20, 2L) # rdd3 should get id 1 now + rdd4 <- parallelize(sc, 1:10, 2L) # rdd4 should get id 2 now + + rm(rdd2) + gc() + + count(rdd3) + count(rdd4) +}) diff --git a/R/pkg/inst/tests/test_includePackage.R b/R/pkg/inst/tests/test_includePackage.R new file mode 100644 index 0000000000000..8152b448d0870 --- /dev/null +++ b/R/pkg/inst/tests/test_includePackage.R @@ -0,0 +1,57 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +context("include R packages") + +# JavaSparkContext handle +sc <- sparkR.init() + +# Partitioned data +nums <- 1:2 +rdd <- parallelize(sc, nums, 2L) + +test_that("include inside function", { + # Only run the test if plyr is installed. + if ("plyr" %in% rownames(installed.packages())) { + suppressPackageStartupMessages(library(plyr)) + generateData <- function(x) { + suppressPackageStartupMessages(library(plyr)) + attach(airquality) + result <- transform(Ozone, logOzone = log(Ozone)) + result + } + + data <- lapplyPartition(rdd, generateData) + actual <- collect(data) + } +}) + +test_that("use include package", { + # Only run the test if plyr is installed. + if ("plyr" %in% rownames(installed.packages())) { + suppressPackageStartupMessages(library(plyr)) + generateData <- function(x) { + attach(airquality) + result <- transform(Ozone, logOzone = log(Ozone)) + result + } + + includePackage(sc, plyr) + data <- lapplyPartition(rdd, generateData) + actual <- collect(data) + } +}) diff --git a/R/pkg/inst/tests/test_parallelize_collect.R b/R/pkg/inst/tests/test_parallelize_collect.R new file mode 100644 index 0000000000000..fff028657db37 --- /dev/null +++ b/R/pkg/inst/tests/test_parallelize_collect.R @@ -0,0 +1,109 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +context("parallelize() and collect()") + +# Mock data +numVector <- c(-10:97) +numList <- list(sqrt(1), sqrt(2), sqrt(3), 4 ** 10) +strVector <- c("Dexter Morgan: I suppose I should be upset, even feel", + "violated, but I'm not. No, in fact, I think this is a friendly", + "message, like \"Hey, wanna play?\" and yes, I want to play. ", + "I really, really do.") +strList <- list("Dexter Morgan: Blood. Sometimes it sets my teeth on edge, ", + "other times it helps me control the chaos.", + "Dexter Morgan: Harry and Dorris Morgan did a wonderful job ", + "raising me. But they're both dead now. I didn't kill them. Honest.") + +numPairs <- list(list(1, 1), list(1, 2), list(2, 2), list(2, 3)) +strPairs <- list(list(strList, strList), list(strList, strList)) + +# JavaSparkContext handle +jsc <- sparkR.init() + +# Tests + +test_that("parallelize() on simple vectors and lists returns an RDD", { + numVectorRDD <- parallelize(jsc, numVector, 1) + numVectorRDD2 <- parallelize(jsc, numVector, 10) + numListRDD <- parallelize(jsc, numList, 1) + numListRDD2 <- parallelize(jsc, numList, 4) + strVectorRDD <- parallelize(jsc, strVector, 2) + strVectorRDD2 <- parallelize(jsc, strVector, 3) + strListRDD <- parallelize(jsc, strList, 4) + strListRDD2 <- parallelize(jsc, strList, 1) + + rdds <- c(numVectorRDD, + numVectorRDD2, + numListRDD, + numListRDD2, + strVectorRDD, + strVectorRDD2, + strListRDD, + strListRDD2) + + for (rdd in rdds) { + expect_true(inherits(rdd, "RDD")) + expect_true(.hasSlot(rdd, "jrdd") + && inherits(rdd@jrdd, "jobj") + && isInstanceOf(rdd@jrdd, "org.apache.spark.api.java.JavaRDD")) + } +}) + +test_that("collect(), following a parallelize(), gives back the original collections", { + numVectorRDD <- parallelize(jsc, numVector, 10) + expect_equal(collect(numVectorRDD), as.list(numVector)) + + numListRDD <- parallelize(jsc, numList, 1) + numListRDD2 <- parallelize(jsc, numList, 4) + expect_equal(collect(numListRDD), as.list(numList)) + expect_equal(collect(numListRDD2), as.list(numList)) + + strVectorRDD <- parallelize(jsc, strVector, 2) + strVectorRDD2 <- parallelize(jsc, strVector, 3) + expect_equal(collect(strVectorRDD), as.list(strVector)) + expect_equal(collect(strVectorRDD2), as.list(strVector)) + + strListRDD <- parallelize(jsc, strList, 4) + strListRDD2 <- parallelize(jsc, strList, 1) + expect_equal(collect(strListRDD), as.list(strList)) + expect_equal(collect(strListRDD2), as.list(strList)) +}) + +test_that("regression: collect() following a parallelize() does not drop elements", { + # 10 %/% 6 = 1, ceiling(10 / 6) = 2 + collLen <- 10 + numPart <- 6 + expected <- runif(collLen) + actual <- collect(parallelize(jsc, expected, numPart)) + expect_equal(actual, as.list(expected)) +}) + +test_that("parallelize() and collect() work for lists of pairs (pairwise data)", { + # use the pairwise logical to indicate pairwise data + numPairsRDDD1 <- parallelize(jsc, numPairs, 1) + numPairsRDDD2 <- parallelize(jsc, numPairs, 2) + numPairsRDDD3 <- parallelize(jsc, numPairs, 3) + expect_equal(collect(numPairsRDDD1), numPairs) + expect_equal(collect(numPairsRDDD2), numPairs) + expect_equal(collect(numPairsRDDD3), numPairs) + # can also leave out the parameter name, if the params are supplied in order + strPairsRDDD1 <- parallelize(jsc, strPairs, 1) + strPairsRDDD2 <- parallelize(jsc, strPairs, 2) + expect_equal(collect(strPairsRDDD1), strPairs) + expect_equal(collect(strPairsRDDD2), strPairs) +}) diff --git a/R/pkg/inst/tests/test_rdd.R b/R/pkg/inst/tests/test_rdd.R new file mode 100644 index 0000000000000..3ba7d1716302a --- /dev/null +++ b/R/pkg/inst/tests/test_rdd.R @@ -0,0 +1,784 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +context("basic RDD functions") + +# JavaSparkContext handle +sc <- sparkR.init() + +# Data +nums <- 1:10 +rdd <- parallelize(sc, nums, 2L) + +intPairs <- list(list(1L, -1), list(2L, 100), list(2L, 1), list(1L, 200)) +intRdd <- parallelize(sc, intPairs, 2L) + +test_that("get number of partitions in RDD", { + expect_equal(numPartitions(rdd), 2) + expect_equal(numPartitions(intRdd), 2) +}) + +test_that("first on RDD", { + expect_true(first(rdd) == 1) + newrdd <- lapply(rdd, function(x) x + 1) + expect_true(first(newrdd) == 2) +}) + +test_that("count and length on RDD", { + expect_equal(count(rdd), 10) + expect_equal(length(rdd), 10) +}) + +test_that("count by values and keys", { + mods <- lapply(rdd, function(x) { x %% 3 }) + actual <- countByValue(mods) + expected <- list(list(0, 3L), list(1, 4L), list(2, 3L)) + expect_equal(sortKeyValueList(actual), sortKeyValueList(expected)) + + actual <- countByKey(intRdd) + expected <- list(list(2L, 2L), list(1L, 2L)) + expect_equal(sortKeyValueList(actual), sortKeyValueList(expected)) +}) + +test_that("lapply on RDD", { + multiples <- lapply(rdd, function(x) { 2 * x }) + actual <- collect(multiples) + expect_equal(actual, as.list(nums * 2)) +}) + +test_that("lapplyPartition on RDD", { + sums <- lapplyPartition(rdd, function(part) { sum(unlist(part)) }) + actual <- collect(sums) + expect_equal(actual, list(15, 40)) +}) + +test_that("mapPartitions on RDD", { + sums <- mapPartitions(rdd, function(part) { sum(unlist(part)) }) + actual <- collect(sums) + expect_equal(actual, list(15, 40)) +}) + +test_that("flatMap() on RDDs", { + flat <- flatMap(intRdd, function(x) { list(x, x) }) + actual <- collect(flat) + expect_equal(actual, rep(intPairs, each=2)) +}) + +test_that("filterRDD on RDD", { + filtered.rdd <- filterRDD(rdd, function(x) { x %% 2 == 0 }) + actual <- collect(filtered.rdd) + expect_equal(actual, list(2, 4, 6, 8, 10)) + + filtered.rdd <- Filter(function(x) { x[[2]] < 0 }, intRdd) + actual <- collect(filtered.rdd) + expect_equal(actual, list(list(1L, -1))) + + # Filter out all elements. + filtered.rdd <- filterRDD(rdd, function(x) { x > 10 }) + actual <- collect(filtered.rdd) + expect_equal(actual, list()) +}) + +test_that("lookup on RDD", { + vals <- lookup(intRdd, 1L) + expect_equal(vals, list(-1, 200)) + + vals <- lookup(intRdd, 3L) + expect_equal(vals, list()) +}) + +test_that("several transformations on RDD (a benchmark on PipelinedRDD)", { + rdd2 <- rdd + for (i in 1:12) + rdd2 <- lapplyPartitionsWithIndex( + rdd2, function(split, part) { + part <- as.list(unlist(part) * split + i) + }) + rdd2 <- lapply(rdd2, function(x) x + x) + actual <- collect(rdd2) + expected <- list(24, 24, 24, 24, 24, + 168, 170, 172, 174, 176) + expect_equal(actual, expected) +}) + +test_that("PipelinedRDD support actions: cache(), persist(), unpersist(), checkpoint()", { + # RDD + rdd2 <- rdd + # PipelinedRDD + rdd2 <- lapplyPartitionsWithIndex( + rdd2, + function(split, part) { + part <- as.list(unlist(part) * split) + }) + + cache(rdd2) + expect_true(rdd2@env$isCached) + rdd2 <- lapply(rdd2, function(x) x) + expect_false(rdd2@env$isCached) + + unpersist(rdd2) + expect_false(rdd2@env$isCached) + + persist(rdd2, "MEMORY_AND_DISK") + expect_true(rdd2@env$isCached) + rdd2 <- lapply(rdd2, function(x) x) + expect_false(rdd2@env$isCached) + + unpersist(rdd2) + expect_false(rdd2@env$isCached) + + tempDir <- tempfile(pattern = "checkpoint") + setCheckpointDir(sc, tempDir) + checkpoint(rdd2) + expect_true(rdd2@env$isCheckpointed) + + rdd2 <- lapply(rdd2, function(x) x) + expect_false(rdd2@env$isCached) + expect_false(rdd2@env$isCheckpointed) + + # make sure the data is collectable + collect(rdd2) + + unlink(tempDir) +}) + +test_that("reduce on RDD", { + sum <- reduce(rdd, "+") + expect_equal(sum, 55) + + # Also test with an inline function + sumInline <- reduce(rdd, function(x, y) { x + y }) + expect_equal(sumInline, 55) +}) + +test_that("lapply with dependency", { + fa <- 5 + multiples <- lapply(rdd, function(x) { fa * x }) + actual <- collect(multiples) + + expect_equal(actual, as.list(nums * 5)) +}) + +test_that("lapplyPartitionsWithIndex on RDDs", { + func <- function(splitIndex, part) { list(splitIndex, Reduce("+", part)) } + actual <- collect(lapplyPartitionsWithIndex(rdd, func), flatten = FALSE) + expect_equal(actual, list(list(0, 15), list(1, 40))) + + pairsRDD <- parallelize(sc, list(list(1, 2), list(3, 4), list(4, 8)), 1L) + partitionByParity <- function(key) { if (key %% 2 == 1) 0 else 1 } + mkTup <- function(splitIndex, part) { list(splitIndex, part) } + actual <- collect(lapplyPartitionsWithIndex( + partitionBy(pairsRDD, 2L, partitionByParity), + mkTup), + FALSE) + expect_equal(actual, list(list(0, list(list(1, 2), list(3, 4))), + list(1, list(list(4, 8))))) +}) + +test_that("sampleRDD() on RDDs", { + expect_equal(unlist(collect(sampleRDD(rdd, FALSE, 1.0, 2014L))), nums) +}) + +test_that("takeSample() on RDDs", { + # ported from RDDSuite.scala, modified seeds + data <- parallelize(sc, 1:100, 2L) + for (seed in 4:5) { + s <- takeSample(data, FALSE, 20L, seed) + expect_equal(length(s), 20L) + expect_equal(length(unique(s)), 20L) + for (elem in s) { + expect_true(elem >= 1 && elem <= 100) + } + } + for (seed in 4:5) { + s <- takeSample(data, FALSE, 200L, seed) + expect_equal(length(s), 100L) + expect_equal(length(unique(s)), 100L) + for (elem in s) { + expect_true(elem >= 1 && elem <= 100) + } + } + for (seed in 4:5) { + s <- takeSample(data, TRUE, 20L, seed) + expect_equal(length(s), 20L) + for (elem in s) { + expect_true(elem >= 1 && elem <= 100) + } + } + for (seed in 4:5) { + s <- takeSample(data, TRUE, 100L, seed) + expect_equal(length(s), 100L) + # Chance of getting all distinct elements is astronomically low, so test we + # got < 100 + expect_true(length(unique(s)) < 100L) + } + for (seed in 4:5) { + s <- takeSample(data, TRUE, 200L, seed) + expect_equal(length(s), 200L) + # Chance of getting all distinct elements is still quite low, so test we + # got < 100 + expect_true(length(unique(s)) < 100L) + } +}) + +test_that("mapValues() on pairwise RDDs", { + multiples <- mapValues(intRdd, function(x) { x * 2 }) + actual <- collect(multiples) + expected <- lapply(intPairs, function(x) { + list(x[[1]], x[[2]] * 2) + }) + expect_equal(sortKeyValueList(actual), sortKeyValueList(expected)) +}) + +test_that("flatMapValues() on pairwise RDDs", { + l <- parallelize(sc, list(list(1, c(1,2)), list(2, c(3,4)))) + actual <- collect(flatMapValues(l, function(x) { x })) + expect_equal(actual, list(list(1,1), list(1,2), list(2,3), list(2,4))) + + # Generate x to x+1 for every value + actual <- collect(flatMapValues(intRdd, function(x) { x:(x + 1) })) + expect_equal(actual, + list(list(1L, -1), list(1L, 0), list(2L, 100), list(2L, 101), + list(2L, 1), list(2L, 2), list(1L, 200), list(1L, 201))) +}) + +test_that("reduceByKeyLocally() on PairwiseRDDs", { + pairs <- parallelize(sc, list(list(1, 2), list(1.1, 3), list(1, 4)), 2L) + actual <- reduceByKeyLocally(pairs, "+") + expect_equal(sortKeyValueList(actual), + sortKeyValueList(list(list(1, 6), list(1.1, 3)))) + + pairs <- parallelize(sc, list(list("abc", 1.2), list(1.1, 0), list("abc", 1.3), + list("bb", 5)), 4L) + actual <- reduceByKeyLocally(pairs, "+") + expect_equal(sortKeyValueList(actual), + sortKeyValueList(list(list("abc", 2.5), list(1.1, 0), list("bb", 5)))) +}) + +test_that("distinct() on RDDs", { + nums.rep2 <- rep(1:10, 2) + rdd.rep2 <- parallelize(sc, nums.rep2, 2L) + uniques <- distinct(rdd.rep2) + actual <- sort(unlist(collect(uniques))) + expect_equal(actual, nums) +}) + +test_that("maximum() on RDDs", { + max <- maximum(rdd) + expect_equal(max, 10) +}) + +test_that("minimum() on RDDs", { + min <- minimum(rdd) + expect_equal(min, 1) +}) + +test_that("sumRDD() on RDDs", { + sum <- sumRDD(rdd) + expect_equal(sum, 55) +}) + +test_that("keyBy on RDDs", { + func <- function(x) { x*x } + keys <- keyBy(rdd, func) + actual <- collect(keys) + expect_equal(actual, lapply(nums, function(x) { list(func(x), x) })) +}) + +test_that("repartition/coalesce on RDDs", { + rdd <- parallelize(sc, 1:20, 4L) # each partition contains 5 elements + + # repartition + r1 <- repartition(rdd, 2) + expect_equal(numPartitions(r1), 2L) + count <- length(collectPartition(r1, 0L)) + expect_true(count >= 8 && count <= 12) + + r2 <- repartition(rdd, 6) + expect_equal(numPartitions(r2), 6L) + count <- length(collectPartition(r2, 0L)) + expect_true(count >=0 && count <= 4) + + # coalesce + r3 <- coalesce(rdd, 1) + expect_equal(numPartitions(r3), 1L) + count <- length(collectPartition(r3, 0L)) + expect_equal(count, 20) +}) + +test_that("sortBy() on RDDs", { + sortedRdd <- sortBy(rdd, function(x) { x * x }, ascending = FALSE) + actual <- collect(sortedRdd) + expect_equal(actual, as.list(sort(nums, decreasing = TRUE))) + + rdd2 <- parallelize(sc, sort(nums, decreasing = TRUE), 2L) + sortedRdd2 <- sortBy(rdd2, function(x) { x * x }) + actual <- collect(sortedRdd2) + expect_equal(actual, as.list(nums)) +}) + +test_that("takeOrdered() on RDDs", { + l <- list(10, 1, 2, 9, 3, 4, 5, 6, 7) + rdd <- parallelize(sc, l) + actual <- takeOrdered(rdd, 6L) + expect_equal(actual, as.list(sort(unlist(l)))[1:6]) + + l <- list("e", "d", "c", "d", "a") + rdd <- parallelize(sc, l) + actual <- takeOrdered(rdd, 3L) + expect_equal(actual, as.list(sort(unlist(l)))[1:3]) +}) + +test_that("top() on RDDs", { + l <- list(10, 1, 2, 9, 3, 4, 5, 6, 7) + rdd <- parallelize(sc, l) + actual <- top(rdd, 6L) + expect_equal(actual, as.list(sort(unlist(l), decreasing = TRUE))[1:6]) + + l <- list("e", "d", "c", "d", "a") + rdd <- parallelize(sc, l) + actual <- top(rdd, 3L) + expect_equal(actual, as.list(sort(unlist(l), decreasing = TRUE))[1:3]) +}) + +test_that("fold() on RDDs", { + actual <- fold(rdd, 0, "+") + expect_equal(actual, Reduce("+", nums, 0)) + + rdd <- parallelize(sc, list()) + actual <- fold(rdd, 0, "+") + expect_equal(actual, 0) +}) + +test_that("aggregateRDD() on RDDs", { + rdd <- parallelize(sc, list(1, 2, 3, 4)) + zeroValue <- list(0, 0) + seqOp <- function(x, y) { list(x[[1]] + y, x[[2]] + 1) } + combOp <- function(x, y) { list(x[[1]] + y[[1]], x[[2]] + y[[2]]) } + actual <- aggregateRDD(rdd, zeroValue, seqOp, combOp) + expect_equal(actual, list(10, 4)) + + rdd <- parallelize(sc, list()) + actual <- aggregateRDD(rdd, zeroValue, seqOp, combOp) + expect_equal(actual, list(0, 0)) +}) + +test_that("zipWithUniqueId() on RDDs", { + rdd <- parallelize(sc, list("a", "b", "c", "d", "e"), 3L) + actual <- collect(zipWithUniqueId(rdd)) + expected <- list(list("a", 0), list("b", 3), list("c", 1), + list("d", 4), list("e", 2)) + expect_equal(actual, expected) + + rdd <- parallelize(sc, list("a", "b", "c", "d", "e"), 1L) + actual <- collect(zipWithUniqueId(rdd)) + expected <- list(list("a", 0), list("b", 1), list("c", 2), + list("d", 3), list("e", 4)) + expect_equal(actual, expected) +}) + +test_that("zipWithIndex() on RDDs", { + rdd <- parallelize(sc, list("a", "b", "c", "d", "e"), 3L) + actual <- collect(zipWithIndex(rdd)) + expected <- list(list("a", 0), list("b", 1), list("c", 2), + list("d", 3), list("e", 4)) + expect_equal(actual, expected) + + rdd <- parallelize(sc, list("a", "b", "c", "d", "e"), 1L) + actual <- collect(zipWithIndex(rdd)) + expected <- list(list("a", 0), list("b", 1), list("c", 2), + list("d", 3), list("e", 4)) + expect_equal(actual, expected) +}) + +test_that("glom() on RDD", { + rdd <- parallelize(sc, as.list(1:4), 2L) + actual <- collect(glom(rdd)) + expect_equal(actual, list(list(1, 2), list(3, 4))) +}) + +test_that("keys() on RDDs", { + keys <- keys(intRdd) + actual <- collect(keys) + expect_equal(actual, lapply(intPairs, function(x) { x[[1]] })) +}) + +test_that("values() on RDDs", { + values <- values(intRdd) + actual <- collect(values) + expect_equal(actual, lapply(intPairs, function(x) { x[[2]] })) +}) + +test_that("pipeRDD() on RDDs", { + actual <- collect(pipeRDD(rdd, "more")) + expected <- as.list(as.character(1:10)) + expect_equal(actual, expected) + + trailed.rdd <- parallelize(sc, c("1", "", "2\n", "3\n\r\n")) + actual <- collect(pipeRDD(trailed.rdd, "sort")) + expected <- list("", "1", "2", "3") + expect_equal(actual, expected) + + rev.nums <- 9:0 + rev.rdd <- parallelize(sc, rev.nums, 2L) + actual <- collect(pipeRDD(rev.rdd, "sort")) + expected <- as.list(as.character(c(5:9, 0:4))) + expect_equal(actual, expected) +}) + +test_that("zipRDD() on RDDs", { + rdd1 <- parallelize(sc, 0:4, 2) + rdd2 <- parallelize(sc, 1000:1004, 2) + actual <- collect(zipRDD(rdd1, rdd2)) + expect_equal(actual, + list(list(0, 1000), list(1, 1001), list(2, 1002), list(3, 1003), list(4, 1004))) + + mockFile = c("Spark is pretty.", "Spark is awesome.") + fileName <- tempfile(pattern="spark-test", fileext=".tmp") + writeLines(mockFile, fileName) + + rdd <- textFile(sc, fileName, 1) + actual <- collect(zipRDD(rdd, rdd)) + expected <- lapply(mockFile, function(x) { list(x ,x) }) + expect_equal(actual, expected) + + rdd1 <- parallelize(sc, 0:1, 1) + actual <- collect(zipRDD(rdd1, rdd)) + expected <- lapply(0:1, function(x) { list(x, mockFile[x + 1]) }) + expect_equal(actual, expected) + + rdd1 <- map(rdd, function(x) { x }) + actual <- collect(zipRDD(rdd, rdd1)) + expected <- lapply(mockFile, function(x) { list(x, x) }) + expect_equal(actual, expected) + + unlink(fileName) +}) + +test_that("cartesian() on RDDs", { + rdd <- parallelize(sc, 1:3) + actual <- collect(cartesian(rdd, rdd)) + expect_equal(sortKeyValueList(actual), + list( + list(1, 1), list(1, 2), list(1, 3), + list(2, 1), list(2, 2), list(2, 3), + list(3, 1), list(3, 2), list(3, 3))) + + # test case where one RDD is empty + emptyRdd <- parallelize(sc, list()) + actual <- collect(cartesian(rdd, emptyRdd)) + expect_equal(actual, list()) + + mockFile = c("Spark is pretty.", "Spark is awesome.") + fileName <- tempfile(pattern="spark-test", fileext=".tmp") + writeLines(mockFile, fileName) + + rdd <- textFile(sc, fileName) + actual <- collect(cartesian(rdd, rdd)) + expected <- list( + list("Spark is awesome.", "Spark is pretty."), + list("Spark is awesome.", "Spark is awesome."), + list("Spark is pretty.", "Spark is pretty."), + list("Spark is pretty.", "Spark is awesome.")) + expect_equal(sortKeyValueList(actual), expected) + + rdd1 <- parallelize(sc, 0:1) + actual <- collect(cartesian(rdd1, rdd)) + expect_equal(sortKeyValueList(actual), + list( + list(0, "Spark is pretty."), + list(0, "Spark is awesome."), + list(1, "Spark is pretty."), + list(1, "Spark is awesome."))) + + rdd1 <- map(rdd, function(x) { x }) + actual <- collect(cartesian(rdd, rdd1)) + expect_equal(sortKeyValueList(actual), expected) + + unlink(fileName) +}) + +test_that("subtract() on RDDs", { + l <- list(1, 1, 2, 2, 3, 4) + rdd1 <- parallelize(sc, l) + + # subtract by itself + actual <- collect(subtract(rdd1, rdd1)) + expect_equal(actual, list()) + + # subtract by an empty RDD + rdd2 <- parallelize(sc, list()) + actual <- collect(subtract(rdd1, rdd2)) + expect_equal(as.list(sort(as.vector(actual, mode="integer"))), + l) + + rdd2 <- parallelize(sc, list(2, 4)) + actual <- collect(subtract(rdd1, rdd2)) + expect_equal(as.list(sort(as.vector(actual, mode="integer"))), + list(1, 1, 3)) + + l <- list("a", "a", "b", "b", "c", "d") + rdd1 <- parallelize(sc, l) + rdd2 <- parallelize(sc, list("b", "d")) + actual <- collect(subtract(rdd1, rdd2)) + expect_equal(as.list(sort(as.vector(actual, mode="character"))), + list("a", "a", "c")) +}) + +test_that("subtractByKey() on pairwise RDDs", { + l <- list(list("a", 1), list("b", 4), + list("b", 5), list("a", 2)) + rdd1 <- parallelize(sc, l) + + # subtractByKey by itself + actual <- collect(subtractByKey(rdd1, rdd1)) + expect_equal(actual, list()) + + # subtractByKey by an empty RDD + rdd2 <- parallelize(sc, list()) + actual <- collect(subtractByKey(rdd1, rdd2)) + expect_equal(sortKeyValueList(actual), + sortKeyValueList(l)) + + rdd2 <- parallelize(sc, list(list("a", 3), list("c", 1))) + actual <- collect(subtractByKey(rdd1, rdd2)) + expect_equal(actual, + list(list("b", 4), list("b", 5))) + + l <- list(list(1, 1), list(2, 4), + list(2, 5), list(1, 2)) + rdd1 <- parallelize(sc, l) + rdd2 <- parallelize(sc, list(list(1, 3), list(3, 1))) + actual <- collect(subtractByKey(rdd1, rdd2)) + expect_equal(actual, + list(list(2, 4), list(2, 5))) +}) + +test_that("intersection() on RDDs", { + # intersection with self + actual <- collect(intersection(rdd, rdd)) + expect_equal(sort(as.integer(actual)), nums) + + # intersection with an empty RDD + emptyRdd <- parallelize(sc, list()) + actual <- collect(intersection(rdd, emptyRdd)) + expect_equal(actual, list()) + + rdd1 <- parallelize(sc, list(1, 10, 2, 3, 4, 5)) + rdd2 <- parallelize(sc, list(1, 6, 2, 3, 7, 8)) + actual <- collect(intersection(rdd1, rdd2)) + expect_equal(sort(as.integer(actual)), 1:3) +}) + +test_that("join() on pairwise RDDs", { + rdd1 <- parallelize(sc, list(list(1,1), list(2,4))) + rdd2 <- parallelize(sc, list(list(1,2), list(1,3))) + actual <- collect(join(rdd1, rdd2, 2L)) + expect_equal(sortKeyValueList(actual), + sortKeyValueList(list(list(1, list(1, 2)), list(1, list(1, 3))))) + + rdd1 <- parallelize(sc, list(list("a",1), list("b",4))) + rdd2 <- parallelize(sc, list(list("a",2), list("a",3))) + actual <- collect(join(rdd1, rdd2, 2L)) + expect_equal(sortKeyValueList(actual), + sortKeyValueList(list(list("a", list(1, 2)), list("a", list(1, 3))))) + + rdd1 <- parallelize(sc, list(list(1,1), list(2,2))) + rdd2 <- parallelize(sc, list(list(3,3), list(4,4))) + actual <- collect(join(rdd1, rdd2, 2L)) + expect_equal(actual, list()) + + rdd1 <- parallelize(sc, list(list("a",1), list("b",2))) + rdd2 <- parallelize(sc, list(list("c",3), list("d",4))) + actual <- collect(join(rdd1, rdd2, 2L)) + expect_equal(actual, list()) +}) + +test_that("leftOuterJoin() on pairwise RDDs", { + rdd1 <- parallelize(sc, list(list(1,1), list(2,4))) + rdd2 <- parallelize(sc, list(list(1,2), list(1,3))) + actual <- collect(leftOuterJoin(rdd1, rdd2, 2L)) + expected <- list(list(1, list(1, 2)), list(1, list(1, 3)), list(2, list(4, NULL))) + expect_equal(sortKeyValueList(actual), + sortKeyValueList(expected)) + + rdd1 <- parallelize(sc, list(list("a",1), list("b",4))) + rdd2 <- parallelize(sc, list(list("a",2), list("a",3))) + actual <- collect(leftOuterJoin(rdd1, rdd2, 2L)) + expected <- list(list("b", list(4, NULL)), list("a", list(1, 2)), list("a", list(1, 3))) + expect_equal(sortKeyValueList(actual), + sortKeyValueList(expected)) + + rdd1 <- parallelize(sc, list(list(1,1), list(2,2))) + rdd2 <- parallelize(sc, list(list(3,3), list(4,4))) + actual <- collect(leftOuterJoin(rdd1, rdd2, 2L)) + expected <- list(list(1, list(1, NULL)), list(2, list(2, NULL))) + expect_equal(sortKeyValueList(actual), + sortKeyValueList(expected)) + + rdd1 <- parallelize(sc, list(list("a",1), list("b",2))) + rdd2 <- parallelize(sc, list(list("c",3), list("d",4))) + actual <- collect(leftOuterJoin(rdd1, rdd2, 2L)) + expected <- list(list("b", list(2, NULL)), list("a", list(1, NULL))) + expect_equal(sortKeyValueList(actual), + sortKeyValueList(expected)) +}) + +test_that("rightOuterJoin() on pairwise RDDs", { + rdd1 <- parallelize(sc, list(list(1,2), list(1,3))) + rdd2 <- parallelize(sc, list(list(1,1), list(2,4))) + actual <- collect(rightOuterJoin(rdd1, rdd2, 2L)) + expected <- list(list(1, list(2, 1)), list(1, list(3, 1)), list(2, list(NULL, 4))) + expect_equal(sortKeyValueList(actual), sortKeyValueList(expected)) + + rdd1 <- parallelize(sc, list(list("a",2), list("a",3))) + rdd2 <- parallelize(sc, list(list("a",1), list("b",4))) + actual <- collect(rightOuterJoin(rdd1, rdd2, 2L)) + expected <- list(list("b", list(NULL, 4)), list("a", list(2, 1)), list("a", list(3, 1))) + expect_equal(sortKeyValueList(actual), + sortKeyValueList(expected)) + + rdd1 <- parallelize(sc, list(list(1,1), list(2,2))) + rdd2 <- parallelize(sc, list(list(3,3), list(4,4))) + actual <- collect(rightOuterJoin(rdd1, rdd2, 2L)) + expect_equal(sortKeyValueList(actual), + sortKeyValueList(list(list(3, list(NULL, 3)), list(4, list(NULL, 4))))) + + rdd1 <- parallelize(sc, list(list("a",1), list("b",2))) + rdd2 <- parallelize(sc, list(list("c",3), list("d",4))) + actual <- collect(rightOuterJoin(rdd1, rdd2, 2L)) + expect_equal(sortKeyValueList(actual), + sortKeyValueList(list(list("d", list(NULL, 4)), list("c", list(NULL, 3))))) +}) + +test_that("fullOuterJoin() on pairwise RDDs", { + rdd1 <- parallelize(sc, list(list(1,2), list(1,3), list(3,3))) + rdd2 <- parallelize(sc, list(list(1,1), list(2,4))) + actual <- collect(fullOuterJoin(rdd1, rdd2, 2L)) + expected <- list(list(1, list(2, 1)), list(1, list(3, 1)), list(2, list(NULL, 4)), list(3, list(3, NULL))) + expect_equal(sortKeyValueList(actual), sortKeyValueList(expected)) + + rdd1 <- parallelize(sc, list(list("a",2), list("a",3), list("c", 1))) + rdd2 <- parallelize(sc, list(list("a",1), list("b",4))) + actual <- collect(fullOuterJoin(rdd1, rdd2, 2L)) + expected <- list(list("b", list(NULL, 4)), list("a", list(2, 1)), list("a", list(3, 1)), list("c", list(1, NULL))) + expect_equal(sortKeyValueList(actual), + sortKeyValueList(expected)) + + rdd1 <- parallelize(sc, list(list(1,1), list(2,2))) + rdd2 <- parallelize(sc, list(list(3,3), list(4,4))) + actual <- collect(fullOuterJoin(rdd1, rdd2, 2L)) + expect_equal(sortKeyValueList(actual), + sortKeyValueList(list(list(1, list(1, NULL)), list(2, list(2, NULL)), list(3, list(NULL, 3)), list(4, list(NULL, 4))))) + + rdd1 <- parallelize(sc, list(list("a",1), list("b",2))) + rdd2 <- parallelize(sc, list(list("c",3), list("d",4))) + actual <- collect(fullOuterJoin(rdd1, rdd2, 2L)) + expect_equal(sortKeyValueList(actual), + sortKeyValueList(list(list("a", list(1, NULL)), list("b", list(2, NULL)), list("d", list(NULL, 4)), list("c", list(NULL, 3))))) +}) + +test_that("sortByKey() on pairwise RDDs", { + numPairsRdd <- map(rdd, function(x) { list (x, x) }) + sortedRdd <- sortByKey(numPairsRdd, ascending = FALSE) + actual <- collect(sortedRdd) + numPairs <- lapply(nums, function(x) { list (x, x) }) + expect_equal(actual, sortKeyValueList(numPairs, decreasing = TRUE)) + + rdd2 <- parallelize(sc, sort(nums, decreasing = TRUE), 2L) + numPairsRdd2 <- map(rdd2, function(x) { list (x, x) }) + sortedRdd2 <- sortByKey(numPairsRdd2) + actual <- collect(sortedRdd2) + expect_equal(actual, numPairs) + + # sort by string keys + l <- list(list("a", 1), list("b", 2), list("1", 3), list("d", 4), list("2", 5)) + rdd3 <- parallelize(sc, l, 2L) + sortedRdd3 <- sortByKey(rdd3) + actual <- collect(sortedRdd3) + expect_equal(actual, list(list("1", 3), list("2", 5), list("a", 1), list("b", 2), list("d", 4))) + + # test on the boundary cases + + # boundary case 1: the RDD to be sorted has only 1 partition + rdd4 <- parallelize(sc, l, 1L) + sortedRdd4 <- sortByKey(rdd4) + actual <- collect(sortedRdd4) + expect_equal(actual, list(list("1", 3), list("2", 5), list("a", 1), list("b", 2), list("d", 4))) + + # boundary case 2: the sorted RDD has only 1 partition + rdd5 <- parallelize(sc, l, 2L) + sortedRdd5 <- sortByKey(rdd5, numPartitions = 1L) + actual <- collect(sortedRdd5) + expect_equal(actual, list(list("1", 3), list("2", 5), list("a", 1), list("b", 2), list("d", 4))) + + # boundary case 3: the RDD to be sorted has only 1 element + l2 <- list(list("a", 1)) + rdd6 <- parallelize(sc, l2, 2L) + sortedRdd6 <- sortByKey(rdd6) + actual <- collect(sortedRdd6) + expect_equal(actual, l2) + + # boundary case 4: the RDD to be sorted has 0 element + l3 <- list() + rdd7 <- parallelize(sc, l3, 2L) + sortedRdd7 <- sortByKey(rdd7) + actual <- collect(sortedRdd7) + expect_equal(actual, l3) +}) + +test_that("collectAsMap() on a pairwise RDD", { + rdd <- parallelize(sc, list(list(1, 2), list(3, 4))) + vals <- collectAsMap(rdd) + expect_equal(vals, list(`1` = 2, `3` = 4)) + + rdd <- parallelize(sc, list(list("a", 1), list("b", 2))) + vals <- collectAsMap(rdd) + expect_equal(vals, list(a = 1, b = 2)) + + rdd <- parallelize(sc, list(list(1.1, 2.2), list(1.2, 2.4))) + vals <- collectAsMap(rdd) + expect_equal(vals, list(`1.1` = 2.2, `1.2` = 2.4)) + + rdd <- parallelize(sc, list(list(1, "a"), list(2, "b"))) + vals <- collectAsMap(rdd) + expect_equal(vals, list(`1` = "a", `2` = "b")) +}) + +test_that("sampleByKey() on pairwise RDDs", { + rdd <- parallelize(sc, 1:2000) + pairsRDD <- lapply(rdd, function(x) { if (x %% 2 == 0) list("a", x) else list("b", x) }) + fractions <- list(a = 0.2, b = 0.1) + sample <- sampleByKey(pairsRDD, FALSE, fractions, 1618L) + expect_equal(100 < length(lookup(sample, "a")) && 300 > length(lookup(sample, "a")), TRUE) + expect_equal(50 < length(lookup(sample, "b")) && 150 > length(lookup(sample, "b")), TRUE) + expect_equal(lookup(sample, "a")[which.min(lookup(sample, "a"))] >= 0, TRUE) + expect_equal(lookup(sample, "a")[which.max(lookup(sample, "a"))] <= 2000, TRUE) + expect_equal(lookup(sample, "b")[which.min(lookup(sample, "b"))] >= 0, TRUE) + expect_equal(lookup(sample, "b")[which.max(lookup(sample, "b"))] <= 2000, TRUE) + + rdd <- parallelize(sc, 1:2000) + pairsRDD <- lapply(rdd, function(x) { if (x %% 2 == 0) list(2, x) else list(3, x) }) + fractions <- list(`2` = 0.2, `3` = 0.1) + sample <- sampleByKey(pairsRDD, TRUE, fractions, 1618L) + expect_equal(100 < length(lookup(sample, 2)) && 300 > length(lookup(sample, 2)), TRUE) + expect_equal(50 < length(lookup(sample, 3)) && 150 > length(lookup(sample, 3)), TRUE) + expect_equal(lookup(sample, 2)[which.min(lookup(sample, 2))] >= 0, TRUE) + expect_equal(lookup(sample, 2)[which.max(lookup(sample, 2))] <= 2000, TRUE) + expect_equal(lookup(sample, 3)[which.min(lookup(sample, 3))] >= 0, TRUE) + expect_equal(lookup(sample, 3)[which.max(lookup(sample, 3))] <= 2000, TRUE) +}) diff --git a/R/pkg/inst/tests/test_shuffle.R b/R/pkg/inst/tests/test_shuffle.R new file mode 100644 index 0000000000000..d7dedda553c56 --- /dev/null +++ b/R/pkg/inst/tests/test_shuffle.R @@ -0,0 +1,221 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +context("partitionBy, groupByKey, reduceByKey etc.") + +# JavaSparkContext handle +sc <- sparkR.init() + +# Data +intPairs <- list(list(1L, -1), list(2L, 100), list(2L, 1), list(1L, 200)) +intRdd <- parallelize(sc, intPairs, 2L) + +doublePairs <- list(list(1.5, -1), list(2.5, 100), list(2.5, 1), list(1.5, 200)) +doubleRdd <- parallelize(sc, doublePairs, 2L) + +numPairs <- list(list(1L, 100), list(2L, 200), list(4L, -1), list(3L, 1), + list(3L, 0)) +numPairsRdd <- parallelize(sc, numPairs, length(numPairs)) + +strList <- list("Dexter Morgan: Blood. Sometimes it sets my teeth on edge and ", + "Dexter Morgan: Harry and Dorris Morgan did a wonderful job ") +strListRDD <- parallelize(sc, strList, 4) + +test_that("groupByKey for integers", { + grouped <- groupByKey(intRdd, 2L) + + actual <- collect(grouped) + + expected <- list(list(2L, list(100, 1)), list(1L, list(-1, 200))) + expect_equal(sortKeyValueList(actual), sortKeyValueList(expected)) +}) + +test_that("groupByKey for doubles", { + grouped <- groupByKey(doubleRdd, 2L) + + actual <- collect(grouped) + + expected <- list(list(1.5, list(-1, 200)), list(2.5, list(100, 1))) + expect_equal(sortKeyValueList(actual), sortKeyValueList(expected)) +}) + +test_that("reduceByKey for ints", { + reduced <- reduceByKey(intRdd, "+", 2L) + + actual <- collect(reduced) + + expected <- list(list(2L, 101), list(1L, 199)) + expect_equal(sortKeyValueList(actual), sortKeyValueList(expected)) +}) + +test_that("reduceByKey for doubles", { + reduced <- reduceByKey(doubleRdd, "+", 2L) + actual <- collect(reduced) + + expected <- list(list(1.5, 199), list(2.5, 101)) + expect_equal(sortKeyValueList(actual), sortKeyValueList(expected)) +}) + +test_that("combineByKey for ints", { + reduced <- combineByKey(intRdd, function(x) { x }, "+", "+", 2L) + + actual <- collect(reduced) + + expected <- list(list(2L, 101), list(1L, 199)) + expect_equal(sortKeyValueList(actual), sortKeyValueList(expected)) +}) + +test_that("combineByKey for doubles", { + reduced <- combineByKey(doubleRdd, function(x) { x }, "+", "+", 2L) + actual <- collect(reduced) + + expected <- list(list(1.5, 199), list(2.5, 101)) + expect_equal(sortKeyValueList(actual), sortKeyValueList(expected)) +}) + +test_that("combineByKey for characters", { + stringKeyRDD <- parallelize(sc, + list(list("max", 1L), list("min", 2L), + list("other", 3L), list("max", 4L)), 2L) + reduced <- combineByKey(stringKeyRDD, + function(x) { x }, "+", "+", 2L) + actual <- collect(reduced) + + expected <- list(list("max", 5L), list("min", 2L), list("other", 3L)) + expect_equal(sortKeyValueList(actual), sortKeyValueList(expected)) +}) + +test_that("aggregateByKey", { + # test aggregateByKey for int keys + rdd <- parallelize(sc, list(list(1, 1), list(1, 2), list(2, 3), list(2, 4))) + + zeroValue <- list(0, 0) + seqOp <- function(x, y) { list(x[[1]] + y, x[[2]] + 1) } + combOp <- function(x, y) { list(x[[1]] + y[[1]], x[[2]] + y[[2]]) } + aggregatedRDD <- aggregateByKey(rdd, zeroValue, seqOp, combOp, 2L) + + actual <- collect(aggregatedRDD) + + expected <- list(list(1, list(3, 2)), list(2, list(7, 2))) + expect_equal(sortKeyValueList(actual), sortKeyValueList(expected)) + + # test aggregateByKey for string keys + rdd <- parallelize(sc, list(list("a", 1), list("a", 2), list("b", 3), list("b", 4))) + + zeroValue <- list(0, 0) + seqOp <- function(x, y) { list(x[[1]] + y, x[[2]] + 1) } + combOp <- function(x, y) { list(x[[1]] + y[[1]], x[[2]] + y[[2]]) } + aggregatedRDD <- aggregateByKey(rdd, zeroValue, seqOp, combOp, 2L) + + actual <- collect(aggregatedRDD) + + expected <- list(list("a", list(3, 2)), list("b", list(7, 2))) + expect_equal(sortKeyValueList(actual), sortKeyValueList(expected)) +}) + +test_that("foldByKey", { + # test foldByKey for int keys + folded <- foldByKey(intRdd, 0, "+", 2L) + + actual <- collect(folded) + + expected <- list(list(2L, 101), list(1L, 199)) + expect_equal(sortKeyValueList(actual), sortKeyValueList(expected)) + + # test foldByKey for double keys + folded <- foldByKey(doubleRdd, 0, "+", 2L) + + actual <- collect(folded) + + expected <- list(list(1.5, 199), list(2.5, 101)) + expect_equal(sortKeyValueList(actual), sortKeyValueList(expected)) + + # test foldByKey for string keys + stringKeyPairs <- list(list("a", -1), list("b", 100), list("b", 1), list("a", 200)) + + stringKeyRDD <- parallelize(sc, stringKeyPairs) + folded <- foldByKey(stringKeyRDD, 0, "+", 2L) + + actual <- collect(folded) + + expected <- list(list("b", 101), list("a", 199)) + expect_equal(sortKeyValueList(actual), sortKeyValueList(expected)) + + # test foldByKey for empty pair RDD + rdd <- parallelize(sc, list()) + folded <- foldByKey(rdd, 0, "+", 2L) + actual <- collect(folded) + expected <- list() + expect_equal(actual, expected) + + # test foldByKey for RDD with only 1 pair + rdd <- parallelize(sc, list(list(1, 1))) + folded <- foldByKey(rdd, 0, "+", 2L) + actual <- collect(folded) + expected <- list(list(1, 1)) + expect_equal(actual, expected) +}) + +test_that("partitionBy() partitions data correctly", { + # Partition by magnitude + partitionByMagnitude <- function(key) { if (key >= 3) 1 else 0 } + + resultRDD <- partitionBy(numPairsRdd, 2L, partitionByMagnitude) + + expected_first <- list(list(1, 100), list(2, 200)) # key < 3 + expected_second <- list(list(4, -1), list(3, 1), list(3, 0)) # key >= 3 + actual_first <- collectPartition(resultRDD, 0L) + actual_second <- collectPartition(resultRDD, 1L) + + expect_equal(sortKeyValueList(actual_first), sortKeyValueList(expected_first)) + expect_equal(sortKeyValueList(actual_second), sortKeyValueList(expected_second)) +}) + +test_that("partitionBy works with dependencies", { + kOne <- 1 + partitionByParity <- function(key) { if (key %% 2 == kOne) 7 else 4 } + + # Partition by parity + resultRDD <- partitionBy(numPairsRdd, numPartitions = 2L, partitionByParity) + + # keys even; 100 %% 2 == 0 + expected_first <- list(list(2, 200), list(4, -1)) + # keys odd; 3 %% 2 == 1 + expected_second <- list(list(1, 100), list(3, 1), list(3, 0)) + actual_first <- collectPartition(resultRDD, 0L) + actual_second <- collectPartition(resultRDD, 1L) + + expect_equal(sortKeyValueList(actual_first), sortKeyValueList(expected_first)) + expect_equal(sortKeyValueList(actual_second), sortKeyValueList(expected_second)) +}) + +test_that("test partitionBy with string keys", { + words <- flatMap(strListRDD, function(line) { strsplit(line, " ")[[1]] }) + wordCount <- lapply(words, function(word) { list(word, 1L) }) + + resultRDD <- partitionBy(wordCount, 2L) + expected_first <- list(list("Dexter", 1), list("Dexter", 1)) + expected_second <- list(list("and", 1), list("and", 1)) + + actual_first <- Filter(function(item) { item[[1]] == "Dexter" }, + collectPartition(resultRDD, 0L)) + actual_second <- Filter(function(item) { item[[1]] == "and" }, + collectPartition(resultRDD, 1L)) + + expect_equal(sortKeyValueList(actual_first), sortKeyValueList(expected_first)) + expect_equal(sortKeyValueList(actual_second), sortKeyValueList(expected_second)) +}) diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R new file mode 100644 index 0000000000000..25831ae2d9e18 --- /dev/null +++ b/R/pkg/inst/tests/test_sparkSQL.R @@ -0,0 +1,704 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +library(testthat) + +context("SparkSQL functions") + +# Tests for SparkSQL functions in SparkR + +sc <- sparkR.init() + +sqlCtx <- sparkRSQL.init(sc) + +mockLines <- c("{\"name\":\"Michael\"}", + "{\"name\":\"Andy\", \"age\":30}", + "{\"name\":\"Justin\", \"age\":19}") +jsonPath <- tempfile(pattern="sparkr-test", fileext=".tmp") +parquetPath <- tempfile(pattern="sparkr-test", fileext=".parquet") +writeLines(mockLines, jsonPath) + +test_that("infer types", { + expect_equal(infer_type(1L), "integer") + expect_equal(infer_type(1.0), "double") + expect_equal(infer_type("abc"), "string") + expect_equal(infer_type(TRUE), "boolean") + expect_equal(infer_type(as.Date("2015-03-11")), "date") + expect_equal(infer_type(as.POSIXlt("2015-03-11 12:13:04.043")), "timestamp") + expect_equal(infer_type(c(1L, 2L)), + list(type = 'array', elementType = "integer", containsNull = TRUE)) + expect_equal(infer_type(list(1L, 2L)), + list(type = 'array', elementType = "integer", containsNull = TRUE)) + expect_equal(infer_type(list(a = 1L, b = "2")), + structType(structField(x = "a", type = "integer", nullable = TRUE), + structField(x = "b", type = "string", nullable = TRUE))) + e <- new.env() + assign("a", 1L, envir = e) + expect_equal(infer_type(e), + list(type = "map", keyType = "string", valueType = "integer", + valueContainsNull = TRUE)) +}) + +test_that("structType and structField", { + testField <- structField("a", "string") + expect_true(inherits(testField, "structField")) + expect_true(testField$name() == "a") + expect_true(testField$nullable()) + + testSchema <- structType(testField, structField("b", "integer")) + expect_true(inherits(testSchema, "structType")) + expect_true(inherits(testSchema$fields()[[2]], "structField")) + expect_true(testSchema$fields()[[1]]$dataType.toString() == "StringType") +}) + +test_that("create DataFrame from RDD", { + rdd <- lapply(parallelize(sc, 1:10), function(x) { list(x, as.character(x)) }) + df <- createDataFrame(sqlCtx, rdd, list("a", "b")) + expect_true(inherits(df, "DataFrame")) + expect_true(count(df) == 10) + expect_equal(columns(df), c("a", "b")) + expect_equal(dtypes(df), list(c("a", "int"), c("b", "string"))) + + df <- createDataFrame(sqlCtx, rdd) + expect_true(inherits(df, "DataFrame")) + expect_equal(columns(df), c("_1", "_2")) + + schema <- structType(structField(x = "a", type = "integer", nullable = TRUE), + structField(x = "b", type = "string", nullable = TRUE)) + df <- createDataFrame(sqlCtx, rdd, schema) + expect_true(inherits(df, "DataFrame")) + expect_equal(columns(df), c("a", "b")) + expect_equal(dtypes(df), list(c("a", "int"), c("b", "string"))) + + rdd <- lapply(parallelize(sc, 1:10), function(x) { list(a = x, b = as.character(x)) }) + df <- createDataFrame(sqlCtx, rdd) + expect_true(inherits(df, "DataFrame")) + expect_true(count(df) == 10) + expect_equal(columns(df), c("a", "b")) + expect_equal(dtypes(df), list(c("a", "int"), c("b", "string"))) +}) + +test_that("toDF", { + rdd <- lapply(parallelize(sc, 1:10), function(x) { list(x, as.character(x)) }) + df <- toDF(rdd, list("a", "b")) + expect_true(inherits(df, "DataFrame")) + expect_true(count(df) == 10) + expect_equal(columns(df), c("a", "b")) + expect_equal(dtypes(df), list(c("a", "int"), c("b", "string"))) + + df <- toDF(rdd) + expect_true(inherits(df, "DataFrame")) + expect_equal(columns(df), c("_1", "_2")) + + schema <- structType(structField(x = "a", type = "integer", nullable = TRUE), + structField(x = "b", type = "string", nullable = TRUE)) + df <- toDF(rdd, schema) + expect_true(inherits(df, "DataFrame")) + expect_equal(columns(df), c("a", "b")) + expect_equal(dtypes(df), list(c("a", "int"), c("b", "string"))) + + rdd <- lapply(parallelize(sc, 1:10), function(x) { list(a = x, b = as.character(x)) }) + df <- toDF(rdd) + expect_true(inherits(df, "DataFrame")) + expect_true(count(df) == 10) + expect_equal(columns(df), c("a", "b")) + expect_equal(dtypes(df), list(c("a", "int"), c("b", "string"))) +}) + +test_that("create DataFrame from list or data.frame", { + l <- list(list(1, 2), list(3, 4)) + df <- createDataFrame(sqlCtx, l, c("a", "b")) + expect_equal(columns(df), c("a", "b")) + + l <- list(list(a=1, b=2), list(a=3, b=4)) + df <- createDataFrame(sqlCtx, l) + expect_equal(columns(df), c("a", "b")) + + a <- 1:3 + b <- c("a", "b", "c") + ldf <- data.frame(a, b) + df <- createDataFrame(sqlCtx, ldf) + expect_equal(columns(df), c("a", "b")) + expect_equal(dtypes(df), list(c("a", "int"), c("b", "string"))) + expect_equal(count(df), 3) + ldf2 <- collect(df) + expect_equal(ldf$a, ldf2$a) +}) + +test_that("create DataFrame with different data types", { + l <- list(a = 1L, b = 2, c = TRUE, d = "ss", e = as.Date("2012-12-13"), + f = as.POSIXct("2015-03-15 12:13:14.056")) + df <- createDataFrame(sqlCtx, list(l)) + expect_equal(dtypes(df), list(c("a", "int"), c("b", "double"), c("c", "boolean"), + c("d", "string"), c("e", "date"), c("f", "timestamp"))) + expect_equal(count(df), 1) + expect_equal(collect(df), data.frame(l, stringsAsFactors = FALSE)) +}) + +# TODO: enable this test after fix serialization for nested object +#test_that("create DataFrame with nested array and struct", { +# e <- new.env() +# assign("n", 3L, envir = e) +# l <- list(1:10, list("a", "b"), e, list(a="aa", b=3L)) +# df <- createDataFrame(sqlCtx, list(l), c("a", "b", "c", "d")) +# expect_equal(dtypes(df), list(c("a", "array"), c("b", "array"), +# c("c", "map"), c("d", "struct"))) +# expect_equal(count(df), 1) +# ldf <- collect(df) +# expect_equal(ldf[1,], l[[1]]) +#}) + +test_that("jsonFile() on a local file returns a DataFrame", { + df <- jsonFile(sqlCtx, jsonPath) + expect_true(inherits(df, "DataFrame")) + expect_true(count(df) == 3) +}) + +test_that("jsonRDD() on a RDD with json string", { + rdd <- parallelize(sc, mockLines) + expect_true(count(rdd) == 3) + df <- jsonRDD(sqlCtx, rdd) + expect_true(inherits(df, "DataFrame")) + expect_true(count(df) == 3) + + rdd2 <- flatMap(rdd, function(x) c(x, x)) + df <- jsonRDD(sqlCtx, rdd2) + expect_true(inherits(df, "DataFrame")) + expect_true(count(df) == 6) +}) + +test_that("test cache, uncache and clearCache", { + df <- jsonFile(sqlCtx, jsonPath) + registerTempTable(df, "table1") + cacheTable(sqlCtx, "table1") + uncacheTable(sqlCtx, "table1") + clearCache(sqlCtx) + dropTempTable(sqlCtx, "table1") +}) + +test_that("test tableNames and tables", { + df <- jsonFile(sqlCtx, jsonPath) + registerTempTable(df, "table1") + expect_true(length(tableNames(sqlCtx)) == 1) + df <- tables(sqlCtx) + expect_true(count(df) == 1) + dropTempTable(sqlCtx, "table1") +}) + +test_that("registerTempTable() results in a queryable table and sql() results in a new DataFrame", { + df <- jsonFile(sqlCtx, jsonPath) + registerTempTable(df, "table1") + newdf <- sql(sqlCtx, "SELECT * FROM table1 where name = 'Michael'") + expect_true(inherits(newdf, "DataFrame")) + expect_true(count(newdf) == 1) + dropTempTable(sqlCtx, "table1") +}) + +test_that("insertInto() on a registered table", { + df <- loadDF(sqlCtx, jsonPath, "json") + saveDF(df, parquetPath, "parquet", "overwrite") + dfParquet <- loadDF(sqlCtx, parquetPath, "parquet") + + lines <- c("{\"name\":\"Bob\", \"age\":24}", + "{\"name\":\"James\", \"age\":35}") + jsonPath2 <- tempfile(pattern="jsonPath2", fileext=".tmp") + parquetPath2 <- tempfile(pattern = "parquetPath2", fileext = ".parquet") + writeLines(lines, jsonPath2) + df2 <- loadDF(sqlCtx, jsonPath2, "json") + saveDF(df2, parquetPath2, "parquet", "overwrite") + dfParquet2 <- loadDF(sqlCtx, parquetPath2, "parquet") + + registerTempTable(dfParquet, "table1") + insertInto(dfParquet2, "table1") + expect_true(count(sql(sqlCtx, "select * from table1")) == 5) + expect_true(first(sql(sqlCtx, "select * from table1 order by age"))$name == "Michael") + dropTempTable(sqlCtx, "table1") + + registerTempTable(dfParquet, "table1") + insertInto(dfParquet2, "table1", overwrite = TRUE) + expect_true(count(sql(sqlCtx, "select * from table1")) == 2) + expect_true(first(sql(sqlCtx, "select * from table1 order by age"))$name == "Bob") + dropTempTable(sqlCtx, "table1") +}) + +test_that("table() returns a new DataFrame", { + df <- jsonFile(sqlCtx, jsonPath) + registerTempTable(df, "table1") + tabledf <- table(sqlCtx, "table1") + expect_true(inherits(tabledf, "DataFrame")) + expect_true(count(tabledf) == 3) + dropTempTable(sqlCtx, "table1") +}) + +test_that("toRDD() returns an RRDD", { + df <- jsonFile(sqlCtx, jsonPath) + testRDD <- toRDD(df) + expect_true(inherits(testRDD, "RDD")) + expect_true(count(testRDD) == 3) +}) + +test_that("union on two RDDs created from DataFrames returns an RRDD", { + df <- jsonFile(sqlCtx, jsonPath) + RDD1 <- toRDD(df) + RDD2 <- toRDD(df) + unioned <- unionRDD(RDD1, RDD2) + expect_true(inherits(unioned, "RDD")) + expect_true(SparkR:::getSerializedMode(unioned) == "byte") + expect_true(collect(unioned)[[2]]$name == "Andy") +}) + +test_that("union on mixed serialization types correctly returns a byte RRDD", { + # Byte RDD + nums <- 1:10 + rdd <- parallelize(sc, nums, 2L) + + # String RDD + textLines <- c("Michael", + "Andy, 30", + "Justin, 19") + textPath <- tempfile(pattern="sparkr-textLines", fileext=".tmp") + writeLines(textLines, textPath) + textRDD <- textFile(sc, textPath) + + df <- jsonFile(sqlCtx, jsonPath) + dfRDD <- toRDD(df) + + unionByte <- unionRDD(rdd, dfRDD) + expect_true(inherits(unionByte, "RDD")) + expect_true(SparkR:::getSerializedMode(unionByte) == "byte") + expect_true(collect(unionByte)[[1]] == 1) + expect_true(collect(unionByte)[[12]]$name == "Andy") + + unionString <- unionRDD(textRDD, dfRDD) + expect_true(inherits(unionString, "RDD")) + expect_true(SparkR:::getSerializedMode(unionString) == "byte") + expect_true(collect(unionString)[[1]] == "Michael") + expect_true(collect(unionString)[[5]]$name == "Andy") +}) + +test_that("objectFile() works with row serialization", { + objectPath <- tempfile(pattern="spark-test", fileext=".tmp") + df <- jsonFile(sqlCtx, jsonPath) + dfRDD <- toRDD(df) + saveAsObjectFile(coalesce(dfRDD, 1L), objectPath) + objectIn <- objectFile(sc, objectPath) + + expect_true(inherits(objectIn, "RDD")) + expect_equal(SparkR:::getSerializedMode(objectIn), "byte") + expect_equal(collect(objectIn)[[2]]$age, 30) +}) + +test_that("lapply() on a DataFrame returns an RDD with the correct columns", { + df <- jsonFile(sqlCtx, jsonPath) + testRDD <- lapply(df, function(row) { + row$newCol <- row$age + 5 + row + }) + expect_true(inherits(testRDD, "RDD")) + collected <- collect(testRDD) + expect_true(collected[[1]]$name == "Michael") + expect_true(collected[[2]]$newCol == "35") +}) + +test_that("collect() returns a data.frame", { + df <- jsonFile(sqlCtx, jsonPath) + rdf <- collect(df) + expect_true(is.data.frame(rdf)) + expect_true(names(rdf)[1] == "age") + expect_true(nrow(rdf) == 3) + expect_true(ncol(rdf) == 2) +}) + +test_that("limit() returns DataFrame with the correct number of rows", { + df <- jsonFile(sqlCtx, jsonPath) + dfLimited <- limit(df, 2) + expect_true(inherits(dfLimited, "DataFrame")) + expect_true(count(dfLimited) == 2) +}) + +test_that("collect() and take() on a DataFrame return the same number of rows and columns", { + df <- jsonFile(sqlCtx, jsonPath) + expect_true(nrow(collect(df)) == nrow(take(df, 10))) + expect_true(ncol(collect(df)) == ncol(take(df, 10))) +}) + +test_that("multiple pipeline transformations starting with a DataFrame result in an RDD with the correct values", { + df <- jsonFile(sqlCtx, jsonPath) + first <- lapply(df, function(row) { + row$age <- row$age + 5 + row + }) + second <- lapply(first, function(row) { + row$testCol <- if (row$age == 35 && !is.na(row$age)) TRUE else FALSE + row + }) + expect_true(inherits(second, "RDD")) + expect_true(count(second) == 3) + expect_true(collect(second)[[2]]$age == 35) + expect_true(collect(second)[[2]]$testCol) + expect_false(collect(second)[[3]]$testCol) +}) + +test_that("cache(), persist(), and unpersist() on a DataFrame", { + df <- jsonFile(sqlCtx, jsonPath) + expect_false(df@env$isCached) + cache(df) + expect_true(df@env$isCached) + + unpersist(df) + expect_false(df@env$isCached) + + persist(df, "MEMORY_AND_DISK") + expect_true(df@env$isCached) + + unpersist(df) + expect_false(df@env$isCached) + + # make sure the data is collectable + expect_true(is.data.frame(collect(df))) +}) + +test_that("schema(), dtypes(), columns(), names() return the correct values/format", { + df <- jsonFile(sqlCtx, jsonPath) + testSchema <- schema(df) + expect_true(length(testSchema$fields()) == 2) + expect_true(testSchema$fields()[[1]]$dataType.toString() == "LongType") + expect_true(testSchema$fields()[[2]]$dataType.simpleString() == "string") + expect_true(testSchema$fields()[[1]]$name() == "age") + + testTypes <- dtypes(df) + expect_true(length(testTypes[[1]]) == 2) + expect_true(testTypes[[1]][1] == "age") + + testCols <- columns(df) + expect_true(length(testCols) == 2) + expect_true(testCols[2] == "name") + + testNames <- names(df) + expect_true(length(testNames) == 2) + expect_true(testNames[2] == "name") +}) + +test_that("head() and first() return the correct data", { + df <- jsonFile(sqlCtx, jsonPath) + testHead <- head(df) + expect_true(nrow(testHead) == 3) + expect_true(ncol(testHead) == 2) + + testHead2 <- head(df, 2) + expect_true(nrow(testHead2) == 2) + expect_true(ncol(testHead2) == 2) + + testFirst <- first(df) + expect_true(nrow(testFirst) == 1) +}) + +test_that("distinct() on DataFrames", { + lines <- c("{\"name\":\"Michael\"}", + "{\"name\":\"Andy\", \"age\":30}", + "{\"name\":\"Justin\", \"age\":19}", + "{\"name\":\"Justin\", \"age\":19}") + jsonPathWithDup <- tempfile(pattern="sparkr-test", fileext=".tmp") + writeLines(lines, jsonPathWithDup) + + df <- jsonFile(sqlCtx, jsonPathWithDup) + uniques <- distinct(df) + expect_true(inherits(uniques, "DataFrame")) + expect_true(count(uniques) == 3) +}) + +test_that("sampleDF on a DataFrame", { + df <- jsonFile(sqlCtx, jsonPath) + sampled <- sampleDF(df, FALSE, 1.0) + expect_equal(nrow(collect(sampled)), count(df)) + expect_true(inherits(sampled, "DataFrame")) + sampled2 <- sampleDF(df, FALSE, 0.1) + expect_true(count(sampled2) < 3) +}) + +test_that("select operators", { + df <- select(jsonFile(sqlCtx, jsonPath), "name", "age") + expect_true(inherits(df$name, "Column")) + expect_true(inherits(df[[2]], "Column")) + expect_true(inherits(df[["age"]], "Column")) + + expect_true(inherits(df[,1], "DataFrame")) + expect_equal(columns(df[,1]), c("name")) + expect_equal(columns(df[,"age"]), c("age")) + df2 <- df[,c("age", "name")] + expect_true(inherits(df2, "DataFrame")) + expect_equal(columns(df2), c("age", "name")) + + df$age2 <- df$age + expect_equal(columns(df), c("name", "age", "age2")) + expect_equal(count(where(df, df$age2 == df$age)), 2) + df$age2 <- df$age * 2 + expect_equal(columns(df), c("name", "age", "age2")) + expect_equal(count(where(df, df$age2 == df$age * 2)), 2) +}) + +test_that("select with column", { + df <- jsonFile(sqlCtx, jsonPath) + df1 <- select(df, "name") + expect_true(columns(df1) == c("name")) + expect_true(count(df1) == 3) + + df2 <- select(df, df$age) + expect_true(columns(df2) == c("age")) + expect_true(count(df2) == 3) +}) + +test_that("selectExpr() on a DataFrame", { + df <- jsonFile(sqlCtx, jsonPath) + selected <- selectExpr(df, "age * 2") + expect_true(names(selected) == "(age * 2)") + expect_equal(collect(selected), collect(select(df, df$age * 2L))) + + selected2 <- selectExpr(df, "name as newName", "abs(age) as age") + expect_equal(names(selected2), c("newName", "age")) + expect_true(count(selected2) == 3) +}) + +test_that("column calculation", { + df <- jsonFile(sqlCtx, jsonPath) + d <- collect(select(df, alias(df$age + 1, "age2"))) + expect_true(names(d) == c("age2")) + df2 <- select(df, lower(df$name), abs(df$age)) + expect_true(inherits(df2, "DataFrame")) + expect_true(count(df2) == 3) +}) + +test_that("load() from json file", { + df <- loadDF(sqlCtx, jsonPath, "json") + expect_true(inherits(df, "DataFrame")) + expect_true(count(df) == 3) +}) + +test_that("save() as parquet file", { + df <- loadDF(sqlCtx, jsonPath, "json") + saveDF(df, parquetPath, "parquet", mode="overwrite") + df2 <- loadDF(sqlCtx, parquetPath, "parquet") + expect_true(inherits(df2, "DataFrame")) + expect_true(count(df2) == 3) +}) + +test_that("test HiveContext", { + hiveCtx <- tryCatch({ + newJObject("org.apache.spark.sql.hive.test.TestHiveContext", ssc) + }, error = function(err) { + skip("Hive is not build with SparkSQL, skipped") + }) + df <- createExternalTable(hiveCtx, "json", jsonPath, "json") + expect_true(inherits(df, "DataFrame")) + expect_true(count(df) == 3) + df2 <- sql(hiveCtx, "select * from json") + expect_true(inherits(df2, "DataFrame")) + expect_true(count(df2) == 3) + + jsonPath2 <- tempfile(pattern="sparkr-test", fileext=".tmp") + saveAsTable(df, "json", "json", "append", path = jsonPath2) + df3 <- sql(hiveCtx, "select * from json") + expect_true(inherits(df3, "DataFrame")) + expect_true(count(df3) == 6) +}) + +test_that("column operators", { + c <- SparkR:::col("a") + c2 <- (- c + 1 - 2) * 3 / 4.0 + c3 <- (c + c2 - c2) * c2 %% c2 + c4 <- (c > c2) & (c2 <= c3) | (c == c2) & (c2 != c3) +}) + +test_that("column functions", { + c <- SparkR:::col("a") + c2 <- min(c) + max(c) + sum(c) + avg(c) + count(c) + abs(c) + sqrt(c) + c3 <- lower(c) + upper(c) + first(c) + last(c) + c4 <- approxCountDistinct(c) + countDistinct(c) + cast(c, "string") +}) + +test_that("string operators", { + df <- jsonFile(sqlCtx, jsonPath) + expect_equal(count(where(df, like(df$name, "A%"))), 1) + expect_equal(count(where(df, startsWith(df$name, "A"))), 1) + expect_equal(first(select(df, substr(df$name, 1, 2)))[[1]], "Mi") + expect_equal(collect(select(df, cast(df$age, "string")))[[2, 1]], "30") +}) + +test_that("group by", { + df <- jsonFile(sqlCtx, jsonPath) + df1 <- agg(df, name = "max", age = "sum") + expect_true(1 == count(df1)) + df1 <- agg(df, age2 = max(df$age)) + expect_true(1 == count(df1)) + expect_equal(columns(df1), c("age2")) + + gd <- groupBy(df, "name") + expect_true(inherits(gd, "GroupedData")) + df2 <- count(gd) + expect_true(inherits(df2, "DataFrame")) + expect_true(3 == count(df2)) + + df3 <- agg(gd, age = "sum") + expect_true(inherits(df3, "DataFrame")) + expect_true(3 == count(df3)) + + df3 <- agg(gd, age = sum(df$age)) + expect_true(inherits(df3, "DataFrame")) + expect_true(3 == count(df3)) + expect_equal(columns(df3), c("name", "age")) + + df4 <- sum(gd, "age") + expect_true(inherits(df4, "DataFrame")) + expect_true(3 == count(df4)) + expect_true(3 == count(mean(gd, "age"))) + expect_true(3 == count(max(gd, "age"))) +}) + +test_that("sortDF() and orderBy() on a DataFrame", { + df <- jsonFile(sqlCtx, jsonPath) + sorted <- sortDF(df, df$age) + expect_true(collect(sorted)[1,2] == "Michael") + + sorted2 <- sortDF(df, "name") + expect_true(collect(sorted2)[2,"age"] == 19) + + sorted3 <- orderBy(df, asc(df$age)) + expect_true(is.na(first(sorted3)$age)) + expect_true(collect(sorted3)[2, "age"] == 19) + + sorted4 <- orderBy(df, desc(df$name)) + expect_true(first(sorted4)$name == "Michael") + expect_true(collect(sorted4)[3,"name"] == "Andy") +}) + +test_that("filter() on a DataFrame", { + df <- jsonFile(sqlCtx, jsonPath) + filtered <- filter(df, "age > 20") + expect_true(count(filtered) == 1) + expect_true(collect(filtered)$name == "Andy") + filtered2 <- where(df, df$name != "Michael") + expect_true(count(filtered2) == 2) + expect_true(collect(filtered2)$age[2] == 19) +}) + +test_that("join() on a DataFrame", { + df <- jsonFile(sqlCtx, jsonPath) + + mockLines2 <- c("{\"name\":\"Michael\", \"test\": \"yes\"}", + "{\"name\":\"Andy\", \"test\": \"no\"}", + "{\"name\":\"Justin\", \"test\": \"yes\"}", + "{\"name\":\"Bob\", \"test\": \"yes\"}") + jsonPath2 <- tempfile(pattern="sparkr-test", fileext=".tmp") + writeLines(mockLines2, jsonPath2) + df2 <- jsonFile(sqlCtx, jsonPath2) + + joined <- join(df, df2) + expect_equal(names(joined), c("age", "name", "name", "test")) + expect_true(count(joined) == 12) + + joined2 <- join(df, df2, df$name == df2$name) + expect_equal(names(joined2), c("age", "name", "name", "test")) + expect_true(count(joined2) == 3) + + joined3 <- join(df, df2, df$name == df2$name, "right_outer") + expect_equal(names(joined3), c("age", "name", "name", "test")) + expect_true(count(joined3) == 4) + expect_true(is.na(collect(orderBy(joined3, joined3$age))$age[2])) + + joined4 <- select(join(df, df2, df$name == df2$name, "outer"), + alias(df$age + 5, "newAge"), df$name, df2$test) + expect_equal(names(joined4), c("newAge", "name", "test")) + expect_true(count(joined4) == 4) + expect_equal(collect(orderBy(joined4, joined4$name))$newAge[3], 24) +}) + +test_that("toJSON() returns an RDD of the correct values", { + df <- jsonFile(sqlCtx, jsonPath) + testRDD <- toJSON(df) + expect_true(inherits(testRDD, "RDD")) + expect_true(SparkR:::getSerializedMode(testRDD) == "string") + expect_equal(collect(testRDD)[[1]], mockLines[1]) +}) + +test_that("showDF()", { + df <- jsonFile(sqlCtx, jsonPath) + expect_output(showDF(df), "age name \nnull Michael\n30 Andy \n19 Justin ") +}) + +test_that("isLocal()", { + df <- jsonFile(sqlCtx, jsonPath) + expect_false(isLocal(df)) +}) + +test_that("unionAll(), except(), and intersect() on a DataFrame", { + df <- jsonFile(sqlCtx, jsonPath) + + lines <- c("{\"name\":\"Bob\", \"age\":24}", + "{\"name\":\"Andy\", \"age\":30}", + "{\"name\":\"James\", \"age\":35}") + jsonPath2 <- tempfile(pattern="sparkr-test", fileext=".tmp") + writeLines(lines, jsonPath2) + df2 <- loadDF(sqlCtx, jsonPath2, "json") + + unioned <- sortDF(unionAll(df, df2), df$age) + expect_true(inherits(unioned, "DataFrame")) + expect_true(count(unioned) == 6) + expect_true(first(unioned)$name == "Michael") + + excepted <- sortDF(except(df, df2), desc(df$age)) + expect_true(inherits(unioned, "DataFrame")) + expect_true(count(excepted) == 2) + expect_true(first(excepted)$name == "Justin") + + intersected <- sortDF(intersect(df, df2), df$age) + expect_true(inherits(unioned, "DataFrame")) + expect_true(count(intersected) == 1) + expect_true(first(intersected)$name == "Andy") +}) + +test_that("withColumn() and withColumnRenamed()", { + df <- jsonFile(sqlCtx, jsonPath) + newDF <- withColumn(df, "newAge", df$age + 2) + expect_true(length(columns(newDF)) == 3) + expect_true(columns(newDF)[3] == "newAge") + expect_true(first(filter(newDF, df$name != "Michael"))$newAge == 32) + + newDF2 <- withColumnRenamed(df, "age", "newerAge") + expect_true(length(columns(newDF2)) == 2) + expect_true(columns(newDF2)[1] == "newerAge") +}) + +test_that("saveDF() on DataFrame and works with parquetFile", { + df <- jsonFile(sqlCtx, jsonPath) + saveDF(df, parquetPath, "parquet", mode="overwrite") + parquetDF <- parquetFile(sqlCtx, parquetPath) + expect_true(inherits(parquetDF, "DataFrame")) + expect_equal(count(df), count(parquetDF)) +}) + +test_that("parquetFile works with multiple input paths", { + df <- jsonFile(sqlCtx, jsonPath) + saveDF(df, parquetPath, "parquet", mode="overwrite") + parquetPath2 <- tempfile(pattern = "parquetPath2", fileext = ".parquet") + saveDF(df, parquetPath2, "parquet", mode="overwrite") + parquetDF <- parquetFile(sqlCtx, parquetPath, parquetPath2) + expect_true(inherits(parquetDF, "DataFrame")) + expect_true(count(parquetDF) == count(df)*2) +}) + +unlink(parquetPath) +unlink(jsonPath) diff --git a/R/pkg/inst/tests/test_take.R b/R/pkg/inst/tests/test_take.R new file mode 100644 index 0000000000000..7f4c7c315d787 --- /dev/null +++ b/R/pkg/inst/tests/test_take.R @@ -0,0 +1,67 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +context("tests RDD function take()") + +# Mock data +numVector <- c(-10:97) +numList <- list(sqrt(1), sqrt(2), sqrt(3), 4 ** 10) +strVector <- c("Dexter Morgan: I suppose I should be upset, even feel", + "violated, but I'm not. No, in fact, I think this is a friendly", + "message, like \"Hey, wanna play?\" and yes, I want to play. ", + "I really, really do.") +strList <- list("Dexter Morgan: Blood. Sometimes it sets my teeth on edge, ", + "other times it helps me control the chaos.", + "Dexter Morgan: Harry and Dorris Morgan did a wonderful job ", + "raising me. But they're both dead now. I didn't kill them. Honest.") + +# JavaSparkContext handle +jsc <- sparkR.init() + +test_that("take() gives back the original elements in correct count and order", { + numVectorRDD <- parallelize(jsc, numVector, 10) + # case: number of elements to take is less than the size of the first partition + expect_equal(take(numVectorRDD, 1), as.list(head(numVector, n = 1))) + # case: number of elements to take is the same as the size of the first partition + expect_equal(take(numVectorRDD, 11), as.list(head(numVector, n = 11))) + # case: number of elements to take is greater than all elements + expect_equal(take(numVectorRDD, length(numVector)), as.list(numVector)) + expect_equal(take(numVectorRDD, length(numVector) + 1), as.list(numVector)) + + numListRDD <- parallelize(jsc, numList, 1) + numListRDD2 <- parallelize(jsc, numList, 4) + expect_equal(take(numListRDD, 3), take(numListRDD2, 3)) + expect_equal(take(numListRDD, 5), take(numListRDD2, 5)) + expect_equal(take(numListRDD, 1), as.list(head(numList, n = 1))) + expect_equal(take(numListRDD2, 999), numList) + + strVectorRDD <- parallelize(jsc, strVector, 2) + strVectorRDD2 <- parallelize(jsc, strVector, 3) + expect_equal(take(strVectorRDD, 4), as.list(strVector)) + expect_equal(take(strVectorRDD2, 2), as.list(head(strVector, n = 2))) + + strListRDD <- parallelize(jsc, strList, 4) + strListRDD2 <- parallelize(jsc, strList, 1) + expect_equal(take(strListRDD, 3), as.list(head(strList, n = 3))) + expect_equal(take(strListRDD2, 1), as.list(head(strList, n = 1))) + + expect_true(length(take(strListRDD, 0)) == 0) + expect_true(length(take(strVectorRDD, 0)) == 0) + expect_true(length(take(numListRDD, 0)) == 0) + expect_true(length(take(numVectorRDD, 0)) == 0) +}) + diff --git a/R/pkg/inst/tests/test_textFile.R b/R/pkg/inst/tests/test_textFile.R new file mode 100644 index 0000000000000..6b87b4b3e0b08 --- /dev/null +++ b/R/pkg/inst/tests/test_textFile.R @@ -0,0 +1,162 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +context("the textFile() function") + +# JavaSparkContext handle +sc <- sparkR.init() + +mockFile = c("Spark is pretty.", "Spark is awesome.") + +test_that("textFile() on a local file returns an RDD", { + fileName <- tempfile(pattern="spark-test", fileext=".tmp") + writeLines(mockFile, fileName) + + rdd <- textFile(sc, fileName) + expect_true(inherits(rdd, "RDD")) + expect_true(count(rdd) > 0) + expect_true(count(rdd) == 2) + + unlink(fileName) +}) + +test_that("textFile() followed by a collect() returns the same content", { + fileName <- tempfile(pattern="spark-test", fileext=".tmp") + writeLines(mockFile, fileName) + + rdd <- textFile(sc, fileName) + expect_equal(collect(rdd), as.list(mockFile)) + + unlink(fileName) +}) + +test_that("textFile() word count works as expected", { + fileName <- tempfile(pattern="spark-test", fileext=".tmp") + writeLines(mockFile, fileName) + + rdd <- textFile(sc, fileName) + + words <- flatMap(rdd, function(line) { strsplit(line, " ")[[1]] }) + wordCount <- lapply(words, function(word) { list(word, 1L) }) + + counts <- reduceByKey(wordCount, "+", 2L) + output <- collect(counts) + expected <- list(list("pretty.", 1), list("is", 2), list("awesome.", 1), + list("Spark", 2)) + expect_equal(sortKeyValueList(output), sortKeyValueList(expected)) + + unlink(fileName) +}) + +test_that("several transformations on RDD created by textFile()", { + fileName <- tempfile(pattern="spark-test", fileext=".tmp") + writeLines(mockFile, fileName) + + rdd <- textFile(sc, fileName) # RDD + for (i in 1:10) { + # PipelinedRDD initially created from RDD + rdd <- lapply(rdd, function(x) paste(x, x)) + } + collect(rdd) + + unlink(fileName) +}) + +test_that("textFile() followed by a saveAsTextFile() returns the same content", { + fileName1 <- tempfile(pattern="spark-test", fileext=".tmp") + fileName2 <- tempfile(pattern="spark-test", fileext=".tmp") + writeLines(mockFile, fileName1) + + rdd <- textFile(sc, fileName1, 1L) + saveAsTextFile(rdd, fileName2) + rdd <- textFile(sc, fileName2) + expect_equal(collect(rdd), as.list(mockFile)) + + unlink(fileName1) + unlink(fileName2) +}) + +test_that("saveAsTextFile() on a parallelized list works as expected", { + fileName <- tempfile(pattern="spark-test", fileext=".tmp") + l <- list(1, 2, 3) + rdd <- parallelize(sc, l, 1L) + saveAsTextFile(rdd, fileName) + rdd <- textFile(sc, fileName) + expect_equal(collect(rdd), lapply(l, function(x) {toString(x)})) + + unlink(fileName) +}) + +test_that("textFile() and saveAsTextFile() word count works as expected", { + fileName1 <- tempfile(pattern="spark-test", fileext=".tmp") + fileName2 <- tempfile(pattern="spark-test", fileext=".tmp") + writeLines(mockFile, fileName1) + + rdd <- textFile(sc, fileName1) + + words <- flatMap(rdd, function(line) { strsplit(line, " ")[[1]] }) + wordCount <- lapply(words, function(word) { list(word, 1L) }) + + counts <- reduceByKey(wordCount, "+", 2L) + + saveAsTextFile(counts, fileName2) + rdd <- textFile(sc, fileName2) + + output <- collect(rdd) + expected <- list(list("awesome.", 1), list("Spark", 2), + list("pretty.", 1), list("is", 2)) + expectedStr <- lapply(expected, function(x) { toString(x) }) + expect_equal(sortKeyValueList(output), sortKeyValueList(expectedStr)) + + unlink(fileName1) + unlink(fileName2) +}) + +test_that("textFile() on multiple paths", { + fileName1 <- tempfile(pattern="spark-test", fileext=".tmp") + fileName2 <- tempfile(pattern="spark-test", fileext=".tmp") + writeLines("Spark is pretty.", fileName1) + writeLines("Spark is awesome.", fileName2) + + rdd <- textFile(sc, c(fileName1, fileName2)) + expect_true(count(rdd) == 2) + + unlink(fileName1) + unlink(fileName2) +}) + +test_that("Pipelined operations on RDDs created using textFile", { + fileName <- tempfile(pattern="spark-test", fileext=".tmp") + writeLines(mockFile, fileName) + + rdd <- textFile(sc, fileName) + + lengths <- lapply(rdd, function(x) { length(x) }) + expect_equal(collect(lengths), list(1, 1)) + + lengthsPipelined <- lapply(lengths, function(x) { x + 10 }) + expect_equal(collect(lengthsPipelined), list(11, 11)) + + lengths30 <- lapply(lengthsPipelined, function(x) { x + 20 }) + expect_equal(collect(lengths30), list(31, 31)) + + lengths20 <- lapply(lengths, function(x) { x + 20 }) + expect_equal(collect(lengths20), list(21, 21)) + + unlink(fileName) +}) + diff --git a/R/pkg/inst/tests/test_utils.R b/R/pkg/inst/tests/test_utils.R new file mode 100644 index 0000000000000..9c5bb427932b4 --- /dev/null +++ b/R/pkg/inst/tests/test_utils.R @@ -0,0 +1,137 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +context("functions in utils.R") + +# JavaSparkContext handle +sc <- sparkR.init() + +test_that("convertJListToRList() gives back (deserializes) the original JLists + of strings and integers", { + # It's hard to manually create a Java List using rJava, since it does not + # support generics well. Instead, we rely on collect() returning a + # JList. + nums <- as.list(1:10) + rdd <- parallelize(sc, nums, 1L) + jList <- callJMethod(rdd@jrdd, "collect") + rList <- convertJListToRList(jList, flatten = TRUE) + expect_equal(rList, nums) + + strs <- as.list("hello", "spark") + rdd <- parallelize(sc, strs, 2L) + jList <- callJMethod(rdd@jrdd, "collect") + rList <- convertJListToRList(jList, flatten = TRUE) + expect_equal(rList, strs) +}) + +test_that("serializeToBytes on RDD", { + # File content + mockFile <- c("Spark is pretty.", "Spark is awesome.") + fileName <- tempfile(pattern="spark-test", fileext=".tmp") + writeLines(mockFile, fileName) + + text.rdd <- textFile(sc, fileName) + expect_true(getSerializedMode(text.rdd) == "string") + ser.rdd <- serializeToBytes(text.rdd) + expect_equal(collect(ser.rdd), as.list(mockFile)) + expect_true(getSerializedMode(ser.rdd) == "byte") + + unlink(fileName) +}) + +test_that("cleanClosure on R functions", { + y <- c(1, 2, 3) + g <- function(x) { x + 1 } + f <- function(x) { g(x) + y } + newF <- cleanClosure(f) + env <- environment(newF) + expect_equal(length(ls(env)), 2) # y, g + actual <- get("y", envir = env, inherits = FALSE) + expect_equal(actual, y) + actual <- get("g", envir = env, inherits = FALSE) + expect_equal(actual, g) + + # Test for nested enclosures and package variables. + env2 <- new.env() + funcEnv <- new.env(parent = env2) + f <- function(x) { log(g(x) + y) } + environment(f) <- funcEnv # enclosing relationship: f -> funcEnv -> env2 -> .GlobalEnv + newF <- cleanClosure(f) + env <- environment(newF) + expect_equal(length(ls(env)), 2) # "min" should not be included + actual <- get("y", envir = env, inherits = FALSE) + expect_equal(actual, y) + actual <- get("g", envir = env, inherits = FALSE) + expect_equal(actual, g) + + base <- c(1, 2, 3) + l <- list(field = matrix(1)) + field <- matrix(2) + defUse <- 3 + g <- function(x) { x + y } + f <- function(x) { + defUse <- base::as.integer(x) + 1 # Test for access operators `::`. + lapply(x, g) + 1 # Test for capturing function call "g"'s closure as a argument of lapply. + l$field[1,1] <- 3 # Test for access operators `$`. + res <- defUse + l$field[1,] # Test for def-use chain of "defUse", and "" symbol. + f(res) # Test for recursive calls. + } + newF <- cleanClosure(f) + env <- environment(newF) + expect_equal(length(ls(env)), 3) # Only "g", "l" and "f". No "base", "field" or "defUse". + expect_true("g" %in% ls(env)) + expect_true("l" %in% ls(env)) + expect_true("f" %in% ls(env)) + expect_equal(get("l", envir = env, inherits = FALSE), l) + # "y" should be in the environemnt of g. + newG <- get("g", envir = env, inherits = FALSE) + env <- environment(newG) + expect_equal(length(ls(env)), 1) + actual <- get("y", envir = env, inherits = FALSE) + expect_equal(actual, y) + + # Test for function (and variable) definitions. + f <- function(x) { + g <- function(y) { y * 2 } + g(x) + } + newF <- cleanClosure(f) + env <- environment(newF) + expect_equal(length(ls(env)), 0) # "y" and "g" should not be included. + + # Test for overriding variables in base namespace (Issue: SparkR-196). + nums <- as.list(1:10) + rdd <- parallelize(sc, nums, 2L) + t = 4 # Override base::t in .GlobalEnv. + f <- function(x) { x > t } + newF <- cleanClosure(f) + env <- environment(newF) + expect_equal(ls(env), "t") + expect_equal(get("t", envir = env, inherits = FALSE), t) + actual <- collect(lapply(rdd, f)) + expected <- as.list(c(rep(FALSE, 4), rep(TRUE, 6))) + expect_equal(actual, expected) + + # Test for broadcast variables. + a <- matrix(nrow=10, ncol=10, data=rnorm(100)) + aBroadcast <- broadcast(sc, a) + normMultiply <- function(x) { norm(aBroadcast$value) * x } + newnormMultiply <- SparkR:::cleanClosure(normMultiply) + env <- environment(newnormMultiply) + expect_equal(ls(env), "aBroadcast") + expect_equal(get("aBroadcast", envir = env, inherits = FALSE), aBroadcast) +}) diff --git a/R/pkg/inst/worker/daemon.R b/R/pkg/inst/worker/daemon.R new file mode 100644 index 0000000000000..3584b418a71a9 --- /dev/null +++ b/R/pkg/inst/worker/daemon.R @@ -0,0 +1,52 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# Worker daemon + +rLibDir <- Sys.getenv("SPARKR_RLIBDIR") +script <- paste(rLibDir, "SparkR/worker/worker.R", sep = "/") + +# preload SparkR package, speedup worker +.libPaths(c(rLibDir, .libPaths())) +suppressPackageStartupMessages(library(SparkR)) + +port <- as.integer(Sys.getenv("SPARKR_WORKER_PORT")) +inputCon <- socketConnection(port = port, open = "rb", blocking = TRUE, timeout = 3600) + +while (TRUE) { + ready <- socketSelect(list(inputCon)) + if (ready) { + port <- SparkR:::readInt(inputCon) + # There is a small chance that it could be interrupted by signal, retry one time + if (length(port) == 0) { + port <- SparkR:::readInt(inputCon) + if (length(port) == 0) { + cat("quitting daemon\n") + quit(save = "no") + } + } + p <- parallel:::mcfork() + if (inherits(p, "masterProcess")) { + close(inputCon) + Sys.setenv(SPARKR_WORKER_PORT = port) + source(script) + # Set SIGUSR1 so that child can exit + tools::pskill(Sys.getpid(), tools::SIGUSR1) + parallel:::mcexit(0L) + } + } +} diff --git a/R/pkg/inst/worker/worker.R b/R/pkg/inst/worker/worker.R new file mode 100644 index 0000000000000..014bf7bd7b3fe --- /dev/null +++ b/R/pkg/inst/worker/worker.R @@ -0,0 +1,177 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# Worker class + +# Get current system time +currentTimeSecs <- function() { + as.numeric(Sys.time()) +} + +# Get elapsed time +elapsedSecs <- function() { + proc.time()[3] +} + +# Constants +specialLengths <- list(END_OF_STERAM = 0L, TIMING_DATA = -1L) + +# Timing R process boot +bootTime <- currentTimeSecs() +bootElap <- elapsedSecs() + +rLibDir <- Sys.getenv("SPARKR_RLIBDIR") +# Set libPaths to include SparkR package as loadNamespace needs this +# TODO: Figure out if we can avoid this by not loading any objects that require +# SparkR namespace +.libPaths(c(rLibDir, .libPaths())) +suppressPackageStartupMessages(library(SparkR)) + +port <- as.integer(Sys.getenv("SPARKR_WORKER_PORT")) +inputCon <- socketConnection(port = port, blocking = TRUE, open = "rb") +outputCon <- socketConnection(port = port, blocking = TRUE, open = "wb") + +# read the index of the current partition inside the RDD +partition <- SparkR:::readInt(inputCon) + +deserializer <- SparkR:::readString(inputCon) +serializer <- SparkR:::readString(inputCon) + +# Include packages as required +packageNames <- unserialize(SparkR:::readRaw(inputCon)) +for (pkg in packageNames) { + suppressPackageStartupMessages(library(as.character(pkg), character.only=TRUE)) +} + +# read function dependencies +funcLen <- SparkR:::readInt(inputCon) +computeFunc <- unserialize(SparkR:::readRawLen(inputCon, funcLen)) +env <- environment(computeFunc) +parent.env(env) <- .GlobalEnv # Attach under global environment. + +# Timing init envs for computing +initElap <- elapsedSecs() + +# Read and set broadcast variables +numBroadcastVars <- SparkR:::readInt(inputCon) +if (numBroadcastVars > 0) { + for (bcast in seq(1:numBroadcastVars)) { + bcastId <- SparkR:::readInt(inputCon) + value <- unserialize(SparkR:::readRaw(inputCon)) + setBroadcastValue(bcastId, value) + } +} + +# Timing broadcast +broadcastElap <- elapsedSecs() + +# If -1: read as normal RDD; if >= 0, treat as pairwise RDD and treat the int +# as number of partitions to create. +numPartitions <- SparkR:::readInt(inputCon) + +isEmpty <- SparkR:::readInt(inputCon) + +if (isEmpty != 0) { + + if (numPartitions == -1) { + if (deserializer == "byte") { + # Now read as many characters as described in funcLen + data <- SparkR:::readDeserialize(inputCon) + } else if (deserializer == "string") { + data <- as.list(readLines(inputCon)) + } else if (deserializer == "row") { + data <- SparkR:::readDeserializeRows(inputCon) + } + # Timing reading input data for execution + inputElap <- elapsedSecs() + + output <- computeFunc(partition, data) + # Timing computing + computeElap <- elapsedSecs() + + if (serializer == "byte") { + SparkR:::writeRawSerialize(outputCon, output) + } else if (serializer == "row") { + SparkR:::writeRowSerialize(outputCon, output) + } else { + # write lines one-by-one with flag + lapply(output, function(line) SparkR:::writeString(outputCon, line)) + } + # Timing output + outputElap <- elapsedSecs() + } else { + if (deserializer == "byte") { + # Now read as many characters as described in funcLen + data <- SparkR:::readDeserialize(inputCon) + } else if (deserializer == "string") { + data <- readLines(inputCon) + } else if (deserializer == "row") { + data <- SparkR:::readDeserializeRows(inputCon) + } + # Timing reading input data for execution + inputElap <- elapsedSecs() + + res <- new.env() + + # Step 1: hash the data to an environment + hashTupleToEnvir <- function(tuple) { + # NOTE: execFunction is the hash function here + hashVal <- computeFunc(tuple[[1]]) + bucket <- as.character(hashVal %% numPartitions) + acc <- res[[bucket]] + # Create a new accumulator + if (is.null(acc)) { + acc <- SparkR:::initAccumulator() + } + SparkR:::addItemToAccumulator(acc, tuple) + res[[bucket]] <- acc + } + invisible(lapply(data, hashTupleToEnvir)) + # Timing computing + computeElap <- elapsedSecs() + + # Step 2: write out all of the environment as key-value pairs. + for (name in ls(res)) { + SparkR:::writeInt(outputCon, 2L) + SparkR:::writeInt(outputCon, as.integer(name)) + # Truncate the accumulator list to the number of elements we have + length(res[[name]]$data) <- res[[name]]$counter + SparkR:::writeRawSerialize(outputCon, res[[name]]$data) + } + # Timing output + outputElap <- elapsedSecs() + } +} else { + inputElap <- broadcastElap + computeElap <- broadcastElap + outputElap <- broadcastElap +} + +# Report timing +SparkR:::writeInt(outputCon, specialLengths$TIMING_DATA) +SparkR:::writeDouble(outputCon, bootTime) +SparkR:::writeDouble(outputCon, initElap - bootElap) # init +SparkR:::writeDouble(outputCon, broadcastElap - initElap) # broadcast +SparkR:::writeDouble(outputCon, inputElap - broadcastElap) # input +SparkR:::writeDouble(outputCon, computeElap - inputElap) # compute +SparkR:::writeDouble(outputCon, outputElap - computeElap) # output + +# End of output +SparkR:::writeInt(outputCon, specialLengths$END_OF_STERAM) + +close(outputCon) +close(inputCon) diff --git a/R/pkg/src/Makefile b/R/pkg/src/Makefile new file mode 100644 index 0000000000000..a55a56fe80e10 --- /dev/null +++ b/R/pkg/src/Makefile @@ -0,0 +1,27 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +all: sharelib + +sharelib: string_hash_code.c + R CMD SHLIB -o SparkR.so string_hash_code.c + +clean: + rm -f *.o + rm -f *.so + +.PHONY: all clean diff --git a/R/pkg/src/Makefile.win b/R/pkg/src/Makefile.win new file mode 100644 index 0000000000000..aa486d8228371 --- /dev/null +++ b/R/pkg/src/Makefile.win @@ -0,0 +1,27 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +all: sharelib + +sharelib: string_hash_code.c + R CMD SHLIB -o SparkR.dll string_hash_code.c + +clean: + rm -f *.o + rm -f *.dll + +.PHONY: all clean diff --git a/R/pkg/src/string_hash_code.c b/R/pkg/src/string_hash_code.c new file mode 100644 index 0000000000000..e3274b9a0c547 --- /dev/null +++ b/R/pkg/src/string_hash_code.c @@ -0,0 +1,49 @@ +/* + Licensed to the Apache Software Foundation (ASF) under one or more + contributor license agreements. See the NOTICE file distributed with + this work for additional information regarding copyright ownership. + The ASF licenses this file to You under the Apache License, Version 2.0 + (the "License"); you may not use this file except in compliance with + the License. You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +*/ + +/* + * A C function for R extension which implements the Java String hash algorithm. + * Refer to http://en.wikipedia.org/wiki/Java_hashCode%28%29#The_java.lang.String_hash_function + * + */ + +#include +#include + +/* for compatibility with R before 3.1 */ +#ifndef IS_SCALAR +#define IS_SCALAR(x, type) (TYPEOF(x) == (type) && XLENGTH(x) == 1) +#endif + +SEXP stringHashCode(SEXP string) { + const char* str; + R_xlen_t len, i; + int hashCode = 0; + + if (!IS_SCALAR(string, STRSXP)) { + error("invalid input"); + } + + str = CHAR(asChar(string)); + len = XLENGTH(asChar(string)); + + for (i = 0; i < len; i++) { + hashCode = (hashCode << 5) - hashCode + *str++; + } + + return ScalarInteger(hashCode); +} diff --git a/R/pkg/tests/run-all.R b/R/pkg/tests/run-all.R new file mode 100644 index 0000000000000..4f8a1ed2d83ef --- /dev/null +++ b/R/pkg/tests/run-all.R @@ -0,0 +1,21 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +library(testthat) +library(SparkR) + +test_package("SparkR") diff --git a/R/run-tests.sh b/R/run-tests.sh new file mode 100755 index 0000000000000..e82ad0ba2cd06 --- /dev/null +++ b/R/run-tests.sh @@ -0,0 +1,39 @@ +#!/bin/bash + +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +FWDIR="$(cd `dirname $0`; pwd)" + +FAILED=0 +LOGFILE=$FWDIR/unit-tests.out +rm -f $LOGFILE + +SPARK_TESTING=1 $FWDIR/../bin/sparkR --driver-java-options "-Dlog4j.configuration=file:$FWDIR/log4j.properties" $FWDIR/pkg/tests/run-all.R 2>&1 | tee -a $LOGFILE +FAILED=$((PIPESTATUS[0]||$FAILED)) + +if [[ $FAILED != 0 ]]; then + cat $LOGFILE + echo -en "\033[31m" # Red + echo "Had test failures; see logs." + echo -en "\033[0m" # No color + exit -1 +else + echo -en "\033[32m" # Green + echo "Tests passed." + echo -en "\033[0m" # No color +fi diff --git a/README.md b/README.md index af02339578195..c3afc4db9c63c 100644 --- a/README.md +++ b/README.md @@ -85,7 +85,7 @@ storage systems. Because the protocols have changed in different versions of Hadoop, you must build Spark against the same version that your cluster runs. Please refer to the build documentation at -["Specifying the Hadoop Version"](http://spark.apache.org/docs/latest/building-with-maven.html#specifying-the-hadoop-version) +["Specifying the Hadoop Version"](http://spark.apache.org/docs/latest/building-spark.html#specifying-the-hadoop-version) for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions. See also ["Third Party Hadoop Distributions"](http://spark.apache.org/docs/latest/hadoop-third-party-distributions.html) diff --git a/assembly/pom.xml b/assembly/pom.xml index 301ff69c2ae3b..f1f8b0d3682e2 100644 --- a/assembly/pom.xml +++ b/assembly/pom.xml @@ -20,8 +20,8 @@ 4.0.0 org.apache.spark - spark-parent - 1.3.0-SNAPSHOT + spark-parent_2.10 + 1.4.0-SNAPSHOT ../pom.xml @@ -36,10 +36,6 @@ scala-${scala.binary.version} spark-assembly-${project.version}-hadoop${hadoop.version}.jar ${project.build.directory}/${spark.jar.dir}/${spark.jar.basename} - spark - /usr/share/spark - root - 755 @@ -217,113 +213,6 @@ - - deb - - - - org.codehaus.mojo - buildnumber-maven-plugin - 1.2 - - - validate - - create - - - 8 - - - - - - org.vafer - jdeb - 0.11 - - - package - - jdeb - - - ${project.build.directory}/${deb.pkg.name}_${project.version}-${buildNumber}_all.deb - false - gzip - - - ${spark.jar} - file - - perm - ${deb.user} - ${deb.user} - ${deb.install.path}/jars - - - - ${basedir}/src/deb/RELEASE - file - - perm - ${deb.user} - ${deb.user} - ${deb.install.path} - - - - ${basedir}/../conf - directory - - perm - ${deb.user} - ${deb.user} - ${deb.install.path}/conf - ${deb.bin.filemode} - - - - ${basedir}/../bin - directory - - perm - ${deb.user} - ${deb.user} - ${deb.install.path}/bin - ${deb.bin.filemode} - - - - ${basedir}/../sbin - directory - - perm - ${deb.user} - ${deb.user} - ${deb.install.path}/sbin - ${deb.bin.filemode} - - - - ${basedir}/../python - directory - - perm - ${deb.user} - ${deb.user} - ${deb.install.path}/python - ${deb.bin.filemode} - - - - - - - - - - kinesis-asl diff --git a/assembly/src/deb/RELEASE b/assembly/src/deb/RELEASE deleted file mode 100644 index aad50ee73aa45..0000000000000 --- a/assembly/src/deb/RELEASE +++ /dev/null @@ -1,2 +0,0 @@ -compute-classpath.sh uses the existence of this file to decide whether to put the assembly jar on the -classpath or instead to use classfiles in the source tree. \ No newline at end of file diff --git a/assembly/src/deb/control/control b/assembly/src/deb/control/control deleted file mode 100644 index a6b4471d485f4..0000000000000 --- a/assembly/src/deb/control/control +++ /dev/null @@ -1,8 +0,0 @@ -Package: [[deb.pkg.name]] -Version: [[version]]-[[buildNumber]] -Section: misc -Priority: extra -Architecture: all -Maintainer: Matei Zaharia -Description: [[name]] -Distribution: development diff --git a/bagel/pom.xml b/bagel/pom.xml index 510e92640eff8..1f3dec91314f2 100644 --- a/bagel/pom.xml +++ b/bagel/pom.xml @@ -20,8 +20,8 @@ 4.0.0 org.apache.spark - spark-parent - 1.3.0-SNAPSHOT + spark-parent_2.10 + 1.4.0-SNAPSHOT ../pom.xml diff --git a/bagel/src/test/resources/log4j.properties b/bagel/src/test/resources/log4j.properties index 853ef0ed2986f..edbecdae92096 100644 --- a/bagel/src/test/resources/log4j.properties +++ b/bagel/src/test/resources/log4j.properties @@ -24,4 +24,4 @@ log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n # Ignore messages below warning level from Jetty, because it's a bit verbose -log4j.logger.org.eclipse.jetty=WARN +log4j.logger.org.spark-project.jetty=WARN diff --git a/bin/compute-classpath.cmd b/bin/compute-classpath.cmd deleted file mode 100644 index 088f993954d9e..0000000000000 --- a/bin/compute-classpath.cmd +++ /dev/null @@ -1,124 +0,0 @@ -@echo off - -rem -rem Licensed to the Apache Software Foundation (ASF) under one or more -rem contributor license agreements. See the NOTICE file distributed with -rem this work for additional information regarding copyright ownership. -rem The ASF licenses this file to You under the Apache License, Version 2.0 -rem (the "License"); you may not use this file except in compliance with -rem the License. You may obtain a copy of the License at -rem -rem http://www.apache.org/licenses/LICENSE-2.0 -rem -rem Unless required by applicable law or agreed to in writing, software -rem distributed under the License is distributed on an "AS IS" BASIS, -rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -rem See the License for the specific language governing permissions and -rem limitations under the License. -rem - -rem This script computes Spark's classpath and prints it to stdout; it's used by both the "run" -rem script and the ExecutorRunner in standalone cluster mode. - -rem If we're called from spark-class2.cmd, it already set enabledelayedexpansion and setting -rem it here would stop us from affecting its copy of the CLASSPATH variable; otherwise we -rem need to set it here because we use !datanucleus_jars! below. -if "%DONT_PRINT_CLASSPATH%"=="1" goto skip_delayed_expansion -setlocal enabledelayedexpansion -:skip_delayed_expansion - -set SCALA_VERSION=2.10 - -rem Figure out where the Spark framework is installed -set FWDIR=%~dp0..\ - -rem Load environment variables from conf\spark-env.cmd, if it exists -if exist "%FWDIR%conf\spark-env.cmd" call "%FWDIR%conf\spark-env.cmd" - -rem Build up classpath -set CLASSPATH=%SPARK_CLASSPATH%;%SPARK_SUBMIT_CLASSPATH% - -if not "x%SPARK_CONF_DIR%"=="x" ( - set CLASSPATH=%CLASSPATH%;%SPARK_CONF_DIR% -) else ( - set CLASSPATH=%CLASSPATH%;%FWDIR%conf -) - -if exist "%FWDIR%RELEASE" ( - for %%d in ("%FWDIR%lib\spark-assembly*.jar") do ( - set ASSEMBLY_JAR=%%d - ) -) else ( - for %%d in ("%FWDIR%assembly\target\scala-%SCALA_VERSION%\spark-assembly*hadoop*.jar") do ( - set ASSEMBLY_JAR=%%d - ) -) - -set CLASSPATH=%CLASSPATH%;%ASSEMBLY_JAR% - -rem When Hive support is needed, Datanucleus jars must be included on the classpath. -rem Datanucleus jars do not work if only included in the uber jar as plugin.xml metadata is lost. -rem Both sbt and maven will populate "lib_managed/jars/" with the datanucleus jars when Spark is -rem built with Hive, so look for them there. -if exist "%FWDIR%RELEASE" ( - set datanucleus_dir=%FWDIR%lib -) else ( - set datanucleus_dir=%FWDIR%lib_managed\jars -) -set "datanucleus_jars=" -for %%d in ("%datanucleus_dir%\datanucleus-*.jar") do ( - set datanucleus_jars=!datanucleus_jars!;%%d -) -set CLASSPATH=%CLASSPATH%;%datanucleus_jars% - -set SPARK_CLASSES=%FWDIR%core\target\scala-%SCALA_VERSION%\classes -set SPARK_CLASSES=%SPARK_CLASSES%;%FWDIR%repl\target\scala-%SCALA_VERSION%\classes -set SPARK_CLASSES=%SPARK_CLASSES%;%FWDIR%mllib\target\scala-%SCALA_VERSION%\classes -set SPARK_CLASSES=%SPARK_CLASSES%;%FWDIR%bagel\target\scala-%SCALA_VERSION%\classes -set SPARK_CLASSES=%SPARK_CLASSES%;%FWDIR%graphx\target\scala-%SCALA_VERSION%\classes -set SPARK_CLASSES=%SPARK_CLASSES%;%FWDIR%streaming\target\scala-%SCALA_VERSION%\classes -set SPARK_CLASSES=%SPARK_CLASSES%;%FWDIR%tools\target\scala-%SCALA_VERSION%\classes -set SPARK_CLASSES=%SPARK_CLASSES%;%FWDIR%sql\catalyst\target\scala-%SCALA_VERSION%\classes -set SPARK_CLASSES=%SPARK_CLASSES%;%FWDIR%sql\core\target\scala-%SCALA_VERSION%\classes -set SPARK_CLASSES=%SPARK_CLASSES%;%FWDIR%sql\hive\target\scala-%SCALA_VERSION%\classes - -set SPARK_TEST_CLASSES=%FWDIR%core\target\scala-%SCALA_VERSION%\test-classes -set SPARK_TEST_CLASSES=%SPARK_TEST_CLASSES%;%FWDIR%repl\target\scala-%SCALA_VERSION%\test-classes -set SPARK_TEST_CLASSES=%SPARK_TEST_CLASSES%;%FWDIR%mllib\target\scala-%SCALA_VERSION%\test-classes -set SPARK_TEST_CLASSES=%SPARK_TEST_CLASSES%;%FWDIR%bagel\target\scala-%SCALA_VERSION%\test-classes -set SPARK_TEST_CLASSES=%SPARK_TEST_CLASSES%;%FWDIR%graphx\target\scala-%SCALA_VERSION%\test-classes -set SPARK_TEST_CLASSES=%SPARK_TEST_CLASSES%;%FWDIR%streaming\target\scala-%SCALA_VERSION%\test-classes -set SPARK_TEST_CLASSES=%SPARK_TEST_CLASSES%;%FWDIR%sql\catalyst\target\scala-%SCALA_VERSION%\test-classes -set SPARK_TEST_CLASSES=%SPARK_TEST_CLASSES%;%FWDIR%sql\core\target\scala-%SCALA_VERSION%\test-classes -set SPARK_TEST_CLASSES=%SPARK_TEST_CLASSES%;%FWDIR%sql\hive\target\scala-%SCALA_VERSION%\test-classes - -if "x%SPARK_TESTING%"=="x1" ( - rem Add test clases to path - note, add SPARK_CLASSES and SPARK_TEST_CLASSES before CLASSPATH - rem so that local compilation takes precedence over assembled jar - set CLASSPATH=%SPARK_CLASSES%;%SPARK_TEST_CLASSES%;%CLASSPATH% -) - -rem Add hadoop conf dir - else FileSystem.*, etc fail -rem Note, this assumes that there is either a HADOOP_CONF_DIR or YARN_CONF_DIR which hosts -rem the configurtion files. -if "x%HADOOP_CONF_DIR%"=="x" goto no_hadoop_conf_dir - set CLASSPATH=%CLASSPATH%;%HADOOP_CONF_DIR% -:no_hadoop_conf_dir - -if "x%YARN_CONF_DIR%"=="x" goto no_yarn_conf_dir - set CLASSPATH=%CLASSPATH%;%YARN_CONF_DIR% -:no_yarn_conf_dir - -rem To allow for distributions to append needed libraries to the classpath (e.g. when -rem using the "hadoop-provided" profile to build Spark), check SPARK_DIST_CLASSPATH and -rem append it to tbe final classpath. -if not "x%$SPARK_DIST_CLASSPATH%"=="x" ( - set CLASSPATH=%CLASSPATH%;%SPARK_DIST_CLASSPATH% -) - -rem A bit of a hack to allow calling this script within run2.cmd without seeing output -if "%DONT_PRINT_CLASSPATH%"=="1" goto exit - -echo %CLASSPATH% - -:exit diff --git a/bin/compute-classpath.sh b/bin/compute-classpath.sh deleted file mode 100755 index a8c344b1ca594..0000000000000 --- a/bin/compute-classpath.sh +++ /dev/null @@ -1,161 +0,0 @@ -#!/usr/bin/env bash - -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -# This script computes Spark's classpath and prints it to stdout; it's used by both the "run" -# script and the ExecutorRunner in standalone cluster mode. - -# Figure out where Spark is installed -FWDIR="$(cd "`dirname "$0"`"/..; pwd)" - -. "$FWDIR"/bin/load-spark-env.sh - -if [ -n "$SPARK_CLASSPATH" ]; then - CLASSPATH="$SPARK_CLASSPATH:$SPARK_SUBMIT_CLASSPATH" -else - CLASSPATH="$SPARK_SUBMIT_CLASSPATH" -fi - -# Build up classpath -if [ -n "$SPARK_CONF_DIR" ]; then - CLASSPATH="$CLASSPATH:$SPARK_CONF_DIR" -else - CLASSPATH="$CLASSPATH:$FWDIR/conf" -fi - -ASSEMBLY_DIR="$FWDIR/assembly/target/scala-$SPARK_SCALA_VERSION" - -if [ -n "$JAVA_HOME" ]; then - JAR_CMD="$JAVA_HOME/bin/jar" -else - JAR_CMD="jar" -fi - -# A developer option to prepend more recently compiled Spark classes -if [ -n "$SPARK_PREPEND_CLASSES" ]; then - echo "NOTE: SPARK_PREPEND_CLASSES is set, placing locally compiled Spark"\ - "classes ahead of assembly." >&2 - # Spark classes - CLASSPATH="$CLASSPATH:$FWDIR/core/target/scala-$SPARK_SCALA_VERSION/classes" - CLASSPATH="$CLASSPATH:$FWDIR/repl/target/scala-$SPARK_SCALA_VERSION/classes" - CLASSPATH="$CLASSPATH:$FWDIR/mllib/target/scala-$SPARK_SCALA_VERSION/classes" - CLASSPATH="$CLASSPATH:$FWDIR/bagel/target/scala-$SPARK_SCALA_VERSION/classes" - CLASSPATH="$CLASSPATH:$FWDIR/graphx/target/scala-$SPARK_SCALA_VERSION/classes" - CLASSPATH="$CLASSPATH:$FWDIR/streaming/target/scala-$SPARK_SCALA_VERSION/classes" - CLASSPATH="$CLASSPATH:$FWDIR/tools/target/scala-$SPARK_SCALA_VERSION/classes" - CLASSPATH="$CLASSPATH:$FWDIR/sql/catalyst/target/scala-$SPARK_SCALA_VERSION/classes" - CLASSPATH="$CLASSPATH:$FWDIR/sql/core/target/scala-$SPARK_SCALA_VERSION/classes" - CLASSPATH="$CLASSPATH:$FWDIR/sql/hive/target/scala-$SPARK_SCALA_VERSION/classes" - CLASSPATH="$CLASSPATH:$FWDIR/sql/hive-thriftserver/target/scala-$SPARK_SCALA_VERSION/classes" - CLASSPATH="$CLASSPATH:$FWDIR/yarn/stable/target/scala-$SPARK_SCALA_VERSION/classes" - # Jars for shaded deps in their original form (copied here during build) - CLASSPATH="$CLASSPATH:$FWDIR/core/target/jars/*" -fi - -# Use spark-assembly jar from either RELEASE or assembly directory -if [ -f "$FWDIR/RELEASE" ]; then - assembly_folder="$FWDIR"/lib -else - assembly_folder="$ASSEMBLY_DIR" -fi - -num_jars=0 - -for f in ${assembly_folder}/spark-assembly*hadoop*.jar; do - if [[ ! -e "$f" ]]; then - echo "Failed to find Spark assembly in $assembly_folder" 1>&2 - echo "You need to build Spark before running this program." 1>&2 - exit 1 - fi - ASSEMBLY_JAR="$f" - num_jars=$((num_jars+1)) -done - -if [ "$num_jars" -gt "1" ]; then - echo "Found multiple Spark assembly jars in $assembly_folder:" 1>&2 - ls ${assembly_folder}/spark-assembly*hadoop*.jar 1>&2 - echo "Please remove all but one jar." 1>&2 - exit 1 -fi - -# Verify that versions of java used to build the jars and run Spark are compatible -jar_error_check=$("$JAR_CMD" -tf "$ASSEMBLY_JAR" nonexistent/class/path 2>&1) -if [[ "$jar_error_check" =~ "invalid CEN header" ]]; then - echo "Loading Spark jar with '$JAR_CMD' failed. " 1>&2 - echo "This is likely because Spark was compiled with Java 7 and run " 1>&2 - echo "with Java 6. (see SPARK-1703). Please use Java 7 to run Spark " 1>&2 - echo "or build Spark with Java 6." 1>&2 - exit 1 -fi - -CLASSPATH="$CLASSPATH:$ASSEMBLY_JAR" - -# When Hive support is needed, Datanucleus jars must be included on the classpath. -# Datanucleus jars do not work if only included in the uber jar as plugin.xml metadata is lost. -# Both sbt and maven will populate "lib_managed/jars/" with the datanucleus jars when Spark is -# built with Hive, so first check if the datanucleus jars exist, and then ensure the current Spark -# assembly is built for Hive, before actually populating the CLASSPATH with the jars. -# Note that this check order is faster (by up to half a second) in the case where Hive is not used. -if [ -f "$FWDIR/RELEASE" ]; then - datanucleus_dir="$FWDIR"/lib -else - datanucleus_dir="$FWDIR"/lib_managed/jars -fi - -datanucleus_jars="$(find "$datanucleus_dir" 2>/dev/null | grep "datanucleus-.*\\.jar$")" -datanucleus_jars="$(echo "$datanucleus_jars" | tr "\n" : | sed s/:$//g)" - -if [ -n "$datanucleus_jars" ]; then - hive_files=$("$JAR_CMD" -tf "$ASSEMBLY_JAR" org/apache/hadoop/hive/ql/exec 2>/dev/null) - if [ -n "$hive_files" ]; then - echo "Spark assembly has been built with Hive, including Datanucleus jars on classpath" 1>&2 - CLASSPATH="$CLASSPATH:$datanucleus_jars" - fi -fi - -# Add test classes if we're running from SBT or Maven with SPARK_TESTING set to 1 -if [[ $SPARK_TESTING == 1 ]]; then - CLASSPATH="$CLASSPATH:$FWDIR/core/target/scala-$SPARK_SCALA_VERSION/test-classes" - CLASSPATH="$CLASSPATH:$FWDIR/repl/target/scala-$SPARK_SCALA_VERSION/test-classes" - CLASSPATH="$CLASSPATH:$FWDIR/mllib/target/scala-$SPARK_SCALA_VERSION/test-classes" - CLASSPATH="$CLASSPATH:$FWDIR/bagel/target/scala-$SPARK_SCALA_VERSION/test-classes" - CLASSPATH="$CLASSPATH:$FWDIR/graphx/target/scala-$SPARK_SCALA_VERSION/test-classes" - CLASSPATH="$CLASSPATH:$FWDIR/streaming/target/scala-$SPARK_SCALA_VERSION/test-classes" - CLASSPATH="$CLASSPATH:$FWDIR/sql/catalyst/target/scala-$SPARK_SCALA_VERSION/test-classes" - CLASSPATH="$CLASSPATH:$FWDIR/sql/core/target/scala-$SPARK_SCALA_VERSION/test-classes" - CLASSPATH="$CLASSPATH:$FWDIR/sql/hive/target/scala-$SPARK_SCALA_VERSION/test-classes" -fi - -# Add hadoop conf dir if given -- otherwise FileSystem.*, etc fail ! -# Note, this assumes that there is either a HADOOP_CONF_DIR or YARN_CONF_DIR which hosts -# the configurtion files. -if [ -n "$HADOOP_CONF_DIR" ]; then - CLASSPATH="$CLASSPATH:$HADOOP_CONF_DIR" -fi -if [ -n "$YARN_CONF_DIR" ]; then - CLASSPATH="$CLASSPATH:$YARN_CONF_DIR" -fi - -# To allow for distributions to append needed libraries to the classpath (e.g. when -# using the "hadoop-provided" profile to build Spark), check SPARK_DIST_CLASSPATH and -# append it to tbe final classpath. -if [ -n "$SPARK_DIST_CLASSPATH" ]; then - CLASSPATH="$CLASSPATH:$SPARK_DIST_CLASSPATH" -fi - -echo "$CLASSPATH" diff --git a/bin/load-spark-env.cmd b/bin/load-spark-env.cmd new file mode 100644 index 0000000000000..36d932c453b6f --- /dev/null +++ b/bin/load-spark-env.cmd @@ -0,0 +1,59 @@ +@echo off + +rem +rem Licensed to the Apache Software Foundation (ASF) under one or more +rem contributor license agreements. See the NOTICE file distributed with +rem this work for additional information regarding copyright ownership. +rem The ASF licenses this file to You under the Apache License, Version 2.0 +rem (the "License"); you may not use this file except in compliance with +rem the License. You may obtain a copy of the License at +rem +rem http://www.apache.org/licenses/LICENSE-2.0 +rem +rem Unless required by applicable law or agreed to in writing, software +rem distributed under the License is distributed on an "AS IS" BASIS, +rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +rem See the License for the specific language governing permissions and +rem limitations under the License. +rem + +rem This script loads spark-env.cmd if it exists, and ensures it is only loaded once. +rem spark-env.cmd is loaded from SPARK_CONF_DIR if set, or within the current directory's +rem conf/ subdirectory. + +if [%SPARK_ENV_LOADED%] == [] ( + set SPARK_ENV_LOADED=1 + + if not [%SPARK_CONF_DIR%] == [] ( + set user_conf_dir=%SPARK_CONF_DIR% + ) else ( + set user_conf_dir=%~dp0..\..\conf + ) + + call :LoadSparkEnv +) + +rem Setting SPARK_SCALA_VERSION if not already set. + +set ASSEMBLY_DIR2=%SPARK_HOME%/assembly/target/scala-2.11 +set ASSEMBLY_DIR1=%SPARK_HOME%/assembly/target/scala-2.10 + +if [%SPARK_SCALA_VERSION%] == [] ( + + if exist %ASSEMBLY_DIR2% if exist %ASSEMBLY_DIR1% ( + echo "Presence of build for both scala versions(SCALA 2.10 and SCALA 2.11) detected." + echo "Either clean one of them or, set SPARK_SCALA_VERSION=2.11 in spark-env.cmd." + exit 1 + ) + if exist %ASSEMBLY_DIR2% ( + set SPARK_SCALA_VERSION=2.11 + ) else ( + set SPARK_SCALA_VERSION=2.10 + ) +) +exit /b 0 + +:LoadSparkEnv +if exist "%user_conf_dir%\spark-env.cmd" ( + call "%user_conf_dir%\spark-env.cmd" +) diff --git a/bin/load-spark-env.sh b/bin/load-spark-env.sh index 356b3d49b2ffe..95779e9ddbb18 100644 --- a/bin/load-spark-env.sh +++ b/bin/load-spark-env.sh @@ -20,6 +20,7 @@ # This script loads spark-env.sh if it exists, and ensures it is only loaded once. # spark-env.sh is loaded from SPARK_CONF_DIR if set, or within the current directory's # conf/ subdirectory. +FWDIR="$(cd "`dirname "$0"`"/..; pwd)" if [ -z "$SPARK_ENV_LOADED" ]; then export SPARK_ENV_LOADED=1 @@ -43,7 +44,7 @@ if [ -z "$SPARK_SCALA_VERSION" ]; then ASSEMBLY_DIR2="$FWDIR/assembly/target/scala-2.11" ASSEMBLY_DIR1="$FWDIR/assembly/target/scala-2.10" - + if [[ -d "$ASSEMBLY_DIR2" && -d "$ASSEMBLY_DIR1" ]]; then echo -e "Presence of build for both scala versions(SCALA 2.10 and SCALA 2.11) detected." 1>&2 echo -e 'Either clean one of them or, export SPARK_SCALA_VERSION=2.11 in spark-env.sh.' 1>&2 @@ -54,5 +55,5 @@ if [ -z "$SPARK_SCALA_VERSION" ]; then export SPARK_SCALA_VERSION="2.11" else export SPARK_SCALA_VERSION="2.10" - fi + fi fi diff --git a/bin/pyspark b/bin/pyspark index 0b4f695dd06dd..8acad6113797d 100755 --- a/bin/pyspark +++ b/bin/pyspark @@ -18,36 +18,24 @@ # # Figure out where Spark is installed -FWDIR="$(cd "`dirname "$0"`"/..; pwd)" +export SPARK_HOME="$(cd "`dirname "$0"`"/..; pwd)" -# Export this as SPARK_HOME -export SPARK_HOME="$FWDIR" - -source "$FWDIR/bin/utils.sh" - -source "$FWDIR"/bin/load-spark-env.sh +source "$SPARK_HOME"/bin/load-spark-env.sh function usage() { + if [ -n "$1" ]; then + echo $1 + fi echo "Usage: ./bin/pyspark [options]" 1>&2 - "$FWDIR"/bin/spark-submit --help 2>&1 | grep -v Usage 1>&2 - exit 0 + "$SPARK_HOME"/bin/spark-submit --help 2>&1 | grep -v Usage 1>&2 + exit $2 } +export -f usage if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then usage fi -# Exit if the user hasn't compiled Spark -if [ ! -f "$FWDIR/RELEASE" ]; then - # Exit if the user hasn't compiled Spark - ls "$FWDIR"/assembly/target/scala-$SPARK_SCALA_VERSION/spark-assembly*hadoop*.jar >& /dev/null - if [[ $? != 0 ]]; then - echo "Failed to find Spark assembly in $FWDIR/assembly/target" 1>&2 - echo "You need to build Spark before running this program" 1>&2 - exit 1 - fi -fi - # In Spark <= 1.1, setting IPYTHON=1 would cause the driver to be launched using the `ipython` # executable, while the worker would still be launched using PYSPARK_PYTHON. # @@ -95,26 +83,13 @@ export PYTHONPATH="$SPARK_HOME/python/lib/py4j-0.8.2.1-src.zip:$PYTHONPATH" # Load the PySpark shell.py script when ./pyspark is used interactively: export OLD_PYTHONSTARTUP="$PYTHONSTARTUP" -export PYTHONSTARTUP="$FWDIR/python/pyspark/shell.py" - -# Build up arguments list manually to preserve quotes and backslashes. -# We export Spark submit arguments as an environment variable because shell.py must run as a -# PYTHONSTARTUP script, which does not take in arguments. This is required for IPython notebooks. -SUBMIT_USAGE_FUNCTION=usage -gatherSparkSubmitOpts "$@" -PYSPARK_SUBMIT_ARGS="" -whitespace="[[:space:]]" -for i in "${SUBMISSION_OPTS[@]}"; do - if [[ $i =~ \" ]]; then i=$(echo $i | sed 's/\"/\\\"/g'); fi - if [[ $i =~ $whitespace ]]; then i=\"$i\"; fi - PYSPARK_SUBMIT_ARGS="$PYSPARK_SUBMIT_ARGS $i" -done -export PYSPARK_SUBMIT_ARGS +export PYTHONSTARTUP="$SPARK_HOME/python/pyspark/shell.py" # For pyspark tests if [[ -n "$SPARK_TESTING" ]]; then unset YARN_CONF_DIR unset HADOOP_CONF_DIR + export PYTHONHASHSEED=0 if [[ -n "$PYSPARK_DOC_TEST" ]]; then exec "$PYSPARK_DRIVER_PYTHON" -m doctest $1 else @@ -123,14 +98,6 @@ if [[ -n "$SPARK_TESTING" ]]; then exit fi -# If a python file is provided, directly run spark-submit. -if [[ "$1" =~ \.py$ ]]; then - echo -e "\nWARNING: Running python applications through ./bin/pyspark is deprecated as of Spark 1.0." 1>&2 - echo -e "Use ./bin/spark-submit \n" 1>&2 - primary="$1" - shift - gatherSparkSubmitOpts "$@" - exec "$FWDIR"/bin/spark-submit "${SUBMISSION_OPTS[@]}" "$primary" "${APPLICATION_OPTS[@]}" -else - exec "$PYSPARK_DRIVER_PYTHON" $PYSPARK_DRIVER_PYTHON_OPTS -fi +export PYSPARK_DRIVER_PYTHON +export PYSPARK_DRIVER_PYTHON_OPTS +exec "$SPARK_HOME"/bin/spark-submit pyspark-shell-main "$@" diff --git a/bin/pyspark2.cmd b/bin/pyspark2.cmd index a542ec80b49d6..09b4149c2a439 100644 --- a/bin/pyspark2.cmd +++ b/bin/pyspark2.cmd @@ -17,59 +17,21 @@ rem See the License for the specific language governing permissions and rem limitations under the License. rem -set SCALA_VERSION=2.10 - rem Figure out where the Spark framework is installed -set FWDIR=%~dp0..\ - -rem Export this as SPARK_HOME -set SPARK_HOME=%FWDIR% - -rem Test whether the user has built Spark -if exist "%FWDIR%RELEASE" goto skip_build_test -set FOUND_JAR=0 -for %%d in ("%FWDIR%assembly\target\scala-%SCALA_VERSION%\spark-assembly*hadoop*.jar") do ( - set FOUND_JAR=1 -) -if [%FOUND_JAR%] == [0] ( - echo Failed to find Spark assembly JAR. - echo You need to build Spark before running this program. - goto exit -) -:skip_build_test +set SPARK_HOME=%~dp0.. -rem Load environment variables from conf\spark-env.cmd, if it exists -if exist "%FWDIR%conf\spark-env.cmd" call "%FWDIR%conf\spark-env.cmd" +call %SPARK_HOME%\bin\load-spark-env.cmd rem Figure out which Python to use. -if [%PYSPARK_PYTHON%] == [] set PYSPARK_PYTHON=python +if "x%PYSPARK_DRIVER_PYTHON%"=="x" ( + set PYSPARK_DRIVER_PYTHON=python + if not [%PYSPARK_PYTHON%] == [] set PYSPARK_DRIVER_PYTHON=%PYSPARK_PYTHON% +) -set PYTHONPATH=%FWDIR%python;%PYTHONPATH% -set PYTHONPATH=%FWDIR%python\lib\py4j-0.8.2.1-src.zip;%PYTHONPATH% +set PYTHONPATH=%SPARK_HOME%\python;%PYTHONPATH% +set PYTHONPATH=%SPARK_HOME%\python\lib\py4j-0.8.2.1-src.zip;%PYTHONPATH% set OLD_PYTHONSTARTUP=%PYTHONSTARTUP% -set PYTHONSTARTUP=%FWDIR%python\pyspark\shell.py -set PYSPARK_SUBMIT_ARGS=%* - -echo Running %PYSPARK_PYTHON% with PYTHONPATH=%PYTHONPATH% - -rem Check whether the argument is a file -for /f %%i in ('echo %1^| findstr /R "\.py"') do ( - set PYTHON_FILE=%%i -) - -if [%PYTHON_FILE%] == [] ( - if [%IPYTHON%] == [1] ( - ipython %IPYTHON_OPTS% - ) else ( - %PYSPARK_PYTHON% - ) -) else ( - echo. - echo WARNING: Running python applications through ./bin/pyspark.cmd is deprecated as of Spark 1.0. - echo Use ./bin/spark-submit ^ - echo. - "%FWDIR%\bin\spark-submit.cmd" %PYSPARK_SUBMIT_ARGS% -) +set PYTHONSTARTUP=%SPARK_HOME%\python\pyspark\shell.py -:exit +call %SPARK_HOME%\bin\spark-submit2.cmd pyspark-shell-main %* diff --git a/bin/run-example b/bin/run-example index c567acf9a6b5c..798e2caeb88ce 100755 --- a/bin/run-example +++ b/bin/run-example @@ -42,7 +42,7 @@ fi JAR_COUNT=0 -for f in ${JAR_PATH}/spark-examples-*hadoop*.jar; do +for f in "${JAR_PATH}"/spark-examples-*hadoop*.jar; do if [[ ! -e "$f" ]]; then echo "Failed to find Spark examples assembly in $FWDIR/lib or $FWDIR/examples/target" 1>&2 echo "You need to build Spark before running this program" 1>&2 @@ -54,7 +54,7 @@ done if [ "$JAR_COUNT" -gt "1" ]; then echo "Found multiple Spark examples assembly jars in ${JAR_PATH}" 1>&2 - ls ${JAR_PATH}/spark-examples-*hadoop*.jar 1>&2 + ls "${JAR_PATH}"/spark-examples-*hadoop*.jar 1>&2 echo "Please remove all but one jar." 1>&2 exit 1 fi @@ -67,7 +67,7 @@ if [[ ! $EXAMPLE_CLASS == org.apache.spark.examples* ]]; then EXAMPLE_CLASS="org.apache.spark.examples.$EXAMPLE_CLASS" fi -"$FWDIR"/bin/spark-submit \ +exec "$FWDIR"/bin/spark-submit \ --master $EXAMPLE_MASTER \ --class $EXAMPLE_CLASS \ "$SPARK_EXAMPLES_JAR" \ diff --git a/bin/run-example2.cmd b/bin/run-example2.cmd index b49d0dcb4ff2d..c3e0221fb62e3 100644 --- a/bin/run-example2.cmd +++ b/bin/run-example2.cmd @@ -25,8 +25,7 @@ set FWDIR=%~dp0..\ rem Export this as SPARK_HOME set SPARK_HOME=%FWDIR% -rem Load environment variables from conf\spark-env.cmd, if it exists -if exist "%FWDIR%conf\spark-env.cmd" call "%FWDIR%conf\spark-env.cmd" +call %SPARK_HOME%\bin\load-spark-env.cmd rem Test that an argument was given if not "x%1"=="x" goto arg_given diff --git a/bin/spark-class b/bin/spark-class index 2f0441bb3c1c2..c49d97ce5cf25 100755 --- a/bin/spark-class +++ b/bin/spark-class @@ -16,89 +16,18 @@ # See the License for the specific language governing permissions and # limitations under the License. # - -# NOTE: Any changes to this file must be reflected in SparkSubmitDriverBootstrapper.scala! - -cygwin=false -case "`uname`" in - CYGWIN*) cygwin=true;; -esac +set -e # Figure out where Spark is installed -FWDIR="$(cd "`dirname "$0"`"/..; pwd)" - -# Export this as SPARK_HOME -export SPARK_HOME="$FWDIR" -export SPARK_CONF_DIR="${SPARK_CONF_DIR:-"$SPARK_HOME/conf"}" +export SPARK_HOME="$(cd "`dirname "$0"`"/..; pwd)" -. "$FWDIR"/bin/load-spark-env.sh +. "$SPARK_HOME"/bin/load-spark-env.sh if [ -z "$1" ]; then echo "Usage: spark-class []" 1>&2 exit 1 fi -if [ -n "$SPARK_MEM" ]; then - echo -e "Warning: SPARK_MEM is deprecated, please use a more specific config option" 1>&2 - echo -e "(e.g., spark.executor.memory or spark.driver.memory)." 1>&2 -fi - -# Use SPARK_MEM or 512m as the default memory, to be overridden by specific options -DEFAULT_MEM=${SPARK_MEM:-512m} - -SPARK_DAEMON_JAVA_OPTS="$SPARK_DAEMON_JAVA_OPTS -Dspark.akka.logLifecycleEvents=true" - -# Add java opts and memory settings for master, worker, history server, executors, and repl. -case "$1" in - # Master, Worker, and HistoryServer use SPARK_DAEMON_JAVA_OPTS (and specific opts) + SPARK_DAEMON_MEMORY. - 'org.apache.spark.deploy.master.Master') - OUR_JAVA_OPTS="$SPARK_DAEMON_JAVA_OPTS $SPARK_MASTER_OPTS" - OUR_JAVA_MEM=${SPARK_DAEMON_MEMORY:-$DEFAULT_MEM} - ;; - 'org.apache.spark.deploy.worker.Worker') - OUR_JAVA_OPTS="$SPARK_DAEMON_JAVA_OPTS $SPARK_WORKER_OPTS" - OUR_JAVA_MEM=${SPARK_DAEMON_MEMORY:-$DEFAULT_MEM} - ;; - 'org.apache.spark.deploy.history.HistoryServer') - OUR_JAVA_OPTS="$SPARK_DAEMON_JAVA_OPTS $SPARK_HISTORY_OPTS" - OUR_JAVA_MEM=${SPARK_DAEMON_MEMORY:-$DEFAULT_MEM} - ;; - - # Executors use SPARK_JAVA_OPTS + SPARK_EXECUTOR_MEMORY. - 'org.apache.spark.executor.CoarseGrainedExecutorBackend') - OUR_JAVA_OPTS="$SPARK_JAVA_OPTS $SPARK_EXECUTOR_OPTS" - OUR_JAVA_MEM=${SPARK_EXECUTOR_MEMORY:-$DEFAULT_MEM} - ;; - 'org.apache.spark.executor.MesosExecutorBackend') - OUR_JAVA_OPTS="$SPARK_JAVA_OPTS $SPARK_EXECUTOR_OPTS" - OUR_JAVA_MEM=${SPARK_EXECUTOR_MEMORY:-$DEFAULT_MEM} - export PYTHONPATH="$FWDIR/python:$PYTHONPATH" - export PYTHONPATH="$FWDIR/python/lib/py4j-0.8.2.1-src.zip:$PYTHONPATH" - ;; - - # Spark submit uses SPARK_JAVA_OPTS + SPARK_SUBMIT_OPTS + - # SPARK_DRIVER_MEMORY + SPARK_SUBMIT_DRIVER_MEMORY. - 'org.apache.spark.deploy.SparkSubmit') - OUR_JAVA_OPTS="$SPARK_JAVA_OPTS $SPARK_SUBMIT_OPTS" - OUR_JAVA_MEM=${SPARK_DRIVER_MEMORY:-$DEFAULT_MEM} - if [ -n "$SPARK_SUBMIT_LIBRARY_PATH" ]; then - if [[ $OSTYPE == darwin* ]]; then - export DYLD_LIBRARY_PATH="$SPARK_SUBMIT_LIBRARY_PATH:$DYLD_LIBRARY_PATH" - else - export LD_LIBRARY_PATH="$SPARK_SUBMIT_LIBRARY_PATH:$LD_LIBRARY_PATH" - fi - fi - if [ -n "$SPARK_SUBMIT_DRIVER_MEMORY" ]; then - OUR_JAVA_MEM="$SPARK_SUBMIT_DRIVER_MEMORY" - fi - ;; - - *) - OUR_JAVA_OPTS="$SPARK_JAVA_OPTS" - OUR_JAVA_MEM=${SPARK_DRIVER_MEMORY:-$DEFAULT_MEM} - ;; -esac - # Find the java binary if [ -n "${JAVA_HOME}" ]; then RUNNER="${JAVA_HOME}/bin/java" @@ -110,83 +39,68 @@ else exit 1 fi fi -JAVA_VERSION=$("$RUNNER" -version 2>&1 | grep 'version' | sed 's/.* version "\(.*\)\.\(.*\)\..*"/\1\2/; 1q') -# Set JAVA_OPTS to be able to load native libraries and to set heap size -if [ "$JAVA_VERSION" -ge 18 ]; then - JAVA_OPTS="$OUR_JAVA_OPTS" +# Find assembly jar +SPARK_ASSEMBLY_JAR= +if [ -f "$SPARK_HOME/RELEASE" ]; then + ASSEMBLY_DIR="$SPARK_HOME/lib" else - JAVA_OPTS="-XX:MaxPermSize=128m $OUR_JAVA_OPTS" + ASSEMBLY_DIR="$SPARK_HOME/assembly/target/scala-$SPARK_SCALA_VERSION" fi -JAVA_OPTS="$JAVA_OPTS -Xms$OUR_JAVA_MEM -Xmx$OUR_JAVA_MEM" -# Load extra JAVA_OPTS from conf/java-opts, if it exists -if [ -e "$SPARK_CONF_DIR/java-opts" ] ; then - JAVA_OPTS="$JAVA_OPTS `cat "$SPARK_CONF_DIR"/java-opts`" -fi - -# Attention: when changing the way the JAVA_OPTS are assembled, the change must be reflected in CommandUtils.scala! - -TOOLS_DIR="$FWDIR"/tools -SPARK_TOOLS_JAR="" -if [ -e "$TOOLS_DIR"/target/scala-$SPARK_SCALA_VERSION/spark-tools*[0-9Tg].jar ]; then - # Use the JAR from the SBT build - export SPARK_TOOLS_JAR="`ls "$TOOLS_DIR"/target/scala-$SPARK_SCALA_VERSION/spark-tools*[0-9Tg].jar`" +num_jars="$(ls -1 "$ASSEMBLY_DIR" | grep "^spark-assembly.*hadoop.*\.jar$" | wc -l)" +if [ "$num_jars" -eq "0" -a -z "$SPARK_ASSEMBLY_JAR" ]; then + echo "Failed to find Spark assembly in $ASSEMBLY_DIR." 1>&2 + echo "You need to build Spark before running this program." 1>&2 + exit 1 fi -if [ -e "$TOOLS_DIR"/target/spark-tools*[0-9Tg].jar ]; then - # Use the JAR from the Maven build - # TODO: this also needs to become an assembly! - export SPARK_TOOLS_JAR="`ls "$TOOLS_DIR"/target/spark-tools*[0-9Tg].jar`" +ASSEMBLY_JARS="$(ls -1 "$ASSEMBLY_DIR" | grep "^spark-assembly.*hadoop.*\.jar$" || true)" +if [ "$num_jars" -gt "1" ]; then + echo "Found multiple Spark assembly jars in $ASSEMBLY_DIR:" 1>&2 + echo "$ASSEMBLY_JARS" 1>&2 + echo "Please remove all but one jar." 1>&2 + exit 1 fi -# Compute classpath using external script -classpath_output=$("$FWDIR"/bin/compute-classpath.sh) -if [[ "$?" != "0" ]]; then - echo "$classpath_output" - exit 1 +SPARK_ASSEMBLY_JAR="${ASSEMBLY_DIR}/${ASSEMBLY_JARS}" + +# Verify that versions of java used to build the jars and run Spark are compatible +if [ -n "$JAVA_HOME" ]; then + JAR_CMD="$JAVA_HOME/bin/jar" else - CLASSPATH="$classpath_output" + JAR_CMD="jar" fi -if [[ "$1" =~ org.apache.spark.tools.* ]]; then - if test -z "$SPARK_TOOLS_JAR"; then - echo "Failed to find Spark Tools Jar in $FWDIR/tools/target/scala-$SPARK_SCALA_VERSION/" 1>&2 - echo "You need to run \"build/sbt tools/package\" before running $1." 1>&2 +if [ $(command -v "$JAR_CMD") ] ; then + jar_error_check=$("$JAR_CMD" -tf "$SPARK_ASSEMBLY_JAR" nonexistent/class/path 2>&1) + if [[ "$jar_error_check" =~ "invalid CEN header" ]]; then + echo "Loading Spark jar with '$JAR_CMD' failed. " 1>&2 + echo "This is likely because Spark was compiled with Java 7 and run " 1>&2 + echo "with Java 6. (see SPARK-1703). Please use Java 7 to run Spark " 1>&2 + echo "or build Spark with Java 6." 1>&2 exit 1 fi - CLASSPATH="$CLASSPATH:$SPARK_TOOLS_JAR" fi -if $cygwin; then - CLASSPATH="`cygpath -wp "$CLASSPATH"`" - if [ "$1" == "org.apache.spark.tools.JavaAPICompletenessChecker" ]; then - export SPARK_TOOLS_JAR="`cygpath -w "$SPARK_TOOLS_JAR"`" - fi +LAUNCH_CLASSPATH="$SPARK_ASSEMBLY_JAR" + +# Add the launcher build dir to the classpath if requested. +if [ -n "$SPARK_PREPEND_CLASSES" ]; then + LAUNCH_CLASSPATH="$SPARK_HOME/launcher/target/scala-$SPARK_SCALA_VERSION/classes:$LAUNCH_CLASSPATH" fi -export CLASSPATH -# In Spark submit client mode, the driver is launched in the same JVM as Spark submit itself. -# Here we must parse the properties file for relevant "spark.driver.*" configs before launching -# the driver JVM itself. Instead of handling this complexity in Bash, we launch a separate JVM -# to prepare the launch environment of this driver JVM. +export _SPARK_ASSEMBLY="$SPARK_ASSEMBLY_JAR" + +# The launcher library will print arguments separated by a NULL character, to allow arguments with +# characters that would be otherwise interpreted by the shell. Read that in a while loop, populating +# an array that will be used to exec the final command. +CMD=() +while IFS= read -d '' -r ARG; do + CMD+=("$ARG") +done < <("$RUNNER" -cp "$LAUNCH_CLASSPATH" org.apache.spark.launcher.Main "$@") -if [ -n "$SPARK_SUBMIT_BOOTSTRAP_DRIVER" ]; then - # This is used only if the properties file actually contains these special configs - # Export the environment variables needed by SparkSubmitDriverBootstrapper - export RUNNER - export CLASSPATH - export JAVA_OPTS - export OUR_JAVA_MEM - export SPARK_CLASS=1 - shift # Ignore main class (org.apache.spark.deploy.SparkSubmit) and use our own - exec "$RUNNER" org.apache.spark.deploy.SparkSubmitDriverBootstrapper "$@" +if [ "${CMD[0]}" = "usage" ]; then + "${CMD[@]}" else - # Note: The format of this command is closely echoed in SparkSubmitDriverBootstrapper.scala - if [ -n "$SPARK_PRINT_LAUNCH_COMMAND" ]; then - echo -n "Spark Command: " 1>&2 - echo "$RUNNER" -cp "$CLASSPATH" $JAVA_OPTS "$@" 1>&2 - echo -e "========================================\n" 1>&2 - fi - exec "$RUNNER" -cp "$CLASSPATH" $JAVA_OPTS "$@" + exec "${CMD[@]}" fi - diff --git a/bin/spark-class2.cmd b/bin/spark-class2.cmd index da46543647efd..3d068dd3a2739 100644 --- a/bin/spark-class2.cmd +++ b/bin/spark-class2.cmd @@ -17,135 +17,51 @@ rem See the License for the specific language governing permissions and rem limitations under the License. rem -rem Any changes to this file must be reflected in SparkSubmitDriverBootstrapper.scala! - -setlocal enabledelayedexpansion - -set SCALA_VERSION=2.10 - rem Figure out where the Spark framework is installed -set FWDIR=%~dp0..\ - -rem Export this as SPARK_HOME -set SPARK_HOME=%FWDIR% +set SPARK_HOME=%~dp0.. -rem Load environment variables from conf\spark-env.cmd, if it exists -if exist "%FWDIR%conf\spark-env.cmd" call "%FWDIR%conf\spark-env.cmd" +call %SPARK_HOME%\bin\load-spark-env.cmd rem Test that an argument was given -if not "x%1"=="x" goto arg_given +if "x%1"=="x" ( echo Usage: spark-class ^ [^] - goto exit -:arg_given - -if not "x%SPARK_MEM%"=="x" ( - echo Warning: SPARK_MEM is deprecated, please use a more specific config option - echo e.g., spark.executor.memory or spark.driver.memory. + exit /b 1 ) -rem Use SPARK_MEM or 512m as the default memory, to be overridden by specific options -set OUR_JAVA_MEM=%SPARK_MEM% -if "x%OUR_JAVA_MEM%"=="x" set OUR_JAVA_MEM=512m - -set SPARK_DAEMON_JAVA_OPTS=%SPARK_DAEMON_JAVA_OPTS% -Dspark.akka.logLifecycleEvents=true - -rem Add java opts and memory settings for master, worker, history server, executors, and repl. -rem Master, Worker and HistoryServer use SPARK_DAEMON_JAVA_OPTS (and specific opts) + SPARK_DAEMON_MEMORY. -if "%1"=="org.apache.spark.deploy.master.Master" ( - set OUR_JAVA_OPTS=%SPARK_DAEMON_JAVA_OPTS% %SPARK_MASTER_OPTS% - if not "x%SPARK_DAEMON_MEMORY%"=="x" set OUR_JAVA_MEM=%SPARK_DAEMON_MEMORY% -) else if "%1"=="org.apache.spark.deploy.worker.Worker" ( - set OUR_JAVA_OPTS=%SPARK_DAEMON_JAVA_OPTS% %SPARK_WORKER_OPTS% - if not "x%SPARK_DAEMON_MEMORY%"=="x" set OUR_JAVA_MEM=%SPARK_DAEMON_MEMORY% -) else if "%1"=="org.apache.spark.deploy.history.HistoryServer" ( - set OUR_JAVA_OPTS=%SPARK_DAEMON_JAVA_OPTS% %SPARK_HISTORY_OPTS% - if not "x%SPARK_DAEMON_MEMORY%"=="x" set OUR_JAVA_MEM=%SPARK_DAEMON_MEMORY% - -rem Executors use SPARK_JAVA_OPTS + SPARK_EXECUTOR_MEMORY. -) else if "%1"=="org.apache.spark.executor.CoarseGrainedExecutorBackend" ( - set OUR_JAVA_OPTS=%SPARK_JAVA_OPTS% %SPARK_EXECUTOR_OPTS% - if not "x%SPARK_EXECUTOR_MEMORY%"=="x" set OUR_JAVA_MEM=%SPARK_EXECUTOR_MEMORY% -) else if "%1"=="org.apache.spark.executor.MesosExecutorBackend" ( - set OUR_JAVA_OPTS=%SPARK_JAVA_OPTS% %SPARK_EXECUTOR_OPTS% - if not "x%SPARK_EXECUTOR_MEMORY%"=="x" set OUR_JAVA_MEM=%SPARK_EXECUTOR_MEMORY% +rem Find assembly jar +set SPARK_ASSEMBLY_JAR=0 -rem Spark submit uses SPARK_JAVA_OPTS + SPARK_SUBMIT_OPTS + -rem SPARK_DRIVER_MEMORY + SPARK_SUBMIT_DRIVER_MEMORY. -rem The repl also uses SPARK_REPL_OPTS. -) else if "%1"=="org.apache.spark.deploy.SparkSubmit" ( - set OUR_JAVA_OPTS=%SPARK_JAVA_OPTS% %SPARK_SUBMIT_OPTS% %SPARK_REPL_OPTS% - if not "x%SPARK_SUBMIT_LIBRARY_PATH%"=="x" ( - set OUR_JAVA_OPTS=!OUR_JAVA_OPTS! -Djava.library.path=%SPARK_SUBMIT_LIBRARY_PATH% - ) else if not "x%SPARK_LIBRARY_PATH%"=="x" ( - set OUR_JAVA_OPTS=!OUR_JAVA_OPTS! -Djava.library.path=%SPARK_LIBRARY_PATH% - ) - if not "x%SPARK_DRIVER_MEMORY%"=="x" set OUR_JAVA_MEM=%SPARK_DRIVER_MEMORY% - if not "x%SPARK_SUBMIT_DRIVER_MEMORY%"=="x" set OUR_JAVA_MEM=%SPARK_SUBMIT_DRIVER_MEMORY% +if exist "%SPARK_HOME%\RELEASE" ( + set ASSEMBLY_DIR=%SPARK_HOME%\lib ) else ( - set OUR_JAVA_OPTS=%SPARK_JAVA_OPTS% - if not "x%SPARK_DRIVER_MEMORY%"=="x" set OUR_JAVA_MEM=%SPARK_DRIVER_MEMORY% + set ASSEMBLY_DIR=%SPARK_HOME%\assembly\target\scala-%SPARK_SCALA_VERSION% ) -rem Set JAVA_OPTS to be able to load native libraries and to set heap size -for /f "tokens=3" %%i in ('java -version 2^>^&1 ^| find "version"') do set jversion=%%i -for /f "tokens=1 delims=_" %%i in ("%jversion:~1,-1%") do set jversion=%%i -if "%jversion%" geq "1.8.0" ( - set JAVA_OPTS=%OUR_JAVA_OPTS% -Xms%OUR_JAVA_MEM% -Xmx%OUR_JAVA_MEM% -) else ( - set JAVA_OPTS=-XX:MaxPermSize=128m %OUR_JAVA_OPTS% -Xms%OUR_JAVA_MEM% -Xmx%OUR_JAVA_MEM% -) -rem Attention: when changing the way the JAVA_OPTS are assembled, the change must be reflected in CommandUtils.scala! - -rem Test whether the user has built Spark -if exist "%FWDIR%RELEASE" goto skip_build_test -set FOUND_JAR=0 -for %%d in ("%FWDIR%assembly\target\scala-%SCALA_VERSION%\spark-assembly*hadoop*.jar") do ( - set FOUND_JAR=1 +for %%d in (%ASSEMBLY_DIR%\spark-assembly*hadoop*.jar) do ( + set SPARK_ASSEMBLY_JAR=%%d ) -if "%FOUND_JAR%"=="0" ( +if "%SPARK_ASSEMBLY_JAR%"=="0" ( echo Failed to find Spark assembly JAR. echo You need to build Spark before running this program. - goto exit + exit /b 1 ) -:skip_build_test -set TOOLS_DIR=%FWDIR%tools -set SPARK_TOOLS_JAR= -for %%d in ("%TOOLS_DIR%\target\scala-%SCALA_VERSION%\spark-tools*assembly*.jar") do ( - set SPARK_TOOLS_JAR=%%d +set LAUNCH_CLASSPATH=%SPARK_ASSEMBLY_JAR% + +rem Add the launcher build dir to the classpath if requested. +if not "x%SPARK_PREPEND_CLASSES%"=="x" ( + set LAUNCH_CLASSPATH=%SPARK_HOME%\launcher\target\scala-%SPARK_SCALA_VERSION%\classes;%LAUNCH_CLASSPATH% ) -rem Compute classpath using external script -set DONT_PRINT_CLASSPATH=1 -call "%FWDIR%bin\compute-classpath.cmd" -set DONT_PRINT_CLASSPATH=0 -set CLASSPATH=%CLASSPATH%;%SPARK_TOOLS_JAR% +set _SPARK_ASSEMBLY=%SPARK_ASSEMBLY_JAR% rem Figure out where java is. set RUNNER=java if not "x%JAVA_HOME%"=="x" set RUNNER=%JAVA_HOME%\bin\java -rem In Spark submit client mode, the driver is launched in the same JVM as Spark submit itself. -rem Here we must parse the properties file for relevant "spark.driver.*" configs before launching -rem the driver JVM itself. Instead of handling this complexity here, we launch a separate JVM -rem to prepare the launch environment of this driver JVM. - -rem In this case, leave out the main class (org.apache.spark.deploy.SparkSubmit) and use our own. -rem Leaving out the first argument is surprisingly difficult to do in Windows. Note that this must -rem be done here because the Windows "shift" command does not work in a conditional block. -set BOOTSTRAP_ARGS= -shift -:start_parse -if "%~1" == "" goto end_parse -set BOOTSTRAP_ARGS=%BOOTSTRAP_ARGS% %~1 -shift -goto start_parse -:end_parse - -if not [%SPARK_SUBMIT_BOOTSTRAP_DRIVER%] == [] ( - set SPARK_CLASS=1 - "%RUNNER%" org.apache.spark.deploy.SparkSubmitDriverBootstrapper %BOOTSTRAP_ARGS% -) else ( - "%RUNNER%" -cp "%CLASSPATH%" %JAVA_OPTS% %* +rem The launcher library prints the command to be executed in a single line suitable for being +rem executed by the batch interpreter. So read all the output of the launcher into a variable. +for /f "tokens=*" %%i in ('cmd /C ""%RUNNER%" -cp %LAUNCH_CLASSPATH% org.apache.spark.launcher.Main %*"') do ( + set SPARK_CMD=%%i ) -:exit +%SPARK_CMD% diff --git a/bin/spark-shell b/bin/spark-shell index cca5aa0676123..b3761b5e1375b 100755 --- a/bin/spark-shell +++ b/bin/spark-shell @@ -28,25 +28,24 @@ esac # Enter posix mode for bash set -o posix -## Global script variables -FWDIR="$(cd "`dirname "$0"`"/..; pwd)" +export FWDIR="$(cd "`dirname "$0"`"/..; pwd)" -function usage() { +usage() { + if [ -n "$1" ]; then + echo "$1" + fi echo "Usage: ./bin/spark-shell [options]" "$FWDIR"/bin/spark-submit --help 2>&1 | grep -v Usage 1>&2 - exit 0 + exit "$2" } +export -f usage if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then - usage + usage "" 0 fi -source "$FWDIR"/bin/utils.sh -SUBMIT_USAGE_FUNCTION=usage -gatherSparkSubmitOpts "$@" - # SPARK-4161: scala does not assume use of the java classpath, -# so we need to add the "-Dscala.usejavacp=true" flag mnually. We +# so we need to add the "-Dscala.usejavacp=true" flag manually. We # do this specifically for the Spark shell because the scala REPL # has its own class loader, and any additional classpath specified # through spark.driver.extraClassPath is not automatically propagated. @@ -61,11 +60,11 @@ function main() { # (see https://github.com/sbt/sbt/issues/562). stty -icanon min 1 -echo > /dev/null 2>&1 export SPARK_SUBMIT_OPTS="$SPARK_SUBMIT_OPTS -Djline.terminal=unix" - "$FWDIR"/bin/spark-submit --class org.apache.spark.repl.Main "${SUBMISSION_OPTS[@]}" spark-shell "${APPLICATION_OPTS[@]}" + "$FWDIR"/bin/spark-submit --class org.apache.spark.repl.Main "$@" stty icanon echo > /dev/null 2>&1 else export SPARK_SUBMIT_OPTS - "$FWDIR"/bin/spark-submit --class org.apache.spark.repl.Main "${SUBMISSION_OPTS[@]}" spark-shell "${APPLICATION_OPTS[@]}" + "$FWDIR"/bin/spark-submit --class org.apache.spark.repl.Main "$@" fi } diff --git a/bin/spark-shell2.cmd b/bin/spark-shell2.cmd index 1d1a40da315eb..02f51fe59a911 100644 --- a/bin/spark-shell2.cmd +++ b/bin/spark-shell2.cmd @@ -25,17 +25,28 @@ if %ERRORLEVEL% equ 0 ( exit /b 0 ) -call %SPARK_HOME%\bin\windows-utils.cmd %* -if %ERRORLEVEL% equ 1 ( +rem SPARK-4161: scala does not assume use of the java classpath, +rem so we need to add the "-Dscala.usejavacp=true" flag manually. We +rem do this specifically for the Spark shell because the scala REPL +rem has its own class loader, and any additional classpath specified +rem through spark.driver.extraClassPath is not automatically propagated. +if "x%SPARK_SUBMIT_OPTS%"=="x" ( + set SPARK_SUBMIT_OPTS=-Dscala.usejavacp=true + goto run_shell +) +set SPARK_SUBMIT_OPTS="%SPARK_SUBMIT_OPTS% -Dscala.usejavacp=true" + +:run_shell +call %SPARK_HOME%\bin\spark-submit2.cmd --class org.apache.spark.repl.Main %* +set SPARK_ERROR_LEVEL=%ERRORLEVEL% +if not "x%SPARK_LAUNCHER_USAGE_ERROR%"=="x" ( call :usage exit /b 1 ) - -cmd /V /E /C %SPARK_HOME%\bin\spark-submit.cmd --class org.apache.spark.repl.Main %SUBMISSION_OPTS% spark-shell %APPLICATION_OPTS% - -exit /b 0 +exit /b %SPARK_ERROR_LEVEL% :usage +echo %SPARK_LAUNCHER_USAGE_ERROR% echo "Usage: .\bin\spark-shell.cmd [options]" >&2 -%SPARK_HOME%\bin\spark-submit --help 2>&1 | findstr /V "Usage" 1>&2 -exit /b 0 +call %SPARK_HOME%\bin\spark-submit2.cmd --help 2>&1 | findstr /V "Usage" 1>&2 +goto :eof diff --git a/bin/spark-sql b/bin/spark-sql index 3b6cc420fea81..ca1729f4cfcb4 100755 --- a/bin/spark-sql +++ b/bin/spark-sql @@ -25,12 +25,15 @@ set -o posix # NOTE: This exact class name is matched downstream by SparkSubmit. # Any changes need to be reflected there. -CLASS="org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver" +export CLASS="org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver" # Figure out where Spark is installed -FWDIR="$(cd "`dirname "$0"`"/..; pwd)" +export FWDIR="$(cd "`dirname "$0"`"/..; pwd)" function usage { + if [ -n "$1" ]; then + echo "$1" + fi echo "Usage: ./bin/spark-sql [options] [cli option]" pattern="usage" pattern+="\|Spark assembly has been built with Hive" @@ -42,16 +45,13 @@ function usage { "$FWDIR"/bin/spark-submit --help 2>&1 | grep -v Usage 1>&2 echo echo "CLI options:" - "$FWDIR"/bin/spark-class $CLASS --help 2>&1 | grep -v "$pattern" 1>&2 + "$FWDIR"/bin/spark-class "$CLASS" --help 2>&1 | grep -v "$pattern" 1>&2 + exit "$2" } +export -f usage if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then - usage - exit 0 + usage "" 0 fi -source "$FWDIR"/bin/utils.sh -SUBMIT_USAGE_FUNCTION=usage -gatherSparkSubmitOpts "$@" - -exec "$FWDIR"/bin/spark-submit --class $CLASS "${SUBMISSION_OPTS[@]}" spark-internal "${APPLICATION_OPTS[@]}" +exec "$FWDIR"/bin/spark-submit --class "$CLASS" "$@" diff --git a/bin/spark-submit b/bin/spark-submit index 3e5cbdbb24394..0e0afe71a0f05 100755 --- a/bin/spark-submit +++ b/bin/spark-submit @@ -17,58 +17,21 @@ # limitations under the License. # -# NOTE: Any changes in this file must be reflected in SparkSubmitDriverBootstrapper.scala! - -export SPARK_HOME="$(cd "`dirname "$0"`"/..; pwd)" -ORIG_ARGS=("$@") - -# Set COLUMNS for progress bar -export COLUMNS=`tput cols` - -while (($#)); do - if [ "$1" = "--deploy-mode" ]; then - SPARK_SUBMIT_DEPLOY_MODE=$2 - elif [ "$1" = "--properties-file" ]; then - SPARK_SUBMIT_PROPERTIES_FILE=$2 - elif [ "$1" = "--driver-memory" ]; then - export SPARK_SUBMIT_DRIVER_MEMORY=$2 - elif [ "$1" = "--driver-library-path" ]; then - export SPARK_SUBMIT_LIBRARY_PATH=$2 - elif [ "$1" = "--driver-class-path" ]; then - export SPARK_SUBMIT_CLASSPATH=$2 - elif [ "$1" = "--driver-java-options" ]; then - export SPARK_SUBMIT_OPTS=$2 - elif [ "$1" = "--master" ]; then - export MASTER=$2 - fi - shift -done - -if [ -z "$SPARK_CONF_DIR" ]; then - export SPARK_CONF_DIR="$SPARK_HOME/conf" -fi -DEFAULT_PROPERTIES_FILE="$SPARK_CONF_DIR/spark-defaults.conf" -if [ "$MASTER" == "yarn-cluster" ]; then - SPARK_SUBMIT_DEPLOY_MODE=cluster +SPARK_HOME="$(cd "`dirname "$0"`"/..; pwd)" + +# disable randomized hash for string in Python 3.3+ +export PYTHONHASHSEED=0 + +# Only define a usage function if an upstream script hasn't done so. +if ! type -t usage >/dev/null 2>&1; then + usage() { + if [ -n "$1" ]; then + echo "$1" + fi + "$SPARK_HOME"/bin/spark-class org.apache.spark.deploy.SparkSubmit --help + exit "$2" + } + export -f usage fi -export SPARK_SUBMIT_DEPLOY_MODE=${SPARK_SUBMIT_DEPLOY_MODE:-"client"} -export SPARK_SUBMIT_PROPERTIES_FILE=${SPARK_SUBMIT_PROPERTIES_FILE:-"$DEFAULT_PROPERTIES_FILE"} - -# For client mode, the driver will be launched in the same JVM that launches -# SparkSubmit, so we may need to read the properties file for any extra class -# paths, library paths, java options and memory early on. Otherwise, it will -# be too late by the time the driver JVM has started. - -if [[ "$SPARK_SUBMIT_DEPLOY_MODE" == "client" && -f "$SPARK_SUBMIT_PROPERTIES_FILE" ]]; then - # Parse the properties file only if the special configs exist - contains_special_configs=$( - grep -e "spark.driver.extra*\|spark.driver.memory" "$SPARK_SUBMIT_PROPERTIES_FILE" | \ - grep -v "^[[:space:]]*#" - ) - if [ -n "$contains_special_configs" ]; then - export SPARK_SUBMIT_BOOTSTRAP_DRIVER=1 - fi -fi - -exec "$SPARK_HOME"/bin/spark-class org.apache.spark.deploy.SparkSubmit "${ORIG_ARGS[@]}" +exec "$SPARK_HOME"/bin/spark-class org.apache.spark.deploy.SparkSubmit "$@" diff --git a/bin/spark-submit2.cmd b/bin/spark-submit2.cmd index 446cbc74b74f9..d3fc4a5cc3f6e 100644 --- a/bin/spark-submit2.cmd +++ b/bin/spark-submit2.cmd @@ -17,62 +17,22 @@ rem See the License for the specific language governing permissions and rem limitations under the License. rem -rem NOTE: Any changes in this file must be reflected in SparkSubmitDriverBootstrapper.scala! - -set SPARK_HOME=%~dp0.. -set ORIG_ARGS=%* - -rem Reset the values of all variables used -set SPARK_SUBMIT_DEPLOY_MODE=client - -if [%SPARK_CONF_DIR%] == [] ( - set SPARK_CONF_DIR=%SPARK_HOME%\conf -) -set SPARK_SUBMIT_PROPERTIES_FILE=%SPARK_CONF_DIR%\spark-defaults.conf -set SPARK_SUBMIT_DRIVER_MEMORY= -set SPARK_SUBMIT_LIBRARY_PATH= -set SPARK_SUBMIT_CLASSPATH= -set SPARK_SUBMIT_OPTS= -set SPARK_SUBMIT_BOOTSTRAP_DRIVER= - -:loop -if [%1] == [] goto continue - if [%1] == [--deploy-mode] ( - set SPARK_SUBMIT_DEPLOY_MODE=%2 - ) else if [%1] == [--properties-file] ( - set SPARK_SUBMIT_PROPERTIES_FILE=%2 - ) else if [%1] == [--driver-memory] ( - set SPARK_SUBMIT_DRIVER_MEMORY=%2 - ) else if [%1] == [--driver-library-path] ( - set SPARK_SUBMIT_LIBRARY_PATH=%2 - ) else if [%1] == [--driver-class-path] ( - set SPARK_SUBMIT_CLASSPATH=%2 - ) else if [%1] == [--driver-java-options] ( - set SPARK_SUBMIT_OPTS=%2 - ) else if [%1] == [--master] ( - set MASTER=%2 - ) - shift -goto loop -:continue - -if [%MASTER%] == [yarn-cluster] ( - set SPARK_SUBMIT_DEPLOY_MODE=cluster -) - -rem For client mode, the driver will be launched in the same JVM that launches -rem SparkSubmit, so we may need to read the properties file for any extra class -rem paths, library paths, java options and memory early on. Otherwise, it will -rem be too late by the time the driver JVM has started. - -if [%SPARK_SUBMIT_DEPLOY_MODE%] == [client] ( - if exist %SPARK_SUBMIT_PROPERTIES_FILE% ( - rem Parse the properties file only if the special configs exist - for /f %%i in ('findstr /r /c:"^[\t ]*spark.driver.memory" /c:"^[\t ]*spark.driver.extra" ^ - %SPARK_SUBMIT_PROPERTIES_FILE%') do ( - set SPARK_SUBMIT_BOOTSTRAP_DRIVER=1 - ) - ) +rem This is the entry point for running Spark submit. To avoid polluting the +rem environment, it just launches a new cmd to do the real work. + +rem disable randomized hash for string in Python 3.3+ +set PYTHONHASHSEED=0 + +set CLASS=org.apache.spark.deploy.SparkSubmit +call %~dp0spark-class2.cmd %CLASS% %* +set SPARK_ERROR_LEVEL=%ERRORLEVEL% +if not "x%SPARK_LAUNCHER_USAGE_ERROR%"=="x" ( + call :usage + exit /b 1 ) +exit /b %SPARK_ERROR_LEVEL% -cmd /V /E /C %SPARK_HOME%\bin\spark-class.cmd org.apache.spark.deploy.SparkSubmit %ORIG_ARGS% +:usage +echo %SPARK_LAUNCHER_USAGE_ERROR% +call %SPARK_HOME%\bin\spark-class2.cmd %CLASS% --help +goto :eof diff --git a/bin/sparkR b/bin/sparkR new file mode 100755 index 0000000000000..8c918e2b09aef --- /dev/null +++ b/bin/sparkR @@ -0,0 +1,39 @@ +#!/bin/bash + +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# Figure out where Spark is installed +export SPARK_HOME="$(cd "`dirname "$0"`"/..; pwd)" + +source "$SPARK_HOME"/bin/load-spark-env.sh + +function usage() { + if [ -n "$1" ]; then + echo $1 + fi + echo "Usage: ./bin/sparkR [options]" 1>&2 + "$SPARK_HOME"/bin/spark-submit --help 2>&1 | grep -v Usage 1>&2 + exit $2 +} +export -f usage + +if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then + usage +fi + +exec "$SPARK_HOME"/bin/spark-submit sparkr-shell-main "$@" diff --git a/bin/sparkR.cmd b/bin/sparkR.cmd new file mode 100644 index 0000000000000..d7b60183ca8e0 --- /dev/null +++ b/bin/sparkR.cmd @@ -0,0 +1,23 @@ +@echo off + +rem +rem Licensed to the Apache Software Foundation (ASF) under one or more +rem contributor license agreements. See the NOTICE file distributed with +rem this work for additional information regarding copyright ownership. +rem The ASF licenses this file to You under the Apache License, Version 2.0 +rem (the "License"); you may not use this file except in compliance with +rem the License. You may obtain a copy of the License at +rem +rem http://www.apache.org/licenses/LICENSE-2.0 +rem +rem Unless required by applicable law or agreed to in writing, software +rem distributed under the License is distributed on an "AS IS" BASIS, +rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +rem See the License for the specific language governing permissions and +rem limitations under the License. +rem + +rem This is the entry point for running SparkR. To avoid polluting the +rem environment, it just launches a new cmd to do the real work. + +cmd /V /E /C %~dp0sparkR2.cmd %* diff --git a/bin/sparkR2.cmd b/bin/sparkR2.cmd new file mode 100644 index 0000000000000..e47f22c7300bb --- /dev/null +++ b/bin/sparkR2.cmd @@ -0,0 +1,26 @@ +@echo off + +rem +rem Licensed to the Apache Software Foundation (ASF) under one or more +rem contributor license agreements. See the NOTICE file distributed with +rem this work for additional information regarding copyright ownership. +rem The ASF licenses this file to You under the Apache License, Version 2.0 +rem (the "License"); you may not use this file except in compliance with +rem the License. You may obtain a copy of the License at +rem +rem http://www.apache.org/licenses/LICENSE-2.0 +rem +rem Unless required by applicable law or agreed to in writing, software +rem distributed under the License is distributed on an "AS IS" BASIS, +rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +rem See the License for the specific language governing permissions and +rem limitations under the License. +rem + +rem Figure out where the Spark framework is installed +set SPARK_HOME=%~dp0.. + +call %SPARK_HOME%\bin\load-spark-env.cmd + + +call %SPARK_HOME%\bin\spark-submit2.cmd sparkr-shell-main %* diff --git a/bin/utils.sh b/bin/utils.sh deleted file mode 100755 index 2241200082018..0000000000000 --- a/bin/utils.sh +++ /dev/null @@ -1,59 +0,0 @@ -#!/usr/bin/env bash - -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -# Gather all spark-submit options into SUBMISSION_OPTS -function gatherSparkSubmitOpts() { - - if [ -z "$SUBMIT_USAGE_FUNCTION" ]; then - echo "Function for printing usage of $0 is not set." 1>&2 - echo "Please set usage function to shell variable 'SUBMIT_USAGE_FUNCTION' in $0" 1>&2 - exit 1 - fi - - # NOTE: If you add or remove spark-submit options, - # modify NOT ONLY this script but also SparkSubmitArgument.scala - SUBMISSION_OPTS=() - APPLICATION_OPTS=() - while (($#)); do - case "$1" in - --master | --deploy-mode | --class | --name | --jars | --packages | --py-files | --files | \ - --conf | --repositories | --properties-file | --driver-memory | --driver-java-options | \ - --driver-library-path | --driver-class-path | --executor-memory | --driver-cores | \ - --total-executor-cores | --executor-cores | --queue | --num-executors | --archives) - if [[ $# -lt 2 ]]; then - "$SUBMIT_USAGE_FUNCTION" - exit 1; - fi - SUBMISSION_OPTS+=("$1"); shift - SUBMISSION_OPTS+=("$1"); shift - ;; - - --verbose | -v | --supervise) - SUBMISSION_OPTS+=("$1"); shift - ;; - - *) - APPLICATION_OPTS+=("$1"); shift - ;; - esac - done - - export SUBMISSION_OPTS - export APPLICATION_OPTS -} diff --git a/bin/windows-utils.cmd b/bin/windows-utils.cmd deleted file mode 100644 index 567b8733f7f77..0000000000000 --- a/bin/windows-utils.cmd +++ /dev/null @@ -1,59 +0,0 @@ -rem -rem Licensed to the Apache Software Foundation (ASF) under one or more -rem contributor license agreements. See the NOTICE file distributed with -rem this work for additional information regarding copyright ownership. -rem The ASF licenses this file to You under the Apache License, Version 2.0 -rem (the "License"); you may not use this file except in compliance with -rem the License. You may obtain a copy of the License at -rem -rem http://www.apache.org/licenses/LICENSE-2.0 -rem -rem Unless required by applicable law or agreed to in writing, software -rem distributed under the License is distributed on an "AS IS" BASIS, -rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -rem See the License for the specific language governing permissions and -rem limitations under the License. -rem - -rem Gather all spark-submit options into SUBMISSION_OPTS - -set SUBMISSION_OPTS= -set APPLICATION_OPTS= - -rem NOTE: If you add or remove spark-sumbmit options, -rem modify NOT ONLY this script but also SparkSubmitArgument.scala - -:OptsLoop -if "x%1"=="x" ( - goto :OptsLoopEnd -) - -SET opts="\<--master\> \<--deploy-mode\> \<--class\> \<--name\> \<--jars\> \<--py-files\> \<--files\>" -SET opts="%opts:~1,-1% \<--conf\> \<--properties-file\> \<--driver-memory\> \<--driver-java-options\>" -SET opts="%opts:~1,-1% \<--driver-library-path\> \<--driver-class-path\> \<--executor-memory\>" -SET opts="%opts:~1,-1% \<--driver-cores\> \<--total-executor-cores\> \<--executor-cores\> \<--queue\>" -SET opts="%opts:~1,-1% \<--num-executors\> \<--archives\> \<--packages\> \<--repositories\>" - -echo %1 | findstr %opts% >nul -if %ERRORLEVEL% equ 0 ( - if "x%2"=="x" ( - echo "%1" requires an argument. >&2 - exit /b 1 - ) - set SUBMISSION_OPTS=%SUBMISSION_OPTS% %1 %2 - shift - shift - goto :OptsLoop -) -echo %1 | findstr "\<--verbose\> \<-v\> \<--supervise\>" >nul -if %ERRORLEVEL% equ 0 ( - set SUBMISSION_OPTS=%SUBMISSION_OPTS% %1 - shift - goto :OptsLoop -) -set APPLICATION_OPTS=%APPLICATION_OPTS% %1 -shift -goto :OptsLoop - -:OptsLoopEnd -exit /b 0 diff --git a/build/mvn b/build/mvn index 53babf54debb6..3561110a4c019 100755 --- a/build/mvn +++ b/build/mvn @@ -21,6 +21,8 @@ _DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" # Preserve the calling directory _CALLING_DIR="$(pwd)" +# Options used during compilation +_COMPILE_JVM_OPTS="-Xmx2g -XX:MaxPermSize=512M -XX:ReservedCodeCacheSize=512m" # Installs any application tarball given a URL, the expected tarball name, # and, optionally, a checkable binary path to determine if the binary has @@ -136,6 +138,7 @@ cd "${_CALLING_DIR}" # Now that zinc is ensured to be installed, check its status and, if its # not running or just installed, start it if [ -n "${ZINC_INSTALL_FLAG}" -o -z "`${ZINC_BIN} -status`" ]; then + export ZINC_OPTS=${ZINC_OPTS:-"$_COMPILE_JVM_OPTS"} ${ZINC_BIN} -shutdown ${ZINC_BIN} -start -port ${ZINC_PORT} \ -scala-compiler "${SCALA_COMPILER}" \ @@ -143,7 +146,7 @@ if [ -n "${ZINC_INSTALL_FLAG}" -o -z "`${ZINC_BIN} -status`" ]; then fi # Set any `mvn` options if not already present -export MAVEN_OPTS=${MAVEN_OPTS:-"-Xmx2g -XX:MaxPermSize=512M -XX:ReservedCodeCacheSize=512m"} +export MAVEN_OPTS=${MAVEN_OPTS:-"$_COMPILE_JVM_OPTS"} # Last, call the `mvn` command as usual ${MVN_BIN} "$@" diff --git a/build/sbt b/build/sbt index 28ebb64f7197c..cc3203d79bccd 100755 --- a/build/sbt +++ b/build/sbt @@ -125,4 +125,32 @@ loadConfigFile() { [[ -f "$etc_sbt_opts_file" ]] && set -- $(loadConfigFile "$etc_sbt_opts_file") "$@" [[ -f "$sbt_opts_file" ]] && set -- $(loadConfigFile "$sbt_opts_file") "$@" +exit_status=127 +saved_stty="" + +restoreSttySettings() { + stty $saved_stty + saved_stty="" +} + +onExit() { + if [[ "$saved_stty" != "" ]]; then + restoreSttySettings + fi + exit $exit_status +} + +saveSttySettings() { + saved_stty=$(stty -g 2>/dev/null) + if [[ ! $? ]]; then + saved_stty="" + fi +} + +saveSttySettings +trap onExit INT + run "$@" + +exit_status=$? +onExit diff --git a/build/sbt-launch-lib.bash b/build/sbt-launch-lib.bash index 5e0c640fa5919..504be48b358fa 100755 --- a/build/sbt-launch-lib.bash +++ b/build/sbt-launch-lib.bash @@ -81,7 +81,7 @@ execRunner () { echo "" } - exec "$@" + "$@" } addJava () { diff --git a/conf/log4j.properties.template b/conf/log4j.properties.template index 89eec7d4b7f61..3a2a88219818f 100644 --- a/conf/log4j.properties.template +++ b/conf/log4j.properties.template @@ -6,7 +6,7 @@ log4j.appender.console.layout=org.apache.log4j.PatternLayout log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n # Settings to quiet third party logs that are too verbose -log4j.logger.org.eclipse.jetty=WARN -log4j.logger.org.eclipse.jetty.util.component.AbstractLifeCycle=ERROR +log4j.logger.org.spark-project.jetty=WARN +log4j.logger.org.spark-project.jetty.util.component.AbstractLifeCycle=ERROR log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO diff --git a/conf/metrics.properties.template b/conf/metrics.properties.template index 464c14457e53f..2e0cb5db170ac 100644 --- a/conf/metrics.properties.template +++ b/conf/metrics.properties.template @@ -122,6 +122,15 @@ #worker.sink.csv.unit=minutes +# Enable Slf4jSink for all instances by class name +#*.sink.slf4j.class=org.apache.spark.metrics.sink.Slf4jSink + +# Polling period for Slf4JSink +#*.sink.sl4j.period=1 + +#*.sink.sl4j.unit=minutes + + # Enable jvm source for instance master, worker, driver and executor #master.source.jvm.class=org.apache.spark.metrics.source.JvmSource diff --git a/conf/spark-env.sh.template b/conf/spark-env.sh.template index 0886b0276fb90..67f81d33361e1 100755 --- a/conf/spark-env.sh.template +++ b/conf/spark-env.sh.template @@ -15,7 +15,7 @@ # - SPARK_PUBLIC_DNS, to set the public DNS name of the driver program # - SPARK_CLASSPATH, default classpath entries to append # - SPARK_LOCAL_DIRS, storage directories to use on this node for shuffle and RDD data -# - MESOS_NATIVE_LIBRARY, to point to your libmesos.so if you use Mesos +# - MESOS_NATIVE_JAVA_LIBRARY, to point to your libmesos.so if you use Mesos # Options read in YARN client mode # - HADOOP_CONF_DIR, to point Spark towards Hadoop configuration files diff --git a/core/pom.xml b/core/pom.xml index 66180035e61f1..e80829b7a7f3d 100644 --- a/core/pom.xml +++ b/core/pom.xml @@ -20,8 +20,8 @@ 4.0.0 org.apache.spark - spark-parent - 1.3.0-SNAPSHOT + spark-parent_2.10 + 1.4.0-SNAPSHOT ../pom.xml @@ -76,6 +76,11 @@ + + org.apache.spark + spark-launcher_${scala.binary.version} + ${project.version} + org.apache.spark spark-network-common_${scala.binary.version} @@ -319,6 +324,12 @@ selenium-java test + + + xml-apis + xml-apis + test + org.mockito mockito-all @@ -329,16 +340,6 @@ scalacheck_${scala.binary.version} test - - org.easymock - easymockclassextension - test - - - asm - asm - test - junit junit @@ -413,7 +414,7 @@ true true - guava,jetty-io,jetty-servlet,jetty-continuation,jetty-http,jetty-plus,jetty-util,jetty-server + guava,jetty-io,jetty-servlet,jetty-continuation,jetty-http,jetty-plus,jetty-util,jetty-server,jetty-security true @@ -441,4 +442,55 @@ + + + Windows + + + Windows + + + + \ + .bat + + + + unix + + + unix + + + + / + .sh + + + + sparkr + + + + org.codehaus.mojo + exec-maven-plugin + 1.3.2 + + + sparkr-pkg + compile + + exec + + + + + ..${path.separator}R${path.separator}install-dev${script.extension} + + + + + + + diff --git a/core/src/main/java/org/apache/spark/util/collection/TimSort.java b/core/src/main/java/org/apache/spark/util/collection/TimSort.java index 409e1a41c5d49..a90cc0e761f62 100644 --- a/core/src/main/java/org/apache/spark/util/collection/TimSort.java +++ b/core/src/main/java/org/apache/spark/util/collection/TimSort.java @@ -425,15 +425,14 @@ private void pushRun(int runBase, int runLen) { private void mergeCollapse() { while (stackSize > 1) { int n = stackSize - 2; - if (n > 0 && runLen[n-1] <= runLen[n] + runLen[n+1]) { + if ( (n >= 1 && runLen[n-1] <= runLen[n] + runLen[n+1]) + || (n >= 2 && runLen[n-2] <= runLen[n] + runLen[n-1])) { if (runLen[n - 1] < runLen[n + 1]) n--; - mergeAt(n); - } else if (runLen[n] <= runLen[n + 1]) { - mergeAt(n); - } else { + } else if (runLen[n] > runLen[n + 1]) { break; // Invariant is established } + mergeAt(n); } } diff --git a/core/src/main/resources/org/apache/spark/log4j-defaults.properties b/core/src/main/resources/org/apache/spark/log4j-defaults.properties index 89eec7d4b7f61..3a2a88219818f 100644 --- a/core/src/main/resources/org/apache/spark/log4j-defaults.properties +++ b/core/src/main/resources/org/apache/spark/log4j-defaults.properties @@ -6,7 +6,7 @@ log4j.appender.console.layout=org.apache.log4j.PatternLayout log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n # Settings to quiet third party logs that are too verbose -log4j.logger.org.eclipse.jetty=WARN -log4j.logger.org.eclipse.jetty.util.component.AbstractLifeCycle=ERROR +log4j.logger.org.spark-project.jetty=WARN +log4j.logger.org.spark-project.jetty.util.component.AbstractLifeCycle=ERROR log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO diff --git a/core/src/main/resources/org/apache/spark/ui/static/additional-metrics.js b/core/src/main/resources/org/apache/spark/ui/static/additional-metrics.js index 14ba37d7c9bd9..013db8df9b363 100644 --- a/core/src/main/resources/org/apache/spark/ui/static/additional-metrics.js +++ b/core/src/main/resources/org/apache/spark/ui/static/additional-metrics.js @@ -30,7 +30,7 @@ $(function() { stripeSummaryTable(); - $("input:checkbox").click(function() { + $('input[type="checkbox"]').click(function() { var column = "table ." + $(this).attr("name"); $(column).toggle(); stripeSummaryTable(); @@ -39,15 +39,15 @@ $(function() { $("#select-all-metrics").click(function() { if (this.checked) { // Toggle all un-checked options. - $('input:checkbox:not(:checked)').trigger('click'); + $('input[type="checkbox"]:not(:checked)').trigger('click'); } else { // Toggle all checked options. - $('input:checkbox:checked').trigger('click'); + $('input[type="checkbox"]:checked').trigger('click'); } }); // Trigger a click on the checkbox if a user clicks the label next to it. $("span.additional-metric-title").click(function() { - $(this).parent().find('input:checkbox').trigger('click'); + $(this).parent().find('input[type="checkbox"]').trigger('click'); }); }); diff --git a/core/src/main/resources/org/apache/spark/ui/static/webui.css b/core/src/main/resources/org/apache/spark/ui/static/webui.css index 68b33b5f0d7c7..4910744d1d790 100644 --- a/core/src/main/resources/org/apache/spark/ui/static/webui.css +++ b/core/src/main/resources/org/apache/spark/ui/static/webui.css @@ -85,17 +85,13 @@ table.sortable td { filter: progid:dximagetransform.microsoft.gradient(startColorstr='#FFA4EDFF', endColorstr='#FF94DDFF', GradientType=0); } -span.kill-link { +a.kill-link { margin-right: 2px; margin-left: 20px; color: gray; float: right; } -span.kill-link a { - color: gray; -} - span.expand-details { font-size: 10pt; cursor: pointer; @@ -196,7 +192,7 @@ span.additional-metric-title { /* Hide all additional metrics by default. This is done here rather than using JavaScript to * avoid slow page loads for stage pages with large numbers (e.g., thousands) of tasks. */ -.scheduler_delay, .deserialization_time, .fetch_wait_time, .serialization_time, -.getting_result_time { +.scheduler_delay, .deserialization_time, .fetch_wait_time, .shuffle_read_remote, +.serialization_time, .getting_result_time { display: none; } diff --git a/core/src/main/scala/org/apache/spark/Accumulators.scala b/core/src/main/scala/org/apache/spark/Accumulators.scala index 5f31bfba3f8d6..330df1d59a9b1 100644 --- a/core/src/main/scala/org/apache/spark/Accumulators.scala +++ b/core/src/main/scala/org/apache/spark/Accumulators.scala @@ -18,11 +18,10 @@ package org.apache.spark import java.io.{ObjectInputStream, Serializable} -import java.util.concurrent.atomic.AtomicLong -import java.lang.ThreadLocal import scala.collection.generic.Growable import scala.collection.mutable.Map +import scala.ref.WeakReference import scala.reflect.ClassTag import org.apache.spark.serializer.JavaSerializer @@ -108,7 +107,7 @@ class Accumulable[R, T] ( * The typical use of this method is to directly mutate the local value, eg., to add * an element to a Set. */ - def localValue = value_ + def localValue: R = value_ /** * Set the accumulator's value; only allowed on master. @@ -136,7 +135,7 @@ class Accumulable[R, T] ( Accumulators.register(this, false) } - override def toString = if (value_ == null) "null" else value_.toString + override def toString: String = if (value_ == null) "null" else value_.toString } /** @@ -256,22 +255,22 @@ object AccumulatorParam { implicit object DoubleAccumulatorParam extends AccumulatorParam[Double] { def addInPlace(t1: Double, t2: Double): Double = t1 + t2 - def zero(initialValue: Double) = 0.0 + def zero(initialValue: Double): Double = 0.0 } implicit object IntAccumulatorParam extends AccumulatorParam[Int] { def addInPlace(t1: Int, t2: Int): Int = t1 + t2 - def zero(initialValue: Int) = 0 + def zero(initialValue: Int): Int = 0 } implicit object LongAccumulatorParam extends AccumulatorParam[Long] { - def addInPlace(t1: Long, t2: Long) = t1 + t2 - def zero(initialValue: Long) = 0L + def addInPlace(t1: Long, t2: Long): Long = t1 + t2 + def zero(initialValue: Long): Long = 0L } implicit object FloatAccumulatorParam extends AccumulatorParam[Float] { - def addInPlace(t1: Float, t2: Float) = t1 + t2 - def zero(initialValue: Float) = 0f + def addInPlace(t1: Float, t2: Float): Float = t1 + t2 + def zero(initialValue: Float): Float = 0f } // TODO: Add AccumulatorParams for other types, e.g. lists and strings @@ -279,13 +278,24 @@ object AccumulatorParam { // TODO: The multi-thread support in accumulators is kind of lame; check // if there's a more intuitive way of doing it right -private[spark] object Accumulators { - // TODO: Use soft references? => need to make readObject work properly then - val originals = Map[Long, Accumulable[_, _]]() - val localAccums = new ThreadLocal[Map[Long, Accumulable[_, _]]]() { +private[spark] object Accumulators extends Logging { + /** + * This global map holds the original accumulator objects that are created on the driver. + * It keeps weak references to these objects so that accumulators can be garbage-collected + * once the RDDs and user-code that reference them are cleaned up. + */ + val originals = Map[Long, WeakReference[Accumulable[_, _]]]() + + /** + * This thread-local map holds per-task copies of accumulators; it is used to collect the set + * of accumulator updates to send back to the driver when tasks complete. After tasks complete, + * this map is cleared by `Accumulators.clear()` (see Executor.scala). + */ + private val localAccums = new ThreadLocal[Map[Long, Accumulable[_, _]]]() { override protected def initialValue() = Map[Long, Accumulable[_, _]]() } - var lastId: Long = 0 + + private var lastId: Long = 0 def newId(): Long = synchronized { lastId += 1 @@ -294,7 +304,7 @@ private[spark] object Accumulators { def register(a: Accumulable[_, _], original: Boolean): Unit = synchronized { if (original) { - originals(a.id) = a + originals(a.id) = new WeakReference[Accumulable[_, _]](a) } else { localAccums.get()(a.id) = a } @@ -303,7 +313,13 @@ private[spark] object Accumulators { // Clear the local (non-original) accumulators for the current thread def clear() { synchronized { - localAccums.get.clear + localAccums.get.clear() + } + } + + def remove(accId: Long) { + synchronized { + originals.remove(accId) } } @@ -320,11 +336,20 @@ private[spark] object Accumulators { def add(values: Map[Long, Any]): Unit = synchronized { for ((id, value) <- values) { if (originals.contains(id)) { - originals(id).asInstanceOf[Accumulable[Any, Any]] ++= value + // Since we are now storing weak references, we must check whether the underlying data + // is valid. + originals(id).get match { + case Some(accum) => accum.asInstanceOf[Accumulable[Any, Any]] ++= value + case None => + throw new IllegalAccessError("Attempted to access garbage collected Accumulator.") + } + } else { + logWarning(s"Ignoring accumulator update for unknown accumulator id $id") } } } - def stringifyPartialValue(partialValue: Any) = "%s".format(partialValue) - def stringifyValue(value: Any) = "%s".format(value) + def stringifyPartialValue(partialValue: Any): String = "%s".format(partialValue) + + def stringifyValue(value: Any): String = "%s".format(value) } diff --git a/core/src/main/scala/org/apache/spark/ContextCleaner.scala b/core/src/main/scala/org/apache/spark/ContextCleaner.scala index ede1e23f4fcc5..37198d887b07b 100644 --- a/core/src/main/scala/org/apache/spark/ContextCleaner.scala +++ b/core/src/main/scala/org/apache/spark/ContextCleaner.scala @@ -22,7 +22,7 @@ import java.lang.ref.{ReferenceQueue, WeakReference} import scala.collection.mutable.{ArrayBuffer, SynchronizedBuffer} import org.apache.spark.broadcast.Broadcast -import org.apache.spark.rdd.RDD +import org.apache.spark.rdd.{RDDCheckpointData, RDD} import org.apache.spark.util.Utils /** @@ -32,6 +32,8 @@ private sealed trait CleanupTask private case class CleanRDD(rddId: Int) extends CleanupTask private case class CleanShuffle(shuffleId: Int) extends CleanupTask private case class CleanBroadcast(broadcastId: Long) extends CleanupTask +private case class CleanAccum(accId: Long) extends CleanupTask +private case class CleanCheckpoint(rddId: Int) extends CleanupTask /** * A WeakReference associated with a CleanupTask. @@ -93,68 +95,95 @@ private[spark] class ContextCleaner(sc: SparkContext) extends Logging { @volatile private var stopped = false /** Attach a listener object to get information of when objects are cleaned. */ - def attachListener(listener: CleanerListener) { + def attachListener(listener: CleanerListener): Unit = { listeners += listener } /** Start the cleaner. */ - def start() { + def start(): Unit = { cleaningThread.setDaemon(true) cleaningThread.setName("Spark Context Cleaner") cleaningThread.start() } - /** Stop the cleaner. */ - def stop() { + /** + * Stop the cleaning thread and wait until the thread has finished running its current task. + */ + def stop(): Unit = { stopped = true + // Interrupt the cleaning thread, but wait until the current task has finished before + // doing so. This guards against the race condition where a cleaning thread may + // potentially clean similarly named variables created by a different SparkContext, + // resulting in otherwise inexplicable block-not-found exceptions (SPARK-6132). + synchronized { + cleaningThread.interrupt() + } + cleaningThread.join() } /** Register a RDD for cleanup when it is garbage collected. */ - def registerRDDForCleanup(rdd: RDD[_]) { + def registerRDDForCleanup(rdd: RDD[_]): Unit = { registerForCleanup(rdd, CleanRDD(rdd.id)) } + def registerAccumulatorForCleanup(a: Accumulable[_, _]): Unit = { + registerForCleanup(a, CleanAccum(a.id)) + } + /** Register a ShuffleDependency for cleanup when it is garbage collected. */ - def registerShuffleForCleanup(shuffleDependency: ShuffleDependency[_, _, _]) { + def registerShuffleForCleanup(shuffleDependency: ShuffleDependency[_, _, _]): Unit = { registerForCleanup(shuffleDependency, CleanShuffle(shuffleDependency.shuffleId)) } /** Register a Broadcast for cleanup when it is garbage collected. */ - def registerBroadcastForCleanup[T](broadcast: Broadcast[T]) { + def registerBroadcastForCleanup[T](broadcast: Broadcast[T]): Unit = { registerForCleanup(broadcast, CleanBroadcast(broadcast.id)) } + /** Register a RDDCheckpointData for cleanup when it is garbage collected. */ + def registerRDDCheckpointDataForCleanup[T](rdd: RDD[_], parentId: Int): Unit = { + registerForCleanup(rdd, CleanCheckpoint(parentId)) + } + /** Register an object for cleanup. */ - private def registerForCleanup(objectForCleanup: AnyRef, task: CleanupTask) { + private def registerForCleanup(objectForCleanup: AnyRef, task: CleanupTask): Unit = { referenceBuffer += new CleanupTaskWeakReference(task, objectForCleanup, referenceQueue) } /** Keep cleaning RDD, shuffle, and broadcast state. */ - private def keepCleaning(): Unit = Utils.logUncaughtExceptions { + private def keepCleaning(): Unit = Utils.tryOrStopSparkContext(sc) { while (!stopped) { try { val reference = Option(referenceQueue.remove(ContextCleaner.REF_QUEUE_POLL_TIMEOUT)) .map(_.asInstanceOf[CleanupTaskWeakReference]) - reference.map(_.task).foreach { task => - logDebug("Got cleaning task " + task) - referenceBuffer -= reference.get - task match { - case CleanRDD(rddId) => - doCleanupRDD(rddId, blocking = blockOnCleanupTasks) - case CleanShuffle(shuffleId) => - doCleanupShuffle(shuffleId, blocking = blockOnShuffleCleanupTasks) - case CleanBroadcast(broadcastId) => - doCleanupBroadcast(broadcastId, blocking = blockOnCleanupTasks) + // Synchronize here to avoid being interrupted on stop() + synchronized { + reference.map(_.task).foreach { task => + logDebug("Got cleaning task " + task) + referenceBuffer -= reference.get + task match { + case CleanRDD(rddId) => + doCleanupRDD(rddId, blocking = blockOnCleanupTasks) + case CleanShuffle(shuffleId) => + doCleanupShuffle(shuffleId, blocking = blockOnShuffleCleanupTasks) + case CleanBroadcast(broadcastId) => + doCleanupBroadcast(broadcastId, blocking = blockOnCleanupTasks) + case CleanAccum(accId) => + doCleanupAccum(accId, blocking = blockOnCleanupTasks) + case CleanCheckpoint(rddId) => + doCleanCheckpoint(rddId) + } } } } catch { + case ie: InterruptedException if stopped => // ignore case e: Exception => logError("Error in cleaning thread", e) } } } /** Perform RDD cleanup. */ - def doCleanupRDD(rddId: Int, blocking: Boolean) { + def doCleanupRDD(rddId: Int, blocking: Boolean): Unit = { try { logDebug("Cleaning RDD " + rddId) sc.unpersistRDD(rddId, blocking) @@ -166,7 +195,7 @@ private[spark] class ContextCleaner(sc: SparkContext) extends Logging { } /** Perform shuffle cleanup, asynchronously. */ - def doCleanupShuffle(shuffleId: Int, blocking: Boolean) { + def doCleanupShuffle(shuffleId: Int, blocking: Boolean): Unit = { try { logDebug("Cleaning shuffle " + shuffleId) mapOutputTrackerMaster.unregisterShuffle(shuffleId) @@ -179,17 +208,42 @@ private[spark] class ContextCleaner(sc: SparkContext) extends Logging { } /** Perform broadcast cleanup. */ - def doCleanupBroadcast(broadcastId: Long, blocking: Boolean) { + def doCleanupBroadcast(broadcastId: Long, blocking: Boolean): Unit = { try { - logDebug("Cleaning broadcast " + broadcastId) + logDebug(s"Cleaning broadcast $broadcastId") broadcastManager.unbroadcast(broadcastId, true, blocking) listeners.foreach(_.broadcastCleaned(broadcastId)) - logInfo("Cleaned broadcast " + broadcastId) + logDebug(s"Cleaned broadcast $broadcastId") } catch { case e: Exception => logError("Error cleaning broadcast " + broadcastId, e) } } + /** Perform accumulator cleanup. */ + def doCleanupAccum(accId: Long, blocking: Boolean): Unit = { + try { + logDebug("Cleaning accumulator " + accId) + Accumulators.remove(accId) + listeners.foreach(_.accumCleaned(accId)) + logInfo("Cleaned accumulator " + accId) + } catch { + case e: Exception => logError("Error cleaning accumulator " + accId, e) + } + } + + /** Perform checkpoint cleanup. */ + def doCleanCheckpoint(rddId: Int): Unit = { + try { + logDebug("Cleaning rdd checkpoint data " + rddId) + RDDCheckpointData.clearRDDCheckpointData(sc, rddId) + listeners.foreach(_.checkpointCleaned(rddId)) + logInfo("Cleaned rdd checkpoint data " + rddId) + } + catch { + case e: Exception => logError("Error cleaning rdd checkpoint data " + rddId, e) + } + } + private def blockManagerMaster = sc.env.blockManager.master private def broadcastManager = sc.env.broadcastManager private def mapOutputTrackerMaster = sc.env.mapOutputTracker.asInstanceOf[MapOutputTrackerMaster] @@ -206,4 +260,6 @@ private[spark] trait CleanerListener { def rddCleaned(rddId: Int) def shuffleCleaned(shuffleId: Int) def broadcastCleaned(broadcastId: Long) + def accumCleaned(accId: Long) + def checkpointCleaned(rddId: Long) } diff --git a/core/src/main/scala/org/apache/spark/Dependency.scala b/core/src/main/scala/org/apache/spark/Dependency.scala index 9a7cd4523e5ab..fc8cdde9348ee 100644 --- a/core/src/main/scala/org/apache/spark/Dependency.scala +++ b/core/src/main/scala/org/apache/spark/Dependency.scala @@ -74,7 +74,7 @@ class ShuffleDependency[K, V, C]( val mapSideCombine: Boolean = false) extends Dependency[Product2[K, V]] { - override def rdd = _rdd.asInstanceOf[RDD[Product2[K, V]]] + override def rdd: RDD[Product2[K, V]] = _rdd.asInstanceOf[RDD[Product2[K, V]]] val shuffleId: Int = _rdd.context.newShuffleId() @@ -91,7 +91,7 @@ class ShuffleDependency[K, V, C]( */ @DeveloperApi class OneToOneDependency[T](rdd: RDD[T]) extends NarrowDependency[T](rdd) { - override def getParents(partitionId: Int) = List(partitionId) + override def getParents(partitionId: Int): List[Int] = List(partitionId) } @@ -107,7 +107,7 @@ class OneToOneDependency[T](rdd: RDD[T]) extends NarrowDependency[T](rdd) { class RangeDependency[T](rdd: RDD[T], inStart: Int, outStart: Int, length: Int) extends NarrowDependency[T](rdd) { - override def getParents(partitionId: Int) = { + override def getParents(partitionId: Int): List[Int] = { if (partitionId >= outStart && partitionId < outStart + length) { List(partitionId - outStart + inStart) } else { diff --git a/core/src/main/scala/org/apache/spark/ExecutorAllocationClient.scala b/core/src/main/scala/org/apache/spark/ExecutorAllocationClient.scala index a46a81eabd965..443830f8d03b6 100644 --- a/core/src/main/scala/org/apache/spark/ExecutorAllocationClient.scala +++ b/core/src/main/scala/org/apache/spark/ExecutorAllocationClient.scala @@ -19,24 +19,32 @@ package org.apache.spark /** * A client that communicates with the cluster manager to request or kill executors. + * This is currently supported only in YARN mode. */ private[spark] trait ExecutorAllocationClient { + /** + * Express a preference to the cluster manager for a given total number of executors. + * This can result in canceling pending requests or filing additional requests. + * @return whether the request is acknowledged by the cluster manager. + */ + private[spark] def requestTotalExecutors(numExecutors: Int): Boolean + /** * Request an additional number of executors from the cluster manager. - * Return whether the request is acknowledged by the cluster manager. + * @return whether the request is acknowledged by the cluster manager. */ def requestExecutors(numAdditionalExecutors: Int): Boolean /** * Request that the cluster manager kill the specified executors. - * Return whether the request is acknowledged by the cluster manager. + * @return whether the request is acknowledged by the cluster manager. */ def killExecutors(executorIds: Seq[String]): Boolean /** * Request that the cluster manager kill the specified executor. - * Return whether the request is acknowledged by the cluster manager. + * @return whether the request is acknowledged by the cluster manager. */ def killExecutor(executorId: String): Boolean = killExecutors(Seq(executorId)) } diff --git a/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala b/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala index 02d54bf3b53cc..4e7bf51fc0622 100644 --- a/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala +++ b/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala @@ -17,9 +17,12 @@ package org.apache.spark +import java.util.concurrent.{Executors, TimeUnit} + import scala.collection.mutable import org.apache.spark.scheduler._ +import org.apache.spark.util.{Clock, SystemClock, Utils} /** * An agent that dynamically allocates and removes executors based on the workload. @@ -77,16 +80,16 @@ private[spark] class ExecutorAllocationManager( Integer.MAX_VALUE) // How long there must be backlogged tasks for before an addition is triggered (seconds) - private val schedulerBacklogTimeout = conf.getLong( - "spark.dynamicAllocation.schedulerBacklogTimeout", 5) + private val schedulerBacklogTimeoutS = conf.getTimeAsSeconds( + "spark.dynamicAllocation.schedulerBacklogTimeout", "5s") - // Same as above, but used only after `schedulerBacklogTimeout` is exceeded - private val sustainedSchedulerBacklogTimeout = conf.getLong( - "spark.dynamicAllocation.sustainedSchedulerBacklogTimeout", schedulerBacklogTimeout) + // Same as above, but used only after `schedulerBacklogTimeoutS` is exceeded + private val sustainedSchedulerBacklogTimeoutS = conf.getTimeAsSeconds( + "spark.dynamicAllocation.sustainedSchedulerBacklogTimeout", s"${schedulerBacklogTimeoutS}s") // How long an executor must be idle for before it is removed (seconds) - private val executorIdleTimeout = conf.getLong( - "spark.dynamicAllocation.executorIdleTimeout", 600) + private val executorIdleTimeoutS = conf.getTimeAsSeconds( + "spark.dynamicAllocation.executorIdleTimeout", "600s") // During testing, the methods to actually kill and add executors are mocked out private val testing = conf.getBoolean("spark.dynamicAllocation.testing", false) @@ -123,11 +126,15 @@ private[spark] class ExecutorAllocationManager( private val intervalMillis: Long = 100 // Clock used to schedule when executors should be added and removed - private var clock: Clock = new RealClock + private var clock: Clock = new SystemClock() // Listener for Spark events that impact the allocation policy private val listener = new ExecutorAllocationListener + // Executor that handles the scheduling task. + private val executor = Executors.newSingleThreadScheduledExecutor( + Utils.namedThreadFactory("spark-dynamic-executor-allocation")) + /** * Verify that the settings specified through the config are valid. * If not, throw an appropriate exception. @@ -143,14 +150,14 @@ private[spark] class ExecutorAllocationManager( throw new SparkException(s"spark.dynamicAllocation.minExecutors ($minNumExecutors) must " + s"be less than or equal to spark.dynamicAllocation.maxExecutors ($maxNumExecutors)!") } - if (schedulerBacklogTimeout <= 0) { + if (schedulerBacklogTimeoutS <= 0) { throw new SparkException("spark.dynamicAllocation.schedulerBacklogTimeout must be > 0!") } - if (sustainedSchedulerBacklogTimeout <= 0) { + if (sustainedSchedulerBacklogTimeoutS <= 0) { throw new SparkException( "spark.dynamicAllocation.sustainedSchedulerBacklogTimeout must be > 0!") } - if (executorIdleTimeout <= 0) { + if (executorIdleTimeoutS <= 0) { throw new SparkException("spark.dynamicAllocation.executorIdleTimeout must be > 0!") } // Require external shuffle service for dynamic allocation @@ -172,47 +179,55 @@ private[spark] class ExecutorAllocationManager( } /** - * Register for scheduler callbacks to decide when to add and remove executors. + * Register for scheduler callbacks to decide when to add and remove executors, and start + * the scheduling task. */ def start(): Unit = { listenerBus.addListener(listener) - startPolling() + + val scheduleTask = new Runnable() { + override def run(): Unit = Utils.logUncaughtExceptions(schedule()) + } + executor.scheduleAtFixedRate(scheduleTask, 0, intervalMillis, TimeUnit.MILLISECONDS) } /** - * Start the main polling thread that keeps track of when to add and remove executors. + * Stop the allocation manager. */ - private def startPolling(): Unit = { - val t = new Thread { - override def run(): Unit = { - while (true) { - try { - schedule() - } catch { - case e: Exception => logError("Exception in dynamic executor allocation thread!", e) - } - Thread.sleep(intervalMillis) - } - } - } - t.setName("spark-dynamic-executor-allocation") - t.setDaemon(true) - t.start() + def stop(): Unit = { + executor.shutdown() + executor.awaitTermination(10, TimeUnit.SECONDS) + } + + /** + * The number of executors we would have if the cluster manager were to fulfill all our existing + * requests. + */ + private def targetNumExecutors(): Int = + numExecutorsPending + executorIds.size - executorsPendingToRemove.size + + /** + * The maximum number of executors we would need under the current load to satisfy all running + * and pending tasks, rounded up. + */ + private def maxNumExecutorsNeeded(): Int = { + val numRunningOrPendingTasks = listener.totalPendingTasks + listener.totalRunningTasks + (numRunningOrPendingTasks + tasksPerExecutor - 1) / tasksPerExecutor } /** - * If the add time has expired, request new executors and refresh the add time. - * If the remove time for an existing executor has expired, kill the executor. + * This is called at a fixed interval to regulate the number of pending executor requests + * and number of executors running. + * + * First, adjust our requested executors based on the add time and our current needs. + * Then, if the remove time for an existing executor has expired, kill the executor. + * * This is factored out into its own method for testing. */ private def schedule(): Unit = synchronized { val now = clock.getTimeMillis - if (addTime != NOT_SET && now >= addTime) { - addExecutors() - logDebug(s"Starting timer to add more executors (to " + - s"expire in $sustainedSchedulerBacklogTimeout seconds)") - addTime += sustainedSchedulerBacklogTimeout * 1000 - } + + addOrCancelExecutorRequests(now) removeTimes.retain { case (executorId, expireTime) => val expired = now >= expireTime @@ -223,59 +238,89 @@ private[spark] class ExecutorAllocationManager( } } + /** + * Check to see whether our existing allocation and the requests we've made previously exceed our + * current needs. If so, let the cluster manager know so that it can cancel pending requests that + * are unneeded. + * + * If not, and the add time has expired, see if we can request new executors and refresh the add + * time. + * + * @return the delta in the target number of executors. + */ + private def addOrCancelExecutorRequests(now: Long): Int = synchronized { + val currentTarget = targetNumExecutors + val maxNeeded = maxNumExecutorsNeeded + + if (maxNeeded < currentTarget) { + // The target number exceeds the number we actually need, so stop adding new + // executors and inform the cluster manager to cancel the extra pending requests. + val newTotalExecutors = math.max(maxNeeded, minNumExecutors) + client.requestTotalExecutors(newTotalExecutors) + numExecutorsToAdd = 1 + updateNumExecutorsPending(newTotalExecutors) + } else if (addTime != NOT_SET && now >= addTime) { + val delta = addExecutors(maxNeeded) + logDebug(s"Starting timer to add more executors (to " + + s"expire in $sustainedSchedulerBacklogTimeoutS seconds)") + addTime += sustainedSchedulerBacklogTimeoutS * 1000 + delta + } else { + 0 + } + } + /** * Request a number of executors from the cluster manager. * If the cap on the number of executors is reached, give up and reset the * number of executors to add next round instead of continuing to double it. - * Return the number actually requested. + * + * @param maxNumExecutorsNeeded the maximum number of executors all currently running or pending + * tasks could fill + * @return the number of additional executors actually requested. */ - private def addExecutors(): Int = synchronized { - // Do not request more executors if we have already reached the upper bound - val numExistingExecutors = executorIds.size + numExecutorsPending - if (numExistingExecutors >= maxNumExecutors) { + private def addExecutors(maxNumExecutorsNeeded: Int): Int = { + // Do not request more executors if it would put our target over the upper bound + val currentTarget = targetNumExecutors + if (currentTarget >= maxNumExecutors) { logDebug(s"Not adding executors because there are already ${executorIds.size} " + s"registered and $numExecutorsPending pending executor(s) (limit $maxNumExecutors)") numExecutorsToAdd = 1 return 0 } - // The number of executors needed to satisfy all pending tasks is the number of tasks pending - // divided by the number of tasks each executor can fit, rounded up. - val maxNumExecutorsPending = - (listener.totalPendingTasks() + tasksPerExecutor - 1) / tasksPerExecutor - if (numExecutorsPending >= maxNumExecutorsPending) { - logDebug(s"Not adding executors because there are already $numExecutorsPending " + - s"pending and pending tasks could only fill $maxNumExecutorsPending") - numExecutorsToAdd = 1 - return 0 - } - - // It's never useful to request more executors than could satisfy all the pending tasks, so - // cap request at that amount. - // Also cap request with respect to the configured upper bound. - val maxNumExecutorsToAdd = math.min( - maxNumExecutorsPending - numExecutorsPending, - maxNumExecutors - numExistingExecutors) - assert(maxNumExecutorsToAdd > 0) - - val actualNumExecutorsToAdd = math.min(numExecutorsToAdd, maxNumExecutorsToAdd) - - val newTotalExecutors = numExistingExecutors + actualNumExecutorsToAdd - val addRequestAcknowledged = testing || client.requestExecutors(actualNumExecutorsToAdd) + val actualMaxNumExecutors = math.min(maxNumExecutors, maxNumExecutorsNeeded) + val newTotalExecutors = math.min(currentTarget + numExecutorsToAdd, actualMaxNumExecutors) + val addRequestAcknowledged = testing || client.requestTotalExecutors(newTotalExecutors) if (addRequestAcknowledged) { - logInfo(s"Requesting $actualNumExecutorsToAdd new executor(s) because " + - s"tasks are backlogged (new desired total will be $newTotalExecutors)") - numExecutorsToAdd = - if (actualNumExecutorsToAdd == numExecutorsToAdd) numExecutorsToAdd * 2 else 1 - numExecutorsPending += actualNumExecutorsToAdd - actualNumExecutorsToAdd + val delta = updateNumExecutorsPending(newTotalExecutors) + logInfo(s"Requesting $delta new executor(s) because tasks are backlogged" + + s" (new desired total will be $newTotalExecutors)") + numExecutorsToAdd = if (delta == numExecutorsToAdd) { + numExecutorsToAdd * 2 + } else { + 1 + } + delta } else { - logWarning(s"Unable to reach the cluster manager " + - s"to request $actualNumExecutorsToAdd executors!") + logWarning( + s"Unable to reach the cluster manager to request $newTotalExecutors total executors!") 0 } } + /** + * Given the new target number of executors, update the number of pending executor requests, + * and return the delta from the old number of pending requests. + */ + private def updateNumExecutorsPending(newTotalExecutors: Int): Int = { + val newNumExecutorsPending = + newTotalExecutors - executorIds.size + executorsPendingToRemove.size + val delta = newNumExecutorsPending - numExecutorsPending + numExecutorsPending = newNumExecutorsPending + delta + } + /** * Request the cluster manager to remove the given executor. * Return whether the request is received. @@ -306,7 +351,7 @@ private[spark] class ExecutorAllocationManager( val removeRequestAcknowledged = testing || client.killExecutor(executorId) if (removeRequestAcknowledged) { logInfo(s"Removing executor $executorId because it has been idle for " + - s"$executorIdleTimeout seconds (new desired total will be ${numExistingExecutors - 1})") + s"$executorIdleTimeoutS seconds (new desired total will be ${numExistingExecutors - 1})") executorsPendingToRemove.add(executorId) true } else { @@ -362,8 +407,8 @@ private[spark] class ExecutorAllocationManager( private def onSchedulerBacklogged(): Unit = synchronized { if (addTime == NOT_SET) { logDebug(s"Starting timer to add executors because pending tasks " + - s"are building up (to expire in $schedulerBacklogTimeout seconds)") - addTime = clock.getTimeMillis + schedulerBacklogTimeout * 1000 + s"are building up (to expire in $schedulerBacklogTimeoutS seconds)") + addTime = clock.getTimeMillis + schedulerBacklogTimeoutS * 1000 } } @@ -386,8 +431,8 @@ private[spark] class ExecutorAllocationManager( if (executorIds.contains(executorId)) { if (!removeTimes.contains(executorId) && !executorsPendingToRemove.contains(executorId)) { logDebug(s"Starting idle timer for $executorId because there are no more tasks " + - s"scheduled to run on the executor (to expire in $executorIdleTimeout seconds)") - removeTimes(executorId) = clock.getTimeMillis + executorIdleTimeout * 1000 + s"scheduled to run on the executor (to expire in $executorIdleTimeoutS seconds)") + removeTimes(executorId) = clock.getTimeMillis + executorIdleTimeoutS * 1000 } } else { logWarning(s"Attempted to mark unknown executor $executorId idle") @@ -415,6 +460,8 @@ private[spark] class ExecutorAllocationManager( private val stageIdToNumTasks = new mutable.HashMap[Int, Int] private val stageIdToTaskIndices = new mutable.HashMap[Int, mutable.HashSet[Int]] private val executorIdToTaskIds = new mutable.HashMap[String, mutable.HashSet[Long]] + // Number of tasks currently running on the cluster. Should be 0 when no stages are active. + private var numRunningTasks: Int = _ override def onStageSubmitted(stageSubmitted: SparkListenerStageSubmitted): Unit = { val stageId = stageSubmitted.stageInfo.stageId @@ -435,6 +482,10 @@ private[spark] class ExecutorAllocationManager( // This is needed in case the stage is aborted for any reason if (stageIdToNumTasks.isEmpty) { allocationManager.onSchedulerQueueEmpty() + if (numRunningTasks != 0) { + logWarning("No stages are running, but numRunningTasks != 0") + numRunningTasks = 0 + } } } } @@ -446,6 +497,7 @@ private[spark] class ExecutorAllocationManager( val executorId = taskStart.taskInfo.executorId allocationManager.synchronized { + numRunningTasks += 1 // This guards against the race condition in which the `SparkListenerTaskStart` // event is posted before the `SparkListenerBlockManagerAdded` event, which is // possible because these events are posted in different threads. (see SPARK-4951) @@ -475,7 +527,8 @@ private[spark] class ExecutorAllocationManager( val executorId = taskEnd.taskInfo.executorId val taskId = taskEnd.taskInfo.taskId allocationManager.synchronized { - // If the executor is no longer running scheduled any tasks, mark it as idle + numRunningTasks -= 1 + // If the executor is no longer running any scheduled tasks, mark it as idle if (executorIdToTaskIds.contains(executorId)) { executorIdToTaskIds(executorId) -= taskId if (executorIdToTaskIds(executorId).isEmpty) { @@ -514,6 +567,11 @@ private[spark] class ExecutorAllocationManager( }.sum } + /** + * The number of tasks currently running across all stages. + */ + def totalRunningTasks(): Int = numRunningTasks + /** * Return true if an executor is not currently running a task, and false otherwise. * @@ -529,28 +587,3 @@ private[spark] class ExecutorAllocationManager( private object ExecutorAllocationManager { val NOT_SET = Long.MaxValue } - -/** - * An abstract clock for measuring elapsed time. - */ -private trait Clock { - def getTimeMillis: Long -} - -/** - * A clock backed by a monotonically increasing time source. - * The time returned by this clock does not correspond to any notion of wall-clock time. - */ -private class RealClock extends Clock { - override def getTimeMillis: Long = System.nanoTime / (1000 * 1000) -} - -/** - * A clock that allows the caller to customize the time. - * This is used mainly for testing. - */ -private class TestClock(startTimeMillis: Long) extends Clock { - private var time: Long = startTimeMillis - override def getTimeMillis: Long = time - def tick(ms: Long): Unit = { time += ms } -} diff --git a/core/src/main/scala/org/apache/spark/FutureAction.scala b/core/src/main/scala/org/apache/spark/FutureAction.scala index e97a7375a267b..91f9ef8ce7185 100644 --- a/core/src/main/scala/org/apache/spark/FutureAction.scala +++ b/core/src/main/scala/org/apache/spark/FutureAction.scala @@ -168,7 +168,7 @@ class SimpleFutureAction[T] private[spark](jobWaiter: JobWaiter[_], resultFunc: } } - def jobIds = Seq(jobWaiter.jobId) + def jobIds: Seq[Int] = Seq(jobWaiter.jobId) } @@ -276,7 +276,7 @@ class ComplexFutureAction[T] extends FutureAction[T] { override def value: Option[Try[T]] = p.future.value - def jobIds = jobs + def jobIds: Seq[Int] = jobs } diff --git a/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala b/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala index 83ae57b7f1516..e3bd16f1cbf24 100644 --- a/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala +++ b/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala @@ -17,33 +17,132 @@ package org.apache.spark -import akka.actor.Actor +import java.util.concurrent.{ScheduledFuture, TimeUnit, Executors} + +import scala.collection.mutable + import org.apache.spark.executor.TaskMetrics +import org.apache.spark.rpc.{ThreadSafeRpcEndpoint, RpcEnv, RpcCallContext} import org.apache.spark.storage.BlockManagerId -import org.apache.spark.scheduler.TaskScheduler -import org.apache.spark.util.ActorLogReceive +import org.apache.spark.scheduler.{SlaveLost, TaskScheduler} +import org.apache.spark.util.Utils /** * A heartbeat from executors to the driver. This is a shared message used by several internal - * components to convey liveness or execution information for in-progress tasks. + * components to convey liveness or execution information for in-progress tasks. It will also + * expire the hosts that have not heartbeated for more than spark.network.timeout. */ private[spark] case class Heartbeat( executorId: String, taskMetrics: Array[(Long, TaskMetrics)], // taskId -> TaskMetrics blockManagerId: BlockManagerId) +/** + * An event that SparkContext uses to notify HeartbeatReceiver that SparkContext.taskScheduler is + * created. + */ +private[spark] case object TaskSchedulerIsSet + +private[spark] case object ExpireDeadHosts + private[spark] case class HeartbeatResponse(reregisterBlockManager: Boolean) /** * Lives in the driver to receive heartbeats from executors.. */ -private[spark] class HeartbeatReceiver(scheduler: TaskScheduler) - extends Actor with ActorLogReceive with Logging { - - override def receiveWithLogging = { - case Heartbeat(executorId, taskMetrics, blockManagerId) => - val response = HeartbeatResponse( - !scheduler.executorHeartbeatReceived(executorId, taskMetrics, blockManagerId)) - sender ! response +private[spark] class HeartbeatReceiver(sc: SparkContext) + extends ThreadSafeRpcEndpoint with Logging { + + override val rpcEnv: RpcEnv = sc.env.rpcEnv + + private[spark] var scheduler: TaskScheduler = null + + // executor ID -> timestamp of when the last heartbeat from this executor was received + private val executorLastSeen = new mutable.HashMap[String, Long] + + // "spark.network.timeout" uses "seconds", while `spark.storage.blockManagerSlaveTimeoutMs` uses + // "milliseconds" + private val slaveTimeoutMs = + sc.conf.getTimeAsMs("spark.storage.blockManagerSlaveTimeoutMs", "120s") + private val executorTimeoutMs = + sc.conf.getTimeAsSeconds("spark.network.timeout", s"${slaveTimeoutMs}ms") * 1000 + + // "spark.network.timeoutInterval" uses "seconds", while + // "spark.storage.blockManagerTimeoutIntervalMs" uses "milliseconds" + private val timeoutIntervalMs = + sc.conf.getTimeAsMs("spark.storage.blockManagerTimeoutIntervalMs", "60s") + private val checkTimeoutIntervalMs = + sc.conf.getTimeAsSeconds("spark.network.timeoutInterval", s"${timeoutIntervalMs}ms") * 1000 + + private var timeoutCheckingTask: ScheduledFuture[_] = null + + private val timeoutCheckingThread = Executors.newSingleThreadScheduledExecutor( + Utils.namedThreadFactory("heartbeat-timeout-checking-thread")) + + private val killExecutorThread = Executors.newSingleThreadExecutor( + Utils.namedThreadFactory("kill-executor-thread")) + + override def onStart(): Unit = { + timeoutCheckingTask = timeoutCheckingThread.scheduleAtFixedRate(new Runnable { + override def run(): Unit = Utils.tryLogNonFatalError { + Option(self).foreach(_.send(ExpireDeadHosts)) + } + }, 0, checkTimeoutIntervalMs, TimeUnit.MILLISECONDS) + } + + override def receive: PartialFunction[Any, Unit] = { + case ExpireDeadHosts => + expireDeadHosts() + case TaskSchedulerIsSet => + scheduler = sc.taskScheduler + } + + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { + case heartbeat @ Heartbeat(executorId, taskMetrics, blockManagerId) => + if (scheduler != null) { + val unknownExecutor = !scheduler.executorHeartbeatReceived( + executorId, taskMetrics, blockManagerId) + val response = HeartbeatResponse(reregisterBlockManager = unknownExecutor) + executorLastSeen(executorId) = System.currentTimeMillis() + context.reply(response) + } else { + // Because Executor will sleep several seconds before sending the first "Heartbeat", this + // case rarely happens. However, if it really happens, log it and ask the executor to + // register itself again. + logWarning(s"Dropping $heartbeat because TaskScheduler is not ready yet") + context.reply(HeartbeatResponse(reregisterBlockManager = true)) + } } + + private def expireDeadHosts(): Unit = { + logTrace("Checking for hosts with no recent heartbeats in HeartbeatReceiver.") + val now = System.currentTimeMillis() + for ((executorId, lastSeenMs) <- executorLastSeen) { + if (now - lastSeenMs > executorTimeoutMs) { + logWarning(s"Removing executor $executorId with no recent heartbeats: " + + s"${now - lastSeenMs} ms exceeds timeout $executorTimeoutMs ms") + scheduler.executorLost(executorId, SlaveLost("Executor heartbeat " + + s"timed out after ${now - lastSeenMs} ms")) + if (sc.supportDynamicAllocation) { + // Asynchronously kill the executor to avoid blocking the current thread + killExecutorThread.submit(new Runnable { + override def run(): Unit = sc.killExecutor(executorId) + }) + } + executorLastSeen.remove(executorId) + } + } + } + + override def onStop(): Unit = { + if (timeoutCheckingTask != null) { + timeoutCheckingTask.cancel(true) + } + timeoutCheckingThread.shutdownNow() + killExecutorThread.shutdownNow() + } +} + +object HeartbeatReceiver { + val ENDPOINT_NAME = "HeartbeatReceiver" } diff --git a/core/src/main/scala/org/apache/spark/HttpFileServer.scala b/core/src/main/scala/org/apache/spark/HttpFileServer.scala index 3f33332a81eaf..7e706bcc42f04 100644 --- a/core/src/main/scala/org/apache/spark/HttpFileServer.scala +++ b/core/src/main/scala/org/apache/spark/HttpFileServer.scala @@ -50,6 +50,15 @@ private[spark] class HttpFileServer( def stop() { httpServer.stop() + + // If we only stop sc, but the driver process still run as a services then we need to delete + // the tmp dir, if not, it will create too many tmp dirs + try { + Utils.deleteRecursively(baseDir) + } catch { + case e: Exception => + logWarning(s"Exception while deleting Spark temp dir: ${baseDir.getAbsolutePath}", e) + } } def addFile(file: File) : String = { diff --git a/core/src/main/scala/org/apache/spark/HttpServer.scala b/core/src/main/scala/org/apache/spark/HttpServer.scala index 09a9ccc226721..8de3a6c04df34 100644 --- a/core/src/main/scala/org/apache/spark/HttpServer.scala +++ b/core/src/main/scala/org/apache/spark/HttpServer.scala @@ -160,7 +160,7 @@ private[spark] class HttpServer( throw new ServerStateException("Server is not started") } else { val scheme = if (securityManager.fileServerSSLOptions.enabled) "https" else "http" - s"$scheme://${Utils.localIpAddress}:$port" + s"$scheme://${Utils.localHostNameForURI()}:$port" } } } diff --git a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala index 6e4edc7c80d7a..d65c94e410662 100644 --- a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala +++ b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala @@ -21,13 +21,11 @@ import java.io._ import java.util.concurrent.ConcurrentHashMap import java.util.zip.{GZIPInputStream, GZIPOutputStream} -import scala.collection.mutable.{HashSet, HashMap, Map} -import scala.concurrent.Await +import scala.collection.mutable.{HashSet, Map} import scala.collection.JavaConversions._ +import scala.reflect.ClassTag -import akka.actor._ -import akka.pattern.ask - +import org.apache.spark.rpc.{RpcEndpointRef, RpcEnv, RpcCallContext, RpcEndpoint} import org.apache.spark.scheduler.MapStatus import org.apache.spark.shuffle.MetadataFetchFailedException import org.apache.spark.storage.BlockManagerId @@ -38,14 +36,15 @@ private[spark] case class GetMapOutputStatuses(shuffleId: Int) extends MapOutputTrackerMessage private[spark] case object StopMapOutputTracker extends MapOutputTrackerMessage -/** Actor class for MapOutputTrackerMaster */ -private[spark] class MapOutputTrackerMasterActor(tracker: MapOutputTrackerMaster, conf: SparkConf) - extends Actor with ActorLogReceive with Logging { +/** RpcEndpoint class for MapOutputTrackerMaster */ +private[spark] class MapOutputTrackerMasterEndpoint( + override val rpcEnv: RpcEnv, tracker: MapOutputTrackerMaster, conf: SparkConf) + extends RpcEndpoint with Logging { val maxAkkaFrameSize = AkkaUtils.maxFrameSizeBytes(conf) - override def receiveWithLogging = { + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { case GetMapOutputStatuses(shuffleId: Int) => - val hostPort = sender.path.address.hostPort + val hostPort = context.sender.address.hostPort logInfo("Asked to send map output locations for shuffle " + shuffleId + " to " + hostPort) val mapOutputStatuses = tracker.getSerializedMapOutputStatuses(shuffleId) val serializedSize = mapOutputStatuses.size @@ -53,19 +52,19 @@ private[spark] class MapOutputTrackerMasterActor(tracker: MapOutputTrackerMaster val msg = s"Map output statuses were $serializedSize bytes which " + s"exceeds spark.akka.frameSize ($maxAkkaFrameSize bytes)." - /* For SPARK-1244 we'll opt for just logging an error and then throwing an exception. - * Note that on exception the actor will just restart. A bigger refactoring (SPARK-1239) - * will ultimately remove this entire code path. */ + /* For SPARK-1244 we'll opt for just logging an error and then sending it to the sender. + * A bigger refactoring (SPARK-1239) will ultimately remove this entire code path. */ val exception = new SparkException(msg) logError(msg, exception) - throw exception + context.sendFailure(exception) + } else { + context.reply(mapOutputStatuses) } - sender ! mapOutputStatuses case StopMapOutputTracker => - logInfo("MapOutputTrackerActor stopped!") - sender ! true - context.stop(self) + logInfo("MapOutputTrackerMasterEndpoint stopped!") + context.reply(true) + stop() } } @@ -75,12 +74,9 @@ private[spark] class MapOutputTrackerMasterActor(tracker: MapOutputTrackerMaster * (driver and executor) use different HashMap to store its metadata. */ private[spark] abstract class MapOutputTracker(conf: SparkConf) extends Logging { - private val timeout = AkkaUtils.askTimeout(conf) - private val retryAttempts = AkkaUtils.numRetries(conf) - private val retryIntervalMs = AkkaUtils.retryWaitMs(conf) - /** Set to the MapOutputTrackerActor living on the driver. */ - var trackerActor: ActorRef = _ + /** Set to the MapOutputTrackerMasterEndpoint living on the driver. */ + var trackerEndpoint: RpcEndpointRef = _ /** * This HashMap has different behavior for the driver and the executors. @@ -105,12 +101,12 @@ private[spark] abstract class MapOutputTracker(conf: SparkConf) extends Logging private val fetching = new HashSet[Int] /** - * Send a message to the trackerActor and get its result within a default timeout, or + * Send a message to the trackerEndpoint and get its result within a default timeout, or * throw a SparkException if this fails. */ - protected def askTracker(message: Any): Any = { + protected def askTracker[T: ClassTag](message: Any): T = { try { - AkkaUtils.askWithReply(message, trackerActor, retryAttempts, retryIntervalMs, timeout) + trackerEndpoint.askWithReply[T](message) } catch { case e: Exception => logError("Error communicating with MapOutputTracker", e) @@ -118,9 +114,9 @@ private[spark] abstract class MapOutputTracker(conf: SparkConf) extends Logging } } - /** Send a one-way message to the trackerActor, to which we expect it to reply with true. */ + /** Send a one-way message to the trackerEndpoint, to which we expect it to reply with true. */ protected def sendTracker(message: Any) { - val response = askTracker(message) + val response = askTracker[Boolean](message) if (response != true) { throw new SparkException( "Error reply received from MapOutputTracker. Expecting true, got " + response.toString) @@ -157,11 +153,10 @@ private[spark] abstract class MapOutputTracker(conf: SparkConf) extends Logging if (fetchedStatuses == null) { // We won the race to fetch the output locs; do so - logInfo("Doing the fetch; tracker actor = " + trackerActor) + logInfo("Doing the fetch; tracker endpoint = " + trackerEndpoint) // This try-finally prevents hangs due to timeouts: try { - val fetchedBytes = - askTracker(GetMapOutputStatuses(shuffleId)).asInstanceOf[Array[Byte]] + val fetchedBytes = askTracker[Array[Byte]](GetMapOutputStatuses(shuffleId)) fetchedStatuses = MapOutputTracker.deserializeMapStatuses(fetchedBytes) logInfo("Got the output locations") mapStatuses.put(shuffleId, fetchedStatuses) @@ -328,7 +323,7 @@ private[spark] class MapOutputTrackerMaster(conf: SparkConf) override def stop() { sendTracker(StopMapOutputTracker) mapStatuses.clear() - trackerActor = null + trackerEndpoint = null metadataCleaner.cancel() cachedSerializedStatuses.clear() } @@ -350,17 +345,22 @@ private[spark] class MapOutputTrackerWorker(conf: SparkConf) extends MapOutputTr private[spark] object MapOutputTracker extends Logging { + val ENDPOINT_NAME = "MapOutputTracker" + // Serialize an array of map output locations into an efficient byte format so that we can send // it to reduce tasks. We do this by compressing the serialized bytes using GZIP. They will // generally be pretty compressible because many map outputs will be on the same hostname. def serializeMapStatuses(statuses: Array[MapStatus]): Array[Byte] = { val out = new ByteArrayOutputStream val objOut = new ObjectOutputStream(new GZIPOutputStream(out)) - // Since statuses can be modified in parallel, sync on it - statuses.synchronized { - objOut.writeObject(statuses) + Utils.tryWithSafeFinally { + // Since statuses can be modified in parallel, sync on it + statuses.synchronized { + objOut.writeObject(statuses) + } + } { + objOut.close() } - objOut.close() out.toByteArray } diff --git a/core/src/main/scala/org/apache/spark/Partitioner.scala b/core/src/main/scala/org/apache/spark/Partitioner.scala index e53a78ead2c0e..b8d244408bc5b 100644 --- a/core/src/main/scala/org/apache/spark/Partitioner.scala +++ b/core/src/main/scala/org/apache/spark/Partitioner.scala @@ -76,7 +76,7 @@ object Partitioner { * produce an unexpected or incorrect result. */ class HashPartitioner(partitions: Int) extends Partitioner { - def numPartitions = partitions + def numPartitions: Int = partitions def getPartition(key: Any): Int = key match { case null => 0 @@ -154,7 +154,7 @@ class RangePartitioner[K : Ordering : ClassTag, V]( } } - def numPartitions = rangeBounds.length + 1 + def numPartitions: Int = rangeBounds.length + 1 private var binarySearch: ((Array[K], K) => Int) = CollectionsUtils.makeBinarySearch[K] diff --git a/core/src/main/scala/org/apache/spark/SecurityManager.scala b/core/src/main/scala/org/apache/spark/SecurityManager.scala index 88d35a4bacc6e..3653f724ba192 100644 --- a/core/src/main/scala/org/apache/spark/SecurityManager.scala +++ b/core/src/main/scala/org/apache/spark/SecurityManager.scala @@ -27,6 +27,7 @@ import org.apache.hadoop.io.Text import org.apache.spark.deploy.SparkHadoopUtil import org.apache.spark.network.sasl.SecretKeyHolder +import org.apache.spark.util.Utils /** * Spark class responsible for security. @@ -203,7 +204,7 @@ private[spark] class SecurityManager(sparkConf: SparkConf) // always add the current user and SPARK_USER to the viewAcls private val defaultAclUsers = Set[String](System.getProperty("user.name", ""), - Option(System.getenv("SPARK_USER")).getOrElse("")).filter(!_.isEmpty) + Utils.getCurrentUserName()) setViewAcls(defaultAclUsers, sparkConf.get("spark.ui.view.acls", "")) setModifyAcls(defaultAclUsers, sparkConf.get("spark.modify.acls", "")) diff --git a/core/src/main/scala/org/apache/spark/SerializableWritable.scala b/core/src/main/scala/org/apache/spark/SerializableWritable.scala index 55cb25946c2ad..cb2cae185256a 100644 --- a/core/src/main/scala/org/apache/spark/SerializableWritable.scala +++ b/core/src/main/scala/org/apache/spark/SerializableWritable.scala @@ -28,8 +28,10 @@ import org.apache.spark.util.Utils @DeveloperApi class SerializableWritable[T <: Writable](@transient var t: T) extends Serializable { - def value = t - override def toString = t.toString + + def value: T = t + + override def toString: String = t.toString private def writeObject(out: ObjectOutputStream): Unit = Utils.tryOrIOException { out.defaultWriteObject() diff --git a/core/src/main/scala/org/apache/spark/SparkConf.scala b/core/src/main/scala/org/apache/spark/SparkConf.scala index 13aa9960ac33a..e3a649d755450 100644 --- a/core/src/main/scala/org/apache/spark/SparkConf.scala +++ b/core/src/main/scala/org/apache/spark/SparkConf.scala @@ -18,6 +18,7 @@ package org.apache.spark import java.util.concurrent.ConcurrentHashMap +import java.util.concurrent.atomic.AtomicBoolean import scala.collection.JavaConverters._ import scala.collection.mutable.LinkedHashSet @@ -67,6 +68,7 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging { if (value == null) { throw new NullPointerException("null value for " + key) } + logDeprecationWarning(key) settings.put(key, value) this } @@ -132,14 +134,16 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging { } /** Set multiple parameters together */ - def setAll(settings: Traversable[(String, String)]) = { - this.settings.putAll(settings.toMap.asJava) + def setAll(settings: Traversable[(String, String)]): SparkConf = { + settings.foreach { case (k, v) => set(k, v) } this } /** Set a parameter if it isn't already configured */ def setIfMissing(key: String, value: String): SparkConf = { - settings.putIfAbsent(key, value) + if (settings.putIfAbsent(key, value) == null) { + logDeprecationWarning(key) + } this } @@ -173,9 +177,44 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging { getOption(key).getOrElse(defaultValue) } + /** + * Get a time parameter as seconds; throws a NoSuchElementException if it's not set. If no + * suffix is provided then seconds are assumed. + * @throws NoSuchElementException + */ + def getTimeAsSeconds(key: String): Long = { + Utils.timeStringAsSeconds(get(key)) + } + + /** + * Get a time parameter as seconds, falling back to a default if not set. If no + * suffix is provided then seconds are assumed. + */ + def getTimeAsSeconds(key: String, defaultValue: String): Long = { + Utils.timeStringAsSeconds(get(key, defaultValue)) + } + + /** + * Get a time parameter as milliseconds; throws a NoSuchElementException if it's not set. If no + * suffix is provided then milliseconds are assumed. + * @throws NoSuchElementException + */ + def getTimeAsMs(key: String): Long = { + Utils.timeStringAsMs(get(key)) + } + + /** + * Get a time parameter as milliseconds, falling back to a default if not set. If no + * suffix is provided then milliseconds are assumed. + */ + def getTimeAsMs(key: String, defaultValue: String): Long = { + Utils.timeStringAsMs(get(key, defaultValue)) + } + + /** Get a parameter as an Option */ def getOption(key: String): Option[String] = { - Option(settings.get(key)) + Option(settings.get(key)).orElse(getDeprecatedConfig(key, this)) } /** Get all parameters as a list of pairs */ @@ -285,7 +324,7 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging { // Validate memory fractions val memoryKeys = Seq( "spark.storage.memoryFraction", - "spark.shuffle.memoryFraction", + "spark.shuffle.memoryFraction", "spark.shuffle.safetyFraction", "spark.storage.unrollFraction", "spark.storage.safetyFraction") @@ -351,9 +390,62 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging { def toDebugString: String = { getAll.sorted.map{case (k, v) => k + "=" + v}.mkString("\n") } + } -private[spark] object SparkConf { +private[spark] object SparkConf extends Logging { + + /** + * Maps deprecated config keys to information about the deprecation. + * + * The extra information is logged as a warning when the config is present in the user's + * configuration. + */ + private val deprecatedConfigs: Map[String, DeprecatedConfig] = { + val configs = Seq( + DeprecatedConfig("spark.cache.class", "0.8", + "The spark.cache.class property is no longer being used! Specify storage levels using " + + "the RDD.persist() method instead."), + DeprecatedConfig("spark.yarn.user.classpath.first", "1.3", + "Please use spark.{driver,executor}.userClassPathFirst instead.")) + Map(configs.map { cfg => (cfg.key -> cfg) }:_*) + } + + /** + * Maps a current config key to alternate keys that were used in previous version of Spark. + * + * The alternates are used in the order defined in this map. If deprecated configs are + * present in the user's configuration, a warning is logged. + */ + private val configsWithAlternatives = Map[String, Seq[AlternateConfig]]( + "spark.executor.userClassPathFirst" -> Seq( + AlternateConfig("spark.files.userClassPathFirst", "1.3")), + "spark.history.fs.update.interval" -> Seq( + AlternateConfig("spark.history.fs.update.interval.seconds", "1.4"), + AlternateConfig("spark.history.fs.updateInterval", "1.3"), + AlternateConfig("spark.history.updateInterval", "1.3")), + "spark.history.fs.cleaner.interval" -> Seq( + AlternateConfig("spark.history.fs.cleaner.interval.seconds", "1.4")), + "spark.history.fs.cleaner.maxAge" -> Seq( + AlternateConfig("spark.history.fs.cleaner.maxAge.seconds", "1.4")), + "spark.yarn.am.waitTime" -> Seq( + AlternateConfig("spark.yarn.applicationMaster.waitTries", "1.3", + // Translate old value to a duration, with 10s wait time per try. + translation = s => s"${s.toLong * 10}s")) + ) + + /** + * A view of `configsWithAlternatives` that makes it more efficient to look up deprecated + * config keys. + * + * Maps the deprecated config name to a 2-tuple (new config name, alternate config info). + */ + private val allAlternatives: Map[String, (String, AlternateConfig)] = { + configsWithAlternatives.keys.flatMap { key => + configsWithAlternatives(key).map { cfg => (cfg.key -> (key -> cfg)) } + }.toMap + } + /** * Return whether the given config is an akka config (e.g. akka.actor.provider). * Note that this does not include spark-specific akka configs (e.g. spark.akka.timeout). @@ -380,4 +472,59 @@ private[spark] object SparkConf { def isSparkPortConf(name: String): Boolean = { (name.startsWith("spark.") && name.endsWith(".port")) || name.startsWith("spark.port.") } + + /** + * Looks for available deprecated keys for the given config option, and return the first + * value available. + */ + def getDeprecatedConfig(key: String, conf: SparkConf): Option[String] = { + configsWithAlternatives.get(key).flatMap { alts => + alts.collectFirst { case alt if conf.contains(alt.key) => + val value = conf.get(alt.key) + if (alt.translation != null) alt.translation(value) else value + } + } + } + + /** + * Logs a warning message if the given config key is deprecated. + */ + def logDeprecationWarning(key: String): Unit = { + deprecatedConfigs.get(key).foreach { cfg => + logWarning( + s"The configuration key '$key' has been deprecated as of Spark ${cfg.version} and " + + s"may be removed in the future. ${cfg.deprecationMessage}") + } + + allAlternatives.get(key).foreach { case (newKey, cfg) => + logWarning( + s"The configuration key '$key' has been deprecated as of Spark ${cfg.version} and " + + s"and may be removed in the future. Please use the new key '$newKey' instead.") + } + } + + /** + * Holds information about keys that have been deprecated and do not have a replacement. + * + * @param key The deprecated key. + * @param version Version of Spark where key was deprecated. + * @param deprecationMessage Message to include in the deprecation warning. + */ + private case class DeprecatedConfig( + key: String, + version: String, + deprecationMessage: String) + + /** + * Information about an alternate configuration key that has been deprecated. + * + * @param key The deprecated config key. + * @param version The Spark version in which the key was deprecated. + * @param translation A translation function for converting old config values into new ones. + */ + private case class AlternateConfig( + key: String, + version: String, + translation: String => String = null) + } diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala index 71bdbc9b38ddb..86269eac52db0 100644 --- a/core/src/main/scala/org/apache/spark/SparkContext.scala +++ b/core/src/main/scala/org/apache/spark/SparkContext.scala @@ -23,7 +23,7 @@ import java.io._ import java.lang.reflect.Constructor import java.net.URI import java.util.{Arrays, Properties, UUID} -import java.util.concurrent.atomic.AtomicInteger +import java.util.concurrent.atomic.{AtomicReference, AtomicBoolean, AtomicInteger} import java.util.UUID.randomUUID import scala.collection.{Map, Set} @@ -31,8 +31,7 @@ import scala.collection.JavaConversions._ import scala.collection.generic.Growable import scala.collection.mutable.HashMap import scala.reflect.{ClassTag, classTag} - -import akka.actor.Props +import scala.util.control.NonFatal import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path @@ -48,11 +47,14 @@ import org.apache.mesos.MesosNativeLibrary import org.apache.spark.annotation.{DeveloperApi, Experimental} import org.apache.spark.broadcast.Broadcast import org.apache.spark.deploy.{LocalSparkCluster, SparkHadoopUtil} -import org.apache.spark.executor.TriggerThreadDump +import org.apache.spark.executor.{ExecutorEndpoint, TriggerThreadDump} import org.apache.spark.input.{StreamInputFormat, PortableDataStream, WholeTextFileInputFormat, FixedLengthBinaryInputFormat} +import org.apache.spark.io.CompressionCodec +import org.apache.spark.metrics.MetricsSystem import org.apache.spark.partial.{ApproximateEvaluator, PartialResult} import org.apache.spark.rdd._ +import org.apache.spark.rpc.{RpcAddress, RpcEndpointRef} import org.apache.spark.scheduler._ import org.apache.spark.scheduler.cluster.{CoarseGrainedSchedulerBackend, SparkDeploySchedulerBackend, SimrSchedulerBackend} @@ -94,10 +96,10 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli val startTime = System.currentTimeMillis() - @volatile private var stopped: Boolean = false + private val stopped: AtomicBoolean = new AtomicBoolean(false) private def assertNotStopped(): Unit = { - if (stopped) { + if (stopped.get()) { throw new IllegalStateException("Cannot call methods on a stopped SparkContext") } } @@ -191,9 +193,43 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli // log out Spark Version in Spark driver log logInfo(s"Running Spark version $SPARK_VERSION") - - private[spark] val conf = config.clone() - conf.validateSettings() + + /* ------------------------------------------------------------------------------------- * + | Private variables. These variables keep the internal state of the context, and are | + | not accessible by the outside world. They're mutable since we want to initialize all | + | of them to some neutral value ahead of time, so that calling "stop()" while the | + | constructor is still running is safe. | + * ------------------------------------------------------------------------------------- */ + + private var _conf: SparkConf = _ + private var _eventLogDir: Option[URI] = None + private var _eventLogCodec: Option[String] = None + private var _env: SparkEnv = _ + private var _metadataCleaner: MetadataCleaner = _ + private var _jobProgressListener: JobProgressListener = _ + private var _statusTracker: SparkStatusTracker = _ + private var _progressBar: Option[ConsoleProgressBar] = None + private var _ui: Option[SparkUI] = None + private var _hadoopConfiguration: Configuration = _ + private var _executorMemory: Int = _ + private var _schedulerBackend: SchedulerBackend = _ + private var _taskScheduler: TaskScheduler = _ + private var _heartbeatReceiver: RpcEndpointRef = _ + @volatile private var _dagScheduler: DAGScheduler = _ + private var _applicationId: String = _ + private var _eventLogger: Option[EventLoggingListener] = None + private var _executorAllocationManager: Option[ExecutorAllocationManager] = None + private var _cleaner: Option[ContextCleaner] = None + private var _listenerBusStarted: Boolean = false + private var _jars: Seq[String] = _ + private var _files: Seq[String] = _ + + /* ------------------------------------------------------------------------------------- * + | Accessors and public fields. These provide access to the internal state of the | + | context. | + * ------------------------------------------------------------------------------------- */ + + private[spark] def conf: SparkConf = _conf /** * Return a copy of this SparkContext's configuration. The configuration ''cannot'' be @@ -201,56 +237,33 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli */ def getConf: SparkConf = conf.clone() - if (!conf.contains("spark.master")) { - throw new SparkException("A master URL must be set in your configuration") - } - if (!conf.contains("spark.app.name")) { - throw new SparkException("An application name must be set in your configuration") - } + def jars: Seq[String] = _jars + def files: Seq[String] = _files + def master: String = _conf.get("spark.master") + def appName: String = _conf.get("spark.app.name") - if (conf.getBoolean("spark.logConf", false)) { - logInfo("Spark configuration:\n" + conf.toDebugString) - } - - // Set Spark driver host and port system properties - conf.setIfMissing("spark.driver.host", Utils.localHostName()) - conf.setIfMissing("spark.driver.port", "0") - - val jars: Seq[String] = - conf.getOption("spark.jars").map(_.split(",")).map(_.filter(_.size != 0)).toSeq.flatten - - val files: Seq[String] = - conf.getOption("spark.files").map(_.split(",")).map(_.filter(_.size != 0)).toSeq.flatten - - val master = conf.get("spark.master") - val appName = conf.get("spark.app.name") - - private[spark] val isEventLogEnabled = conf.getBoolean("spark.eventLog.enabled", false) - private[spark] val eventLogDir: Option[String] = { - if (isEventLogEnabled) { - Some(conf.get("spark.eventLog.dir", EventLoggingListener.DEFAULT_LOG_DIR).stripSuffix("/")) - } else { - None - } - } + private[spark] def isEventLogEnabled: Boolean = _conf.getBoolean("spark.eventLog.enabled", false) + private[spark] def eventLogDir: Option[URI] = _eventLogDir + private[spark] def eventLogCodec: Option[String] = _eventLogCodec // Generate the random name for a temp folder in Tachyon // Add a timestamp as the suffix here to make it more safe val tachyonFolderName = "spark-" + randomUUID.toString() - conf.set("spark.tachyonStore.folderName", tachyonFolderName) - - val isLocal = (master == "local" || master.startsWith("local[")) - if (master == "yarn-client") System.setProperty("SPARK_YARN_MODE", "true") + def isLocal: Boolean = (master == "local" || master.startsWith("local[")) // An asynchronous listener bus for Spark events private[spark] val listenerBus = new LiveListenerBus - conf.set("spark.executor.id", SparkContext.DRIVER_IDENTIFIER) + // This function allows components created by SparkEnv to be mocked in unit tests: + private[spark] def createSparkEnv( + conf: SparkConf, + isLocal: Boolean, + listenerBus: LiveListenerBus): SparkEnv = { + SparkEnv.createDriverEnv(conf, isLocal, listenerBus) + } - // Create the Spark execution environment (cache, map output tracker, etc) - private[spark] val env = SparkEnv.createDriverEnv(conf, isLocal, listenerBus) - SparkEnv.set(env) + private[spark] def env: SparkEnv = _env // Used to store a URL for each static file/jar together with the file's local timestamp private[spark] val addedFiles = HashMap[String, Long]() @@ -258,35 +271,14 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli // Keeps track of all persisted RDDs private[spark] val persistentRdds = new TimeStampedWeakValueHashMap[Int, RDD[_]] - private[spark] val metadataCleaner = - new MetadataCleaner(MetadataCleanerType.SPARK_CONTEXT, this.cleanup, conf) - - - private[spark] val jobProgressListener = new JobProgressListener(conf) - listenerBus.addListener(jobProgressListener) - - val statusTracker = new SparkStatusTracker(this) + private[spark] def metadataCleaner: MetadataCleaner = _metadataCleaner + private[spark] def jobProgressListener: JobProgressListener = _jobProgressListener - private[spark] val progressBar: Option[ConsoleProgressBar] = - if (conf.getBoolean("spark.ui.showConsoleProgress", true) && !log.isInfoEnabled) { - Some(new ConsoleProgressBar(this)) - } else { - None - } + def statusTracker: SparkStatusTracker = _statusTracker - // Initialize the Spark UI - private[spark] val ui: Option[SparkUI] = - if (conf.getBoolean("spark.ui.enabled", true)) { - Some(SparkUI.createLiveUI(this, conf, listenerBus, jobProgressListener, - env.securityManager,appName)) - } else { - // For tests, do not enable the UI - None - } + private[spark] def progressBar: Option[ConsoleProgressBar] = _progressBar - // Bind the UI before starting the task scheduler to communicate - // the bound port to the cluster manager properly - ui.foreach(_.bind()) + private[spark] def ui: Option[SparkUI] = _ui /** * A default Hadoop Configuration for the Hadoop code (e.g. file systems) that we reuse. @@ -294,131 +286,248 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * '''Note:''' As it will be reused in all Hadoop RDDs, it's better not to modify it unless you * plan to set some global configurations for all Hadoop RDDs. */ - val hadoopConfiguration = SparkHadoopUtil.get.newConfiguration(conf) + def hadoopConfiguration: Configuration = _hadoopConfiguration - // Add each JAR given through the constructor - if (jars != null) { - jars.foreach(addJar) + private[spark] def executorMemory: Int = _executorMemory + + // Environment variables to pass to our executors. + private[spark] val executorEnvs = HashMap[String, String]() + + // Set SPARK_USER for user who is running SparkContext. + val sparkUser = Utils.getCurrentUserName() + + private[spark] def schedulerBackend: SchedulerBackend = _schedulerBackend + private[spark] def schedulerBackend_=(sb: SchedulerBackend): Unit = { + _schedulerBackend = sb + } + + private[spark] def taskScheduler: TaskScheduler = _taskScheduler + private[spark] def taskScheduler_=(ts: TaskScheduler): Unit = { + _taskScheduler = ts + } + + private[spark] def dagScheduler: DAGScheduler = _dagScheduler + private[spark] def dagScheduler_=(ds: DAGScheduler): Unit = { + _dagScheduler = ds } - if (files != null) { - files.foreach(addFile) + def applicationId: String = _applicationId + + def metricsSystem: MetricsSystem = if (_env != null) _env.metricsSystem else null + + private[spark] def eventLogger: Option[EventLoggingListener] = _eventLogger + + private[spark] def executorAllocationManager: Option[ExecutorAllocationManager] = + _executorAllocationManager + + private[spark] def cleaner: Option[ContextCleaner] = _cleaner + + private[spark] var checkpointDir: Option[String] = None + + // Thread Local variable that can be used by users to pass information down the stack + private val localProperties = new InheritableThreadLocal[Properties] { + override protected def childValue(parent: Properties): Properties = new Properties(parent) + override protected def initialValue(): Properties = new Properties() } + /* ------------------------------------------------------------------------------------- * + | Initialization. This code initializes the context in a manner that is exception-safe. | + | All internal fields holding state are initialized here, and any error prompts the | + | stop() method to be called. | + * ------------------------------------------------------------------------------------- */ + private def warnSparkMem(value: String): String = { logWarning("Using SPARK_MEM to set amount of memory to use per executor process is " + "deprecated, please use spark.executor.memory instead.") value } - private[spark] val executorMemory = conf.getOption("spark.executor.memory") - .orElse(Option(System.getenv("SPARK_EXECUTOR_MEMORY"))) - .orElse(Option(System.getenv("SPARK_MEM")).map(warnSparkMem)) - .map(Utils.memoryStringToMb) - .getOrElse(512) + try { + _conf = config.clone() + _conf.validateSettings() - // Environment variables to pass to our executors. - private[spark] val executorEnvs = HashMap[String, String]() + if (!_conf.contains("spark.master")) { + throw new SparkException("A master URL must be set in your configuration") + } + if (!_conf.contains("spark.app.name")) { + throw new SparkException("An application name must be set in your configuration") + } - // Convert java options to env vars as a work around - // since we can't set env vars directly in sbt. - for { (envKey, propKey) <- Seq(("SPARK_TESTING", "spark.testing")) - value <- Option(System.getenv(envKey)).orElse(Option(System.getProperty(propKey)))} { - executorEnvs(envKey) = value - } - Option(System.getenv("SPARK_PREPEND_CLASSES")).foreach { v => - executorEnvs("SPARK_PREPEND_CLASSES") = v - } - // The Mesos scheduler backend relies on this environment variable to set executor memory. - // TODO: Set this only in the Mesos scheduler. - executorEnvs("SPARK_EXECUTOR_MEMORY") = executorMemory + "m" - executorEnvs ++= conf.getExecutorEnv + if (_conf.getBoolean("spark.logConf", false)) { + logInfo("Spark configuration:\n" + _conf.toDebugString) + } - // Set SPARK_USER for user who is running SparkContext. - val sparkUser = Option { - Option(System.getenv("SPARK_USER")).getOrElse(System.getProperty("user.name")) - }.getOrElse { - SparkContext.SPARK_UNKNOWN_USER - } - executorEnvs("SPARK_USER") = sparkUser - - // Create and start the scheduler - private[spark] var (schedulerBackend, taskScheduler) = - SparkContext.createTaskScheduler(this, master) - private val heartbeatReceiver = env.actorSystem.actorOf( - Props(new HeartbeatReceiver(taskScheduler)), "HeartbeatReceiver") - @volatile private[spark] var dagScheduler: DAGScheduler = _ - try { - dagScheduler = new DAGScheduler(this) - } catch { - case e: Exception => { - try { - stop() - } finally { - throw new SparkException("Error while constructing DAGScheduler", e) + // Set Spark driver host and port system properties + _conf.setIfMissing("spark.driver.host", Utils.localHostName()) + _conf.setIfMissing("spark.driver.port", "0") + + _conf.set("spark.executor.id", SparkContext.DRIVER_IDENTIFIER) + + _jars =_conf.getOption("spark.jars").map(_.split(",")).map(_.filter(_.size != 0)).toSeq.flatten + _files = _conf.getOption("spark.files").map(_.split(",")).map(_.filter(_.size != 0)) + .toSeq.flatten + + _eventLogDir = + if (isEventLogEnabled) { + val unresolvedDir = conf.get("spark.eventLog.dir", EventLoggingListener.DEFAULT_LOG_DIR) + .stripSuffix("/") + Some(Utils.resolveURI(unresolvedDir)) + } else { + None + } + + _eventLogCodec = { + val compress = _conf.getBoolean("spark.eventLog.compress", false) + if (compress && isEventLogEnabled) { + Some(CompressionCodec.getCodecName(_conf)).map(CompressionCodec.getShortName) + } else { + None } } - } - // start TaskScheduler after taskScheduler sets DAGScheduler reference in DAGScheduler's - // constructor - taskScheduler.start() + _conf.set("spark.tachyonStore.folderName", tachyonFolderName) - val applicationId: String = taskScheduler.applicationId() - conf.set("spark.app.id", applicationId) + if (master == "yarn-client") System.setProperty("SPARK_YARN_MODE", "true") - env.blockManager.initialize(applicationId) + // Create the Spark execution environment (cache, map output tracker, etc) + _env = createSparkEnv(_conf, isLocal, listenerBus) + SparkEnv.set(_env) - val metricsSystem = env.metricsSystem + _metadataCleaner = new MetadataCleaner(MetadataCleanerType.SPARK_CONTEXT, this.cleanup, _conf) - // The metrics system for Driver need to be set spark.app.id to app ID. - // So it should start after we get app ID from the task scheduler and set spark.app.id. - metricsSystem.start() - // Attach the driver metrics servlet handler to the web ui after the metrics system is started. - metricsSystem.getServletHandlers.foreach(handler => ui.foreach(_.attachHandler(handler))) + _jobProgressListener = new JobProgressListener(_conf) + listenerBus.addListener(jobProgressListener) - // Optionally log Spark events - private[spark] val eventLogger: Option[EventLoggingListener] = { - if (isEventLogEnabled) { - val logger = - new EventLoggingListener(applicationId, eventLogDir.get, conf, hadoopConfiguration) - logger.start() - listenerBus.addListener(logger) - Some(logger) - } else None - } + _statusTracker = new SparkStatusTracker(this) - // Optionally scale number of executors dynamically based on workload. Exposed for testing. - private val dynamicAllocationEnabled = conf.getBoolean("spark.dynamicAllocation.enabled", false) - private val dynamicAllocationTesting = conf.getBoolean("spark.dynamicAllocation.testing", false) - private[spark] val executorAllocationManager: Option[ExecutorAllocationManager] = - if (dynamicAllocationEnabled) { - assert(master.contains("yarn") || dynamicAllocationTesting, - "Dynamic allocation of executors is currently only supported in YARN mode") - Some(new ExecutorAllocationManager(this, listenerBus, conf)) - } else { - None + _progressBar = + if (_conf.getBoolean("spark.ui.showConsoleProgress", true) && !log.isInfoEnabled) { + Some(new ConsoleProgressBar(this)) + } else { + None + } + + _ui = + if (conf.getBoolean("spark.ui.enabled", true)) { + Some(SparkUI.createLiveUI(this, _conf, listenerBus, _jobProgressListener, + _env.securityManager,appName)) + } else { + // For tests, do not enable the UI + None + } + // Bind the UI before starting the task scheduler to communicate + // the bound port to the cluster manager properly + _ui.foreach(_.bind()) + + _hadoopConfiguration = SparkHadoopUtil.get.newConfiguration(_conf) + + // Add each JAR given through the constructor + if (jars != null) { + jars.foreach(addJar) } - executorAllocationManager.foreach(_.start()) - private[spark] val cleaner: Option[ContextCleaner] = { - if (conf.getBoolean("spark.cleaner.referenceTracking", true)) { - Some(new ContextCleaner(this)) - } else { - None + if (files != null) { + files.foreach(addFile) } - } - cleaner.foreach(_.start()) - setupAndStartListenerBus() - postEnvironmentUpdate() - postApplicationStart() + _executorMemory = _conf.getOption("spark.executor.memory") + .orElse(Option(System.getenv("SPARK_EXECUTOR_MEMORY"))) + .orElse(Option(System.getenv("SPARK_MEM")) + .map(warnSparkMem)) + .map(Utils.memoryStringToMb) + .getOrElse(512) + + // Convert java options to env vars as a work around + // since we can't set env vars directly in sbt. + for { (envKey, propKey) <- Seq(("SPARK_TESTING", "spark.testing")) + value <- Option(System.getenv(envKey)).orElse(Option(System.getProperty(propKey)))} { + executorEnvs(envKey) = value + } + Option(System.getenv("SPARK_PREPEND_CLASSES")).foreach { v => + executorEnvs("SPARK_PREPEND_CLASSES") = v + } + // The Mesos scheduler backend relies on this environment variable to set executor memory. + // TODO: Set this only in the Mesos scheduler. + executorEnvs("SPARK_EXECUTOR_MEMORY") = executorMemory + "m" + executorEnvs ++= _conf.getExecutorEnv + executorEnvs("SPARK_USER") = sparkUser + + // We need to register "HeartbeatReceiver" before "createTaskScheduler" because Executor will + // retrieve "HeartbeatReceiver" in the constructor. (SPARK-6640) + _heartbeatReceiver = env.rpcEnv.setupEndpoint( + HeartbeatReceiver.ENDPOINT_NAME, new HeartbeatReceiver(this)) + + // Create and start the scheduler + val (sched, ts) = SparkContext.createTaskScheduler(this, master) + _schedulerBackend = sched + _taskScheduler = ts + _dagScheduler = new DAGScheduler(this) + _heartbeatReceiver.send(TaskSchedulerIsSet) + + // start TaskScheduler after taskScheduler sets DAGScheduler reference in DAGScheduler's + // constructor + _taskScheduler.start() + + _applicationId = _taskScheduler.applicationId() + _conf.set("spark.app.id", _applicationId) + _env.blockManager.initialize(_applicationId) + + // The metrics system for Driver need to be set spark.app.id to app ID. + // So it should start after we get app ID from the task scheduler and set spark.app.id. + metricsSystem.start() + // Attach the driver metrics servlet handler to the web ui after the metrics system is started. + metricsSystem.getServletHandlers.foreach(handler => ui.foreach(_.attachHandler(handler))) + + _eventLogger = + if (isEventLogEnabled) { + val logger = + new EventLoggingListener(_applicationId, _eventLogDir.get, _conf, _hadoopConfiguration) + logger.start() + listenerBus.addListener(logger) + Some(logger) + } else { + None + } - private[spark] var checkpointDir: Option[String] = None + // Optionally scale number of executors dynamically based on workload. Exposed for testing. + val dynamicAllocationEnabled = _conf.getBoolean("spark.dynamicAllocation.enabled", false) + _executorAllocationManager = + if (dynamicAllocationEnabled) { + assert(supportDynamicAllocation, + "Dynamic allocation of executors is currently only supported in YARN mode") + Some(new ExecutorAllocationManager(this, listenerBus, _conf)) + } else { + None + } + _executorAllocationManager.foreach(_.start()) - // Thread Local variable that can be used by users to pass information down the stack - private val localProperties = new InheritableThreadLocal[Properties] { - override protected def childValue(parent: Properties): Properties = new Properties(parent) + _cleaner = + if (_conf.getBoolean("spark.cleaner.referenceTracking", true)) { + Some(new ContextCleaner(this)) + } else { + None + } + _cleaner.foreach(_.start()) + + setupAndStartListenerBus() + postEnvironmentUpdate() + postApplicationStart() + + // Post init + _taskScheduler.postStartHook() + _env.metricsSystem.registerSource(new DAGSchedulerSource(dagScheduler)) + _env.metricsSystem.registerSource(new BlockManagerSource(_env.blockManager)) + } catch { + case NonFatal(e) => + logError("Error initializing SparkContext.", e) + try { + stop() + } catch { + case NonFatal(inner) => + logError("Error stopping SparkContext after init error.", inner) + } finally { + throw e + } } /** @@ -432,10 +541,12 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli if (executorId == SparkContext.DRIVER_IDENTIFIER) { Some(Utils.getThreadDump()) } else { - val (host, port) = env.blockManager.master.getActorSystemHostPortForExecutor(executorId).get - val actorRef = AkkaUtils.makeExecutorRef("ExecutorActor", conf, host, port, env.actorSystem) - Some(AkkaUtils.askWithReply[Array[ThreadStackTrace]](TriggerThreadDump, actorRef, - AkkaUtils.numRetries(conf), AkkaUtils.retryWaitMs(conf), AkkaUtils.askTimeout(conf))) + val (host, port) = env.blockManager.master.getRpcHostPortForExecutor(executorId).get + val endpointRef = env.rpcEnv.setupEndpointRef( + SparkEnv.executorActorSystemName, + RpcAddress(host, port), + ExecutorEndpoint.EXECUTOR_ENDPOINT_NAME) + Some(endpointRef.askWithReply[Array[ThreadStackTrace]](TriggerThreadDump)) } } catch { case e: Exception => @@ -460,9 +571,6 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * Spark fair scheduler pool. */ def setLocalProperty(key: String, value: String) { - if (localProperties.get() == null) { - localProperties.set(new Properties()) - } if (value == null) { localProperties.get.remove(key) } else { @@ -523,19 +631,6 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli setLocalProperty(SparkContext.SPARK_JOB_INTERRUPT_ON_CANCEL, null) } - // Post init - taskScheduler.postStartHook() - - private val dagSchedulerSource = new DAGSchedulerSource(this.dagScheduler) - private val blockManagerSource = new BlockManagerSource(SparkEnv.get.blockManager) - - private def initDriverMetrics() { - SparkEnv.get.metricsSystem.registerSource(dagSchedulerSource) - SparkEnv.get.metricsSystem.registerSource(blockManagerSource) - } - - initDriverMetrics() - // Methods for creating RDDs /** Distribute a local Scala collection to form an RDD. @@ -543,6 +638,8 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * @note Parallelize acts lazily. If `seq` is a mutable collection and is altered after the call * to parallelize and before the first action on the RDD, the resultant RDD will reflect the * modified collection. Pass a copy of the argument to avoid this. + * @note avoid using `parallelize(Seq())` to create an empty `RDD`. Consider `emptyRDD` for an + * RDD with no partitions, or `parallelize(Seq[T]())` for an RDD of `T` with empty partitions. */ def parallelize[T: ClassTag](seq: Seq[T], numSlices: Int = defaultParallelism): RDD[T] = { assertNotStopped() @@ -826,7 +923,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli vClass: Class[V], conf: Configuration = hadoopConfiguration): RDD[(K, V)] = { assertNotStopped() - // The call to new NewHadoopJob automatically adds security credentials to conf, + // The call to new NewHadoopJob automatically adds security credentials to conf, // so we don't need to explicitly add them ourselves val job = new NewHadoopJob(conf) NewFileInputFormat.addInputPath(job, new Path(path)) @@ -956,14 +1053,21 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli } /** Build the union of a list of RDDs. */ - def union[T: ClassTag](rdds: Seq[RDD[T]]): RDD[T] = new UnionRDD(this, rdds) + def union[T: ClassTag](rdds: Seq[RDD[T]]): RDD[T] = { + val partitioners = rdds.flatMap(_.partitioner).toSet + if (partitioners.size == 1) { + new PartitionerAwareUnionRDD(this, rdds) + } else { + new UnionRDD(this, rdds) + } + } /** Build the union of a list of RDDs passed as variable-length arguments. */ def union[T: ClassTag](first: RDD[T], rest: RDD[T]*): RDD[T] = - new UnionRDD(this, Seq(first) ++ rest) + union(Seq(first) ++ rest) /** Get an RDD that has no partitions or elements. */ - def emptyRDD[T: ClassTag] = new EmptyRDD[T](this) + def emptyRDD[T: ClassTag]: EmptyRDD[T] = new EmptyRDD[T](this) // Methods for creating shared variables @@ -971,16 +1075,23 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * Create an [[org.apache.spark.Accumulator]] variable of a given type, which tasks can "add" * values to using the `+=` method. Only the driver can access the accumulator's `value`. */ - def accumulator[T](initialValue: T)(implicit param: AccumulatorParam[T]) = - new Accumulator(initialValue, param) + def accumulator[T](initialValue: T)(implicit param: AccumulatorParam[T]): Accumulator[T] = + { + val acc = new Accumulator(initialValue, param) + cleaner.foreach(_.registerAccumulatorForCleanup(acc)) + acc + } /** * Create an [[org.apache.spark.Accumulator]] variable of a given type, with a name for display * in the Spark UI. Tasks can "add" values to the accumulator using the `+=` method. Only the * driver can access the accumulator's `value`. */ - def accumulator[T](initialValue: T, name: String)(implicit param: AccumulatorParam[T]) = { - new Accumulator(initialValue, param, Some(name)) + def accumulator[T](initialValue: T, name: String)(implicit param: AccumulatorParam[T]) + : Accumulator[T] = { + val acc = new Accumulator(initialValue, param, Some(name)) + cleaner.foreach(_.registerAccumulatorForCleanup(acc)) + acc } /** @@ -989,8 +1100,12 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * @tparam R accumulator result type * @tparam T type that can be added to the accumulator */ - def accumulable[R, T](initialValue: R)(implicit param: AccumulableParam[R, T]) = - new Accumulable(initialValue, param) + def accumulable[R, T](initialValue: R)(implicit param: AccumulableParam[R, T]) + : Accumulable[R, T] = { + val acc = new Accumulable(initialValue, param) + cleaner.foreach(_.registerAccumulatorForCleanup(acc)) + acc + } /** * Create an [[org.apache.spark.Accumulable]] shared variable, with a name for display in the @@ -999,8 +1114,12 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * @tparam R accumulator result type * @tparam T type that can be added to the accumulator */ - def accumulable[R, T](initialValue: R, name: String)(implicit param: AccumulableParam[R, T]) = - new Accumulable(initialValue, param, Some(name)) + def accumulable[R, T](initialValue: R, name: String)(implicit param: AccumulableParam[R, T]) + : Accumulable[R, T] = { + val acc = new Accumulable(initialValue, param, Some(name)) + cleaner.foreach(_.registerAccumulatorForCleanup(acc)) + acc + } /** * Create an accumulator from a "mutable collection" type. @@ -1011,7 +1130,9 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli def accumulableCollection[R <% Growable[T] with TraversableOnce[T] with Serializable: ClassTag, T] (initialValue: R): Accumulable[R, T] = { val param = new GrowableAccumulableParam[R,T] - new Accumulable(initialValue, param) + val acc = new Accumulable(initialValue, param) + cleaner.foreach(_.registerAccumulatorForCleanup(acc)) + acc } /** @@ -1056,7 +1177,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli def addFile(path: String, recursive: Boolean): Unit = { val uri = new URI(path) val schemeCorrectedPath = uri.getScheme match { - case null | "local" => "file:" + uri.getPath + case null | "local" => new File(path).getCanonicalFile.toURI.toString case _ => path } @@ -1067,7 +1188,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli if (!fs.exists(hadoopPath)) { throw new FileNotFoundException(s"Added file $hadoopPath does not exist.") } - val isDir = fs.isDirectory(hadoopPath) + val isDir = fs.getFileStatus(hadoopPath).isDir if (!isLocal && scheme == "file" && isDir) { throw new SparkException(s"addFile does not support local directories when not running " + "local mode.") @@ -1094,6 +1215,13 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli postEnvironmentUpdate() } + /** + * Return whether dynamically adjusting the amount of resources allocated to + * this application is supported. This is currently only available for YARN. + */ + private[spark] def supportDynamicAllocation = + master.contains("yarn") || _conf.getBoolean("spark.dynamicAllocation.testing", false) + /** * :: DeveloperApi :: * Register a listener to receive up-calls from events that happen during execution. @@ -1103,14 +1231,31 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli listenerBus.addListener(listener) } + /** + * Express a preference to the cluster manager for a given total number of executors. + * This can result in canceling pending requests or filing additional requests. + * This is currently only supported in YARN mode. Return whether the request is received. + */ + private[spark] override def requestTotalExecutors(numExecutors: Int): Boolean = { + assert(supportDynamicAllocation, + "Requesting executors is currently only supported in YARN mode") + schedulerBackend match { + case b: CoarseGrainedSchedulerBackend => + b.requestTotalExecutors(numExecutors) + case _ => + logWarning("Requesting executors is only supported in coarse-grained mode") + false + } + } + /** * :: DeveloperApi :: * Request an additional number of executors from the cluster manager. - * This is currently only supported in Yarn mode. Return whether the request is received. + * This is currently only supported in YARN mode. Return whether the request is received. */ @DeveloperApi override def requestExecutors(numAdditionalExecutors: Int): Boolean = { - assert(master.contains("yarn") || dynamicAllocationTesting, + assert(supportDynamicAllocation, "Requesting executors is currently only supported in YARN mode") schedulerBackend match { case b: CoarseGrainedSchedulerBackend => @@ -1124,11 +1269,11 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli /** * :: DeveloperApi :: * Request that the cluster manager kill the specified executors. - * This is currently only supported in Yarn mode. Return whether the request is received. + * This is currently only supported in YARN mode. Return whether the request is received. */ @DeveloperApi override def killExecutors(executorIds: Seq[String]): Boolean = { - assert(master.contains("yarn") || dynamicAllocationTesting, + assert(supportDynamicAllocation, "Killing executors is currently only supported in YARN mode") schedulerBackend match { case b: CoarseGrainedSchedulerBackend => @@ -1148,7 +1293,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli override def killExecutor(executorId: String): Boolean = super.killExecutor(executorId) /** The version of Spark on which this application is running. */ - def version = SPARK_VERSION + def version: String = SPARK_VERSION /** * Return a map from the slave to the max memory available for caching and the remaining @@ -1328,31 +1473,46 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli addedJars.clear() } - /** Shut down the SparkContext. */ + // Shut down the SparkContext. def stop() { - SparkContext.SPARK_CONTEXT_CONSTRUCTOR_LOCK.synchronized { - postApplicationEnd() - ui.foreach(_.stop()) - if (!stopped) { - stopped = true - env.metricsSystem.report() - metadataCleaner.cancel() - env.actorSystem.stop(heartbeatReceiver) - cleaner.foreach(_.stop()) - dagScheduler.stop() - dagScheduler = null - taskScheduler = null - // TODO: Cache.stop()? - env.stop() - SparkEnv.set(null) - listenerBus.stop() - eventLogger.foreach(_.stop()) - logInfo("Successfully stopped SparkContext") - SparkContext.clearActiveContext() - } else { - logInfo("SparkContext already stopped") - } + // Use the stopping variable to ensure no contention for the stop scenario. + // Still track the stopped variable for use elsewhere in the code. + if (!stopped.compareAndSet(false, true)) { + logInfo("SparkContext already stopped.") + return + } + + postApplicationEnd() + _ui.foreach(_.stop()) + if (env != null) { + env.metricsSystem.report() + } + if (metadataCleaner != null) { + metadataCleaner.cancel() + } + _cleaner.foreach(_.stop()) + _executorAllocationManager.foreach(_.stop()) + if (_dagScheduler != null) { + _dagScheduler.stop() + _dagScheduler = null + } + if (_listenerBusStarted) { + listenerBus.stop() + _listenerBusStarted = false + } + _eventLogger.foreach(_.stop()) + if (env != null && _heartbeatReceiver != null) { + env.rpcEnv.stop(_heartbeatReceiver) } + _progressBar.foreach(_.stop()) + _taskScheduler = null + // TODO: Cache.stop()? + if (_env != null) { + _env.stop() + SparkEnv.set(null) + } + SparkContext.clearActiveContext() + logInfo("Successfully stopped SparkContext") } @@ -1414,12 +1574,15 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli partitions: Seq[Int], allowLocal: Boolean, resultHandler: (Int, U) => Unit) { - if (stopped) { + if (stopped.get()) { throw new IllegalStateException("SparkContext has been shutdown") } val callSite = getCallSite val cleanedFunc = clean(func) logInfo("Starting job: " + callSite.shortForm) + if (conf.getBoolean("spark.logLineage", false)) { + logInfo("RDD's recursive dependencies:\n" + rdd.toDebugString) + } dagScheduler.runJob(rdd, cleanedFunc, partitions, callSite, allowLocal, resultHandler, localProperties.get) progressBar.foreach(_.finishAll()) @@ -1594,7 +1757,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli } } - def getCheckpointDir = checkpointDir + def getCheckpointDir: Option[String] = checkpointDir /** Default level of parallelism to use when not given by user (e.g. parallelize and makeRDD). */ def defaultParallelism: Int = { @@ -1606,8 +1769,8 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli @deprecated("use defaultMinPartitions", "1.0.0") def defaultMinSplits: Int = math.min(defaultParallelism, 2) - /** - * Default min number of partitions for Hadoop RDDs when not given by user + /** + * Default min number of partitions for Hadoop RDDs when not given by user * Notice that we use math.min so the "defaultMinPartitions" cannot be higher than 2. * The reasons for this are discussed in https://github.com/mesos/spark/pull/718 */ @@ -1671,7 +1834,8 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli } } - listenerBus.start() + listenerBus.start(this) + _listenerBusStarted = true } /** Post the application start event */ @@ -1723,11 +1887,12 @@ object SparkContext extends Logging { private val SPARK_CONTEXT_CONSTRUCTOR_LOCK = new Object() /** - * The active, fully-constructed SparkContext. If no SparkContext is active, then this is `None`. + * The active, fully-constructed SparkContext. If no SparkContext is active, then this is `null`. * - * Access to this field is guarded by SPARK_CONTEXT_CONSTRUCTOR_LOCK + * Access to this field is guarded by SPARK_CONTEXT_CONSTRUCTOR_LOCK. */ - private var activeContext: Option[SparkContext] = None + private val activeContext: AtomicReference[SparkContext] = + new AtomicReference[SparkContext](null) /** * Points to a partially-constructed SparkContext if some thread is in the SparkContext @@ -1762,7 +1927,8 @@ object SparkContext extends Logging { logWarning(warnMsg) } - activeContext.foreach { ctx => + if (activeContext.get() != null) { + val ctx = activeContext.get() val errMsg = "Only one SparkContext may be running in this JVM (see SPARK-2243)." + " To ignore this error, set spark.driver.allowMultipleContexts = true. " + s"The currently running SparkContext was created at:\n${ctx.creationSite.longForm}" @@ -1777,6 +1943,39 @@ object SparkContext extends Logging { } } + /** + * This function may be used to get or instantiate a SparkContext and register it as a + * singleton object. Because we can only have one active SparkContext per JVM, + * this is useful when applications may wish to share a SparkContext. + * + * Note: This function cannot be used to create multiple SparkContext instances + * even if multiple contexts are allowed. + */ + def getOrCreate(config: SparkConf): SparkContext = { + // Synchronize to ensure that multiple create requests don't trigger an exception + // from assertNoOtherContextIsRunning within setActiveContext + SPARK_CONTEXT_CONSTRUCTOR_LOCK.synchronized { + if (activeContext.get() == null) { + setActiveContext(new SparkContext(config), allowMultipleContexts = false) + } + activeContext.get() + } + } + + /** + * This function may be used to get or instantiate a SparkContext and register it as a + * singleton object. Because we can only have one active SparkContext per JVM, + * this is useful when applications may wish to share a SparkContext. + * + * This method allows not passing a SparkConf (useful if just retrieving). + * + * Note: This function cannot be used to create multiple SparkContext instances + * even if multiple contexts are allowed. + */ + def getOrCreate(): SparkContext = { + getOrCreate(new SparkConf()) + } + /** * Called at the beginning of the SparkContext constructor to ensure that no SparkContext is * running. Throws an exception if a running context is detected and logs a warning if another @@ -1803,7 +2002,7 @@ object SparkContext extends Logging { SPARK_CONTEXT_CONSTRUCTOR_LOCK.synchronized { assertNoOtherContextIsRunning(sc, allowMultipleContexts) contextBeingConstructed = None - activeContext = Some(sc) + activeContext.set(sc) } } @@ -1814,7 +2013,7 @@ object SparkContext extends Logging { */ private[spark] def clearActiveContext(): Unit = { SPARK_CONTEXT_CONSTRUCTOR_LOCK.synchronized { - activeContext = None + activeContext.set(null) } } @@ -1824,9 +2023,17 @@ object SparkContext extends Logging { private[spark] val SPARK_JOB_INTERRUPT_ON_CANCEL = "spark.job.interruptOnCancel" - private[spark] val SPARK_UNKNOWN_USER = "" + /** + * Executor id for the driver. In earlier versions of Spark, this was ``, but this was + * changed to `driver` because the angle brackets caused escaping issues in URLs and XML (see + * SPARK-6716 for more details). + */ + private[spark] val DRIVER_IDENTIFIER = "driver" - private[spark] val DRIVER_IDENTIFIER = "" + /** + * Legacy version of DRIVER_IDENTIFIER, retained for backwards-compatibility. + */ + private[spark] val LEGACY_DRIVER_IDENTIFIER = "" // The following deprecated objects have already been copied to `object AccumulatorParam` to // make the compiler find them automatically. They are duplicate codes only for backward @@ -1837,28 +2044,28 @@ object SparkContext extends Logging { "backward compatibility.", "1.3.0") object DoubleAccumulatorParam extends AccumulatorParam[Double] { def addInPlace(t1: Double, t2: Double): Double = t1 + t2 - def zero(initialValue: Double) = 0.0 + def zero(initialValue: Double): Double = 0.0 } @deprecated("Replaced by implicit objects in AccumulatorParam. This is kept here only for " + "backward compatibility.", "1.3.0") object IntAccumulatorParam extends AccumulatorParam[Int] { def addInPlace(t1: Int, t2: Int): Int = t1 + t2 - def zero(initialValue: Int) = 0 + def zero(initialValue: Int): Int = 0 } @deprecated("Replaced by implicit objects in AccumulatorParam. This is kept here only for " + "backward compatibility.", "1.3.0") object LongAccumulatorParam extends AccumulatorParam[Long] { - def addInPlace(t1: Long, t2: Long) = t1 + t2 - def zero(initialValue: Long) = 0L + def addInPlace(t1: Long, t2: Long): Long = t1 + t2 + def zero(initialValue: Long): Long = 0L } @deprecated("Replaced by implicit objects in AccumulatorParam. This is kept here only for " + "backward compatibility.", "1.3.0") object FloatAccumulatorParam extends AccumulatorParam[Float] { - def addInPlace(t1: Float, t2: Float) = t1 + t2 - def zero(initialValue: Float) = 0f + def addInPlace(t1: Float, t2: Float): Float = t1 + t2 + def zero(initialValue: Float): Float = 0f } // The following deprecated functions have already been moved to `object RDD` to @@ -1868,18 +2075,18 @@ object SparkContext extends Logging { @deprecated("Replaced by implicit functions in the RDD companion object. This is " + "kept here only for backward compatibility.", "1.3.0") def rddToPairRDDFunctions[K, V](rdd: RDD[(K, V)]) - (implicit kt: ClassTag[K], vt: ClassTag[V], ord: Ordering[K] = null) = { + (implicit kt: ClassTag[K], vt: ClassTag[V], ord: Ordering[K] = null): PairRDDFunctions[K, V] = RDD.rddToPairRDDFunctions(rdd) - } @deprecated("Replaced by implicit functions in the RDD companion object. This is " + "kept here only for backward compatibility.", "1.3.0") - def rddToAsyncRDDActions[T: ClassTag](rdd: RDD[T]) = RDD.rddToAsyncRDDActions(rdd) + def rddToAsyncRDDActions[T: ClassTag](rdd: RDD[T]): AsyncRDDActions[T] = + RDD.rddToAsyncRDDActions(rdd) @deprecated("Replaced by implicit functions in the RDD companion object. This is " + "kept here only for backward compatibility.", "1.3.0") def rddToSequenceFileRDDFunctions[K <% Writable: ClassTag, V <% Writable: ClassTag]( - rdd: RDD[(K, V)]) = { + rdd: RDD[(K, V)]): SequenceFileRDDFunctions[K, V] = { val kf = implicitly[K => Writable] val vf = implicitly[V => Writable] // Set the Writable class to null and `SequenceFileRDDFunctions` will use Reflection to get it @@ -1891,16 +2098,17 @@ object SparkContext extends Logging { @deprecated("Replaced by implicit functions in the RDD companion object. This is " + "kept here only for backward compatibility.", "1.3.0") def rddToOrderedRDDFunctions[K : Ordering : ClassTag, V: ClassTag]( - rdd: RDD[(K, V)]) = + rdd: RDD[(K, V)]): OrderedRDDFunctions[K, V, (K, V)] = RDD.rddToOrderedRDDFunctions(rdd) @deprecated("Replaced by implicit functions in the RDD companion object. This is " + "kept here only for backward compatibility.", "1.3.0") - def doubleRDDToDoubleRDDFunctions(rdd: RDD[Double]) = RDD.doubleRDDToDoubleRDDFunctions(rdd) + def doubleRDDToDoubleRDDFunctions(rdd: RDD[Double]): DoubleRDDFunctions = + RDD.doubleRDDToDoubleRDDFunctions(rdd) @deprecated("Replaced by implicit functions in the RDD companion object. This is " + "kept here only for backward compatibility.", "1.3.0") - def numericRDDToDoubleRDDFunctions[T](rdd: RDD[T])(implicit num: Numeric[T]) = + def numericRDDToDoubleRDDFunctions[T](rdd: RDD[T])(implicit num: Numeric[T]): DoubleRDDFunctions = RDD.numericRDDToDoubleRDDFunctions(rdd) // The following deprecated functions have already been moved to `object WritableFactory` to @@ -2066,29 +2274,29 @@ object SparkContext extends Logging { master match { case "local" => val scheduler = new TaskSchedulerImpl(sc, MAX_LOCAL_TASK_FAILURES, isLocal = true) - val backend = new LocalBackend(scheduler, 1) + val backend = new LocalBackend(sc.getConf, scheduler, 1) scheduler.initialize(backend) (backend, scheduler) case LOCAL_N_REGEX(threads) => - def localCpuCount = Runtime.getRuntime.availableProcessors() + def localCpuCount: Int = Runtime.getRuntime.availableProcessors() // local[*] estimates the number of cores on the machine; local[N] uses exactly N threads. val threadCount = if (threads == "*") localCpuCount else threads.toInt if (threadCount <= 0) { throw new SparkException(s"Asked to run locally with $threadCount threads") } val scheduler = new TaskSchedulerImpl(sc, MAX_LOCAL_TASK_FAILURES, isLocal = true) - val backend = new LocalBackend(scheduler, threadCount) + val backend = new LocalBackend(sc.getConf, scheduler, threadCount) scheduler.initialize(backend) (backend, scheduler) case LOCAL_N_FAILURES_REGEX(threads, maxFailures) => - def localCpuCount = Runtime.getRuntime.availableProcessors() + def localCpuCount: Int = Runtime.getRuntime.availableProcessors() // local[*, M] means the number of cores on the computer with M failures // local[N, M] means exactly N threads with M failures val threadCount = if (threads == "*") localCpuCount else threads.toInt val scheduler = new TaskSchedulerImpl(sc, maxFailures.toInt, isLocal = true) - val backend = new LocalBackend(scheduler, threadCount) + val backend = new LocalBackend(sc.getConf, scheduler, threadCount) scheduler.initialize(backend) (backend, scheduler) diff --git a/core/src/main/scala/org/apache/spark/SparkEnv.scala b/core/src/main/scala/org/apache/spark/SparkEnv.scala index f25db7f8de565..959aefabd8de4 100644 --- a/core/src/main/scala/org/apache/spark/SparkEnv.scala +++ b/core/src/main/scala/org/apache/spark/SparkEnv.scala @@ -24,7 +24,6 @@ import scala.collection.JavaConversions._ import scala.collection.mutable import scala.util.Properties -import akka.actor._ import com.google.common.collect.MapMaker import org.apache.spark.annotation.DeveloperApi @@ -34,11 +33,14 @@ import org.apache.spark.metrics.MetricsSystem import org.apache.spark.network.BlockTransferService import org.apache.spark.network.netty.NettyBlockTransferService import org.apache.spark.network.nio.NioBlockTransferService -import org.apache.spark.scheduler.LiveListenerBus +import org.apache.spark.rpc.{RpcEndpointRef, RpcEndpoint, RpcEnv} +import org.apache.spark.rpc.akka.AkkaRpcEnv +import org.apache.spark.scheduler.{OutputCommitCoordinator, LiveListenerBus} +import org.apache.spark.scheduler.OutputCommitCoordinator.OutputCommitCoordinatorEndpoint import org.apache.spark.serializer.Serializer import org.apache.spark.shuffle.{ShuffleMemoryManager, ShuffleManager} import org.apache.spark.storage._ -import org.apache.spark.util.{AkkaUtils, Utils} +import org.apache.spark.util.{RpcUtils, Utils} /** * :: DeveloperApi :: @@ -53,7 +55,7 @@ import org.apache.spark.util.{AkkaUtils, Utils} @DeveloperApi class SparkEnv ( val executorId: String, - val actorSystem: ActorSystem, + private[spark] val rpcEnv: RpcEnv, val serializer: Serializer, val closureSerializer: Serializer, val cacheManager: CacheManager, @@ -67,8 +69,12 @@ class SparkEnv ( val sparkFilesDir: String, val metricsSystem: MetricsSystem, val shuffleMemoryManager: ShuffleMemoryManager, + val outputCommitCoordinator: OutputCommitCoordinator, val conf: SparkConf) extends Logging { + // TODO Remove actorSystem + val actorSystem = rpcEnv.asInstanceOf[AkkaRpcEnv].actorSystem + private[spark] var isStopped = false private val pythonWorkers = mutable.HashMap[(String, Map[String, String]), PythonWorkerFactory]() @@ -76,6 +82,8 @@ class SparkEnv ( // (e.g., HadoopFileRDD uses this to cache JobConfs and InputFormats). private[spark] val hadoopJobMetadata = new MapMaker().softValues().makeMap[String, Any]() + private var driverTmpDirToDelete: Option[String] = None + private[spark] def stop() { isStopped = true pythonWorkers.foreach { case(key, worker) => worker.stop() } @@ -86,13 +94,31 @@ class SparkEnv ( blockManager.stop() blockManager.master.stop() metricsSystem.stop() - actorSystem.shutdown() + outputCommitCoordinator.stop() + rpcEnv.shutdown() + // Unfortunately Akka's awaitTermination doesn't actually wait for the Netty server to shut // down, but let's call it anyway in case it gets fixed in a later release // UPDATE: In Akka 2.1.x, this hangs if there are remote actors, so we can't call it. // actorSystem.awaitTermination() // Note that blockTransferService is stopped by BlockManager since it is started by it. + + // If we only stop sc, but the driver process still run as a services then we need to delete + // the tmp dir, if not, it will create too many tmp dirs. + // We only need to delete the tmp dir create by driver, because sparkFilesDir is point to the + // current working dir in executor which we do not need to delete. + driverTmpDirToDelete match { + case Some(path) => { + try { + Utils.deleteRecursively(new File(path)) + } catch { + case e: Exception => + logWarning(s"Exception while deleting Spark temp dir: $path", e) + } + } + case None => // We just need to delete tmp dir created by driver, so do nothing on executor + } } private[spark] @@ -151,7 +177,8 @@ object SparkEnv extends Logging { private[spark] def createDriverEnv( conf: SparkConf, isLocal: Boolean, - listenerBus: LiveListenerBus): SparkEnv = { + listenerBus: LiveListenerBus, + mockOutputCommitCoordinator: Option[OutputCommitCoordinator] = None): SparkEnv = { assert(conf.contains("spark.driver.host"), "spark.driver.host is not set on the driver!") assert(conf.contains("spark.driver.port"), "spark.driver.port is not set on the driver!") val hostname = conf.get("spark.driver.host") @@ -163,7 +190,8 @@ object SparkEnv extends Logging { port, isDriver = true, isLocal = isLocal, - listenerBus = listenerBus + listenerBus = listenerBus, + mockOutputCommitCoordinator = mockOutputCommitCoordinator ) } @@ -202,7 +230,8 @@ object SparkEnv extends Logging { isDriver: Boolean, isLocal: Boolean, listenerBus: LiveListenerBus = null, - numUsableCores: Int = 0): SparkEnv = { + numUsableCores: Int = 0, + mockOutputCommitCoordinator: Option[OutputCommitCoordinator] = None): SparkEnv = { // Listener bus is only used on the driver if (isDriver) { @@ -212,16 +241,15 @@ object SparkEnv extends Logging { val securityManager = new SecurityManager(conf) // Create the ActorSystem for Akka and get the port it binds to. - val (actorSystem, boundPort) = { - val actorSystemName = if (isDriver) driverActorSystemName else executorActorSystemName - AkkaUtils.createActorSystem(actorSystemName, hostname, port, conf, securityManager) - } + val actorSystemName = if (isDriver) driverActorSystemName else executorActorSystemName + val rpcEnv = RpcEnv.create(actorSystemName, hostname, port, conf, securityManager) + val actorSystem = rpcEnv.asInstanceOf[AkkaRpcEnv].actorSystem // Figure out which port Akka actually bound to in case the original port is 0 or occupied. if (isDriver) { - conf.set("spark.driver.port", boundPort.toString) + conf.set("spark.driver.port", rpcEnv.address.port.toString) } else { - conf.set("spark.executor.port", boundPort.toString) + conf.set("spark.executor.port", rpcEnv.address.port.toString) } // Create an instance of the class with the given name, possibly initializing it with our conf @@ -257,12 +285,14 @@ object SparkEnv extends Logging { val closureSerializer = instantiateClassFromConf[Serializer]( "spark.closure.serializer", "org.apache.spark.serializer.JavaSerializer") - def registerOrLookup(name: String, newActor: => Actor): ActorRef = { + def registerOrLookupEndpoint( + name: String, endpointCreator: => RpcEndpoint): + RpcEndpointRef = { if (isDriver) { logInfo("Registering " + name) - actorSystem.actorOf(Props(newActor), name = name) + rpcEnv.setupEndpoint(name, endpointCreator) } else { - AkkaUtils.makeDriverRef(name, conf, actorSystem) + RpcUtils.makeDriverRef(name, conf, rpcEnv) } } @@ -274,9 +304,9 @@ object SparkEnv extends Logging { // Have to assign trackerActor after initialization as MapOutputTrackerActor // requires the MapOutputTracker itself - mapOutputTracker.trackerActor = registerOrLookup( - "MapOutputTracker", - new MapOutputTrackerMasterActor(mapOutputTracker.asInstanceOf[MapOutputTrackerMaster], conf)) + mapOutputTracker.trackerEndpoint = registerOrLookupEndpoint(MapOutputTracker.ENDPOINT_NAME, + new MapOutputTrackerMasterEndpoint( + rpcEnv, mapOutputTracker.asInstanceOf[MapOutputTrackerMaster], conf)) // Let the user specify short names for shuffle managers val shortShuffleMgrNames = Map( @@ -296,12 +326,13 @@ object SparkEnv extends Logging { new NioBlockTransferService(conf, securityManager) } - val blockManagerMaster = new BlockManagerMaster(registerOrLookup( - "BlockManagerMaster", - new BlockManagerMasterActor(isLocal, conf, listenerBus)), conf, isDriver) + val blockManagerMaster = new BlockManagerMaster(registerOrLookupEndpoint( + BlockManagerMaster.DRIVER_ENDPOINT_NAME, + new BlockManagerMasterEndpoint(rpcEnv, isLocal, conf, listenerBus)), + conf, isDriver) // NB: blockManager is not valid until initialize() is called later. - val blockManager = new BlockManager(executorId, actorSystem, blockManagerMaster, + val blockManager = new BlockManager(executorId, rpcEnv, blockManagerMaster, serializer, conf, mapOutputTracker, shuffleManager, blockTransferService, securityManager, numUsableCores) @@ -344,15 +375,16 @@ object SparkEnv extends Logging { "." } - // Warn about deprecated spark.cache.class property - if (conf.contains("spark.cache.class")) { - logWarning("The spark.cache.class property is no longer being used! Specify storage " + - "levels using the RDD.persist() method instead.") + val outputCommitCoordinator = mockOutputCommitCoordinator.getOrElse { + new OutputCommitCoordinator(conf) } + val outputCommitCoordinatorRef = registerOrLookupEndpoint("OutputCommitCoordinator", + new OutputCommitCoordinatorEndpoint(rpcEnv, outputCommitCoordinator)) + outputCommitCoordinator.coordinatorRef = Some(outputCommitCoordinatorRef) - new SparkEnv( + val envInstance = new SparkEnv( executorId, - actorSystem, + rpcEnv, serializer, closureSerializer, cacheManager, @@ -366,7 +398,17 @@ object SparkEnv extends Logging { sparkFilesDir, metricsSystem, shuffleMemoryManager, + outputCommitCoordinator, conf) + + // Add a reference to tmp dir created by driver, we will delete this tmp dir when stop() is + // called, and we only need to do it for driver. Because driver may run as a service, and if we + // don't delete this tmp dir when sc is stopped, then will create too many tmp dirs. + if (isDriver) { + envInstance.driverTmpDirToDelete = Some(sparkFilesDir) + } + + envInstance } /** diff --git a/core/src/main/scala/org/apache/spark/SparkHadoopWriter.scala b/core/src/main/scala/org/apache/spark/SparkHadoopWriter.scala index 40237596570de..2ec42d3aea169 100644 --- a/core/src/main/scala/org/apache/spark/SparkHadoopWriter.scala +++ b/core/src/main/scala/org/apache/spark/SparkHadoopWriter.scala @@ -103,26 +103,11 @@ class SparkHadoopWriter(@transient jobConf: JobConf) } def commit() { - val taCtxt = getTaskContext() - val cmtr = getOutputCommitter() - if (cmtr.needsTaskCommit(taCtxt)) { - try { - cmtr.commitTask(taCtxt) - logInfo (taID + ": Committed") - } catch { - case e: IOException => { - logError("Error committing the output of task: " + taID.value, e) - cmtr.abortTask(taCtxt) - throw e - } - } - } else { - logInfo ("No need to commit output of task: " + taID.value) - } + SparkHadoopMapRedUtil.commitTask( + getOutputCommitter(), getTaskContext(), jobID, splitID, attemptID) } def commitJob() { - // always ? Or if cmtr.needsTaskCommit ? val cmtr = getOutputCommitter() cmtr.commitJob(getJobContext()) } diff --git a/core/src/main/scala/org/apache/spark/SparkStatusTracker.scala b/core/src/main/scala/org/apache/spark/SparkStatusTracker.scala index edbdda8a0bcb6..34ee3a48f8e74 100644 --- a/core/src/main/scala/org/apache/spark/SparkStatusTracker.scala +++ b/core/src/main/scala/org/apache/spark/SparkStatusTracker.scala @@ -45,8 +45,7 @@ class SparkStatusTracker private[spark] (sc: SparkContext) { */ def getJobIdsForGroup(jobGroup: String): Array[Int] = { jobProgressListener.synchronized { - val jobData = jobProgressListener.jobIdToData.valuesIterator - jobData.filter(_.jobGroup.orNull == jobGroup).map(_.jobId).toArray + jobProgressListener.jobGroupToJobIds.getOrElse(jobGroup, Seq.empty).toArray } } diff --git a/core/src/main/scala/org/apache/spark/TaskEndReason.scala b/core/src/main/scala/org/apache/spark/TaskEndReason.scala index af5fd8e0ac00c..48fd3e7e23d52 100644 --- a/core/src/main/scala/org/apache/spark/TaskEndReason.scala +++ b/core/src/main/scala/org/apache/spark/TaskEndReason.scala @@ -146,6 +146,16 @@ case object TaskKilled extends TaskFailedReason { override def toErrorString: String = "TaskKilled (killed intentionally)" } +/** + * :: DeveloperApi :: + * Task requested the driver to commit, but was denied. + */ +@DeveloperApi +case class TaskCommitDenied(jobID: Int, partitionID: Int, attemptID: Int) extends TaskFailedReason { + override def toErrorString: String = s"TaskCommitDenied (Driver denied task commit)" + + s" for job: $jobID, partition: $partitionID, attempt: $attemptID" +} + /** * :: DeveloperApi :: * The task failed because the executor that it was running on was lost. This may happen because diff --git a/core/src/main/scala/org/apache/spark/TaskState.scala b/core/src/main/scala/org/apache/spark/TaskState.scala index 0bf1e4a5e2ccd..fe19f07e32d1b 100644 --- a/core/src/main/scala/org/apache/spark/TaskState.scala +++ b/core/src/main/scala/org/apache/spark/TaskState.scala @@ -27,7 +27,9 @@ private[spark] object TaskState extends Enumeration { type TaskState = Value - def isFinished(state: TaskState) = FINISHED_STATES.contains(state) + def isFailed(state: TaskState): Boolean = (LOST == state) || (FAILED == state) + + def isFinished(state: TaskState): Boolean = FINISHED_STATES.contains(state) def toMesos(state: TaskState): MesosTaskState = state match { case LAUNCHING => MesosTaskState.TASK_STARTING @@ -46,5 +48,6 @@ private[spark] object TaskState extends Enumeration { case MesosTaskState.TASK_FAILED => FAILED case MesosTaskState.TASK_KILLED => KILLED case MesosTaskState.TASK_LOST => LOST + case MesosTaskState.TASK_ERROR => LOST } } diff --git a/core/src/main/scala/org/apache/spark/TestUtils.scala b/core/src/main/scala/org/apache/spark/TestUtils.scala index be081c3825566..398ca41e16151 100644 --- a/core/src/main/scala/org/apache/spark/TestUtils.scala +++ b/core/src/main/scala/org/apache/spark/TestUtils.scala @@ -17,12 +17,13 @@ package org.apache.spark -import java.io.{File, FileInputStream, FileOutputStream} +import java.io.{ByteArrayInputStream, File, FileInputStream, FileOutputStream} import java.net.{URI, URL} import java.util.jar.{JarEntry, JarOutputStream} import scala.collection.JavaConversions._ +import com.google.common.base.Charsets.UTF_8 import com.google.common.io.{ByteStreams, Files} import javax.tools.{JavaFileObject, SimpleJavaFileObject, ToolProvider} @@ -59,6 +60,22 @@ private[spark] object TestUtils { createJar(files1 ++ files2, jarFile) } + /** + * Create a jar file containing multiple files. The `files` map contains a mapping of + * file names in the jar file to their contents. + */ + def createJarWithFiles(files: Map[String, String], dir: File = null): URL = { + val tempDir = Option(dir).getOrElse(Utils.createTempDir()) + val jarFile = File.createTempFile("testJar", ".jar", tempDir) + val jarStream = new JarOutputStream(new FileOutputStream(jarFile)) + files.foreach { case (k, v) => + val entry = new JarEntry(k) + jarStream.putNextEntry(entry) + ByteStreams.copy(new ByteArrayInputStream(v.getBytes(UTF_8)), jarStream) + } + jarStream.close() + jarFile.toURI.toURL + } /** * Create a jar file that contains this set of files. All files will be located at the root @@ -90,7 +107,7 @@ private[spark] object TestUtils { private class JavaSourceFromString(val name: String, val code: String) extends SimpleJavaFileObject(createURI(name), SOURCE) { - override def getCharContent(ignoreEncodingErrors: Boolean) = code + override def getCharContent(ignoreEncodingErrors: Boolean): String = code } /** Creates a compiled class with the given name. Class file will be placed in destDir. */ diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaDoubleRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaDoubleRDD.scala index 8e8f7f6c4fda2..61af867b11b9c 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaDoubleRDD.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaDoubleRDD.scala @@ -32,7 +32,8 @@ import org.apache.spark.storage.StorageLevel import org.apache.spark.util.StatCounter import org.apache.spark.util.Utils -class JavaDoubleRDD(val srdd: RDD[scala.Double]) extends JavaRDDLike[JDouble, JavaDoubleRDD] { +class JavaDoubleRDD(val srdd: RDD[scala.Double]) + extends AbstractJavaRDDLike[JDouble, JavaDoubleRDD] { override val classTag: ClassTag[JDouble] = implicitly[ClassTag[JDouble]] @@ -162,6 +163,20 @@ class JavaDoubleRDD(val srdd: RDD[scala.Double]) extends JavaRDDLike[JDouble, Ja /** Add up the elements in this RDD. */ def sum(): JDouble = srdd.sum() + /** + * Returns the minimum element from this RDD as defined by + * the default comparator natural order. + * @return the minimum of the RDD + */ + def min(): JDouble = min(com.google.common.collect.Ordering.natural()) + + /** + * Returns the maximum element from this RDD as defined by + * the default comparator natural order. + * @return the maximum of the RDD + */ + def max(): JDouble = max(com.google.common.collect.Ordering.natural()) + /** * Return a [[org.apache.spark.util.StatCounter]] object that captures the mean, variance and * count of the RDD's elements in one operation. diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala index 7af3538262fd6..8441bb3a3047e 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala @@ -39,12 +39,13 @@ import org.apache.spark.api.java.function.{Function => JFunction, Function2 => J import org.apache.spark.partial.{BoundedDouble, PartialResult} import org.apache.spark.rdd.{OrderedRDDFunctions, RDD} import org.apache.spark.rdd.RDD.rddToPairRDDFunctions +import org.apache.spark.serializer.Serializer import org.apache.spark.storage.StorageLevel import org.apache.spark.util.Utils class JavaPairRDD[K, V](val rdd: RDD[(K, V)]) (implicit val kClassTag: ClassTag[K], implicit val vClassTag: ClassTag[V]) - extends JavaRDDLike[(K, V), JavaPairRDD[K, V]] { + extends AbstractJavaRDDLike[(K, V), JavaPairRDD[K, V]] { override def wrapRDD(rdd: RDD[(K, V)]): JavaPairRDD[K, V] = JavaPairRDD.fromRDD(rdd) @@ -227,24 +228,51 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)]) * - `mergeValue`, to merge a V into a C (e.g., adds it to the end of a list) * - `mergeCombiners`, to combine two C's into a single one. * - * In addition, users can control the partitioning of the output RDD, and whether to perform - * map-side aggregation (if a mapper can produce multiple items with the same key). + * In addition, users can control the partitioning of the output RDD, the serializer that is use + * for the shuffle, and whether to perform map-side aggregation (if a mapper can produce multiple + * items with the same key). */ def combineByKey[C](createCombiner: JFunction[V, C], - mergeValue: JFunction2[C, V, C], - mergeCombiners: JFunction2[C, C, C], - partitioner: Partitioner): JavaPairRDD[K, C] = { - implicit val ctag: ClassTag[C] = fakeClassTag + mergeValue: JFunction2[C, V, C], + mergeCombiners: JFunction2[C, C, C], + partitioner: Partitioner, + mapSideCombine: Boolean, + serializer: Serializer): JavaPairRDD[K, C] = { + implicit val ctag: ClassTag[C] = fakeClassTag fromRDD(rdd.combineByKey( createCombiner, mergeValue, mergeCombiners, - partitioner + partitioner, + mapSideCombine, + serializer )) } /** - * Simplified version of combineByKey that hash-partitions the output RDD. + * Generic function to combine the elements for each key using a custom set of aggregation + * functions. Turns a JavaPairRDD[(K, V)] into a result of type JavaPairRDD[(K, C)], for a + * "combined type" C * Note that V and C can be different -- for example, one might group an + * RDD of type (Int, Int) into an RDD of type (Int, List[Int]). Users provide three + * functions: + * + * - `createCombiner`, which turns a V into a C (e.g., creates a one-element list) + * - `mergeValue`, to merge a V into a C (e.g., adds it to the end of a list) + * - `mergeCombiners`, to combine two C's into a single one. + * + * In addition, users can control the partitioning of the output RDD. This method automatically + * uses map-side aggregation in shuffling the RDD. + */ + def combineByKey[C](createCombiner: JFunction[V, C], + mergeValue: JFunction2[C, V, C], + mergeCombiners: JFunction2[C, C, C], + partitioner: Partitioner): JavaPairRDD[K, C] = { + combineByKey(createCombiner, mergeValue, mergeCombiners, partitioner, true, null) + } + + /** + * Simplified version of combineByKey that hash-partitions the output RDD and uses map-side + * aggregation. */ def combineByKey[C](createCombiner: JFunction[V, C], mergeValue: JFunction2[C, V, C], @@ -488,7 +516,7 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)]) /** * Simplified version of combineByKey that hash-partitions the resulting RDD using the existing - * partitioner/parallelism level. + * partitioner/parallelism level and using map-side aggregation. */ def combineByKey[C](createCombiner: JFunction[V, C], mergeValue: JFunction2[C, V, C], @@ -633,7 +661,7 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)]) */ def flatMapValues[U](f: JFunction[V, java.lang.Iterable[U]]): JavaPairRDD[K, U] = { import scala.collection.JavaConverters._ - def fn = (x: V) => f.call(x).asScala + def fn: (V) => Iterable[U] = (x: V) => f.call(x).asScala implicit val ctag: ClassTag[U] = fakeClassTag fromRDD(rdd.flatMapValues(fn)) } diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala index 86fb374bef1e3..db4e996feb31c 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala @@ -30,7 +30,7 @@ import org.apache.spark.storage.StorageLevel import org.apache.spark.util.Utils class JavaRDD[T](val rdd: RDD[T])(implicit val classTag: ClassTag[T]) - extends JavaRDDLike[T, JavaRDD[T]] { + extends AbstractJavaRDDLike[T, JavaRDD[T]] { override def wrapRDD(rdd: RDD[T]): JavaRDD[T] = JavaRDD.fromRDD(rdd) @@ -101,12 +101,23 @@ class JavaRDD[T](val rdd: RDD[T])(implicit val classTag: ClassTag[T]) /** * Return a sampled subset of this RDD. + * + * @param withReplacement can elements be sampled multiple times (replaced when sampled out) + * @param fraction expected size of the sample as a fraction of this RDD's size + * without replacement: probability that each element is chosen; fraction must be [0, 1] + * with replacement: expected number of times each element is chosen; fraction must be >= 0 */ def sample(withReplacement: Boolean, fraction: Double): JavaRDD[T] = sample(withReplacement, fraction, Utils.random.nextLong) /** * Return a sampled subset of this RDD. + * + * @param withReplacement can elements be sampled multiple times (replaced when sampled out) + * @param fraction expected size of the sample as a fraction of this RDD's size + * without replacement: probability that each element is chosen; fraction must be [0, 1] + * with replacement: expected number of times each element is chosen; fraction must be >= 0 + * @param seed seed for the random number generator */ def sample(withReplacement: Boolean, fraction: Double, seed: Long): JavaRDD[T] = wrapRDD(rdd.sample(withReplacement, fraction, seed)) @@ -168,7 +179,7 @@ class JavaRDD[T](val rdd: RDD[T])(implicit val classTag: ClassTag[T]) def subtract(other: JavaRDD[T], p: Partitioner): JavaRDD[T] = wrapRDD(rdd.subtract(other, p)) - override def toString = rdd.toString + override def toString: String = rdd.toString /** Assign a name to this RDD */ def setName(name: String): JavaRDD[T] = { @@ -181,7 +192,7 @@ class JavaRDD[T](val rdd: RDD[T])(implicit val classTag: ClassTag[T]) */ def sortBy[S](f: JFunction[T, S], ascending: Boolean, numPartitions: Int): JavaRDD[T] = { import scala.collection.JavaConverters._ - def fn = (x: T) => f.call(x) + def fn: (T) => S = (x: T) => f.call(x) import com.google.common.collect.Ordering // shadows scala.math.Ordering implicit val ordering = Ordering.natural().asInstanceOf[Ordering[S]] implicit val ctag: ClassTag[S] = fakeClassTag diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala index 0f91c942ecd50..8bf0627fc420d 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala @@ -17,8 +17,9 @@ package org.apache.spark.api.java -import java.util.{Comparator, List => JList, Iterator => JIterator} +import java.{lang => jl} import java.lang.{Iterable => JIterable, Long => JLong} +import java.util.{Comparator, List => JList, Iterator => JIterator} import scala.collection.JavaConversions._ import scala.collection.JavaConverters._ @@ -38,6 +39,14 @@ import org.apache.spark.rdd.RDD import org.apache.spark.storage.StorageLevel import org.apache.spark.util.Utils +/** + * As a workaround for https://issues.scala-lang.org/browse/SI-8905, implementations + * of JavaRDDLike should extend this dummy abstract class instead of directly inheriting + * from the trait. See SPARK-3266 for additional details. + */ +private[spark] abstract class AbstractJavaRDDLike[T, This <: JavaRDDLike[T, This]] + extends JavaRDDLike[T, This] + /** * Defines operations common to several Java RDD implementations. * Note that this trait is not intended to be implemented by user code. @@ -85,7 +94,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { * of the original partition. */ def mapPartitionsWithIndex[R]( - f: JFunction2[java.lang.Integer, java.util.Iterator[T], java.util.Iterator[R]], + f: JFunction2[jl.Integer, java.util.Iterator[T], java.util.Iterator[R]], preservesPartitioning: Boolean = false): JavaRDD[R] = new JavaRDD(rdd.mapPartitionsWithIndex(((a,b) => f(a,asJavaIterator(b))), preservesPartitioning)(fakeClassTag))(fakeClassTag) @@ -101,7 +110,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { * Return a new RDD by applying a function to all elements of this RDD. */ def mapToPair[K2, V2](f: PairFunction[T, K2, V2]): JavaPairRDD[K2, V2] = { - def cm = implicitly[ClassTag[(K2, V2)]] + def cm: ClassTag[(K2, V2)] = implicitly[ClassTag[(K2, V2)]] new JavaPairRDD(rdd.map[(K2, V2)](f)(cm))(fakeClassTag[K2], fakeClassTag[V2]) } @@ -111,7 +120,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { */ def flatMap[U](f: FlatMapFunction[T, U]): JavaRDD[U] = { import scala.collection.JavaConverters._ - def fn = (x: T) => f.call(x).asScala + def fn: (T) => Iterable[U] = (x: T) => f.call(x).asScala JavaRDD.fromRDD(rdd.flatMap(fn)(fakeClassTag[U]))(fakeClassTag[U]) } @@ -121,8 +130,8 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { */ def flatMapToDouble(f: DoubleFlatMapFunction[T]): JavaDoubleRDD = { import scala.collection.JavaConverters._ - def fn = (x: T) => f.call(x).asScala - new JavaDoubleRDD(rdd.flatMap(fn).map((x: java.lang.Double) => x.doubleValue())) + def fn: (T) => Iterable[jl.Double] = (x: T) => f.call(x).asScala + new JavaDoubleRDD(rdd.flatMap(fn).map((x: jl.Double) => x.doubleValue())) } /** @@ -131,8 +140,8 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { */ def flatMapToPair[K2, V2](f: PairFlatMapFunction[T, K2, V2]): JavaPairRDD[K2, V2] = { import scala.collection.JavaConverters._ - def fn = (x: T) => f.call(x).asScala - def cm = implicitly[ClassTag[(K2, V2)]] + def fn: (T) => Iterable[(K2, V2)] = (x: T) => f.call(x).asScala + def cm: ClassTag[(K2, V2)] = implicitly[ClassTag[(K2, V2)]] JavaPairRDD.fromRDD(rdd.flatMap(fn)(cm))(fakeClassTag[K2], fakeClassTag[V2]) } @@ -140,7 +149,9 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { * Return a new RDD by applying a function to each partition of this RDD. */ def mapPartitions[U](f: FlatMapFunction[java.util.Iterator[T], U]): JavaRDD[U] = { - def fn = (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator()) + def fn: (Iterator[T]) => Iterator[U] = { + (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator()) + } JavaRDD.fromRDD(rdd.mapPartitions(fn)(fakeClassTag[U]))(fakeClassTag[U]) } @@ -149,7 +160,9 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { */ def mapPartitions[U](f: FlatMapFunction[java.util.Iterator[T], U], preservesPartitioning: Boolean): JavaRDD[U] = { - def fn = (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator()) + def fn: (Iterator[T]) => Iterator[U] = { + (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator()) + } JavaRDD.fromRDD( rdd.mapPartitions(fn, preservesPartitioning)(fakeClassTag[U]))(fakeClassTag[U]) } @@ -158,8 +171,10 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { * Return a new RDD by applying a function to each partition of this RDD. */ def mapPartitionsToDouble(f: DoubleFlatMapFunction[java.util.Iterator[T]]): JavaDoubleRDD = { - def fn = (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator()) - new JavaDoubleRDD(rdd.mapPartitions(fn).map((x: java.lang.Double) => x.doubleValue())) + def fn: (Iterator[T]) => Iterator[jl.Double] = { + (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator()) + } + new JavaDoubleRDD(rdd.mapPartitions(fn).map((x: jl.Double) => x.doubleValue())) } /** @@ -167,7 +182,9 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { */ def mapPartitionsToPair[K2, V2](f: PairFlatMapFunction[java.util.Iterator[T], K2, V2]): JavaPairRDD[K2, V2] = { - def fn = (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator()) + def fn: (Iterator[T]) => Iterator[(K2, V2)] = { + (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator()) + } JavaPairRDD.fromRDD(rdd.mapPartitions(fn))(fakeClassTag[K2], fakeClassTag[V2]) } @@ -176,7 +193,9 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { */ def mapPartitionsToDouble(f: DoubleFlatMapFunction[java.util.Iterator[T]], preservesPartitioning: Boolean): JavaDoubleRDD = { - def fn = (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator()) + def fn: (Iterator[T]) => Iterator[jl.Double] = { + (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator()) + } new JavaDoubleRDD(rdd.mapPartitions(fn, preservesPartitioning) .map(x => x.doubleValue())) } @@ -186,7 +205,9 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { */ def mapPartitionsToPair[K2, V2](f: PairFlatMapFunction[java.util.Iterator[T], K2, V2], preservesPartitioning: Boolean): JavaPairRDD[K2, V2] = { - def fn = (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator()) + def fn: (Iterator[T]) => Iterator[(K2, V2)] = { + (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator()) + } JavaPairRDD.fromRDD( rdd.mapPartitions(fn, preservesPartitioning))(fakeClassTag[K2], fakeClassTag[V2]) } @@ -269,8 +290,10 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { def zipPartitions[U, V]( other: JavaRDDLike[U, _], f: FlatMapFunction2[java.util.Iterator[T], java.util.Iterator[U], V]): JavaRDD[V] = { - def fn = (x: Iterator[T], y: Iterator[U]) => asScalaIterator( - f.call(asJavaIterator(x), asJavaIterator(y)).iterator()) + def fn: (Iterator[T], Iterator[U]) => Iterator[V] = { + (x: Iterator[T], y: Iterator[U]) => asScalaIterator( + f.call(asJavaIterator(x), asJavaIterator(y)).iterator()) + } JavaRDD.fromRDD( rdd.zipPartitions(other.rdd)(fn)(other.classTag, fakeClassTag[V]))(fakeClassTag[V]) } @@ -433,8 +456,8 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { * Return the count of each unique value in this RDD as a map of (value, count) pairs. The final * combine step happens locally on the master, equivalent to running a single reduce task. */ - def countByValue(): java.util.Map[T, java.lang.Long] = - mapAsSerializableJavaMap(rdd.countByValue().map((x => (x._1, new java.lang.Long(x._2))))) + def countByValue(): java.util.Map[T, jl.Long] = + mapAsSerializableJavaMap(rdd.countByValue().map((x => (x._1, new jl.Long(x._2))))) /** * (Experimental) Approximate version of countByValue(). diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala index 6d6ed693be752..3be6783bba49d 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala @@ -108,7 +108,7 @@ class JavaSparkContext(val sc: SparkContext) private[spark] val env = sc.env - def statusTracker = new JavaSparkStatusTracker(sc) + def statusTracker: JavaSparkStatusTracker = new JavaSparkStatusTracker(sc) def isLocal: java.lang.Boolean = sc.isLocal diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaUtils.scala b/core/src/main/scala/org/apache/spark/api/java/JavaUtils.scala index 71b26737b8c02..8f9647eea9e25 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaUtils.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaUtils.scala @@ -17,6 +17,8 @@ package org.apache.spark.api.java +import java.util.Map.Entry + import com.google.common.base.Optional import java.{util => ju} @@ -30,8 +32,8 @@ private[spark] object JavaUtils { } // Workaround for SPARK-3926 / SI-8911 - def mapAsSerializableJavaMap[A, B](underlying: collection.Map[A, B]) = - new SerializableMapWrapper(underlying) + def mapAsSerializableJavaMap[A, B](underlying: collection.Map[A, B]): SerializableMapWrapper[A, B] + = new SerializableMapWrapper(underlying) // Implementation is copied from scala.collection.convert.Wrappers.MapWrapper, // but implements java.io.Serializable. It can't just be subclassed to make it @@ -40,36 +42,33 @@ private[spark] object JavaUtils { class SerializableMapWrapper[A, B](underlying: collection.Map[A, B]) extends ju.AbstractMap[A, B] with java.io.Serializable { self => - override def size = underlying.size + override def size: Int = underlying.size override def get(key: AnyRef): B = try { - underlying get key.asInstanceOf[A] match { - case None => null.asInstanceOf[B] - case Some(v) => v - } + underlying.getOrElse(key.asInstanceOf[A], null.asInstanceOf[B]) } catch { case ex: ClassCastException => null.asInstanceOf[B] } override def entrySet: ju.Set[ju.Map.Entry[A, B]] = new ju.AbstractSet[ju.Map.Entry[A, B]] { - def size = self.size + override def size: Int = self.size - def iterator = new ju.Iterator[ju.Map.Entry[A, B]] { + override def iterator: ju.Iterator[ju.Map.Entry[A, B]] = new ju.Iterator[ju.Map.Entry[A, B]] { val ui = underlying.iterator var prev : Option[A] = None - def hasNext = ui.hasNext + def hasNext: Boolean = ui.hasNext - def next() = { - val (k, v) = ui.next + def next(): Entry[A, B] = { + val (k, v) = ui.next() prev = Some(k) new ju.Map.Entry[A, B] { import scala.util.hashing.byteswap32 - def getKey = k - def getValue = v - def setValue(v1 : B) = self.put(k, v1) - override def hashCode = byteswap32(k.hashCode) + (byteswap32(v.hashCode) << 16) - override def equals(other: Any) = other match { + override def getKey: A = k + override def getValue: B = v + override def setValue(v1 : B): B = self.put(k, v1) + override def hashCode: Int = byteswap32(k.hashCode) + (byteswap32(v.hashCode) << 16) + override def equals(other: Any): Boolean = other match { case e: ju.Map.Entry[_, _] => k == e.getKey && v == e.getValue case _ => false } @@ -81,7 +80,7 @@ private[spark] object JavaUtils { case Some(k) => underlying match { case mm: mutable.Map[A, _] => - mm remove k + mm.remove(k) prev = None case _ => throw new UnsupportedOperationException("remove") diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonGatewayServer.scala b/core/src/main/scala/org/apache/spark/api/python/PythonGatewayServer.scala new file mode 100644 index 0000000000000..164e95081583f --- /dev/null +++ b/core/src/main/scala/org/apache/spark/api/python/PythonGatewayServer.scala @@ -0,0 +1,64 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.api.python + +import java.io.DataOutputStream +import java.net.Socket + +import py4j.GatewayServer + +import org.apache.spark.Logging +import org.apache.spark.util.Utils + +/** + * Process that starts a Py4J GatewayServer on an ephemeral port and communicates the bound port + * back to its caller via a callback port specified by the caller. + * + * This process is launched (via SparkSubmit) by the PySpark driver (see java_gateway.py). + */ +private[spark] object PythonGatewayServer extends Logging { + def main(args: Array[String]): Unit = Utils.tryOrExit { + // Start a GatewayServer on an ephemeral port + val gatewayServer: GatewayServer = new GatewayServer(null, 0) + gatewayServer.start() + val boundPort: Int = gatewayServer.getListeningPort + if (boundPort == -1) { + logError("GatewayServer failed to bind; exiting") + System.exit(1) + } else { + logDebug(s"Started PythonGatewayServer on port $boundPort") + } + + // Communicate the bound port back to the caller via the caller-specified callback port + val callbackHost = sys.env("_PYSPARK_DRIVER_CALLBACK_HOST") + val callbackPort = sys.env("_PYSPARK_DRIVER_CALLBACK_PORT").toInt + logDebug(s"Communicating GatewayServer port to Python driver at $callbackHost:$callbackPort") + val callbackSocket = new Socket(callbackHost, callbackPort) + val dos = new DataOutputStream(callbackSocket.getOutputStream) + dos.writeInt(boundPort) + dos.close() + callbackSocket.close() + + // Exit on EOF or broken pipe to ensure that this process dies when the Python driver dies: + while (System.in.read() != -1) { + // Do nothing + } + logDebug("Exiting due to broken pipe from Python driver") + System.exit(0) + } +} diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala index b89effc16d36d..7409dc2d866f6 100644 --- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala +++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala @@ -19,26 +19,27 @@ package org.apache.spark.api.python import java.io._ import java.net._ -import java.util.{List => JList, ArrayList => JArrayList, Map => JMap, UUID, Collections} - -import org.apache.spark.input.PortableDataStream +import java.util.{Collections, ArrayList => JArrayList, List => JList, Map => JMap} import scala.collection.JavaConversions._ import scala.collection.mutable import scala.language.existentials import com.google.common.base.Charsets.UTF_8 - import org.apache.hadoop.conf.Configuration import org.apache.hadoop.io.compress.CompressionCodec -import org.apache.hadoop.mapred.{InputFormat, OutputFormat, JobConf} +import org.apache.hadoop.mapred.{InputFormat, JobConf, OutputFormat} import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat, OutputFormat => NewOutputFormat} + import org.apache.spark._ -import org.apache.spark.api.java.{JavaSparkContext, JavaPairRDD, JavaRDD} +import org.apache.spark.api.java.{JavaPairRDD, JavaRDD, JavaSparkContext} import org.apache.spark.broadcast.Broadcast +import org.apache.spark.input.PortableDataStream import org.apache.spark.rdd.RDD import org.apache.spark.util.Utils +import scala.util.control.NonFatal + private[spark] class PythonRDD( @transient parent: RDD[_], command: Array[Byte], @@ -53,9 +54,11 @@ private[spark] class PythonRDD( val bufferSize = conf.getInt("spark.buffer.size", 65536) val reuse_worker = conf.getBoolean("spark.python.worker.reuse", true) - override def getPartitions = firstParent.partitions + override def getPartitions: Array[Partition] = firstParent.partitions - override val partitioner = if (preservePartitoning) firstParent.partitioner else None + override val partitioner: Option[Partitioner] = { + if (preservePartitoning) firstParent.partitioner else None + } override def compute(split: Partition, context: TaskContext): Iterator[Array[Byte]] = { val startTime = System.currentTimeMillis @@ -75,7 +78,6 @@ private[spark] class PythonRDD( context.addTaskCompletionListener { context => writerThread.shutdownOnTaskCompletion() - writerThread.join() if (!reuse_worker || !released) { try { worker.close() @@ -92,7 +94,7 @@ private[spark] class PythonRDD( // Return an iterator that read lines from the process's stdout val stream = new DataInputStream(new BufferedInputStream(worker.getInputStream, bufferSize)) val stdoutIterator = new Iterator[Array[Byte]] { - def next(): Array[Byte] = { + override def next(): Array[Byte] = { val obj = _nextObj if (hasNext) { _nextObj = read() @@ -175,7 +177,7 @@ private[spark] class PythonRDD( var _nextObj = read() - def hasNext = _nextObj != null + override def hasNext: Boolean = _nextObj != null } new InterruptibleIterator(context, stdoutIterator) } @@ -219,14 +221,13 @@ private[spark] class PythonRDD( val oldBids = PythonRDD.getWorkerBroadcasts(worker) val newBids = broadcastVars.map(_.id).toSet // number of different broadcasts - val cnt = oldBids.diff(newBids).size + newBids.diff(oldBids).size + val toRemove = oldBids.diff(newBids) + val cnt = toRemove.size + newBids.diff(oldBids).size dataOut.writeInt(cnt) - for (bid <- oldBids) { - if (!newBids.contains(bid)) { - // remove the broadcast from worker - dataOut.writeLong(- bid - 1) // bid >= 0 - oldBids.remove(bid) - } + for (bid <- toRemove) { + // remove the broadcast from worker + dataOut.writeLong(- bid - 1) // bid >= 0 + oldBids.remove(bid) } for (broadcast <- broadcastVars) { if (!oldBids.contains(broadcast.id)) { @@ -248,13 +249,17 @@ private[spark] class PythonRDD( } catch { case e: Exception if context.isCompleted || context.isInterrupted => logDebug("Exception thrown after task completion (likely due to cleanup)", e) - worker.shutdownOutput() + if (!worker.isClosed) { + Utils.tryLog(worker.shutdownOutput()) + } case e: Exception => // We must avoid throwing exceptions here, because the thread uncaught exception handler // will kill the whole executor (see org.apache.spark.executor.Executor). _exception = e - worker.shutdownOutput() + if (!worker.isClosed) { + Utils.tryLog(worker.shutdownOutput()) + } } finally { // Release memory used by this thread for shuffles env.shuffleMemoryManager.releaseMemoryForThisThread() @@ -300,10 +305,10 @@ private class PythonException(msg: String, cause: Exception) extends RuntimeExce * Form an RDD[(Array[Byte], Array[Byte])] from key-value pairs returned from Python. * This is used by PySpark's shuffle operations. */ -private class PairwiseRDD(prev: RDD[Array[Byte]]) extends - RDD[(Long, Array[Byte])](prev) { - override def getPartitions = prev.partitions - override def compute(split: Partition, context: TaskContext) = +private class PairwiseRDD(prev: RDD[Array[Byte]]) extends RDD[(Long, Array[Byte])](prev) { + override def getPartitions: Array[Partition] = prev.partitions + override val partitioner: Option[Partitioner] = prev.partitioner + override def compute(split: Partition, context: TaskContext): Iterator[(Long, Array[Byte])] = prev.iterator(split, context).grouped(2).map { case Seq(a, b) => (Utils.deserializeLongValue(a), b) case x => throw new SparkException("PairwiseRDD: unexpected value: " + x) @@ -329,24 +334,45 @@ private[spark] object PythonRDD extends Logging { } } + /** + * Return an RDD of values from an RDD of (Long, Array[Byte]), with preservePartitions=true + * + * This is useful for PySpark to have the partitioner after partitionBy() + */ + def valueOfPair(pair: JavaPairRDD[Long, Array[Byte]]): JavaRDD[Array[Byte]] = { + pair.rdd.mapPartitions(it => it.map(_._2), true) + } + /** * Adapter for calling SparkContext#runJob from Python. * - * This method will return an iterator of an array that contains all elements in the RDD + * This method will serve an iterator of an array that contains all elements in the RDD * (effectively a collect()), but allows you to run on a certain subset of partitions, * or to enable local execution. + * + * @return the port number of a local socket which serves the data collected from this job. */ def runJob( sc: SparkContext, rdd: JavaRDD[Array[Byte]], partitions: JArrayList[Int], - allowLocal: Boolean): Iterator[Array[Byte]] = { + allowLocal: Boolean): Int = { type ByteArray = Array[Byte] type UnrolledPartition = Array[ByteArray] val allPartitions: Array[UnrolledPartition] = sc.runJob(rdd, (x: Iterator[ByteArray]) => x.toArray, partitions, allowLocal) val flattenedPartition: UnrolledPartition = Array.concat(allPartitions: _*) - flattenedPartition.iterator + serveIterator(flattenedPartition.iterator, + s"serve RDD ${rdd.id} with partitions ${partitions.mkString(",")}") + } + + /** + * A helper function to collect an RDD as an iterator, then serve it via socket. + * + * @return the port number of a local socket which serves the data collected from this job. + */ + def collectAndServe[T](rdd: RDD[T]): Int = { + serveIterator(rdd.collect().iterator, s"serve RDD ${rdd.id}") } def readRDDFromFile(sc: JavaSparkContext, filename: String, parallelism: Int): @@ -410,7 +436,7 @@ private[spark] object PythonRDD extends Logging { keyConverterClass: String, valueConverterClass: String, minSplits: Int, - batchSize: Int) = { + batchSize: Int): JavaRDD[Array[Byte]] = { val keyClass = Option(keyClassMaybeNull).getOrElse("org.apache.hadoop.io.Text") val valueClass = Option(valueClassMaybeNull).getOrElse("org.apache.hadoop.io.Text") val kc = Utils.classForName(keyClass).asInstanceOf[Class[K]] @@ -437,7 +463,7 @@ private[spark] object PythonRDD extends Logging { keyConverterClass: String, valueConverterClass: String, confAsMap: java.util.HashMap[String, String], - batchSize: Int) = { + batchSize: Int): JavaRDD[Array[Byte]] = { val mergedConf = getMergedConf(confAsMap, sc.hadoopConfiguration()) val rdd = newAPIHadoopRDDFromClassNames[K, V, F](sc, @@ -463,7 +489,7 @@ private[spark] object PythonRDD extends Logging { keyConverterClass: String, valueConverterClass: String, confAsMap: java.util.HashMap[String, String], - batchSize: Int) = { + batchSize: Int): JavaRDD[Array[Byte]] = { val conf = PythonHadoopUtil.mapToConf(confAsMap) val rdd = newAPIHadoopRDDFromClassNames[K, V, F](sc, @@ -480,7 +506,7 @@ private[spark] object PythonRDD extends Logging { inputFormatClass: String, keyClass: String, valueClass: String, - conf: Configuration) = { + conf: Configuration): RDD[(K, V)] = { val kc = Utils.classForName(keyClass).asInstanceOf[Class[K]] val vc = Utils.classForName(valueClass).asInstanceOf[Class[V]] val fc = Utils.classForName(inputFormatClass).asInstanceOf[Class[F]] @@ -506,7 +532,7 @@ private[spark] object PythonRDD extends Logging { keyConverterClass: String, valueConverterClass: String, confAsMap: java.util.HashMap[String, String], - batchSize: Int) = { + batchSize: Int): JavaRDD[Array[Byte]] = { val mergedConf = getMergedConf(confAsMap, sc.hadoopConfiguration()) val rdd = hadoopRDDFromClassNames[K, V, F](sc, @@ -532,7 +558,7 @@ private[spark] object PythonRDD extends Logging { keyConverterClass: String, valueConverterClass: String, confAsMap: java.util.HashMap[String, String], - batchSize: Int) = { + batchSize: Int): JavaRDD[Array[Byte]] = { val conf = PythonHadoopUtil.mapToConf(confAsMap) val rdd = hadoopRDDFromClassNames[K, V, F](sc, @@ -566,15 +592,43 @@ private[spark] object PythonRDD extends Logging { dataOut.write(bytes) } - def writeToFile[T](items: java.util.Iterator[T], filename: String) { - import scala.collection.JavaConverters._ - writeToFile(items.asScala, filename) - } + /** + * Create a socket server and a background thread to serve the data in `items`, + * + * The socket server can only accept one connection, or close if no connection + * in 3 seconds. + * + * Once a connection comes in, it tries to serialize all the data in `items` + * and send them into this connection. + * + * The thread will terminate after all the data are sent or any exceptions happen. + */ + private def serveIterator[T](items: Iterator[T], threadName: String): Int = { + val serverSocket = new ServerSocket(0, 1, InetAddress.getByName("localhost")) + // Close the socket if no connection in 3 seconds + serverSocket.setSoTimeout(3000) + + new Thread(threadName) { + setDaemon(true) + override def run() { + try { + val sock = serverSocket.accept() + val out = new DataOutputStream(new BufferedOutputStream(sock.getOutputStream)) + Utils.tryWithSafeFinally { + writeIteratorToStream(items, out) + } { + out.close() + } + } catch { + case NonFatal(e) => + logError(s"Error while sending iterator", e) + } finally { + serverSocket.close() + } + } + }.start() - def writeToFile[T](items: Iterator[T], filename: String) { - val file = new DataOutputStream(new FileOutputStream(filename)) - writeIteratorToStream(items, file) - file.close() + serverSocket.getLocalPort } private def getMergedConf(confAsMap: java.util.HashMap[String, String], @@ -632,7 +686,7 @@ private[spark] object PythonRDD extends Logging { pyRDD: JavaRDD[Array[Byte]], batchSerialized: Boolean, path: String, - compressionCodecClass: String) = { + compressionCodecClass: String): Unit = { saveAsHadoopFile( pyRDD, batchSerialized, path, "org.apache.hadoop.mapred.SequenceFileOutputFormat", null, null, null, null, new java.util.HashMap(), compressionCodecClass) @@ -657,7 +711,7 @@ private[spark] object PythonRDD extends Logging { keyConverterClass: String, valueConverterClass: String, confAsMap: java.util.HashMap[String, String], - compressionCodecClass: String) = { + compressionCodecClass: String): Unit = { val rdd = SerDeUtil.pythonToPairRDD(pyRDD, batchSerialized) val (kc, vc) = getKeyValueTypes(keyClass, valueClass).getOrElse( inferKeyValueTypes(rdd, keyConverterClass, valueConverterClass)) @@ -687,7 +741,7 @@ private[spark] object PythonRDD extends Logging { valueClass: String, keyConverterClass: String, valueConverterClass: String, - confAsMap: java.util.HashMap[String, String]) = { + confAsMap: java.util.HashMap[String, String]): Unit = { val rdd = SerDeUtil.pythonToPairRDD(pyRDD, batchSerialized) val (kc, vc) = getKeyValueTypes(keyClass, valueClass).getOrElse( inferKeyValueTypes(rdd, keyConverterClass, valueConverterClass)) @@ -712,7 +766,7 @@ private[spark] object PythonRDD extends Logging { confAsMap: java.util.HashMap[String, String], keyConverterClass: String, valueConverterClass: String, - useNewAPI: Boolean) = { + useNewAPI: Boolean): Unit = { val conf = PythonHadoopUtil.mapToConf(confAsMap) val converted = convertRDD(SerDeUtil.pythonToPairRDD(pyRDD, batchSerialized), keyConverterClass, valueConverterClass, new JavaToWritableConverter) @@ -808,9 +862,9 @@ private[spark] class PythonBroadcast(@transient var path: String) extends Serial val file = File.createTempFile("broadcast", "", dir) path = file.getAbsolutePath val out = new FileOutputStream(file) - try { + Utils.tryWithSafeFinally { Utils.copyStream(in, out) - } finally { + } { out.close() } } diff --git a/core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala b/core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala index fb52a960e0765..257491e90dd66 100644 --- a/core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala +++ b/core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala @@ -84,7 +84,7 @@ private[spark] object SerDeUtil extends Logging { private var initialized = false // This should be called before trying to unpickle array.array from Python // In cluster mode, this should be put in closure - def initialize() = { + def initialize(): Unit = { synchronized{ if (!initialized) { Unpickler.registerConstructor("array", "array", new ArrayConstructor()) diff --git a/core/src/main/scala/org/apache/spark/api/python/WriteInputFormatTestDataGenerator.scala b/core/src/main/scala/org/apache/spark/api/python/WriteInputFormatTestDataGenerator.scala index cf289fb3ae39f..8f30ff9202c83 100644 --- a/core/src/main/scala/org/apache/spark/api/python/WriteInputFormatTestDataGenerator.scala +++ b/core/src/main/scala/org/apache/spark/api/python/WriteInputFormatTestDataGenerator.scala @@ -18,38 +18,37 @@ package org.apache.spark.api.python import java.io.{DataOutput, DataInput} +import java.{util => ju} import com.google.common.base.Charsets.UTF_8 import org.apache.hadoop.io._ import org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat + +import org.apache.spark.SparkException import org.apache.spark.api.java.JavaSparkContext -import org.apache.spark.{SparkContext, SparkException} /** * A class to test Pyrolite serialization on the Scala side, that will be deserialized * in Python - * @param str - * @param int - * @param double */ case class TestWritable(var str: String, var int: Int, var double: Double) extends Writable { def this() = this("", 0, 0.0) - def getStr = str + def getStr: String = str def setStr(str: String) { this.str = str } - def getInt = int + def getInt: Int = int def setInt(int: Int) { this.int = int } - def getDouble = double + def getDouble: Double = double def setDouble(double: Double) { this.double = double } - def write(out: DataOutput) = { + def write(out: DataOutput): Unit = { out.writeUTF(str) out.writeInt(int) out.writeDouble(double) } - def readFields(in: DataInput) = { + def readFields(in: DataInput): Unit = { str = in.readUTF() int = in.readInt() double = in.readDouble() @@ -57,28 +56,28 @@ case class TestWritable(var str: String, var int: Int, var double: Double) exten } private[python] class TestInputKeyConverter extends Converter[Any, Any] { - override def convert(obj: Any) = { + override def convert(obj: Any): Char = { obj.asInstanceOf[IntWritable].get().toChar } } private[python] class TestInputValueConverter extends Converter[Any, Any] { import collection.JavaConversions._ - override def convert(obj: Any) = { + override def convert(obj: Any): ju.List[Double] = { val m = obj.asInstanceOf[MapWritable] seqAsJavaList(m.keySet.map(w => w.asInstanceOf[DoubleWritable].get()).toSeq) } } private[python] class TestOutputKeyConverter extends Converter[Any, Any] { - override def convert(obj: Any) = { + override def convert(obj: Any): Text = { new Text(obj.asInstanceOf[Int].toString) } } private[python] class TestOutputValueConverter extends Converter[Any, Any] { import collection.JavaConversions._ - override def convert(obj: Any) = { + override def convert(obj: Any): DoubleWritable = { new DoubleWritable(obj.asInstanceOf[java.util.Map[Double, _]].keySet().head) } } @@ -86,7 +85,7 @@ private[python] class TestOutputValueConverter extends Converter[Any, Any] { private[python] class DoubleArrayWritable extends ArrayWritable(classOf[DoubleWritable]) private[python] class DoubleArrayToWritableConverter extends Converter[Any, Writable] { - override def convert(obj: Any) = obj match { + override def convert(obj: Any): DoubleArrayWritable = obj match { case arr if arr.getClass.isArray && arr.getClass.getComponentType == classOf[Double] => val daw = new DoubleArrayWritable daw.set(arr.asInstanceOf[Array[Double]].map(new DoubleWritable(_))) diff --git a/core/src/main/scala/org/apache/spark/api/r/RBackend.scala b/core/src/main/scala/org/apache/spark/api/r/RBackend.scala new file mode 100644 index 0000000000000..3a2c94bd9d875 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/api/r/RBackend.scala @@ -0,0 +1,145 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.api.r + +import java.io.{DataOutputStream, File, FileOutputStream, IOException} +import java.net.{InetSocketAddress, ServerSocket} +import java.util.concurrent.TimeUnit + +import io.netty.bootstrap.ServerBootstrap +import io.netty.channel.{ChannelFuture, ChannelInitializer, EventLoopGroup} +import io.netty.channel.nio.NioEventLoopGroup +import io.netty.channel.socket.SocketChannel +import io.netty.channel.socket.nio.NioServerSocketChannel +import io.netty.handler.codec.LengthFieldBasedFrameDecoder +import io.netty.handler.codec.bytes.{ByteArrayDecoder, ByteArrayEncoder} + +import org.apache.spark.Logging + +/** + * Netty-based backend server that is used to communicate between R and Java. + */ +private[spark] class RBackend { + + private[this] var channelFuture: ChannelFuture = null + private[this] var bootstrap: ServerBootstrap = null + private[this] var bossGroup: EventLoopGroup = null + + def init(): Int = { + bossGroup = new NioEventLoopGroup(2) + val workerGroup = bossGroup + val handler = new RBackendHandler(this) + + bootstrap = new ServerBootstrap() + .group(bossGroup, workerGroup) + .channel(classOf[NioServerSocketChannel]) + + bootstrap.childHandler(new ChannelInitializer[SocketChannel]() { + def initChannel(ch: SocketChannel): Unit = { + ch.pipeline() + .addLast("encoder", new ByteArrayEncoder()) + .addLast("frameDecoder", + // maxFrameLength = 2G + // lengthFieldOffset = 0 + // lengthFieldLength = 4 + // lengthAdjustment = 0 + // initialBytesToStrip = 4, i.e. strip out the length field itself + new LengthFieldBasedFrameDecoder(Integer.MAX_VALUE, 0, 4, 0, 4)) + .addLast("decoder", new ByteArrayDecoder()) + .addLast("handler", handler) + } + }) + + channelFuture = bootstrap.bind(new InetSocketAddress(0)) + channelFuture.syncUninterruptibly() + channelFuture.channel().localAddress().asInstanceOf[InetSocketAddress].getPort() + } + + def run(): Unit = { + channelFuture.channel.closeFuture().syncUninterruptibly() + } + + def close(): Unit = { + if (channelFuture != null) { + // close is a local operation and should finish within milliseconds; timeout just to be safe + channelFuture.channel().close().awaitUninterruptibly(10, TimeUnit.SECONDS) + channelFuture = null + } + if (bootstrap != null && bootstrap.group() != null) { + bootstrap.group().shutdownGracefully() + } + if (bootstrap != null && bootstrap.childGroup() != null) { + bootstrap.childGroup().shutdownGracefully() + } + bootstrap = null + } + +} + +private[spark] object RBackend extends Logging { + def main(args: Array[String]): Unit = { + if (args.length < 1) { + System.err.println("Usage: RBackend ") + System.exit(-1) + } + val sparkRBackend = new RBackend() + try { + // bind to random port + val boundPort = sparkRBackend.init() + val serverSocket = new ServerSocket(0, 1) + val listenPort = serverSocket.getLocalPort() + + // tell the R process via temporary file + val path = args(0) + val f = new File(path + ".tmp") + val dos = new DataOutputStream(new FileOutputStream(f)) + dos.writeInt(boundPort) + dos.writeInt(listenPort) + dos.close() + f.renameTo(new File(path)) + + // wait for the end of stdin, then exit + new Thread("wait for socket to close") { + setDaemon(true) + override def run(): Unit = { + // any un-catched exception will also shutdown JVM + val buf = new Array[Byte](1024) + // shutdown JVM if R does not connect back in 10 seconds + serverSocket.setSoTimeout(10000) + try { + val inSocket = serverSocket.accept() + serverSocket.close() + // wait for the end of socket, closed if R process die + inSocket.getInputStream().read(buf) + } finally { + sparkRBackend.close() + System.exit(0) + } + } + }.start() + + sparkRBackend.run() + } catch { + case e: IOException => + logError("Server shutting down: failed with exception ", e) + sparkRBackend.close() + System.exit(1) + } + System.exit(0) + } +} diff --git a/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala b/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala new file mode 100644 index 0000000000000..0075d963711f1 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala @@ -0,0 +1,223 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.api.r + +import java.io.{ByteArrayInputStream, ByteArrayOutputStream, DataInputStream, DataOutputStream} + +import scala.collection.mutable.HashMap + +import io.netty.channel.ChannelHandler.Sharable +import io.netty.channel.{ChannelHandlerContext, SimpleChannelInboundHandler} + +import org.apache.spark.Logging +import org.apache.spark.api.r.SerDe._ + +/** + * Handler for RBackend + * TODO: This is marked as sharable to get a handle to RBackend. Is it safe to re-use + * this across connections ? + */ +@Sharable +private[r] class RBackendHandler(server: RBackend) + extends SimpleChannelInboundHandler[Array[Byte]] with Logging { + + override def channelRead0(ctx: ChannelHandlerContext, msg: Array[Byte]): Unit = { + val bis = new ByteArrayInputStream(msg) + val dis = new DataInputStream(bis) + + val bos = new ByteArrayOutputStream() + val dos = new DataOutputStream(bos) + + // First bit is isStatic + val isStatic = readBoolean(dis) + val objId = readString(dis) + val methodName = readString(dis) + val numArgs = readInt(dis) + + if (objId == "SparkRHandler") { + methodName match { + case "stopBackend" => + writeInt(dos, 0) + writeType(dos, "void") + server.close() + case "rm" => + try { + val t = readObjectType(dis) + assert(t == 'c') + val objToRemove = readString(dis) + JVMObjectTracker.remove(objToRemove) + writeInt(dos, 0) + writeObject(dos, null) + } catch { + case e: Exception => + logError(s"Removing $objId failed", e) + writeInt(dos, -1) + } + case _ => dos.writeInt(-1) + } + } else { + handleMethodCall(isStatic, objId, methodName, numArgs, dis, dos) + } + + val reply = bos.toByteArray + ctx.write(reply) + } + + override def channelReadComplete(ctx: ChannelHandlerContext): Unit = { + ctx.flush() + } + + override def exceptionCaught(ctx: ChannelHandlerContext, cause: Throwable): Unit = { + // Close the connection when an exception is raised. + cause.printStackTrace() + ctx.close() + } + + def handleMethodCall( + isStatic: Boolean, + objId: String, + methodName: String, + numArgs: Int, + dis: DataInputStream, + dos: DataOutputStream): Unit = { + var obj: Object = null + try { + val cls = if (isStatic) { + Class.forName(objId) + } else { + JVMObjectTracker.get(objId) match { + case None => throw new IllegalArgumentException("Object not found " + objId) + case Some(o) => + obj = o + o.getClass + } + } + + val args = readArgs(numArgs, dis) + + val methods = cls.getMethods + val selectedMethods = methods.filter(m => m.getName == methodName) + if (selectedMethods.length > 0) { + val methods = selectedMethods.filter { x => + matchMethod(numArgs, args, x.getParameterTypes) + } + if (methods.isEmpty) { + logWarning(s"cannot find matching method ${cls}.$methodName. " + + s"Candidates are:") + selectedMethods.foreach { method => + logWarning(s"$methodName(${method.getParameterTypes.mkString(",")})") + } + throw new Exception(s"No matched method found for $cls.$methodName") + } + val ret = methods.head.invoke(obj, args:_*) + + // Write status bit + writeInt(dos, 0) + writeObject(dos, ret.asInstanceOf[AnyRef]) + } else if (methodName == "") { + // methodName should be "" for constructor + val ctor = cls.getConstructors.filter { x => + matchMethod(numArgs, args, x.getParameterTypes) + }.head + + val obj = ctor.newInstance(args:_*) + + writeInt(dos, 0) + writeObject(dos, obj.asInstanceOf[AnyRef]) + } else { + throw new IllegalArgumentException("invalid method " + methodName + " for object " + objId) + } + } catch { + case e: Exception => + logError(s"$methodName on $objId failed", e) + writeInt(dos, -1) + } + } + + // Read a number of arguments from the data input stream + def readArgs(numArgs: Int, dis: DataInputStream): Array[java.lang.Object] = { + (0 until numArgs).map { arg => + readObject(dis) + }.toArray + } + + // Checks if the arguments passed in args matches the parameter types. + // NOTE: Currently we do exact match. We may add type conversions later. + def matchMethod( + numArgs: Int, + args: Array[java.lang.Object], + parameterTypes: Array[Class[_]]): Boolean = { + if (parameterTypes.length != numArgs) { + return false + } + + for (i <- 0 to numArgs - 1) { + val parameterType = parameterTypes(i) + var parameterWrapperType = parameterType + + // Convert native parameters to Object types as args is Array[Object] here + if (parameterType.isPrimitive) { + parameterWrapperType = parameterType match { + case java.lang.Integer.TYPE => classOf[java.lang.Integer] + case java.lang.Double.TYPE => classOf[java.lang.Double] + case java.lang.Boolean.TYPE => classOf[java.lang.Boolean] + case _ => parameterType + } + } + if (!parameterWrapperType.isInstance(args(i))) { + return false + } + } + true + } +} + +/** + * Helper singleton that tracks JVM objects returned to R. + * This is useful for referencing these objects in RPC calls. + */ +private[r] object JVMObjectTracker { + + // TODO: This map should be thread-safe if we want to support multiple + // connections at the same time + private[this] val objMap = new HashMap[String, Object] + + // TODO: We support only one connection now, so an integer is fine. + // Investigate using use atomic integer in the future. + private[this] var objCounter: Int = 0 + + def getObject(id: String): Object = { + objMap(id) + } + + def get(id: String): Option[Object] = { + objMap.get(id) + } + + def put(obj: Object): String = { + val objId = objCounter.toString + objCounter = objCounter + 1 + objMap.put(objId, obj) + objId + } + + def remove(id: String): Option[Object] = { + objMap.remove(id) + } + +} diff --git a/core/src/main/scala/org/apache/spark/api/r/RRDD.scala b/core/src/main/scala/org/apache/spark/api/r/RRDD.scala new file mode 100644 index 0000000000000..6fea5e1144f2f --- /dev/null +++ b/core/src/main/scala/org/apache/spark/api/r/RRDD.scala @@ -0,0 +1,453 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.api.r + +import java.io._ +import java.net.ServerSocket +import java.util.{Map => JMap} + +import scala.collection.JavaConversions._ +import scala.io.Source +import scala.reflect.ClassTag +import scala.util.Try + +import org.apache.spark._ +import org.apache.spark.api.java.{JavaPairRDD, JavaRDD, JavaSparkContext} +import org.apache.spark.broadcast.Broadcast +import org.apache.spark.rdd.RDD +import org.apache.spark.util.Utils + +private abstract class BaseRRDD[T: ClassTag, U: ClassTag]( + parent: RDD[T], + numPartitions: Int, + func: Array[Byte], + deserializer: String, + serializer: String, + packageNames: Array[Byte], + rLibDir: String, + broadcastVars: Array[Broadcast[Object]]) + extends RDD[U](parent) with Logging { + protected var dataStream: DataInputStream = _ + private var bootTime: Double = _ + override def getPartitions: Array[Partition] = parent.partitions + + override def compute(partition: Partition, context: TaskContext): Iterator[U] = { + + // Timing start + bootTime = System.currentTimeMillis / 1000.0 + + // The parent may be also an RRDD, so we should launch it first. + val parentIterator = firstParent[T].iterator(partition, context) + + // we expect two connections + val serverSocket = new ServerSocket(0, 2) + val listenPort = serverSocket.getLocalPort() + + // The stdout/stderr is shared by multiple tasks, because we use one daemon + // to launch child process as worker. + val errThread = RRDD.createRWorker(rLibDir, listenPort) + + // We use two sockets to separate input and output, then it's easy to manage + // the lifecycle of them to avoid deadlock. + // TODO: optimize it to use one socket + + // the socket used to send out the input of task + serverSocket.setSoTimeout(10000) + val inSocket = serverSocket.accept() + startStdinThread(inSocket.getOutputStream(), parentIterator, partition.index) + + // the socket used to receive the output of task + val outSocket = serverSocket.accept() + val inputStream = new BufferedInputStream(outSocket.getInputStream) + dataStream = new DataInputStream(inputStream) + serverSocket.close() + + try { + + return new Iterator[U] { + def next(): U = { + val obj = _nextObj + if (hasNext) { + _nextObj = read() + } + obj + } + + var _nextObj = read() + + def hasNext(): Boolean = { + val hasMore = (_nextObj != null) + if (!hasMore) { + dataStream.close() + } + hasMore + } + } + } catch { + case e: Exception => + throw new SparkException("R computation failed with\n " + errThread.getLines()) + } + } + + /** + * Start a thread to write RDD data to the R process. + */ + private def startStdinThread[T]( + output: OutputStream, + iter: Iterator[T], + partition: Int): Unit = { + + val env = SparkEnv.get + val bufferSize = System.getProperty("spark.buffer.size", "65536").toInt + val stream = new BufferedOutputStream(output, bufferSize) + + new Thread("writer for R") { + override def run(): Unit = { + try { + SparkEnv.set(env) + val dataOut = new DataOutputStream(stream) + dataOut.writeInt(partition) + + SerDe.writeString(dataOut, deserializer) + SerDe.writeString(dataOut, serializer) + + dataOut.writeInt(packageNames.length) + dataOut.write(packageNames) + + dataOut.writeInt(func.length) + dataOut.write(func) + + dataOut.writeInt(broadcastVars.length) + broadcastVars.foreach { broadcast => + // TODO(shivaram): Read a Long in R to avoid this cast + dataOut.writeInt(broadcast.id.toInt) + // TODO: Pass a byte array from R to avoid this cast ? + val broadcastByteArr = broadcast.value.asInstanceOf[Array[Byte]] + dataOut.writeInt(broadcastByteArr.length) + dataOut.write(broadcastByteArr) + } + + dataOut.writeInt(numPartitions) + + if (!iter.hasNext) { + dataOut.writeInt(0) + } else { + dataOut.writeInt(1) + } + + val printOut = new PrintStream(stream) + + def writeElem(elem: Any): Unit = { + if (deserializer == SerializationFormats.BYTE) { + val elemArr = elem.asInstanceOf[Array[Byte]] + dataOut.writeInt(elemArr.length) + dataOut.write(elemArr) + } else if (deserializer == SerializationFormats.ROW) { + dataOut.write(elem.asInstanceOf[Array[Byte]]) + } else if (deserializer == SerializationFormats.STRING) { + // write string(for StringRRDD) + printOut.println(elem) + } + } + + for (elem <- iter) { + elem match { + case (key, value) => + writeElem(key) + writeElem(value) + case _ => + writeElem(elem) + } + } + stream.flush() + } catch { + // TODO: We should propogate this error to the task thread + case e: Exception => + logError("R Writer thread got an exception", e) + } finally { + Try(output.close()) + } + } + }.start() + } + + protected def readData(length: Int): U + + protected def read(): U = { + try { + val length = dataStream.readInt() + + length match { + case SpecialLengths.TIMING_DATA => + // Timing data from R worker + val boot = dataStream.readDouble - bootTime + val init = dataStream.readDouble + val broadcast = dataStream.readDouble + val input = dataStream.readDouble + val compute = dataStream.readDouble + val output = dataStream.readDouble + logInfo( + ("Times: boot = %.3f s, init = %.3f s, broadcast = %.3f s, " + + "read-input = %.3f s, compute = %.3f s, write-output = %.3f s, " + + "total = %.3f s").format( + boot, + init, + broadcast, + input, + compute, + output, + boot + init + broadcast + input + compute + output)) + read() + case length if length >= 0 => + readData(length) + } + } catch { + case eof: EOFException => + throw new SparkException("R worker exited unexpectedly (cranshed)", eof) + } + } +} + +/** + * Form an RDD[(Int, Array[Byte])] from key-value pairs returned from R. + * This is used by SparkR's shuffle operations. + */ +private class PairwiseRRDD[T: ClassTag]( + parent: RDD[T], + numPartitions: Int, + hashFunc: Array[Byte], + deserializer: String, + packageNames: Array[Byte], + rLibDir: String, + broadcastVars: Array[Object]) + extends BaseRRDD[T, (Int, Array[Byte])]( + parent, numPartitions, hashFunc, deserializer, + SerializationFormats.BYTE, packageNames, rLibDir, + broadcastVars.map(x => x.asInstanceOf[Broadcast[Object]])) { + + override protected def readData(length: Int): (Int, Array[Byte]) = { + length match { + case length if length == 2 => + val hashedKey = dataStream.readInt() + val contentPairsLength = dataStream.readInt() + val contentPairs = new Array[Byte](contentPairsLength) + dataStream.readFully(contentPairs) + (hashedKey, contentPairs) + case _ => null + } + } + + lazy val asJavaPairRDD : JavaPairRDD[Int, Array[Byte]] = JavaPairRDD.fromRDD(this) +} + +/** + * An RDD that stores serialized R objects as Array[Byte]. + */ +private class RRDD[T: ClassTag]( + parent: RDD[T], + func: Array[Byte], + deserializer: String, + serializer: String, + packageNames: Array[Byte], + rLibDir: String, + broadcastVars: Array[Object]) + extends BaseRRDD[T, Array[Byte]]( + parent, -1, func, deserializer, serializer, packageNames, rLibDir, + broadcastVars.map(x => x.asInstanceOf[Broadcast[Object]])) { + + override protected def readData(length: Int): Array[Byte] = { + length match { + case length if length > 0 => + val obj = new Array[Byte](length) + dataStream.readFully(obj) + obj + case _ => null + } + } + + lazy val asJavaRDD : JavaRDD[Array[Byte]] = JavaRDD.fromRDD(this) +} + +/** + * An RDD that stores R objects as Array[String]. + */ +private class StringRRDD[T: ClassTag]( + parent: RDD[T], + func: Array[Byte], + deserializer: String, + packageNames: Array[Byte], + rLibDir: String, + broadcastVars: Array[Object]) + extends BaseRRDD[T, String]( + parent, -1, func, deserializer, SerializationFormats.STRING, packageNames, rLibDir, + broadcastVars.map(x => x.asInstanceOf[Broadcast[Object]])) { + + override protected def readData(length: Int): String = { + length match { + case length if length > 0 => + SerDe.readStringBytes(dataStream, length) + case _ => null + } + } + + lazy val asJavaRDD : JavaRDD[String] = JavaRDD.fromRDD(this) +} + +private object SpecialLengths { + val TIMING_DATA = -1 +} + +private[r] class BufferedStreamThread( + in: InputStream, + name: String, + errBufferSize: Int) extends Thread(name) with Logging { + val lines = new Array[String](errBufferSize) + var lineIdx = 0 + override def run() { + for (line <- Source.fromInputStream(in).getLines) { + synchronized { + lines(lineIdx) = line + lineIdx = (lineIdx + 1) % errBufferSize + } + logInfo(line) + } + } + + def getLines(): String = synchronized { + (0 until errBufferSize).filter { x => + lines((x + lineIdx) % errBufferSize) != null + }.map { x => + lines((x + lineIdx) % errBufferSize) + }.mkString("\n") + } +} + +private[r] object RRDD { + // Because forking processes from Java is expensive, we prefer to launch + // a single R daemon (daemon.R) and tell it to fork new workers for our tasks. + // This daemon currently only works on UNIX-based systems now, so we should + // also fall back to launching workers (worker.R) directly. + private[this] var errThread: BufferedStreamThread = _ + private[this] var daemonChannel: DataOutputStream = _ + + def createSparkContext( + master: String, + appName: String, + sparkHome: String, + jars: Array[String], + sparkEnvirMap: JMap[Object, Object], + sparkExecutorEnvMap: JMap[Object, Object]): JavaSparkContext = { + + val sparkConf = new SparkConf().setAppName(appName) + .setSparkHome(sparkHome) + .setJars(jars) + + // Override `master` if we have a user-specified value + if (master != "") { + sparkConf.setMaster(master) + } else { + // If conf has no master set it to "local" to maintain + // backwards compatibility + sparkConf.setIfMissing("spark.master", "local") + } + + for ((name, value) <- sparkEnvirMap) { + sparkConf.set(name.asInstanceOf[String], value.asInstanceOf[String]) + } + for ((name, value) <- sparkExecutorEnvMap) { + sparkConf.setExecutorEnv(name.asInstanceOf[String], value.asInstanceOf[String]) + } + + new JavaSparkContext(sparkConf) + } + + /** + * Start a thread to print the process's stderr to ours + */ + private def startStdoutThread(proc: Process): BufferedStreamThread = { + val BUFFER_SIZE = 100 + val thread = new BufferedStreamThread(proc.getInputStream, "stdout reader for R", BUFFER_SIZE) + thread.setDaemon(true) + thread.start() + thread + } + + private def createRProcess(rLibDir: String, port: Int, script: String): BufferedStreamThread = { + val rCommand = "Rscript" + val rOptions = "--vanilla" + val rExecScript = rLibDir + "/SparkR/worker/" + script + val pb = new ProcessBuilder(List(rCommand, rOptions, rExecScript)) + // Unset the R_TESTS environment variable for workers. + // This is set by R CMD check as startup.Rs + // (http://svn.r-project.org/R/trunk/src/library/tools/R/testing.R) + // and confuses worker script which tries to load a non-existent file + pb.environment().put("R_TESTS", "") + pb.environment().put("SPARKR_RLIBDIR", rLibDir) + pb.environment().put("SPARKR_WORKER_PORT", port.toString) + pb.redirectErrorStream(true) // redirect stderr into stdout + val proc = pb.start() + val errThread = startStdoutThread(proc) + errThread + } + + /** + * ProcessBuilder used to launch worker R processes. + */ + def createRWorker(rLibDir: String, port: Int): BufferedStreamThread = { + val useDaemon = SparkEnv.get.conf.getBoolean("spark.sparkr.use.daemon", true) + if (!Utils.isWindows && useDaemon) { + synchronized { + if (daemonChannel == null) { + // we expect one connections + val serverSocket = new ServerSocket(0, 1) + val daemonPort = serverSocket.getLocalPort + errThread = createRProcess(rLibDir, daemonPort, "daemon.R") + // the socket used to send out the input of task + serverSocket.setSoTimeout(10000) + val sock = serverSocket.accept() + daemonChannel = new DataOutputStream(new BufferedOutputStream(sock.getOutputStream)) + serverSocket.close() + } + try { + daemonChannel.writeInt(port) + daemonChannel.flush() + } catch { + case e: IOException => + // daemon process died + daemonChannel.close() + daemonChannel = null + errThread = null + // fail the current task, retry by scheduler + throw e + } + errThread + } + } else { + createRProcess(rLibDir, port, "worker.R") + } + } + + /** + * Create an RRDD given a sequence of byte arrays. Used to create RRDD when `parallelize` is + * called from R. + */ + def createRDDFromArray(jsc: JavaSparkContext, arr: Array[Array[Byte]]): JavaRDD[Array[Byte]] = { + JavaRDD.fromRDD(jsc.sc.parallelize(arr, arr.length)) + } + +} diff --git a/core/src/main/scala/org/apache/spark/api/r/SerDe.scala b/core/src/main/scala/org/apache/spark/api/r/SerDe.scala new file mode 100644 index 0000000000000..371dfe454d1a2 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/api/r/SerDe.scala @@ -0,0 +1,344 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.api.r + +import java.io.{DataInputStream, DataOutputStream} +import java.sql.{Date, Time} + +import scala.collection.JavaConversions._ + +/** + * Utility functions to serialize, deserialize objects to / from R + */ +private[spark] object SerDe { + + // Type mapping from R to Java + // + // NULL -> void + // integer -> Int + // character -> String + // logical -> Boolean + // double, numeric -> Double + // raw -> Array[Byte] + // Date -> Date + // POSIXlt/POSIXct -> Time + // + // list[T] -> Array[T], where T is one of above mentioned types + // environment -> Map[String, T], where T is a native type + // jobj -> Object, where jobj is an object created in the backend + + def readObjectType(dis: DataInputStream): Char = { + dis.readByte().toChar + } + + def readObject(dis: DataInputStream): Object = { + val dataType = readObjectType(dis) + readTypedObject(dis, dataType) + } + + def readTypedObject( + dis: DataInputStream, + dataType: Char): Object = { + dataType match { + case 'n' => null + case 'i' => new java.lang.Integer(readInt(dis)) + case 'd' => new java.lang.Double(readDouble(dis)) + case 'b' => new java.lang.Boolean(readBoolean(dis)) + case 'c' => readString(dis) + case 'e' => readMap(dis) + case 'r' => readBytes(dis) + case 'l' => readList(dis) + case 'D' => readDate(dis) + case 't' => readTime(dis) + case 'j' => JVMObjectTracker.getObject(readString(dis)) + case _ => throw new IllegalArgumentException(s"Invalid type $dataType") + } + } + + def readBytes(in: DataInputStream): Array[Byte] = { + val len = readInt(in) + val out = new Array[Byte](len) + val bytesRead = in.readFully(out) + out + } + + def readInt(in: DataInputStream): Int = { + in.readInt() + } + + def readDouble(in: DataInputStream): Double = { + in.readDouble() + } + + def readStringBytes(in: DataInputStream, len: Int): String = { + val bytes = new Array[Byte](len) + in.readFully(bytes) + assert(bytes(len - 1) == 0) + val str = new String(bytes.dropRight(1), "UTF-8") + str + } + + def readString(in: DataInputStream): String = { + val len = in.readInt() + readStringBytes(in, len) + } + + def readBoolean(in: DataInputStream): Boolean = { + val intVal = in.readInt() + if (intVal == 0) false else true + } + + def readDate(in: DataInputStream): Date = { + Date.valueOf(readString(in)) + } + + def readTime(in: DataInputStream): Time = { + val t = in.readDouble() + new Time((t * 1000L).toLong) + } + + def readBytesArr(in: DataInputStream): Array[Array[Byte]] = { + val len = readInt(in) + (0 until len).map(_ => readBytes(in)).toArray + } + + def readIntArr(in: DataInputStream): Array[Int] = { + val len = readInt(in) + (0 until len).map(_ => readInt(in)).toArray + } + + def readDoubleArr(in: DataInputStream): Array[Double] = { + val len = readInt(in) + (0 until len).map(_ => readDouble(in)).toArray + } + + def readBooleanArr(in: DataInputStream): Array[Boolean] = { + val len = readInt(in) + (0 until len).map(_ => readBoolean(in)).toArray + } + + def readStringArr(in: DataInputStream): Array[String] = { + val len = readInt(in) + (0 until len).map(_ => readString(in)).toArray + } + + def readList(dis: DataInputStream): Array[_] = { + val arrType = readObjectType(dis) + arrType match { + case 'i' => readIntArr(dis) + case 'c' => readStringArr(dis) + case 'd' => readDoubleArr(dis) + case 'b' => readBooleanArr(dis) + case 'j' => readStringArr(dis).map(x => JVMObjectTracker.getObject(x)) + case 'r' => readBytesArr(dis) + case _ => throw new IllegalArgumentException(s"Invalid array type $arrType") + } + } + + def readMap(in: DataInputStream): java.util.Map[Object, Object] = { + val len = readInt(in) + if (len > 0) { + val keysType = readObjectType(in) + val keysLen = readInt(in) + val keys = (0 until keysLen).map(_ => readTypedObject(in, keysType)) + + val valuesType = readObjectType(in) + val valuesLen = readInt(in) + val values = (0 until valuesLen).map(_ => readTypedObject(in, valuesType)) + mapAsJavaMap(keys.zip(values).toMap) + } else { + new java.util.HashMap[Object, Object]() + } + } + + // Methods to write out data from Java to R + // + // Type mapping from Java to R + // + // void -> NULL + // Int -> integer + // String -> character + // Boolean -> logical + // Double -> double + // Long -> double + // Array[Byte] -> raw + // Date -> Date + // Time -> POSIXct + // + // Array[T] -> list() + // Object -> jobj + + def writeType(dos: DataOutputStream, typeStr: String): Unit = { + typeStr match { + case "void" => dos.writeByte('n') + case "character" => dos.writeByte('c') + case "double" => dos.writeByte('d') + case "integer" => dos.writeByte('i') + case "logical" => dos.writeByte('b') + case "date" => dos.writeByte('D') + case "time" => dos.writeByte('t') + case "raw" => dos.writeByte('r') + case "list" => dos.writeByte('l') + case "jobj" => dos.writeByte('j') + case _ => throw new IllegalArgumentException(s"Invalid type $typeStr") + } + } + + def writeObject(dos: DataOutputStream, value: Object): Unit = { + if (value == null) { + writeType(dos, "void") + } else { + value.getClass.getName match { + case "java.lang.String" => + writeType(dos, "character") + writeString(dos, value.asInstanceOf[String]) + case "long" | "java.lang.Long" => + writeType(dos, "double") + writeDouble(dos, value.asInstanceOf[Long].toDouble) + case "double" | "java.lang.Double" => + writeType(dos, "double") + writeDouble(dos, value.asInstanceOf[Double]) + case "int" | "java.lang.Integer" => + writeType(dos, "integer") + writeInt(dos, value.asInstanceOf[Int]) + case "boolean" | "java.lang.Boolean" => + writeType(dos, "logical") + writeBoolean(dos, value.asInstanceOf[Boolean]) + case "java.sql.Date" => + writeType(dos, "date") + writeDate(dos, value.asInstanceOf[Date]) + case "java.sql.Time" => + writeType(dos, "time") + writeTime(dos, value.asInstanceOf[Time]) + case "[B" => + writeType(dos, "raw") + writeBytes(dos, value.asInstanceOf[Array[Byte]]) + // TODO: Types not handled right now include + // byte, char, short, float + + // Handle arrays + case "[Ljava.lang.String;" => + writeType(dos, "list") + writeStringArr(dos, value.asInstanceOf[Array[String]]) + case "[I" => + writeType(dos, "list") + writeIntArr(dos, value.asInstanceOf[Array[Int]]) + case "[J" => + writeType(dos, "list") + writeDoubleArr(dos, value.asInstanceOf[Array[Long]].map(_.toDouble)) + case "[D" => + writeType(dos, "list") + writeDoubleArr(dos, value.asInstanceOf[Array[Double]]) + case "[Z" => + writeType(dos, "list") + writeBooleanArr(dos, value.asInstanceOf[Array[Boolean]]) + case "[[B" => + writeType(dos, "list") + writeBytesArr(dos, value.asInstanceOf[Array[Array[Byte]]]) + case otherName => + // Handle array of objects + if (otherName.startsWith("[L")) { + val objArr = value.asInstanceOf[Array[Object]] + writeType(dos, "list") + writeType(dos, "jobj") + dos.writeInt(objArr.length) + objArr.foreach(o => writeJObj(dos, o)) + } else { + writeType(dos, "jobj") + writeJObj(dos, value) + } + } + } + } + + def writeInt(out: DataOutputStream, value: Int): Unit = { + out.writeInt(value) + } + + def writeDouble(out: DataOutputStream, value: Double): Unit = { + out.writeDouble(value) + } + + def writeBoolean(out: DataOutputStream, value: Boolean): Unit = { + val intValue = if (value) 1 else 0 + out.writeInt(intValue) + } + + def writeDate(out: DataOutputStream, value: Date): Unit = { + writeString(out, value.toString) + } + + def writeTime(out: DataOutputStream, value: Time): Unit = { + out.writeDouble(value.getTime.toDouble / 1000.0) + } + + + // NOTE: Only works for ASCII right now + def writeString(out: DataOutputStream, value: String): Unit = { + val len = value.length + out.writeInt(len + 1) // For the \0 + out.writeBytes(value) + out.writeByte(0) + } + + def writeBytes(out: DataOutputStream, value: Array[Byte]): Unit = { + out.writeInt(value.length) + out.write(value) + } + + def writeJObj(out: DataOutputStream, value: Object): Unit = { + val objId = JVMObjectTracker.put(value) + writeString(out, objId) + } + + def writeIntArr(out: DataOutputStream, value: Array[Int]): Unit = { + writeType(out, "integer") + out.writeInt(value.length) + value.foreach(v => out.writeInt(v)) + } + + def writeDoubleArr(out: DataOutputStream, value: Array[Double]): Unit = { + writeType(out, "double") + out.writeInt(value.length) + value.foreach(v => out.writeDouble(v)) + } + + def writeBooleanArr(out: DataOutputStream, value: Array[Boolean]): Unit = { + writeType(out, "logical") + out.writeInt(value.length) + value.foreach(v => writeBoolean(out, v)) + } + + def writeStringArr(out: DataOutputStream, value: Array[String]): Unit = { + writeType(out, "character") + out.writeInt(value.length) + value.foreach(v => writeString(out, v)) + } + + def writeBytesArr(out: DataOutputStream, value: Array[Array[Byte]]): Unit = { + writeType(out, "raw") + out.writeInt(value.length) + value.foreach(v => writeBytes(out, v)) + } +} + +private[r] object SerializationFormats { + val BYTE = "byte" + val STRING = "string" + val ROW = "row" +} diff --git a/core/src/main/scala/org/apache/spark/broadcast/Broadcast.scala b/core/src/main/scala/org/apache/spark/broadcast/Broadcast.scala index a5ea478f231d7..12d79f6ed311b 100644 --- a/core/src/main/scala/org/apache/spark/broadcast/Broadcast.scala +++ b/core/src/main/scala/org/apache/spark/broadcast/Broadcast.scala @@ -146,5 +146,5 @@ abstract class Broadcast[T: ClassTag](val id: Long) extends Serializable with Lo } } - override def toString = "Broadcast(" + id + ")" + override def toString: String = "Broadcast(" + id + ")" } diff --git a/core/src/main/scala/org/apache/spark/broadcast/BroadcastManager.scala b/core/src/main/scala/org/apache/spark/broadcast/BroadcastManager.scala index 8f8a0b11f9f2e..685313ac009ba 100644 --- a/core/src/main/scala/org/apache/spark/broadcast/BroadcastManager.scala +++ b/core/src/main/scala/org/apache/spark/broadcast/BroadcastManager.scala @@ -58,7 +58,7 @@ private[spark] class BroadcastManager( private val nextBroadcastId = new AtomicLong(0) - def newBroadcast[T: ClassTag](value_ : T, isLocal: Boolean) = { + def newBroadcast[T: ClassTag](value_ : T, isLocal: Boolean): Broadcast[T] = { broadcastFactory.newBroadcast[T](value_, isLocal, nextBroadcastId.getAndIncrement()) } diff --git a/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala b/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala index 1444c0dd3d2d6..4457c75e8b0fc 100644 --- a/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala +++ b/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala @@ -160,12 +160,12 @@ private[broadcast] object HttpBroadcast extends Logging { logInfo("Broadcast server started at " + serverUri) } - def getFile(id: Long) = new File(broadcastDir, BroadcastBlockId(id).name) + def getFile(id: Long): File = new File(broadcastDir, BroadcastBlockId(id).name) private def write(id: Long, value: Any) { val file = getFile(id) val fileOutputStream = new FileOutputStream(file) - try { + Utils.tryWithSafeFinally { val out: OutputStream = { if (compress) { compressionCodec.compressedOutputStream(fileOutputStream) @@ -175,10 +175,13 @@ private[broadcast] object HttpBroadcast extends Logging { } val ser = SparkEnv.get.serializer.newInstance() val serOut = ser.serializeStream(out) - serOut.writeObject(value) - serOut.close() + Utils.tryWithSafeFinally { + serOut.writeObject(value) + } { + serOut.close() + } files += file - } finally { + } { fileOutputStream.close() } } @@ -212,9 +215,11 @@ private[broadcast] object HttpBroadcast extends Logging { } val ser = SparkEnv.get.serializer.newInstance() val serIn = ser.deserializeStream(in) - val obj = serIn.readObject[T]() - serIn.close() - obj + Utils.tryWithSafeFinally { + serIn.readObject[T]() + } { + serIn.close() + } } /** @@ -222,7 +227,7 @@ private[broadcast] object HttpBroadcast extends Logging { * If removeFromDriver is true, also remove these persisted blocks on the driver * and delete the associated broadcast file. */ - def unpersist(id: Long, removeFromDriver: Boolean, blocking: Boolean) = synchronized { + def unpersist(id: Long, removeFromDriver: Boolean, blocking: Boolean): Unit = synchronized { SparkEnv.get.blockManager.master.removeBroadcast(id, removeFromDriver, blocking) if (removeFromDriver) { val file = getFile(id) diff --git a/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcastFactory.scala b/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcastFactory.scala index c7ef02d572a19..cf3ae36f27949 100644 --- a/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcastFactory.scala +++ b/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcastFactory.scala @@ -31,7 +31,7 @@ class HttpBroadcastFactory extends BroadcastFactory { HttpBroadcast.initialize(isDriver, conf, securityMgr) } - override def newBroadcast[T: ClassTag](value_ : T, isLocal: Boolean, id: Long) = + override def newBroadcast[T: ClassTag](value_ : T, isLocal: Boolean, id: Long): Broadcast[T] = new HttpBroadcast[T](value_, isLocal, id) override def stop() { HttpBroadcast.stop() } diff --git a/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala index 94142d33369c7..23b02e60338fb 100644 --- a/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala +++ b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala @@ -222,7 +222,7 @@ private object TorrentBroadcast extends Logging { * Remove all persisted blocks associated with this torrent broadcast on the executors. * If removeFromDriver is true, also remove these persisted blocks on the driver. */ - def unpersist(id: Long, removeFromDriver: Boolean, blocking: Boolean) = { + def unpersist(id: Long, removeFromDriver: Boolean, blocking: Boolean): Unit = { logDebug(s"Unpersisting TorrentBroadcast $id") SparkEnv.get.blockManager.master.removeBroadcast(id, removeFromDriver, blocking) } diff --git a/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcastFactory.scala b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcastFactory.scala index fb024c12094f2..96d8dd79908c8 100644 --- a/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcastFactory.scala +++ b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcastFactory.scala @@ -30,7 +30,7 @@ class TorrentBroadcastFactory extends BroadcastFactory { override def initialize(isDriver: Boolean, conf: SparkConf, securityMgr: SecurityManager) { } - override def newBroadcast[T: ClassTag](value_ : T, isLocal: Boolean, id: Long) = { + override def newBroadcast[T: ClassTag](value_ : T, isLocal: Boolean, id: Long): Broadcast[T] = { new TorrentBroadcast[T](value_, id) } diff --git a/core/src/main/scala/org/apache/spark/deploy/ApplicationDescription.scala b/core/src/main/scala/org/apache/spark/deploy/ApplicationDescription.scala index ae55b4ff40b74..ae99432f5ce86 100644 --- a/core/src/main/scala/org/apache/spark/deploy/ApplicationDescription.scala +++ b/core/src/main/scala/org/apache/spark/deploy/ApplicationDescription.scala @@ -17,13 +17,18 @@ package org.apache.spark.deploy +import java.net.URI + private[spark] class ApplicationDescription( val name: String, val maxCores: Option[Int], - val memoryPerSlave: Int, + val memoryPerExecutorMB: Int, val command: Command, var appUiUrl: String, - val eventLogDir: Option[String] = None) + val eventLogDir: Option[URI] = None, + // short name of compression codec used when writing event logs, if any (e.g. lzf) + val eventLogCodec: Option[String] = None, + val coresPerExecutor: Option[Int] = None) extends Serializable { val user = System.getProperty("user.name", "") @@ -31,11 +36,13 @@ private[spark] class ApplicationDescription( def copy( name: String = name, maxCores: Option[Int] = maxCores, - memoryPerSlave: Int = memoryPerSlave, + memoryPerExecutorMB: Int = memoryPerExecutorMB, command: Command = command, appUiUrl: String = appUiUrl, - eventLogDir: Option[String] = eventLogDir): ApplicationDescription = - new ApplicationDescription(name, maxCores, memoryPerSlave, command, appUiUrl, eventLogDir) + eventLogDir: Option[URI] = eventLogDir, + eventLogCodec: Option[String] = eventLogCodec): ApplicationDescription = + new ApplicationDescription( + name, maxCores, memoryPerExecutorMB, command, appUiUrl, eventLogDir, eventLogCodec) override def toString: String = "ApplicationDescription(" + name + ")" } diff --git a/core/src/main/scala/org/apache/spark/deploy/Client.scala b/core/src/main/scala/org/apache/spark/deploy/Client.scala index 38b3da0b13756..8d13b2a2cd4f3 100644 --- a/core/src/main/scala/org/apache/spark/deploy/Client.scala +++ b/core/src/main/scala/org/apache/spark/deploy/Client.scala @@ -38,7 +38,7 @@ private class ClientActor(driverArgs: ClientArguments, conf: SparkConf) var masterActor: ActorSelection = _ val timeout = AkkaUtils.askTimeout(conf) - override def preStart() = { + override def preStart(): Unit = { masterActor = context.actorSelection( Master.toAkkaUrl(driverArgs.master, AkkaUtils.protocol(context.system))) @@ -68,8 +68,9 @@ private class ClientActor(driverArgs: ClientArguments, conf: SparkConf) .map(Utils.splitCommandString).getOrElse(Seq.empty) val sparkJavaOpts = Utils.sparkJavaOpts(conf) val javaOpts = sparkJavaOpts ++ extraJavaOpts - val command = new Command(mainClass, Seq("{{WORKER_URL}}", driverArgs.mainClass) ++ - driverArgs.driverOptions, sys.env, classPathEntries, libraryPathEntries, javaOpts) + val command = new Command(mainClass, + Seq("{{WORKER_URL}}", "{{USER_JAR}}", driverArgs.mainClass) ++ driverArgs.driverOptions, + sys.env, classPathEntries, libraryPathEntries, javaOpts) val driverDescription = new DriverDescription( driverArgs.jarUrl, @@ -88,7 +89,7 @@ private class ClientActor(driverArgs: ClientArguments, conf: SparkConf) /* Find out driver status then exit the JVM */ def pollAndReportStatus(driverId: String) { - println(s"... waiting before polling master for driver state") + println("... waiting before polling master for driver state") Thread.sleep(5000) println("... polling master for driver state") val statusFuture = (masterActor ? RequestDriverStatus(driverId))(timeout) @@ -117,7 +118,7 @@ private class ClientActor(driverArgs: ClientArguments, conf: SparkConf) } } - override def receiveWithLogging = { + override def receiveWithLogging: PartialFunction[Any, Unit] = { case SubmitDriverResponse(success, driverId, message) => println(message) diff --git a/core/src/main/scala/org/apache/spark/deploy/ClientArguments.scala b/core/src/main/scala/org/apache/spark/deploy/ClientArguments.scala index 415bd50591692..5cbac787dceeb 100644 --- a/core/src/main/scala/org/apache/spark/deploy/ClientArguments.scala +++ b/core/src/main/scala/org/apache/spark/deploy/ClientArguments.scala @@ -28,7 +28,7 @@ import org.apache.spark.util.{IntParam, MemoryParam} /** * Command-line parser for the driver client. */ -private[spark] class ClientArguments(args: Array[String]) { +private[deploy] class ClientArguments(args: Array[String]) { import ClientArguments._ var cmd: String = "" // 'launch' or 'kill' @@ -42,7 +42,7 @@ private[spark] class ClientArguments(args: Array[String]) { var memory: Int = DEFAULT_MEMORY var cores: Int = DEFAULT_CORES private var _driverOptions = ListBuffer[String]() - def driverOptions = _driverOptions.toSeq + def driverOptions: Seq[String] = _driverOptions.toSeq // kill parameters var driverId: String = "" @@ -96,7 +96,7 @@ private[spark] class ClientArguments(args: Array[String]) { /** * Print usage and exit JVM with the given exit code. */ - def printUsageAndExit(exitCode: Int) { + private def printUsageAndExit(exitCode: Int) { // TODO: It wouldn't be too hard to allow users to submit their app and dependency jars // separately similar to in the YARN client. val usage = @@ -116,10 +116,10 @@ private[spark] class ClientArguments(args: Array[String]) { } } -object ClientArguments { - private[spark] val DEFAULT_CORES = 1 - private[spark] val DEFAULT_MEMORY = 512 // MB - private[spark] val DEFAULT_SUPERVISE = false +private[deploy] object ClientArguments { + val DEFAULT_CORES = 1 + val DEFAULT_MEMORY = 512 // MB + val DEFAULT_SUPERVISE = false def isValidJarUrl(s: String): Boolean = { try { diff --git a/core/src/main/scala/org/apache/spark/deploy/DeployMessage.scala b/core/src/main/scala/org/apache/spark/deploy/DeployMessage.scala index 7f600d89604a2..9db6fd1ac4dbe 100644 --- a/core/src/main/scala/org/apache/spark/deploy/DeployMessage.scala +++ b/core/src/main/scala/org/apache/spark/deploy/DeployMessage.scala @@ -101,6 +101,8 @@ private[deploy] object DeployMessages { case class RegisterApplication(appDescription: ApplicationDescription) extends DeployMessage + case class UnregisterApplication(appId: String) + case class MasterChangeAcknowledged(appId: String) // Master to AppClient @@ -162,7 +164,7 @@ private[deploy] object DeployMessages { Utils.checkHost(host, "Required hostname") assert (port > 0) - def uri = "spark://" + host + ":" + port + def uri: String = "spark://" + host + ":" + port def restUri: Option[String] = restPort.map { p => "spark://" + host + ":" + p } } diff --git a/core/src/main/scala/org/apache/spark/deploy/DriverDescription.scala b/core/src/main/scala/org/apache/spark/deploy/DriverDescription.scala index b056a19ce6598..659fb434a80f5 100644 --- a/core/src/main/scala/org/apache/spark/deploy/DriverDescription.scala +++ b/core/src/main/scala/org/apache/spark/deploy/DriverDescription.scala @@ -17,7 +17,7 @@ package org.apache.spark.deploy -private[spark] class DriverDescription( +private[deploy] class DriverDescription( val jarUrl: String, val mem: Int, val cores: Int, diff --git a/core/src/main/scala/org/apache/spark/deploy/ExecutorDescription.scala b/core/src/main/scala/org/apache/spark/deploy/ExecutorDescription.scala index 2abf0b69dddb3..ec23371b52f93 100644 --- a/core/src/main/scala/org/apache/spark/deploy/ExecutorDescription.scala +++ b/core/src/main/scala/org/apache/spark/deploy/ExecutorDescription.scala @@ -22,7 +22,7 @@ package org.apache.spark.deploy * This state is sufficient for the Master to reconstruct its internal data structures during * failover. */ -private[spark] class ExecutorDescription( +private[deploy] class ExecutorDescription( val appId: String, val execId: Int, val cores: Int, diff --git a/core/src/main/scala/org/apache/spark/deploy/ExecutorState.scala b/core/src/main/scala/org/apache/spark/deploy/ExecutorState.scala index 9f34d01e6db48..efa88c62e1f5d 100644 --- a/core/src/main/scala/org/apache/spark/deploy/ExecutorState.scala +++ b/core/src/main/scala/org/apache/spark/deploy/ExecutorState.scala @@ -17,7 +17,7 @@ package org.apache.spark.deploy -private[spark] object ExecutorState extends Enumeration { +private[deploy] object ExecutorState extends Enumeration { val LAUNCHING, LOADING, RUNNING, KILLED, FAILED, LOST, EXITED = Value diff --git a/core/src/main/scala/org/apache/spark/deploy/FaultToleranceTest.scala b/core/src/main/scala/org/apache/spark/deploy/FaultToleranceTest.scala index 47dbcd87c35b5..a7c89276a045e 100644 --- a/core/src/main/scala/org/apache/spark/deploy/FaultToleranceTest.scala +++ b/core/src/main/scala/org/apache/spark/deploy/FaultToleranceTest.scala @@ -33,6 +33,7 @@ import org.json4s.jackson.JsonMethods import org.apache.spark.{Logging, SparkConf, SparkContext} import org.apache.spark.deploy.master.{RecoveryState, SparkCuratorUtil} +import org.apache.spark.util.Utils /** * This suite tests the fault tolerance of the Spark standalone scheduler, mainly the Master. @@ -55,29 +56,29 @@ import org.apache.spark.deploy.master.{RecoveryState, SparkCuratorUtil} * - The docker images tagged spark-test-master and spark-test-worker are built from the * docker/ directory. Run 'docker/spark-test/build' to generate these. */ -private[spark] object FaultToleranceTest extends App with Logging { +private object FaultToleranceTest extends App with Logging { - val conf = new SparkConf() - val ZK_DIR = conf.get("spark.deploy.zookeeper.dir", "/spark") + private val conf = new SparkConf() + private val ZK_DIR = conf.get("spark.deploy.zookeeper.dir", "/spark") - val masters = ListBuffer[TestMasterInfo]() - val workers = ListBuffer[TestWorkerInfo]() - var sc: SparkContext = _ + private val masters = ListBuffer[TestMasterInfo]() + private val workers = ListBuffer[TestWorkerInfo]() + private var sc: SparkContext = _ - val zk = SparkCuratorUtil.newClient(conf) + private val zk = SparkCuratorUtil.newClient(conf) - var numPassed = 0 - var numFailed = 0 + private var numPassed = 0 + private var numFailed = 0 - val sparkHome = System.getenv("SPARK_HOME") + private val sparkHome = System.getenv("SPARK_HOME") assertTrue(sparkHome != null, "Run with a valid SPARK_HOME") - val containerSparkHome = "/opt/spark" - val dockerMountDir = "%s:%s".format(sparkHome, containerSparkHome) + private val containerSparkHome = "/opt/spark" + private val dockerMountDir = "%s:%s".format(sparkHome, containerSparkHome) System.setProperty("spark.driver.host", "172.17.42.1") // default docker host ip - def afterEach() { + private def afterEach() { if (sc != null) { sc.stop() sc = null @@ -179,7 +180,7 @@ private[spark] object FaultToleranceTest extends App with Logging { } } - def test(name: String)(fn: => Unit) { + private def test(name: String)(fn: => Unit) { try { fn numPassed += 1 @@ -197,19 +198,19 @@ private[spark] object FaultToleranceTest extends App with Logging { afterEach() } - def addMasters(num: Int) { + private def addMasters(num: Int) { logInfo(s">>>>> ADD MASTERS $num <<<<<") (1 to num).foreach { _ => masters += SparkDocker.startMaster(dockerMountDir) } } - def addWorkers(num: Int) { + private def addWorkers(num: Int) { logInfo(s">>>>> ADD WORKERS $num <<<<<") val masterUrls = getMasterUrls(masters) (1 to num).foreach { _ => workers += SparkDocker.startWorker(dockerMountDir, masterUrls) } } /** Creates a SparkContext, which constructs a Client to interact with our cluster. */ - def createClient() = { + private def createClient() = { logInfo(">>>>> CREATE CLIENT <<<<<") if (sc != null) { sc.stop() } // Counter-hack: Because of a hack in SparkEnv#create() that changes this @@ -218,17 +219,17 @@ private[spark] object FaultToleranceTest extends App with Logging { sc = new SparkContext(getMasterUrls(masters), "fault-tolerance", containerSparkHome) } - def getMasterUrls(masters: Seq[TestMasterInfo]): String = { + private def getMasterUrls(masters: Seq[TestMasterInfo]): String = { "spark://" + masters.map(master => master.ip + ":7077").mkString(",") } - def getLeader: TestMasterInfo = { + private def getLeader: TestMasterInfo = { val leaders = masters.filter(_.state == RecoveryState.ALIVE) assertTrue(leaders.size == 1) leaders(0) } - def killLeader(): Unit = { + private def killLeader(): Unit = { logInfo(">>>>> KILL LEADER <<<<<") masters.foreach(_.readState()) val leader = getLeader @@ -236,9 +237,9 @@ private[spark] object FaultToleranceTest extends App with Logging { leader.kill() } - def delay(secs: Duration = 5.seconds) = Thread.sleep(secs.toMillis) + private def delay(secs: Duration = 5.seconds) = Thread.sleep(secs.toMillis) - def terminateCluster() { + private def terminateCluster() { logInfo(">>>>> TERMINATE CLUSTER <<<<<") masters.foreach(_.kill()) workers.foreach(_.kill()) @@ -247,7 +248,7 @@ private[spark] object FaultToleranceTest extends App with Logging { } /** This includes Client retry logic, so it may take a while if the cluster is recovering. */ - def assertUsable() = { + private def assertUsable() = { val f = future { try { val res = sc.parallelize(0 until 10).collect() @@ -269,7 +270,7 @@ private[spark] object FaultToleranceTest extends App with Logging { * Asserts that the cluster is usable and that the expected masters and workers * are all alive in a proper configuration (e.g., only one leader). */ - def assertValidClusterState() = { + private def assertValidClusterState() = { logInfo(">>>>> ASSERT VALID CLUSTER STATE <<<<<") assertUsable() var numAlive = 0 @@ -325,7 +326,7 @@ private[spark] object FaultToleranceTest extends App with Logging { } } - def assertTrue(bool: Boolean, message: String = "") { + private def assertTrue(bool: Boolean, message: String = "") { if (!bool) { throw new IllegalStateException("Assertion failed: " + message) } @@ -335,7 +336,7 @@ private[spark] object FaultToleranceTest extends App with Logging { numFailed)) } -private[spark] class TestMasterInfo(val ip: String, val dockerId: DockerId, val logFile: File) +private class TestMasterInfo(val ip: String, val dockerId: DockerId, val logFile: File) extends Logging { implicit val formats = org.json4s.DefaultFormats @@ -377,7 +378,7 @@ private[spark] class TestMasterInfo(val ip: String, val dockerId: DockerId, val format(ip, dockerId.id, logFile.getAbsolutePath, state) } -private[spark] class TestWorkerInfo(val ip: String, val dockerId: DockerId, val logFile: File) +private class TestWorkerInfo(val ip: String, val dockerId: DockerId, val logFile: File) extends Logging { implicit val formats = org.json4s.DefaultFormats @@ -390,7 +391,7 @@ private[spark] class TestWorkerInfo(val ip: String, val dockerId: DockerId, val "[ip=%s, id=%s, logFile=%s]".format(ip, dockerId, logFile.getAbsolutePath) } -private[spark] object SparkDocker { +private object SparkDocker { def startMaster(mountDir: String): TestMasterInfo = { val cmd = Docker.makeRunCmd("spark-test-master", mountDir = mountDir) val (ip, id, outFile) = startNode(cmd) @@ -405,8 +406,7 @@ private[spark] object SparkDocker { private def startNode(dockerCmd: ProcessBuilder) : (String, DockerId, File) = { val ipPromise = promise[String]() - val outFile = File.createTempFile("fault-tolerance-test", "") - outFile.deleteOnExit() + val outFile = File.createTempFile("fault-tolerance-test", "", Utils.createTempDir()) val outStream: FileWriter = new FileWriter(outFile) def findIpAndLog(line: String): Unit = { if (line.startsWith("CONTAINER_IP=")) { @@ -425,11 +425,11 @@ private[spark] object SparkDocker { } } -private[spark] class DockerId(val id: String) { - override def toString = id +private class DockerId(val id: String) { + override def toString: String = id } -private[spark] object Docker extends Logging { +private object Docker extends Logging { def makeRunCmd(imageTag: String, args: String = "", mountDir: String = ""): ProcessBuilder = { val mountCmd = if (mountDir != "") { " -v " + mountDir } else "" diff --git a/core/src/main/scala/org/apache/spark/deploy/JsonProtocol.scala b/core/src/main/scala/org/apache/spark/deploy/JsonProtocol.scala index 696f32a6f5730..2954f932b4f41 100644 --- a/core/src/main/scala/org/apache/spark/deploy/JsonProtocol.scala +++ b/core/src/main/scala/org/apache/spark/deploy/JsonProtocol.scala @@ -17,14 +17,15 @@ package org.apache.spark.deploy +import org.json4s.JsonAST.JObject import org.json4s.JsonDSL._ import org.apache.spark.deploy.DeployMessages.{MasterStateResponse, WorkerStateResponse} import org.apache.spark.deploy.master.{ApplicationInfo, DriverInfo, WorkerInfo} import org.apache.spark.deploy.worker.ExecutorRunner -private[spark] object JsonProtocol { - def writeWorkerInfo(obj: WorkerInfo) = { +private[deploy] object JsonProtocol { + def writeWorkerInfo(obj: WorkerInfo): JObject = { ("id" -> obj.id) ~ ("host" -> obj.host) ~ ("port" -> obj.port) ~ @@ -39,34 +40,34 @@ private[spark] object JsonProtocol { ("lastheartbeat" -> obj.lastHeartbeat) } - def writeApplicationInfo(obj: ApplicationInfo) = { + def writeApplicationInfo(obj: ApplicationInfo): JObject = { ("starttime" -> obj.startTime) ~ ("id" -> obj.id) ~ ("name" -> obj.desc.name) ~ ("cores" -> obj.desc.maxCores) ~ ("user" -> obj.desc.user) ~ - ("memoryperslave" -> obj.desc.memoryPerSlave) ~ + ("memoryperslave" -> obj.desc.memoryPerExecutorMB) ~ ("submitdate" -> obj.submitDate.toString) ~ ("state" -> obj.state.toString) ~ ("duration" -> obj.duration) } - def writeApplicationDescription(obj: ApplicationDescription) = { + def writeApplicationDescription(obj: ApplicationDescription): JObject = { ("name" -> obj.name) ~ ("cores" -> obj.maxCores) ~ - ("memoryperslave" -> obj.memoryPerSlave) ~ + ("memoryperslave" -> obj.memoryPerExecutorMB) ~ ("user" -> obj.user) ~ ("command" -> obj.command.toString) } - def writeExecutorRunner(obj: ExecutorRunner) = { + def writeExecutorRunner(obj: ExecutorRunner): JObject = { ("id" -> obj.execId) ~ ("memory" -> obj.memory) ~ ("appid" -> obj.appId) ~ ("appdesc" -> writeApplicationDescription(obj.appDesc)) } - def writeDriverInfo(obj: DriverInfo) = { + def writeDriverInfo(obj: DriverInfo): JObject = { ("id" -> obj.id) ~ ("starttime" -> obj.startTime.toString) ~ ("state" -> obj.state.toString) ~ @@ -74,7 +75,7 @@ private[spark] object JsonProtocol { ("memory" -> obj.desc.mem) } - def writeMasterState(obj: MasterStateResponse) = { + def writeMasterState(obj: MasterStateResponse): JObject = { ("url" -> obj.uri) ~ ("workers" -> obj.workers.toList.map(writeWorkerInfo)) ~ ("cores" -> obj.workers.map(_.cores).sum) ~ @@ -87,7 +88,7 @@ private[spark] object JsonProtocol { ("status" -> obj.status.toString) } - def writeWorkerState(obj: WorkerStateResponse) = { + def writeWorkerState(obj: WorkerStateResponse): JObject = { ("id" -> obj.workerId) ~ ("masterurl" -> obj.masterUrl) ~ ("masterwebuiurl" -> obj.masterWebUiUrl) ~ diff --git a/core/src/main/scala/org/apache/spark/deploy/LocalSparkCluster.scala b/core/src/main/scala/org/apache/spark/deploy/LocalSparkCluster.scala index 0401b15446a7b..f0e77c2ba982b 100644 --- a/core/src/main/scala/org/apache/spark/deploy/LocalSparkCluster.scala +++ b/core/src/main/scala/org/apache/spark/deploy/LocalSparkCluster.scala @@ -53,13 +53,13 @@ class LocalSparkCluster( /* Start the Master */ val (masterSystem, masterPort, _, _) = Master.startSystemAndActor(localHostname, 0, 0, _conf) masterActorSystems += masterSystem - val masterUrl = "spark://" + localHostname + ":" + masterPort + val masterUrl = "spark://" + Utils.localHostNameForURI() + ":" + masterPort val masters = Array(masterUrl) /* Start the Workers */ for (workerNum <- 1 to numWorkers) { val (workerSystem, _) = Worker.startSystemAndActor(localHostname, 0, 0, coresPerWorker, - memoryPerWorker, masters, null, Some(workerNum)) + memoryPerWorker, masters, null, Some(workerNum), _conf) workerActorSystems += workerSystem } diff --git a/core/src/main/scala/org/apache/spark/deploy/RRunner.scala b/core/src/main/scala/org/apache/spark/deploy/RRunner.scala new file mode 100644 index 0000000000000..e99779f299785 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/deploy/RRunner.scala @@ -0,0 +1,92 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.deploy + +import java.io._ +import java.util.concurrent.{Semaphore, TimeUnit} + +import scala.collection.JavaConversions._ + +import org.apache.hadoop.fs.Path + +import org.apache.spark.api.r.RBackend +import org.apache.spark.util.RedirectThread + +/** + * Main class used to launch SparkR applications using spark-submit. It executes R as a + * subprocess and then has it connect back to the JVM to access system properties etc. + */ +object RRunner { + def main(args: Array[String]): Unit = { + val rFile = PythonRunner.formatPath(args(0)) + + val otherArgs = args.slice(1, args.length) + + // Time to wait for SparkR backend to initialize in seconds + val backendTimeout = sys.env.getOrElse("SPARKR_BACKEND_TIMEOUT", "120").toInt + val rCommand = "Rscript" + + // Check if the file path exists. + // If not, change directory to current working directory for YARN cluster mode + val rF = new File(rFile) + val rFileNormalized = if (!rF.exists()) { + new Path(rFile).getName + } else { + rFile + } + + // Launch a SparkR backend server for the R process to connect to; this will let it see our + // Java system properties etc. + val sparkRBackend = new RBackend() + @volatile var sparkRBackendPort = 0 + val initialized = new Semaphore(0) + val sparkRBackendThread = new Thread("SparkR backend") { + override def run() { + sparkRBackendPort = sparkRBackend.init() + initialized.release() + sparkRBackend.run() + } + } + + sparkRBackendThread.start() + // Wait for RBackend initialization to finish + if (initialized.tryAcquire(backendTimeout, TimeUnit.SECONDS)) { + // Launch R + val returnCode = try { + val builder = new ProcessBuilder(Seq(rCommand, rFileNormalized) ++ otherArgs) + val env = builder.environment() + env.put("EXISTING_SPARKR_BACKEND_PORT", sparkRBackendPort.toString) + val sparkHome = System.getenv("SPARK_HOME") + env.put("R_PROFILE_USER", + Seq(sparkHome, "R", "lib", "SparkR", "profile", "general.R").mkString(File.separator)) + builder.redirectErrorStream(true) // Ugly but needed for stdout and stderr to synchronize + val process = builder.start() + + new RedirectThread(process.getInputStream, System.out, "redirect R output").start() + + process.waitFor() + } finally { + sparkRBackend.close() + } + System.exit(returnCode) + } else { + System.err.println("SparkR backend did not initialize in " + backendTimeout + " seconds") + System.exit(-1) + } + } +} diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala index 03238e9fa0088..cfaebf9ea5050 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala @@ -24,11 +24,10 @@ import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.{FileStatus, FileSystem, Path} import org.apache.hadoop.fs.FileSystem.Statistics import org.apache.hadoop.mapred.JobConf -import org.apache.hadoop.mapreduce.{JobContext, TaskAttemptContext} -import org.apache.hadoop.security.Credentials -import org.apache.hadoop.security.UserGroupInformation +import org.apache.hadoop.mapreduce.JobContext +import org.apache.hadoop.security.{Credentials, UserGroupInformation} -import org.apache.spark.{Logging, SparkContext, SparkConf, SparkException} +import org.apache.spark.{Logging, SparkConf, SparkException} import org.apache.spark.annotation.DeveloperApi import org.apache.spark.util.Utils @@ -52,18 +51,13 @@ class SparkHadoopUtil extends Logging { * do a FileSystem.closeAllForUGI in order to avoid leaking Filesystems */ def runAsSparkUser(func: () => Unit) { - val user = Option(System.getenv("SPARK_USER")).getOrElse(SparkContext.SPARK_UNKNOWN_USER) - if (user != SparkContext.SPARK_UNKNOWN_USER) { - logDebug("running as user: " + user) - val ugi = UserGroupInformation.createRemoteUser(user) - transferCredentials(UserGroupInformation.getCurrentUser(), ugi) - ugi.doAs(new PrivilegedExceptionAction[Unit] { - def run: Unit = func() - }) - } else { - logDebug("running as SPARK_UNKNOWN_USER") - func() - } + val user = Utils.getCurrentUserName() + logDebug("running as user: " + user) + val ugi = UserGroupInformation.createRemoteUser(user) + transferCredentials(UserGroupInformation.getCurrentUser(), ugi) + ugi.doAs(new PrivilegedExceptionAction[Unit] { + def run: Unit = func() + }) } def transferCredentials(source: UserGroupInformation, dest: UserGroupInformation) { @@ -198,7 +192,7 @@ class SparkHadoopUtil extends Logging { * that file. */ def listLeafStatuses(fs: FileSystem, basePath: Path): Seq[FileStatus] = { - def recurse(path: Path) = { + def recurse(path: Path): Array[FileStatus] = { val (directories, leaves) = fs.listStatus(path).partition(_.isDir) leaves ++ directories.flatMap(f => listLeafStatuses(fs, f.getPath)) } @@ -206,6 +200,37 @@ class SparkHadoopUtil extends Logging { val baseStatus = fs.getFileStatus(basePath) if (baseStatus.isDir) recurse(basePath) else Array(baseStatus) } + + private val HADOOP_CONF_PATTERN = "(\\$\\{hadoopconf-[^\\}\\$\\s]+\\})".r.unanchored + + /** + * Substitute variables by looking them up in Hadoop configs. Only variables that match the + * ${hadoopconf- .. } pattern are substituted. + */ + def substituteHadoopVariables(text: String, hadoopConf: Configuration): String = { + text match { + case HADOOP_CONF_PATTERN(matched) => { + logDebug(text + " matched " + HADOOP_CONF_PATTERN) + val key = matched.substring(13, matched.length() - 1) // remove ${hadoopconf- .. } + val eval = Option[String](hadoopConf.get(key)) + .map { value => + logDebug("Substituted " + matched + " with " + value) + text.replace(matched, value) + } + if (eval.isEmpty) { + // The variable was not found in Hadoop configs, so return text as is. + text + } else { + // Continue to substitute more variables. + substituteHadoopVariables(eval.get, hadoopConf) + } + } + case _ => { + logDebug(text + " didn't match " + HADOOP_CONF_PATTERN) + text + } + } + } } object SparkHadoopUtil { diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala index 6d213926f3d7b..296a0764b8baf 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala @@ -18,12 +18,14 @@ package org.apache.spark.deploy import java.io.{File, PrintStream} -import java.lang.reflect.{InvocationTargetException, Modifier} +import java.lang.reflect.{InvocationTargetException, Modifier, UndeclaredThrowableException} import java.net.URL +import java.security.PrivilegedExceptionAction import scala.collection.mutable.{ArrayBuffer, HashMap, Map} import org.apache.hadoop.fs.Path +import org.apache.hadoop.security.UserGroupInformation import org.apache.ivy.Ivy import org.apache.ivy.core.LogOptions import org.apache.ivy.core.module.descriptor._ @@ -35,15 +37,15 @@ import org.apache.ivy.core.settings.IvySettings import org.apache.ivy.plugins.matcher.GlobPatternMatcher import org.apache.ivy.plugins.resolver.{ChainResolver, IBiblioResolver} +import org.apache.spark.SPARK_VERSION import org.apache.spark.deploy.rest._ -import org.apache.spark.executor._ -import org.apache.spark.util.Utils +import org.apache.spark.util.{ChildFirstURLClassLoader, MutableURLClassLoader, Utils} /** * Whether to submit, kill, or request the status of an application. * The latter two operations are currently supported only for standalone cluster mode. */ -private[spark] object SparkSubmitAction extends Enumeration { +private[deploy] object SparkSubmitAction extends Enumeration { type SparkSubmitAction = Value val SUBMIT, KILL, REQUEST_STATUS = Value } @@ -75,20 +77,32 @@ object SparkSubmit { // Special primary resource names that represent shells rather than application jars. private val SPARK_SHELL = "spark-shell" private val PYSPARK_SHELL = "pyspark-shell" + private val SPARKR_SHELL = "sparkr-shell" private val CLASS_NOT_FOUND_EXIT_STATUS = 101 // Exposed for testing - private[spark] var exitFn: () => Unit = () => System.exit(-1) + private[spark] var exitFn: () => Unit = () => System.exit(1) private[spark] var printStream: PrintStream = System.err - private[spark] def printWarning(str: String) = printStream.println("Warning: " + str) - private[spark] def printErrorAndExit(str: String) = { + private[spark] def printWarning(str: String): Unit = printStream.println("Warning: " + str) + private[spark] def printErrorAndExit(str: String): Unit = { printStream.println("Error: " + str) printStream.println("Run with --help for usage help or --verbose for debug output") exitFn() } + private[spark] def printVersionAndExit(): Unit = { + printStream.println("""Welcome to + ____ __ + / __/__ ___ _____/ /__ + _\ \/ _ \/ _ `/ __/ '_/ + /___/ .__/\_,_/_/ /_/\_\ version %s + /_/ + """.format(SPARK_VERSION)) + printStream.println("Type --help for more information.") + exitFn() + } - def main(args: Array[String]) { + def main(args: Array[String]): Unit = { val appArgs = new SparkSubmitArguments(args) if (appArgs.verbose) { printStream.println(appArgs) @@ -124,8 +138,36 @@ object SparkSubmit { * Second, we use this launch environment to invoke the main method of the child * main class. */ - private[spark] def submit(args: SparkSubmitArguments): Unit = { + private def submit(args: SparkSubmitArguments): Unit = { val (childArgs, childClasspath, sysProps, childMainClass) = prepareSubmitEnvironment(args) + + def doRunMain(): Unit = { + if (args.proxyUser != null) { + val proxyUser = UserGroupInformation.createProxyUser(args.proxyUser, + UserGroupInformation.getCurrentUser()) + try { + proxyUser.doAs(new PrivilegedExceptionAction[Unit]() { + override def run(): Unit = { + runMain(childArgs, childClasspath, sysProps, childMainClass, args.verbose) + } + }) + } catch { + case e: Exception => + // Hadoop's AuthorizationException suppresses the exception's stack trace, which + // makes the message printed to the output by the JVM not very helpful. Instead, + // detect exceptions with empty stack traces here, and treat them differently. + if (e.getStackTrace().length == 0) { + printStream.println(s"ERROR: ${e.getClass().getName()}: ${e.getMessage()}") + exitFn() + } else { + throw e + } + } + } else { + runMain(childArgs, childClasspath, sysProps, childMainClass, args.verbose) + } + } + // In standalone cluster mode, there are two submission gateways: // (1) The traditional Akka gateway using o.a.s.deploy.Client as a wrapper // (2) The new REST-based gateway introduced in Spark 1.3 @@ -134,7 +176,7 @@ object SparkSubmit { if (args.isStandaloneCluster && args.useRest) { try { printStream.println("Running Spark using the REST application submission protocol.") - runMain(childArgs, childClasspath, sysProps, childMainClass) + doRunMain() } catch { // Fail over to use the legacy submission gateway case e: SubmitRestConnectionException => @@ -145,7 +187,7 @@ object SparkSubmit { } // In all other modes, just run the main class as prepared } else { - runMain(childArgs, childClasspath, sysProps, childMainClass) + doRunMain() } } @@ -158,7 +200,7 @@ object SparkSubmit { * (4) the main class for the child * Exposed for testing. */ - private[spark] def prepareSubmitEnvironment(args: SparkSubmitArguments) + private[deploy] def prepareSubmitEnvironment(args: SparkSubmitArguments) : (Seq[String], Seq[String], Map[String, String], String) = { // Return values val childArgs = new ArrayBuffer[String]() @@ -211,6 +253,26 @@ object SparkSubmit { val isYarnCluster = clusterManager == YARN && deployMode == CLUSTER + // Resolve maven dependencies if there are any and add classpath to jars. Add them to py-files + // too for packages that include Python code + val resolvedMavenCoordinates = + SparkSubmitUtils.resolveMavenCoordinates( + args.packages, Option(args.repositories), Option(args.ivyRepoPath)) + if (!resolvedMavenCoordinates.trim.isEmpty) { + if (args.jars == null || args.jars.trim.isEmpty) { + args.jars = resolvedMavenCoordinates + } else { + args.jars += s",$resolvedMavenCoordinates" + } + if (args.isPython) { + if (args.pyFiles == null || args.pyFiles.trim.isEmpty) { + args.pyFiles = resolvedMavenCoordinates + } else { + args.pyFiles += s",$resolvedMavenCoordinates" + } + } + } + // Require all python files to be local, so we can add them to the PYTHONPATH // In YARN cluster mode, python files are distributed as regular files, which can be non-local if (args.isPython && !isYarnCluster) { @@ -223,6 +285,13 @@ object SparkSubmit { } } + // Require all R files to be local + if (args.isR && !isYarnCluster) { + if (Utils.nonLocalPaths(args.primaryResource).nonEmpty) { + printErrorAndExit(s"Only local R files are supported: $args.primaryResource") + } + } + // The following modes are not supported or applicable (clusterManager, deployMode) match { case (MESOS, CLUSTER) => @@ -230,6 +299,9 @@ object SparkSubmit { case (STANDALONE, CLUSTER) if args.isPython => printErrorAndExit("Cluster deploy mode is currently not supported for python " + "applications on standalone clusters.") + case (STANDALONE, CLUSTER) if args.isR => + printErrorAndExit("Cluster deploy mode is currently not supported for R " + + "applications on standalone clusters.") case (_, CLUSTER) if isShell(args.primaryResource) => printErrorAndExit("Cluster deploy mode is not applicable to Spark shells.") case (_, CLUSTER) if isSqlShell(args.mainClass) => @@ -242,8 +314,7 @@ object SparkSubmit { // If we're running a python app, set the main class to our specific python runner if (args.isPython && deployMode == CLIENT) { if (args.primaryResource == PYSPARK_SHELL) { - args.mainClass = "py4j.GatewayServer" - args.childArgs = ArrayBuffer("--die-on-broken-pipe", "0") + args.mainClass = "org.apache.spark.api.python.PythonGatewayServer" } else { // If a python file is provided, add it to the child arguments and list of files to deploy. // Usage: PythonAppRunner
[app arguments] @@ -257,28 +328,37 @@ object SparkSubmit { } } - // In yarn-cluster mode for a python app, add primary resource and pyFiles to files - // that can be distributed with the job - if (args.isPython && isYarnCluster) { - args.files = mergeFileLists(args.files, args.primaryResource) - args.files = mergeFileLists(args.files, args.pyFiles) + // If we're running a R app, set the main class to our specific R runner + if (args.isR && deployMode == CLIENT) { + if (args.primaryResource == SPARKR_SHELL) { + args.mainClass = "org.apache.spark.api.r.RBackend" + } else { + // If a R file is provided, add it to the child arguments and list of files to deploy. + // Usage: RRunner
[app arguments] + args.mainClass = "org.apache.spark.deploy.RRunner" + args.childArgs = ArrayBuffer(args.primaryResource) ++ args.childArgs + args.files = mergeFileLists(args.files, args.primaryResource) + } } - // Special flag to avoid deprecation warnings at the client - sysProps("SPARK_SUBMIT") = "true" + if (isYarnCluster) { + // In yarn-cluster mode for a python app, add primary resource and pyFiles to files + // that can be distributed with the job + if (args.isPython) { + args.files = mergeFileLists(args.files, args.primaryResource) + args.files = mergeFileLists(args.files, args.pyFiles) + } - // Resolve maven dependencies if there are any and add classpath to jars - val resolvedMavenCoordinates = - SparkSubmitUtils.resolveMavenCoordinates( - args.packages, Option(args.repositories), Option(args.ivyRepoPath)) - if (!resolvedMavenCoordinates.trim.isEmpty) { - if (args.jars == null || args.jars.trim.isEmpty) { - args.jars = resolvedMavenCoordinates - } else { - args.jars += s",$resolvedMavenCoordinates" + // In yarn-cluster mode for a R app, add primary resource to files + // that can be distributed with the job + if (args.isR) { + args.files = mergeFileLists(args.files, args.primaryResource) } } + // Special flag to avoid deprecation warnings at the client + sysProps("SPARK_SUBMIT") = "true" + // A list of rules to map each argument to system properties or command-line options in // each deploy mode; we iterate through these below val options = List[OptionAssigner]( @@ -326,6 +406,8 @@ object SparkSubmit { OptionAssigner(args.jars, YARN, CLUSTER, clOption = "--addJars"), // Other options + OptionAssigner(args.executorCores, STANDALONE, ALL_DEPLOY_MODES, + sysProp = "spark.executor.cores"), OptionAssigner(args.executorMemory, STANDALONE | MESOS | YARN, ALL_DEPLOY_MODES, sysProp = "spark.executor.memory"), OptionAssigner(args.totalExecutorCores, STANDALONE | MESOS, ALL_DEPLOY_MODES, @@ -357,8 +439,8 @@ object SparkSubmit { // Add the application jar automatically so the user doesn't have to call sc.addJar // For YARN cluster mode, the jar is already distributed on each node as "app.jar" - // For python files, the primary resource is already distributed as a regular file - if (!isYarnCluster && !args.isPython) { + // For python and R files, the primary resource is already distributed as a regular file + if (!isYarnCluster && !args.isPython && !args.isR) { var jars = sysProps.get("spark.jars").map(x => x.split(",").toSeq).getOrElse(Seq.empty) if (isUserJar(args.primaryResource)) { jars = jars ++ Seq(args.primaryResource) @@ -399,6 +481,10 @@ object SparkSubmit { childArgs += ("--py-files", pyFilesNames) } childArgs += ("--class", "org.apache.spark.deploy.PythonRunner") + } else if (args.isR) { + val mainFile = new Path(args.primaryResource).getName + childArgs += ("--primary-r-file", mainFile) + childArgs += ("--class", "org.apache.spark.deploy.RRunner") } else { if (args.primaryResource != SPARK_INTERNAL) { childArgs += ("--jar", args.primaryResource) @@ -457,7 +543,7 @@ object SparkSubmit { childClasspath: Seq[String], sysProps: Map[String, String], childMainClass: String, - verbose: Boolean = false) { + verbose: Boolean): Unit = { if (verbose) { printStream.println(s"Main class:\n$childMainClass") printStream.println(s"Arguments:\n${childArgs.mkString("\n")}") @@ -467,11 +553,11 @@ object SparkSubmit { } val loader = - if (sysProps.getOrElse("spark.files.userClassPathFirst", "false").toBoolean) { - new ChildExecutorURLClassLoader(new Array[URL](0), + if (sysProps.getOrElse("spark.driver.userClassPathFirst", "false").toBoolean) { + new ChildFirstURLClassLoader(new Array[URL](0), Thread.currentThread.getContextClassLoader) } else { - new ExecutorURLClassLoader(new Array[URL](0), + new MutableURLClassLoader(new Array[URL](0), Thread.currentThread.getContextClassLoader) } Thread.currentThread.setContextClassLoader(loader) @@ -507,13 +593,21 @@ object SparkSubmit { if (!Modifier.isStatic(mainMethod.getModifiers)) { throw new IllegalStateException("The main method in the given main class must be static") } + + def findCause(t: Throwable): Throwable = t match { + case e: UndeclaredThrowableException => + if (e.getCause() != null) findCause(e.getCause()) else e + case e: InvocationTargetException => + if (e.getCause() != null) findCause(e.getCause()) else e + case e: Throwable => + e + } + try { mainMethod.invoke(null, childArgs.toArray) } catch { - case e: InvocationTargetException => e.getCause match { - case cause: Throwable => throw cause - case null => throw e - } + case t: Throwable => + throw findCause(t) } } @@ -535,47 +629,54 @@ object SparkSubmit { /** * Return whether the given primary resource represents a user jar. */ - private def isUserJar(primaryResource: String): Boolean = { - !isShell(primaryResource) && !isPython(primaryResource) && !isInternal(primaryResource) + private[deploy] def isUserJar(res: String): Boolean = { + !isShell(res) && !isPython(res) && !isInternal(res) && !isR(res) } /** * Return whether the given primary resource represents a shell. */ - private[spark] def isShell(primaryResource: String): Boolean = { - primaryResource == SPARK_SHELL || primaryResource == PYSPARK_SHELL + private[deploy] def isShell(res: String): Boolean = { + (res == SPARK_SHELL || res == PYSPARK_SHELL || res == SPARKR_SHELL) } /** * Return whether the given main class represents a sql shell. */ - private[spark] def isSqlShell(mainClass: String): Boolean = { + private def isSqlShell(mainClass: String): Boolean = { mainClass == "org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver" } /** * Return whether the given main class represents a thrift server. */ - private[spark] def isThriftServer(mainClass: String): Boolean = { + private def isThriftServer(mainClass: String): Boolean = { mainClass == "org.apache.spark.sql.hive.thriftserver.HiveThriftServer2" } /** * Return whether the given primary resource requires running python. */ - private[spark] def isPython(primaryResource: String): Boolean = { - primaryResource.endsWith(".py") || primaryResource == PYSPARK_SHELL + private[deploy] def isPython(res: String): Boolean = { + res != null && res.endsWith(".py") || res == PYSPARK_SHELL } - private[spark] def isInternal(primaryResource: String): Boolean = { - primaryResource == SPARK_INTERNAL + /** + * Return whether the given primary resource requires running R. + */ + private[deploy] def isR(res: String): Boolean = { + res != null && res.endsWith(".R") || res == SPARKR_SHELL + } + + private[deploy] def isInternal(res: String): Boolean = { + res == SPARK_INTERNAL } /** * Merge a sequence of comma-separated file lists, some of which may be null to indicate * no files, into a single comma-separated string. */ - private[spark] def mergeFileLists(lists: String*): String = { + private def mergeFileLists(lists: String*): String = { val merged = lists.filter(_ != null) .flatMap(_.split(",")) .mkString(",") @@ -584,10 +685,10 @@ object SparkSubmit { } /** Provides utility functions to be used inside SparkSubmit. */ -private[spark] object SparkSubmitUtils { +private[deploy] object SparkSubmitUtils { // Exposed for testing - private[spark] var printStream = SparkSubmit.printStream + var printStream = SparkSubmit.printStream /** * Represents a Maven Coordinate @@ -595,16 +696,17 @@ private[spark] object SparkSubmitUtils { * @param artifactId the artifactId of the coordinate * @param version the version of the coordinate */ - private[spark] case class MavenCoordinate(groupId: String, artifactId: String, version: String) + private[deploy] case class MavenCoordinate(groupId: String, artifactId: String, version: String) /** - * Extracts maven coordinates from a comma-delimited string + * Extracts maven coordinates from a comma-delimited string. Coordinates should be provided + * in the format `groupId:artifactId:version` or `groupId/artifactId:version`. * @param coordinates Comma-delimited string of maven coordinates * @return Sequence of Maven coordinates */ - private[spark] def extractMavenCoordinates(coordinates: String): Seq[MavenCoordinate] = { + def extractMavenCoordinates(coordinates: String): Seq[MavenCoordinate] = { coordinates.split(",").map { p => - val splits = p.split(":") + val splits = p.replace("/", ":").split(":") require(splits.length == 3, s"Provided Maven Coordinates must be in the form " + s"'groupId:artifactId:version'. The coordinate provided is: $p") require(splits(0) != null && splits(0).trim.nonEmpty, s"The groupId cannot be null or " + @@ -622,7 +724,7 @@ private[spark] object SparkSubmitUtils { * @param remoteRepos Comma-delimited string of remote repositories * @return A ChainResolver used by Ivy to search for and resolve dependencies. */ - private[spark] def createRepoResolvers(remoteRepos: Option[String]): ChainResolver = { + def createRepoResolvers(remoteRepos: Option[String]): ChainResolver = { // We need a chain resolver if we want to check multiple repositories val cr = new ChainResolver cr.setName("list") @@ -634,6 +736,13 @@ private[spark] object SparkSubmitUtils { br.setName("central") cr.add(br) + val sp: IBiblioResolver = new IBiblioResolver + sp.setM2compatible(true) + sp.setUsepoms(true) + sp.setRoot("http://dl.bintray.com/spark-packages/maven") + sp.setName("spark-packages") + cr.add(sp) + val repositoryList = remoteRepos.getOrElse("") // add any other remote repositories other than maven central if (repositoryList.trim.nonEmpty) { @@ -658,7 +767,7 @@ private[spark] object SparkSubmitUtils { * @param cacheDirectory directory where jars are cached * @return a comma-delimited list of paths for the dependencies */ - private[spark] def resolveDependencyPaths( + def resolveDependencyPaths( artifacts: Array[AnyRef], cacheDirectory: File): String = { artifacts.map { artifactInfo => @@ -670,7 +779,7 @@ private[spark] object SparkSubmitUtils { } /** Adds the given maven coordinates to Ivy's module descriptor. */ - private[spark] def addDependenciesToIvy( + def addDependenciesToIvy( md: DefaultModuleDescriptor, artifacts: Seq[MavenCoordinate], ivyConfName: String): Unit = { @@ -682,9 +791,38 @@ private[spark] object SparkSubmitUtils { md.addDependency(dd) } } + + /** Add exclusion rules for dependencies already included in the spark-assembly */ + def addExclusionRules( + ivySettings: IvySettings, + ivyConfName: String, + md: DefaultModuleDescriptor): Unit = { + // Add scala exclusion rule + val scalaArtifacts = new ArtifactId(new ModuleId("*", "scala-library"), "*", "*", "*") + val scalaDependencyExcludeRule = + new DefaultExcludeRule(scalaArtifacts, ivySettings.getMatcher("glob"), null) + scalaDependencyExcludeRule.addConfiguration(ivyConfName) + md.addExcludeRule(scalaDependencyExcludeRule) + + // We need to specify each component explicitly, otherwise we miss spark-streaming-kafka and + // other spark-streaming utility components. Underscore is there to differentiate between + // spark-streaming_2.1x and spark-streaming-kafka-assembly_2.1x + val components = Seq("bagel_", "catalyst_", "core_", "graphx_", "hive_", "mllib_", "repl_", + "sql_", "streaming_", "yarn_", "network-common_", "network-shuffle_", "network-yarn_") + + components.foreach { comp => + val sparkArtifacts = + new ArtifactId(new ModuleId("org.apache.spark", s"spark-$comp*"), "*", "*", "*") + val sparkDependencyExcludeRule = + new DefaultExcludeRule(sparkArtifacts, ivySettings.getMatcher("glob"), null) + sparkDependencyExcludeRule.addConfiguration(ivyConfName) + + md.addExcludeRule(sparkDependencyExcludeRule) + } + } /** A nice function to use in tests as well. Values are dummy strings. */ - private[spark] def getModuleDescriptor = DefaultModuleDescriptor.newDefaultInstance( + def getModuleDescriptor: DefaultModuleDescriptor = DefaultModuleDescriptor.newDefaultInstance( ModuleRevisionId.newInstance("org.apache.spark", "spark-submit-parent", "1.0")) /** @@ -695,7 +833,7 @@ private[spark] object SparkSubmitUtils { * @return The comma-delimited path to the jars of the given maven artifacts including their * transitive dependencies */ - private[spark] def resolveMavenCoordinates( + def resolveMavenCoordinates( coordinates: String, remoteRepos: Option[String], ivyPath: Option[String], @@ -703,6 +841,9 @@ private[spark] object SparkSubmitUtils { if (coordinates == null || coordinates.trim.isEmpty) { "" } else { + val sysOut = System.out + // To prevent ivy from logging to system out + System.setOut(printStream) val artifacts = extractMavenCoordinates(coordinates) // Default configuration name for ivy val ivyConfName = "default" @@ -746,14 +887,9 @@ private[spark] object SparkSubmitUtils { val md = getModuleDescriptor md.setDefaultConf(ivyConfName) - // Add an exclusion rule for Spark - val sparkArtifacts = new ArtifactId(new ModuleId("org.apache.spark", "*"), "*", "*", "*") - val sparkDependencyExcludeRule = - new DefaultExcludeRule(sparkArtifacts, ivySettings.getMatcher("glob"), null) - sparkDependencyExcludeRule.addConfiguration(ivyConfName) - - // Exclude any Spark dependencies, and add all supplied maven artifacts as dependencies - md.addExcludeRule(sparkDependencyExcludeRule) + // Add exclusion rules for Spark and Scala Library + addExclusionRules(ivySettings, ivyConfName, md) + // add all supplied maven artifacts as dependencies addDependenciesToIvy(md, artifacts, ivyConfName) // resolve dependencies @@ -765,7 +901,7 @@ private[spark] object SparkSubmitUtils { ivy.retrieve(rr.getModuleDescriptor.getModuleRevisionId, packagesDirectory.getAbsolutePath + File.separator + "[artifact](-[classifier]).[ext]", retrieveOptions.setConfs(Array(ivyConfName))) - + System.setOut(sysOut) resolveDependencyPaths(rr.getArtifacts.toArray, packagesDirectory) } } diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala index bd0ae26fd8210..faa8780288ea3 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala @@ -18,18 +18,22 @@ package org.apache.spark.deploy import java.net.URI +import java.util.{List => JList} import java.util.jar.JarFile +import scala.collection.JavaConversions._ import scala.collection.mutable.{ArrayBuffer, HashMap} import org.apache.spark.deploy.SparkSubmitAction._ +import org.apache.spark.launcher.SparkSubmitArgumentsParser import org.apache.spark.util.Utils /** * Parses and encapsulates arguments from the spark-submit script. * The env argument is used for testing. */ -private[spark] class SparkSubmitArguments(args: Seq[String], env: Map[String, String] = sys.env) { +private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, String] = sys.env) + extends SparkSubmitArgumentsParser { var master: String = null var deployMode: String = null var executorMemory: String = null @@ -55,8 +59,10 @@ private[spark] class SparkSubmitArguments(args: Seq[String], env: Map[String, St var verbose: Boolean = false var isPython: Boolean = false var pyFiles: String = null + var isR: Boolean = false var action: SparkSubmitAction = null val sparkProperties: HashMap[String, String] = new HashMap[String, String]() + var proxyUser: String = null // Standalone cluster mode only var supervise: Boolean = false @@ -83,7 +89,12 @@ private[spark] class SparkSubmitArguments(args: Seq[String], env: Map[String, St } // Set parameters from command line arguments - parseOpts(args.toList) + try { + parse(args.toList) + } catch { + case e: IllegalArgumentException => + SparkSubmit.printErrorAndExit(e.getMessage()) + } // Populate `sparkProperties` map from properties file mergeDefaultSparkProperties() // Use `sparkProperties` map along with env vars to fill in any missing parameters @@ -148,7 +159,7 @@ private[spark] class SparkSubmitArguments(args: Seq[String], env: Map[String, St .getOrElse(sparkProperties.get("spark.executor.instances").orNull) // Try to set main class from JAR if no --class argument is given - if (mainClass == null && !isPython && primaryResource != null) { + if (mainClass == null && !isPython && !isR && primaryResource != null) { val uri = new URI(primaryResource) val uriScheme = uri.getScheme() @@ -201,9 +212,9 @@ private[spark] class SparkSubmitArguments(args: Seq[String], env: Map[String, St printUsageAndExit(-1) } if (primaryResource == null) { - SparkSubmit.printErrorAndExit("Must specify a primary resource (JAR or Python file)") + SparkSubmit.printErrorAndExit("Must specify a primary resource (JAR or Python or R file)") } - if (mainClass == null && !isPython) { + if (mainClass == null && SparkSubmit.isUserJar(primaryResource)) { SparkSubmit.printErrorAndExit("No main class set in JAR; please specify one with --class") } if (pyFiles != null && !isPython) { @@ -242,7 +253,7 @@ private[spark] class SparkSubmitArguments(args: Seq[String], env: Map[String, St master.startsWith("spark://") && deployMode == "cluster" } - override def toString = { + override def toString: String = { s"""Parsed arguments: | master $master | deployMode $deployMode @@ -276,160 +287,140 @@ private[spark] class SparkSubmitArguments(args: Seq[String], env: Map[String, St """.stripMargin } - /** - * Fill in values by parsing user options. - * NOTE: Any changes here must be reflected in YarnClientSchedulerBackend. - */ - private def parseOpts(opts: Seq[String]): Unit = { - val EQ_SEPARATED_OPT="""(--[^=]+)=(.+)""".r - - // Delineates parsing of Spark options from parsing of user options. - parse(opts) - - /** - * NOTE: If you add or remove spark-submit options, - * modify NOT ONLY this file but also utils.sh - */ - def parse(opts: Seq[String]): Unit = opts match { - case ("--name") :: value :: tail => + /** Fill in values by parsing user options. */ + override protected def handle(opt: String, value: String): Boolean = { + opt match { + case NAME => name = value - parse(tail) - case ("--master") :: value :: tail => + case MASTER => master = value - parse(tail) - case ("--class") :: value :: tail => + case CLASS => mainClass = value - parse(tail) - case ("--deploy-mode") :: value :: tail => + case DEPLOY_MODE => if (value != "client" && value != "cluster") { SparkSubmit.printErrorAndExit("--deploy-mode must be either \"client\" or \"cluster\"") } deployMode = value - parse(tail) - case ("--num-executors") :: value :: tail => + case NUM_EXECUTORS => numExecutors = value - parse(tail) - case ("--total-executor-cores") :: value :: tail => + case TOTAL_EXECUTOR_CORES => totalExecutorCores = value - parse(tail) - case ("--executor-cores") :: value :: tail => + case EXECUTOR_CORES => executorCores = value - parse(tail) - case ("--executor-memory") :: value :: tail => + case EXECUTOR_MEMORY => executorMemory = value - parse(tail) - case ("--driver-memory") :: value :: tail => + case DRIVER_MEMORY => driverMemory = value - parse(tail) - case ("--driver-cores") :: value :: tail => + case DRIVER_CORES => driverCores = value - parse(tail) - case ("--driver-class-path") :: value :: tail => + case DRIVER_CLASS_PATH => driverExtraClassPath = value - parse(tail) - case ("--driver-java-options") :: value :: tail => + case DRIVER_JAVA_OPTIONS => driverExtraJavaOptions = value - parse(tail) - case ("--driver-library-path") :: value :: tail => + case DRIVER_LIBRARY_PATH => driverExtraLibraryPath = value - parse(tail) - case ("--properties-file") :: value :: tail => + case PROPERTIES_FILE => propertiesFile = value - parse(tail) - case ("--kill") :: value :: tail => + case KILL_SUBMISSION => submissionToKill = value if (action != null) { SparkSubmit.printErrorAndExit(s"Action cannot be both $action and $KILL.") } action = KILL - parse(tail) - case ("--status") :: value :: tail => + case STATUS => submissionToRequestStatusFor = value if (action != null) { SparkSubmit.printErrorAndExit(s"Action cannot be both $action and $REQUEST_STATUS.") } action = REQUEST_STATUS - parse(tail) - case ("--supervise") :: tail => + case SUPERVISE => supervise = true - parse(tail) - case ("--queue") :: value :: tail => + case QUEUE => queue = value - parse(tail) - case ("--files") :: value :: tail => + case FILES => files = Utils.resolveURIs(value) - parse(tail) - case ("--py-files") :: value :: tail => + case PY_FILES => pyFiles = Utils.resolveURIs(value) - parse(tail) - case ("--archives") :: value :: tail => + case ARCHIVES => archives = Utils.resolveURIs(value) - parse(tail) - case ("--jars") :: value :: tail => + case JARS => jars = Utils.resolveURIs(value) - parse(tail) - case ("--packages") :: value :: tail => + case PACKAGES => packages = value - parse(tail) - case ("--repositories") :: value :: tail => + case REPOSITORIES => repositories = value - parse(tail) - case ("--conf" | "-c") :: value :: tail => + case CONF => value.split("=", 2).toSeq match { case Seq(k, v) => sparkProperties(k) = v case _ => SparkSubmit.printErrorAndExit(s"Spark config without '=': $value") } - parse(tail) - case ("--help" | "-h") :: tail => + case PROXY_USER => + proxyUser = value + + case HELP => printUsageAndExit(0) - case ("--verbose" | "-v") :: tail => + case VERBOSE => verbose = true - parse(tail) - case EQ_SEPARATED_OPT(opt, value) :: tail => - parse(opt :: value :: tail) + case VERSION => + SparkSubmit.printVersionAndExit() - case value :: tail if value.startsWith("-") => - SparkSubmit.printErrorAndExit(s"Unrecognized option '$value'.") - - case value :: tail => - primaryResource = - if (!SparkSubmit.isShell(value) && !SparkSubmit.isInternal(value)) { - Utils.resolveURI(value).toString - } else { - value - } - isPython = SparkSubmit.isPython(value) - childArgs ++= tail + case _ => + throw new IllegalArgumentException(s"Unexpected argument '$opt'.") + } + true + } - case Nil => + /** + * Handle unrecognized command line options. + * + * The first unrecognized option is treated as the "primary resource". Everything else is + * treated as application arguments. + */ + override protected def handleUnknown(opt: String): Boolean = { + if (opt.startsWith("-")) { + SparkSubmit.printErrorAndExit(s"Unrecognized option '$opt'.") } + + primaryResource = + if (!SparkSubmit.isShell(opt) && !SparkSubmit.isInternal(opt)) { + Utils.resolveURI(opt).toString + } else { + opt + } + isPython = SparkSubmit.isPython(opt) + isR = SparkSubmit.isR(opt) + false + } + + override protected def handleExtraArgs(extra: JList[String]): Unit = { + childArgs ++= extra } private def printUsageAndExit(exitCode: Int, unknownParam: Any = null): Unit = { @@ -476,8 +467,11 @@ private[spark] class SparkSubmitArguments(args: Seq[String], env: Map[String, St | | --executor-memory MEM Memory per executor (e.g. 1000M, 2G) (Default: 1G). | + | --proxy-user NAME User to impersonate when submitting the application. + | | --help, -h Show this help message and exit | --verbose, -v Print additional debug output + | --version, Print the version of current Spark | | Spark standalone with cluster deploy mode only: | --driver-cores NUM Cores for driver (Default: 1). @@ -488,10 +482,13 @@ private[spark] class SparkSubmitArguments(args: Seq[String], env: Map[String, St | Spark standalone and Mesos only: | --total-executor-cores NUM Total cores for all executors. | + | Spark standalone and YARN only: + | --executor-cores NUM Number of cores per executor. (Default: 1 in YARN mode, + | or all available cores on the worker in standalone mode) + | | YARN-only: | --driver-cores NUM Number of cores used by the driver, only in cluster mode | (Default: 1). - | --executor-cores NUM Number of cores per executor (Default: 1). | --queue QUEUE_NAME The YARN queue to submit to (Default: "default"). | --num-executors NUM Number of executors to launch (Default: 2). | --archives ARCHIVES Comma separated list of archives to be extracted into the diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitDriverBootstrapper.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitDriverBootstrapper.scala deleted file mode 100644 index 2eab9981845e8..0000000000000 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitDriverBootstrapper.scala +++ /dev/null @@ -1,170 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.deploy - -import java.io.File - -import scala.collection.JavaConversions._ - -import org.apache.spark.util.{RedirectThread, Utils} - -/** - * Launch an application through Spark submit in client mode with the appropriate classpath, - * library paths, java options and memory. These properties of the JVM must be set before the - * driver JVM is launched. The sole purpose of this class is to avoid handling the complexity - * of parsing the properties file for such relevant configs in Bash. - * - * Usage: org.apache.spark.deploy.SparkSubmitDriverBootstrapper - */ -private[spark] object SparkSubmitDriverBootstrapper { - - // Note: This class depends on the behavior of `bin/spark-class` and `bin/spark-submit`. - // Any changes made there must be reflected in this file. - - def main(args: Array[String]): Unit = { - - // This should be called only from `bin/spark-class` - if (!sys.env.contains("SPARK_CLASS")) { - System.err.println("SparkSubmitDriverBootstrapper must be called from `bin/spark-class`!") - System.exit(1) - } - - val submitArgs = args - val runner = sys.env("RUNNER") - val classpath = sys.env("CLASSPATH") - val javaOpts = sys.env("JAVA_OPTS") - val defaultDriverMemory = sys.env("OUR_JAVA_MEM") - - // Spark submit specific environment variables - val deployMode = sys.env("SPARK_SUBMIT_DEPLOY_MODE") - val propertiesFile = sys.env("SPARK_SUBMIT_PROPERTIES_FILE") - val bootstrapDriver = sys.env("SPARK_SUBMIT_BOOTSTRAP_DRIVER") - val submitDriverMemory = sys.env.get("SPARK_SUBMIT_DRIVER_MEMORY") - val submitLibraryPath = sys.env.get("SPARK_SUBMIT_LIBRARY_PATH") - val submitClasspath = sys.env.get("SPARK_SUBMIT_CLASSPATH") - val submitJavaOpts = sys.env.get("SPARK_SUBMIT_OPTS") - - assume(runner != null, "RUNNER must be set") - assume(classpath != null, "CLASSPATH must be set") - assume(javaOpts != null, "JAVA_OPTS must be set") - assume(defaultDriverMemory != null, "OUR_JAVA_MEM must be set") - assume(deployMode == "client", "SPARK_SUBMIT_DEPLOY_MODE must be \"client\"!") - assume(propertiesFile != null, "SPARK_SUBMIT_PROPERTIES_FILE must be set") - assume(bootstrapDriver != null, "SPARK_SUBMIT_BOOTSTRAP_DRIVER must be set") - - // Parse the properties file for the equivalent spark.driver.* configs - val properties = Utils.getPropertiesFromFile(propertiesFile) - val confDriverMemory = properties.get("spark.driver.memory") - val confLibraryPath = properties.get("spark.driver.extraLibraryPath") - val confClasspath = properties.get("spark.driver.extraClassPath") - val confJavaOpts = properties.get("spark.driver.extraJavaOptions") - - // Favor Spark submit arguments over the equivalent configs in the properties file. - // Note that we do not actually use the Spark submit values for library path, classpath, - // and Java opts here, because we have already captured them in Bash. - - val newDriverMemory = submitDriverMemory - .orElse(confDriverMemory) - .getOrElse(defaultDriverMemory) - - val newClasspath = - if (submitClasspath.isDefined) { - classpath - } else { - classpath + confClasspath.map(sys.props("path.separator") + _).getOrElse("") - } - - val newJavaOpts = - if (submitJavaOpts.isDefined) { - // SPARK_SUBMIT_OPTS is already captured in JAVA_OPTS - javaOpts - } else { - javaOpts + confJavaOpts.map(" " + _).getOrElse("") - } - - val filteredJavaOpts = Utils.splitCommandString(newJavaOpts) - .filterNot(_.startsWith("-Xms")) - .filterNot(_.startsWith("-Xmx")) - - // Build up command - val command: Seq[String] = - Seq(runner) ++ - Seq("-cp", newClasspath) ++ - filteredJavaOpts ++ - Seq(s"-Xms$newDriverMemory", s"-Xmx$newDriverMemory") ++ - Seq("org.apache.spark.deploy.SparkSubmit") ++ - submitArgs - - // Print the launch command. This follows closely the format used in `bin/spark-class`. - if (sys.env.contains("SPARK_PRINT_LAUNCH_COMMAND")) { - System.err.print("Spark Command: ") - System.err.println(command.mkString(" ")) - System.err.println("========================================\n") - } - - // Start the driver JVM - val filteredCommand = command.filter(_.nonEmpty) - val builder = new ProcessBuilder(filteredCommand) - val env = builder.environment() - - if (submitLibraryPath.isEmpty && confLibraryPath.nonEmpty) { - val libraryPaths = confLibraryPath ++ sys.env.get(Utils.libraryPathEnvName) - env.put(Utils.libraryPathEnvName, libraryPaths.mkString(sys.props("path.separator"))) - } - - val process = builder.start() - - // If we kill an app while it's running, its sub-process should be killed too. - Runtime.getRuntime().addShutdownHook(new Thread() { - override def run() = { - if (process != null) { - process.destroy() - process.waitFor() - } - } - }) - - // Redirect stdout and stderr from the child JVM - val stdoutThread = new RedirectThread(process.getInputStream, System.out, "redirect stdout") - val stderrThread = new RedirectThread(process.getErrorStream, System.err, "redirect stderr") - stdoutThread.start() - stderrThread.start() - - // Redirect stdin to child JVM only if we're not running Windows. This is because the - // subprocess there already reads directly from our stdin, so we should avoid spawning a - // thread that contends with the subprocess in reading from System.in. - val isWindows = Utils.isWindows - val isSubprocess = sys.env.contains("IS_SUBPROCESS") - if (!isWindows) { - val stdinThread = new RedirectThread(System.in, process.getOutputStream, "redirect stdin", - propagateEof = true) - stdinThread.start() - // Spark submit (JVM) may run as a subprocess, and so this JVM should terminate on - // broken pipe, signaling that the parent process has exited. This is the case if the - // application is launched directly from python, as in the PySpark shell. In Windows, - // the termination logic is handled in java_gateway.py - if (isSubprocess) { - stdinThread.join() - process.destroy() - } - } - val returnCode = process.waitFor() - sys.exit(returnCode) - } - -} diff --git a/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala b/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala index ffe940fbda2fb..4f06d7f96c46e 100644 --- a/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala +++ b/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala @@ -47,18 +47,18 @@ private[spark] class AppClient( conf: SparkConf) extends Logging { - val masterAkkaUrls = masterUrls.map(Master.toAkkaUrl(_, AkkaUtils.protocol(actorSystem))) + private val masterAkkaUrls = masterUrls.map(Master.toAkkaUrl(_, AkkaUtils.protocol(actorSystem))) - val REGISTRATION_TIMEOUT = 20.seconds - val REGISTRATION_RETRIES = 3 + private val REGISTRATION_TIMEOUT = 20.seconds + private val REGISTRATION_RETRIES = 3 - var masterAddress: Address = null - var actor: ActorRef = null - var appId: String = null - var registered = false - var activeMasterUrl: String = null + private var masterAddress: Address = null + private var actor: ActorRef = null + private var appId: String = null + private var registered = false + private var activeMasterUrl: String = null - class ClientActor extends Actor with ActorLogReceive with Logging { + private class ClientActor extends Actor with ActorLogReceive with Logging { var master: ActorSelection = null var alreadyDisconnected = false // To avoid calling listener.disconnected() multiple times var alreadyDead = false // To avoid calling listener.dead() multiple times @@ -116,7 +116,7 @@ private[spark] class AppClient( masterAkkaUrls.map(AddressFromURIString(_).hostPort).contains(remoteUrl.hostPort) } - override def receiveWithLogging = { + override def receiveWithLogging: PartialFunction[Any, Unit] = { case RegisteredApplication(appId_, masterUrl) => appId = appId_ registered = true @@ -157,6 +157,7 @@ private[spark] class AppClient( case StopAppClient => markDead("Application has been stopped.") + master ! UnregisterApplication(appId) sender ! true context.stop(self) } diff --git a/core/src/main/scala/org/apache/spark/deploy/client/TestClient.scala b/core/src/main/scala/org/apache/spark/deploy/client/TestClient.scala index 88a0862b96afe..40835b9550586 100644 --- a/core/src/main/scala/org/apache/spark/deploy/client/TestClient.scala +++ b/core/src/main/scala/org/apache/spark/deploy/client/TestClient.scala @@ -23,7 +23,7 @@ import org.apache.spark.util.{AkkaUtils, Utils} private[spark] object TestClient { - class TestListener extends AppClientListener with Logging { + private class TestListener extends AppClientListener with Logging { def connected(id: String) { logInfo("Connected to master, got app ID " + id) } @@ -46,7 +46,7 @@ private[spark] object TestClient { def main(args: Array[String]) { val url = args(0) val conf = new SparkConf - val (actorSystem, _) = AkkaUtils.createActorSystem("spark", Utils.localIpAddress, 0, + val (actorSystem, _) = AkkaUtils.createActorSystem("spark", Utils.localHostName(), 0, conf = conf, securityManager = new SecurityManager(conf)) val desc = new ApplicationDescription("TestClient", Some(1), 512, Command("spark.deploy.client.TestExecutor", Seq(), Map(), Seq(), Seq(), Seq()), "ignored") diff --git a/core/src/main/scala/org/apache/spark/deploy/history/ApplicationHistoryProvider.scala b/core/src/main/scala/org/apache/spark/deploy/history/ApplicationHistoryProvider.scala index 553bf3cb945ab..ea6c85ee511d5 100644 --- a/core/src/main/scala/org/apache/spark/deploy/history/ApplicationHistoryProvider.scala +++ b/core/src/main/scala/org/apache/spark/deploy/history/ApplicationHistoryProvider.scala @@ -19,7 +19,7 @@ package org.apache.spark.deploy.history import org.apache.spark.ui.SparkUI -private[spark] case class ApplicationHistoryInfo( +private[history] case class ApplicationHistoryInfo( id: String, name: String, startTime: Long, @@ -28,7 +28,7 @@ private[spark] case class ApplicationHistoryInfo( sparkUser: String, completed: Boolean = false) -private[spark] abstract class ApplicationHistoryProvider { +private[history] abstract class ApplicationHistoryProvider { /** * Returns a list of applications available for the history server to show. diff --git a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala index 868c63d30a202..47bdd7749ec3d 100644 --- a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala +++ b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala @@ -17,19 +17,24 @@ package org.apache.spark.deploy.history -import java.io.{BufferedInputStream, FileNotFoundException, InputStream} +import java.io.{IOException, BufferedInputStream, FileNotFoundException, InputStream} +import java.util.concurrent.{ExecutorService, Executors, TimeUnit} import scala.collection.mutable +import scala.concurrent.duration.Duration -import org.apache.hadoop.fs.{FileStatus, Path} -import org.apache.hadoop.fs.permission.AccessControlException +import com.google.common.util.concurrent.ThreadFactoryBuilder -import org.apache.spark.{Logging, SecurityManager, SparkConf} +import com.google.common.util.concurrent.MoreExecutors +import org.apache.hadoop.fs.permission.AccessControlException +import org.apache.hadoop.fs.{FileStatus, Path} import org.apache.spark.deploy.SparkHadoopUtil import org.apache.spark.io.CompressionCodec import org.apache.spark.scheduler._ import org.apache.spark.ui.SparkUI import org.apache.spark.util.Utils +import org.apache.spark.{Logging, SecurityManager, SparkConf} + /** * A class that provides application history from event logs stored in the file system. @@ -44,8 +49,10 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis private val NOT_STARTED = "" // Interval between each check for event log updates - private val UPDATE_INTERVAL_MS = conf.getInt("spark.history.fs.updateInterval", - conf.getInt("spark.history.updateInterval", 10)) * 1000 + private val UPDATE_INTERVAL_S = conf.getTimeAsSeconds("spark.history.fs.update.interval", "10s") + + // Interval between each cleaner checks for event logs to delete + private val CLEAN_INTERVAL_S = conf.getTimeAsSeconds("spark.history.fs.cleaner.interval", "1d") private val logDir = conf.getOption("spark.history.fs.logDirectory") .map { d => Utils.resolveURI(d).toString } @@ -53,8 +60,11 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis private val fs = Utils.getHadoopFileSystem(logDir, SparkHadoopUtil.get.newConfiguration(conf)) - // A timestamp of when the disk was last accessed to check for log updates - private var lastLogCheckTimeMs = -1L + // Used by check event thread and clean log thread. + // Scheduled thread pool size must be one, otherwise it will have concurrent issues about fs + // and applications between check task and clean task. + private val pool = Executors.newScheduledThreadPool(1, new ThreadFactoryBuilder() + .setNameFormat("spark-history-task-%d").setDaemon(true).build()) // The modification time of the newest log detected during the last scan. This is used // to ignore logs that are older during subsequent scans, to avoid processing data that @@ -68,34 +78,33 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis // Constants used to parse Spark 1.0.0 log directories. private[history] val LOG_PREFIX = "EVENT_LOG_" - private[history] val SPARK_VERSION_PREFIX = "SPARK_VERSION_" - private[history] val COMPRESSION_CODEC_PREFIX = "COMPRESSION_CODEC_" + private[history] val SPARK_VERSION_PREFIX = EventLoggingListener.SPARK_VERSION_KEY + "_" + private[history] val COMPRESSION_CODEC_PREFIX = EventLoggingListener.COMPRESSION_CODEC_KEY + "_" private[history] val APPLICATION_COMPLETE = "APPLICATION_COMPLETE" /** - * A background thread that periodically checks for event log updates on disk. - * - * If a log check is invoked manually in the middle of a period, this thread re-adjusts the - * time at which it performs the next log check to maintain the same period as before. - * - * TODO: Add a mechanism to update manually. + * Return a runnable that performs the given operation on the event logs. + * This operation is expected to be executed periodically. */ - private val logCheckingThread = new Thread("LogCheckingThread") { - override def run() = Utils.logUncaughtExceptions { - while (true) { - val now = getMonotonicTimeMs() - if (now - lastLogCheckTimeMs > UPDATE_INTERVAL_MS) { - Thread.sleep(UPDATE_INTERVAL_MS) - } else { - // If the user has manually checked for logs recently, wait until - // UPDATE_INTERVAL_MS after the last check time - Thread.sleep(lastLogCheckTimeMs + UPDATE_INTERVAL_MS - now) - } - checkForLogs() + private def getRunner(operateFun: () => Unit): Runnable = { + new Runnable() { + override def run(): Unit = Utils.tryOrExit { + operateFun() } } } + /** + * An Executor to fetch and parse log files. + */ + private val replayExecutor: ExecutorService = { + if (!conf.contains("spark.testing")) { + Executors.newSingleThreadExecutor(Utils.namedThreadFactory("log-replay-executor")) + } else { + MoreExecutors.sameThreadExecutor() + } + } + initialize() private def initialize(): Unit = { @@ -104,7 +113,7 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis if (!fs.exists(path)) { var msg = s"Log directory specified does not exist: $logDir." if (logDir == DEFAULT_LOG_DIR) { - msg += " Did you configure the correct one through spark.fs.history.logDirectory?" + msg += " Did you configure the correct one through spark.history.fs.logDirectory?" } throw new IllegalArgumentException(msg) } @@ -113,16 +122,19 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis "Logging directory specified is not a directory: %s".format(logDir)) } - checkForLogs() - // Disable the background thread during tests. if (!conf.contains("spark.testing")) { - logCheckingThread.setDaemon(true) - logCheckingThread.start() + // A task that periodically checks for event log updates on disk. + pool.scheduleAtFixedRate(getRunner(checkForLogs), 0, UPDATE_INTERVAL_S, TimeUnit.SECONDS) + + if (conf.getBoolean("spark.history.fs.cleaner.enabled", false)) { + // A task that periodically cleans event logs on disk. + pool.scheduleAtFixedRate(getRunner(cleanLogs), 0, CLEAN_INTERVAL_S, TimeUnit.SECONDS) + } } } - override def getListing() = applications.values + override def getListing(): Iterable[FsApplicationHistoryInfo] = applications.values override def getAppUI(appId: String): Option[SparkUI] = { try { @@ -163,14 +175,11 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis * applications that haven't been updated since last time the logs were checked. */ private[history] def checkForLogs(): Unit = { - lastLogCheckTimeMs = getMonotonicTimeMs() - logDebug("Checking for logs. Time is now %d.".format(lastLogCheckTimeMs)) - try { - var newLastModifiedTime = lastModifiedTime val statusList = Option(fs.listStatus(new Path(logDir))).map(_.toSeq) .getOrElse(Seq[FileStatus]()) - val logInfos = statusList + var newLastModifiedTime = lastModifiedTime + val logInfos: Seq[FileStatus] = statusList .filter { entry => try { getModificationTime(entry).map { time => @@ -185,48 +194,108 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis false } } - .flatMap { entry => - try { - Some(replay(entry, new ReplayListenerBus())) - } catch { - case e: Exception => - logError(s"Failed to load application log data from $entry.", e) - None - } - } - .sortWith(compareAppInfo) + .flatMap { entry => Some(entry) } + .sortWith { case (entry1, entry2) => + val mod1 = getModificationTime(entry1).getOrElse(-1L) + val mod2 = getModificationTime(entry2).getOrElse(-1L) + mod1 >= mod2 + } + + logInfos.sliding(20, 20).foreach { batch => + replayExecutor.submit(new Runnable { + override def run(): Unit = mergeApplicationListing(batch) + }) + } lastModifiedTime = newLastModifiedTime + } catch { + case e: Exception => logError("Exception in checking for event log updates", e) + } + } - // When there are new logs, merge the new list with the existing one, maintaining - // the expected ordering (descending end time). Maintaining the order is important - // to avoid having to sort the list every time there is a request for the log list. - if (!logInfos.isEmpty) { - val newApps = new mutable.LinkedHashMap[String, FsApplicationHistoryInfo]() - def addIfAbsent(info: FsApplicationHistoryInfo) = { - if (!newApps.contains(info.id) || - newApps(info.id).logPath.endsWith(EventLoggingListener.IN_PROGRESS) && - !info.logPath.endsWith(EventLoggingListener.IN_PROGRESS)) { - newApps += (info.id -> info) - } + /** + * Replay the log files in the list and merge the list of old applications with new ones + */ + private def mergeApplicationListing(logs: Seq[FileStatus]): Unit = { + val bus = new ReplayListenerBus() + val newApps = logs.flatMap { fileStatus => + try { + val res = replay(fileStatus, bus) + logInfo(s"Application log ${res.logPath} loaded successfully.") + Some(res) + } catch { + case e: Exception => + logError( + s"Exception encountered when attempting to load application log ${fileStatus.getPath}", + e) + None + } + }.toSeq.sortWith(compareAppInfo) + + // When there are new logs, merge the new list with the existing one, maintaining + // the expected ordering (descending end time). Maintaining the order is important + // to avoid having to sort the list every time there is a request for the log list. + if (newApps.nonEmpty) { + val mergedApps = new mutable.LinkedHashMap[String, FsApplicationHistoryInfo]() + def addIfAbsent(info: FsApplicationHistoryInfo): Unit = { + if (!mergedApps.contains(info.id) || + mergedApps(info.id).logPath.endsWith(EventLoggingListener.IN_PROGRESS) && + !info.logPath.endsWith(EventLoggingListener.IN_PROGRESS)) { + mergedApps += (info.id -> info) } + } - val newIterator = logInfos.iterator.buffered - val oldIterator = applications.values.iterator.buffered - while (newIterator.hasNext && oldIterator.hasNext) { - if (compareAppInfo(newIterator.head, oldIterator.head)) { - addIfAbsent(newIterator.next) - } else { - addIfAbsent(oldIterator.next) - } + val newIterator = newApps.iterator.buffered + val oldIterator = applications.values.iterator.buffered + while (newIterator.hasNext && oldIterator.hasNext) { + if (compareAppInfo(newIterator.head, oldIterator.head)) { + addIfAbsent(newIterator.next()) + } else { + addIfAbsent(oldIterator.next()) } - newIterator.foreach(addIfAbsent) - oldIterator.foreach(addIfAbsent) + } + newIterator.foreach(addIfAbsent) + oldIterator.foreach(addIfAbsent) + + applications = mergedApps + } + } - applications = newApps + /** + * Delete event logs from the log directory according to the clean policy defined by the user. + */ + private def cleanLogs(): Unit = { + try { + val statusList = Option(fs.listStatus(new Path(logDir))).map(_.toSeq) + .getOrElse(Seq[FileStatus]()) + val maxAge = conf.getTimeAsSeconds("spark.history.fs.cleaner.maxAge", "7d") * 1000 + + val now = System.currentTimeMillis() + val appsToRetain = new mutable.LinkedHashMap[String, FsApplicationHistoryInfo]() + + applications.values.foreach { info => + if (now - info.lastUpdated <= maxAge) { + appsToRetain += (info.id -> info) + } + } + + applications = appsToRetain + + // Scan all logs from the log directory. + // Only directories older than the specified max age will be deleted + statusList.foreach { dir => + try { + if (now - dir.getModificationTime() > maxAge) { + // if path is a directory and set to true, + // the directory is deleted else throws an exception + fs.delete(dir.getPath, true) + } + } catch { + case t: IOException => logError(s"IOException in cleaning logs of $dir", t) + } } } catch { - case e: Exception => logError("Exception in checking for event log updates", e) + case t: Exception => logError("Exception in cleaning logs", t) } } @@ -247,7 +316,8 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis */ private def replay(eventLog: FileStatus, bus: ReplayListenerBus): FsApplicationHistoryInfo = { val logPath = eventLog.getPath() - val (logInput, sparkVersion) = + logInfo(s"Replaying log path: $logPath") + val logInput = if (isLegacyLogDirectory(eventLog)) { openLegacyEventLog(logPath) } else { @@ -256,7 +326,7 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis try { val appListener = new ApplicationEventListener bus.addListener(appListener) - bus.replay(logInput, sparkVersion) + bus.replay(logInput, logPath.toString) new FsApplicationHistoryInfo( logPath.getName(), appListener.appId.getOrElse(logPath.getName()), @@ -276,30 +346,24 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis * log file (along with other metadata files), which is the case for directories generated by * the code in previous releases. * - * @return 2-tuple of (input stream of the events, version of Spark which wrote the log) + * @return input stream that holds one JSON record per line. */ - private[history] def openLegacyEventLog(dir: Path): (InputStream, String) = { + private[history] def openLegacyEventLog(dir: Path): InputStream = { val children = fs.listStatus(dir) var eventLogPath: Path = null var codecName: Option[String] = None - var sparkVersion: String = null children.foreach { child => child.getPath().getName() match { case name if name.startsWith(LOG_PREFIX) => eventLogPath = child.getPath() - case codec if codec.startsWith(COMPRESSION_CODEC_PREFIX) => codecName = Some(codec.substring(COMPRESSION_CODEC_PREFIX.length())) - - case version if version.startsWith(SPARK_VERSION_PREFIX) => - sparkVersion = version.substring(SPARK_VERSION_PREFIX.length()) - case _ => } } - if (eventLogPath == null || sparkVersion == null) { + if (eventLogPath == null) { throw new IllegalArgumentException(s"$dir is not a Spark application log directory.") } @@ -311,7 +375,7 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis } val in = new BufferedInputStream(fs.open(eventLogPath)) - (codec.map(_.compressedInputStream(in)).getOrElse(in), sparkVersion) + codec.map(_.compressedInputStream(in)).getOrElse(in) } /** @@ -335,9 +399,6 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis } } - /** Returns the system's mononotically increasing time. */ - private def getMonotonicTimeMs(): Long = System.nanoTime() / (1000 * 1000) - /** * Return true when the application has completed. */ diff --git a/core/src/main/scala/org/apache/spark/deploy/history/HistoryPage.scala b/core/src/main/scala/org/apache/spark/deploy/history/HistoryPage.scala index e4e7bc2216014..3781b4e8c12bd 100644 --- a/core/src/main/scala/org/apache/spark/deploy/history/HistoryPage.scala +++ b/core/src/main/scala/org/apache/spark/deploy/history/HistoryPage.scala @@ -23,7 +23,7 @@ import scala.xml.Node import org.apache.spark.ui.{WebUIPage, UIUtils} -private[spark] class HistoryPage(parent: HistoryServer) extends WebUIPage("") { +private[history] class HistoryPage(parent: HistoryServer) extends WebUIPage("") { private val pageSize = 20 private val plusOrMinus = 2 @@ -61,9 +61,10 @@ private[spark] class HistoryPage(parent: HistoryServer) extends WebUIPage("") { // page, `...` will be displayed. if (allApps.size > 0) { val leftSideIndices = - rangeIndices(actualPage - plusOrMinus until actualPage, 1 < _) + rangeIndices(actualPage - plusOrMinus until actualPage, 1 < _, requestedIncomplete) val rightSideIndices = - rangeIndices(actualPage + 1 to actualPage + plusOrMinus, _ < pageCount) + rangeIndices(actualPage + 1 to actualPage + plusOrMinus, _ < pageCount, + requestedIncomplete)

Showing {actualFirst + 1}-{last + 1} of {allApps.size} @@ -89,6 +90,8 @@ private[spark] class HistoryPage(parent: HistoryServer) extends WebUIPage("") {

++ appTable + } else if (requestedIncomplete) { +

No incomplete applications found!

} else {

No completed applications found!

++

Did you specify the correct logging directory? @@ -122,8 +125,10 @@ private[spark] class HistoryPage(parent: HistoryServer) extends WebUIPage("") { "Spark User", "Last Updated") - private def rangeIndices(range: Seq[Int], condition: Int => Boolean): Seq[Node] = { - range.filter(condition).map(nextPage => {nextPage} ) + private def rangeIndices(range: Seq[Int], condition: Int => Boolean, showIncomplete: Boolean): + Seq[Node] = { + range.filter(condition).map(nextPage => + {nextPage} ) } private def appRow(info: ApplicationHistoryInfo): Seq[Node] = { diff --git a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala index fa9bfe5426b6c..72f6048239297 100644 --- a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala +++ b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala @@ -61,7 +61,7 @@ class HistoryServer( private val appCache = CacheBuilder.newBuilder() .maximumSize(retainedApplications) .removalListener(new RemovalListener[String, SparkUI] { - override def onRemoval(rm: RemovalNotification[String, SparkUI]) = { + override def onRemoval(rm: RemovalNotification[String, SparkUI]): Unit = { detachSparkUI(rm.getValue()) } }) @@ -96,6 +96,10 @@ class HistoryServer( } } } + // SPARK-5983 ensure TRACE is not supported + protected override def doTrace(req: HttpServletRequest, res: HttpServletResponse): Unit = { + res.sendError(HttpServletResponse.SC_METHOD_NOT_ALLOWED) + } } initialize() @@ -145,14 +149,14 @@ class HistoryServer( * * @return List of all known applications. */ - def getApplicationList() = provider.getListing() + def getApplicationList(): Iterable[ApplicationHistoryInfo] = provider.getListing() /** * Returns the provider configuration to show in the listing page. * * @return A map with the provider's configuration. */ - def getProviderConfig() = provider.getConfig() + def getProviderConfig(): Map[String, String] = provider.getConfig() } @@ -191,9 +195,7 @@ object HistoryServer extends Logging { server.bind() Runtime.getRuntime().addShutdownHook(new Thread("HistoryServerStopper") { - override def run() = { - server.stop() - } + override def run(): Unit = server.stop() }) // Wait until the end of the world... or if the HistoryServer process is manually stopped diff --git a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServerArguments.scala b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServerArguments.scala index b1270ade9f750..a2a97a7877ce7 100644 --- a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServerArguments.scala +++ b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServerArguments.scala @@ -23,7 +23,8 @@ import org.apache.spark.util.Utils /** * Command-line parser for the master. */ -private[spark] class HistoryServerArguments(conf: SparkConf, args: Array[String]) extends Logging { +private[history] class HistoryServerArguments(conf: SparkConf, args: Array[String]) + extends Logging { private var propertiesFile: String = null parse(args.toList) diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala b/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala index ede0a9dbefb8d..f59d550d4f3b3 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala @@ -28,7 +28,7 @@ import org.apache.spark.annotation.DeveloperApi import org.apache.spark.deploy.ApplicationDescription import org.apache.spark.util.Utils -private[spark] class ApplicationInfo( +private[deploy] class ApplicationInfo( val startTime: Long, val id: String, val desc: ApplicationDescription, @@ -75,14 +75,17 @@ private[spark] class ApplicationInfo( } } - def addExecutor(worker: WorkerInfo, cores: Int, useID: Option[Int] = None): ExecutorDesc = { - val exec = new ExecutorDesc(newExecutorId(useID), this, worker, cores, desc.memoryPerSlave) + private[master] def addExecutor( + worker: WorkerInfo, + cores: Int, + useID: Option[Int] = None): ExecutorDesc = { + val exec = new ExecutorDesc(newExecutorId(useID), this, worker, cores, desc.memoryPerExecutorMB) executors(exec.id) = exec coresGranted += cores exec } - def removeExecutor(exec: ExecutorDesc) { + private[master] def removeExecutor(exec: ExecutorDesc) { if (executors.contains(exec.id)) { removedExecutors += executors(exec.id) executors -= exec.id @@ -90,26 +93,30 @@ private[spark] class ApplicationInfo( } } - private val myMaxCores = desc.maxCores.getOrElse(defaultCores) + private val requestedCores = desc.maxCores.getOrElse(defaultCores) - def coresLeft: Int = myMaxCores - coresGranted + private[master] def coresLeft: Int = requestedCores - coresGranted private var _retryCount = 0 - def retryCount = _retryCount + private[master] def retryCount = _retryCount - def incrementRetryCount() = { + private[master] def incrementRetryCount() = { _retryCount += 1 _retryCount } - def resetRetryCount() = _retryCount = 0 + private[master] def resetRetryCount() = _retryCount = 0 - def markFinished(endState: ApplicationState.Value) { + private[master] def markFinished(endState: ApplicationState.Value) { state = endState endTime = System.currentTimeMillis() } + private[master] def isFinished: Boolean = { + state != ApplicationState.WAITING && state != ApplicationState.RUNNING + } + def duration: Long = { if (endTime != -1) { endTime - startTime diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ApplicationSource.scala b/core/src/main/scala/org/apache/spark/deploy/master/ApplicationSource.scala index 38db02cd2421b..017e8b55cbe7f 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ApplicationSource.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ApplicationSource.scala @@ -21,7 +21,7 @@ import com.codahale.metrics.{Gauge, MetricRegistry} import org.apache.spark.metrics.source.Source -class ApplicationSource(val application: ApplicationInfo) extends Source { +private[master] class ApplicationSource(val application: ApplicationInfo) extends Source { override val metricRegistry = new MetricRegistry() override val sourceName = "%s.%s.%s".format("application", application.desc.name, System.currentTimeMillis()) diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ApplicationState.scala b/core/src/main/scala/org/apache/spark/deploy/master/ApplicationState.scala index 67e6c5d66af0e..37bfcdfdf4777 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ApplicationState.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ApplicationState.scala @@ -17,11 +17,11 @@ package org.apache.spark.deploy.master -private[spark] object ApplicationState extends Enumeration { +private[master] object ApplicationState extends Enumeration { type ApplicationState = Value - val WAITING, RUNNING, FINISHED, FAILED, UNKNOWN = Value + val WAITING, RUNNING, FINISHED, FAILED, KILLED, UNKNOWN = Value val MAX_NUM_RETRY = 10 } diff --git a/core/src/main/scala/org/apache/spark/deploy/master/DriverInfo.scala b/core/src/main/scala/org/apache/spark/deploy/master/DriverInfo.scala index 9d3d7938c6ccb..b197dbcbfe294 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/DriverInfo.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/DriverInfo.scala @@ -23,7 +23,7 @@ import org.apache.spark.annotation.DeveloperApi import org.apache.spark.deploy.DriverDescription import org.apache.spark.util.Utils -private[spark] class DriverInfo( +private[deploy] class DriverInfo( val startTime: Long, val id: String, val desc: DriverDescription, diff --git a/core/src/main/scala/org/apache/spark/deploy/master/DriverState.scala b/core/src/main/scala/org/apache/spark/deploy/master/DriverState.scala index 26a68bade3c60..35ff33a61653c 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/DriverState.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/DriverState.scala @@ -17,7 +17,7 @@ package org.apache.spark.deploy.master -private[spark] object DriverState extends Enumeration { +private[deploy] object DriverState extends Enumeration { type DriverState = Value diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ExecutorDesc.scala b/core/src/main/scala/org/apache/spark/deploy/master/ExecutorDesc.scala index 5d620dfcabad5..fc62b094def67 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ExecutorDesc.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ExecutorDesc.scala @@ -19,7 +19,7 @@ package org.apache.spark.deploy.master import org.apache.spark.deploy.{ExecutorDescription, ExecutorState} -private[spark] class ExecutorDesc( +private[master] class ExecutorDesc( val id: Int, val application: ApplicationInfo, val worker: WorkerInfo, diff --git a/core/src/main/scala/org/apache/spark/deploy/master/FileSystemPersistenceEngine.scala b/core/src/main/scala/org/apache/spark/deploy/master/FileSystemPersistenceEngine.scala index 36a2e2c6a6349..f459ed5b3a1a1 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/FileSystemPersistenceEngine.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/FileSystemPersistenceEngine.scala @@ -24,6 +24,7 @@ import scala.reflect.ClassTag import akka.serialization.Serialization import org.apache.spark.Logging +import org.apache.spark.util.Utils /** @@ -33,7 +34,7 @@ import org.apache.spark.Logging * @param dir Directory to store files. Created if non-existent (but not recursively). * @param serialization Used to serialize our objects. */ -private[spark] class FileSystemPersistenceEngine( +private[master] class FileSystemPersistenceEngine( val dir: String, val serialization: Serialization) extends PersistenceEngine with Logging { @@ -48,7 +49,7 @@ private[spark] class FileSystemPersistenceEngine( new File(dir + File.separator + name).delete() } - override def read[T: ClassTag](prefix: String) = { + override def read[T: ClassTag](prefix: String): Seq[T] = { val files = new File(dir).listFiles().filter(_.getName.startsWith(prefix)) files.map(deserializeFromFile[T]) } @@ -59,9 +60,9 @@ private[spark] class FileSystemPersistenceEngine( val serializer = serialization.findSerializerFor(value) val serialized = serializer.toBinary(value) val out = new FileOutputStream(file) - try { + Utils.tryWithSafeFinally { out.write(serialized) - } finally { + } { out.close() } } diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala index b8b1a25abff2e..c5a6b1beac9be 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala @@ -49,7 +49,7 @@ import org.apache.spark.scheduler.{EventLoggingListener, ReplayListenerBus} import org.apache.spark.ui.SparkUI import org.apache.spark.util.{ActorLogReceive, AkkaUtils, SignalLogger, Utils} -private[spark] class Master( +private[master] class Master( host: String, port: Int, webUiPort: Int, @@ -59,65 +59,68 @@ private[spark] class Master( import context.dispatcher // to use Akka's scheduler.schedule() - val hadoopConf = SparkHadoopUtil.get.newConfiguration(conf) + private val hadoopConf = SparkHadoopUtil.get.newConfiguration(conf) - def createDateFormat = new SimpleDateFormat("yyyyMMddHHmmss") // For application IDs - val WORKER_TIMEOUT = conf.getLong("spark.worker.timeout", 60) * 1000 - val RETAINED_APPLICATIONS = conf.getInt("spark.deploy.retainedApplications", 200) - val RETAINED_DRIVERS = conf.getInt("spark.deploy.retainedDrivers", 200) - val REAPER_ITERATIONS = conf.getInt("spark.dead.worker.persistence", 15) - val RECOVERY_MODE = conf.get("spark.deploy.recoveryMode", "NONE") + private def createDateFormat = new SimpleDateFormat("yyyyMMddHHmmss") // For application IDs + + private val WORKER_TIMEOUT = conf.getLong("spark.worker.timeout", 60) * 1000 + private val RETAINED_APPLICATIONS = conf.getInt("spark.deploy.retainedApplications", 200) + private val RETAINED_DRIVERS = conf.getInt("spark.deploy.retainedDrivers", 200) + private val REAPER_ITERATIONS = conf.getInt("spark.dead.worker.persistence", 15) + private val RECOVERY_MODE = conf.get("spark.deploy.recoveryMode", "NONE") val workers = new HashSet[WorkerInfo] - val idToWorker = new HashMap[String, WorkerInfo] - val addressToWorker = new HashMap[Address, WorkerInfo] - - val apps = new HashSet[ApplicationInfo] val idToApp = new HashMap[String, ApplicationInfo] - val actorToApp = new HashMap[ActorRef, ApplicationInfo] - val addressToApp = new HashMap[Address, ApplicationInfo] val waitingApps = new ArrayBuffer[ApplicationInfo] - val completedApps = new ArrayBuffer[ApplicationInfo] - var nextAppNumber = 0 - val appIdToUI = new HashMap[String, SparkUI] + val apps = new HashSet[ApplicationInfo] + + private val idToWorker = new HashMap[String, WorkerInfo] + private val addressToWorker = new HashMap[Address, WorkerInfo] - val drivers = new HashSet[DriverInfo] - val completedDrivers = new ArrayBuffer[DriverInfo] - val waitingDrivers = new ArrayBuffer[DriverInfo] // Drivers currently spooled for scheduling - var nextDriverNumber = 0 + private val actorToApp = new HashMap[ActorRef, ApplicationInfo] + private val addressToApp = new HashMap[Address, ApplicationInfo] + private val completedApps = new ArrayBuffer[ApplicationInfo] + private var nextAppNumber = 0 + private val appIdToUI = new HashMap[String, SparkUI] + + private val drivers = new HashSet[DriverInfo] + private val completedDrivers = new ArrayBuffer[DriverInfo] + // Drivers currently spooled for scheduling + private val waitingDrivers = new ArrayBuffer[DriverInfo] + private var nextDriverNumber = 0 Utils.checkHost(host, "Expected hostname") - val masterMetricsSystem = MetricsSystem.createMetricsSystem("master", conf, securityMgr) - val applicationMetricsSystem = MetricsSystem.createMetricsSystem("applications", conf, + private val masterMetricsSystem = MetricsSystem.createMetricsSystem("master", conf, securityMgr) + private val applicationMetricsSystem = MetricsSystem.createMetricsSystem("applications", conf, securityMgr) - val masterSource = new MasterSource(this) + private val masterSource = new MasterSource(this) - val webUi = new MasterWebUI(this, webUiPort) + private val webUi = new MasterWebUI(this, webUiPort) - val masterPublicAddress = { - val envVar = System.getenv("SPARK_PUBLIC_DNS") + private val masterPublicAddress = { + val envVar = conf.getenv("SPARK_PUBLIC_DNS") if (envVar != null) envVar else host } - val masterUrl = "spark://" + host + ":" + port - var masterWebUiUrl: String = _ + private val masterUrl = "spark://" + host + ":" + port + private var masterWebUiUrl: String = _ - var state = RecoveryState.STANDBY + private var state = RecoveryState.STANDBY - var persistenceEngine: PersistenceEngine = _ + private var persistenceEngine: PersistenceEngine = _ - var leaderElectionAgent: LeaderElectionAgent = _ + private var leaderElectionAgent: LeaderElectionAgent = _ private var recoveryCompletionTask: Cancellable = _ // As a temporary workaround before better ways of configuring memory, we allow users to set // a flag that will perform round-robin scheduling across the nodes (spreading out each app // among all the nodes) instead of trying to consolidate each app onto a small # of nodes. - val spreadOutApps = conf.getBoolean("spark.deploy.spreadOut", true) + private val spreadOutApps = conf.getBoolean("spark.deploy.spreadOut", true) // Default maxCores for applications that don't specify it (i.e. pass Int.MaxValue) - val defaultCores = conf.getInt("spark.deploy.defaultCores", Int.MaxValue) + private val defaultCores = conf.getInt("spark.deploy.defaultCores", Int.MaxValue) if (defaultCores < 1) { throw new SparkException("spark.deploy.defaultCores must be positive") } @@ -201,7 +204,7 @@ private[spark] class Master( self ! RevokedLeadership } - override def receiveWithLogging = { + override def receiveWithLogging: PartialFunction[Any, Unit] = { case ElectedLeader => { val (storedApps, storedDrivers, storedWorkers) = persistenceEngine.readPersistedData() state = if (storedApps.isEmpty && storedDrivers.isEmpty && storedWorkers.isEmpty) { @@ -336,7 +339,11 @@ private[spark] class Master( if (ExecutorState.isFinished(state)) { // Remove this executor from the worker and app logInfo(s"Removing executor ${exec.fullId} because it is $state") - appInfo.removeExecutor(exec) + // If an application has already finished, preserve its + // state to display its information properly on the UI + if (!appInfo.isFinished) { + appInfo.removeExecutor(exec) + } exec.worker.removeExecutor(exec) val normalExit = exitStatus == Some(0) @@ -425,6 +432,10 @@ private[spark] class Master( if (canCompleteRecovery) { completeRecovery() } } + case UnregisterApplication(applicationId) => + logInfo(s"Received unregister request from application $applicationId") + idToApp.get(applicationId).foreach(finishApplication) + case DisassociatedEvent(_, address, _) => { // The disconnected client could've been either a worker or an app; remove whichever it was logInfo(s"$address got disassociated, removing it.") @@ -449,11 +460,11 @@ private[spark] class Master( } } - def canCompleteRecovery = + private def canCompleteRecovery = workers.count(_.state == WorkerState.UNKNOWN) == 0 && apps.count(_.state == ApplicationState.UNKNOWN) == 0 - def beginRecovery(storedApps: Seq[ApplicationInfo], storedDrivers: Seq[DriverInfo], + private def beginRecovery(storedApps: Seq[ApplicationInfo], storedDrivers: Seq[DriverInfo], storedWorkers: Seq[WorkerInfo]) { for (app <- storedApps) { logInfo("Trying to recover app: " + app.id) @@ -484,7 +495,7 @@ private[spark] class Master( } } - def completeRecovery() { + private def completeRecovery() { // Ensure "only-once" recovery semantics using a short synchronization period. synchronized { if (state != RecoveryState.RECOVERING) { return } @@ -513,52 +524,28 @@ private[spark] class Master( } /** - * Can an app use the given worker? True if the worker has enough memory and we haven't already - * launched an executor for the app on it (right now the standalone backend doesn't like having - * two executors on the same worker). - */ - def canUse(app: ApplicationInfo, worker: WorkerInfo): Boolean = { - worker.memoryFree >= app.desc.memoryPerSlave && !worker.hasExecutor(app) - } - - /** - * Schedule the currently available resources among waiting apps. This method will be called - * every time a new app joins or resource availability changes. + * Schedule executors to be launched on the workers. + * + * There are two modes of launching executors. The first attempts to spread out an application's + * executors on as many workers as possible, while the second does the opposite (i.e. launch them + * on as few workers as possible). The former is usually better for data locality purposes and is + * the default. + * + * The number of cores assigned to each executor is configurable. When this is explicitly set, + * multiple executors from the same application may be launched on the same worker if the worker + * has enough cores and memory. Otherwise, each executor grabs all the cores available on the + * worker by default, in which case only one executor may be launched on each worker. */ - private def schedule() { - if (state != RecoveryState.ALIVE) { return } - - // First schedule drivers, they take strict precedence over applications - // Randomization helps balance drivers - val shuffledAliveWorkers = Random.shuffle(workers.toSeq.filter(_.state == WorkerState.ALIVE)) - val numWorkersAlive = shuffledAliveWorkers.size - var curPos = 0 - - for (driver <- waitingDrivers.toList) { // iterate over a copy of waitingDrivers - // We assign workers to each waiting driver in a round-robin fashion. For each driver, we - // start from the last worker that was assigned a driver, and continue onwards until we have - // explored all alive workers. - var launched = false - var numWorkersVisited = 0 - while (numWorkersVisited < numWorkersAlive && !launched) { - val worker = shuffledAliveWorkers(curPos) - numWorkersVisited += 1 - if (worker.memoryFree >= driver.desc.mem && worker.coresFree >= driver.desc.cores) { - launchDriver(worker, driver) - waitingDrivers -= driver - launched = true - } - curPos = (curPos + 1) % numWorkersAlive - } - } - + private def startExecutorsOnWorkers(): Unit = { // Right now this is a very simple FIFO scheduler. We keep trying to fit in the first app // in the queue, then the second app, etc. if (spreadOutApps) { - // Try to spread out each app among all the nodes, until it has all its cores + // Try to spread out each app among all the workers, until it has all its cores for (app <- waitingApps if app.coresLeft > 0) { val usableWorkers = workers.toArray.filter(_.state == WorkerState.ALIVE) - .filter(canUse(app, _)).sortBy(_.coresFree).reverse + .filter(worker => worker.memoryFree >= app.desc.memoryPerExecutorMB && + worker.coresFree >= app.desc.coresPerExecutor.getOrElse(1)) + .sortBy(_.coresFree).reverse val numUsable = usableWorkers.length val assigned = new Array[Int](numUsable) // Number of cores to give on each node var toAssign = math.min(app.coresLeft, usableWorkers.map(_.coresFree).sum) @@ -571,32 +558,61 @@ private[spark] class Master( pos = (pos + 1) % numUsable } // Now that we've decided how many cores to give on each node, let's actually give them - for (pos <- 0 until numUsable) { - if (assigned(pos) > 0) { - val exec = app.addExecutor(usableWorkers(pos), assigned(pos)) - launchExecutor(usableWorkers(pos), exec) - app.state = ApplicationState.RUNNING - } + for (pos <- 0 until numUsable if assigned(pos) > 0) { + allocateWorkerResourceToExecutors(app, assigned(pos), usableWorkers(pos)) } } } else { - // Pack each app into as few nodes as possible until we've assigned all its cores + // Pack each app into as few workers as possible until we've assigned all its cores for (worker <- workers if worker.coresFree > 0 && worker.state == WorkerState.ALIVE) { for (app <- waitingApps if app.coresLeft > 0) { - if (canUse(app, worker)) { - val coresToUse = math.min(worker.coresFree, app.coresLeft) - if (coresToUse > 0) { - val exec = app.addExecutor(worker, coresToUse) - launchExecutor(worker, exec) - app.state = ApplicationState.RUNNING - } - } + allocateWorkerResourceToExecutors(app, app.coresLeft, worker) } } } } - def launchExecutor(worker: WorkerInfo, exec: ExecutorDesc) { + /** + * Allocate a worker's resources to one or more executors. + * @param app the info of the application which the executors belong to + * @param coresToAllocate cores on this worker to be allocated to this application + * @param worker the worker info + */ + private def allocateWorkerResourceToExecutors( + app: ApplicationInfo, + coresToAllocate: Int, + worker: WorkerInfo): Unit = { + val memoryPerExecutor = app.desc.memoryPerExecutorMB + val coresPerExecutor = app.desc.coresPerExecutor.getOrElse(coresToAllocate) + var coresLeft = coresToAllocate + while (coresLeft >= coresPerExecutor && worker.memoryFree >= memoryPerExecutor) { + val exec = app.addExecutor(worker, coresPerExecutor) + coresLeft -= coresPerExecutor + launchExecutor(worker, exec) + app.state = ApplicationState.RUNNING + } + } + + /** + * Schedule the currently available resources among waiting apps. This method will be called + * every time a new app joins or resource availability changes. + */ + private def schedule(): Unit = { + if (state != RecoveryState.ALIVE) { return } + // Drivers take strict precedence over executors + val shuffledWorkers = Random.shuffle(workers) // Randomization helps balance drivers + for (worker <- shuffledWorkers if worker.state == WorkerState.ALIVE) { + for (driver <- waitingDrivers) { + if (worker.memoryFree >= driver.desc.mem && worker.coresFree >= driver.desc.cores) { + launchDriver(worker, driver) + waitingDrivers -= driver + } + } + } + startExecutorsOnWorkers() + } + + private def launchExecutor(worker: WorkerInfo, exec: ExecutorDesc): Unit = { logInfo("Launching executor " + exec.fullId + " on worker " + worker.id) worker.addExecutor(exec) worker.actor ! LaunchExecutor(masterUrl, @@ -605,7 +621,7 @@ private[spark] class Master( exec.id, worker.id, worker.hostPort, exec.cores, exec.memory) } - def registerWorker(worker: WorkerInfo): Boolean = { + private def registerWorker(worker: WorkerInfo): Boolean = { // There may be one or more refs to dead workers on this same node (w/ different ID's), // remove them. workers.filter { w => @@ -633,7 +649,7 @@ private[spark] class Master( true } - def removeWorker(worker: WorkerInfo) { + private def removeWorker(worker: WorkerInfo) { logInfo("Removing worker " + worker.id + " on " + worker.host + ":" + worker.port) worker.setState(WorkerState.DEAD) idToWorker -= worker.id @@ -656,22 +672,22 @@ private[spark] class Master( persistenceEngine.removeWorker(worker) } - def relaunchDriver(driver: DriverInfo) { + private def relaunchDriver(driver: DriverInfo) { driver.worker = None driver.state = DriverState.RELAUNCHING waitingDrivers += driver schedule() } - def createApplication(desc: ApplicationDescription, driver: ActorRef): ApplicationInfo = { + private def createApplication(desc: ApplicationDescription, driver: ActorRef): ApplicationInfo = { val now = System.currentTimeMillis() val date = new Date(now) new ApplicationInfo(now, newApplicationId(date), desc, date, driver, defaultCores) } - def registerApplication(app: ApplicationInfo): Unit = { + private def registerApplication(app: ApplicationInfo): Unit = { val appAddress = app.driver.path.address - if (addressToWorker.contains(appAddress)) { + if (addressToApp.contains(appAddress)) { logInfo("Attempted to re-register application at same address: " + appAddress) return } @@ -684,7 +700,7 @@ private[spark] class Master( waitingApps += app } - def finishApplication(app: ApplicationInfo) { + private def finishApplication(app: ApplicationInfo) { removeApplication(app, ApplicationState.FINISHED) } @@ -732,36 +748,41 @@ private[spark] class Master( * Rebuild a new SparkUI from the given application's event logs. * Return whether this is successful. */ - def rebuildSparkUI(app: ApplicationInfo): Boolean = { + private def rebuildSparkUI(app: ApplicationInfo): Boolean = { val appName = app.desc.name val notFoundBasePath = HistoryServer.UI_PATH_PREFIX + "/not-found" try { - val eventLogFile = app.desc.eventLogDir - .map { dir => EventLoggingListener.getLogPath(dir, app.id) } + val eventLogDir = app.desc.eventLogDir .getOrElse { // Event logging is not enabled for this application app.desc.appUiUrl = notFoundBasePath return false } - - val fs = Utils.getHadoopFileSystem(eventLogFile, hadoopConf) - - if (fs.exists(new Path(eventLogFile + EventLoggingListener.IN_PROGRESS))) { + + val eventLogFilePrefix = EventLoggingListener.getLogPath( + eventLogDir, app.id, app.desc.eventLogCodec) + val fs = Utils.getHadoopFileSystem(eventLogDir, hadoopConf) + val inProgressExists = fs.exists(new Path(eventLogFilePrefix + + EventLoggingListener.IN_PROGRESS)) + + if (inProgressExists) { // Event logging is enabled for this application, but the application is still in progress - val title = s"Application history not found (${app.id})" - var msg = s"Application $appName is still in progress." - logWarning(msg) - msg = URLEncoder.encode(msg, "UTF-8") - app.desc.appUiUrl = notFoundBasePath + s"?msg=$msg&title=$title" - return false + logWarning(s"Application $appName is still in progress, it may be terminated abnormally.") } - - val (logInput, sparkVersion) = EventLoggingListener.openEventLog(new Path(eventLogFile), fs) + + val (eventLogFile, status) = if (inProgressExists) { + (eventLogFilePrefix + EventLoggingListener.IN_PROGRESS, " (in progress)") + } else { + (eventLogFilePrefix, " (completed)") + } + + val logInput = EventLoggingListener.openEventLog(new Path(eventLogFile), fs) val replayBus = new ReplayListenerBus() val ui = SparkUI.createHistoryUI(new SparkConf, replayBus, new SecurityManager(conf), - appName + " (completed)", HistoryServer.UI_PATH_PREFIX + s"/${app.id}") + appName + status, HistoryServer.UI_PATH_PREFIX + s"/${app.id}") + val maybeTruncated = eventLogFile.endsWith(EventLoggingListener.IN_PROGRESS) try { - replayBus.replay(logInput, sparkVersion) + replayBus.replay(logInput, eventLogFile, maybeTruncated) } finally { logInput.close() } @@ -774,7 +795,7 @@ private[spark] class Master( case fnf: FileNotFoundException => // Event logging is enabled for this application, but no event logs are found val title = s"Application history not found (${app.id})" - var msg = s"No event logs found for application $appName in ${app.desc.eventLogDir}." + var msg = s"No event logs found for application $appName in ${app.desc.eventLogDir.get}." logWarning(msg) msg += " Did you specify the correct logging directory?" msg = URLEncoder.encode(msg, "UTF-8") @@ -793,14 +814,14 @@ private[spark] class Master( } /** Generate a new app ID given a app's submission date */ - def newApplicationId(submitDate: Date): String = { + private def newApplicationId(submitDate: Date): String = { val appId = "app-%s-%04d".format(createDateFormat.format(submitDate), nextAppNumber) nextAppNumber += 1 appId } /** Check for, and remove, any timed-out workers */ - def timeOutDeadWorkers() { + private def timeOutDeadWorkers() { // Copy the workers into an array so we don't modify the hashset while iterating through it val currentTime = System.currentTimeMillis() val toRemove = workers.filter(_.lastHeartbeat < currentTime - WORKER_TIMEOUT).toArray @@ -817,19 +838,19 @@ private[spark] class Master( } } - def newDriverId(submitDate: Date): String = { + private def newDriverId(submitDate: Date): String = { val appId = "driver-%s-%04d".format(createDateFormat.format(submitDate), nextDriverNumber) nextDriverNumber += 1 appId } - def createDriver(desc: DriverDescription): DriverInfo = { + private def createDriver(desc: DriverDescription): DriverInfo = { val now = System.currentTimeMillis() val date = new Date(now) new DriverInfo(now, newDriverId(date), desc, date) } - def launchDriver(worker: WorkerInfo, driver: DriverInfo) { + private def launchDriver(worker: WorkerInfo, driver: DriverInfo) { logInfo("Launching driver " + driver.id + " on worker " + worker.id) worker.addDriver(driver) driver.worker = Some(worker) @@ -837,7 +858,10 @@ private[spark] class Master( driver.state = DriverState.RUNNING } - def removeDriver(driverId: String, finalState: DriverState, exception: Option[Exception]) { + private def removeDriver( + driverId: String, + finalState: DriverState, + exception: Option[Exception]) { drivers.find(d => d.id == driverId) match { case Some(driver) => logInfo(s"Removing driver: $driverId") @@ -858,7 +882,7 @@ private[spark] class Master( } } -private[spark] object Master extends Logging { +private[deploy] object Master extends Logging { val systemName = "sparkMaster" private val actorName = "Master" diff --git a/core/src/main/scala/org/apache/spark/deploy/master/MasterArguments.scala b/core/src/main/scala/org/apache/spark/deploy/master/MasterArguments.scala index e34bee7854292..435b9b12f83b8 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/MasterArguments.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/MasterArguments.scala @@ -23,7 +23,7 @@ import org.apache.spark.util.{IntParam, Utils} /** * Command-line parser for the master. */ -private[spark] class MasterArguments(args: Array[String], conf: SparkConf) { +private[master] class MasterArguments(args: Array[String], conf: SparkConf) { var host = Utils.localHostName() var port = 7077 var webUiPort = 8080 @@ -49,7 +49,7 @@ private[spark] class MasterArguments(args: Array[String], conf: SparkConf) { webUiPort = conf.get("spark.master.ui.port").toInt } - def parse(args: List[String]): Unit = args match { + private def parse(args: List[String]): Unit = args match { case ("--ip" | "-i") :: value :: tail => Utils.checkHost(value, "ip no longer supported, please use hostname " + value) host = value @@ -84,7 +84,7 @@ private[spark] class MasterArguments(args: Array[String], conf: SparkConf) { /** * Print usage and exit JVM with the given exit code. */ - def printUsageAndExit(exitCode: Int) { + private def printUsageAndExit(exitCode: Int) { System.err.println( "Usage: Master [options]\n" + "\n" + diff --git a/core/src/main/scala/org/apache/spark/deploy/master/PersistenceEngine.scala b/core/src/main/scala/org/apache/spark/deploy/master/PersistenceEngine.scala index 2e0e1e7036ac8..da5060778edeb 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/PersistenceEngine.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/PersistenceEngine.scala @@ -87,7 +87,7 @@ trait PersistenceEngine { def close() {} } -private[spark] class BlackHolePersistenceEngine extends PersistenceEngine { +private[master] class BlackHolePersistenceEngine extends PersistenceEngine { override def persist(name: String, obj: Object): Unit = {} diff --git a/core/src/main/scala/org/apache/spark/deploy/master/RecoveryModeFactory.scala b/core/src/main/scala/org/apache/spark/deploy/master/RecoveryModeFactory.scala index 1096eb0368357..351db8fab2041 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/RecoveryModeFactory.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/RecoveryModeFactory.scala @@ -49,22 +49,29 @@ abstract class StandaloneRecoveryModeFactory(conf: SparkConf, serializer: Serial * LeaderAgent in this case is a no-op. Since leader is forever leader as the actual * recovery is made by restoring from filesystem. */ -private[spark] class FileSystemRecoveryModeFactory(conf: SparkConf, serializer: Serialization) +private[master] class FileSystemRecoveryModeFactory(conf: SparkConf, serializer: Serialization) extends StandaloneRecoveryModeFactory(conf, serializer) with Logging { + val RECOVERY_DIR = conf.get("spark.deploy.recoveryDirectory", "") - def createPersistenceEngine() = { + def createPersistenceEngine(): PersistenceEngine = { logInfo("Persisting recovery state to directory: " + RECOVERY_DIR) new FileSystemPersistenceEngine(RECOVERY_DIR, serializer) } - def createLeaderElectionAgent(master: LeaderElectable) = new MonarchyLeaderAgent(master) + def createLeaderElectionAgent(master: LeaderElectable): LeaderElectionAgent = { + new MonarchyLeaderAgent(master) + } } -private[spark] class ZooKeeperRecoveryModeFactory(conf: SparkConf, serializer: Serialization) +private[master] class ZooKeeperRecoveryModeFactory(conf: SparkConf, serializer: Serialization) extends StandaloneRecoveryModeFactory(conf, serializer) { - def createPersistenceEngine() = new ZooKeeperPersistenceEngine(conf, serializer) - def createLeaderElectionAgent(master: LeaderElectable) = + def createPersistenceEngine(): PersistenceEngine = { + new ZooKeeperPersistenceEngine(conf, serializer) + } + + def createLeaderElectionAgent(master: LeaderElectable): LeaderElectionAgent = { new ZooKeeperLeaderElectionAgent(master, conf) + } } diff --git a/core/src/main/scala/org/apache/spark/deploy/master/RecoveryState.scala b/core/src/main/scala/org/apache/spark/deploy/master/RecoveryState.scala index 256a5a7c28e47..aa0f02fa625cc 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/RecoveryState.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/RecoveryState.scala @@ -17,7 +17,7 @@ package org.apache.spark.deploy.master -private[spark] object RecoveryState extends Enumeration { +private[deploy] object RecoveryState extends Enumeration { type MasterState = Value val STANDBY, ALIVE, RECOVERING, COMPLETING_RECOVERY = Value diff --git a/core/src/main/scala/org/apache/spark/deploy/master/SparkCuratorUtil.scala b/core/src/main/scala/org/apache/spark/deploy/master/SparkCuratorUtil.scala index 4781a80d470e1..5b22481ea8c5f 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/SparkCuratorUtil.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/SparkCuratorUtil.scala @@ -25,12 +25,12 @@ import org.apache.zookeeper.KeeperException import org.apache.spark.{Logging, SparkConf} -object SparkCuratorUtil extends Logging { +private[deploy] object SparkCuratorUtil extends Logging { - val ZK_CONNECTION_TIMEOUT_MILLIS = 15000 - val ZK_SESSION_TIMEOUT_MILLIS = 60000 - val RETRY_WAIT_MILLIS = 5000 - val MAX_RECONNECT_ATTEMPTS = 3 + private val ZK_CONNECTION_TIMEOUT_MILLIS = 15000 + private val ZK_SESSION_TIMEOUT_MILLIS = 60000 + private val RETRY_WAIT_MILLIS = 5000 + private val MAX_RECONNECT_ATTEMPTS = 3 def newClient(conf: SparkConf): CuratorFramework = { val ZK_URL = conf.get("spark.deploy.zookeeper.url") diff --git a/core/src/main/scala/org/apache/spark/deploy/master/WorkerInfo.scala b/core/src/main/scala/org/apache/spark/deploy/master/WorkerInfo.scala index e94aae93e4495..9b3d48c6edc84 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/WorkerInfo.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/WorkerInfo.scala @@ -104,7 +104,7 @@ private[spark] class WorkerInfo( "http://" + this.publicAddress + ":" + this.webUiPort } - def setState(state: WorkerState.Value) = { + def setState(state: WorkerState.Value): Unit = { this.state = state } } diff --git a/core/src/main/scala/org/apache/spark/deploy/master/WorkerState.scala b/core/src/main/scala/org/apache/spark/deploy/master/WorkerState.scala index 0b36ef60051fc..b60baaadfb4bc 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/WorkerState.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/WorkerState.scala @@ -17,7 +17,7 @@ package org.apache.spark.deploy.master -private[spark] object WorkerState extends Enumeration { +private[master] object WorkerState extends Enumeration { type WorkerState = Value val ALIVE, DEAD, DECOMMISSIONED, UNKNOWN = Value diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperLeaderElectionAgent.scala b/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperLeaderElectionAgent.scala index 8eaa0ad948519..4823fd7cac0cb 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperLeaderElectionAgent.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperLeaderElectionAgent.scala @@ -24,7 +24,7 @@ import org.apache.spark.deploy.master.MasterMessages._ import org.apache.curator.framework.CuratorFramework import org.apache.curator.framework.recipes.leader.{LeaderLatchListener, LeaderLatch} -private[spark] class ZooKeeperLeaderElectionAgent(val masterActor: LeaderElectable, +private[master] class ZooKeeperLeaderElectionAgent(val masterActor: LeaderElectable, conf: SparkConf) extends LeaderLatchListener with LeaderElectionAgent with Logging { val WORKING_DIR = conf.get("spark.deploy.zookeeper.dir", "/spark") + "/leader_election" @@ -35,7 +35,7 @@ private[spark] class ZooKeeperLeaderElectionAgent(val masterActor: LeaderElectab start() - def start() { + private def start() { logInfo("Starting ZooKeeper LeaderElection agent") zk = SparkCuratorUtil.newClient(conf) leaderLatch = new LeaderLatch(zk, WORKING_DIR) @@ -72,7 +72,7 @@ private[spark] class ZooKeeperLeaderElectionAgent(val masterActor: LeaderElectab } } - def updateLeadershipStatus(isLeader: Boolean) { + private def updateLeadershipStatus(isLeader: Boolean) { if (isLeader && status == LeadershipStatus.NOT_LEADER) { status = LeadershipStatus.LEADER masterActor.electedLeader() diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperPersistenceEngine.scala b/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperPersistenceEngine.scala index e11ac031fb9c6..a285783f72000 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperPersistenceEngine.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperPersistenceEngine.scala @@ -28,12 +28,12 @@ import org.apache.zookeeper.CreateMode import org.apache.spark.{Logging, SparkConf} -private[spark] class ZooKeeperPersistenceEngine(conf: SparkConf, val serialization: Serialization) +private[master] class ZooKeeperPersistenceEngine(conf: SparkConf, val serialization: Serialization) extends PersistenceEngine - with Logging -{ - val WORKING_DIR = conf.get("spark.deploy.zookeeper.dir", "/spark") + "/master_status" - val zk: CuratorFramework = SparkCuratorUtil.newClient(conf) + with Logging { + + private val WORKING_DIR = conf.get("spark.deploy.zookeeper.dir", "/spark") + "/master_status" + private val zk: CuratorFramework = SparkCuratorUtil.newClient(conf) SparkCuratorUtil.mkdir(zk, WORKING_DIR) @@ -46,7 +46,7 @@ private[spark] class ZooKeeperPersistenceEngine(conf: SparkConf, val serializati zk.delete().forPath(WORKING_DIR + "/" + name) } - override def read[T: ClassTag](prefix: String) = { + override def read[T: ClassTag](prefix: String): Seq[T] = { val file = zk.getChildren.forPath(WORKING_DIR).filter(_.startsWith(prefix)) file.map(deserializeFromFile[T]).flatten } @@ -61,7 +61,7 @@ private[spark] class ZooKeeperPersistenceEngine(conf: SparkConf, val serializati zk.create().withMode(CreateMode.PERSISTENT).forPath(path, serialized) } - def deserializeFromFile[T](filename: String)(implicit m: ClassTag[T]): Option[T] = { + private def deserializeFromFile[T](filename: String)(implicit m: ClassTag[T]): Option[T] = { val fileData = zk.getData().forPath(WORKING_DIR + "/" + filename) val clazz = m.runtimeClass.asInstanceOf[Class[T]] val serializer = serialization.serializerFor(clazz) diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ui/ApplicationPage.scala b/core/src/main/scala/org/apache/spark/deploy/master/ui/ApplicationPage.scala index 3aae2b95d7396..273f077bd8f57 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ui/ApplicationPage.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ui/ApplicationPage.scala @@ -24,6 +24,7 @@ import scala.xml.Node import akka.pattern.ask import org.json4s.JValue +import org.json4s.JsonAST.JNothing import org.apache.spark.deploy.{ExecutorState, JsonProtocol} import org.apache.spark.deploy.DeployMessages.{MasterStateResponse, RequestMasterState} @@ -31,7 +32,7 @@ import org.apache.spark.deploy.master.ExecutorDesc import org.apache.spark.ui.{UIUtils, WebUIPage} import org.apache.spark.util.Utils -private[spark] class ApplicationPage(parent: MasterWebUI) extends WebUIPage("app") { +private[ui] class ApplicationPage(parent: MasterWebUI) extends WebUIPage("app") { private val master = parent.masterActorRef private val timeout = parent.timeout @@ -44,7 +45,11 @@ private[spark] class ApplicationPage(parent: MasterWebUI) extends WebUIPage("app val app = state.activeApps.find(_.id == appId).getOrElse({ state.completedApps.find(_.id == appId).getOrElse(null) }) - JsonProtocol.writeApplicationInfo(app) + if (app == null) { + JNothing + } else { + JsonProtocol.writeApplicationInfo(app) + } } /** Executor details for a particular application */ @@ -55,6 +60,10 @@ private[spark] class ApplicationPage(parent: MasterWebUI) extends WebUIPage("app val app = state.activeApps.find(_.id == appId).getOrElse({ state.completedApps.find(_.id == appId).getOrElse(null) }) + if (app == null) { + val msg =

No running application with ID {appId}
+ return UIUtils.basicSparkPage(msg, "Not Found") + } val executorHeaders = Seq("ExecutorID", "Worker", "Cores", "Memory", "State", "Logs") val allExecutors = (app.executors.values ++ app.removedExecutors).toSet.toSeq @@ -85,7 +94,7 @@ private[spark] class ApplicationPage(parent: MasterWebUI) extends WebUIPage("app
  • Executor Memory: - {Utils.megabytesToString(app.desc.memoryPerSlave)} + {Utils.megabytesToString(app.desc.memoryPerExecutorMB)}
  • Submit Date: {app.submitDate}
  • State: {app.state}
    • diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ui/HistoryNotFoundPage.scala b/core/src/main/scala/org/apache/spark/deploy/master/ui/HistoryNotFoundPage.scala index d8daff3e7fb9c..e021f1eef794f 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ui/HistoryNotFoundPage.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ui/HistoryNotFoundPage.scala @@ -24,7 +24,7 @@ import scala.xml.Node import org.apache.spark.ui.{UIUtils, WebUIPage} -private[spark] class HistoryNotFoundPage(parent: MasterWebUI) +private[ui] class HistoryNotFoundPage(parent: MasterWebUI) extends WebUIPage("history/not-found") { /** diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala index b47a081053e77..1f2c3fdbfb2bc 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala @@ -26,12 +26,12 @@ import akka.pattern.ask import org.json4s.JValue import org.apache.spark.deploy.JsonProtocol -import org.apache.spark.deploy.DeployMessages.{MasterStateResponse, RequestMasterState} -import org.apache.spark.deploy.master.{ApplicationInfo, DriverInfo, WorkerInfo} +import org.apache.spark.deploy.DeployMessages.{RequestKillDriver, MasterStateResponse, RequestMasterState} +import org.apache.spark.deploy.master._ import org.apache.spark.ui.{WebUIPage, UIUtils} import org.apache.spark.util.Utils -private[spark] class MasterPage(parent: MasterWebUI) extends WebUIPage("") { +private[ui] class MasterPage(parent: MasterWebUI) extends WebUIPage("") { private val master = parent.masterActorRef private val timeout = parent.timeout @@ -41,6 +41,31 @@ private[spark] class MasterPage(parent: MasterWebUI) extends WebUIPage("") { JsonProtocol.writeMasterState(state) } + def handleAppKillRequest(request: HttpServletRequest): Unit = { + handleKillRequest(request, id => { + parent.master.idToApp.get(id).foreach { app => + parent.master.removeApplication(app, ApplicationState.KILLED) + } + }) + } + + def handleDriverKillRequest(request: HttpServletRequest): Unit = { + handleKillRequest(request, id => { master ! RequestKillDriver(id) }) + } + + private def handleKillRequest(request: HttpServletRequest, action: String => Unit): Unit = { + if (parent.killEnabled && + parent.master.securityMgr.checkModifyPermissions(request.getRemoteUser)) { + val killFlag = Option(request.getParameter("terminate")).getOrElse("false").toBoolean + val id = Option(request.getParameter("id")) + if (id.isDefined && killFlag) { + action(id.get) + } + + Thread.sleep(100) + } + } + /** Index view listing applications and executors */ def render(request: HttpServletRequest): Seq[Node] = { val stateFuture = (master ? RequestMasterState)(timeout).mapTo[MasterStateResponse] @@ -66,7 +91,7 @@ private[spark] class MasterPage(parent: MasterWebUI) extends WebUIPage("") { // For now we only show driver information if the user has submitted drivers to the cluster. // This is until we integrate the notion of drivers and applications in the UI. - def hasDrivers = activeDrivers.length > 0 || completedDrivers.length > 0 + def hasDrivers: Boolean = activeDrivers.length > 0 || completedDrivers.length > 0 val content =
    @@ -163,9 +188,21 @@ private[spark] class MasterPage(parent: MasterWebUI) extends WebUIPage("") { } private def appRow(app: ApplicationInfo): Seq[Node] = { + val killLink = if (parent.killEnabled && + (app.state == ApplicationState.RUNNING || app.state == ApplicationState.WAITING)) { + val confirm = + s"if (window.confirm('Are you sure you want to kill application ${app.id} ?')) " + + "{ this.parentNode.submit(); return true; } else { return false; }" +
    + + + (kill) +
    + } {app.id} + {killLink} {app.desc.name} @@ -173,8 +210,8 @@ private[spark] class MasterPage(parent: MasterWebUI) extends WebUIPage("") { {app.coresGranted} - - {Utils.megabytesToString(app.desc.memoryPerSlave)} + + {Utils.megabytesToString(app.desc.memoryPerExecutorMB)} {UIUtils.formatDate(app.submitDate)} {app.desc.user} @@ -184,8 +221,21 @@ private[spark] class MasterPage(parent: MasterWebUI) extends WebUIPage("") { } private def driverRow(driver: DriverInfo): Seq[Node] = { + val killLink = if (parent.killEnabled && + (driver.state == DriverState.RUNNING || + driver.state == DriverState.SUBMITTED || + driver.state == DriverState.RELAUNCHING)) { + val confirm = + s"if (window.confirm('Are you sure you want to kill driver ${driver.id} ?')) " + + "{ this.parentNode.submit(); return true; } else { return false; }" +
    + + + (kill) +
    + } - {driver.id} + {driver.id} {killLink} {driver.submitDate} {driver.worker.map(w => {w.id.toString}).getOrElse("None")} @@ -196,7 +246,7 @@ private[spark] class MasterPage(parent: MasterWebUI) extends WebUIPage("") { {Utils.megabytesToString(driver.desc.mem.toLong)} - {driver.desc.command.arguments(1)} + {driver.desc.command.arguments(2)} } } diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala index 73400c5affb5d..bb11e0642ddc6 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala @@ -26,21 +26,27 @@ import org.apache.spark.util.AkkaUtils /** * Web UI server for the standalone master. */ -private[spark] +private[master] class MasterWebUI(val master: Master, requestedPort: Int) extends WebUI(master.securityMgr, requestedPort, master.conf, name = "MasterUI") with Logging { val masterActorRef = master.self val timeout = AkkaUtils.askTimeout(master.conf) + val killEnabled = master.conf.getBoolean("spark.ui.killEnabled", true) initialize() /** Initialize all components of the server. */ def initialize() { + val masterPage = new MasterPage(this) attachPage(new ApplicationPage(this)) attachPage(new HistoryNotFoundPage(this)) - attachPage(new MasterPage(this)) + attachPage(masterPage) attachHandler(createStaticHandler(MasterWebUI.STATIC_RESOURCE_DIR, "/static")) + attachHandler(createRedirectHandler( + "/app/kill", "/", masterPage.handleAppKillRequest, httpMethod = "POST")) + attachHandler(createRedirectHandler( + "/driver/kill", "/", masterPage.handleDriverKillRequest, httpMethod = "POST")) } /** Attach a reconstructed UI to this Master UI. Only valid after bind(). */ @@ -56,6 +62,6 @@ class MasterWebUI(val master: Master, requestedPort: Int) } } -private[spark] object MasterWebUI { - val STATIC_RESOURCE_DIR = SparkUI.STATIC_RESOURCE_DIR +private[master] object MasterWebUI { + private val STATIC_RESOURCE_DIR = SparkUI.STATIC_RESOURCE_DIR } diff --git a/core/src/main/scala/org/apache/spark/deploy/rest/StandaloneRestClient.scala b/core/src/main/scala/org/apache/spark/deploy/rest/StandaloneRestClient.scala index 115aa5278bb62..b8fd406fb6f9a 100644 --- a/core/src/main/scala/org/apache/spark/deploy/rest/StandaloneRestClient.scala +++ b/core/src/main/scala/org/apache/spark/deploy/rest/StandaloneRestClient.scala @@ -19,13 +19,15 @@ package org.apache.spark.deploy.rest import java.io.{DataOutputStream, FileNotFoundException} import java.net.{HttpURLConnection, SocketException, URL} +import javax.servlet.http.HttpServletResponse import scala.io.Source -import com.fasterxml.jackson.databind.JsonMappingException +import com.fasterxml.jackson.core.JsonProcessingException import com.google.common.base.Charsets import org.apache.spark.{Logging, SparkConf, SPARK_VERSION => sparkVersion} +import org.apache.spark.util.Utils /** * A client that submits applications to the standalone Master using a REST protocol. @@ -51,7 +53,7 @@ import org.apache.spark.{Logging, SparkConf, SPARK_VERSION => sparkVersion} * implementation of this client can use that information to retry using the version specified * by the server. */ -private[spark] class StandaloneRestClient extends Logging { +private[deploy] class StandaloneRestClient extends Logging { import StandaloneRestClient._ /** @@ -60,7 +62,7 @@ private[spark] class StandaloneRestClient extends Logging { * If the submission was successful, poll the status of the submission and report * it to the user. Otherwise, report the error message provided by the server. */ - def createSubmission( + private[rest] def createSubmission( master: String, request: CreateSubmissionRequest): SubmitRestProtocolResponse = { logInfo(s"Submitting a request to launch an application in $master.") @@ -105,7 +107,7 @@ private[spark] class StandaloneRestClient extends Logging { } /** Construct a message that captures the specified parameters for submitting an application. */ - def constructSubmitRequest( + private[rest] def constructSubmitRequest( appResource: String, mainClass: String, appArgs: Array[String], @@ -147,18 +149,32 @@ private[spark] class StandaloneRestClient extends Logging { conn.setRequestProperty("charset", "utf-8") conn.setDoOutput(true) val out = new DataOutputStream(conn.getOutputStream) - out.write(json.getBytes(Charsets.UTF_8)) - out.close() + Utils.tryWithSafeFinally { + out.write(json.getBytes(Charsets.UTF_8)) + } { + out.close() + } readResponse(conn) } /** * Read the response from the server and return it as a validated [[SubmitRestProtocolResponse]]. * If the response represents an error, report the embedded message to the user. + * Exposed for testing. */ - private def readResponse(connection: HttpURLConnection): SubmitRestProtocolResponse = { + private[rest] def readResponse(connection: HttpURLConnection): SubmitRestProtocolResponse = { try { - val responseJson = Source.fromInputStream(connection.getInputStream).mkString + val dataStream = + if (connection.getResponseCode == HttpServletResponse.SC_OK) { + connection.getInputStream + } else { + connection.getErrorStream + } + // If the server threw an exception while writing a response, it will not have a body + if (dataStream == null) { + throw new SubmitRestProtocolException("Server returned empty body") + } + val responseJson = Source.fromInputStream(dataStream).mkString logDebug(s"Response from the server:\n$responseJson") val response = SubmitRestProtocolMessage.fromJson(responseJson) response.validate() @@ -177,7 +193,7 @@ private[spark] class StandaloneRestClient extends Logging { case unreachable @ (_: FileNotFoundException | _: SocketException) => throw new SubmitRestConnectionException( s"Unable to connect to server ${connection.getURL}", unreachable) - case malformed @ (_: SubmitRestProtocolException | _: JsonMappingException) => + case malformed @ (_: JsonProcessingException | _: SubmitRestProtocolException) => throw new SubmitRestProtocolException( "Malformed response received from server", malformed) } @@ -229,7 +245,7 @@ private[spark] class StandaloneRestClient extends Logging { } } else { val failMessage = Option(submitResponse.message).map { ": " + _ }.getOrElse("") - logError("Application submission failed" + failMessage) + logError(s"Application submission failed$failMessage") } } @@ -279,12 +295,32 @@ private[spark] class StandaloneRestClient extends Logging { } } -private[spark] object StandaloneRestClient { - val REPORT_DRIVER_STATUS_INTERVAL = 1000 - val REPORT_DRIVER_STATUS_MAX_TRIES = 10 +private[rest] object StandaloneRestClient { + private val REPORT_DRIVER_STATUS_INTERVAL = 1000 + private val REPORT_DRIVER_STATUS_MAX_TRIES = 10 val PROTOCOL_VERSION = "v1" - /** Submit an application, assuming Spark parameters are specified through system properties. */ + /** + * Submit an application, assuming Spark parameters are specified through the given config. + * This is abstracted to its own method for testing purposes. + */ + def run( + appResource: String, + mainClass: String, + appArgs: Array[String], + conf: SparkConf, + env: Map[String, String] = sys.env): SubmitRestProtocolResponse = { + val master = conf.getOption("spark.master").getOrElse { + throw new IllegalArgumentException("'spark.master' must be set.") + } + val sparkProperties = conf.getAll.toMap + val environmentVariables = env.filter { case (k, _) => k.startsWith("SPARK_") } + val client = new StandaloneRestClient + val submitRequest = client.constructSubmitRequest( + appResource, mainClass, appArgs, sparkProperties, environmentVariables) + client.createSubmission(master, submitRequest) + } + def main(args: Array[String]): Unit = { if (args.size < 2) { sys.error("Usage: StandaloneRestClient [app resource] [main class] [app args*]") @@ -294,14 +330,6 @@ private[spark] object StandaloneRestClient { val mainClass = args(1) val appArgs = args.slice(2, args.size) val conf = new SparkConf - val master = conf.getOption("spark.master").getOrElse { - throw new IllegalArgumentException("'spark.master' must be set.") - } - val sparkProperties = conf.getAll.toMap - val environmentVariables = sys.env.filter { case (k, _) => k.startsWith("SPARK_") } - val client = new StandaloneRestClient - val submitRequest = client.constructSubmitRequest( - appResource, mainClass, appArgs, sparkProperties, environmentVariables) - client.createSubmission(master, submitRequest) + run(appResource, mainClass, appArgs, conf) } } diff --git a/core/src/main/scala/org/apache/spark/deploy/rest/StandaloneRestServer.scala b/core/src/main/scala/org/apache/spark/deploy/rest/StandaloneRestServer.scala index 2033d67e1f394..4f19af59f409f 100644 --- a/core/src/main/scala/org/apache/spark/deploy/rest/StandaloneRestServer.scala +++ b/core/src/main/scala/org/apache/spark/deploy/rest/StandaloneRestServer.scala @@ -17,15 +17,14 @@ package org.apache.spark.deploy.rest -import java.io.{DataOutputStream, File} +import java.io.File import java.net.InetSocketAddress import javax.servlet.http.{HttpServlet, HttpServletRequest, HttpServletResponse} import scala.io.Source import akka.actor.ActorRef -import com.fasterxml.jackson.databind.JsonMappingException -import com.google.common.base.Charsets +import com.fasterxml.jackson.core.JsonProcessingException import org.eclipse.jetty.server.Server import org.eclipse.jetty.servlet.{ServletHolder, ServletContextHandler} import org.eclipse.jetty.util.thread.QueuedThreadPool @@ -59,7 +58,7 @@ import org.apache.spark.deploy.ClientArguments._ * @param masterUrl the URL of the Master new drivers will attempt to connect to * @param masterConf the conf used by the Master */ -private[spark] class StandaloneRestServer( +private[deploy] class StandaloneRestServer( host: String, requestedPort: Int, masterActor: ActorRef, @@ -70,14 +69,14 @@ private[spark] class StandaloneRestServer( import StandaloneRestServer._ private var _server: Option[Server] = None - private val baseContext = s"/$PROTOCOL_VERSION/submissions" - - // A mapping from servlets to the URL prefixes they are responsible for - private val servletToContext = Map[StandaloneRestServlet, String]( - new SubmitRequestServlet(masterActor, masterUrl, masterConf) -> s"$baseContext/create/*", - new KillRequestServlet(masterActor, masterConf) -> s"$baseContext/kill/*", - new StatusRequestServlet(masterActor, masterConf) -> s"$baseContext/status/*", - new ErrorServlet -> "/" // default handler + + // A mapping from URL prefixes to servlets that serve them. Exposed for testing. + protected val baseContext = s"/$PROTOCOL_VERSION/submissions" + protected val contextToServlet = Map[String, StandaloneRestServlet]( + s"$baseContext/create/*" -> new SubmitRequestServlet(masterActor, masterUrl, masterConf), + s"$baseContext/kill/*" -> new KillRequestServlet(masterActor, masterConf), + s"$baseContext/status/*" -> new StatusRequestServlet(masterActor, masterConf), + "/*" -> new ErrorServlet // default handler ) /** Start the server and return the bound port. */ @@ -99,7 +98,7 @@ private[spark] class StandaloneRestServer( server.setThreadPool(threadPool) val mainHandler = new ServletContextHandler mainHandler.setContextPath("/") - servletToContext.foreach { case (servlet, prefix) => + contextToServlet.foreach { case (prefix, servlet) => mainHandler.addServlet(new ServletHolder(servlet), prefix) } server.setHandler(mainHandler) @@ -113,7 +112,7 @@ private[spark] class StandaloneRestServer( } } -private object StandaloneRestServer { +private[rest] object StandaloneRestServer { val PROTOCOL_VERSION = StandaloneRestClient.PROTOCOL_VERSION val SC_UNKNOWN_PROTOCOL_VERSION = 468 } @@ -121,20 +120,7 @@ private object StandaloneRestServer { /** * An abstract servlet for handling requests passed to the [[StandaloneRestServer]]. */ -private abstract class StandaloneRestServlet extends HttpServlet with Logging { - - /** Service a request. If an exception is thrown in the process, indicate server error. */ - protected override def service( - request: HttpServletRequest, - response: HttpServletResponse): Unit = { - try { - super.service(request, response) - } catch { - case e: Exception => - logError("Exception while handling request", e) - response.setStatus(HttpServletResponse.SC_INTERNAL_SERVER_ERROR) - } - } +private[rest] abstract class StandaloneRestServlet extends HttpServlet with Logging { /** * Serialize the given response message to JSON and send it through the response servlet. @@ -146,11 +132,7 @@ private abstract class StandaloneRestServlet extends HttpServlet with Logging { val message = validateResponse(responseMessage, responseServlet) responseServlet.setContentType("application/json") responseServlet.setCharacterEncoding("utf-8") - responseServlet.setStatus(HttpServletResponse.SC_OK) - val content = message.toJson.getBytes(Charsets.UTF_8) - val out = new DataOutputStream(responseServlet.getOutputStream) - out.write(content) - out.close() + responseServlet.getWriter.write(message.toJson) } /** @@ -186,6 +168,19 @@ private abstract class StandaloneRestServlet extends HttpServlet with Logging { e } + /** + * Parse a submission ID from the relative path, assuming it is the first part of the path. + * For instance, we expect the path to take the form /[submission ID]/maybe/something/else. + * The returned submission ID cannot be empty. If the path is unexpected, return None. + */ + protected def parseSubmissionId(path: String): Option[String] = { + if (path == null || path.isEmpty) { + None + } else { + path.stripPrefix("/").split("/").headOption.filter(_.nonEmpty) + } + } + /** * Validate the response to ensure that it is correctly constructed. * @@ -209,7 +204,7 @@ private abstract class StandaloneRestServlet extends HttpServlet with Logging { /** * A servlet for handling kill requests passed to the [[StandaloneRestServer]]. */ -private class KillRequestServlet(masterActor: ActorRef, conf: SparkConf) +private[rest] class KillRequestServlet(masterActor: ActorRef, conf: SparkConf) extends StandaloneRestServlet { /** @@ -219,18 +214,15 @@ private class KillRequestServlet(masterActor: ActorRef, conf: SparkConf) protected override def doPost( request: HttpServletRequest, response: HttpServletResponse): Unit = { - val submissionId = request.getPathInfo.stripPrefix("/") - val responseMessage = - if (submissionId.nonEmpty) { - handleKill(submissionId) - } else { - response.setStatus(HttpServletResponse.SC_BAD_REQUEST) - handleError("Submission ID is missing in kill request.") - } + val submissionId = parseSubmissionId(request.getPathInfo) + val responseMessage = submissionId.map(handleKill).getOrElse { + response.setStatus(HttpServletResponse.SC_BAD_REQUEST) + handleError("Submission ID is missing in kill request.") + } sendResponse(responseMessage, response) } - private def handleKill(submissionId: String): KillSubmissionResponse = { + protected def handleKill(submissionId: String): KillSubmissionResponse = { val askTimeout = AkkaUtils.askTimeout(conf) val response = AkkaUtils.askWithReply[DeployMessages.KillDriverResponse]( DeployMessages.RequestKillDriver(submissionId), masterActor, askTimeout) @@ -246,7 +238,7 @@ private class KillRequestServlet(masterActor: ActorRef, conf: SparkConf) /** * A servlet for handling status requests passed to the [[StandaloneRestServer]]. */ -private class StatusRequestServlet(masterActor: ActorRef, conf: SparkConf) +private[rest] class StatusRequestServlet(masterActor: ActorRef, conf: SparkConf) extends StandaloneRestServlet { /** @@ -256,18 +248,15 @@ private class StatusRequestServlet(masterActor: ActorRef, conf: SparkConf) protected override def doGet( request: HttpServletRequest, response: HttpServletResponse): Unit = { - val submissionId = request.getPathInfo.stripPrefix("/") - val responseMessage = - if (submissionId.nonEmpty) { - handleStatus(submissionId) - } else { - response.setStatus(HttpServletResponse.SC_BAD_REQUEST) - handleError("Submission ID is missing in status request.") - } + val submissionId = parseSubmissionId(request.getPathInfo) + val responseMessage = submissionId.map(handleStatus).getOrElse { + response.setStatus(HttpServletResponse.SC_BAD_REQUEST) + handleError("Submission ID is missing in status request.") + } sendResponse(responseMessage, response) } - private def handleStatus(submissionId: String): SubmissionStatusResponse = { + protected def handleStatus(submissionId: String): SubmissionStatusResponse = { val askTimeout = AkkaUtils.askTimeout(conf) val response = AkkaUtils.askWithReply[DeployMessages.DriverStatusResponse]( DeployMessages.RequestDriverStatus(submissionId), masterActor, askTimeout) @@ -287,7 +276,7 @@ private class StatusRequestServlet(masterActor: ActorRef, conf: SparkConf) /** * A servlet for handling submit requests passed to the [[StandaloneRestServer]]. */ -private class SubmitRequestServlet( +private[rest] class SubmitRequestServlet( masterActor: ActorRef, masterUrl: String, conf: SparkConf) @@ -313,7 +302,7 @@ private class SubmitRequestServlet( handleSubmit(requestMessageJson, requestMessage, responseServlet) } catch { // The client failed to provide a valid JSON, so this is not our fault - case e @ (_: JsonMappingException | _: SubmitRestProtocolException) => + case e @ (_: JsonProcessingException | _: SubmitRestProtocolException) => responseServlet.setStatus(HttpServletResponse.SC_BAD_REQUEST) handleError("Malformed request: " + formatException(e)) } @@ -392,7 +381,7 @@ private class SubmitRequestServlet( val javaOpts = sparkJavaOpts ++ extraJavaOpts val command = new Command( "org.apache.spark.deploy.worker.DriverWrapper", - Seq("{{WORKER_URL}}", mainClass) ++ appArgs, // args to the DriverWrapper + Seq("{{WORKER_URL}}", "{{USER_JAR}}", mainClass) ++ appArgs, // args to the DriverWrapper environmentVariables, extraClassPath, extraLibraryPath, javaOpts) val actualDriverMemory = driverMemory.map(Utils.memoryStringToMb).getOrElse(DEFAULT_MEMORY) val actualDriverCores = driverCores.map(_.toInt).getOrElse(DEFAULT_CORES) @@ -413,7 +402,7 @@ private class ErrorServlet extends StandaloneRestServlet { request: HttpServletRequest, response: HttpServletResponse): Unit = { val path = request.getPathInfo - val parts = path.stripPrefix("/").split("/").toSeq + val parts = path.stripPrefix("/").split("/").filter(_.nonEmpty).toList var versionMismatch = false var msg = parts match { @@ -423,10 +412,10 @@ private class ErrorServlet extends StandaloneRestServlet { case `serverVersion` :: Nil => // http://host:port/correct-version "Missing the /submissions prefix." - case `serverVersion` :: "submissions" :: Nil => - // http://host:port/correct-version/submissions + case `serverVersion` :: "submissions" :: tail => + // http://host:port/correct-version/submissions/* "Missing an action: please specify one of /create, /kill, or /status." - case unknownVersion :: _ => + case unknownVersion :: tail => // http://host:port/unknown-version/* versionMismatch = true s"Unknown protocol version '$unknownVersion'." diff --git a/core/src/main/scala/org/apache/spark/deploy/rest/SubmitRestProtocolException.scala b/core/src/main/scala/org/apache/spark/deploy/rest/SubmitRestProtocolException.scala index d7a0bdbe10778..b97921ec934a0 100644 --- a/core/src/main/scala/org/apache/spark/deploy/rest/SubmitRestProtocolException.scala +++ b/core/src/main/scala/org/apache/spark/deploy/rest/SubmitRestProtocolException.scala @@ -20,17 +20,17 @@ package org.apache.spark.deploy.rest /** * An exception thrown in the REST application submission protocol. */ -private[spark] class SubmitRestProtocolException(message: String, cause: Throwable = null) +private[rest] class SubmitRestProtocolException(message: String, cause: Throwable = null) extends Exception(message, cause) /** * An exception thrown if a field is missing from a [[SubmitRestProtocolMessage]]. */ -private[spark] class SubmitRestMissingFieldException(message: String) +private[rest] class SubmitRestMissingFieldException(message: String) extends SubmitRestProtocolException(message) /** * An exception thrown if the REST client cannot reach the REST server. */ -private[spark] class SubmitRestConnectionException(message: String, cause: Throwable) +private[deploy] class SubmitRestConnectionException(message: String, cause: Throwable) extends SubmitRestProtocolException(message, cause) diff --git a/core/src/main/scala/org/apache/spark/deploy/rest/SubmitRestProtocolMessage.scala b/core/src/main/scala/org/apache/spark/deploy/rest/SubmitRestProtocolMessage.scala index b877898231e3e..e6615a3174ce1 100644 --- a/core/src/main/scala/org/apache/spark/deploy/rest/SubmitRestProtocolMessage.scala +++ b/core/src/main/scala/org/apache/spark/deploy/rest/SubmitRestProtocolMessage.scala @@ -17,8 +17,6 @@ package org.apache.spark.deploy.rest -import scala.util.Try - import com.fasterxml.jackson.annotation._ import com.fasterxml.jackson.annotation.JsonAutoDetect.Visibility import com.fasterxml.jackson.annotation.JsonInclude.Include @@ -41,7 +39,7 @@ import org.apache.spark.util.Utils @JsonInclude(Include.NON_NULL) @JsonAutoDetect(getterVisibility = Visibility.ANY, setterVisibility = Visibility.ANY) @JsonPropertyOrder(alphabetic = true) -private[spark] abstract class SubmitRestProtocolMessage { +private[rest] abstract class SubmitRestProtocolMessage { @JsonIgnore val messageType = Utils.getFormattedClassName(this) @@ -111,12 +109,14 @@ private[spark] object SubmitRestProtocolMessage { * If the action field is not found, throw a [[SubmitRestMissingFieldException]]. */ def parseAction(json: String): String = { - parse(json).asInstanceOf[JObject].obj - .find { case (f, _) => f == "action" } - .map { case (_, v) => v.asInstanceOf[JString].s } - .getOrElse { - throw new SubmitRestMissingFieldException(s"Action field not found in JSON:\n$json") - } + val value: Option[String] = parse(json) match { + case JObject(fields) => + fields.collectFirst { case ("action", v) => v }.collect { case JString(s) => s } + case _ => None + } + value.getOrElse { + throw new SubmitRestMissingFieldException(s"Action field not found in JSON:\n$json") + } } /** diff --git a/core/src/main/scala/org/apache/spark/deploy/rest/SubmitRestProtocolRequest.scala b/core/src/main/scala/org/apache/spark/deploy/rest/SubmitRestProtocolRequest.scala index 9e1fd8c40cabd..d80abdf15fb34 100644 --- a/core/src/main/scala/org/apache/spark/deploy/rest/SubmitRestProtocolRequest.scala +++ b/core/src/main/scala/org/apache/spark/deploy/rest/SubmitRestProtocolRequest.scala @@ -24,7 +24,7 @@ import org.apache.spark.util.Utils /** * An abstract request sent from the client in the REST application submission protocol. */ -private[spark] abstract class SubmitRestProtocolRequest extends SubmitRestProtocolMessage { +private[rest] abstract class SubmitRestProtocolRequest extends SubmitRestProtocolMessage { var clientSparkVersion: String = null protected override def doValidate(): Unit = { super.doValidate() @@ -35,7 +35,7 @@ private[spark] abstract class SubmitRestProtocolRequest extends SubmitRestProtoc /** * A request to launch a new application in the REST application submission protocol. */ -private[spark] class CreateSubmissionRequest extends SubmitRestProtocolRequest { +private[rest] class CreateSubmissionRequest extends SubmitRestProtocolRequest { var appResource: String = null var mainClass: String = null var appArgs: Array[String] = null diff --git a/core/src/main/scala/org/apache/spark/deploy/rest/SubmitRestProtocolResponse.scala b/core/src/main/scala/org/apache/spark/deploy/rest/SubmitRestProtocolResponse.scala index 16dfe041d4bea..8fde8c142a4c1 100644 --- a/core/src/main/scala/org/apache/spark/deploy/rest/SubmitRestProtocolResponse.scala +++ b/core/src/main/scala/org/apache/spark/deploy/rest/SubmitRestProtocolResponse.scala @@ -22,7 +22,7 @@ import java.lang.Boolean /** * An abstract response sent from the server in the REST application submission protocol. */ -private[spark] abstract class SubmitRestProtocolResponse extends SubmitRestProtocolMessage { +private[rest] abstract class SubmitRestProtocolResponse extends SubmitRestProtocolMessage { var serverSparkVersion: String = null var success: Boolean = null var unknownFields: Array[String] = null @@ -35,7 +35,7 @@ private[spark] abstract class SubmitRestProtocolResponse extends SubmitRestProto /** * A response to a [[CreateSubmissionRequest]] in the REST application submission protocol. */ -private[spark] class CreateSubmissionResponse extends SubmitRestProtocolResponse { +private[rest] class CreateSubmissionResponse extends SubmitRestProtocolResponse { var submissionId: String = null protected override def doValidate(): Unit = { super.doValidate() @@ -46,7 +46,7 @@ private[spark] class CreateSubmissionResponse extends SubmitRestProtocolResponse /** * A response to a kill request in the REST application submission protocol. */ -private[spark] class KillSubmissionResponse extends SubmitRestProtocolResponse { +private[rest] class KillSubmissionResponse extends SubmitRestProtocolResponse { var submissionId: String = null protected override def doValidate(): Unit = { super.doValidate() @@ -58,7 +58,7 @@ private[spark] class KillSubmissionResponse extends SubmitRestProtocolResponse { /** * A response to a status request in the REST application submission protocol. */ -private[spark] class SubmissionStatusResponse extends SubmitRestProtocolResponse { +private[rest] class SubmissionStatusResponse extends SubmitRestProtocolResponse { var submissionId: String = null var driverState: String = null var workerId: String = null @@ -74,7 +74,7 @@ private[spark] class SubmissionStatusResponse extends SubmitRestProtocolResponse /** * An error response message used in the REST application submission protocol. */ -private[spark] class ErrorResponse extends SubmitRestProtocolResponse { +private[rest] class ErrorResponse extends SubmitRestProtocolResponse { // The highest protocol version that the server knows about // This is set when the client specifies an unknown version var highestProtocolVersion: String = null diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala b/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala index 3e013c32096c5..0a1d60f58bc58 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala @@ -20,16 +20,18 @@ package org.apache.spark.deploy.worker import java.io.{File, FileOutputStream, InputStream, IOException} import java.lang.System._ +import scala.collection.JavaConversions._ import scala.collection.Map import org.apache.spark.Logging import org.apache.spark.deploy.Command +import org.apache.spark.launcher.WorkerCommandBuilder import org.apache.spark.util.Utils /** ** Utilities for running commands with the spark classpath. */ -private[spark] +private[deploy] object CommandUtils extends Logging { /** @@ -54,12 +56,10 @@ object CommandUtils extends Logging { } private def buildCommandSeq(command: Command, memory: Int, sparkHome: String): Seq[String] = { - val runner = sys.env.get("JAVA_HOME").map(_ + "/bin/java").getOrElse("java") - // SPARK-698: do not call the run.cmd script, as process.destroy() // fails to kill a process tree on Windows - Seq(runner) ++ buildJavaOpts(command, memory, sparkHome) ++ Seq(command.mainClass) ++ - command.arguments + val cmd = new WorkerCommandBuilder(sparkHome, memory, command).buildCommand() + cmd.toSeq ++ Seq(command.mainClass) ++ command.arguments } /** @@ -92,44 +92,6 @@ object CommandUtils extends Logging { command.javaOpts) } - /** - * Attention: this must always be aligned with the environment variables in the run scripts and - * the way the JAVA_OPTS are assembled there. - */ - private def buildJavaOpts(command: Command, memory: Int, sparkHome: String): Seq[String] = { - val memoryOpts = Seq(s"-Xms${memory}M", s"-Xmx${memory}M") - - // Exists for backwards compatibility with older Spark versions - val workerLocalOpts = Option(getenv("SPARK_JAVA_OPTS")).map(Utils.splitCommandString) - .getOrElse(Nil) - if (workerLocalOpts.length > 0) { - logWarning("SPARK_JAVA_OPTS was set on the worker. It is deprecated in Spark 1.0.") - logWarning("Set SPARK_LOCAL_DIRS for node-specific storage locations.") - } - - // Figure out our classpath with the external compute-classpath script - val ext = if (System.getProperty("os.name").startsWith("Windows")) ".cmd" else ".sh" - val classPath = Utils.executeAndGetOutput( - Seq(sparkHome + "/bin/compute-classpath" + ext), - extraEnvironment = command.environment) - val userClassPath = command.classPathEntries ++ Seq(classPath) - - val javaVersion = System.getProperty("java.version") - - val javaOpts = workerLocalOpts ++ command.javaOpts - - val permGenOpt = - if (!javaVersion.startsWith("1.8") && !javaOpts.exists(_.startsWith("-XX:MaxPermSize="))) { - // do not specify -XX:MaxPermSize if it was already specified by user - Some("-XX:MaxPermSize=128m") - } else { - None - } - - Seq("-cp", userClassPath.filterNot(_.isEmpty).mkString(File.pathSeparator)) ++ - permGenOpt ++ javaOpts ++ memoryOpts - } - /** Spawn a thread that will redirect a given stream to a file */ def redirectStream(in: InputStream, file: File) { val out = new FileOutputStream(file, true) diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala b/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala index 28cab36c7b9e2..ef7a703bffe67 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala @@ -20,64 +20,73 @@ package org.apache.spark.deploy.worker import java.io._ import scala.collection.JavaConversions._ -import scala.collection.Map import akka.actor.ActorRef import com.google.common.base.Charsets.UTF_8 import com.google.common.io.Files -import org.apache.hadoop.conf.Configuration -import org.apache.hadoop.fs.{FileUtil, Path} +import org.apache.hadoop.fs.Path -import org.apache.spark.{Logging, SparkConf} -import org.apache.spark.deploy.{Command, DriverDescription, SparkHadoopUtil} +import org.apache.spark.{Logging, SparkConf, SecurityManager} +import org.apache.spark.deploy.{DriverDescription, SparkHadoopUtil} import org.apache.spark.deploy.DeployMessages.DriverStateChanged import org.apache.spark.deploy.master.DriverState import org.apache.spark.deploy.master.DriverState.DriverState +import org.apache.spark.util.{Utils, Clock, SystemClock} /** * Manages the execution of one driver, including automatically restarting the driver on failure. * This is currently only used in standalone cluster deploy mode. */ -private[spark] class DriverRunner( - val conf: SparkConf, +private[deploy] class DriverRunner( + conf: SparkConf, val driverId: String, val workDir: File, val sparkHome: File, val driverDesc: DriverDescription, val worker: ActorRef, - val workerUrl: String) + val workerUrl: String, + val securityManager: SecurityManager) extends Logging { - @volatile var process: Option[Process] = None - @volatile var killed = false + @volatile private var process: Option[Process] = None + @volatile private var killed = false // Populated once finished - var finalState: Option[DriverState] = None - var finalException: Option[Exception] = None - var finalExitCode: Option[Int] = None + private[worker] var finalState: Option[DriverState] = None + private[worker] var finalException: Option[Exception] = None + private var finalExitCode: Option[Int] = None // Decoupled for testing - private[deploy] def setClock(_clock: Clock) = clock = _clock - private[deploy] def setSleeper(_sleeper: Sleeper) = sleeper = _sleeper - private var clock = new Clock { - def currentTimeMillis(): Long = System.currentTimeMillis() + def setClock(_clock: Clock): Unit = { + clock = _clock } + + def setSleeper(_sleeper: Sleeper): Unit = { + sleeper = _sleeper + } + + private var clock: Clock = new SystemClock() private var sleeper = new Sleeper { def sleep(seconds: Int): Unit = (0 until seconds).takeWhile(f => {Thread.sleep(1000); !killed}) } /** Starts a thread to run and manage the driver. */ - def start() = { + private[worker] def start() = { new Thread("DriverRunner for " + driverId) { override def run() { try { val driverDir = createWorkingDirectory() val localJarFilename = downloadUserJar(driverDir) - // Make sure user application jar is on the classpath + def substituteVariables(argument: String): String = argument match { + case "{{WORKER_URL}}" => workerUrl + case "{{USER_JAR}}" => localJarFilename + case other => other + } + // TODO: If we add ability to submit multiple jars they should also be added here val builder = CommandUtils.buildProcessBuilder(driverDesc.command, driverDesc.mem, - sparkHome.getAbsolutePath, substituteVariables, Seq(localJarFilename)) + sparkHome.getAbsolutePath, substituteVariables) launchDriver(builder, driverDir, driverDesc.supervise) } catch { @@ -104,19 +113,13 @@ private[spark] class DriverRunner( } /** Terminate this driver (or prevent it from ever starting if not yet started) */ - def kill() { + private[worker] def kill() { synchronized { process.foreach(p => p.destroy()) killed = true } } - /** Replace variables in a command argument passed to us */ - private def substituteVariables(argument: String): String = argument match { - case "{{WORKER_URL}}" => workerUrl - case other => other - } - /** * Creates the working directory for this driver. * Will throw an exception if there are errors preparing the directory. @@ -134,12 +137,9 @@ private[spark] class DriverRunner( * Will throw an exception if there are errors downloading the jar. */ private def downloadUserJar(driverDir: File): String = { - val jarPath = new Path(driverDesc.jarUrl) val hadoopConf = SparkHadoopUtil.get.newConfiguration(conf) - val jarFileSystem = jarPath.getFileSystem(hadoopConf) - val destPath = new File(driverDir.getAbsolutePath, jarPath.getName) val jarFileName = jarPath.getName val localJarFile = new File(driverDir, jarFileName) @@ -147,7 +147,14 @@ private[spark] class DriverRunner( if (!localJarFile.exists()) { // May already exist if running multiple workers on one node logInfo(s"Copying user jar $jarPath to $destPath") - FileUtil.copy(jarFileSystem, jarPath, destPath, false, hadoopConf) + Utils.fetchFile( + driverDesc.jarUrl, + driverDir, + conf, + securityManager, + hadoopConf, + System.currentTimeMillis(), + useCache = false) } if (!localJarFile.exists()) { // Verify copy succeeded @@ -159,7 +166,7 @@ private[spark] class DriverRunner( private def launchDriver(builder: ProcessBuilder, baseDir: File, supervise: Boolean) { builder.directory(baseDir) - def initialize(process: Process) = { + def initialize(process: Process): Unit = { // Redirect stdout and stderr to files val stdout = new File(baseDir, "stdout") CommandUtils.redirectStream(process.getInputStream, stdout) @@ -173,8 +180,8 @@ private[spark] class DriverRunner( runCommandWithRetry(ProcessBuilderLike(builder), initialize, supervise) } - private[deploy] def runCommandWithRetry(command: ProcessBuilderLike, initialize: Process => Unit, - supervise: Boolean) { + def runCommandWithRetry( + command: ProcessBuilderLike, initialize: Process => Unit, supervise: Boolean): Unit = { // Time to wait between submission retries. var waitSeconds = 1 // A run of this many seconds resets the exponential back-off. @@ -191,9 +198,9 @@ private[spark] class DriverRunner( initialize(process.get) } - val processStart = clock.currentTimeMillis() + val processStart = clock.getTimeMillis() val exitCode = process.get.waitFor() - if (clock.currentTimeMillis() - processStart > successfulRunDuration * 1000) { + if (clock.getTimeMillis() - processStart > successfulRunDuration * 1000) { waitSeconds = 1 } @@ -209,10 +216,6 @@ private[spark] class DriverRunner( } } -private[deploy] trait Clock { - def currentTimeMillis(): Long -} - private[deploy] trait Sleeper { def sleep(seconds: Int) } @@ -224,8 +227,8 @@ private[deploy] trait ProcessBuilderLike { } private[deploy] object ProcessBuilderLike { - def apply(processBuilder: ProcessBuilder) = new ProcessBuilderLike { - def start() = processBuilder.start() - def command = processBuilder.command() + def apply(processBuilder: ProcessBuilder): ProcessBuilderLike = new ProcessBuilderLike { + override def start(): Process = processBuilder.start() + override def command: Seq[String] = processBuilder.command() } } diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/DriverWrapper.scala b/core/src/main/scala/org/apache/spark/deploy/worker/DriverWrapper.scala index 05e242e6df702..d1a12b01e78f7 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/DriverWrapper.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/DriverWrapper.scala @@ -17,32 +17,50 @@ package org.apache.spark.deploy.worker -import akka.actor._ +import java.io.File import org.apache.spark.{SecurityManager, SparkConf} -import org.apache.spark.util.{AkkaUtils, Utils} +import org.apache.spark.rpc.RpcEnv +import org.apache.spark.util.{ChildFirstURLClassLoader, MutableURLClassLoader, Utils} /** * Utility object for launching driver programs such that they share fate with the Worker process. + * This is used in standalone cluster mode only. */ object DriverWrapper { def main(args: Array[String]) { args.toList match { - case workerUrl :: mainClass :: extraArgs => + /* + * IMPORTANT: Spark 1.3 provides a stable application submission gateway that is both + * backward and forward compatible across future Spark versions. Because this gateway + * uses this class to launch the driver, the ordering and semantics of the arguments + * here must also remain consistent across versions. + */ + case workerUrl :: userJar :: mainClass :: extraArgs => val conf = new SparkConf() - val (actorSystem, _) = AkkaUtils.createActorSystem("Driver", + val rpcEnv = RpcEnv.create("Driver", Utils.localHostName(), 0, conf, new SecurityManager(conf)) - actorSystem.actorOf(Props(classOf[WorkerWatcher], workerUrl), name = "workerWatcher") + rpcEnv.setupEndpoint("workerWatcher", new WorkerWatcher(rpcEnv, workerUrl)) + + val currentLoader = Thread.currentThread.getContextClassLoader + val userJarUrl = new File(userJar).toURI().toURL() + val loader = + if (sys.props.getOrElse("spark.driver.userClassPathFirst", "false").toBoolean) { + new ChildFirstURLClassLoader(Array(userJarUrl), currentLoader) + } else { + new MutableURLClassLoader(Array(userJarUrl), currentLoader) + } + Thread.currentThread.setContextClassLoader(loader) // Delegate to supplied main class - val clazz = Class.forName(args(1)) + val clazz = Class.forName(mainClass, true, loader) val mainMethod = clazz.getMethod("main", classOf[Array[String]]) mainMethod.invoke(null, extraArgs.toArray[String]) - actorSystem.shutdown() + rpcEnv.shutdown() case _ => - System.err.println("Usage: DriverWrapper [options]") + System.err.println("Usage: DriverWrapper [options]") System.exit(-1) } } diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala b/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala index 0add3064da452..7d5acabb95a48 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala @@ -34,7 +34,7 @@ import org.apache.spark.util.logging.FileAppender * Manages the execution of one executor process. * This is currently only used in standalone mode. */ -private[spark] class ExecutorRunner( +private[deploy] class ExecutorRunner( val appId: String, val execId: Int, val appDesc: ApplicationDescription, @@ -44,25 +44,26 @@ private[spark] class ExecutorRunner( val workerId: String, val host: String, val webUiPort: Int, + val publicAddress: String, val sparkHome: File, val executorDir: File, val workerUrl: String, - val conf: SparkConf, + conf: SparkConf, val appLocalDirs: Seq[String], - var state: ExecutorState.Value) + @volatile var state: ExecutorState.Value) extends Logging { - val fullId = appId + "/" + execId - var workerThread: Thread = null - var process: Process = null - var stdoutAppender: FileAppender = null - var stderrAppender: FileAppender = null + private val fullId = appId + "/" + execId + private var workerThread: Thread = null + private var process: Process = null + private var stdoutAppender: FileAppender = null + private var stderrAppender: FileAppender = null // NOTE: This is now redundant with the automated shut-down enforced by the Executor. It might // make sense to remove this in the future. - var shutdownHook: Thread = null + private var shutdownHook: Thread = null - def start() { + private[worker] def start() { workerThread = new Thread("ExecutorRunner for " + fullId) { override def run() { fetchAndRunExecutor() } } @@ -85,32 +86,35 @@ private[spark] class ExecutorRunner( var exitCode: Option[Int] = None if (process != null) { logInfo("Killing process!") - process.destroy() - process.waitFor() if (stdoutAppender != null) { stdoutAppender.stop() } if (stderrAppender != null) { stderrAppender.stop() } + process.destroy() exitCode = Some(process.waitFor()) } worker ! ExecutorStateChanged(appId, execId, state, message, exitCode) } /** Stop this executor runner, including killing the process it launched */ - def kill() { + private[worker] def kill() { if (workerThread != null) { // the workerThread will kill the child process when interrupted workerThread.interrupt() workerThread = null state = ExecutorState.KILLED - Runtime.getRuntime.removeShutdownHook(shutdownHook) + try { + Runtime.getRuntime.removeShutdownHook(shutdownHook) + } catch { + case e: IllegalStateException => None + } } } /** Replace variables such as {{EXECUTOR_ID}} and {{CORES}} in a command argument passed to us */ - def substituteVariables(argument: String): String = argument match { + private[worker] def substituteVariables(argument: String): String = argument match { case "{{WORKER_URL}}" => workerUrl case "{{EXECUTOR_ID}}" => execId.toString case "{{HOSTNAME}}" => host @@ -122,7 +126,7 @@ private[spark] class ExecutorRunner( /** * Download and run the executor described in our ApplicationDescription */ - def fetchAndRunExecutor() { + private def fetchAndRunExecutor() { try { // Launch the process val builder = CommandUtils.buildProcessBuilder(appDesc.command, memory, @@ -131,13 +135,14 @@ private[spark] class ExecutorRunner( logInfo("Launch command: " + command.mkString("\"", "\" \"", "\"")) builder.directory(executorDir) - builder.environment.put("SPARK_LOCAL_DIRS", appLocalDirs.mkString(",")) + builder.environment.put("SPARK_EXECUTOR_DIRS", appLocalDirs.mkString(File.pathSeparator)) // In case we are running this from within the Spark Shell, avoid creating a "scala" // parent process for the executor command builder.environment.put("SPARK_LAUNCH_WITH_SCALA", "0") // Add webUI log urls - val baseUrl = s"http://$host:$webUiPort/logPage/?appId=$appId&executorId=$execId&logType=" + val baseUrl = + s"http://$publicAddress:$webUiPort/logPage/?appId=$appId&executorId=$execId&logType=" builder.environment.put("SPARK_LOG_URL_STDERR", s"${baseUrl}stderr") builder.environment.put("SPARK_LOG_URL_STDOUT", s"${baseUrl}stdout") diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala index 10929eb516041..3ee2eb69e8a4e 100755 --- a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala @@ -42,7 +42,7 @@ import org.apache.spark.util.{ActorLogReceive, AkkaUtils, SignalLogger, Utils} /** * @param masterAkkaUrls Each url should be a valid akka url. */ -private[spark] class Worker( +private[worker] class Worker( host: String, port: Int, webUiPort: Int, @@ -60,85 +60,90 @@ private[spark] class Worker( Utils.checkHost(host, "Expected hostname") assert (port > 0) - def createDateFormat = new SimpleDateFormat("yyyyMMddHHmmss") // For worker and executor IDs + // For worker and executor IDs + private def createDateFormat = new SimpleDateFormat("yyyyMMddHHmmss") // Send a heartbeat every (heartbeat timeout) / 4 milliseconds - val HEARTBEAT_MILLIS = conf.getLong("spark.worker.timeout", 60) * 1000 / 4 + private val HEARTBEAT_MILLIS = conf.getLong("spark.worker.timeout", 60) * 1000 / 4 // Model retries to connect to the master, after Hadoop's model. // The first six attempts to reconnect are in shorter intervals (between 5 and 15 seconds) // Afterwards, the next 10 attempts are between 30 and 90 seconds. // A bit of randomness is introduced so that not all of the workers attempt to reconnect at // the same time. - val INITIAL_REGISTRATION_RETRIES = 6 - val TOTAL_REGISTRATION_RETRIES = INITIAL_REGISTRATION_RETRIES + 10 - val FUZZ_MULTIPLIER_INTERVAL_LOWER_BOUND = 0.500 - val REGISTRATION_RETRY_FUZZ_MULTIPLIER = { + private val INITIAL_REGISTRATION_RETRIES = 6 + private val TOTAL_REGISTRATION_RETRIES = INITIAL_REGISTRATION_RETRIES + 10 + private val FUZZ_MULTIPLIER_INTERVAL_LOWER_BOUND = 0.500 + private val REGISTRATION_RETRY_FUZZ_MULTIPLIER = { val randomNumberGenerator = new Random(UUID.randomUUID.getMostSignificantBits) randomNumberGenerator.nextDouble + FUZZ_MULTIPLIER_INTERVAL_LOWER_BOUND } - val INITIAL_REGISTRATION_RETRY_INTERVAL = (math.round(10 * + private val INITIAL_REGISTRATION_RETRY_INTERVAL = (math.round(10 * REGISTRATION_RETRY_FUZZ_MULTIPLIER)).seconds - val PROLONGED_REGISTRATION_RETRY_INTERVAL = (math.round(60 + private val PROLONGED_REGISTRATION_RETRY_INTERVAL = (math.round(60 * REGISTRATION_RETRY_FUZZ_MULTIPLIER)).seconds - val CLEANUP_ENABLED = conf.getBoolean("spark.worker.cleanup.enabled", false) + private val CLEANUP_ENABLED = conf.getBoolean("spark.worker.cleanup.enabled", false) // How often worker will clean up old app folders - val CLEANUP_INTERVAL_MILLIS = conf.getLong("spark.worker.cleanup.interval", 60 * 30) * 1000 + private val CLEANUP_INTERVAL_MILLIS = + conf.getLong("spark.worker.cleanup.interval", 60 * 30) * 1000 // TTL for app folders/data; after TTL expires it will be cleaned up - val APP_DATA_RETENTION_SECS = conf.getLong("spark.worker.cleanup.appDataTtl", 7 * 24 * 3600) - - val testing: Boolean = sys.props.contains("spark.testing") - var master: ActorSelection = null - var masterAddress: Address = null - var activeMasterUrl: String = "" - var activeMasterWebUiUrl : String = "" - val akkaUrl = AkkaUtils.address( + private val APP_DATA_RETENTION_SECS = + conf.getLong("spark.worker.cleanup.appDataTtl", 7 * 24 * 3600) + + private val testing: Boolean = sys.props.contains("spark.testing") + private var master: ActorSelection = null + private var masterAddress: Address = null + private var activeMasterUrl: String = "" + private[worker] var activeMasterWebUiUrl : String = "" + private val akkaUrl = AkkaUtils.address( AkkaUtils.protocol(context.system), actorSystemName, host, port, actorName) - @volatile var registered = false - @volatile var connected = false - val workerId = generateWorkerId() - val sparkHome = + @volatile private var registered = false + @volatile private var connected = false + private val workerId = generateWorkerId() + private val sparkHome = if (testing) { assert(sys.props.contains("spark.test.home"), "spark.test.home is not set!") new File(sys.props("spark.test.home")) } else { new File(sys.env.get("SPARK_HOME").getOrElse(".")) } + var workDir: File = null - val executors = new HashMap[String, ExecutorRunner] val finishedExecutors = new HashMap[String, ExecutorRunner] val drivers = new HashMap[String, DriverRunner] + val executors = new HashMap[String, ExecutorRunner] val finishedDrivers = new HashMap[String, DriverRunner] val appDirectories = new HashMap[String, Seq[String]] val finishedApps = new HashSet[String] // The shuffle service is not actually started unless configured. - val shuffleService = new StandaloneWorkerShuffleService(conf, securityMgr) + private val shuffleService = new StandaloneWorkerShuffleService(conf, securityMgr) - val publicAddress = { - val envVar = System.getenv("SPARK_PUBLIC_DNS") + private val publicAddress = { + val envVar = conf.getenv("SPARK_PUBLIC_DNS") if (envVar != null) envVar else host } - var webUi: WorkerWebUI = null + private var webUi: WorkerWebUI = null - var coresUsed = 0 - var memoryUsed = 0 - var connectionAttemptCount = 0 + private var connectionAttemptCount = 0 - val metricsSystem = MetricsSystem.createMetricsSystem("worker", conf, securityMgr) - val workerSource = new WorkerSource(this) + private val metricsSystem = MetricsSystem.createMetricsSystem("worker", conf, securityMgr) + private val workerSource = new WorkerSource(this) + + private var registrationRetryTimer: Option[Cancellable] = None - var registrationRetryTimer: Option[Cancellable] = None + var coresUsed = 0 + var memoryUsed = 0 def coresFree: Int = cores - coresUsed def memoryFree: Int = memory - memoryUsed - def createWorkDir() { + private def createWorkDir() { workDir = Option(workDirPath).map(new File(_)).getOrElse(new File(sparkHome, "work")) try { // This sporadically fails - not sure why ... !workDir.exists() && !workDir.mkdirs() @@ -175,7 +180,7 @@ private[spark] class Worker( metricsSystem.getServletHandlers.foreach(webUi.attachHandler) } - def changeMaster(url: String, uiUrl: String) { + private def changeMaster(url: String, uiUrl: String) { // activeMasterUrl it's a valid Spark url since we receive it from master. activeMasterUrl = url activeMasterWebUiUrl = uiUrl @@ -252,7 +257,7 @@ private[spark] class Worker( } } - def registerWithMaster() { + private def registerWithMaster() { // DisassociatedEvent may be triggered multiple times, so don't attempt registration // if there are outstanding registration attempts scheduled. registrationRetryTimer match { @@ -270,7 +275,7 @@ private[spark] class Worker( } } - override def receiveWithLogging = { + override def receiveWithLogging: PartialFunction[Any, Unit] = { case RegisteredWorker(masterUrl, masterWebUiUrl) => logInfo("Successfully registered with master " + masterUrl) registered = true @@ -345,11 +350,11 @@ private[spark] class Worker( } // Create local dirs for the executor. These are passed to the executor via the - // SPARK_LOCAL_DIRS environment variable, and deleted by the Worker when the + // SPARK_EXECUTOR_DIRS environment variable, and deleted by the Worker when the // application finishes. val appLocalDirs = appDirectories.get(appId).getOrElse { Utils.getOrCreateLocalRootDirs(conf).map { dir => - Utils.createDirectory(dir).getAbsolutePath() + Utils.createDirectory(dir, namePrefix = "executor").getAbsolutePath() }.toSeq } appDirectories(appId) = appLocalDirs @@ -362,7 +367,8 @@ private[spark] class Worker( self, workerId, host, - webUiPort, + webUi.boundPort, + publicAddress, sparkHome, executorDir, akkaUrl, @@ -430,7 +436,8 @@ private[spark] class Worker( sparkHome, driverDesc.copy(command = Worker.maybeUpdateSSLSettings(driverDesc.command, conf)), self, - akkaUrl) + akkaUrl, + securityMgr) drivers(driverId) = driver driver.start() @@ -505,7 +512,7 @@ private[spark] class Worker( } } - def generateWorkerId(): String = { + private def generateWorkerId(): String = { "worker-%s-%s-%d".format(createDateFormat.format(new Date), host, port) } @@ -520,7 +527,7 @@ private[spark] class Worker( } } -private[spark] object Worker extends Logging { +private[deploy] object Worker extends Logging { def main(argStrings: Array[String]) { SignalLogger.register(log) val conf = new SparkConf @@ -538,10 +545,10 @@ private[spark] object Worker extends Logging { memory: Int, masterUrls: Array[String], workDir: String, - workerNumber: Option[Int] = None): (ActorSystem, Int) = { + workerNumber: Option[Int] = None, + conf: SparkConf = new SparkConf): (ActorSystem, Int) = { // The LocalSparkCluster runs multiple local sparkWorkerX actor systems - val conf = new SparkConf val systemName = "sparkWorker" + workerNumber.map(_.toString).getOrElse("") val actorName = "Worker" val securityMgr = new SecurityManager(conf) @@ -553,7 +560,7 @@ private[spark] object Worker extends Logging { (actorSystem, boundPort) } - private[spark] def isUseLocalNodeSSLConfig(cmd: Command): Boolean = { + def isUseLocalNodeSSLConfig(cmd: Command): Boolean = { val pattern = """\-Dspark\.ssl\.useNodeLocalConf\=(.+)""".r val result = cmd.javaOpts.collectFirst { case pattern(_result) => _result.toBoolean @@ -561,7 +568,7 @@ private[spark] object Worker extends Logging { result.getOrElse(false) } - private[spark] def maybeUpdateSSLSettings(cmd: Command, conf: SparkConf): Command = { + def maybeUpdateSSLSettings(cmd: Command, conf: SparkConf): Command = { val prefix = "spark.ssl." val useNLC = "spark.ssl.useNodeLocalConf" if (isUseLocalNodeSSLConfig(cmd)) { diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/WorkerArguments.scala b/core/src/main/scala/org/apache/spark/deploy/worker/WorkerArguments.scala index 019cd70f2a229..88f9d880ac209 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/WorkerArguments.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/WorkerArguments.scala @@ -25,7 +25,7 @@ import org.apache.spark.SparkConf /** * Command-line parser for the worker. */ -private[spark] class WorkerArguments(args: Array[String], conf: SparkConf) { +private[worker] class WorkerArguments(args: Array[String], conf: SparkConf) { var host = Utils.localHostName() var port = 0 var webUiPort = 8081 @@ -63,7 +63,7 @@ private[spark] class WorkerArguments(args: Array[String], conf: SparkConf) { checkWorkerMemory() - def parse(args: List[String]): Unit = args match { + private def parse(args: List[String]): Unit = args match { case ("--ip" | "-i") :: value :: tail => Utils.checkHost(value, "ip no longer supported, please use hostname " + value) host = value diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/WorkerSource.scala b/core/src/main/scala/org/apache/spark/deploy/worker/WorkerSource.scala index df1e01b23b932..b36023bc40c3d 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/WorkerSource.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/WorkerSource.scala @@ -21,7 +21,7 @@ import com.codahale.metrics.{Gauge, MetricRegistry} import org.apache.spark.metrics.source.Source -private[spark] class WorkerSource(val worker: Worker) extends Source { +private[worker] class WorkerSource(val worker: Worker) extends Source { override val sourceName = "worker" override val metricRegistry = new MetricRegistry() diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/WorkerWatcher.scala b/core/src/main/scala/org/apache/spark/deploy/worker/WorkerWatcher.scala index 63a8ac817b618..83fb991891a41 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/WorkerWatcher.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/WorkerWatcher.scala @@ -17,58 +17,63 @@ package org.apache.spark.deploy.worker -import akka.actor.{Actor, Address, AddressFromURIString} -import akka.remote.{AssociatedEvent, AssociationErrorEvent, AssociationEvent, DisassociatedEvent, RemotingLifecycleEvent} - import org.apache.spark.Logging import org.apache.spark.deploy.DeployMessages.SendHeartbeat -import org.apache.spark.util.ActorLogReceive +import org.apache.spark.rpc._ /** * Actor which connects to a worker process and terminates the JVM if the connection is severed. * Provides fate sharing between a worker and its associated child processes. */ -private[spark] class WorkerWatcher(workerUrl: String) - extends Actor with ActorLogReceive with Logging { - - override def preStart() { - context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent]) +private[spark] class WorkerWatcher(override val rpcEnv: RpcEnv, workerUrl: String) + extends RpcEndpoint with Logging { + override def onStart() { logInfo(s"Connecting to worker $workerUrl") - val worker = context.actorSelection(workerUrl) - worker ! SendHeartbeat // need to send a message here to initiate connection + if (!isTesting) { + rpcEnv.asyncSetupEndpointRefByURI(workerUrl) + } } // Used to avoid shutting down JVM during tests + // In the normal case, exitNonZero will call `System.exit(-1)` to shutdown the JVM. In the unit + // test, the user should call `setTesting(true)` so that `exitNonZero` will set `isShutDown` to + // true rather than calling `System.exit`. The user can check `isShutDown` to know if + // `exitNonZero` is called. private[deploy] var isShutDown = false private[deploy] def setTesting(testing: Boolean) = isTesting = testing private var isTesting = false // Lets us filter events only from the worker's actor system - private val expectedHostPort = AddressFromURIString(workerUrl).hostPort - private def isWorker(address: Address) = address.hostPort == expectedHostPort + private val expectedAddress = RpcAddress.fromURIString(workerUrl) + private def isWorker(address: RpcAddress) = expectedAddress == address - def exitNonZero() = if (isTesting) isShutDown = true else System.exit(-1) + private def exitNonZero() = if (isTesting) isShutDown = true else System.exit(-1) - override def receiveWithLogging = { - case AssociatedEvent(localAddress, remoteAddress, inbound) if isWorker(remoteAddress) => - logInfo(s"Successfully connected to $workerUrl") + override def receive: PartialFunction[Any, Unit] = { + case e => logWarning(s"Received unexpected message: $e") + } - case AssociationErrorEvent(cause, localAddress, remoteAddress, inbound, _) - if isWorker(remoteAddress) => - // These logs may not be seen if the worker (and associated pipe) has died - logError(s"Could not initialize connection to worker $workerUrl. Exiting.") - logError(s"Error was: $cause") - exitNonZero() + override def onConnected(remoteAddress: RpcAddress): Unit = { + if (isWorker(remoteAddress)) { + logInfo(s"Successfully connected to $workerUrl") + } + } - case DisassociatedEvent(localAddress, remoteAddress, inbound) if isWorker(remoteAddress) => + override def onDisconnected(remoteAddress: RpcAddress): Unit = { + if (isWorker(remoteAddress)) { // This log message will never be seen logError(s"Lost connection to worker actor $workerUrl. Exiting.") exitNonZero() + } + } - case e: AssociationEvent => - // pass through association events relating to other remote actor systems - - case e => logWarning(s"Received unexpected actor system event: $e") + override def onNetworkError(cause: Throwable, remoteAddress: RpcAddress): Unit = { + if (isWorker(remoteAddress)) { + // These logs may not be seen if the worker (and associated pipe) has died + logError(s"Could not initialize connection to worker $workerUrl. Exiting.") + logError(s"Error was: $cause") + exitNonZero() + } } } diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/ui/LogPage.scala b/core/src/main/scala/org/apache/spark/deploy/worker/ui/LogPage.scala index ecb358c399819..88170d4df3053 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/ui/LogPage.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/ui/LogPage.scala @@ -26,7 +26,7 @@ import org.apache.spark.util.Utils import org.apache.spark.Logging import org.apache.spark.util.logging.RollingFileAppender -private[spark] class LogPage(parent: WorkerWebUI) extends WebUIPage("logPage") with Logging { +private[ui] class LogPage(parent: WorkerWebUI) extends WebUIPage("logPage") with Logging { private val worker = parent.worker private val workDir = parent.workDir diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerPage.scala b/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerPage.scala index 327b905032800..9f9f27d71e1ae 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerPage.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerPage.scala @@ -31,10 +31,9 @@ import org.apache.spark.deploy.worker.{DriverRunner, ExecutorRunner} import org.apache.spark.ui.{WebUIPage, UIUtils} import org.apache.spark.util.Utils -private[spark] class WorkerPage(parent: WorkerWebUI) extends WebUIPage("") { - val workerActor = parent.worker.self - val worker = parent.worker - val timeout = parent.timeout +private[ui] class WorkerPage(parent: WorkerWebUI) extends WebUIPage("") { + private val workerActor = parent.worker.self + private val timeout = parent.timeout override def renderJson(request: HttpServletRequest): JValue = { val stateFuture = (workerActor ? RequestWorkerState)(timeout).mapTo[WorkerStateResponse] @@ -134,7 +133,7 @@ private[spark] class WorkerPage(parent: WorkerWebUI) extends WebUIPage("") { def driverRow(driver: DriverRunner): Seq[Node] = { {driver.driverId} - {driver.driverDesc.command.arguments(1)} + {driver.driverDesc.command.arguments(2)} {driver.finalState.getOrElse(DriverState.RUNNING)} {driver.driverDesc.cores.toString} diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerWebUI.scala b/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerWebUI.scala index 7ac81a2d87efd..de6423beb543e 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerWebUI.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerWebUI.scala @@ -30,7 +30,7 @@ import org.apache.spark.util.AkkaUtils /** * Web UI server for the standalone worker. */ -private[spark] +private[worker] class WorkerWebUI( val worker: Worker, val workDir: File, @@ -38,7 +38,7 @@ class WorkerWebUI( extends WebUI(worker.securityMgr, requestedPort, worker.conf, name = "WorkerUI") with Logging { - val timeout = AkkaUtils.askTimeout(worker.conf) + private[ui] val timeout = AkkaUtils.askTimeout(worker.conf) initialize() @@ -53,6 +53,6 @@ class WorkerWebUI( } } -private[spark] object WorkerWebUI { +private[ui] object WorkerWebUI { val STATIC_RESOURCE_BASE = SparkUI.STATIC_RESOURCE_DIR } diff --git a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala index 3a42f8b157977..8300f9f2190b9 100644 --- a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala +++ b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala @@ -17,40 +17,49 @@ package org.apache.spark.executor +import java.net.URL import java.nio.ByteBuffer -import scala.concurrent.Await +import scala.collection.mutable +import scala.util.{Failure, Success} -import akka.actor.{Actor, ActorSelection, Props} -import akka.pattern.Patterns -import akka.remote.{RemotingLifecycleEvent, DisassociatedEvent} - -import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkEnv} +import org.apache.spark.rpc._ +import org.apache.spark._ import org.apache.spark.TaskState.TaskState import org.apache.spark.deploy.SparkHadoopUtil import org.apache.spark.deploy.worker.WorkerWatcher import org.apache.spark.scheduler.TaskDescription import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._ -import org.apache.spark.util.{ActorLogReceive, AkkaUtils, SignalLogger, Utils} +import org.apache.spark.util.{SignalLogger, Utils} private[spark] class CoarseGrainedExecutorBackend( + override val rpcEnv: RpcEnv, driverUrl: String, executorId: String, hostPort: String, cores: Int, + userClassPath: Seq[URL], env: SparkEnv) - extends Actor with ActorLogReceive with ExecutorBackend with Logging { + extends ThreadSafeRpcEndpoint with ExecutorBackend with Logging { Utils.checkHostPort(hostPort, "Expected hostport") var executor: Executor = null - var driver: ActorSelection = null + @volatile var driver: Option[RpcEndpointRef] = None - override def preStart() { + override def onStart() { + import scala.concurrent.ExecutionContext.Implicits.global logInfo("Connecting to driver: " + driverUrl) - driver = context.actorSelection(driverUrl) - driver ! RegisterExecutor(executorId, hostPort, cores, extractLogUrls) - context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent]) + rpcEnv.asyncSetupEndpointRefByURI(driverUrl).flatMap { ref => + driver = Some(ref) + ref.sendWithReply[RegisteredExecutor.type]( + RegisterExecutor(executorId, self, hostPort, cores, extractLogUrls)) + } onComplete { + case Success(msg) => Utils.tryLogNonFatalError { + Option(self).foreach(_.send(msg)) // msg must be RegisteredExecutor + } + case Failure(e) => logError(s"Cannot register with driver: $driverUrl", e) + } } def extractLogUrls: Map[String, String] = { @@ -59,11 +68,11 @@ private[spark] class CoarseGrainedExecutorBackend( .map(e => (e._1.substring(prefix.length).toLowerCase, e._2)) } - override def receiveWithLogging = { + override def receive: PartialFunction[Any, Unit] = { case RegisteredExecutor => logInfo("Successfully registered with driver") val (hostname, _) = Utils.parseHostPort(hostPort) - executor = new Executor(executorId, hostname, env, isLocal = false) + executor = new Executor(executorId, hostname, env, userClassPath, isLocal = false) case RegisterExecutorFailed(message) => logError("Slave registration failed: " + message) @@ -89,23 +98,28 @@ private[spark] class CoarseGrainedExecutorBackend( executor.killTask(taskId, interruptThread) } - case x: DisassociatedEvent => - if (x.remoteAddress == driver.anchorPath.address) { - logError(s"Driver $x disassociated! Shutting down.") - System.exit(1) - } else { - logWarning(s"Received irrelevant DisassociatedEvent $x") - } - case StopExecutor => logInfo("Driver commanded a shutdown") executor.stop() - context.stop(self) - context.system.shutdown() + stop() + rpcEnv.shutdown() + } + + override def onDisconnected(remoteAddress: RpcAddress): Unit = { + if (driver.exists(_.address == remoteAddress)) { + logError(s"Driver $remoteAddress disassociated! Shutting down.") + System.exit(1) + } else { + logWarning(s"An unknown ($remoteAddress) driver disconnected.") + } } override def statusUpdate(taskId: Long, state: TaskState, data: ByteBuffer) { - driver ! StatusUpdate(executorId, taskId, state, data) + val msg = StatusUpdate(executorId, taskId, state, data) + driver match { + case Some(driverRef) => driverRef.send(msg) + case None => logWarning(s"Drop $msg because has not yet connected to driver") + } } } @@ -117,7 +131,8 @@ private[spark] object CoarseGrainedExecutorBackend extends Logging { hostname: String, cores: Int, appId: String, - workerUrl: Option[String]) { + workerUrl: Option[String], + userClassPath: Seq[URL]) { SignalLogger.register(log) @@ -128,16 +143,14 @@ private[spark] object CoarseGrainedExecutorBackend extends Logging { // Bootstrap to fetch the driver's Spark properties. val executorConf = new SparkConf val port = executorConf.getInt("spark.executor.port", 0) - val (fetcher, _) = AkkaUtils.createActorSystem( + val fetcher = RpcEnv.create( "driverPropsFetcher", hostname, port, executorConf, new SecurityManager(executorConf)) - val driver = fetcher.actorSelection(driverUrl) - val timeout = AkkaUtils.askTimeout(executorConf) - val fut = Patterns.ask(driver, RetrieveSparkProps, timeout) - val props = Await.result(fut, timeout).asInstanceOf[Seq[(String, String)]] ++ + val driver = fetcher.setupEndpointRefByURI(driverUrl) + val props = driver.askWithReply[Seq[(String, String)]](RetrieveSparkProps) ++ Seq[(String, String)](("spark.app.id", appId)) fetcher.shutdown() @@ -158,34 +171,81 @@ private[spark] object CoarseGrainedExecutorBackend extends Logging { val boundPort = env.conf.getInt("spark.executor.port", 0) assert(boundPort != 0) - // Start the CoarseGrainedExecutorBackend actor. + // Start the CoarseGrainedExecutorBackend endpoint. val sparkHostPort = hostname + ":" + boundPort - env.actorSystem.actorOf( - Props(classOf[CoarseGrainedExecutorBackend], - driverUrl, executorId, sparkHostPort, cores, env), - name = "Executor") + env.rpcEnv.setupEndpoint("Executor", new CoarseGrainedExecutorBackend( + env.rpcEnv, driverUrl, executorId, sparkHostPort, cores, userClassPath, env)) workerUrl.foreach { url => - env.actorSystem.actorOf(Props(classOf[WorkerWatcher], url), name = "WorkerWatcher") + env.rpcEnv.setupEndpoint("WorkerWatcher", new WorkerWatcher(env.rpcEnv, url)) } - env.actorSystem.awaitTermination() + env.rpcEnv.awaitTermination() } } def main(args: Array[String]) { - args.length match { - case x if x < 5 => - System.err.println( + var driverUrl: String = null + var executorId: String = null + var hostname: String = null + var cores: Int = 0 + var appId: String = null + var workerUrl: Option[String] = None + val userClassPath = new mutable.ListBuffer[URL]() + + var argv = args.toList + while (!argv.isEmpty) { + argv match { + case ("--driver-url") :: value :: tail => + driverUrl = value + argv = tail + case ("--executor-id") :: value :: tail => + executorId = value + argv = tail + case ("--hostname") :: value :: tail => + hostname = value + argv = tail + case ("--cores") :: value :: tail => + cores = value.toInt + argv = tail + case ("--app-id") :: value :: tail => + appId = value + argv = tail + case ("--worker-url") :: value :: tail => // Worker url is used in spark standalone mode to enforce fate-sharing with worker - "Usage: CoarseGrainedExecutorBackend " + - " [] ") - System.exit(1) + workerUrl = Some(value) + argv = tail + case ("--user-class-path") :: value :: tail => + userClassPath += new URL(value) + argv = tail + case Nil => + case tail => + System.err.println(s"Unrecognized options: ${tail.mkString(" ")}") + printUsageAndExit() + } + } - // NB: These arguments are provided by SparkDeploySchedulerBackend (for standalone mode) - // and CoarseMesosSchedulerBackend (for mesos mode). - case 5 => - run(args(0), args(1), args(2), args(3).toInt, args(4), None) - case x if x > 5 => - run(args(0), args(1), args(2), args(3).toInt, args(4), Some(args(5))) + if (driverUrl == null || executorId == null || hostname == null || cores <= 0 || + appId == null) { + printUsageAndExit() } + + run(driverUrl, executorId, hostname, cores, appId, workerUrl, userClassPath) + } + + private def printUsageAndExit() = { + System.err.println( + """ + |"Usage: CoarseGrainedExecutorBackend [options] + | + | Options are: + | --driver-url + | --executor-id + | --hostname + | --cores + | --app-id + | --worker-url + | --user-class-path + |""".stripMargin) + System.exit(1) } + } diff --git a/core/src/main/scala/org/apache/spark/executor/CommitDeniedException.scala b/core/src/main/scala/org/apache/spark/executor/CommitDeniedException.scala new file mode 100644 index 0000000000000..f47d7ef511da1 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/executor/CommitDeniedException.scala @@ -0,0 +1,33 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.executor + +import org.apache.spark.{TaskCommitDenied, TaskEndReason} + +/** + * Exception thrown when a task attempts to commit output to HDFS but is denied by the driver. + */ +private[spark] class CommitDeniedException( + msg: String, + jobID: Int, + splitID: Int, + attemptID: Int) + extends Exception(msg) { + + def toTaskEndReason: TaskEndReason = TaskCommitDenied(jobID, splitID, attemptID) +} diff --git a/core/src/main/scala/org/apache/spark/executor/Executor.scala b/core/src/main/scala/org/apache/spark/executor/Executor.scala index 5141483d1e745..327d155b38c22 100644 --- a/core/src/main/scala/org/apache/spark/executor/Executor.scala +++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala @@ -19,33 +19,35 @@ package org.apache.spark.executor import java.io.File import java.lang.management.ManagementFactory +import java.net.URL import java.nio.ByteBuffer -import java.util.concurrent._ +import java.util.concurrent.{ConcurrentHashMap, Executors, TimeUnit} import scala.collection.JavaConversions._ import scala.collection.mutable.{ArrayBuffer, HashMap} import scala.util.control.NonFatal -import akka.actor.Props - import org.apache.spark._ import org.apache.spark.deploy.SparkHadoopUtil -import org.apache.spark.scheduler._ +import org.apache.spark.scheduler.{DirectTaskResult, IndirectTaskResult, Task} import org.apache.spark.shuffle.FetchFailedException import org.apache.spark.storage.{StorageLevel, TaskResultBlockId} -import org.apache.spark.util.{SparkUncaughtExceptionHandler, AkkaUtils, Utils} +import org.apache.spark.util._ /** - * Spark executor used with Mesos, YARN, and the standalone scheduler. - * In coarse-grained mode, an existing actor system is provided. + * Spark executor, backed by a threadpool to run tasks. + * + * This can be used with Mesos, YARN, and the standalone scheduler. + * An internal RPC interface (at the moment Akka) is used for communication with the driver, + * except in the case of Mesos fine-grained mode. */ private[spark] class Executor( executorId: String, executorHostname: String, env: SparkEnv, + userClassPath: Seq[URL] = Nil, isLocal: Boolean = false) - extends Logging -{ + extends Logging { logInfo(s"Starting executor ID $executorId on host $executorHostname") @@ -58,8 +60,6 @@ private[spark] class Executor( private val conf = env.conf - @volatile private var isStopped = false - // No ip or host:port - just hostname Utils.checkHost(executorHostname, "Expected executed slave to be a hostname") // must not have port specified. @@ -76,18 +76,20 @@ private[spark] class Executor( } // Start worker thread pool - val threadPool = Utils.newDaemonCachedThreadPool("Executor task launch worker") - - val executorSource = new ExecutorSource(this, executorId) + private val threadPool = Utils.newDaemonCachedThreadPool("Executor task launch worker") + private val executorSource = new ExecutorSource(threadPool, executorId) if (!isLocal) { env.metricsSystem.registerSource(executorSource) env.blockManager.initialize(conf.getAppId) } - // Create an actor for receiving RPCs from the driver - private val executorActor = env.actorSystem.actorOf( - Props(new ExecutorActor(executorId)), "ExecutorActor") + // Create an RpcEndpoint for receiving RPCs from the driver + private val executorEndpoint = env.rpcEnv.setupEndpoint( + ExecutorEndpoint.EXECUTOR_ENDPOINT_NAME, new ExecutorEndpoint(env.rpcEnv, executorId)) + + // Whether to load classes in user jars before those in Spark jars + private val userClassPathFirst = conf.getBoolean("spark.executor.userClassPathFirst", false) // Create our ClassLoader // do this after SparkEnv creation so can access the SecurityManager @@ -95,7 +97,7 @@ private[spark] class Executor( private val replClassLoader = addReplClassLoaderIfNeeded(urlClassLoader) // Set the classloader for serializer - env.serializer.setDefaultClassLoader(urlClassLoader) + env.serializer.setDefaultClassLoader(replClassLoader) // Akka's message frame size. If task result is bigger than this, we use the block manager // to send the result back. @@ -107,6 +109,10 @@ private[spark] class Executor( // Maintains the list of running tasks. private val runningTasks = new ConcurrentHashMap[Long, TaskRunner] + // Executor for the heartbeat task. + private val heartbeater = Executors.newSingleThreadScheduledExecutor( + Utils.namedThreadFactory("driver-heartbeater")) + startDriverHeartbeater() def launchTask( @@ -114,31 +120,35 @@ private[spark] class Executor( taskId: Long, attemptNumber: Int, taskName: String, - serializedTask: ByteBuffer) { + serializedTask: ByteBuffer): Unit = { val tr = new TaskRunner(context, taskId = taskId, attemptNumber = attemptNumber, taskName, serializedTask) runningTasks.put(taskId, tr) threadPool.execute(tr) } - def killTask(taskId: Long, interruptThread: Boolean) { + def killTask(taskId: Long, interruptThread: Boolean): Unit = { val tr = runningTasks.get(taskId) if (tr != null) { tr.kill(interruptThread) } } - def stop() { + def stop(): Unit = { env.metricsSystem.report() - env.actorSystem.stop(executorActor) - isStopped = true + env.rpcEnv.stop(executorEndpoint) + heartbeater.shutdown() + heartbeater.awaitTermination(10, TimeUnit.SECONDS) threadPool.shutdown() if (!isLocal) { env.stop() } } - private def gcTime = ManagementFactory.getGarbageCollectorMXBeans.map(_.getCollectionTime).sum + /** Returns the total amount of time this JVM process has spent in garbage collection. */ + private def computeTotalGcTime(): Long = { + ManagementFactory.getGarbageCollectorMXBeans.map(_.getCollectionTime).sum + } class TaskRunner( execBackend: ExecutorBackend, @@ -148,12 +158,19 @@ private[spark] class Executor( serializedTask: ByteBuffer) extends Runnable { + /** Whether this task has been killed. */ @volatile private var killed = false - @volatile var task: Task[Any] = _ - @volatile var attemptedTask: Option[Task[Any]] = None + + /** How much the JVM process has spent in GC when the task starts to run. */ @volatile var startGCTime: Long = _ - def kill(interruptThread: Boolean) { + /** + * The task to run. This will be set in run() by deserializing the task binary coming + * from the driver. Once it is set, it will never be changed. + */ + @volatile var task: Task[Any] = _ + + def kill(interruptThread: Boolean): Unit = { logInfo(s"Executor is trying to kill $taskName (TID $taskId)") killed = true if (task != null) { @@ -161,14 +178,14 @@ private[spark] class Executor( } } - override def run() { + override def run(): Unit = { val deserializeStartTime = System.currentTimeMillis() Thread.currentThread.setContextClassLoader(replClassLoader) val ser = env.closureSerializer.newInstance() logInfo(s"Running $taskName (TID $taskId)") execBackend.statusUpdate(taskId, TaskState.RUNNING, EMPTY_BYTE_BUFFER) var taskStart: Long = 0 - startGCTime = gcTime + startGCTime = computeTotalGcTime() try { val (taskFiles, taskJars, taskBytes) = Task.deserializeWithDependencies(serializedTask) @@ -185,7 +202,6 @@ private[spark] class Executor( throw new TaskKilledException } - attemptedTask = Some(task) logDebug("Task " + taskId + "'s epoch is " + task.epoch) env.mapOutputTracker.updateEpoch(task.epoch) @@ -207,18 +223,17 @@ private[spark] class Executor( for (m <- task.metrics) { m.setExecutorDeserializeTime(taskStart - deserializeStartTime) m.setExecutorRunTime(taskFinish - taskStart) - m.setJvmGCTime(gcTime - startGCTime) + m.setJvmGCTime(computeTotalGcTime() - startGCTime) m.setResultSerializationTime(afterSerialization - beforeSerialization) } val accumUpdates = Accumulators.values - val directResult = new DirectTaskResult(valueBytes, accumUpdates, task.metrics.orNull) val serializedDirectResult = ser.serialize(directResult) val resultSize = serializedDirectResult.limit // directSend = sending directly back to the driver - val serializedResult = { + val serializedResult: ByteBuffer = { if (maxResultSize > 0 && resultSize > maxResultSize) { logWarning(s"Finished $taskName (TID $taskId). Result is larger than maxResultSize " + s"(${Utils.bytesToString(resultSize)} > ${Utils.bytesToString(maxResultSize)}), " + @@ -240,37 +255,40 @@ private[spark] class Executor( execBackend.statusUpdate(taskId, TaskState.FINISHED, serializedResult) } catch { - case ffe: FetchFailedException => { + case ffe: FetchFailedException => val reason = ffe.toTaskEndReason execBackend.statusUpdate(taskId, TaskState.FAILED, ser.serialize(reason)) - } - case _: TaskKilledException | _: InterruptedException if task.killed => { + case _: TaskKilledException | _: InterruptedException if task.killed => logInfo(s"Executor killed $taskName (TID $taskId)") execBackend.statusUpdate(taskId, TaskState.KILLED, ser.serialize(TaskKilled)) - } - case t: Throwable => { + case cDE: CommitDeniedException => + val reason = cDE.toTaskEndReason + execBackend.statusUpdate(taskId, TaskState.FAILED, ser.serialize(reason)) + + case t: Throwable => // Attempt to exit cleanly by informing the driver of our failure. // If anything goes wrong (or this was a fatal exception), we will delegate to // the default uncaught exception handler, which will terminate the Executor. logError(s"Exception in $taskName (TID $taskId)", t) - val serviceTime = System.currentTimeMillis() - taskStart - val metrics = attemptedTask.flatMap(t => t.metrics) - for (m <- metrics) { - m.setExecutorRunTime(serviceTime) - m.setJvmGCTime(gcTime - startGCTime) + val metrics: Option[TaskMetrics] = Option(task).flatMap { task => + task.metrics.map { m => + m.setExecutorRunTime(System.currentTimeMillis() - taskStart) + m.setJvmGCTime(computeTotalGcTime() - startGCTime) + m + } } - val reason = new ExceptionFailure(t, metrics) - execBackend.statusUpdate(taskId, TaskState.FAILED, ser.serialize(reason)) + val taskEndReason = new ExceptionFailure(t, metrics) + execBackend.statusUpdate(taskId, TaskState.FAILED, ser.serialize(taskEndReason)) // Don't forcibly exit unless the exception was inherently fatal, to avoid // stopping other tasks unnecessarily. if (Utils.isFatalError(t)) { SparkUncaughtExceptionHandler.uncaughtException(t) } - } + } finally { // Release memory used by this thread for shuffles env.shuffleMemoryManager.releaseMemoryForThisThread() @@ -288,17 +306,23 @@ private[spark] class Executor( * created by the interpreter to the search path */ private def createClassLoader(): MutableURLClassLoader = { + // Bootstrap the list of jars with the user class path. + val now = System.currentTimeMillis() + userClassPath.foreach { url => + currentJars(url.getPath().split("/").last) = now + } + val currentLoader = Utils.getContextOrSparkClassLoader // For each of the jars in the jarSet, add them to the class loader. // We assume each of the files has already been fetched. - val urls = currentJars.keySet.map { uri => + val urls = userClassPath.toArray ++ currentJars.keySet.map { uri => new File(uri.split("/").last).toURI.toURL - }.toArray - val userClassPathFirst = conf.getBoolean("spark.files.userClassPathFirst", false) - userClassPathFirst match { - case true => new ChildExecutorURLClassLoader(urls, currentLoader) - case false => new ExecutorURLClassLoader(urls, currentLoader) + } + if (userClassPathFirst) { + new ChildFirstURLClassLoader(urls, currentLoader) + } else { + new MutableURLClassLoader(urls, currentLoader) } } @@ -310,14 +334,13 @@ private[spark] class Executor( val classUri = conf.get("spark.repl.class.uri", null) if (classUri != null) { logInfo("Using REPL class URI: " + classUri) - val userClassPathFirst: java.lang.Boolean = - conf.getBoolean("spark.files.userClassPathFirst", false) try { + val _userClassPathFirst: java.lang.Boolean = userClassPathFirst val klass = Class.forName("org.apache.spark.repl.ExecutorClassLoader") .asInstanceOf[Class[_ <: ClassLoader]] val constructor = klass.getConstructor(classOf[SparkConf], classOf[String], classOf[ClassLoader], classOf[Boolean]) - constructor.newInstance(conf, classUri, parent, userClassPathFirst) + constructor.newInstance(conf, classUri, parent, _userClassPathFirst) } catch { case _: ClassNotFoundException => logError("Could not find org.apache.spark.repl.ExecutorClassLoader on classpath!") @@ -340,82 +363,86 @@ private[spark] class Executor( for ((name, timestamp) <- newFiles if currentFiles.getOrElse(name, -1L) < timestamp) { logInfo("Fetching " + name + " with timestamp " + timestamp) // Fetch file with useCache mode, close cache for local mode. - Utils.fetchFile(name, new File(SparkFiles.getRootDirectory), conf, + Utils.fetchFile(name, new File(SparkFiles.getRootDirectory()), conf, env.securityManager, hadoopConf, timestamp, useCache = !isLocal) currentFiles(name) = timestamp } - for ((name, timestamp) <- newJars if currentJars.getOrElse(name, -1L) < timestamp) { - logInfo("Fetching " + name + " with timestamp " + timestamp) - // Fetch file with useCache mode, close cache for local mode. - Utils.fetchFile(name, new File(SparkFiles.getRootDirectory), conf, - env.securityManager, hadoopConf, timestamp, useCache = !isLocal) - currentJars(name) = timestamp - // Add it to our class loader + for ((name, timestamp) <- newJars) { val localName = name.split("/").last - val url = new File(SparkFiles.getRootDirectory, localName).toURI.toURL - if (!urlClassLoader.getURLs.contains(url)) { - logInfo("Adding " + url + " to class loader") - urlClassLoader.addURL(url) + val currentTimeStamp = currentJars.get(name) + .orElse(currentJars.get(localName)) + .getOrElse(-1L) + if (currentTimeStamp < timestamp) { + logInfo("Fetching " + name + " with timestamp " + timestamp) + // Fetch file with useCache mode, close cache for local mode. + Utils.fetchFile(name, new File(SparkFiles.getRootDirectory()), conf, + env.securityManager, hadoopConf, timestamp, useCache = !isLocal) + currentJars(name) = timestamp + // Add it to our class loader + val url = new File(SparkFiles.getRootDirectory(), localName).toURI.toURL + if (!urlClassLoader.getURLs().contains(url)) { + logInfo("Adding " + url + " to class loader") + urlClassLoader.addURL(url) + } } } } } - def startDriverHeartbeater() { - val interval = conf.getInt("spark.executor.heartbeatInterval", 10000) - val timeout = AkkaUtils.lookupTimeout(conf) - val retryAttempts = AkkaUtils.numRetries(conf) - val retryIntervalMs = AkkaUtils.retryWaitMs(conf) - val heartbeatReceiverRef = AkkaUtils.makeDriverRef("HeartbeatReceiver", conf, env.actorSystem) - - val t = new Thread() { - override def run() { - // Sleep a random interval so the heartbeats don't end up in sync - Thread.sleep(interval + (math.random * interval).asInstanceOf[Int]) - - while (!isStopped) { - val tasksMetrics = new ArrayBuffer[(Long, TaskMetrics)]() - val curGCTime = gcTime - - for (taskRunner <- runningTasks.values()) { - if (taskRunner.attemptedTask.nonEmpty) { - Option(taskRunner.task).flatMap(_.metrics).foreach { metrics => - metrics.updateShuffleReadMetrics() - metrics.updateInputMetrics() - metrics.setJvmGCTime(curGCTime - taskRunner.startGCTime) - - if (isLocal) { - // JobProgressListener will hold an reference of it during - // onExecutorMetricsUpdate(), then JobProgressListener can not see - // the changes of metrics any more, so make a deep copy of it - val copiedMetrics = Utils.deserialize[TaskMetrics](Utils.serialize(metrics)) - tasksMetrics += ((taskRunner.taskId, copiedMetrics)) - } else { - // It will be copied by serialization - tasksMetrics += ((taskRunner.taskId, metrics)) - } - } - } - } - - val message = Heartbeat(executorId, tasksMetrics.toArray, env.blockManager.blockManagerId) - try { - val response = AkkaUtils.askWithReply[HeartbeatResponse](message, heartbeatReceiverRef, - retryAttempts, retryIntervalMs, timeout) - if (response.reregisterBlockManager) { - logWarning("Told to re-register on heartbeat") - env.blockManager.reregister() - } - } catch { - case NonFatal(t) => logWarning("Issue communicating with driver in heartbeater", t) + private val heartbeatReceiverRef = + RpcUtils.makeDriverRef(HeartbeatReceiver.ENDPOINT_NAME, conf, env.rpcEnv) + + /** Reports heartbeat and metrics for active tasks to the driver. */ + private def reportHeartBeat(): Unit = { + // list of (task id, metrics) to send back to the driver + val tasksMetrics = new ArrayBuffer[(Long, TaskMetrics)]() + val curGCTime = computeTotalGcTime() + + for (taskRunner <- runningTasks.values()) { + if (taskRunner.task != null) { + taskRunner.task.metrics.foreach { metrics => + metrics.updateShuffleReadMetrics() + metrics.updateInputMetrics() + metrics.setJvmGCTime(curGCTime - taskRunner.startGCTime) + + if (isLocal) { + // JobProgressListener will hold an reference of it during + // onExecutorMetricsUpdate(), then JobProgressListener can not see + // the changes of metrics any more, so make a deep copy of it + val copiedMetrics = Utils.deserialize[TaskMetrics](Utils.serialize(metrics)) + tasksMetrics += ((taskRunner.taskId, copiedMetrics)) + } else { + // It will be copied by serialization + tasksMetrics += ((taskRunner.taskId, metrics)) } - - Thread.sleep(interval) } } } - t.setDaemon(true) - t.setName("Driver Heartbeater") - t.start() + + val message = Heartbeat(executorId, tasksMetrics.toArray, env.blockManager.blockManagerId) + try { + val response = heartbeatReceiverRef.askWithReply[HeartbeatResponse](message) + if (response.reregisterBlockManager) { + logWarning("Told to re-register on heartbeat") + env.blockManager.reregister() + } + } catch { + case NonFatal(e) => logWarning("Issue communicating with driver in heartbeater", e) + } + } + + /** + * Schedules a task to report heartbeat and partial metrics for active tasks to driver. + */ + private def startDriverHeartbeater(): Unit = { + val intervalMs = conf.getTimeAsMs("spark.executor.heartbeatInterval", "10s") + + // Wait a random interval so the heartbeats don't end up in sync + val initialDelay = intervalMs + (math.random * intervalMs).asInstanceOf[Int] + + val heartbeatTask = new Runnable() { + override def run(): Unit = Utils.logUncaughtExceptions(reportHeartBeat()) + } + heartbeater.scheduleAtFixedRate(heartbeatTask, initialDelay, intervalMs, TimeUnit.MILLISECONDS) } } diff --git a/core/src/main/scala/org/apache/spark/executor/ExecutorActor.scala b/core/src/main/scala/org/apache/spark/executor/ExecutorEndpoint.scala similarity index 67% rename from core/src/main/scala/org/apache/spark/executor/ExecutorActor.scala rename to core/src/main/scala/org/apache/spark/executor/ExecutorEndpoint.scala index 41925f7e97e84..cf362f8464735 100644 --- a/core/src/main/scala/org/apache/spark/executor/ExecutorActor.scala +++ b/core/src/main/scala/org/apache/spark/executor/ExecutorEndpoint.scala @@ -17,10 +17,8 @@ package org.apache.spark.executor -import akka.actor.Actor -import org.apache.spark.Logging - -import org.apache.spark.util.{Utils, ActorLogReceive} +import org.apache.spark.rpc.{RpcEnv, RpcCallContext, RpcEndpoint} +import org.apache.spark.util.Utils /** * Driver -> Executor message to trigger a thread dump. @@ -28,14 +26,18 @@ import org.apache.spark.util.{Utils, ActorLogReceive} private[spark] case object TriggerThreadDump /** - * Actor that runs inside of executors to enable driver -> executor RPC. + * [[RpcEndpoint]] that runs inside of executors to enable driver -> executor RPC. */ private[spark] -class ExecutorActor(executorId: String) extends Actor with ActorLogReceive with Logging { +class ExecutorEndpoint(override val rpcEnv: RpcEnv, executorId: String) extends RpcEndpoint { - override def receiveWithLogging = { + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { case TriggerThreadDump => - sender ! Utils.getThreadDump() + context.reply(Utils.getThreadDump()) } } + +object ExecutorEndpoint { + val EXECUTOR_ENDPOINT_NAME = "ExecutorEndpoint" +} diff --git a/core/src/main/scala/org/apache/spark/executor/ExecutorSource.scala b/core/src/main/scala/org/apache/spark/executor/ExecutorSource.scala index c4d73622c4727..293c512f8b70c 100644 --- a/core/src/main/scala/org/apache/spark/executor/ExecutorSource.scala +++ b/core/src/main/scala/org/apache/spark/executor/ExecutorSource.scala @@ -17,6 +17,8 @@ package org.apache.spark.executor +import java.util.concurrent.ThreadPoolExecutor + import scala.collection.JavaConversions._ import com.codahale.metrics.{Gauge, MetricRegistry} @@ -24,9 +26,11 @@ import org.apache.hadoop.fs.FileSystem import org.apache.spark.metrics.source.Source -private[spark] class ExecutorSource(val executor: Executor, executorId: String) extends Source { +private[spark] +class ExecutorSource(threadPool: ThreadPoolExecutor, executorId: String) extends Source { + private def fileStats(scheme: String) : Option[FileSystem.Statistics] = - FileSystem.getAllStatistics().filter(s => s.getScheme.equals(scheme)).headOption + FileSystem.getAllStatistics().find(s => s.getScheme.equals(scheme)) private def registerFileSystemStat[T]( scheme: String, name: String, f: FileSystem.Statistics => T, defaultValue: T) = { @@ -41,23 +45,23 @@ private[spark] class ExecutorSource(val executor: Executor, executorId: String) // Gauge for executor thread pool's actively executing task counts metricRegistry.register(MetricRegistry.name("threadpool", "activeTasks"), new Gauge[Int] { - override def getValue: Int = executor.threadPool.getActiveCount() + override def getValue: Int = threadPool.getActiveCount() }) // Gauge for executor thread pool's approximate total number of tasks that have been completed metricRegistry.register(MetricRegistry.name("threadpool", "completeTasks"), new Gauge[Long] { - override def getValue: Long = executor.threadPool.getCompletedTaskCount() + override def getValue: Long = threadPool.getCompletedTaskCount() }) // Gauge for executor thread pool's current number of threads metricRegistry.register(MetricRegistry.name("threadpool", "currentPool_size"), new Gauge[Int] { - override def getValue: Int = executor.threadPool.getPoolSize() + override def getValue: Int = threadPool.getPoolSize() }) // Gauge got executor thread pool's largest number of threads that have ever simultaneously // been in th pool metricRegistry.register(MetricRegistry.name("threadpool", "maxPool_size"), new Gauge[Int] { - override def getValue: Int = executor.threadPool.getMaximumPoolSize() + override def getValue: Int = threadPool.getMaximumPoolSize() }) // Gauge for file system stats of this executor diff --git a/core/src/main/scala/org/apache/spark/executor/ExecutorURLClassLoader.scala b/core/src/main/scala/org/apache/spark/executor/ExecutorURLClassLoader.scala deleted file mode 100644 index 8011e75944aac..0000000000000 --- a/core/src/main/scala/org/apache/spark/executor/ExecutorURLClassLoader.scala +++ /dev/null @@ -1,84 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.executor - -import java.net.{URLClassLoader, URL} - -import org.apache.spark.util.ParentClassLoader - -/** - * The addURL method in URLClassLoader is protected. We subclass it to make this accessible. - * We also make changes so user classes can come before the default classes. - */ - -private[spark] trait MutableURLClassLoader extends ClassLoader { - def addURL(url: URL) - def getURLs: Array[URL] -} - -private[spark] class ChildExecutorURLClassLoader(urls: Array[URL], parent: ClassLoader) - extends MutableURLClassLoader { - - private object userClassLoader extends URLClassLoader(urls, null){ - override def addURL(url: URL) { - super.addURL(url) - } - override def findClass(name: String): Class[_] = { - val loaded = super.findLoadedClass(name) - if (loaded != null) { - return loaded - } - try { - super.findClass(name) - } catch { - case e: ClassNotFoundException => { - parentClassLoader.loadClass(name) - } - } - } - } - - private val parentClassLoader = new ParentClassLoader(parent) - - override def findClass(name: String): Class[_] = { - try { - userClassLoader.findClass(name) - } catch { - case e: ClassNotFoundException => { - parentClassLoader.loadClass(name) - } - } - } - - def addURL(url: URL) { - userClassLoader.addURL(url) - } - - def getURLs() = { - userClassLoader.getURLs() - } -} - -private[spark] class ExecutorURLClassLoader(urls: Array[URL], parent: ClassLoader) - extends URLClassLoader(urls, parent) with MutableURLClassLoader { - - override def addURL(url: URL) { - super.addURL(url) - } -} - diff --git a/core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala b/core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala index d05659193b334..06152f16ae618 100644 --- a/core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala +++ b/core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala @@ -17,13 +17,10 @@ package org.apache.spark.executor -import java.util.concurrent.atomic.AtomicLong - -import org.apache.spark.executor.DataReadMethod.DataReadMethod - import scala.collection.mutable.ArrayBuffer import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.executor.DataReadMethod.DataReadMethod import org.apache.spark.storage.{BlockId, BlockStatus} /** @@ -44,14 +41,14 @@ class TaskMetrics extends Serializable { * Host's name the task runs on */ private var _hostname: String = _ - def hostname = _hostname + def hostname: String = _hostname private[spark] def setHostname(value: String) = _hostname = value /** * Time taken on the executor to deserialize this task */ private var _executorDeserializeTime: Long = _ - def executorDeserializeTime = _executorDeserializeTime + def executorDeserializeTime: Long = _executorDeserializeTime private[spark] def setExecutorDeserializeTime(value: Long) = _executorDeserializeTime = value @@ -59,14 +56,14 @@ class TaskMetrics extends Serializable { * Time the executor spends actually running the task (including fetching shuffle data) */ private var _executorRunTime: Long = _ - def executorRunTime = _executorRunTime + def executorRunTime: Long = _executorRunTime private[spark] def setExecutorRunTime(value: Long) = _executorRunTime = value /** * The number of bytes this task transmitted back to the driver as the TaskResult */ private var _resultSize: Long = _ - def resultSize = _resultSize + def resultSize: Long = _resultSize private[spark] def setResultSize(value: Long) = _resultSize = value @@ -74,31 +71,31 @@ class TaskMetrics extends Serializable { * Amount of time the JVM spent in garbage collection while executing this task */ private var _jvmGCTime: Long = _ - def jvmGCTime = _jvmGCTime + def jvmGCTime: Long = _jvmGCTime private[spark] def setJvmGCTime(value: Long) = _jvmGCTime = value /** * Amount of time spent serializing the task result */ private var _resultSerializationTime: Long = _ - def resultSerializationTime = _resultSerializationTime + def resultSerializationTime: Long = _resultSerializationTime private[spark] def setResultSerializationTime(value: Long) = _resultSerializationTime = value /** * The number of in-memory bytes spilled by this task */ private var _memoryBytesSpilled: Long = _ - def memoryBytesSpilled = _memoryBytesSpilled - private[spark] def incMemoryBytesSpilled(value: Long) = _memoryBytesSpilled += value - private[spark] def decMemoryBytesSpilled(value: Long) = _memoryBytesSpilled -= value + def memoryBytesSpilled: Long = _memoryBytesSpilled + private[spark] def incMemoryBytesSpilled(value: Long): Unit = _memoryBytesSpilled += value + private[spark] def decMemoryBytesSpilled(value: Long): Unit = _memoryBytesSpilled -= value /** * The number of on-disk bytes spilled by this task */ private var _diskBytesSpilled: Long = _ - def diskBytesSpilled = _diskBytesSpilled - def incDiskBytesSpilled(value: Long) = _diskBytesSpilled += value - def decDiskBytesSpilled(value: Long) = _diskBytesSpilled -= value + def diskBytesSpilled: Long = _diskBytesSpilled + def incDiskBytesSpilled(value: Long): Unit = _diskBytesSpilled += value + def decDiskBytesSpilled(value: Long): Unit = _diskBytesSpilled -= value /** * If this task reads from a HadoopRDD or from persisted data, metrics on how much data was read @@ -106,7 +103,7 @@ class TaskMetrics extends Serializable { */ private var _inputMetrics: Option[InputMetrics] = None - def inputMetrics = _inputMetrics + def inputMetrics: Option[InputMetrics] = _inputMetrics /** * This should only be used when recreating TaskMetrics, not when updating input metrics in @@ -128,7 +125,7 @@ class TaskMetrics extends Serializable { */ private var _shuffleReadMetrics: Option[ShuffleReadMetrics] = None - def shuffleReadMetrics = _shuffleReadMetrics + def shuffleReadMetrics: Option[ShuffleReadMetrics] = _shuffleReadMetrics /** * This should only be used when recreating TaskMetrics, not when updating read metrics in @@ -177,17 +174,18 @@ class TaskMetrics extends Serializable { * Once https://issues.apache.org/jira/browse/SPARK-5225 is addressed, * we can store all the different inputMetrics (one per readMethod). */ - private[spark] def getInputMetricsForReadMethod(readMethod: DataReadMethod): - InputMetrics =synchronized { - _inputMetrics match { - case None => - val metrics = new InputMetrics(readMethod) - _inputMetrics = Some(metrics) - metrics - case Some(metrics @ InputMetrics(method)) if method == readMethod => - metrics - case Some(InputMetrics(method)) => - new InputMetrics(readMethod) + private[spark] def getInputMetricsForReadMethod(readMethod: DataReadMethod): InputMetrics = { + synchronized { + _inputMetrics match { + case None => + val metrics = new InputMetrics(readMethod) + _inputMetrics = Some(metrics) + metrics + case Some(metrics @ InputMetrics(method)) if method == readMethod => + metrics + case Some(InputMetrics(method)) => + new InputMetrics(readMethod) + } } } @@ -195,15 +193,18 @@ class TaskMetrics extends Serializable { * Aggregates shuffle read metrics for all registered dependencies into shuffleReadMetrics. */ private[spark] def updateShuffleReadMetrics(): Unit = synchronized { - val merged = new ShuffleReadMetrics() - for (depMetrics <- depsShuffleReadMetrics) { - merged.incFetchWaitTime(depMetrics.fetchWaitTime) - merged.incLocalBlocksFetched(depMetrics.localBlocksFetched) - merged.incRemoteBlocksFetched(depMetrics.remoteBlocksFetched) - merged.incRemoteBytesRead(depMetrics.remoteBytesRead) - merged.incRecordsRead(depMetrics.recordsRead) + if (!depsShuffleReadMetrics.isEmpty) { + val merged = new ShuffleReadMetrics() + for (depMetrics <- depsShuffleReadMetrics) { + merged.incFetchWaitTime(depMetrics.fetchWaitTime) + merged.incLocalBlocksFetched(depMetrics.localBlocksFetched) + merged.incRemoteBlocksFetched(depMetrics.remoteBlocksFetched) + merged.incRemoteBytesRead(depMetrics.remoteBytesRead) + merged.incLocalBytesRead(depMetrics.localBytesRead) + merged.incRecordsRead(depMetrics.recordsRead) + } + _shuffleReadMetrics = Some(merged) } - _shuffleReadMetrics = Some(merged) } private[spark] def updateInputMetrics(): Unit = synchronized { @@ -253,14 +254,14 @@ case class InputMetrics(readMethod: DataReadMethod.Value) { */ private var _bytesRead: Long = _ def bytesRead: Long = _bytesRead - def incBytesRead(bytes: Long) = _bytesRead += bytes + def incBytesRead(bytes: Long): Unit = _bytesRead += bytes /** * Total records read. */ private var _recordsRead: Long = _ def recordsRead: Long = _recordsRead - def incRecordsRead(records: Long) = _recordsRead += records + def incRecordsRead(records: Long): Unit = _recordsRead += records /** * Invoke the bytesReadCallback and mutate bytesRead. @@ -290,15 +291,15 @@ case class OutputMetrics(writeMethod: DataWriteMethod.Value) { * Total bytes written */ private var _bytesWritten: Long = _ - def bytesWritten = _bytesWritten - private[spark] def setBytesWritten(value : Long) = _bytesWritten = value + def bytesWritten: Long = _bytesWritten + private[spark] def setBytesWritten(value : Long): Unit = _bytesWritten = value /** * Total records written */ private var _recordsWritten: Long = 0L - def recordsWritten = _recordsWritten - private[spark] def setRecordsWritten(value: Long) = _recordsWritten = value + def recordsWritten: Long = _recordsWritten + private[spark] def setRecordsWritten(value: Long): Unit = _recordsWritten = value } /** @@ -311,7 +312,7 @@ class ShuffleReadMetrics extends Serializable { * Number of remote blocks fetched in this shuffle by this task */ private var _remoteBlocksFetched: Int = _ - def remoteBlocksFetched = _remoteBlocksFetched + def remoteBlocksFetched: Int = _remoteBlocksFetched private[spark] def incRemoteBlocksFetched(value: Int) = _remoteBlocksFetched += value private[spark] def decRemoteBlocksFetched(value: Int) = _remoteBlocksFetched -= value @@ -319,7 +320,7 @@ class ShuffleReadMetrics extends Serializable { * Number of local blocks fetched in this shuffle by this task */ private var _localBlocksFetched: Int = _ - def localBlocksFetched = _localBlocksFetched + def localBlocksFetched: Int = _localBlocksFetched private[spark] def incLocalBlocksFetched(value: Int) = _localBlocksFetched += value private[spark] def decLocalBlocksFetched(value: Int) = _localBlocksFetched -= value @@ -329,7 +330,7 @@ class ShuffleReadMetrics extends Serializable { * still not finished processing block A, it is not considered to be blocking on block B. */ private var _fetchWaitTime: Long = _ - def fetchWaitTime = _fetchWaitTime + def fetchWaitTime: Long = _fetchWaitTime private[spark] def incFetchWaitTime(value: Long) = _fetchWaitTime += value private[spark] def decFetchWaitTime(value: Long) = _fetchWaitTime -= value @@ -337,20 +338,32 @@ class ShuffleReadMetrics extends Serializable { * Total number of remote bytes read from the shuffle by this task */ private var _remoteBytesRead: Long = _ - def remoteBytesRead = _remoteBytesRead + def remoteBytesRead: Long = _remoteBytesRead private[spark] def incRemoteBytesRead(value: Long) = _remoteBytesRead += value private[spark] def decRemoteBytesRead(value: Long) = _remoteBytesRead -= value + /** + * Shuffle data that was read from the local disk (as opposed to from a remote executor). + */ + private var _localBytesRead: Long = _ + def localBytesRead: Long = _localBytesRead + private[spark] def incLocalBytesRead(value: Long) = _localBytesRead += value + + /** + * Total bytes fetched in the shuffle by this task (both remote and local). + */ + def totalBytesRead: Long = _remoteBytesRead + _localBytesRead + /** * Number of blocks fetched in this shuffle by this task (remote or local) */ - def totalBlocksFetched = _remoteBlocksFetched + _localBlocksFetched + def totalBlocksFetched: Int = _remoteBlocksFetched + _localBlocksFetched /** * Total number of records read from the shuffle by this task */ private var _recordsRead: Long = _ - def recordsRead = _recordsRead + def recordsRead: Long = _recordsRead private[spark] def incRecordsRead(value: Long) = _recordsRead += value private[spark] def decRecordsRead(value: Long) = _recordsRead -= value } @@ -365,7 +378,7 @@ class ShuffleWriteMetrics extends Serializable { * Number of bytes written for the shuffle by this task */ @volatile private var _shuffleBytesWritten: Long = _ - def shuffleBytesWritten = _shuffleBytesWritten + def shuffleBytesWritten: Long = _shuffleBytesWritten private[spark] def incShuffleBytesWritten(value: Long) = _shuffleBytesWritten += value private[spark] def decShuffleBytesWritten(value: Long) = _shuffleBytesWritten -= value @@ -373,7 +386,7 @@ class ShuffleWriteMetrics extends Serializable { * Time the task spent blocking on writes to disk or buffer cache, in nanoseconds */ @volatile private var _shuffleWriteTime: Long = _ - def shuffleWriteTime= _shuffleWriteTime + def shuffleWriteTime: Long = _shuffleWriteTime private[spark] def incShuffleWriteTime(value: Long) = _shuffleWriteTime += value private[spark] def decShuffleWriteTime(value: Long) = _shuffleWriteTime -= value @@ -381,7 +394,7 @@ class ShuffleWriteMetrics extends Serializable { * Total number of records written to the shuffle by this task */ @volatile private var _shuffleRecordsWritten: Long = _ - def shuffleRecordsWritten = _shuffleRecordsWritten + def shuffleRecordsWritten: Long = _shuffleRecordsWritten private[spark] def incShuffleRecordsWritten(value: Long) = _shuffleRecordsWritten += value private[spark] def decShuffleRecordsWritten(value: Long) = _shuffleRecordsWritten -= value private[spark] def setShuffleRecordsWritten(value: Long) = _shuffleRecordsWritten = value diff --git a/core/src/main/scala/org/apache/spark/input/PortableDataStream.scala b/core/src/main/scala/org/apache/spark/input/PortableDataStream.scala index 593a62b3e3b32..6cda7772f77bc 100644 --- a/core/src/main/scala/org/apache/spark/input/PortableDataStream.scala +++ b/core/src/main/scala/org/apache/spark/input/PortableDataStream.scala @@ -73,16 +73,16 @@ private[spark] abstract class StreamBasedRecordReader[T]( private var key = "" private var value: T = null.asInstanceOf[T] - override def initialize(split: InputSplit, context: TaskAttemptContext) = {} - override def close() = {} + override def initialize(split: InputSplit, context: TaskAttemptContext): Unit = {} + override def close(): Unit = {} - override def getProgress = if (processed) 1.0f else 0.0f + override def getProgress: Float = if (processed) 1.0f else 0.0f - override def getCurrentKey = key + override def getCurrentKey: String = key - override def getCurrentValue = value + override def getCurrentValue: T = value - override def nextKeyValue = { + override def nextKeyValue: Boolean = { if (!processed) { val fileIn = new PortableDataStream(split, context, index) value = parseStream(fileIn) @@ -119,7 +119,8 @@ private[spark] class StreamRecordReader( * The format for the PortableDataStream files */ private[spark] class StreamInputFormat extends StreamFileInputFormat[PortableDataStream] { - override def createRecordReader(split: InputSplit, taContext: TaskAttemptContext) = { + override def createRecordReader(split: InputSplit, taContext: TaskAttemptContext) + : CombineFileRecordReader[String, PortableDataStream] = { new CombineFileRecordReader[String, PortableDataStream]( split.asInstanceOf[CombineFileSplit], taContext, classOf[StreamRecordReader]) } @@ -204,7 +205,7 @@ class PortableDataStream( /** * Close the file (if it is currently open) */ - def close() = { + def close(): Unit = { if (isOpen) { try { fileIn.close() diff --git a/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala b/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala index f856890d279f4..0709b6d689e86 100644 --- a/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala +++ b/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala @@ -26,7 +26,6 @@ import org.xerial.snappy.{Snappy, SnappyInputStream, SnappyOutputStream} import org.apache.spark.SparkConf import org.apache.spark.annotation.DeveloperApi import org.apache.spark.util.Utils -import org.apache.spark.Logging /** * :: DeveloperApi :: @@ -53,8 +52,12 @@ private[spark] object CompressionCodec { "lzf" -> classOf[LZFCompressionCodec].getName, "snappy" -> classOf[SnappyCompressionCodec].getName) + def getCodecName(conf: SparkConf): String = { + conf.get(configKey, DEFAULT_COMPRESSION_CODEC) + } + def createCodec(conf: SparkConf): CompressionCodec = { - createCodec(conf, conf.get(configKey, DEFAULT_COMPRESSION_CODEC)) + createCodec(conf, getCodecName(conf)) } def createCodec(conf: SparkConf, codecName: String): CompressionCodec = { @@ -71,6 +74,20 @@ private[spark] object CompressionCodec { s"Consider setting $configKey=$FALLBACK_COMPRESSION_CODEC")) } + /** + * Return the short version of the given codec name. + * If it is already a short name, just return it. + */ + def getShortName(codecName: String): String = { + if (shortCompressionCodecNames.contains(codecName)) { + codecName + } else { + shortCompressionCodecNames + .collectFirst { case (k, v) if v == codecName => k } + .getOrElse { throw new IllegalArgumentException(s"No short name for codec $codecName.") } + } + } + val FALLBACK_COMPRESSION_CODEC = "lzf" val DEFAULT_COMPRESSION_CODEC = "snappy" val ALL_COMPRESSION_CODECS = shortCompressionCodecNames.values.toSeq diff --git a/core/src/main/scala/org/apache/spark/TaskContextHelper.scala b/core/src/main/scala/org/apache/spark/launcher/SparkSubmitArgumentsParser.scala similarity index 70% rename from core/src/main/scala/org/apache/spark/TaskContextHelper.scala rename to core/src/main/scala/org/apache/spark/launcher/SparkSubmitArgumentsParser.scala index 4636c4600a01a..a835012531052 100644 --- a/core/src/main/scala/org/apache/spark/TaskContextHelper.scala +++ b/core/src/main/scala/org/apache/spark/launcher/SparkSubmitArgumentsParser.scala @@ -15,15 +15,11 @@ * limitations under the License. */ -package org.apache.spark +package org.apache.spark.launcher /** - * This class exists to restrict the visibility of TaskContext setters. + * This class makes SparkSubmitOptionParser visible for Spark code outside of the `launcher` + * package, since Java doesn't have a feature similar to `private[spark]`, and we don't want + * that class to be public. */ -private [spark] object TaskContextHelper { - - def setTaskContext(tc: TaskContext): Unit = TaskContext.setTaskContext(tc) - - def unset(): Unit = TaskContext.unset() - -} +private[spark] abstract class SparkSubmitArgumentsParser extends SparkSubmitOptionParser diff --git a/core/src/main/scala/org/apache/spark/launcher/WorkerCommandBuilder.scala b/core/src/main/scala/org/apache/spark/launcher/WorkerCommandBuilder.scala new file mode 100644 index 0000000000000..9be98723aed14 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/launcher/WorkerCommandBuilder.scala @@ -0,0 +1,50 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.launcher + +import java.io.File +import java.util.{HashMap => JHashMap, List => JList, Map => JMap} + +import scala.collection.JavaConversions._ + +import org.apache.spark.deploy.Command + +/** + * This class is used by CommandUtils. It uses some package-private APIs in SparkLauncher, and since + * Java doesn't have a feature similar to `private[spark]`, and we don't want that class to be + * public, needs to live in the same package as the rest of the library. + */ +private[spark] class WorkerCommandBuilder(sparkHome: String, memoryMb: Int, command: Command) + extends AbstractCommandBuilder { + + childEnv.putAll(command.environment) + childEnv.put(CommandBuilderUtils.ENV_SPARK_HOME, sparkHome) + + override def buildCommand(env: JMap[String, String]): JList[String] = { + val cmd = buildJavaCommand(command.classPathEntries.mkString(File.pathSeparator)) + cmd.add(s"-Xms${memoryMb}M") + cmd.add(s"-Xmx${memoryMb}M") + command.javaOpts.foreach(cmd.add) + addPermGenSizeOpt(cmd) + addOptionString(cmd, getenv("SPARK_JAVA_OPTS")) + cmd + } + + def buildCommand(): JList[String] = buildCommand(new JHashMap[String, String]()) + +} diff --git a/core/src/main/scala/org/apache/spark/mapred/SparkHadoopMapRedUtil.scala b/core/src/main/scala/org/apache/spark/mapred/SparkHadoopMapRedUtil.scala index 21b782edd2a9e..818f7a4c8d422 100644 --- a/core/src/main/scala/org/apache/spark/mapred/SparkHadoopMapRedUtil.scala +++ b/core/src/main/scala/org/apache/spark/mapred/SparkHadoopMapRedUtil.scala @@ -17,9 +17,15 @@ package org.apache.spark.mapred +import java.io.IOException import java.lang.reflect.Modifier -import org.apache.hadoop.mapred.{TaskAttemptID, JobID, JobConf, JobContext, TaskAttemptContext} +import org.apache.hadoop.mapred._ +import org.apache.hadoop.mapreduce.{TaskAttemptContext => MapReduceTaskAttemptContext} +import org.apache.hadoop.mapreduce.{OutputCommitter => MapReduceOutputCommitter} + +import org.apache.spark.executor.CommitDeniedException +import org.apache.spark.{Logging, SparkEnv, TaskContext} private[spark] trait SparkHadoopMapRedUtil { @@ -52,7 +58,7 @@ trait SparkHadoopMapRedUtil { jobId: Int, isMap: Boolean, taskId: Int, - attemptId: Int) = { + attemptId: Int): TaskAttemptID = { new TaskAttemptID(jtIdentifier, jobId, isMap, taskId, attemptId) } @@ -65,3 +71,86 @@ trait SparkHadoopMapRedUtil { } } } + +object SparkHadoopMapRedUtil extends Logging { + /** + * Commits a task output. Before committing the task output, we need to know whether some other + * task attempt might be racing to commit the same output partition. Therefore, coordinate with + * the driver in order to determine whether this attempt can commit (please see SPARK-4879 for + * details). + * + * Output commit coordinator is only contacted when the following two configurations are both set + * to `true`: + * + * - `spark.speculation` + * - `spark.hadoop.outputCommitCoordination.enabled` + */ + def commitTask( + committer: MapReduceOutputCommitter, + mrTaskContext: MapReduceTaskAttemptContext, + jobId: Int, + splitId: Int, + attemptId: Int): Unit = { + + val mrTaskAttemptID = mrTaskContext.getTaskAttemptID + + // Called after we have decided to commit + def performCommit(): Unit = { + try { + committer.commitTask(mrTaskContext) + logInfo(s"$mrTaskAttemptID: Committed") + } catch { + case cause: IOException => + logError(s"Error committing the output of task: $mrTaskAttemptID", cause) + committer.abortTask(mrTaskContext) + throw cause + } + } + + // First, check whether the task's output has already been committed by some other attempt + if (committer.needsTaskCommit(mrTaskContext)) { + val shouldCoordinateWithDriver: Boolean = { + val sparkConf = SparkEnv.get.conf + // We only need to coordinate with the driver if there are multiple concurrent task + // attempts, which should only occur if speculation is enabled + val speculationEnabled = sparkConf.getBoolean("spark.speculation", defaultValue = false) + // This (undocumented) setting is an escape-hatch in case the commit code introduces bugs + sparkConf.getBoolean("spark.hadoop.outputCommitCoordination.enabled", speculationEnabled) + } + + if (shouldCoordinateWithDriver) { + val outputCommitCoordinator = SparkEnv.get.outputCommitCoordinator + val canCommit = outputCommitCoordinator.canCommit(jobId, splitId, attemptId) + + if (canCommit) { + performCommit() + } else { + val message = + s"$mrTaskAttemptID: Not committed because the driver did not authorize commit" + logInfo(message) + // We need to abort the task so that the driver can reschedule new attempts, if necessary + committer.abortTask(mrTaskContext) + throw new CommitDeniedException(message, jobId, splitId, attemptId) + } + } else { + // Speculation is disabled or a user has chosen to manually bypass the commit coordination + performCommit() + } + } else { + // Some other attempt committed the output, so we do nothing and signal success + logInfo(s"No need to commit output of task because needsTaskCommit=false: $mrTaskAttemptID") + } + } + + def commitTask( + committer: MapReduceOutputCommitter, + mrTaskContext: MapReduceTaskAttemptContext, + sparkTaskContext: TaskContext): Unit = { + commitTask( + committer, + mrTaskContext, + sparkTaskContext.stageId(), + sparkTaskContext.partitionId(), + sparkTaskContext.attemptNumber()) + } +} diff --git a/core/src/main/scala/org/apache/spark/mapreduce/SparkHadoopMapReduceUtil.scala b/core/src/main/scala/org/apache/spark/mapreduce/SparkHadoopMapReduceUtil.scala index 3340673f91156..cfd20392d12f1 100644 --- a/core/src/main/scala/org/apache/spark/mapreduce/SparkHadoopMapReduceUtil.scala +++ b/core/src/main/scala/org/apache/spark/mapreduce/SparkHadoopMapReduceUtil.scala @@ -45,7 +45,7 @@ trait SparkHadoopMapReduceUtil { jobId: Int, isMap: Boolean, taskId: Int, - attemptId: Int) = { + attemptId: Int): TaskAttemptID = { val klass = Class.forName("org.apache.hadoop.mapreduce.TaskAttemptID") try { // First, attempt to use the old-style constructor that takes a boolean isMap diff --git a/core/src/main/scala/org/apache/spark/metrics/MetricsConfig.scala b/core/src/main/scala/org/apache/spark/metrics/MetricsConfig.scala index 1b7a5d1f1980a..8edf493780687 100644 --- a/core/src/main/scala/org/apache/spark/metrics/MetricsConfig.scala +++ b/core/src/main/scala/org/apache/spark/metrics/MetricsConfig.scala @@ -28,12 +28,12 @@ import org.apache.spark.util.Utils private[spark] class MetricsConfig(val configFile: Option[String]) extends Logging { - val DEFAULT_PREFIX = "*" - val INSTANCE_REGEX = "^(\\*|[a-zA-Z]+)\\.(.+)".r - val METRICS_CONF = "metrics.properties" + private val DEFAULT_PREFIX = "*" + private val INSTANCE_REGEX = "^(\\*|[a-zA-Z]+)\\.(.+)".r + private val DEFAULT_METRICS_CONF_FILENAME = "metrics.properties" - val properties = new Properties() - var propertyCategories: mutable.HashMap[String, Properties] = null + private[metrics] val properties = new Properties() + private[metrics] var propertyCategories: mutable.HashMap[String, Properties] = null private def setDefaultProperties(prop: Properties) { prop.setProperty("*.sink.servlet.class", "org.apache.spark.metrics.sink.MetricsServlet") @@ -47,20 +47,22 @@ private[spark] class MetricsConfig(val configFile: Option[String]) extends Loggi setDefaultProperties(properties) // If spark.metrics.conf is not set, try to get file in class path - var is: InputStream = null - try { - is = configFile match { - case Some(f) => new FileInputStream(f) - case None => Utils.getSparkClassLoader.getResourceAsStream(METRICS_CONF) + val isOpt: Option[InputStream] = configFile.map(new FileInputStream(_)).orElse { + try { + Option(Utils.getSparkClassLoader.getResourceAsStream(DEFAULT_METRICS_CONF_FILENAME)) + } catch { + case e: Exception => + logError("Error loading default configuration file", e) + None } + } - if (is != null) { + isOpt.foreach { is => + try { properties.load(is) + } finally { + is.close() } - } catch { - case e: Exception => logError("Error loading configure file", e) - } finally { - if (is != null) is.close() } propertyCategories = subProperties(properties, INSTANCE_REGEX) diff --git a/core/src/main/scala/org/apache/spark/metrics/MetricsSystem.scala b/core/src/main/scala/org/apache/spark/metrics/MetricsSystem.scala index 83e8eb71260eb..9150ad35712a1 100644 --- a/core/src/main/scala/org/apache/spark/metrics/MetricsSystem.scala +++ b/core/src/main/scala/org/apache/spark/metrics/MetricsSystem.scala @@ -23,6 +23,7 @@ import java.util.concurrent.TimeUnit import scala.collection.mutable import com.codahale.metrics.{Metric, MetricFilter, MetricRegistry} +import org.eclipse.jetty.servlet.ServletContextHandler import org.apache.spark.{Logging, SecurityManager, SparkConf} import org.apache.spark.metrics.sink.{MetricsServlet, Sink} @@ -84,7 +85,7 @@ private[spark] class MetricsSystem private ( /** * Get any UI handlers used by this metrics system; can only be called after start(). */ - def getServletHandlers = { + def getServletHandlers: Array[ServletContextHandler] = { require(running, "Can only call getServletHandlers on a running MetricsSystem") metricsServlet.map(_.getHandlers).getOrElse(Array()) } @@ -191,7 +192,10 @@ private[spark] class MetricsSystem private ( sinks += sink.asInstanceOf[Sink] } } catch { - case e: Exception => logError("Sink class " + classPath + " cannot be instantialized", e) + case e: Exception => { + logError("Sink class " + classPath + " cannot be instantialized") + throw e + } } } } diff --git a/core/src/main/scala/org/apache/spark/metrics/sink/MetricsServlet.scala b/core/src/main/scala/org/apache/spark/metrics/sink/MetricsServlet.scala index 2f65bc8b46609..0c2e212a33074 100644 --- a/core/src/main/scala/org/apache/spark/metrics/sink/MetricsServlet.scala +++ b/core/src/main/scala/org/apache/spark/metrics/sink/MetricsServlet.scala @@ -30,8 +30,12 @@ import org.eclipse.jetty.servlet.ServletContextHandler import org.apache.spark.SecurityManager import org.apache.spark.ui.JettyUtils._ -private[spark] class MetricsServlet(val property: Properties, val registry: MetricRegistry, - securityMgr: SecurityManager) extends Sink { +private[spark] class MetricsServlet( + val property: Properties, + val registry: MetricRegistry, + securityMgr: SecurityManager) + extends Sink { + val SERVLET_KEY_PATH = "path" val SERVLET_KEY_SAMPLE = "sample" @@ -45,10 +49,12 @@ private[spark] class MetricsServlet(val property: Properties, val registry: Metr val mapper = new ObjectMapper().registerModule( new MetricsModule(TimeUnit.SECONDS, TimeUnit.MILLISECONDS, servletShowSample)) - def getHandlers = Array[ServletContextHandler]( - createServletHandler(servletPath, - new ServletParams(request => getMetricsSnapshot(request), "text/json"), securityMgr) - ) + def getHandlers: Array[ServletContextHandler] = { + Array[ServletContextHandler]( + createServletHandler(servletPath, + new ServletParams(request => getMetricsSnapshot(request), "text/json"), securityMgr) + ) + } def getMetricsSnapshot(request: HttpServletRequest): String = { mapper.writeValueAsString(registry) diff --git a/core/src/main/scala/org/apache/spark/metrics/sink/Sink.scala b/core/src/main/scala/org/apache/spark/metrics/sink/Sink.scala index 0d83d8c425ca4..9fad4e7deacb6 100644 --- a/core/src/main/scala/org/apache/spark/metrics/sink/Sink.scala +++ b/core/src/main/scala/org/apache/spark/metrics/sink/Sink.scala @@ -18,7 +18,7 @@ package org.apache.spark.metrics.sink private[spark] trait Sink { - def start: Unit - def stop: Unit + def start(): Unit + def stop(): Unit def report(): Unit } diff --git a/core/src/main/scala/org/apache/spark/metrics/sink/Slf4jSink.scala b/core/src/main/scala/org/apache/spark/metrics/sink/Slf4jSink.scala new file mode 100644 index 0000000000000..e8b3074e8f1a6 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/metrics/sink/Slf4jSink.scala @@ -0,0 +1,68 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.metrics.sink + +import java.util.Properties +import java.util.concurrent.TimeUnit + +import com.codahale.metrics.{Slf4jReporter, MetricRegistry} + +import org.apache.spark.SecurityManager +import org.apache.spark.metrics.MetricsSystem + +private[spark] class Slf4jSink( + val property: Properties, + val registry: MetricRegistry, + securityMgr: SecurityManager) + extends Sink { + val SLF4J_DEFAULT_PERIOD = 10 + val SLF4J_DEFAULT_UNIT = "SECONDS" + + val SLF4J_KEY_PERIOD = "period" + val SLF4J_KEY_UNIT = "unit" + + val pollPeriod = Option(property.getProperty(SLF4J_KEY_PERIOD)) match { + case Some(s) => s.toInt + case None => SLF4J_DEFAULT_PERIOD + } + + val pollUnit: TimeUnit = Option(property.getProperty(SLF4J_KEY_UNIT)) match { + case Some(s) => TimeUnit.valueOf(s.toUpperCase()) + case None => TimeUnit.valueOf(SLF4J_DEFAULT_UNIT) + } + + MetricsSystem.checkMinimalPollingPeriod(pollUnit, pollPeriod) + + val reporter: Slf4jReporter = Slf4jReporter.forRegistry(registry) + .convertDurationsTo(TimeUnit.MILLISECONDS) + .convertRatesTo(TimeUnit.SECONDS) + .build() + + override def start() { + reporter.start(pollPeriod, pollUnit) + } + + override def stop() { + reporter.stop() + } + + override def report() { + reporter.report() + } +} + diff --git a/core/src/main/scala/org/apache/spark/network/nio/BlockMessageArray.scala b/core/src/main/scala/org/apache/spark/network/nio/BlockMessageArray.scala index a1a2c00ed1542..1ba25aa74aa02 100644 --- a/core/src/main/scala/org/apache/spark/network/nio/BlockMessageArray.scala +++ b/core/src/main/scala/org/apache/spark/network/nio/BlockMessageArray.scala @@ -32,11 +32,11 @@ class BlockMessageArray(var blockMessages: Seq[BlockMessage]) def this() = this(null.asInstanceOf[Seq[BlockMessage]]) - def apply(i: Int) = blockMessages(i) + def apply(i: Int): BlockMessage = blockMessages(i) - def iterator = blockMessages.iterator + def iterator: Iterator[BlockMessage] = blockMessages.iterator - def length = blockMessages.length + def length: Int = blockMessages.length def set(bufferMessage: BufferMessage) { val startTime = System.currentTimeMillis diff --git a/core/src/main/scala/org/apache/spark/network/nio/BufferMessage.scala b/core/src/main/scala/org/apache/spark/network/nio/BufferMessage.scala index 3b245c5c7a4f3..9a9e22b0c2366 100644 --- a/core/src/main/scala/org/apache/spark/network/nio/BufferMessage.scala +++ b/core/src/main/scala/org/apache/spark/network/nio/BufferMessage.scala @@ -31,9 +31,9 @@ class BufferMessage(id_ : Int, val buffers: ArrayBuffer[ByteBuffer], var ackId: val initialSize = currentSize() var gotChunkForSendingOnce = false - def size = initialSize + def size: Int = initialSize - def currentSize() = { + def currentSize(): Int = { if (buffers == null || buffers.isEmpty) { 0 } else { @@ -100,11 +100,11 @@ class BufferMessage(id_ : Int, val buffers: ArrayBuffer[ByteBuffer], var ackId: buffers.foreach(_.flip) } - def hasAckId() = (ackId != 0) + def hasAckId(): Boolean = ackId != 0 - def isCompletelyReceived() = !buffers(0).hasRemaining + def isCompletelyReceived: Boolean = !buffers(0).hasRemaining - override def toString = { + override def toString: String = { if (hasAckId) { "BufferAckMessage(aid = " + ackId + ", id = " + id + ", size = " + size + ")" } else { diff --git a/core/src/main/scala/org/apache/spark/network/nio/Connection.scala b/core/src/main/scala/org/apache/spark/network/nio/Connection.scala index c2d9578be7ebb..6b898bd4bfc1b 100644 --- a/core/src/main/scala/org/apache/spark/network/nio/Connection.scala +++ b/core/src/main/scala/org/apache/spark/network/nio/Connection.scala @@ -101,9 +101,11 @@ abstract class Connection(val channel: SocketChannel, val selector: Selector, socketRemoteConnectionManagerId } - def key() = channel.keyFor(selector) + def key(): SelectionKey = channel.keyFor(selector) - def getRemoteAddress() = channel.socket.getRemoteSocketAddress().asInstanceOf[InetSocketAddress] + def getRemoteAddress(): InetSocketAddress = { + channel.socket.getRemoteSocketAddress().asInstanceOf[InetSocketAddress] + } // Returns whether we have to register for further reads or not. def read(): Boolean = { @@ -179,7 +181,7 @@ abstract class Connection(val channel: SocketChannel, val selector: Selector, buffer.get(bytes) bytes.foreach(x => print(x + " ")) buffer.position(curPosition) - print(" (" + bytes.size + ")") + print(" (" + bytes.length + ")") } def printBuffer(buffer: ByteBuffer, position: Int, length: Int) { @@ -280,7 +282,7 @@ class SendingConnection(val address: InetSocketAddress, selector_ : Selector, /* channel.socket.setSendBufferSize(256 * 1024) */ - override def getRemoteAddress() = address + override def getRemoteAddress(): InetSocketAddress = address val DEFAULT_INTEREST = SelectionKey.OP_READ diff --git a/core/src/main/scala/org/apache/spark/network/nio/ConnectionId.scala b/core/src/main/scala/org/apache/spark/network/nio/ConnectionId.scala index 764dc5e5503ed..b3b281ff465f1 100644 --- a/core/src/main/scala/org/apache/spark/network/nio/ConnectionId.scala +++ b/core/src/main/scala/org/apache/spark/network/nio/ConnectionId.scala @@ -18,7 +18,9 @@ package org.apache.spark.network.nio private[nio] case class ConnectionId(connectionManagerId: ConnectionManagerId, uniqId: Int) { - override def toString = connectionManagerId.host + "_" + connectionManagerId.port + "_" + uniqId + override def toString: String = { + connectionManagerId.host + "_" + connectionManagerId.port + "_" + uniqId + } } private[nio] object ConnectionId { diff --git a/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala b/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala index ee22c6656e69e..1a68e621eaee7 100644 --- a/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala +++ b/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala @@ -82,14 +82,15 @@ private[nio] class ConnectionManager( new HashedWheelTimer(Utils.namedThreadFactory("AckTimeoutMonitor")) private val ackTimeout = - conf.getInt("spark.core.connection.ack.wait.timeout", conf.getInt("spark.network.timeout", 120)) + conf.getTimeAsSeconds("spark.core.connection.ack.wait.timeout", + conf.get("spark.network.timeout", "120s")) // Get the thread counts from the Spark Configuration. - // + // // Even though the ThreadPoolExecutor constructor takes both a minimum and maximum value, // we only query for the minimum value because we are using LinkedBlockingDeque. - // - // The JavaDoc for ThreadPoolExecutor points out that when using a LinkedBlockingDeque (which is + // + // The JavaDoc for ThreadPoolExecutor points out that when using a LinkedBlockingDeque (which is // an unbounded queue) no more than corePoolSize threads will ever be created, so only the "min" // parameter is necessary. private val handlerThreadCount = conf.getInt("spark.core.connection.handler.threads.min", 20) @@ -187,8 +188,9 @@ private[nio] class ConnectionManager( private val writeRunnableStarted: HashSet[SelectionKey] = new HashSet[SelectionKey]() private val readRunnableStarted: HashSet[SelectionKey] = new HashSet[SelectionKey]() + @volatile private var isActive = true private val selectorThread = new Thread("connection-manager-thread") { - override def run() = ConnectionManager.this.run() + override def run(): Unit = ConnectionManager.this.run() } selectorThread.setDaemon(true) // start this thread last, since it invokes run(), which accesses members above @@ -341,7 +343,7 @@ private[nio] class ConnectionManager( def run() { try { - while(!selectorThread.isInterrupted) { + while (isActive) { while (!registerRequests.isEmpty) { val conn: SendingConnection = registerRequests.dequeue() addListeners(conn) @@ -397,7 +399,7 @@ private[nio] class ConnectionManager( } catch { // Explicitly only dealing with CancelledKeyException here since other exceptions // should be dealt with differently. - case e: CancelledKeyException => { + case e: CancelledKeyException => // Some keys within the selectors list are invalid/closed. clear them. val allKeys = selector.keys().iterator() @@ -419,8 +421,11 @@ private[nio] class ConnectionManager( } } } - } - 0 + 0 + + case e: ClosedSelectorException => + logDebug("Failed select() as selector is closed.", e) + return } if (selectedKeysCount == 0) { @@ -987,10 +992,11 @@ private[nio] class ConnectionManager( } def stop() { + isActive = false ackTimeoutMonitor.stop() + selector.close() selectorThread.interrupt() selectorThread.join() - selector.close() val connections = connectionsByKey.values connections.foreach(_.close()) if (connectionsByKey.size != 0) { diff --git a/core/src/main/scala/org/apache/spark/network/nio/ConnectionManagerId.scala b/core/src/main/scala/org/apache/spark/network/nio/ConnectionManagerId.scala index cbb37ec5ced1f..1cd13d887c6f6 100644 --- a/core/src/main/scala/org/apache/spark/network/nio/ConnectionManagerId.scala +++ b/core/src/main/scala/org/apache/spark/network/nio/ConnectionManagerId.scala @@ -26,7 +26,7 @@ private[nio] case class ConnectionManagerId(host: String, port: Int) { Utils.checkHost(host) assert (port > 0) - def toSocketAddress() = new InetSocketAddress(host, port) + def toSocketAddress(): InetSocketAddress = new InetSocketAddress(host, port) } diff --git a/core/src/main/scala/org/apache/spark/network/nio/Message.scala b/core/src/main/scala/org/apache/spark/network/nio/Message.scala index fb4a979b824c3..85d2fe2bf9c20 100644 --- a/core/src/main/scala/org/apache/spark/network/nio/Message.scala +++ b/core/src/main/scala/org/apache/spark/network/nio/Message.scala @@ -42,7 +42,9 @@ private[nio] abstract class Message(val typ: Long, val id: Int) { def timeTaken(): String = (finishTime - startTime).toString + " ms" - override def toString = this.getClass.getSimpleName + "(id = " + id + ", size = " + size + ")" + override def toString: String = { + this.getClass.getSimpleName + "(id = " + id + ", size = " + size + ")" + } } @@ -51,7 +53,7 @@ private[nio] object Message { var lastId = 1 - def getNewId() = synchronized { + def getNewId(): Int = synchronized { lastId += 1 if (lastId == 0) { lastId += 1 diff --git a/core/src/main/scala/org/apache/spark/network/nio/MessageChunk.scala b/core/src/main/scala/org/apache/spark/network/nio/MessageChunk.scala index 278c5ac356ef2..a4568e849fa13 100644 --- a/core/src/main/scala/org/apache/spark/network/nio/MessageChunk.scala +++ b/core/src/main/scala/org/apache/spark/network/nio/MessageChunk.scala @@ -24,9 +24,9 @@ import scala.collection.mutable.ArrayBuffer private[nio] class MessageChunk(val header: MessageChunkHeader, val buffer: ByteBuffer) { - val size = if (buffer == null) 0 else buffer.remaining + val size: Int = if (buffer == null) 0 else buffer.remaining - lazy val buffers = { + lazy val buffers: ArrayBuffer[ByteBuffer] = { val ab = new ArrayBuffer[ByteBuffer]() ab += header.buffer if (buffer != null) { @@ -35,7 +35,7 @@ class MessageChunk(val header: MessageChunkHeader, val buffer: ByteBuffer) { ab } - override def toString = { + override def toString: String = { "" + this.getClass.getSimpleName + " (id = " + header.id + ", size = " + size + ")" } } diff --git a/core/src/main/scala/org/apache/spark/network/nio/MessageChunkHeader.scala b/core/src/main/scala/org/apache/spark/network/nio/MessageChunkHeader.scala index 6e20f291c5cec..7b3da4bb9d5ee 100644 --- a/core/src/main/scala/org/apache/spark/network/nio/MessageChunkHeader.scala +++ b/core/src/main/scala/org/apache/spark/network/nio/MessageChunkHeader.scala @@ -50,8 +50,10 @@ private[nio] class MessageChunkHeader( flip.asInstanceOf[ByteBuffer] } - override def toString = "" + this.getClass.getSimpleName + ":" + id + " of type " + typ + + override def toString: String = { + "" + this.getClass.getSimpleName + ":" + id + " of type " + typ + " and sizes " + totalSize + " / " + chunkSize + " bytes, securityNeg: " + securityNeg + } } diff --git a/core/src/main/scala/org/apache/spark/package.scala b/core/src/main/scala/org/apache/spark/package.scala index b6249b492150a..2ab41ba488ff6 100644 --- a/core/src/main/scala/org/apache/spark/package.scala +++ b/core/src/main/scala/org/apache/spark/package.scala @@ -43,5 +43,5 @@ package org.apache package object spark { // For package docs only - val SPARK_VERSION = "1.3.0-SNAPSHOT" + val SPARK_VERSION = "1.4.0-SNAPSHOT" } diff --git a/core/src/main/scala/org/apache/spark/partial/PartialResult.scala b/core/src/main/scala/org/apache/spark/partial/PartialResult.scala index cadd0c7ed19ba..53c4b32c95ab3 100644 --- a/core/src/main/scala/org/apache/spark/partial/PartialResult.scala +++ b/core/src/main/scala/org/apache/spark/partial/PartialResult.scala @@ -99,7 +99,7 @@ class PartialResult[R](initialVal: R, isFinal: Boolean) { case None => "(partial: " + initialValue + ")" } } - def getFinalValueInternal() = PartialResult.this.getFinalValueInternal().map(f) + def getFinalValueInternal(): Option[T] = PartialResult.this.getFinalValueInternal().map(f) } } diff --git a/core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala b/core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala index 646df283ac069..3406a7e97e368 100644 --- a/core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala @@ -45,7 +45,7 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi } result }, - Range(0, self.partitions.size), + Range(0, self.partitions.length), (index: Int, data: Long) => totalCount.addAndGet(data), totalCount.get()) } @@ -54,8 +54,8 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi * Returns a future for retrieving all elements of this RDD. */ def collectAsync(): FutureAction[Seq[T]] = { - val results = new Array[Array[T]](self.partitions.size) - self.context.submitJob[T, Array[T], Seq[T]](self, _.toArray, Range(0, self.partitions.size), + val results = new Array[Array[T]](self.partitions.length) + self.context.submitJob[T, Array[T], Seq[T]](self, _.toArray, Range(0, self.partitions.length), (index, data) => results(index) = data, results.flatten.toSeq) } @@ -111,7 +111,7 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi */ def foreachAsync(f: T => Unit): FutureAction[Unit] = { val cleanF = self.context.clean(f) - self.context.submitJob[T, Unit, Unit](self, _.foreach(cleanF), Range(0, self.partitions.size), + self.context.submitJob[T, Unit, Unit](self, _.foreach(cleanF), Range(0, self.partitions.length), (index, data) => Unit, Unit) } @@ -119,7 +119,7 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi * Applies a function f to each partition of this RDD. */ def foreachPartitionAsync(f: Iterator[T] => Unit): FutureAction[Unit] = { - self.context.submitJob[T, Unit, Unit](self, f, Range(0, self.partitions.size), + self.context.submitJob[T, Unit, Unit](self, f, Range(0, self.partitions.length), (index, data) => Unit, Unit) } } diff --git a/core/src/main/scala/org/apache/spark/rdd/BlockRDD.scala b/core/src/main/scala/org/apache/spark/rdd/BlockRDD.scala index fffa1911f5bc2..71578d1210fde 100644 --- a/core/src/main/scala/org/apache/spark/rdd/BlockRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/BlockRDD.scala @@ -36,7 +36,7 @@ class BlockRDD[T: ClassTag](@transient sc: SparkContext, @transient val blockIds override def getPartitions: Array[Partition] = { assertValid() - (0 until blockIds.size).map(i => { + (0 until blockIds.length).map(i => { new BlockRDDPartition(blockIds(i), i).asInstanceOf[Partition] }).toArray } diff --git a/core/src/main/scala/org/apache/spark/rdd/CartesianRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CartesianRDD.scala index 1cbd684224b7c..c1d6971787572 100644 --- a/core/src/main/scala/org/apache/spark/rdd/CartesianRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/CartesianRDD.scala @@ -53,11 +53,11 @@ class CartesianRDD[T: ClassTag, U: ClassTag]( extends RDD[Pair[T, U]](sc, Nil) with Serializable { - val numPartitionsInRdd2 = rdd2.partitions.size + val numPartitionsInRdd2 = rdd2.partitions.length override def getPartitions: Array[Partition] = { // create the cross product split - val array = new Array[Partition](rdd1.partitions.size * rdd2.partitions.size) + val array = new Array[Partition](rdd1.partitions.length * rdd2.partitions.length) for (s1 <- rdd1.partitions; s2 <- rdd2.partitions) { val idx = s1.index * numPartitionsInRdd2 + s2.index array(idx) = new CartesianPartition(idx, rdd1, rdd2, s1.index, s2.index) @@ -70,7 +70,7 @@ class CartesianRDD[T: ClassTag, U: ClassTag]( (rdd1.preferredLocations(currSplit.s1) ++ rdd2.preferredLocations(currSplit.s2)).distinct } - override def compute(split: Partition, context: TaskContext) = { + override def compute(split: Partition, context: TaskContext): Iterator[(T, U)] = { val currSplit = split.asInstanceOf[CartesianPartition] for (x <- rdd1.iterator(currSplit.s1, context); y <- rdd2.iterator(currSplit.s2, context)) yield (x, y) diff --git a/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala index 1c13e2c372845..0d130dd4c7a60 100644 --- a/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala @@ -27,6 +27,7 @@ import org.apache.hadoop.fs.Path import org.apache.spark._ import org.apache.spark.broadcast.Broadcast import org.apache.spark.deploy.SparkHadoopUtil +import org.apache.spark.util.Utils private[spark] class CheckpointRDDPartition(val index: Int) extends Partition {} @@ -48,7 +49,7 @@ class CheckpointRDD[T: ClassTag](sc: SparkContext, val checkpointPath: String) if (fs.exists(cpath)) { val dirContents = fs.listStatus(cpath).map(_.getPath) val partitionFiles = dirContents.filter(_.getName.startsWith("part-")).map(_.toString).sorted - val numPart = partitionFiles.size + val numPart = partitionFiles.length if (numPart > 0 && (! partitionFiles(0).endsWith(CheckpointRDD.splitIdToFile(0)) || ! partitionFiles(numPart-1).endsWith(CheckpointRDD.splitIdToFile(numPart-1)))) { throw new SparkException("Invalid checkpoint directory: " + checkpointPath) @@ -112,8 +113,11 @@ private[spark] object CheckpointRDD extends Logging { } val serializer = env.serializer.newInstance() val serializeStream = serializer.serializeStream(fileOutputStream) - serializeStream.writeAll(iterator) - serializeStream.close() + Utils.tryWithSafeFinally { + serializeStream.writeAll(iterator) + } { + serializeStream.close() + } if (!fs.rename(tempOutputPath, finalOutputPath)) { if (!fs.exists(finalOutputPath)) { diff --git a/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala index 07398a6fa62f6..7021a339e879b 100644 --- a/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala @@ -99,7 +99,7 @@ class CoGroupedRDD[K](@transient var rdds: Seq[RDD[_ <: Product2[K, _]]], part: override def getPartitions: Array[Partition] = { val array = new Array[Partition](part.numPartitions) - for (i <- 0 until array.size) { + for (i <- 0 until array.length) { // Each CoGroupPartition will have a dependency per contributing RDD array(i) = new CoGroupPartition(i, rdds.zipWithIndex.map { case (rdd, j) => // Assume each RDD contributed a single dependency, and get it @@ -120,7 +120,7 @@ class CoGroupedRDD[K](@transient var rdds: Seq[RDD[_ <: Product2[K, _]]], part: val sparkConf = SparkEnv.get.conf val externalSorting = sparkConf.getBoolean("spark.shuffle.spill", true) val split = s.asInstanceOf[CoGroupPartition] - val numRdds = split.deps.size + val numRdds = split.deps.length // A list of (rdd iterator, dependency number) pairs val rddIterators = new ArrayBuffer[(Iterator[Product2[K, Any]], Int)] diff --git a/core/src/main/scala/org/apache/spark/rdd/CoalescedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CoalescedRDD.scala index b073eba8a1574..0c1b02c07d09f 100644 --- a/core/src/main/scala/org/apache/spark/rdd/CoalescedRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/CoalescedRDD.scala @@ -166,7 +166,7 @@ private class PartitionCoalescer(maxPartitions: Int, prev: RDD[_], balanceSlack: // determines the tradeoff between load-balancing the partitions sizes and their locality // e.g. balanceSlack=0.10 means that it allows up to 10% imbalance in favor of locality - val slack = (balanceSlack * prev.partitions.size).toInt + val slack = (balanceSlack * prev.partitions.length).toInt var noLocality = true // if true if no preferredLocations exists for parent RDD @@ -186,7 +186,7 @@ private class PartitionCoalescer(maxPartitions: Int, prev: RDD[_], balanceSlack: override val isEmpty = !it.hasNext // initializes/resets to start iterating from the beginning - def resetIterator() = { + def resetIterator(): Iterator[(String, Partition)] = { val iterators = (0 to 2).map( x => prev.partitions.iterator.flatMap(p => { if (currPrefLocs(p).size > x) Some((currPrefLocs(p)(x), p)) else None @@ -196,10 +196,10 @@ private class PartitionCoalescer(maxPartitions: Int, prev: RDD[_], balanceSlack: } // hasNext() is false iff there are no preferredLocations for any of the partitions of the RDD - def hasNext(): Boolean = { !isEmpty } + override def hasNext: Boolean = { !isEmpty } // return the next preferredLocation of some partition of the RDD - def next(): (String, Partition) = { + override def next(): (String, Partition) = { if (it.hasNext) { it.next() } else { @@ -237,7 +237,7 @@ private class PartitionCoalescer(maxPartitions: Int, prev: RDD[_], balanceSlack: val rotIt = new LocationIterator(prev) // deal with empty case, just create targetLen partition groups with no preferred location - if (!rotIt.hasNext()) { + if (!rotIt.hasNext) { (1 to targetLen).foreach(x => groupArr += PartitionGroup()) return } @@ -343,7 +343,7 @@ private class PartitionCoalescer(maxPartitions: Int, prev: RDD[_], balanceSlack: private case class PartitionGroup(prefLoc: Option[String] = None) { var arr = mutable.ArrayBuffer[Partition]() - def size = arr.size + def size: Int = arr.size } private object PartitionGroup { diff --git a/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala index e66f83bb34e30..843a893235e56 100644 --- a/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala @@ -31,7 +31,7 @@ import org.apache.spark.util.StatCounter class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable { /** Add up the elements in this RDD. */ def sum(): Double = { - self.reduce(_ + _) + self.fold(0.0)(_ + _) } /** @@ -70,7 +70,7 @@ class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable { @Experimental def meanApprox(timeout: Long, confidence: Double = 0.95): PartialResult[BoundedDouble] = { val processPartition = (ctx: TaskContext, ns: Iterator[Double]) => StatCounter(ns) - val evaluator = new MeanEvaluator(self.partitions.size, confidence) + val evaluator = new MeanEvaluator(self.partitions.length, confidence) self.context.runApproximateJob(self, processPartition, evaluator, timeout) } @@ -81,7 +81,7 @@ class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable { @Experimental def sumApprox(timeout: Long, confidence: Double = 0.95): PartialResult[BoundedDouble] = { val processPartition = (ctx: TaskContext, ns: Iterator[Double]) => StatCounter(ns) - val evaluator = new SumEvaluator(self.partitions.size, confidence) + val evaluator = new SumEvaluator(self.partitions.length, confidence) self.context.runApproximateJob(self, processPartition, evaluator, timeout) } @@ -191,29 +191,34 @@ class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable { } } // Determine the bucket function in constant time. Requires that buckets are evenly spaced - def fastBucketFunction(min: Double, increment: Double, count: Int)(e: Double): Option[Int] = { + def fastBucketFunction(min: Double, max: Double, count: Int)(e: Double): Option[Int] = { // If our input is not a number unless the increment is also NaN then we fail fast - if (e.isNaN()) { - return None - } - val bucketNumber = (e - min)/(increment) - // We do this rather than buckets.lengthCompare(bucketNumber) - // because Array[Double] fails to override it (for now). - if (bucketNumber > count || bucketNumber < 0) { + if (e.isNaN || e < min || e > max) { None } else { - Some(bucketNumber.toInt.min(count - 1)) + // Compute ratio of e's distance along range to total range first, for better precision + val bucketNumber = (((e - min) / (max - min)) * count).toInt + // should be less than count, but will equal count if e == max, in which case + // it's part of the last end-range-inclusive bucket, so return count-1 + Some(math.min(bucketNumber, count - 1)) } } // Decide which bucket function to pass to histogramPartition. We decide here - // rather than having a general function so that the decission need only be made + // rather than having a general function so that the decision need only be made // once rather than once per shard val bucketFunction = if (evenBuckets) { - fastBucketFunction(buckets(0), buckets(1)-buckets(0), buckets.length-1) _ + fastBucketFunction(buckets.head, buckets.last, buckets.length - 1) _ } else { basicBucketFunction _ } - self.mapPartitions(histogramPartition(bucketFunction)).reduce(mergeCounters) + if (self.partitions.length == 0) { + new Array[Long](buckets.length - 1) + } else { + // reduce() requires a non-empty RDD. This works because the mapPartitions will make + // non-empty partitions out of empty ones. But it doesn't handle the no-partitions case, + // which is below + self.mapPartitions(histogramPartition(bucketFunction)).reduce(mergeCounters) + } } } diff --git a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala index 486e86ce1bb19..f77abac42b623 100644 --- a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala @@ -215,8 +215,7 @@ class HadoopRDD[K, V]( logInfo("Input split: " + split.inputSplit) val jobConf = getJobConf() - val inputMetrics = context.taskMetrics - .getInputMetricsForReadMethod(DataReadMethod.Hadoop) + val inputMetrics = context.taskMetrics.getInputMetricsForReadMethod(DataReadMethod.Hadoop) // Find a function that will return the FileSystem bytes read by this thread. Do this before // creating RecordReader, because RecordReader's constructor might read some bytes @@ -240,7 +239,7 @@ class HadoopRDD[K, V]( val key: K = reader.createKey() val value: V = reader.createValue() - override def getNext() = { + override def getNext(): (K, V) = { try { finished = !reader.next(key, value) } catch { @@ -337,11 +336,11 @@ private[spark] object HadoopRDD extends Logging { * The three methods below are helpers for accessing the local map, a property of the SparkEnv of * the local process. */ - def getCachedMetadata(key: String) = SparkEnv.get.hadoopJobMetadata.get(key) + def getCachedMetadata(key: String): Any = SparkEnv.get.hadoopJobMetadata.get(key) - def containsCachedMetadata(key: String) = SparkEnv.get.hadoopJobMetadata.containsKey(key) + def containsCachedMetadata(key: String): Boolean = SparkEnv.get.hadoopJobMetadata.containsKey(key) - def putCachedMetadata(key: String, value: Any) = + private def putCachedMetadata(key: String, value: Any): Unit = SparkEnv.get.hadoopJobMetadata.put(key, value) /** Add Hadoop configuration specific to a single partition and attempt. */ @@ -371,7 +370,7 @@ private[spark] object HadoopRDD extends Logging { override def getPartitions: Array[Partition] = firstParent[T].partitions - override def compute(split: Partition, context: TaskContext) = { + override def compute(split: Partition, context: TaskContext): Iterator[U] = { val partition = split.asInstanceOf[HadoopPartition] val inputSplit = partition.inputSplit.value f(inputSplit, firstParent[T].iterator(split, context)) diff --git a/core/src/main/scala/org/apache/spark/rdd/JdbcRDD.scala b/core/src/main/scala/org/apache/spark/rdd/JdbcRDD.scala index 642a12c1edf6c..0c28f045e46e9 100644 --- a/core/src/main/scala/org/apache/spark/rdd/JdbcRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/JdbcRDD.scala @@ -17,7 +17,7 @@ package org.apache.spark.rdd -import java.sql.{Connection, ResultSet} +import java.sql.{PreparedStatement, Connection, ResultSet} import scala.reflect.ClassTag @@ -28,8 +28,9 @@ import org.apache.spark.util.NextIterator import org.apache.spark.{Logging, Partition, SparkContext, TaskContext} private[spark] class JdbcPartition(idx: Int, val lower: Long, val upper: Long) extends Partition { - override def index = idx + override def index: Int = idx } + // TODO: Expose a jdbcRDD function in SparkContext and mark this as semi-private /** * An RDD that executes an SQL query on a JDBC connection and reads results. @@ -62,15 +63,16 @@ class JdbcRDD[T: ClassTag]( override def getPartitions: Array[Partition] = { // bounds are inclusive, hence the + 1 here and - 1 on end - val length = 1 + upperBound - lowerBound + val length = BigInt(1) + upperBound - lowerBound (0 until numPartitions).map(i => { - val start = lowerBound + ((i * length) / numPartitions).toLong - val end = lowerBound + (((i + 1) * length) / numPartitions).toLong - 1 - new JdbcPartition(i, start, end) + val start = lowerBound + ((i * length) / numPartitions) + val end = lowerBound + (((i + 1) * length) / numPartitions) - 1 + new JdbcPartition(i, start.toLong, end.toLong) }).toArray } - override def compute(thePart: Partition, context: TaskContext) = new NextIterator[T] { + override def compute(thePart: Partition, context: TaskContext): Iterator[T] = new NextIterator[T] + { context.addTaskCompletionListener{ context => closeIfNeeded() } val part = thePart.asInstanceOf[JdbcPartition] val conn = getConnection() @@ -88,7 +90,7 @@ class JdbcRDD[T: ClassTag]( stmt.setLong(2, part.upper) val rs = stmt.executeQuery() - override def getNext: T = { + override def getNext(): T = { if (rs.next()) { mapRow(rs) } else { @@ -99,21 +101,21 @@ class JdbcRDD[T: ClassTag]( override def close() { try { - if (null != rs && ! rs.isClosed()) { + if (null != rs) { rs.close() } } catch { case e: Exception => logWarning("Exception closing resultset", e) } try { - if (null != stmt && ! stmt.isClosed()) { + if (null != stmt) { stmt.close() } } catch { case e: Exception => logWarning("Exception closing statement", e) } try { - if (null != conn && ! conn.isClosed()) { + if (null != conn) { conn.close() } logInfo("closed connection") diff --git a/core/src/main/scala/org/apache/spark/rdd/MapPartitionsRDD.scala b/core/src/main/scala/org/apache/spark/rdd/MapPartitionsRDD.scala index 4883fb828814c..a838aac6e8d1a 100644 --- a/core/src/main/scala/org/apache/spark/rdd/MapPartitionsRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/MapPartitionsRDD.scala @@ -31,6 +31,6 @@ private[spark] class MapPartitionsRDD[U: ClassTag, T: ClassTag]( override def getPartitions: Array[Partition] = firstParent[T].partitions - override def compute(split: Partition, context: TaskContext) = + override def compute(split: Partition, context: TaskContext): Iterator[U] = f(context, split.index, firstParent[T].iterator(split, context)) } diff --git a/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala index 7fb94840df99c..2ab967f4bb313 100644 --- a/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala @@ -238,7 +238,7 @@ private[spark] object NewHadoopRDD { override def getPartitions: Array[Partition] = firstParent[T].partitions - override def compute(split: Partition, context: TaskContext) = { + override def compute(split: Partition, context: TaskContext): Iterator[U] = { val partition = split.asInstanceOf[NewHadoopPartition] val inputSplit = partition.serializableHadoopSplit.value f(inputSplit, firstParent[T].iterator(split, context)) diff --git a/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala index 144f679a59460..6afe50161dacd 100644 --- a/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala @@ -56,7 +56,7 @@ class OrderedRDDFunctions[K : Ordering : ClassTag, * order of the keys). */ // TODO: this currently doesn't work on P other than Tuple2! - def sortByKey(ascending: Boolean = true, numPartitions: Int = self.partitions.size) + def sortByKey(ascending: Boolean = true, numPartitions: Int = self.partitions.length) : RDD[(K, V)] = { val part = new RangePartitioner(numPartitions, self, ascending) @@ -75,4 +75,27 @@ class OrderedRDDFunctions[K : Ordering : ClassTag, new ShuffledRDD[K, V, V](self, partitioner).setKeyOrdering(ordering) } + /** + * Returns an RDD containing only the elements in the the inclusive range `lower` to `upper`. + * If the RDD has been partitioned using a `RangePartitioner`, then this operation can be + * performed efficiently by only scanning the partitions that might contain matching elements. + * Otherwise, a standard `filter` is applied to all partitions. + */ + def filterByRange(lower: K, upper: K): RDD[P] = { + + def inRange(k: K): Boolean = ordering.gteq(k, lower) && ordering.lteq(k, upper) + + val rddToFilter: RDD[P] = self.partitioner match { + case Some(rp: RangePartitioner[K, V]) => { + val partitionIndicies = (rp.getPartition(lower), rp.getPartition(upper)) match { + case (l, u) => Math.min(l, u) to Math.max(l, u) + } + PartitionPruningRDD.create(self, partitionIndicies.contains) + } + case _ => + self + } + rddToFilter.filter { case (k, v) => inRange(k) } + } + } diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala index 955b42c3baaa1..05351ba4ff76b 100644 --- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala @@ -823,7 +823,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * RDD will be <= us. */ def subtractByKey[W: ClassTag](other: RDD[(K, W)]): RDD[(K, V)] = - subtractByKey(other, self.partitioner.getOrElse(new HashPartitioner(self.partitions.size))) + subtractByKey(other, self.partitioner.getOrElse(new HashPartitioner(self.partitions.length))) /** Return an RDD with the pairs from `this` whose keys are not in `other`. */ def subtractByKey[W: ClassTag](other: RDD[(K, W)], numPartitions: Int): RDD[(K, V)] = @@ -993,8 +993,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) val (outputMetrics, bytesWrittenCallback) = initHadoopOutputMetrics(context) val writer = format.getRecordWriter(hadoopContext).asInstanceOf[NewRecordWriter[K,V]] + require(writer != null, "Unable to obtain RecordWriter") var recordsWritten = 0L - try { + Utils.tryWithSafeFinally { while (iter.hasNext) { val pair = iter.next() writer.write(pair._1, pair._2) @@ -1003,7 +1004,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) maybeUpdateOutputMetrics(bytesWrittenCallback, outputMetrics, recordsWritten) recordsWritten += 1 } - } finally { + } { writer.close(hadoopContext) } committer.commitTask(hadoopContext) @@ -1067,7 +1068,8 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) writer.setup(context.stageId, context.partitionId, taskAttemptId) writer.open() var recordsWritten = 0L - try { + + Utils.tryWithSafeFinally { while (iter.hasNext) { val record = iter.next() writer.write(record._1.asInstanceOf[AnyRef], record._2.asInstanceOf[AnyRef]) @@ -1076,7 +1078,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) maybeUpdateOutputMetrics(bytesWrittenCallback, outputMetrics, recordsWritten) recordsWritten += 1 } - } finally { + } { writer.close() } writer.commit() diff --git a/core/src/main/scala/org/apache/spark/rdd/ParallelCollectionRDD.scala b/core/src/main/scala/org/apache/spark/rdd/ParallelCollectionRDD.scala index f12d0cffaba34..e2394e28f8d26 100644 --- a/core/src/main/scala/org/apache/spark/rdd/ParallelCollectionRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/ParallelCollectionRDD.scala @@ -98,7 +98,7 @@ private[spark] class ParallelCollectionRDD[T: ClassTag]( slices.indices.map(i => new ParallelCollectionPartition(id, i, slices(i))).toArray } - override def compute(s: Partition, context: TaskContext) = { + override def compute(s: Partition, context: TaskContext): Iterator[T] = { new InterruptibleIterator(context, s.asInstanceOf[ParallelCollectionPartition[T]].iterator) } diff --git a/core/src/main/scala/org/apache/spark/rdd/PartitionPruningRDD.scala b/core/src/main/scala/org/apache/spark/rdd/PartitionPruningRDD.scala index f781a8d776f2a..a00f4c1cdff91 100644 --- a/core/src/main/scala/org/apache/spark/rdd/PartitionPruningRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/PartitionPruningRDD.scala @@ -40,7 +40,7 @@ private[spark] class PruneDependency[T](rdd: RDD[T], @transient partitionFilterF .filter(s => partitionFilterFunc(s.index)).zipWithIndex .map { case(split, idx) => new PartitionPruningRDDPartition(idx, split) : Partition } - override def getParents(partitionId: Int) = { + override def getParents(partitionId: Int): List[Int] = { List(partitions(partitionId).asInstanceOf[PartitionPruningRDDPartition].parentSplit.index) } } @@ -59,8 +59,10 @@ class PartitionPruningRDD[T: ClassTag]( @transient partitionFilterFunc: Int => Boolean) extends RDD[T](prev.context, List(new PruneDependency(prev, partitionFilterFunc))) { - override def compute(split: Partition, context: TaskContext) = firstParent[T].iterator( - split.asInstanceOf[PartitionPruningRDDPartition].parentSplit, context) + override def compute(split: Partition, context: TaskContext): Iterator[T] = { + firstParent[T].iterator( + split.asInstanceOf[PartitionPruningRDDPartition].parentSplit, context) + } override protected def getPartitions: Array[Partition] = getDependencies.head.asInstanceOf[PruneDependency[T]].partitions @@ -74,7 +76,7 @@ object PartitionPruningRDD { * Create a PartitionPruningRDD. This function can be used to create the PartitionPruningRDD * when its type T is not known at compile time. */ - def create[T](rdd: RDD[T], partitionFilterFunc: Int => Boolean) = { + def create[T](rdd: RDD[T], partitionFilterFunc: Int => Boolean): PartitionPruningRDD[T] = { new PartitionPruningRDD[T](rdd, partitionFilterFunc)(rdd.elementClassTag) } } diff --git a/core/src/main/scala/org/apache/spark/rdd/PipedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/PipedRDD.scala index ed79032893d33..dc60d48927624 100644 --- a/core/src/main/scala/org/apache/spark/rdd/PipedRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/PipedRDD.scala @@ -149,10 +149,10 @@ private[spark] class PipedRDD[T: ClassTag]( }.start() // Return an iterator that read lines from the process's stdout - val lines = Source.fromInputStream(proc.getInputStream).getLines + val lines = Source.fromInputStream(proc.getInputStream).getLines() new Iterator[String] { - def next() = lines.next() - def hasNext = { + def next(): String = lines.next() + def hasNext: Boolean = { if (lines.hasNext) { true } else { @@ -162,7 +162,7 @@ private[spark] class PipedRDD[T: ClassTag]( } // cleanup task working directory if used - if (workInTaskDirectory == true) { + if (workInTaskDirectory) { scala.util.control.Exception.ignoring(classOf[IOException]) { Utils.deleteRecursively(new File(taskDirectory)) } diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala index fe55a5124f3b6..d80d94a588346 100644 --- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala @@ -186,7 +186,7 @@ abstract class RDD[T: ClassTag]( } /** Get the RDD's current storage level, or StorageLevel.NONE if none is set. */ - def getStorageLevel = storageLevel + def getStorageLevel: StorageLevel = storageLevel // Our dependencies and partitions will be gotten by calling subclass's methods below, and will // be overwritten when we're checkpointed @@ -316,7 +316,7 @@ abstract class RDD[T: ClassTag]( /** * Return a new RDD containing the distinct elements in this RDD. */ - def distinct(): RDD[T] = distinct(partitions.size) + def distinct(): RDD[T] = distinct(partitions.length) /** * Return a new RDD that has exactly numPartitions partitions. @@ -377,6 +377,12 @@ abstract class RDD[T: ClassTag]( /** * Return a sampled subset of this RDD. + * + * @param withReplacement can elements be sampled multiple times (replaced when sampled out) + * @param fraction expected size of the sample as a fraction of this RDD's size + * without replacement: probability that each element is chosen; fraction must be [0, 1] + * with replacement: expected number of times each element is chosen; fraction must be >= 0 + * @param seed seed for the random number generator */ def sample(withReplacement: Boolean, fraction: Double, @@ -462,7 +468,13 @@ abstract class RDD[T: ClassTag]( * Return the union of this RDD and another one. Any identical elements will appear multiple * times (use `.distinct()` to eliminate them). */ - def union(other: RDD[T]): RDD[T] = new UnionRDD(sc, Array(this, other)) + def union(other: RDD[T]): RDD[T] = { + if (partitioner.isDefined && other.partitioner == partitioner) { + new PartitionerAwareUnionRDD(sc, Array(this, other)) + } else { + new UnionRDD(sc, Array(this, other)) + } + } /** * Return the union of this RDD and another one. Any identical elements will appear multiple @@ -476,7 +488,7 @@ abstract class RDD[T: ClassTag]( def sortBy[K]( f: (T) => K, ascending: Boolean = true, - numPartitions: Int = this.partitions.size) + numPartitions: Int = this.partitions.length) (implicit ord: Ordering[K], ctag: ClassTag[K]): RDD[T] = this.keyBy[K](f) .sortByKey(ascending, numPartitions) @@ -734,13 +746,13 @@ abstract class RDD[T: ClassTag]( def zip[U: ClassTag](other: RDD[U]): RDD[(T, U)] = { zipPartitions(other, preservesPartitioning = false) { (thisIter, otherIter) => new Iterator[(T, U)] { - def hasNext = (thisIter.hasNext, otherIter.hasNext) match { + def hasNext: Boolean = (thisIter.hasNext, otherIter.hasNext) match { case (true, true) => true case (false, false) => false case _ => throw new SparkException("Can only zip RDDs with " + "same number of elements in each partition") } - def next = (thisIter.next, otherIter.next) + def next(): (T, U) = (thisIter.next(), otherIter.next()) } } } @@ -840,7 +852,7 @@ abstract class RDD[T: ClassTag]( * RDD will be <= us. */ def subtract(other: RDD[T]): RDD[T] = - subtract(other, partitioner.getOrElse(new HashPartitioner(partitions.size))) + subtract(other, partitioner.getOrElse(new HashPartitioner(partitions.length))) /** * Return an RDD with the elements from `this` that are not in `other`. @@ -856,8 +868,8 @@ abstract class RDD[T: ClassTag]( // Our partitioner knows how to handle T (which, since we have a partitioner, is // really (K, V)) so make a new Partitioner that will de-tuple our fake tuples val p2 = new Partitioner() { - override def numPartitions = p.numPartitions - override def getPartition(k: Any) = p.getPartition(k.asInstanceOf[(Any, _)]._1) + override def numPartitions: Int = p.numPartitions + override def getPartition(k: Any): Int = p.getPartition(k.asInstanceOf[(Any, _)]._1) } // Unfortunately, since we're making a new p2, we'll get ShuffleDependencies // anyway, and when calling .keys, will not have a partitioner set, even though @@ -954,7 +966,7 @@ abstract class RDD[T: ClassTag]( */ def aggregate[U: ClassTag](zeroValue: U)(seqOp: (U, T) => U, combOp: (U, U) => U): U = { // Clone the zero value since we will also be serializing it as part of tasks - var jobResult = Utils.clone(zeroValue, sc.env.closureSerializer.newInstance()) + var jobResult = Utils.clone(zeroValue, sc.env.serializer.newInstance()) val cleanSeqOp = sc.clean(seqOp) val cleanCombOp = sc.clean(combOp) val aggregatePartition = (it: Iterator[T]) => it.aggregate(zeroValue)(cleanSeqOp, cleanCombOp) @@ -974,14 +986,14 @@ abstract class RDD[T: ClassTag]( combOp: (U, U) => U, depth: Int = 2): U = { require(depth >= 1, s"Depth must be greater than or equal to 1 but got $depth.") - if (partitions.size == 0) { + if (partitions.length == 0) { return Utils.clone(zeroValue, context.env.closureSerializer.newInstance()) } val cleanSeqOp = context.clean(seqOp) val cleanCombOp = context.clean(combOp) val aggregatePartition = (it: Iterator[T]) => it.aggregate(zeroValue)(cleanSeqOp, cleanCombOp) var partiallyAggregated = mapPartitions(it => Iterator(aggregatePartition(it))) - var numPartitions = partiallyAggregated.partitions.size + var numPartitions = partiallyAggregated.partitions.length val scale = math.max(math.ceil(math.pow(numPartitions, 1.0 / depth)).toInt, 2) // If creating an extra level doesn't help reduce the wall-clock time, we stop tree aggregation. while (numPartitions > scale + numPartitions / scale) { @@ -1014,7 +1026,7 @@ abstract class RDD[T: ClassTag]( } result } - val evaluator = new CountEvaluator(partitions.size, confidence) + val evaluator = new CountEvaluator(partitions.length, confidence) sc.runApproximateJob(this, countElements, evaluator, timeout) } @@ -1049,7 +1061,7 @@ abstract class RDD[T: ClassTag]( } map } - val evaluator = new GroupedCountEvaluator[T](partitions.size, confidence) + val evaluator = new GroupedCountEvaluator[T](partitions.length, confidence) sc.runApproximateJob(this, countPartition, evaluator, timeout) } @@ -1128,7 +1140,7 @@ abstract class RDD[T: ClassTag]( * the same index assignments, you should sort the RDD with sortByKey() or save it to a file. */ def zipWithUniqueId(): RDD[(T, Long)] = { - val n = this.partitions.size.toLong + val n = this.partitions.length.toLong this.mapPartitionsWithIndex { case (k, iter) => iter.zipWithIndex.map { case (item, i) => (item, i * n + k) @@ -1140,6 +1152,9 @@ abstract class RDD[T: ClassTag]( * Take the first num elements of the RDD. It works by first scanning one partition, and use the * results from that partition to estimate the number of additional partitions needed to satisfy * the limit. + * + * @note due to complications in the internal implementation, this method will raise + * an exception if called on an RDD of `Nothing` or `Null`. */ def take(num: Int): Array[T] = { if (num == 0) { @@ -1228,7 +1243,7 @@ abstract class RDD[T: ClassTag]( queue ++= util.collection.Utils.takeOrdered(items, num)(ord) Iterator.single(queue) } - if (mapRDDs.partitions.size == 0) { + if (mapRDDs.partitions.length == 0) { Array.empty } else { mapRDDs.reduce { (queue1, queue2) => @@ -1252,6 +1267,10 @@ abstract class RDD[T: ClassTag]( def min()(implicit ord: Ordering[T]): T = this.reduce(ord.min) /** + * @note due to complications in the internal implementation, this method will raise an + * exception if called on an RDD of `Nothing` or `Null`. This may be come up in practice + * because, for example, the type of `parallelize(Seq())` is `RDD[Nothing]`. + * (`parallelize(Seq())` should be avoided anyway in favor of `parallelize(Seq[T]())`.) * @return true if and only if the RDD contains no elements at all. Note that an RDD * may be empty even when it has at least 1 partition. */ @@ -1375,7 +1394,7 @@ abstract class RDD[T: ClassTag]( } /** The [[org.apache.spark.SparkContext]] that this RDD was created on. */ - def context = sc + def context: SparkContext = sc /** * Private API for changing an RDD's ClassTag. @@ -1470,7 +1489,7 @@ abstract class RDD[T: ClassTag]( } // The first RDD in the dependency stack has no parents, so no need for a +- def firstDebugString(rdd: RDD[_]): Seq[String] = { - val partitionStr = "(" + rdd.partitions.size + ")" + val partitionStr = "(" + rdd.partitions.length + ")" val leftOffset = (partitionStr.length - 1) / 2 val nextPrefix = (" " * leftOffset) + "|" + (" " * (partitionStr.length - leftOffset)) @@ -1480,7 +1499,7 @@ abstract class RDD[T: ClassTag]( } ++ debugChildren(rdd, nextPrefix) } def shuffleDebugString(rdd: RDD[_], prefix: String = "", isLastChild: Boolean): Seq[String] = { - val partitionStr = "(" + rdd.partitions.size + ")" + val partitionStr = "(" + rdd.partitions.length + ")" val leftOffset = (partitionStr.length - 1) / 2 val thisPrefix = prefix.replaceAll("\\|\\s+$", "") val nextPrefix = ( diff --git a/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala b/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala index f67e5f1857979..1722c27e55003 100644 --- a/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala +++ b/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala @@ -21,7 +21,7 @@ import scala.reflect.ClassTag import org.apache.hadoop.fs.Path -import org.apache.spark.{Logging, Partition, SerializableWritable, SparkException} +import org.apache.spark._ import org.apache.spark.scheduler.{ResultTask, ShuffleMapTask} /** @@ -83,7 +83,7 @@ private[spark] class RDDCheckpointData[T: ClassTag](@transient rdd: RDD[T]) } // Create the output path for the checkpoint - val path = new Path(rdd.context.checkpointDir.get, "rdd-" + rdd.id) + val path = RDDCheckpointData.rddCheckpointDataPath(rdd.context, rdd.id).get val fs = path.getFileSystem(rdd.context.hadoopConfiguration) if (!fs.mkdirs(path)) { throw new SparkException("Failed to create checkpoint path " + path) @@ -92,12 +92,17 @@ private[spark] class RDDCheckpointData[T: ClassTag](@transient rdd: RDD[T]) // Save to file, and reload it as an RDD val broadcastedConf = rdd.context.broadcast( new SerializableWritable(rdd.context.hadoopConfiguration)) - rdd.context.runJob(rdd, CheckpointRDD.writeToFile[T](path.toString, broadcastedConf) _) val newRDD = new CheckpointRDD[T](rdd.context, path.toString) - if (newRDD.partitions.size != rdd.partitions.size) { + if (rdd.conf.getBoolean("spark.cleaner.referenceTracking.cleanCheckpoints", false)) { + rdd.context.cleaner.foreach { cleaner => + cleaner.registerRDDCheckpointDataForCleanup(newRDD, rdd.id) + } + } + rdd.context.runJob(rdd, CheckpointRDD.writeToFile[T](path.toString, broadcastedConf) _) + if (newRDD.partitions.length != rdd.partitions.length) { throw new SparkException( - "Checkpoint RDD " + newRDD + "(" + newRDD.partitions.size + ") has different " + - "number of partitions than original RDD " + rdd + "(" + rdd.partitions.size + ")") + "Checkpoint RDD " + newRDD + "(" + newRDD.partitions.length + ") has different " + + "number of partitions than original RDD " + rdd + "(" + rdd.partitions.length + ")") } // Change the dependencies and partitions of the RDD @@ -130,5 +135,17 @@ private[spark] class RDDCheckpointData[T: ClassTag](@transient rdd: RDD[T]) } } -// Used for synchronization -private[spark] object RDDCheckpointData +private[spark] object RDDCheckpointData { + def rddCheckpointDataPath(sc: SparkContext, rddId: Int): Option[Path] = { + sc.checkpointDir.map { dir => new Path(dir, "rdd-" + rddId) } + } + + def clearRDDCheckpointData(sc: SparkContext, rddId: Int): Unit = { + rddCheckpointDataPath(sc, rddId).foreach { path => + val fs = path.getFileSystem(sc.hadoopConfiguration) + if (fs.exists(path)) { + fs.delete(path, true) + } + } + } +} diff --git a/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala b/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala index d9fe6847254fa..2dc47f95937cb 100644 --- a/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala @@ -17,14 +17,12 @@ package org.apache.spark.rdd -import scala.reflect.ClassTag - import org.apache.spark._ import org.apache.spark.annotation.DeveloperApi import org.apache.spark.serializer.Serializer private[spark] class ShuffledRDDPartition(val idx: Int) extends Partition { - override val index = idx + override val index: Int = idx override def hashCode(): Int = idx } diff --git a/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala index ed24ea22a661c..e9d745588ee9a 100644 --- a/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala @@ -76,7 +76,7 @@ private[spark] class SubtractedRDD[K: ClassTag, V: ClassTag, W: ClassTag]( override def getPartitions: Array[Partition] = { val array = new Array[Partition](part.numPartitions) - for (i <- 0 until array.size) { + for (i <- 0 until array.length) { // Each CoGroupPartition will depend on rdd1 and rdd2 array(i) = new CoGroupPartition(i, Seq(rdd1, rdd2).zipWithIndex.map { case (rdd, j) => dependencies(j) match { @@ -105,7 +105,7 @@ private[spark] class SubtractedRDD[K: ClassTag, V: ClassTag, W: ClassTag]( seq } } - def integrate(dep: CoGroupSplitDep, op: Product2[K, V] => Unit) = dep match { + def integrate(dep: CoGroupSplitDep, op: Product2[K, V] => Unit): Unit = dep match { case NarrowCoGroupSplitDep(rdd, _, itsSplit) => rdd.iterator(itsSplit, context).asInstanceOf[Iterator[Product2[K, V]]].foreach(op) diff --git a/core/src/main/scala/org/apache/spark/rdd/UnionRDD.scala b/core/src/main/scala/org/apache/spark/rdd/UnionRDD.scala index aece683ff3199..3986645350a82 100644 --- a/core/src/main/scala/org/apache/spark/rdd/UnionRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/UnionRDD.scala @@ -44,7 +44,7 @@ private[spark] class UnionPartition[T: ClassTag]( var parentPartition: Partition = rdd.partitions(parentRddPartitionIndex) - def preferredLocations() = rdd.preferredLocations(parentPartition) + def preferredLocations(): Seq[String] = rdd.preferredLocations(parentPartition) override val index: Int = idx @@ -63,7 +63,7 @@ class UnionRDD[T: ClassTag]( extends RDD[T](sc, Nil) { // Nil since we implement getDependencies override def getPartitions: Array[Partition] = { - val array = new Array[Partition](rdds.map(_.partitions.size).sum) + val array = new Array[Partition](rdds.map(_.partitions.length).sum) var pos = 0 for ((rdd, rddIndex) <- rdds.zipWithIndex; split <- rdd.partitions) { array(pos) = new UnionPartition(pos, rdd, rddIndex, split.index) @@ -76,8 +76,8 @@ class UnionRDD[T: ClassTag]( val deps = new ArrayBuffer[Dependency[_]] var pos = 0 for (rdd <- rdds) { - deps += new RangeDependency(rdd, 0, pos, rdd.partitions.size) - pos += rdd.partitions.size + deps += new RangeDependency(rdd, 0, pos, rdd.partitions.length) + pos += rdd.partitions.length } deps } diff --git a/core/src/main/scala/org/apache/spark/rdd/ZippedPartitionsRDD.scala b/core/src/main/scala/org/apache/spark/rdd/ZippedPartitionsRDD.scala index 95b2dd954e9f4..a96b6c3d23454 100644 --- a/core/src/main/scala/org/apache/spark/rdd/ZippedPartitionsRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/ZippedPartitionsRDD.scala @@ -32,7 +32,7 @@ private[spark] class ZippedPartitionsPartition( override val index: Int = idx var partitionValues = rdds.map(rdd => rdd.partitions(idx)) - def partitions = partitionValues + def partitions: Seq[Partition] = partitionValues @throws(classOf[IOException]) private def writeObject(oos: ObjectOutputStream): Unit = Utils.tryOrIOException { @@ -52,8 +52,8 @@ private[spark] abstract class ZippedPartitionsBaseRDD[V: ClassTag]( if (preservesPartitioning) firstParent[Any].partitioner else None override def getPartitions: Array[Partition] = { - val numParts = rdds.head.partitions.size - if (!rdds.forall(rdd => rdd.partitions.size == numParts)) { + val numParts = rdds.head.partitions.length + if (!rdds.forall(rdd => rdd.partitions.length == numParts)) { throw new IllegalArgumentException("Can't zip RDDs with unequal numbers of partitions") } Array.tabulate[Partition](numParts) { i => diff --git a/core/src/main/scala/org/apache/spark/rdd/ZippedWithIndexRDD.scala b/core/src/main/scala/org/apache/spark/rdd/ZippedWithIndexRDD.scala index 8c43a559409f2..523aaf2b860b5 100644 --- a/core/src/main/scala/org/apache/spark/rdd/ZippedWithIndexRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/ZippedWithIndexRDD.scala @@ -41,7 +41,7 @@ class ZippedWithIndexRDD[T: ClassTag](@transient prev: RDD[T]) extends RDD[(T, L /** The start index of each partition. */ @transient private val startIndices: Array[Long] = { - val n = prev.partitions.size + val n = prev.partitions.length if (n == 0) { Array[Long]() } else if (n == 1) { diff --git a/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala b/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala new file mode 100644 index 0000000000000..f2c1c86af767e --- /dev/null +++ b/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala @@ -0,0 +1,429 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.rpc + +import java.net.URI + +import scala.concurrent.{Await, Future} +import scala.concurrent.duration._ +import scala.language.postfixOps +import scala.reflect.ClassTag + +import org.apache.spark.{Logging, SparkException, SecurityManager, SparkConf} +import org.apache.spark.util.{AkkaUtils, Utils} + +/** + * An RPC environment. [[RpcEndpoint]]s need to register itself with a name to [[RpcEnv]] to + * receives messages. Then [[RpcEnv]] will process messages sent from [[RpcEndpointRef]] or remote + * nodes, and deliver them to corresponding [[RpcEndpoint]]s. For uncaught exceptions caught by + * [[RpcEnv]], [[RpcEnv]] will use [[RpcCallContext.sendFailure]] to send exceptions back to the + * sender, or logging them if no such sender or `NotSerializableException`. + * + * [[RpcEnv]] also provides some methods to retrieve [[RpcEndpointRef]]s given name or uri. + */ +private[spark] abstract class RpcEnv(conf: SparkConf) { + + private[spark] val defaultLookupTimeout = AkkaUtils.lookupTimeout(conf) + + /** + * Return RpcEndpointRef of the registered [[RpcEndpoint]]. Will be used to implement + * [[RpcEndpoint.self]]. Return `null` if the corresponding [[RpcEndpointRef]] does not exist. + */ + private[rpc] def endpointRef(endpoint: RpcEndpoint): RpcEndpointRef + + /** + * Return the address that [[RpcEnv]] is listening to. + */ + def address: RpcAddress + + /** + * Register a [[RpcEndpoint]] with a name and return its [[RpcEndpointRef]]. [[RpcEnv]] does not + * guarantee thread-safety. + */ + def setupEndpoint(name: String, endpoint: RpcEndpoint): RpcEndpointRef + + /** + * Retrieve the [[RpcEndpointRef]] represented by `uri` asynchronously. + */ + def asyncSetupEndpointRefByURI(uri: String): Future[RpcEndpointRef] + + /** + * Retrieve the [[RpcEndpointRef]] represented by `uri`. This is a blocking action. + */ + def setupEndpointRefByURI(uri: String): RpcEndpointRef = { + Await.result(asyncSetupEndpointRefByURI(uri), defaultLookupTimeout) + } + + /** + * Retrieve the [[RpcEndpointRef]] represented by `systemName`, `address` and `endpointName` + * asynchronously. + */ + def asyncSetupEndpointRef( + systemName: String, address: RpcAddress, endpointName: String): Future[RpcEndpointRef] = { + asyncSetupEndpointRefByURI(uriOf(systemName, address, endpointName)) + } + + /** + * Retrieve the [[RpcEndpointRef]] represented by `systemName`, `address` and `endpointName`. + * This is a blocking action. + */ + def setupEndpointRef( + systemName: String, address: RpcAddress, endpointName: String): RpcEndpointRef = { + setupEndpointRefByURI(uriOf(systemName, address, endpointName)) + } + + /** + * Stop [[RpcEndpoint]] specified by `endpoint`. + */ + def stop(endpoint: RpcEndpointRef): Unit + + /** + * Shutdown this [[RpcEnv]] asynchronously. If need to make sure [[RpcEnv]] exits successfully, + * call [[awaitTermination()]] straight after [[shutdown()]]. + */ + def shutdown(): Unit + + /** + * Wait until [[RpcEnv]] exits. + * + * TODO do we need a timeout parameter? + */ + def awaitTermination(): Unit + + /** + * Create a URI used to create a [[RpcEndpointRef]]. Use this one to create the URI instead of + * creating it manually because different [[RpcEnv]] may have different formats. + */ + def uriOf(systemName: String, address: RpcAddress, endpointName: String): String +} + +private[spark] case class RpcEnvConfig( + conf: SparkConf, + name: String, + host: String, + port: Int, + securityManager: SecurityManager) + +/** + * A RpcEnv implementation must have a [[RpcEnvFactory]] implementation with an empty constructor + * so that it can be created via Reflection. + */ +private[spark] object RpcEnv { + + private def getRpcEnvFactory(conf: SparkConf): RpcEnvFactory = { + // Add more RpcEnv implementations here + val rpcEnvNames = Map("akka" -> "org.apache.spark.rpc.akka.AkkaRpcEnvFactory") + val rpcEnvName = conf.get("spark.rpc", "akka") + val rpcEnvFactoryClassName = rpcEnvNames.getOrElse(rpcEnvName.toLowerCase, rpcEnvName) + Class.forName(rpcEnvFactoryClassName, true, Utils.getContextOrSparkClassLoader). + newInstance().asInstanceOf[RpcEnvFactory] + } + + def create( + name: String, + host: String, + port: Int, + conf: SparkConf, + securityManager: SecurityManager): RpcEnv = { + // Using Reflection to create the RpcEnv to avoid to depend on Akka directly + val config = RpcEnvConfig(conf, name, host, port, securityManager) + getRpcEnvFactory(conf).create(config) + } + +} + +/** + * A factory class to create the [[RpcEnv]]. It must have an empty constructor so that it can be + * created using Reflection. + */ +private[spark] trait RpcEnvFactory { + + def create(config: RpcEnvConfig): RpcEnv +} + +/** + * An end point for the RPC that defines what functions to trigger given a message. + * + * It is guaranteed that `onStart`, `receive` and `onStop` will be called in sequence. + * + * The lift-cycle will be: + * + * constructor onStart receive* onStop + * + * Note: `receive` can be called concurrently. If you want `receive` is thread-safe, please use + * [[ThreadSafeRpcEndpoint]] + * + * If any error is thrown from one of [[RpcEndpoint]] methods except `onError`, `onError` will be + * invoked with the cause. If `onError` throws an error, [[RpcEnv]] will ignore it. + */ +private[spark] trait RpcEndpoint { + + /** + * The [[RpcEnv]] that this [[RpcEndpoint]] is registered to. + */ + val rpcEnv: RpcEnv + + /** + * The [[RpcEndpointRef]] of this [[RpcEndpoint]]. `self` will become valid when `onStart` is + * called. And `self` will become `null` when `onStop` is called. + * + * Note: Because before `onStart`, [[RpcEndpoint]] has not yet been registered and there is not + * valid [[RpcEndpointRef]] for it. So don't call `self` before `onStart` is called. + */ + final def self: RpcEndpointRef = { + require(rpcEnv != null, "rpcEnv has not been initialized") + rpcEnv.endpointRef(this) + } + + /** + * Process messages from [[RpcEndpointRef.send]] or [[RpcCallContext.reply)]]. If receiving a + * unmatched message, [[SparkException]] will be thrown and sent to `onError`. + */ + def receive: PartialFunction[Any, Unit] = { + case _ => throw new SparkException(self + " does not implement 'receive'") + } + + /** + * Process messages from [[RpcEndpointRef.sendWithReply]]. If receiving a unmatched message, + * [[SparkException]] will be thrown and sent to `onError`. + */ + def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { + case _ => context.sendFailure(new SparkException(self + " won't reply anything")) + } + + /** + * Call onError when any exception is thrown during handling messages. + * + * @param cause + */ + def onError(cause: Throwable): Unit = { + // By default, throw e and let RpcEnv handle it + throw cause + } + + /** + * Invoked before [[RpcEndpoint]] starts to handle any message. + */ + def onStart(): Unit = { + // By default, do nothing. + } + + /** + * Invoked when [[RpcEndpoint]] is stopping. + */ + def onStop(): Unit = { + // By default, do nothing. + } + + /** + * Invoked when `remoteAddress` is connected to the current node. + */ + def onConnected(remoteAddress: RpcAddress): Unit = { + // By default, do nothing. + } + + /** + * Invoked when `remoteAddress` is lost. + */ + def onDisconnected(remoteAddress: RpcAddress): Unit = { + // By default, do nothing. + } + + /** + * Invoked when some network error happens in the connection between the current node and + * `remoteAddress`. + */ + def onNetworkError(cause: Throwable, remoteAddress: RpcAddress): Unit = { + // By default, do nothing. + } + + /** + * A convenient method to stop [[RpcEndpoint]]. + */ + final def stop(): Unit = { + val _self = self + if (_self != null) { + rpcEnv.stop(self) + } + } +} + +/** + * A trait that requires RpcEnv thread-safely sending messages to it. + * + * Thread-safety means processing of one message happens before processing of the next message by + * the same [[ThreadSafeRpcEndpoint]]. In the other words, changes to internal fields of a + * [[ThreadSafeRpcEndpoint]] are visible when processing the next message, and fields in the + * [[ThreadSafeRpcEndpoint]] need not be volatile or equivalent. + * + * However, there is no guarantee that the same thread will be executing the same + * [[ThreadSafeRpcEndpoint]] for different messages. + */ +trait ThreadSafeRpcEndpoint extends RpcEndpoint + +/** + * A reference for a remote [[RpcEndpoint]]. [[RpcEndpointRef]] is thread-safe. + */ +private[spark] abstract class RpcEndpointRef(@transient conf: SparkConf) + extends Serializable with Logging { + + private[this] val maxRetries = conf.getInt("spark.akka.num.retries", 3) + private[this] val retryWaitMs = conf.getLong("spark.akka.retry.wait", 3000) + private[this] val defaultAskTimeout = conf.getLong("spark.akka.askTimeout", 30) seconds + + /** + * return the address for the [[RpcEndpointRef]] + */ + def address: RpcAddress + + def name: String + + /** + * Sends a one-way asynchronous message. Fire-and-forget semantics. + */ + def send(message: Any): Unit + + /** + * Send a message to the corresponding [[RpcEndpoint.receiveAndReply)]] and return a `Future` to + * receive the reply within a default timeout. + * + * This method only sends the message once and never retries. + */ + def sendWithReply[T: ClassTag](message: Any): Future[T] = + sendWithReply(message, defaultAskTimeout) + + /** + * Send a message to the corresponding [[RpcEndpoint.receiveAndReply)]] and return a `Future` to + * receive the reply within the specified timeout. + * + * This method only sends the message once and never retries. + */ + def sendWithReply[T: ClassTag](message: Any, timeout: FiniteDuration): Future[T] + + /** + * Send a message to the corresponding [[RpcEndpoint]] and get its result within a default + * timeout, or throw a SparkException if this fails even after the default number of retries. + * The default `timeout` will be used in every trial of calling `sendWithReply`. Because this + * method retries, the message handling in the receiver side should be idempotent. + * + * Note: this is a blocking action which may cost a lot of time, so don't call it in an message + * loop of [[RpcEndpoint]]. + * + * @param message the message to send + * @tparam T type of the reply message + * @return the reply message from the corresponding [[RpcEndpoint]] + */ + def askWithReply[T: ClassTag](message: Any): T = askWithReply(message, defaultAskTimeout) + + /** + * Send a message to the corresponding [[RpcEndpoint.receive]] and get its result within a + * specified timeout, throw a SparkException if this fails even after the specified number of + * retries. `timeout` will be used in every trial of calling `sendWithReply`. Because this method + * retries, the message handling in the receiver side should be idempotent. + * + * Note: this is a blocking action which may cost a lot of time, so don't call it in an message + * loop of [[RpcEndpoint]]. + * + * @param message the message to send + * @param timeout the timeout duration + * @tparam T type of the reply message + * @return the reply message from the corresponding [[RpcEndpoint]] + */ + def askWithReply[T: ClassTag](message: Any, timeout: FiniteDuration): T = { + // TODO: Consider removing multiple attempts + var attempts = 0 + var lastException: Exception = null + while (attempts < maxRetries) { + attempts += 1 + try { + val future = sendWithReply[T](message, timeout) + val result = Await.result(future, timeout) + if (result == null) { + throw new SparkException("Actor returned null") + } + return result + } catch { + case ie: InterruptedException => throw ie + case e: Exception => + lastException = e + logWarning(s"Error sending message [message = $message] in $attempts attempts", e) + } + Thread.sleep(retryWaitMs) + } + + throw new SparkException( + s"Error sending message [message = $message]", lastException) + } + +} + +/** + * Represent a host with a port + */ +private[spark] case class RpcAddress(host: String, port: Int) { + // TODO do we need to add the type of RpcEnv in the address? + + val hostPort: String = host + ":" + port + + override val toString: String = hostPort +} + +private[spark] object RpcAddress { + + /** + * Return the [[RpcAddress]] represented by `uri`. + */ + def fromURI(uri: URI): RpcAddress = { + RpcAddress(uri.getHost, uri.getPort) + } + + /** + * Return the [[RpcAddress]] represented by `uri`. + */ + def fromURIString(uri: String): RpcAddress = { + fromURI(new java.net.URI(uri)) + } + + def fromSparkURL(sparkUrl: String): RpcAddress = { + val (host, port) = Utils.extractHostPortFromSparkUrl(sparkUrl) + RpcAddress(host, port) + } +} + +/** + * A callback that [[RpcEndpoint]] can use it to send back a message or failure. It's thread-safe + * and can be called in any thread. + */ +private[spark] trait RpcCallContext { + + /** + * Reply a message to the sender. If the sender is [[RpcEndpoint]], its [[RpcEndpoint.receive]] + * will be called. + */ + def reply(response: Any): Unit + + /** + * Report a failure to the sender. + */ + def sendFailure(e: Throwable): Unit + + /** + * The sender of this message. + */ + def sender: RpcEndpointRef +} diff --git a/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala b/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala new file mode 100644 index 0000000000000..652e52f2b2e73 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala @@ -0,0 +1,325 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.rpc.akka + +import java.util.concurrent.ConcurrentHashMap + +import scala.concurrent.Future +import scala.concurrent.duration._ +import scala.language.postfixOps +import scala.reflect.ClassTag +import scala.util.control.NonFatal + +import akka.actor.{ActorSystem, ExtendedActorSystem, Actor, ActorRef, Props, Address} +import akka.event.Logging.Error +import akka.pattern.{ask => akkaAsk} +import akka.remote.{AssociationEvent, AssociatedEvent, DisassociatedEvent, AssociationErrorEvent} +import org.apache.spark.{SparkException, Logging, SparkConf} +import org.apache.spark.rpc._ +import org.apache.spark.util.{ActorLogReceive, AkkaUtils} + +/** + * A RpcEnv implementation based on Akka. + * + * TODO Once we remove all usages of Akka in other place, we can move this file to a new project and + * remove Akka from the dependencies. + * + * @param actorSystem + * @param conf + * @param boundPort + */ +private[spark] class AkkaRpcEnv private[akka] ( + val actorSystem: ActorSystem, conf: SparkConf, boundPort: Int) + extends RpcEnv(conf) with Logging { + + private val defaultAddress: RpcAddress = { + val address = actorSystem.asInstanceOf[ExtendedActorSystem].provider.getDefaultAddress + // In some test case, ActorSystem doesn't bind to any address. + // So just use some default value since they are only some unit tests + RpcAddress(address.host.getOrElse("localhost"), address.port.getOrElse(boundPort)) + } + + override val address: RpcAddress = defaultAddress + + /** + * A lookup table to search a [[RpcEndpointRef]] for a [[RpcEndpoint]]. We need it to make + * [[RpcEndpoint.self]] work. + */ + private val endpointToRef = new ConcurrentHashMap[RpcEndpoint, RpcEndpointRef]() + + /** + * Need this map to remove `RpcEndpoint` from `endpointToRef` via a `RpcEndpointRef` + */ + private val refToEndpoint = new ConcurrentHashMap[RpcEndpointRef, RpcEndpoint]() + + private def registerEndpoint(endpoint: RpcEndpoint, endpointRef: RpcEndpointRef): Unit = { + endpointToRef.put(endpoint, endpointRef) + refToEndpoint.put(endpointRef, endpoint) + } + + private def unregisterEndpoint(endpointRef: RpcEndpointRef): Unit = { + val endpoint = refToEndpoint.remove(endpointRef) + if (endpoint != null) { + endpointToRef.remove(endpoint) + } + } + + /** + * Retrieve the [[RpcEndpointRef]] of `endpoint`. + */ + override def endpointRef(endpoint: RpcEndpoint): RpcEndpointRef = endpointToRef.get(endpoint) + + override def setupEndpoint(name: String, endpoint: RpcEndpoint): RpcEndpointRef = { + @volatile var endpointRef: AkkaRpcEndpointRef = null + // Use lazy because the Actor needs to use `endpointRef`. + // So `actorRef` should be created after assigning `endpointRef`. + lazy val actorRef = actorSystem.actorOf(Props(new Actor with ActorLogReceive with Logging { + + assert(endpointRef != null) + + override def preStart(): Unit = { + // Listen for remote client network events + context.system.eventStream.subscribe(self, classOf[AssociationEvent]) + safelyCall(endpoint) { + endpoint.onStart() + } + } + + override def receiveWithLogging: Receive = { + case AssociatedEvent(_, remoteAddress, _) => + safelyCall(endpoint) { + endpoint.onConnected(akkaAddressToRpcAddress(remoteAddress)) + } + + case DisassociatedEvent(_, remoteAddress, _) => + safelyCall(endpoint) { + endpoint.onDisconnected(akkaAddressToRpcAddress(remoteAddress)) + } + + case AssociationErrorEvent(cause, localAddress, remoteAddress, inbound, _) => + safelyCall(endpoint) { + endpoint.onNetworkError(cause, akkaAddressToRpcAddress(remoteAddress)) + } + + case e: AssociationEvent => + // TODO ignore? + + case m: AkkaMessage => + logDebug(s"Received RPC message: $m") + safelyCall(endpoint) { + processMessage(endpoint, m, sender) + } + + case AkkaFailure(e) => + safelyCall(endpoint) { + throw e + } + + case message: Any => { + logWarning(s"Unknown message: $message") + } + + } + + override def postStop(): Unit = { + unregisterEndpoint(endpoint.self) + safelyCall(endpoint) { + endpoint.onStop() + } + } + + }), name = name) + endpointRef = new AkkaRpcEndpointRef(defaultAddress, actorRef, conf, initInConstructor = false) + registerEndpoint(endpoint, endpointRef) + // Now actorRef can be created safely + endpointRef.init() + endpointRef + } + + private def processMessage(endpoint: RpcEndpoint, m: AkkaMessage, _sender: ActorRef): Unit = { + val message = m.message + val needReply = m.needReply + val pf: PartialFunction[Any, Unit] = + if (needReply) { + endpoint.receiveAndReply(new RpcCallContext { + override def sendFailure(e: Throwable): Unit = { + _sender ! AkkaFailure(e) + } + + override def reply(response: Any): Unit = { + _sender ! AkkaMessage(response, false) + } + + // Some RpcEndpoints need to know the sender's address + override val sender: RpcEndpointRef = + new AkkaRpcEndpointRef(defaultAddress, _sender, conf) + }) + } else { + endpoint.receive + } + try { + pf.applyOrElse[Any, Unit](message, { message => + throw new SparkException(s"Unmatched message $message from ${_sender}") + }) + } catch { + case NonFatal(e) => + if (needReply) { + // If the sender asks a reply, we should send the error back to the sender + _sender ! AkkaFailure(e) + } else { + throw e + } + } + } + + /** + * Run `action` safely to avoid to crash the thread. If any non-fatal exception happens, it will + * call `endpoint.onError`. If `endpoint.onError` throws any non-fatal exception, just log it. + */ + private def safelyCall(endpoint: RpcEndpoint)(action: => Unit): Unit = { + try { + action + } catch { + case NonFatal(e) => { + try { + endpoint.onError(e) + } catch { + case NonFatal(e) => logError(s"Ignore error: ${e.getMessage}", e) + } + } + } + } + + private def akkaAddressToRpcAddress(address: Address): RpcAddress = { + RpcAddress(address.host.getOrElse(defaultAddress.host), + address.port.getOrElse(defaultAddress.port)) + } + + override def asyncSetupEndpointRefByURI(uri: String): Future[RpcEndpointRef] = { + import actorSystem.dispatcher + actorSystem.actorSelection(uri).resolveOne(defaultLookupTimeout). + map(new AkkaRpcEndpointRef(defaultAddress, _, conf)) + } + + override def uriOf(systemName: String, address: RpcAddress, endpointName: String): String = { + AkkaUtils.address( + AkkaUtils.protocol(actorSystem), systemName, address.host, address.port, endpointName) + } + + override def shutdown(): Unit = { + actorSystem.shutdown() + } + + override def stop(endpoint: RpcEndpointRef): Unit = { + require(endpoint.isInstanceOf[AkkaRpcEndpointRef]) + actorSystem.stop(endpoint.asInstanceOf[AkkaRpcEndpointRef].actorRef) + } + + override def awaitTermination(): Unit = { + actorSystem.awaitTermination() + } + + override def toString: String = s"${getClass.getSimpleName}($actorSystem)" +} + +private[spark] class AkkaRpcEnvFactory extends RpcEnvFactory { + + def create(config: RpcEnvConfig): RpcEnv = { + val (actorSystem, boundPort) = AkkaUtils.createActorSystem( + config.name, config.host, config.port, config.conf, config.securityManager) + actorSystem.actorOf(Props(classOf[ErrorMonitor]), "ErrorMonitor") + new AkkaRpcEnv(actorSystem, config.conf, boundPort) + } +} + +/** + * Monitor errors reported by Akka and log them. + */ +private[akka] class ErrorMonitor extends Actor with ActorLogReceive with Logging { + + override def preStart(): Unit = { + context.system.eventStream.subscribe(self, classOf[Error]) + } + + override def receiveWithLogging: Actor.Receive = { + case Error(cause: Throwable, _, _, message: String) => logError(message, cause) + } +} + +private[akka] class AkkaRpcEndpointRef( + @transient defaultAddress: RpcAddress, + @transient _actorRef: => ActorRef, + @transient conf: SparkConf, + @transient initInConstructor: Boolean = true) + extends RpcEndpointRef(conf) with Logging { + + lazy val actorRef = _actorRef + + override lazy val address: RpcAddress = { + val akkaAddress = actorRef.path.address + RpcAddress(akkaAddress.host.getOrElse(defaultAddress.host), + akkaAddress.port.getOrElse(defaultAddress.port)) + } + + override lazy val name: String = actorRef.path.name + + private[akka] def init(): Unit = { + // Initialize the lazy vals + actorRef + address + name + } + + if (initInConstructor) { + init() + } + + override def send(message: Any): Unit = { + actorRef ! AkkaMessage(message, false) + } + + override def sendWithReply[T: ClassTag](message: Any, timeout: FiniteDuration): Future[T] = { + import scala.concurrent.ExecutionContext.Implicits.global + actorRef.ask(AkkaMessage(message, true))(timeout).flatMap { + case msg @ AkkaMessage(message, reply) => + if (reply) { + logError(s"Receive $msg but the sender cannot reply") + Future.failed(new SparkException(s"Receive $msg but the sender cannot reply")) + } else { + Future.successful(message) + } + case AkkaFailure(e) => + Future.failed(e) + }.mapTo[T] + } + + override def toString: String = s"${getClass.getSimpleName}($actorRef)" + +} + +/** + * A wrapper to `message` so that the receiver knows if the sender expects a reply. + * @param message + * @param needReply if the sender expects a reply message + */ +private[akka] case class AkkaMessage(message: Any, needReply: Boolean) + +/** + * A reply with the failure error from the receiver to the sender + */ +private[akka] case class AkkaFailure(e: Throwable) diff --git a/core/src/main/scala/org/apache/spark/scheduler/AccumulableInfo.scala b/core/src/main/scala/org/apache/spark/scheduler/AccumulableInfo.scala index fa83372bb4d11..e0edd7d4ae968 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/AccumulableInfo.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/AccumulableInfo.scala @@ -39,8 +39,11 @@ class AccumulableInfo ( } object AccumulableInfo { - def apply(id: Long, name: String, update: Option[String], value: String) = + def apply(id: Long, name: String, update: Option[String], value: String): AccumulableInfo = { new AccumulableInfo(id, name, update, value) + } - def apply(id: Long, name: String, value: String) = new AccumulableInfo(id, name, None, value) + def apply(id: Long, name: String, value: String): AccumulableInfo = { + new AccumulableInfo(id, name, None, value) + } } diff --git a/core/src/main/scala/org/apache/spark/scheduler/ActiveJob.scala b/core/src/main/scala/org/apache/spark/scheduler/ActiveJob.scala index b755d8fb15757..50a69379412d2 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/ActiveJob.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/ActiveJob.scala @@ -27,7 +27,7 @@ import org.apache.spark.util.CallSite */ private[spark] class ActiveJob( val jobId: Int, - val finalStage: Stage, + val finalStage: ResultStage, val func: (TaskContext, Iterator[_]) => _, val partitions: Array[Int], val callSite: CallSite, diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala index 1cfe98673773a..4a32f8936fb0e 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala @@ -23,22 +23,18 @@ import java.util.concurrent.{TimeUnit, Executors} import java.util.concurrent.atomic.AtomicInteger import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet, Map, Stack} -import scala.concurrent.Await import scala.concurrent.duration._ +import scala.language.existentials import scala.language.postfixOps -import scala.reflect.ClassTag import scala.util.control.NonFatal -import akka.pattern.ask -import akka.util.Timeout - import org.apache.spark._ import org.apache.spark.broadcast.Broadcast import org.apache.spark.executor.TaskMetrics import org.apache.spark.partial.{ApproximateActionListener, ApproximateEvaluator, PartialResult} import org.apache.spark.rdd.RDD import org.apache.spark.storage._ -import org.apache.spark.util.{CallSite, EventLoop, SystemClock, Clock, Utils} +import org.apache.spark.util._ import org.apache.spark.storage.BlockManagerMessages.BlockManagerHeartbeat /** @@ -54,6 +50,10 @@ import org.apache.spark.storage.BlockManagerMessages.BlockManagerHeartbeat * not caused by shuffle file loss are handled by the TaskScheduler, which will retry each task * a small number of times before cancelling the whole stage. * + * Here's a checklist to use when making or reviewing changes to this class: + * + * - When adding a new data structure, update `DAGSchedulerSuite.assertDataStructuresEmpty` to + * include the new structure. This will help to catch memory leaks. */ private[spark] class DAGScheduler( @@ -63,7 +63,7 @@ class DAGScheduler( mapOutputTracker: MapOutputTrackerMaster, blockManagerMaster: BlockManagerMaster, env: SparkEnv, - clock: Clock = SystemClock) + clock: Clock = new SystemClock()) extends Logging { def this(sc: SparkContext, taskScheduler: TaskScheduler) = { @@ -84,7 +84,7 @@ class DAGScheduler( private[scheduler] val jobIdToStageIds = new HashMap[Int, HashSet[Int]] private[scheduler] val stageIdToStage = new HashMap[Int, Stage] - private[scheduler] val shuffleToMapStage = new HashMap[Int, Stage] + private[scheduler] val shuffleToMapStage = new HashMap[Int, ShuffleMapStage] private[scheduler] val jobIdToActiveJob = new HashMap[Int, ActiveJob] // Stages we need to run whose parents aren't done @@ -98,8 +98,14 @@ class DAGScheduler( private[scheduler] val activeJobs = new HashSet[ActiveJob] - // Contains the locations that each RDD's partitions are cached on - private val cacheLocs = new HashMap[Int, Array[Seq[TaskLocation]]] + /** + * Contains the locations that each RDD's partitions are cached on. This map's keys are RDD ids + * and its values are arrays indexed by partition numbers. Each array value is the set of + * locations where that RDD partition is cached. + * + * All accesses to this map should be guarded by synchronizing on it (see SPARK-4454). + */ + private val cacheLocs = new HashMap[Int, Seq[Seq[TaskLocation]]] // For tracking failed nodes, we use the MapOutputTracker's epoch number, which is sent with // every task. When we detect a node failing, we note the current epoch number and failed @@ -109,6 +115,8 @@ class DAGScheduler( // stray messages to detect. private val failedEpoch = new HashMap[String, Long] + private [scheduler] val outputCommitCoordinator = env.outputCommitCoordinator + // A closure serializer that we reuse. // This is only safe because DAGScheduler runs in a single thread. private val closureSerializer = SparkEnv.get.closureSerializer.newInstance() @@ -143,7 +151,7 @@ class DAGScheduler( result: Any, accumUpdates: Map[Long, Any], taskInfo: TaskInfo, - taskMetrics: TaskMetrics) { + taskMetrics: TaskMetrics): Unit = { eventProcessLoop.post( CompletionEvent(task, reason, result, accumUpdates, taskInfo, taskMetrics)) } @@ -158,41 +166,40 @@ class DAGScheduler( taskMetrics: Array[(Long, Int, Int, TaskMetrics)], // (taskId, stageId, stateAttempt, metrics) blockManagerId: BlockManagerId): Boolean = { listenerBus.post(SparkListenerExecutorMetricsUpdate(execId, taskMetrics)) - implicit val timeout = Timeout(600 seconds) - - Await.result( - blockManagerMaster.driverActor ? BlockManagerHeartbeat(blockManagerId), - timeout.duration).asInstanceOf[Boolean] + blockManagerMaster.driverEndpoint.askWithReply[Boolean]( + BlockManagerHeartbeat(blockManagerId), 600 seconds) } // Called by TaskScheduler when an executor fails. - def executorLost(execId: String) { + def executorLost(execId: String): Unit = { eventProcessLoop.post(ExecutorLost(execId)) } // Called by TaskScheduler when a host is added - def executorAdded(execId: String, host: String) { + def executorAdded(execId: String, host: String): Unit = { eventProcessLoop.post(ExecutorAdded(execId, host)) } // Called by TaskScheduler to cancel an entire TaskSet due to either repeated failures or // cancellation of the job itself. - def taskSetFailed(taskSet: TaskSet, reason: String) { + def taskSetFailed(taskSet: TaskSet, reason: String): Unit = { eventProcessLoop.post(TaskSetFailed(taskSet, reason)) } - private def getCacheLocs(rdd: RDD[_]): Array[Seq[TaskLocation]] = { + private[scheduler] + def getCacheLocs(rdd: RDD[_]): Seq[Seq[TaskLocation]] = cacheLocs.synchronized { + // Note: this doesn't use `getOrElse()` because this method is called O(num tasks) times if (!cacheLocs.contains(rdd.id)) { val blockIds = rdd.partitions.indices.map(index => RDDBlockId(rdd.id, index)).toArray[BlockId] - val locs = BlockManager.blockIdsToBlockManagers(blockIds, env, blockManagerMaster) - cacheLocs(rdd.id) = blockIds.map { id => - locs.getOrElse(id, Nil).map(bm => TaskLocation(bm.host, bm.executorId)) + val locs: Seq[Seq[TaskLocation]] = blockManagerMaster.getLocations(blockIds).map { bms => + bms.map(bm => TaskLocation(bm.host, bm.executorId)) } + cacheLocs(rdd.id) = locs } cacheLocs(rdd.id) } - private def clearCacheLocs() { + private def clearCacheLocs(): Unit = cacheLocs.synchronized { cacheLocs.clear() } @@ -201,40 +208,65 @@ class DAGScheduler( * The jobId value passed in will be used if the stage doesn't already exist with * a lower jobId (jobId always increases across jobs.) */ - private def getShuffleMapStage(shuffleDep: ShuffleDependency[_, _, _], jobId: Int): Stage = { + private def getShuffleMapStage( + shuffleDep: ShuffleDependency[_, _, _], + jobId: Int): ShuffleMapStage = { shuffleToMapStage.get(shuffleDep.shuffleId) match { case Some(stage) => stage case None => // We are going to register ancestor shuffle dependencies registerShuffleDependencies(shuffleDep, jobId) // Then register current shuffleDep - val stage = - newOrUsedStage( - shuffleDep.rdd, shuffleDep.rdd.partitions.size, shuffleDep, jobId, - shuffleDep.rdd.creationSite) + val stage = newOrUsedShuffleStage(shuffleDep, jobId) shuffleToMapStage(shuffleDep.shuffleId) = stage - + stage } } /** - * Create a Stage -- either directly for use as a result stage, or as part of the (re)-creation - * of a shuffle map stage in newOrUsedStage. The stage will be associated with the provided - * jobId. Production of shuffle map stages should always use newOrUsedStage, not newStage - * directly. + * Helper function to eliminate some code re-use when creating new stages. */ - private def newStage( + private def getParentStagesAndId(rdd: RDD[_], jobId: Int): (List[Stage], Int) = { + val parentStages = getParentStages(rdd, jobId) + val id = nextStageId.getAndIncrement() + (parentStages, id) + } + + /** + * Create a ShuffleMapStage as part of the (re)-creation of a shuffle map stage in + * newOrUsedShuffleStage. The stage will be associated with the provided jobId. + * Production of shuffle map stages should always use newOrUsedShuffleStage, not + * newShuffleMapStage directly. + */ + private def newShuffleMapStage( rdd: RDD[_], numTasks: Int, - shuffleDep: Option[ShuffleDependency[_, _, _]], + shuffleDep: ShuffleDependency[_, _, _], jobId: Int, - callSite: CallSite) - : Stage = - { - val parentStages = getParentStages(rdd, jobId) - val id = nextStageId.getAndIncrement() - val stage = new Stage(id, rdd, numTasks, shuffleDep, parentStages, jobId, callSite) + callSite: CallSite): ShuffleMapStage = { + val (parentStages: List[Stage], id: Int) = getParentStagesAndId(rdd, jobId) + val stage: ShuffleMapStage = new ShuffleMapStage(id, rdd, numTasks, parentStages, + jobId, callSite, shuffleDep) + + stageIdToStage(id) = stage + updateJobIdStageIdMaps(jobId, stage) + stage + } + + /** + * Create a ResultStage -- either directly for use as a result stage, or as part of the + * (re)-creation of a shuffle map stage in newOrUsedShuffleStage. The stage will be associated + * with the provided jobId. + */ + private def newResultStage( + rdd: RDD[_], + numTasks: Int, + jobId: Int, + callSite: CallSite): ResultStage = { + val (parentStages: List[Stage], id: Int) = getParentStagesAndId(rdd, jobId) + val stage: ResultStage = new ResultStage(id, rdd, numTasks, parentStages, jobId, callSite) + stageIdToStage(id) = stage updateJobIdStageIdMaps(jobId, stage) stage @@ -246,20 +278,17 @@ class DAGScheduler( * present in the MapOutputTracker, then the number and location of available outputs are * recovered from the MapOutputTracker */ - private def newOrUsedStage( - rdd: RDD[_], - numTasks: Int, + private def newOrUsedShuffleStage( shuffleDep: ShuffleDependency[_, _, _], - jobId: Int, - callSite: CallSite) - : Stage = - { - val stage = newStage(rdd, numTasks, Some(shuffleDep), jobId, callSite) + jobId: Int): ShuffleMapStage = { + val rdd = shuffleDep.rdd + val numTasks = rdd.partitions.size + val stage = newShuffleMapStage(rdd, numTasks, shuffleDep, jobId, rdd.creationSite) if (mapOutputTracker.containsShuffle(shuffleDep.shuffleId)) { val serLocs = mapOutputTracker.getSerializedMapOutputStatuses(shuffleDep.shuffleId) val locs = MapOutputTracker.deserializeMapStatuses(serLocs) for (i <- 0 until locs.size) { - stage.outputLocs(i) = Option(locs(i)).toList // locs(i) will be null if missing + stage.outputLocs(i) = Option(locs(i)).toList // locs(i) will be null if missing } stage.numAvailableOutputs = locs.count(_ != null) } else { @@ -297,26 +326,23 @@ class DAGScheduler( } } waitingForVisit.push(rdd) - while (!waitingForVisit.isEmpty) { + while (waitingForVisit.nonEmpty) { visit(waitingForVisit.pop()) } parents.toList } - // Find ancestor missing shuffle dependencies and register into shuffleToMapStage - private def registerShuffleDependencies(shuffleDep: ShuffleDependency[_, _, _], jobId: Int) = { + /** Find ancestor missing shuffle dependencies and register into shuffleToMapStage */ + private def registerShuffleDependencies(shuffleDep: ShuffleDependency[_, _, _], jobId: Int) { val parentsWithNoMapStage = getAncestorShuffleDependencies(shuffleDep.rdd) - while (!parentsWithNoMapStage.isEmpty) { + while (parentsWithNoMapStage.nonEmpty) { val currentShufDep = parentsWithNoMapStage.pop() - val stage = - newOrUsedStage( - currentShufDep.rdd, currentShufDep.rdd.partitions.size, currentShufDep, jobId, - currentShufDep.rdd.creationSite) + val stage = newOrUsedShuffleStage(currentShufDep, jobId) shuffleToMapStage(currentShufDep.shuffleId) = stage } } - // Find ancestor shuffle dependencies that are not registered in shuffleToMapStage yet + /** Find ancestor shuffle dependencies that are not registered in shuffleToMapStage yet */ private def getAncestorShuffleDependencies(rdd: RDD[_]): Stack[ShuffleDependency[_, _, _]] = { val parents = new Stack[ShuffleDependency[_, _, _]] val visited = new HashSet[RDD[_]] @@ -342,7 +368,7 @@ class DAGScheduler( } waitingForVisit.push(rdd) - while (!waitingForVisit.isEmpty) { + while (waitingForVisit.nonEmpty) { visit(waitingForVisit.pop()) } parents @@ -373,7 +399,7 @@ class DAGScheduler( } } waitingForVisit.push(stage.rdd) - while (!waitingForVisit.isEmpty) { + while (waitingForVisit.nonEmpty) { visit(waitingForVisit.pop()) } missing.toList @@ -383,7 +409,7 @@ class DAGScheduler( * Registers the given jobId among the jobs that need the given stage and * all of that stage's ancestors. */ - private def updateJobIdStageIdMaps(jobId: Int, stage: Stage) { + private def updateJobIdStageIdMaps(jobId: Int, stage: Stage): Unit = { def updateJobIdStageIdMapsList(stages: List[Stage]) { if (stages.nonEmpty) { val s = stages.head @@ -403,7 +429,7 @@ class DAGScheduler( * * @param job The job whose state to cleanup. */ - private def cleanupStateForJobAndIndependentStages(job: ActiveJob) { + private def cleanupStateForJobAndIndependentStages(job: ActiveJob): Unit = { val registeredStages = jobIdToStageIds.get(job.jobId) if (registeredStages.isEmpty || registeredStages.get.isEmpty) { logError("No stages registered for job " + job.jobId) @@ -465,8 +491,7 @@ class DAGScheduler( callSite: CallSite, allowLocal: Boolean, resultHandler: (Int, U) => Unit, - properties: Properties = null): JobWaiter[U] = - { + properties: Properties): JobWaiter[U] = { // Check to make sure we are not launching a task on a partition that does not exist. val maxPartitions = rdd.partitions.length partitions.find(p => p >= maxPartitions || p < 0).foreach { p => @@ -488,22 +513,20 @@ class DAGScheduler( waiter } - def runJob[T, U: ClassTag]( + def runJob[T, U]( rdd: RDD[T], func: (TaskContext, Iterator[T]) => U, partitions: Seq[Int], callSite: CallSite, allowLocal: Boolean, resultHandler: (Int, U) => Unit, - properties: Properties = null) - { + properties: Properties): Unit = { val start = System.nanoTime val waiter = submitJob(rdd, func, partitions, callSite, allowLocal, resultHandler, properties) waiter.awaitResult() match { - case JobSucceeded => { + case JobSucceeded => logInfo("Job %d finished: %s, took %f s".format (waiter.jobId, callSite.shortForm, (System.nanoTime - start) / 1e9)) - } case JobFailed(exception: Exception) => logInfo("Job %d failed: %s, took %f s".format (waiter.jobId, callSite.shortForm, (System.nanoTime - start) / 1e9)) @@ -517,9 +540,7 @@ class DAGScheduler( evaluator: ApproximateEvaluator[U, R], callSite: CallSite, timeout: Long, - properties: Properties = null) - : PartialResult[R] = - { + properties: Properties): PartialResult[R] = { val listener = new ApproximateActionListener(rdd, func, evaluator, timeout) val func2 = func.asInstanceOf[(TaskContext, Iterator[_]) => _] val partitions = (0 until rdd.partitions.size).toArray @@ -532,12 +553,12 @@ class DAGScheduler( /** * Cancel a job that is running or waiting in the queue. */ - def cancelJob(jobId: Int) { + def cancelJob(jobId: Int): Unit = { logInfo("Asked to cancel job " + jobId) eventProcessLoop.post(JobCancelled(jobId)) } - def cancelJobGroup(groupId: String) { + def cancelJobGroup(groupId: String): Unit = { logInfo("Asked to cancel job group " + groupId) eventProcessLoop.post(JobGroupCancelled(groupId)) } @@ -545,7 +566,7 @@ class DAGScheduler( /** * Cancel all jobs that are running or waiting in the queue. */ - def cancelAllJobs() { + def cancelAllJobs(): Unit = { eventProcessLoop.post(AllJobsCancelled) } @@ -624,13 +645,13 @@ class DAGScheduler( val split = rdd.partitions(job.partitions(0)) val taskContext = new TaskContextImpl(job.finalStage.id, job.partitions(0), taskAttemptId = 0, attemptNumber = 0, runningLocally = true) - TaskContextHelper.setTaskContext(taskContext) + TaskContext.setTaskContext(taskContext) try { val result = job.func(taskContext, rdd.iterator(split, taskContext)) job.listener.taskSucceeded(0, result) } finally { taskContext.markTaskCompleted() - TaskContextHelper.unset() + TaskContext.unset() } } catch { case e: Exception => @@ -648,7 +669,7 @@ class DAGScheduler( // completion events or stage abort stageIdToStage -= s.id jobIdToStageIds -= job.jobId - listenerBus.post(SparkListenerJobEnd(job.jobId, clock.getTime(), jobResult)) + listenerBus.post(SparkListenerJobEnd(job.jobId, clock.getTimeMillis(), jobResult)) } } @@ -666,7 +687,7 @@ class DAGScheduler( // Cancel all jobs belonging to this job group. // First finds all active jobs with this group id, and then kill stages for them. val activeInGroup = activeJobs.filter(activeJob => - groupId == activeJob.properties.get(SparkContext.SPARK_JOB_GROUP_ID)) + Option(activeJob.properties).exists(_.get(SparkContext.SPARK_JOB_GROUP_ID) == groupId)) val jobIds = activeInGroup.map(_.jobId) jobIds.foreach(handleJobCancellation(_, "part of cancelled job group %s".format(groupId))) submitWaitingStages() @@ -693,11 +714,12 @@ class DAGScheduler( // cancelling the stages because if the DAG scheduler is stopped, the entire application // is in the process of getting stopped. val stageFailedMessage = "Stage cancelled because SparkContext was shut down" - runningStages.foreach { stage => - stage.latestInfo.stageFailed(stageFailedMessage) - listenerBus.post(SparkListenerStageCompleted(stage.latestInfo)) + // The `toArray` here is necessary so that we don't iterate over `runningStages` while + // mutating it. + runningStages.toArray.foreach { stage => + markStageAsFinished(stage, Some(stageFailedMessage)) } - listenerBus.post(SparkListenerJobEnd(job.jobId, clock.getTime(), JobFailed(error))) + listenerBus.post(SparkListenerJobEnd(job.jobId, clock.getTimeMillis(), JobFailed(error))) } } @@ -713,13 +735,12 @@ class DAGScheduler( allowLocal: Boolean, callSite: CallSite, listener: JobListener, - properties: Properties = null) - { - var finalStage: Stage = null + properties: Properties) { + var finalStage: ResultStage = null try { // New stage creation may throw an exception if, for example, jobs are run on a // HadoopRDD whose underlying HDFS files have been deleted. - finalStage = newStage(finalRDD, partitions.size, None, jobId, callSite) + finalStage = newResultStage(finalRDD, partitions.size, jobId, callSite) } catch { case e: Exception => logWarning("Creating new stage failed due to exception - job: " + jobId, e) @@ -736,7 +757,7 @@ class DAGScheduler( logInfo("Missing parents: " + getMissingParentStages(finalStage)) val shouldRunLocally = localExecutionEnabled && allowLocal && finalStage.parents.isEmpty && partitions.length == 1 - val jobSubmissionTime = clock.getTime() + val jobSubmissionTime = clock.getTimeMillis() if (shouldRunLocally) { // Compute very short actions like first() or take() with no parent stages locally. listenerBus.post( @@ -764,7 +785,7 @@ class DAGScheduler( if (!waitingStages(stage) && !runningStages(stage) && !failedStages(stage)) { val missing = getMissingParentStages(stage).sortBy(_.id) logDebug("missing: " + missing) - if (missing == Nil) { + if (missing.isEmpty) { logInfo("Submitting " + stage + " (" + stage.rdd + "), which has no missing parents") submitMissingTasks(stage, jobId.get) } else { @@ -785,22 +806,19 @@ class DAGScheduler( // Get our pending tasks and remember them in our pendingTasks entry stage.pendingTasks.clear() + // First figure out the indexes of partition ids to compute. val partitionsToCompute: Seq[Int] = { - if (stage.isShuffleMap) { - (0 until stage.numPartitions).filter(id => stage.outputLocs(id) == Nil) - } else { - val job = stage.resultOfJob.get - (0 until job.numPartitions).filter(id => !job.finished(id)) + stage match { + case stage: ShuffleMapStage => + (0 until stage.numPartitions).filter(id => stage.outputLocs(id).isEmpty) + case stage: ResultStage => + val job = stage.resultOfJob.get + (0 until job.numPartitions).filter(id => !job.finished(id)) } } - val properties = if (jobIdToActiveJob.contains(jobId)) { - jobIdToActiveJob(stage.jobId).properties - } else { - // this stage will be assigned to "default" pool - null - } + val properties = jobIdToActiveJob.get(stage.jobId).map(_.properties).orNull runningStages += stage // SparkListenerStageSubmitted should be posted before testing whether tasks are @@ -808,6 +826,7 @@ class DAGScheduler( // will be posted, which should always come after a corresponding SparkListenerStageSubmitted // event. stage.latestInfo = StageInfo.fromStage(stage, Some(partitionsToCompute.size)) + outputCommitCoordinator.stageStart(stage.id) listenerBus.post(SparkListenerStageSubmitted(stage.latestInfo, properties)) // TODO: Maybe we can keep the taskBinary in Stage to avoid serializing it multiple times. @@ -820,18 +839,21 @@ class DAGScheduler( try { // For ShuffleMapTask, serialize and broadcast (rdd, shuffleDep). // For ResultTask, serialize and broadcast (rdd, func). - val taskBinaryBytes: Array[Byte] = - if (stage.isShuffleMap) { - closureSerializer.serialize((stage.rdd, stage.shuffleDep.get) : AnyRef).array() - } else { - closureSerializer.serialize((stage.rdd, stage.resultOfJob.get.func) : AnyRef).array() - } + val taskBinaryBytes: Array[Byte] = stage match { + case stage: ShuffleMapStage => + closureSerializer.serialize((stage.rdd, stage.shuffleDep): AnyRef).array() + case stage: ResultStage => + closureSerializer.serialize((stage.rdd, stage.resultOfJob.get.func): AnyRef).array() + } + taskBinary = sc.broadcast(taskBinaryBytes) } catch { // In the case of a failure during serialization, abort the stage. case e: NotSerializableException => abortStage(stage, "Task not serializable: " + e.toString) runningStages -= stage + + // Abort execution return case NonFatal(e) => abortStage(stage, s"Task serialization failed: $e\n${e.getStackTraceString}") @@ -839,20 +861,22 @@ class DAGScheduler( return } - val tasks: Seq[Task[_]] = if (stage.isShuffleMap) { - partitionsToCompute.map { id => - val locs = getPreferredLocs(stage.rdd, id) - val part = stage.rdd.partitions(id) - new ShuffleMapTask(stage.id, taskBinary, part, locs) - } - } else { - val job = stage.resultOfJob.get - partitionsToCompute.map { id => - val p: Int = job.partitions(id) - val part = stage.rdd.partitions(p) - val locs = getPreferredLocs(stage.rdd, p) - new ResultTask(stage.id, taskBinary, part, locs, id) - } + val tasks: Seq[Task[_]] = stage match { + case stage: ShuffleMapStage => + partitionsToCompute.map { id => + val locs = getPreferredLocs(stage.rdd, id) + val part = stage.rdd.partitions(id) + new ShuffleMapTask(stage.id, taskBinary, part, locs) + } + + case stage: ResultStage => + val job = stage.resultOfJob.get + partitionsToCompute.map { id => + val p: Int = job.partitions(id) + val part = stage.rdd.partitions(p) + val locs = getPreferredLocs(stage.rdd, p) + new ResultTask(stage.id, taskBinary, part, locs, id) + } } if (tasks.size > 0) { @@ -861,14 +885,22 @@ class DAGScheduler( logDebug("New pending tasks: " + stage.pendingTasks) taskScheduler.submitTasks( new TaskSet(tasks.toArray, stage.id, stage.newAttemptId(), stage.jobId, properties)) - stage.latestInfo.submissionTime = Some(clock.getTime()) + stage.latestInfo.submissionTime = Some(clock.getTimeMillis()) } else { - // Because we posted SparkListenerStageSubmitted earlier, we should post - // SparkListenerStageCompleted here in case there are no tasks to run. - listenerBus.post(SparkListenerStageCompleted(stage.latestInfo)) - logDebug("Stage " + stage + " is actually done; %b %d %d".format( - stage.isAvailable, stage.numAvailableOutputs, stage.numPartitions)) - runningStages -= stage + // Because we posted SparkListenerStageSubmitted earlier, we should mark + // the stage as completed here in case there are no tasks to run + markStageAsFinished(stage, None) + + val debugString = stage match { + case stage: ShuffleMapStage => + s"Stage ${stage} is actually done; " + + s"(available: ${stage.isAvailable}," + + s"available outputs: ${stage.numAvailableOutputs}," + + s"partitions: ${stage.numPartitions})" + case stage : ResultStage => + s"Stage ${stage} is actually done; (partitions: ${stage.numPartitions})" + } + logDebug(debugString) } } @@ -879,8 +911,16 @@ class DAGScheduler( if (event.accumUpdates != null) { try { Accumulators.add(event.accumUpdates) + event.accumUpdates.foreach { case (id, partialValue) => - val acc = Accumulators.originals(id).asInstanceOf[Accumulable[Any, Any]] + // In this instance, although the reference in Accumulators.originals is a WeakRef, + // it's guaranteed to exist since the event.accumUpdates Map exists + + val acc = Accumulators.originals(id).get match { + case Some(accum) => accum.asInstanceOf[Accumulable[Any, Any]] + case None => throw new NullPointerException("Non-existent reference to Accumulator") + } + // To avoid UI cruft, ignore cases where value wasn't updated if (acc.name.isDefined && partialValue != acc.zero) { val name = acc.name.get @@ -909,6 +949,9 @@ class DAGScheduler( val stageId = task.stageId val taskType = Utils.getFormattedClassName(task) + outputCommitCoordinator.taskCompleted(stageId, task.partitionId, + event.taskInfo.attempt, event.reason) + // The success case is dealt with separately below, since we need to compute accumulator // updates before posting. if (event.reason != Success) { @@ -921,23 +964,8 @@ class DAGScheduler( // Skip all the actions if the stage has been cancelled. return } - val stage = stageIdToStage(task.stageId) - def markStageAsFinished(stage: Stage, errorMessage: Option[String] = None) = { - val serviceTime = stage.latestInfo.submissionTime match { - case Some(t) => "%.03f".format((clock.getTime() - t) / 1000.0) - case _ => "Unknown" - } - if (errorMessage.isEmpty) { - logInfo("%s (%s) finished in %s s".format(stage, stage.name, serviceTime)) - stage.latestInfo.completionTime = Some(clock.getTime()) - } else { - stage.latestInfo.stageFailed(errorMessage.get) - logInfo("%s (%s) failed in %s s".format(stage, stage.name, serviceTime)) - } - listenerBus.post(SparkListenerStageCompleted(stage.latestInfo)) - runningStages -= stage - } + val stage = stageIdToStage(task.stageId) event.reason match { case Success => listenerBus.post(SparkListenerTaskEnd(stageId, stage.latestInfo.attemptId, taskType, @@ -945,7 +973,10 @@ class DAGScheduler( stage.pendingTasks -= task task match { case rt: ResultTask[_, _] => - stage.resultOfJob match { + // Cast to ResultStage here because it's part of the ResultTask + // TODO Refactor this out to a function that accepts a ResultStage + val resultStage = stage.asInstanceOf[ResultStage] + resultStage.resultOfJob match { case Some(job) => if (!job.finished(rt.outputId)) { updateAccumulators(event) @@ -953,10 +984,10 @@ class DAGScheduler( job.numFinished += 1 // If the whole job has finished, remove it if (job.numFinished == job.numPartitions) { - markStageAsFinished(stage) + markStageAsFinished(resultStage) cleanupStateForJobAndIndependentStages(job) listenerBus.post( - SparkListenerJobEnd(job.jobId, clock.getTime(), JobSucceeded)) + SparkListenerJobEnd(job.jobId, clock.getTimeMillis(), JobSucceeded)) } // taskSucceeded runs some user code that might throw an exception. Make sure @@ -965,7 +996,7 @@ class DAGScheduler( job.listener.taskSucceeded(rt.outputId, event.result) } catch { case e: Exception => - // TODO: Perhaps we want to mark the stage as failed? + // TODO: Perhaps we want to mark the resultStage as failed? job.listener.jobFailed(new SparkDriverExecutionException(e)) } } @@ -974,6 +1005,7 @@ class DAGScheduler( } case smt: ShuffleMapTask => + val shuffleStage = stage.asInstanceOf[ShuffleMapStage] updateAccumulators(event) val status = event.result.asInstanceOf[MapStatus] val execId = status.location.executorId @@ -981,50 +1013,54 @@ class DAGScheduler( if (failedEpoch.contains(execId) && smt.epoch <= failedEpoch(execId)) { logInfo("Ignoring possibly bogus ShuffleMapTask completion from " + execId) } else { - stage.addOutputLoc(smt.partitionId, status) + shuffleStage.addOutputLoc(smt.partitionId, status) } - if (runningStages.contains(stage) && stage.pendingTasks.isEmpty) { - markStageAsFinished(stage) + if (runningStages.contains(shuffleStage) && shuffleStage.pendingTasks.isEmpty) { + markStageAsFinished(shuffleStage) logInfo("looking for newly runnable stages") logInfo("running: " + runningStages) logInfo("waiting: " + waitingStages) logInfo("failed: " + failedStages) - if (stage.shuffleDep.isDefined) { - // We supply true to increment the epoch number here in case this is a - // recomputation of the map outputs. In that case, some nodes may have cached - // locations with holes (from when we detected the error) and will need the - // epoch incremented to refetch them. - // TODO: Only increment the epoch number if this is not the first time - // we registered these map outputs. - mapOutputTracker.registerMapOutputs( - stage.shuffleDep.get.shuffleId, - stage.outputLocs.map(list => if (list.isEmpty) null else list.head).toArray, - changeEpoch = true) - } + + // We supply true to increment the epoch number here in case this is a + // recomputation of the map outputs. In that case, some nodes may have cached + // locations with holes (from when we detected the error) and will need the + // epoch incremented to refetch them. + // TODO: Only increment the epoch number if this is not the first time + // we registered these map outputs. + mapOutputTracker.registerMapOutputs( + shuffleStage.shuffleDep.shuffleId, + shuffleStage.outputLocs.map(list => if (list.isEmpty) null else list.head).toArray, + changeEpoch = true) + clearCacheLocs() - if (stage.outputLocs.exists(_ == Nil)) { - // Some tasks had failed; let's resubmit this stage + if (shuffleStage.outputLocs.contains(Nil)) { + // Some tasks had failed; let's resubmit this shuffleStage // TODO: Lower-level scheduler should also deal with this - logInfo("Resubmitting " + stage + " (" + stage.name + + logInfo("Resubmitting " + shuffleStage + " (" + shuffleStage.name + ") because some of its tasks had failed: " + - stage.outputLocs.zipWithIndex.filter(_._1 == Nil).map(_._2).mkString(", ")) - submitStage(stage) + shuffleStage.outputLocs.zipWithIndex.filter(_._1.isEmpty) + .map(_._2).mkString(", ")) + submitStage(shuffleStage) } else { val newlyRunnable = new ArrayBuffer[Stage] - for (stage <- waitingStages) { - logInfo("Missing parents for " + stage + ": " + getMissingParentStages(stage)) + for (shuffleStage <- waitingStages) { + logInfo("Missing parents for " + shuffleStage + ": " + + getMissingParentStages(shuffleStage)) } - for (stage <- waitingStages if getMissingParentStages(stage) == Nil) { - newlyRunnable += stage + for (shuffleStage <- waitingStages if getMissingParentStages(shuffleStage).isEmpty) + { + newlyRunnable += shuffleStage } waitingStages --= newlyRunnable runningStages ++= newlyRunnable for { - stage <- newlyRunnable.sortBy(_.id) - jobId <- activeJobForStage(stage) + shuffleStage <- newlyRunnable.sortBy(_.id) + jobId <- activeJobForStage(shuffleStage) } { - logInfo("Submitting " + stage + " (" + stage.rdd + "), which is now runnable") - submitMissingTasks(stage, jobId) + logInfo("Submitting " + shuffleStage + " (" + + shuffleStage.rdd + "), which is now runnable") + submitMissingTasks(shuffleStage, jobId) } } } @@ -1045,7 +1081,6 @@ class DAGScheduler( logInfo(s"Marking $failedStage (${failedStage.name}) as failed " + s"due to a fetch failure from $mapStage (${mapStage.name})") markStageAsFinished(failedStage, Some(failureMessage)) - runningStages -= failedStage } if (disallowStageRetryForTest) { @@ -1073,6 +1108,9 @@ class DAGScheduler( handleExecutorLost(bmAddress.executorId, fetchFailed = true, Some(task.epoch)) } + case commitDenied: TaskCommitDenied => + // Do nothing here, left up to the TaskScheduler to decide how to handle denied commits + case ExceptionFailure(className, description, stackTrace, fullStackTrace, metrics) => // Do nothing here, left up to the TaskScheduler to decide how to handle user failures @@ -1158,6 +1196,26 @@ class DAGScheduler( submitWaitingStages() } + /** + * Marks a stage as finished and removes it from the list of running stages. + */ + private def markStageAsFinished(stage: Stage, errorMessage: Option[String] = None): Unit = { + val serviceTime = stage.latestInfo.submissionTime match { + case Some(t) => "%.03f".format((clock.getTimeMillis() - t) / 1000.0) + case _ => "Unknown" + } + if (errorMessage.isEmpty) { + logInfo("%s (%s) finished in %s s".format(stage, stage.name, serviceTime)) + stage.latestInfo.completionTime = Some(clock.getTimeMillis()) + } else { + stage.latestInfo.stageFailed(errorMessage.get) + logInfo("%s (%s) failed in %s s".format(stage, stage.name, serviceTime)) + } + outputCommitCoordinator.stageEnd(stage.id) + listenerBus.post(SparkListenerStageCompleted(stage.latestInfo)) + runningStages -= stage + } + /** * Aborts all jobs depending on a particular Stage. This is called in response to a task set * being canceled by the TaskScheduler. Use taskSetFailed() to inject this event from outside. @@ -1169,7 +1227,7 @@ class DAGScheduler( } val dependentJobs: Seq[ActiveJob] = activeJobs.filter(job => stageDependsOn(job.finalStage, failedStage)).toSeq - failedStage.latestInfo.completionTime = Some(clock.getTime()) + failedStage.latestInfo.completionTime = Some(clock.getTimeMillis()) for (job <- dependentJobs) { failJobAndIndependentStages(job, s"Job aborted due to stage failure: $reason") } @@ -1178,9 +1236,7 @@ class DAGScheduler( } } - /** - * Fails a job and all stages that are only used by that job, and cleans up relevant state. - */ + /** Fails a job and all stages that are only used by that job, and cleans up relevant state. */ private def failJobAndIndependentStages(job: ActiveJob, failureReason: String) { val error = new SparkException(failureReason) var ableToCancelStages = true @@ -1209,8 +1265,7 @@ class DAGScheduler( if (runningStages.contains(stage)) { try { // cancelTasks will fail if a SchedulerBackend does not implement killTask taskScheduler.cancelTasks(stageId, shouldInterruptThread) - stage.latestInfo.stageFailed(failureReason) - listenerBus.post(SparkListenerStageCompleted(stage.latestInfo)) + markStageAsFinished(stage, Some(failureReason)) } catch { case e: UnsupportedOperationException => logInfo(s"Could not cancel tasks for stage $stageId", e) @@ -1224,19 +1279,16 @@ class DAGScheduler( if (ableToCancelStages) { job.listener.jobFailed(error) cleanupStateForJobAndIndependentStages(job) - listenerBus.post(SparkListenerJobEnd(job.jobId, clock.getTime(), JobFailed(error))) + listenerBus.post(SparkListenerJobEnd(job.jobId, clock.getTimeMillis(), JobFailed(error))) } } - /** - * Return true if one of stage's ancestors is target. - */ + /** Return true if one of stage's ancestors is target. */ private def stageDependsOn(stage: Stage, target: Stage): Boolean = { if (stage == target) { return true } val visitedRdds = new HashSet[RDD[_]] - val visitedStages = new HashSet[Stage] // We are manually maintaining a stack here to prevent StackOverflowError // caused by recursively visiting val waitingForVisit = new Stack[RDD[_]] @@ -1248,7 +1300,6 @@ class DAGScheduler( case shufDep: ShuffleDependency[_, _, _] => val mapStage = getShuffleMapStage(shufDep, stage.jobId) if (!mapStage.isAvailable) { - visitedStages += mapStage waitingForVisit.push(mapStage.rdd) } // Otherwise there's no need to follow the dependency back case narrowDep: NarrowDependency[_] => @@ -1258,30 +1309,37 @@ class DAGScheduler( } } waitingForVisit.push(stage.rdd) - while (!waitingForVisit.isEmpty) { + while (waitingForVisit.nonEmpty) { visit(waitingForVisit.pop()) } visitedRdds.contains(target.rdd) } /** - * Synchronized method that might be called from other threads. + * Gets the locality information associated with a partition of a particular RDD. + * + * This method is thread-safe and is called from both DAGScheduler and SparkContext. + * * @param rdd whose partitions are to be looked at * @param partition to lookup locality information for * @return list of machines that are preferred by the partition */ private[spark] - def getPreferredLocs(rdd: RDD[_], partition: Int): Seq[TaskLocation] = synchronized { + def getPreferredLocs(rdd: RDD[_], partition: Int): Seq[TaskLocation] = { getPreferredLocsInternal(rdd, partition, new HashSet) } - /** Recursive implementation for getPreferredLocs. */ + /** + * Recursive implementation for getPreferredLocs. + * + * This method is thread-safe because it only accesses DAGScheduler state through thread-safe + * methods (getCacheLocs()); please be careful when modifying this method, because any new + * DAGScheduler state accessed by it may require additional synchronization. + */ private def getPreferredLocsInternal( rdd: RDD[_], partition: Int, - visited: HashSet[(RDD[_],Int)]) - : Seq[TaskLocation] = - { + visited: HashSet[(RDD[_],Int)]): Seq[TaskLocation] = { // If the partition has already been visited, no need to re-visit. // This avoids exponential path exploration. SPARK-695 if (!visited.add((rdd,partition))) { @@ -1290,12 +1348,12 @@ class DAGScheduler( } // If the partition is cached, return the cache locations val cached = getCacheLocs(rdd)(partition) - if (!cached.isEmpty) { + if (cached.nonEmpty) { return cached } // If the RDD has some placement preferences (as is the case for input RDDs), get those val rddPrefs = rdd.preferredLocations(rdd.partitions(partition)).toList - if (!rddPrefs.isEmpty) { + if (rddPrefs.nonEmpty) { return rddPrefs.map(TaskLocation(_)) } // If the RDD has narrow dependencies, pick the first partition of the first narrow dep @@ -1379,7 +1437,7 @@ private[scheduler] class DAGSchedulerEventProcessLoop(dagScheduler: DAGScheduler dagScheduler.sc.stop() } - override def onStop() { + override def onStop(): Unit = { // Cancel any active jobs in postStop hook dagScheduler.cleanUpAfterSchedulerStop() } diff --git a/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala b/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala index 30075c172bdb1..08e7727db2fde 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala @@ -47,21 +47,30 @@ import org.apache.spark.util.{JsonProtocol, Utils} */ private[spark] class EventLoggingListener( appId: String, - logBaseDir: String, + logBaseDir: URI, sparkConf: SparkConf, hadoopConf: Configuration) extends SparkListener with Logging { import EventLoggingListener._ - def this(appId: String, logBaseDir: String, sparkConf: SparkConf) = + def this(appId: String, logBaseDir: URI, sparkConf: SparkConf) = this(appId, logBaseDir, sparkConf, SparkHadoopUtil.get.newConfiguration(sparkConf)) private val shouldCompress = sparkConf.getBoolean("spark.eventLog.compress", false) private val shouldOverwrite = sparkConf.getBoolean("spark.eventLog.overwrite", false) private val testing = sparkConf.getBoolean("spark.eventLog.testing", false) private val outputBufferSize = sparkConf.getInt("spark.eventLog.buffer.kb", 100) * 1024 - private val fileSystem = Utils.getHadoopFileSystem(new URI(logBaseDir), hadoopConf) + private val fileSystem = Utils.getHadoopFileSystem(logBaseDir, hadoopConf) + private val compressionCodec = + if (shouldCompress) { + Some(CompressionCodec.createCodec(sparkConf)) + } else { + None + } + private val compressionCodecName = compressionCodec.map { c => + CompressionCodec.getShortName(c.getClass.getName) + } // Only defined if the file system scheme is not local private var hadoopDataStream: Option[FSDataOutputStream] = None @@ -80,13 +89,13 @@ private[spark] class EventLoggingListener( private[scheduler] val loggedEvents = new ArrayBuffer[JValue] // Visible for tests only. - private[scheduler] val logPath = getLogPath(logBaseDir, appId) + private[scheduler] val logPath = getLogPath(logBaseDir, appId, compressionCodecName) /** * Creates the log file in the configured log directory. */ def start() { - if (!fileSystem.isDirectory(new Path(logBaseDir))) { + if (!fileSystem.getFileStatus(new Path(logBaseDir)).isDir) { throw new IllegalArgumentException(s"Log directory $logBaseDir does not exist.") } @@ -111,19 +120,19 @@ private[spark] class EventLoggingListener( hadoopDataStream.get } - val compressionCodec = - if (shouldCompress) { - Some(CompressionCodec.createCodec(sparkConf)) - } else { - None - } - - fileSystem.setPermission(path, LOG_FILE_PERMISSIONS) - val logStream = initEventLog(new BufferedOutputStream(dstream, outputBufferSize), - compressionCodec) - writer = Some(new PrintWriter(logStream)) + try { + val cstream = compressionCodec.map(_.compressedOutputStream(dstream)).getOrElse(dstream) + val bstream = new BufferedOutputStream(cstream, outputBufferSize) - logInfo("Logging events to %s".format(logPath)) + EventLoggingListener.initEventLog(bstream) + fileSystem.setPermission(path, LOG_FILE_PERMISSIONS) + writer = Some(new PrintWriter(bstream)) + logInfo("Logging events to %s".format(logPath)) + } catch { + case e: Exception => + dstream.close() + throw e + } } /** Log the event as JSON. */ @@ -140,47 +149,60 @@ private[spark] class EventLoggingListener( } // Events that do not trigger a flush - override def onStageSubmitted(event: SparkListenerStageSubmitted) = - logEvent(event) - override def onTaskStart(event: SparkListenerTaskStart) = - logEvent(event) - override def onTaskGettingResult(event: SparkListenerTaskGettingResult) = - logEvent(event) - override def onTaskEnd(event: SparkListenerTaskEnd) = - logEvent(event) - override def onEnvironmentUpdate(event: SparkListenerEnvironmentUpdate) = - logEvent(event) + override def onStageSubmitted(event: SparkListenerStageSubmitted): Unit = logEvent(event) + + override def onTaskStart(event: SparkListenerTaskStart): Unit = logEvent(event) + + override def onTaskGettingResult(event: SparkListenerTaskGettingResult): Unit = logEvent(event) + + override def onTaskEnd(event: SparkListenerTaskEnd): Unit = logEvent(event) + + override def onEnvironmentUpdate(event: SparkListenerEnvironmentUpdate): Unit = logEvent(event) // Events that trigger a flush - override def onStageCompleted(event: SparkListenerStageCompleted) = - logEvent(event, flushLogger = true) - override def onJobStart(event: SparkListenerJobStart) = + override def onStageCompleted(event: SparkListenerStageCompleted): Unit = { logEvent(event, flushLogger = true) - override def onJobEnd(event: SparkListenerJobEnd) = - logEvent(event, flushLogger = true) - override def onBlockManagerAdded(event: SparkListenerBlockManagerAdded) = + } + + override def onJobStart(event: SparkListenerJobStart): Unit = logEvent(event, flushLogger = true) + + override def onJobEnd(event: SparkListenerJobEnd): Unit = logEvent(event, flushLogger = true) + + override def onBlockManagerAdded(event: SparkListenerBlockManagerAdded): Unit = { logEvent(event, flushLogger = true) - override def onBlockManagerRemoved(event: SparkListenerBlockManagerRemoved) = + } + + override def onBlockManagerRemoved(event: SparkListenerBlockManagerRemoved): Unit = { logEvent(event, flushLogger = true) - override def onUnpersistRDD(event: SparkListenerUnpersistRDD) = + } + + override def onUnpersistRDD(event: SparkListenerUnpersistRDD): Unit = { logEvent(event, flushLogger = true) - override def onApplicationStart(event: SparkListenerApplicationStart) = + } + + override def onApplicationStart(event: SparkListenerApplicationStart): Unit = { logEvent(event, flushLogger = true) - override def onApplicationEnd(event: SparkListenerApplicationEnd) = + } + + override def onApplicationEnd(event: SparkListenerApplicationEnd): Unit = { logEvent(event, flushLogger = true) - override def onExecutorAdded(event: SparkListenerExecutorAdded) = + } + override def onExecutorAdded(event: SparkListenerExecutorAdded): Unit = { logEvent(event, flushLogger = true) - override def onExecutorRemoved(event: SparkListenerExecutorRemoved) = + } + + override def onExecutorRemoved(event: SparkListenerExecutorRemoved): Unit = { logEvent(event, flushLogger = true) + } // No-op because logging every update would be overkill - override def onExecutorMetricsUpdate(event: SparkListenerExecutorMetricsUpdate) { } + override def onExecutorMetricsUpdate(event: SparkListenerExecutorMetricsUpdate): Unit = { } /** * Stop logging events. The event log file will be renamed so that it loses the * ".inprogress" suffix. */ - def stop() = { + def stop(): Unit = { writer.foreach(_.close()) val target = new Path(logPath) @@ -201,77 +223,57 @@ private[spark] object EventLoggingListener extends Logging { // Suffix applied to the names of files still being written by applications. val IN_PROGRESS = ".inprogress" val DEFAULT_LOG_DIR = "/tmp/spark-events" + val SPARK_VERSION_KEY = "SPARK_VERSION" + val COMPRESSION_CODEC_KEY = "COMPRESSION_CODEC" private val LOG_FILE_PERMISSIONS = new FsPermission(Integer.parseInt("770", 8).toShort) - // Marker for the end of header data in a log file. After this marker, log data, potentially - // compressed, will be found. - private val HEADER_END_MARKER = "=== LOG_HEADER_END ===" - - // To avoid corrupted files causing the heap to fill up. Value is arbitrary. - private val MAX_HEADER_LINE_LENGTH = 4096 - // A cache for compression codecs to avoid creating the same codec many times private val codecMap = new mutable.HashMap[String, CompressionCodec] /** - * Write metadata about the event log to the given stream. - * - * The header is a serialized version of a map, except it does not use Java serialization to - * avoid incompatibilities between different JDKs. It writes one map entry per line, in - * "key=value" format. - * - * The very last entry in the header is the `HEADER_END_MARKER` marker, so that the parsing code - * can know when to stop. + * Write metadata about an event log to the given stream. + * The metadata is encoded in the first line of the event log as JSON. * - * The format needs to be kept in sync with the openEventLog() method below. Also, it cannot - * change in new Spark versions without some other way of detecting the change (like some - * metadata encoded in the file name). - * - * @param logStream Raw output stream to the even log file. - * @param compressionCodec Optional compression codec to use. - * @return A stream where to write event log data. This may be a wrapper around the original - * stream (for example, when compression is enabled). + * @param logStream Raw output stream to the event log file. */ - def initEventLog( - logStream: OutputStream, - compressionCodec: Option[CompressionCodec]): OutputStream = { - val meta = mutable.HashMap(("version" -> SPARK_VERSION)) - compressionCodec.foreach { codec => - meta += ("compressionCodec" -> codec.getClass().getName()) - } - - def write(entry: String) = { - val bytes = entry.getBytes(Charsets.UTF_8) - if (bytes.length > MAX_HEADER_LINE_LENGTH) { - throw new IOException(s"Header entry too long: ${entry}") - } - logStream.write(bytes, 0, bytes.length) - } - - meta.foreach { case (k, v) => write(s"$k=$v\n") } - write(s"$HEADER_END_MARKER\n") - compressionCodec.map(_.compressedOutputStream(logStream)).getOrElse(logStream) + def initEventLog(logStream: OutputStream): Unit = { + val metadata = SparkListenerLogStart(SPARK_VERSION) + val metadataJson = compact(JsonProtocol.logStartToJson(metadata)) + "\n" + logStream.write(metadataJson.getBytes(Charsets.UTF_8)) } /** * Return a file-system-safe path to the log file for the given application. * + * Note that because we currently only create a single log file for each application, + * we must encode all the information needed to parse this event log in the file name + * instead of within the file itself. Otherwise, if the file is compressed, for instance, + * we won't know which codec to use to decompress the metadata needed to open the file in + * the first place. + * * @param logBaseDir Directory where the log file will be written. * @param appId A unique app ID. + * @param compressionCodecName Name to identify the codec used to compress the contents + * of the log, or None if compression is not enabled. * @return A path which consists of file-system-safe characters. */ - def getLogPath(logBaseDir: String, appId: String): String = { - val name = appId.replaceAll("[ :/]", "-").replaceAll("[${}'\"]", "_").toLowerCase - Utils.resolveURI(logBaseDir) + "/" + name.stripSuffix("/") + def getLogPath( + logBaseDir: URI, + appId: String, + compressionCodecName: Option[String] = None): String = { + val sanitizedAppId = appId.replaceAll("[ :/]", "-").replaceAll("[.${}'\"]", "_").toLowerCase + // e.g. app_123, app_123.lzf + val logName = sanitizedAppId + compressionCodecName.map { "." + _ }.getOrElse("") + logBaseDir.toString.stripSuffix("/") + "/" + logName } /** - * Opens an event log file and returns an input stream to the event data. + * Opens an event log file and returns an input stream that contains the event data. * - * @return 2-tuple (event input stream, Spark version of event data) + * @return input stream that holds one JSON record per line. */ - def openEventLog(log: Path, fs: FileSystem): (InputStream, String) = { + def openEventLog(log: Path, fs: FileSystem): InputStream = { // It's not clear whether FileSystem.open() throws FileNotFoundException or just plain // IOException when a file does not exist, so try our best to throw a proper exception. if (!fs.exists(log)) { @@ -279,52 +281,17 @@ private[spark] object EventLoggingListener extends Logging { } val in = new BufferedInputStream(fs.open(log)) - // Read a single line from the input stream without buffering. - // We cannot use BufferedReader because we must avoid reading - // beyond the end of the header, after which the content of the - // file may be compressed. - def readLine(): String = { - val bytes = new ByteArrayOutputStream() - var next = in.read() - var count = 0 - while (next != '\n') { - if (next == -1) { - throw new IOException("Unexpected end of file.") - } - bytes.write(next) - count = count + 1 - if (count > MAX_HEADER_LINE_LENGTH) { - throw new IOException("Maximum header line length exceeded.") - } - next = in.read() - } - new String(bytes.toByteArray(), Charsets.UTF_8) + + // Compression codec is encoded as an extension, e.g. app_123.lzf + // Since we sanitize the app ID to not include periods, it is safe to split on it + val logName = log.getName.stripSuffix(IN_PROGRESS) + val codecName: Option[String] = logName.split("\\.").tail.lastOption + val codec = codecName.map { c => + codecMap.getOrElseUpdate(c, CompressionCodec.createCodec(new SparkConf, c)) } - // Parse the header metadata in the form of k=v pairs - // This assumes that every line before the header end marker follows this format try { - val meta = new mutable.HashMap[String, String]() - var foundEndMarker = false - while (!foundEndMarker) { - readLine() match { - case HEADER_END_MARKER => - foundEndMarker = true - case entry => - val prop = entry.split("=", 2) - if (prop.length != 2) { - throw new IllegalArgumentException("Invalid metadata in log file.") - } - meta += (prop(0) -> prop(1)) - } - } - - val sparkVersion = meta.get("version").getOrElse( - throw new IllegalArgumentException("Missing Spark version in log metadata.")) - val codec = meta.get("compressionCodec").map { codecName => - codecMap.getOrElseUpdate(codecName, CompressionCodec.createCodec(new SparkConf, codecName)) - } - (codec.map(_.compressedInputStream(in)).getOrElse(in), sparkVersion) + codec.map(_.compressedInputStream(in)).getOrElse(in) } catch { case e: Exception => in.close() diff --git a/core/src/main/scala/org/apache/spark/scheduler/JobLogger.scala b/core/src/main/scala/org/apache/spark/scheduler/JobLogger.scala index 3bb54855bae44..e55b76c36cc5f 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/JobLogger.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/JobLogger.scala @@ -57,7 +57,7 @@ class JobLogger(val user: String, val logDirName: String) extends SparkListener private val stageIdToJobId = new HashMap[Int, Int] private val jobIdToStageIds = new HashMap[Int, Seq[Int]] private val dateFormat = new ThreadLocal[SimpleDateFormat]() { - override def initialValue() = new SimpleDateFormat("yyyy/MM/dd HH:mm:ss") + override def initialValue(): SimpleDateFormat = new SimpleDateFormat("yyyy/MM/dd HH:mm:ss") } createLogDir() @@ -169,7 +169,8 @@ class JobLogger(val user: String, val logDirName: String) extends SparkListener " BLOCK_FETCHED_LOCAL=" + metrics.localBlocksFetched + " BLOCK_FETCHED_REMOTE=" + metrics.remoteBlocksFetched + " REMOTE_FETCH_WAIT_TIME=" + metrics.fetchWaitTime + - " REMOTE_BYTES_READ=" + metrics.remoteBytesRead + " REMOTE_BYTES_READ=" + metrics.remoteBytesRead + + " LOCAL_BYTES_READ=" + metrics.localBytesRead case None => "" } val writeMetrics = taskMetrics.shuffleWriteMetrics match { diff --git a/core/src/main/scala/org/apache/spark/scheduler/JobWaiter.scala b/core/src/main/scala/org/apache/spark/scheduler/JobWaiter.scala index 29879b374b801..382b09422a4a0 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/JobWaiter.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/JobWaiter.scala @@ -34,7 +34,7 @@ private[spark] class JobWaiter[T]( @volatile private var _jobFinished = totalTasks == 0 - def jobFinished = _jobFinished + def jobFinished: Boolean = _jobFinished // If the job is finished, this will be its result. In the case of 0 task jobs (e.g. zero // partition RDDs), we set the jobResult directly to JobSucceeded. diff --git a/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala b/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala new file mode 100644 index 0000000000000..7c184b1dcb308 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala @@ -0,0 +1,180 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.scheduler + +import scala.collection.mutable + +import org.apache.spark._ +import org.apache.spark.rpc.{RpcCallContext, RpcEndpointRef, RpcEnv, RpcEndpoint} + +private sealed trait OutputCommitCoordinationMessage extends Serializable + +private case object StopCoordinator extends OutputCommitCoordinationMessage +private case class AskPermissionToCommitOutput(stage: Int, task: Long, taskAttempt: Long) + +/** + * Authority that decides whether tasks can commit output to HDFS. Uses a "first committer wins" + * policy. + * + * OutputCommitCoordinator is instantiated in both the drivers and executors. On executors, it is + * configured with a reference to the driver's OutputCommitCoordinatorEndpoint, so requests to + * commit output will be forwarded to the driver's OutputCommitCoordinator. + * + * This class was introduced in SPARK-4879; see that JIRA issue (and the associated pull requests) + * for an extensive design discussion. + */ +private[spark] class OutputCommitCoordinator(conf: SparkConf) extends Logging { + + // Initialized by SparkEnv + var coordinatorRef: Option[RpcEndpointRef] = None + + private type StageId = Int + private type PartitionId = Long + private type TaskAttemptId = Long + + /** + * Map from active stages's id => partition id => task attempt with exclusive lock on committing + * output for that partition. + * + * Entries are added to the top-level map when stages start and are removed they finish + * (either successfully or unsuccessfully). + * + * Access to this map should be guarded by synchronizing on the OutputCommitCoordinator instance. + */ + private val authorizedCommittersByStage: CommittersByStageMap = mutable.Map() + private type CommittersByStageMap = mutable.Map[StageId, mutable.Map[PartitionId, TaskAttemptId]] + + /** + * Returns whether the OutputCommitCoordinator's internal data structures are all empty. + */ + def isEmpty: Boolean = { + authorizedCommittersByStage.isEmpty + } + + /** + * Called by tasks to ask whether they can commit their output to HDFS. + * + * If a task attempt has been authorized to commit, then all other attempts to commit the same + * task will be denied. If the authorized task attempt fails (e.g. due to its executor being + * lost), then a subsequent task attempt may be authorized to commit its output. + * + * @param stage the stage number + * @param partition the partition number + * @param attempt a unique identifier for this task attempt + * @return true if this task is authorized to commit, false otherwise + */ + def canCommit( + stage: StageId, + partition: PartitionId, + attempt: TaskAttemptId): Boolean = { + val msg = AskPermissionToCommitOutput(stage, partition, attempt) + coordinatorRef match { + case Some(endpointRef) => + endpointRef.askWithReply[Boolean](msg) + case None => + logError( + "canCommit called after coordinator was stopped (is SparkEnv shutdown in progress)?") + false + } + } + + // Called by DAGScheduler + private[scheduler] def stageStart(stage: StageId): Unit = synchronized { + authorizedCommittersByStage(stage) = mutable.HashMap[PartitionId, TaskAttemptId]() + } + + // Called by DAGScheduler + private[scheduler] def stageEnd(stage: StageId): Unit = synchronized { + authorizedCommittersByStage.remove(stage) + } + + // Called by DAGScheduler + private[scheduler] def taskCompleted( + stage: StageId, + partition: PartitionId, + attempt: TaskAttemptId, + reason: TaskEndReason): Unit = synchronized { + val authorizedCommitters = authorizedCommittersByStage.getOrElse(stage, { + logDebug(s"Ignoring task completion for completed stage") + return + }) + reason match { + case Success => + // The task output has been committed successfully + case denied: TaskCommitDenied => + logInfo( + s"Task was denied committing, stage: $stage, partition: $partition, attempt: $attempt") + case otherReason => + if (authorizedCommitters.get(partition).exists(_ == attempt)) { + logDebug(s"Authorized committer $attempt (stage=$stage, partition=$partition) failed;" + + s" clearing lock") + authorizedCommitters.remove(partition) + } + } + } + + def stop(): Unit = synchronized { + coordinatorRef.foreach(_ send StopCoordinator) + coordinatorRef = None + authorizedCommittersByStage.clear() + } + + // Marked private[scheduler] instead of private so this can be mocked in tests + private[scheduler] def handleAskPermissionToCommit( + stage: StageId, + partition: PartitionId, + attempt: TaskAttemptId): Boolean = synchronized { + authorizedCommittersByStage.get(stage) match { + case Some(authorizedCommitters) => + authorizedCommitters.get(partition) match { + case Some(existingCommitter) => + logDebug(s"Denying $attempt to commit for stage=$stage, partition=$partition; " + + s"existingCommitter = $existingCommitter") + false + case None => + logDebug(s"Authorizing $attempt to commit for stage=$stage, partition=$partition") + authorizedCommitters(partition) = attempt + true + } + case None => + logDebug(s"Stage $stage has completed, so not allowing task attempt $attempt to commit") + false + } + } +} + +private[spark] object OutputCommitCoordinator { + + // This actor is used only for RPC + private[spark] class OutputCommitCoordinatorEndpoint( + override val rpcEnv: RpcEnv, outputCommitCoordinator: OutputCommitCoordinator) + extends RpcEndpoint with Logging { + + override def receive: PartialFunction[Any, Unit] = { + case StopCoordinator => + logInfo("OutputCommitCoordinator stopped!") + stop() + } + + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { + case AskPermissionToCommitOutput(stage, partition, taskAttempt) => + context.reply( + outputCommitCoordinator.handleAskPermissionToCommit(stage, partition, taskAttempt)) + } + } +} diff --git a/core/src/main/scala/org/apache/spark/scheduler/ReplayListenerBus.scala b/core/src/main/scala/org/apache/spark/scheduler/ReplayListenerBus.scala index 584f4e7789d1a..86f357abb8723 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/ReplayListenerBus.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/ReplayListenerBus.scala @@ -21,6 +21,7 @@ import java.io.{InputStream, IOException} import scala.io.Source +import com.fasterxml.jackson.core.JsonParseException import org.json4s.jackson.JsonMethods._ import org.apache.spark.Logging @@ -39,22 +40,40 @@ private[spark] class ReplayListenerBus extends SparkListenerBus with Logging { * error is thrown by this method. * * @param logData Stream containing event log data. - * @param version Spark version that generated the events. + * @param sourceName Filename (or other source identifier) from whence @logData is being read + * @param maybeTruncated Indicate whether log file might be truncated (some abnormal situations + * encountered, log file might not finished writing) or not */ - def replay(logData: InputStream, version: String) { + def replay( + logData: InputStream, + sourceName: String, + maybeTruncated: Boolean = false): Unit = { var currentLine: String = null + var lineNumber: Int = 1 try { val lines = Source.fromInputStream(logData).getLines() - lines.foreach { line => - currentLine = line - postToAll(JsonProtocol.sparkEventFromJson(parse(line))) + while (lines.hasNext) { + currentLine = lines.next() + try { + postToAll(JsonProtocol.sparkEventFromJson(parse(currentLine))) + } catch { + case jpe: JsonParseException => + // We can only ignore exception from last line of the file that might be truncated + if (!maybeTruncated || lines.hasNext) { + throw jpe + } else { + logWarning(s"Got JsonParseException from log file $sourceName" + + s" at line $lineNumber, the file might not have finished writing cleanly.") + } + } + lineNumber += 1 } } catch { case ioe: IOException => throw ioe case e: Exception => - logError("Exception in parsing Spark event log.", e) - logError("Malformed line: %s\n".format(currentLine)) + logError(s"Exception parsing Spark event log: $sourceName", e) + logError(s"Malformed line #$lineNumber: $currentLine\n") } } diff --git a/core/src/main/scala/org/apache/spark/scheduler/ResultStage.scala b/core/src/main/scala/org/apache/spark/scheduler/ResultStage.scala new file mode 100644 index 0000000000000..c0f3d5a13d623 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/scheduler/ResultStage.scala @@ -0,0 +1,40 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.scheduler + +import org.apache.spark.rdd.RDD +import org.apache.spark.util.CallSite + +/** + * The ResultStage represents the final stage in a job. + */ +private[spark] class ResultStage( + id: Int, + rdd: RDD[_], + numTasks: Int, + parents: List[Stage], + jobId: Int, + callSite: CallSite) + extends Stage(id, rdd, numTasks, parents, jobId, callSite) { + + // The active job for this result stage. Will be empty if the job has already finished + // (e.g., because the job was cancelled). + var resultOfJob: Option[ActiveJob] = None + + override def toString: String = "ResultStage " + id +} diff --git a/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala b/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala index 4a9ff918afe25..e074ce6ebff0b 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala @@ -64,5 +64,5 @@ private[spark] class ResultTask[T, U]( // This is only callable on the driver side. override def preferredLocations: Seq[TaskLocation] = preferredLocs - override def toString = "ResultTask(" + stageId + ", " + partitionId + ")" + override def toString: String = "ResultTask(" + stageId + ", " + partitionId + ")" } diff --git a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapStage.scala b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapStage.scala new file mode 100644 index 0000000000000..d02210743484c --- /dev/null +++ b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapStage.scala @@ -0,0 +1,84 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.scheduler + +import org.apache.spark.ShuffleDependency +import org.apache.spark.rdd.RDD +import org.apache.spark.storage.BlockManagerId +import org.apache.spark.util.CallSite + +/** + * The ShuffleMapStage represents the intermediate stages in a job. + */ +private[spark] class ShuffleMapStage( + id: Int, + rdd: RDD[_], + numTasks: Int, + parents: List[Stage], + jobId: Int, + callSite: CallSite, + val shuffleDep: ShuffleDependency[_, _, _]) + extends Stage(id, rdd, numTasks, parents, jobId, callSite) { + + override def toString: String = "ShuffleMapStage " + id + + var numAvailableOutputs: Long = 0 + + def isAvailable: Boolean = numAvailableOutputs == numPartitions + + val outputLocs = Array.fill[List[MapStatus]](numPartitions)(Nil) + + def addOutputLoc(partition: Int, status: MapStatus): Unit = { + val prevList = outputLocs(partition) + outputLocs(partition) = status :: prevList + if (prevList == Nil) { + numAvailableOutputs += 1 + } + } + + def removeOutputLoc(partition: Int, bmAddress: BlockManagerId): Unit = { + val prevList = outputLocs(partition) + val newList = prevList.filterNot(_.location == bmAddress) + outputLocs(partition) = newList + if (prevList != Nil && newList == Nil) { + numAvailableOutputs -= 1 + } + } + + /** + * Removes all shuffle outputs associated with this executor. Note that this will also remove + * outputs which are served by an external shuffle server (if one exists), as they are still + * registered with this execId. + */ + def removeOutputsOnExecutor(execId: String): Unit = { + var becameUnavailable = false + for (partition <- 0 until numPartitions) { + val prevList = outputLocs(partition) + val newList = prevList.filterNot(_.location.executorId == execId) + outputLocs(partition) = newList + if (prevList != Nil && newList == Nil) { + becameUnavailable = true + numAvailableOutputs -= 1 + } + } + if (becameUnavailable) { + logInfo("%s is now unavailable on executor %s (%d/%d, %s)".format( + this, execId, numAvailableOutputs, numPartitions, isAvailable)) + } + } +} diff --git a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala index 79709089c0da4..6c7d00069acb2 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala @@ -33,7 +33,7 @@ import org.apache.spark.shuffle.ShuffleWriter * See [[org.apache.spark.scheduler.Task]] for more information. * * @param stageId id of the stage this task belongs to - * @param taskBinary broadcast version of of the RDD and the ShuffleDependency. Once deserialized, + * @param taskBinary broadcast version of the RDD and the ShuffleDependency. Once deserialized, * the type should be (RDD[_], ShuffleDependency[_, _, _]). * @param partition partition of the RDD this task is associated with * @param locs preferred task execution locations for locality scheduling @@ -47,7 +47,7 @@ private[spark] class ShuffleMapTask( /** A constructor used only in test suites. This does not require passing in an RDD. */ def this(partitionId: Int) { - this(0, null, new Partition { override def index = 0 }, null) + this(0, null, new Partition { override def index: Int = 0 }, null) } @transient private val preferredLocs: Seq[TaskLocation] = { @@ -83,5 +83,5 @@ private[spark] class ShuffleMapTask( override def preferredLocations: Seq[TaskLocation] = preferredLocs - override def toString = "ShuffleMapTask(%d, %d)".format(stageId, partitionId) + override def toString: String = "ShuffleMapTask(%d, %d)".format(stageId, partitionId) } diff --git a/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala b/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala index dd28ddb31de1f..b711ff209af94 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala @@ -116,6 +116,11 @@ case class SparkListenerApplicationStart(appName: String, appId: Option[String], @DeveloperApi case class SparkListenerApplicationEnd(time: Long) extends SparkListenerEvent +/** + * An internal class that describes the metadata of an event log. + * This event is not meant to be posted to listeners downstream. + */ +private[spark] case class SparkListenerLogStart(sparkVersion: String) extends SparkListenerEvent /** * :: DeveloperApi :: @@ -295,7 +300,7 @@ private[spark] object StatsReportListener extends Logging { } def showDistribution(heading: String, dOpt: Option[Distribution], format:String) { - def f(d: Double) = format.format(d) + def f(d: Double): String = format.format(d) showDistribution(heading, dOpt, f _) } @@ -341,7 +346,7 @@ private[spark] object StatsReportListener extends Logging { /** * Reformat a time interval in milliseconds to a prettier format for output */ - def millisToString(ms: Long) = { + def millisToString(ms: Long): String = { val (size, units) = if (ms > hours) { (ms.toDouble / hours, "hours") diff --git a/core/src/main/scala/org/apache/spark/scheduler/SparkListenerBus.scala b/core/src/main/scala/org/apache/spark/scheduler/SparkListenerBus.scala index fe8a19a2c0cb9..61e69ecc08387 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/SparkListenerBus.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/SparkListenerBus.scala @@ -58,6 +58,7 @@ private[spark] trait SparkListenerBus extends ListenerBus[SparkListener, SparkLi listener.onExecutorAdded(executorAdded) case executorRemoved: SparkListenerExecutorRemoved => listener.onExecutorRemoved(executorRemoved) + case logStart: SparkListenerLogStart => // ignore event log metadata } } diff --git a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala index cc13f57a49b89..5d0ddb8377c33 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala @@ -21,7 +21,6 @@ import scala.collection.mutable.HashSet import org.apache.spark._ import org.apache.spark.rdd.RDD -import org.apache.spark.storage.BlockManagerId import org.apache.spark.util.CallSite /** @@ -47,29 +46,23 @@ import org.apache.spark.util.CallSite * be updated for each attempt. * */ -private[spark] class Stage( +private[spark] abstract class Stage( val id: Int, val rdd: RDD[_], val numTasks: Int, - val shuffleDep: Option[ShuffleDependency[_, _, _]], // Output shuffle if stage is a map stage val parents: List[Stage], val jobId: Int, val callSite: CallSite) extends Logging { - val isShuffleMap = shuffleDep.isDefined val numPartitions = rdd.partitions.size - val outputLocs = Array.fill[List[MapStatus]](numPartitions)(Nil) - var numAvailableOutputs = 0 /** Set of jobs that this stage belongs to. */ val jobIds = new HashSet[Int] - /** For stages that are the final (consists of only ResultTasks), link to the ActiveJob. */ - var resultOfJob: Option[ActiveJob] = None var pendingTasks = new HashSet[Task[_]] - private var nextAttemptId = 0 + private var nextAttemptId: Int = 0 val name = callSite.shortForm val details = callSite.longForm @@ -77,53 +70,6 @@ private[spark] class Stage( /** Pointer to the latest [StageInfo] object, set by DAGScheduler. */ var latestInfo: StageInfo = StageInfo.fromStage(this) - def isAvailable: Boolean = { - if (!isShuffleMap) { - true - } else { - numAvailableOutputs == numPartitions - } - } - - def addOutputLoc(partition: Int, status: MapStatus) { - val prevList = outputLocs(partition) - outputLocs(partition) = status :: prevList - if (prevList == Nil) { - numAvailableOutputs += 1 - } - } - - def removeOutputLoc(partition: Int, bmAddress: BlockManagerId) { - val prevList = outputLocs(partition) - val newList = prevList.filterNot(_.location == bmAddress) - outputLocs(partition) = newList - if (prevList != Nil && newList == Nil) { - numAvailableOutputs -= 1 - } - } - - /** - * Removes all shuffle outputs associated with this executor. Note that this will also remove - * outputs which are served by an external shuffle server (if one exists), as they are still - * registered with this execId. - */ - def removeOutputsOnExecutor(execId: String) { - var becameUnavailable = false - for (partition <- 0 until numPartitions) { - val prevList = outputLocs(partition) - val newList = prevList.filterNot(_.location.executorId == execId) - outputLocs(partition) = newList - if (prevList != Nil && newList == Nil) { - becameUnavailable = true - numAvailableOutputs -= 1 - } - } - if (becameUnavailable) { - logInfo("%s is now unavailable on executor %s (%d/%d, %s)".format( - this, execId, numAvailableOutputs, numPartitions, isAvailable)) - } - } - /** Return a new attempt id, starting with 0. */ def newAttemptId(): Int = { val id = nextAttemptId @@ -133,11 +79,8 @@ private[spark] class Stage( def attemptId: Int = nextAttemptId - override def toString = "Stage " + id - - override def hashCode(): Int = id - - override def equals(other: Any): Boolean = other match { + override final def hashCode(): Int = id + override final def equals(other: Any): Boolean = other match { case stage: Stage => stage != null && stage.id == id case _ => false } diff --git a/core/src/main/scala/org/apache/spark/scheduler/Task.scala b/core/src/main/scala/org/apache/spark/scheduler/Task.scala index 847a4912eec13..8b592867ee31d 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/Task.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/Task.scala @@ -22,7 +22,7 @@ import java.nio.ByteBuffer import scala.collection.mutable.HashMap -import org.apache.spark.{TaskContextHelper, TaskContextImpl, TaskContext} +import org.apache.spark.{TaskContextImpl, TaskContext} import org.apache.spark.executor.TaskMetrics import org.apache.spark.serializer.SerializerInstance import org.apache.spark.util.ByteBufferInputStream @@ -45,7 +45,7 @@ import org.apache.spark.util.Utils private[spark] abstract class Task[T](val stageId: Int, var partitionId: Int) extends Serializable { /** - * Called by Executor to run this task. + * Called by [[Executor]] to run this task. * * @param taskAttemptId an identifier for this task attempt that is unique within a SparkContext. * @param attemptNumber how many times this task has been attempted (0 for the first attempt) @@ -54,7 +54,7 @@ private[spark] abstract class Task[T](val stageId: Int, var partitionId: Int) ex final def run(taskAttemptId: Long, attemptNumber: Int): T = { context = new TaskContextImpl(stageId = stageId, partitionId = partitionId, taskAttemptId = taskAttemptId, attemptNumber = attemptNumber, runningLocally = false) - TaskContextHelper.setTaskContext(context) + TaskContext.setTaskContext(context) context.taskMetrics.setHostname(Utils.localHostName()) taskThread = Thread.currentThread() if (_killed) { @@ -64,7 +64,7 @@ private[spark] abstract class Task[T](val stageId: Int, var partitionId: Int) ex runTask(context) } finally { context.markTaskCompleted() - TaskContextHelper.unset() + TaskContext.unset() } } diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskInfo.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskInfo.scala index 6fa1f2c880f7a..132a9ced77700 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskInfo.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskInfo.scala @@ -81,9 +81,11 @@ class TaskInfo( def status: String = { if (running) { - "RUNNING" - } else if (gettingResult) { - "GET RESULT" + if (gettingResult) { + "GET RESULT" + } else { + "RUNNING" + } } else if (failed) { "FAILED" } else if (successful) { diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskLocation.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskLocation.scala index 10c685f29d3ac..da07ce2c6ea49 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskLocation.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskLocation.scala @@ -29,23 +29,22 @@ private[spark] sealed trait TaskLocation { /** * A location that includes both a host and an executor id on that host. */ -private [spark] case class ExecutorCacheTaskLocation(override val host: String, - val executorId: String) extends TaskLocation { -} +private [spark] +case class ExecutorCacheTaskLocation(override val host: String, executorId: String) + extends TaskLocation /** * A location on a host. */ private [spark] case class HostTaskLocation(override val host: String) extends TaskLocation { - override def toString = host + override def toString: String = host } /** * A location on a host that is cached by HDFS. */ -private [spark] case class HDFSCacheTaskLocation(override val host: String) - extends TaskLocation { - override def toString = TaskLocation.inMemoryLocationTag + host +private [spark] case class HDFSCacheTaskLocation(override val host: String) extends TaskLocation { + override def toString: String = TaskLocation.inMemoryLocationTag + host } private[spark] object TaskLocation { @@ -54,14 +53,16 @@ private[spark] object TaskLocation { // confusion. See RFC 952 and RFC 1123 for information about the format of hostnames. val inMemoryLocationTag = "hdfs_cache_" - def apply(host: String, executorId: String) = new ExecutorCacheTaskLocation(host, executorId) + def apply(host: String, executorId: String): TaskLocation = { + new ExecutorCacheTaskLocation(host, executorId) + } /** * Create a TaskLocation from a string returned by getPreferredLocations. * These strings have the form [hostname] or hdfs_cache_[hostname], depending on whether the * location is cached. */ - def apply(str: String) = { + def apply(str: String): TaskLocation = { val hstr = str.stripPrefix(inMemoryLocationTag) if (hstr.equals(str)) { new HostTaskLocation(str) diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskResultGetter.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskResultGetter.scala index 774f3d8cdb275..3938580aeea59 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskResultGetter.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskResultGetter.scala @@ -18,6 +18,7 @@ package org.apache.spark.scheduler import java.nio.ByteBuffer +import java.util.concurrent.RejectedExecutionException import scala.language.existentials import scala.util.control.NonFatal @@ -95,25 +96,30 @@ private[spark] class TaskResultGetter(sparkEnv: SparkEnv, scheduler: TaskSchedul def enqueueFailedTask(taskSetManager: TaskSetManager, tid: Long, taskState: TaskState, serializedData: ByteBuffer) { var reason : TaskEndReason = UnknownReason - getTaskResultExecutor.execute(new Runnable { - override def run(): Unit = Utils.logUncaughtExceptions { - try { - if (serializedData != null && serializedData.limit() > 0) { - reason = serializer.get().deserialize[TaskEndReason]( - serializedData, Utils.getSparkClassLoader) + try { + getTaskResultExecutor.execute(new Runnable { + override def run(): Unit = Utils.logUncaughtExceptions { + try { + if (serializedData != null && serializedData.limit() > 0) { + reason = serializer.get().deserialize[TaskEndReason]( + serializedData, Utils.getSparkClassLoader) + } + } catch { + case cnd: ClassNotFoundException => + // Log an error but keep going here -- the task failed, so not catastrophic + // if we can't deserialize the reason. + val loader = Utils.getContextOrSparkClassLoader + logError( + "Could not deserialize TaskEndReason: ClassNotFound with classloader " + loader) + case ex: Exception => {} } - } catch { - case cnd: ClassNotFoundException => - // Log an error but keep going here -- the task failed, so not catastrophic if we can't - // deserialize the reason. - val loader = Utils.getContextOrSparkClassLoader - logError( - "Could not deserialize TaskEndReason: ClassNotFound with classloader " + loader) - case ex: Exception => {} + scheduler.handleFailedTask(taskSetManager, tid, taskState, reason) } - scheduler.handleFailedTask(taskSetManager, tid, taskState, reason) - } - }) + }) + } catch { + case e: RejectedExecutionException if sparkEnv.isStopped => + // ignore it + } } def stop() { diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskScheduler.scala index f095915352b17..ed3418676e077 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskScheduler.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskScheduler.scala @@ -73,5 +73,9 @@ private[spark] trait TaskScheduler { * @return An application ID */ def applicationId(): String = appId - + + /** + * Process a lost executor + */ + def executorLost(executorId: String, reason: ExecutorLossReason): Unit } diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala index 79f84e70df9d5..13a52d836f32f 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala @@ -62,10 +62,10 @@ private[spark] class TaskSchedulerImpl( val conf = sc.conf // How often to check for speculative tasks - val SPECULATION_INTERVAL = conf.getLong("spark.speculation.interval", 100) + val SPECULATION_INTERVAL_MS = conf.getTimeAsMs("spark.speculation.interval", "100ms") // Threshold above which we warn user initial TaskSet may be starved - val STARVATION_TIMEOUT = conf.getLong("spark.starvation.timeout", 15000) + val STARVATION_TIMEOUT_MS = conf.getTimeAsMs("spark.starvation.timeout", "15s") // CPUs to request per task val CPUS_PER_TASK = conf.getInt("spark.task.cpus", 1) @@ -142,11 +142,10 @@ private[spark] class TaskSchedulerImpl( if (!isLocal && conf.getBoolean("spark.speculation", false)) { logInfo("Starting speculative execution thread") - import sc.env.actorSystem.dispatcher - sc.env.actorSystem.scheduler.schedule(SPECULATION_INTERVAL milliseconds, - SPECULATION_INTERVAL milliseconds) { - Utils.tryOrExit { checkSpeculatableTasks() } - } + sc.env.actorSystem.scheduler.schedule(SPECULATION_INTERVAL_MS milliseconds, + SPECULATION_INTERVAL_MS milliseconds) { + Utils.tryOrStopSparkContext(sc) { checkSpeculatableTasks() } + }(sc.env.actorSystem.dispatcher) } } @@ -158,7 +157,7 @@ private[spark] class TaskSchedulerImpl( val tasks = taskSet.tasks logInfo("Adding task set " + taskSet.id + " with " + tasks.length + " tasks") this.synchronized { - val manager = new TaskSetManager(this, taskSet, maxTaskFailures) + val manager = createTaskSetManager(taskSet, maxTaskFailures) activeTaskSets(taskSet.id) = manager schedulableBuilder.addTaskSetManager(manager, manager.taskSet.properties) @@ -173,13 +172,20 @@ private[spark] class TaskSchedulerImpl( this.cancel() } } - }, STARVATION_TIMEOUT, STARVATION_TIMEOUT) + }, STARVATION_TIMEOUT_MS, STARVATION_TIMEOUT_MS) } hasReceivedTask = true } backend.reviveOffers() } + // Label as private[scheduler] to allow tests to swap in different task set managers if necessary + private[scheduler] def createTaskSetManager( + taskSet: TaskSet, + maxTaskFailures: Int): TaskSetManager = { + new TaskSetManager(this, taskSet, maxTaskFailures) + } + override def cancelTasks(stageId: Int, interruptThread: Boolean): Unit = synchronized { logInfo("Cancelling stage " + stageId) activeTaskSets.find(_._2.stageId == stageId).foreach { case (_, tsm) => @@ -366,17 +372,17 @@ private[spark] class TaskSchedulerImpl( } def handleSuccessfulTask( - taskSetManager: TaskSetManager, - tid: Long, - taskResult: DirectTaskResult[_]) = synchronized { + taskSetManager: TaskSetManager, + tid: Long, + taskResult: DirectTaskResult[_]): Unit = synchronized { taskSetManager.handleSuccessfulTask(tid, taskResult) } def handleFailedTask( - taskSetManager: TaskSetManager, - tid: Long, - taskState: TaskState, - reason: TaskEndReason) = synchronized { + taskSetManager: TaskSetManager, + tid: Long, + taskState: TaskState, + reason: TaskEndReason): Unit = synchronized { taskSetManager.handleFailedTask(tid, taskState, reason) if (!taskSetManager.isZombie && taskState != TaskState.KILLED) { // Need to revive offers again now that the task set manager state has been updated to @@ -387,7 +393,7 @@ private[spark] class TaskSchedulerImpl( def error(message: String) { synchronized { - if (activeTaskSets.size > 0) { + if (activeTaskSets.nonEmpty) { // Have each task set throw a SparkException with the error for ((taskSetId, manager) <- activeTaskSets) { try { @@ -400,8 +406,7 @@ private[spark] class TaskSchedulerImpl( // No task sets are active but we still got an error. Just exit since this // must mean the error is during registration. // It might be good to do something smarter here in the future. - logError("Exiting due to error from cluster scheduler: " + message) - System.exit(1) + throw new SparkException(s"Exiting due to error from cluster scheduler: $message") } } } @@ -416,7 +421,7 @@ private[spark] class TaskSchedulerImpl( starvationTimer.cancel() } - override def defaultParallelism() = backend.defaultParallelism() + override def defaultParallelism(): Int = backend.defaultParallelism() // Check for speculatable tasks in all our active jobs. def checkSpeculatableTasks() { @@ -429,7 +434,7 @@ private[spark] class TaskSchedulerImpl( } } - def executorLost(executorId: String, reason: ExecutorLossReason) { + override def executorLost(executorId: String, reason: ExecutorLossReason): Unit = { var failedExecutor: Option[String] = None synchronized { diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala index 55024ecd55e61..7dc325283d961 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala @@ -20,6 +20,7 @@ package org.apache.spark.scheduler import java.io.NotSerializableException import java.nio.ByteBuffer import java.util.Arrays +import java.util.concurrent.ConcurrentLinkedQueue import scala.collection.mutable.ArrayBuffer import scala.collection.mutable.HashMap @@ -29,6 +30,7 @@ import scala.util.control.NonFatal import org.apache.spark._ import org.apache.spark.executor.TaskMetrics +import org.apache.spark.scheduler.SchedulingMode._ import org.apache.spark.TaskState.TaskState import org.apache.spark.util.{Clock, SystemClock, Utils} @@ -51,7 +53,7 @@ private[spark] class TaskSetManager( sched: TaskSchedulerImpl, val taskSet: TaskSet, val maxTaskFailures: Int, - clock: Clock = SystemClock) + clock: Clock = new SystemClock()) extends Schedulable with Logging { val conf = sched.sc.conf @@ -97,7 +99,8 @@ private[spark] class TaskSetManager( var calculatedTasks = 0 val runningTasksSet = new HashSet[Long] - override def runningTasks = runningTasksSet.size + + override def runningTasks: Int = runningTasksSet.size // True once no more tasks should be launched for this task set manager. TaskSetManagers enter // the zombie state once at least one attempt of each task has completed successfully, or if the @@ -166,11 +169,11 @@ private[spark] class TaskSetManager( // last launched a task at that level, and move up a level when localityWaits[curLevel] expires. // We then move down if we manage to launch a "more local" task. var currentLocalityIndex = 0 // Index of our current locality level in validLocalityLevels - var lastLaunchTime = clock.getTime() // Time we last launched a task at this level + var lastLaunchTime = clock.getTimeMillis() // Time we last launched a task at this level - override def schedulableQueue = null + override def schedulableQueue: ConcurrentLinkedQueue[Schedulable] = null - override def schedulingMode = SchedulingMode.NONE + override def schedulingMode: SchedulingMode = SchedulingMode.NONE var emittedTaskSizeWarning = false @@ -281,7 +284,7 @@ private[spark] class TaskSetManager( val failed = failedExecutors.get(taskId).get return failed.contains(execId) && - clock.getTime() - failed.get(execId).get < EXECUTOR_TASK_BLACKLIST_TIMEOUT + clock.getTimeMillis() - failed.get(execId).get < EXECUTOR_TASK_BLACKLIST_TIMEOUT } false @@ -292,7 +295,8 @@ private[spark] class TaskSetManager( * an attempt running on this host, in case the host is slow. In addition, the task should meet * the given locality constraint. */ - private def dequeueSpeculativeTask(execId: String, host: String, locality: TaskLocality.Value) + // Labeled as protected to allow tests to override providing speculative tasks if necessary + protected def dequeueSpeculativeTask(execId: String, host: String, locality: TaskLocality.Value) : Option[(Int, TaskLocality.Value)] = { speculatableTasks.retain(index => !successful(index)) // Remove finished tasks from set @@ -427,7 +431,7 @@ private[spark] class TaskSetManager( : Option[TaskDescription] = { if (!isZombie) { - val curTime = clock.getTime() + val curTime = clock.getTimeMillis() var allowedLocality = maxLocality @@ -458,7 +462,7 @@ private[spark] class TaskSetManager( lastLaunchTime = curTime } // Serialize and return the task - val startTime = clock.getTime() + val startTime = clock.getTimeMillis() val serializedTask: ByteBuffer = try { Task.serializeWithDependencies(task, sched.sc.addedFiles, sched.sc.addedJars, ser) } catch { @@ -584,7 +588,7 @@ private[spark] class TaskSetManager( /** * Marks the task as getting result and notifies the DAG Scheduler */ - def handleTaskGettingResult(tid: Long) = { + def handleTaskGettingResult(tid: Long): Unit = { val info = taskInfos(tid) info.markGettingResult() sched.dagScheduler.taskGettingResult(info) @@ -611,7 +615,7 @@ private[spark] class TaskSetManager( /** * Marks the task as successful and notifies the DAGScheduler that a task has ended. */ - def handleSuccessfulTask(tid: Long, result: DirectTaskResult[_]) = { + def handleSuccessfulTask(tid: Long, result: DirectTaskResult[_]): Unit = { val info = taskInfos(tid) val index = info.index info.markSuccessful() @@ -673,7 +677,7 @@ private[spark] class TaskSetManager( return } val key = ef.description - val now = clock.getTime() + val now = clock.getTimeMillis() val (printFull, dupCount) = { if (recentExceptions.contains(key)) { val (dupCount, printTime) = recentExceptions(key) @@ -705,10 +709,13 @@ private[spark] class TaskSetManager( } // always add to failed executors failedExecutors.getOrElseUpdate(index, new HashMap[String, Long]()). - put(info.executorId, clock.getTime()) + put(info.executorId, clock.getTimeMillis()) sched.dagScheduler.taskEnded(tasks(index), reason, null, null, info, taskMetrics) addPendingTask(index) - if (!isZombie && state != TaskState.KILLED) { + if (!isZombie && state != TaskState.KILLED && !reason.isInstanceOf[TaskCommitDenied]) { + // If a task failed because its attempt to commit was denied, do not count this failure + // towards failing the stage. This is intended to prevent spurious stage failures in cases + // where many speculative tasks are launched and denied to commit. assert (null != failureReason) numFailures(index) += 1 if (numFailures(index) >= maxTaskFailures) { @@ -817,7 +824,7 @@ private[spark] class TaskSetManager( val minFinishedForSpeculation = (SPECULATION_QUANTILE * numTasks).floor.toInt logDebug("Checking for speculative tasks: minFinished = " + minFinishedForSpeculation) if (tasksSuccessful >= minFinishedForSpeculation && tasksSuccessful > 0) { - val time = clock.getTime() + val time = clock.getTimeMillis() val durations = taskInfos.values.filter(_.successful).map(_.duration).toArray Arrays.sort(durations) val medianDuration = durations(min((0.5 * tasksSuccessful).round.toInt, durations.size - 1)) @@ -841,15 +848,18 @@ private[spark] class TaskSetManager( } private def getLocalityWait(level: TaskLocality.TaskLocality): Long = { - val defaultWait = conf.get("spark.locality.wait", "3000") - level match { - case TaskLocality.PROCESS_LOCAL => - conf.get("spark.locality.wait.process", defaultWait).toLong - case TaskLocality.NODE_LOCAL => - conf.get("spark.locality.wait.node", defaultWait).toLong - case TaskLocality.RACK_LOCAL => - conf.get("spark.locality.wait.rack", defaultWait).toLong - case _ => 0L + val defaultWait = conf.get("spark.locality.wait", "3s") + val localityWaitKey = level match { + case TaskLocality.PROCESS_LOCAL => "spark.locality.wait.process" + case TaskLocality.NODE_LOCAL => "spark.locality.wait.node" + case TaskLocality.RACK_LOCAL => "spark.locality.wait.rack" + case _ => null + } + + if (localityWaitKey != null) { + conf.getTimeAsMs(localityWaitKey, defaultWait) + } else { + 0L } } diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedClusterMessage.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedClusterMessage.scala index 9bf74f4be198d..70364cea62a80 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedClusterMessage.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedClusterMessage.scala @@ -20,6 +20,7 @@ package org.apache.spark.scheduler.cluster import java.nio.ByteBuffer import org.apache.spark.TaskState.TaskState +import org.apache.spark.rpc.RpcEndpointRef import org.apache.spark.util.{SerializableBuffer, Utils} private[spark] sealed trait CoarseGrainedClusterMessage extends Serializable @@ -41,6 +42,7 @@ private[spark] object CoarseGrainedClusterMessages { // Executors to driver case class RegisterExecutor( executorId: String, + executorRef: RpcEndpointRef, hostPort: String, cores: Int, logUrls: Map[String, String]) @@ -70,6 +72,8 @@ private[spark] object CoarseGrainedClusterMessages { case class RemoveExecutor(executorId: String, reason: String) extends CoarseGrainedClusterMessage + case class SetupDriver(driver: RpcEndpointRef) extends CoarseGrainedClusterMessage + // Exchanged between the driver and the AM in Yarn client mode case class AddWebUIFilter(filterName:String, filterParams: Map[String, String], proxyBase: String) extends CoarseGrainedClusterMessage @@ -77,7 +81,7 @@ private[spark] object CoarseGrainedClusterMessages { // Messages exchanged between the driver and the cluster manager for executor allocation // In Yarn mode, these are exchanged between the driver and the AM - case object RegisterClusterManager extends CoarseGrainedClusterMessage + case class RegisterClusterManager(am: RpcEndpointRef) extends CoarseGrainedClusterMessage // Request executors by specifying the new total number of executors desired // This includes executors already pending or running diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala index 9d2fb4f3b4729..63987dfb32695 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala @@ -17,20 +17,16 @@ package org.apache.spark.scheduler.cluster +import java.util.concurrent.{TimeUnit, Executors} import java.util.concurrent.atomic.AtomicInteger import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet} -import scala.concurrent.Await -import scala.concurrent.duration._ - -import akka.actor._ -import akka.pattern.ask -import akka.remote.{DisassociatedEvent, RemotingLifecycleEvent} +import org.apache.spark.rpc._ import org.apache.spark.{ExecutorAllocationClient, Logging, SparkEnv, SparkException, TaskState} import org.apache.spark.scheduler._ import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._ -import org.apache.spark.util.{ActorLogReceive, SerializableBuffer, AkkaUtils, Utils} +import org.apache.spark.util.{SerializableBuffer, AkkaUtils, Utils} /** * A scheduler backend that waits for coarse grained executors to connect to it through Akka. @@ -41,7 +37,7 @@ import org.apache.spark.util.{ActorLogReceive, SerializableBuffer, AkkaUtils, Ut * (spark.deploy.*). */ private[spark] -class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val actorSystem: ActorSystem) +class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: RpcEnv) extends ExecutorAllocationClient with SchedulerBackend with Logging { // Use an atomic variable to track total number of cores in the cluster for simplicity and speed @@ -49,7 +45,6 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val actorSyste // Total number of executors that are currently registered var totalRegisteredExecutors = new AtomicInteger(0) val conf = scheduler.sc.conf - private val timeout = AkkaUtils.askTimeout(conf) private val akkaFrameSize = AkkaUtils.maxFrameSizeBytes(conf) // Submit tasks only after (registered resources / total expected resources) // is equal to at least this value, that is double between 0 and 1. @@ -57,8 +52,8 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val actorSyste math.min(1, conf.getDouble("spark.scheduler.minRegisteredResourcesRatio", 0)) // Submit tasks after maxRegisteredWaitingTime milliseconds // if minRegisteredRatio has not yet been reached - val maxRegisteredWaitingTime = - conf.getInt("spark.scheduler.maxRegisteredResourcesWaitingTime", 30000) + val maxRegisteredWaitingTimeMs = + conf.getTimeAsMs("spark.scheduler.maxRegisteredResourcesWaitingTime", "30s") val createTime = System.currentTimeMillis() private val executorDataMap = new HashMap[String, ExecutorData] @@ -71,48 +66,27 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val actorSyste // Executors we have requested the cluster manager to kill that have not died yet private val executorsPendingToRemove = new HashSet[String] - class DriverActor(sparkProperties: Seq[(String, String)]) extends Actor with ActorLogReceive { + class DriverEndpoint(override val rpcEnv: RpcEnv, sparkProperties: Seq[(String, String)]) + extends ThreadSafeRpcEndpoint with Logging { override protected def log = CoarseGrainedSchedulerBackend.this.log - private val addressToExecutorId = new HashMap[Address, String] - - override def preStart() { - // Listen for remote client disconnection events, since they don't go through Akka's watch() - context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent]) - // Periodically revive offers to allow delay scheduling to work - val reviveInterval = conf.getLong("spark.scheduler.revive.interval", 1000) - import context.dispatcher - context.system.scheduler.schedule(0.millis, reviveInterval.millis, self, ReviveOffers) - } + private val addressToExecutorId = new HashMap[RpcAddress, String] - def receiveWithLogging = { - case RegisterExecutor(executorId, hostPort, cores, logUrls) => - Utils.checkHostPort(hostPort, "Host port expected " + hostPort) - if (executorDataMap.contains(executorId)) { - sender ! RegisterExecutorFailed("Duplicate executor ID: " + executorId) - } else { - logInfo("Registered executor: " + sender + " with ID " + executorId) - sender ! RegisteredExecutor + private val reviveThread = + Executors.newSingleThreadScheduledExecutor(Utils.namedThreadFactory("driver-revive-thread")) - addressToExecutorId(sender.path.address) = executorId - totalCoreCount.addAndGet(cores) - totalRegisteredExecutors.addAndGet(1) - val (host, _) = Utils.parseHostPort(hostPort) - val data = new ExecutorData(sender, sender.path.address, host, cores, cores, logUrls) - // This must be synchronized because variables mutated - // in this block are read when requesting executors - CoarseGrainedSchedulerBackend.this.synchronized { - executorDataMap.put(executorId, data) - if (numPendingExecutors > 0) { - numPendingExecutors -= 1 - logDebug(s"Decremented number of pending executors ($numPendingExecutors left)") - } - } - listenerBus.post( - SparkListenerExecutorAdded(System.currentTimeMillis(), executorId, data)) - makeOffers() + override def onStart() { + // Periodically revive offers to allow delay scheduling to work + val reviveIntervalMs = conf.getTimeAsMs("spark.scheduler.revive.interval", "1s") + + reviveThread.scheduleAtFixedRate(new Runnable { + override def run(): Unit = Utils.tryLogNonFatalError { + Option(self).foreach(_.send(ReviveOffers)) } + }, 0, reviveIntervalMs, TimeUnit.MILLISECONDS) + } + override def receive: PartialFunction[Any, Unit] = { case StatusUpdate(executorId, taskId, state, data) => scheduler.statusUpdate(taskId, state, data.value) if (TaskState.isFinished(state)) { @@ -133,33 +107,58 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val actorSyste case KillTask(taskId, executorId, interruptThread) => executorDataMap.get(executorId) match { case Some(executorInfo) => - executorInfo.executorActor ! KillTask(taskId, executorId, interruptThread) + executorInfo.executorEndpoint.send(KillTask(taskId, executorId, interruptThread)) case None => // Ignoring the task kill since the executor is not registered. logWarning(s"Attempted to kill task $taskId for unknown executor $executorId.") } + } + + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { + case RegisterExecutor(executorId, executorRef, hostPort, cores, logUrls) => + Utils.checkHostPort(hostPort, "Host port expected " + hostPort) + if (executorDataMap.contains(executorId)) { + context.reply(RegisterExecutorFailed("Duplicate executor ID: " + executorId)) + } else { + logInfo("Registered executor: " + executorRef + " with ID " + executorId) + context.reply(RegisteredExecutor) + + addressToExecutorId(executorRef.address) = executorId + totalCoreCount.addAndGet(cores) + totalRegisteredExecutors.addAndGet(1) + val (host, _) = Utils.parseHostPort(hostPort) + val data = new ExecutorData(executorRef, executorRef.address, host, cores, cores, logUrls) + // This must be synchronized because variables mutated + // in this block are read when requesting executors + CoarseGrainedSchedulerBackend.this.synchronized { + executorDataMap.put(executorId, data) + if (numPendingExecutors > 0) { + numPendingExecutors -= 1 + logDebug(s"Decremented number of pending executors ($numPendingExecutors left)") + } + } + listenerBus.post( + SparkListenerExecutorAdded(System.currentTimeMillis(), executorId, data)) + makeOffers() + } case StopDriver => - sender ! true - context.stop(self) + context.reply(true) + stop() case StopExecutors => logInfo("Asking each executor to shut down") for ((_, executorData) <- executorDataMap) { - executorData.executorActor ! StopExecutor + executorData.executorEndpoint.send(StopExecutor) } - sender ! true + context.reply(true) case RemoveExecutor(executorId, reason) => removeExecutor(executorId, reason) - sender ! true - - case DisassociatedEvent(_, address, _) => - addressToExecutorId.get(address).foreach(removeExecutor(_, - "remote Akka client disassociated")) + context.reply(true) case RetrieveSparkProps => - sender ! sparkProperties + context.reply(sparkProperties) } // Make fake resource offers on all executors @@ -169,6 +168,11 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val actorSyste }.toSeq)) } + override def onDisconnected(remoteAddress: RpcAddress): Unit = { + addressToExecutorId.get(remoteAddress).foreach(removeExecutor(_, + "remote Rpc client disassociated")) + } + // Make fake resource offers on just one executor def makeOffers(executorId: String) { val executorData = executorDataMap(executorId) @@ -199,7 +203,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val actorSyste else { val executorData = executorDataMap(task.executorId) executorData.freeCores -= scheduler.CPUS_PER_TASK - executorData.executorActor ! LaunchTask(new SerializableBuffer(serializedTask)) + executorData.executorEndpoint.send(LaunchTask(new SerializableBuffer(serializedTask))) } } } @@ -211,6 +215,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val actorSyste // This must be synchronized because variables mutated // in this block are read when requesting executors CoarseGrainedSchedulerBackend.this.synchronized { + addressToExecutorId -= executorInfo.executorAddress executorDataMap -= executorId executorsPendingToRemove -= executorId } @@ -222,9 +227,13 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val actorSyste case None => logError(s"Asked to remove non-existent executor $executorId") } } + + override def onStop() { + reviveThread.shutdownNow() + } } - var driverActor: ActorRef = null + var driverEndpoint: RpcEndpointRef = null val taskIdsOnSlave = new HashMap[String, HashSet[String]] override def start() { @@ -235,16 +244,15 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val actorSyste } } // TODO (prashant) send conf instead of properties - driverActor = actorSystem.actorOf( - Props(new DriverActor(properties)), name = CoarseGrainedSchedulerBackend.ACTOR_NAME) + driverEndpoint = rpcEnv.setupEndpoint( + CoarseGrainedSchedulerBackend.ENDPOINT_NAME, new DriverEndpoint(rpcEnv, properties)) } def stopExecutors() { try { - if (driverActor != null) { + if (driverEndpoint != null) { logInfo("Shutting down all executors") - val future = driverActor.ask(StopExecutors)(timeout) - Await.ready(future, timeout) + driverEndpoint.askWithReply[Boolean](StopExecutors) } } catch { case e: Exception => @@ -255,22 +263,21 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val actorSyste override def stop() { stopExecutors() try { - if (driverActor != null) { - val future = driverActor.ask(StopDriver)(timeout) - Await.ready(future, timeout) + if (driverEndpoint != null) { + driverEndpoint.askWithReply[Boolean](StopDriver) } } catch { case e: Exception => - throw new SparkException("Error stopping standalone scheduler's driver actor", e) + throw new SparkException("Error stopping standalone scheduler's driver endpoint", e) } } override def reviveOffers() { - driverActor ! ReviveOffers + driverEndpoint.send(ReviveOffers) } override def killTask(taskId: Long, executorId: String, interruptThread: Boolean) { - driverActor ! KillTask(taskId, executorId, interruptThread) + driverEndpoint.send(KillTask(taskId, executorId, interruptThread)) } override def defaultParallelism(): Int = { @@ -280,11 +287,10 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val actorSyste // Called by subclasses when notified of a lost worker def removeExecutor(executorId: String, reason: String) { try { - val future = driverActor.ask(RemoveExecutor(executorId, reason))(timeout) - Await.ready(future, timeout) + driverEndpoint.askWithReply[Boolean](RemoveExecutor(executorId, reason)) } catch { case e: Exception => - throw new SparkException("Error notifying standalone scheduler's driver actor", e) + throw new SparkException("Error notifying standalone scheduler's driver endpoint", e) } } @@ -296,9 +302,9 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val actorSyste s"reached minRegisteredResourcesRatio: $minRegisteredRatio") return true } - if ((System.currentTimeMillis() - createTime) >= maxRegisteredWaitingTime) { + if ((System.currentTimeMillis() - createTime) >= maxRegisteredWaitingTimeMs) { logInfo("SchedulerBackend is ready for scheduling beginning after waiting " + - s"maxRegisteredResourcesWaitingTime: $maxRegisteredWaitingTime(ms)") + s"maxRegisteredResourcesWaitingTime: $maxRegisteredWaitingTimeMs(ms)") return true } false @@ -311,9 +317,14 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val actorSyste /** * Request an additional number of executors from the cluster manager. - * Return whether the request is acknowledged. + * @return whether the request is acknowledged. */ final override def requestExecutors(numAdditionalExecutors: Int): Boolean = synchronized { + if (numAdditionalExecutors < 0) { + throw new IllegalArgumentException( + "Attempted to request a negative number of additional executor(s) " + + s"$numAdditionalExecutors from the cluster manager. Please specify a positive number!") + } logInfo(s"Requesting $numAdditionalExecutors additional executor(s) from the cluster manager") logDebug(s"Number of pending executors is now $numPendingExecutors") numPendingExecutors += numAdditionalExecutors @@ -322,6 +333,22 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val actorSyste doRequestTotalExecutors(newTotal) } + /** + * Express a preference to the cluster manager for a given total number of executors. This can + * result in canceling pending requests or filing additional requests. + * @return whether the request is acknowledged. + */ + final override def requestTotalExecutors(numExecutors: Int): Boolean = synchronized { + if (numExecutors < 0) { + throw new IllegalArgumentException( + "Attempted to request a negative number of executor(s) " + + s"$numExecutors from the cluster manager. Please specify a positive number!") + } + numPendingExecutors = + math.max(numExecutors - numExistingExecutors + executorsPendingToRemove.size, 0) + doRequestTotalExecutors(numExecutors) + } + /** * Request executors from the cluster manager by specifying the total number desired, * including existing pending and running executors. @@ -332,7 +359,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val actorSyste * insufficient resources to satisfy the first request. We make the assumption here that the * cluster manager will eventually fulfill all requests when resources free up. * - * Return whether the request is acknowledged. + * @return whether the request is acknowledged. */ protected def doRequestTotalExecutors(requestedTotal: Int): Boolean = false @@ -350,6 +377,12 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val actorSyste logWarning(s"Executor to kill $id does not exist!") } } + // Killing executors means effectively that we want less executors than before, so also update + // the target number of executors to avoid having the backend allocate new ones. + val newTotal = (numExistingExecutors + numPendingExecutors - executorsPendingToRemove.size + - filteredExecutorIds.size) + doRequestTotalExecutors(newTotal) + executorsPendingToRemove ++= filteredExecutorIds doKillExecutors(filteredExecutorIds) } @@ -363,5 +396,5 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val actorSyste } private[spark] object CoarseGrainedSchedulerBackend { - val ACTOR_NAME = "CoarseGrainedScheduler" + val ENDPOINT_NAME = "CoarseGrainedScheduler" } diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/ExecutorData.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/ExecutorData.scala index 5e571efe76720..26e72c0bff38d 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/ExecutorData.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/ExecutorData.scala @@ -17,20 +17,20 @@ package org.apache.spark.scheduler.cluster -import akka.actor.{Address, ActorRef} +import org.apache.spark.rpc.{RpcEndpointRef, RpcAddress} /** * Grouping of data for an executor used by CoarseGrainedSchedulerBackend. * - * @param executorActor The ActorRef representing this executor + * @param executorEndpoint The ActorRef representing this executor * @param executorAddress The network address of this executor * @param executorHost The hostname that this executor is running on * @param freeCores The current number of cores available for work on the executor * @param totalCores The total number of cores available to the executor */ private[cluster] class ExecutorData( - val executorActor: ActorRef, - val executorAddress: Address, + val executorEndpoint: RpcEndpointRef, + val executorAddress: RpcAddress, override val executorHost: String, var freeCores: Int, override val totalCores: Int, diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/SimrSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/SimrSchedulerBackend.scala index 06786a59524e7..0324c9dab910b 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/SimrSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/SimrSchedulerBackend.scala @@ -19,16 +19,16 @@ package org.apache.spark.scheduler.cluster import org.apache.hadoop.fs.{Path, FileSystem} +import org.apache.spark.rpc.RpcAddress import org.apache.spark.{Logging, SparkContext, SparkEnv} import org.apache.spark.deploy.SparkHadoopUtil import org.apache.spark.scheduler.TaskSchedulerImpl -import org.apache.spark.util.AkkaUtils private[spark] class SimrSchedulerBackend( scheduler: TaskSchedulerImpl, sc: SparkContext, driverFilePath: String) - extends CoarseGrainedSchedulerBackend(scheduler, sc.env.actorSystem) + extends CoarseGrainedSchedulerBackend(scheduler, sc.env.rpcEnv) with Logging { val tmpPath = new Path(driverFilePath + "_tmp") @@ -39,12 +39,9 @@ private[spark] class SimrSchedulerBackend( override def start() { super.start() - val driverUrl = AkkaUtils.address( - AkkaUtils.protocol(actorSystem), - SparkEnv.driverActorSystemName, - sc.conf.get("spark.driver.host"), - sc.conf.get("spark.driver.port"), - CoarseGrainedSchedulerBackend.ACTOR_NAME) + val driverUrl = rpcEnv.uriOf(SparkEnv.driverActorSystemName, + RpcAddress(sc.conf.get("spark.driver.host"), sc.conf.get("spark.driver.port").toInt), + CoarseGrainedSchedulerBackend.ENDPOINT_NAME) val conf = SparkHadoopUtil.get.newConfiguration(sc.conf) val fs = FileSystem.get(conf) diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala index d2e1680a5fd1b..ccf1dc5af6120 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala @@ -17,43 +17,48 @@ package org.apache.spark.scheduler.cluster +import java.util.concurrent.Semaphore + +import org.apache.spark.rpc.RpcAddress import org.apache.spark.{Logging, SparkConf, SparkContext, SparkEnv} import org.apache.spark.deploy.{ApplicationDescription, Command} import org.apache.spark.deploy.client.{AppClient, AppClientListener} import org.apache.spark.scheduler.{ExecutorExited, ExecutorLossReason, SlaveLost, TaskSchedulerImpl} -import org.apache.spark.util.{AkkaUtils, Utils} +import org.apache.spark.util.Utils private[spark] class SparkDeploySchedulerBackend( scheduler: TaskSchedulerImpl, sc: SparkContext, masters: Array[String]) - extends CoarseGrainedSchedulerBackend(scheduler, sc.env.actorSystem) + extends CoarseGrainedSchedulerBackend(scheduler, sc.env.rpcEnv) with AppClientListener with Logging { - var client: AppClient = null - var stopping = false - var shutdownCallback : (SparkDeploySchedulerBackend) => Unit = _ - @volatile var appId: String = _ + private var client: AppClient = null + private var stopping = false + + @volatile var shutdownCallback: SparkDeploySchedulerBackend => Unit = _ + @volatile private var appId: String = _ - val registrationLock = new Object() - var registrationDone = false + private val registrationBarrier = new Semaphore(0) - val maxCores = conf.getOption("spark.cores.max").map(_.toInt) - val totalExpectedCores = maxCores.getOrElse(0) + private val maxCores = conf.getOption("spark.cores.max").map(_.toInt) + private val totalExpectedCores = maxCores.getOrElse(0) override def start() { super.start() // The endpoint for executors to talk to us - val driverUrl = AkkaUtils.address( - AkkaUtils.protocol(actorSystem), - SparkEnv.driverActorSystemName, - conf.get("spark.driver.host"), - conf.get("spark.driver.port"), - CoarseGrainedSchedulerBackend.ACTOR_NAME) - val args = Seq(driverUrl, "{{EXECUTOR_ID}}", "{{HOSTNAME}}", "{{CORES}}", "{{APP_ID}}", - "{{WORKER_URL}}") + val driverUrl = rpcEnv.uriOf(SparkEnv.driverActorSystemName, + RpcAddress(sc.conf.get("spark.driver.host"), sc.conf.get("spark.driver.port").toInt), + CoarseGrainedSchedulerBackend.ENDPOINT_NAME) + val args = Seq( + "--driver-url", driverUrl, + "--executor-id", "{{EXECUTOR_ID}}", + "--hostname", "{{HOSTNAME}}", + "--cores", "{{CORES}}", + "--app-id", "{{APP_ID}}", + "--worker-url", "{{WORKER_URL}}") val extraJavaOpts = sc.conf.getOption("spark.executor.extraJavaOptions") .map(Utils.splitCommandString).getOrElse(Seq.empty) val classPathEntries = sc.conf.getOption("spark.executor.extraClassPath") @@ -77,12 +82,11 @@ private[spark] class SparkDeploySchedulerBackend( val command = Command("org.apache.spark.executor.CoarseGrainedExecutorBackend", args, sc.executorEnvs, classPathEntries ++ testingClassPath, libraryPathEntries, javaOpts) val appUIAddress = sc.ui.map(_.appUIAddress).getOrElse("") - val appDesc = new ApplicationDescription(sc.appName, maxCores, sc.executorMemory, command, - appUIAddress, sc.eventLogDir) - + val coresPerExecutor = conf.getOption("spark.executor.cores").map(_.toInt) + val appDesc = new ApplicationDescription(sc.appName, maxCores, sc.executorMemory, + command, appUIAddress, sc.eventLogDir, sc.eventLogCodec, coresPerExecutor) client = new AppClient(sc.env.actorSystem, masters, appDesc, this, conf) client.start() - waitForRegistration() } @@ -90,8 +94,10 @@ private[spark] class SparkDeploySchedulerBackend( stopping = true super.stop() client.stop() - if (shutdownCallback != null) { - shutdownCallback(this) + + val callback = shutdownCallback + if (callback != null) { + callback(this) } } @@ -112,9 +118,12 @@ private[spark] class SparkDeploySchedulerBackend( notifyContext() if (!stopping) { logError("Application has been killed. Reason: " + reason) - scheduler.error(reason) - // Ensure the application terminates, as we can no longer run jobs. - sc.stop() + try { + scheduler.error(reason) + } finally { + // Ensure the application terminates, as we can no longer run jobs. + sc.stop() + } } } @@ -144,18 +153,11 @@ private[spark] class SparkDeploySchedulerBackend( } private def waitForRegistration() = { - registrationLock.synchronized { - while (!registrationDone) { - registrationLock.wait() - } - } + registrationBarrier.acquire() } private def notifyContext() = { - registrationLock.synchronized { - registrationDone = true - registrationLock.notifyAll() - } + registrationBarrier.release() } } diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala index f14aaeea0a25c..f72566c370a6f 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala @@ -19,10 +19,8 @@ package org.apache.spark.scheduler.cluster import scala.concurrent.{Future, ExecutionContext} -import akka.actor.{Actor, ActorRef, Props} -import akka.remote.{DisassociatedEvent, RemotingLifecycleEvent} - -import org.apache.spark.SparkContext +import org.apache.spark.{Logging, SparkContext} +import org.apache.spark.rpc._ import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._ import org.apache.spark.scheduler.TaskSchedulerImpl import org.apache.spark.ui.JettyUtils @@ -37,7 +35,7 @@ import scala.util.control.NonFatal private[spark] abstract class YarnSchedulerBackend( scheduler: TaskSchedulerImpl, sc: SparkContext) - extends CoarseGrainedSchedulerBackend(scheduler, sc.env.actorSystem) { + extends CoarseGrainedSchedulerBackend(scheduler, sc.env.rpcEnv) { if (conf.getOption("spark.scheduler.minRegisteredResourcesRatio").isEmpty) { minRegisteredRatio = 0.8 @@ -45,10 +43,8 @@ private[spark] abstract class YarnSchedulerBackend( protected var totalExpectedExecutors = 0 - private val yarnSchedulerActor: ActorRef = - actorSystem.actorOf( - Props(new YarnSchedulerActor), - name = YarnSchedulerBackend.ACTOR_NAME) + private val yarnSchedulerEndpoint = rpcEnv.setupEndpoint( + YarnSchedulerBackend.ENDPOINT_NAME, new YarnSchedulerEndpoint(rpcEnv)) private implicit val askTimeout = AkkaUtils.askTimeout(sc.conf) @@ -57,16 +53,14 @@ private[spark] abstract class YarnSchedulerBackend( * This includes executors already pending or running. */ override def doRequestTotalExecutors(requestedTotal: Int): Boolean = { - AkkaUtils.askWithReply[Boolean]( - RequestExecutors(requestedTotal), yarnSchedulerActor, askTimeout) + yarnSchedulerEndpoint.askWithReply[Boolean](RequestExecutors(requestedTotal)) } /** * Request that the ApplicationMaster kill the specified executors. */ override def doKillExecutors(executorIds: Seq[String]): Boolean = { - AkkaUtils.askWithReply[Boolean]( - KillExecutors(executorIds), yarnSchedulerActor, askTimeout) + yarnSchedulerEndpoint.askWithReply[Boolean](KillExecutors(executorIds)) } override def sufficientResourcesRegistered(): Boolean = { @@ -96,64 +90,71 @@ private[spark] abstract class YarnSchedulerBackend( } /** - * An actor that communicates with the ApplicationMaster. + * An [[RpcEndpoint]] that communicates with the ApplicationMaster. */ - private class YarnSchedulerActor extends Actor { - private var amActor: Option[ActorRef] = None + private class YarnSchedulerEndpoint(override val rpcEnv: RpcEnv) + extends ThreadSafeRpcEndpoint with Logging { + private var amEndpoint: Option[RpcEndpointRef] = None - implicit val askAmActorExecutor = ExecutionContext.fromExecutor( - Utils.newDaemonCachedThreadPool("yarn-scheduler-ask-am-executor")) + private val askAmThreadPool = + Utils.newDaemonCachedThreadPool("yarn-scheduler-ask-am-thread-pool") + implicit val askAmExecutor = ExecutionContext.fromExecutor(askAmThreadPool) - override def preStart(): Unit = { - // Listen for disassociation events - context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent]) - } + override def receive: PartialFunction[Any, Unit] = { + case RegisterClusterManager(am) => + logInfo(s"ApplicationMaster registered as $am") + amEndpoint = Some(am) + + case AddWebUIFilter(filterName, filterParams, proxyBase) => + addWebUIFilter(filterName, filterParams, proxyBase) - override def receive = { - case RegisterClusterManager => - logInfo(s"ApplicationMaster registered as $sender") - amActor = Some(sender) + } + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { case r: RequestExecutors => - amActor match { - case Some(actor) => - val driverActor = sender + amEndpoint match { + case Some(am) => Future { - driverActor ! AkkaUtils.askWithReply[Boolean](r, actor, askTimeout) + context.reply(am.askWithReply[Boolean](r)) } onFailure { - case NonFatal(e) => logError(s"Sending $r to AM was unsuccessful", e) + case NonFatal(e) => + logError(s"Sending $r to AM was unsuccessful", e) + context.sendFailure(e) } case None => logWarning("Attempted to request executors before the AM has registered!") - sender ! false + context.reply(false) } case k: KillExecutors => - amActor match { - case Some(actor) => - val driverActor = sender + amEndpoint match { + case Some(am) => Future { - driverActor ! AkkaUtils.askWithReply[Boolean](k, actor, askTimeout) + context.reply(am.askWithReply[Boolean](k)) } onFailure { - case NonFatal(e) => logError(s"Sending $k to AM was unsuccessful", e) + case NonFatal(e) => + logError(s"Sending $k to AM was unsuccessful", e) + context.sendFailure(e) } case None => logWarning("Attempted to kill executors before the AM has registered!") - sender ! false + context.reply(false) } - case AddWebUIFilter(filterName, filterParams, proxyBase) => - addWebUIFilter(filterName, filterParams, proxyBase) - sender ! true + } - case d: DisassociatedEvent => - if (amActor.isDefined && sender == amActor.get) { - logWarning(s"ApplicationMaster has disassociated: $d") - } + override def onDisconnected(remoteAddress: RpcAddress): Unit = { + if (amEndpoint.exists(_.address == remoteAddress)) { + logWarning(s"ApplicationMaster has disassociated: $remoteAddress") + } + } + + override def onStop(): Unit ={ + askAmThreadPool.shutdownNow() } } } private[spark] object YarnSchedulerBackend { - val ACTOR_NAME = "YarnScheduler" + val ENDPOINT_NAME = "YarnScheduler" } diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala index 0d1c2a916ca7f..b037a4966ced0 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala @@ -28,7 +28,7 @@ import org.apache.mesos.{Scheduler => MScheduler} import org.apache.mesos._ import org.apache.mesos.Protos.{TaskInfo => MesosTaskInfo, TaskState => MesosTaskState, _} -import org.apache.spark.{Logging, SparkContext, SparkEnv, SparkException} +import org.apache.spark.{Logging, SparkContext, SparkEnv, SparkException, TaskState} import org.apache.spark.scheduler.TaskSchedulerImpl import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend import org.apache.spark.util.{Utils, AkkaUtils} @@ -47,7 +47,7 @@ private[spark] class CoarseMesosSchedulerBackend( scheduler: TaskSchedulerImpl, sc: SparkContext, master: String) - extends CoarseGrainedSchedulerBackend(scheduler, sc.env.actorSystem) + extends CoarseGrainedSchedulerBackend(scheduler, sc.env.rpcEnv) with MScheduler with Logging { @@ -148,24 +148,31 @@ private[spark] class CoarseMesosSchedulerBackend( SparkEnv.driverActorSystemName, conf.get("spark.driver.host"), conf.get("spark.driver.port"), - CoarseGrainedSchedulerBackend.ACTOR_NAME) + CoarseGrainedSchedulerBackend.ENDPOINT_NAME) val uri = conf.get("spark.executor.uri", null) if (uri == null) { val runScript = new File(executorSparkHome, "./bin/spark-class").getCanonicalPath command.setValue( - "%s \"%s\" org.apache.spark.executor.CoarseGrainedExecutorBackend %s %s %s %d %s".format( - prefixEnv, runScript, driverUrl, offer.getSlaveId.getValue, - offer.getHostname, numCores, appId)) + "%s \"%s\" org.apache.spark.executor.CoarseGrainedExecutorBackend" + .format(prefixEnv, runScript) + + s" --driver-url $driverUrl" + + s" --executor-id ${offer.getSlaveId.getValue}" + + s" --hostname ${offer.getHostname}" + + s" --cores $numCores" + + s" --app-id $appId") } else { // Grab everything to the first '.'. We'll use that and '*' to // glob the directory "correctly". val basename = uri.split('/').last.split('.').head command.setValue( - ("cd %s*; %s " + - "./bin/spark-class org.apache.spark.executor.CoarseGrainedExecutorBackend %s %s %s %d %s") - .format(basename, prefixEnv, driverUrl, offer.getSlaveId.getValue, - offer.getHostname, numCores, appId)) + s"cd $basename*; $prefixEnv " + + "./bin/spark-class org.apache.spark.executor.CoarseGrainedExecutorBackend" + + s" --driver-url $driverUrl" + + s" --executor-id ${offer.getSlaveId.getValue}" + + s" --hostname ${offer.getHostname}" + + s" --cores $numCores" + + s" --app-id $appId") command.addUris(CommandInfo.URI.newBuilder().setValue(uri)) } command.build() @@ -255,20 +262,12 @@ private[spark] class CoarseMesosSchedulerBackend( .build() } - /** Check whether a Mesos task state represents a finished task */ - private def isFinished(state: MesosTaskState) = { - state == MesosTaskState.TASK_FINISHED || - state == MesosTaskState.TASK_FAILED || - state == MesosTaskState.TASK_KILLED || - state == MesosTaskState.TASK_LOST - } - override def statusUpdate(d: SchedulerDriver, status: TaskStatus) { val taskId = status.getTaskId.getValue.toInt val state = status.getState logInfo("Mesos task " + taskId + " is now " + state) synchronized { - if (isFinished(state)) { + if (TaskState.isFinished(TaskState.fromMesos(state))) { val slaveId = taskIdToSlaveId(taskId) slaveIdsWithExecutors -= slaveId taskIdToSlaveId -= taskId @@ -278,7 +277,7 @@ private[spark] class CoarseMesosSchedulerBackend( coresByTaskId -= taskId } // If it was a failure, mark the slave as failed for blacklisting purposes - if (state == MesosTaskState.TASK_FAILED || state == MesosTaskState.TASK_LOST) { + if (TaskState.isFailed(TaskState.fromMesos(state))) { failuresBySlaveId(slaveId) = failuresBySlaveId.getOrElse(slaveId, 0) + 1 if (failuresBySlaveId(slaveId) >= MAX_SLAVE_FAILURES) { logInfo("Blacklisting Mesos slave " + slaveId + " due to too many failures; " + diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MemoryUtils.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MemoryUtils.scala index 5101ec8352e79..8df4f3b554c41 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MemoryUtils.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MemoryUtils.scala @@ -21,15 +21,11 @@ import org.apache.spark.SparkContext private[spark] object MemoryUtils { // These defaults copied from YARN - val OVERHEAD_FRACTION = 1.07 + val OVERHEAD_FRACTION = 0.10 val OVERHEAD_MINIMUM = 384 - def calculateTotalMemory(sc: SparkContext) = { - math.max( - sc.conf.getOption("spark.mesos.executor.memoryOverhead") - .getOrElse(OVERHEAD_MINIMUM.toString) - .toInt + sc.executorMemory, - OVERHEAD_FRACTION * sc.executorMemory - ) + def calculateTotalMemory(sc: SparkContext): Int = { + sc.conf.getInt("spark.mesos.executor.memoryOverhead", + math.max(OVERHEAD_FRACTION * sc.executorMemory, OVERHEAD_MINIMUM).toInt) + sc.executorMemory } } diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala index cfb6592e14aa8..d9d62b0e287ed 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala @@ -67,6 +67,8 @@ private[spark] class MesosSchedulerBackend( // The listener bus to publish executor added/removed events. val listenerBus = sc.listenerBus + + private[mesos] val mesosExecutorCores = sc.conf.getDouble("spark.mesos.mesosExecutor.cores", 1) @volatile var appId: String = _ @@ -139,7 +141,7 @@ private[spark] class MesosSchedulerBackend( .setName("cpus") .setType(Value.Type.SCALAR) .setScalar(Value.Scalar.newBuilder() - .setValue(scheduler.CPUS_PER_TASK).build()) + .setValue(mesosExecutorCores).build()) .build() val memory = Resource.newBuilder() .setName("mem") @@ -220,10 +222,9 @@ private[spark] class MesosSchedulerBackend( val mem = getResource(o.getResourcesList, "mem") val cpus = getResource(o.getResourcesList, "cpus") val slaveId = o.getSlaveId.getValue - // TODO(pwendell): Should below be 1 + scheduler.CPUS_PER_TASK? (mem >= MemoryUtils.calculateTotalMemory(sc) && // need at least 1 for executor, 1 for task - cpus >= 2 * scheduler.CPUS_PER_TASK) || + cpus >= (mesosExecutorCores + scheduler.CPUS_PER_TASK)) || (slaveIdsWithExecutors.contains(slaveId) && cpus >= scheduler.CPUS_PER_TASK) } @@ -232,10 +233,9 @@ private[spark] class MesosSchedulerBackend( val cpus = if (slaveIdsWithExecutors.contains(o.getSlaveId.getValue)) { getResource(o.getResourcesList, "cpus").toInt } else { - // If the executor doesn't exist yet, subtract CPU for executor - // TODO(pwendell): Should below just subtract "1"? - getResource(o.getResourcesList, "cpus").toInt - - scheduler.CPUS_PER_TASK + // If the Mesos executor has not been started on this slave yet, set aside a few + // cores for the Mesos executor by offering fewer cores to the Spark executor + (getResource(o.getResourcesList, "cpus") - mesosExecutorCores).toInt } new WorkerOffer( o.getSlaveId.getValue, @@ -313,24 +313,17 @@ private[spark] class MesosSchedulerBackend( .build() } - /** Check whether a Mesos task state represents a finished task */ - def isFinished(state: MesosTaskState) = { - state == MesosTaskState.TASK_FINISHED || - state == MesosTaskState.TASK_FAILED || - state == MesosTaskState.TASK_KILLED || - state == MesosTaskState.TASK_LOST - } - override def statusUpdate(d: SchedulerDriver, status: TaskStatus) { inClassLoader() { val tid = status.getTaskId.getValue.toLong val state = TaskState.fromMesos(status.getState) synchronized { - if (status.getState == MesosTaskState.TASK_LOST && taskIdToSlaveId.contains(tid)) { + if (TaskState.isFailed(TaskState.fromMesos(status.getState)) + && taskIdToSlaveId.contains(tid)) { // We lost the executor on this slave, so remember that it's gone removeExecutor(taskIdToSlaveId(tid), "Lost executor") } - if (isFinished(status.getState)) { + if (TaskState.isFinished(state)) { taskIdToSlaveId.remove(tid) } } @@ -394,7 +387,7 @@ private[spark] class MesosSchedulerBackend( } // TODO: query Mesos for number of cores - override def defaultParallelism() = sc.conf.getInt("spark.default.parallelism", 8) + override def defaultParallelism(): Int = sc.conf.getInt("spark.default.parallelism", 8) override def applicationId(): String = Option(appId).getOrElse { diff --git a/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala index 4676b828d3d89..50ba0b9d5a612 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala @@ -18,16 +18,14 @@ package org.apache.spark.scheduler.local import java.nio.ByteBuffer +import java.util.concurrent.{Executors, TimeUnit} -import scala.concurrent.duration._ - -import akka.actor.{Actor, ActorRef, Props} - -import org.apache.spark.{Logging, SparkContext, SparkEnv, TaskState} +import org.apache.spark.{Logging, SparkConf, SparkContext, SparkEnv, TaskState} import org.apache.spark.TaskState.TaskState import org.apache.spark.executor.{Executor, ExecutorBackend} +import org.apache.spark.rpc.{ThreadSafeRpcEndpoint, RpcCallContext, RpcEndpointRef, RpcEnv} import org.apache.spark.scheduler.{SchedulerBackend, TaskSchedulerImpl, WorkerOffer} -import org.apache.spark.util.ActorLogReceive +import org.apache.spark.util.Utils private case class ReviveOffers() @@ -38,17 +36,19 @@ private case class KillTask(taskId: Long, interruptThread: Boolean) private case class StopExecutor() /** - * Calls to LocalBackend are all serialized through LocalActor. Using an actor makes the calls on - * LocalBackend asynchronous, which is necessary to prevent deadlock between LocalBackend + * Calls to LocalBackend are all serialized through LocalEndpoint. Using an RpcEndpoint makes the + * calls on LocalBackend asynchronous, which is necessary to prevent deadlock between LocalBackend * and the TaskSchedulerImpl. */ -private[spark] class LocalActor( +private[spark] class LocalEndpoint( + override val rpcEnv: RpcEnv, scheduler: TaskSchedulerImpl, executorBackend: LocalBackend, private val totalCores: Int) - extends Actor with ActorLogReceive with Logging { + extends ThreadSafeRpcEndpoint with Logging { - import context.dispatcher // to use Akka's scheduler.scheduleOnce() + private val reviveThread = Executors.newSingleThreadScheduledExecutor( + Utils.namedThreadFactory("local-revive-thread")) private var freeCores = totalCores @@ -58,7 +58,7 @@ private[spark] class LocalActor( private val executor = new Executor( localExecutorId, localExecutorHostname, SparkEnv.get, isLocal = true) - override def receiveWithLogging = { + override def receive: PartialFunction[Any, Unit] = { case ReviveOffers => reviveOffers() @@ -71,11 +71,15 @@ private[spark] class LocalActor( case KillTask(taskId, interruptThread) => executor.killTask(taskId, interruptThread) + } + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { case StopExecutor => executor.stop() + context.reply(true) } + def reviveOffers() { val offers = Seq(new WorkerOffer(localExecutorId, localExecutorHostname, freeCores)) val tasks = scheduler.resourceOffers(offers).flatten @@ -86,9 +90,17 @@ private[spark] class LocalActor( } if (tasks.isEmpty && scheduler.activeTaskSets.nonEmpty) { // Try to reviveOffer after 1 second, because scheduler may wait for locality timeout - context.system.scheduler.scheduleOnce(1000 millis, self, ReviveOffers) + reviveThread.schedule(new Runnable { + override def run(): Unit = Utils.tryLogNonFatalError { + Option(self).foreach(_.send(ReviveOffers)) + } + }, 1000, TimeUnit.MILLISECONDS) } } + + override def onStop(): Unit = { + reviveThread.shutdownNow() + } } /** @@ -96,35 +108,37 @@ private[spark] class LocalActor( * master all run in the same JVM. It sits behind a TaskSchedulerImpl and handles launching tasks * on a single Executor (created by the LocalBackend) running locally. */ -private[spark] class LocalBackend(scheduler: TaskSchedulerImpl, val totalCores: Int) - extends SchedulerBackend with ExecutorBackend { +private[spark] class LocalBackend( + conf: SparkConf, + scheduler: TaskSchedulerImpl, + val totalCores: Int) + extends SchedulerBackend with ExecutorBackend with Logging { private val appId = "local-" + System.currentTimeMillis - var localActor: ActorRef = null + var localEndpoint: RpcEndpointRef = null override def start() { - localActor = SparkEnv.get.actorSystem.actorOf( - Props(new LocalActor(scheduler, this, totalCores)), - "LocalBackendActor") + localEndpoint = SparkEnv.get.rpcEnv.setupEndpoint( + "LocalBackendEndpoint", new LocalEndpoint(SparkEnv.get.rpcEnv, scheduler, this, totalCores)) } override def stop() { - localActor ! StopExecutor + localEndpoint.sendWithReply(StopExecutor) } override def reviveOffers() { - localActor ! ReviveOffers + localEndpoint.send(ReviveOffers) } - override def defaultParallelism() = + override def defaultParallelism(): Int = scheduler.conf.getInt("spark.default.parallelism", totalCores) override def killTask(taskId: Long, executorId: String, interruptThread: Boolean) { - localActor ! KillTask(taskId, interruptThread) + localEndpoint.send(KillTask(taskId, interruptThread)) } override def statusUpdate(taskId: Long, state: TaskState, serializedData: ByteBuffer) { - localActor ! StatusUpdate(taskId, state, serializedData) + localEndpoint.send(StatusUpdate(taskId, state, serializedData)) } override def applicationId(): String = appId diff --git a/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala b/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala index 1baa0e009f3ae..dfbde7c8a1b0d 100644 --- a/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala +++ b/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala @@ -59,9 +59,10 @@ private[spark] class JavaSerializationStream( } private[spark] class JavaDeserializationStream(in: InputStream, loader: ClassLoader) -extends DeserializationStream { + extends DeserializationStream { + private val objIn = new ObjectInputStream(in) { - override def resolveClass(desc: ObjectStreamClass) = + override def resolveClass(desc: ObjectStreamClass): Class[_] = Class.forName(desc.getName, false, loader) } diff --git a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala index 02158aa0f866e..579fb6624e692 100644 --- a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala +++ b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala @@ -20,22 +20,23 @@ package org.apache.spark.serializer import java.io.{EOFException, InputStream, OutputStream} import java.nio.ByteBuffer +import scala.reflect.ClassTag + import com.esotericsoftware.kryo.{Kryo, KryoException} import com.esotericsoftware.kryo.io.{Input => KryoInput, Output => KryoOutput} import com.esotericsoftware.kryo.serializers.{JavaSerializer => KryoJavaSerializer} import com.twitter.chill.{AllScalaRegistrar, EmptyScalaKryoInstantiator} +import org.roaringbitmap.{ArrayContainer, BitmapContainer, RoaringArray, RoaringBitmap} import org.apache.spark._ import org.apache.spark.api.python.PythonBroadcast import org.apache.spark.broadcast.HttpBroadcast -import org.apache.spark.network.nio.{PutBlock, GotBlock, GetBlock} +import org.apache.spark.network.nio.{GetBlock, GotBlock, PutBlock} import org.apache.spark.scheduler.{CompressedMapStatus, HighlyCompressedMapStatus} import org.apache.spark.storage._ import org.apache.spark.util.BoundedPriorityQueue import org.apache.spark.util.collection.CompactBuffer -import scala.reflect.ClassTag - /** * A Spark serializer that uses the [[https://code.google.com/p/kryo/ Kryo serialization library]]. * @@ -48,10 +49,20 @@ class KryoSerializer(conf: SparkConf) with Logging with Serializable { - private val bufferSize = - (conf.getDouble("spark.kryoserializer.buffer.mb", 0.064) * 1024 * 1024).toInt + private val bufferSizeMb = conf.getDouble("spark.kryoserializer.buffer.mb", 0.064) + if (bufferSizeMb >= 2048) { + throw new IllegalArgumentException("spark.kryoserializer.buffer.mb must be less than " + + s"2048 mb, got: + $bufferSizeMb mb.") + } + private val bufferSize = (bufferSizeMb * 1024 * 1024).toInt + + val maxBufferSizeMb = conf.getInt("spark.kryoserializer.buffer.max.mb", 64) + if (maxBufferSizeMb >= 2048) { + throw new IllegalArgumentException("spark.kryoserializer.buffer.max.mb must be less than " + + s"2048 mb, got: + $maxBufferSizeMb mb.") + } + private val maxBufferSize = maxBufferSizeMb * 1024 * 1024 - private val maxBufferSize = conf.getInt("spark.kryoserializer.buffer.max.mb", 64) * 1024 * 1024 private val referenceTracking = conf.getBoolean("spark.kryo.referenceTracking", true) private val registrationRequired = conf.getBoolean("spark.kryo.registrationRequired", false) private val userRegistrator = conf.getOption("spark.kryo.registrator") @@ -59,7 +70,7 @@ class KryoSerializer(conf: SparkConf) .split(',') .filter(!_.isEmpty) - def newKryoOutput() = new KryoOutput(bufferSize, math.max(bufferSize, maxBufferSize)) + def newKryoOutput(): KryoOutput = new KryoOutput(bufferSize, math.max(bufferSize, maxBufferSize)) def newKryo(): Kryo = { val instantiator = new EmptyScalaKryoInstantiator @@ -157,7 +168,13 @@ private[spark] class KryoSerializerInstance(ks: KryoSerializer) extends Serializ override def serialize[T: ClassTag](t: T): ByteBuffer = { output.clear() - kryo.writeClassAndObject(output, t) + try { + kryo.writeClassAndObject(output, t) + } catch { + case e: KryoException if e.getMessage.startsWith("Buffer overflow") => + throw new SparkException(s"Kryo serialization failed: ${e.getMessage}. To avoid this, " + + "increase spark.kryoserializer.buffer.max.mb value.") + } ByteBuffer.wrap(output.toBytes) } @@ -202,9 +219,17 @@ private[serializer] object KryoSerializer { classOf[GetBlock], classOf[CompressedMapStatus], classOf[HighlyCompressedMapStatus], + classOf[RoaringBitmap], + classOf[RoaringArray], + classOf[RoaringArray.Element], + classOf[Array[RoaringArray.Element]], + classOf[ArrayContainer], + classOf[BitmapContainer], classOf[CompactBuffer[_]], classOf[BlockManagerId], classOf[Array[Byte]], + classOf[Array[Short]], + classOf[Array[Long]], classOf[BoundedPriorityQueue[_]], classOf[SparkConf] ) diff --git a/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockManager.scala b/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockManager.scala index 7de2f9cbb2866..5be3ed771e534 100644 --- a/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockManager.scala +++ b/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockManager.scala @@ -67,7 +67,7 @@ private[spark] trait ShuffleWriterGroup { // org.apache.spark.network.shuffle.StandaloneShuffleBlockManager#getHashBasedShuffleBlockData(). private[spark] class FileShuffleBlockManager(conf: SparkConf) - extends ShuffleBlockManager with Logging { + extends ShuffleBlockResolver with Logging { private val transportConf = SparkTransportConf.fromSparkConf(conf) @@ -106,12 +106,13 @@ class FileShuffleBlockManager(conf: SparkConf) * when the writers are closed successfully */ def forMapTask(shuffleId: Int, mapId: Int, numBuckets: Int, serializer: Serializer, - writeMetrics: ShuffleWriteMetrics) = { + writeMetrics: ShuffleWriteMetrics): ShuffleWriterGroup = { new ShuffleWriterGroup { shuffleStates.putIfAbsent(shuffleId, new ShuffleState(numBuckets)) private val shuffleState = shuffleStates(shuffleId) private var fileGroup: ShuffleFileGroup = null + val openStartTime = System.nanoTime val writers: Array[BlockObjectWriter] = if (consolidateShuffleFiles) { fileGroup = getUnusedFileGroup() Array.tabulate[BlockObjectWriter](numBuckets) { bucketId => @@ -135,6 +136,9 @@ class FileShuffleBlockManager(conf: SparkConf) blockManager.getDiskWriter(blockId, blockFile, serializer, bufferSize, writeMetrics) } } + // Creating the file to write to and creating a disk writer both involve interacting with + // the disk, so should be included in the shuffle write time. + writeMetrics.incShuffleWriteTime(System.nanoTime - openStartTime) override def releaseWriters(success: Boolean) { if (consolidateShuffleFiles) { @@ -171,11 +175,6 @@ class FileShuffleBlockManager(conf: SparkConf) } } - override def getBytes(blockId: ShuffleBlockId): Option[ByteBuffer] = { - val segment = getBlockData(blockId) - Some(segment.nioByteBuffer()) - } - override def getBlockData(blockId: ShuffleBlockId): ManagedBuffer = { if (consolidateShuffleFiles) { // Search all file groups associated with this shuffle. @@ -268,7 +267,7 @@ object FileShuffleBlockManager { new PrimitiveVector[Long]() } - def apply(bucketId: Int) = files(bucketId) + def apply(bucketId: Int): File = files(bucketId) def recordMapOutput(mapId: Int, offsets: Array[Long], lengths: Array[Long]) { assert(offsets.length == lengths.length) diff --git a/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockManager.scala b/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockManager.scala index b292587d37028..a1741e2875c16 100644 --- a/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockManager.scala +++ b/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockManager.scala @@ -26,6 +26,9 @@ import org.apache.spark.{SparkConf, SparkEnv} import org.apache.spark.network.buffer.{FileSegmentManagedBuffer, ManagedBuffer} import org.apache.spark.network.netty.SparkTransportConf import org.apache.spark.storage._ +import org.apache.spark.util.Utils + +import IndexShuffleBlockManager.NOOP_REDUCE_ID /** * Create and maintain the shuffle blocks' mapping between logic block and physical file location. @@ -39,25 +42,18 @@ import org.apache.spark.storage._ // Note: Changes to the format in this file should be kept in sync with // org.apache.spark.network.shuffle.StandaloneShuffleBlockManager#getSortBasedShuffleBlockData(). private[spark] -class IndexShuffleBlockManager(conf: SparkConf) extends ShuffleBlockManager { +class IndexShuffleBlockManager(conf: SparkConf) extends ShuffleBlockResolver { private lazy val blockManager = SparkEnv.get.blockManager private val transportConf = SparkTransportConf.fromSparkConf(conf) - /** - * Mapping to a single shuffleBlockId with reduce ID 0. - * */ - def consolidateId(shuffleId: Int, mapId: Int): ShuffleBlockId = { - ShuffleBlockId(shuffleId, mapId, 0) - } - def getDataFile(shuffleId: Int, mapId: Int): File = { - blockManager.diskBlockManager.getFile(ShuffleDataBlockId(shuffleId, mapId, 0)) + blockManager.diskBlockManager.getFile(ShuffleDataBlockId(shuffleId, mapId, NOOP_REDUCE_ID)) } private def getIndexFile(shuffleId: Int, mapId: Int): File = { - blockManager.diskBlockManager.getFile(ShuffleIndexBlockId(shuffleId, mapId, 0)) + blockManager.diskBlockManager.getFile(ShuffleIndexBlockId(shuffleId, mapId, NOOP_REDUCE_ID)) } /** @@ -80,27 +76,22 @@ class IndexShuffleBlockManager(conf: SparkConf) extends ShuffleBlockManager { * end of the output file. This will be used by getBlockLocation to figure out where each block * begins and ends. * */ - def writeIndexFile(shuffleId: Int, mapId: Int, lengths: Array[Long]) = { + def writeIndexFile(shuffleId: Int, mapId: Int, lengths: Array[Long]): Unit = { val indexFile = getIndexFile(shuffleId, mapId) val out = new DataOutputStream(new BufferedOutputStream(new FileOutputStream(indexFile))) - try { + Utils.tryWithSafeFinally { // We take in lengths of each block, need to convert it to offsets. var offset = 0L out.writeLong(offset) - for (length <- lengths) { offset += length out.writeLong(offset) } - } finally { + } { out.close() } } - override def getBytes(blockId: ShuffleBlockId): Option[ByteBuffer] = { - Some(getBlockData(blockId).nioByteBuffer()) - } - override def getBlockData(blockId: ShuffleBlockId): ManagedBuffer = { // The block is actually going to be a range of a single map output file for this map, so // find out the consolidated file, then the offset within that from our index @@ -121,5 +112,13 @@ class IndexShuffleBlockManager(conf: SparkConf) extends ShuffleBlockManager { } } - override def stop() = {} + override def stop(): Unit = {} +} + +private[spark] object IndexShuffleBlockManager { + // No-op reduce ID used in interactions with disk store and BlockObjectWriter. + // The disk store currently expects puts to relate to a (map, reduce) pair, but in the sort + // shuffle outputs for several reduces are glommed into a single file. + // TODO: Avoid this entirely by having the DiskBlockObjectWriter not require a BlockId. + val NOOP_REDUCE_ID = 0 } diff --git a/core/src/main/scala/org/apache/spark/shuffle/ShuffleBlockManager.scala b/core/src/main/scala/org/apache/spark/shuffle/ShuffleBlockResolver.scala similarity index 68% rename from core/src/main/scala/org/apache/spark/shuffle/ShuffleBlockManager.scala rename to core/src/main/scala/org/apache/spark/shuffle/ShuffleBlockResolver.scala index b521f0c7fc77e..4342b0d598b16 100644 --- a/core/src/main/scala/org/apache/spark/shuffle/ShuffleBlockManager.scala +++ b/core/src/main/scala/org/apache/spark/shuffle/ShuffleBlockResolver.scala @@ -22,15 +22,19 @@ import org.apache.spark.network.buffer.ManagedBuffer import org.apache.spark.storage.ShuffleBlockId private[spark] -trait ShuffleBlockManager { +/** + * Implementers of this trait understand how to retrieve block data for a logical shuffle block + * identifier (i.e. map, reduce, and shuffle). Implementations may use files or file segments to + * encapsulate shuffle data. This is used by the BlockStore to abstract over different shuffle + * implementations when shuffle data is retrieved. + */ +trait ShuffleBlockResolver { type ShuffleId = Int /** - * Get shuffle block data managed by the local ShuffleBlockManager. - * @return Some(ByteBuffer) if block found, otherwise None. + * Retrieve the data for the specified block. If the data for that block is not available, + * throws an unspecified exception. */ - def getBytes(blockId: ShuffleBlockId): Option[ByteBuffer] - def getBlockData(blockId: ShuffleBlockId): ManagedBuffer def stop(): Unit diff --git a/core/src/main/scala/org/apache/spark/shuffle/ShuffleManager.scala b/core/src/main/scala/org/apache/spark/shuffle/ShuffleManager.scala index a44a8e1249256..978366d1a1d1b 100644 --- a/core/src/main/scala/org/apache/spark/shuffle/ShuffleManager.scala +++ b/core/src/main/scala/org/apache/spark/shuffle/ShuffleManager.scala @@ -55,7 +55,10 @@ private[spark] trait ShuffleManager { */ def unregisterShuffle(shuffleId: Int): Boolean - def shuffleBlockManager: ShuffleBlockManager + /** + * Return a resolver capable of retrieving shuffle block data based on block coordinates. + */ + def shuffleBlockResolver: ShuffleBlockResolver /** Shut down this ShuffleManager. */ def stop(): Unit diff --git a/core/src/main/scala/org/apache/spark/shuffle/ShuffleWriter.scala b/core/src/main/scala/org/apache/spark/shuffle/ShuffleWriter.scala index b934480cfb9be..f6e6fe5defe09 100644 --- a/core/src/main/scala/org/apache/spark/shuffle/ShuffleWriter.scala +++ b/core/src/main/scala/org/apache/spark/shuffle/ShuffleWriter.scala @@ -23,7 +23,7 @@ import org.apache.spark.scheduler.MapStatus * Obtained inside a map task to write out records to the shuffle system. */ private[spark] trait ShuffleWriter[K, V] { - /** Write a bunch of records to this task's output */ + /** Write a sequence of records to this task's output */ def write(records: Iterator[_ <: Product2[K, V]]): Unit /** Close this writer, passing along whether the map completed */ diff --git a/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleManager.scala b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleManager.scala index 62e0629b34400..2a7df8dd5bd83 100644 --- a/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleManager.scala +++ b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleManager.scala @@ -53,20 +53,20 @@ private[spark] class HashShuffleManager(conf: SparkConf) extends ShuffleManager override def getWriter[K, V](handle: ShuffleHandle, mapId: Int, context: TaskContext) : ShuffleWriter[K, V] = { new HashShuffleWriter( - shuffleBlockManager, handle.asInstanceOf[BaseShuffleHandle[K, V, _]], mapId, context) + shuffleBlockResolver, handle.asInstanceOf[BaseShuffleHandle[K, V, _]], mapId, context) } /** Remove a shuffle's metadata from the ShuffleManager. */ override def unregisterShuffle(shuffleId: Int): Boolean = { - shuffleBlockManager.removeShuffle(shuffleId) + shuffleBlockResolver.removeShuffle(shuffleId) } - override def shuffleBlockManager: FileShuffleBlockManager = { + override def shuffleBlockResolver: FileShuffleBlockManager = { fileShuffleBlockManager } /** Shut down this ShuffleManager. */ override def stop(): Unit = { - shuffleBlockManager.stop() + shuffleBlockResolver.stop() } } diff --git a/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleManager.scala b/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleManager.scala index bda30a56d808e..0497036192154 100644 --- a/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleManager.scala +++ b/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleManager.scala @@ -58,7 +58,7 @@ private[spark] class SortShuffleManager(conf: SparkConf) extends ShuffleManager val baseShuffleHandle = handle.asInstanceOf[BaseShuffleHandle[K, V, _]] shuffleMapNumber.putIfAbsent(baseShuffleHandle.shuffleId, baseShuffleHandle.numMaps) new SortShuffleWriter( - shuffleBlockManager, baseShuffleHandle, mapId, context) + shuffleBlockResolver, baseShuffleHandle, mapId, context) } /** Remove a shuffle's metadata from the ShuffleManager. */ @@ -66,18 +66,19 @@ private[spark] class SortShuffleManager(conf: SparkConf) extends ShuffleManager if (shuffleMapNumber.containsKey(shuffleId)) { val numMaps = shuffleMapNumber.remove(shuffleId) (0 until numMaps).map{ mapId => - shuffleBlockManager.removeDataByMap(shuffleId, mapId) + shuffleBlockResolver.removeDataByMap(shuffleId, mapId) } } true } - override def shuffleBlockManager: IndexShuffleBlockManager = { + override def shuffleBlockResolver: IndexShuffleBlockManager = { indexShuffleBlockManager } /** Shut down this ShuffleManager. */ override def stop(): Unit = { - shuffleBlockManager.stop() + shuffleBlockResolver.stop() } } + diff --git a/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleWriter.scala b/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleWriter.scala index 27496c5a289cb..a066435df6fb0 100644 --- a/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleWriter.scala +++ b/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleWriter.scala @@ -58,13 +58,15 @@ private[spark] class SortShuffleWriter[K, V, C]( // In this case we pass neither an aggregator nor an ordering to the sorter, because we don't // care whether the keys get sorted in each partition; that will be done on the reduce side // if the operation being run is sortByKey. - sorter = new ExternalSorter[K, V, V]( - None, Some(dep.partitioner), None, dep.serializer) + sorter = new ExternalSorter[K, V, V](None, Some(dep.partitioner), None, dep.serializer) sorter.insertAll(records) } + // Don't bother including the time to open the merged output file in the shuffle write time, + // because it just opens a single file, so is typically too fast to measure accurately + // (see SPARK-3570). val outputFile = shuffleBlockManager.getDataFile(dep.shuffleId, mapId) - val blockId = shuffleBlockManager.consolidateId(dep.shuffleId, mapId) + val blockId = ShuffleBlockId(dep.shuffleId, mapId, IndexShuffleBlockManager.NOOP_REDUCE_ID) val partitionLengths = sorter.writePartitionedFile(blockId, context, outputFile) shuffleBlockManager.writeIndexFile(dep.shuffleId, mapId, partitionLengths) @@ -88,9 +90,13 @@ private[spark] class SortShuffleWriter[K, V, C]( } finally { // Clean up our sorter, which may have its own intermediate files if (sorter != null) { + val startTime = System.nanoTime() sorter.stop() + context.taskMetrics.shuffleWriteMetrics.foreach( + _.incShuffleWriteTime(System.nanoTime - startTime)) sorter = null } } } } + diff --git a/core/src/main/scala/org/apache/spark/storage/BlockId.scala b/core/src/main/scala/org/apache/spark/storage/BlockId.scala index 1f012941c85ab..c186fd360fef6 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockId.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockId.scala @@ -35,13 +35,13 @@ sealed abstract class BlockId { def name: String // convenience methods - def asRDDId = if (isRDD) Some(asInstanceOf[RDDBlockId]) else None - def isRDD = isInstanceOf[RDDBlockId] - def isShuffle = isInstanceOf[ShuffleBlockId] - def isBroadcast = isInstanceOf[BroadcastBlockId] + def asRDDId: Option[RDDBlockId] = if (isRDD) Some(asInstanceOf[RDDBlockId]) else None + def isRDD: Boolean = isInstanceOf[RDDBlockId] + def isShuffle: Boolean = isInstanceOf[ShuffleBlockId] + def isBroadcast: Boolean = isInstanceOf[BroadcastBlockId] - override def toString = name - override def hashCode = name.hashCode + override def toString: String = name + override def hashCode: Int = name.hashCode override def equals(other: Any): Boolean = other match { case o: BlockId => getClass == o.getClass && name.equals(o.name) case _ => false @@ -50,54 +50,54 @@ sealed abstract class BlockId { @DeveloperApi case class RDDBlockId(rddId: Int, splitIndex: Int) extends BlockId { - def name = "rdd_" + rddId + "_" + splitIndex + override def name: String = "rdd_" + rddId + "_" + splitIndex } // Format of the shuffle block ids (including data and index) should be kept in sync with // org.apache.spark.network.shuffle.StandaloneShuffleBlockManager#getBlockData(). @DeveloperApi case class ShuffleBlockId(shuffleId: Int, mapId: Int, reduceId: Int) extends BlockId { - def name = "shuffle_" + shuffleId + "_" + mapId + "_" + reduceId + override def name: String = "shuffle_" + shuffleId + "_" + mapId + "_" + reduceId } @DeveloperApi case class ShuffleDataBlockId(shuffleId: Int, mapId: Int, reduceId: Int) extends BlockId { - def name = "shuffle_" + shuffleId + "_" + mapId + "_" + reduceId + ".data" + override def name: String = "shuffle_" + shuffleId + "_" + mapId + "_" + reduceId + ".data" } @DeveloperApi case class ShuffleIndexBlockId(shuffleId: Int, mapId: Int, reduceId: Int) extends BlockId { - def name = "shuffle_" + shuffleId + "_" + mapId + "_" + reduceId + ".index" + override def name: String = "shuffle_" + shuffleId + "_" + mapId + "_" + reduceId + ".index" } @DeveloperApi case class BroadcastBlockId(broadcastId: Long, field: String = "") extends BlockId { - def name = "broadcast_" + broadcastId + (if (field == "") "" else "_" + field) + override def name: String = "broadcast_" + broadcastId + (if (field == "") "" else "_" + field) } @DeveloperApi case class TaskResultBlockId(taskId: Long) extends BlockId { - def name = "taskresult_" + taskId + override def name: String = "taskresult_" + taskId } @DeveloperApi case class StreamBlockId(streamId: Int, uniqueId: Long) extends BlockId { - def name = "input-" + streamId + "-" + uniqueId + override def name: String = "input-" + streamId + "-" + uniqueId } /** Id associated with temporary local data managed as blocks. Not serializable. */ private[spark] case class TempLocalBlockId(id: UUID) extends BlockId { - def name = "temp_local_" + id + override def name: String = "temp_local_" + id } /** Id associated with temporary shuffle data managed as blocks. Not serializable. */ private[spark] case class TempShuffleBlockId(id: UUID) extends BlockId { - def name = "temp_shuffle_" + id + override def name: String = "temp_shuffle_" + id } // Intended only for testing purposes private[spark] case class TestBlockId(id: String) extends BlockId { - def name = "test_" + id + override def name: String = "test_" + id } @DeveloperApi @@ -112,7 +112,7 @@ object BlockId { val TEST = "test_(.*)".r /** Converts a BlockId "name" String back into a BlockId. */ - def apply(id: String) = id match { + def apply(id: String): BlockId = id match { case RDD(rddId, splitIndex) => RDDBlockId(rddId.toInt, splitIndex.toInt) case SHUFFLE(shuffleId, mapId, reduceId) => diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala index 86dbd89f0ffb8..1aa0ef18de118 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala @@ -26,7 +26,6 @@ import scala.concurrent.ExecutionContext.Implicits.global import scala.concurrent.duration._ import scala.util.Random -import akka.actor.{ActorSystem, Props} import sun.nio.ch.DirectBuffer import org.apache.spark._ @@ -37,6 +36,7 @@ import org.apache.spark.network.buffer.{ManagedBuffer, NioManagedBuffer} import org.apache.spark.network.netty.SparkTransportConf import org.apache.spark.network.shuffle.ExternalShuffleClient import org.apache.spark.network.shuffle.protocol.ExecutorShuffleInfo +import org.apache.spark.rpc.RpcEnv import org.apache.spark.serializer.Serializer import org.apache.spark.shuffle.ShuffleManager import org.apache.spark.shuffle.hash.HashShuffleManager @@ -64,7 +64,7 @@ private[spark] class BlockResult( */ private[spark] class BlockManager( executorId: String, - actorSystem: ActorSystem, + rpcEnv: RpcEnv, val master: BlockManagerMaster, defaultSerializer: Serializer, maxMemory: Long, @@ -136,9 +136,9 @@ private[spark] class BlockManager( // Whether to compress shuffle output temporarily spilled to disk private val compressShuffleSpill = conf.getBoolean("spark.shuffle.spill.compress", true) - private val slaveActor = actorSystem.actorOf( - Props(new BlockManagerSlaveActor(this, mapOutputTracker)), - name = "BlockManagerActor" + BlockManager.ID_GENERATOR.next) + private val slaveEndpoint = rpcEnv.setupEndpoint( + "BlockManagerEndpoint" + BlockManager.ID_GENERATOR.next, + new BlockManagerSlaveEndpoint(rpcEnv, this, mapOutputTracker)) // Pending re-registration action being executed asynchronously or null if none is pending. // Accesses should synchronize on asyncReregisterLock. @@ -167,7 +167,7 @@ private[spark] class BlockManager( */ def this( execId: String, - actorSystem: ActorSystem, + rpcEnv: RpcEnv, master: BlockManagerMaster, serializer: Serializer, conf: SparkConf, @@ -176,7 +176,7 @@ private[spark] class BlockManager( blockTransferService: BlockTransferService, securityManager: SecurityManager, numUsableCores: Int) = { - this(execId, actorSystem, master, serializer, BlockManager.getMaxMemory(conf), + this(execId, rpcEnv, master, serializer, BlockManager.getMaxMemory(conf), conf, mapOutputTracker, shuffleManager, blockTransferService, securityManager, numUsableCores) } @@ -186,7 +186,7 @@ private[spark] class BlockManager( * where it is only learned after registration with the TaskScheduler). * * This method initializes the BlockTransferService and ShuffleClient, registers with the - * BlockManagerMaster, starts the BlockManagerWorker actor, and registers with a local shuffle + * BlockManagerMaster, starts the BlockManagerWorker endpoint, and registers with a local shuffle * service if configured. */ def initialize(appId: String): Unit = { @@ -202,7 +202,7 @@ private[spark] class BlockManager( blockManagerId } - master.registerBlockManager(blockManagerId, maxMemory, slaveActor) + master.registerBlockManager(blockManagerId, maxMemory, slaveEndpoint) // Register Executors' configuration with the local shuffle service, if one should exist. if (externalShuffleServiceEnabled && !blockManagerId.isDriver) { @@ -265,7 +265,7 @@ private[spark] class BlockManager( def reregister(): Unit = { // TODO: We might need to rate limit re-registering. logInfo("BlockManager re-registering with master") - master.registerBlockManager(blockManagerId, maxMemory, slaveActor) + master.registerBlockManager(blockManagerId, maxMemory, slaveEndpoint) reportAllBlocks() } @@ -301,7 +301,7 @@ private[spark] class BlockManager( */ override def getBlockData(blockId: BlockId): ManagedBuffer = { if (blockId.isShuffle) { - shuffleManager.shuffleBlockManager.getBlockData(blockId.asInstanceOf[ShuffleBlockId]) + shuffleManager.shuffleBlockResolver.getBlockData(blockId.asInstanceOf[ShuffleBlockId]) } else { val blockBytesOpt = doGetLocal(blockId, asBlockResult = false) .asInstanceOf[Option[ByteBuffer]] @@ -439,14 +439,10 @@ private[spark] class BlockManager( // As an optimization for map output fetches, if the block is for a shuffle, return it // without acquiring a lock; the disk store never deletes (recent) items so this should work if (blockId.isShuffle) { - val shuffleBlockManager = shuffleManager.shuffleBlockManager - shuffleBlockManager.getBytes(blockId.asInstanceOf[ShuffleBlockId]) match { - case Some(bytes) => - Some(bytes) - case None => - throw new BlockException( - blockId, s"Block $blockId not found on disk, though it should be") - } + val shuffleBlockManager = shuffleManager.shuffleBlockResolver + // TODO: This should gracefully handle case where local block is not available. Currently + // downstream code will throw an exception. + Option(shuffleBlockManager.getBlockData(blockId.asInstanceOf[ShuffleBlockId]).nioByteBuffer()) } else { doGetLocal(blockId, asBlockResult = false).asInstanceOf[Option[ByteBuffer]] } @@ -535,9 +531,14 @@ private[spark] class BlockManager( /* We'll store the bytes in memory if the block's storage level includes * "memory serialized", or if it should be cached as objects in memory * but we only requested its serialized bytes. */ - val copyForMemory = ByteBuffer.allocate(bytes.limit) - copyForMemory.put(bytes) - memoryStore.putBytes(blockId, copyForMemory, level) + memoryStore.putBytes(blockId, bytes.limit, () => { + // https://issues.apache.org/jira/browse/SPARK-6076 + // If the file size is bigger than the free memory, OOM will happen. So if we cannot + // put it into MemoryStore, copyForMemory should not be created. That's why this + // action is put into a `() => ByteBuffer` and created lazily. + val copyForMemory = ByteBuffer.allocate(bytes.limit) + copyForMemory.put(bytes) + }) bytes.rewind() } if (!asBlockResult) { @@ -991,15 +992,23 @@ private[spark] class BlockManager( putIterator(blockId, Iterator(value), level, tellMaster) } + def dropFromMemory( + blockId: BlockId, + data: Either[Array[Any], ByteBuffer]): Option[BlockStatus] = { + dropFromMemory(blockId, () => data) + } + /** * Drop a block from memory, possibly putting it on disk if applicable. Called when the memory * store reaches its limit and needs to free up space. * + * If `data` is not put on disk, it won't be created. + * * Return the block status if the given block has been updated, else None. */ def dropFromMemory( blockId: BlockId, - data: Either[Array[Any], ByteBuffer]): Option[BlockStatus] = { + data: () => Either[Array[Any], ByteBuffer]): Option[BlockStatus] = { logInfo(s"Dropping block $blockId from memory") val info = blockInfo.get(blockId).orNull @@ -1023,7 +1032,7 @@ private[spark] class BlockManager( // Drop to disk, if storage level requires if (level.useDisk && !diskStore.contains(blockId)) { logInfo(s"Writing block $blockId to disk") - data match { + data() match { case Left(elements) => diskStore.putArray(blockId, elements, level, returnValues = false) case Right(bytes) => @@ -1074,7 +1083,7 @@ private[spark] class BlockManager( * Remove all blocks belonging to the given broadcast. */ def removeBroadcast(broadcastId: Long, tellMaster: Boolean): Int = { - logInfo(s"Removing broadcast $broadcastId") + logDebug(s"Removing broadcast $broadcastId") val blocksToRemove = blockInfo.keys.collect { case bid @ BroadcastBlockId(`broadcastId`, _) => bid } @@ -1086,7 +1095,7 @@ private[spark] class BlockManager( * Remove a block from both memory and disk. */ def removeBlock(blockId: BlockId, tellMaster: Boolean = true): Unit = { - logInfo(s"Removing block $blockId") + logDebug(s"Removing block $blockId") val info = blockInfo.get(blockId).orNull if (info != null) { info.synchronized { @@ -1206,7 +1215,7 @@ private[spark] class BlockManager( shuffleClient.close() } diskBlockManager.stop() - actorSystem.stop(slaveActor) + rpcEnv.stop(slaveEndpoint) blockInfo.clear() memoryStore.clear() diskStore.clear() @@ -1245,10 +1254,10 @@ private[spark] object BlockManager extends Logging { } } - def blockIdsToBlockManagers( + def blockIdsToHosts( blockIds: Array[BlockId], env: SparkEnv, - blockManagerMaster: BlockManagerMaster = null): Map[BlockId, Seq[BlockManagerId]] = { + blockManagerMaster: BlockManagerMaster = null): Map[BlockId, Seq[String]] = { // blockManagerMaster != null is used in tests assert(env != null || blockManagerMaster != null) @@ -1258,24 +1267,10 @@ private[spark] object BlockManager extends Logging { blockManagerMaster.getLocations(blockIds) } - val blockManagers = new HashMap[BlockId, Seq[BlockManagerId]] + val blockManagers = new HashMap[BlockId, Seq[String]] for (i <- 0 until blockIds.length) { - blockManagers(blockIds(i)) = blockLocations(i) + blockManagers(blockIds(i)) = blockLocations(i).map(_.host) } blockManagers.toMap } - - def blockIdsToExecutorIds( - blockIds: Array[BlockId], - env: SparkEnv, - blockManagerMaster: BlockManagerMaster = null): Map[BlockId, Seq[String]] = { - blockIdsToBlockManagers(blockIds, env, blockManagerMaster).mapValues(s => s.map(_.executorId)) - } - - def blockIdsToHosts( - blockIds: Array[BlockId], - env: SparkEnv, - blockManagerMaster: BlockManagerMaster = null): Map[BlockId, Seq[String]] = { - blockIdsToBlockManagers(blockIds, env, blockManagerMaster).mapValues(s => s.map(_.host)) - } } diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerId.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerId.scala index b177a59c721df..69ac37511e730 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerId.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerId.scala @@ -60,7 +60,10 @@ class BlockManagerId private ( def port: Int = port_ - def isDriver: Boolean = { executorId == SparkContext.DRIVER_IDENTIFIER } + def isDriver: Boolean = { + executorId == SparkContext.DRIVER_IDENTIFIER || + executorId == SparkContext.LEGACY_DRIVER_IDENTIFIER + } override def writeExternal(out: ObjectOutput): Unit = Utils.tryOrIOException { out.writeUTF(executorId_) @@ -77,11 +80,11 @@ class BlockManagerId private ( @throws(classOf[IOException]) private def readResolve(): Object = BlockManagerId.getCachedBlockManagerId(this) - override def toString = s"BlockManagerId($executorId, $host, $port)" + override def toString: String = s"BlockManagerId($executorId, $host, $port)" override def hashCode: Int = (executorId.hashCode * 41 + host.hashCode) * 41 + port - override def equals(that: Any) = that match { + override def equals(that: Any): Boolean = that match { case id: BlockManagerId => executorId == id.executorId && port == id.port && host == id.host case _ => @@ -100,10 +103,10 @@ private[spark] object BlockManagerId { * @param port Port of the block manager. * @return A new [[org.apache.spark.storage.BlockManagerId]]. */ - def apply(execId: String, host: String, port: Int) = + def apply(execId: String, host: String, port: Int): BlockManagerId = getCachedBlockManagerId(new BlockManagerId(execId, host, port)) - def apply(in: ObjectInput) = { + def apply(in: ObjectInput): BlockManagerId = { val obj = new BlockManagerId() obj.readExternal(in) getCachedBlockManagerId(obj) diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala index b63c7f191155c..ceacf043029f3 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala @@ -20,35 +20,31 @@ package org.apache.spark.storage import scala.concurrent.{Await, Future} import scala.concurrent.ExecutionContext.Implicits.global -import akka.actor._ - +import org.apache.spark.rpc.RpcEndpointRef import org.apache.spark.{Logging, SparkConf, SparkException} import org.apache.spark.storage.BlockManagerMessages._ import org.apache.spark.util.AkkaUtils private[spark] class BlockManagerMaster( - var driverActor: ActorRef, + var driverEndpoint: RpcEndpointRef, conf: SparkConf, isDriver: Boolean) extends Logging { - private val AKKA_RETRY_ATTEMPTS: Int = AkkaUtils.numRetries(conf) - private val AKKA_RETRY_INTERVAL_MS: Int = AkkaUtils.retryWaitMs(conf) - - val DRIVER_AKKA_ACTOR_NAME = "BlockManagerMaster" val timeout = AkkaUtils.askTimeout(conf) - /** Remove a dead executor from the driver actor. This is only called on the driver side. */ + /** Remove a dead executor from the driver endpoint. This is only called on the driver side. */ def removeExecutor(execId: String) { tell(RemoveExecutor(execId)) logInfo("Removed " + execId + " successfully in removeExecutor") } /** Register the BlockManager's id with the driver. */ - def registerBlockManager(blockManagerId: BlockManagerId, maxMemSize: Long, slaveActor: ActorRef) { + def registerBlockManager( + blockManagerId: BlockManagerId, maxMemSize: Long, slaveEndpoint: RpcEndpointRef): Unit = { logInfo("Trying to register BlockManager") - tell(RegisterBlockManager(blockManagerId, maxMemSize, slaveActor)) + tell(RegisterBlockManager(blockManagerId, maxMemSize, slaveEndpoint)) logInfo("Registered BlockManager") } @@ -59,37 +55,37 @@ class BlockManagerMaster( memSize: Long, diskSize: Long, tachyonSize: Long): Boolean = { - val res = askDriverWithReply[Boolean]( + val res = driverEndpoint.askWithReply[Boolean]( UpdateBlockInfo(blockManagerId, blockId, storageLevel, memSize, diskSize, tachyonSize)) - logInfo("Updated info of block " + blockId) + logDebug(s"Updated info of block $blockId") res } /** Get locations of the blockId from the driver */ def getLocations(blockId: BlockId): Seq[BlockManagerId] = { - askDriverWithReply[Seq[BlockManagerId]](GetLocations(blockId)) + driverEndpoint.askWithReply[Seq[BlockManagerId]](GetLocations(blockId)) } /** Get locations of multiple blockIds from the driver */ def getLocations(blockIds: Array[BlockId]): Seq[Seq[BlockManagerId]] = { - askDriverWithReply[Seq[Seq[BlockManagerId]]](GetLocationsMultipleBlockIds(blockIds)) + driverEndpoint.askWithReply[Seq[Seq[BlockManagerId]]](GetLocationsMultipleBlockIds(blockIds)) } /** * Check if block manager master has a block. Note that this can be used to check for only * those blocks that are reported to block manager master. */ - def contains(blockId: BlockId) = { + def contains(blockId: BlockId): Boolean = { !getLocations(blockId).isEmpty } /** Get ids of other nodes in the cluster from the driver */ def getPeers(blockManagerId: BlockManagerId): Seq[BlockManagerId] = { - askDriverWithReply[Seq[BlockManagerId]](GetPeers(blockManagerId)) + driverEndpoint.askWithReply[Seq[BlockManagerId]](GetPeers(blockManagerId)) } - def getActorSystemHostPortForExecutor(executorId: String): Option[(String, Int)] = { - askDriverWithReply[Option[(String, Int)]](GetActorSystemHostPortForExecutor(executorId)) + def getRpcHostPortForExecutor(executorId: String): Option[(String, Int)] = { + driverEndpoint.askWithReply[Option[(String, Int)]](GetRpcHostPortForExecutor(executorId)) } /** @@ -97,12 +93,12 @@ class BlockManagerMaster( * blocks that the driver knows about. */ def removeBlock(blockId: BlockId) { - askDriverWithReply(RemoveBlock(blockId)) + driverEndpoint.askWithReply[Boolean](RemoveBlock(blockId)) } /** Remove all blocks belonging to the given RDD. */ def removeRdd(rddId: Int, blocking: Boolean) { - val future = askDriverWithReply[Future[Seq[Int]]](RemoveRdd(rddId)) + val future = driverEndpoint.askWithReply[Future[Seq[Int]]](RemoveRdd(rddId)) future.onFailure { case e: Exception => logWarning(s"Failed to remove RDD $rddId - ${e.getMessage}}") @@ -114,7 +110,7 @@ class BlockManagerMaster( /** Remove all blocks belonging to the given shuffle. */ def removeShuffle(shuffleId: Int, blocking: Boolean) { - val future = askDriverWithReply[Future[Seq[Boolean]]](RemoveShuffle(shuffleId)) + val future = driverEndpoint.askWithReply[Future[Seq[Boolean]]](RemoveShuffle(shuffleId)) future.onFailure { case e: Exception => logWarning(s"Failed to remove shuffle $shuffleId - ${e.getMessage}}") @@ -126,7 +122,7 @@ class BlockManagerMaster( /** Remove all blocks belonging to the given broadcast. */ def removeBroadcast(broadcastId: Long, removeFromMaster: Boolean, blocking: Boolean) { - val future = askDriverWithReply[Future[Seq[Int]]]( + val future = driverEndpoint.askWithReply[Future[Seq[Int]]]( RemoveBroadcast(broadcastId, removeFromMaster)) future.onFailure { case e: Exception => @@ -145,11 +141,11 @@ class BlockManagerMaster( * amount of remaining memory. */ def getMemoryStatus: Map[BlockManagerId, (Long, Long)] = { - askDriverWithReply[Map[BlockManagerId, (Long, Long)]](GetMemoryStatus) + driverEndpoint.askWithReply[Map[BlockManagerId, (Long, Long)]](GetMemoryStatus) } def getStorageStatus: Array[StorageStatus] = { - askDriverWithReply[Array[StorageStatus]](GetStorageStatus) + driverEndpoint.askWithReply[Array[StorageStatus]](GetStorageStatus) } /** @@ -165,11 +161,12 @@ class BlockManagerMaster( askSlaves: Boolean = true): Map[BlockManagerId, BlockStatus] = { val msg = GetBlockStatus(blockId, askSlaves) /* - * To avoid potential deadlocks, the use of Futures is necessary, because the master actor + * To avoid potential deadlocks, the use of Futures is necessary, because the master endpoint * should not block on waiting for a block manager, which can in turn be waiting for the - * master actor for a response to a prior message. + * master endpoint for a response to a prior message. */ - val response = askDriverWithReply[Map[BlockManagerId, Future[Option[BlockStatus]]]](msg) + val response = driverEndpoint. + askWithReply[Map[BlockManagerId, Future[Option[BlockStatus]]]](msg) val (blockManagerIds, futures) = response.unzip val result = Await.result(Future.sequence(futures), timeout) if (result == null) { @@ -193,33 +190,28 @@ class BlockManagerMaster( filter: BlockId => Boolean, askSlaves: Boolean): Seq[BlockId] = { val msg = GetMatchingBlockIds(filter, askSlaves) - val future = askDriverWithReply[Future[Seq[BlockId]]](msg) + val future = driverEndpoint.askWithReply[Future[Seq[BlockId]]](msg) Await.result(future, timeout) } - /** Stop the driver actor, called only on the Spark driver node */ + /** Stop the driver endpoint, called only on the Spark driver node */ def stop() { - if (driverActor != null && isDriver) { + if (driverEndpoint != null && isDriver) { tell(StopBlockManagerMaster) - driverActor = null + driverEndpoint = null logInfo("BlockManagerMaster stopped") } } - /** Send a one-way message to the master actor, to which we expect it to reply with true. */ + /** Send a one-way message to the master endpoint, to which we expect it to reply with true. */ private def tell(message: Any) { - if (!askDriverWithReply[Boolean](message)) { - throw new SparkException("BlockManagerMasterActor returned false, expected true.") + if (!driverEndpoint.askWithReply[Boolean](message)) { + throw new SparkException("BlockManagerMasterEndpoint returned false, expected true.") } } - /** - * Send a message to the driver actor and get its result within a default timeout, or - * throw a SparkException if this fails. - */ - private def askDriverWithReply[T](message: Any): T = { - AkkaUtils.askWithReply(message, driverActor, AKKA_RETRY_ATTEMPTS, AKKA_RETRY_INTERVAL_MS, - timeout) - } +} +private[spark] object BlockManagerMaster { + val DRIVER_ENDPOINT_NAME = "BlockManagerMaster" } diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala similarity index 78% rename from core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala rename to core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala index 64133464d8daa..28c73a7d543ff 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala @@ -21,25 +21,26 @@ import java.util.{HashMap => JHashMap} import scala.collection.mutable import scala.collection.JavaConversions._ -import scala.concurrent.Future -import scala.concurrent.duration._ +import scala.concurrent.{ExecutionContext, Future} -import akka.actor.{Actor, ActorRef, Cancellable} -import akka.pattern.ask - -import org.apache.spark.{Logging, SparkConf, SparkException} +import org.apache.spark.rpc.{RpcEndpointRef, RpcEnv, RpcCallContext, ThreadSafeRpcEndpoint} +import org.apache.spark.{Logging, SparkConf} import org.apache.spark.annotation.DeveloperApi import org.apache.spark.scheduler._ import org.apache.spark.storage.BlockManagerMessages._ -import org.apache.spark.util.{ActorLogReceive, AkkaUtils, Utils} +import org.apache.spark.util.Utils /** - * BlockManagerMasterActor is an actor on the master node to track statuses of - * all slaves' block managers. + * BlockManagerMasterEndpoint is an [[ThreadSafeRpcEndpoint]] on the master node to track statuses + * of all slaves' block managers. */ private[spark] -class BlockManagerMasterActor(val isLocal: Boolean, conf: SparkConf, listenerBus: LiveListenerBus) - extends Actor with ActorLogReceive with Logging { +class BlockManagerMasterEndpoint( + override val rpcEnv: RpcEnv, + val isLocal: Boolean, + conf: SparkConf, + listenerBus: LiveListenerBus) + extends ThreadSafeRpcEndpoint with Logging { // Mapping from block manager id to the block manager's information. private val blockManagerInfo = new mutable.HashMap[BlockManagerId, BlockManagerInfo] @@ -50,87 +51,67 @@ class BlockManagerMasterActor(val isLocal: Boolean, conf: SparkConf, listenerBus // Mapping from block id to the set of block managers that have the block. private val blockLocations = new JHashMap[BlockId, mutable.HashSet[BlockManagerId]] - private val akkaTimeout = AkkaUtils.askTimeout(conf) - - val slaveTimeout = conf.getLong("spark.storage.blockManagerSlaveTimeoutMs", 120 * 1000) + private val askThreadPool = Utils.newDaemonCachedThreadPool("block-manager-ask-thread-pool") + private implicit val askExecutionContext = ExecutionContext.fromExecutorService(askThreadPool) - val checkTimeoutInterval = conf.getLong("spark.storage.blockManagerTimeoutIntervalMs", 60000) - - var timeoutCheckingTask: Cancellable = null - - override def preStart() { - import context.dispatcher - timeoutCheckingTask = context.system.scheduler.schedule(0.seconds, - checkTimeoutInterval.milliseconds, self, ExpireDeadHosts) - super.preStart() - } - - override def receiveWithLogging = { - case RegisterBlockManager(blockManagerId, maxMemSize, slaveActor) => - register(blockManagerId, maxMemSize, slaveActor) - sender ! true + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { + case RegisterBlockManager(blockManagerId, maxMemSize, slaveEndpoint) => + register(blockManagerId, maxMemSize, slaveEndpoint) + context.reply(true) case UpdateBlockInfo( blockManagerId, blockId, storageLevel, deserializedSize, size, tachyonSize) => - sender ! updateBlockInfo( - blockManagerId, blockId, storageLevel, deserializedSize, size, tachyonSize) + context.reply(updateBlockInfo( + blockManagerId, blockId, storageLevel, deserializedSize, size, tachyonSize)) case GetLocations(blockId) => - sender ! getLocations(blockId) + context.reply(getLocations(blockId)) case GetLocationsMultipleBlockIds(blockIds) => - sender ! getLocationsMultipleBlockIds(blockIds) + context.reply(getLocationsMultipleBlockIds(blockIds)) case GetPeers(blockManagerId) => - sender ! getPeers(blockManagerId) + context.reply(getPeers(blockManagerId)) - case GetActorSystemHostPortForExecutor(executorId) => - sender ! getActorSystemHostPortForExecutor(executorId) + case GetRpcHostPortForExecutor(executorId) => + context.reply(getRpcHostPortForExecutor(executorId)) case GetMemoryStatus => - sender ! memoryStatus + context.reply(memoryStatus) case GetStorageStatus => - sender ! storageStatus + context.reply(storageStatus) case GetBlockStatus(blockId, askSlaves) => - sender ! blockStatus(blockId, askSlaves) + context.reply(blockStatus(blockId, askSlaves)) case GetMatchingBlockIds(filter, askSlaves) => - sender ! getMatchingBlockIds(filter, askSlaves) + context.reply(getMatchingBlockIds(filter, askSlaves)) case RemoveRdd(rddId) => - sender ! removeRdd(rddId) + context.reply(removeRdd(rddId)) case RemoveShuffle(shuffleId) => - sender ! removeShuffle(shuffleId) + context.reply(removeShuffle(shuffleId)) case RemoveBroadcast(broadcastId, removeFromDriver) => - sender ! removeBroadcast(broadcastId, removeFromDriver) + context.reply(removeBroadcast(broadcastId, removeFromDriver)) case RemoveBlock(blockId) => removeBlockFromWorkers(blockId) - sender ! true + context.reply(true) case RemoveExecutor(execId) => removeExecutor(execId) - sender ! true + context.reply(true) case StopBlockManagerMaster => - sender ! true - if (timeoutCheckingTask != null) { - timeoutCheckingTask.cancel() - } - context.stop(self) - - case ExpireDeadHosts => - expireDeadHosts() + context.reply(true) + stop() case BlockManagerHeartbeat(blockManagerId) => - sender ! heartbeatReceived(blockManagerId) + context.reply(heartbeatReceived(blockManagerId)) - case other => - logWarning("Got unknown message: " + other) } private def removeRdd(rddId: Int): Future[Seq[Int]] = { @@ -148,22 +129,20 @@ class BlockManagerMasterActor(val isLocal: Boolean, conf: SparkConf, listenerBus // Ask the slaves to remove the RDD, and put the result in a sequence of Futures. // The dispatcher is used as an implicit argument into the Future sequence construction. - import context.dispatcher val removeMsg = RemoveRdd(rddId) Future.sequence( blockManagerInfo.values.map { bm => - bm.slaveActor.ask(removeMsg)(akkaTimeout).mapTo[Int] + bm.slaveEndpoint.sendWithReply[Int](removeMsg) }.toSeq ) } private def removeShuffle(shuffleId: Int): Future[Seq[Boolean]] = { - // Nothing to do in the BlockManagerMasterActor data structures - import context.dispatcher + // Nothing to do in the BlockManagerMasterEndpoint data structures val removeMsg = RemoveShuffle(shuffleId) Future.sequence( blockManagerInfo.values.map { bm => - bm.slaveActor.ask(removeMsg)(akkaTimeout).mapTo[Boolean] + bm.slaveEndpoint.sendWithReply[Boolean](removeMsg) }.toSeq ) } @@ -174,14 +153,13 @@ class BlockManagerMasterActor(val isLocal: Boolean, conf: SparkConf, listenerBus * from the executors, but not from the driver. */ private def removeBroadcast(broadcastId: Long, removeFromDriver: Boolean): Future[Seq[Int]] = { - import context.dispatcher val removeMsg = RemoveBroadcast(broadcastId, removeFromDriver) val requiredBlockManagers = blockManagerInfo.values.filter { info => removeFromDriver || !info.blockManagerId.isDriver } Future.sequence( requiredBlockManagers.map { bm => - bm.slaveActor.ask(removeMsg)(akkaTimeout).mapTo[Int] + bm.slaveEndpoint.sendWithReply[Int](removeMsg) }.toSeq ) } @@ -207,21 +185,6 @@ class BlockManagerMasterActor(val isLocal: Boolean, conf: SparkConf, listenerBus logInfo(s"Removing block manager $blockManagerId") } - private def expireDeadHosts() { - logTrace("Checking for hosts with no recent heart beats in BlockManagerMaster.") - val now = System.currentTimeMillis() - val minSeenTime = now - slaveTimeout - val toRemove = new mutable.HashSet[BlockManagerId] - for (info <- blockManagerInfo.values) { - if (info.lastSeenMs < minSeenTime && !info.blockManagerId.isDriver) { - logWarning("Removing BlockManager " + info.blockManagerId + " with no recent heart beats: " - + (now - info.lastSeenMs) + "ms exceeds " + slaveTimeout + "ms") - toRemove += info.blockManagerId - } - } - toRemove.foreach(removeBlockManager) - } - private def removeExecutor(execId: String) { logInfo("Trying to remove executor " + execId + " from BlockManagerMaster.") blockManagerIdByExecutor.get(execId).foreach(removeBlockManager) @@ -251,7 +214,7 @@ class BlockManagerMasterActor(val isLocal: Boolean, conf: SparkConf, listenerBus // Remove the block from the slave's BlockManager. // Doesn't actually wait for a confirmation and the message might get lost. // If message loss becomes frequent, we should add retry logic here. - blockManager.get.slaveActor.ask(RemoveBlock(blockId))(akkaTimeout) + blockManager.get.slaveEndpoint.sendWithReply[Boolean](RemoveBlock(blockId)) } } } @@ -281,17 +244,16 @@ class BlockManagerMasterActor(val isLocal: Boolean, conf: SparkConf, listenerBus private def blockStatus( blockId: BlockId, askSlaves: Boolean): Map[BlockManagerId, Future[Option[BlockStatus]]] = { - import context.dispatcher val getBlockStatus = GetBlockStatus(blockId) /* - * Rather than blocking on the block status query, master actor should simply return + * Rather than blocking on the block status query, master endpoint should simply return * Futures to avoid potential deadlocks. This can arise if there exists a block manager - * that is also waiting for this master actor's response to a previous message. + * that is also waiting for this master endpoint's response to a previous message. */ blockManagerInfo.values.map { info => val blockStatusFuture = if (askSlaves) { - info.slaveActor.ask(getBlockStatus)(akkaTimeout).mapTo[Option[BlockStatus]] + info.slaveEndpoint.sendWithReply[Option[BlockStatus]](getBlockStatus) } else { Future { info.getStatus(blockId) } } @@ -310,13 +272,12 @@ class BlockManagerMasterActor(val isLocal: Boolean, conf: SparkConf, listenerBus private def getMatchingBlockIds( filter: BlockId => Boolean, askSlaves: Boolean): Future[Seq[BlockId]] = { - import context.dispatcher val getMatchingBlockIds = GetMatchingBlockIds(filter) Future.sequence( blockManagerInfo.values.map { info => val future = if (askSlaves) { - info.slaveActor.ask(getMatchingBlockIds)(akkaTimeout).mapTo[Seq[BlockId]] + info.slaveEndpoint.sendWithReply[Seq[BlockId]](getMatchingBlockIds) } else { Future { info.blocks.keys.filter(filter).toSeq } } @@ -325,7 +286,7 @@ class BlockManagerMasterActor(val isLocal: Boolean, conf: SparkConf, listenerBus ).map(_.flatten.toSeq) } - private def register(id: BlockManagerId, maxMemSize: Long, slaveActor: ActorRef) { + private def register(id: BlockManagerId, maxMemSize: Long, slaveEndpoint: RpcEndpointRef) { val time = System.currentTimeMillis() if (!blockManagerInfo.contains(id)) { blockManagerIdByExecutor.get(id.executorId) match { @@ -342,7 +303,7 @@ class BlockManagerMasterActor(val isLocal: Boolean, conf: SparkConf, listenerBus blockManagerIdByExecutor(id.executorId) = id blockManagerInfo(id) = new BlockManagerInfo( - id, System.currentTimeMillis(), maxMemSize, slaveActor) + id, System.currentTimeMillis(), maxMemSize, slaveEndpoint) } listenerBus.post(SparkListenerBlockManagerAdded(time, id, maxMemSize)) } @@ -413,19 +374,21 @@ class BlockManagerMasterActor(val isLocal: Boolean, conf: SparkConf, listenerBus } /** - * Returns the hostname and port of an executor's actor system, based on the Akka address of its - * BlockManagerSlaveActor. + * Returns the hostname and port of an executor, based on the [[RpcEnv]] address of its + * [[BlockManagerSlaveEndpoint]]. */ - private def getActorSystemHostPortForExecutor(executorId: String): Option[(String, Int)] = { + private def getRpcHostPortForExecutor(executorId: String): Option[(String, Int)] = { for ( blockManagerId <- blockManagerIdByExecutor.get(executorId); - info <- blockManagerInfo.get(blockManagerId); - host <- info.slaveActor.path.address.host; - port <- info.slaveActor.path.address.port + info <- blockManagerInfo.get(blockManagerId) ) yield { - (host, port) + (info.slaveEndpoint.address.host, info.slaveEndpoint.address.port) } } + + override def onStop(): Unit = { + askThreadPool.shutdownNow() + } } @DeveloperApi @@ -446,7 +409,7 @@ private[spark] class BlockManagerInfo( val blockManagerId: BlockManagerId, timeMs: Long, val maxMem: Long, - val slaveActor: ActorRef) + val slaveEndpoint: RpcEndpointRef) extends Logging { private var _lastSeenMs: Long = timeMs @@ -455,7 +418,7 @@ private[spark] class BlockManagerInfo( // Mapping from block id to its status. private val _blocks = new JHashMap[BlockId, BlockStatus] - def getStatus(blockId: BlockId) = Option(_blocks.get(blockId)) + def getStatus(blockId: BlockId): Option[BlockStatus] = Option(_blocks.get(blockId)) def updateLastSeenMs() { _lastSeenMs = System.currentTimeMillis() diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMessages.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMessages.scala index 3f32099d08cc9..f89d8d7493f7c 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMessages.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMessages.scala @@ -19,8 +19,7 @@ package org.apache.spark.storage import java.io.{Externalizable, ObjectInput, ObjectOutput} -import akka.actor.ActorRef - +import org.apache.spark.rpc.RpcEndpointRef import org.apache.spark.util.Utils private[spark] object BlockManagerMessages { @@ -52,7 +51,7 @@ private[spark] object BlockManagerMessages { case class RegisterBlockManager( blockManagerId: BlockManagerId, maxMemSize: Long, - sender: ActorRef) + sender: RpcEndpointRef) extends ToBlockManagerMaster case class UpdateBlockInfo( @@ -92,7 +91,7 @@ private[spark] object BlockManagerMessages { case class GetPeers(blockManagerId: BlockManagerId) extends ToBlockManagerMaster - case class GetActorSystemHostPortForExecutor(executorId: String) extends ToBlockManagerMaster + case class GetRpcHostPortForExecutor(executorId: String) extends ToBlockManagerMaster case class RemoveExecutor(execId: String) extends ToBlockManagerMaster @@ -109,6 +108,4 @@ private[spark] object BlockManagerMessages { extends ToBlockManagerMaster case class BlockManagerHeartbeat(blockManagerId: BlockManagerId) extends ToBlockManagerMaster - - case object ExpireDeadHosts extends ToBlockManagerMaster } diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerSlaveActor.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerSlaveEndpoint.scala similarity index 61% rename from core/src/main/scala/org/apache/spark/storage/BlockManagerSlaveActor.scala rename to core/src/main/scala/org/apache/spark/storage/BlockManagerSlaveEndpoint.scala index 8462871e798a5..8980fa8eb70e2 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerSlaveActor.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerSlaveEndpoint.scala @@ -17,41 +17,43 @@ package org.apache.spark.storage -import scala.concurrent.Future - -import akka.actor.{ActorRef, Actor} +import scala.concurrent.{ExecutionContext, Future} +import org.apache.spark.rpc.{RpcEnv, RpcCallContext, RpcEndpoint} +import org.apache.spark.util.Utils import org.apache.spark.{Logging, MapOutputTracker, SparkEnv} import org.apache.spark.storage.BlockManagerMessages._ -import org.apache.spark.util.ActorLogReceive /** - * An actor to take commands from the master to execute options. For example, + * An RpcEndpoint to take commands from the master to execute options. For example, * this is used to remove blocks from the slave's BlockManager. */ private[storage] -class BlockManagerSlaveActor( +class BlockManagerSlaveEndpoint( + override val rpcEnv: RpcEnv, blockManager: BlockManager, mapOutputTracker: MapOutputTracker) - extends Actor with ActorLogReceive with Logging { + extends RpcEndpoint with Logging { - import context.dispatcher + private val asyncThreadPool = + Utils.newDaemonCachedThreadPool("block-manager-slave-async-thread-pool") + private implicit val asyncExecutionContext = ExecutionContext.fromExecutorService(asyncThreadPool) // Operations that involve removing blocks may be slow and should be done asynchronously - override def receiveWithLogging = { + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { case RemoveBlock(blockId) => - doAsync[Boolean]("removing block " + blockId, sender) { + doAsync[Boolean]("removing block " + blockId, context) { blockManager.removeBlock(blockId) true } case RemoveRdd(rddId) => - doAsync[Int]("removing RDD " + rddId, sender) { + doAsync[Int]("removing RDD " + rddId, context) { blockManager.removeRdd(rddId) } case RemoveShuffle(shuffleId) => - doAsync[Boolean]("removing shuffle " + shuffleId, sender) { + doAsync[Boolean]("removing shuffle " + shuffleId, context) { if (mapOutputTracker != null) { mapOutputTracker.unregisterShuffle(shuffleId) } @@ -59,30 +61,34 @@ class BlockManagerSlaveActor( } case RemoveBroadcast(broadcastId, _) => - doAsync[Int]("removing broadcast " + broadcastId, sender) { + doAsync[Int]("removing broadcast " + broadcastId, context) { blockManager.removeBroadcast(broadcastId, tellMaster = true) } case GetBlockStatus(blockId, _) => - sender ! blockManager.getStatus(blockId) + context.reply(blockManager.getStatus(blockId)) case GetMatchingBlockIds(filter, _) => - sender ! blockManager.getMatchingBlockIds(filter) + context.reply(blockManager.getMatchingBlockIds(filter)) } - private def doAsync[T](actionMessage: String, responseActor: ActorRef)(body: => T) { + private def doAsync[T](actionMessage: String, context: RpcCallContext)(body: => T) { val future = Future { logDebug(actionMessage) body } future.onSuccess { case response => logDebug("Done " + actionMessage + ", response is " + response) - responseActor ! response - logDebug("Sent response: " + response + " to " + responseActor) + context.reply(response) + logDebug("Sent response: " + response + " to " + context.sender) } future.onFailure { case t: Throwable => logError("Error in " + actionMessage, t) - responseActor ! null.asInstanceOf[T] + context.sendFailure(t) } } + + override def onStop(): Unit = { + asyncThreadPool.shutdownNow() + } } diff --git a/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala b/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala index 81164178b9e8e..0dfc91dfaff85 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala @@ -23,6 +23,7 @@ import java.nio.channels.FileChannel import org.apache.spark.Logging import org.apache.spark.serializer.{SerializationStream, Serializer} import org.apache.spark.executor.ShuffleWriteMetrics +import org.apache.spark.util.Utils /** * An interface for writing JVM objects to some underlying storage. This interface allows @@ -82,11 +83,13 @@ private[spark] class DiskBlockObjectWriter( { /** Intercepts write calls and tracks total time spent writing. Not thread safe. */ private class TimeTrackingOutputStream(out: OutputStream) extends OutputStream { - def write(i: Int): Unit = callWithTiming(out.write(i)) - override def write(b: Array[Byte]) = callWithTiming(out.write(b)) - override def write(b: Array[Byte], off: Int, len: Int) = callWithTiming(out.write(b, off, len)) - override def close() = out.close() - override def flush() = out.flush() + override def write(i: Int): Unit = callWithTiming(out.write(i)) + override def write(b: Array[Byte]): Unit = callWithTiming(out.write(b)) + override def write(b: Array[Byte], off: Int, len: Int): Unit = { + callWithTiming(out.write(b, off, len)) + } + override def close(): Unit = out.close() + override def flush(): Unit = out.flush() } /** The file channel, used for repositioning / truncating the file. */ @@ -138,13 +141,17 @@ private[spark] class DiskBlockObjectWriter( override def close() { if (initialized) { - if (syncWrites) { - // Force outstanding writes to disk and track how long it takes - objOut.flush() - def sync = fos.getFD.sync() - callWithTiming(sync) + Utils.tryWithSafeFinally { + if (syncWrites) { + // Force outstanding writes to disk and track how long it takes + objOut.flush() + callWithTiming { + fos.getFD.sync() + } + } + } { + objOut.close() } - objOut.close() channel = null bs = null diff --git a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala index 53eaedacbf291..2883137872600 100644 --- a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala @@ -47,9 +47,11 @@ private[spark] class DiskBlockManager(blockManager: BlockManager, conf: SparkCon logError("Failed to create any local dir.") System.exit(ExecutorExitCode.DISK_STORE_FAILED_TO_CREATE_DIR) } + // The content of subDirs is immutable but the content of subDirs(i) is mutable. And the content + // of subDirs(i) is protected by the lock of subDirs(i) private val subDirs = Array.fill(localDirs.length)(new Array[File](subDirsPerLocalDir)) - addShutdownHook() + private val shutdownHook = addShutdownHook() /** Looks up a file by hashing it into one of our local subdirectories. */ // This method should be kept in sync with @@ -61,20 +63,17 @@ private[spark] class DiskBlockManager(blockManager: BlockManager, conf: SparkCon val subDirId = (hash / localDirs.length) % subDirsPerLocalDir // Create the subdirectory if it doesn't already exist - var subDir = subDirs(dirId)(subDirId) - if (subDir == null) { - subDir = subDirs(dirId).synchronized { - val old = subDirs(dirId)(subDirId) - if (old != null) { - old - } else { - val newDir = new File(localDirs(dirId), "%02x".format(subDirId)) - if (!newDir.exists() && !newDir.mkdir()) { - throw new IOException(s"Failed to create local dir in $newDir.") - } - subDirs(dirId)(subDirId) = newDir - newDir + val subDir = subDirs(dirId).synchronized { + val old = subDirs(dirId)(subDirId) + if (old != null) { + old + } else { + val newDir = new File(localDirs(dirId), "%02x".format(subDirId)) + if (!newDir.exists() && !newDir.mkdir()) { + throw new IOException(s"Failed to create local dir in $newDir.") } + subDirs(dirId)(subDirId) = newDir + newDir } } @@ -91,7 +90,12 @@ private[spark] class DiskBlockManager(blockManager: BlockManager, conf: SparkCon /** List all the files currently stored on disk by the disk manager. */ def getAllFiles(): Seq[File] = { // Get all the files inside the array of array of directories - subDirs.flatten.filter(_ != null).flatMap { dir => + subDirs.flatMap { dir => + dir.synchronized { + // Copy the content of dir because it may be modified in other threads + dir.clone() + } + }.filter(_ != null).flatMap { dir => val files = dir.listFiles() if (files != null) files else Seq.empty } @@ -134,17 +138,29 @@ private[spark] class DiskBlockManager(blockManager: BlockManager, conf: SparkCon } } - private def addShutdownHook() { - Runtime.getRuntime.addShutdownHook(new Thread("delete Spark local dirs") { + private def addShutdownHook(): Thread = { + val shutdownHook = new Thread("delete Spark local dirs") { override def run(): Unit = Utils.logUncaughtExceptions { logDebug("Shutdown hook called") - DiskBlockManager.this.stop() + DiskBlockManager.this.doStop() } - }) + } + Runtime.getRuntime.addShutdownHook(shutdownHook) + shutdownHook } /** Cleanup local dirs and stop shuffle sender. */ private[spark] def stop() { + // Remove the shutdown hook. It causes memory leaks if we leave it around. + try { + Runtime.getRuntime.removeShutdownHook(shutdownHook) + } catch { + case e: IllegalStateException => None + } + doStop() + } + + private def doStop(): Unit = { // Only perform cleanup if an external service is not serving our shuffle files. if (!blockManager.externalShuffleServiceEnabled || blockManager.blockManagerId.isDriver) { localDirs.foreach { localDir => diff --git a/core/src/main/scala/org/apache/spark/storage/DiskStore.scala b/core/src/main/scala/org/apache/spark/storage/DiskStore.scala index 61ef5ff168791..4b232ae7d3180 100644 --- a/core/src/main/scala/org/apache/spark/storage/DiskStore.scala +++ b/core/src/main/scala/org/apache/spark/storage/DiskStore.scala @@ -46,10 +46,13 @@ private[spark] class DiskStore(blockManager: BlockManager, diskManager: DiskBloc val startTime = System.currentTimeMillis val file = diskManager.getFile(blockId) val channel = new FileOutputStream(file).getChannel - while (bytes.remaining > 0) { - channel.write(bytes) + Utils.tryWithSafeFinally { + while (bytes.remaining > 0) { + channel.write(bytes) + } + } { + channel.close() } - channel.close() val finishTime = System.currentTimeMillis logDebug("Block %s stored as %s file on disk in %d ms".format( file.getName, Utils.bytesToString(bytes.limit), finishTime - startTime)) @@ -75,9 +78,9 @@ private[spark] class DiskStore(blockManager: BlockManager, diskManager: DiskBloc val file = diskManager.getFile(blockId) val outputStream = new FileOutputStream(file) try { - try { + Utils.tryWithSafeFinally { blockManager.dataSerializeStream(blockId, outputStream, values) - } finally { + } { // Close outputStream here because it should be closed before file is deleted. outputStream.close() } @@ -106,8 +109,7 @@ private[spark] class DiskStore(blockManager: BlockManager, diskManager: DiskBloc private def getBytes(file: File, offset: Long, length: Long): Option[ByteBuffer] = { val channel = new RandomAccessFile(file, "r").getChannel - - try { + Utils.tryWithSafeFinally { // For small files, directly read rather than memory map if (length < minMemoryMapBytes) { val buf = ByteBuffer.allocate(length.toInt) @@ -123,7 +125,7 @@ private[spark] class DiskStore(blockManager: BlockManager, diskManager: DiskBloc } else { Some(channel.map(MapMode.READ_ONLY, offset, length)) } - } finally { + } { channel.close() } } diff --git a/core/src/main/scala/org/apache/spark/storage/FileSegment.scala b/core/src/main/scala/org/apache/spark/storage/FileSegment.scala index 132502b75f8cd..95e2d688d9b17 100644 --- a/core/src/main/scala/org/apache/spark/storage/FileSegment.scala +++ b/core/src/main/scala/org/apache/spark/storage/FileSegment.scala @@ -24,5 +24,7 @@ import java.io.File * based off an offset and a length. */ private[spark] class FileSegment(val file: File, val offset: Long, val length: Long) { - override def toString = "(name=%s, offset=%d, length=%d)".format(file.getName, offset, length) + override def toString: String = { + "(name=%s, offset=%d, length=%d)".format(file.getName, offset, length) + } } diff --git a/core/src/main/scala/org/apache/spark/storage/MemoryStore.scala b/core/src/main/scala/org/apache/spark/storage/MemoryStore.scala index 71305a46bf570..ed609772e6979 100644 --- a/core/src/main/scala/org/apache/spark/storage/MemoryStore.scala +++ b/core/src/main/scala/org/apache/spark/storage/MemoryStore.scala @@ -46,6 +46,14 @@ private[spark] class MemoryStore(blockManager: BlockManager, maxMemory: Long) // A mapping from thread ID to amount of memory used for unrolling a block (in bytes) // All accesses of this map are assumed to have manually synchronized on `accountingLock` private val unrollMemoryMap = mutable.HashMap[Long, Long]() + // Same as `unrollMemoryMap`, but for pending unroll memory as defined below. + // Pending unroll memory refers to the intermediate memory occupied by a thread + // after the unroll but before the actual putting of the block in the cache. + // This chunk of memory is expected to be released *as soon as* we finish + // caching the corresponding block as opposed to until after the task finishes. + // This is only used if a block is successfully unrolled in its entirety in + // memory (SPARK-4777). + private val pendingUnrollMemoryMap = mutable.HashMap[Long, Long]() /** * The amount of space ensured for unrolling values in memory, shared across all cores. @@ -90,6 +98,26 @@ private[spark] class MemoryStore(blockManager: BlockManager, maxMemory: Long) } } + /** + * Use `size` to test if there is enough space in MemoryStore. If so, create the ByteBuffer and + * put it into MemoryStore. Otherwise, the ByteBuffer won't be created. + * + * The caller should guarantee that `size` is correct. + */ + def putBytes(blockId: BlockId, size: Long, _bytes: () => ByteBuffer): PutResult = { + // Work on a duplicate - since the original input might be used elsewhere. + lazy val bytes = _bytes().duplicate().rewind().asInstanceOf[ByteBuffer] + val putAttempt = tryToPut(blockId, () => bytes, size, deserialized = false) + val data = + if (putAttempt.success) { + assert(bytes.limit == size) + Right(bytes.duplicate()) + } else { + null + } + PutResult(size, data, putAttempt.droppedBlocks) + } + override def putArray( blockId: BlockId, values: Array[Any], @@ -184,7 +212,7 @@ private[spark] class MemoryStore(blockManager: BlockManager, maxMemory: Long) val entry = entries.remove(blockId) if (entry != null) { currentMemory -= entry.size - logInfo(s"Block $blockId of size ${entry.size} dropped from memory (free $freeMemory)") + logDebug(s"Block $blockId of size ${entry.size} dropped from memory (free $freeMemory)") true } else { false @@ -283,12 +311,16 @@ private[spark] class MemoryStore(blockManager: BlockManager, maxMemory: Long) } } finally { - // If we return an array, the values returned do not depend on the underlying vector and - // we can immediately free up space for other threads. Otherwise, if we return an iterator, - // we release the memory claimed by this thread later on when the task finishes. + // If we return an array, the values returned will later be cached in `tryToPut`. + // In this case, we should release the memory after we cache the block there. + // Otherwise, if we return an iterator, we release the memory reserved here + // later when the task finishes. if (keepUnrolling) { - val amountToRelease = currentUnrollMemoryForThisThread - previousMemoryReserved - releaseUnrollMemoryForThisThread(amountToRelease) + accountingLock.synchronized { + val amountToRelease = currentUnrollMemoryForThisThread - previousMemoryReserved + releaseUnrollMemoryForThisThread(amountToRelease) + reservePendingUnrollMemoryForThisThread(amountToRelease) + } } } } @@ -300,11 +332,22 @@ private[spark] class MemoryStore(blockManager: BlockManager, maxMemory: Long) blockId.asRDDId.map(_.rddId) } + private def tryToPut( + blockId: BlockId, + value: Any, + size: Long, + deserialized: Boolean): ResultWithDroppedBlocks = { + tryToPut(blockId, () => value, size, deserialized) + } + /** * Try to put in a set of values, if we can free up enough space. The value should either be * an Array if deserialized is true or a ByteBuffer otherwise. Its (possibly estimated) size * must also be passed by the caller. * + * `value` will be lazily created. If it cannot be put into MemoryStore or disk, `value` won't be + * created to avoid OOM since it may be a big ByteBuffer. + * * Synchronize on `accountingLock` to ensure that all the put requests and its associated block * dropping is done by only on thread at a time. Otherwise while one thread is dropping * blocks to free memory for one block, another thread may use up the freed space for @@ -314,7 +357,7 @@ private[spark] class MemoryStore(blockManager: BlockManager, maxMemory: Long) */ private def tryToPut( blockId: BlockId, - value: Any, + value: () => Any, size: Long, deserialized: Boolean): ResultWithDroppedBlocks = { @@ -333,7 +376,7 @@ private[spark] class MemoryStore(blockManager: BlockManager, maxMemory: Long) droppedBlocks ++= freeSpaceResult.droppedBlocks if (enoughFreeSpace) { - val entry = new MemoryEntry(value, size, deserialized) + val entry = new MemoryEntry(value(), size, deserialized) entries.synchronized { entries.put(blockId, entry) currentMemory += size @@ -345,14 +388,16 @@ private[spark] class MemoryStore(blockManager: BlockManager, maxMemory: Long) } else { // Tell the block manager that we couldn't put it in memory so that it can drop it to // disk if the block allows disk storage. - val data = if (deserialized) { - Left(value.asInstanceOf[Array[Any]]) + lazy val data = if (deserialized) { + Left(value().asInstanceOf[Array[Any]]) } else { - Right(value.asInstanceOf[ByteBuffer].duplicate()) + Right(value().asInstanceOf[ByteBuffer].duplicate()) } - val droppedBlockStatus = blockManager.dropFromMemory(blockId, data) + val droppedBlockStatus = blockManager.dropFromMemory(blockId, () => data) droppedBlockStatus.foreach { status => droppedBlocks += ((blockId, status)) } } + // Release the unroll memory used because we no longer need the underlying Array + releasePendingUnrollMemoryForThisThread() } ResultWithDroppedBlocks(putSuccess, droppedBlocks) } @@ -381,7 +426,10 @@ private[spark] class MemoryStore(blockManager: BlockManager, maxMemory: Long) } // Take into account the amount of memory currently occupied by unrolling blocks - val actualFreeMemory = freeMemory - currentUnrollMemory + // and minus the pending unroll memory for that block on current thread. + val threadId = Thread.currentThread().getId + val actualFreeMemory = freeMemory - currentUnrollMemory + + pendingUnrollMemoryMap.getOrElse(threadId, 0L) if (actualFreeMemory < space) { val rddToAdd = getRddId(blockIdToAdd) @@ -468,11 +516,32 @@ private[spark] class MemoryStore(blockManager: BlockManager, maxMemory: Long) } } + /** + * Reserve the unroll memory of current unroll successful block used by this thread + * until actually put the block into memory entry. + */ + def reservePendingUnrollMemoryForThisThread(memory: Long): Unit = { + val threadId = Thread.currentThread().getId + accountingLock.synchronized { + pendingUnrollMemoryMap(threadId) = pendingUnrollMemoryMap.getOrElse(threadId, 0L) + memory + } + } + + /** + * Release pending unroll memory of current unroll successful block used by this thread + */ + def releasePendingUnrollMemoryForThisThread(): Unit = { + val threadId = Thread.currentThread().getId + accountingLock.synchronized { + pendingUnrollMemoryMap.remove(threadId) + } + } + /** * Return the amount of memory currently occupied for unrolling blocks across all threads. */ def currentUnrollMemory: Long = accountingLock.synchronized { - unrollMemoryMap.values.sum + unrollMemoryMap.values.sum + pendingUnrollMemoryMap.values.sum } /** diff --git a/core/src/main/scala/org/apache/spark/storage/RDDInfo.scala b/core/src/main/scala/org/apache/spark/storage/RDDInfo.scala index 120c327a7e580..034525b56f59c 100644 --- a/core/src/main/scala/org/apache/spark/storage/RDDInfo.scala +++ b/core/src/main/scala/org/apache/spark/storage/RDDInfo.scala @@ -36,7 +36,7 @@ class RDDInfo( def isCached: Boolean = (memSize + diskSize + tachyonSize > 0) && numCachedPartitions > 0 - override def toString = { + override def toString: String = { import Utils.bytesToString ("RDD \"%s\" (%d) StorageLevel: %s; CachedPartitions: %d; TotalPartitions: %d; " + "MemorySize: %s; TachyonSize: %s; DiskSize: %s").format( @@ -44,7 +44,7 @@ class RDDInfo( bytesToString(memSize), bytesToString(tachyonSize), bytesToString(diskSize)) } - override def compare(that: RDDInfo) = { + override def compare(that: RDDInfo): Int = { this.id - that.id } } @@ -52,6 +52,6 @@ class RDDInfo( private[spark] object RDDInfo { def fromRdd(rdd: RDD[_]): RDDInfo = { val rddName = Option(rdd.name).getOrElse(rdd.id.toString) - new RDDInfo(rdd.id, rddName, rdd.partitions.size, rdd.getStorageLevel) + new RDDInfo(rdd.id, rddName, rdd.partitions.length, rdd.getStorageLevel) } } diff --git a/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala b/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala index ab9ee4f0096bf..8f28ef49a8a6f 100644 --- a/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala +++ b/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala @@ -234,6 +234,7 @@ final class ShuffleBlockFetcherIterator( try { val buf = blockManager.getBlockData(blockId) shuffleMetrics.incLocalBlocksFetched(1) + shuffleMetrics.incLocalBytesRead(buf.size) buf.retain() results.put(new SuccessFetchResult(blockId, 0, buf)) } catch { diff --git a/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala b/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala index e5e1cf5a69a19..134abea866218 100644 --- a/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala +++ b/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala @@ -50,11 +50,11 @@ class StorageLevel private( def this() = this(false, true, false, false) // For deserialization - def useDisk = _useDisk - def useMemory = _useMemory - def useOffHeap = _useOffHeap - def deserialized = _deserialized - def replication = _replication + def useDisk: Boolean = _useDisk + def useMemory: Boolean = _useMemory + def useOffHeap: Boolean = _useOffHeap + def deserialized: Boolean = _deserialized + def replication: Int = _replication assert(replication < 40, "Replication restricted to be less than 40 for calculating hash codes") @@ -80,7 +80,7 @@ class StorageLevel private( false } - def isValid = (useMemory || useDisk || useOffHeap) && (replication > 0) + def isValid: Boolean = (useMemory || useDisk || useOffHeap) && (replication > 0) def toInt: Int = { var ret = 0 @@ -183,7 +183,7 @@ object StorageLevel { useMemory: Boolean, useOffHeap: Boolean, deserialized: Boolean, - replication: Int) = { + replication: Int): StorageLevel = { getCachedStorageLevel( new StorageLevel(useDisk, useMemory, useOffHeap, deserialized, replication)) } @@ -197,7 +197,7 @@ object StorageLevel { useDisk: Boolean, useMemory: Boolean, deserialized: Boolean, - replication: Int = 1) = { + replication: Int = 1): StorageLevel = { getCachedStorageLevel(new StorageLevel(useDisk, useMemory, false, deserialized, replication)) } diff --git a/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala b/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala index def49e80a3605..7d75929b96f75 100644 --- a/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala +++ b/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala @@ -19,7 +19,6 @@ package org.apache.spark.storage import scala.collection.mutable -import org.apache.spark.SparkContext import org.apache.spark.annotation.DeveloperApi import org.apache.spark.scheduler._ @@ -32,7 +31,7 @@ class StorageStatusListener extends SparkListener { // This maintains only blocks that are cached (i.e. storage level is not StorageLevel.NONE) private[storage] val executorIdToStorageStatus = mutable.Map[String, StorageStatus]() - def storageStatusList = executorIdToStorageStatus.values.toSeq + def storageStatusList: Seq[StorageStatus] = executorIdToStorageStatus.values.toSeq /** Update storage status list to reflect updated block statuses */ private def updateStorageStatus(execId: String, updatedBlocks: Seq[(BlockId, BlockStatus)]) { @@ -56,7 +55,7 @@ class StorageStatusListener extends SparkListener { } } - override def onTaskEnd(taskEnd: SparkListenerTaskEnd) = synchronized { + override def onTaskEnd(taskEnd: SparkListenerTaskEnd): Unit = synchronized { val info = taskEnd.taskInfo val metrics = taskEnd.taskMetrics if (info != null && metrics != null) { @@ -67,7 +66,7 @@ class StorageStatusListener extends SparkListener { } } - override def onUnpersistRDD(unpersistRDD: SparkListenerUnpersistRDD) = synchronized { + override def onUnpersistRDD(unpersistRDD: SparkListenerUnpersistRDD): Unit = synchronized { updateStorageStatus(unpersistRDD.rddId) } diff --git a/core/src/main/scala/org/apache/spark/storage/TachyonFileSegment.scala b/core/src/main/scala/org/apache/spark/storage/TachyonFileSegment.scala index b86abbda1d3e7..65fa81704c365 100644 --- a/core/src/main/scala/org/apache/spark/storage/TachyonFileSegment.scala +++ b/core/src/main/scala/org/apache/spark/storage/TachyonFileSegment.scala @@ -24,5 +24,7 @@ import tachyon.client.TachyonFile * a length. */ private[spark] class TachyonFileSegment(val file: TachyonFile, val offset: Long, val length: Long) { - override def toString = "(name=%s, offset=%d, length=%d)".format(file.getPath(), offset, length) + override def toString: String = { + "(name=%s, offset=%d, length=%d)".format(file.getPath(), offset, length) + } } diff --git a/core/src/main/scala/org/apache/spark/ui/ConsoleProgressBar.scala b/core/src/main/scala/org/apache/spark/ui/ConsoleProgressBar.scala index 27ba9e18237b5..77c0bc8b5360a 100644 --- a/core/src/main/scala/org/apache/spark/ui/ConsoleProgressBar.scala +++ b/core/src/main/scala/org/apache/spark/ui/ConsoleProgressBar.scala @@ -28,7 +28,6 @@ import org.apache.spark._ * of them will be combined together, showed in one line. */ private[spark] class ConsoleProgressBar(sc: SparkContext) extends Logging { - // Carrige return val CR = '\r' // Update period of progress bar, in milliseconds @@ -66,7 +65,7 @@ private[spark] class ConsoleProgressBar(sc: SparkContext) extends Logging { val stageIds = sc.statusTracker.getActiveStageIds() val stages = stageIds.map(sc.statusTracker.getStageInfo).flatten.filter(_.numTasks() > 1) .filter(now - _.submissionTime() > FIRST_DELAY).sortBy(_.stageId()) - if (stages.size > 0) { + if (stages.length > 0) { show(now, stages.take(3)) // display at most 3 stages in same time } } @@ -82,7 +81,7 @@ private[spark] class ConsoleProgressBar(sc: SparkContext) extends Logging { val total = s.numTasks() val header = s"[Stage ${s.stageId()}:" val tailer = s"(${s.numCompletedTasks()} + ${s.numActiveTasks()}) / $total]" - val w = width - header.size - tailer.size + val w = width - header.length - tailer.length val bar = if (w > 0) { val percent = w * s.numCompletedTasks() / total (0 until w).map { i => @@ -121,4 +120,10 @@ private[spark] class ConsoleProgressBar(sc: SparkContext) extends Logging { clear() lastFinishTime = System.currentTimeMillis() } + + /** + * Tear down the timer thread. The timer thread is a GC root, and it retains the entire + * SparkContext if it's not terminated. + */ + def stop(): Unit = timer.cancel() } diff --git a/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala b/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala index bf4b24e98b134..a091ca650c60c 100644 --- a/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala +++ b/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala @@ -80,6 +80,10 @@ private[spark] object JettyUtils extends Logging { response.sendError(HttpServletResponse.SC_BAD_REQUEST, e.getMessage) } } + // SPARK-5983 ensure TRACE is not supported + protected override def doTrace(req: HttpServletRequest, res: HttpServletResponse): Unit = { + res.sendError(HttpServletResponse.SC_METHOD_NOT_ALLOWED) + } } } @@ -110,15 +114,32 @@ private[spark] object JettyUtils extends Logging { srcPath: String, destPath: String, beforeRedirect: HttpServletRequest => Unit = x => (), - basePath: String = ""): ServletContextHandler = { + basePath: String = "", + httpMethod: String = "GET"): ServletContextHandler = { val prefixedDestPath = attachPrefix(basePath, destPath) val servlet = new HttpServlet { - override def doGet(request: HttpServletRequest, response: HttpServletResponse) { + override def doGet(request: HttpServletRequest, response: HttpServletResponse): Unit = { + httpMethod match { + case "GET" => doRequest(request, response) + case _ => response.sendError(HttpServletResponse.SC_METHOD_NOT_ALLOWED) + } + } + override def doPost(request: HttpServletRequest, response: HttpServletResponse): Unit = { + httpMethod match { + case "POST" => doRequest(request, response) + case _ => response.sendError(HttpServletResponse.SC_METHOD_NOT_ALLOWED) + } + } + private def doRequest(request: HttpServletRequest, response: HttpServletResponse): Unit = { beforeRedirect(request) // Make sure we don't end up with "//" in the middle val newUrl = new URL(new URL(request.getRequestURL.toString), prefixedDestPath).toString response.sendRedirect(newUrl) } + // SPARK-5983 ensure TRACE is not supported + protected override def doTrace(req: HttpServletRequest, res: HttpServletResponse): Unit = { + res.sendError(HttpServletResponse.SC_METHOD_NOT_ALLOWED) + } } createServletHandler(srcPath, servlet, basePath) } diff --git a/core/src/main/scala/org/apache/spark/ui/SparkUI.scala b/core/src/main/scala/org/apache/spark/ui/SparkUI.scala index 0c24ad2760e08..580ab8b1325f8 100644 --- a/core/src/main/scala/org/apache/spark/ui/SparkUI.scala +++ b/core/src/main/scala/org/apache/spark/ui/SparkUI.scala @@ -55,12 +55,12 @@ private[spark] class SparkUI private ( attachTab(new ExecutorsTab(this)) attachHandler(createStaticHandler(SparkUI.STATIC_RESOURCE_DIR, "/static")) attachHandler(createRedirectHandler("/", "/jobs", basePath = basePath)) - attachHandler( - createRedirectHandler("/stages/stage/kill", "/stages", stagesTab.handleKillRequest)) + attachHandler(createRedirectHandler( + "/stages/stage/kill", "/stages", stagesTab.handleKillRequest, httpMethod = "POST")) } initialize() - def getAppName = appName + def getAppName: String = appName /** Set the app name for this UI. */ def setAppName(name: String) { diff --git a/core/src/main/scala/org/apache/spark/ui/ToolTips.scala b/core/src/main/scala/org/apache/spark/ui/ToolTips.scala index 3a15e603b1969..cae6870c2ab20 100644 --- a/core/src/main/scala/org/apache/spark/ui/ToolTips.scala +++ b/core/src/main/scala/org/apache/spark/ui/ToolTips.scala @@ -37,8 +37,12 @@ private[spark] object ToolTips { "Bytes and records written to disk in order to be read by a shuffle in a future stage." val SHUFFLE_READ = - """Bytes and records read from remote executors. Typically less than shuffle write bytes - because this does not include shuffle data read locally.""" + """Total shuffle bytes and records read (includes both data read locally and data read from + remote executors). """ + + val SHUFFLE_READ_REMOTE_SIZE = + """Total shuffle bytes read from remote executors. This is a subset of the shuffle + read bytes; the remaining shuffle data is read locally. """ val GETTING_RESULT_TIME = """Time that the driver spends fetching task results from workers. If this is large, consider diff --git a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala index b5022fe853c49..f07864141a21c 100644 --- a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala +++ b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala @@ -149,9 +149,11 @@ private[spark] object UIUtils extends Logging { } } - def prependBaseUri(basePath: String = "", resource: String = "") = uiRoot + basePath + resource + def prependBaseUri(basePath: String = "", resource: String = ""): String = { + uiRoot + basePath + resource + } - def commonHeaderNodes = { + def commonHeaderNodes: Seq[Node] = { diff --git a/core/src/main/scala/org/apache/spark/ui/UIWorkloadGenerator.scala b/core/src/main/scala/org/apache/spark/ui/UIWorkloadGenerator.scala index fc1844600f1cb..5fbcd6bb8ad94 100644 --- a/core/src/main/scala/org/apache/spark/ui/UIWorkloadGenerator.scala +++ b/core/src/main/scala/org/apache/spark/ui/UIWorkloadGenerator.scala @@ -17,6 +17,8 @@ package org.apache.spark.ui +import java.util.concurrent.Semaphore + import scala.util.Random import org.apache.spark.{SparkConf, SparkContext} @@ -51,7 +53,7 @@ private[spark] object UIWorkloadGenerator { val nJobSet = args(2).toInt val sc = new SparkContext(conf) - def setProperties(s: String) = { + def setProperties(s: String): Unit = { if(schedulingMode == SchedulingMode.FAIR) { sc.setLocalProperty("spark.scheduler.pool", s) } @@ -59,7 +61,7 @@ private[spark] object UIWorkloadGenerator { } val baseData = sc.makeRDD(1 to NUM_PARTITIONS * 10, NUM_PARTITIONS) - def nextFloat() = new Random().nextFloat() + def nextFloat(): Float = new Random().nextFloat() val jobs = Seq[(String, () => Long)]( ("Count", baseData.count), @@ -88,6 +90,8 @@ private[spark] object UIWorkloadGenerator { ("Job with delays", baseData.map(x => Thread.sleep(100)).count) ) + val barrier = new Semaphore(-nJobSet * jobs.size + 1) + (1 to nJobSet).foreach { _ => for ((desc, job) <- jobs) { new Thread { @@ -99,12 +103,17 @@ private[spark] object UIWorkloadGenerator { } catch { case e: Exception => println("Job Failed: " + desc) + } finally { + barrier.release() } } }.start Thread.sleep(INTER_JOB_WAIT_MS) } } + + // Waiting for threads. + barrier.acquire() sc.stop() } } diff --git a/core/src/main/scala/org/apache/spark/ui/WebUI.scala b/core/src/main/scala/org/apache/spark/ui/WebUI.scala index 9be65a4a39a09..f9860d1a5ce76 100644 --- a/core/src/main/scala/org/apache/spark/ui/WebUI.scala +++ b/core/src/main/scala/org/apache/spark/ui/WebUI.scala @@ -20,14 +20,15 @@ package org.apache.spark.ui import javax.servlet.http.HttpServletRequest import scala.collection.mutable.ArrayBuffer +import scala.collection.mutable.HashMap import scala.xml.Node import org.eclipse.jetty.servlet.ServletContextHandler import org.json4s.JsonAST.{JNothing, JValue} -import org.apache.spark.{Logging, SecurityManager, SparkConf} import org.apache.spark.ui.JettyUtils._ import org.apache.spark.util.Utils +import org.apache.spark.{Logging, SecurityManager, SparkConf} /** * The top level component of the UI hierarchy that contains the server. @@ -45,9 +46,10 @@ private[spark] abstract class WebUI( protected val tabs = ArrayBuffer[WebUITab]() protected val handlers = ArrayBuffer[ServletContextHandler]() + protected val pageToHandlers = new HashMap[WebUIPage, ArrayBuffer[ServletContextHandler]] protected var serverInfo: Option[ServerInfo] = None - protected val localHostName = Utils.localHostName() - protected val publicHostName = Option(System.getenv("SPARK_PUBLIC_DNS")).getOrElse(localHostName) + protected val localHostName = Utils.localHostNameForURI() + protected val publicHostName = Option(conf.getenv("SPARK_PUBLIC_DNS")).getOrElse(localHostName) private val className = Utils.getFormattedClassName(this) def getBasePath: String = basePath @@ -60,14 +62,30 @@ private[spark] abstract class WebUI( tab.pages.foreach(attachPage) tabs += tab } + + def detachTab(tab: WebUITab) { + tab.pages.foreach(detachPage) + tabs -= tab + } + + def detachPage(page: WebUIPage) { + pageToHandlers.remove(page).foreach(_.foreach(detachHandler)) + } /** Attach a page to this UI. */ def attachPage(page: WebUIPage) { val pagePath = "/" + page.prefix - attachHandler(createServletHandler(pagePath, - (request: HttpServletRequest) => page.render(request), securityManager, basePath)) - attachHandler(createServletHandler(pagePath.stripSuffix("/") + "/json", - (request: HttpServletRequest) => page.renderJson(request), securityManager, basePath)) + val renderHandler = createServletHandler(pagePath, + (request: HttpServletRequest) => page.render(request), securityManager, basePath) + val renderJsonHandler = createServletHandler(pagePath.stripSuffix("/") + "/json", + (request: HttpServletRequest) => page.renderJson(request), securityManager, basePath) + attachHandler(renderHandler) + attachHandler(renderJsonHandler) + pageToHandlers.getOrElseUpdate(page, ArrayBuffer[ServletContextHandler]()) + .append(renderHandler) + pageToHandlers.getOrElseUpdate(page, ArrayBuffer[ServletContextHandler]()) + .append(renderJsonHandler) + } /** Attach a handler to this UI. */ diff --git a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsTab.scala b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsTab.scala index 3afd7ef07d7c9..69053fe44d7e4 100644 --- a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsTab.scala +++ b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsTab.scala @@ -22,7 +22,7 @@ import scala.collection.mutable.HashMap import org.apache.spark.ExceptionFailure import org.apache.spark.annotation.DeveloperApi import org.apache.spark.scheduler._ -import org.apache.spark.storage.StorageStatusListener +import org.apache.spark.storage.{StorageStatus, StorageStatusListener} import org.apache.spark.ui.{SparkUI, SparkUITab} private[ui] class ExecutorsTab(parent: SparkUI) extends SparkUITab(parent, "executors") { @@ -55,19 +55,19 @@ class ExecutorsListener(storageStatusListener: StorageStatusListener) extends Sp val executorToShuffleWrite = HashMap[String, Long]() val executorToLogUrls = HashMap[String, Map[String, String]]() - def storageStatusList = storageStatusListener.storageStatusList + def storageStatusList: Seq[StorageStatus] = storageStatusListener.storageStatusList - override def onExecutorAdded(executorAdded: SparkListenerExecutorAdded) = synchronized { + override def onExecutorAdded(executorAdded: SparkListenerExecutorAdded): Unit = synchronized { val eid = executorAdded.executorId executorToLogUrls(eid) = executorAdded.executorInfo.logUrlMap } - override def onTaskStart(taskStart: SparkListenerTaskStart) = synchronized { + override def onTaskStart(taskStart: SparkListenerTaskStart): Unit = synchronized { val eid = taskStart.taskInfo.executorId executorToTasksActive(eid) = executorToTasksActive.getOrElse(eid, 0) + 1 } - override def onTaskEnd(taskEnd: SparkListenerTaskEnd) = synchronized { + override def onTaskEnd(taskEnd: SparkListenerTaskEnd): Unit = synchronized { val info = taskEnd.taskInfo if (info != null) { val eid = info.executorId diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala index 045c69da06feb..bd923d78a86ce 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala @@ -42,7 +42,9 @@ private[ui] class AllJobsPage(parent: JobsTab) extends WebUIPage("") { } def makeRow(job: JobUIData): Seq[Node] = { - val lastStageInfo = listener.stageIdToInfo.get(job.stageIds.max) + val lastStageInfo = Option(job.stageIds) + .filter(_.nonEmpty) + .flatMap { ids => listener.stageIdToInfo.get(ids.max) } val lastStageData = lastStageInfo.flatMap { s => listener.stageIdToData.get((s.stageId, s.attemptId)) } diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala b/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala index f463f8d7c7215..625596885faa1 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala @@ -44,6 +44,7 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging { // These type aliases are public because they're used in the types of public fields: type JobId = Int + type JobGroupId = String type StageId = Int type StageAttemptId = Int type PoolName = String @@ -54,6 +55,7 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging { val completedJobs = ListBuffer[JobUIData]() val failedJobs = ListBuffer[JobUIData]() val jobIdToData = new HashMap[JobId, JobUIData] + val jobGroupToJobIds = new HashMap[JobGroupId, HashSet[JobId]] // Stages: val pendingStages = new HashMap[StageId, StageInfo] @@ -73,7 +75,7 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging { // Misc: val executorIdToBlockManagerId = HashMap[ExecutorId, BlockManagerId]() - def blockManagerIds = executorIdToBlockManagerId.values.toSeq + def blockManagerIds: Seq[BlockManagerId] = executorIdToBlockManagerId.values.toSeq var schedulingMode: Option[SchedulingMode] = None @@ -119,7 +121,10 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging { Map( "jobIdToData" -> jobIdToData.size, "stageIdToData" -> stageIdToData.size, - "stageIdToStageInfo" -> stageIdToInfo.size + "stageIdToStageInfo" -> stageIdToInfo.size, + "jobGroupToJobIds" -> jobGroupToJobIds.values.map(_.size).sum, + // Since jobGroupToJobIds is map of sets, check that we don't leak keys with empty values: + "jobGroupToJobIds keySet" -> jobGroupToJobIds.keys.size ) } @@ -140,13 +145,25 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging { if (jobs.size > retainedJobs) { val toRemove = math.max(retainedJobs / 10, 1) jobs.take(toRemove).foreach { job => - jobIdToData.remove(job.jobId) + // Remove the job's UI data, if it exists + jobIdToData.remove(job.jobId).foreach { removedJob => + // A null jobGroupId is used for jobs that are run without a job group + val jobGroupId = removedJob.jobGroup.orNull + // Remove the job group -> job mapping entry, if it exists + jobGroupToJobIds.get(jobGroupId).foreach { jobsInGroup => + jobsInGroup.remove(job.jobId) + // If this was the last job in this job group, remove the map entry for the job group + if (jobsInGroup.isEmpty) { + jobGroupToJobIds.remove(jobGroupId) + } + } + } } jobs.trimStart(toRemove) } } - override def onJobStart(jobStart: SparkListenerJobStart) = synchronized { + override def onJobStart(jobStart: SparkListenerJobStart): Unit = synchronized { val jobGroup = for ( props <- Option(jobStart.properties); group <- Option(props.getProperty(SparkContext.SPARK_JOB_GROUP_ID)) @@ -158,6 +175,8 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging { stageIds = jobStart.stageIds, jobGroup = jobGroup, status = JobExecutionStatus.RUNNING) + // A null jobGroupId is used for jobs that are run without a job group + jobGroupToJobIds.getOrElseUpdate(jobGroup.orNull, new HashSet[JobId]).add(jobStart.jobId) jobStart.stageInfos.foreach(x => pendingStages(x.stageId) = x) // Compute (a potential underestimate of) the number of tasks that will be run by this job. // This may be an underestimate because the job start event references all of the result @@ -182,7 +201,7 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging { } } - override def onJobEnd(jobEnd: SparkListenerJobEnd) = synchronized { + override def onJobEnd(jobEnd: SparkListenerJobEnd): Unit = synchronized { val jobData = activeJobs.remove(jobEnd.jobId).getOrElse { logWarning(s"Job completed for unknown job ${jobEnd.jobId}") new JobUIData(jobId = jobEnd.jobId) @@ -203,6 +222,9 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging { for (stageId <- jobData.stageIds) { stageIdToActiveJobIds.get(stageId).foreach { jobsUsingStage => jobsUsingStage.remove(jobEnd.jobId) + if (jobsUsingStage.isEmpty) { + stageIdToActiveJobIds.remove(stageId) + } stageIdToInfo.get(stageId).foreach { stageInfo => if (stageInfo.submissionTime.isEmpty) { // if this stage is pending, it won't complete, so mark it as "skipped": @@ -216,7 +238,7 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging { } } - override def onStageCompleted(stageCompleted: SparkListenerStageCompleted) = synchronized { + override def onStageCompleted(stageCompleted: SparkListenerStageCompleted): Unit = synchronized { val stage = stageCompleted.stageInfo stageIdToInfo(stage.stageId) = stage val stageData = stageIdToData.getOrElseUpdate((stage.stageId, stage.attemptId), { @@ -257,7 +279,7 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging { } /** For FIFO, all stages are contained by "default" pool but "default" pool here is meaningless */ - override def onStageSubmitted(stageSubmitted: SparkListenerStageSubmitted) = synchronized { + override def onStageSubmitted(stageSubmitted: SparkListenerStageSubmitted): Unit = synchronized { val stage = stageSubmitted.stageInfo activeStages(stage.stageId) = stage pendingStages.remove(stage.stageId) @@ -285,7 +307,7 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging { } } - override def onTaskStart(taskStart: SparkListenerTaskStart) = synchronized { + override def onTaskStart(taskStart: SparkListenerTaskStart): Unit = synchronized { val taskInfo = taskStart.taskInfo if (taskInfo != null) { val stageData = stageIdToData.getOrElseUpdate((taskStart.stageId, taskStart.stageAttemptId), { @@ -309,7 +331,7 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging { // stageToTaskInfos already has the updated status. } - override def onTaskEnd(taskEnd: SparkListenerTaskEnd) = synchronized { + override def onTaskEnd(taskEnd: SparkListenerTaskEnd): Unit = synchronized { val info = taskEnd.taskInfo // If stage attempt id is -1, it means the DAGScheduler had no idea which attempt this task // completion event is for. Let's just drop it here. This means we might have some speculation @@ -401,9 +423,9 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging { execSummary.shuffleWriteRecords += shuffleWriteRecordsDelta val shuffleReadDelta = - (taskMetrics.shuffleReadMetrics.map(_.remoteBytesRead).getOrElse(0L) - - oldMetrics.flatMap(_.shuffleReadMetrics).map(_.remoteBytesRead).getOrElse(0L)) - stageData.shuffleReadBytes += shuffleReadDelta + (taskMetrics.shuffleReadMetrics.map(_.totalBytesRead).getOrElse(0L) + - oldMetrics.flatMap(_.shuffleReadMetrics).map(_.totalBytesRead).getOrElse(0L)) + stageData.shuffleReadTotalBytes += shuffleReadDelta execSummary.shuffleRead += shuffleReadDelta val shuffleReadRecordsDelta = diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/JobsTab.scala b/core/src/main/scala/org/apache/spark/ui/jobs/JobsTab.scala index b2bbfdee56946..7ffcf291b5cc6 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/JobsTab.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/JobsTab.scala @@ -24,7 +24,7 @@ import org.apache.spark.ui.{SparkUI, SparkUITab} private[ui] class JobsTab(parent: SparkUI) extends SparkUITab(parent, "jobs") { val sc = parent.sc val killEnabled = parent.killEnabled - def isFairScheduler = listener.schedulingMode.exists(_ == SchedulingMode.FAIR) + def isFairScheduler: Boolean = listener.schedulingMode.exists(_ == SchedulingMode.FAIR) val listener = parent.jobProgressListener attachPage(new AllJobsPage(this)) diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala index 05ffd5bc58fbb..797c9404bc449 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala @@ -20,7 +20,7 @@ package org.apache.spark.ui.jobs import java.util.Date import javax.servlet.http.HttpServletRequest -import scala.xml.{Node, Unparsed} +import scala.xml.{Elem, Node, Unparsed} import org.apache.commons.lang3.StringEscapeUtils @@ -85,7 +85,7 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") { {if (stageData.hasShuffleRead) {
  • Shuffle read: - {s"${Utils.bytesToString(stageData.shuffleReadBytes)} / " + + {s"${Utils.bytesToString(stageData.shuffleReadTotalBytes)} / " + s"${stageData.shuffleReadRecords}"}
    • }} @@ -143,6 +143,13 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") { Shuffle Read Blocked Time +
  • + + + Shuffle Remote Reads + +
    • }}
  • val accumulableHeaders: Seq[String] = Seq("Accumulable", "Value") - def accumulableRow(acc: AccumulableInfo) = {acc.name}{acc.value} + def accumulableRow(acc: AccumulableInfo): Elem = + {acc.name}{acc.value} val accumulableTable = UIUtils.listingTable(accumulableHeaders, accumulableRow, accumulables.values.toSeq) @@ -181,7 +189,8 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") { {if (stageData.hasOutput) Seq(("Output Size / Records", "")) else Nil} ++ {if (stageData.hasShuffleRead) { Seq(("Shuffle Read Blocked Time", TaskDetailsClassNames.SHUFFLE_READ_BLOCKED_TIME), - ("Shuffle Read Size / Records", "")) + ("Shuffle Read Size / Records", ""), + ("Shuffle Remote Reads", TaskDetailsClassNames.SHUFFLE_READ_REMOTE_SIZE)) } else { Nil }} ++ @@ -260,11 +269,7 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") { +: getFormattedTimeQuantiles(serializationTimes) val gettingResultTimes = validTasks.map { case TaskUIData(info, _, _) => - if (info.gettingResultTime > 0) { - (info.finishTime - info.gettingResultTime).toDouble - } else { - 0.0 - } + getGettingResultTime(info).toDouble } val gettingResultQuantiles = @@ -285,10 +290,11 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") { val schedulerDelayQuantiles = schedulerDelayTitle +: getFormattedTimeQuantiles(schedulerDelays) - def getFormattedSizeQuantiles(data: Seq[Double]) = + def getFormattedSizeQuantiles(data: Seq[Double]): Seq[Elem] = getDistributionQuantiles(data).map(d => {Utils.bytesToString(d.toLong)}) - def getFormattedSizeQuantilesWithRecords(data: Seq[Double], records: Seq[Double]) = { + def getFormattedSizeQuantilesWithRecords(data: Seq[Double], records: Seq[Double]) + : Seq[Elem] = { val recordDist = getDistributionQuantiles(records).iterator getDistributionQuantiles(data).map(d => {s"${Utils.bytesToString(d.toLong)} / ${recordDist.next().toLong}"} @@ -320,19 +326,41 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") { val shuffleReadBlockedTimes = validTasks.map { case TaskUIData(_, metrics, _) => metrics.get.shuffleReadMetrics.map(_.fetchWaitTime).getOrElse(0L).toDouble } - val shuffleReadBlockedQuantiles = Shuffle Read Blocked Time +: + val shuffleReadBlockedQuantiles = + + + Shuffle Read Blocked Time + + +: getFormattedTimeQuantiles(shuffleReadBlockedTimes) - val shuffleReadSizes = validTasks.map { case TaskUIData(_, metrics, _) => - metrics.get.shuffleReadMetrics.map(_.remoteBytesRead).getOrElse(0L).toDouble + val shuffleReadTotalSizes = validTasks.map { case TaskUIData(_, metrics, _) => + metrics.get.shuffleReadMetrics.map(_.totalBytesRead).getOrElse(0L).toDouble } - - val shuffleReadRecords = validTasks.map { case TaskUIData(_, metrics, _) => + val shuffleReadTotalRecords = validTasks.map { case TaskUIData(_, metrics, _) => metrics.get.shuffleReadMetrics.map(_.recordsRead).getOrElse(0L).toDouble } + val shuffleReadTotalQuantiles = + + + Shuffle Read Size / Records + + +: + getFormattedSizeQuantilesWithRecords(shuffleReadTotalSizes, shuffleReadTotalRecords) - val shuffleReadQuantiles = Shuffle Read Size / Records (Remote) +: - getFormattedSizeQuantilesWithRecords(shuffleReadSizes, shuffleReadRecords) + val shuffleReadRemoteSizes = validTasks.map { case TaskUIData(_, metrics, _) => + metrics.get.shuffleReadMetrics.map(_.remoteBytesRead).getOrElse(0L).toDouble + } + val shuffleReadRemoteQuantiles = + + + Shuffle Remote Reads + + +: + getFormattedSizeQuantiles(shuffleReadRemoteSizes) val shuffleWriteSizes = validTasks.map { case TaskUIData(_, metrics, _) => metrics.get.shuffleWriteMetrics.map(_.shuffleBytesWritten).getOrElse(0L).toDouble @@ -374,7 +402,10 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") { {shuffleReadBlockedQuantiles} - {shuffleReadQuantiles} + {shuffleReadTotalQuantiles} + + {shuffleReadRemoteQuantiles} + } else { Nil }, @@ -429,7 +460,7 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") { val gcTime = metrics.map(_.jvmGCTime).getOrElse(0L) val taskDeserializationTime = metrics.map(_.executorDeserializeTime).getOrElse(0L) val serializationTime = metrics.map(_.resultSerializationTime).getOrElse(0L) - val gettingResultTime = info.gettingResultTime + val gettingResultTime = getGettingResultTime(info) val maybeAccumulators = info.accumulables val accumulatorsReadable = maybeAccumulators.map{acc => s"${acc.name}: ${acc.update.get}"} @@ -454,11 +485,15 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") { val shuffleReadBlockedTimeReadable = maybeShuffleRead.map(ms => UIUtils.formatDuration(ms.fetchWaitTime)).getOrElse("") - val shuffleReadSortable = maybeShuffleRead.map(_.remoteBytesRead.toString).getOrElse("") - val shuffleReadReadable = maybeShuffleRead - .map(m => s"${Utils.bytesToString(m.remoteBytesRead)}").getOrElse("") + val totalShuffleBytes = maybeShuffleRead.map(_.totalBytesRead) + val shuffleReadSortable = totalShuffleBytes.map(_.toString).getOrElse("") + val shuffleReadReadable = totalShuffleBytes.map(Utils.bytesToString).getOrElse("") val shuffleReadRecords = maybeShuffleRead.map(_.recordsRead.toString).getOrElse("") + val remoteShuffleBytes = maybeShuffleRead.map(_.remoteBytesRead) + val shuffleReadRemoteSortable = remoteShuffleBytes.map(_.toString).getOrElse("") + val shuffleReadRemoteReadable = remoteShuffleBytes.map(Utils.bytesToString).getOrElse("") + val maybeShuffleWrite = metrics.flatMap(_.shuffleWriteMetrics) val shuffleWriteSortable = maybeShuffleWrite.map(_.shuffleBytesWritten.toString).getOrElse("") val shuffleWriteReadable = maybeShuffleWrite @@ -536,6 +571,10 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") { {s"$shuffleReadReadable / $shuffleReadRecords"} + + {shuffleReadRemoteReadable} + }} {if (hasShuffleWrite) { @@ -584,16 +623,32 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") { {errorSummary}{details} } - private def getSchedulerDelay(info: TaskInfo, metrics: TaskMetrics): Long = { - val totalExecutionTime = { - if (info.gettingResultTime > 0) { - (info.gettingResultTime - info.launchTime) + private def getGettingResultTime(info: TaskInfo): Long = { + if (info.gettingResultTime > 0) { + if (info.finishTime > 0) { + info.finishTime - info.gettingResultTime } else { - (info.finishTime - info.launchTime) + // The task is still fetching the result. + System.currentTimeMillis - info.gettingResultTime } + } else { + 0L } + } + + private def getSchedulerDelay(info: TaskInfo, metrics: TaskMetrics): Long = { + val totalExecutionTime = + if (info.gettingResult) { + info.gettingResultTime - info.launchTime + } else if (info.finished) { + info.finishTime - info.launchTime + } else { + 0 + } val executorOverhead = (metrics.executorDeserializeTime + metrics.resultSerializationTime) - totalExecutionTime - metrics.executorRunTime - executorOverhead + math.max( + 0, + totalExecutionTime - metrics.executorRunTime - executorOverhead - getGettingResultTime(info)) } } diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala index 703d43f9c640d..cb72890a0fd20 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala @@ -73,20 +73,21 @@ private[ui] class StageTableBase( } private def makeDescription(s: StageInfo): Seq[Node] = { - // scalastyle:off + val basePathUri = UIUtils.prependBaseUri(basePath) + val killLink = if (killEnabled) { - val killLinkUri = "%s/stages/stage/kill?id=%s&terminate=true" - .format(UIUtils.prependBaseUri(basePath), s.stageId) - val confirm = "return window.confirm('Are you sure you want to kill stage %s ?');" - .format(s.stageId) - - (kill) - + val killLinkUri = s"$basePathUri/stages/stage/kill/" + val confirm = + s"if (window.confirm('Are you sure you want to kill stage ${s.stageId} ?')) " + + "{ this.parentNode.submit(); return true; } else { return false; }" +
    + + + (kill) +
    } - // scalastyle:on - val nameLinkUri ="%s/stages/stage?id=%s&attempt=%s" - .format(UIUtils.prependBaseUri(basePath), s.stageId, s.attemptId) + val nameLinkUri = s"$basePathUri/stages/stage?id=${s.stageId}&attempt=${s.attemptId}" val nameLink = {s.name} val cachedRddInfos = s.rddInfos.filter(_.numCachedPartitions > 0) @@ -98,11 +99,9 @@ private[ui] class StageTableBase( @@ -138,7 +137,7 @@ private[ui] class StageTableBase( val inputReadWithUnit = if (inputRead > 0) Utils.bytesToString(inputRead) else "" val outputWrite = stageData.outputBytes val outputWriteWithUnit = if (outputWrite > 0) Utils.bytesToString(outputWrite) else "" - val shuffleRead = stageData.shuffleReadBytes + val shuffleRead = stageData.shuffleReadTotalBytes val shuffleReadWithUnit = if (shuffleRead > 0) Utils.bytesToString(shuffleRead) else "" val shuffleWrite = stageData.shuffleWriteBytes val shuffleWriteWithUnit = if (shuffleWrite > 0) Utils.bytesToString(shuffleWrite) else "" diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StagesTab.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StagesTab.scala index 937261de00e3a..1bd2d87e00796 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/StagesTab.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/StagesTab.scala @@ -32,10 +32,10 @@ private[ui] class StagesTab(parent: SparkUI) extends SparkUITab(parent, "stages" attachPage(new StagePage(this)) attachPage(new PoolPage(this)) - def isFairScheduler = listener.schedulingMode.exists(_ == SchedulingMode.FAIR) + def isFairScheduler: Boolean = listener.schedulingMode.exists(_ == SchedulingMode.FAIR) - def handleKillRequest(request: HttpServletRequest) = { - if ((killEnabled) && (parent.securityManager.checkModifyPermissions(request.getRemoteUser))) { + def handleKillRequest(request: HttpServletRequest): Unit = { + if (killEnabled && parent.securityManager.checkModifyPermissions(request.getRemoteUser)) { val killFlag = Option(request.getParameter("terminate")).getOrElse("false").toBoolean val stageId = Option(request.getParameter("id")).getOrElse("-1").toInt if (stageId >= 0 && killFlag && listener.activeStages.contains(stageId)) { diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/TaskDetailsClassNames.scala b/core/src/main/scala/org/apache/spark/ui/jobs/TaskDetailsClassNames.scala index 37cf2c207ba40..9bf67db8acde1 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/TaskDetailsClassNames.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/TaskDetailsClassNames.scala @@ -28,6 +28,7 @@ private[spark] object TaskDetailsClassNames { val SCHEDULER_DELAY = "scheduler_delay" val TASK_DESERIALIZATION_TIME = "deserialization_time" val SHUFFLE_READ_BLOCKED_TIME = "fetch_wait_time" + val SHUFFLE_READ_REMOTE_SIZE = "shuffle_read_remote" val RESULT_SERIALIZATION_TIME = "serialization_time" val GETTING_RESULT_TIME = "getting_result_time" } diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/UIData.scala b/core/src/main/scala/org/apache/spark/ui/jobs/UIData.scala index 69aac6c862de5..711a3697bda15 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/UIData.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/UIData.scala @@ -80,7 +80,7 @@ private[jobs] object UIData { var inputRecords: Long = _ var outputBytes: Long = _ var outputRecords: Long = _ - var shuffleReadBytes: Long = _ + var shuffleReadTotalBytes: Long = _ var shuffleReadRecords : Long = _ var shuffleWriteBytes: Long = _ var shuffleWriteRecords: Long = _ @@ -94,11 +94,11 @@ private[jobs] object UIData { var taskData = new HashMap[Long, TaskUIData] var executorSummary = new HashMap[String, ExecutorSummary] - def hasInput = inputBytes > 0 - def hasOutput = outputBytes > 0 - def hasShuffleRead = shuffleReadBytes > 0 - def hasShuffleWrite = shuffleWriteBytes > 0 - def hasBytesSpilled = memoryBytesSpilled > 0 && diskBytesSpilled > 0 + def hasInput: Boolean = inputBytes > 0 + def hasOutput: Boolean = outputBytes > 0 + def hasShuffleRead: Boolean = shuffleReadTotalBytes > 0 + def hasShuffleWrite: Boolean = shuffleWriteBytes > 0 + def hasBytesSpilled: Boolean = memoryBytesSpilled > 0 && diskBytesSpilled > 0 } /** diff --git a/core/src/main/scala/org/apache/spark/ui/storage/StorageTab.scala b/core/src/main/scala/org/apache/spark/ui/storage/StorageTab.scala index a81291d505583..045bd784990d1 100644 --- a/core/src/main/scala/org/apache/spark/ui/storage/StorageTab.scala +++ b/core/src/main/scala/org/apache/spark/ui/storage/StorageTab.scala @@ -40,10 +40,10 @@ private[ui] class StorageTab(parent: SparkUI) extends SparkUITab(parent, "storag class StorageListener(storageStatusListener: StorageStatusListener) extends SparkListener { private[ui] val _rddInfoMap = mutable.Map[Int, RDDInfo]() // exposed for testing - def storageStatusList = storageStatusListener.storageStatusList + def storageStatusList: Seq[StorageStatus] = storageStatusListener.storageStatusList /** Filter RDD info to include only those with cached partitions */ - def rddInfoList = _rddInfoMap.values.filter(_.numCachedPartitions > 0).toSeq + def rddInfoList: Seq[RDDInfo] = _rddInfoMap.values.filter(_.numCachedPartitions > 0).toSeq /** Update the storage info of the RDDs whose blocks are among the given updated blocks */ private def updateRDDInfo(updatedBlocks: Seq[(BlockId, BlockStatus)]): Unit = { @@ -56,19 +56,19 @@ class StorageListener(storageStatusListener: StorageStatusListener) extends Spar * Assumes the storage status list is fully up-to-date. This implies the corresponding * StorageStatusSparkListener must process the SparkListenerTaskEnd event before this listener. */ - override def onTaskEnd(taskEnd: SparkListenerTaskEnd) = synchronized { + override def onTaskEnd(taskEnd: SparkListenerTaskEnd): Unit = synchronized { val metrics = taskEnd.taskMetrics if (metrics != null && metrics.updatedBlocks.isDefined) { updateRDDInfo(metrics.updatedBlocks.get) } } - override def onStageSubmitted(stageSubmitted: SparkListenerStageSubmitted) = synchronized { + override def onStageSubmitted(stageSubmitted: SparkListenerStageSubmitted): Unit = synchronized { val rddInfos = stageSubmitted.stageInfo.rddInfos rddInfos.foreach { info => _rddInfoMap.getOrElseUpdate(info.id, info) } } - override def onStageCompleted(stageCompleted: SparkListenerStageCompleted) = synchronized { + override def onStageCompleted(stageCompleted: SparkListenerStageCompleted): Unit = synchronized { // Remove all partitions that are no longer cached in current completed stage val completedRddIds = stageCompleted.stageInfo.rddInfos.map(r => r.id).toSet _rddInfoMap.retain { case (id, info) => @@ -76,7 +76,7 @@ class StorageListener(storageStatusListener: StorageStatusListener) extends Spar } } - override def onUnpersistRDD(unpersistRDD: SparkListenerUnpersistRDD) = synchronized { + override def onUnpersistRDD(unpersistRDD: SparkListenerUnpersistRDD): Unit = synchronized { _rddInfoMap.remove(unpersistRDD.rddId) } } diff --git a/core/src/main/scala/org/apache/spark/util/ActorLogReceive.scala b/core/src/main/scala/org/apache/spark/util/ActorLogReceive.scala index 332d0cbb2dc0c..81a7cbde01ce5 100644 --- a/core/src/main/scala/org/apache/spark/util/ActorLogReceive.scala +++ b/core/src/main/scala/org/apache/spark/util/ActorLogReceive.scala @@ -43,7 +43,13 @@ private[spark] trait ActorLogReceive { private val _receiveWithLogging = receiveWithLogging - override def isDefinedAt(o: Any): Boolean = _receiveWithLogging.isDefinedAt(o) + override def isDefinedAt(o: Any): Boolean = { + val handled = _receiveWithLogging.isDefinedAt(o) + if (!handled) { + log.debug(s"Received unexpected actor system event: $o") + } + handled + } override def apply(o: Any): Unit = { if (log.isDebugEnabled) { diff --git a/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala b/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala index 3d9c6192ff7f7..8e8cc7cc6389e 100644 --- a/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala +++ b/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala @@ -20,7 +20,6 @@ package org.apache.spark.util import scala.collection.JavaConversions.mapAsJavaMap import scala.concurrent.Await import scala.concurrent.duration.{Duration, FiniteDuration} -import scala.util.Try import akka.actor.{ActorRef, ActorSystem, ExtendedActorSystem} import akka.pattern.ask @@ -66,7 +65,8 @@ private[spark] object AkkaUtils extends Logging { val akkaThreads = conf.getInt("spark.akka.threads", 4) val akkaBatchSize = conf.getInt("spark.akka.batchSize", 15) - val akkaTimeout = conf.getInt("spark.akka.timeout", conf.getInt("spark.network.timeout", 120)) + val akkaTimeoutS = conf.getTimeAsSeconds("spark.akka.timeout", + conf.get("spark.network.timeout", "120s")) val akkaFrameSize = maxFrameSizeBytes(conf) val akkaLogLifecycleEvents = conf.getBoolean("spark.akka.logLifecycleEvents", false) val lifecycleEvents = if (akkaLogLifecycleEvents) "on" else "off" @@ -78,10 +78,8 @@ private[spark] object AkkaUtils extends Logging { val logAkkaConfig = if (conf.getBoolean("spark.akka.logAkkaConfig", false)) "on" else "off" - val akkaHeartBeatPauses = conf.getInt("spark.akka.heartbeat.pauses", 6000) - val akkaFailureDetector = - conf.getDouble("spark.akka.failure-detector.threshold", 300.0) - val akkaHeartBeatInterval = conf.getInt("spark.akka.heartbeat.interval", 1000) + val akkaHeartBeatPausesS = conf.getTimeAsSeconds("spark.akka.heartbeat.pauses", "6000s") + val akkaHeartBeatIntervalS = conf.getTimeAsSeconds("spark.akka.heartbeat.interval", "1000s") val secretKey = securityManager.getSecretKey() val isAuthOn = securityManager.isAuthenticationEnabled() @@ -104,15 +102,14 @@ private[spark] object AkkaUtils extends Logging { |akka.jvm-exit-on-fatal-error = off |akka.remote.require-cookie = "$requireCookie" |akka.remote.secure-cookie = "$secureCookie" - |akka.remote.transport-failure-detector.heartbeat-interval = $akkaHeartBeatInterval s - |akka.remote.transport-failure-detector.acceptable-heartbeat-pause = $akkaHeartBeatPauses s - |akka.remote.transport-failure-detector.threshold = $akkaFailureDetector + |akka.remote.transport-failure-detector.heartbeat-interval = $akkaHeartBeatIntervalS s + |akka.remote.transport-failure-detector.acceptable-heartbeat-pause = $akkaHeartBeatPausesS s |akka.actor.provider = "akka.remote.RemoteActorRefProvider" |akka.remote.netty.tcp.transport-class = "akka.remote.transport.netty.NettyTransport" |akka.remote.netty.tcp.hostname = "$host" |akka.remote.netty.tcp.port = $port |akka.remote.netty.tcp.tcp-nodelay = on - |akka.remote.netty.tcp.connection-timeout = $akkaTimeout s + |akka.remote.netty.tcp.connection-timeout = $akkaTimeoutS s |akka.remote.netty.tcp.maximum-frame-size = ${akkaFrameSize}B |akka.remote.netty.tcp.execution-pool-size = $akkaThreads |akka.actor.default-dispatcher.throughput = $akkaBatchSize @@ -182,7 +179,7 @@ private[spark] object AkkaUtils extends Logging { message: Any, actor: ActorRef, maxAttempts: Int, - retryInterval: Int, + retryInterval: Long, timeout: FiniteDuration): T = { // TODO: Consider removing multiple attempts if (actor == null) { diff --git a/core/src/main/scala/org/apache/spark/util/AsynchronousListenerBus.scala b/core/src/main/scala/org/apache/spark/util/AsynchronousListenerBus.scala index 18c627e8c7a15..ce7887b76ff96 100644 --- a/core/src/main/scala/org/apache/spark/util/AsynchronousListenerBus.scala +++ b/core/src/main/scala/org/apache/spark/util/AsynchronousListenerBus.scala @@ -21,6 +21,7 @@ import java.util.concurrent._ import java.util.concurrent.atomic.AtomicBoolean import com.google.common.annotations.VisibleForTesting +import org.apache.spark.SparkContext /** * Asynchronously passes events to registered listeners. @@ -38,6 +39,8 @@ private[spark] abstract class AsynchronousListenerBus[L <: AnyRef, E](name: Stri self => + private var sparkContext: SparkContext = null + /* Cap the capacity of the event queue so we get an explicit error (rather than * an OOM exception) if it's perpetually being added to more quickly than it's being drained. */ private val EVENT_QUEUE_CAPACITY = 10000 @@ -57,7 +60,7 @@ private[spark] abstract class AsynchronousListenerBus[L <: AnyRef, E](name: Stri private val listenerThread = new Thread(name) { setDaemon(true) - override def run(): Unit = Utils.logUncaughtExceptions { + override def run(): Unit = Utils.tryOrStopSparkContext(sparkContext) { while (true) { eventLock.acquire() self.synchronized { @@ -89,9 +92,12 @@ private[spark] abstract class AsynchronousListenerBus[L <: AnyRef, E](name: Stri * This first sends out all buffered events posted before this listener bus has started, then * listens for any additional events asynchronously while the listener bus is still running. * This should only be called once. + * + * @param sc Used to stop the SparkContext in case the listener thread dies. */ - def start() { + def start(sc: SparkContext) { if (started.compareAndSet(false, true)) { + sparkContext = sc listenerThread.start() } else { throw new IllegalStateException(s"$name already started!") diff --git a/core/src/main/scala/org/apache/spark/util/Clock.scala b/core/src/main/scala/org/apache/spark/util/Clock.scala index 97c2b45aabf28..e92ed11bd165b 100644 --- a/core/src/main/scala/org/apache/spark/util/Clock.scala +++ b/core/src/main/scala/org/apache/spark/util/Clock.scala @@ -21,9 +21,47 @@ package org.apache.spark.util * An interface to represent clocks, so that they can be mocked out in unit tests. */ private[spark] trait Clock { - def getTime(): Long + def getTimeMillis(): Long + def waitTillTime(targetTime: Long): Long } -private[spark] object SystemClock extends Clock { - def getTime(): Long = System.currentTimeMillis() +/** + * A clock backed by the actual time from the OS as reported by the `System` API. + */ +private[spark] class SystemClock extends Clock { + + val minPollTime = 25L + + /** + * @return the same time (milliseconds since the epoch) + * as is reported by `System.currentTimeMillis()` + */ + def getTimeMillis(): Long = System.currentTimeMillis() + + /** + * @param targetTime block until the current time is at least this value + * @return current system time when wait has completed + */ + def waitTillTime(targetTime: Long): Long = { + var currentTime = 0L + currentTime = System.currentTimeMillis() + + var waitTime = targetTime - currentTime + if (waitTime <= 0) { + return currentTime + } + + val pollTime = math.max(waitTime / 10.0, minPollTime).toLong + + while (true) { + currentTime = System.currentTimeMillis() + waitTime = targetTime - currentTime + if (waitTime <= 0) { + return currentTime + } + val sleepTime = math.min(waitTime, pollTime) + Thread.sleep(sleepTime) + } + -1 + } } diff --git a/core/src/main/scala/org/apache/spark/util/CompletionIterator.scala b/core/src/main/scala/org/apache/spark/util/CompletionIterator.scala index 390310243ee0a..9044aaeef2d48 100644 --- a/core/src/main/scala/org/apache/spark/util/CompletionIterator.scala +++ b/core/src/main/scala/org/apache/spark/util/CompletionIterator.scala @@ -27,8 +27,8 @@ abstract class CompletionIterator[ +A, +I <: Iterator[A]](sub: I) extends Iterat // scalastyle:on private[this] var completed = false - def next() = sub.next() - def hasNext = { + def next(): A = sub.next() + def hasNext: Boolean = { val r = sub.hasNext if (!r && !completed) { completed = true @@ -37,13 +37,13 @@ abstract class CompletionIterator[ +A, +I <: Iterator[A]](sub: I) extends Iterat r } - def completion() + def completion(): Unit } private[spark] object CompletionIterator { - def apply[A, I <: Iterator[A]](sub: I, completionFunction: => Unit) : CompletionIterator[A,I] = { + def apply[A, I <: Iterator[A]](sub: I, completionFunction: => Unit) : CompletionIterator[A, I] = { new CompletionIterator[A,I](sub) { - def completion() = completionFunction + def completion(): Unit = completionFunction } } } diff --git a/core/src/main/scala/org/apache/spark/util/Distribution.scala b/core/src/main/scala/org/apache/spark/util/Distribution.scala index a465298c8c5ab..9aea8efa38c7a 100644 --- a/core/src/main/scala/org/apache/spark/util/Distribution.scala +++ b/core/src/main/scala/org/apache/spark/util/Distribution.scala @@ -57,7 +57,7 @@ private[spark] class Distribution(val data: Array[Double], val startIdx: Int, va out.println } - def statCounter = StatCounter(data.slice(startIdx, endIdx)) + def statCounter: StatCounter = StatCounter(data.slice(startIdx, endIdx)) /** * print a summary of this distribution to the given PrintStream. diff --git a/core/src/main/scala/org/apache/spark/util/EventLoop.scala b/core/src/main/scala/org/apache/spark/util/EventLoop.scala index b0ed908b84424..e9b2b8d24b476 100644 --- a/core/src/main/scala/org/apache/spark/util/EventLoop.scala +++ b/core/src/main/scala/org/apache/spark/util/EventLoop.scala @@ -76,9 +76,21 @@ private[spark] abstract class EventLoop[E](name: String) extends Logging { def stop(): Unit = { if (stopped.compareAndSet(false, true)) { eventThread.interrupt() - eventThread.join() - // Call onStop after the event thread exits to make sure onReceive happens before onStop - onStop() + var onStopCalled = false + try { + eventThread.join() + // Call onStop after the event thread exits to make sure onReceive happens before onStop + onStopCalled = true + onStop() + } catch { + case ie: InterruptedException => + Thread.currentThread().interrupt() + if (!onStopCalled) { + // ie is thrown from `eventThread.join()`. Otherwise, we should not call `onStop` since + // it's already called. + onStop() + } + } } else { // Keep quiet to allow calling `stop` multiple times. } diff --git a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala index b0b545640f5aa..474f79fb756f6 100644 --- a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala +++ b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala @@ -89,6 +89,8 @@ private[spark] object JsonProtocol { executorAddedToJson(executorAdded) case executorRemoved: SparkListenerExecutorRemoved => executorRemovedToJson(executorRemoved) + case logStart: SparkListenerLogStart => + logStartToJson(logStart) // These aren't used, but keeps compiler happy case SparkListenerExecutorMetricsUpdate(_, _) => JNothing } @@ -214,6 +216,11 @@ private[spark] object JsonProtocol { ("Removed Reason" -> executorRemoved.reason) } + def logStartToJson(logStart: SparkListenerLogStart): JValue = { + ("Event" -> Utils.getFormattedClassName(logStart)) ~ + ("Spark Version" -> SPARK_VERSION) + } + /** ------------------------------------------------------------------- * * JSON serialization methods for classes SparkListenerEvents depend on | * -------------------------------------------------------------------- */ @@ -294,6 +301,7 @@ private[spark] object JsonProtocol { ("Local Blocks Fetched" -> shuffleReadMetrics.localBlocksFetched) ~ ("Fetch Wait Time" -> shuffleReadMetrics.fetchWaitTime) ~ ("Remote Bytes Read" -> shuffleReadMetrics.remoteBytesRead) ~ + ("Local Bytes Read" -> shuffleReadMetrics.localBytesRead) ~ ("Total Records Read" -> shuffleReadMetrics.recordsRead) } @@ -446,6 +454,7 @@ private[spark] object JsonProtocol { val applicationEnd = Utils.getFormattedClassName(SparkListenerApplicationEnd) val executorAdded = Utils.getFormattedClassName(SparkListenerExecutorAdded) val executorRemoved = Utils.getFormattedClassName(SparkListenerExecutorRemoved) + val logStart = Utils.getFormattedClassName(SparkListenerLogStart) (json \ "Event").extract[String] match { case `stageSubmitted` => stageSubmittedFromJson(json) @@ -463,6 +472,7 @@ private[spark] object JsonProtocol { case `applicationEnd` => applicationEndFromJson(json) case `executorAdded` => executorAddedFromJson(json) case `executorRemoved` => executorRemovedFromJson(json) + case `logStart` => logStartFromJson(json) } } @@ -573,6 +583,11 @@ private[spark] object JsonProtocol { SparkListenerExecutorRemoved(time, executorId, reason) } + def logStartFromJson(json: JValue): SparkListenerLogStart = { + val sparkVersion = (json \ "Spark Version").extract[String] + SparkListenerLogStart(sparkVersion) + } + /** --------------------------------------------------------------------- * * JSON deserialization methods for classes SparkListenerEvents depend on | * ---------------------------------------------------------------------- */ @@ -674,6 +689,7 @@ private[spark] object JsonProtocol { metrics.incLocalBlocksFetched((json \ "Local Blocks Fetched").extract[Int]) metrics.incFetchWaitTime((json \ "Fetch Wait Time").extract[Long]) metrics.incRemoteBytesRead((json \ "Remote Bytes Read").extract[Long]) + metrics.incLocalBytesRead((json \ "Local Bytes Read").extractOpt[Long].getOrElse(0)) metrics.incRecordsRead((json \ "Total Records Read").extractOpt[Long].getOrElse(0)) metrics } diff --git a/core/src/main/scala/org/apache/spark/util/ListenerBus.scala b/core/src/main/scala/org/apache/spark/util/ListenerBus.scala index d60b8b9a31a9b..a725767d08cc2 100644 --- a/core/src/main/scala/org/apache/spark/util/ListenerBus.scala +++ b/core/src/main/scala/org/apache/spark/util/ListenerBus.scala @@ -19,9 +19,12 @@ package org.apache.spark.util import java.util.concurrent.CopyOnWriteArrayList +import scala.collection.JavaConversions._ +import scala.reflect.ClassTag import scala.util.control.NonFatal import org.apache.spark.Logging +import org.apache.spark.scheduler.SparkListener /** * An event bus which posts events to its listeners. @@ -64,4 +67,9 @@ private[spark] trait ListenerBus[L <: AnyRef, E] extends Logging { */ def onPostEvent(listener: L, event: E): Unit + private[spark] def findListenersByClass[T <: L : ClassTag](): Seq[T] = { + val c = implicitly[ClassTag[T]].runtimeClass + listeners.filter(_.getClass == c).map(_.asInstanceOf[T]).toSeq + } + } diff --git a/core/src/main/scala/org/apache/spark/util/ManualClock.scala b/core/src/main/scala/org/apache/spark/util/ManualClock.scala new file mode 100644 index 0000000000000..1718554061985 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/util/ManualClock.scala @@ -0,0 +1,65 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.util + +/** + * A `Clock` whose time can be manually set and modified. Its reported time does not change + * as time elapses, but only as its time is modified by callers. This is mainly useful for + * testing. + * + * @param time initial time (in milliseconds since the epoch) + */ +private[spark] class ManualClock(private var time: Long) extends Clock { + + /** + * @return `ManualClock` with initial time 0 + */ + def this() = this(0L) + + def getTimeMillis(): Long = + synchronized { + time + } + + /** + * @param timeToSet new time (in milliseconds) that the clock should represent + */ + def setTime(timeToSet: Long): Unit = synchronized { + time = timeToSet + notifyAll() + } + + /** + * @param timeToAdd time (in milliseconds) to add to the clock's time + */ + def advance(timeToAdd: Long): Unit = synchronized { + time += timeToAdd + notifyAll() + } + + /** + * @param targetTime block until the clock time is set or advanced to at least this time + * @return current time reported by the clock when waiting finishes + */ + def waitTillTime(targetTime: Long): Long = synchronized { + while (time < targetTime) { + wait(100) + } + getTimeMillis() + } +} diff --git a/core/src/main/scala/org/apache/spark/util/MetadataCleaner.scala b/core/src/main/scala/org/apache/spark/util/MetadataCleaner.scala index ac40f19ed6799..2bbfc988a99a8 100644 --- a/core/src/main/scala/org/apache/spark/util/MetadataCleaner.scala +++ b/core/src/main/scala/org/apache/spark/util/MetadataCleaner.scala @@ -67,15 +67,16 @@ private[spark] object MetadataCleanerType extends Enumeration { type MetadataCleanerType = Value - def systemProperty(which: MetadataCleanerType.MetadataCleanerType) = - "spark.cleaner.ttl." + which.toString + def systemProperty(which: MetadataCleanerType.MetadataCleanerType): String = { + "spark.cleaner.ttl." + which.toString + } } // TODO: This mutates a Conf to set properties right now, which is kind of ugly when used in the // initialization of StreamingContext. It's okay for users trying to configure stuff themselves. private[spark] object MetadataCleaner { - def getDelaySeconds(conf: SparkConf) = { - conf.getInt("spark.cleaner.ttl", -1) + def getDelaySeconds(conf: SparkConf): Int = { + conf.getTimeAsSeconds("spark.cleaner.ttl", "-1").toInt } def getDelaySeconds( diff --git a/core/src/main/scala/org/apache/spark/util/MutablePair.scala b/core/src/main/scala/org/apache/spark/util/MutablePair.scala index 74fa77b68de0b..dad888548ed10 100644 --- a/core/src/main/scala/org/apache/spark/util/MutablePair.scala +++ b/core/src/main/scala/org/apache/spark/util/MutablePair.scala @@ -43,7 +43,7 @@ case class MutablePair[@specialized(Int, Long, Double, Char, Boolean/* , AnyRef this } - override def toString = "(" + _1 + "," + _2 + ")" + override def toString: String = "(" + _1 + "," + _2 + ")" override def canEqual(that: Any): Boolean = that.isInstanceOf[MutablePair[_,_]] } diff --git a/core/src/main/scala/org/apache/spark/util/MutableURLClassLoader.scala b/core/src/main/scala/org/apache/spark/util/MutableURLClassLoader.scala new file mode 100644 index 0000000000000..1e0ba5c28754a --- /dev/null +++ b/core/src/main/scala/org/apache/spark/util/MutableURLClassLoader.scala @@ -0,0 +1,101 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.util + +import java.net.{URLClassLoader, URL} +import java.util.Enumeration +import java.util.concurrent.ConcurrentHashMap + +import scala.collection.JavaConversions._ + +/** + * URL class loader that exposes the `addURL` and `getURLs` methods in URLClassLoader. + */ +private[spark] class MutableURLClassLoader(urls: Array[URL], parent: ClassLoader) + extends URLClassLoader(urls, parent) { + + override def addURL(url: URL): Unit = { + super.addURL(url) + } + + override def getURLs(): Array[URL] = { + super.getURLs() + } + +} + +/** + * A mutable class loader that gives preference to its own URLs over the parent class loader + * when loading classes and resources. + */ +private[spark] class ChildFirstURLClassLoader(urls: Array[URL], parent: ClassLoader) + extends MutableURLClassLoader(urls, null) { + + private val parentClassLoader = new ParentClassLoader(parent) + + /** + * Used to implement fine-grained class loading locks similar to what is done by Java 7. This + * prevents deadlock issues when using non-hierarchical class loaders. + * + * Note that due to Java 6 compatibility (and some issues with implementing class loaders in + * Scala), Java 7's `ClassLoader.registerAsParallelCapable` method is not called. + */ + private val locks = new ConcurrentHashMap[String, Object]() + + override def loadClass(name: String, resolve: Boolean): Class[_] = { + var lock = locks.get(name) + if (lock == null) { + val newLock = new Object() + lock = locks.putIfAbsent(name, newLock) + if (lock == null) { + lock = newLock + } + } + + lock.synchronized { + try { + super.loadClass(name, resolve) + } catch { + case e: ClassNotFoundException => + parentClassLoader.loadClass(name, resolve) + } + } + } + + override def getResource(name: String): URL = { + val url = super.findResource(name) + val res = if (url != null) url else parentClassLoader.getResource(name) + res + } + + override def getResources(name: String): Enumeration[URL] = { + val urls = super.findResources(name) + val res = + if (urls != null && urls.hasMoreElements()) { + urls + } else { + parentClassLoader.getResources(name) + } + res + } + + override def addURL(url: URL) { + super.addURL(url) + } + +} diff --git a/core/src/main/scala/org/apache/spark/util/ParentClassLoader.scala b/core/src/main/scala/org/apache/spark/util/ParentClassLoader.scala index 3abc12681fe9a..73d126ff6254e 100644 --- a/core/src/main/scala/org/apache/spark/util/ParentClassLoader.scala +++ b/core/src/main/scala/org/apache/spark/util/ParentClassLoader.scala @@ -18,15 +18,20 @@ package org.apache.spark.util /** - * A class loader which makes findClass accesible to the child + * A class loader which makes some protected methods in ClassLoader accesible. */ private[spark] class ParentClassLoader(parent: ClassLoader) extends ClassLoader(parent) { - override def findClass(name: String) = { + override def findClass(name: String): Class[_] = { super.findClass(name) } override def loadClass(name: String): Class[_] = { super.loadClass(name) } + + override def loadClass(name: String, resolve: Boolean): Class[_] = { + super.loadClass(name, resolve) + } + } diff --git a/core/src/main/scala/org/apache/spark/util/RpcUtils.scala b/core/src/main/scala/org/apache/spark/util/RpcUtils.scala new file mode 100644 index 0000000000000..6665b17c3d5df --- /dev/null +++ b/core/src/main/scala/org/apache/spark/util/RpcUtils.scala @@ -0,0 +1,35 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.util + +import org.apache.spark.{SparkEnv, SparkConf} +import org.apache.spark.rpc.{RpcAddress, RpcEndpointRef, RpcEnv} + +object RpcUtils { + + /** + * Retrieve a [[RpcEndpointRef]] which is located in the driver via its name. + */ + def makeDriverRef(name: String, conf: SparkConf, rpcEnv: RpcEnv): RpcEndpointRef = { + val driverActorSystemName = SparkEnv.driverActorSystemName + val driverHost: String = conf.get("spark.driver.host", "localhost") + val driverPort: Int = conf.getInt("spark.driver.port", 7077) + Utils.checkHost(driverHost, "Expected hostname") + rpcEnv.setupEndpointRef(driverActorSystemName, RpcAddress(driverHost, driverPort), name) + } +} diff --git a/core/src/main/scala/org/apache/spark/util/SerializableBuffer.scala b/core/src/main/scala/org/apache/spark/util/SerializableBuffer.scala index 770ff9d5ad6ae..a06b6f84ef11b 100644 --- a/core/src/main/scala/org/apache/spark/util/SerializableBuffer.scala +++ b/core/src/main/scala/org/apache/spark/util/SerializableBuffer.scala @@ -27,7 +27,7 @@ import java.nio.channels.Channels */ private[spark] class SerializableBuffer(@transient var buffer: ByteBuffer) extends Serializable { - def value = buffer + def value: ByteBuffer = buffer private def readObject(in: ObjectInputStream): Unit = Utils.tryOrIOException { val length = in.readInt() diff --git a/core/src/main/scala/org/apache/spark/util/SizeEstimator.scala b/core/src/main/scala/org/apache/spark/util/SizeEstimator.scala index bce3b3afe9aba..26ffbf9350388 100644 --- a/core/src/main/scala/org/apache/spark/util/SizeEstimator.scala +++ b/core/src/main/scala/org/apache/spark/util/SizeEstimator.scala @@ -18,18 +18,16 @@ package org.apache.spark.util import java.lang.management.ManagementFactory -import java.lang.reflect.{Array => JArray} -import java.lang.reflect.Field -import java.lang.reflect.Modifier -import java.util.IdentityHashMap -import java.util.Random +import java.lang.reflect.{Field, Modifier} +import java.util.{IdentityHashMap, Random} import java.util.concurrent.ConcurrentHashMap - import scala.collection.mutable.ArrayBuffer +import scala.runtime.ScalaRunTime import org.apache.spark.Logging import org.apache.spark.util.collection.OpenHashSet + /** * Estimates the sizes of Java objects (number of bytes of memory they occupy), for use in * memory-aware caches. @@ -184,9 +182,9 @@ private[spark] object SizeEstimator extends Logging { private val ARRAY_SIZE_FOR_SAMPLING = 200 private val ARRAY_SAMPLE_SIZE = 100 // should be lower than ARRAY_SIZE_FOR_SAMPLING - private def visitArray(array: AnyRef, cls: Class[_], state: SearchState) { - val length = JArray.getLength(array) - val elementClass = cls.getComponentType + private def visitArray(array: AnyRef, arrayClass: Class[_], state: SearchState) { + val length = ScalaRunTime.array_length(array) + val elementClass = arrayClass.getComponentType() // Arrays have object header and length field which is an integer var arrSize: Long = alignSize(objectSize + INT_SIZE) @@ -199,22 +197,26 @@ private[spark] object SizeEstimator extends Logging { state.size += arrSize if (length <= ARRAY_SIZE_FOR_SAMPLING) { - for (i <- 0 until length) { - state.enqueue(JArray.get(array, i)) + var arrayIndex = 0 + while (arrayIndex < length) { + state.enqueue(ScalaRunTime.array_apply(array, arrayIndex).asInstanceOf[AnyRef]) + arrayIndex += 1 } } else { // Estimate the size of a large array by sampling elements without replacement. var size = 0.0 val rand = new Random(42) val drawn = new OpenHashSet[Int](ARRAY_SAMPLE_SIZE) - for (i <- 0 until ARRAY_SAMPLE_SIZE) { + var numElementsDrawn = 0 + while (numElementsDrawn < ARRAY_SAMPLE_SIZE) { var index = 0 do { index = rand.nextInt(length) } while (drawn.contains(index)) drawn.add(index) - val elem = JArray.get(array, index) + val elem = ScalaRunTime.array_apply(array, index).asInstanceOf[AnyRef] size += SizeEstimator.estimate(elem, state.visited) + numElementsDrawn += 1 } state.size += ((length / (ARRAY_SAMPLE_SIZE * 1.0)) * size).toLong } diff --git a/core/src/main/scala/org/apache/spark/util/StatCounter.scala b/core/src/main/scala/org/apache/spark/util/StatCounter.scala index d80eed455c427..8586da1996cf3 100644 --- a/core/src/main/scala/org/apache/spark/util/StatCounter.scala +++ b/core/src/main/scala/org/apache/spark/util/StatCounter.scala @@ -141,8 +141,8 @@ class StatCounter(values: TraversableOnce[Double]) extends Serializable { object StatCounter { /** Build a StatCounter from a list of values. */ - def apply(values: TraversableOnce[Double]) = new StatCounter(values) + def apply(values: TraversableOnce[Double]): StatCounter = new StatCounter(values) /** Build a StatCounter from a list of values passed as variable-length arguments. */ - def apply(values: Double*) = new StatCounter(values) + def apply(values: Double*): StatCounter = new StatCounter(values) } diff --git a/core/src/main/scala/org/apache/spark/util/TimeStampedWeakValueHashMap.scala b/core/src/main/scala/org/apache/spark/util/TimeStampedWeakValueHashMap.scala index f5be5856c2109..310c0c109416c 100644 --- a/core/src/main/scala/org/apache/spark/util/TimeStampedWeakValueHashMap.scala +++ b/core/src/main/scala/org/apache/spark/util/TimeStampedWeakValueHashMap.scala @@ -82,7 +82,7 @@ private[spark] class TimeStampedWeakValueHashMap[A, B](updateTimeStampOnGet: Boo this } - override def update(key: A, value: B) = this += ((key, value)) + override def update(key: A, value: B): Unit = this += ((key, value)) override def apply(key: A): B = internalMap.apply(key) @@ -92,14 +92,14 @@ private[spark] class TimeStampedWeakValueHashMap[A, B](updateTimeStampOnGet: Boo override def size: Int = internalMap.size - override def foreach[U](f: ((A, B)) => U) = nonNullReferenceMap.foreach(f) + override def foreach[U](f: ((A, B)) => U): Unit = nonNullReferenceMap.foreach(f) def putIfAbsent(key: A, value: B): Option[B] = internalMap.putIfAbsent(key, value) def toMap: Map[A, B] = iterator.toMap /** Remove old key-value pairs with timestamps earlier than `threshTime`. */ - def clearOldValues(threshTime: Long) = internalMap.clearOldValues(threshTime) + def clearOldValues(threshTime: Long): Unit = internalMap.clearOldValues(threshTime) /** Remove entries with values that are no longer strongly reachable. */ def clearNullValues() { diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala index 61d287ca9c3ac..1029b0f9fce1e 100644 --- a/core/src/main/scala/org/apache/spark/util/Utils.scala +++ b/core/src/main/scala/org/apache/spark/util/Utils.scala @@ -22,7 +22,7 @@ import java.lang.management.ManagementFactory import java.net._ import java.nio.ByteBuffer import java.util.{Properties, Locale, Random, UUID} -import java.util.concurrent.{ThreadFactory, ConcurrentHashMap, Executors, ThreadPoolExecutor} +import java.util.concurrent._ import javax.net.ssl.HttpsURLConnection import scala.collection.JavaConversions._ @@ -34,10 +34,12 @@ import scala.util.Try import scala.util.control.{ControlThrowable, NonFatal} import com.google.common.io.{ByteStreams, Files} +import com.google.common.net.InetAddresses import com.google.common.util.concurrent.ThreadFactoryBuilder import org.apache.commons.lang3.SystemUtils import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.{FileSystem, FileUtil, Path} +import org.apache.hadoop.security.UserGroupInformation import org.apache.log4j.PropertyConfigurator import org.eclipse.jetty.util.MultiException import org.json4s._ @@ -45,6 +47,7 @@ import tachyon.client.{TachyonFS, TachyonFile} import org.apache.spark._ import org.apache.spark.deploy.SparkHadoopUtil +import org.apache.spark.network.util.JavaUtils import org.apache.spark.serializer.{DeserializationStream, SerializationStream, SerializerInstance} /** CallSite represents a place in user code. It can have a short and a long form. */ @@ -62,6 +65,7 @@ private[spark] object Utils extends Logging { val random = new Random() private val MAX_DIR_CREATION_ATTEMPTS: Int = 10 + @volatile private var localRootDirs: Array[String] = null /** Serialize an object using Java serialization */ def serialize[T](o: T): Array[Byte] = { @@ -83,7 +87,7 @@ private[spark] object Utils extends Logging { def deserialize[T](bytes: Array[Byte], loader: ClassLoader): T = { val bis = new ByteArrayInputStream(bytes) val ois = new ObjectInputStream(bis) { - override def resolveClass(desc: ObjectStreamClass) = + override def resolveClass(desc: ObjectStreamClass): Class[_] = Class.forName(desc.getName, false, loader) } ois.readObject.asInstanceOf[T] @@ -104,11 +108,10 @@ private[spark] object Utils extends Logging { /** Serialize via nested stream using specific serializer */ def serializeViaNestedStream(os: OutputStream, ser: SerializerInstance)( - f: SerializationStream => Unit) = { + f: SerializationStream => Unit): Unit = { val osWrapper = ser.serializeStream(new OutputStream { - def write(b: Int) = os.write(b) - - override def write(b: Array[Byte], off: Int, len: Int) = os.write(b, off, len) + override def write(b: Int): Unit = os.write(b) + override def write(b: Array[Byte], off: Int, len: Int): Unit = os.write(b, off, len) }) try { f(osWrapper) @@ -119,10 +122,9 @@ private[spark] object Utils extends Logging { /** Deserialize via nested stream using specific serializer */ def deserializeViaNestedStream(is: InputStream, ser: SerializerInstance)( - f: DeserializationStream => Unit) = { + f: DeserializationStream => Unit): Unit = { val isWrapper = ser.deserializeStream(new InputStream { - def read(): Int = is.read() - + override def read(): Int = is.read() override def read(b: Array[Byte], off: Int, len: Int): Int = is.read(b, off, len) }) try { @@ -135,7 +137,7 @@ private[spark] object Utils extends Logging { /** * Get the ClassLoader which loaded Spark. */ - def getSparkClassLoader = getClass.getClassLoader + def getSparkClassLoader: ClassLoader = getClass.getClassLoader /** * Get the Context ClassLoader on this thread or, if not present, the ClassLoader that @@ -144,7 +146,7 @@ private[spark] object Utils extends Logging { * This should be used whenever passing a ClassLoader to Class.ForName or finding the currently * active loader when setting up ClassLoader delegation chains. */ - def getContextOrSparkClassLoader = + def getContextOrSparkClassLoader: ClassLoader = Option(Thread.currentThread().getContextClassLoader).getOrElse(getSparkClassLoader) /** Determines whether the provided class is loadable in the current thread. */ @@ -153,12 +155,14 @@ private[spark] object Utils extends Logging { } /** Preferred alternative to Class.forName(className) */ - def classForName(className: String) = Class.forName(className, true, getContextOrSparkClassLoader) + def classForName(className: String): Class[_] = { + Class.forName(className, true, getContextOrSparkClassLoader) + } /** * Primitive often used when writing [[java.nio.ByteBuffer]] to [[java.io.DataOutput]] */ - def writeByteBuffer(bb: ByteBuffer, out: ObjectOutput) = { + def writeByteBuffer(bb: ByteBuffer, out: ObjectOutput): Unit = { if (bb.hasArray) { out.write(bb.array(), bb.arrayOffset() + bb.position(), bb.remaining()) } else { @@ -212,8 +216,8 @@ private[spark] object Utils extends Logging { // Is the path already registered to be deleted via a shutdown hook ? def hasShutdownDeleteTachyonDir(file: TachyonFile): Boolean = { val absolutePath = file.getPath() - shutdownDeletePaths.synchronized { - shutdownDeletePaths.contains(absolutePath) + shutdownDeleteTachyonPaths.synchronized { + shutdownDeleteTachyonPaths.contains(absolutePath) } } @@ -282,18 +286,11 @@ private[spark] object Utils extends Logging { dir = new File(root, namePrefix + "-" + UUID.randomUUID.toString) if (dir.exists() || !dir.mkdirs()) { dir = null - } else { - // Restrict file permissions via chmod if available. - // For Windows this step is ignored. - if (!isWindows && !chmod700(dir)) { - dir.delete() - dir = null - } } } catch { case e: SecurityException => dir = null; } } - dir + dir.getCanonicalFile } /** @@ -318,7 +315,7 @@ private[spark] object Utils extends Logging { transferToEnabled: Boolean = false): Long = { var count = 0L - try { + tryWithSafeFinally { if (in.isInstanceOf[FileInputStream] && out.isInstanceOf[FileOutputStream] && transferToEnabled) { // When both streams are File stream, use transferTo to improve copy performance. @@ -358,7 +355,7 @@ private[spark] object Utils extends Logging { } } count - } finally { + } { if (closeStreams) { try { in.close() @@ -408,7 +405,8 @@ private[spark] object Utils extends Logging { useCache: Boolean) { val fileName = url.split("/").last val targetFile = new File(targetDir, fileName) - if (useCache) { + val fetchCacheEnabled = conf.getBoolean("spark.files.useFetchCache", defaultValue = true) + if (useCache && fetchCacheEnabled) { val cachedFileName = s"${url.hashCode}${timestamp}_cache" val lockFileName = s"${url.hashCode}${timestamp}_lock" val localDir = new File(getLocalDir(conf)) @@ -446,6 +444,12 @@ private[spark] object Utils extends Logging { } // Make the file executable - That's necessary for scripts FileUtil.chmod(targetFile.getAbsolutePath, "a+x") + + // Windows does not grant read permission by default to non-admin users + // Add read permission to owner explicitly + if (isWindows) { + FileUtil.chmod(targetFile.getAbsolutePath, "u+r") + } } /** @@ -609,9 +613,10 @@ private[spark] object Utils extends Logging { } Utils.setupSecureURLConnection(uc, securityMgr) - val timeout = conf.getInt("spark.files.fetchTimeout", 60) * 1000 - uc.setConnectTimeout(timeout) - uc.setReadTimeout(timeout) + val timeoutMs = + conf.getTimeAsSeconds("spark.files.fetchTimeout", "60s").toInt * 1000 + uc.setConnectTimeout(timeoutMs) + uc.setReadTimeout(timeoutMs) uc.connect() val in = uc.getInputStream() downloadFile(url, in, targetFile, fileOverwrite) @@ -623,7 +628,8 @@ private[spark] object Utils extends Logging { case _ => val fs = getHadoopFileSystem(uri, hadoopConf) val path = new Path(uri) - fetchHcfsFile(path, new File(targetDir, path.getName), fs, conf, hadoopConf, fileOverwrite) + fetchHcfsFile(path, targetDir, fs, conf, hadoopConf, fileOverwrite, + filename = Some(filename)) } } @@ -638,19 +644,22 @@ private[spark] object Utils extends Logging { fs: FileSystem, conf: SparkConf, hadoopConf: Configuration, - fileOverwrite: Boolean): Unit = { - if (!targetDir.mkdir()) { + fileOverwrite: Boolean, + filename: Option[String] = None): Unit = { + if (!targetDir.exists() && !targetDir.mkdir()) { throw new IOException(s"Failed to create directory ${targetDir.getPath}") } - fs.listStatus(path).foreach { fileStatus => - val innerPath = fileStatus.getPath - if (fileStatus.isDir) { - fetchHcfsFile(innerPath, new File(targetDir, innerPath.getName), fs, conf, hadoopConf, - fileOverwrite) - } else { - val in = fs.open(innerPath) - val targetFile = new File(targetDir, innerPath.getName) - downloadFile(innerPath.toString, in, targetFile, fileOverwrite) + val dest = new File(targetDir, filename.getOrElse(path.getName)) + if (fs.isFile(path)) { + val in = fs.open(path) + try { + downloadFile(path.toString, in, dest, fileOverwrite) + } finally { + in.close() + } + } else { + fs.listStatus(path).foreach { fileStatus => + fetchHcfsFile(fileStatus.getPath(), dest, fs, conf, hadoopConf, fileOverwrite) } } } @@ -683,14 +692,31 @@ private[spark] object Utils extends Logging { * and returns only the directories that exist / could be created. * * If no directories could be created, this will return an empty list. + * + * This method will cache the local directories for the application when it's first invoked. + * So calling it multiple times with a different configuration will always return the same + * set of directories. */ private[spark] def getOrCreateLocalRootDirs(conf: SparkConf): Array[String] = { + if (localRootDirs == null) { + this.synchronized { + if (localRootDirs == null) { + localRootDirs = getOrCreateLocalRootDirsImpl(conf) + } + } + } + localRootDirs + } + + private def getOrCreateLocalRootDirsImpl(conf: SparkConf): Array[String] = { if (isRunningInYarnContainer(conf)) { // If we are in yarn mode, systems can have different disk layouts so we must set it // to what Yarn on this system said was available. Note this assumes that Yarn has // created the directories already, and that they are secured so that only the // user has access to them. getYarnLocalDirs(conf).split(",") + } else if (conf.getenv("SPARK_EXECUTOR_DIRS") != null) { + conf.getenv("SPARK_EXECUTOR_DIRS").split(File.pathSeparator) } else { // In non-Yarn mode (or for the driver in yarn-client mode), we cannot trust the user // configuration to point to a secure directory. So create a subdirectory with restricted @@ -702,7 +728,9 @@ private[spark] object Utils extends Logging { try { val rootDir = new File(root) if (rootDir.exists || rootDir.mkdirs()) { - Some(createDirectory(root).getAbsolutePath()) + val dir = createTempDir(root) + chmod700(dir) + Some(dir.getAbsolutePath) } else { logError(s"Failed to create dir in $root. Ignoring this directory.") None @@ -732,6 +760,11 @@ private[spark] object Utils extends Logging { localDirs } + /** Used by unit tests. Do not call from other places. */ + private[spark] def clearLocalRootDirs(): Unit = { + localRootDirs = null + } + /** * Shuffle the elements of a collection into a random order, returning the * result in a new collection. Unlike scala.util.Random.shuffle, this method @@ -759,13 +792,12 @@ private[spark] object Utils extends Logging { * Get the local host's IP address in dotted-quad format (e.g. 1.2.3.4). * Note, this is typically not used from within core spark. */ - lazy val localIpAddress: String = findLocalIpAddress() - lazy val localIpAddressHostname: String = getAddressHostName(localIpAddress) + private lazy val localIpAddress: InetAddress = findLocalInetAddress() - private def findLocalIpAddress(): String = { + private def findLocalInetAddress(): InetAddress = { val defaultIpOverride = System.getenv("SPARK_LOCAL_IP") if (defaultIpOverride != null) { - defaultIpOverride + InetAddress.getByName(defaultIpOverride) } else { val address = InetAddress.getLocalHost if (address.isLoopbackAddress) { @@ -776,15 +808,20 @@ private[spark] object Utils extends Logging { // It's more proper to pick ip address following system output order. val activeNetworkIFs = NetworkInterface.getNetworkInterfaces.toList val reOrderedNetworkIFs = if (isWindows) activeNetworkIFs else activeNetworkIFs.reverse + for (ni <- reOrderedNetworkIFs) { - for (addr <- ni.getInetAddresses if !addr.isLinkLocalAddress && - !addr.isLoopbackAddress && addr.isInstanceOf[Inet4Address]) { + val addresses = ni.getInetAddresses.toList + .filterNot(addr => addr.isLinkLocalAddress || addr.isLoopbackAddress) + if (addresses.nonEmpty) { + val addr = addresses.find(_.isInstanceOf[Inet4Address]).getOrElse(addresses.head) + // because of Inet6Address.toHostName may add interface at the end if it knows about it + val strippedAddress = InetAddress.getByAddress(addr.getAddress) // We've found an address that looks reasonable! logWarning("Your hostname, " + InetAddress.getLocalHost.getHostName + " resolves to" + - " a loopback address: " + address.getHostAddress + "; using " + addr.getHostAddress + - " instead (on interface " + ni.getName + ")") + " a loopback address: " + address.getHostAddress + "; using " + + strippedAddress.getHostAddress + " instead (on interface " + ni.getName + ")") logWarning("Set SPARK_LOCAL_IP if you need to bind to another address") - return addr.getHostAddress + return strippedAddress } } logWarning("Your hostname, " + InetAddress.getLocalHost.getHostName + " resolves to" + @@ -792,7 +829,7 @@ private[spark] object Utils extends Logging { " external IP address!") logWarning("Set SPARK_LOCAL_IP if you need to bind to another address") } - address.getHostAddress + address } } @@ -812,11 +849,14 @@ private[spark] object Utils extends Logging { * Get the local machine's hostname. */ def localHostName(): String = { - customHostname.getOrElse(localIpAddressHostname) + customHostname.getOrElse(localIpAddress.getHostAddress) } - def getAddressHostName(address: String): String = { - InetAddress.getByName(address).getHostName + /** + * Get the local machine's URI. + */ + def localHostNameForURI(): String = { + customHostname.getOrElse(InetAddresses.toUriString(localIpAddress)) } def checkHost(host: String, message: String = "") { @@ -980,6 +1020,22 @@ private[spark] object Utils extends Logging { ) } + /** + * Convert a time parameter such as (50s, 100ms, or 250us) to microseconds for internal use. If + * no suffix is provided, the passed number is assumed to be in ms. + */ + def timeStringAsMs(str: String): Long = { + JavaUtils.timeStringAsMs(str) + } + + /** + * Convert a time parameter such as (50s, 100ms, or 250us) to microseconds for internal use. If + * no suffix is provided, the passed number is assumed to be in seconds. + */ + def timeStringAsSeconds(str: String): Long = { + JavaUtils.timeStringAsSec(str) + } + /** * Convert a Java memory parameter passed to -Xmx (such as 300m or 1g) to a number of megabytes. */ @@ -1116,6 +1172,8 @@ private[spark] object Utils extends Logging { /** * Execute a block of code that evaluates to Unit, forwarding any uncaught exceptions to the * default UncaughtExceptionHandler + * + * NOTE: This method is to be called by the spark-started JVM process. */ def tryOrExit(block: => Unit) { try { @@ -1126,6 +1184,32 @@ private[spark] object Utils extends Logging { } } + /** + * Execute a block of code that evaluates to Unit, stop SparkContext is there is any uncaught + * exception + * + * NOTE: This method is to be called by the driver-side components to avoid stopping the + * user-started JVM process completely; in contrast, tryOrExit is to be called in the + * spark-started JVM process . + */ + def tryOrStopSparkContext(sc: SparkContext)(block: => Unit) { + try { + block + } catch { + case e: ControlThrowable => throw e + case t: Throwable => + val currentThreadName = Thread.currentThread().getName + if (sc != null) { + logError(s"uncaught error in thread $currentThreadName, stopping SparkContext", t) + sc.stop() + } + if (!NonFatal(t)) { + logError(s"throw uncaught fatal error in thread $currentThreadName", t) + throw t + } + } + } + /** * Execute a block of code that evaluates to Unit, re-throwing any non-fatal uncaught * exceptions as IOException. This is used when implementing Externalizable and Serializable's @@ -1156,6 +1240,54 @@ private[spark] object Utils extends Logging { } } + /** Executes the given block. Log non-fatal errors if any, and only throw fatal errors */ + def tryLogNonFatalError(block: => Unit) { + try { + block + } catch { + case NonFatal(t) => + logError(s"Uncaught exception in thread ${Thread.currentThread().getName}", t) + } + } + + /** + * Execute a block of code, then a finally block, but if exceptions happen in + * the finally block, do not suppress the original exception. + * + * This is primarily an issue with `finally { out.close() }` blocks, where + * close needs to be called to clean up `out`, but if an exception happened + * in `out.write`, it's likely `out` may be corrupted and `out.close` will + * fail as well. This would then suppress the original/likely more meaningful + * exception from the original `out.write` call. + */ + def tryWithSafeFinally[T](block: => T)(finallyBlock: => Unit): T = { + // It would be nice to find a method on Try that did this + var originalThrowable: Throwable = null + try { + block + } catch { + case t: Throwable => + // Purposefully not using NonFatal, because even fatal exceptions + // we don't want to have our finallyBlock suppress + originalThrowable = t + throw originalThrowable + } finally { + try { + finallyBlock + } catch { + case t: Throwable => + if (originalThrowable != null) { + // We could do originalThrowable.addSuppressed(t), but it's + // not available in JDK 1.6. + logWarning(s"Suppressing exception in finally: " + t.getMessage, t) + throw originalThrowable + } else { + throw t + } + } + } + } + /** Default filtering function for finding call sites using `getCallSite`. */ private def coreExclusionFunction(className: String): Boolean = { // A regular expression to match classes of the "core" Spark API that we want to skip when @@ -1499,7 +1631,7 @@ private[spark] object Utils extends Logging { /** Return the class name of the given object, removing all dollar signs */ - def getFormattedClassName(obj: AnyRef) = { + def getFormattedClassName(obj: AnyRef): String = { obj.getClass.getSimpleName.replace("$", "") } @@ -1512,7 +1644,7 @@ private[spark] object Utils extends Logging { } /** Return an empty JSON object */ - def emptyJson = JObject(List[JField]()) + def emptyJson: JsonAST.JObject = JObject(List[JField]()) /** * Return a Hadoop FileSystem with the scheme encoded in the given path. @@ -1560,7 +1692,7 @@ private[spark] object Utils extends Logging { /** * Indicates whether Spark is currently running unit tests. */ - def isTesting = { + def isTesting: Boolean = { sys.env.contains("SPARK_TESTING") || sys.props.contains("spark.testing") } @@ -1818,6 +1950,10 @@ private[spark] object Utils extends Logging { startService: Int => (T, Int), conf: SparkConf, serviceName: String = ""): (T, Int) = { + + require(startPort == 0 || (1024 <= startPort && startPort < 65536), + "startPort should be between 1024 and 65535 (inclusive), or 0 for a random free port.") + val serviceString = if (serviceName.isEmpty) "" else s" '$serviceName'" val maxRetries = portMaxRetries(conf) for (offset <- 0 to maxRetries) { @@ -1986,6 +2122,16 @@ private[spark] object Utils extends Logging { throw new SparkException("Invalid master URL: " + sparkUrl, e) } } + + /** + * Returns the current user name. This is the currently logged in user, unless that's been + * overridden by the `SPARK_USER` environment variable. + */ + def getCurrentUserName(): String = { + Option(System.getenv("SPARK_USER")) + .getOrElse(UserGroupInformation.getCurrentUser().getShortUserName()) + } + } /** @@ -2002,7 +2148,7 @@ private[spark] class RedirectThread( override def run() { scala.util.control.Exception.ignoring(classOf[IOException]) { // FIXME: We copy the stream on the level of bytes to avoid encoding problems. - try { + Utils.tryWithSafeFinally { val buf = new Array[Byte](1024) var len = in.read(buf) while (len != -1) { @@ -2010,7 +2156,7 @@ private[spark] class RedirectThread( out.flush() len = in.read(buf) } - } finally { + } { if (propagateEof) { out.close() } diff --git a/core/src/main/scala/org/apache/spark/util/collection/BitSet.scala b/core/src/main/scala/org/apache/spark/util/collection/BitSet.scala index af1f64649f354..41cb8cfe2afa3 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/BitSet.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/BitSet.scala @@ -39,7 +39,7 @@ class BitSet(numBits: Int) extends Serializable { val wordIndex = bitIndex >> 6 // divide by 64 var i = 0 while(i < wordIndex) { words(i) = -1; i += 1 } - if(wordIndex < words.size) { + if(wordIndex < words.length) { // Set the remaining bits (note that the mask could still be zero) val mask = ~(-1L << (bitIndex & 0x3f)) words(wordIndex) |= mask @@ -156,10 +156,10 @@ class BitSet(numBits: Int) extends Serializable { /** * Get an iterator over the set bits. */ - def iterator = new Iterator[Int] { + def iterator: Iterator[Int] = new Iterator[Int] { var ind = nextSetBit(0) override def hasNext: Boolean = ind >= 0 - override def next() = { + override def next(): Int = { val tmp = ind ind = nextSetBit(ind + 1) tmp diff --git a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala index 8a0f5a602de12..9ff4744593d4d 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala @@ -159,7 +159,7 @@ class ExternalAppendOnlyMap[K, V, C]( val batchSizes = new ArrayBuffer[Long] // Flush the disk writer's contents to disk, and update relevant variables - def flush() = { + def flush(): Unit = { val w = writer writer = null w.commitAndClose() @@ -355,7 +355,7 @@ class ExternalAppendOnlyMap[K, V, C]( val pairs: ArrayBuffer[(K, C)]) extends Comparable[StreamBuffer] { - def isEmpty = pairs.length == 0 + def isEmpty: Boolean = pairs.length == 0 // Invalid if there are no more pairs in this stream def minKeyHash: Int = { diff --git a/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala b/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala index eaec5a71e6819..035f3767ff554 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala @@ -283,7 +283,7 @@ private[spark] class ExternalSorter[K, V, C]( // Flush the disk writer's contents to disk, and update relevant variables. // The writer is closed at the end of this process, and cannot be reused. - def flush() = { + def flush(): Unit = { val w = writer writer = null w.commitAndClose() @@ -352,6 +352,7 @@ private[spark] class ExternalSorter[K, V, C]( // Create our file writers if we haven't done so yet if (partitionWriters == null) { curWriteMetrics = new ShuffleWriteMetrics() + val openStartTime = System.nanoTime partitionWriters = Array.fill(numPartitions) { // Because these files may be read during shuffle, their compression must be controlled by // spark.shuffle.compress instead of spark.shuffle.spill.compress, so we need to use @@ -359,6 +360,10 @@ private[spark] class ExternalSorter[K, V, C]( val (blockId, file) = diskBlockManager.createTempShuffleBlock() blockManager.getDiskWriter(blockId, file, ser, fileBufferSize, curWriteMetrics).open() } + // Creating the file to write to and creating a disk writer both involve interacting with + // the disk, and can take a long time in aggregate when we open many files, so should be + // included in the shuffle write time. + curWriteMetrics.incShuffleWriteTime(System.nanoTime - openStartTime) } // No need to sort stuff, just write each element out @@ -659,6 +664,8 @@ private[spark] class ExternalSorter[K, V, C]( } /** + * Exposed for testing purposes. + * * Return an iterator over all the data written to this object, grouped by partition and * aggregated by the requested aggregator. For each partition we then have an iterator over its * contents, and these are expected to be accessed in order (you can't "skip ahead" to one @@ -668,7 +675,7 @@ private[spark] class ExternalSorter[K, V, C]( * For now, we just merge all the spilled files in once pass, but this can be modified to * support hierarchical merging. */ - def partitionedIterator: Iterator[(Int, Iterator[Product2[K, C]])] = { + def partitionedIterator: Iterator[(Int, Iterator[Product2[K, C]])] = { val usingMap = aggregator.isDefined val collection: SizeTrackingPairCollection[(Int, K), C] = if (usingMap) map else buffer if (spills.isEmpty && partitionWriters == null) { @@ -721,24 +728,21 @@ private[spark] class ExternalSorter[K, V, C]( // this simple we spill out the current in-memory collection so that everything is in files. spillToPartitionFiles(if (aggregator.isDefined) map else buffer) partitionWriters.foreach(_.commitAndClose()) - var out: FileOutputStream = null - var in: FileInputStream = null - try { - out = new FileOutputStream(outputFile, true) + val out = new FileOutputStream(outputFile, true) + val writeStartTime = System.nanoTime + util.Utils.tryWithSafeFinally { for (i <- 0 until numPartitions) { - in = new FileInputStream(partitionWriters(i).fileSegment().file) - val size = org.apache.spark.util.Utils.copyStream(in, out, false, transferToEnabled) - in.close() - in = null - lengths(i) = size - } - } finally { - if (out != null) { - out.close() - } - if (in != null) { - in.close() + val in = new FileInputStream(partitionWriters(i).fileSegment().file) + util.Utils.tryWithSafeFinally { + lengths(i) = org.apache.spark.util.Utils.copyStream(in, out, false, transferToEnabled) + } { + in.close() + } } + } { + out.close() + context.taskMetrics.shuffleWriteMetrics.foreach( + _.incShuffleWriteTime(System.nanoTime - writeStartTime)) } } else { // Either we're not bypassing merge-sort or we have only in-memory data; get an iterator by @@ -773,7 +777,7 @@ private[spark] class ExternalSorter[K, V, C]( /** * Read a partition file back as an iterator (used in our iterator method) */ - def readPartitionFile(writer: BlockObjectWriter): Iterator[Product2[K, C]] = { + private def readPartitionFile(writer: BlockObjectWriter): Iterator[Product2[K, C]] = { if (writer.isOpen) { writer.commitAndClose() } diff --git a/core/src/main/scala/org/apache/spark/util/collection/OpenHashMap.scala b/core/src/main/scala/org/apache/spark/util/collection/OpenHashMap.scala index b8de4ff9aa494..efc2482c74ddf 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/OpenHashMap.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/OpenHashMap.scala @@ -53,6 +53,15 @@ class OpenHashMap[K : ClassTag, @specialized(Long, Int, Double) V: ClassTag]( override def size: Int = if (haveNullValue) _keySet.size + 1 else _keySet.size + /** Tests whether this map contains a binding for a key. */ + def contains(k: K): Boolean = { + if (k == null) { + haveNullValue + } else { + _keySet.getPos(k) != OpenHashSet.INVALID_POS + } + } + /** Get the value for a given key */ def apply(k: K): V = { if (k == null) { @@ -109,7 +118,7 @@ class OpenHashMap[K : ClassTag, @specialized(Long, Int, Double) V: ClassTag]( } } - override def iterator = new Iterator[(K, V)] { + override def iterator: Iterator[(K, V)] = new Iterator[(K, V)] { var pos = -1 var nextPair: (K, V) = computeNextPair() @@ -132,9 +141,9 @@ class OpenHashMap[K : ClassTag, @specialized(Long, Int, Double) V: ClassTag]( } } - def hasNext = nextPair != null + def hasNext: Boolean = nextPair != null - def next() = { + def next(): (K, V) = { val pair = nextPair nextPair = computeNextPair() pair diff --git a/core/src/main/scala/org/apache/spark/util/collection/OpenHashSet.scala b/core/src/main/scala/org/apache/spark/util/collection/OpenHashSet.scala index 4e363b74f4bef..1501111a06655 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/OpenHashSet.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/OpenHashSet.scala @@ -85,7 +85,7 @@ class OpenHashSet[@specialized(Long, Int) T: ClassTag]( protected var _bitset = new BitSet(_capacity) - def getBitSet = _bitset + def getBitSet: BitSet = _bitset // Init of the array in constructor (instead of in declaration) to work around a Scala compiler // specialization bug that would generate two arrays (one for Object and one for specialized T). @@ -122,7 +122,7 @@ class OpenHashSet[@specialized(Long, Int) T: ClassTag]( */ def addWithoutResize(k: T): Int = { var pos = hashcode(hasher.hash(k)) & _mask - var i = 1 + var delta = 1 while (true) { if (!_bitset.get(pos)) { // This is a new key. @@ -134,14 +134,12 @@ class OpenHashSet[@specialized(Long, Int) T: ClassTag]( // Found an existing key. return pos } else { - val delta = i + // quadratic probing with values increase by 1, 2, 3, ... pos = (pos + delta) & _mask - i += 1 + delta += 1 } } - // Never reached here - assert(INVALID_POS != INVALID_POS) - INVALID_POS + throw new RuntimeException("Should never reach here.") } /** @@ -163,27 +161,25 @@ class OpenHashSet[@specialized(Long, Int) T: ClassTag]( */ def getPos(k: T): Int = { var pos = hashcode(hasher.hash(k)) & _mask - var i = 1 - val maxProbe = _data.size - while (i < maxProbe) { + var delta = 1 + while (true) { if (!_bitset.get(pos)) { return INVALID_POS } else if (k == _data(pos)) { return pos } else { - val delta = i + // quadratic probing with values increase by 1, 2, 3, ... pos = (pos + delta) & _mask - i += 1 + delta += 1 } } - // Never reached here - INVALID_POS + throw new RuntimeException("Should never reach here.") } /** Return the value at the specified position. */ def getValue(pos: Int): T = _data(pos) - def iterator = new Iterator[T] { + def iterator: Iterator[T] = new Iterator[T] { var pos = nextPos(0) override def hasNext: Boolean = pos != INVALID_POS override def next(): T = { diff --git a/core/src/main/scala/org/apache/spark/util/collection/PrimitiveKeyOpenHashMap.scala b/core/src/main/scala/org/apache/spark/util/collection/PrimitiveKeyOpenHashMap.scala index 2e1ef06cbc4e1..b4ec4ea521253 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/PrimitiveKeyOpenHashMap.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/PrimitiveKeyOpenHashMap.scala @@ -46,7 +46,12 @@ class PrimitiveKeyOpenHashMap[@specialized(Long, Int) K: ClassTag, private var _oldValues: Array[V] = null - override def size = _keySet.size + override def size: Int = _keySet.size + + /** Tests whether this map contains a binding for a key. */ + def contains(k: K): Boolean = { + _keySet.getPos(k) != OpenHashSet.INVALID_POS + } /** Get the value for a given key */ def apply(k: K): V = { @@ -87,7 +92,7 @@ class PrimitiveKeyOpenHashMap[@specialized(Long, Int) K: ClassTag, } } - override def iterator = new Iterator[(K, V)] { + override def iterator: Iterator[(K, V)] = new Iterator[(K, V)] { var pos = 0 var nextPair: (K, V) = computeNextPair() @@ -103,9 +108,9 @@ class PrimitiveKeyOpenHashMap[@specialized(Long, Int) K: ClassTag, } } - def hasNext = nextPair != null + def hasNext: Boolean = nextPair != null - def next() = { + def next(): (K, V) = { val pair = nextPair nextPair = computeNextPair() pair diff --git a/core/src/main/scala/org/apache/spark/util/collection/PrimitiveVector.scala b/core/src/main/scala/org/apache/spark/util/collection/PrimitiveVector.scala index 7e76d060d6000..b6c380a8eea9f 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/PrimitiveVector.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/PrimitiveVector.scala @@ -71,12 +71,21 @@ class PrimitiveVector[@specialized(Long, Int, Double) V: ClassTag](initialSize: /** Resizes the array, dropping elements if the total length decreases. */ def resize(newLength: Int): PrimitiveVector[V] = { - val newArray = new Array[V](newLength) - _array.copyToArray(newArray) - _array = newArray + _array = copyArrayWithLength(newLength) if (newLength < _numElements) { _numElements = newLength } this } + + /** Return a trimmed version of the underlying array. */ + def toArray: Array[V] = { + copyArrayWithLength(size) + } + + private def copyArrayWithLength(length: Int): Array[V] = { + val copy = new Array[V](length) + _array.copyToArray(copy) + copy + } } diff --git a/core/src/main/scala/org/apache/spark/util/collection/SizeTrackingVector.scala b/core/src/main/scala/org/apache/spark/util/collection/SizeTrackingVector.scala index 65a7b4e0d497b..dfcfb66af8613 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/SizeTrackingVector.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/SizeTrackingVector.scala @@ -36,11 +36,4 @@ private[spark] class SizeTrackingVector[T: ClassTag] resetSamples() this } - - /** - * Return a trimmed version of the underlying array. - */ - def toArray: Array[T] = { - super.iterator.toArray - } } diff --git a/core/src/main/scala/org/apache/spark/util/collection/Spillable.scala b/core/src/main/scala/org/apache/spark/util/collection/Spillable.scala index 9f54312074856..747ecf075a397 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/Spillable.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/Spillable.scala @@ -42,9 +42,6 @@ private[spark] trait Spillable[C] extends Logging { // Memory manager that can be used to acquire/release memory private[this] val shuffleMemoryManager = SparkEnv.get.shuffleMemoryManager - // Threshold for `elementsRead` before we start tracking this collection's memory usage - private[this] val trackMemoryThreshold = 1000 - // Initial threshold for the size of a collection before we start tracking its memory usage // Exposed for testing private[this] val initialMemoryThreshold: Long = @@ -72,8 +69,7 @@ private[spark] trait Spillable[C] extends Logging { * @return true if `collection` was spilled to disk; false otherwise */ protected def maybeSpill(collection: C, currentMemory: Long): Boolean = { - if (elementsRead > trackMemoryThreshold && elementsRead % 32 == 0 && - currentMemory >= myMemoryThreshold) { + if (elementsRead % 32 == 0 && currentMemory >= myMemoryThreshold) { // Claim up to double our current memory from the shuffle memory pool val amountToRequest = 2 * currentMemory - myMemoryThreshold val granted = shuffleMemoryManager.tryToAcquire(amountToRequest) diff --git a/core/src/main/scala/org/apache/spark/util/collection/Utils.scala b/core/src/main/scala/org/apache/spark/util/collection/Utils.scala index c5268c0fae0ef..bdbca00a00622 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/Utils.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/Utils.scala @@ -32,7 +32,7 @@ private[spark] object Utils { */ def takeOrdered[T](input: Iterator[T], num: Int)(implicit ord: Ordering[T]): Iterator[T] = { val ordering = new GuavaOrdering[T] { - override def compare(l: T, r: T) = ord.compare(l, r) + override def compare(l: T, r: T): Int = ord.compare(l, r) } collectionAsScalaIterable(ordering.leastOf(asJavaIterator(input), num)).iterator } diff --git a/core/src/main/scala/org/apache/spark/util/logging/FileAppender.scala b/core/src/main/scala/org/apache/spark/util/logging/FileAppender.scala index 1d5467060623c..14b6ba4af489a 100644 --- a/core/src/main/scala/org/apache/spark/util/logging/FileAppender.scala +++ b/core/src/main/scala/org/apache/spark/util/logging/FileAppender.scala @@ -121,7 +121,7 @@ private[spark] object FileAppender extends Logging { val rollingSizeBytes = conf.get(SIZE_PROPERTY, STRATEGY_DEFAULT) val rollingInterval = conf.get(INTERVAL_PROPERTY, INTERVAL_DEFAULT) - def createTimeBasedAppender() = { + def createTimeBasedAppender(): FileAppender = { val validatedParams: Option[(Long, String)] = rollingInterval match { case "daily" => logInfo(s"Rolling executor logs enabled for $file with daily rolling") @@ -149,7 +149,7 @@ private[spark] object FileAppender extends Logging { } } - def createSizeBasedAppender() = { + def createSizeBasedAppender(): FileAppender = { rollingSizeBytes match { case IntParam(bytes) => logInfo(s"Rolling executor logs enabled for $file with rolling every $bytes bytes") diff --git a/core/src/main/scala/org/apache/spark/util/random/RandomSampler.scala b/core/src/main/scala/org/apache/spark/util/random/RandomSampler.scala index 76e7a2760bcd1..786b97ad7b9ec 100644 --- a/core/src/main/scala/org/apache/spark/util/random/RandomSampler.scala +++ b/core/src/main/scala/org/apache/spark/util/random/RandomSampler.scala @@ -105,7 +105,7 @@ class BernoulliCellSampler[T](lb: Double, ub: Double, complement: Boolean = fals private val rng: Random = new XORShiftRandom - override def setSeed(seed: Long) = rng.setSeed(seed) + override def setSeed(seed: Long): Unit = rng.setSeed(seed) override def sample(items: Iterator[T]): Iterator[T] = { if (ub - lb <= 0.0) { @@ -131,7 +131,7 @@ class BernoulliCellSampler[T](lb: Double, ub: Double, complement: Boolean = fals def cloneComplement(): BernoulliCellSampler[T] = new BernoulliCellSampler[T](lb, ub, !complement) - override def clone = new BernoulliCellSampler[T](lb, ub, complement) + override def clone: BernoulliCellSampler[T] = new BernoulliCellSampler[T](lb, ub, complement) } @@ -153,7 +153,7 @@ class BernoulliSampler[T: ClassTag](fraction: Double) extends RandomSampler[T, T private val rng: Random = RandomSampler.newDefaultRNG - override def setSeed(seed: Long) = rng.setSeed(seed) + override def setSeed(seed: Long): Unit = rng.setSeed(seed) override def sample(items: Iterator[T]): Iterator[T] = { if (fraction <= 0.0) { @@ -167,7 +167,7 @@ class BernoulliSampler[T: ClassTag](fraction: Double) extends RandomSampler[T, T } } - override def clone = new BernoulliSampler[T](fraction) + override def clone: BernoulliSampler[T] = new BernoulliSampler[T](fraction) } @@ -209,7 +209,7 @@ class PoissonSampler[T: ClassTag](fraction: Double) extends RandomSampler[T, T] } } - override def clone = new PoissonSampler[T](fraction) + override def clone: PoissonSampler[T] = new PoissonSampler[T](fraction) } @@ -228,15 +228,18 @@ class GapSamplingIterator[T: ClassTag]( val arrayClass = Array.empty[T].iterator.getClass val arrayBufferClass = ArrayBuffer.empty[T].iterator.getClass data.getClass match { - case `arrayClass` => ((n: Int) => { data = data.drop(n) }) - case `arrayBufferClass` => ((n: Int) => { data = data.drop(n) }) - case _ => ((n: Int) => { + case `arrayClass` => + (n: Int) => { data = data.drop(n) } + case `arrayBufferClass` => + (n: Int) => { data = data.drop(n) } + case _ => + (n: Int) => { var j = 0 while (j < n && data.hasNext) { data.next() j += 1 } - }) + } } } @@ -244,21 +247,21 @@ class GapSamplingIterator[T: ClassTag]( override def next(): T = { val r = data.next() - advance + advance() r } private val lnq = math.log1p(-f) /** skip elements that won't be sampled, according to geometric dist P(k) = (f)(1-f)^k. */ - private def advance: Unit = { + private def advance(): Unit = { val u = math.max(rng.nextDouble(), epsilon) val k = (math.log(u) / lnq).toInt iterDrop(k) } /** advance to first sample as part of object construction. */ - advance + advance() // Attempting to invoke this closer to the top with other object initialization // was causing it to break in strange ways, so I'm invoking it last, which seems to // work reliably. @@ -279,15 +282,18 @@ class GapSamplingReplacementIterator[T: ClassTag]( val arrayClass = Array.empty[T].iterator.getClass val arrayBufferClass = ArrayBuffer.empty[T].iterator.getClass data.getClass match { - case `arrayClass` => ((n: Int) => { data = data.drop(n) }) - case `arrayBufferClass` => ((n: Int) => { data = data.drop(n) }) - case _ => ((n: Int) => { + case `arrayClass` => + (n: Int) => { data = data.drop(n) } + case `arrayBufferClass` => + (n: Int) => { data = data.drop(n) } + case _ => + (n: Int) => { var j = 0 while (j < n && data.hasNext) { data.next() j += 1 } - }) + } } } @@ -300,7 +306,7 @@ class GapSamplingReplacementIterator[T: ClassTag]( override def next(): T = { val r = v rep -= 1 - if (rep <= 0) advance + if (rep <= 0) advance() r } @@ -309,7 +315,7 @@ class GapSamplingReplacementIterator[T: ClassTag]( * Samples 'k' from geometric distribution P(k) = (1-q)(q)^k, where q = e^(-f), that is * q is the probabililty of Poisson(0; f) */ - private def advance: Unit = { + private def advance(): Unit = { val u = math.max(rng.nextDouble(), epsilon) val k = (math.log(u) / (-f)).toInt iterDrop(k) @@ -343,7 +349,7 @@ class GapSamplingReplacementIterator[T: ClassTag]( } /** advance to first sample as part of object construction. */ - advance + advance() // Attempting to invoke this closer to the top with other object initialization // was causing it to break in strange ways, so I'm invoking it last, which seems to // work reliably. diff --git a/core/src/main/scala/org/apache/spark/util/random/StratifiedSamplingUtils.scala b/core/src/main/scala/org/apache/spark/util/random/StratifiedSamplingUtils.scala index 2ae308dacf1ae..9e29bf9d61f17 100644 --- a/core/src/main/scala/org/apache/spark/util/random/StratifiedSamplingUtils.scala +++ b/core/src/main/scala/org/apache/spark/util/random/StratifiedSamplingUtils.scala @@ -311,7 +311,7 @@ private[random] class AcceptanceResult(var numItems: Long = 0L, var numAccepted: var acceptBound: Double = Double.NaN // upper bound for accepting item instantly var waitListBound: Double = Double.NaN // upper bound for adding item to waitlist - def areBoundsEmpty = acceptBound.isNaN || waitListBound.isNaN + def areBoundsEmpty: Boolean = acceptBound.isNaN || waitListBound.isNaN def merge(other: Option[AcceptanceResult]): Unit = { if (other.isDefined) { diff --git a/core/src/main/scala/org/apache/spark/util/random/XORShiftRandom.scala b/core/src/main/scala/org/apache/spark/util/random/XORShiftRandom.scala index 467b890fb4bb9..c4a7b4441c85c 100644 --- a/core/src/main/scala/org/apache/spark/util/random/XORShiftRandom.scala +++ b/core/src/main/scala/org/apache/spark/util/random/XORShiftRandom.scala @@ -83,7 +83,7 @@ private[spark] object XORShiftRandom { * @return Map of execution times for {@link java.util.Random java.util.Random} * and XORShift */ - def benchmark(numIters: Int) = { + def benchmark(numIters: Int): Map[String, Long] = { val seed = 1L val million = 1e6.toInt diff --git a/core/src/test/java/org/apache/spark/JavaAPISuite.java b/core/src/test/java/org/apache/spark/JavaAPISuite.java index b16a1e9460286..8a4f2a08fe701 100644 --- a/core/src/test/java/org/apache/spark/JavaAPISuite.java +++ b/core/src/test/java/org/apache/spark/JavaAPISuite.java @@ -24,11 +24,12 @@ import java.util.*; import java.util.concurrent.*; -import org.apache.spark.input.PortableDataStream; +import scala.collection.JavaConversions; import scala.Tuple2; import scala.Tuple3; import scala.Tuple4; +import com.google.common.collect.ImmutableMap; import com.google.common.collect.Iterables; import com.google.common.collect.Iterators; import com.google.common.collect.Lists; @@ -51,8 +52,11 @@ import org.apache.spark.api.java.*; import org.apache.spark.api.java.function.*; import org.apache.spark.executor.TaskMetrics; +import org.apache.spark.input.PortableDataStream; import org.apache.spark.partial.BoundedDouble; import org.apache.spark.partial.PartialResult; +import org.apache.spark.rdd.RDD; +import org.apache.spark.serializer.KryoSerializer; import org.apache.spark.storage.StorageLevel; import org.apache.spark.util.StatCounter; @@ -267,6 +271,22 @@ public void call(String s) throws IOException { Assert.assertEquals(2, accum.value().intValue()); } + @Test + public void foreachPartition() { + final Accumulator accum = sc.accumulator(0); + JavaRDD rdd = sc.parallelize(Arrays.asList("Hello", "World")); + rdd.foreachPartition(new VoidFunction>() { + @Override + public void call(Iterator iter) throws IOException { + while (iter.hasNext()) { + iter.next(); + accum.add(1); + } + } + }); + Assert.assertEquals(2, accum.value().intValue()); + } + @Test public void toLocalIterator() { List correct = Arrays.asList(1, 2, 3, 4); @@ -657,6 +677,13 @@ public Boolean call(Integer i) { }).isEmpty()); } + @Test + public void toArray() { + JavaRDD rdd = sc.parallelize(Arrays.asList(1, 2, 3)); + List list = rdd.toArray(); + Assert.assertEquals(Arrays.asList(1, 2, 3), list); + } + @Test public void cartesian() { JavaDoubleRDD doubleRDD = sc.parallelizeDoubles(Arrays.asList(1.0, 1.0, 2.0, 3.0, 5.0, 8.0)); @@ -703,11 +730,103 @@ public void javaDoubleRDDHistoGram() { Tuple2 results = rdd.histogram(2); double[] expected_buckets = {1.0, 2.5, 4.0}; long[] expected_counts = {2, 2}; - Assert.assertArrayEquals(expected_buckets, results._1, 0.1); - Assert.assertArrayEquals(expected_counts, results._2); + Assert.assertArrayEquals(expected_buckets, results._1(), 0.1); + Assert.assertArrayEquals(expected_counts, results._2()); // Test with provided buckets long[] histogram = rdd.histogram(expected_buckets); Assert.assertArrayEquals(expected_counts, histogram); + // SPARK-5744 + Assert.assertArrayEquals( + new long[] {0}, + sc.parallelizeDoubles(new ArrayList(0), 1).histogram(new double[]{0.0, 1.0})); + } + + private static class DoubleComparator implements Comparator, Serializable { + public int compare(Double o1, Double o2) { + return o1.compareTo(o2); + } + } + + @Test + public void max() { + JavaDoubleRDD rdd = sc.parallelizeDoubles(Arrays.asList(1.0, 2.0, 3.0, 4.0)); + double max = rdd.max(new DoubleComparator()); + Assert.assertEquals(4.0, max, 0.001); + } + + @Test + public void min() { + JavaDoubleRDD rdd = sc.parallelizeDoubles(Arrays.asList(1.0, 2.0, 3.0, 4.0)); + double max = rdd.min(new DoubleComparator()); + Assert.assertEquals(1.0, max, 0.001); + } + + @Test + public void naturalMax() { + JavaDoubleRDD rdd = sc.parallelizeDoubles(Arrays.asList(1.0, 2.0, 3.0, 4.0)); + double max = rdd.max(); + Assert.assertTrue(4.0 == max); + } + + @Test + public void naturalMin() { + JavaDoubleRDD rdd = sc.parallelizeDoubles(Arrays.asList(1.0, 2.0, 3.0, 4.0)); + double max = rdd.min(); + Assert.assertTrue(1.0 == max); + } + + @Test + public void takeOrdered() { + JavaDoubleRDD rdd = sc.parallelizeDoubles(Arrays.asList(1.0, 2.0, 3.0, 4.0)); + Assert.assertEquals(Arrays.asList(1.0, 2.0), rdd.takeOrdered(2, new DoubleComparator())); + Assert.assertEquals(Arrays.asList(1.0, 2.0), rdd.takeOrdered(2)); + } + + @Test + public void top() { + JavaRDD rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4)); + List top2 = rdd.top(2); + Assert.assertEquals(Arrays.asList(4, 3), top2); + } + + private static class AddInts implements Function2 { + @Override + public Integer call(Integer a, Integer b) { + return a + b; + } + } + + @Test + public void reduce() { + JavaRDD rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4)); + int sum = rdd.reduce(new AddInts()); + Assert.assertEquals(10, sum); + } + + @Test + public void reduceOnJavaDoubleRDD() { + JavaDoubleRDD rdd = sc.parallelizeDoubles(Arrays.asList(1.0, 2.0, 3.0, 4.0)); + double sum = rdd.reduce(new Function2() { + @Override + public Double call(Double v1, Double v2) throws Exception { + return v1 + v2; + } + }); + Assert.assertEquals(10.0, sum, 0.001); + } + + @Test + public void fold() { + JavaRDD rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4)); + int sum = rdd.fold(0, new AddInts()); + Assert.assertEquals(10, sum); + } + + @Test + public void aggregate() { + JavaRDD rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4)); + int sum = rdd.aggregate(0, new AddInts(), new AddInts()); + Assert.assertEquals(10, sum); } @Test @@ -826,6 +945,25 @@ public Iterable call(Iterator iter) { Assert.assertEquals("[3, 7]", partitionSums.collect().toString()); } + + @Test + public void mapPartitionsWithIndex() { + JavaRDD rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4), 2); + JavaRDD partitionSums = rdd.mapPartitionsWithIndex( + new Function2, Iterator>() { + @Override + public Iterator call(Integer index, Iterator iter) throws Exception { + int sum = 0; + while (iter.hasNext()) { + sum += iter.next(); + } + return Collections.singletonList(sum).iterator(); + } + }, false); + Assert.assertEquals("[3, 7]", partitionSums.collect().toString()); + } + + @Test public void repartition() { // Shrinking number of partitions @@ -1304,6 +1442,49 @@ public void checkpointAndRestore() { Assert.assertEquals(Arrays.asList(1, 2, 3, 4, 5), recovered.collect()); } + @Test + public void combineByKey() { + JavaRDD originalRDD = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5, 6)); + Function keyFunction = new Function() { + @Override + public Integer call(Integer v1) throws Exception { + return v1 % 3; + } + }; + Function createCombinerFunction = new Function() { + @Override + public Integer call(Integer v1) throws Exception { + return v1; + } + }; + + Function2 mergeValueFunction = new Function2() { + @Override + public Integer call(Integer v1, Integer v2) throws Exception { + return v1 + v2; + } + }; + + JavaPairRDD combinedRDD = originalRDD.keyBy(keyFunction) + .combineByKey(createCombinerFunction, mergeValueFunction, mergeValueFunction); + Map results = combinedRDD.collectAsMap(); + ImmutableMap expected = ImmutableMap.of(0, 9, 1, 5, 2, 7); + Assert.assertEquals(expected, results); + + Partitioner defaultPartitioner = Partitioner.defaultPartitioner( + combinedRDD.rdd(), JavaConversions.asScalaBuffer(Lists.>newArrayList())); + combinedRDD = originalRDD.keyBy(keyFunction) + .combineByKey( + createCombinerFunction, + mergeValueFunction, + mergeValueFunction, + defaultPartitioner, + false, + new KryoSerializer(new SparkConf())); + results = combinedRDD.collectAsMap(); + Assert.assertEquals(expected, results); + } + @SuppressWarnings("unchecked") @Test public void mapOnPairRDD() { @@ -1512,6 +1693,19 @@ public void collectAsync() throws Exception { Assert.assertEquals(1, future.jobIds().size()); } + @Test + public void takeAsync() throws Exception { + List data = Arrays.asList(1, 2, 3, 4, 5); + JavaRDD rdd = sc.parallelize(data, 1); + JavaFutureAction> future = rdd.takeAsync(1); + List result = future.get(); + Assert.assertEquals(1, result.size()); + Assert.assertEquals((Integer) 1, result.get(0)); + Assert.assertFalse(future.isCancelled()); + Assert.assertTrue(future.isDone()); + Assert.assertEquals(1, future.jobIds().size()); + } + @Test public void foreachAsync() throws Exception { List data = Arrays.asList(1, 2, 3, 4, 5); diff --git a/core/src/test/java/org/apache/spark/util/collection/TestTimSort.java b/core/src/test/java/org/apache/spark/util/collection/TestTimSort.java new file mode 100644 index 0000000000000..45772b6d3c20d --- /dev/null +++ b/core/src/test/java/org/apache/spark/util/collection/TestTimSort.java @@ -0,0 +1,134 @@ +/** + * Copyright 2015 Stijn de Gouw + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * + */ +package org.apache.spark.util.collection; + +import java.util.*; + +/** + * This codes generates a int array which fails the standard TimSort. + * + * The blog that reported the bug + * http://www.envisage-project.eu/timsort-specification-and-verification/ + * + * This codes was originally wrote by Stijn de Gouw, modified by Evan Yu to adapt to + * our test suite. + * + * https://github.com/abstools/java-timsort-bug + * https://github.com/abstools/java-timsort-bug/blob/master/LICENSE + */ +public class TestTimSort { + + private static final int MIN_MERGE = 32; + + /** + * Returns an array of integers that demonstrate the bug in TimSort + */ + public static int[] getTimSortBugTestSet(int length) { + int minRun = minRunLength(length); + List runs = runsJDKWorstCase(minRun, length); + return createArray(runs, length); + } + + private static int minRunLength(int n) { + int r = 0; // Becomes 1 if any 1 bits are shifted off + while (n >= MIN_MERGE) { + r |= (n & 1); + n >>= 1; + } + return n + r; + } + + private static int[] createArray(List runs, int length) { + int[] a = new int[length]; + Arrays.fill(a, 0); + int endRun = -1; + for (long len : runs) { + a[endRun += len] = 1; + } + a[length - 1] = 0; + return a; + } + + /** + * Fills runs with a sequence of run lengths of the form
    + * Y_n x_{n,1} x_{n,2} ... x_{n,l_n}
    + * Y_{n-1} x_{n-1,1} x_{n-1,2} ... x_{n-1,l_{n-1}}
    + * ...
    + * Y_1 x_{1,1} x_{1,2} ... x_{1,l_1}
    + * The Y_i's are chosen to satisfy the invariant throughout execution, + * but the x_{i,j}'s are merged (by TimSort.mergeCollapse) + * into an X_i that violates the invariant. + * + * @param length The sum of all run lengths that will be added to runs. + */ + private static List runsJDKWorstCase(int minRun, int length) { + List runs = new ArrayList(); + + long runningTotal = 0, Y = minRun + 4, X = minRun; + + while (runningTotal + Y + X <= length) { + runningTotal += X + Y; + generateJDKWrongElem(runs, minRun, X); + runs.add(0, Y); + // X_{i+1} = Y_i + x_{i,1} + 1, since runs.get(1) = x_{i,1} + X = Y + runs.get(1) + 1; + // Y_{i+1} = X_{i+1} + Y_i + 1 + Y += X + 1; + } + + if (runningTotal + X <= length) { + runningTotal += X; + generateJDKWrongElem(runs, minRun, X); + } + + runs.add(length - runningTotal); + return runs; + } + + /** + * Adds a sequence x_1, ..., x_n of run lengths to runs such that:
    + * 1. X = x_1 + ... + x_n
    + * 2. x_j >= minRun for all j
    + * 3. x_1 + ... + x_{j-2} < x_j < x_1 + ... + x_{j-1} for all j
    + * These conditions guarantee that TimSort merges all x_j's one by one + * (resulting in X) using only merges on the second-to-last element. + * + * @param X The sum of the sequence that should be added to runs. + */ + private static void generateJDKWrongElem(List runs, int minRun, long X) { + for (long newTotal; X >= 2 * minRun + 1; X = newTotal) { + //Default strategy + newTotal = X / 2 + 1; + //Specialized strategies + if (3 * minRun + 3 <= X && X <= 4 * minRun + 1) { + // add x_1=MIN+1, x_2=MIN, x_3=X-newTotal to runs + newTotal = 2 * minRun + 1; + } else if (5 * minRun + 5 <= X && X <= 6 * minRun + 5) { + // add x_1=MIN+1, x_2=MIN, x_3=MIN+2, x_4=X-newTotal to runs + newTotal = 3 * minRun + 3; + } else if (8 * minRun + 9 <= X && X <= 10 * minRun + 9) { + // add x_1=MIN+1, x_2=MIN, x_3=MIN+2, x_4=2MIN+2, x_5=X-newTotal to runs + newTotal = 5 * minRun + 5; + } else if (13 * minRun + 15 <= X && X <= 16 * minRun + 17) { + // add x_1=MIN+1, x_2=MIN, x_3=MIN+2, x_4=2MIN+2, x_5=3MIN+4, x_6=X-newTotal to runs + newTotal = 8 * minRun + 9; + } + runs.add(0, X - newTotal); + } + runs.add(0, X); + } +} diff --git a/core/src/test/resources/log4j.properties b/core/src/test/resources/log4j.properties index 287c8e3563503..eb3b1999eb996 100644 --- a/core/src/test/resources/log4j.properties +++ b/core/src/test/resources/log4j.properties @@ -24,5 +24,5 @@ log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n # Ignore messages below warning level from Jetty, because it's a bit verbose -log4j.logger.org.eclipse.jetty=WARN -org.eclipse.jetty.LEVEL=WARN +log4j.logger.org.spark-project.jetty=WARN +org.spark-project.jetty.LEVEL=WARN diff --git a/core/src/test/resources/test_metrics_system.properties b/core/src/test/resources/test_metrics_system.properties index 35d0bd3b8d0b8..4e8b8465696e5 100644 --- a/core/src/test/resources/test_metrics_system.properties +++ b/core/src/test/resources/test_metrics_system.properties @@ -18,7 +18,5 @@ *.sink.console.period = 10 *.sink.console.unit = seconds test.sink.console.class = org.apache.spark.metrics.sink.ConsoleSink -test.sink.dummy.class = org.apache.spark.metrics.sink.DummySink -test.source.dummy.class = org.apache.spark.metrics.source.DummySource test.sink.console.period = 20 test.sink.console.unit = minutes diff --git a/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala b/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala index f087fc550dde3..75399461f2a5f 100644 --- a/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala +++ b/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala @@ -18,6 +18,7 @@ package org.apache.spark import scala.collection.mutable +import scala.ref.WeakReference import org.scalatest.FunSuite import org.scalatest.Matchers @@ -26,19 +27,20 @@ import org.scalatest.Matchers class AccumulatorSuite extends FunSuite with Matchers with LocalSparkContext { - implicit def setAccum[A] = new AccumulableParam[mutable.Set[A], A] { - def addInPlace(t1: mutable.Set[A], t2: mutable.Set[A]) : mutable.Set[A] = { - t1 ++= t2 - t1 - } - def addAccumulator(t1: mutable.Set[A], t2: A) : mutable.Set[A] = { - t1 += t2 - t1 - } - def zero(t: mutable.Set[A]) : mutable.Set[A] = { - new mutable.HashSet[A]() + implicit def setAccum[A]: AccumulableParam[mutable.Set[A], A] = + new AccumulableParam[mutable.Set[A], A] { + def addInPlace(t1: mutable.Set[A], t2: mutable.Set[A]) : mutable.Set[A] = { + t1 ++= t2 + t1 + } + def addAccumulator(t1: mutable.Set[A], t2: A) : mutable.Set[A] = { + t1 += t2 + t1 + } + def zero(t: mutable.Set[A]) : mutable.Set[A] = { + new mutable.HashSet[A]() + } } - } test ("basic accumulation"){ sc = new SparkContext("local", "test") @@ -48,11 +50,10 @@ class AccumulatorSuite extends FunSuite with Matchers with LocalSparkContext { d.foreach{x => acc += x} acc.value should be (210) - - val longAcc = sc.accumulator(0l) + val longAcc = sc.accumulator(0L) val maxInt = Integer.MAX_VALUE.toLong d.foreach{x => longAcc += maxInt + x} - longAcc.value should be (210l + maxInt * 20) + longAcc.value should be (210L + maxInt * 20) } test ("value not assignable from tasks") { @@ -136,4 +137,23 @@ class AccumulatorSuite extends FunSuite with Matchers with LocalSparkContext { } } + test ("garbage collection") { + // Create an accumulator and let it go out of scope to test that it's properly garbage collected + sc = new SparkContext("local", "test") + var acc: Accumulable[mutable.Set[Any], Any] = sc.accumulable(new mutable.HashSet[Any]()) + val accId = acc.id + val ref = WeakReference(acc) + + // Ensure the accumulator is present + assert(ref.get.isDefined) + + // Remove the explicit reference to it and allow weak reference to get garbage collected + acc = null + System.gc() + assert(ref.get.isEmpty) + + Accumulators.remove(accId) + assert(!Accumulators.originals.get(accId).isDefined) + } + } diff --git a/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala b/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala index d7d9dc7b50f30..70529d9216591 100644 --- a/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala @@ -17,16 +17,18 @@ package org.apache.spark +import org.mockito.Mockito._ import org.scalatest.{BeforeAndAfter, FunSuite} -import org.scalatest.mock.EasyMockSugar +import org.scalatest.mock.MockitoSugar -import org.apache.spark.executor.{DataReadMethod, TaskMetrics} +import org.apache.spark.executor.DataReadMethod import org.apache.spark.rdd.RDD import org.apache.spark.storage._ // TODO: Test the CacheManager's thread-safety aspects -class CacheManagerSuite extends FunSuite with BeforeAndAfter with EasyMockSugar { - var sc : SparkContext = _ +class CacheManagerSuite extends FunSuite with LocalSparkContext with BeforeAndAfter + with MockitoSugar { + var blockManager: BlockManager = _ var cacheManager: CacheManager = _ var split: Partition = _ @@ -43,24 +45,21 @@ class CacheManagerSuite extends FunSuite with BeforeAndAfter with EasyMockSugar rdd = new RDD[Int](sc, Nil) { override def getPartitions: Array[Partition] = Array(split) override val getDependencies = List[Dependency[_]]() - override def compute(split: Partition, context: TaskContext) = Array(1, 2, 3, 4).iterator + override def compute(split: Partition, context: TaskContext): Iterator[Int] = + Array(1, 2, 3, 4).iterator } rdd2 = new RDD[Int](sc, List(new OneToOneDependency(rdd))) { override def getPartitions: Array[Partition] = firstParent[Int].partitions - override def compute(split: Partition, context: TaskContext) = + override def compute(split: Partition, context: TaskContext): Iterator[Int] = firstParent[Int].iterator(split, context) }.cache() rdd3 = new RDD[Int](sc, List(new OneToOneDependency(rdd2))) { override def getPartitions: Array[Partition] = firstParent[Int].partitions - override def compute(split: Partition, context: TaskContext) = + override def compute(split: Partition, context: TaskContext): Iterator[Int] = firstParent[Int].iterator(split, context) }.cache() } - after { - sc.stop() - } - test("get uncached rdd") { // Do not mock this test, because attempting to match Array[Any], which is not covariant, // in blockManager.put is a losing battle. You have been warned. @@ -75,29 +74,21 @@ class CacheManagerSuite extends FunSuite with BeforeAndAfter with EasyMockSugar } test("get cached rdd") { - expecting { - val result = new BlockResult(Array(5, 6, 7).iterator, DataReadMethod.Memory, 12) - blockManager.get(RDDBlockId(0, 0)).andReturn(Some(result)) - } + val result = new BlockResult(Array(5, 6, 7).iterator, DataReadMethod.Memory, 12) + when(blockManager.get(RDDBlockId(0, 0))).thenReturn(Some(result)) - whenExecuting(blockManager) { - val context = new TaskContextImpl(0, 0, 0, 0) - val value = cacheManager.getOrCompute(rdd, split, context, StorageLevel.MEMORY_ONLY) - assert(value.toList === List(5, 6, 7)) - } + val context = new TaskContextImpl(0, 0, 0, 0) + val value = cacheManager.getOrCompute(rdd, split, context, StorageLevel.MEMORY_ONLY) + assert(value.toList === List(5, 6, 7)) } test("get uncached local rdd") { - expecting { - // Local computation should not persist the resulting value, so don't expect a put(). - blockManager.get(RDDBlockId(0, 0)).andReturn(None) - } + // Local computation should not persist the resulting value, so don't expect a put(). + when(blockManager.get(RDDBlockId(0, 0))).thenReturn(None) - whenExecuting(blockManager) { - val context = new TaskContextImpl(0, 0, 0, 0, true) - val value = cacheManager.getOrCompute(rdd, split, context, StorageLevel.MEMORY_ONLY) - assert(value.toList === List(1, 2, 3, 4)) - } + val context = new TaskContextImpl(0, 0, 0, 0, true) + val value = cacheManager.getOrCompute(rdd, split, context, StorageLevel.MEMORY_ONLY) + assert(value.toList === List(1, 2, 3, 4)) } test("verify task metrics updated correctly") { diff --git a/core/src/test/scala/org/apache/spark/CheckpointSuite.scala b/core/src/test/scala/org/apache/spark/CheckpointSuite.scala index 3b10b3a042317..e1faddeabec79 100644 --- a/core/src/test/scala/org/apache/spark/CheckpointSuite.scala +++ b/core/src/test/scala/org/apache/spark/CheckpointSuite.scala @@ -33,8 +33,7 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging { override def beforeEach() { super.beforeEach() - checkpointDir = File.createTempFile("temp", "") - checkpointDir.deleteOnExit() + checkpointDir = File.createTempFile("temp", "", Utils.createTempDir()) checkpointDir.delete() sc = new SparkContext("local", "test") sc.setCheckpointDir(checkpointDir.toString) @@ -76,7 +75,8 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging { assert(sc.checkpointFile[Int](parCollection.getCheckpointFile.get).collect() === result) assert(parCollection.dependencies != Nil) assert(parCollection.partitions.length === numPartitions) - assert(parCollection.partitions.toList === parCollection.checkpointData.get.getPartitions.toList) + assert(parCollection.partitions.toList === + parCollection.checkpointData.get.getPartitions.toList) assert(parCollection.collect() === result) } @@ -103,13 +103,13 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging { } test("UnionRDD") { - def otherRDD = sc.makeRDD(1 to 10, 1) + def otherRDD: RDD[Int] = sc.makeRDD(1 to 10, 1) testRDD(_.union(otherRDD)) testRDDPartitions(_.union(otherRDD)) } test("CartesianRDD") { - def otherRDD = sc.makeRDD(1 to 10, 1) + def otherRDD: RDD[Int] = sc.makeRDD(1 to 10, 1) testRDD(new CartesianRDD(sc, _, otherRDD)) testRDDPartitions(new CartesianRDD(sc, _, otherRDD)) @@ -224,7 +224,8 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging { val partitionAfterCheckpoint = serializeDeserialize( unionRDD.partitions.head.asInstanceOf[PartitionerAwareUnionRDDPartition]) assert( - partitionBeforeCheckpoint.parents.head.getClass != partitionAfterCheckpoint.parents.head.getClass, + partitionBeforeCheckpoint.parents.head.getClass != + partitionAfterCheckpoint.parents.head.getClass, "PartitionerAwareUnionRDDPartition.parents not updated after parent RDD is checkpointed" ) } @@ -359,7 +360,7 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging { * Generate an pair RDD (with partitioner) such that both the RDD and its partitions * have large size. */ - def generateFatPairRDD() = { + def generateFatPairRDD(): RDD[(Int, Int)] = { new FatPairRDD(sc.makeRDD(1 to 100, 4), partitioner).mapValues(x => x) } @@ -446,7 +447,8 @@ class FatPairRDD(parent: RDD[Int], _partitioner: Partitioner) extends RDD[(Int, object CheckpointSuite { // This is a custom cogroup function that does not use mapValues like // the PairRDDFunctions.cogroup() - def cogroup[K, V](first: RDD[(K, V)], second: RDD[(K, V)], part: Partitioner) = { + def cogroup[K, V](first: RDD[(K, V)], second: RDD[(K, V)], part: Partitioner) + : RDD[(K, Array[Iterable[V]])] = { new CoGroupedRDD[K]( Seq(first.asInstanceOf[RDD[(K, _)]], second.asInstanceOf[RDD[(K, _)]]), part diff --git a/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala b/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala index ae2ae7ed0d3aa..c7868ddcf770f 100644 --- a/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala +++ b/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala @@ -28,7 +28,8 @@ import org.scalatest.concurrent.{PatienceConfiguration, Eventually} import org.scalatest.concurrent.Eventually._ import org.scalatest.time.SpanSugar._ -import org.apache.spark.rdd.RDD +import org.apache.spark.SparkContext._ +import org.apache.spark.rdd.{RDDCheckpointData, RDD} import org.apache.spark.storage._ import org.apache.spark.shuffle.hash.HashShuffleManager import org.apache.spark.shuffle.sort.SortShuffleManager @@ -64,7 +65,7 @@ abstract class ContextCleanerSuiteBase(val shuffleManager: Class[_] = classOf[Ha } } - //------ Helper functions ------ + // ------ Helper functions ------ protected def newRDD() = sc.makeRDD(1 to 10) protected def newPairRDD() = newRDD().map(_ -> 1) @@ -205,6 +206,52 @@ class ContextCleanerSuite extends ContextCleanerSuiteBase { postGCTester.assertCleanup() } + test("automatically cleanup checkpoint") { + val checkpointDir = java.io.File.createTempFile("temp", "") + checkpointDir.deleteOnExit() + checkpointDir.delete() + var rdd = newPairRDD + sc.setCheckpointDir(checkpointDir.toString) + rdd.checkpoint() + rdd.cache() + rdd.collect() + var rddId = rdd.id + + // Confirm the checkpoint directory exists + assert(RDDCheckpointData.rddCheckpointDataPath(sc, rddId).isDefined) + val path = RDDCheckpointData.rddCheckpointDataPath(sc, rddId).get + val fs = path.getFileSystem(sc.hadoopConfiguration) + assert(fs.exists(path)) + + // the checkpoint is not cleaned by default (without the configuration set) + var postGCTester = new CleanerTester(sc, Seq(rddId), Nil, Nil, Nil) + rdd = null // Make RDD out of scope + runGC() + postGCTester.assertCleanup() + assert(fs.exists(RDDCheckpointData.rddCheckpointDataPath(sc, rddId).get)) + + sc.stop() + val conf = new SparkConf().setMaster("local[2]").setAppName("cleanupCheckpoint"). + set("spark.cleaner.referenceTracking.cleanCheckpoints", "true") + sc = new SparkContext(conf) + rdd = newPairRDD + sc.setCheckpointDir(checkpointDir.toString) + rdd.checkpoint() + rdd.cache() + rdd.collect() + rddId = rdd.id + + // Confirm the checkpoint directory exists + assert(fs.exists(RDDCheckpointData.rddCheckpointDataPath(sc, rddId).get)) + + // Test that GC causes checkpoint data cleanup after dereferencing the RDD + postGCTester = new CleanerTester(sc, Seq(rddId), Nil, Nil, Seq(rddId)) + rdd = null // Make RDD out of scope + runGC() + postGCTester.assertCleanup() + assert(!fs.exists(RDDCheckpointData.rddCheckpointDataPath(sc, rddId).get)) + } + test("automatically cleanup RDD + shuffle + broadcast") { val numRdds = 100 val numBroadcasts = 4 // Broadcasts are more costly @@ -359,18 +406,20 @@ class CleanerTester( sc: SparkContext, rddIds: Seq[Int] = Seq.empty, shuffleIds: Seq[Int] = Seq.empty, - broadcastIds: Seq[Long] = Seq.empty) + broadcastIds: Seq[Long] = Seq.empty, + checkpointIds: Seq[Long] = Seq.empty) extends Logging { val toBeCleanedRDDIds = new HashSet[Int] with SynchronizedSet[Int] ++= rddIds val toBeCleanedShuffleIds = new HashSet[Int] with SynchronizedSet[Int] ++= shuffleIds val toBeCleanedBroadcstIds = new HashSet[Long] with SynchronizedSet[Long] ++= broadcastIds + val toBeCheckpointIds = new HashSet[Long] with SynchronizedSet[Long] ++= checkpointIds val isDistributed = !sc.isLocal val cleanerListener = new CleanerListener { def rddCleaned(rddId: Int): Unit = { toBeCleanedRDDIds -= rddId - logInfo("RDD "+ rddId + " cleaned") + logInfo("RDD " + rddId + " cleaned") } def shuffleCleaned(shuffleId: Int): Unit = { @@ -380,7 +429,16 @@ class CleanerTester( def broadcastCleaned(broadcastId: Long): Unit = { toBeCleanedBroadcstIds -= broadcastId - logInfo("Broadcast" + broadcastId + " cleaned") + logInfo("Broadcast " + broadcastId + " cleaned") + } + + def accumCleaned(accId: Long): Unit = { + logInfo("Cleaned accId " + accId + " cleaned") + } + + def checkpointCleaned(rddId: Long): Unit = { + toBeCheckpointIds -= rddId + logInfo("checkpoint " + rddId + " cleaned") } } @@ -405,7 +463,8 @@ class CleanerTester( /** Verify that RDDs, shuffles, etc. occupy resources */ private def preCleanupValidate() { - assert(rddIds.nonEmpty || shuffleIds.nonEmpty || broadcastIds.nonEmpty, "Nothing to cleanup") + assert(rddIds.nonEmpty || shuffleIds.nonEmpty || broadcastIds.nonEmpty || + checkpointIds.nonEmpty, "Nothing to cleanup") // Verify the RDDs have been persisted and blocks are present rddIds.foreach { rddId => @@ -496,7 +555,8 @@ class CleanerTester( private def isAllCleanedUp = toBeCleanedRDDIds.isEmpty && toBeCleanedShuffleIds.isEmpty && - toBeCleanedBroadcstIds.isEmpty + toBeCleanedBroadcstIds.isEmpty && + toBeCheckpointIds.isEmpty private def getRDDBlocks(rddId: Int): Seq[BlockId] = { blockManager.master.getMatchingBlockIds( _ match { diff --git a/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala b/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala index 9eb87f016068d..22acc270b983e 100644 --- a/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala @@ -19,18 +19,29 @@ package org.apache.spark import scala.collection.mutable -import org.scalatest.{FunSuite, PrivateMethodTester} +import org.scalatest.{BeforeAndAfter, FunSuite, PrivateMethodTester} import org.apache.spark.executor.TaskMetrics import org.apache.spark.scheduler._ import org.apache.spark.scheduler.cluster.ExecutorInfo +import org.apache.spark.util.ManualClock /** * Test add and remove behavior of ExecutorAllocationManager. */ -class ExecutorAllocationManagerSuite extends FunSuite with LocalSparkContext { +class ExecutorAllocationManagerSuite extends FunSuite with LocalSparkContext with BeforeAndAfter { import ExecutorAllocationManager._ import ExecutorAllocationManagerSuite._ + private val contexts = new mutable.ListBuffer[SparkContext]() + + before { + contexts.clear() + } + + after { + contexts.foreach(_.stop()) + } + test("verify min/max executors") { val conf = new SparkConf() .setMaster("local") @@ -38,25 +49,20 @@ class ExecutorAllocationManagerSuite extends FunSuite with LocalSparkContext { .set("spark.dynamicAllocation.enabled", "true") .set("spark.dynamicAllocation.testing", "true") val sc0 = new SparkContext(conf) + contexts += sc0 assert(sc0.executorAllocationManager.isDefined) sc0.stop() // Min < 0 val conf1 = conf.clone().set("spark.dynamicAllocation.minExecutors", "-1") - intercept[SparkException] { new SparkContext(conf1) } - SparkEnv.get.stop() - SparkContext.clearActiveContext() + intercept[SparkException] { contexts += new SparkContext(conf1) } // Max < 0 val conf2 = conf.clone().set("spark.dynamicAllocation.maxExecutors", "-1") - intercept[SparkException] { new SparkContext(conf2) } - SparkEnv.get.stop() - SparkContext.clearActiveContext() + intercept[SparkException] { contexts += new SparkContext(conf2) } // Both min and max, but min > max intercept[SparkException] { createSparkContext(2, 1) } - SparkEnv.get.stop() - SparkContext.clearActiveContext() // Both min and max, and min == max val sc1 = createSparkContext(1, 1) @@ -175,6 +181,33 @@ class ExecutorAllocationManagerSuite extends FunSuite with LocalSparkContext { assert(numExecutorsPending(manager) === 9) } + test("cancel pending executors when no longer needed") { + sc = createSparkContext(1, 10) + val manager = sc.executorAllocationManager.get + sc.listenerBus.postToAll(SparkListenerStageSubmitted(createStageInfo(2, 5))) + + assert(numExecutorsPending(manager) === 0) + assert(numExecutorsToAdd(manager) === 1) + assert(addExecutors(manager) === 1) + assert(numExecutorsPending(manager) === 1) + assert(numExecutorsToAdd(manager) === 2) + assert(addExecutors(manager) === 2) + assert(numExecutorsPending(manager) === 3) + + val task1Info = createTaskInfo(0, 0, "executor-1") + sc.listenerBus.postToAll(SparkListenerTaskStart(2, 0, task1Info)) + + assert(numExecutorsToAdd(manager) === 4) + assert(addExecutors(manager) === 2) + + val task2Info = createTaskInfo(1, 0, "executor-1") + sc.listenerBus.postToAll(SparkListenerTaskStart(2, 0, task2Info)) + sc.listenerBus.postToAll(SparkListenerTaskEnd(2, 0, null, null, task1Info, null)) + sc.listenerBus.postToAll(SparkListenerTaskEnd(2, 0, null, null, task2Info, null)) + + assert(adjustRequestedExecutors(manager) === -1) + } + test("remove executors") { sc = createSparkContext(5, 10) val manager = sc.executorAllocationManager.get @@ -270,15 +303,15 @@ class ExecutorAllocationManagerSuite extends FunSuite with LocalSparkContext { assert(removeExecutor(manager, "5")) assert(removeExecutor(manager, "6")) assert(executorIds(manager).size === 10) - assert(addExecutors(manager) === 0) // still at upper limit + assert(addExecutors(manager) === 1) onExecutorRemoved(manager, "3") onExecutorRemoved(manager, "4") assert(executorIds(manager).size === 8) // Add succeeds again, now that we are no longer at the upper limit // Number of executors added restarts at 1 - assert(addExecutors(manager) === 1) - assert(addExecutors(manager) === 1) // upper limit reached again + assert(addExecutors(manager) === 2) + assert(addExecutors(manager) === 1) // upper limit reached assert(addExecutors(manager) === 0) assert(executorIds(manager).size === 8) onExecutorRemoved(manager, "5") @@ -286,9 +319,7 @@ class ExecutorAllocationManagerSuite extends FunSuite with LocalSparkContext { onExecutorAdded(manager, "13") onExecutorAdded(manager, "14") assert(executorIds(manager).size === 8) - assert(addExecutors(manager) === 1) - assert(addExecutors(manager) === 1) // upper limit reached again - assert(addExecutors(manager) === 0) + assert(addExecutors(manager) === 0) // still at upper limit onExecutorAdded(manager, "15") onExecutorAdded(manager, "16") assert(executorIds(manager).size === 10) @@ -296,7 +327,7 @@ class ExecutorAllocationManagerSuite extends FunSuite with LocalSparkContext { test("starting/canceling add timer") { sc = createSparkContext(2, 10) - val clock = new TestClock(8888L) + val clock = new ManualClock(8888L) val manager = sc.executorAllocationManager.get manager.setClock(clock) @@ -305,21 +336,21 @@ class ExecutorAllocationManagerSuite extends FunSuite with LocalSparkContext { onSchedulerBacklogged(manager) val firstAddTime = addTime(manager) assert(firstAddTime === clock.getTimeMillis + schedulerBacklogTimeout * 1000) - clock.tick(100L) + clock.advance(100L) onSchedulerBacklogged(manager) assert(addTime(manager) === firstAddTime) // timer is already started - clock.tick(200L) + clock.advance(200L) onSchedulerBacklogged(manager) assert(addTime(manager) === firstAddTime) onSchedulerQueueEmpty(manager) // Restart add timer - clock.tick(1000L) + clock.advance(1000L) assert(addTime(manager) === NOT_SET) onSchedulerBacklogged(manager) val secondAddTime = addTime(manager) assert(secondAddTime === clock.getTimeMillis + schedulerBacklogTimeout * 1000) - clock.tick(100L) + clock.advance(100L) onSchedulerBacklogged(manager) assert(addTime(manager) === secondAddTime) // timer is already started assert(addTime(manager) !== firstAddTime) @@ -328,7 +359,7 @@ class ExecutorAllocationManagerSuite extends FunSuite with LocalSparkContext { test("starting/canceling remove timers") { sc = createSparkContext(2, 10) - val clock = new TestClock(14444L) + val clock = new ManualClock(14444L) val manager = sc.executorAllocationManager.get manager.setClock(clock) @@ -341,17 +372,17 @@ class ExecutorAllocationManagerSuite extends FunSuite with LocalSparkContext { assert(removeTimes(manager).contains("1")) val firstRemoveTime = removeTimes(manager)("1") assert(firstRemoveTime === clock.getTimeMillis + executorIdleTimeout * 1000) - clock.tick(100L) + clock.advance(100L) onExecutorIdle(manager, "1") assert(removeTimes(manager)("1") === firstRemoveTime) // timer is already started - clock.tick(200L) + clock.advance(200L) onExecutorIdle(manager, "1") assert(removeTimes(manager)("1") === firstRemoveTime) - clock.tick(300L) + clock.advance(300L) onExecutorIdle(manager, "2") assert(removeTimes(manager)("2") !== firstRemoveTime) // different executor assert(removeTimes(manager)("2") === clock.getTimeMillis + executorIdleTimeout * 1000) - clock.tick(400L) + clock.advance(400L) onExecutorIdle(manager, "3") assert(removeTimes(manager)("3") !== firstRemoveTime) assert(removeTimes(manager)("3") === clock.getTimeMillis + executorIdleTimeout * 1000) @@ -360,7 +391,7 @@ class ExecutorAllocationManagerSuite extends FunSuite with LocalSparkContext { assert(removeTimes(manager).contains("3")) // Restart remove timer - clock.tick(1000L) + clock.advance(1000L) onExecutorBusy(manager, "1") assert(removeTimes(manager).size === 2) onExecutorIdle(manager, "1") @@ -376,7 +407,7 @@ class ExecutorAllocationManagerSuite extends FunSuite with LocalSparkContext { test("mock polling loop with no events") { sc = createSparkContext(1, 20) val manager = sc.executorAllocationManager.get - val clock = new TestClock(2020L) + val clock = new ManualClock(2020L) manager.setClock(clock) // No events - we should not be adding or removing @@ -385,15 +416,15 @@ class ExecutorAllocationManagerSuite extends FunSuite with LocalSparkContext { schedule(manager) assert(numExecutorsPending(manager) === 0) assert(executorsPendingToRemove(manager).isEmpty) - clock.tick(100L) + clock.advance(100L) schedule(manager) assert(numExecutorsPending(manager) === 0) assert(executorsPendingToRemove(manager).isEmpty) - clock.tick(1000L) + clock.advance(1000L) schedule(manager) assert(numExecutorsPending(manager) === 0) assert(executorsPendingToRemove(manager).isEmpty) - clock.tick(10000L) + clock.advance(10000L) schedule(manager) assert(numExecutorsPending(manager) === 0) assert(executorsPendingToRemove(manager).isEmpty) @@ -401,57 +432,57 @@ class ExecutorAllocationManagerSuite extends FunSuite with LocalSparkContext { test("mock polling loop add behavior") { sc = createSparkContext(1, 20) - val clock = new TestClock(2020L) + val clock = new ManualClock(2020L) val manager = sc.executorAllocationManager.get manager.setClock(clock) sc.listenerBus.postToAll(SparkListenerStageSubmitted(createStageInfo(0, 1000))) // Scheduler queue backlogged onSchedulerBacklogged(manager) - clock.tick(schedulerBacklogTimeout * 1000 / 2) + clock.advance(schedulerBacklogTimeout * 1000 / 2) schedule(manager) assert(numExecutorsPending(manager) === 0) // timer not exceeded yet - clock.tick(schedulerBacklogTimeout * 1000) + clock.advance(schedulerBacklogTimeout * 1000) schedule(manager) assert(numExecutorsPending(manager) === 1) // first timer exceeded - clock.tick(sustainedSchedulerBacklogTimeout * 1000 / 2) + clock.advance(sustainedSchedulerBacklogTimeout * 1000 / 2) schedule(manager) assert(numExecutorsPending(manager) === 1) // second timer not exceeded yet - clock.tick(sustainedSchedulerBacklogTimeout * 1000) + clock.advance(sustainedSchedulerBacklogTimeout * 1000) schedule(manager) assert(numExecutorsPending(manager) === 1 + 2) // second timer exceeded - clock.tick(sustainedSchedulerBacklogTimeout * 1000) + clock.advance(sustainedSchedulerBacklogTimeout * 1000) schedule(manager) assert(numExecutorsPending(manager) === 1 + 2 + 4) // third timer exceeded // Scheduler queue drained onSchedulerQueueEmpty(manager) - clock.tick(sustainedSchedulerBacklogTimeout * 1000) + clock.advance(sustainedSchedulerBacklogTimeout * 1000) schedule(manager) assert(numExecutorsPending(manager) === 7) // timer is canceled - clock.tick(sustainedSchedulerBacklogTimeout * 1000) + clock.advance(sustainedSchedulerBacklogTimeout * 1000) schedule(manager) assert(numExecutorsPending(manager) === 7) // Scheduler queue backlogged again onSchedulerBacklogged(manager) - clock.tick(schedulerBacklogTimeout * 1000) + clock.advance(schedulerBacklogTimeout * 1000) schedule(manager) assert(numExecutorsPending(manager) === 7 + 1) // timer restarted - clock.tick(sustainedSchedulerBacklogTimeout * 1000) + clock.advance(sustainedSchedulerBacklogTimeout * 1000) schedule(manager) assert(numExecutorsPending(manager) === 7 + 1 + 2) - clock.tick(sustainedSchedulerBacklogTimeout * 1000) + clock.advance(sustainedSchedulerBacklogTimeout * 1000) schedule(manager) assert(numExecutorsPending(manager) === 7 + 1 + 2 + 4) - clock.tick(sustainedSchedulerBacklogTimeout * 1000) + clock.advance(sustainedSchedulerBacklogTimeout * 1000) schedule(manager) assert(numExecutorsPending(manager) === 20) // limit reached } test("mock polling loop remove behavior") { sc = createSparkContext(1, 20) - val clock = new TestClock(2020L) + val clock = new ManualClock(2020L) val manager = sc.executorAllocationManager.get manager.setClock(clock) @@ -461,11 +492,11 @@ class ExecutorAllocationManagerSuite extends FunSuite with LocalSparkContext { onExecutorAdded(manager, "executor-3") assert(removeTimes(manager).size === 3) assert(executorsPendingToRemove(manager).isEmpty) - clock.tick(executorIdleTimeout * 1000 / 2) + clock.advance(executorIdleTimeout * 1000 / 2) schedule(manager) assert(removeTimes(manager).size === 3) // idle threshold not reached yet assert(executorsPendingToRemove(manager).isEmpty) - clock.tick(executorIdleTimeout * 1000) + clock.advance(executorIdleTimeout * 1000) schedule(manager) assert(removeTimes(manager).isEmpty) // idle threshold exceeded assert(executorsPendingToRemove(manager).size === 2) // limit reached (1 executor remaining) @@ -486,7 +517,7 @@ class ExecutorAllocationManagerSuite extends FunSuite with LocalSparkContext { assert(!removeTimes(manager).contains("executor-5")) assert(!removeTimes(manager).contains("executor-6")) assert(executorsPendingToRemove(manager).size === 2) - clock.tick(executorIdleTimeout * 1000) + clock.advance(executorIdleTimeout * 1000) schedule(manager) assert(removeTimes(manager).isEmpty) // idle executors are removed assert(executorsPendingToRemove(manager).size === 4) @@ -504,7 +535,7 @@ class ExecutorAllocationManagerSuite extends FunSuite with LocalSparkContext { assert(removeTimes(manager).contains("executor-5")) assert(removeTimes(manager).contains("executor-6")) assert(executorsPendingToRemove(manager).size === 4) - clock.tick(executorIdleTimeout * 1000) + clock.advance(executorIdleTimeout * 1000) schedule(manager) assert(removeTimes(manager).isEmpty) assert(executorsPendingToRemove(manager).size === 6) // limit reached (1 executor remaining) @@ -639,16 +670,6 @@ class ExecutorAllocationManagerSuite extends FunSuite with LocalSparkContext { assert(removeTimes(manager).contains("executor-2")) assert(!removeTimes(manager).contains("executor-1")) } -} - -/** - * Helper methods for testing ExecutorAllocationManager. - * This includes methods to access private methods and fields in ExecutorAllocationManager. - */ -private object ExecutorAllocationManagerSuite extends PrivateMethodTester { - private val schedulerBacklogTimeout = 1L - private val sustainedSchedulerBacklogTimeout = 2L - private val executorIdleTimeout = 3L private def createSparkContext(minExecutors: Int = 1, maxExecutors: Int = 5): SparkContext = { val conf = new SparkConf() @@ -657,14 +678,28 @@ private object ExecutorAllocationManagerSuite extends PrivateMethodTester { .set("spark.dynamicAllocation.enabled", "true") .set("spark.dynamicAllocation.minExecutors", minExecutors.toString) .set("spark.dynamicAllocation.maxExecutors", maxExecutors.toString) - .set("spark.dynamicAllocation.schedulerBacklogTimeout", schedulerBacklogTimeout.toString) + .set("spark.dynamicAllocation.schedulerBacklogTimeout", + s"${schedulerBacklogTimeout.toString}s") .set("spark.dynamicAllocation.sustainedSchedulerBacklogTimeout", - sustainedSchedulerBacklogTimeout.toString) - .set("spark.dynamicAllocation.executorIdleTimeout", executorIdleTimeout.toString) + s"${sustainedSchedulerBacklogTimeout.toString}s") + .set("spark.dynamicAllocation.executorIdleTimeout", s"${executorIdleTimeout.toString}s") .set("spark.dynamicAllocation.testing", "true") - new SparkContext(conf) + val sc = new SparkContext(conf) + contexts += sc + sc } +} + +/** + * Helper methods for testing ExecutorAllocationManager. + * This includes methods to access private methods and fields in ExecutorAllocationManager. + */ +private object ExecutorAllocationManagerSuite extends PrivateMethodTester { + private val schedulerBacklogTimeout = 1L + private val sustainedSchedulerBacklogTimeout = 2L + private val executorIdleTimeout = 3L + private def createStageInfo(stageId: Int, numTasks: Int): StageInfo = { new StageInfo(stageId, 0, "name", numTasks, Seq.empty, "no details") } @@ -679,6 +714,7 @@ private object ExecutorAllocationManagerSuite extends PrivateMethodTester { private val _numExecutorsToAdd = PrivateMethod[Int]('numExecutorsToAdd) private val _numExecutorsPending = PrivateMethod[Int]('numExecutorsPending) + private val _maxNumExecutorsNeeded = PrivateMethod[Int]('maxNumExecutorsNeeded) private val _executorsPendingToRemove = PrivateMethod[collection.Set[String]]('executorsPendingToRemove) private val _executorIds = PrivateMethod[collection.Set[String]]('executorIds) @@ -686,6 +722,7 @@ private object ExecutorAllocationManagerSuite extends PrivateMethodTester { private val _removeTimes = PrivateMethod[collection.Map[String, Long]]('removeTimes) private val _schedule = PrivateMethod[Unit]('schedule) private val _addExecutors = PrivateMethod[Int]('addExecutors) + private val _addOrCancelExecutorRequests = PrivateMethod[Int]('addOrCancelExecutorRequests) private val _removeExecutor = PrivateMethod[Boolean]('removeExecutor) private val _onExecutorAdded = PrivateMethod[Unit]('onExecutorAdded) private val _onExecutorRemoved = PrivateMethod[Unit]('onExecutorRemoved) @@ -724,7 +761,12 @@ private object ExecutorAllocationManagerSuite extends PrivateMethodTester { } private def addExecutors(manager: ExecutorAllocationManager): Int = { - manager invokePrivate _addExecutors() + val maxNumExecutorsNeeded = manager invokePrivate _maxNumExecutorsNeeded() + manager invokePrivate _addExecutors(maxNumExecutorsNeeded) + } + + private def adjustRequestedExecutors(manager: ExecutorAllocationManager): Int = { + manager invokePrivate _addOrCancelExecutorRequests(0L) } private def removeExecutor(manager: ExecutorAllocationManager, id: String): Boolean = { diff --git a/core/src/test/scala/org/apache/spark/FileServerSuite.scala b/core/src/test/scala/org/apache/spark/FileServerSuite.scala index 5fdf6bc2777e3..a69e9b761f9a7 100644 --- a/core/src/test/scala/org/apache/spark/FileServerSuite.scala +++ b/core/src/test/scala/org/apache/spark/FileServerSuite.scala @@ -20,7 +20,7 @@ package org.apache.spark import java.io._ import java.net.URI import java.util.jar.{JarEntry, JarOutputStream} -import javax.net.ssl.SSLHandshakeException +import javax.net.ssl.SSLException import com.google.common.io.ByteStreams import org.apache.commons.io.{FileUtils, IOUtils} @@ -228,7 +228,7 @@ class FileServerSuite extends FunSuite with LocalSparkContext { try { server.initialize() - intercept[SSLHandshakeException] { + intercept[SSLException] { fileTransferTest(server) } } finally { diff --git a/core/src/test/scala/org/apache/spark/FileSuite.scala b/core/src/test/scala/org/apache/spark/FileSuite.scala index 7acd27c735727..c8f08eed47c76 100644 --- a/core/src/test/scala/org/apache/spark/FileSuite.scala +++ b/core/src/test/scala/org/apache/spark/FileSuite.scala @@ -222,7 +222,7 @@ class FileSuite extends FunSuite with LocalSparkContext { val nums = sc.makeRDD(1 to 3).map(x => (new IntWritable(x), new Text("a" * x))) nums.saveAsSequenceFile(outputDir) val output = - sc.newAPIHadoopFile[IntWritable, Text, SequenceFileInputFormat[IntWritable, Text]](outputDir) + sc.newAPIHadoopFile[IntWritable, Text, SequenceFileInputFormat[IntWritable, Text]](outputDir) assert(output.map(_.toString).collect().toList === List("(1,a)", "(2,aa)", "(3,aaa)")) } @@ -451,7 +451,8 @@ class FileSuite extends FunSuite with LocalSparkContext { test ("prevent user from overwriting the empty directory (new Hadoop API)") { sc = new SparkContext("local", "test") - val randomRDD = sc.parallelize(Array(("key1", "a"), ("key2", "a"), ("key3", "b"), ("key4", "c")), 1) + val randomRDD = sc.parallelize( + Array(("key1", "a"), ("key2", "a"), ("key3", "b"), ("key4", "c")), 1) intercept[FileAlreadyExistsException] { randomRDD.saveAsNewAPIHadoopFile[NewTextOutputFormat[String, String]](tempDir.getPath) } @@ -459,8 +460,10 @@ class FileSuite extends FunSuite with LocalSparkContext { test ("prevent user from overwriting the non-empty directory (new Hadoop API)") { sc = new SparkContext("local", "test") - val randomRDD = sc.parallelize(Array(("key1", "a"), ("key2", "a"), ("key3", "b"), ("key4", "c")), 1) - randomRDD.saveAsNewAPIHadoopFile[NewTextOutputFormat[String, String]](tempDir.getPath + "/output") + val randomRDD = sc.parallelize( + Array(("key1", "a"), ("key2", "a"), ("key3", "b"), ("key4", "c")), 1) + randomRDD.saveAsNewAPIHadoopFile[NewTextOutputFormat[String, String]]( + tempDir.getPath + "/output") assert(new File(tempDir.getPath + "/output/part-r-00000").exists() === true) intercept[FileAlreadyExistsException] { randomRDD.saveAsNewAPIHadoopFile[NewTextOutputFormat[String, String]](tempDir.getPath) @@ -471,16 +474,20 @@ class FileSuite extends FunSuite with LocalSparkContext { val sf = new SparkConf() sf.setAppName("test").setMaster("local").set("spark.hadoop.validateOutputSpecs", "false") sc = new SparkContext(sf) - val randomRDD = sc.parallelize(Array(("key1", "a"), ("key2", "a"), ("key3", "b"), ("key4", "c")), 1) - randomRDD.saveAsNewAPIHadoopFile[NewTextOutputFormat[String, String]](tempDir.getPath + "/output") + val randomRDD = sc.parallelize( + Array(("key1", "a"), ("key2", "a"), ("key3", "b"), ("key4", "c")), 1) + randomRDD.saveAsNewAPIHadoopFile[NewTextOutputFormat[String, String]]( + tempDir.getPath + "/output") assert(new File(tempDir.getPath + "/output/part-r-00000").exists() === true) - randomRDD.saveAsNewAPIHadoopFile[NewTextOutputFormat[String, String]](tempDir.getPath + "/output") + randomRDD.saveAsNewAPIHadoopFile[NewTextOutputFormat[String, String]]( + tempDir.getPath + "/output") assert(new File(tempDir.getPath + "/output/part-r-00000").exists() === true) } test ("save Hadoop Dataset through old Hadoop API") { sc = new SparkContext("local", "test") - val randomRDD = sc.parallelize(Array(("key1", "a"), ("key2", "a"), ("key3", "b"), ("key4", "c")), 1) + val randomRDD = sc.parallelize( + Array(("key1", "a"), ("key2", "a"), ("key3", "b"), ("key4", "c")), 1) val job = new JobConf() job.setOutputKeyClass(classOf[String]) job.setOutputValueClass(classOf[String]) @@ -492,7 +499,8 @@ class FileSuite extends FunSuite with LocalSparkContext { test ("save Hadoop Dataset through new Hadoop API") { sc = new SparkContext("local", "test") - val randomRDD = sc.parallelize(Array(("key1", "a"), ("key2", "a"), ("key3", "b"), ("key4", "c")), 1) + val randomRDD = sc.parallelize( + Array(("key1", "a"), ("key2", "a"), ("key3", "b"), ("key4", "c")), 1) val job = new Job(sc.hadoopConfiguration) job.setOutputKeyClass(classOf[String]) job.setOutputValueClass(classOf[String]) diff --git a/core/src/test/scala/org/apache/spark/HeartbeatReceiverSuite.scala b/core/src/test/scala/org/apache/spark/HeartbeatReceiverSuite.scala new file mode 100644 index 0000000000000..0fd570e5297d9 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/HeartbeatReceiverSuite.scala @@ -0,0 +1,81 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark + +import scala.concurrent.duration._ +import scala.language.postfixOps + +import org.apache.spark.executor.TaskMetrics +import org.apache.spark.storage.BlockManagerId +import org.scalatest.FunSuite +import org.mockito.Mockito.{mock, spy, verify, when} +import org.mockito.Matchers +import org.mockito.Matchers._ + +import org.apache.spark.scheduler.TaskScheduler +import org.apache.spark.util.RpcUtils +import org.scalatest.concurrent.Eventually._ + +class HeartbeatReceiverSuite extends FunSuite with LocalSparkContext { + + test("HeartbeatReceiver") { + sc = spy(new SparkContext("local[2]", "test")) + val scheduler = mock(classOf[TaskScheduler]) + when(scheduler.executorHeartbeatReceived(any(), any(), any())).thenReturn(true) + when(sc.taskScheduler).thenReturn(scheduler) + + val heartbeatReceiver = new HeartbeatReceiver(sc) + sc.env.rpcEnv.setupEndpoint("heartbeat", heartbeatReceiver).send(TaskSchedulerIsSet) + eventually(timeout(5 seconds), interval(5 millis)) { + assert(heartbeatReceiver.scheduler != null) + } + val receiverRef = RpcUtils.makeDriverRef("heartbeat", sc.conf, sc.env.rpcEnv) + + val metrics = new TaskMetrics + val blockManagerId = BlockManagerId("executor-1", "localhost", 12345) + val response = receiverRef.askWithReply[HeartbeatResponse]( + Heartbeat("executor-1", Array(1L -> metrics), blockManagerId)) + + verify(scheduler).executorHeartbeatReceived( + Matchers.eq("executor-1"), Matchers.eq(Array(1L -> metrics)), Matchers.eq(blockManagerId)) + assert(false === response.reregisterBlockManager) + } + + test("HeartbeatReceiver re-register") { + sc = spy(new SparkContext("local[2]", "test")) + val scheduler = mock(classOf[TaskScheduler]) + when(scheduler.executorHeartbeatReceived(any(), any(), any())).thenReturn(false) + when(sc.taskScheduler).thenReturn(scheduler) + + val heartbeatReceiver = new HeartbeatReceiver(sc) + sc.env.rpcEnv.setupEndpoint("heartbeat", heartbeatReceiver).send(TaskSchedulerIsSet) + eventually(timeout(5 seconds), interval(5 millis)) { + assert(heartbeatReceiver.scheduler != null) + } + val receiverRef = RpcUtils.makeDriverRef("heartbeat", sc.conf, sc.env.rpcEnv) + + val metrics = new TaskMetrics + val blockManagerId = BlockManagerId("executor-1", "localhost", 12345) + val response = receiverRef.askWithReply[HeartbeatResponse]( + Heartbeat("executor-1", Array(1L -> metrics), blockManagerId)) + + verify(scheduler).executorHeartbeatReceived( + Matchers.eq("executor-1"), Matchers.eq(Array(1L -> metrics)), Matchers.eq(blockManagerId)) + assert(true === response.reregisterBlockManager) + } +} diff --git a/core/src/test/scala/org/apache/spark/ImplicitOrderingSuite.scala b/core/src/test/scala/org/apache/spark/ImplicitOrderingSuite.scala index d895230ecf330..51348c039b5c9 100644 --- a/core/src/test/scala/org/apache/spark/ImplicitOrderingSuite.scala +++ b/core/src/test/scala/org/apache/spark/ImplicitOrderingSuite.scala @@ -51,7 +51,7 @@ private object ImplicitOrderingSuite { override def compare(o: OrderedClass): Int = ??? } - def basicMapExpectations(rdd: RDD[Int]) = { + def basicMapExpectations(rdd: RDD[Int]): List[(Boolean, String)] = { List((rdd.map(x => (x, x)).keyOrdering.isDefined, "rdd.map(x => (x, x)).keyOrdering.isDefined"), (rdd.map(x => (1, x)).keyOrdering.isDefined, @@ -68,7 +68,7 @@ private object ImplicitOrderingSuite { "rdd.map(x => (new OrderedClass, x)).keyOrdering.isDefined")) } - def otherRDDMethodExpectations(rdd: RDD[Int]) = { + def otherRDDMethodExpectations(rdd: RDD[Int]): List[(Boolean, String)] = { List((rdd.groupBy(x => x).keyOrdering.isDefined, "rdd.groupBy(x => x).keyOrdering.isDefined"), (rdd.groupBy(x => new NonOrderedClass).keyOrdering.isEmpty, @@ -82,4 +82,4 @@ private object ImplicitOrderingSuite { (rdd.groupBy((x: Int) => x, new HashPartitioner(5)).keyOrdering.isDefined, "rdd.groupBy((x: Int) => x, new HashPartitioner(5)).keyOrdering.isDefined")) } -} \ No newline at end of file +} diff --git a/core/src/test/scala/org/apache/spark/JobCancellationSuite.scala b/core/src/test/scala/org/apache/spark/JobCancellationSuite.scala index 21487bc24d58a..4d3e09793faff 100644 --- a/core/src/test/scala/org/apache/spark/JobCancellationSuite.scala +++ b/core/src/test/scala/org/apache/spark/JobCancellationSuite.scala @@ -188,7 +188,7 @@ class JobCancellationSuite extends FunSuite with Matchers with BeforeAndAfter val rdd = sc.parallelize(1 to 10, 2).map { i => JobCancellationSuite.twoJobsSharingStageSemaphore.acquire() (i, i) - }.reduceByKey(_+_) + }.reduceByKey(_ + _) val f1 = rdd.collectAsync() val f2 = rdd.countAsync() diff --git a/core/src/test/scala/org/apache/spark/LocalSparkContext.scala b/core/src/test/scala/org/apache/spark/LocalSparkContext.scala index 53e367a61715b..8bf2e55defd02 100644 --- a/core/src/test/scala/org/apache/spark/LocalSparkContext.scala +++ b/core/src/test/scala/org/apache/spark/LocalSparkContext.scala @@ -37,7 +37,7 @@ trait LocalSparkContext extends BeforeAndAfterEach with BeforeAndAfterAll { self super.afterEach() } - def resetSparkContext() = { + def resetSparkContext(): Unit = { LocalSparkContext.stop(sc) sc = null } @@ -54,7 +54,7 @@ object LocalSparkContext { } /** Runs `f` by passing in `sc` and ensures that `sc` is stopped. */ - def withSpark[T](sc: SparkContext)(f: SparkContext => T) = { + def withSpark[T](sc: SparkContext)(f: SparkContext => T): T = { try { f(sc) } finally { diff --git a/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala b/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala index ccfe0678cb1c3..6295d34be5ca9 100644 --- a/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala +++ b/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala @@ -17,34 +17,37 @@ package org.apache.spark -import scala.concurrent.Await - -import akka.actor._ -import akka.testkit.TestActorRef +import org.mockito.Mockito._ +import org.mockito.Matchers.{any, isA} import org.scalatest.FunSuite +import org.apache.spark.rpc.{RpcAddress, RpcEndpointRef, RpcCallContext, RpcEnv} import org.apache.spark.scheduler.{CompressedMapStatus, MapStatus} import org.apache.spark.shuffle.FetchFailedException import org.apache.spark.storage.BlockManagerId -import org.apache.spark.util.AkkaUtils class MapOutputTrackerSuite extends FunSuite { private val conf = new SparkConf + def createRpcEnv(name: String, host: String = "localhost", port: Int = 0, + securityManager: SecurityManager = new SecurityManager(conf)): RpcEnv = { + RpcEnv.create(name, host, port, conf, securityManager) + } + test("master start and stop") { - val actorSystem = ActorSystem("test") + val rpcEnv = createRpcEnv("test") val tracker = new MapOutputTrackerMaster(conf) - tracker.trackerActor = - actorSystem.actorOf(Props(new MapOutputTrackerMasterActor(tracker, conf))) + tracker.trackerEndpoint = rpcEnv.setupEndpoint(MapOutputTracker.ENDPOINT_NAME, + new MapOutputTrackerMasterEndpoint(rpcEnv, tracker, conf)) tracker.stop() - actorSystem.shutdown() + rpcEnv.shutdown() } test("master register shuffle and fetch") { - val actorSystem = ActorSystem("test") + val rpcEnv = createRpcEnv("test") val tracker = new MapOutputTrackerMaster(conf) - tracker.trackerActor = - actorSystem.actorOf(Props(new MapOutputTrackerMasterActor(tracker, conf))) + tracker.trackerEndpoint = rpcEnv.setupEndpoint(MapOutputTracker.ENDPOINT_NAME, + new MapOutputTrackerMasterEndpoint(rpcEnv, tracker, conf)) tracker.registerShuffle(10, 2) assert(tracker.containsShuffle(10)) val size1000 = MapStatus.decompressSize(MapStatus.compressSize(1000L)) @@ -57,13 +60,14 @@ class MapOutputTrackerSuite extends FunSuite { assert(statuses.toSeq === Seq((BlockManagerId("a", "hostA", 1000), size1000), (BlockManagerId("b", "hostB", 1000), size10000))) tracker.stop() - actorSystem.shutdown() + rpcEnv.shutdown() } test("master register and unregister shuffle") { - val actorSystem = ActorSystem("test") + val rpcEnv = createRpcEnv("test") val tracker = new MapOutputTrackerMaster(conf) - tracker.trackerActor = actorSystem.actorOf(Props(new MapOutputTrackerMasterActor(tracker, conf))) + tracker.trackerEndpoint = rpcEnv.setupEndpoint(MapOutputTracker.ENDPOINT_NAME, + new MapOutputTrackerMasterEndpoint(rpcEnv, tracker, conf)) tracker.registerShuffle(10, 2) val compressedSize1000 = MapStatus.compressSize(1000L) val compressedSize10000 = MapStatus.compressSize(10000L) @@ -78,14 +82,14 @@ class MapOutputTrackerSuite extends FunSuite { assert(tracker.getServerStatuses(10, 0).isEmpty) tracker.stop() - actorSystem.shutdown() + rpcEnv.shutdown() } test("master register shuffle and unregister map output and fetch") { - val actorSystem = ActorSystem("test") + val rpcEnv = createRpcEnv("test") val tracker = new MapOutputTrackerMaster(conf) - tracker.trackerActor = - actorSystem.actorOf(Props(new MapOutputTrackerMasterActor(tracker, conf))) + tracker.trackerEndpoint = rpcEnv.setupEndpoint(MapOutputTracker.ENDPOINT_NAME, + new MapOutputTrackerMasterEndpoint(rpcEnv, tracker, conf)) tracker.registerShuffle(10, 2) val compressedSize1000 = MapStatus.compressSize(1000L) val compressedSize10000 = MapStatus.compressSize(10000L) @@ -104,25 +108,21 @@ class MapOutputTrackerSuite extends FunSuite { intercept[FetchFailedException] { tracker.getServerStatuses(10, 1) } tracker.stop() - actorSystem.shutdown() + rpcEnv.shutdown() } test("remote fetch") { val hostname = "localhost" - val (actorSystem, boundPort) = AkkaUtils.createActorSystem("spark", hostname, 0, conf = conf, - securityManager = new SecurityManager(conf)) + val rpcEnv = createRpcEnv("spark", hostname, 0, new SecurityManager(conf)) val masterTracker = new MapOutputTrackerMaster(conf) - masterTracker.trackerActor = actorSystem.actorOf( - Props(new MapOutputTrackerMasterActor(masterTracker, conf)), "MapOutputTracker") + masterTracker.trackerEndpoint = rpcEnv.setupEndpoint(MapOutputTracker.ENDPOINT_NAME, + new MapOutputTrackerMasterEndpoint(rpcEnv, masterTracker, conf)) - val (slaveSystem, _) = AkkaUtils.createActorSystem("spark-slave", hostname, 0, conf = conf, - securityManager = new SecurityManager(conf)) + val slaveRpcEnv = createRpcEnv("spark-slave", hostname, 0, new SecurityManager(conf)) val slaveTracker = new MapOutputTrackerWorker(conf) - val selection = slaveSystem.actorSelection( - AkkaUtils.address(AkkaUtils.protocol(slaveSystem), "spark", "localhost", boundPort, "MapOutputTracker")) - val timeout = AkkaUtils.lookupTimeout(conf) - slaveTracker.trackerActor = Await.result(selection.resolveOne(timeout), timeout) + slaveTracker.trackerEndpoint = + slaveRpcEnv.setupEndpointRef("spark", rpcEnv.address, MapOutputTracker.ENDPOINT_NAME) masterTracker.registerShuffle(10, 1) masterTracker.incrementEpoch() @@ -147,8 +147,8 @@ class MapOutputTrackerSuite extends FunSuite { masterTracker.stop() slaveTracker.stop() - actorSystem.shutdown() - slaveSystem.shutdown() + rpcEnv.shutdown() + slaveRpcEnv.shutdown() } test("remote fetch below akka frame size") { @@ -157,19 +157,24 @@ class MapOutputTrackerSuite extends FunSuite { newConf.set("spark.akka.askTimeout", "1") // Fail fast val masterTracker = new MapOutputTrackerMaster(conf) - val actorSystem = ActorSystem("test") - val actorRef = TestActorRef[MapOutputTrackerMasterActor]( - Props(new MapOutputTrackerMasterActor(masterTracker, newConf)))(actorSystem) - val masterActor = actorRef.underlyingActor + val rpcEnv = createRpcEnv("spark") + val masterEndpoint = new MapOutputTrackerMasterEndpoint(rpcEnv, masterTracker, newConf) + rpcEnv.setupEndpoint(MapOutputTracker.ENDPOINT_NAME, masterEndpoint) // Frame size should be ~123B, and no exception should be thrown masterTracker.registerShuffle(10, 1) masterTracker.registerMapOutput(10, 0, MapStatus( BlockManagerId("88", "mph", 1000), Array.fill[Long](10)(0))) - masterActor.receive(GetMapOutputStatuses(10)) + val sender = mock(classOf[RpcEndpointRef]) + when(sender.address).thenReturn(RpcAddress("localhost", 12345)) + val rpcCallContext = mock(classOf[RpcCallContext]) + when(rpcCallContext.sender).thenReturn(sender) + masterEndpoint.receiveAndReply(rpcCallContext)(GetMapOutputStatuses(10)) + verify(rpcCallContext).reply(any()) + verify(rpcCallContext, never()).sendFailure(any()) // masterTracker.stop() // this throws an exception - actorSystem.shutdown() + rpcEnv.shutdown() } test("remote fetch exceeds akka frame size") { @@ -178,12 +183,11 @@ class MapOutputTrackerSuite extends FunSuite { newConf.set("spark.akka.askTimeout", "1") // Fail fast val masterTracker = new MapOutputTrackerMaster(conf) - val actorSystem = ActorSystem("test") - val actorRef = TestActorRef[MapOutputTrackerMasterActor]( - Props(new MapOutputTrackerMasterActor(masterTracker, newConf)))(actorSystem) - val masterActor = actorRef.underlyingActor + val rpcEnv = createRpcEnv("test") + val masterEndpoint = new MapOutputTrackerMasterEndpoint(rpcEnv, masterTracker, newConf) + rpcEnv.setupEndpoint(MapOutputTracker.ENDPOINT_NAME, masterEndpoint) - // Frame size should be ~1.1MB, and MapOutputTrackerMasterActor should throw exception. + // Frame size should be ~1.1MB, and MapOutputTrackerMasterEndpoint should throw exception. // Note that the size is hand-selected here because map output statuses are compressed before // being sent. masterTracker.registerShuffle(20, 100) @@ -191,9 +195,15 @@ class MapOutputTrackerSuite extends FunSuite { masterTracker.registerMapOutput(20, i, new CompressedMapStatus( BlockManagerId("999", "mps", 1000), Array.fill[Long](4000000)(0))) } - intercept[SparkException] { masterActor.receive(GetMapOutputStatuses(20)) } + val sender = mock(classOf[RpcEndpointRef]) + when(sender.address).thenReturn(RpcAddress("localhost", 12345)) + val rpcCallContext = mock(classOf[RpcCallContext]) + when(rpcCallContext.sender).thenReturn(sender) + masterEndpoint.receiveAndReply(rpcCallContext)(GetMapOutputStatuses(20)) + verify(rpcCallContext, never()).reply(any()) + verify(rpcCallContext).sendFailure(isA(classOf[SparkException])) // masterTracker.stop() // this throws an exception - actorSystem.shutdown() + rpcEnv.shutdown() } } diff --git a/core/src/test/scala/org/apache/spark/PartitioningSuite.scala b/core/src/test/scala/org/apache/spark/PartitioningSuite.scala index b7532314ada01..47e3bf6e1ac41 100644 --- a/core/src/test/scala/org/apache/spark/PartitioningSuite.scala +++ b/core/src/test/scala/org/apache/spark/PartitioningSuite.scala @@ -92,7 +92,7 @@ class PartitioningSuite extends FunSuite with SharedSparkContext with PrivateMet test("RangePartitioner for keys that are not Comparable (but with Ordering)") { // Row does not extend Comparable, but has an implicit Ordering defined. implicit object RowOrdering extends Ordering[Row] { - override def compare(x: Row, y: Row) = x.value - y.value + override def compare(x: Row, y: Row): Int = x.value - y.value } val rdd = sc.parallelize(1 to 4500).map(x => (Row(x), Row(x))) @@ -212,20 +212,24 @@ class PartitioningSuite extends FunSuite with SharedSparkContext with PrivateMet val arrPairs: RDD[(Array[Int], Int)] = sc.parallelize(Array(1, 2, 3, 4), 2).map(x => (Array(x), x)) - assert(intercept[SparkException]{ arrs.distinct() }.getMessage.contains("array")) + def verify(testFun: => Unit): Unit = { + intercept[SparkException](testFun).getMessage.contains("array") + } + + verify(arrs.distinct()) // We can't catch all usages of arrays, since they might occur inside other collections: // assert(fails { arrPairs.distinct() }) - assert(intercept[SparkException]{ arrPairs.partitionBy(new HashPartitioner(2)) }.getMessage.contains("array")) - assert(intercept[SparkException]{ arrPairs.join(arrPairs) }.getMessage.contains("array")) - assert(intercept[SparkException]{ arrPairs.leftOuterJoin(arrPairs) }.getMessage.contains("array")) - assert(intercept[SparkException]{ arrPairs.rightOuterJoin(arrPairs) }.getMessage.contains("array")) - assert(intercept[SparkException]{ arrPairs.fullOuterJoin(arrPairs) }.getMessage.contains("array")) - assert(intercept[SparkException]{ arrPairs.groupByKey() }.getMessage.contains("array")) - assert(intercept[SparkException]{ arrPairs.countByKey() }.getMessage.contains("array")) - assert(intercept[SparkException]{ arrPairs.countByKeyApprox(1) }.getMessage.contains("array")) - assert(intercept[SparkException]{ arrPairs.cogroup(arrPairs) }.getMessage.contains("array")) - assert(intercept[SparkException]{ arrPairs.reduceByKeyLocally(_ + _) }.getMessage.contains("array")) - assert(intercept[SparkException]{ arrPairs.reduceByKey(_ + _) }.getMessage.contains("array")) + verify(arrPairs.partitionBy(new HashPartitioner(2))) + verify(arrPairs.join(arrPairs)) + verify(arrPairs.leftOuterJoin(arrPairs)) + verify(arrPairs.rightOuterJoin(arrPairs)) + verify(arrPairs.fullOuterJoin(arrPairs)) + verify(arrPairs.groupByKey()) + verify(arrPairs.countByKey()) + verify(arrPairs.countByKeyApprox(1)) + verify(arrPairs.cogroup(arrPairs)) + verify(arrPairs.reduceByKeyLocally(_ + _)) + verify(arrPairs.reduceByKey(_ + _)) } test("zero-length partitions should be correctly handled") { diff --git a/core/src/test/scala/org/apache/spark/SSLOptionsSuite.scala b/core/src/test/scala/org/apache/spark/SSLOptionsSuite.scala index 444a33371bd71..93f46ef11c0e2 100644 --- a/core/src/test/scala/org/apache/spark/SSLOptionsSuite.scala +++ b/core/src/test/scala/org/apache/spark/SSLOptionsSuite.scala @@ -36,7 +36,8 @@ class SSLOptionsSuite extends FunSuite with BeforeAndAfterAll { conf.set("spark.ssl.keyPassword", "password") conf.set("spark.ssl.trustStore", trustStorePath) conf.set("spark.ssl.trustStorePassword", "password") - conf.set("spark.ssl.enabledAlgorithms", "TLS_RSA_WITH_AES_128_CBC_SHA, TLS_RSA_WITH_AES_256_CBC_SHA") + conf.set("spark.ssl.enabledAlgorithms", + "TLS_RSA_WITH_AES_128_CBC_SHA, TLS_RSA_WITH_AES_256_CBC_SHA") conf.set("spark.ssl.protocol", "SSLv3") val opts = SSLOptions.parse(conf, "spark.ssl") @@ -52,7 +53,8 @@ class SSLOptionsSuite extends FunSuite with BeforeAndAfterAll { assert(opts.keyStorePassword === Some("password")) assert(opts.keyPassword === Some("password")) assert(opts.protocol === Some("SSLv3")) - assert(opts.enabledAlgorithms === Set("TLS_RSA_WITH_AES_128_CBC_SHA", "TLS_RSA_WITH_AES_256_CBC_SHA")) + assert(opts.enabledAlgorithms === + Set("TLS_RSA_WITH_AES_128_CBC_SHA", "TLS_RSA_WITH_AES_256_CBC_SHA")) } test("test resolving property with defaults specified ") { @@ -66,7 +68,8 @@ class SSLOptionsSuite extends FunSuite with BeforeAndAfterAll { conf.set("spark.ssl.keyPassword", "password") conf.set("spark.ssl.trustStore", trustStorePath) conf.set("spark.ssl.trustStorePassword", "password") - conf.set("spark.ssl.enabledAlgorithms", "TLS_RSA_WITH_AES_128_CBC_SHA, TLS_RSA_WITH_AES_256_CBC_SHA") + conf.set("spark.ssl.enabledAlgorithms", + "TLS_RSA_WITH_AES_128_CBC_SHA, TLS_RSA_WITH_AES_256_CBC_SHA") conf.set("spark.ssl.protocol", "SSLv3") val defaultOpts = SSLOptions.parse(conf, "spark.ssl", defaults = None) @@ -83,7 +86,8 @@ class SSLOptionsSuite extends FunSuite with BeforeAndAfterAll { assert(opts.keyStorePassword === Some("password")) assert(opts.keyPassword === Some("password")) assert(opts.protocol === Some("SSLv3")) - assert(opts.enabledAlgorithms === Set("TLS_RSA_WITH_AES_128_CBC_SHA", "TLS_RSA_WITH_AES_256_CBC_SHA")) + assert(opts.enabledAlgorithms === + Set("TLS_RSA_WITH_AES_128_CBC_SHA", "TLS_RSA_WITH_AES_256_CBC_SHA")) } test("test whether defaults can be overridden ") { @@ -99,7 +103,8 @@ class SSLOptionsSuite extends FunSuite with BeforeAndAfterAll { conf.set("spark.ssl.keyPassword", "password") conf.set("spark.ssl.trustStore", trustStorePath) conf.set("spark.ssl.trustStorePassword", "password") - conf.set("spark.ssl.enabledAlgorithms", "TLS_RSA_WITH_AES_128_CBC_SHA, TLS_RSA_WITH_AES_256_CBC_SHA") + conf.set("spark.ssl.enabledAlgorithms", + "TLS_RSA_WITH_AES_128_CBC_SHA, TLS_RSA_WITH_AES_256_CBC_SHA") conf.set("spark.ui.ssl.enabledAlgorithms", "ABC, DEF") conf.set("spark.ssl.protocol", "SSLv3") diff --git a/core/src/test/scala/org/apache/spark/SSLSampleConfigs.scala b/core/src/test/scala/org/apache/spark/SSLSampleConfigs.scala index ace8123a8961f..308b9ea17708d 100644 --- a/core/src/test/scala/org/apache/spark/SSLSampleConfigs.scala +++ b/core/src/test/scala/org/apache/spark/SSLSampleConfigs.scala @@ -21,10 +21,11 @@ import java.io.File object SSLSampleConfigs { val keyStorePath = new File(this.getClass.getResource("/keystore").toURI).getAbsolutePath - val untrustedKeyStorePath = new File(this.getClass.getResource("/untrusted-keystore").toURI).getAbsolutePath + val untrustedKeyStorePath = new File( + this.getClass.getResource("/untrusted-keystore").toURI).getAbsolutePath val trustStorePath = new File(this.getClass.getResource("/truststore").toURI).getAbsolutePath - def sparkSSLConfig() = { + def sparkSSLConfig(): SparkConf = { val conf = new SparkConf(loadDefaults = false) conf.set("spark.ssl.enabled", "true") conf.set("spark.ssl.keyStore", keyStorePath) @@ -38,7 +39,7 @@ object SSLSampleConfigs { conf } - def sparkSSLConfigUntrusted() = { + def sparkSSLConfigUntrusted(): SparkConf = { val conf = new SparkConf(loadDefaults = false) conf.set("spark.ssl.enabled", "true") conf.set("spark.ssl.keyStore", untrustedKeyStorePath) diff --git a/core/src/test/scala/org/apache/spark/SecurityManagerSuite.scala b/core/src/test/scala/org/apache/spark/SecurityManagerSuite.scala index 43fbd3ff3f756..62cb7649c0284 100644 --- a/core/src/test/scala/org/apache/spark/SecurityManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/SecurityManagerSuite.scala @@ -21,6 +21,8 @@ import java.io.File import org.scalatest.FunSuite +import org.apache.spark.util.Utils + class SecurityManagerSuite extends FunSuite { test("set security with conf") { @@ -160,8 +162,7 @@ class SecurityManagerSuite extends FunSuite { } test("ssl off setup") { - val file = File.createTempFile("SSLOptionsSuite", "conf") - file.deleteOnExit() + val file = File.createTempFile("SSLOptionsSuite", "conf", Utils.createTempDir()) System.setProperty("spark.ssl.configFile", file.getAbsolutePath) val conf = new SparkConf() diff --git a/core/src/test/scala/org/apache/spark/ShuffleSuite.scala b/core/src/test/scala/org/apache/spark/ShuffleSuite.scala index f57921b768310..d7180516029d5 100644 --- a/core/src/test/scala/org/apache/spark/ShuffleSuite.scala +++ b/core/src/test/scala/org/apache/spark/ShuffleSuite.scala @@ -142,7 +142,7 @@ abstract class ShuffleSuite extends FunSuite with Matchers with LocalSparkContex test("shuffle on mutable pairs") { // Use a local cluster with 2 processes to make sure there are both local and remote blocks sc = new SparkContext("local-cluster[2,1,512]", "test", conf) - def p[T1, T2](_1: T1, _2: T2) = MutablePair(_1, _2) + def p[T1, T2](_1: T1, _2: T2): MutablePair[T1, T2] = MutablePair(_1, _2) val data = Array(p(1, 1), p(1, 2), p(1, 3), p(2, 1)) val pairs: RDD[MutablePair[Int, Int]] = sc.parallelize(data, 2) val results = new ShuffledRDD[Int, Int, Int](pairs, @@ -155,7 +155,7 @@ abstract class ShuffleSuite extends FunSuite with Matchers with LocalSparkContex // This is not in SortingSuite because of the local cluster setup. // Use a local cluster with 2 processes to make sure there are both local and remote blocks sc = new SparkContext("local-cluster[2,1,512]", "test", conf) - def p[T1, T2](_1: T1, _2: T2) = MutablePair(_1, _2) + def p[T1, T2](_1: T1, _2: T2): MutablePair[T1, T2] = MutablePair(_1, _2) val data = Array(p(1, 11), p(3, 33), p(100, 100), p(2, 22)) val pairs: RDD[MutablePair[Int, Int]] = sc.parallelize(data, 2) val results = new OrderedRDDFunctions[Int, Int, MutablePair[Int, Int]](pairs) @@ -169,7 +169,7 @@ abstract class ShuffleSuite extends FunSuite with Matchers with LocalSparkContex test("cogroup using mutable pairs") { // Use a local cluster with 2 processes to make sure there are both local and remote blocks sc = new SparkContext("local-cluster[2,1,512]", "test", conf) - def p[T1, T2](_1: T1, _2: T2) = MutablePair(_1, _2) + def p[T1, T2](_1: T1, _2: T2): MutablePair[T1, T2] = MutablePair(_1, _2) val data1 = Seq(p(1, 1), p(1, 2), p(1, 3), p(2, 1)) val data2 = Seq(p(1, "11"), p(1, "12"), p(2, "22"), p(3, "3")) val pairs1: RDD[MutablePair[Int, Int]] = sc.parallelize(data1, 2) @@ -196,7 +196,7 @@ abstract class ShuffleSuite extends FunSuite with Matchers with LocalSparkContex test("subtract mutable pairs") { // Use a local cluster with 2 processes to make sure there are both local and remote blocks sc = new SparkContext("local-cluster[2,1,512]", "test", conf) - def p[T1, T2](_1: T1, _2: T2) = MutablePair(_1, _2) + def p[T1, T2](_1: T1, _2: T2): MutablePair[T1, T2] = MutablePair(_1, _2) val data1 = Seq(p(1, 1), p(1, 2), p(1, 3), p(2, 1), p(3, 33)) val data2 = Seq(p(1, "11"), p(1, "12"), p(2, "22")) val pairs1: RDD[MutablePair[Int, Int]] = sc.parallelize(data1, 2) @@ -242,14 +242,14 @@ abstract class ShuffleSuite extends FunSuite with Matchers with LocalSparkContex shuffleSpillCompress <- Set(true, false); shuffleCompress <- Set(true, false) ) { - val conf = new SparkConf() + val myConf = conf.clone() .setAppName("test") .setMaster("local") .set("spark.shuffle.spill.compress", shuffleSpillCompress.toString) .set("spark.shuffle.compress", shuffleCompress.toString) .set("spark.shuffle.memoryFraction", "0.001") resetSparkContext() - sc = new SparkContext(conf) + sc = new SparkContext(myConf) try { sc.parallelize(0 until 100000).map(i => (i / 4, i)).groupByKey().collect() } catch { diff --git a/core/src/test/scala/org/apache/spark/SparkConfSuite.scala b/core/src/test/scala/org/apache/spark/SparkConfSuite.scala index e08210ae60d17..8e6c200c4ba00 100644 --- a/core/src/test/scala/org/apache/spark/SparkConfSuite.scala +++ b/core/src/test/scala/org/apache/spark/SparkConfSuite.scala @@ -197,6 +197,31 @@ class SparkConfSuite extends FunSuite with LocalSparkContext with ResetSystemPro serializer.newInstance().serialize(new StringBuffer()) } + test("deprecated configs") { + val conf = new SparkConf() + val newName = "spark.history.fs.update.interval" + + assert(!conf.contains(newName)) + + conf.set("spark.history.updateInterval", "1") + assert(conf.get(newName) === "1") + + conf.set("spark.history.fs.updateInterval", "2") + assert(conf.get(newName) === "2") + + conf.set("spark.history.fs.update.interval.seconds", "3") + assert(conf.get(newName) === "3") + + conf.set(newName, "4") + assert(conf.get(newName) === "4") + + val count = conf.getAll.filter { case (k, v) => k.startsWith("spark.history.") }.size + assert(count === 4) + + conf.set("spark.yarn.applicationMaster.waitTries", "42") + assert(conf.getTimeAsSeconds("spark.yarn.am.waitTime") === 420) + } + } class Class1 {} diff --git a/core/src/test/scala/org/apache/spark/SparkContextSuite.scala b/core/src/test/scala/org/apache/spark/SparkContextSuite.scala index 50f347f1954de..728558a424780 100644 --- a/core/src/test/scala/org/apache/spark/SparkContextSuite.scala +++ b/core/src/test/scala/org/apache/spark/SparkContextSuite.scala @@ -18,6 +18,7 @@ package org.apache.spark import java.io.File +import java.util.concurrent.TimeUnit import com.google.common.base.Charsets._ import com.google.common.io.Files @@ -25,9 +26,11 @@ import com.google.common.io.Files import org.scalatest.FunSuite import org.apache.hadoop.io.BytesWritable - import org.apache.spark.util.Utils +import scala.concurrent.Await +import scala.concurrent.duration.Duration + class SparkContextSuite extends FunSuite with LocalSparkContext { test("Only one SparkContext may be active at a time") { @@ -64,6 +67,26 @@ class SparkContextSuite extends FunSuite with LocalSparkContext { } } + test("Test getOrCreate") { + var sc2: SparkContext = null + SparkContext.clearActiveContext() + val conf = new SparkConf().setAppName("test").setMaster("local") + + sc = SparkContext.getOrCreate(conf) + + assert(sc.getConf.get("spark.app.name").equals("test")) + sc2 = SparkContext.getOrCreate(new SparkConf().setAppName("test2").setMaster("local")) + assert(sc2.getConf.get("spark.app.name").equals("test")) + assert(sc === sc2) + assert(sc eq sc2) + + // Try creating second context to confirm that it's still possible, if desired + sc2 = new SparkContext(new SparkConf().setAppName("test3").setMaster("local") + .set("spark.driver.allowMultipleContexts", "true")) + + sc2.stop() + } + test("BytesWritable implicit conversion is correct") { // Regression test for SPARK-3121 val bytesWritable = new BytesWritable() @@ -81,24 +104,50 @@ class SparkContextSuite extends FunSuite with LocalSparkContext { } test("addFile works") { - val file = File.createTempFile("someprefix", "somesuffix") - val absolutePath = file.getAbsolutePath + val dir = Utils.createTempDir() + + val file1 = File.createTempFile("someprefix1", "somesuffix1", dir) + val absolutePath1 = file1.getAbsolutePath + + val file2 = File.createTempFile("someprefix2", "somesuffix2", dir) + val relativePath = file2.getParent + "/../" + file2.getParentFile.getName + "/" + file2.getName + val absolutePath2 = file2.getAbsolutePath + try { - Files.write("somewords", file, UTF_8) - val length = file.length() + Files.write("somewords1", file1, UTF_8) + Files.write("somewords2", file2, UTF_8) + val length1 = file1.length() + val length2 = file2.length() + sc = new SparkContext(new SparkConf().setAppName("test").setMaster("local")) - sc.addFile(file.getAbsolutePath) + sc.addFile(file1.getAbsolutePath) + sc.addFile(relativePath) sc.parallelize(Array(1), 1).map(x => { - val gotten = new File(SparkFiles.get(file.getName)) - if (!gotten.exists()) { - throw new SparkException("file doesn't exist") + val gotten1 = new File(SparkFiles.get(file1.getName)) + val gotten2 = new File(SparkFiles.get(file2.getName)) + if (!gotten1.exists()) { + throw new SparkException("file doesn't exist : " + absolutePath1) } - if (length != gotten.length()) { + if (!gotten2.exists()) { + throw new SparkException("file doesn't exist : " + absolutePath2) + } + + if (length1 != gotten1.length()) { throw new SparkException( - s"file has different length $length than added file ${gotten.length()}") + s"file has different length $length1 than added file ${gotten1.length()} : " + + absolutePath1) } - if (absolutePath == gotten.getAbsolutePath) { - throw new SparkException("file should have been copied") + if (length2 != gotten2.length()) { + throw new SparkException( + s"file has different length $length2 than added file ${gotten2.length()} : " + + absolutePath2) + } + + if (absolutePath1 == gotten1.getAbsolutePath) { + throw new SparkException("file should have been copied :" + absolutePath1) + } + if (absolutePath2 == gotten2.getAbsolutePath) { + throw new SparkException("file should have been copied : " + absolutePath2) } x }).count() @@ -149,4 +198,19 @@ class SparkContextSuite extends FunSuite with LocalSparkContext { sc.stop() } } + + test("Cancelling job group should not cause SparkContext to shutdown (SPARK-6414)") { + try { + sc = new SparkContext(new SparkConf().setAppName("test").setMaster("local")) + val future = sc.parallelize(Seq(0)).foreachAsync(_ => {Thread.sleep(1000L)}) + sc.cancelJobGroup("nonExistGroupId") + Await.ready(future, Duration(2, TimeUnit.SECONDS)) + + // In SPARK-6414, sc.cancelJobGroup will cause NullPointerException and cause + // SparkContext to shutdown, so the following assertion will fail. + assert(sc.parallelize(1 to 10).count() == 10L) + } finally { + sc.stop() + } + } } diff --git a/core/src/test/scala/org/apache/spark/StatusTrackerSuite.scala b/core/src/test/scala/org/apache/spark/StatusTrackerSuite.scala index 41d6ea29d5b06..084eb237d70d1 100644 --- a/core/src/test/scala/org/apache/spark/StatusTrackerSuite.scala +++ b/core/src/test/scala/org/apache/spark/StatusTrackerSuite.scala @@ -82,7 +82,8 @@ class StatusTrackerSuite extends FunSuite with Matchers with LocalSparkContext { secondJobFuture.jobIds.head } eventually(timeout(10 seconds)) { - sc.statusTracker.getJobIdsForGroup("my-job-group").toSet should be (Set(firstJobId, secondJobId)) + sc.statusTracker.getJobIdsForGroup("my-job-group").toSet should be ( + Set(firstJobId, secondJobId)) } } -} \ No newline at end of file +} diff --git a/core/src/test/scala/org/apache/spark/broadcast/BroadcastSuite.scala b/core/src/test/scala/org/apache/spark/broadcast/BroadcastSuite.scala index af3272692d7a1..c8fdfa693912e 100644 --- a/core/src/test/scala/org/apache/spark/broadcast/BroadcastSuite.scala +++ b/core/src/test/scala/org/apache/spark/broadcast/BroadcastSuite.scala @@ -33,7 +33,7 @@ class DummyBroadcastClass(rdd: RDD[Int]) extends Serializable { val broadcast = rdd.context.broadcast(list) val bid = broadcast.id - def doSomething() = { + def doSomething(): Set[(Int, Boolean)] = { rdd.map { x => val bm = SparkEnv.get.blockManager // Check if broadcast block was fetched diff --git a/core/src/test/scala/org/apache/spark/deploy/ClientSuite.scala b/core/src/test/scala/org/apache/spark/deploy/ClientSuite.scala index 518073dcbb64e..745f9eeee7536 100644 --- a/core/src/test/scala/org/apache/spark/deploy/ClientSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/ClientSuite.scala @@ -46,5 +46,4 @@ class ClientSuite extends FunSuite with Matchers { // Invalid syntax. ClientArguments.isValidJarUrl("hdfs:") should be (false) } - } diff --git a/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala b/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala index e955636cf5b59..b58d62567afe1 100644 --- a/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala @@ -28,7 +28,7 @@ import org.scalatest.FunSuite import org.apache.spark.deploy.DeployMessages.{MasterStateResponse, WorkerStateResponse} import org.apache.spark.deploy.master.{ApplicationInfo, DriverInfo, RecoveryState, WorkerInfo} import org.apache.spark.deploy.worker.{DriverRunner, ExecutorRunner} -import org.apache.spark.SparkConf +import org.apache.spark.{SecurityManager, SparkConf} class JsonProtocolSuite extends FunSuite { @@ -100,13 +100,13 @@ class JsonProtocolSuite extends FunSuite { appInfo } - def createDriverCommand() = new Command( + def createDriverCommand(): Command = new Command( "org.apache.spark.FakeClass", Seq("some arg --and-some options -g foo"), Map(("K1", "V1"), ("K2", "V2")), Seq("cp1", "cp2"), Seq("lp1", "lp2"), Seq("-Dfoo") ) - def createDriverDesc() = new DriverDescription("hdfs://some-dir/some.jar", 100, 3, - false, createDriverCommand()) + def createDriverDesc(): DriverDescription = + new DriverDescription("hdfs://some-dir/some.jar", 100, 3, false, createDriverCommand()) def createDriverInfo(): DriverInfo = new DriverInfo(3, "driver-3", createDriverDesc(), new Date()) @@ -119,13 +119,14 @@ class JsonProtocolSuite extends FunSuite { def createExecutorRunner(): ExecutorRunner = { new ExecutorRunner("appId", 123, createAppDesc(), 4, 1234, null, "workerId", "host", 123, - new File("sparkHome"), new File("workDir"), "akka://worker", + "publicAddress", new File("sparkHome"), new File("workDir"), "akka://worker", new SparkConf, Seq("localDir"), ExecutorState.RUNNING) } def createDriverRunner(): DriverRunner = { - new DriverRunner(new SparkConf(), "driverId", new File("workDir"), new File("sparkHome"), - createDriverDesc(), null, "akka://worker") + val conf = new SparkConf() + new DriverRunner(conf, "driverId", new File("workDir"), new File("sparkHome"), + createDriverDesc(), null, "akka://worker", new SecurityManager(conf)) } def assertValidJson(json: JValue) { diff --git a/core/src/test/scala/org/apache/spark/deploy/LogUrlsStandaloneSuite.scala b/core/src/test/scala/org/apache/spark/deploy/LogUrlsStandaloneSuite.scala index f33bdc73e40ac..c93d16f8a1586 100644 --- a/core/src/test/scala/org/apache/spark/deploy/LogUrlsStandaloneSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/LogUrlsStandaloneSuite.scala @@ -17,43 +17,79 @@ package org.apache.spark.deploy +import java.net.URL + +import scala.collection.JavaConversions._ import scala.collection.mutable +import scala.io.Source -import org.scalatest.{BeforeAndAfter, FunSuite} +import org.scalatest.FunSuite import org.apache.spark.scheduler.cluster.ExecutorInfo import org.apache.spark.scheduler.{SparkListenerExecutorAdded, SparkListener} -import org.apache.spark.{SparkContext, LocalSparkContext} +import org.apache.spark.{SparkConf, SparkContext, LocalSparkContext} -class LogUrlsStandaloneSuite extends FunSuite with LocalSparkContext with BeforeAndAfter { +class LogUrlsStandaloneSuite extends FunSuite with LocalSparkContext { /** Length of time to wait while draining listener events. */ - val WAIT_TIMEOUT_MILLIS = 10000 + private val WAIT_TIMEOUT_MILLIS = 10000 - before { + test("verify that correct log urls get propagated from workers") { sc = new SparkContext("local-cluster[2,1,512]", "test") - } - test("verify log urls get propagated from workers") { val listener = new SaveExecutorInfo sc.addSparkListener(listener) - val rdd1 = sc.parallelize(1 to 100, 4) - val rdd2 = rdd1.map(_.toString) - rdd2.setName("Target RDD") - rdd2.count() + // Trigger a job so that executors get added + sc.parallelize(1 to 100, 4).map(_.toString).count() assert(sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)) listener.addedExecutorInfos.values.foreach { info => assert(info.logUrlMap.nonEmpty) + // Browse to each URL to check that it's valid + info.logUrlMap.foreach { case (logType, logUrl) => + val html = Source.fromURL(logUrl).mkString + assert(html.contains(s"$logType log page")) + } } } - private class SaveExecutorInfo extends SparkListener { - val addedExecutorInfos = mutable.Map[String, ExecutorInfo]() + test("verify that log urls reflect SPARK_PUBLIC_DNS (SPARK-6175)") { + val SPARK_PUBLIC_DNS = "public_dns" + class MySparkConf extends SparkConf(false) { + override def getenv(name: String): String = { + if (name == "SPARK_PUBLIC_DNS") SPARK_PUBLIC_DNS + else super.getenv(name) + } + + override def clone: SparkConf = { + new MySparkConf().setAll(getAll) + } + } + val conf = new MySparkConf().set( + "spark.extraListeners", classOf[SaveExecutorInfo].getName) + sc = new SparkContext("local-cluster[2,1,512]", "test", conf) + + // Trigger a job so that executors get added + sc.parallelize(1 to 100, 4).map(_.toString).count() - override def onExecutorAdded(executor: SparkListenerExecutorAdded) { - addedExecutorInfos(executor.executorId) = executor.executorInfo + assert(sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)) + val listeners = sc.listenerBus.findListenersByClass[SaveExecutorInfo] + assert(listeners.size === 1) + val listener = listeners(0) + listener.addedExecutorInfos.values.foreach { info => + assert(info.logUrlMap.nonEmpty) + info.logUrlMap.values.foreach { logUrl => + assert(new URL(logUrl).getHost === SPARK_PUBLIC_DNS) + } } } } + +private[spark] class SaveExecutorInfo extends SparkListener { + val addedExecutorInfos = mutable.Map[String, ExecutorInfo]() + + override def onExecutorAdded(executor: SparkListenerExecutorAdded) { + addedExecutorInfos(executor.executorId) = executor.executorInfo + } +} diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala index 1ddccae1262bc..4561e5b8e9663 100644 --- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala @@ -21,6 +21,8 @@ import java.io._ import scala.collection.mutable.ArrayBuffer +import com.google.common.base.Charsets.UTF_8 +import com.google.common.io.ByteStreams import org.scalatest.FunSuite import org.scalatest.Matchers import org.scalatest.concurrent.Timeouts @@ -400,8 +402,10 @@ class SparkSubmitSuite extends FunSuite with Matchers with ResetSystemProperties val archives = "file:/archive1,archive2" // spark.yarn.dist.archives val pyFiles = "py-file1,py-file2" // spark.submit.pyFiles + val tmpDir = Utils.createTempDir() + // Test jars and files - val f1 = File.createTempFile("test-submit-jars-files", "") + val f1 = File.createTempFile("test-submit-jars-files", "", tmpDir) val writer1 = new PrintWriter(f1) writer1.println("spark.jars " + jars) writer1.println("spark.files " + files) @@ -418,7 +422,7 @@ class SparkSubmitSuite extends FunSuite with Matchers with ResetSystemProperties sysProps("spark.files") should be(Utils.resolveURIs(files)) // Test files and archives (Yarn) - val f2 = File.createTempFile("test-submit-files-archives", "") + val f2 = File.createTempFile("test-submit-files-archives", "", tmpDir) val writer2 = new PrintWriter(f2) writer2.println("spark.yarn.dist.files " + files) writer2.println("spark.yarn.dist.archives " + archives) @@ -435,7 +439,7 @@ class SparkSubmitSuite extends FunSuite with Matchers with ResetSystemProperties sysProps2("spark.yarn.dist.archives") should be(Utils.resolveURIs(archives)) // Test python files - val f3 = File.createTempFile("test-submit-python-files", "") + val f3 = File.createTempFile("test-submit-python-files", "", tmpDir) val writer3 = new PrintWriter(f3) writer3.println("spark.submit.pyFiles " + pyFiles) writer3.close() @@ -450,6 +454,19 @@ class SparkSubmitSuite extends FunSuite with Matchers with ResetSystemProperties PythonRunner.formatPaths(Utils.resolveURIs(pyFiles)).mkString(",")) } + test("user classpath first in driver") { + val systemJar = TestUtils.createJarWithFiles(Map("test.resource" -> "SYSTEM")) + val userJar = TestUtils.createJarWithFiles(Map("test.resource" -> "USER")) + val args = Seq( + "--class", UserClasspathFirstTest.getClass.getName.stripSuffix("$"), + "--name", "testApp", + "--master", "local", + "--conf", "spark.driver.extraClassPath=" + systemJar, + "--conf", "spark.driver.userClassPathFirst=true", + userJar.toString) + runSparkSubmit(args) + } + test("SPARK_CONF_DIR overrides spark-defaults.conf") { forConfDir(Map("spark.executor.memory" -> "2.3g")) { path => val unusedJar = TestUtils.createJarWithClasses(Seq.empty) @@ -541,3 +558,15 @@ object SimpleApplicationTest { } } } + +object UserClasspathFirstTest { + def main(args: Array[String]) { + val ccl = Thread.currentThread().getContextClassLoader() + val resource = ccl.getResourceAsStream("test.resource") + val bytes = ByteStreams.toByteArray(resource) + val contents = new String(bytes, 0, bytes.length, UTF_8) + if (contents != "USER") { + throw new SparkException("Should have read user resource, but instead read: " + contents) + } + } +} diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala index 53665350013cd..8bcca926097a1 100644 --- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala @@ -57,20 +57,23 @@ class SparkSubmitUtilsSuite extends FunSuite with BeforeAndAfterAll { test("create repo resolvers") { val resolver1 = SparkSubmitUtils.createRepoResolvers(None) - // should have central by default - assert(resolver1.getResolvers.size() === 1) + // should have central and spark-packages by default + assert(resolver1.getResolvers.size() === 2) assert(resolver1.getResolvers.get(0).asInstanceOf[IBiblioResolver].getName === "central") + assert(resolver1.getResolvers.get(1).asInstanceOf[IBiblioResolver].getName === "spark-packages") val repos = "a/1,b/2,c/3" val resolver2 = SparkSubmitUtils.createRepoResolvers(Option(repos)) - assert(resolver2.getResolvers.size() === 4) + assert(resolver2.getResolvers.size() === 5) val expected = repos.split(",").map(r => s"$r/") resolver2.getResolvers.toArray.zipWithIndex.foreach { case (resolver: IBiblioResolver, i) => if (i == 0) { assert(resolver.getName === "central") + } else if (i == 1) { + assert(resolver.getName === "spark-packages") } else { - assert(resolver.getName === s"repo-$i") - assert(resolver.getRoot === expected(i - 1)) + assert(resolver.getName === s"repo-${i - 1}") + assert(resolver.getRoot === expected(i - 2)) } } } @@ -114,8 +117,20 @@ class SparkSubmitUtilsSuite extends FunSuite with BeforeAndAfterAll { } test("neglects Spark and Spark's dependencies") { - val path = SparkSubmitUtils.resolveMavenCoordinates( - "org.apache.spark:spark-core_2.10:1.2.0", None, None, true) + val components = Seq("bagel_", "catalyst_", "core_", "graphx_", "hive_", "mllib_", "repl_", + "sql_", "streaming_", "yarn_", "network-common_", "network-shuffle_", "network-yarn_") + + val coordinates = + components.map(comp => s"org.apache.spark:spark-${comp}2.10:1.2.0").mkString(",") + + ",org.apache.spark:spark-core_fake:1.2.0" + + val path = SparkSubmitUtils.resolveMavenCoordinates(coordinates, None, None, true) assert(path === "", "should return empty path") + // Should not exclude the following dependency. Will throw an error, because it doesn't exist, + // but the fact that it is checking means that it wasn't excluded. + intercept[RuntimeException] { + SparkSubmitUtils.resolveMavenCoordinates(coordinates + + ",org.apache.spark:spark-streaming-kafka-assembly_2.10:1.2.0", None, None, true) + } } } diff --git a/core/src/test/scala/org/apache/spark/deploy/history/FsHistoryProviderSuite.scala b/core/src/test/scala/org/apache/spark/deploy/history/FsHistoryProviderSuite.scala index 85939eaadccc7..fcae603c7d18e 100644 --- a/core/src/test/scala/org/apache/spark/deploy/history/FsHistoryProviderSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/history/FsHistoryProviderSuite.scala @@ -17,18 +17,17 @@ package org.apache.spark.deploy.history -import java.io.{File, FileOutputStream, OutputStreamWriter} +import java.io.{BufferedOutputStream, File, FileOutputStream, OutputStreamWriter} +import java.net.URI import scala.io.Source -import com.google.common.io.Files import org.apache.hadoop.fs.Path import org.json4s.jackson.JsonMethods._ import org.scalatest.{BeforeAndAfter, FunSuite} import org.scalatest.Matchers import org.apache.spark.{Logging, SparkConf} -import org.apache.spark.deploy.SparkHadoopUtil import org.apache.spark.io._ import org.apache.spark.scheduler._ import org.apache.spark.util.{JsonProtocol, Utils} @@ -45,18 +44,35 @@ class FsHistoryProviderSuite extends FunSuite with BeforeAndAfter with Matchers Utils.deleteRecursively(testDir) } + /** Create a fake log file using the new log format used in Spark 1.3+ */ + private def newLogFile( + appId: String, + inProgress: Boolean, + codec: Option[String] = None): File = { + val ip = if (inProgress) EventLoggingListener.IN_PROGRESS else "" + val logUri = EventLoggingListener.getLogPath(testDir.toURI, appId) + val logPath = new URI(logUri).getPath + ip + new File(logPath) + } + test("Parse new and old application logs") { val provider = new FsHistoryProvider(createTestConf()) // Write a new-style application log. - val newAppComplete = new File(testDir, "new1") + val newAppComplete = newLogFile("new1", inProgress = false) writeFile(newAppComplete, true, None, SparkListenerApplicationStart("new-app-complete", None, 1L, "test"), - SparkListenerApplicationEnd(4L) + SparkListenerApplicationEnd(5L) ) + // Write a new-style application log. + val newAppCompressedComplete = newLogFile("new1compressed", inProgress = false, Some("lzf")) + writeFile(newAppCompressedComplete, true, None, + SparkListenerApplicationStart("new-app-compressed-complete", None, 1L, "test"), + SparkListenerApplicationEnd(4L)) + // Write an unfinished app, new-style. - val newAppIncomplete = new File(testDir, "new2" + EventLoggingListener.IN_PROGRESS) + val newAppIncomplete = newLogFile("new2", inProgress = true) writeFile(newAppIncomplete, true, None, SparkListenerApplicationStart("new-app-incomplete", None, 1L, "test") ) @@ -89,16 +105,18 @@ class FsHistoryProviderSuite extends FunSuite with BeforeAndAfter with Matchers val list = provider.getListing().toSeq list should not be (null) - list.size should be (4) - list.count(e => e.completed) should be (2) + list.size should be (5) + list.count(_.completed) should be (3) - list(0) should be (ApplicationHistoryInfo(newAppComplete.getName(), "new-app-complete", 1L, 4L, + list(0) should be (ApplicationHistoryInfo(newAppComplete.getName(), "new-app-complete", 1L, 5L, newAppComplete.lastModified(), "test", true)) - list(1) should be (ApplicationHistoryInfo(oldAppComplete.getName(), "old-app-complete", 2L, 3L, + list(1) should be (ApplicationHistoryInfo(newAppCompressedComplete.getName(), + "new-app-compressed-complete", 1L, 4L, newAppCompressedComplete.lastModified(), "test", true)) + list(2) should be (ApplicationHistoryInfo(oldAppComplete.getName(), "old-app-complete", 2L, 3L, oldAppComplete.lastModified(), "test", true)) - list(2) should be (ApplicationHistoryInfo(oldAppIncomplete.getName(), "old-app-incomplete", 2L, + list(3) should be (ApplicationHistoryInfo(oldAppIncomplete.getName(), "old-app-incomplete", 2L, -1L, oldAppIncomplete.lastModified(), "test", false)) - list(3) should be (ApplicationHistoryInfo(newAppIncomplete.getName(), "new-app-incomplete", 1L, + list(4) should be (ApplicationHistoryInfo(newAppIncomplete.getName(), "new-app-incomplete", 1L, -1L, newAppIncomplete.lastModified(), "test", false)) // Make sure the UI can be rendered. @@ -127,7 +145,7 @@ class FsHistoryProviderSuite extends FunSuite with BeforeAndAfter with Matchers val logPath = new Path(logDir.getAbsolutePath()) try { - val (logInput, sparkVersion) = provider.openLegacyEventLog(logPath) + val logInput = provider.openLegacyEventLog(logPath) try { Source.fromInputStream(logInput).getLines().toSeq.size should be (2) } finally { @@ -141,12 +159,12 @@ class FsHistoryProviderSuite extends FunSuite with BeforeAndAfter with Matchers } test("SPARK-3697: ignore directories that cannot be read.") { - val logFile1 = new File(testDir, "new1") + val logFile1 = newLogFile("new1", inProgress = false) writeFile(logFile1, true, None, SparkListenerApplicationStart("app1-1", None, 1L, "test"), SparkListenerApplicationEnd(2L) ) - val logFile2 = new File(testDir, "new2") + val logFile2 = newLogFile("new2", inProgress = false) writeFile(logFile2, true, None, SparkListenerApplicationStart("app1-2", None, 1L, "test"), SparkListenerApplicationEnd(2L) @@ -164,7 +182,7 @@ class FsHistoryProviderSuite extends FunSuite with BeforeAndAfter with Matchers test("history file is renamed from inprogress to completed") { val provider = new FsHistoryProvider(createTestConf()) - val logFile1 = new File(testDir, "app1" + EventLoggingListener.IN_PROGRESS) + val logFile1 = newLogFile("app1", inProgress = true) writeFile(logFile1, true, None, SparkListenerApplicationStart("app1", Some("app1"), 1L, "test"), SparkListenerApplicationEnd(2L) @@ -174,7 +192,7 @@ class FsHistoryProviderSuite extends FunSuite with BeforeAndAfter with Matchers appListBeforeRename.size should be (1) appListBeforeRename.head.logPath should endWith(EventLoggingListener.IN_PROGRESS) - logFile1.renameTo(new File(testDir, "app1")) + logFile1.renameTo(newLogFile("app1", inProgress = false)) provider.checkForLogs() val appListAfterRename = provider.getListing() appListAfterRename.size should be (1) @@ -184,7 +202,7 @@ class FsHistoryProviderSuite extends FunSuite with BeforeAndAfter with Matchers test("SPARK-5582: empty log directory") { val provider = new FsHistoryProvider(createTestConf()) - val logFile1 = new File(testDir, "app1" + EventLoggingListener.IN_PROGRESS) + val logFile1 = newLogFile("app1", inProgress = true) writeFile(logFile1, true, None, SparkListenerApplicationStart("app1", Some("app1"), 1L, "test"), SparkListenerApplicationEnd(2L)) @@ -199,14 +217,13 @@ class FsHistoryProviderSuite extends FunSuite with BeforeAndAfter with Matchers private def writeFile(file: File, isNewFormat: Boolean, codec: Option[CompressionCodec], events: SparkListenerEvent*) = { - val out = - if (isNewFormat) { - EventLoggingListener.initEventLog(new FileOutputStream(file), codec) - } else { - val fileStream = new FileOutputStream(file) - codec.map(_.compressedOutputStream(fileStream)).getOrElse(fileStream) - } - val writer = new OutputStreamWriter(out, "UTF-8") + val fstream = new FileOutputStream(file) + val cstream = codec.map(_.compressedOutputStream(fstream)).getOrElse(fstream) + val bstream = new BufferedOutputStream(cstream) + if (isNewFormat) { + EventLoggingListener.initEventLog(new FileOutputStream(file)) + } + val writer = new OutputStreamWriter(bstream, "UTF-8") try { events.foreach(e => writer.write(compact(render(JsonProtocol.sparkEventToJson(e))) + "\n")) } finally { diff --git a/core/src/test/scala/org/apache/spark/deploy/history/HistoryServerSuite.scala b/core/src/test/scala/org/apache/spark/deploy/history/HistoryServerSuite.scala new file mode 100644 index 0000000000000..20de46fdab909 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/deploy/history/HistoryServerSuite.scala @@ -0,0 +1,56 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.deploy.history + +import javax.servlet.http.HttpServletRequest + +import scala.collection.mutable + +import org.apache.hadoop.fs.Path +import org.mockito.Mockito.{when} +import org.scalatest.FunSuite +import org.scalatest.Matchers +import org.scalatest.mock.MockitoSugar + +import org.apache.spark.ui.SparkUI + +class HistoryServerSuite extends FunSuite with Matchers with MockitoSugar { + + test("generate history page with relative links") { + val historyServer = mock[HistoryServer] + val request = mock[HttpServletRequest] + val ui = mock[SparkUI] + val link = "/history/app1" + val info = new ApplicationHistoryInfo("app1", "app1", 0, 2, 1, "xxx", true) + when(historyServer.getApplicationList()).thenReturn(Seq(info)) + when(ui.basePath).thenReturn(link) + when(historyServer.getProviderConfig()).thenReturn(Map[String, String]()) + val page = new HistoryPage(historyServer) + + // when + val response = page.render(request) + + // then + val links = response \\ "a" + val justHrefs = for { + l <- links + attrs <- l.attribute("href") + } yield (attrs.toString) + justHrefs should contain(link) + } +} diff --git a/core/src/test/scala/org/apache/spark/deploy/rest/StandaloneRestSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/rest/StandaloneRestSubmitSuite.scala index 29aed89b67aa7..8e09976636386 100644 --- a/core/src/test/scala/org/apache/spark/deploy/rest/StandaloneRestSubmitSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/rest/StandaloneRestSubmitSuite.scala @@ -17,141 +17,416 @@ package org.apache.spark.deploy.rest -import java.io.{File, FileInputStream, FileOutputStream, PrintWriter} -import java.util.jar.{JarEntry, JarOutputStream} -import java.util.zip.ZipEntry +import java.io.DataOutputStream +import java.net.{HttpURLConnection, URL} +import javax.servlet.http.HttpServletResponse -import scala.collection.mutable.ArrayBuffer -import scala.io.Source +import scala.collection.mutable -import akka.actor.ActorSystem -import com.google.common.io.ByteStreams -import org.scalatest.{BeforeAndAfterAll, BeforeAndAfterEach, FunSuite} -import org.scalatest.exceptions.TestFailedException +import akka.actor.{Actor, ActorRef, ActorSystem, Props} +import com.google.common.base.Charsets +import org.scalatest.{BeforeAndAfterEach, FunSuite} +import org.json4s.JsonAST._ +import org.json4s.jackson.JsonMethods._ import org.apache.spark._ -import org.apache.spark.util.Utils +import org.apache.spark.util.{AkkaUtils, Utils} +import org.apache.spark.deploy.DeployMessages._ import org.apache.spark.deploy.{SparkSubmit, SparkSubmitArguments} -import org.apache.spark.deploy.master.{DriverState, Master} -import org.apache.spark.deploy.worker.Worker +import org.apache.spark.deploy.master.DriverState._ /** - * End-to-end tests for the REST application submission protocol in standalone mode. + * Tests for the REST application submission protocol used in standalone cluster mode. */ -class StandaloneRestSubmitSuite extends FunSuite with BeforeAndAfterAll with BeforeAndAfterEach { - private val systemsToStop = new ArrayBuffer[ActorSystem] - private val masterRestUrl = startLocalCluster() +class StandaloneRestSubmitSuite extends FunSuite with BeforeAndAfterEach { private val client = new StandaloneRestClient - private val mainJar = StandaloneRestSubmitSuite.createJar() - private val mainClass = StandaloneRestApp.getClass.getName.stripSuffix("$") + private var actorSystem: Option[ActorSystem] = None + private var server: Option[StandaloneRestServer] = None - override def afterAll() { - systemsToStop.foreach(_.shutdown()) + override def afterEach() { + actorSystem.foreach(_.shutdown()) + server.foreach(_.stop()) } - test("simple submit until completion") { - val resultsFile = File.createTempFile("test-submit", ".txt") - val numbers = Seq(1, 2, 3) - val size = 500 - val submissionId = submitApplication(resultsFile, numbers, size) - waitUntilFinished(submissionId) - validateResult(resultsFile, numbers, size) + test("construct submit request") { + val appArgs = Array("one", "two", "three") + val sparkProperties = Map("spark.app.name" -> "pi") + val environmentVariables = Map("SPARK_ONE" -> "UN", "SPARK_TWO" -> "DEUX") + val request = client.constructSubmitRequest( + "my-app-resource", "my-main-class", appArgs, sparkProperties, environmentVariables) + assert(request.action === Utils.getFormattedClassName(request)) + assert(request.clientSparkVersion === SPARK_VERSION) + assert(request.appResource === "my-app-resource") + assert(request.mainClass === "my-main-class") + assert(request.appArgs === appArgs) + assert(request.sparkProperties === sparkProperties) + assert(request.environmentVariables === environmentVariables) } - test("kill empty submission") { - val response = client.killSubmission(masterRestUrl, "submission-that-does-not-exist") - val killResponse = getKillResponse(response) - val killSuccess = killResponse.success - assert(!killSuccess) + test("create submission") { + val submittedDriverId = "my-driver-id" + val submitMessage = "your driver is submitted" + val masterUrl = startDummyServer(submitId = submittedDriverId, submitMessage = submitMessage) + val appArgs = Array("one", "two", "four") + val request = constructSubmitRequest(masterUrl, appArgs) + assert(request.appArgs === appArgs) + assert(request.sparkProperties("spark.master") === masterUrl) + val response = client.createSubmission(masterUrl, request) + val submitResponse = getSubmitResponse(response) + assert(submitResponse.action === Utils.getFormattedClassName(submitResponse)) + assert(submitResponse.serverSparkVersion === SPARK_VERSION) + assert(submitResponse.message === submitMessage) + assert(submitResponse.submissionId === submittedDriverId) + assert(submitResponse.success) + } + + test("create submission from main method") { + val submittedDriverId = "your-driver-id" + val submitMessage = "my driver is submitted" + val masterUrl = startDummyServer(submitId = submittedDriverId, submitMessage = submitMessage) + val conf = new SparkConf(loadDefaults = false) + conf.set("spark.master", masterUrl) + conf.set("spark.app.name", "dreamer") + val appArgs = Array("one", "two", "six") + // main method calls this + val response = StandaloneRestClient.run("app-resource", "main-class", appArgs, conf) + val submitResponse = getSubmitResponse(response) + assert(submitResponse.action === Utils.getFormattedClassName(submitResponse)) + assert(submitResponse.serverSparkVersion === SPARK_VERSION) + assert(submitResponse.message === submitMessage) + assert(submitResponse.submissionId === submittedDriverId) + assert(submitResponse.success) } - test("kill running submission") { - val resultsFile = File.createTempFile("test-kill", ".txt") - val numbers = Seq(1, 2, 3) - val size = 500 - val submissionId = submitApplication(resultsFile, numbers, size) - val response = client.killSubmission(masterRestUrl, submissionId) + test("kill submission") { + val submissionId = "my-lyft-driver" + val killMessage = "your driver is killed" + val masterUrl = startDummyServer(killMessage = killMessage) + val response = client.killSubmission(masterUrl, submissionId) val killResponse = getKillResponse(response) - val killSuccess = killResponse.success - waitUntilFinished(submissionId) - val response2 = client.requestSubmissionStatus(masterRestUrl, submissionId) - val statusResponse = getStatusResponse(response2) - val statusSuccess = statusResponse.success - val driverState = statusResponse.driverState - assert(killSuccess) - assert(statusSuccess) - assert(driverState === DriverState.KILLED.toString) - // we should not see the expected results because we killed the submission - intercept[TestFailedException] { validateResult(resultsFile, numbers, size) } + assert(killResponse.action === Utils.getFormattedClassName(killResponse)) + assert(killResponse.serverSparkVersion === SPARK_VERSION) + assert(killResponse.message === killMessage) + assert(killResponse.submissionId === submissionId) + assert(killResponse.success) } - test("request status for empty submission") { - val response = client.requestSubmissionStatus(masterRestUrl, "submission-that-does-not-exist") + test("request submission status") { + val submissionId = "my-uber-driver" + val submissionState = KILLED + val submissionException = new Exception("there was an irresponsible mix of alcohol and cars") + val masterUrl = startDummyServer(state = submissionState, exception = Some(submissionException)) + val response = client.requestSubmissionStatus(masterUrl, submissionId) val statusResponse = getStatusResponse(response) - val statusSuccess = statusResponse.success - assert(!statusSuccess) + assert(statusResponse.action === Utils.getFormattedClassName(statusResponse)) + assert(statusResponse.serverSparkVersion === SPARK_VERSION) + assert(statusResponse.message.contains(submissionException.getMessage)) + assert(statusResponse.submissionId === submissionId) + assert(statusResponse.driverState === submissionState.toString) + assert(statusResponse.success) + } + + test("create then kill") { + val masterUrl = startSmartServer() + val request = constructSubmitRequest(masterUrl) + val response1 = client.createSubmission(masterUrl, request) + val submitResponse = getSubmitResponse(response1) + assert(submitResponse.success) + assert(submitResponse.submissionId != null) + // kill submission that was just created + val submissionId = submitResponse.submissionId + val response2 = client.killSubmission(masterUrl, submissionId) + val killResponse = getKillResponse(response2) + assert(killResponse.success) + assert(killResponse.submissionId === submissionId) + } + + test("create then request status") { + val masterUrl = startSmartServer() + val request = constructSubmitRequest(masterUrl) + val response1 = client.createSubmission(masterUrl, request) + val submitResponse = getSubmitResponse(response1) + assert(submitResponse.success) + assert(submitResponse.submissionId != null) + // request status of submission that was just created + val submissionId = submitResponse.submissionId + val response2 = client.requestSubmissionStatus(masterUrl, submissionId) + val statusResponse = getStatusResponse(response2) + assert(statusResponse.success) + assert(statusResponse.submissionId === submissionId) + assert(statusResponse.driverState === RUNNING.toString) + } + + test("create then kill then request status") { + val masterUrl = startSmartServer() + val request = constructSubmitRequest(masterUrl) + val response1 = client.createSubmission(masterUrl, request) + val response2 = client.createSubmission(masterUrl, request) + val submitResponse1 = getSubmitResponse(response1) + val submitResponse2 = getSubmitResponse(response2) + assert(submitResponse1.success) + assert(submitResponse2.success) + assert(submitResponse1.submissionId != null) + assert(submitResponse2.submissionId != null) + val submissionId1 = submitResponse1.submissionId + val submissionId2 = submitResponse2.submissionId + // kill only submission 1, but not submission 2 + val response3 = client.killSubmission(masterUrl, submissionId1) + val killResponse = getKillResponse(response3) + assert(killResponse.success) + assert(killResponse.submissionId === submissionId1) + // request status for both submissions: 1 should be KILLED but 2 should be RUNNING still + val response4 = client.requestSubmissionStatus(masterUrl, submissionId1) + val response5 = client.requestSubmissionStatus(masterUrl, submissionId2) + val statusResponse1 = getStatusResponse(response4) + val statusResponse2 = getStatusResponse(response5) + assert(statusResponse1.submissionId === submissionId1) + assert(statusResponse2.submissionId === submissionId2) + assert(statusResponse1.driverState === KILLED.toString) + assert(statusResponse2.driverState === RUNNING.toString) + } + + test("kill or request status before create") { + val masterUrl = startSmartServer() + val doesNotExist = "does-not-exist" + // kill a non-existent submission + val response1 = client.killSubmission(masterUrl, doesNotExist) + val killResponse = getKillResponse(response1) + assert(!killResponse.success) + assert(killResponse.submissionId === doesNotExist) + // request status for a non-existent submission + val response2 = client.requestSubmissionStatus(masterUrl, doesNotExist) + val statusResponse = getStatusResponse(response2) + assert(!statusResponse.success) + assert(statusResponse.submissionId === doesNotExist) + } + + /* ---------------------------------------- * + | Aberrant client / server behavior | + * ---------------------------------------- */ + + test("good request paths") { + val masterUrl = startSmartServer() + val httpUrl = masterUrl.replace("spark://", "http://") + val v = StandaloneRestServer.PROTOCOL_VERSION + val json = constructSubmitRequest(masterUrl).toJson + val submitRequestPath = s"$httpUrl/$v/submissions/create" + val killRequestPath = s"$httpUrl/$v/submissions/kill" + val statusRequestPath = s"$httpUrl/$v/submissions/status" + val (response1, code1) = sendHttpRequestWithResponse(submitRequestPath, "POST", json) + val (response2, code2) = sendHttpRequestWithResponse(s"$killRequestPath/anything", "POST") + val (response3, code3) = sendHttpRequestWithResponse(s"$killRequestPath/any/thing", "POST") + val (response4, code4) = sendHttpRequestWithResponse(s"$statusRequestPath/anything", "GET") + val (response5, code5) = sendHttpRequestWithResponse(s"$statusRequestPath/any/thing", "GET") + // these should all succeed and the responses should be of the correct types + getSubmitResponse(response1) + val killResponse1 = getKillResponse(response2) + val killResponse2 = getKillResponse(response3) + val statusResponse1 = getStatusResponse(response4) + val statusResponse2 = getStatusResponse(response5) + assert(killResponse1.submissionId === "anything") + assert(killResponse2.submissionId === "any") + assert(statusResponse1.submissionId === "anything") + assert(statusResponse2.submissionId === "any") + assert(code1 === HttpServletResponse.SC_OK) + assert(code2 === HttpServletResponse.SC_OK) + assert(code3 === HttpServletResponse.SC_OK) + assert(code4 === HttpServletResponse.SC_OK) + assert(code5 === HttpServletResponse.SC_OK) + } + + test("good request paths, bad requests") { + val masterUrl = startSmartServer() + val httpUrl = masterUrl.replace("spark://", "http://") + val v = StandaloneRestServer.PROTOCOL_VERSION + val submitRequestPath = s"$httpUrl/$v/submissions/create" + val killRequestPath = s"$httpUrl/$v/submissions/kill" + val statusRequestPath = s"$httpUrl/$v/submissions/status" + val goodJson = constructSubmitRequest(masterUrl).toJson + val badJson1 = goodJson.replaceAll("action", "fraction") // invalid JSON + val badJson2 = goodJson.substring(goodJson.size / 2) // malformed JSON + val notJson = "\"hello, world\"" + val (response1, code1) = sendHttpRequestWithResponse(submitRequestPath, "POST") // missing JSON + val (response2, code2) = sendHttpRequestWithResponse(submitRequestPath, "POST", badJson1) + val (response3, code3) = sendHttpRequestWithResponse(submitRequestPath, "POST", badJson2) + val (response4, code4) = sendHttpRequestWithResponse(killRequestPath, "POST") // missing ID + val (response5, code5) = sendHttpRequestWithResponse(s"$killRequestPath/", "POST") + val (response6, code6) = sendHttpRequestWithResponse(statusRequestPath, "GET") // missing ID + val (response7, code7) = sendHttpRequestWithResponse(s"$statusRequestPath/", "GET") + val (response8, code8) = sendHttpRequestWithResponse(submitRequestPath, "POST", notJson) + // these should all fail as error responses + getErrorResponse(response1) + getErrorResponse(response2) + getErrorResponse(response3) + getErrorResponse(response4) + getErrorResponse(response5) + getErrorResponse(response6) + getErrorResponse(response7) + getErrorResponse(response8) + assert(code1 === HttpServletResponse.SC_BAD_REQUEST) + assert(code2 === HttpServletResponse.SC_BAD_REQUEST) + assert(code3 === HttpServletResponse.SC_BAD_REQUEST) + assert(code4 === HttpServletResponse.SC_BAD_REQUEST) + assert(code5 === HttpServletResponse.SC_BAD_REQUEST) + assert(code6 === HttpServletResponse.SC_BAD_REQUEST) + assert(code7 === HttpServletResponse.SC_BAD_REQUEST) + assert(code8 === HttpServletResponse.SC_BAD_REQUEST) + } + + test("bad request paths") { + val masterUrl = startSmartServer() + val httpUrl = masterUrl.replace("spark://", "http://") + val v = StandaloneRestServer.PROTOCOL_VERSION + val (response1, code1) = sendHttpRequestWithResponse(httpUrl, "GET") + val (response2, code2) = sendHttpRequestWithResponse(s"$httpUrl/", "GET") + val (response3, code3) = sendHttpRequestWithResponse(s"$httpUrl/$v", "GET") + val (response4, code4) = sendHttpRequestWithResponse(s"$httpUrl/$v/", "GET") + val (response5, code5) = sendHttpRequestWithResponse(s"$httpUrl/$v/submissions", "GET") + val (response6, code6) = sendHttpRequestWithResponse(s"$httpUrl/$v/submissions/", "GET") + val (response7, code7) = sendHttpRequestWithResponse(s"$httpUrl/$v/submissions/bad", "GET") + val (response8, code8) = sendHttpRequestWithResponse(s"$httpUrl/bad-version", "GET") + assert(code1 === HttpServletResponse.SC_BAD_REQUEST) + assert(code2 === HttpServletResponse.SC_BAD_REQUEST) + assert(code3 === HttpServletResponse.SC_BAD_REQUEST) + assert(code4 === HttpServletResponse.SC_BAD_REQUEST) + assert(code5 === HttpServletResponse.SC_BAD_REQUEST) + assert(code6 === HttpServletResponse.SC_BAD_REQUEST) + assert(code7 === HttpServletResponse.SC_BAD_REQUEST) + assert(code8 === StandaloneRestServer.SC_UNKNOWN_PROTOCOL_VERSION) + // all responses should be error responses + val errorResponse1 = getErrorResponse(response1) + val errorResponse2 = getErrorResponse(response2) + val errorResponse3 = getErrorResponse(response3) + val errorResponse4 = getErrorResponse(response4) + val errorResponse5 = getErrorResponse(response5) + val errorResponse6 = getErrorResponse(response6) + val errorResponse7 = getErrorResponse(response7) + val errorResponse8 = getErrorResponse(response8) + // only the incompatible version response should have server protocol version set + assert(errorResponse1.highestProtocolVersion === null) + assert(errorResponse2.highestProtocolVersion === null) + assert(errorResponse3.highestProtocolVersion === null) + assert(errorResponse4.highestProtocolVersion === null) + assert(errorResponse5.highestProtocolVersion === null) + assert(errorResponse6.highestProtocolVersion === null) + assert(errorResponse7.highestProtocolVersion === null) + assert(errorResponse8.highestProtocolVersion === StandaloneRestServer.PROTOCOL_VERSION) + } + + test("server returns unknown fields") { + val masterUrl = startSmartServer() + val httpUrl = masterUrl.replace("spark://", "http://") + val v = StandaloneRestServer.PROTOCOL_VERSION + val submitRequestPath = s"$httpUrl/$v/submissions/create" + val oldJson = constructSubmitRequest(masterUrl).toJson + val oldFields = parse(oldJson).asInstanceOf[JObject].obj + val newFields = oldFields ++ Seq( + JField("tomato", JString("not-a-fruit")), + JField("potato", JString("not-po-tah-to")) + ) + val newJson = pretty(render(JObject(newFields))) + // send two requests, one with the unknown fields and the other without + val (response1, code1) = sendHttpRequestWithResponse(submitRequestPath, "POST", oldJson) + val (response2, code2) = sendHttpRequestWithResponse(submitRequestPath, "POST", newJson) + val submitResponse1 = getSubmitResponse(response1) + val submitResponse2 = getSubmitResponse(response2) + assert(code1 === HttpServletResponse.SC_OK) + assert(code2 === HttpServletResponse.SC_OK) + // only the response to the modified request should have unknown fields set + assert(submitResponse1.unknownFields === null) + assert(submitResponse2.unknownFields === Array("tomato", "potato")) + } + + test("client handles faulty server") { + val masterUrl = startFaultyServer() + val httpUrl = masterUrl.replace("spark://", "http://") + val v = StandaloneRestServer.PROTOCOL_VERSION + val submitRequestPath = s"$httpUrl/$v/submissions/create" + val killRequestPath = s"$httpUrl/$v/submissions/kill/anything" + val statusRequestPath = s"$httpUrl/$v/submissions/status/anything" + val json = constructSubmitRequest(masterUrl).toJson + // server returns malformed response unwittingly + // client should throw an appropriate exception to indicate server failure + val conn1 = sendHttpRequest(submitRequestPath, "POST", json) + intercept[SubmitRestProtocolException] { client.readResponse(conn1) } + // server attempts to send invalid response, but fails internally on validation + // client should receive an error response as server is able to recover + val conn2 = sendHttpRequest(killRequestPath, "POST") + val response2 = client.readResponse(conn2) + getErrorResponse(response2) + assert(conn2.getResponseCode === HttpServletResponse.SC_INTERNAL_SERVER_ERROR) + // server explodes internally beyond recovery + // client should throw an appropriate exception to indicate server failure + val conn3 = sendHttpRequest(statusRequestPath, "GET") + intercept[SubmitRestProtocolException] { client.readResponse(conn3) } // empty response + assert(conn3.getResponseCode === HttpServletResponse.SC_INTERNAL_SERVER_ERROR) + } + + /* --------------------- * + | Helper methods | + * --------------------- */ + + /** Start a dummy server that responds to requests using the specified parameters. */ + private def startDummyServer( + submitId: String = "fake-driver-id", + submitMessage: String = "driver is submitted", + killMessage: String = "driver is killed", + state: DriverState = FINISHED, + exception: Option[Exception] = None): String = { + startServer(new DummyMaster(submitId, submitMessage, killMessage, state, exception)) + } + + /** Start a smarter dummy server that keeps track of submitted driver states. */ + private def startSmartServer(): String = { + startServer(new SmarterMaster) + } + + /** Start a dummy server that is faulty in many ways... */ + private def startFaultyServer(): String = { + startServer(new DummyMaster, faulty = true) } /** - * Start a local cluster containing one Master and a few Workers. - * Do not use [[org.apache.spark.deploy.LocalSparkCluster]] here because we want the REST URL. - * Return the Master's REST URL to which applications should be submitted. + * Start a [[StandaloneRestServer]] that communicates with the given actor. + * If `faulty` is true, start an [[FaultyStandaloneRestServer]] instead. + * Return the master URL that corresponds to the address of this server. */ - private def startLocalCluster(): String = { - val conf = new SparkConf(false) - .set("spark.master.rest.enabled", "true") - .set("spark.master.rest.port", "0") - val (numWorkers, coresPerWorker, memPerWorker) = (2, 1, 512) - val localHostName = Utils.localHostName() - val (masterSystem, masterPort, _, _masterRestPort) = - Master.startSystemAndActor(localHostName, 0, 0, conf) - val masterRestPort = _masterRestPort.getOrElse { fail("REST server not started on Master!") } - val masterUrl = "spark://" + localHostName + ":" + masterPort - val masterRestUrl = "spark://" + localHostName + ":" + masterRestPort - (1 to numWorkers).foreach { n => - val (workerSystem, _) = Worker.startSystemAndActor( - localHostName, 0, 0, coresPerWorker, memPerWorker, Array(masterUrl), null, Some(n)) - systemsToStop.append(workerSystem) - } - systemsToStop.append(masterSystem) - masterRestUrl + private def startServer(makeFakeMaster: => Actor, faulty: Boolean = false): String = { + val name = "test-standalone-rest-protocol" + val conf = new SparkConf + val localhost = Utils.localHostName() + val securityManager = new SecurityManager(conf) + val (_actorSystem, _) = AkkaUtils.createActorSystem(name, localhost, 0, conf, securityManager) + val fakeMasterRef = _actorSystem.actorOf(Props(makeFakeMaster)) + val _server = + if (faulty) { + new FaultyStandaloneRestServer(localhost, 0, fakeMasterRef, "spark://fake:7077", conf) + } else { + new StandaloneRestServer(localhost, 0, fakeMasterRef, "spark://fake:7077", conf) + } + val port = _server.start() + // set these to clean them up after every test + actorSystem = Some(_actorSystem) + server = Some(_server) + s"spark://$localhost:$port" } - /** Submit the [[StandaloneRestApp]] and return the corresponding submission ID. */ - private def submitApplication(resultsFile: File, numbers: Seq[Int], size: Int): String = { - val appArgs = Seq(resultsFile.getAbsolutePath) ++ numbers.map(_.toString) ++ Seq(size.toString) + /** Create a submit request with real parameters using Spark submit. */ + private def constructSubmitRequest( + masterUrl: String, + appArgs: Array[String] = Array.empty): CreateSubmissionRequest = { + val mainClass = "main-class-not-used" + val mainJar = "dummy-jar-not-used.jar" val commandLineArgs = Array( "--deploy-mode", "cluster", - "--master", masterRestUrl, + "--master", masterUrl, "--name", mainClass, "--class", mainClass, mainJar) ++ appArgs val args = new SparkSubmitArguments(commandLineArgs) val (_, _, sparkProperties, _) = SparkSubmit.prepareSubmitEnvironment(args) - val request = client.constructSubmitRequest( - mainJar, mainClass, appArgs.toArray, sparkProperties.toMap, Map.empty) - val response = client.createSubmission(masterRestUrl, request) - val submitResponse = getSubmitResponse(response) - val submissionId = submitResponse.submissionId - assert(submissionId != null, "Application submission was unsuccessful!") - submissionId - } - - /** Wait until the given submission has finished running up to the specified timeout. */ - private def waitUntilFinished(submissionId: String, maxSeconds: Int = 30): Unit = { - var finished = false - val expireTime = System.currentTimeMillis + maxSeconds * 1000 - while (!finished) { - val response = client.requestSubmissionStatus(masterRestUrl, submissionId) - val statusResponse = getStatusResponse(response) - val driverState = statusResponse.driverState - finished = - driverState != DriverState.SUBMITTED.toString && - driverState != DriverState.RUNNING.toString - if (System.currentTimeMillis > expireTime) { - fail(s"Driver $submissionId did not finish within $maxSeconds seconds.") - } - } + client.constructSubmitRequest( + mainJar, mainClass, appArgs, sparkProperties.toMap, Map.empty) } /** Return the response as a submit response, or fail with error otherwise. */ @@ -181,85 +456,151 @@ class StandaloneRestSubmitSuite extends FunSuite with BeforeAndAfterAll with Bef } } - /** Validate whether the application produced the corrupt output. */ - private def validateResult(resultsFile: File, numbers: Seq[Int], size: Int): Unit = { - val lines = Source.fromFile(resultsFile.getAbsolutePath).getLines().toSeq - val unexpectedContent = - if (lines.nonEmpty) { - "[\n" + lines.map { l => " " + l }.mkString("\n") + "\n]" - } else { - "[EMPTY]" - } - assert(lines.size === 2, s"Unexpected content in file: $unexpectedContent") - assert(lines(0).toInt === numbers.sum, s"Sum of ${numbers.mkString(",")} is incorrect") - assert(lines(1).toInt === (size / 2) + 1, "Result of Spark job is incorrect") + /** Return the response as an error response, or fail if the response was not an error. */ + private def getErrorResponse(response: SubmitRestProtocolResponse): ErrorResponse = { + response match { + case e: ErrorResponse => e + case r => fail(s"Expected error response. Actual: ${r.toJson}") + } } -} - -private object StandaloneRestSubmitSuite { - private val pathPrefix = this.getClass.getPackage.getName.replaceAll("\\.", "/") /** - * Create a jar that contains all the class files needed for running the [[StandaloneRestApp]]. - * Return the absolute path to that jar. + * Send an HTTP request to the given URL using the method and the body specified. + * Return the connection object. */ - def createJar(): String = { - val jarFile = File.createTempFile("test-standalone-rest-protocol", ".jar") - val jarFileStream = new FileOutputStream(jarFile) - val jarStream = new JarOutputStream(jarFileStream, new java.util.jar.Manifest) - jarStream.putNextEntry(new ZipEntry(pathPrefix)) - getClassFiles.foreach { cf => - jarStream.putNextEntry(new JarEntry(pathPrefix + "/" + cf.getName)) - val in = new FileInputStream(cf) - ByteStreams.copy(in, jarStream) - in.close() + private def sendHttpRequest( + url: String, + method: String, + body: String = ""): HttpURLConnection = { + val conn = new URL(url).openConnection().asInstanceOf[HttpURLConnection] + conn.setRequestMethod(method) + if (body.nonEmpty) { + conn.setDoOutput(true) + val out = new DataOutputStream(conn.getOutputStream) + out.write(body.getBytes(Charsets.UTF_8)) + out.close() } - jarStream.close() - jarFileStream.close() - jarFile.getAbsolutePath + conn } /** - * Return a list of class files compiled for [[StandaloneRestApp]]. - * This includes all the anonymous classes used in the application. + * Send an HTTP request to the given URL using the method and the body specified. + * Return a 2-tuple of the response message from the server and the response code. */ - private def getClassFiles: Seq[File] = { - val className = Utils.getFormattedClassName(StandaloneRestApp) - val clazz = StandaloneRestApp.getClass - val basePath = clazz.getProtectionDomain.getCodeSource.getLocation.toURI.getPath - val baseDir = new File(basePath + "/" + pathPrefix) - baseDir.listFiles().filter(_.getName.contains(className)) + private def sendHttpRequestWithResponse( + url: String, + method: String, + body: String = ""): (SubmitRestProtocolResponse, Int) = { + val conn = sendHttpRequest(url, method, body) + (client.readResponse(conn), conn.getResponseCode) } } /** - * Sample application to be submitted to the cluster using the REST gateway. - * All relevant classes will be packaged into a jar at run time. + * A mock standalone Master that responds with dummy messages. + * In all responses, the success parameter is always true. */ -object StandaloneRestApp { - // Usage: [path to results file] [num1] [num2] [num3] [rddSize] - // The first line of the results file should be (num1 + num2 + num3) - // The second line should be (rddSize / 2) + 1 - def main(args: Array[String]) { - assert(args.size == 5, s"Expected exactly 5 arguments: ${args.mkString(",")}") - val resultFile = new File(args(0)) - val writer = new PrintWriter(resultFile) - try { - val conf = new SparkConf() - val sc = new SparkContext(conf) - val firstLine = args(1).toInt + args(2).toInt + args(3).toInt - val secondLine = sc.parallelize(1 to args(4).toInt) - .map { i => (i / 2, i) } - .reduceByKey(_ + _) - .count() - writer.println(firstLine) - writer.println(secondLine) - } catch { - case e: Exception => - writer.println(e) - e.getStackTrace.foreach { l => writer.println(" " + l) } - } finally { - writer.close() +private class DummyMaster( + submitId: String = "fake-driver-id", + submitMessage: String = "submitted", + killMessage: String = "killed", + state: DriverState = FINISHED, + exception: Option[Exception] = None) + extends Actor { + + override def receive: PartialFunction[Any, Unit] = { + case RequestSubmitDriver(driverDesc) => + sender ! SubmitDriverResponse(success = true, Some(submitId), submitMessage) + case RequestKillDriver(driverId) => + sender ! KillDriverResponse(driverId, success = true, killMessage) + case RequestDriverStatus(driverId) => + sender ! DriverStatusResponse(found = true, Some(state), None, None, exception) + } +} + +/** + * A mock standalone Master that keeps track of drivers that have been submitted. + * + * If a driver is submitted, its state is immediately set to RUNNING. + * If an existing driver is killed, its state is immediately set to KILLED. + * If an existing driver's status is requested, its state is returned in the response. + * Submits are always successful while kills and status requests are successful only + * if the driver was submitted in the past. + */ +private class SmarterMaster extends Actor { + private var counter: Int = 0 + private val submittedDrivers = new mutable.HashMap[String, DriverState] + + override def receive: PartialFunction[Any, Unit] = { + case RequestSubmitDriver(driverDesc) => + val driverId = s"driver-$counter" + submittedDrivers(driverId) = RUNNING + counter += 1 + sender ! SubmitDriverResponse(success = true, Some(driverId), "submitted") + + case RequestKillDriver(driverId) => + val success = submittedDrivers.contains(driverId) + if (success) { + submittedDrivers(driverId) = KILLED + } + sender ! KillDriverResponse(driverId, success, "killed") + + case RequestDriverStatus(driverId) => + val found = submittedDrivers.contains(driverId) + val state = submittedDrivers.get(driverId) + sender ! DriverStatusResponse(found, state, None, None, None) + } +} + +/** + * A [[StandaloneRestServer]] that is faulty in many ways. + * + * When handling a submit request, the server returns a malformed JSON. + * When handling a kill request, the server returns an invalid JSON. + * When handling a status request, the server throws an internal exception. + * The purpose of this class is to test that client handles these cases gracefully. + */ +private class FaultyStandaloneRestServer( + host: String, + requestedPort: Int, + masterActor: ActorRef, + masterUrl: String, + masterConf: SparkConf) + extends StandaloneRestServer(host, requestedPort, masterActor, masterUrl, masterConf) { + + protected override val contextToServlet = Map[String, StandaloneRestServlet]( + s"$baseContext/create/*" -> new MalformedSubmitServlet, + s"$baseContext/kill/*" -> new InvalidKillServlet, + s"$baseContext/status/*" -> new ExplodingStatusServlet, + "/*" -> new ErrorServlet + ) + + /** A faulty servlet that produces malformed responses. */ + class MalformedSubmitServlet extends SubmitRequestServlet(masterActor, masterUrl, masterConf) { + protected override def sendResponse( + responseMessage: SubmitRestProtocolResponse, + responseServlet: HttpServletResponse): Unit = { + val badJson = responseMessage.toJson.drop(10).dropRight(20) + responseServlet.getWriter.write(badJson) + } + } + + /** A faulty servlet that produces invalid responses. */ + class InvalidKillServlet extends KillRequestServlet(masterActor, masterConf) { + protected override def handleKill(submissionId: String): KillSubmissionResponse = { + val k = super.handleKill(submissionId) + k.submissionId = null + k + } + } + + /** A faulty status servlet that explodes. */ + class ExplodingStatusServlet extends StatusRequestServlet(masterActor, masterConf) { + private def explode: Int = 1 / 0 + protected override def handleStatus(submissionId: String): SubmissionStatusResponse = { + val s = super.handleStatus(submissionId) + s.workerId = explode.toString + s } } } diff --git a/core/src/test/scala/org/apache/spark/deploy/rest/SubmitRestProtocolSuite.scala b/core/src/test/scala/org/apache/spark/deploy/rest/SubmitRestProtocolSuite.scala index 1d64ec201e647..61071ee17256c 100644 --- a/core/src/test/scala/org/apache/spark/deploy/rest/SubmitRestProtocolSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/rest/SubmitRestProtocolSuite.scala @@ -129,7 +129,8 @@ class SubmitRestProtocolSuite extends FunSuite { assert(newMessage.sparkProperties("spark.files") === "fireball.png") assert(newMessage.sparkProperties("spark.driver.memory") === "512m") assert(newMessage.sparkProperties("spark.driver.cores") === "180") - assert(newMessage.sparkProperties("spark.driver.extraJavaOptions") === " -Dslices=5 -Dcolor=mostly_red") + assert(newMessage.sparkProperties("spark.driver.extraJavaOptions") === + " -Dslices=5 -Dcolor=mostly_red") assert(newMessage.sparkProperties("spark.driver.extraClassPath") === "food-coloring.jar") assert(newMessage.sparkProperties("spark.driver.extraLibraryPath") === "pickle.jar") assert(newMessage.sparkProperties("spark.driver.supervise") === "false") diff --git a/core/src/test/scala/org/apache/spark/deploy/CommandUtilsSuite.scala b/core/src/test/scala/org/apache/spark/deploy/worker/CommandUtilsSuite.scala similarity index 94% rename from core/src/test/scala/org/apache/spark/deploy/CommandUtilsSuite.scala rename to core/src/test/scala/org/apache/spark/deploy/worker/CommandUtilsSuite.scala index 7915ee75d8778..1c27d83cf876c 100644 --- a/core/src/test/scala/org/apache/spark/deploy/CommandUtilsSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/worker/CommandUtilsSuite.scala @@ -15,11 +15,10 @@ * limitations under the License. */ -package org.apache.spark.deploy +package org.apache.spark.deploy.worker -import org.apache.spark.deploy.worker.CommandUtils +import org.apache.spark.deploy.Command import org.apache.spark.util.Utils - import org.scalatest.{FunSuite, Matchers} class CommandUtilsSuite extends FunSuite with Matchers { diff --git a/core/src/test/scala/org/apache/spark/deploy/worker/DriverRunnerTest.scala b/core/src/test/scala/org/apache/spark/deploy/worker/DriverRunnerTest.scala index b6f4411e0587a..2159fd8c16c6f 100644 --- a/core/src/test/scala/org/apache/spark/deploy/worker/DriverRunnerTest.scala +++ b/core/src/test/scala/org/apache/spark/deploy/worker/DriverRunnerTest.scala @@ -25,15 +25,17 @@ import org.mockito.invocation.InvocationOnMock import org.mockito.stubbing.Answer import org.scalatest.FunSuite -import org.apache.spark.SparkConf +import org.apache.spark.{SecurityManager, SparkConf} import org.apache.spark.deploy.{Command, DriverDescription} +import org.apache.spark.util.Clock class DriverRunnerTest extends FunSuite { private def createDriverRunner() = { val command = new Command("mainClass", Seq(), Map(), Seq(), Seq(), Seq()) val driverDescription = new DriverDescription("jarUrl", 512, 1, true, command) - new DriverRunner(new SparkConf(), "driverId", new File("workDir"), new File("sparkHome"), - driverDescription, null, "akka://1.2.3.4/worker/") + val conf = new SparkConf() + new DriverRunner(conf, "driverId", new File("workDir"), new File("sparkHome"), + driverDescription, null, "akka://1.2.3.4/worker/", new SecurityManager(conf)) } private def createProcessBuilderAndProcess(): (ProcessBuilderLike, Process) = { @@ -129,7 +131,7 @@ class DriverRunnerTest extends FunSuite { .thenReturn(-1) // fail 3 .thenReturn(-1) // fail 4 .thenReturn(0) // success - when(clock.currentTimeMillis()) + when(clock.getTimeMillis()) .thenReturn(0).thenReturn(1000) // fail 1 (short) .thenReturn(1000).thenReturn(2000) // fail 2 (short) .thenReturn(2000).thenReturn(10000) // fail 3 (long) diff --git a/core/src/test/scala/org/apache/spark/deploy/worker/ExecutorRunnerTest.scala b/core/src/test/scala/org/apache/spark/deploy/worker/ExecutorRunnerTest.scala index 76511699e5ac5..a8b9df227c996 100644 --- a/core/src/test/scala/org/apache/spark/deploy/worker/ExecutorRunnerTest.scala +++ b/core/src/test/scala/org/apache/spark/deploy/worker/ExecutorRunnerTest.scala @@ -33,9 +33,10 @@ class ExecutorRunnerTest extends FunSuite { val appDesc = new ApplicationDescription("app name", Some(8), 500, Command("foo", Seq(appId), Map(), Seq(), Seq(), Seq()), "appUiUrl") val er = new ExecutorRunner(appId, 1, appDesc, 8, 500, null, "blah", "worker321", 123, - new File(sparkHome), new File("ooga"), "blah", new SparkConf, Seq("localDir"), + "publicAddr", new File(sparkHome), new File("ooga"), "blah", new SparkConf, Seq("localDir"), ExecutorState.RUNNING) - val builder = CommandUtils.buildProcessBuilder(appDesc.command, 512, sparkHome, er.substituteVariables) + val builder = CommandUtils.buildProcessBuilder( + appDesc.command, 512, sparkHome, er.substituteVariables) assert(builder.command().last === appId) } } diff --git a/core/src/test/scala/org/apache/spark/deploy/worker/WorkerArgumentsTest.scala b/core/src/test/scala/org/apache/spark/deploy/worker/WorkerArgumentsTest.scala index 372d7aa453008..7cc2104281464 100644 --- a/core/src/test/scala/org/apache/spark/deploy/worker/WorkerArgumentsTest.scala +++ b/core/src/test/scala/org/apache/spark/deploy/worker/WorkerArgumentsTest.scala @@ -37,7 +37,7 @@ class WorkerArgumentsTest extends FunSuite { val args = Array("spark://localhost:0000 ") class MySparkConf extends SparkConf(false) { - override def getenv(name: String) = { + override def getenv(name: String): String = { if (name == "SPARK_WORKER_MEMORY") "50000" else super.getenv(name) } @@ -56,7 +56,7 @@ class WorkerArgumentsTest extends FunSuite { val args = Array("spark://localhost:0000 ") class MySparkConf extends SparkConf(false) { - override def getenv(name: String) = { + override def getenv(name: String): String = { if (name == "SPARK_WORKER_MEMORY") "5G" else super.getenv(name) } diff --git a/core/src/test/scala/org/apache/spark/deploy/worker/WorkerSuite.scala b/core/src/test/scala/org/apache/spark/deploy/worker/WorkerSuite.scala index 84e2fd7ad936d..450fba21f4b5c 100644 --- a/core/src/test/scala/org/apache/spark/deploy/worker/WorkerSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/worker/WorkerSuite.scala @@ -24,8 +24,10 @@ import org.scalatest.{Matchers, FunSuite} class WorkerSuite extends FunSuite with Matchers { - def cmd(javaOpts: String*) = Command("", Seq.empty, Map.empty, Seq.empty, Seq.empty, Seq(javaOpts:_*)) - def conf(opts: (String, String)*) = new SparkConf(loadDefaults = false).setAll(opts) + def cmd(javaOpts: String*): Command = { + Command("", Seq.empty, Map.empty, Seq.empty, Seq.empty, Seq(javaOpts:_*)) + } + def conf(opts: (String, String)*): SparkConf = new SparkConf(loadDefaults = false).setAll(opts) test("test isUseLocalNodeSSLConfig") { Worker.isUseLocalNodeSSLConfig(cmd("-Dasdf=dfgh")) shouldBe false diff --git a/core/src/test/scala/org/apache/spark/deploy/worker/WorkerWatcherSuite.scala b/core/src/test/scala/org/apache/spark/deploy/worker/WorkerWatcherSuite.scala index 5e538d6fab2a1..6a6f29dd613cd 100644 --- a/core/src/test/scala/org/apache/spark/deploy/worker/WorkerWatcherSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/worker/WorkerWatcherSuite.scala @@ -17,32 +17,38 @@ package org.apache.spark.deploy.worker -import akka.actor.{ActorSystem, AddressFromURIString, Props} -import akka.testkit.TestActorRef -import akka.remote.DisassociatedEvent +import akka.actor.AddressFromURIString +import org.apache.spark.SparkConf +import org.apache.spark.SecurityManager +import org.apache.spark.rpc.{RpcAddress, RpcEnv} import org.scalatest.FunSuite class WorkerWatcherSuite extends FunSuite { test("WorkerWatcher shuts down on valid disassociation") { - val actorSystem = ActorSystem("test") - val targetWorkerUrl = "akka://1.2.3.4/user/Worker" + val conf = new SparkConf() + val rpcEnv = RpcEnv.create("test", "localhost", 12345, conf, new SecurityManager(conf)) + val targetWorkerUrl = "akka://test@1.2.3.4:1234/user/Worker" val targetWorkerAddress = AddressFromURIString(targetWorkerUrl) - val actorRef = TestActorRef[WorkerWatcher](Props(classOf[WorkerWatcher], targetWorkerUrl))(actorSystem) - val workerWatcher = actorRef.underlyingActor + val workerWatcher = new WorkerWatcher(rpcEnv, targetWorkerUrl) workerWatcher.setTesting(testing = true) - actorRef.underlyingActor.receive(new DisassociatedEvent(null, targetWorkerAddress, false)) - assert(actorRef.underlyingActor.isShutDown) + rpcEnv.setupEndpoint("worker-watcher", workerWatcher) + workerWatcher.onDisconnected( + RpcAddress(targetWorkerAddress.host.get, targetWorkerAddress.port.get)) + assert(workerWatcher.isShutDown) + rpcEnv.shutdown() } test("WorkerWatcher stays alive on invalid disassociation") { - val actorSystem = ActorSystem("test") - val targetWorkerUrl = "akka://1.2.3.4/user/Worker" - val otherAkkaURL = "akka://4.3.2.1/user/OtherActor" + val conf = new SparkConf() + val rpcEnv = RpcEnv.create("test", "localhost", 12345, conf, new SecurityManager(conf)) + val targetWorkerUrl = "akka://test@1.2.3.4:1234/user/Worker" + val otherAkkaURL = "akka://test@4.3.2.1:1234/user/OtherActor" val otherAkkaAddress = AddressFromURIString(otherAkkaURL) - val actorRef = TestActorRef[WorkerWatcher](Props(classOf[WorkerWatcher], targetWorkerUrl))(actorSystem) - val workerWatcher = actorRef.underlyingActor + val workerWatcher = new WorkerWatcher(rpcEnv, targetWorkerUrl) workerWatcher.setTesting(testing = true) - actorRef.underlyingActor.receive(new DisassociatedEvent(null, otherAkkaAddress, false)) - assert(!actorRef.underlyingActor.isShutDown) + rpcEnv.setupEndpoint("worker-watcher", workerWatcher) + workerWatcher.onDisconnected(RpcAddress(otherAkkaAddress.host.get, otherAkkaAddress.port.get)) + assert(!workerWatcher.isShutDown) + rpcEnv.shutdown() } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JavaJDBCTrampoline.scala b/core/src/test/scala/org/apache/spark/executor/TaskMetricsSuite.scala similarity index 67% rename from sql/core/src/main/scala/org/apache/spark/sql/jdbc/JavaJDBCTrampoline.scala rename to core/src/test/scala/org/apache/spark/executor/TaskMetricsSuite.scala index 86bb67ec74256..326e203afe136 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JavaJDBCTrampoline.scala +++ b/core/src/test/scala/org/apache/spark/executor/TaskMetricsSuite.scala @@ -15,16 +15,14 @@ * limitations under the License. */ -package org.apache.spark.sql.jdbc +package org.apache.spark.executor -import org.apache.spark.sql.DataFrame +import org.scalatest.FunSuite -private[jdbc] class JavaJDBCTrampoline { - def createJDBCTable(rdd: DataFrame, url: String, table: String, allowExisting: Boolean) { - rdd.createJDBCTable(url, table, allowExisting); - } - - def insertIntoJDBC(rdd: DataFrame, url: String, table: String, overwrite: Boolean) { - rdd.insertIntoJDBC(url, table, overwrite); +class TaskMetricsSuite extends FunSuite { + test("[SPARK-5701] updateShuffleReadMetrics: ShuffleReadMetrics not added when no shuffle deps") { + val taskMetrics = new TaskMetrics() + taskMetrics.updateShuffleReadMetrics() + assert(taskMetrics.shuffleReadMetrics.isEmpty) } } diff --git a/core/src/test/scala/org/apache/spark/input/WholeTextFileRecordReaderSuite.scala b/core/src/test/scala/org/apache/spark/input/WholeTextFileRecordReaderSuite.scala index 98b0a16ce88ba..2e58c159a2ed8 100644 --- a/core/src/test/scala/org/apache/spark/input/WholeTextFileRecordReaderSuite.scala +++ b/core/src/test/scala/org/apache/spark/input/WholeTextFileRecordReaderSuite.scala @@ -28,7 +28,7 @@ import org.scalatest.FunSuite import org.apache.hadoop.io.Text -import org.apache.spark.SparkContext +import org.apache.spark.{SparkConf, SparkContext} import org.apache.spark.util.Utils import org.apache.hadoop.io.compress.{DefaultCodec, CompressionCodecFactory, GzipCodec} @@ -42,7 +42,15 @@ class WholeTextFileRecordReaderSuite extends FunSuite with BeforeAndAfterAll { private var factory: CompressionCodecFactory = _ override def beforeAll() { - sc = new SparkContext("local", "test") + // Hadoop's FileSystem caching does not use the Configuration as part of its cache key, which + // can cause Filesystem.get(Configuration) to return a cached instance created with a different + // configuration than the one passed to get() (see HADOOP-8490 for more details). This caused + // hard-to-reproduce test failures, since any suites that were run after this one would inherit + // the new value of "fs.local.block.size" (see SPARK-5227 and SPARK-5679). To work around this, + // we disable FileSystem caching in this suite. + val conf = new SparkConf().set("spark.hadoop.fs.file.impl.disable.cache", "true") + + sc = new SparkContext("local", "test", conf) // Set the block size of local file system to test whether files are split right or not. sc.hadoopConfiguration.setLong("fs.local.block.size", 32) diff --git a/core/src/test/scala/org/apache/spark/metrics/InputOutputMetricsSuite.scala b/core/src/test/scala/org/apache/spark/metrics/InputOutputMetricsSuite.scala index 78fa98a3b9065..190b08d950a02 100644 --- a/core/src/test/scala/org/apache/spark/metrics/InputOutputMetricsSuite.scala +++ b/core/src/test/scala/org/apache/spark/metrics/InputOutputMetricsSuite.scala @@ -238,7 +238,7 @@ class InputOutputMetricsSuite extends FunSuite with SharedSparkContext sc.textFile(tmpFilePath, 4) .map(key => (key, 1)) - .reduceByKey(_+_) + .reduceByKey(_ + _) .saveAsTextFile("file://" + tmpFile.getAbsolutePath) sc.listenerBus.waitUntilEmpty(500) diff --git a/core/src/test/scala/org/apache/spark/metrics/MetricsConfigSuite.scala b/core/src/test/scala/org/apache/spark/metrics/MetricsConfigSuite.scala index 1a9ce8c607dcd..100ac77dec1f7 100644 --- a/core/src/test/scala/org/apache/spark/metrics/MetricsConfigSuite.scala +++ b/core/src/test/scala/org/apache/spark/metrics/MetricsConfigSuite.scala @@ -27,7 +27,7 @@ class MetricsConfigSuite extends FunSuite with BeforeAndAfter { } test("MetricsConfig with default properties") { - val conf = new MetricsConfig(Option("dummy-file")) + val conf = new MetricsConfig(None) conf.initialize() assert(conf.properties.size() === 4) @@ -35,7 +35,8 @@ class MetricsConfigSuite extends FunSuite with BeforeAndAfter { val property = conf.getInstance("random") assert(property.size() === 2) - assert(property.getProperty("sink.servlet.class") === "org.apache.spark.metrics.sink.MetricsServlet") + assert(property.getProperty("sink.servlet.class") === + "org.apache.spark.metrics.sink.MetricsServlet") assert(property.getProperty("sink.servlet.path") === "/metrics/json") } @@ -47,16 +48,20 @@ class MetricsConfigSuite extends FunSuite with BeforeAndAfter { assert(masterProp.size() === 5) assert(masterProp.getProperty("sink.console.period") === "20") assert(masterProp.getProperty("sink.console.unit") === "minutes") - assert(masterProp.getProperty("source.jvm.class") === "org.apache.spark.metrics.source.JvmSource") - assert(masterProp.getProperty("sink.servlet.class") === "org.apache.spark.metrics.sink.MetricsServlet") + assert(masterProp.getProperty("source.jvm.class") === + "org.apache.spark.metrics.source.JvmSource") + assert(masterProp.getProperty("sink.servlet.class") === + "org.apache.spark.metrics.sink.MetricsServlet") assert(masterProp.getProperty("sink.servlet.path") === "/metrics/master/json") val workerProp = conf.getInstance("worker") assert(workerProp.size() === 5) assert(workerProp.getProperty("sink.console.period") === "10") assert(workerProp.getProperty("sink.console.unit") === "seconds") - assert(workerProp.getProperty("source.jvm.class") === "org.apache.spark.metrics.source.JvmSource") - assert(workerProp.getProperty("sink.servlet.class") === "org.apache.spark.metrics.sink.MetricsServlet") + assert(workerProp.getProperty("source.jvm.class") === + "org.apache.spark.metrics.source.JvmSource") + assert(workerProp.getProperty("sink.servlet.class") === + "org.apache.spark.metrics.sink.MetricsServlet") assert(workerProp.getProperty("sink.servlet.path") === "/metrics/json") } diff --git a/core/src/test/scala/org/apache/spark/network/nio/ConnectionManagerSuite.scala b/core/src/test/scala/org/apache/spark/network/nio/ConnectionManagerSuite.scala index 716f875d30b8a..02424c59d6831 100644 --- a/core/src/test/scala/org/apache/spark/network/nio/ConnectionManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/network/nio/ConnectionManagerSuite.scala @@ -260,8 +260,8 @@ class ConnectionManagerSuite extends FunSuite { test("sendMessageReliably timeout") { val clientConf = new SparkConf clientConf.set("spark.authenticate", "false") - val ackTimeout = 30 - clientConf.set("spark.core.connection.ack.wait.timeout", s"${ackTimeout}") + val ackTimeoutS = 30 + clientConf.set("spark.core.connection.ack.wait.timeout", s"${ackTimeoutS}s") val clientSecurityManager = new SecurityManager(clientConf) val manager = new ConnectionManager(0, clientConf, clientSecurityManager) @@ -272,7 +272,7 @@ class ConnectionManagerSuite extends FunSuite { val managerServer = new ConnectionManager(0, serverConf, serverSecurityManager) managerServer.onReceiveMessage((msg: Message, id: ConnectionManagerId) => { // sleep 60 sec > ack timeout for simulating server slow down or hang up - Thread.sleep(ackTimeout * 3 * 1000) + Thread.sleep(ackTimeoutS * 3 * 1000) None }) @@ -287,7 +287,7 @@ class ConnectionManagerSuite extends FunSuite { // Otherwise TimeoutExcepton is thrown from Await.result. // We expect TimeoutException is not thrown. intercept[IOException] { - Await.result(future, (ackTimeout * 2) second) + Await.result(future, (ackTimeoutS * 2) second) } manager.stop() diff --git a/core/src/test/scala/org/apache/spark/rdd/DoubleRDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/DoubleRDDSuite.scala index de306533752c1..01039b9449daf 100644 --- a/core/src/test/scala/org/apache/spark/rdd/DoubleRDDSuite.scala +++ b/core/src/test/scala/org/apache/spark/rdd/DoubleRDDSuite.scala @@ -22,6 +22,12 @@ import org.scalatest.FunSuite import org.apache.spark._ class DoubleRDDSuite extends FunSuite with SharedSparkContext { + test("sum") { + assert(sc.parallelize(Seq.empty[Double]).sum() === 0.0) + assert(sc.parallelize(Seq(1.0)).sum() === 1.0) + assert(sc.parallelize(Seq(1.0, 2.0)).sum() === 3.0) + } + // Verify tests on the histogram functionality. We test with both evenly // and non-evenly spaced buckets as the bucket lookup function changes. test("WorksOnEmpty") { @@ -33,6 +39,9 @@ class DoubleRDDSuite extends FunSuite with SharedSparkContext { val expectedHistogramResults = Array(0) assert(histogramResults === expectedHistogramResults) assert(histogramResults2 === expectedHistogramResults) + val emptyRDD: RDD[Double] = sc.emptyRDD + assert(emptyRDD.histogram(buckets) === expectedHistogramResults) + assert(emptyRDD.histogram(buckets, true) === expectedHistogramResults) } test("WorksWithOutOfRangeWithOneBucket") { @@ -232,6 +241,12 @@ class DoubleRDDSuite extends FunSuite with SharedSparkContext { assert(histogramBuckets === expectedHistogramBuckets) } + test("WorksWithDoubleValuesAtMinMax") { + val rdd = sc.parallelize(Seq(1, 1, 1, 2, 3, 3)) + assert(Array(3, 0, 1, 2) === rdd.map(_.toDouble).histogram(4)._2) + assert(Array(3, 1, 2) === rdd.map(_.toDouble).histogram(3)._2) + } + test("WorksWithoutBucketsWithMoreRequestedThanElements") { // Verify the basic case of one bucket and all elements in that bucket works val rdd = sc.parallelize(Seq(1, 2)) @@ -245,7 +260,7 @@ class DoubleRDDSuite extends FunSuite with SharedSparkContext { } test("WorksWithoutBucketsForLargerDatasets") { - // Verify the case of slighly larger datasets + // Verify the case of slightly larger datasets val rdd = sc.parallelize(6 to 99) val (histogramBuckets, histogramResults) = rdd.histogram(8) val expectedHistogramResults = @@ -256,17 +271,27 @@ class DoubleRDDSuite extends FunSuite with SharedSparkContext { assert(histogramBuckets === expectedHistogramBuckets) } - test("WorksWithoutBucketsWithIrrationalBucketEdges") { - // Verify the case of buckets with irrational edges. See #SPARK-2862. + test("WorksWithoutBucketsWithNonIntegralBucketEdges") { + // Verify the case of buckets with nonintegral edges. See #SPARK-2862. val rdd = sc.parallelize(6 to 99) val (histogramBuckets, histogramResults) = rdd.histogram(9) + // Buckets are 6.0, 16.333333333333336, 26.666666666666668, 37.0, 47.333333333333336 ... val expectedHistogramResults = - Array(11, 10, 11, 10, 10, 11, 10, 10, 11) + Array(11, 10, 10, 11, 10, 10, 11, 10, 11) assert(histogramResults === expectedHistogramResults) assert(histogramBuckets(0) === 6.0) assert(histogramBuckets(9) === 99.0) } + test("WorksWithHugeRange") { + val rdd = sc.parallelize(Array(0, 1.0e24, 1.0e30)) + val histogramResults = rdd.histogram(1000000)._2 + assert(histogramResults(0) === 1) + assert(histogramResults(1) === 1) + assert(histogramResults.last === 1) + assert((2 to histogramResults.length - 2).forall(i => histogramResults(i) == 0)) + } + // Test the failure mode with an invalid RDD test("ThrowsExceptionOnInvalidRDDs") { // infinity diff --git a/core/src/test/scala/org/apache/spark/rdd/JdbcRDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/JdbcRDDSuite.scala index 6138d0bbd57f6..be8467354b222 100644 --- a/core/src/test/scala/org/apache/spark/rdd/JdbcRDDSuite.scala +++ b/core/src/test/scala/org/apache/spark/rdd/JdbcRDDSuite.scala @@ -29,22 +29,42 @@ class JdbcRDDSuite extends FunSuite with BeforeAndAfter with LocalSparkContext { Class.forName("org.apache.derby.jdbc.EmbeddedDriver") val conn = DriverManager.getConnection("jdbc:derby:target/JdbcRDDSuiteDb;create=true") try { - val create = conn.createStatement - create.execute(""" - CREATE TABLE FOO( - ID INTEGER NOT NULL GENERATED ALWAYS AS IDENTITY (START WITH 1, INCREMENT BY 1), - DATA INTEGER - )""") - create.close() - val insert = conn.prepareStatement("INSERT INTO FOO(DATA) VALUES(?)") - (1 to 100).foreach { i => - insert.setInt(1, i * 2) - insert.executeUpdate + + try { + val create = conn.createStatement + create.execute(""" + CREATE TABLE FOO( + ID INTEGER NOT NULL GENERATED ALWAYS AS IDENTITY (START WITH 1, INCREMENT BY 1), + DATA INTEGER + )""") + create.close() + val insert = conn.prepareStatement("INSERT INTO FOO(DATA) VALUES(?)") + (1 to 100).foreach { i => + insert.setInt(1, i * 2) + insert.executeUpdate + } + insert.close() + } catch { + case e: SQLException if e.getSQLState == "X0Y32" => + // table exists } - insert.close() - } catch { - case e: SQLException if e.getSQLState == "X0Y32" => + + try { + val create = conn.createStatement + create.execute("CREATE TABLE BIGINT_TEST(ID BIGINT NOT NULL, DATA INTEGER)") + create.close() + val insert = conn.prepareStatement("INSERT INTO BIGINT_TEST VALUES(?,?)") + (1 to 100).foreach { i => + insert.setLong(1, 100000000000000000L + 4000000000000000L * i) + insert.setInt(2, i) + insert.executeUpdate + } + insert.close() + } catch { + case e: SQLException if e.getSQLState == "X0Y32" => // table exists + } + } finally { conn.close() } @@ -60,7 +80,19 @@ class JdbcRDDSuite extends FunSuite with BeforeAndAfter with LocalSparkContext { (r: ResultSet) => { r.getInt(1) } ).cache() assert(rdd.count === 100) - assert(rdd.reduce(_+_) === 10100) + assert(rdd.reduce(_ + _) === 10100) + } + + test("large id overflow") { + sc = new SparkContext("local", "test") + val rdd = new JdbcRDD( + sc, + () => { DriverManager.getConnection("jdbc:derby:target/JdbcRDDSuiteDb") }, + "SELECT DATA FROM BIGINT_TEST WHERE ? <= ID AND ID <= ?", + 1131544775L, 567279358897692673L, 20, + (r: ResultSet) => { r.getInt(1) } ).cache() + assert(rdd.count === 100) + assert(rdd.reduce(_ + _) === 5050) } after { diff --git a/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala b/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala index 108f70af43f37..ca0d953d306d8 100644 --- a/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala +++ b/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala @@ -168,13 +168,13 @@ class PairRDDFunctionsSuite extends FunSuite with SharedSparkContext { test("reduceByKey") { val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (1, 1), (2, 1))) - val sums = pairs.reduceByKey(_+_).collect() + val sums = pairs.reduceByKey(_ + _).collect() assert(sums.toSet === Set((1, 7), (2, 1))) } test("reduceByKey with collectAsMap") { val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (1, 1), (2, 1))) - val sums = pairs.reduceByKey(_+_).collectAsMap() + val sums = pairs.reduceByKey(_ + _).collectAsMap() assert(sums.size === 2) assert(sums(1) === 7) assert(sums(2) === 1) @@ -182,7 +182,7 @@ class PairRDDFunctionsSuite extends FunSuite with SharedSparkContext { test("reduceByKey with many output partitons") { val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (1, 1), (2, 1))) - val sums = pairs.reduceByKey(_+_, 10).collect() + val sums = pairs.reduceByKey(_ + _, 10).collect() assert(sums.toSet === Set((1, 7), (2, 1))) } @@ -192,7 +192,7 @@ class PairRDDFunctionsSuite extends FunSuite with SharedSparkContext { def getPartition(key: Any) = key.asInstanceOf[Int] } val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 1), (0, 1))).partitionBy(p) - val sums = pairs.reduceByKey(_+_) + val sums = pairs.reduceByKey(_ + _) assert(sums.collect().toSet === Set((1, 4), (0, 1))) assert(sums.partitioner === Some(p)) // count the dependencies to make sure there is only 1 ShuffledRDD @@ -208,7 +208,7 @@ class PairRDDFunctionsSuite extends FunSuite with SharedSparkContext { } test("countApproxDistinctByKey") { - def error(est: Long, size: Long) = math.abs(est - size) / size.toDouble + def error(est: Long, size: Long): Double = math.abs(est - size) / size.toDouble /* Since HyperLogLog unique counting is approximate, and the relative standard deviation is * only a statistical bound, the tests can fail for large values of relativeSD. We will be using @@ -465,7 +465,7 @@ class PairRDDFunctionsSuite extends FunSuite with SharedSparkContext { test("foldByKey") { val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (1, 1), (2, 1))) - val sums = pairs.foldByKey(0)(_+_).collect() + val sums = pairs.foldByKey(0)(_ + _).collect() assert(sums.toSet === Set((1, 7), (2, 1))) } @@ -505,7 +505,8 @@ class PairRDDFunctionsSuite extends FunSuite with SharedSparkContext { conf.setOutputCommitter(classOf[FakeOutputCommitter]) FakeOutputCommitter.ran = false - pairs.saveAsHadoopFile("ignored", pairs.keyClass, pairs.valueClass, classOf[FakeOutputFormat], conf) + pairs.saveAsHadoopFile( + "ignored", pairs.keyClass, pairs.valueClass, classOf[FakeOutputFormat], conf) assert(FakeOutputCommitter.ran, "OutputCommitter was never called") } @@ -552,7 +553,7 @@ class PairRDDFunctionsSuite extends FunSuite with SharedSparkContext { } private object StratifiedAuxiliary { - def stratifier (fractionPositive: Double) = { + def stratifier (fractionPositive: Double): (Int) => String = { (x: Int) => if (x % 10 < (10 * fractionPositive).toInt) "1" else "0" } @@ -572,7 +573,7 @@ class PairRDDFunctionsSuite extends FunSuite with SharedSparkContext { def testSampleExact(stratifiedData: RDD[(String, Int)], samplingRate: Double, seed: Long, - n: Long) = { + n: Long): Unit = { testBernoulli(stratifiedData, true, samplingRate, seed, n) testPoisson(stratifiedData, true, samplingRate, seed, n) } @@ -580,7 +581,7 @@ class PairRDDFunctionsSuite extends FunSuite with SharedSparkContext { def testSample(stratifiedData: RDD[(String, Int)], samplingRate: Double, seed: Long, - n: Long) = { + n: Long): Unit = { testBernoulli(stratifiedData, false, samplingRate, seed, n) testPoisson(stratifiedData, false, samplingRate, seed, n) } @@ -590,7 +591,7 @@ class PairRDDFunctionsSuite extends FunSuite with SharedSparkContext { exact: Boolean, samplingRate: Double, seed: Long, - n: Long) = { + n: Long): Unit = { val expectedSampleSize = stratifiedData.countByKey() .mapValues(count => math.ceil(count * samplingRate).toInt) val fractions = Map("1" -> samplingRate, "0" -> samplingRate) @@ -612,7 +613,7 @@ class PairRDDFunctionsSuite extends FunSuite with SharedSparkContext { exact: Boolean, samplingRate: Double, seed: Long, - n: Long) = { + n: Long): Unit = { val expectedSampleSize = stratifiedData.countByKey().mapValues(count => math.ceil(count * samplingRate).toInt) val fractions = Map("1" -> samplingRate, "0" -> samplingRate) @@ -701,27 +702,27 @@ class FakeOutputFormat() extends OutputFormat[Integer, Integer]() { */ class NewFakeWriter extends NewRecordWriter[Integer, Integer] { - def close(p1: NewTaskAttempContext) = () + def close(p1: NewTaskAttempContext): Unit = () - def write(p1: Integer, p2: Integer) = () + def write(p1: Integer, p2: Integer): Unit = () } class NewFakeCommitter extends NewOutputCommitter { - def setupJob(p1: NewJobContext) = () + def setupJob(p1: NewJobContext): Unit = () def needsTaskCommit(p1: NewTaskAttempContext): Boolean = false - def setupTask(p1: NewTaskAttempContext) = () + def setupTask(p1: NewTaskAttempContext): Unit = () - def commitTask(p1: NewTaskAttempContext) = () + def commitTask(p1: NewTaskAttempContext): Unit = () - def abortTask(p1: NewTaskAttempContext) = () + def abortTask(p1: NewTaskAttempContext): Unit = () } class NewFakeFormat() extends NewOutputFormat[Integer, Integer]() { - def checkOutputSpecs(p1: NewJobContext) = () + def checkOutputSpecs(p1: NewJobContext): Unit = () def getRecordWriter(p1: NewTaskAttempContext): NewRecordWriter[Integer, Integer] = { new NewFakeWriter() @@ -735,7 +736,7 @@ class NewFakeFormat() extends NewOutputFormat[Integer, Integer]() { class ConfigTestFormat() extends NewFakeFormat() with Configurable { var setConfCalled = false - def setConf(p1: Configuration) = { + def setConf(p1: Configuration): Unit = { setConfCalled = true () } diff --git a/core/src/test/scala/org/apache/spark/rdd/ParallelCollectionSplitSuite.scala b/core/src/test/scala/org/apache/spark/rdd/ParallelCollectionSplitSuite.scala index cd193ae4f5238..1880364581c1a 100644 --- a/core/src/test/scala/org/apache/spark/rdd/ParallelCollectionSplitSuite.scala +++ b/core/src/test/scala/org/apache/spark/rdd/ParallelCollectionSplitSuite.scala @@ -100,7 +100,7 @@ class ParallelCollectionSplitSuite extends FunSuite with Checkers { val data = 1 until 100 val slices = ParallelCollectionRDD.slice(data, 3) assert(slices.size === 3) - assert(slices.map(_.size).reduceLeft(_+_) === 99) + assert(slices.map(_.size).reduceLeft(_ + _) === 99) assert(slices.forall(_.isInstanceOf[Range])) } @@ -108,7 +108,7 @@ class ParallelCollectionSplitSuite extends FunSuite with Checkers { val data = 1 to 100 val slices = ParallelCollectionRDD.slice(data, 3) assert(slices.size === 3) - assert(slices.map(_.size).reduceLeft(_+_) === 100) + assert(slices.map(_.size).reduceLeft(_ + _) === 100) assert(slices.forall(_.isInstanceOf[Range])) } @@ -139,7 +139,7 @@ class ParallelCollectionSplitSuite extends FunSuite with Checkers { assert(slices(i).isInstanceOf[Range]) val range = slices(i).asInstanceOf[Range] assert(range.start === i * (N / 40), "slice " + i + " start") - assert(range.end === (i+1) * (N / 40), "slice " + i + " end") + assert(range.end === (i + 1) * (N / 40), "slice " + i + " end") assert(range.step === 1, "slice " + i + " step") } } @@ -156,7 +156,7 @@ class ParallelCollectionSplitSuite extends FunSuite with Checkers { val slices = ParallelCollectionRDD.slice(d, n) ("n slices" |: slices.size == n) && ("concat to d" |: Seq.concat(slices: _*).mkString(",") == d.mkString(",")) && - ("equal sizes" |: slices.map(_.size).forall(x => x==d.size/n || x==d.size/n+1)) + ("equal sizes" |: slices.map(_.size).forall(x => x == d.size / n || x == d.size /n + 1)) } check(prop) } @@ -174,7 +174,7 @@ class ParallelCollectionSplitSuite extends FunSuite with Checkers { ("n slices" |: slices.size == n) && ("all ranges" |: slices.forall(_.isInstanceOf[Range])) && ("concat to d" |: Seq.concat(slices: _*).mkString(",") == d.mkString(",")) && - ("equal sizes" |: slices.map(_.size).forall(x => x==d.size/n || x==d.size/n+1)) + ("equal sizes" |: slices.map(_.size).forall(x => x == d.size / n || x == d.size / n + 1)) } check(prop) } @@ -192,7 +192,7 @@ class ParallelCollectionSplitSuite extends FunSuite with Checkers { ("n slices" |: slices.size == n) && ("all ranges" |: slices.forall(_.isInstanceOf[Range])) && ("concat to d" |: Seq.concat(slices: _*).mkString(",") == d.mkString(",")) && - ("equal sizes" |: slices.map(_.size).forall(x => x==d.size/n || x==d.size/n+1)) + ("equal sizes" |: slices.map(_.size).forall(x => x == d.size / n || x == d.size / n + 1)) } check(prop) } @@ -201,7 +201,7 @@ class ParallelCollectionSplitSuite extends FunSuite with Checkers { val data = 1L until 100L val slices = ParallelCollectionRDD.slice(data, 3) assert(slices.size === 3) - assert(slices.map(_.size).reduceLeft(_+_) === 99) + assert(slices.map(_.size).reduceLeft(_ + _) === 99) assert(slices.forall(_.isInstanceOf[NumericRange[_]])) } @@ -209,7 +209,7 @@ class ParallelCollectionSplitSuite extends FunSuite with Checkers { val data = 1L to 100L val slices = ParallelCollectionRDD.slice(data, 3) assert(slices.size === 3) - assert(slices.map(_.size).reduceLeft(_+_) === 100) + assert(slices.map(_.size).reduceLeft(_ + _) === 100) assert(slices.forall(_.isInstanceOf[NumericRange[_]])) } @@ -217,7 +217,7 @@ class ParallelCollectionSplitSuite extends FunSuite with Checkers { val data = 1.0 until 100.0 by 1.0 val slices = ParallelCollectionRDD.slice(data, 3) assert(slices.size === 3) - assert(slices.map(_.size).reduceLeft(_+_) === 99) + assert(slices.map(_.size).reduceLeft(_ + _) === 99) assert(slices.forall(_.isInstanceOf[NumericRange[_]])) } @@ -225,7 +225,7 @@ class ParallelCollectionSplitSuite extends FunSuite with Checkers { val data = 1.0 to 100.0 by 1.0 val slices = ParallelCollectionRDD.slice(data, 3) assert(slices.size === 3) - assert(slices.map(_.size).reduceLeft(_+_) === 100) + assert(slices.map(_.size).reduceLeft(_ + _) === 100) assert(slices.forall(_.isInstanceOf[NumericRange[_]])) } diff --git a/core/src/test/scala/org/apache/spark/rdd/PartitionPruningRDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/PartitionPruningRDDSuite.scala index 8408d7e785c65..465068c6cbb16 100644 --- a/core/src/test/scala/org/apache/spark/rdd/PartitionPruningRDDSuite.scala +++ b/core/src/test/scala/org/apache/spark/rdd/PartitionPruningRDDSuite.scala @@ -23,7 +23,6 @@ import org.apache.spark.{Partition, SharedSparkContext, TaskContext} class PartitionPruningRDDSuite extends FunSuite with SharedSparkContext { - test("Pruned Partitions inherit locality prefs correctly") { val rdd = new RDD[Int](sc, Nil) { @@ -74,8 +73,6 @@ class PartitionPruningRDDSuite extends FunSuite with SharedSparkContext { } class TestPartition(i: Int, value: Int) extends Partition with Serializable { - def index = i - - def testValue = this.value - + def index: Int = i + def testValue: Int = this.value } diff --git a/core/src/test/scala/org/apache/spark/rdd/PartitionwiseSampledRDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/PartitionwiseSampledRDDSuite.scala index a0483886f8db3..0d1369c19c69e 100644 --- a/core/src/test/scala/org/apache/spark/rdd/PartitionwiseSampledRDDSuite.scala +++ b/core/src/test/scala/org/apache/spark/rdd/PartitionwiseSampledRDDSuite.scala @@ -35,7 +35,7 @@ class MockSampler extends RandomSampler[Long, Long] { Iterator(s) } - override def clone = new MockSampler + override def clone: MockSampler = new MockSampler } class PartitionwiseSampledRDDSuite extends FunSuite with SharedSparkContext { diff --git a/core/src/test/scala/org/apache/spark/rdd/PipedRDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/PipedRDDSuite.scala index 1a9a0e857e546..aea76c1adcc09 100644 --- a/core/src/test/scala/org/apache/spark/rdd/PipedRDDSuite.scala +++ b/core/src/test/scala/org/apache/spark/rdd/PipedRDDSuite.scala @@ -22,7 +22,6 @@ import java.io.File import org.apache.hadoop.fs.Path import org.apache.hadoop.io.{LongWritable, Text} import org.apache.hadoop.mapred.{FileSplit, JobConf, TextInputFormat} -import org.apache.spark._ import org.scalatest.FunSuite import scala.collection.Map @@ -30,6 +29,9 @@ import scala.language.postfixOps import scala.sys.process._ import scala.util.Try +import org.apache.spark._ +import org.apache.spark.util.Utils + class PipedRDDSuite extends FunSuite with SharedSparkContext { test("basic pipe") { @@ -141,7 +143,7 @@ class PipedRDDSuite extends FunSuite with SharedSparkContext { // make sure symlinks were created assert(pipedLs.length > 0) // clean up top level tasks directory - new File("tasks").delete() + Utils.deleteRecursively(new File("tasks")) } else { assert(true) } diff --git a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala index bede1ffb3e2d0..df42faab64505 100644 --- a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala +++ b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala @@ -82,7 +82,7 @@ class RDDSuite extends FunSuite with SharedSparkContext { test("countApproxDistinct") { - def error(est: Long, size: Long) = math.abs(est - size) / size.toDouble + def error(est: Long, size: Long): Double = math.abs(est - size) / size.toDouble val size = 1000 val uniformDistro = for (i <- 1 to 5000) yield i % size @@ -100,7 +100,7 @@ class RDDSuite extends FunSuite with SharedSparkContext { } test("partitioner aware union") { - def makeRDDWithPartitioner(seq: Seq[Int]) = { + def makeRDDWithPartitioner(seq: Seq[Int]): RDD[Int] = { sc.makeRDD(seq, 1) .map(x => (x, null)) .partitionBy(new HashPartitioner(2)) @@ -159,8 +159,8 @@ class RDDSuite extends FunSuite with SharedSparkContext { test("treeAggregate") { val rdd = sc.makeRDD(-1000 until 1000, 10) - def seqOp = (c: Long, x: Int) => c + x - def combOp = (c1: Long, c2: Long) => c1 + c2 + def seqOp: (Long, Int) => Long = (c: Long, x: Int) => c + x + def combOp: (Long, Long) => Long = (c1: Long, c2: Long) => c1 + c2 for (depth <- 1 until 10) { val sum = rdd.treeAggregate(0L)(seqOp, combOp, depth) assert(sum === -1000L) @@ -204,7 +204,7 @@ class RDDSuite extends FunSuite with SharedSparkContext { assert(empty.collect().size === 0) val thrown = intercept[UnsupportedOperationException]{ - empty.reduce(_+_) + empty.reduce(_ + _) } assert(thrown.getMessage.contains("empty")) @@ -321,7 +321,7 @@ class RDDSuite extends FunSuite with SharedSparkContext { assert(list3.sorted === Array("a","b","c"), "Locality preferences are dropped") // RDD with locality preferences spread (non-randomly) over 6 machines, m0 through m5 - val data = sc.makeRDD((1 to 9).map(i => (i, (i to (i+2)).map{ j => "m" + (j%6)}))) + val data = sc.makeRDD((1 to 9).map(i => (i, (i to (i + 2)).map{ j => "m" + (j%6)}))) val coalesced1 = data.coalesce(3) assert(coalesced1.collect().toList.sorted === (1 to 9).toList, "Data got *lost* in coalescing") @@ -921,15 +921,17 @@ class RDDSuite extends FunSuite with SharedSparkContext { test("task serialization exception should not hang scheduler") { class BadSerializable extends Serializable { @throws(classOf[IOException]) - private def writeObject(out: ObjectOutputStream): Unit = throw new KryoException("Bad serialization") + private def writeObject(out: ObjectOutputStream): Unit = + throw new KryoException("Bad serialization") @throws(classOf[IOException]) private def readObject(in: ObjectInputStream): Unit = {} } - // Note that in the original bug, SPARK-4349, that this verifies, the job would only hang if there were - // more threads in the Spark Context than there were number of objects in this sequence. + // Note that in the original bug, SPARK-4349, that this verifies, the job would only hang if + // there were more threads in the Spark Context than there were number of objects in this + // sequence. intercept[Throwable] { - sc.parallelize(Seq(new BadSerializable, new BadSerializable)).collect + sc.parallelize(Seq(new BadSerializable, new BadSerializable)).collect() } // Check that the context has not crashed sc.parallelize(1 to 100).map(x => x*2).collect diff --git a/core/src/test/scala/org/apache/spark/rdd/RDDSuiteUtils.scala b/core/src/test/scala/org/apache/spark/rdd/RDDSuiteUtils.scala index 4762fc17855ce..fe695d85e29dd 100644 --- a/core/src/test/scala/org/apache/spark/rdd/RDDSuiteUtils.scala +++ b/core/src/test/scala/org/apache/spark/rdd/RDDSuiteUtils.scala @@ -21,11 +21,11 @@ object RDDSuiteUtils { case class Person(first: String, last: String, age: Int) object AgeOrdering extends Ordering[Person] { - def compare(a:Person, b:Person) = a.age compare b.age + def compare(a:Person, b:Person): Int = a.age.compare(b.age) } object NameOrdering extends Ordering[Person] { - def compare(a:Person, b:Person) = + def compare(a:Person, b:Person): Int = implicitly[Ordering[Tuple2[String,String]]].compare((a.last, a.first), (b.last, b.first)) } } diff --git a/core/src/test/scala/org/apache/spark/rdd/SortingSuite.scala b/core/src/test/scala/org/apache/spark/rdd/SortingSuite.scala index a40f2ffeffdf9..64b1c24c47168 100644 --- a/core/src/test/scala/org/apache/spark/rdd/SortingSuite.scala +++ b/core/src/test/scala/org/apache/spark/rdd/SortingSuite.scala @@ -119,5 +119,33 @@ class SortingSuite extends FunSuite with SharedSparkContext with Matchers with L partitions(1).last should be > partitions(2).head partitions(2).last should be > partitions(3).head } + + test("get a range of elements in a sorted RDD that is on one partition") { + val pairArr = (1 to 1000).map(x => (x, x)).toArray + val sorted = sc.parallelize(pairArr, 10).sortByKey() + val range = sorted.filterByRange(20, 40).collect() + assert((20 to 40).toArray === range.map(_._1)) + } + + test("get a range of elements over multiple partitions in a descendingly sorted RDD") { + val pairArr = (1000 to 1 by -1).map(x => (x, x)).toArray + val sorted = sc.parallelize(pairArr, 10).sortByKey(false) + val range = sorted.filterByRange(200, 800).collect() + assert((800 to 200 by -1).toArray === range.map(_._1)) + } + + test("get a range of elements in an array not partitioned by a range partitioner") { + val pairArr = util.Random.shuffle((1 to 1000).toList).map(x => (x, x)) + val pairs = sc.parallelize(pairArr,10) + val range = pairs.filterByRange(200, 800).collect() + assert((800 to 200 by -1).toArray.sorted === range.map(_._1).sorted) + } + + test("get a range of elements over multiple partitions but not taking up full partitions") { + val pairArr = (1000 to 1 by -1).map(x => (x, x)).toArray + val sorted = sc.parallelize(pairArr, 10).sortByKey(false) + val range = sorted.filterByRange(250, 850).collect() + assert((850 to 250 by -1).toArray === range.map(_._1)) + } } diff --git a/core/src/test/scala/org/apache/spark/rpc/RpcEnvSuite.scala b/core/src/test/scala/org/apache/spark/rpc/RpcEnvSuite.scala new file mode 100644 index 0000000000000..ada07ef11cd7a --- /dev/null +++ b/core/src/test/scala/org/apache/spark/rpc/RpcEnvSuite.scala @@ -0,0 +1,548 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.rpc + +import java.util.concurrent.{TimeUnit, CountDownLatch, TimeoutException} + +import scala.collection.mutable +import scala.concurrent.Await +import scala.concurrent.duration._ +import scala.language.postfixOps + +import org.scalatest.{BeforeAndAfterAll, FunSuite} +import org.scalatest.concurrent.Eventually._ + +import org.apache.spark.{SparkException, SparkConf} + +/** + * Common tests for an RpcEnv implementation. + */ +abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { + + var env: RpcEnv = _ + + override def beforeAll(): Unit = { + val conf = new SparkConf() + env = createRpcEnv(conf, "local", 12345) + } + + override def afterAll(): Unit = { + if(env != null) { + env.shutdown() + } + } + + def createRpcEnv(conf: SparkConf, name: String, port: Int): RpcEnv + + test("send a message locally") { + @volatile var message: String = null + val rpcEndpointRef = env.setupEndpoint("send-locally", new RpcEndpoint { + override val rpcEnv = env + + override def receive = { + case msg: String => message = msg + } + }) + rpcEndpointRef.send("hello") + eventually(timeout(5 seconds), interval(10 millis)) { + assert("hello" === message) + } + } + + test("send a message remotely") { + @volatile var message: String = null + // Set up a RpcEndpoint using env + env.setupEndpoint("send-remotely", new RpcEndpoint { + override val rpcEnv = env + + override def receive: PartialFunction[Any, Unit] = { + case msg: String => message = msg + } + }) + + val anotherEnv = createRpcEnv(new SparkConf(), "remote" ,13345) + // Use anotherEnv to find out the RpcEndpointRef + val rpcEndpointRef = anotherEnv.setupEndpointRef("local", env.address, "send-remotely") + try { + rpcEndpointRef.send("hello") + eventually(timeout(5 seconds), interval(10 millis)) { + assert("hello" === message) + } + } finally { + anotherEnv.shutdown() + anotherEnv.awaitTermination() + } + } + + test("send a RpcEndpointRef") { + val endpoint = new RpcEndpoint { + override val rpcEnv = env + + override def receiveAndReply(context: RpcCallContext) = { + case "Hello" => context.reply(self) + case "Echo" => context.reply("Echo") + } + } + val rpcEndpointRef = env.setupEndpoint("send-ref", endpoint) + + val newRpcEndpointRef = rpcEndpointRef.askWithReply[RpcEndpointRef]("Hello") + val reply = newRpcEndpointRef.askWithReply[String]("Echo") + assert("Echo" === reply) + } + + test("ask a message locally") { + val rpcEndpointRef = env.setupEndpoint("ask-locally", new RpcEndpoint { + override val rpcEnv = env + + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { + case msg: String => { + context.reply(msg) + } + } + }) + val reply = rpcEndpointRef.askWithReply[String]("hello") + assert("hello" === reply) + } + + test("ask a message remotely") { + env.setupEndpoint("ask-remotely", new RpcEndpoint { + override val rpcEnv = env + + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { + case msg: String => { + context.reply(msg) + } + } + }) + + val anotherEnv = createRpcEnv(new SparkConf(), "remote", 13345) + // Use anotherEnv to find out the RpcEndpointRef + val rpcEndpointRef = anotherEnv.setupEndpointRef("local", env.address, "ask-remotely") + try { + val reply = rpcEndpointRef.askWithReply[String]("hello") + assert("hello" === reply) + } finally { + anotherEnv.shutdown() + anotherEnv.awaitTermination() + } + } + + test("ask a message timeout") { + env.setupEndpoint("ask-timeout", new RpcEndpoint { + override val rpcEnv = env + + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { + case msg: String => { + Thread.sleep(100) + context.reply(msg) + } + } + }) + + val conf = new SparkConf() + conf.set("spark.akka.retry.wait", "0") + conf.set("spark.akka.num.retries", "1") + val anotherEnv = createRpcEnv(conf, "remote", 13345) + // Use anotherEnv to find out the RpcEndpointRef + val rpcEndpointRef = anotherEnv.setupEndpointRef("local", env.address, "ask-timeout") + try { + val e = intercept[Exception] { + rpcEndpointRef.askWithReply[String]("hello", 1 millis) + } + assert(e.isInstanceOf[TimeoutException] || e.getCause.isInstanceOf[TimeoutException]) + } finally { + anotherEnv.shutdown() + anotherEnv.awaitTermination() + } + } + + test("onStart and onStop") { + val stopLatch = new CountDownLatch(1) + val calledMethods = mutable.ArrayBuffer[String]() + + val endpoint = new RpcEndpoint { + override val rpcEnv = env + + override def onStart(): Unit = { + calledMethods += "start" + } + + override def receive: PartialFunction[Any, Unit] = { + case msg: String => + } + + override def onStop(): Unit = { + calledMethods += "stop" + stopLatch.countDown() + } + } + val rpcEndpointRef = env.setupEndpoint("start-stop-test", endpoint) + env.stop(rpcEndpointRef) + stopLatch.await(10, TimeUnit.SECONDS) + assert(List("start", "stop") === calledMethods) + } + + test("onError: error in onStart") { + @volatile var e: Throwable = null + env.setupEndpoint("onError-onStart", new RpcEndpoint { + override val rpcEnv = env + + override def onStart(): Unit = { + throw new RuntimeException("Oops!") + } + + override def receive: PartialFunction[Any, Unit] = { + case m => + } + + override def onError(cause: Throwable): Unit = { + e = cause + } + }) + + eventually(timeout(5 seconds), interval(10 millis)) { + assert(e.getMessage === "Oops!") + } + } + + test("onError: error in onStop") { + @volatile var e: Throwable = null + val endpointRef = env.setupEndpoint("onError-onStop", new RpcEndpoint { + override val rpcEnv = env + + override def receive: PartialFunction[Any, Unit] = { + case m => + } + + override def onError(cause: Throwable): Unit = { + e = cause + } + + override def onStop(): Unit = { + throw new RuntimeException("Oops!") + } + }) + + env.stop(endpointRef) + + eventually(timeout(5 seconds), interval(10 millis)) { + assert(e.getMessage === "Oops!") + } + } + + test("onError: error in receive") { + @volatile var e: Throwable = null + val endpointRef = env.setupEndpoint("onError-receive", new RpcEndpoint { + override val rpcEnv = env + + override def receive: PartialFunction[Any, Unit] = { + case m => throw new RuntimeException("Oops!") + } + + override def onError(cause: Throwable): Unit = { + e = cause + } + }) + + endpointRef.send("Foo") + + eventually(timeout(5 seconds), interval(10 millis)) { + assert(e.getMessage === "Oops!") + } + } + + test("self: call in onStart") { + @volatile var callSelfSuccessfully = false + + env.setupEndpoint("self-onStart", new RpcEndpoint { + override val rpcEnv = env + + override def onStart(): Unit = { + self + callSelfSuccessfully = true + } + + override def receive: PartialFunction[Any, Unit] = { + case m => + } + }) + + eventually(timeout(5 seconds), interval(10 millis)) { + // Calling `self` in `onStart` is fine + assert(callSelfSuccessfully === true) + } + } + + test("self: call in receive") { + @volatile var callSelfSuccessfully = false + + val endpointRef = env.setupEndpoint("self-receive", new RpcEndpoint { + override val rpcEnv = env + + override def receive: PartialFunction[Any, Unit] = { + case m => { + self + callSelfSuccessfully = true + } + } + }) + + endpointRef.send("Foo") + + eventually(timeout(5 seconds), interval(10 millis)) { + // Calling `self` in `receive` is fine + assert(callSelfSuccessfully === true) + } + } + + test("self: call in onStop") { + @volatile var selfOption: Option[RpcEndpointRef] = null + + val endpointRef = env.setupEndpoint("self-onStop", new RpcEndpoint { + override val rpcEnv = env + + override def receive: PartialFunction[Any, Unit] = { + case m => + } + + override def onStop(): Unit = { + selfOption = Option(self) + } + + override def onError(cause: Throwable): Unit = { + } + }) + + env.stop(endpointRef) + + eventually(timeout(5 seconds), interval(10 millis)) { + // Calling `self` in `onStop` will return null, so selfOption will be None + assert(selfOption == None) + } + } + + test("call receive in sequence") { + // If a RpcEnv implementation breaks the `receive` contract, hope this test can expose it + for(i <- 0 until 100) { + @volatile var result = 0 + val endpointRef = env.setupEndpoint(s"receive-in-sequence-$i", new ThreadSafeRpcEndpoint { + override val rpcEnv = env + + override def receive: PartialFunction[Any, Unit] = { + case m => result += 1 + } + + }) + + (0 until 10) foreach { _ => + new Thread { + override def run() { + (0 until 100) foreach { _ => + endpointRef.send("Hello") + } + } + }.start() + } + + eventually(timeout(5 seconds), interval(5 millis)) { + assert(result == 1000) + } + + env.stop(endpointRef) + } + } + + test("stop(RpcEndpointRef) reentrant") { + @volatile var onStopCount = 0 + val endpointRef = env.setupEndpoint("stop-reentrant", new RpcEndpoint { + override val rpcEnv = env + + override def receive: PartialFunction[Any, Unit] = { + case m => + } + + override def onStop(): Unit = { + onStopCount += 1 + } + }) + + env.stop(endpointRef) + env.stop(endpointRef) + + eventually(timeout(5 seconds), interval(5 millis)) { + // Calling stop twice should only trigger onStop once. + assert(onStopCount == 1) + } + } + + test("sendWithReply") { + val endpointRef = env.setupEndpoint("sendWithReply", new RpcEndpoint { + override val rpcEnv = env + + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { + case m => context.reply("ack") + } + }) + + val f = endpointRef.sendWithReply[String]("Hi") + val ack = Await.result(f, 5 seconds) + assert("ack" === ack) + + env.stop(endpointRef) + } + + test("sendWithReply: remotely") { + env.setupEndpoint("sendWithReply-remotely", new RpcEndpoint { + override val rpcEnv = env + + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { + case m => context.reply("ack") + } + }) + + val anotherEnv = createRpcEnv(new SparkConf(), "remote", 13345) + // Use anotherEnv to find out the RpcEndpointRef + val rpcEndpointRef = anotherEnv.setupEndpointRef("local", env.address, "sendWithReply-remotely") + try { + val f = rpcEndpointRef.sendWithReply[String]("hello") + val ack = Await.result(f, 5 seconds) + assert("ack" === ack) + } finally { + anotherEnv.shutdown() + anotherEnv.awaitTermination() + } + } + + test("sendWithReply: error") { + val endpointRef = env.setupEndpoint("sendWithReply-error", new RpcEndpoint { + override val rpcEnv = env + + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { + case m => context.sendFailure(new SparkException("Oops")) + } + }) + + val f = endpointRef.sendWithReply[String]("Hi") + val e = intercept[SparkException] { + Await.result(f, 5 seconds) + } + assert("Oops" === e.getMessage) + + env.stop(endpointRef) + } + + test("sendWithReply: remotely error") { + env.setupEndpoint("sendWithReply-remotely-error", new RpcEndpoint { + override val rpcEnv = env + + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { + case msg: String => context.sendFailure(new SparkException("Oops")) + } + }) + + val anotherEnv = createRpcEnv(new SparkConf(), "remote", 13345) + // Use anotherEnv to find out the RpcEndpointRef + val rpcEndpointRef = anotherEnv.setupEndpointRef( + "local", env.address, "sendWithReply-remotely-error") + try { + val f = rpcEndpointRef.sendWithReply[String]("hello") + val e = intercept[SparkException] { + Await.result(f, 5 seconds) + } + assert("Oops" === e.getMessage) + } finally { + anotherEnv.shutdown() + anotherEnv.awaitTermination() + } + } + + test("network events") { + val events = new mutable.ArrayBuffer[(Any, Any)] with mutable.SynchronizedBuffer[(Any, Any)] + env.setupEndpoint("network-events", new ThreadSafeRpcEndpoint { + override val rpcEnv = env + + override def receive: PartialFunction[Any, Unit] = { + case "hello" => + case m => events += "receive" -> m + } + + override def onConnected(remoteAddress: RpcAddress): Unit = { + events += "onConnected" -> remoteAddress + } + + override def onDisconnected(remoteAddress: RpcAddress): Unit = { + events += "onDisconnected" -> remoteAddress + } + + override def onNetworkError(cause: Throwable, remoteAddress: RpcAddress): Unit = { + events += "onNetworkError" -> remoteAddress + } + + }) + + val anotherEnv = createRpcEnv(new SparkConf(), "remote", 13345) + // Use anotherEnv to find out the RpcEndpointRef + val rpcEndpointRef = anotherEnv.setupEndpointRef( + "local", env.address, "network-events") + val remoteAddress = anotherEnv.address + rpcEndpointRef.send("hello") + eventually(timeout(5 seconds), interval(5 millis)) { + assert(events === List(("onConnected", remoteAddress))) + } + + anotherEnv.shutdown() + anotherEnv.awaitTermination() + eventually(timeout(5 seconds), interval(5 millis)) { + assert(events === List( + ("onConnected", remoteAddress), + ("onNetworkError", remoteAddress), + ("onDisconnected", remoteAddress))) + } + } + + test("sendWithReply: unserializable error") { + env.setupEndpoint("sendWithReply-unserializable-error", new RpcEndpoint { + override val rpcEnv = env + + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { + case msg: String => context.sendFailure(new UnserializableException) + } + }) + + val anotherEnv = createRpcEnv(new SparkConf(), "remote", 13345) + // Use anotherEnv to find out the RpcEndpointRef + val rpcEndpointRef = anotherEnv.setupEndpointRef( + "local", env.address, "sendWithReply-unserializable-error") + try { + val f = rpcEndpointRef.sendWithReply[String]("hello") + intercept[TimeoutException] { + Await.result(f, 1 seconds) + } + } finally { + anotherEnv.shutdown() + anotherEnv.awaitTermination() + } + } + +} + +class UnserializableClass + +class UnserializableException extends Exception { + private val unserializableField = new UnserializableClass +} diff --git a/core/src/test/scala/org/apache/spark/rpc/akka/AkkaRpcEnvSuite.scala b/core/src/test/scala/org/apache/spark/rpc/akka/AkkaRpcEnvSuite.scala new file mode 100644 index 0000000000000..58214c0637235 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/rpc/akka/AkkaRpcEnvSuite.scala @@ -0,0 +1,50 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.rpc.akka + +import org.apache.spark.rpc._ +import org.apache.spark.{SecurityManager, SparkConf} + +class AkkaRpcEnvSuite extends RpcEnvSuite { + + override def createRpcEnv(conf: SparkConf, name: String, port: Int): RpcEnv = { + new AkkaRpcEnvFactory().create( + RpcEnvConfig(conf, name, "localhost", port, new SecurityManager(conf))) + } + + test("setupEndpointRef: systemName, address, endpointName") { + val ref = env.setupEndpoint("test_endpoint", new RpcEndpoint { + override val rpcEnv = env + + override def receive = { + case _ => + } + }) + val conf = new SparkConf() + val newRpcEnv = new AkkaRpcEnvFactory().create( + RpcEnvConfig(conf, "test", "localhost", 12346, new SecurityManager(conf))) + try { + val newRef = newRpcEnv.setupEndpointRef("local", ref.address, "test_endpoint") + assert("akka.tcp://local@localhost:12345/user/test_endpoint" === + newRef.asInstanceOf[AkkaRpcEndpointRef].actorRef.path.toString) + } finally { + newRpcEnv.shutdown() + } + } + +} diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala index eb116213f69fc..3c52a8c4460c6 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala @@ -57,20 +57,18 @@ class MyRDD( locations: Seq[Seq[String]] = Nil) extends RDD[(Int, Int)](sc, dependencies) with Serializable { override def compute(split: Partition, context: TaskContext): Iterator[(Int, Int)] = throw new RuntimeException("should not be reached") - override def getPartitions = (0 until numPartitions).map(i => new Partition { - override def index = i + override def getPartitions: Array[Partition] = (0 until numPartitions).map(i => new Partition { + override def index: Int = i }).toArray override def getPreferredLocations(split: Partition): Seq[String] = - if (locations.isDefinedAt(split.index)) - locations(split.index) - else - Nil + if (locations.isDefinedAt(split.index)) locations(split.index) else Nil override def toString: String = "DAGSchedulerSuiteRDD " + id } class DAGSchedulerSuiteDummyException extends Exception -class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSparkContext with Timeouts { +class DAGSchedulerSuite + extends FunSuiteLike with BeforeAndAfter with LocalSparkContext with Timeouts { val conf = new SparkConf /** Set of TaskSets the DAGScheduler has requested executed. */ @@ -96,6 +94,7 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar } override def setDAGScheduler(dagScheduler: DAGScheduler) = {} override def defaultParallelism() = 2 + override def executorLost(executorId: String, reason: ExecutorLossReason): Unit = {} } /** Length of time to wait while draining listener events. */ @@ -208,7 +207,8 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar assert(taskSet.tasks.size >= results.size) for ((result, i) <- results.zipWithIndex) { if (i < taskSet.tasks.size) { - runEvent(CompletionEvent(taskSet.tasks(i), result._1, result._2, null, null, null)) + runEvent(CompletionEvent( + taskSet.tasks(i), result._1, result._2, null, createFakeTaskInfo(), null)) } } } @@ -219,7 +219,7 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar for ((result, i) <- results.zipWithIndex) { if (i < taskSet.tasks.size) { runEvent(CompletionEvent(taskSet.tasks(i), result._1, result._2, - Map[Long, Any]((accumId, 1)), null, null)) + Map[Long, Any]((accumId, 1)), createFakeTaskInfo(), null)) } } } @@ -268,21 +268,23 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar submit(new MyRDD(sc, 1, Nil), Array(0)) complete(taskSets(0), List((Success, 42))) assert(results === Map(0 -> 42)) - assertDataStructuresEmpty + assertDataStructuresEmpty() } test("local job") { val rdd = new PairOfIntsRDD(sc, Nil) { override def compute(split: Partition, context: TaskContext): Iterator[(Int, Int)] = Array(42 -> 0).iterator - override def getPartitions = Array( new Partition { override def index = 0 } ) - override def getPreferredLocations(split: Partition) = Nil - override def toString = "DAGSchedulerSuite Local RDD" + override def getPartitions: Array[Partition] = + Array( new Partition { override def index: Int = 0 } ) + override def getPreferredLocations(split: Partition): List[String] = Nil + override def toString: String = "DAGSchedulerSuite Local RDD" } val jobId = scheduler.nextJobId.getAndIncrement() - runEvent(JobSubmitted(jobId, rdd, jobComputeFunc, Array(0), true, CallSite("", ""), jobListener)) + runEvent( + JobSubmitted(jobId, rdd, jobComputeFunc, Array(0), true, CallSite("", ""), jobListener)) assert(results === Map(0 -> 42)) - assertDataStructuresEmpty + assertDataStructuresEmpty() } test("local job oom") { @@ -294,9 +296,10 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar override def toString = "DAGSchedulerSuite Local RDD" } val jobId = scheduler.nextJobId.getAndIncrement() - runEvent(JobSubmitted(jobId, rdd, jobComputeFunc, Array(0), true, CallSite("", ""), jobListener)) + runEvent( + JobSubmitted(jobId, rdd, jobComputeFunc, Array(0), true, CallSite("", ""), jobListener)) assert(results.size == 0) - assertDataStructuresEmpty + assertDataStructuresEmpty() } test("run trivial job w/ dependency") { @@ -305,7 +308,7 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar submit(finalRdd, Array(0)) complete(taskSets(0), Seq((Success, 42))) assert(results === Map(0 -> 42)) - assertDataStructuresEmpty + assertDataStructuresEmpty() } test("cache location preferences w/ dependency") { @@ -318,11 +321,23 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar assertLocations(taskSet, Seq(Seq("hostA", "hostB"))) complete(taskSet, Seq((Success, 42))) assert(results === Map(0 -> 42)) - assertDataStructuresEmpty + assertDataStructuresEmpty() + } + + test("regression test for getCacheLocs") { + val rdd = new MyRDD(sc, 3, Nil) + cacheLocations(rdd.id -> 0) = + Seq(makeBlockManagerId("hostA"), makeBlockManagerId("hostB")) + cacheLocations(rdd.id -> 1) = + Seq(makeBlockManagerId("hostB"), makeBlockManagerId("hostC")) + cacheLocations(rdd.id -> 2) = + Seq(makeBlockManagerId("hostC"), makeBlockManagerId("hostD")) + val locs = scheduler.getCacheLocs(rdd).map(_.map(_.host)) + assert(locs === Seq(Seq("hostA", "hostB"), Seq("hostB", "hostC"), Seq("hostC", "hostD"))) } test("avoid exponential blowup when getting preferred locs list") { - // Build up a complex dependency graph with repeated zip operations, without preferred locations. + // Build up a complex dependency graph with repeated zip operations, without preferred locations var rdd: RDD[_] = new MyRDD(sc, 1, Nil) (1 to 30).foreach(_ => rdd = rdd.zip(rdd)) // getPreferredLocs runs quickly, indicating that exponential graph traversal is avoided. @@ -344,7 +359,7 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar assert(sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)) assert(sparkListener.failedStages.contains(0)) assert(sparkListener.failedStages.size === 1) - assertDataStructuresEmpty + assertDataStructuresEmpty() } test("trivial job failure") { @@ -354,7 +369,7 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar assert(sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)) assert(sparkListener.failedStages.contains(0)) assert(sparkListener.failedStages.size === 1) - assertDataStructuresEmpty + assertDataStructuresEmpty() } test("trivial job cancellation") { @@ -365,7 +380,7 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar assert(sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)) assert(sparkListener.failedStages.contains(0)) assert(sparkListener.failedStages.size === 1) - assertDataStructuresEmpty + assertDataStructuresEmpty() } test("job cancellation no-kill backend") { @@ -374,18 +389,21 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar val noKillTaskScheduler = new TaskScheduler() { override def rootPool: Pool = null override def schedulingMode: SchedulingMode = SchedulingMode.NONE - override def start() = {} - override def stop() = {} - override def submitTasks(taskSet: TaskSet) = { + override def start(): Unit = {} + override def stop(): Unit = {} + override def submitTasks(taskSet: TaskSet): Unit = { taskSets += taskSet } override def cancelTasks(stageId: Int, interruptThread: Boolean) { throw new UnsupportedOperationException } - override def setDAGScheduler(dagScheduler: DAGScheduler) = {} - override def defaultParallelism() = 2 - override def executorHeartbeatReceived(execId: String, taskMetrics: Array[(Long, TaskMetrics)], - blockManagerId: BlockManagerId): Boolean = true + override def setDAGScheduler(dagScheduler: DAGScheduler): Unit = {} + override def defaultParallelism(): Int = 2 + override def executorHeartbeatReceived( + execId: String, + taskMetrics: Array[(Long, TaskMetrics)], + blockManagerId: BlockManagerId): Boolean = true + override def executorLost(executorId: String, reason: ExecutorLossReason): Unit = {} } val noKillScheduler = new DAGScheduler( sc, @@ -408,7 +426,7 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar // When the task set completes normally, state should be correctly updated. complete(taskSets(0), Seq((Success, 42))) assert(results === Map(0 -> 42)) - assertDataStructuresEmpty + assertDataStructuresEmpty() assert(sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)) assert(sparkListener.failedStages.isEmpty) @@ -428,7 +446,7 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar Array(makeBlockManagerId("hostA"), makeBlockManagerId("hostB"))) complete(taskSets(1), Seq((Success, 42))) assert(results === Map(0 -> 42)) - assertDataStructuresEmpty + assertDataStructuresEmpty() } test("run trivial shuffle with fetch failure") { @@ -451,10 +469,11 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar // have the 2nd attempt pass complete(taskSets(2), Seq((Success, makeMapStatus("hostA", 1)))) // we can see both result blocks now - assert(mapOutputTracker.getServerStatuses(shuffleId, 0).map(_._1.host) === Array("hostA", "hostB")) + assert(mapOutputTracker.getServerStatuses(shuffleId, 0).map(_._1.host) === + Array("hostA", "hostB")) complete(taskSets(3), Seq((Success, 43))) assert(results === Map(0 -> 42, 1 -> 43)) - assertDataStructuresEmpty + assertDataStructuresEmpty() } test("trivial shuffle with multiple fetch failures") { @@ -476,7 +495,7 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar FetchFailed(makeBlockManagerId("hostA"), shuffleId, 0, 0, "ignored"), null, Map[Long, Any](), - null, + createFakeTaskInfo(), null)) assert(sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)) assert(sparkListener.failedStages.contains(1)) @@ -487,7 +506,7 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar FetchFailed(makeBlockManagerId("hostA"), shuffleId, 1, 1, "ignored"), null, Map[Long, Any](), - null, + createFakeTaskInfo(), null)) // The SparkListener should not receive redundant failure events. assert(sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)) @@ -507,19 +526,23 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar assert(newEpoch > oldEpoch) val taskSet = taskSets(0) // should be ignored for being too old - runEvent(CompletionEvent(taskSet.tasks(0), Success, makeMapStatus("hostA", 1), null, null, null)) + runEvent(CompletionEvent( + taskSet.tasks(0), Success, makeMapStatus("hostA", 1), null, createFakeTaskInfo(), null)) // should work because it's a non-failed host - runEvent(CompletionEvent(taskSet.tasks(0), Success, makeMapStatus("hostB", 1), null, null, null)) + runEvent(CompletionEvent( + taskSet.tasks(0), Success, makeMapStatus("hostB", 1), null, createFakeTaskInfo(), null)) // should be ignored for being too old - runEvent(CompletionEvent(taskSet.tasks(0), Success, makeMapStatus("hostA", 1), null, null, null)) + runEvent(CompletionEvent( + taskSet.tasks(0), Success, makeMapStatus("hostA", 1), null, createFakeTaskInfo(), null)) // should work because it's a new epoch taskSet.tasks(1).epoch = newEpoch - runEvent(CompletionEvent(taskSet.tasks(1), Success, makeMapStatus("hostA", 1), null, null, null)) + runEvent(CompletionEvent( + taskSet.tasks(1), Success, makeMapStatus("hostA", 1), null, createFakeTaskInfo(), null)) assert(mapOutputTracker.getServerStatuses(shuffleId, 0).map(_._1) === Array(makeBlockManagerId("hostB"), makeBlockManagerId("hostA"))) complete(taskSets(1), Seq((Success, 42), (Success, 43))) assert(results === Map(0 -> 42, 1 -> 43)) - assertDataStructuresEmpty + assertDataStructuresEmpty() } test("run shuffle with map stage failure") { @@ -538,7 +561,7 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar assert(sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)) assert(sparkListener.failedStages.toSet === Set(0)) - assertDataStructuresEmpty + assertDataStructuresEmpty() } /** @@ -572,7 +595,7 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar class FailureRecordingJobListener() extends JobListener { var failureMessage: String = _ override def taskSucceeded(index: Int, result: Any) {} - override def jobFailed(exception: Exception) = { failureMessage = exception.getMessage } + override def jobFailed(exception: Exception): Unit = { failureMessage = exception.getMessage } } val listener1 = new FailureRecordingJobListener() val listener2 = new FailureRecordingJobListener() @@ -592,7 +615,7 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar assert(listener1.failureMessage === s"Job aborted due to stage failure: $stageFailureMessage") assert(listener2.failureMessage === s"Job aborted due to stage failure: $stageFailureMessage") - assertDataStructuresEmpty + assertDataStructuresEmpty() } test("run trivial shuffle with out-of-band failure and retry") { @@ -615,7 +638,7 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar Array(makeBlockManagerId("hostC"), makeBlockManagerId("hostB"))) complete(taskSets(2), Seq((Success, 42))) assert(results === Map(0 -> 42)) - assertDataStructuresEmpty + assertDataStructuresEmpty() } test("recursive shuffle failures") { @@ -644,7 +667,7 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar complete(taskSets(4), Seq((Success, makeMapStatus("hostA", 1)))) complete(taskSets(5), Seq((Success, 42))) assert(results === Map(0 -> 42)) - assertDataStructuresEmpty + assertDataStructuresEmpty() } test("cached post-shuffle") { @@ -676,7 +699,7 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar complete(taskSets(3), Seq((Success, makeMapStatus("hostD", 1)))) complete(taskSets(4), Seq((Success, 42))) assert(results === Map(0 -> 42)) - assertDataStructuresEmpty + assertDataStructuresEmpty() } test("misbehaved accumulator should not crash DAGScheduler and SparkContext") { @@ -728,15 +751,19 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar } test("accumulator not calculated for resubmitted result stage") { - //just for register + // just for register val accum = new Accumulator[Int](0, AccumulatorParam.IntAccumulatorParam) val finalRdd = new MyRDD(sc, 1, Nil) submit(finalRdd, Array(0)) completeWithAccumulator(accum.id, taskSets(0), Seq((Success, 42))) completeWithAccumulator(accum.id, taskSets(0), Seq((Success, 42))) assert(results === Map(0 -> 42)) - assert(Accumulators.originals(accum.id).value === 1) - assertDataStructuresEmpty + + val accVal = Accumulators.originals(accum.id).get.get.value + + assert(accVal === 1) + + assertDataStructuresEmpty() } /** @@ -756,7 +783,7 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar private def makeBlockManagerId(host: String): BlockManagerId = BlockManagerId("exec-" + host, host, 12345) - private def assertDataStructuresEmpty = { + private def assertDataStructuresEmpty(): Unit = { assert(scheduler.activeJobs.isEmpty) assert(scheduler.failedStages.isEmpty) assert(scheduler.jobIdToActiveJob.isEmpty) @@ -765,6 +792,16 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar assert(scheduler.runningStages.isEmpty) assert(scheduler.shuffleToMapStage.isEmpty) assert(scheduler.waitingStages.isEmpty) + assert(scheduler.outputCommitCoordinator.isEmpty) } + + // Nothing in this test should break if the task info's fields are null, but + // OutputCommitCoordinator requires the task info itself to not be null. + private def createFakeTaskInfo(): TaskInfo = { + val info = new TaskInfo(0, 0, 0, 0L, "", "", TaskLocality.ANY, false) + info.finishTime = 1 // to prevent spurious errors in JobProgressListener + info + } + } diff --git a/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala index 437d8693c0b1f..6d25edb7d20dc 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala @@ -18,15 +18,16 @@ package org.apache.spark.scheduler import java.io.{File, FileOutputStream, InputStream, IOException} +import java.net.URI import scala.collection.mutable import scala.io.Source import org.apache.hadoop.fs.Path import org.json4s.jackson.JsonMethods._ -import org.scalatest.{BeforeAndAfter, FunSuite} +import org.scalatest.{FunSuiteLike, BeforeAndAfter, FunSuite} -import org.apache.spark.{Logging, SparkConf, SparkContext} +import org.apache.spark._ import org.apache.spark.deploy.SparkHadoopUtil import org.apache.spark.io._ import org.apache.spark.util.{JsonProtocol, Utils} @@ -38,7 +39,8 @@ import org.apache.spark.util.{JsonProtocol, Utils} * logging events, whether the parsing of the file names is correct, and whether the logged events * can be read and deserialized into actual SparkListenerEvents. */ -class EventLoggingListenerSuite extends FunSuite with BeforeAndAfter with Logging { +class EventLoggingListenerSuite extends FunSuite with LocalSparkContext with BeforeAndAfter + with Logging { import EventLoggingListenerSuite._ private val fileSystem = Utils.getHadoopFileSystem("/", @@ -59,7 +61,7 @@ class EventLoggingListenerSuite extends FunSuite with BeforeAndAfter with Loggin test("Verify log file exist") { // Verify logging directory exists val conf = getLoggingConf(testDirPath) - val eventLogger = new EventLoggingListener("test", testDirPath.toUri().toString(), conf) + val eventLogger = new EventLoggingListener("test", testDirPath.toUri(), conf) eventLogger.start() val logPath = new Path(eventLogger.logPath + EventLoggingListener.IN_PROGRESS) @@ -78,7 +80,7 @@ class EventLoggingListenerSuite extends FunSuite with BeforeAndAfter with Loggin test("Basic event logging with compression") { CompressionCodec.ALL_COMPRESSION_CODECS.foreach { codec => - testEventLogging(compressionCodec = Some(codec)) + testEventLogging(compressionCodec = Some(CompressionCodec.getShortName(codec))) } } @@ -88,25 +90,38 @@ class EventLoggingListenerSuite extends FunSuite with BeforeAndAfter with Loggin test("End-to-end event logging with compression") { CompressionCodec.ALL_COMPRESSION_CODECS.foreach { codec => - testApplicationEventLogging(compressionCodec = Some(codec)) + testApplicationEventLogging(compressionCodec = Some(CompressionCodec.getShortName(codec))) } } test("Log overwriting") { - val log = new FileOutputStream(new File(testDir, "test")) - log.close() - try { - testEventLogging() - assert(false) - } catch { - case e: IOException => - // Expected, since we haven't enabled log overwrite. - } - + val logUri = EventLoggingListener.getLogPath(testDir.toURI, "test") + val logPath = new URI(logUri).getPath + // Create file before writing the event log + new FileOutputStream(new File(logPath)).close() + // Expected IOException, since we haven't enabled log overwrite. + intercept[IOException] { testEventLogging() } // Try again, but enable overwriting. testEventLogging(extraConf = Map("spark.eventLog.overwrite" -> "true")) } + test("Event log name") { + // without compression + assert(s"file:/base-dir/app1" === EventLoggingListener.getLogPath( + Utils.resolveURI("/base-dir"), "app1")) + // with compression + assert(s"file:/base-dir/app1.lzf" === + EventLoggingListener.getLogPath(Utils.resolveURI("/base-dir"), "app1", Some("lzf"))) + // illegal characters in app ID + assert(s"file:/base-dir/a-fine-mind_dollar_bills__1" === + EventLoggingListener.getLogPath(Utils.resolveURI("/base-dir"), + "a fine:mind$dollar{bills}.1")) + // illegal characters in app ID with compression + assert(s"file:/base-dir/a-fine-mind_dollar_bills__1.lz4" === + EventLoggingListener.getLogPath(Utils.resolveURI("/base-dir"), + "a fine:mind$dollar{bills}.1", Some("lz4"))) + } + /* ----------------- * * Actual test logic * * ----------------- */ @@ -125,7 +140,7 @@ class EventLoggingListenerSuite extends FunSuite with BeforeAndAfter with Loggin val conf = getLoggingConf(testDirPath, compressionCodec) extraConf.foreach { case (k, v) => conf.set(k, v) } val logName = compressionCodec.map("test-" + _).getOrElse("test") - val eventLogger = new EventLoggingListener(logName, testDirPath.toUri().toString(), conf) + val eventLogger = new EventLoggingListener(logName, testDirPath.toUri(), conf) val listenerBus = new LiveListenerBus val applicationStart = SparkListenerApplicationStart("Greatest App (N)ever", None, 125L, "Mickey") @@ -133,22 +148,24 @@ class EventLoggingListenerSuite extends FunSuite with BeforeAndAfter with Loggin // A comprehensive test on JSON de/serialization of all events is in JsonProtocolSuite eventLogger.start() - listenerBus.start() + listenerBus.start(sc) listenerBus.addListener(eventLogger) listenerBus.postToAll(applicationStart) listenerBus.postToAll(applicationEnd) eventLogger.stop() // Verify file contains exactly the two events logged - val (logData, version) = EventLoggingListener.openEventLog(new Path(eventLogger.logPath), - fileSystem) + val logData = EventLoggingListener.openEventLog(new Path(eventLogger.logPath), fileSystem) try { val lines = readLines(logData) - assert(lines.size === 2) - assert(lines(0).contains("SparkListenerApplicationStart")) - assert(lines(1).contains("SparkListenerApplicationEnd")) - assert(JsonProtocol.sparkEventFromJson(parse(lines(0))) === applicationStart) - assert(JsonProtocol.sparkEventFromJson(parse(lines(1))) === applicationEnd) + val logStart = SparkListenerLogStart(SPARK_VERSION) + assert(lines.size === 3) + assert(lines(0).contains("SparkListenerLogStart")) + assert(lines(1).contains("SparkListenerApplicationStart")) + assert(lines(2).contains("SparkListenerApplicationEnd")) + assert(JsonProtocol.sparkEventFromJson(parse(lines(0))) === logStart) + assert(JsonProtocol.sparkEventFromJson(parse(lines(1))) === applicationStart) + assert(JsonProtocol.sparkEventFromJson(parse(lines(2))) === applicationEnd) } finally { logData.close() } @@ -159,12 +176,17 @@ class EventLoggingListenerSuite extends FunSuite with BeforeAndAfter with Loggin * This runs a simple Spark job and asserts that the expected events are logged when expected. */ private def testApplicationEventLogging(compressionCodec: Option[String] = None) { + // Set defaultFS to something that would cause an exception, to make sure we don't run + // into SPARK-6688. val conf = getLoggingConf(testDirPath, compressionCodec) + .set("spark.hadoop.fs.defaultFS", "unsupported://example.com") val sc = new SparkContext("local-cluster[2,2,512]", "test", conf) assert(sc.eventLogger.isDefined) val eventLogger = sc.eventLogger.get - val expectedLogDir = testDir.toURI().toString() - assert(eventLogger.logPath.startsWith(expectedLogDir + "/")) + val eventLogPath = eventLogger.logPath + val expectedLogDir = testDir.toURI() + assert(eventLogPath === EventLoggingListener.getLogPath( + expectedLogDir, sc.applicationId, compressionCodec.map(CompressionCodec.getShortName))) // Begin listening for events that trigger asserts val eventExistenceListener = new EventExistenceListener(eventLogger) @@ -178,8 +200,8 @@ class EventLoggingListenerSuite extends FunSuite with BeforeAndAfter with Loggin eventExistenceListener.assertAllCallbacksInvoked() // Make sure expected events exist in the log file. - val (logData, version) = EventLoggingListener.openEventLog(new Path(eventLogger.logPath), - fileSystem) + val logData = EventLoggingListener.openEventLog(new Path(eventLogger.logPath), fileSystem) + val logStart = SparkListenerLogStart(SPARK_VERSION) val lines = readLines(logData) val eventSet = mutable.Set( SparkListenerApplicationStart, @@ -204,6 +226,7 @@ class EventLoggingListenerSuite extends FunSuite with BeforeAndAfter with Loggin } } } + assert(JsonProtocol.sparkEventFromJson(parse(lines(0))) === logStart) assert(eventSet.isEmpty, "The following events are missing: " + eventSet.toSeq) } @@ -245,7 +268,7 @@ class EventLoggingListenerSuite extends FunSuite with BeforeAndAfter with Loggin object EventLoggingListenerSuite { /** Get a SparkConf with event logging enabled. */ - def getLoggingConf(logDir: Path, compressionCodec: Option[String] = None) = { + def getLoggingConf(logDir: Path, compressionCodec: Option[String] = None): SparkConf = { val conf = new SparkConf conf.set("spark.eventLog.enabled", "true") conf.set("spark.eventLog.testing", "true") @@ -257,5 +280,5 @@ object EventLoggingListenerSuite { conf } - def getUniqueApplicationId = "test-" + System.currentTimeMillis + def getUniqueApplicationId: String = "test-" + System.currentTimeMillis } diff --git a/core/src/test/scala/org/apache/spark/scheduler/NotSerializableFakeTask.scala b/core/src/test/scala/org/apache/spark/scheduler/NotSerializableFakeTask.scala index 6b75c98839e03..9b92f8de56759 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/NotSerializableFakeTask.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/NotSerializableFakeTask.scala @@ -24,7 +24,9 @@ import org.apache.spark.TaskContext /** * A Task implementation that fails to serialize. */ -private[spark] class NotSerializableFakeTask(myId: Int, stageId: Int) extends Task[Array[Byte]](stageId, 0) { +private[spark] class NotSerializableFakeTask(myId: Int, stageId: Int) + extends Task[Array[Byte]](stageId, 0) { + override def runTask(context: TaskContext): Array[Byte] = Array.empty[Byte] override def preferredLocations: Seq[TaskLocation] = Seq[TaskLocation]() diff --git a/core/src/test/scala/org/apache/spark/scheduler/OutputCommitCoordinatorSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/OutputCommitCoordinatorSuite.scala new file mode 100644 index 0000000000000..cf97707946706 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/scheduler/OutputCommitCoordinatorSuite.scala @@ -0,0 +1,238 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.scheduler + +import java.io.File +import java.util.concurrent.TimeoutException + +import org.mockito.Matchers +import org.mockito.Mockito._ +import org.mockito.invocation.InvocationOnMock +import org.mockito.stubbing.Answer +import org.scalatest.{BeforeAndAfter, FunSuite} + +import org.apache.hadoop.mapred.{TaskAttemptID, JobConf, TaskAttemptContext, OutputCommitter} + +import org.apache.spark._ +import org.apache.spark.rdd.{RDD, FakeOutputCommitter} +import org.apache.spark.util.Utils + +import scala.concurrent.Await +import scala.concurrent.duration._ +import scala.language.postfixOps + +/** + * Unit tests for the output commit coordination functionality. + * + * The unit test makes both the original task and the speculated task + * attempt to commit, where committing is emulated by creating a + * directory. If both tasks create directories then the end result is + * a failure. + * + * Note that there are some aspects of this test that are less than ideal. + * In particular, the test mocks the speculation-dequeuing logic to always + * dequeue a task and consider it as speculated. Immediately after initially + * submitting the tasks and calling reviveOffers(), reviveOffers() is invoked + * again to pick up the speculated task. This may be hacking the original + * behavior in too much of an unrealistic fashion. + * + * Also, the validation is done by checking the number of files in a directory. + * Ideally, an accumulator would be used for this, where we could increment + * the accumulator in the output committer's commitTask() call. If the call to + * commitTask() was called twice erroneously then the test would ideally fail because + * the accumulator would be incremented twice. + * + * The problem with this test implementation is that when both a speculated task and + * its original counterpart complete, only one of the accumulator's increments is + * captured. This results in a paradox where if the OutputCommitCoordinator logic + * was not in SparkHadoopWriter, the tests would still pass because only one of the + * increments would be captured even though the commit in both tasks was executed + * erroneously. + */ +class OutputCommitCoordinatorSuite extends FunSuite with BeforeAndAfter { + + var outputCommitCoordinator: OutputCommitCoordinator = null + var tempDir: File = null + var sc: SparkContext = null + + before { + tempDir = Utils.createTempDir() + val conf = new SparkConf() + .setMaster("local[4]") + .setAppName(classOf[OutputCommitCoordinatorSuite].getSimpleName) + .set("spark.speculation", "true") + sc = new SparkContext(conf) { + override private[spark] def createSparkEnv( + conf: SparkConf, + isLocal: Boolean, + listenerBus: LiveListenerBus): SparkEnv = { + outputCommitCoordinator = spy(new OutputCommitCoordinator(conf)) + // Use Mockito.spy() to maintain the default infrastructure everywhere else. + // This mocking allows us to control the coordinator responses in test cases. + SparkEnv.createDriverEnv(conf, isLocal, listenerBus, Some(outputCommitCoordinator)) + } + } + // Use Mockito.spy() to maintain the default infrastructure everywhere else + val mockTaskScheduler = spy(sc.taskScheduler.asInstanceOf[TaskSchedulerImpl]) + + doAnswer(new Answer[Unit]() { + override def answer(invoke: InvocationOnMock): Unit = { + // Submit the tasks, then force the task scheduler to dequeue the + // speculated task + invoke.callRealMethod() + mockTaskScheduler.backend.reviveOffers() + } + }).when(mockTaskScheduler).submitTasks(Matchers.any()) + + doAnswer(new Answer[TaskSetManager]() { + override def answer(invoke: InvocationOnMock): TaskSetManager = { + val taskSet = invoke.getArguments()(0).asInstanceOf[TaskSet] + new TaskSetManager(mockTaskScheduler, taskSet, 4) { + var hasDequeuedSpeculatedTask = false + override def dequeueSpeculativeTask( + execId: String, + host: String, + locality: TaskLocality.Value): Option[(Int, TaskLocality.Value)] = { + if (!hasDequeuedSpeculatedTask) { + hasDequeuedSpeculatedTask = true + Some(0, TaskLocality.PROCESS_LOCAL) + } else { + None + } + } + } + } + }).when(mockTaskScheduler).createTaskSetManager(Matchers.any(), Matchers.any()) + + sc.taskScheduler = mockTaskScheduler + val dagSchedulerWithMockTaskScheduler = new DAGScheduler(sc, mockTaskScheduler) + sc.taskScheduler.setDAGScheduler(dagSchedulerWithMockTaskScheduler) + sc.dagScheduler = dagSchedulerWithMockTaskScheduler + } + + after { + sc.stop() + tempDir.delete() + outputCommitCoordinator = null + } + + test("Only one of two duplicate commit tasks should commit") { + val rdd = sc.parallelize(Seq(1), 1) + sc.runJob(rdd, OutputCommitFunctions(tempDir.getAbsolutePath).commitSuccessfully _, + 0 until rdd.partitions.size, allowLocal = false) + assert(tempDir.list().size === 1) + } + + test("If commit fails, if task is retried it should not be locked, and will succeed.") { + val rdd = sc.parallelize(Seq(1), 1) + sc.runJob(rdd, OutputCommitFunctions(tempDir.getAbsolutePath).failFirstCommitAttempt _, + 0 until rdd.partitions.size, allowLocal = false) + assert(tempDir.list().size === 1) + } + + test("Job should not complete if all commits are denied") { + // Create a mock OutputCommitCoordinator that denies all attempts to commit + doReturn(false).when(outputCommitCoordinator).handleAskPermissionToCommit( + Matchers.any(), Matchers.any(), Matchers.any()) + val rdd: RDD[Int] = sc.parallelize(Seq(1), 1) + def resultHandler(x: Int, y: Unit): Unit = {} + val futureAction: SimpleFutureAction[Unit] = sc.submitJob[Int, Unit, Unit](rdd, + OutputCommitFunctions(tempDir.getAbsolutePath).commitSuccessfully, + 0 until rdd.partitions.size, resultHandler, () => Unit) + // It's an error if the job completes successfully even though no committer was authorized, + // so throw an exception if the job was allowed to complete. + intercept[TimeoutException] { + Await.result(futureAction, 5 seconds) + } + assert(tempDir.list().size === 0) + } + + test("Only authorized committer failures can clear the authorized committer lock (SPARK-6614)") { + val stage: Int = 1 + val partition: Long = 2 + val authorizedCommitter: Long = 3 + val nonAuthorizedCommitter: Long = 100 + outputCommitCoordinator.stageStart(stage) + assert(outputCommitCoordinator.canCommit(stage, partition, attempt = authorizedCommitter)) + assert(!outputCommitCoordinator.canCommit(stage, partition, attempt = nonAuthorizedCommitter)) + // The non-authorized committer fails + outputCommitCoordinator.taskCompleted( + stage, partition, attempt = nonAuthorizedCommitter, reason = TaskKilled) + // New tasks should still not be able to commit because the authorized committer has not failed + assert( + !outputCommitCoordinator.canCommit(stage, partition, attempt = nonAuthorizedCommitter + 1)) + // The authorized committer now fails, clearing the lock + outputCommitCoordinator.taskCompleted( + stage, partition, attempt = authorizedCommitter, reason = TaskKilled) + // A new task should now be allowed to become the authorized committer + assert( + outputCommitCoordinator.canCommit(stage, partition, attempt = nonAuthorizedCommitter + 2)) + // There can only be one authorized committer + assert( + !outputCommitCoordinator.canCommit(stage, partition, attempt = nonAuthorizedCommitter + 3)) + } +} + +/** + * Class with methods that can be passed to runJob to test commits with a mock committer. + */ +private case class OutputCommitFunctions(tempDirPath: String) { + + // Mock output committer that simulates a successful commit (after commit is authorized) + private def successfulOutputCommitter = new FakeOutputCommitter { + override def commitTask(context: TaskAttemptContext): Unit = { + Utils.createDirectory(tempDirPath) + } + } + + // Mock output committer that simulates a failed commit (after commit is authorized) + private def failingOutputCommitter = new FakeOutputCommitter { + override def commitTask(taskAttemptContext: TaskAttemptContext) { + throw new RuntimeException + } + } + + def commitSuccessfully(iter: Iterator[Int]): Unit = { + val ctx = TaskContext.get() + runCommitWithProvidedCommitter(ctx, iter, successfulOutputCommitter) + } + + def failFirstCommitAttempt(iter: Iterator[Int]): Unit = { + val ctx = TaskContext.get() + runCommitWithProvidedCommitter(ctx, iter, + if (ctx.attemptNumber == 0) failingOutputCommitter else successfulOutputCommitter) + } + + private def runCommitWithProvidedCommitter( + ctx: TaskContext, + iter: Iterator[Int], + outputCommitter: OutputCommitter): Unit = { + def jobConf = new JobConf { + override def getOutputCommitter(): OutputCommitter = outputCommitter + } + val sparkHadoopWriter = new SparkHadoopWriter(jobConf) { + override def newTaskAttemptContext( + conf: JobConf, + attemptId: TaskAttemptID): TaskAttemptContext = { + mock(classOf[TaskAttemptContext]) + } + } + sparkHadoopWriter.setup(ctx.stageId, ctx.partitionId, ctx.attemptNumber) + sparkHadoopWriter.commit() + } +} diff --git a/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala index 7e360cc6082ec..6de6d2fec622a 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala @@ -18,6 +18,7 @@ package org.apache.spark.scheduler import java.io.{File, PrintWriter} +import java.net.URI import org.json4s.jackson.JsonMethods._ import org.scalatest.{BeforeAndAfter, FunSuite} @@ -61,7 +62,7 @@ class ReplayListenerSuite extends FunSuite with BeforeAndAfter { try { val replayer = new ReplayListenerBus() replayer.addListener(eventMonster) - replayer.replay(logData, SPARK_VERSION) + replayer.replay(logData, logFilePath.toString) } finally { logData.close() } @@ -115,12 +116,12 @@ class ReplayListenerSuite extends FunSuite with BeforeAndAfter { assert(!eventLog.isDir) // Replay events - val (logData, version) = EventLoggingListener.openEventLog(eventLog.getPath(), fileSystem) + val logData = EventLoggingListener.openEventLog(eventLog.getPath(), fileSystem) val eventMonster = new EventMonster(conf) try { val replayer = new ReplayListenerBus() replayer.addListener(eventMonster) - replayer.replay(logData, version) + replayer.replay(logData, eventLog.getPath().toString) } finally { logData.close() } @@ -145,16 +146,9 @@ class ReplayListenerSuite extends FunSuite with BeforeAndAfter { * log the events. */ private class EventMonster(conf: SparkConf) - extends EventLoggingListener("test", "testdir", conf) { + extends EventLoggingListener("test", new URI("testdir"), conf) { override def start() { } } - - private def getCompressionCodec(codecName: String) = { - val conf = new SparkConf - conf.set("spark.io.compression.codec", codecName) - CompressionCodec.createCodec(conf) - } - } diff --git a/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala index 3a41ee8d4ae0c..825c616c0c3e0 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala @@ -46,7 +46,7 @@ class SparkListenerSuite extends FunSuite with LocalSparkContext with Matchers assert(counter.count === 0) // Starting listener bus should flush all buffered events - bus.start() + bus.start(sc) assert(bus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)) assert(counter.count === 5) @@ -58,8 +58,8 @@ class SparkListenerSuite extends FunSuite with LocalSparkContext with Matchers // Listener bus must not be started twice intercept[IllegalStateException] { val bus = new LiveListenerBus - bus.start() - bus.start() + bus.start(sc) + bus.start(sc) } // ... or stopped before starting @@ -85,7 +85,7 @@ class SparkListenerSuite extends FunSuite with LocalSparkContext with Matchers val stopperReturned = new Semaphore(0) class BlockingListener extends SparkListener { - override def onJobEnd(jobEnd: SparkListenerJobEnd) = { + override def onJobEnd(jobEnd: SparkListenerJobEnd): Unit = { listenerStarted.release() listenerWait.acquire() drained = true @@ -96,7 +96,7 @@ class SparkListenerSuite extends FunSuite with LocalSparkContext with Matchers val blockingListener = new BlockingListener bus.addListener(blockingListener) - bus.start() + bus.start(sc) bus.post(SparkListenerJobEnd(0, jobCompletionTime, JobSucceeded)) listenerStarted.acquire() @@ -206,8 +206,9 @@ class SparkListenerSuite extends FunSuite with LocalSparkContext with Matchers sc.addSparkListener(new StatsReportListener) // just to make sure some of the tasks take a noticeable amount of time val w = { i: Int => - if (i == 0) + if (i == 0) { Thread.sleep(100) + } i } @@ -247,12 +248,12 @@ class SparkListenerSuite extends FunSuite with LocalSparkContext with Matchers */ taskInfoMetrics.foreach { case (taskInfo, taskMetrics) => - taskMetrics.resultSize should be > (0l) + taskMetrics.resultSize should be > (0L) if (stageInfo.rddInfos.exists(info => info.name == d2.name || info.name == d3.name)) { taskMetrics.inputMetrics should not be ('defined) taskMetrics.outputMetrics should not be ('defined) taskMetrics.shuffleWriteMetrics should be ('defined) - taskMetrics.shuffleWriteMetrics.get.shuffleBytesWritten should be > (0l) + taskMetrics.shuffleWriteMetrics.get.shuffleBytesWritten should be > (0L) } if (stageInfo.rddInfos.exists(_.name == d4.name)) { taskMetrics.shuffleReadMetrics should be ('defined) @@ -260,7 +261,7 @@ class SparkListenerSuite extends FunSuite with LocalSparkContext with Matchers sm.totalBlocksFetched should be (128) sm.localBlocksFetched should be (128) sm.remoteBlocksFetched should be (0) - sm.remoteBytesRead should be (0l) + sm.remoteBytesRead should be (0L) } } } @@ -347,7 +348,7 @@ class SparkListenerSuite extends FunSuite with LocalSparkContext with Matchers bus.addListener(badListener) bus.addListener(jobCounter1) bus.addListener(jobCounter2) - bus.start() + bus.start(sc) // Post events to all listeners, and wait until the queue is drained (1 to 5).foreach { _ => bus.post(SparkListenerJobEnd(0, jobCompletionTime, JobSucceeded)) } @@ -406,12 +407,12 @@ class SparkListenerSuite extends FunSuite with LocalSparkContext with Matchers val startedGettingResultTasks = new mutable.HashSet[Int]() val endedTasks = new mutable.HashSet[Int]() - override def onTaskStart(taskStart: SparkListenerTaskStart) = synchronized { + override def onTaskStart(taskStart: SparkListenerTaskStart): Unit = synchronized { startedTasks += taskStart.taskInfo.index notify() } - override def onTaskEnd(taskEnd: SparkListenerTaskEnd) = synchronized { + override def onTaskEnd(taskEnd: SparkListenerTaskEnd): Unit = synchronized { endedTasks += taskEnd.taskInfo.index notify() } @@ -425,7 +426,7 @@ class SparkListenerSuite extends FunSuite with LocalSparkContext with Matchers * A simple listener that throws an exception on job end. */ private class BadListener extends SparkListener { - override def onJobEnd(jobEnd: SparkListenerJobEnd) = { throw new Exception } + override def onJobEnd(jobEnd: SparkListenerJobEnd): Unit = { throw new Exception } } } @@ -438,10 +439,10 @@ class SparkListenerSuite extends FunSuite with LocalSparkContext with Matchers */ private class BasicJobCounter extends SparkListener { var count = 0 - override def onJobEnd(job: SparkListenerJobEnd) = count += 1 + override def onJobEnd(job: SparkListenerJobEnd): Unit = count += 1 } private class ListenerThatAcceptsSparkConf(conf: SparkConf) extends SparkListener { var count = 0 - override def onJobEnd(job: SparkListenerJobEnd) = count += 1 + override def onJobEnd(job: SparkListenerJobEnd): Unit = count += 1 } diff --git a/core/src/test/scala/org/apache/spark/scheduler/TaskSchedulerImplSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/TaskSchedulerImplSuite.scala index add13f5b21765..ffa4381969b68 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/TaskSchedulerImplSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/TaskSchedulerImplSuite.scala @@ -17,8 +17,6 @@ package org.apache.spark.scheduler -import java.util.Properties - import org.scalatest.FunSuite import org.apache.spark._ @@ -27,7 +25,7 @@ class FakeSchedulerBackend extends SchedulerBackend { def start() {} def stop() {} def reviveOffers() {} - def defaultParallelism() = 1 + def defaultParallelism(): Int = 1 } class TaskSchedulerImplSuite extends FunSuite with LocalSparkContext with Logging { @@ -115,7 +113,8 @@ class TaskSchedulerImplSuite extends FunSuite with LocalSparkContext with Loggin } val numFreeCores = 1 taskScheduler.setDAGScheduler(dagScheduler) - var taskSet = new TaskSet(Array(new NotSerializableFakeTask(1, 0), new NotSerializableFakeTask(0, 1)), 0, 0, 0, null) + val taskSet = new TaskSet( + Array(new NotSerializableFakeTask(1, 0), new NotSerializableFakeTask(0, 1)), 0, 0, 0, null) val multiCoreWorkerOffers = Seq(new WorkerOffer("executor0", "host0", taskCpus), new WorkerOffer("executor1", "host1", numFreeCores)) taskScheduler.submitTasks(taskSet) @@ -123,7 +122,8 @@ class TaskSchedulerImplSuite extends FunSuite with LocalSparkContext with Loggin assert(0 === taskDescriptions.length) // Now check that we can still submit tasks - // Even if one of the tasks has not-serializable tasks, the other task set should still be processed without error + // Even if one of the tasks has not-serializable tasks, the other task set should + // still be processed without error taskScheduler.submitTasks(taskSet) taskScheduler.submitTasks(FakeTask.createTaskSet(1)) taskDescriptions = taskScheduler.resourceOffers(multiCoreWorkerOffers).flatten diff --git a/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala index 59580561cb45a..6198cea46ddf8 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala @@ -17,7 +17,6 @@ package org.apache.spark.scheduler -import java.io.{ObjectInputStream, ObjectOutputStream, IOException} import java.util.Random import scala.collection.mutable.ArrayBuffer @@ -27,7 +26,7 @@ import org.scalatest.FunSuite import org.apache.spark._ import org.apache.spark.executor.TaskMetrics -import org.apache.spark.util.FakeClock +import org.apache.spark.util.{ManualClock, Utils} class FakeDAGScheduler(sc: SparkContext, taskScheduler: FakeTaskScheduler) extends DAGScheduler(sc) { @@ -67,7 +66,7 @@ object FakeRackUtil { hostToRack(host) = rack } - def getRackForHost(host: String) = { + def getRackForHost(host: String): Option[String] = { hostToRack.get(host) } } @@ -152,7 +151,7 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { private val conf = new SparkConf - val LOCALITY_WAIT = conf.getLong("spark.locality.wait", 3000) + val LOCALITY_WAIT_MS = conf.getTimeAsMs("spark.locality.wait", "3s") val MAX_TASK_FAILURES = 4 override def beforeEach() { @@ -164,7 +163,7 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { sc = new SparkContext("local", "test") val sched = new FakeTaskScheduler(sc, ("exec1", "host1")) val taskSet = FakeTask.createTaskSet(1) - val clock = new FakeClock + val clock = new ManualClock val manager = new TaskSetManager(sched, taskSet, MAX_TASK_FAILURES, clock) // Offer a host with NO_PREF as the constraint, @@ -213,7 +212,7 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { sc = new SparkContext("local", "test") val sched = new FakeTaskScheduler(sc, ("execA", "host1"), ("execC", "host2")) val taskSet = FakeTask.createTaskSet(1, Seq(TaskLocation("host1", "execB"))) - val clock = new FakeClock + val clock = new ManualClock val manager = new TaskSetManager(sched, taskSet, MAX_TASK_FAILURES, clock) // An executor that is not NODE_LOCAL should be rejected. @@ -234,13 +233,13 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { Seq(TaskLocation("host1"), TaskLocation("host2", "exec2")), Seq() // Last task has no locality prefs ) - val clock = new FakeClock + val clock = new ManualClock val manager = new TaskSetManager(sched, taskSet, MAX_TASK_FAILURES, clock) // First offer host1, exec1: first task should be chosen assert(manager.resourceOffer("exec1", "host1", ANY).get.index === 0) assert(manager.resourceOffer("exec1", "host1", PROCESS_LOCAL) == None) - clock.advance(LOCALITY_WAIT) + clock.advance(LOCALITY_WAIT_MS) // Offer host1, exec1 again, at NODE_LOCAL level: the node local (task 2) should // get chosen before the noPref task assert(manager.resourceOffer("exec1", "host1", NODE_LOCAL).get.index == 2) @@ -251,7 +250,7 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { // Offer host2, exec3 again, at NODE_LOCAL level: we should get noPref task // after failing to find a node_Local task assert(manager.resourceOffer("exec2", "host2", NODE_LOCAL) == None) - clock.advance(LOCALITY_WAIT) + clock.advance(LOCALITY_WAIT_MS) assert(manager.resourceOffer("exec2", "host2", NO_PREF).get.index == 3) } @@ -263,7 +262,7 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { Seq(TaskLocation("host2", "exec3")), Seq() // Last task has no locality prefs ) - val clock = new FakeClock + val clock = new ManualClock val manager = new TaskSetManager(sched, taskSet, MAX_TASK_FAILURES, clock) // First offer host1, exec1: first task should be chosen assert(manager.resourceOffer("exec1", "host1", PROCESS_LOCAL).get.index === 0) @@ -283,7 +282,7 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { Seq(TaskLocation("host3")), Seq(TaskLocation("host2")) ) - val clock = new FakeClock + val clock = new ManualClock val manager = new TaskSetManager(sched, taskSet, MAX_TASK_FAILURES, clock) // First offer host1: first task should be chosen @@ -292,7 +291,7 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { // Offer host1 again: nothing should get chosen assert(manager.resourceOffer("exec1", "host1", ANY) === None) - clock.advance(LOCALITY_WAIT) + clock.advance(LOCALITY_WAIT_MS) // Offer host1 again: second task (on host2) should get chosen assert(manager.resourceOffer("exec1", "host1", ANY).get.index === 1) @@ -306,7 +305,7 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { // Now that we've launched a local task, we should no longer launch the task for host3 assert(manager.resourceOffer("exec2", "host2", ANY) === None) - clock.advance(LOCALITY_WAIT) + clock.advance(LOCALITY_WAIT_MS) // After another delay, we can go ahead and launch that task non-locally assert(manager.resourceOffer("exec2", "host2", ANY).get.index === 3) @@ -321,14 +320,14 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { Seq(TaskLocation("host2")), Seq(TaskLocation("host3")) ) - val clock = new FakeClock + val clock = new ManualClock val manager = new TaskSetManager(sched, taskSet, MAX_TASK_FAILURES, clock) // First offer host1: first task should be chosen assert(manager.resourceOffer("exec1", "host1", ANY).get.index === 0) - // After this, nothing should get chosen, because we have separated tasks with unavailable preference - // from the noPrefPendingTasks + // After this, nothing should get chosen, because we have separated tasks with unavailable + // preference from the noPrefPendingTasks assert(manager.resourceOffer("exec1", "host1", ANY) === None) // Now mark host2 as dead @@ -338,7 +337,7 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { // nothing should be chosen assert(manager.resourceOffer("exec1", "host1", ANY) === None) - clock.advance(LOCALITY_WAIT * 2) + clock.advance(LOCALITY_WAIT_MS * 2) // task 1 and 2 would be scheduled as nonLocal task assert(manager.resourceOffer("exec1", "host1", ANY).get.index === 1) @@ -353,7 +352,7 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { sc = new SparkContext("local", "test") val sched = new FakeTaskScheduler(sc, ("exec1", "host1")) val taskSet = FakeTask.createTaskSet(1) - val clock = new FakeClock + val clock = new ManualClock val manager = new TaskSetManager(sched, taskSet, MAX_TASK_FAILURES, clock) assert(manager.resourceOffer("exec1", "host1", ANY).get.index === 0) @@ -370,7 +369,7 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { sc = new SparkContext("local", "test") val sched = new FakeTaskScheduler(sc, ("exec1", "host1")) val taskSet = FakeTask.createTaskSet(1) - val clock = new FakeClock + val clock = new ManualClock val manager = new TaskSetManager(sched, taskSet, MAX_TASK_FAILURES, clock) // Fail the task MAX_TASK_FAILURES times, and check that the task set is aborted @@ -402,7 +401,7 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { ("exec1.1", "host1"), ("exec2", "host2")) // affinity to exec1 on host1 - which we will fail. val taskSet = FakeTask.createTaskSet(1, Seq(TaskLocation("host1", "exec1"))) - val clock = new FakeClock + val clock = new ManualClock val manager = new TaskSetManager(sched, taskSet, 4, clock) { @@ -486,7 +485,7 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { Seq(TaskLocation("host1", "execB")), Seq(TaskLocation("host2", "execC")), Seq()) - val clock = new FakeClock + val clock = new ManualClock val manager = new TaskSetManager(sched, taskSet, MAX_TASK_FAILURES, clock) // Only ANY is valid assert(manager.myLocalityLevels.sameElements(Array(NO_PREF, ANY))) @@ -499,7 +498,8 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { sched.addExecutor("execC", "host2") manager.executorAdded() // Valid locality should contain PROCESS_LOCAL, NODE_LOCAL, RACK_LOCAL and ANY - assert(manager.myLocalityLevels.sameElements(Array(PROCESS_LOCAL, NODE_LOCAL, NO_PREF, RACK_LOCAL, ANY))) + assert(manager.myLocalityLevels.sameElements( + Array(PROCESS_LOCAL, NODE_LOCAL, NO_PREF, RACK_LOCAL, ANY))) // test if the valid locality is recomputed when the executor is lost sched.removeExecutor("execC") manager.executorLost("execC", "host2") @@ -522,12 +522,12 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { val taskSet = FakeTask.createTaskSet(2, Seq(TaskLocation("host1", "execA")), Seq(TaskLocation("host1", "execA"))) - val clock = new FakeClock + val clock = new ManualClock val manager = new TaskSetManager(sched, taskSet, MAX_TASK_FAILURES, clock) assert(manager.myLocalityLevels.sameElements(Array(PROCESS_LOCAL, NODE_LOCAL, RACK_LOCAL, ANY))) // Set allowed locality to ANY - clock.advance(LOCALITY_WAIT * 3) + clock.advance(LOCALITY_WAIT_MS * 3) // Offer host3 // No task is scheduled if we restrict locality to RACK_LOCAL assert(manager.resourceOffer("execC", "host3", RACK_LOCAL) === None) @@ -569,7 +569,8 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { sc = new SparkContext("local", "test") val sched = new FakeTaskScheduler(sc, ("exec1", "host1")) - val taskSet = new TaskSet(Array(new NotSerializableFakeTask(1, 0), new NotSerializableFakeTask(0, 1)), 0, 0, 0, null) + val taskSet = new TaskSet( + Array(new NotSerializableFakeTask(1, 0), new NotSerializableFakeTask(0, 1)), 0, 0, 0, null) val manager = new TaskSetManager(sched, taskSet, MAX_TASK_FAILURES) intercept[TaskNotSerializableException] { @@ -582,7 +583,7 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { val conf = new SparkConf().set("spark.driver.maxResultSize", "2m") sc = new SparkContext("local", "test", conf) - def genBytes(size: Int) = { (x: Int) => + def genBytes(size: Int): (Int) => Array[Byte] = { (x: Int) => val bytes = Array.ofDim[Byte](size) scala.util.Random.nextBytes(bytes) bytes @@ -605,13 +606,14 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { test("speculative and noPref task should be scheduled after node-local") { sc = new SparkContext("local", "test") - val sched = new FakeTaskScheduler(sc, ("execA", "host1"), ("execB", "host2"), ("execC", "host3")) + val sched = new FakeTaskScheduler( + sc, ("execA", "host1"), ("execB", "host2"), ("execC", "host3")) val taskSet = FakeTask.createTaskSet(4, Seq(TaskLocation("host1", "execA")), Seq(TaskLocation("host2"), TaskLocation("host1")), Seq(), Seq(TaskLocation("host3", "execC"))) - val clock = new FakeClock + val clock = new ManualClock val manager = new TaskSetManager(sched, taskSet, MAX_TASK_FAILURES, clock) assert(manager.resourceOffer("execA", "host1", PROCESS_LOCAL).get.index === 0) @@ -619,25 +621,27 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { assert(manager.resourceOffer("execA", "host1", NO_PREF).get.index == 1) manager.speculatableTasks += 1 - clock.advance(LOCALITY_WAIT) + clock.advance(LOCALITY_WAIT_MS) // schedule the nonPref task assert(manager.resourceOffer("execA", "host1", NO_PREF).get.index === 2) // schedule the speculative task assert(manager.resourceOffer("execB", "host2", NO_PREF).get.index === 1) - clock.advance(LOCALITY_WAIT * 3) + clock.advance(LOCALITY_WAIT_MS * 3) // schedule non-local tasks assert(manager.resourceOffer("execB", "host2", ANY).get.index === 3) } - test("node-local tasks should be scheduled right away when there are only node-local and no-preference tasks") { + test("node-local tasks should be scheduled right away " + + "when there are only node-local and no-preference tasks") { sc = new SparkContext("local", "test") - val sched = new FakeTaskScheduler(sc, ("execA", "host1"), ("execB", "host2"), ("execC", "host3")) + val sched = new FakeTaskScheduler( + sc, ("execA", "host1"), ("execB", "host2"), ("execC", "host3")) val taskSet = FakeTask.createTaskSet(4, Seq(TaskLocation("host1")), Seq(TaskLocation("host2")), Seq(), Seq(TaskLocation("host3"))) - val clock = new FakeClock + val clock = new ManualClock val manager = new TaskSetManager(sched, taskSet, MAX_TASK_FAILURES, clock) // node-local tasks are scheduled without delay @@ -650,7 +654,8 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { assert(manager.resourceOffer("execA", "host3", NO_PREF).get.index === 2) } - test("SPARK-4939: node-local tasks should be scheduled right after process-local tasks finished") { + test("SPARK-4939: node-local tasks should be scheduled right after process-local tasks finished") + { sc = new SparkContext("local", "test") val sched = new FakeTaskScheduler(sc, ("execA", "host1"), ("execB", "host2")) val taskSet = FakeTask.createTaskSet(4, @@ -658,7 +663,7 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { Seq(TaskLocation("host2")), Seq(ExecutorCacheTaskLocation("host1", "execA")), Seq(ExecutorCacheTaskLocation("host2", "execB"))) - val clock = new FakeClock + val clock = new ManualClock val manager = new TaskSetManager(sched, taskSet, MAX_TASK_FAILURES, clock) // process-local tasks are scheduled first @@ -678,7 +683,7 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { Seq(), Seq(ExecutorCacheTaskLocation("host1", "execA")), Seq(ExecutorCacheTaskLocation("host2", "execB"))) - val clock = new FakeClock + val clock = new ManualClock val manager = new TaskSetManager(sched, taskSet, MAX_TASK_FAILURES, clock) // process-local tasks are scheduled first @@ -698,7 +703,7 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { val taskSet = FakeTask.createTaskSet(2, Seq(TaskLocation("host1", "execA")), Seq(TaskLocation("host2", "execB.1"))) - val clock = new FakeClock + val clock = new ManualClock val manager = new TaskSetManager(sched, taskSet, MAX_TASK_FAILURES, clock) // Only ANY is valid assert(manager.myLocalityLevels.sameElements(Array(ANY))) @@ -710,13 +715,13 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { // Valid locality should contain PROCESS_LOCAL, NODE_LOCAL and ANY assert(manager.myLocalityLevels.sameElements(Array(PROCESS_LOCAL, NODE_LOCAL, ANY))) assert(manager.resourceOffer("execA", "host1", ANY) !== None) - clock.advance(LOCALITY_WAIT * 4) + clock.advance(LOCALITY_WAIT_MS * 4) assert(manager.resourceOffer("execB.2", "host2", ANY) !== None) sched.removeExecutor("execA") sched.removeExecutor("execB.2") manager.executorLost("execA", "host1") manager.executorLost("execB.2", "host2") - clock.advance(LOCALITY_WAIT * 4) + clock.advance(LOCALITY_WAIT_MS * 4) sched.addExecutor("execC", "host3") manager.executorAdded() // Prior to the fix, this line resulted in an ArrayIndexOutOfBoundsException: @@ -732,7 +737,7 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { Seq(HostTaskLocation("host1")), Seq(HostTaskLocation("host2")), Seq(HDFSCacheTaskLocation("host3"))) - val clock = new FakeClock + val clock = new ManualClock val manager = new TaskSetManager(sched, taskSet, MAX_TASK_FAILURES, clock) assert(manager.myLocalityLevels.sameElements(Array(PROCESS_LOCAL, NODE_LOCAL, ANY))) sched.removeExecutor("execA") diff --git a/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MemoryUtilsSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MemoryUtilsSuite.scala new file mode 100644 index 0000000000000..3fa0115e68259 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MemoryUtilsSuite.scala @@ -0,0 +1,47 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.scheduler.cluster.mesos + +import org.mockito.Mockito._ +import org.scalatest.FunSuite +import org.scalatest.mock.MockitoSugar + +import org.apache.spark.{SparkConf, SparkContext} + +class MemoryUtilsSuite extends FunSuite with MockitoSugar { + test("MesosMemoryUtils should always override memoryOverhead when it's set") { + val sparkConf = new SparkConf + + val sc = mock[SparkContext] + when(sc.conf).thenReturn(sparkConf) + + // 384 > sc.executorMemory * 0.1 => 512 + 384 = 896 + when(sc.executorMemory).thenReturn(512) + assert(MemoryUtils.calculateTotalMemory(sc) === 896) + + // 384 < sc.executorMemory * 0.1 => 4096 + (4096 * 0.1) = 4505.6 + when(sc.executorMemory).thenReturn(4096) + assert(MemoryUtils.calculateTotalMemory(sc) === 4505) + + // set memoryOverhead + sparkConf.set("spark.mesos.executor.memoryOverhead", "100") + assert(MemoryUtils.calculateTotalMemory(sc) === 4196) + sparkConf.set("spark.mesos.executor.memoryOverhead", "400") + assert(MemoryUtils.calculateTotalMemory(sc) === 4496) + } +} diff --git a/core/src/test/scala/org/apache/spark/scheduler/mesos/MesosSchedulerBackendSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala similarity index 50% rename from core/src/test/scala/org/apache/spark/scheduler/mesos/MesosSchedulerBackendSuite.scala rename to core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala index 46ab02bfef780..cdd7be0fbe5dd 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/mesos/MesosSchedulerBackendSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala @@ -15,62 +15,66 @@ * limitations under the License. */ -package org.apache.spark.scheduler.mesos +package org.apache.spark.scheduler.cluster.mesos -import org.apache.spark.executor.MesosExecutorBackend -import org.scalatest.FunSuite -import org.apache.spark.{SparkConf, SparkContext, LocalSparkContext} -import org.apache.spark.scheduler.{SparkListenerExecutorAdded, LiveListenerBus, - TaskDescription, WorkerOffer, TaskSchedulerImpl} -import org.apache.spark.scheduler.cluster.ExecutorInfo -import org.apache.spark.scheduler.cluster.mesos.{MemoryUtils, MesosSchedulerBackend} -import org.apache.mesos.SchedulerDriver -import org.apache.mesos.Protos.{ExecutorInfo => MesosExecutorInfo, _} -import org.apache.mesos.Protos.Value.Scalar -import org.easymock.{Capture, EasyMock} import java.nio.ByteBuffer -import java.util.Collections import java.util -import org.scalatest.mock.EasyMockSugar +import java.util.Collections import scala.collection.mutable import scala.collection.mutable.ArrayBuffer -class MesosSchedulerBackendSuite extends FunSuite with LocalSparkContext with EasyMockSugar { +import org.apache.mesos.Protos.Value.Scalar +import org.apache.mesos.Protos._ +import org.apache.mesos.SchedulerDriver +import org.mockito.Matchers._ +import org.mockito.Mockito._ +import org.mockito.{ArgumentCaptor, Matchers} +import org.scalatest.FunSuite +import org.scalatest.mock.MockitoSugar + +import org.apache.spark.executor.MesosExecutorBackend +import org.apache.spark.scheduler.cluster.ExecutorInfo +import org.apache.spark.scheduler.{LiveListenerBus, SparkListenerExecutorAdded, + TaskDescription, TaskSchedulerImpl, WorkerOffer} +import org.apache.spark.{LocalSparkContext, SparkConf, SparkContext} + +class MesosSchedulerBackendSuite extends FunSuite with LocalSparkContext with MockitoSugar { test("check spark-class location correctly") { val conf = new SparkConf conf.set("spark.mesos.executor.home" , "/mesos-home") - val listenerBus = EasyMock.createMock(classOf[LiveListenerBus]) - listenerBus.post(SparkListenerExecutorAdded(EasyMock.anyLong, "s1", new ExecutorInfo("host1", 2, Map.empty))) - EasyMock.replay(listenerBus) - - val sc = EasyMock.createMock(classOf[SparkContext]) - EasyMock.expect(sc.getSparkHome()).andReturn(Option("/spark-home")).anyTimes() - EasyMock.expect(sc.conf).andReturn(conf).anyTimes() - EasyMock.expect(sc.executorEnvs).andReturn(new mutable.HashMap).anyTimes() - EasyMock.expect(sc.executorMemory).andReturn(100).anyTimes() - EasyMock.expect(sc.listenerBus).andReturn(listenerBus) - EasyMock.replay(sc) - val taskScheduler = EasyMock.createMock(classOf[TaskSchedulerImpl]) - EasyMock.expect(taskScheduler.CPUS_PER_TASK).andReturn(2).anyTimes() - EasyMock.replay(taskScheduler) + val listenerBus = mock[LiveListenerBus] + listenerBus.post( + SparkListenerExecutorAdded(anyLong, "s1", new ExecutorInfo("host1", 2, Map.empty))) + + val sc = mock[SparkContext] + when(sc.getSparkHome()).thenReturn(Option("/spark-home")) + + when(sc.conf).thenReturn(conf) + when(sc.executorEnvs).thenReturn(new mutable.HashMap[String, String]) + when(sc.executorMemory).thenReturn(100) + when(sc.listenerBus).thenReturn(listenerBus) + val taskScheduler = mock[TaskSchedulerImpl] + when(taskScheduler.CPUS_PER_TASK).thenReturn(2) val mesosSchedulerBackend = new MesosSchedulerBackend(taskScheduler, sc, "master") // uri is null. val executorInfo = mesosSchedulerBackend.createExecutorInfo("test-id") - assert(executorInfo.getCommand.getValue === s" /mesos-home/bin/spark-class ${classOf[MesosExecutorBackend].getName}") + assert(executorInfo.getCommand.getValue === + s" /mesos-home/bin/spark-class ${classOf[MesosExecutorBackend].getName}") // uri exists. conf.set("spark.executor.uri", "hdfs:///test-app-1.0.0.tgz") val executorInfo1 = mesosSchedulerBackend.createExecutorInfo("test-id") - assert(executorInfo1.getCommand.getValue === s"cd test-app-1*; ./bin/spark-class ${classOf[MesosExecutorBackend].getName}") + assert(executorInfo1.getCommand.getValue === + s"cd test-app-1*; ./bin/spark-class ${classOf[MesosExecutorBackend].getName}") } test("mesos resource offers result in launching tasks") { - def createOffer(id: Int, mem: Int, cpu: Int) = { + def createOffer(id: Int, mem: Int, cpu: Int): Offer = { val builder = Offer.newBuilder() builder.addResourcesBuilder() .setName("mem") @@ -80,24 +84,25 @@ class MesosSchedulerBackendSuite extends FunSuite with LocalSparkContext with Ea .setName("cpus") .setType(Value.Type.SCALAR) .setScalar(Scalar.newBuilder().setValue(cpu)) - builder.setId(OfferID.newBuilder().setValue(s"o${id.toString}").build()).setFrameworkId(FrameworkID.newBuilder().setValue("f1")) - .setSlaveId(SlaveID.newBuilder().setValue(s"s${id.toString}")).setHostname(s"host${id.toString}").build() + builder.setId(OfferID.newBuilder().setValue(s"o${id.toString}").build()) + .setFrameworkId(FrameworkID.newBuilder().setValue("f1")) + .setSlaveId(SlaveID.newBuilder().setValue(s"s${id.toString}")) + .setHostname(s"host${id.toString}").build() } - val driver = EasyMock.createMock(classOf[SchedulerDriver]) - val taskScheduler = EasyMock.createMock(classOf[TaskSchedulerImpl]) + val driver = mock[SchedulerDriver] + val taskScheduler = mock[TaskSchedulerImpl] - val listenerBus = EasyMock.createMock(classOf[LiveListenerBus]) - listenerBus.post(SparkListenerExecutorAdded(EasyMock.anyLong, "s1", new ExecutorInfo("host1", 2, Map.empty))) - EasyMock.replay(listenerBus) + val listenerBus = mock[LiveListenerBus] + listenerBus.post( + SparkListenerExecutorAdded(anyLong, "s1", new ExecutorInfo("host1", 2, Map.empty))) - val sc = EasyMock.createMock(classOf[SparkContext]) - EasyMock.expect(sc.executorMemory).andReturn(100).anyTimes() - EasyMock.expect(sc.getSparkHome()).andReturn(Option("/path")).anyTimes() - EasyMock.expect(sc.executorEnvs).andReturn(new mutable.HashMap).anyTimes() - EasyMock.expect(sc.conf).andReturn(new SparkConf).anyTimes() - EasyMock.expect(sc.listenerBus).andReturn(listenerBus) - EasyMock.replay(sc) + val sc = mock[SparkContext] + when(sc.executorMemory).thenReturn(100) + when(sc.getSparkHome()).thenReturn(Option("/path")) + when(sc.executorEnvs).thenReturn(new mutable.HashMap[String, String]) + when(sc.conf).thenReturn(new SparkConf) + when(sc.listenerBus).thenReturn(listenerBus) val minMem = MemoryUtils.calculateTotalMemory(sc).toInt val minCpu = 4 @@ -113,33 +118,37 @@ class MesosSchedulerBackendSuite extends FunSuite with LocalSparkContext with Ea expectedWorkerOffers.append(new WorkerOffer( mesosOffers.get(0).getSlaveId.getValue, mesosOffers.get(0).getHostname, - 2 + (minCpu - backend.mesosExecutorCores).toInt )) expectedWorkerOffers.append(new WorkerOffer( mesosOffers.get(2).getSlaveId.getValue, mesosOffers.get(2).getHostname, - 2 + (minCpu - backend.mesosExecutorCores).toInt )) val taskDesc = new TaskDescription(1L, 0, "s1", "n1", 0, ByteBuffer.wrap(new Array[Byte](0))) - EasyMock.expect(taskScheduler.resourceOffers(EasyMock.eq(expectedWorkerOffers))).andReturn(Seq(Seq(taskDesc))) - EasyMock.expect(taskScheduler.CPUS_PER_TASK).andReturn(2).anyTimes() - EasyMock.replay(taskScheduler) + when(taskScheduler.resourceOffers(expectedWorkerOffers)).thenReturn(Seq(Seq(taskDesc))) + when(taskScheduler.CPUS_PER_TASK).thenReturn(2) - val capture = new Capture[util.Collection[TaskInfo]] - EasyMock.expect( + val capture = ArgumentCaptor.forClass(classOf[util.Collection[TaskInfo]]) + when( driver.launchTasks( - EasyMock.eq(Collections.singleton(mesosOffers.get(0).getId)), - EasyMock.capture(capture), - EasyMock.anyObject(classOf[Filters]) + Matchers.eq(Collections.singleton(mesosOffers.get(0).getId)), + capture.capture(), + any(classOf[Filters]) ) - ).andReturn(Status.valueOf(1)).once - EasyMock.expect(driver.declineOffer(mesosOffers.get(1).getId)).andReturn(Status.valueOf(1)).times(1) - EasyMock.expect(driver.declineOffer(mesosOffers.get(2).getId)).andReturn(Status.valueOf(1)).times(1) - EasyMock.replay(driver) + ).thenReturn(Status.valueOf(1)) + when(driver.declineOffer(mesosOffers.get(1).getId)).thenReturn(Status.valueOf(1)) + when(driver.declineOffer(mesosOffers.get(2).getId)).thenReturn(Status.valueOf(1)) backend.resourceOffers(driver, mesosOffers) - EasyMock.verify(driver) + verify(driver, times(1)).launchTasks( + Matchers.eq(Collections.singleton(mesosOffers.get(0).getId)), + capture.capture(), + any(classOf[Filters]) + ) + verify(driver, times(1)).declineOffer(mesosOffers.get(1).getId) + verify(driver, times(1)).declineOffer(mesosOffers.get(2).getId) assert(capture.getValue.size() == 1) val taskInfo = capture.getValue.iterator().next() assert(taskInfo.getName.equals("n1")) @@ -151,15 +160,13 @@ class MesosSchedulerBackendSuite extends FunSuite with LocalSparkContext with Ea // Unwanted resources offered on an existing node. Make sure they are declined val mesosOffers2 = new java.util.ArrayList[Offer] mesosOffers2.add(createOffer(1, minMem, minCpu)) - EasyMock.reset(taskScheduler) - EasyMock.reset(driver) - EasyMock.expect(taskScheduler.resourceOffers(EasyMock.anyObject(classOf[Seq[WorkerOffer]])).andReturn(Seq(Seq()))) - EasyMock.expect(taskScheduler.CPUS_PER_TASK).andReturn(2).anyTimes() - EasyMock.replay(taskScheduler) - EasyMock.expect(driver.declineOffer(mesosOffers2.get(0).getId)).andReturn(Status.valueOf(1)).times(1) - EasyMock.replay(driver) + reset(taskScheduler) + reset(driver) + when(taskScheduler.resourceOffers(any(classOf[Seq[WorkerOffer]]))).thenReturn(Seq(Seq())) + when(taskScheduler.CPUS_PER_TASK).thenReturn(2) + when(driver.declineOffer(mesosOffers2.get(0).getId)).thenReturn(Status.valueOf(1)) backend.resourceOffers(driver, mesosOffers2) - EasyMock.verify(driver) + verify(driver, times(1)).declineOffer(mesosOffers2.get(0).getId) } } diff --git a/core/src/test/scala/org/apache/spark/scheduler/mesos/MesosTaskLaunchDataSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosTaskLaunchDataSuite.scala similarity index 92% rename from core/src/test/scala/org/apache/spark/scheduler/mesos/MesosTaskLaunchDataSuite.scala rename to core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosTaskLaunchDataSuite.scala index 86a42a7398e4d..eebcba40f8a1c 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/mesos/MesosTaskLaunchDataSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosTaskLaunchDataSuite.scala @@ -15,14 +15,12 @@ * limitations under the License. */ -package org.apache.spark.scheduler.mesos +package org.apache.spark.scheduler.cluster.mesos import java.nio.ByteBuffer import org.scalatest.FunSuite -import org.apache.spark.scheduler.cluster.mesos.MesosTaskLaunchData - class MesosTaskLaunchDataSuite extends FunSuite { test("serialize and deserialize data must be same") { val serializedTask = ByteBuffer.allocate(40) diff --git a/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala b/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala index a70f67af2e62e..b070a54aa989b 100644 --- a/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala +++ b/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala @@ -23,9 +23,10 @@ import scala.reflect.ClassTag import com.esotericsoftware.kryo.Kryo import org.scalatest.FunSuite -import org.apache.spark.{SparkConf, SharedSparkContext} +import org.apache.spark.{SharedSparkContext, SparkConf} +import org.apache.spark.scheduler.HighlyCompressedMapStatus import org.apache.spark.serializer.KryoTest._ - +import org.apache.spark.storage.BlockManagerId class KryoSerializerSuite extends FunSuite with SharedSparkContext { conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer") @@ -105,7 +106,9 @@ class KryoSerializerSuite extends FunSuite with SharedSparkContext { check(mutable.HashMap(1 -> "one", 2 -> "two")) check(mutable.HashMap("one" -> 1, "two" -> 2)) check(List(Some(mutable.HashMap(1->1, 2->2)), None, Some(mutable.HashMap(3->4)))) - check(List(mutable.HashMap("one" -> 1, "two" -> 2),mutable.HashMap(1->"one",2->"two",3->"three"))) + check(List( + mutable.HashMap("one" -> 1, "two" -> 2), + mutable.HashMap(1->"one",2->"two",3->"three"))) } test("ranges") { @@ -168,7 +171,10 @@ class KryoSerializerSuite extends FunSuite with SharedSparkContext { test("kryo with collect") { val control = 1 :: 2 :: Nil - val result = sc.parallelize(control, 2).map(new ClassWithoutNoArgConstructor(_)).collect().map(_.x) + val result = sc.parallelize(control, 2) + .map(new ClassWithoutNoArgConstructor(_)) + .collect() + .map(_.x) assert(control === result.toSeq) } @@ -236,12 +242,44 @@ class KryoSerializerSuite extends FunSuite with SharedSparkContext { // Set a special, broken ClassLoader and make sure we get an exception on deserialization ser.setDefaultClassLoader(new ClassLoader() { - override def loadClass(name: String) = throw new UnsupportedOperationException + override def loadClass(name: String): Class[_] = throw new UnsupportedOperationException }) intercept[UnsupportedOperationException] { ser.newInstance().deserialize[ClassLoaderTestingObject](bytes) } } + + test("registration of HighlyCompressedMapStatus") { + val conf = new SparkConf(false) + conf.set("spark.kryo.registrationRequired", "true") + + // these cases require knowing the internals of RoaringBitmap a little. Blocks span 2^16 + // values, and they use a bitmap (dense) if they have more than 4096 values, and an + // array (sparse) if they use less. So we just create two cases, one sparse and one dense. + // and we use a roaring bitmap for the empty blocks, so we trigger the dense case w/ mostly + // empty blocks + + val ser = new KryoSerializer(conf).newInstance() + val denseBlockSizes = new Array[Long](5000) + val sparseBlockSizes = Array[Long](0L, 1L, 0L, 2L) + Seq(denseBlockSizes, sparseBlockSizes).foreach { blockSizes => + ser.serialize(HighlyCompressedMapStatus(BlockManagerId("exec-1", "host", 1234), blockSizes)) + } + } + + test("serialization buffer overflow reporting") { + import org.apache.spark.SparkException + val kryoBufferMaxProperty = "spark.kryoserializer.buffer.max.mb" + + val largeObject = (1 to 1000000).toArray + + val conf = new SparkConf(false) + conf.set(kryoBufferMaxProperty, "1") + + val ser = new KryoSerializer(conf).newInstance() + val thrown = intercept[SparkException](ser.serialize(largeObject)) + assert(thrown.getMessage.contains(kryoBufferMaxProperty)) + } } @@ -254,14 +292,14 @@ object KryoTest { class ClassWithNoArgConstructor { var x: Int = 0 - override def equals(other: Any) = other match { + override def equals(other: Any): Boolean = other match { case c: ClassWithNoArgConstructor => x == c.x case _ => false } } class ClassWithoutNoArgConstructor(val x: Int) { - override def equals(other: Any) = other match { + override def equals(other: Any): Boolean = other match { case c: ClassWithoutNoArgConstructor => x == c.x case _ => false } diff --git a/core/src/test/scala/org/apache/spark/serializer/ProactiveClosureSerializationSuite.scala b/core/src/test/scala/org/apache/spark/serializer/ProactiveClosureSerializationSuite.scala index d037e2c19a64d..433fd6bb4a11d 100644 --- a/core/src/test/scala/org/apache/spark/serializer/ProactiveClosureSerializationSuite.scala +++ b/core/src/test/scala/org/apache/spark/serializer/ProactiveClosureSerializationSuite.scala @@ -24,14 +24,16 @@ import org.apache.spark.rdd.RDD /* A trivial (but unserializable) container for trivial functions */ class UnserializableClass { - def op[T](x: T) = x.toString + def op[T](x: T): String = x.toString - def pred[T](x: T) = x.toString.length % 2 == 0 + def pred[T](x: T): Boolean = x.toString.length % 2 == 0 } class ProactiveClosureSerializationSuite extends FunSuite with SharedSparkContext { - def fixture = (sc.parallelize(0 until 1000).map(_.toString), new UnserializableClass) + def fixture: (RDD[String], UnserializableClass) = { + (sc.parallelize(0 until 1000).map(_.toString), new UnserializableClass) + } test("throws expected serialization exceptions on actions") { val (data, uc) = fixture diff --git a/core/src/test/scala/org/apache/spark/serializer/TestSerializer.scala b/core/src/test/scala/org/apache/spark/serializer/TestSerializer.scala index 0ade1bab18d7e..963264cef3a71 100644 --- a/core/src/test/scala/org/apache/spark/serializer/TestSerializer.scala +++ b/core/src/test/scala/org/apache/spark/serializer/TestSerializer.scala @@ -27,7 +27,7 @@ import scala.reflect.ClassTag * A serializer implementation that always return a single element in a deserialization stream. */ class TestSerializer extends Serializer { - override def newInstance() = new TestSerializerInstance + override def newInstance(): TestSerializerInstance = new TestSerializerInstance } @@ -36,7 +36,8 @@ class TestSerializerInstance extends SerializerInstance { override def serializeStream(s: OutputStream): SerializationStream = ??? - override def deserializeStream(s: InputStream) = new TestDeserializationStream + override def deserializeStream(s: InputStream): TestDeserializationStream = + new TestDeserializationStream override def deserialize[T: ClassTag](bytes: ByteBuffer): T = ??? diff --git a/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleManagerSuite.scala b/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleManagerSuite.scala index 6790388f96603..7d76435cd75e7 100644 --- a/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleManagerSuite.scala @@ -54,7 +54,7 @@ class HashShuffleManagerSuite extends FunSuite with LocalSparkContext { sc = new SparkContext("local", "test", conf) val shuffleBlockManager = - SparkEnv.get.shuffleManager.shuffleBlockManager.asInstanceOf[FileShuffleBlockManager] + SparkEnv.get.shuffleManager.shuffleBlockResolver.asInstanceOf[FileShuffleBlockManager] val shuffle1 = shuffleBlockManager.forMapTask(1, 1, 1, new JavaSerializer(conf), new ShuffleWriteMetrics) @@ -85,8 +85,8 @@ class HashShuffleManagerSuite extends FunSuite with LocalSparkContext { // Now comes the test : // Write to shuffle 3; and close it, but before registering it, check if the file lengths for // previous task (forof shuffle1) is the same as 'segments'. Earlier, we were inferring length - // of block based on remaining data in file : which could mess things up when there is concurrent read - // and writes happening to the same shuffle group. + // of block based on remaining data in file : which could mess things up when there is + // concurrent read and writes happening to the same shuffle group. val shuffle3 = shuffleBlockManager.forMapTask(1, 3, 1, new JavaSerializer(testConf), new ShuffleWriteMetrics) diff --git a/core/src/test/scala/org/apache/spark/storage/BlockManagerReplicationSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockManagerReplicationSuite.scala index c2903c8597997..ffa5162a31841 100644 --- a/core/src/test/scala/org/apache/spark/storage/BlockManagerReplicationSuite.scala +++ b/core/src/test/scala/org/apache/spark/storage/BlockManagerReplicationSuite.scala @@ -22,11 +22,11 @@ import scala.concurrent.duration._ import scala.language.implicitConversions import scala.language.postfixOps -import akka.actor.{ActorSystem, Props} import org.mockito.Mockito.{mock, when} -import org.scalatest.{BeforeAndAfter, FunSuite, Matchers, PrivateMethodTester} +import org.scalatest.{BeforeAndAfter, FunSuite, Matchers} import org.scalatest.concurrent.Eventually._ +import org.apache.spark.rpc.RpcEnv import org.apache.spark.{MapOutputTrackerMaster, SparkConf, SparkContext, SecurityManager} import org.apache.spark.network.BlockTransferService import org.apache.spark.network.nio.NioBlockTransferService @@ -34,13 +34,12 @@ import org.apache.spark.scheduler.LiveListenerBus import org.apache.spark.serializer.KryoSerializer import org.apache.spark.shuffle.hash.HashShuffleManager import org.apache.spark.storage.StorageLevel._ -import org.apache.spark.util.{AkkaUtils, SizeEstimator} /** Testsuite that tests block replication in BlockManager */ class BlockManagerReplicationSuite extends FunSuite with Matchers with BeforeAndAfter { private val conf = new SparkConf(false) - var actorSystem: ActorSystem = null + var rpcEnv: RpcEnv = null var master: BlockManagerMaster = null val securityMgr = new SecurityManager(conf) val mapOutputTracker = new MapOutputTrackerMaster(conf) @@ -61,7 +60,7 @@ class BlockManagerReplicationSuite extends FunSuite with Matchers with BeforeAnd maxMem: Long, name: String = SparkContext.DRIVER_IDENTIFIER): BlockManager = { val transfer = new NioBlockTransferService(conf, securityMgr) - val store = new BlockManager(name, actorSystem, master, serializer, maxMem, conf, + val store = new BlockManager(name, rpcEnv, master, serializer, maxMem, conf, mapOutputTracker, shuffleManager, transfer, securityMgr, 0) store.initialize("app-id") allStores += store @@ -69,32 +68,29 @@ class BlockManagerReplicationSuite extends FunSuite with Matchers with BeforeAnd } before { - val (actorSystem, boundPort) = AkkaUtils.createActorSystem( - "test", "localhost", 0, conf = conf, securityManager = securityMgr) - this.actorSystem = actorSystem + rpcEnv = RpcEnv.create("test", "localhost", 0, conf, securityMgr) conf.set("spark.authenticate", "false") - conf.set("spark.driver.port", boundPort.toString) + conf.set("spark.driver.port", rpcEnv.address.port.toString) conf.set("spark.storage.unrollFraction", "0.4") conf.set("spark.storage.unrollMemoryThreshold", "512") // to make a replication attempt to inactive store fail fast - conf.set("spark.core.connection.ack.wait.timeout", "1") + conf.set("spark.core.connection.ack.wait.timeout", "1s") // to make cached peers refresh frequently conf.set("spark.storage.cachedPeersTtl", "10") - master = new BlockManagerMaster( - actorSystem.actorOf(Props(new BlockManagerMasterActor(true, conf, new LiveListenerBus))), - conf, true) + master = new BlockManagerMaster(rpcEnv.setupEndpoint("blockmanager", + new BlockManagerMasterEndpoint(rpcEnv, true, conf, new LiveListenerBus)), conf, true) allStores.clear() } after { allStores.foreach { _.stop() } allStores.clear() - actorSystem.shutdown() - actorSystem.awaitTermination() - actorSystem = null + rpcEnv.shutdown() + rpcEnv.awaitTermination() + rpcEnv = null master = null } @@ -262,7 +258,7 @@ class BlockManagerReplicationSuite extends FunSuite with Matchers with BeforeAnd val failableTransfer = mock(classOf[BlockTransferService]) // this wont actually work when(failableTransfer.hostName).thenReturn("some-hostname") when(failableTransfer.port).thenReturn(1000) - val failableStore = new BlockManager("failable-store", actorSystem, master, serializer, + val failableStore = new BlockManager("failable-store", rpcEnv, master, serializer, 10000, conf, mapOutputTracker, shuffleManager, failableTransfer, securityMgr, 0) failableStore.initialize("app-id") allStores += failableStore // so that this gets stopped after test diff --git a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala index ffe6f039145ea..545722b050ee8 100644 --- a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala @@ -19,24 +19,18 @@ package org.apache.spark.storage import java.nio.{ByteBuffer, MappedByteBuffer} import java.util.Arrays -import java.util.concurrent.TimeUnit import scala.collection.mutable.ArrayBuffer -import scala.concurrent.Await import scala.concurrent.duration._ import scala.language.implicitConversions import scala.language.postfixOps -import akka.actor._ -import akka.pattern.ask -import akka.util.Timeout - import org.mockito.Mockito.{mock, when} - import org.scalatest._ import org.scalatest.concurrent.Eventually._ import org.scalatest.concurrent.Timeouts._ +import org.apache.spark.rpc.RpcEnv import org.apache.spark.{MapOutputTrackerMaster, SparkConf, SparkContext, SecurityManager} import org.apache.spark.executor.DataReadMethod import org.apache.spark.network.nio.NioBlockTransferService @@ -53,7 +47,7 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach private val conf = new SparkConf(false) var store: BlockManager = null var store2: BlockManager = null - var actorSystem: ActorSystem = null + var rpcEnv: RpcEnv = null var master: BlockManagerMaster = null conf.set("spark.authenticate", "false") val securityMgr = new SecurityManager(conf) @@ -66,34 +60,31 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach // Implicitly convert strings to BlockIds for test clarity. implicit def StringToBlockId(value: String): BlockId = new TestBlockId(value) - def rdd(rddId: Int, splitId: Int) = RDDBlockId(rddId, splitId) + def rdd(rddId: Int, splitId: Int): RDDBlockId = RDDBlockId(rddId, splitId) private def makeBlockManager( maxMem: Long, name: String = SparkContext.DRIVER_IDENTIFIER): BlockManager = { val transfer = new NioBlockTransferService(conf, securityMgr) - val manager = new BlockManager(name, actorSystem, master, serializer, maxMem, conf, + val manager = new BlockManager(name, rpcEnv, master, serializer, maxMem, conf, mapOutputTracker, shuffleManager, transfer, securityMgr, 0) manager.initialize("app-id") manager } override def beforeEach(): Unit = { - val (actorSystem, boundPort) = AkkaUtils.createActorSystem( - "test", "localhost", 0, conf = conf, securityManager = securityMgr) - this.actorSystem = actorSystem + rpcEnv = RpcEnv.create("test", "localhost", 0, conf, securityMgr) // Set the arch to 64-bit and compressedOops to true to get a deterministic test-case System.setProperty("os.arch", "amd64") conf.set("os.arch", "amd64") conf.set("spark.test.useCompressedOops", "true") - conf.set("spark.driver.port", boundPort.toString) + conf.set("spark.driver.port", rpcEnv.address.port.toString) conf.set("spark.storage.unrollFraction", "0.4") conf.set("spark.storage.unrollMemoryThreshold", "512") - master = new BlockManagerMaster( - actorSystem.actorOf(Props(new BlockManagerMasterActor(true, conf, new LiveListenerBus))), - conf, true) + master = new BlockManagerMaster(rpcEnv.setupEndpoint("blockmanager", + new BlockManagerMasterEndpoint(rpcEnv, true, conf, new LiveListenerBus)), conf, true) val initialize = PrivateMethod[Unit]('initialize) SizeEstimator invokePrivate initialize() @@ -108,16 +99,18 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach store2.stop() store2 = null } - actorSystem.shutdown() - actorSystem.awaitTermination() - actorSystem = null + rpcEnv.shutdown() + rpcEnv.awaitTermination() + rpcEnv = null master = null } test("StorageLevel object caching") { val level1 = StorageLevel(false, false, false, false, 3) - val level2 = StorageLevel(false, false, false, false, 3) // this should return the same object as level1 - val level3 = StorageLevel(false, false, false, false, 2) // this should return a different object + // this should return the same object as level1 + val level2 = StorageLevel(false, false, false, false, 3) + // this should return a different object + val level3 = StorageLevel(false, false, false, false, 2) assert(level2 === level1, "level2 is not same as level1") assert(level2.eq(level1), "level2 is not the same object as level1") assert(level3 != level1, "level3 is same as level1") @@ -148,6 +141,12 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach assert(id2_.eq(id1), "Deserialized id2 is not the same object as original id1") } + test("BlockManagerId.isDriver() backwards-compatibility with legacy driver ids (SPARK-6716)") { + assert(BlockManagerId(SparkContext.DRIVER_IDENTIFIER, "XXX", 1).isDriver) + assert(BlockManagerId(SparkContext.LEGACY_DRIVER_IDENTIFIER, "XXX", 1).isDriver) + assert(!BlockManagerId("notADriverIdentifier", "XXX", 1).isDriver) + } + test("master + 1 manager interaction") { store = makeBlockManager(20000) val a1 = new Array[Byte](4000) @@ -170,8 +169,8 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach assert(master.getLocations("a3").size === 0, "master was told about a3") // Drop a1 and a2 from memory; this should be reported back to the master - store.dropFromMemory("a1", null) - store.dropFromMemory("a2", null) + store.dropFromMemory("a1", null: Either[Array[Any], ByteBuffer]) + store.dropFromMemory("a2", null: Either[Array[Any], ByteBuffer]) assert(store.getSingle("a1") === None, "a1 not removed from store") assert(store.getSingle("a2") === None, "a2 not removed from store") assert(master.getLocations("a1").size === 0, "master did not remove a1") @@ -357,10 +356,8 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach master.removeExecutor(store.blockManagerId.executorId) assert(master.getLocations("a1").size == 0, "a1 was not removed from master") - implicit val timeout = Timeout(30, TimeUnit.SECONDS) - val reregister = !Await.result( - master.driverActor ? BlockManagerHeartbeat(store.blockManagerId), - timeout.duration).asInstanceOf[Boolean] + val reregister = !master.driverEndpoint.askWithReply[Boolean]( + BlockManagerHeartbeat(store.blockManagerId)) assert(reregister == true) } @@ -413,8 +410,8 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach t2.join() t3.join() - store.dropFromMemory("a1", null) - store.dropFromMemory("a2", null) + store.dropFromMemory("a1", null: Either[Array[Any], ByteBuffer]) + store.dropFromMemory("a2", null: Either[Array[Any], ByteBuffer]) store.waitForAsyncReregister() } } @@ -785,7 +782,7 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach test("block store put failure") { // Use Java serializer so we can create an unserializable error. val transfer = new NioBlockTransferService(conf, securityMgr) - store = new BlockManager(SparkContext.DRIVER_IDENTIFIER, actorSystem, master, + store = new BlockManager(SparkContext.DRIVER_IDENTIFIER, rpcEnv, master, new JavaSerializer(conf), 1200, conf, mapOutputTracker, shuffleManager, transfer, securityMgr, 0) @@ -807,7 +804,7 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach // Create a non-trivial (not all zeros) byte array var counter = 0.toByte - def incr = {counter = (counter + 1).toByte; counter;} + def incr: Byte = {counter = (counter + 1).toByte; counter;} val bytes = Array.fill[Byte](1000)(incr) val byteBuffer = ByteBuffer.wrap(bytes) @@ -961,8 +958,10 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach store.putIterator("list3", list.iterator, StorageLevel.MEMORY_AND_DISK, tellMaster = true) // getLocations and getBlockStatus should yield the same locations - assert(store.master.getMatchingBlockIds(_.toString.contains("list"), askSlaves = false).size === 3) - assert(store.master.getMatchingBlockIds(_.toString.contains("list1"), askSlaves = false).size === 1) + assert(store.master.getMatchingBlockIds(_.toString.contains("list"), askSlaves = false).size + === 3) + assert(store.master.getMatchingBlockIds(_.toString.contains("list1"), askSlaves = false).size + === 1) // insert some more blocks store.putIterator("newlist1", list.iterator, StorageLevel.MEMORY_AND_DISK, tellMaster = true) @@ -970,8 +969,10 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach store.putIterator("newlist3", list.iterator, StorageLevel.MEMORY_AND_DISK, tellMaster = false) // getLocations and getBlockStatus should yield the same locations - assert(store.master.getMatchingBlockIds(_.toString.contains("newlist"), askSlaves = false).size === 1) - assert(store.master.getMatchingBlockIds(_.toString.contains("newlist"), askSlaves = true).size === 3) + assert(store.master.getMatchingBlockIds(_.toString.contains("newlist"), askSlaves = false).size + === 1) + assert(store.master.getMatchingBlockIds(_.toString.contains("newlist"), askSlaves = true).size + === 3) val blockIds = Seq(RDDBlockId(1, 0), RDDBlockId(1, 1), RDDBlockId(2, 0)) blockIds.foreach { blockId => @@ -1064,6 +1065,7 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach var unrollResult = memoryStore.unrollSafely("unroll", smallList.iterator, droppedBlocks) verifyUnroll(smallList.iterator, unrollResult, shouldBeArray = true) assert(memoryStore.currentUnrollMemoryForThisThread === 0) + memoryStore.releasePendingUnrollMemoryForThisThread() // Unroll with not enough space. This should succeed after kicking out someBlock1. store.putIterator("someBlock1", smallList.iterator, StorageLevel.MEMORY_ONLY) @@ -1074,6 +1076,7 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach assert(droppedBlocks.size === 1) assert(droppedBlocks.head._1 === TestBlockId("someBlock1")) droppedBlocks.clear() + memoryStore.releasePendingUnrollMemoryForThisThread() // Unroll huge block with not enough space. Even after ensuring free space of 12000 * 0.4 = // 4800 bytes, there is still not enough room to unroll this block. This returns an iterator. @@ -1093,8 +1096,8 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach val memoryStore = store.memoryStore val smallList = List.fill(40)(new Array[Byte](100)) val bigList = List.fill(40)(new Array[Byte](1000)) - def smallIterator = smallList.iterator.asInstanceOf[Iterator[Any]] - def bigIterator = bigList.iterator.asInstanceOf[Iterator[Any]] + def smallIterator: Iterator[Any] = smallList.iterator.asInstanceOf[Iterator[Any]] + def bigIterator: Iterator[Any] = bigList.iterator.asInstanceOf[Iterator[Any]] assert(memoryStore.currentUnrollMemoryForThisThread === 0) // Unroll with plenty of space. This should succeed and cache both blocks. @@ -1147,8 +1150,8 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach val diskStore = store.diskStore val smallList = List.fill(40)(new Array[Byte](100)) val bigList = List.fill(40)(new Array[Byte](1000)) - def smallIterator = smallList.iterator.asInstanceOf[Iterator[Any]] - def bigIterator = bigList.iterator.asInstanceOf[Iterator[Any]] + def smallIterator: Iterator[Any] = smallList.iterator.asInstanceOf[Iterator[Any]] + def bigIterator: Iterator[Any] = bigList.iterator.asInstanceOf[Iterator[Any]] assert(memoryStore.currentUnrollMemoryForThisThread === 0) store.putIterator("b1", smallIterator, memAndDisk) @@ -1190,7 +1193,7 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach val memOnly = StorageLevel.MEMORY_ONLY val memoryStore = store.memoryStore val smallList = List.fill(40)(new Array[Byte](100)) - def smallIterator = smallList.iterator.asInstanceOf[Iterator[Any]] + def smallIterator: Iterator[Any] = smallList.iterator.asInstanceOf[Iterator[Any]] assert(memoryStore.currentUnrollMemoryForThisThread === 0) // All unroll memory used is released because unrollSafely returned an array @@ -1221,4 +1224,30 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach assert(unrollMemoryAfterB6 === unrollMemoryAfterB4) assert(unrollMemoryAfterB7 === unrollMemoryAfterB4) } + + test("lazily create a big ByteBuffer to avoid OOM if it cannot be put into MemoryStore") { + store = makeBlockManager(12000) + val memoryStore = store.memoryStore + val blockId = BlockId("rdd_3_10") + val result = memoryStore.putBytes(blockId, 13000, () => { + fail("A big ByteBuffer that cannot be put into MemoryStore should not be created") + }) + assert(result.size === 13000) + assert(result.data === null) + assert(result.droppedBlocks === Nil) + } + + test("put a small ByteBuffer to MemoryStore") { + store = makeBlockManager(12000) + val memoryStore = store.memoryStore + val blockId = BlockId("rdd_3_10") + var bytes: ByteBuffer = null + val result = memoryStore.putBytes(blockId, 10000, () => { + bytes = ByteBuffer.allocate(10000) + bytes + }) + assert(result.size === 10000) + assert(result.data === Right(bytes)) + assert(result.droppedBlocks === Nil) + } } diff --git a/core/src/test/scala/org/apache/spark/storage/BlockObjectWriterSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockObjectWriterSuite.scala index c21c92b63ad13..78bbc4ec2c620 100644 --- a/core/src/test/scala/org/apache/spark/storage/BlockObjectWriterSuite.scala +++ b/core/src/test/scala/org/apache/spark/storage/BlockObjectWriterSuite.scala @@ -16,16 +16,18 @@ */ package org.apache.spark.storage -import org.scalatest.FunSuite import java.io.File + +import org.scalatest.FunSuite + +import org.apache.spark.SparkConf import org.apache.spark.executor.ShuffleWriteMetrics import org.apache.spark.serializer.JavaSerializer -import org.apache.spark.SparkConf +import org.apache.spark.util.Utils class BlockObjectWriterSuite extends FunSuite { test("verify write metrics") { - val file = new File("somefile") - file.deleteOnExit() + val file = new File(Utils.createTempDir(), "somefile") val writeMetrics = new ShuffleWriteMetrics() val writer = new DiskBlockObjectWriter(new TestBlockId("0"), file, new JavaSerializer(new SparkConf()), 1024, os => os, true, writeMetrics) @@ -47,8 +49,7 @@ class BlockObjectWriterSuite extends FunSuite { } test("verify write metrics on revert") { - val file = new File("somefile") - file.deleteOnExit() + val file = new File(Utils.createTempDir(), "somefile") val writeMetrics = new ShuffleWriteMetrics() val writer = new DiskBlockObjectWriter(new TestBlockId("0"), file, new JavaSerializer(new SparkConf()), 1024, os => os, true, writeMetrics) @@ -71,8 +72,7 @@ class BlockObjectWriterSuite extends FunSuite { } test("Reopening a closed block writer") { - val file = new File("somefile") - file.deleteOnExit() + val file = new File(Utils.createTempDir(), "somefile") val writeMetrics = new ShuffleWriteMetrics() val writer = new DiskBlockObjectWriter(new TestBlockId("0"), file, new JavaSerializer(new SparkConf()), 1024, os => os, true, writeMetrics) diff --git a/core/src/test/scala/org/apache/spark/storage/LocalDirsSuite.scala b/core/src/test/scala/org/apache/spark/storage/LocalDirsSuite.scala index 8cf951adb354b..b47157f8331cc 100644 --- a/core/src/test/scala/org/apache/spark/storage/LocalDirsSuite.scala +++ b/core/src/test/scala/org/apache/spark/storage/LocalDirsSuite.scala @@ -20,7 +20,7 @@ package org.apache.spark.storage import java.io.File import org.apache.spark.util.Utils -import org.scalatest.FunSuite +import org.scalatest.{BeforeAndAfter, FunSuite} import org.apache.spark.SparkConf @@ -28,7 +28,11 @@ import org.apache.spark.SparkConf /** * Tests for the spark.local.dir and SPARK_LOCAL_DIRS configuration options. */ -class LocalDirsSuite extends FunSuite { +class LocalDirsSuite extends FunSuite with BeforeAndAfter { + + before { + Utils.clearLocalRootDirs() + } test("Utils.getLocalDir() returns a valid directory, even if some local dirs are missing") { // Regression test for SPARK-2974 @@ -43,7 +47,7 @@ class LocalDirsSuite extends FunSuite { assert(!new File("/NONEXISTENT_DIR").exists()) // SPARK_LOCAL_DIRS is a valid directory: class MySparkConf extends SparkConf(false) { - override def getenv(name: String) = { + override def getenv(name: String): String = { if (name == "SPARK_LOCAL_DIRS") System.getProperty("java.io.tmpdir") else super.getenv(name) } diff --git a/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala b/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala index 6a972381faf14..eb9db550fd74c 100644 --- a/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala +++ b/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala @@ -17,20 +17,25 @@ package org.apache.spark.ui +import java.net.{HttpURLConnection, URL} +import javax.servlet.http.HttpServletRequest + import scala.collection.JavaConversions._ +import scala.xml.Node -import org.openqa.selenium.{By, WebDriver} import org.openqa.selenium.htmlunit.HtmlUnitDriver +import org.openqa.selenium.{By, WebDriver} import org.scalatest._ import org.scalatest.concurrent.Eventually._ import org.scalatest.selenium.WebBrowser import org.scalatest.time.SpanSugar._ -import org.apache.spark._ import org.apache.spark.LocalSparkContext._ +import org.apache.spark._ import org.apache.spark.api.java.StorageLevels import org.apache.spark.shuffle.FetchFailedException + /** * Selenium tests for the Spark Web UI. */ @@ -52,12 +57,13 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before * Create a test SparkContext with the SparkUI enabled. * It is safe to `get` the SparkUI directly from the SparkContext returned here. */ - private def newSparkContext(): SparkContext = { + private def newSparkContext(killEnabled: Boolean = true): SparkContext = { val conf = new SparkConf() .setMaster("local") .setAppName("test") .set("spark.ui.enabled", "true") .set("spark.ui.port", "0") + .set("spark.ui.killEnabled", killEnabled.toString) val sc = new SparkContext(conf) assert(sc.ui.isDefined) sc @@ -124,21 +130,12 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before } test("spark.ui.killEnabled should properly control kill button display") { - def getSparkContext(killEnabled: Boolean): SparkContext = { - val conf = new SparkConf() - .setMaster("local") - .setAppName("test") - .set("spark.ui.enabled", "true") - .set("spark.ui.killEnabled", killEnabled.toString) - new SparkContext(conf) - } - - def hasKillLink = find(className("kill-link")).isDefined + def hasKillLink: Boolean = find(className("kill-link")).isDefined def runSlowJob(sc: SparkContext) { sc.parallelize(1 to 10).map{x => Thread.sleep(10000); x}.countAsync() } - withSpark(getSparkContext(killEnabled = true)) { sc => + withSpark(newSparkContext(killEnabled = true)) { sc => runSlowJob(sc) eventually(timeout(5 seconds), interval(50 milliseconds)) { go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/stages") @@ -146,7 +143,7 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before } } - withSpark(getSparkContext(killEnabled = false)) { sc => + withSpark(newSparkContext(killEnabled = false)) { sc => runSlowJob(sc) eventually(timeout(5 seconds), interval(50 milliseconds)) { go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/stages") @@ -229,7 +226,7 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before // because someone could change the error message and cause this test to pass by accident. // Instead, it's safer to check that each row contains a link to a stage details page. findAll(cssSelector("tbody tr")).foreach { row => - val link = row.underlying.findElement(By.xpath(".//a")) + val link = row.underlying.findElement(By.xpath("./td/div/a")) link.getAttribute("href") should include ("stage") } } @@ -310,4 +307,67 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before } } } + + test("attaching and detaching a new tab") { + withSpark(newSparkContext()) { sc => + val sparkUI = sc.ui.get + + val newTab = new WebUITab(sparkUI, "foo") { + attachPage(new WebUIPage("") { + def render(request: HttpServletRequest): Seq[Node] = { + "html magic" + } + }) + } + sparkUI.attachTab(newTab) + eventually(timeout(10 seconds), interval(50 milliseconds)) { + go to (sc.ui.get.appUIAddress.stripSuffix("/")) + find(cssSelector("""ul li a[href*="jobs"]""")) should not be(None) + find(cssSelector("""ul li a[href*="stages"]""")) should not be(None) + find(cssSelector("""ul li a[href*="storage"]""")) should not be(None) + find(cssSelector("""ul li a[href*="environment"]""")) should not be(None) + find(cssSelector("""ul li a[href*="foo"]""")) should not be(None) + } + eventually(timeout(10 seconds), interval(50 milliseconds)) { + // check whether new page exists + go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/foo") + find(cssSelector("b")).get.text should include ("html magic") + } + sparkUI.detachTab(newTab) + eventually(timeout(10 seconds), interval(50 milliseconds)) { + go to (sc.ui.get.appUIAddress.stripSuffix("/")) + find(cssSelector("""ul li a[href*="jobs"]""")) should not be(None) + find(cssSelector("""ul li a[href*="stages"]""")) should not be(None) + find(cssSelector("""ul li a[href*="storage"]""")) should not be(None) + find(cssSelector("""ul li a[href*="environment"]""")) should not be(None) + find(cssSelector("""ul li a[href*="foo"]""")) should be(None) + } + eventually(timeout(10 seconds), interval(50 milliseconds)) { + // check new page not exist + go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/foo") + find(cssSelector("b")) should be(None) + } + } + } + + test("kill stage is POST only") { + def getResponseCode(url: URL, method: String): Int = { + val connection = url.openConnection().asInstanceOf[HttpURLConnection] + connection.setRequestMethod(method) + connection.connect() + val code = connection.getResponseCode() + connection.disconnect() + code + } + + withSpark(newSparkContext(killEnabled = true)) { sc => + sc.parallelize(1 to 10).map{x => Thread.sleep(10000); x}.countAsync() + eventually(timeout(5 seconds), interval(50 milliseconds)) { + val url = new URL( + sc.ui.get.appUIAddress.stripSuffix("/") + "/stages/stage/kill/?id=0&terminate=true") + getResponseCode(url, "GET") should be (405) + getResponseCode(url, "POST") should be (200) + } + } + } } diff --git a/core/src/test/scala/org/apache/spark/ui/UISuite.scala b/core/src/test/scala/org/apache/spark/ui/UISuite.scala index 92a21f82f3c21..77a038dc1720d 100644 --- a/core/src/test/scala/org/apache/spark/ui/UISuite.scala +++ b/core/src/test/scala/org/apache/spark/ui/UISuite.scala @@ -18,7 +18,6 @@ package org.apache.spark.ui import java.net.ServerSocket -import javax.servlet.http.HttpServletRequest import scala.io.Source import scala.util.{Failure, Success, Try} @@ -28,9 +27,8 @@ import org.scalatest.FunSuite import org.scalatest.concurrent.Eventually._ import org.scalatest.time.SpanSugar._ -import org.apache.spark.{SparkContext, SparkConf} import org.apache.spark.LocalSparkContext._ -import scala.xml.Node +import org.apache.spark.{SparkConf, SparkContext} class UISuite extends FunSuite { @@ -72,40 +70,6 @@ class UISuite extends FunSuite { } } - ignore("attaching a new tab") { - withSpark(newSparkContext()) { sc => - val sparkUI = sc.ui.get - - val newTab = new WebUITab(sparkUI, "foo") { - attachPage(new WebUIPage("") { - def render(request: HttpServletRequest): Seq[Node] = { - "html magic" - } - }) - } - sparkUI.attachTab(newTab) - eventually(timeout(10 seconds), interval(50 milliseconds)) { - val html = Source.fromURL(sparkUI.appUIAddress).mkString - assert(!html.contains("random data that should not be present")) - - // check whether new page exists - assert(html.toLowerCase.contains("foo")) - - // check whether other pages still exist - assert(html.toLowerCase.contains("stages")) - assert(html.toLowerCase.contains("storage")) - assert(html.toLowerCase.contains("environment")) - assert(html.toLowerCase.contains("executors")) - } - - eventually(timeout(10 seconds), interval(50 milliseconds)) { - val html = Source.fromURL(sparkUI.appUIAddress.stripSuffix("/") + "/foo").mkString - // check whether new page exists - assert(html.contains("magic")) - } - } - } - test("jetty selects different port under contention") { val server = new ServerSocket(0) val startPort = server.getLocalPort diff --git a/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala b/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala index e8405baa8e3ea..21d8267114133 100644 --- a/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala +++ b/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala @@ -17,6 +17,8 @@ package org.apache.spark.ui.jobs +import java.util.Properties + import org.scalatest.FunSuite import org.scalatest.Matchers @@ -44,11 +46,19 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matc SparkListenerStageCompleted(stageInfo) } - private def createJobStartEvent(jobId: Int, stageIds: Seq[Int]) = { + private def createJobStartEvent( + jobId: Int, + stageIds: Seq[Int], + jobGroup: Option[String] = None): SparkListenerJobStart = { val stageInfos = stageIds.map { stageId => new StageInfo(stageId, 0, stageId.toString, 0, null, "") } - SparkListenerJobStart(jobId, jobSubmissionTime, stageInfos) + val properties: Option[Properties] = jobGroup.map { groupId => + val props = new Properties() + props.setProperty(SparkContext.SPARK_JOB_GROUP_ID, groupId) + props + } + SparkListenerJobStart(jobId, jobSubmissionTime, stageInfos, properties.orNull) } private def createJobEndEvent(jobId: Int, failed: Boolean = false) = { @@ -88,6 +98,45 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matc listener.completedStages.map(_.stageId).toSet should be (Set(50, 49, 48, 47, 46)) } + test("test clearing of stageIdToActiveJobs") { + val conf = new SparkConf() + conf.set("spark.ui.retainedStages", 5.toString) + val listener = new JobProgressListener(conf) + val jobId = 0 + val stageIds = 1 to 50 + // Start a job with 50 stages + listener.onJobStart(createJobStartEvent(jobId, stageIds)) + for (stageId <- stageIds) { + listener.onStageSubmitted(createStageStartEvent(stageId)) + } + listener.stageIdToActiveJobIds.size should be > 0 + + // Complete the stages and job + for (stageId <- stageIds) { + listener.onStageCompleted(createStageEndEvent(stageId, failed = false)) + } + listener.onJobEnd(createJobEndEvent(jobId, false)) + assertActiveJobsStateIsEmpty(listener) + listener.stageIdToActiveJobIds.size should be (0) + } + + test("test clearing of jobGroupToJobIds") { + val conf = new SparkConf() + conf.set("spark.ui.retainedJobs", 5.toString) + val listener = new JobProgressListener(conf) + + // Run 50 jobs, each with one stage + for (jobId <- 0 to 50) { + listener.onJobStart(createJobStartEvent(jobId, Seq(0), jobGroup = Some(jobId.toString))) + listener.onStageSubmitted(createStageStartEvent(0)) + listener.onStageCompleted(createStageEndEvent(0, failed = false)) + listener.onJobEnd(createJobEndEvent(jobId, false)) + } + assertActiveJobsStateIsEmpty(listener) + // This collection won't become empty, but it should be bounded by spark.ui.retainedJobs + listener.jobGroupToJobIds.size should be (5) + } + test("test LRU eviction of jobs") { val conf = new SparkConf() conf.set("spark.ui.retainedStages", 5.toString) @@ -220,13 +269,14 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matc val taskType = Utils.getFormattedClassName(new ShuffleMapTask(0)) val execId = "exe-1" - def makeTaskMetrics(base: Int) = { + def makeTaskMetrics(base: Int): TaskMetrics = { val taskMetrics = new TaskMetrics() val shuffleReadMetrics = new ShuffleReadMetrics() val shuffleWriteMetrics = new ShuffleWriteMetrics() taskMetrics.setShuffleReadMetrics(Some(shuffleReadMetrics)) taskMetrics.shuffleWriteMetrics = Some(shuffleWriteMetrics) shuffleReadMetrics.incRemoteBytesRead(base + 1) + shuffleReadMetrics.incLocalBytesRead(base + 9) shuffleReadMetrics.incRemoteBlocksFetched(base + 2) shuffleWriteMetrics.incShuffleBytesWritten(base + 3) taskMetrics.setExecutorRunTime(base + 4) @@ -241,7 +291,7 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matc taskMetrics } - def makeTaskInfo(taskId: Long, finishTime: Int = 0) = { + def makeTaskInfo(taskId: Long, finishTime: Int = 0): TaskInfo = { val taskInfo = new TaskInfo(taskId, 0, 1, 0L, execId, "host1", TaskLocality.NODE_LOCAL, false) taskInfo.finishTime = finishTime @@ -260,8 +310,8 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matc var stage0Data = listener.stageIdToData.get((0, 0)).get var stage1Data = listener.stageIdToData.get((1, 0)).get - assert(stage0Data.shuffleReadBytes == 102) - assert(stage1Data.shuffleReadBytes == 201) + assert(stage0Data.shuffleReadTotalBytes == 220) + assert(stage1Data.shuffleReadTotalBytes == 410) assert(stage0Data.shuffleWriteBytes == 106) assert(stage1Data.shuffleWriteBytes == 203) assert(stage0Data.executorRunTime == 108) @@ -290,8 +340,11 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matc stage0Data = listener.stageIdToData.get((0, 0)).get stage1Data = listener.stageIdToData.get((1, 0)).get - assert(stage0Data.shuffleReadBytes == 402) - assert(stage1Data.shuffleReadBytes == 602) + // Task 1235 contributed (100+1)+(100+9) = 210 shuffle bytes, and task 1234 contributed + // (300+1)+(300+9) = 610 total shuffle bytes, so the total for the stage is 820. + assert(stage0Data.shuffleReadTotalBytes == 820) + // Task 1236 contributed 410 shuffle bytes, and task 1237 contributed 810 shuffle bytes. + assert(stage1Data.shuffleReadTotalBytes == 1220) assert(stage0Data.shuffleWriteBytes == 406) assert(stage1Data.shuffleWriteBytes == 606) assert(stage0Data.executorRunTime == 408) diff --git a/core/src/test/scala/org/apache/spark/ui/storage/StorageTabSuite.scala b/core/src/test/scala/org/apache/spark/ui/storage/StorageTabSuite.scala index e1bc1379b5d80..3744e479d2f05 100644 --- a/core/src/test/scala/org/apache/spark/ui/storage/StorageTabSuite.scala +++ b/core/src/test/scala/org/apache/spark/ui/storage/StorageTabSuite.scala @@ -107,7 +107,8 @@ class StorageTabSuite extends FunSuite with BeforeAndAfter { val myRddInfo0 = rddInfo0 val myRddInfo1 = rddInfo1 val myRddInfo2 = rddInfo2 - val stageInfo0 = new StageInfo(0, 0, "0", 100, Seq(myRddInfo0, myRddInfo1, myRddInfo2), "details") + val stageInfo0 = new StageInfo( + 0, 0, "0", 100, Seq(myRddInfo0, myRddInfo1, myRddInfo2), "details") bus.postToAll(SparkListenerBlockManagerAdded(1L, bm1, 1000L)) bus.postToAll(SparkListenerStageSubmitted(stageInfo0)) assert(storageListener._rddInfoMap.size === 3) diff --git a/core/src/test/scala/org/apache/spark/util/AkkaUtilsSuite.scala b/core/src/test/scala/org/apache/spark/util/AkkaUtilsSuite.scala index 2cc5817758cf7..bec79fc4dc8f7 100644 --- a/core/src/test/scala/org/apache/spark/util/AkkaUtilsSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/AkkaUtilsSuite.scala @@ -19,13 +19,11 @@ package org.apache.spark.util import java.util.concurrent.TimeoutException -import scala.concurrent.Await - -import akka.actor._ - +import akka.actor.ActorNotFound import org.scalatest.FunSuite import org.apache.spark._ +import org.apache.spark.rpc.RpcEnv import org.apache.spark.scheduler.MapStatus import org.apache.spark.storage.BlockManagerId import org.apache.spark.SSLSampleConfigs._ @@ -38,39 +36,37 @@ class AkkaUtilsSuite extends FunSuite with LocalSparkContext with ResetSystemPro test("remote fetch security bad password") { val conf = new SparkConf + conf.set("spark.rpc", "akka") conf.set("spark.authenticate", "true") conf.set("spark.authenticate.secret", "good") val securityManager = new SecurityManager(conf) val hostname = "localhost" - val (actorSystem, boundPort) = AkkaUtils.createActorSystem("spark", hostname, 0, - conf = conf, securityManager = securityManager) - System.setProperty("spark.hostPort", hostname + ":" + boundPort) + val rpcEnv = RpcEnv.create("spark", hostname, 0, conf, securityManager) + System.setProperty("spark.hostPort", rpcEnv.address.hostPort) assert(securityManager.isAuthenticationEnabled() === true) val masterTracker = new MapOutputTrackerMaster(conf) - masterTracker.trackerActor = actorSystem.actorOf( - Props(new MapOutputTrackerMasterActor(masterTracker, conf)), "MapOutputTracker") + masterTracker.trackerEndpoint = rpcEnv.setupEndpoint(MapOutputTracker.ENDPOINT_NAME, + new MapOutputTrackerMasterEndpoint(rpcEnv, masterTracker, conf)) val badconf = new SparkConf + badconf.set("spark.rpc", "akka") badconf.set("spark.authenticate", "true") badconf.set("spark.authenticate.secret", "bad") val securityManagerBad = new SecurityManager(badconf) assert(securityManagerBad.isAuthenticationEnabled() === true) - val (slaveSystem, _) = AkkaUtils.createActorSystem("spark-slave", hostname, 0, - conf = conf, securityManager = securityManagerBad) + val slaveRpcEnv = RpcEnv.create("spark-slave", hostname, 0, conf, securityManagerBad) val slaveTracker = new MapOutputTrackerWorker(conf) - val selection = slaveSystem.actorSelection( - AkkaUtils.address(AkkaUtils.protocol(slaveSystem), "spark", "localhost", boundPort, "MapOutputTracker")) - val timeout = AkkaUtils.lookupTimeout(conf) intercept[akka.actor.ActorNotFound] { - slaveTracker.trackerActor = Await.result(selection.resolveOne(timeout), timeout) + slaveTracker.trackerEndpoint = + slaveRpcEnv.setupEndpointRef("spark", rpcEnv.address, MapOutputTracker.ENDPOINT_NAME) } - actorSystem.shutdown() - slaveSystem.shutdown() + rpcEnv.shutdown() + slaveRpcEnv.shutdown() } test("remote fetch security off") { @@ -80,28 +76,24 @@ class AkkaUtilsSuite extends FunSuite with LocalSparkContext with ResetSystemPro val securityManager = new SecurityManager(conf) val hostname = "localhost" - val (actorSystem, boundPort) = AkkaUtils.createActorSystem("spark", hostname, 0, - conf = conf, securityManager = securityManager) - System.setProperty("spark.hostPort", hostname + ":" + boundPort) + val rpcEnv = RpcEnv.create("spark", hostname, 0, conf, securityManager) + System.setProperty("spark.hostPort", rpcEnv.address.hostPort) assert(securityManager.isAuthenticationEnabled() === false) val masterTracker = new MapOutputTrackerMaster(conf) - masterTracker.trackerActor = actorSystem.actorOf( - Props(new MapOutputTrackerMasterActor(masterTracker, conf)), "MapOutputTracker") + masterTracker.trackerEndpoint = rpcEnv.setupEndpoint(MapOutputTracker.ENDPOINT_NAME, + new MapOutputTrackerMasterEndpoint(rpcEnv, masterTracker, conf)) val badconf = new SparkConf badconf.set("spark.authenticate", "false") badconf.set("spark.authenticate.secret", "good") val securityManagerBad = new SecurityManager(badconf) - val (slaveSystem, _) = AkkaUtils.createActorSystem("spark-slave", hostname, 0, - conf = badconf, securityManager = securityManagerBad) + val slaveRpcEnv = RpcEnv.create("spark-slave", hostname, 0, badconf, securityManagerBad) val slaveTracker = new MapOutputTrackerWorker(conf) - val selection = slaveSystem.actorSelection( - AkkaUtils.address(AkkaUtils.protocol(slaveSystem), "spark", "localhost", boundPort, "MapOutputTracker")) - val timeout = AkkaUtils.lookupTimeout(conf) - slaveTracker.trackerActor = Await.result(selection.resolveOne(timeout), timeout) + slaveTracker.trackerEndpoint = + slaveRpcEnv.setupEndpointRef("spark", rpcEnv.address, MapOutputTracker.ENDPOINT_NAME) assert(securityManagerBad.isAuthenticationEnabled() === false) @@ -119,8 +111,8 @@ class AkkaUtilsSuite extends FunSuite with LocalSparkContext with ResetSystemPro assert(slaveTracker.getServerStatuses(10, 0).toSeq === Seq((BlockManagerId("a", "hostA", 1000), size1000))) - actorSystem.shutdown() - slaveSystem.shutdown() + rpcEnv.shutdown() + slaveRpcEnv.shutdown() } test("remote fetch security pass") { @@ -130,15 +122,14 @@ class AkkaUtilsSuite extends FunSuite with LocalSparkContext with ResetSystemPro val securityManager = new SecurityManager(conf) val hostname = "localhost" - val (actorSystem, boundPort) = AkkaUtils.createActorSystem("spark", hostname, 0, - conf = conf, securityManager = securityManager) - System.setProperty("spark.hostPort", hostname + ":" + boundPort) + val rpcEnv = RpcEnv.create("spark", hostname, 0, conf, securityManager) + System.setProperty("spark.hostPort", rpcEnv.address.hostPort) assert(securityManager.isAuthenticationEnabled() === true) val masterTracker = new MapOutputTrackerMaster(conf) - masterTracker.trackerActor = actorSystem.actorOf( - Props(new MapOutputTrackerMasterActor(masterTracker, conf)), "MapOutputTracker") + masterTracker.trackerEndpoint = rpcEnv.setupEndpoint(MapOutputTracker.ENDPOINT_NAME, + new MapOutputTrackerMasterEndpoint(rpcEnv, masterTracker, conf)) val goodconf = new SparkConf goodconf.set("spark.authenticate", "true") @@ -147,13 +138,10 @@ class AkkaUtilsSuite extends FunSuite with LocalSparkContext with ResetSystemPro assert(securityManagerGood.isAuthenticationEnabled() === true) - val (slaveSystem, _) = AkkaUtils.createActorSystem("spark-slave", hostname, 0, - conf = goodconf, securityManager = securityManagerGood) + val slaveRpcEnv =RpcEnv.create("spark-slave", hostname, 0, goodconf, securityManagerGood) val slaveTracker = new MapOutputTrackerWorker(conf) - val selection = slaveSystem.actorSelection( - AkkaUtils.address(AkkaUtils.protocol(slaveSystem), "spark", "localhost", boundPort, "MapOutputTracker")) - val timeout = AkkaUtils.lookupTimeout(conf) - slaveTracker.trackerActor = Await.result(selection.resolveOne(timeout), timeout) + slaveTracker.trackerEndpoint = + slaveRpcEnv.setupEndpointRef("spark", rpcEnv.address, MapOutputTracker.ENDPOINT_NAME) masterTracker.registerShuffle(10, 1) masterTracker.incrementEpoch() @@ -169,47 +157,45 @@ class AkkaUtilsSuite extends FunSuite with LocalSparkContext with ResetSystemPro assert(slaveTracker.getServerStatuses(10, 0).toSeq === Seq((BlockManagerId("a", "hostA", 1000), size1000))) - actorSystem.shutdown() - slaveSystem.shutdown() + rpcEnv.shutdown() + slaveRpcEnv.shutdown() } test("remote fetch security off client") { val conf = new SparkConf + conf.set("spark.rpc", "akka") conf.set("spark.authenticate", "true") conf.set("spark.authenticate.secret", "good") val securityManager = new SecurityManager(conf) val hostname = "localhost" - val (actorSystem, boundPort) = AkkaUtils.createActorSystem("spark", hostname, 0, - conf = conf, securityManager = securityManager) - System.setProperty("spark.hostPort", hostname + ":" + boundPort) + val rpcEnv = RpcEnv.create("spark", hostname, 0, conf, securityManager) + System.setProperty("spark.hostPort", rpcEnv.address.hostPort) assert(securityManager.isAuthenticationEnabled() === true) val masterTracker = new MapOutputTrackerMaster(conf) - masterTracker.trackerActor = actorSystem.actorOf( - Props(new MapOutputTrackerMasterActor(masterTracker, conf)), "MapOutputTracker") + masterTracker.trackerEndpoint = rpcEnv.setupEndpoint(MapOutputTracker.ENDPOINT_NAME, + new MapOutputTrackerMasterEndpoint(rpcEnv, masterTracker, conf)) val badconf = new SparkConf + badconf.set("spark.rpc", "akka") badconf.set("spark.authenticate", "false") badconf.set("spark.authenticate.secret", "bad") val securityManagerBad = new SecurityManager(badconf) assert(securityManagerBad.isAuthenticationEnabled() === false) - val (slaveSystem, _) = AkkaUtils.createActorSystem("spark-slave", hostname, 0, - conf = badconf, securityManager = securityManagerBad) + val slaveRpcEnv = RpcEnv.create("spark-slave", hostname, 0, badconf, securityManagerBad) val slaveTracker = new MapOutputTrackerWorker(conf) - val selection = slaveSystem.actorSelection( - AkkaUtils.address(AkkaUtils.protocol(slaveSystem), "spark", "localhost", boundPort, "MapOutputTracker")) - val timeout = AkkaUtils.lookupTimeout(conf) intercept[akka.actor.ActorNotFound] { - slaveTracker.trackerActor = Await.result(selection.resolveOne(timeout), timeout) + slaveTracker.trackerEndpoint = + slaveRpcEnv.setupEndpointRef("spark", rpcEnv.address, MapOutputTracker.ENDPOINT_NAME) } - actorSystem.shutdown() - slaveSystem.shutdown() + rpcEnv.shutdown() + slaveRpcEnv.shutdown() } test("remote fetch ssl on") { @@ -217,26 +203,22 @@ class AkkaUtilsSuite extends FunSuite with LocalSparkContext with ResetSystemPro val securityManager = new SecurityManager(conf) val hostname = "localhost" - val (actorSystem, boundPort) = AkkaUtils.createActorSystem("spark", hostname, 0, - conf = conf, securityManager = securityManager) - System.setProperty("spark.hostPort", hostname + ":" + boundPort) + val rpcEnv = RpcEnv.create("spark", hostname, 0, conf, securityManager) + System.setProperty("spark.hostPort", rpcEnv.address.hostPort) assert(securityManager.isAuthenticationEnabled() === false) val masterTracker = new MapOutputTrackerMaster(conf) - masterTracker.trackerActor = actorSystem.actorOf( - Props(new MapOutputTrackerMasterActor(masterTracker, conf)), "MapOutputTracker") + masterTracker.trackerEndpoint = rpcEnv.setupEndpoint(MapOutputTracker.ENDPOINT_NAME, + new MapOutputTrackerMasterEndpoint(rpcEnv, masterTracker, conf)) val slaveConf = sparkSSLConfig() val securityManagerBad = new SecurityManager(slaveConf) - val (slaveSystem, _) = AkkaUtils.createActorSystem("spark-slave", hostname, 0, - conf = slaveConf, securityManager = securityManagerBad) + val slaveRpcEnv = RpcEnv.create("spark-slaves", hostname, 0, slaveConf, securityManagerBad) val slaveTracker = new MapOutputTrackerWorker(conf) - val selection = slaveSystem.actorSelection( - AkkaUtils.address(AkkaUtils.protocol(slaveSystem), "spark", "localhost", boundPort, "MapOutputTracker")) - val timeout = AkkaUtils.lookupTimeout(conf) - slaveTracker.trackerActor = Await.result(selection.resolveOne(timeout), timeout) + slaveTracker.trackerEndpoint = + slaveRpcEnv.setupEndpointRef("spark", rpcEnv.address, MapOutputTracker.ENDPOINT_NAME) assert(securityManagerBad.isAuthenticationEnabled() === false) @@ -254,8 +236,8 @@ class AkkaUtilsSuite extends FunSuite with LocalSparkContext with ResetSystemPro assert(slaveTracker.getServerStatuses(10, 0).toSeq === Seq((BlockManagerId("a", "hostA", 1000), size1000))) - actorSystem.shutdown() - slaveSystem.shutdown() + rpcEnv.shutdown() + slaveRpcEnv.shutdown() } @@ -266,28 +248,24 @@ class AkkaUtilsSuite extends FunSuite with LocalSparkContext with ResetSystemPro val securityManager = new SecurityManager(conf) val hostname = "localhost" - val (actorSystem, boundPort) = AkkaUtils.createActorSystem("spark", hostname, 0, - conf = conf, securityManager = securityManager) - System.setProperty("spark.hostPort", hostname + ":" + boundPort) + val rpcEnv = RpcEnv.create("spark", hostname, 0, conf, securityManager) + System.setProperty("spark.hostPort", rpcEnv.address.hostPort) assert(securityManager.isAuthenticationEnabled() === true) val masterTracker = new MapOutputTrackerMaster(conf) - masterTracker.trackerActor = actorSystem.actorOf( - Props(new MapOutputTrackerMasterActor(masterTracker, conf)), "MapOutputTracker") + masterTracker.trackerEndpoint = rpcEnv.setupEndpoint(MapOutputTracker.ENDPOINT_NAME, + new MapOutputTrackerMasterEndpoint(rpcEnv, masterTracker, conf)) val slaveConf = sparkSSLConfig() slaveConf.set("spark.authenticate", "true") slaveConf.set("spark.authenticate.secret", "good") val securityManagerBad = new SecurityManager(slaveConf) - val (slaveSystem, _) = AkkaUtils.createActorSystem("spark-slave", hostname, 0, - conf = slaveConf, securityManager = securityManagerBad) + val slaveRpcEnv = RpcEnv.create("spark-slave", hostname, 0, slaveConf, securityManagerBad) val slaveTracker = new MapOutputTrackerWorker(conf) - val selection = slaveSystem.actorSelection( - AkkaUtils.address(AkkaUtils.protocol(slaveSystem), "spark", "localhost", boundPort, "MapOutputTracker")) - val timeout = AkkaUtils.lookupTimeout(conf) - slaveTracker.trackerActor = Await.result(selection.resolveOne(timeout), timeout) + slaveTracker.trackerEndpoint = + slaveRpcEnv.setupEndpointRef("spark", rpcEnv.address, MapOutputTracker.ENDPOINT_NAME) assert(securityManagerBad.isAuthenticationEnabled() === true) @@ -304,45 +282,43 @@ class AkkaUtilsSuite extends FunSuite with LocalSparkContext with ResetSystemPro assert(slaveTracker.getServerStatuses(10, 0).toSeq === Seq((BlockManagerId("a", "hostA", 1000), size1000))) - actorSystem.shutdown() - slaveSystem.shutdown() + rpcEnv.shutdown() + slaveRpcEnv.shutdown() } test("remote fetch ssl on and security enabled - bad credentials") { val conf = sparkSSLConfig() + conf.set("spark.rpc", "akka") conf.set("spark.authenticate", "true") conf.set("spark.authenticate.secret", "good") val securityManager = new SecurityManager(conf) val hostname = "localhost" - val (actorSystem, boundPort) = AkkaUtils.createActorSystem("spark", hostname, 0, - conf = conf, securityManager = securityManager) - System.setProperty("spark.hostPort", hostname + ":" + boundPort) + val rpcEnv = RpcEnv.create("spark", hostname, 0, conf, securityManager) + System.setProperty("spark.hostPort", rpcEnv.address.hostPort) assert(securityManager.isAuthenticationEnabled() === true) val masterTracker = new MapOutputTrackerMaster(conf) - masterTracker.trackerActor = actorSystem.actorOf( - Props(new MapOutputTrackerMasterActor(masterTracker, conf)), "MapOutputTracker") + masterTracker.trackerEndpoint = rpcEnv.setupEndpoint(MapOutputTracker.ENDPOINT_NAME, + new MapOutputTrackerMasterEndpoint(rpcEnv, masterTracker, conf)) val slaveConf = sparkSSLConfig() + slaveConf.set("spark.rpc", "akka") slaveConf.set("spark.authenticate", "true") slaveConf.set("spark.authenticate.secret", "bad") val securityManagerBad = new SecurityManager(slaveConf) - val (slaveSystem, _) = AkkaUtils.createActorSystem("spark-slave", hostname, 0, - conf = slaveConf, securityManager = securityManagerBad) + val slaveRpcEnv = RpcEnv.create("spark-slave", hostname, 0, slaveConf, securityManagerBad) val slaveTracker = new MapOutputTrackerWorker(conf) - val selection = slaveSystem.actorSelection( - AkkaUtils.address(AkkaUtils.protocol(slaveSystem), "spark", "localhost", boundPort, "MapOutputTracker")) - val timeout = AkkaUtils.lookupTimeout(conf) intercept[akka.actor.ActorNotFound] { - slaveTracker.trackerActor = Await.result(selection.resolveOne(timeout), timeout) + slaveTracker.trackerEndpoint = + slaveRpcEnv.setupEndpointRef("spark", rpcEnv.address, MapOutputTracker.ENDPOINT_NAME) } - actorSystem.shutdown() - slaveSystem.shutdown() + rpcEnv.shutdown() + slaveRpcEnv.shutdown() } @@ -351,31 +327,30 @@ class AkkaUtilsSuite extends FunSuite with LocalSparkContext with ResetSystemPro val securityManager = new SecurityManager(conf) val hostname = "localhost" - val (actorSystem, boundPort) = AkkaUtils.createActorSystem("spark", hostname, 0, - conf = conf, securityManager = securityManager) - System.setProperty("spark.hostPort", hostname + ":" + boundPort) + val rpcEnv = RpcEnv.create("spark", hostname, 0, conf, securityManager) + System.setProperty("spark.hostPort", rpcEnv.address.hostPort) assert(securityManager.isAuthenticationEnabled() === false) val masterTracker = new MapOutputTrackerMaster(conf) - masterTracker.trackerActor = actorSystem.actorOf( - Props(new MapOutputTrackerMasterActor(masterTracker, conf)), "MapOutputTracker") + masterTracker.trackerEndpoint = rpcEnv.setupEndpoint(MapOutputTracker.ENDPOINT_NAME, + new MapOutputTrackerMasterEndpoint(rpcEnv, masterTracker, conf)) val slaveConf = sparkSSLConfig() val securityManagerBad = new SecurityManager(slaveConf) - val (slaveSystem, _) = AkkaUtils.createActorSystem("spark-slave", hostname, 0, - conf = slaveConf, securityManager = securityManagerBad) + val slaveRpcEnv = RpcEnv.create("spark-slave", hostname, 0, slaveConf, securityManagerBad) val slaveTracker = new MapOutputTrackerWorker(conf) - val selection = slaveSystem.actorSelection( - AkkaUtils.address(AkkaUtils.protocol(slaveSystem), "spark", "localhost", boundPort, "MapOutputTracker")) - val timeout = AkkaUtils.lookupTimeout(conf) - intercept[TimeoutException] { - slaveTracker.trackerActor = Await.result(selection.resolveOne(timeout * 2), timeout) + try { + slaveRpcEnv.setupEndpointRef("spark", rpcEnv.address, MapOutputTracker.ENDPOINT_NAME) + fail("should receive either ActorNotFound or TimeoutException") + } catch { + case e: ActorNotFound => + case e: TimeoutException => } - actorSystem.shutdown() - slaveSystem.shutdown() + rpcEnv.shutdown() + slaveRpcEnv.shutdown() } } diff --git a/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite.scala b/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite.scala index 054ef54e746a5..c47162779bbba 100644 --- a/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite.scala @@ -83,7 +83,7 @@ object TestObject { class TestClass extends Serializable { var x = 5 - def getX = x + def getX: Int = x def run(): Int = { var nonSer = new NonSerializable @@ -95,7 +95,7 @@ class TestClass extends Serializable { } class TestClassWithoutDefaultConstructor(x: Int) extends Serializable { - def getX = x + def getX: Int = x def run(): Int = { var nonSer = new NonSerializable @@ -164,7 +164,7 @@ object TestObjectWithNesting { } class TestClassWithNesting(val y: Int) extends Serializable { - def getY = y + def getY: Int = y def run(): Int = { var nonSer = new NonSerializable diff --git a/core/src/test/scala/org/apache/spark/util/EventLoopSuite.scala b/core/src/test/scala/org/apache/spark/util/EventLoopSuite.scala index 1026cb2aa7cae..47b535206c949 100644 --- a/core/src/test/scala/org/apache/spark/util/EventLoopSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/EventLoopSuite.scala @@ -203,4 +203,76 @@ class EventLoopSuite extends FunSuite with Timeouts { assert(!eventLoop.isActive) } } + + test("EventLoop: stop() in onStart should call onStop") { + @volatile var onStopCalled: Boolean = false + val eventLoop = new EventLoop[Int]("test") { + + override def onStart(): Unit = { + stop() + } + + override def onReceive(event: Int): Unit = { + } + + override def onError(e: Throwable): Unit = { + } + + override def onStop(): Unit = { + onStopCalled = true + } + } + eventLoop.start() + eventually(timeout(5 seconds), interval(5 millis)) { + assert(!eventLoop.isActive) + } + assert(onStopCalled) + } + + test("EventLoop: stop() in onReceive should call onStop") { + @volatile var onStopCalled: Boolean = false + val eventLoop = new EventLoop[Int]("test") { + + override def onReceive(event: Int): Unit = { + stop() + } + + override def onError(e: Throwable): Unit = { + } + + override def onStop(): Unit = { + onStopCalled = true + } + } + eventLoop.start() + eventLoop.post(1) + eventually(timeout(5 seconds), interval(5 millis)) { + assert(!eventLoop.isActive) + } + assert(onStopCalled) + } + + test("EventLoop: stop() in onError should call onStop") { + @volatile var onStopCalled: Boolean = false + val eventLoop = new EventLoop[Int]("test") { + + override def onReceive(event: Int): Unit = { + throw new RuntimeException("Oops") + } + + override def onError(e: Throwable): Unit = { + stop() + } + + override def onStop(): Unit = { + onStopCalled = true + } + } + eventLoop.start() + eventLoop.post(1) + eventually(timeout(5 seconds), interval(5 millis)) { + assert(!eventLoop.isActive) + } + assert(onStopCalled) + } } diff --git a/core/src/test/scala/org/apache/spark/util/FileAppenderSuite.scala b/core/src/test/scala/org/apache/spark/util/FileAppenderSuite.scala index 4dc5b6103db74..c05317534cddf 100644 --- a/core/src/test/scala/org/apache/spark/util/FileAppenderSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/FileAppenderSuite.scala @@ -32,7 +32,7 @@ import org.apache.spark.util.logging.{RollingFileAppender, SizeBasedRollingPolic class FileAppenderSuite extends FunSuite with BeforeAndAfter with Logging { - val testFile = new File("FileAppenderSuite-test-" + System.currentTimeMillis).getAbsoluteFile + val testFile = new File(Utils.createTempDir(), "FileAppenderSuite-test").getAbsoluteFile before { cleanup() @@ -109,7 +109,8 @@ class FileAppenderSuite extends FunSuite with BeforeAndAfter with Logging { // verify whether the earliest file has been deleted val rolledOverFiles = allGeneratedFiles.filter { _ != testFile.toString }.toArray.sorted - logInfo(s"All rolled over files generated:${rolledOverFiles.size}\n" + rolledOverFiles.mkString("\n")) + logInfo(s"All rolled over files generated:${rolledOverFiles.size}\n" + + rolledOverFiles.mkString("\n")) assert(rolledOverFiles.size > 2) val earliestRolledOverFile = rolledOverFiles.head val existingRolledOverFiles = RollingFileAppender.getSortedRolledOverFiles( @@ -135,7 +136,7 @@ class FileAppenderSuite extends FunSuite with BeforeAndAfter with Logging { val testOutputStream = new PipedOutputStream() val testInputStream = new PipedInputStream(testOutputStream) val appender = FileAppender(testInputStream, testFile, conf) - //assert(appender.getClass === classTag[ExpectedAppender].getClass) + // assert(appender.getClass === classTag[ExpectedAppender].getClass) assert(appender.getClass.getSimpleName === classTag[ExpectedAppender].runtimeClass.getSimpleName) if (appender.isInstanceOf[RollingFileAppender]) { @@ -153,9 +154,11 @@ class FileAppenderSuite extends FunSuite with BeforeAndAfter with Logging { import RollingFileAppender._ - def rollingStrategy(strategy: String) = Seq(STRATEGY_PROPERTY -> strategy) - def rollingSize(size: String) = Seq(SIZE_PROPERTY -> size) - def rollingInterval(interval: String) = Seq(INTERVAL_PROPERTY -> interval) + def rollingStrategy(strategy: String): Seq[(String, String)] = + Seq(STRATEGY_PROPERTY -> strategy) + def rollingSize(size: String): Seq[(String, String)] = Seq(SIZE_PROPERTY -> size) + def rollingInterval(interval: String): Seq[(String, String)] = + Seq(INTERVAL_PROPERTY -> interval) val msInDay = 24 * 60 * 60 * 1000L val msInHour = 60 * 60 * 1000L diff --git a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala index f3017dc42cd5c..a2be724254d7c 100644 --- a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala @@ -260,6 +260,18 @@ class JsonProtocolSuite extends FunSuite { assert(expectedFetchFailed === JsonProtocol.taskEndReasonFromJson(oldEvent)) } + test("ShuffleReadMetrics: Local bytes read and time taken backwards compatibility") { + // Metrics about local shuffle bytes read and local read time were added in 1.3.1. + val metrics = makeTaskMetrics(1L, 2L, 3L, 4L, 5, 6, + hasHadoopInput = false, hasOutput = false, hasRecords = false) + assert(metrics.shuffleReadMetrics.nonEmpty) + val newJson = JsonProtocol.taskMetricsToJson(metrics) + val oldJson = newJson.removeField { case (field, _) => field == "Local Bytes Read" } + .removeField { case (field, _) => field == "Local Read Time" } + val newMetrics = JsonProtocol.taskMetricsFromJson(oldJson) + assert(newMetrics.shuffleReadMetrics.get.localBytesRead == 0) + } + test("SparkListenerApplicationStart backwards compatibility") { // SparkListenerApplicationStart in Spark 1.0.0 do not have an "appId" property. val applicationStart = SparkListenerApplicationStart("test", None, 1L, "user") @@ -695,6 +707,7 @@ class JsonProtocolSuite extends FunSuite { sr.incFetchWaitTime(a + d) sr.incRemoteBlocksFetched(f) sr.incRecordsRead(if (hasRecords) (b + d) / 100 else -1) + sr.incLocalBytesRead(a + f) t.setShuffleReadMetrics(Some(sr)) } if (hasOutput) { @@ -941,6 +954,7 @@ class JsonProtocolSuite extends FunSuite { | "Local Blocks Fetched": 700, | "Fetch Wait Time": 900, | "Remote Bytes Read": 1000, + | "Local Bytes Read": 1100, | "Total Records Read" : 10 | }, | "Shuffle Write Metrics": { diff --git a/core/src/test/scala/org/apache/spark/executor/ExecutorURLClassLoaderSuite.scala b/core/src/test/scala/org/apache/spark/util/MutableURLClassLoaderSuite.scala similarity index 87% rename from core/src/test/scala/org/apache/spark/executor/ExecutorURLClassLoaderSuite.scala rename to core/src/test/scala/org/apache/spark/util/MutableURLClassLoaderSuite.scala index b7912c09d1410..87de90bb0dfb0 100644 --- a/core/src/test/scala/org/apache/spark/executor/ExecutorURLClassLoaderSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/MutableURLClassLoaderSuite.scala @@ -15,16 +15,15 @@ * limitations under the License. */ -package org.apache.spark.executor +package org.apache.spark.util import java.net.URLClassLoader import org.scalatest.FunSuite -import org.apache.spark.{LocalSparkContext, SparkContext, SparkException, TestUtils} -import org.apache.spark.util.Utils +import org.apache.spark.{SparkContext, SparkException, TestUtils} -class ExecutorURLClassLoaderSuite extends FunSuite { +class MutableURLClassLoaderSuite extends FunSuite { val urls2 = List(TestUtils.createJarWithClasses( classNames = Seq("FakeClass1", "FakeClass2", "FakeClass3"), @@ -37,7 +36,7 @@ class ExecutorURLClassLoaderSuite extends FunSuite { test("child first") { val parentLoader = new URLClassLoader(urls2, null) - val classLoader = new ChildExecutorURLClassLoader(urls, parentLoader) + val classLoader = new ChildFirstURLClassLoader(urls, parentLoader) val fakeClass = classLoader.loadClass("FakeClass2").newInstance() val fakeClassVersion = fakeClass.toString assert(fakeClassVersion === "1") @@ -47,7 +46,7 @@ class ExecutorURLClassLoaderSuite extends FunSuite { test("parent first") { val parentLoader = new URLClassLoader(urls2, null) - val classLoader = new ExecutorURLClassLoader(urls, parentLoader) + val classLoader = new MutableURLClassLoader(urls, parentLoader) val fakeClass = classLoader.loadClass("FakeClass1").newInstance() val fakeClassVersion = fakeClass.toString assert(fakeClassVersion === "2") @@ -57,7 +56,7 @@ class ExecutorURLClassLoaderSuite extends FunSuite { test("child first can fall back") { val parentLoader = new URLClassLoader(urls2, null) - val classLoader = new ChildExecutorURLClassLoader(urls, parentLoader) + val classLoader = new ChildFirstURLClassLoader(urls, parentLoader) val fakeClass = classLoader.loadClass("FakeClass3").newInstance() val fakeClassVersion = fakeClass.toString assert(fakeClassVersion === "2") @@ -65,7 +64,7 @@ class ExecutorURLClassLoaderSuite extends FunSuite { test("child first can fail") { val parentLoader = new URLClassLoader(urls2, null) - val classLoader = new ChildExecutorURLClassLoader(urls, parentLoader) + val classLoader = new ChildFirstURLClassLoader(urls, parentLoader) intercept[java.lang.ClassNotFoundException] { classLoader.loadClass("FakeClassDoesNotExist").newInstance() } diff --git a/core/src/test/scala/org/apache/spark/util/NextIteratorSuite.scala b/core/src/test/scala/org/apache/spark/util/NextIteratorSuite.scala index 72e81f3f1a884..403dcb03bd6e5 100644 --- a/core/src/test/scala/org/apache/spark/util/NextIteratorSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/NextIteratorSuite.scala @@ -71,7 +71,7 @@ class NextIteratorSuite extends FunSuite with Matchers { class StubIterator(ints: Buffer[Int]) extends NextIterator[Int] { var closeCalled = 0 - override def getNext() = { + override def getNext(): Int = { if (ints.size == 0) { finished = true 0 diff --git a/core/src/test/scala/org/apache/spark/util/SizeEstimatorSuite.scala b/core/src/test/scala/org/apache/spark/util/SizeEstimatorSuite.scala index 7424c2e91d4f2..67a9f75ff2187 100644 --- a/core/src/test/scala/org/apache/spark/util/SizeEstimatorSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/SizeEstimatorSuite.scala @@ -98,8 +98,10 @@ class SizeEstimatorSuite // If an array contains the *same* element many times, we should only count it once. val d1 = new DummyClass1 - assertResult(72)(SizeEstimator.estimate(Array.fill(10)(d1))) // 10 pointers plus 8-byte object - assertResult(432)(SizeEstimator.estimate(Array.fill(100)(d1))) // 100 pointers plus 8-byte object + // 10 pointers plus 8-byte object + assertResult(72)(SizeEstimator.estimate(Array.fill(10)(d1))) + // 100 pointers plus 8-byte object + assertResult(432)(SizeEstimator.estimate(Array.fill(100)(d1))) // Same thing with huge array containing the same element many times. Note that this won't // return exactly 4032 because it can't tell that *all* the elements will equal the first diff --git a/core/src/test/scala/org/apache/spark/util/TimeStampedHashMapSuite.scala b/core/src/test/scala/org/apache/spark/util/TimeStampedHashMapSuite.scala index c1c605cdb487b..8b72fe665c214 100644 --- a/core/src/test/scala/org/apache/spark/util/TimeStampedHashMapSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/TimeStampedHashMapSuite.scala @@ -63,7 +63,7 @@ class TimeStampedHashMapSuite extends FunSuite { assert(map1.getTimestamp("k1").get < threshTime1) assert(map1.getTimestamp("k2").isDefined) assert(map1.getTimestamp("k2").get >= threshTime1) - map1.clearOldValues(threshTime1) //should only clear k1 + map1.clearOldValues(threshTime1) // should only clear k1 assert(map1.get("k1") === None) assert(map1.get("k2").isDefined) } @@ -93,7 +93,7 @@ class TimeStampedHashMapSuite extends FunSuite { assert(map1.getTimestamp("k1").get < threshTime1) assert(map1.getTimestamp("k2").isDefined) assert(map1.getTimestamp("k2").get >= threshTime1) - map1.clearOldValues(threshTime1) //should only clear k1 + map1.clearOldValues(threshTime1) // should only clear k1 assert(map1.get("k1") === None) assert(map1.get("k2").isDefined) } diff --git a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala index fe2b644251157..fb97e650ff95c 100644 --- a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala @@ -23,6 +23,7 @@ import java.io.{File, ByteArrayOutputStream, ByteArrayInputStream, FileOutputStr import java.net.{BindException, ServerSocket, URI} import java.nio.{ByteBuffer, ByteOrder} import java.text.DecimalFormatSymbols +import java.util.concurrent.TimeUnit import java.util.Locale import com.google.common.base.Charsets.UTF_8 @@ -35,7 +36,50 @@ import org.apache.hadoop.fs.Path import org.apache.spark.SparkConf class UtilsSuite extends FunSuite with ResetSystemProperties { + + test("timeConversion") { + // Test -1 + assert(Utils.timeStringAsSeconds("-1") === -1) + + // Test zero + assert(Utils.timeStringAsSeconds("0") === 0) + + assert(Utils.timeStringAsSeconds("1") === 1) + assert(Utils.timeStringAsSeconds("1s") === 1) + assert(Utils.timeStringAsSeconds("1000ms") === 1) + assert(Utils.timeStringAsSeconds("1000000us") === 1) + assert(Utils.timeStringAsSeconds("1m") === TimeUnit.MINUTES.toSeconds(1)) + assert(Utils.timeStringAsSeconds("1min") === TimeUnit.MINUTES.toSeconds(1)) + assert(Utils.timeStringAsSeconds("1h") === TimeUnit.HOURS.toSeconds(1)) + assert(Utils.timeStringAsSeconds("1d") === TimeUnit.DAYS.toSeconds(1)) + + assert(Utils.timeStringAsMs("1") === 1) + assert(Utils.timeStringAsMs("1ms") === 1) + assert(Utils.timeStringAsMs("1000us") === 1) + assert(Utils.timeStringAsMs("1s") === TimeUnit.SECONDS.toMillis(1)) + assert(Utils.timeStringAsMs("1m") === TimeUnit.MINUTES.toMillis(1)) + assert(Utils.timeStringAsMs("1min") === TimeUnit.MINUTES.toMillis(1)) + assert(Utils.timeStringAsMs("1h") === TimeUnit.HOURS.toMillis(1)) + assert(Utils.timeStringAsMs("1d") === TimeUnit.DAYS.toMillis(1)) + + // Test invalid strings + intercept[NumberFormatException] { + Utils.timeStringAsMs("This breaks 600s") + } + + intercept[NumberFormatException] { + Utils.timeStringAsMs("This breaks 600ds") + } + intercept[NumberFormatException] { + Utils.timeStringAsMs("600s This breaks") + } + + intercept[NumberFormatException] { + Utils.timeStringAsMs("This 123s breaks") + } + } + test("bytesToString") { assert(Utils.bytesToString(10) === "10.0 B") assert(Utils.bytesToString(1500) === "1500.0 B") @@ -106,7 +150,7 @@ class UtilsSuite extends FunSuite with ResetSystemProperties { val second = 1000 val minute = second * 60 val hour = minute * 60 - def str = Utils.msDurationToString(_) + def str: (Long) => String = Utils.msDurationToString(_) val sep = new DecimalFormatSymbols(Locale.getDefault()).getDecimalSeparator() @@ -122,7 +166,6 @@ class UtilsSuite extends FunSuite with ResetSystemProperties { test("reading offset bytes of a file") { val tmpDir2 = Utils.createTempDir() - tmpDir2.deleteOnExit() val f1Path = tmpDir2 + "/f1" val f1 = new FileOutputStream(f1Path) f1.write("1\n2\n3\n4\n5\n6\n7\n8\n9\n".getBytes(UTF_8)) @@ -151,7 +194,6 @@ class UtilsSuite extends FunSuite with ResetSystemProperties { test("reading offset bytes across multiple files") { val tmpDir = Utils.createTempDir() - tmpDir.deleteOnExit() val files = (1 to 3).map(i => new File(tmpDir, i.toString)) Files.write("0123456789", files(0), UTF_8) Files.write("abcdefghij", files(1), UTF_8) @@ -201,25 +243,26 @@ class UtilsSuite extends FunSuite with ResetSystemProperties { test("doesDirectoryContainFilesNewerThan") { // create some temporary directories and files val parent: File = Utils.createTempDir() - val child1: File = Utils.createTempDir(parent.getCanonicalPath) // The parent directory has two child directories + // The parent directory has two child directories + val child1: File = Utils.createTempDir(parent.getCanonicalPath) val child2: File = Utils.createTempDir(parent.getCanonicalPath) val child3: File = Utils.createTempDir(child1.getCanonicalPath) // set the last modified time of child1 to 30 secs old child1.setLastModified(System.currentTimeMillis() - (1000 * 30)) // although child1 is old, child2 is still new so return true - assert(Utils.doesDirectoryContainAnyNewFiles(parent, 5)) + assert(Utils.doesDirectoryContainAnyNewFiles(parent, 5)) child2.setLastModified(System.currentTimeMillis - (1000 * 30)) - assert(Utils.doesDirectoryContainAnyNewFiles(parent, 5)) + assert(Utils.doesDirectoryContainAnyNewFiles(parent, 5)) parent.setLastModified(System.currentTimeMillis - (1000 * 30)) // although parent and its immediate children are new, child3 is still old // we expect a full recursive search for new files. - assert(Utils.doesDirectoryContainAnyNewFiles(parent, 5)) + assert(Utils.doesDirectoryContainAnyNewFiles(parent, 5)) child3.setLastModified(System.currentTimeMillis - (1000 * 30)) - assert(!Utils.doesDirectoryContainAnyNewFiles(parent, 5)) + assert(!Utils.doesDirectoryContainAnyNewFiles(parent, 5)) } test("resolveURI") { @@ -339,25 +382,26 @@ class UtilsSuite extends FunSuite with ResetSystemProperties { assert(!tempDir1.exists()) val tempDir2 = Utils.createTempDir() - val tempFile1 = new File(tempDir2, "foo.txt") - Files.touch(tempFile1) - assert(tempFile1.exists()) - Utils.deleteRecursively(tempFile1) - assert(!tempFile1.exists()) + val sourceFile1 = new File(tempDir2, "foo.txt") + Files.touch(sourceFile1) + assert(sourceFile1.exists()) + Utils.deleteRecursively(sourceFile1) + assert(!sourceFile1.exists()) val tempDir3 = new File(tempDir2, "subdir") assert(tempDir3.mkdir()) - val tempFile2 = new File(tempDir3, "bar.txt") - Files.touch(tempFile2) - assert(tempFile2.exists()) + val sourceFile2 = new File(tempDir3, "bar.txt") + Files.touch(sourceFile2) + assert(sourceFile2.exists()) Utils.deleteRecursively(tempDir2) assert(!tempDir2.exists()) assert(!tempDir3.exists()) - assert(!tempFile2.exists()) + assert(!sourceFile2.exists()) } test("loading properties from file") { - val outFile = File.createTempFile("test-load-spark-properties", "test") + val tmpDir = Utils.createTempDir() + val outFile = File.createTempFile("test-load-spark-properties", "test", tmpDir) try { System.setProperty("spark.test.fileNameLoadB", "2") Files.write("spark.test.fileNameLoadA true\n" + @@ -370,7 +414,7 @@ class UtilsSuite extends FunSuite with ResetSystemProperties { assert(sparkConf.getBoolean("spark.test.fileNameLoadA", false) === true) assert(sparkConf.getInt("spark.test.fileNameLoadB", 1) === 2) } finally { - outFile.delete() + Utils.deleteRecursively(tmpDir) } } @@ -387,29 +431,39 @@ class UtilsSuite extends FunSuite with ResetSystemProperties { test("fetch hcfs dir") { val tempDir = Utils.createTempDir() - val innerTempDir = Utils.createTempDir(tempDir.getPath) - val tempFile = File.createTempFile("someprefix", "somesuffix", innerTempDir) - val targetDir = new File("target-dir") - Files.write("some text", tempFile, UTF_8) - - try { - val path = new Path("file://" + tempDir.getAbsolutePath) - val conf = new Configuration() - val fs = Utils.getHadoopFileSystem(path.toString, conf) - Utils.fetchHcfsFile(path, targetDir, fs, new SparkConf(), conf, false) - assert(targetDir.exists()) - assert(targetDir.isDirectory()) - val newInnerDir = new File(targetDir, innerTempDir.getName) - println("inner temp dir: " + innerTempDir.getName) - targetDir.listFiles().map(_.getName).foreach(println) - assert(newInnerDir.exists()) - assert(newInnerDir.isDirectory()) - val newInnerFile = new File(newInnerDir, tempFile.getName) - assert(newInnerFile.exists()) - assert(newInnerFile.isFile()) - } finally { - Utils.deleteRecursively(tempDir) - Utils.deleteRecursively(targetDir) - } + val sourceDir = new File(tempDir, "source-dir") + val innerSourceDir = Utils.createTempDir(root=sourceDir.getPath) + val sourceFile = File.createTempFile("someprefix", "somesuffix", innerSourceDir) + val targetDir = new File(tempDir, "target-dir") + Files.write("some text", sourceFile, UTF_8) + + val path = new Path("file://" + sourceDir.getAbsolutePath) + val conf = new Configuration() + val fs = Utils.getHadoopFileSystem(path.toString, conf) + + assert(!targetDir.isDirectory()) + Utils.fetchHcfsFile(path, targetDir, fs, new SparkConf(), conf, false) + assert(targetDir.isDirectory()) + + // Copy again to make sure it doesn't error if the dir already exists. + Utils.fetchHcfsFile(path, targetDir, fs, new SparkConf(), conf, false) + + val destDir = new File(targetDir, sourceDir.getName()) + assert(destDir.isDirectory()) + + val destInnerDir = new File(destDir, innerSourceDir.getName) + assert(destInnerDir.isDirectory()) + + val destInnerFile = new File(destInnerDir, sourceFile.getName) + assert(destInnerFile.isFile()) + + val filePath = new Path("file://" + sourceFile.getAbsolutePath) + val testFileDir = new File(tempDir, "test-filename") + val testFileName = "testFName" + val testFilefs = Utils.getHadoopFileSystem(filePath.toString, conf) + Utils.fetchHcfsFile(filePath, testFileDir, testFilefs, new SparkConf(), + conf, false, Some(testFileName)) + val newFileName = new File(testFileDir, testFileName) + assert(newFileName.isFile()) } } diff --git a/core/src/test/scala/org/apache/spark/util/VectorSuite.scala b/core/src/test/scala/org/apache/spark/util/VectorSuite.scala index 794a55d61750b..ce2968728a996 100644 --- a/core/src/test/scala/org/apache/spark/util/VectorSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/VectorSuite.scala @@ -27,7 +27,7 @@ import org.scalatest.FunSuite @deprecated("suppress compile time deprecation warning", "1.0.0") class VectorSuite extends FunSuite { - def verifyVector(vector: Vector, expectedLength: Int) = { + def verifyVector(vector: Vector, expectedLength: Int): Unit = { assert(vector.length == expectedLength) assert(vector.elements.min > 0.0) assert(vector.elements.max < 1.0) diff --git a/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala index 48f79ea651018..dff8f3ddc816f 100644 --- a/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala @@ -185,7 +185,7 @@ class ExternalAppendOnlyMapSuite extends FunSuite with LocalSparkContext { // reduceByKey val rdd = sc.parallelize(1 to 10).map(i => (i%2, 1)) - val result1 = rdd.reduceByKey(_+_).collect() + val result1 = rdd.reduceByKey(_ + _).collect() assert(result1.toSet === Set[(Int, Int)]((0, 5), (1, 5))) // groupByKey diff --git a/core/src/test/scala/org/apache/spark/util/collection/ExternalSorterSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/ExternalSorterSuite.scala index 72d96798b1141..9ff067f86af44 100644 --- a/core/src/test/scala/org/apache/spark/util/collection/ExternalSorterSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/collection/ExternalSorterSuite.scala @@ -553,10 +553,10 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe conf.set("spark.shuffle.memoryFraction", "0.001") sc = new SparkContext("local-cluster[1,1,512]", "test", conf) - def createCombiner(i: String) = ArrayBuffer[String](i) - def mergeValue(buffer: ArrayBuffer[String], i: String) = buffer += i - def mergeCombiners(buffer1: ArrayBuffer[String], buffer2: ArrayBuffer[String]) = - buffer1 ++= buffer2 + def createCombiner(i: String): ArrayBuffer[String] = ArrayBuffer[String](i) + def mergeValue(buffer: ArrayBuffer[String], i: String): ArrayBuffer[String] = buffer += i + def mergeCombiners(buffer1: ArrayBuffer[String], buffer2: ArrayBuffer[String]) + : ArrayBuffer[String] = buffer1 ++= buffer2 val agg = new Aggregator[String, String, ArrayBuffer[String]]( createCombiner _, mergeValue _, mergeCombiners _) @@ -633,14 +633,17 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe conf.set("spark.shuffle.memoryFraction", "0.001") sc = new SparkContext("local-cluster[1,1,512]", "test", conf) - def createCombiner(i: Int) = ArrayBuffer[Int](i) - def mergeValue(buffer: ArrayBuffer[Int], i: Int) = buffer += i - def mergeCombiners(buf1: ArrayBuffer[Int], buf2: ArrayBuffer[Int]) = buf1 ++= buf2 + def createCombiner(i: Int): ArrayBuffer[Int] = ArrayBuffer[Int](i) + def mergeValue(buffer: ArrayBuffer[Int], i: Int): ArrayBuffer[Int] = buffer += i + def mergeCombiners(buf1: ArrayBuffer[Int], buf2: ArrayBuffer[Int]): ArrayBuffer[Int] = { + buf1 ++= buf2 + } val agg = new Aggregator[Int, Int, ArrayBuffer[Int]](createCombiner, mergeValue, mergeCombiners) val sorter = new ExternalSorter[Int, Int, ArrayBuffer[Int]](Some(agg), None, None, None) - sorter.insertAll((1 to 100000).iterator.map(i => (i, i)) ++ Iterator((Int.MaxValue, Int.MaxValue))) + sorter.insertAll( + (1 to 100000).iterator.map(i => (i, i)) ++ Iterator((Int.MaxValue, Int.MaxValue))) val it = sorter.iterator while (it.hasNext) { @@ -654,9 +657,10 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe conf.set("spark.shuffle.memoryFraction", "0.001") sc = new SparkContext("local-cluster[1,1,512]", "test", conf) - def createCombiner(i: String) = ArrayBuffer[String](i) - def mergeValue(buffer: ArrayBuffer[String], i: String) = buffer += i - def mergeCombiners(buf1: ArrayBuffer[String], buf2: ArrayBuffer[String]) = buf1 ++= buf2 + def createCombiner(i: String): ArrayBuffer[String] = ArrayBuffer[String](i) + def mergeValue(buffer: ArrayBuffer[String], i: String): ArrayBuffer[String] = buffer += i + def mergeCombiners(buf1: ArrayBuffer[String], buf2: ArrayBuffer[String]): ArrayBuffer[String] = + buf1 ++= buf2 val agg = new Aggregator[String, String, ArrayBuffer[String]]( createCombiner, mergeValue, mergeCombiners) @@ -720,7 +724,7 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe // Using wrongOrdering to show integer overflow introduced exception. val rand = new Random(100L) val wrongOrdering = new Ordering[String] { - override def compare(a: String, b: String) = { + override def compare(a: String, b: String): Int = { val h1 = if (a == null) 0 else a.hashCode() val h2 = if (b == null) 0 else b.hashCode() h1 - h2 @@ -742,9 +746,10 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe // Using aggregation and external spill to make sure ExternalSorter using // partitionKeyComparator. - def createCombiner(i: String) = ArrayBuffer(i) - def mergeValue(c: ArrayBuffer[String], i: String) = c += i - def mergeCombiners(c1: ArrayBuffer[String], c2: ArrayBuffer[String]) = c1 ++= c2 + def createCombiner(i: String): ArrayBuffer[String] = ArrayBuffer(i) + def mergeValue(c: ArrayBuffer[String], i: String): ArrayBuffer[String] = c += i + def mergeCombiners(c1: ArrayBuffer[String], c2: ArrayBuffer[String]): ArrayBuffer[String] = + c1 ++= c2 val agg = new Aggregator[String, String, ArrayBuffer[String]]( createCombiner, mergeValue, mergeCombiners) diff --git a/core/src/test/scala/org/apache/spark/util/collection/OpenHashMapSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/OpenHashMapSuite.scala index 6a70877356409..ef890d2ba60f3 100644 --- a/core/src/test/scala/org/apache/spark/util/collection/OpenHashMapSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/collection/OpenHashMapSuite.scala @@ -176,4 +176,14 @@ class OpenHashMapSuite extends FunSuite with Matchers { assert(map(i.toString) === i.toString) } } + + test("contains") { + val map = new OpenHashMap[String, Int](2) + map("a") = 1 + assert(map.contains("a")) + assert(!map.contains("b")) + assert(!map.contains(null)) + map(null) = 0 + assert(map.contains(null)) + } } diff --git a/core/src/test/scala/org/apache/spark/util/collection/PrimitiveKeyOpenHashMapSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/PrimitiveKeyOpenHashMapSuite.scala index 8c7df7d73dcd3..caf378fec8b3e 100644 --- a/core/src/test/scala/org/apache/spark/util/collection/PrimitiveKeyOpenHashMapSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/collection/PrimitiveKeyOpenHashMapSuite.scala @@ -118,4 +118,11 @@ class PrimitiveKeyOpenHashMapSuite extends FunSuite with Matchers { assert(map(i.toLong) === i.toString) } } + + test("contains") { + val map = new PrimitiveKeyOpenHashMap[Int, Int](1) + map(0) = 0 + assert(map.contains(0)) + assert(!map.contains(1)) + } } diff --git a/core/src/test/scala/org/apache/spark/util/collection/SorterSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/SorterSuite.scala index 0cb1ed7397655..e0d6cc16bde05 100644 --- a/core/src/test/scala/org/apache/spark/util/collection/SorterSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/collection/SorterSuite.scala @@ -65,6 +65,13 @@ class SorterSuite extends FunSuite { } } + // http://www.envisage-project.eu/timsort-specification-and-verification/ + test("SPARK-5984 TimSort bug") { + val data = TestTimSort.getTimSortBugTestSet(67108864) + new Sorter(new IntArraySortDataFormat).sort(data, 0, data.length, Ordering.Int) + (0 to data.length - 2).foreach(i => assert(data(i) <= data(i + 1))) + } + /** Runs an experiment several times. */ def runExperiment(name: String, skip: Boolean = false)(f: => Unit, prepare: () => Unit): Unit = { if (skip) { diff --git a/core/src/test/scala/org/apache/spark/util/random/XORShiftRandomSuite.scala b/core/src/test/scala/org/apache/spark/util/random/XORShiftRandomSuite.scala index ef7178bcdf5c2..03f5f2d1b8528 100644 --- a/core/src/test/scala/org/apache/spark/util/random/XORShiftRandomSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/random/XORShiftRandomSuite.scala @@ -28,7 +28,7 @@ import scala.language.reflectiveCalls class XORShiftRandomSuite extends FunSuite with Matchers { - def fixture = new { + def fixture: Object {val seed: Long; val hundMil: Int; val xorRand: XORShiftRandom} = new { val seed = 1L val xorRand = new XORShiftRandom(seed) val hundMil = 1e8.toInt diff --git a/data/mllib/als/sample_movielens_movies.txt b/data/mllib/als/sample_movielens_movies.txt new file mode 100644 index 0000000000000..934a0253849e1 --- /dev/null +++ b/data/mllib/als/sample_movielens_movies.txt @@ -0,0 +1,100 @@ +0::Movie 0::Romance|Comedy +1::Movie 1::Action|Anime +2::Movie 2::Romance|Thriller +3::Movie 3::Action|Romance +4::Movie 4::Anime|Comedy +5::Movie 5::Action|Action +6::Movie 6::Action|Comedy +7::Movie 7::Anime|Comedy +8::Movie 8::Comedy|Action +9::Movie 9::Anime|Thriller +10::Movie 10::Action|Anime +11::Movie 11::Action|Anime +12::Movie 12::Anime|Comedy +13::Movie 13::Thriller|Action +14::Movie 14::Anime|Comedy +15::Movie 15::Comedy|Thriller +16::Movie 16::Anime|Romance +17::Movie 17::Thriller|Action +18::Movie 18::Action|Comedy +19::Movie 19::Anime|Romance +20::Movie 20::Action|Anime +21::Movie 21::Romance|Thriller +22::Movie 22::Romance|Romance +23::Movie 23::Comedy|Comedy +24::Movie 24::Anime|Action +25::Movie 25::Comedy|Comedy +26::Movie 26::Anime|Romance +27::Movie 27::Anime|Anime +28::Movie 28::Thriller|Anime +29::Movie 29::Anime|Romance +30::Movie 30::Thriller|Romance +31::Movie 31::Thriller|Romance +32::Movie 32::Comedy|Anime +33::Movie 33::Comedy|Comedy +34::Movie 34::Anime|Anime +35::Movie 35::Action|Thriller +36::Movie 36::Anime|Romance +37::Movie 37::Romance|Anime +38::Movie 38::Thriller|Romance +39::Movie 39::Romance|Comedy +40::Movie 40::Action|Anime +41::Movie 41::Comedy|Thriller +42::Movie 42::Comedy|Action +43::Movie 43::Thriller|Anime +44::Movie 44::Anime|Action +45::Movie 45::Comedy|Romance +46::Movie 46::Comedy|Action 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+0.56297807,0.74 +0.56603774,0.75 +0.57929628,0.76 +0.64762876,0.77 +0.66241713,0.78 +0.69301377,0.79 +0.65119837,0.80 +0.68332483,0.81 +0.66598674,0.82 +0.73890872,0.83 +0.73992861,0.84 +0.84242733,0.85 +0.91330954,0.86 +0.88016318,0.87 +0.90719021,0.88 +0.93115757,0.89 +0.93115757,0.90 +0.91942886,0.91 +0.92911780,0.92 +0.95665477,0.93 +0.95002550,0.94 +0.96940337,0.95 +1.00000000,0.96 +0.89801122,0.97 +0.90311066,0.98 +0.90362060,0.99 +0.83477817,1.0 \ No newline at end of file diff --git a/data/mllib/sample_lda_data.txt b/data/mllib/sample_lda_data.txt new file mode 100644 index 0000000000000..2e76702ca9d67 --- /dev/null +++ b/data/mllib/sample_lda_data.txt @@ -0,0 +1,12 @@ +1 2 6 0 2 3 1 1 0 0 3 +1 3 0 1 3 0 0 2 0 0 1 +1 4 1 0 0 4 9 0 1 2 0 +2 1 0 3 0 0 5 0 2 3 9 +3 1 1 9 3 0 2 0 0 1 3 +4 2 0 3 4 5 1 1 1 4 0 +2 1 0 3 0 0 5 0 2 2 9 +1 1 1 9 2 1 2 0 0 1 3 +4 4 0 3 4 2 1 3 0 0 0 +2 8 2 0 3 0 2 0 2 7 2 +1 1 1 9 0 2 2 0 0 3 3 +4 1 0 0 4 5 1 3 0 1 0 diff --git a/dev/.gitignore b/dev/.gitignore new file mode 100644 index 0000000000000..4a6027429e0d3 --- /dev/null +++ b/dev/.gitignore @@ -0,0 +1 @@ +pep8*.py diff --git a/dev/audit-release/sbt_app_sql/src/main/scala/SqlApp.scala b/dev/audit-release/sbt_app_sql/src/main/scala/SqlApp.scala index d888de929fdda..cc86ef45858c9 100644 --- a/dev/audit-release/sbt_app_sql/src/main/scala/SqlApp.scala +++ b/dev/audit-release/sbt_app_sql/src/main/scala/SqlApp.scala @@ -36,8 +36,10 @@ object SparkSqlExample { val sc = new SparkContext(conf) val sqlContext = new SQLContext(sc) + import sqlContext.implicits._ import sqlContext._ - val people = sc.makeRDD(1 to 100, 10).map(x => Person(s"Name$x", x)) + + val people = sc.makeRDD(1 to 100, 10).map(x => Person(s"Name$x", x)).toDF() people.registerTempTable("people") val teenagers = sql("SELECT name FROM people WHERE age >= 13 AND age <= 19") val teenagerNames = teenagers.map(t => "Name: " + t(0)).collect() diff --git a/dev/change-version-to-2.10.sh b/dev/change-version-to-2.10.sh index 7473c20d28e09..15e0c73b4295e 100755 --- a/dev/change-version-to-2.10.sh +++ b/dev/change-version-to-2.10.sh @@ -16,5 +16,11 @@ # See the License for the specific language governing permissions and # limitations under the License. # + +# Note that this will not necessarily work as intended with non-GNU sed (e.g. OS X) + find . -name 'pom.xml' | grep -v target \ - | xargs -I {} sed -i -e 's|\(artifactId.*\)_2.11|\1_2.10|g' {} + | xargs -I {} sed -i -e 's/\(artifactId.*\)_2.11/\1_2.10/g' {} + +# Also update in parent POM +sed -i -e '0,/2.112.10 in parent POM +sed -i -e '0,/2.102.11 "$JAR_DL" && mv "$JAR_DL" "$JAR" - elif [ $(command -v wget) ]; then - wget --quiet ${URL} -O "$JAR_DL" && mv "$JAR_DL" "$JAR" - else - printf "You do not have curl or wget installed, please install rat manually.\n" - exit -1 - fi - fi - - unzip -tq $JAR &> /dev/null - if [ $? -ne 0 ]; then - # We failed to download - printf "Our attempt to download rat locally to ${JAR} failed. Please install rat manually.\n" + # Download rat launch jar if it hasn't been downloaded yet + if [ ! -f "$JAR" ]; then + # Download + printf "Attempting to fetch rat\n" + JAR_DL="${JAR}.part" + if [ $(command -v curl) ]; then + curl -L --silent "${URL}" > "$JAR_DL" && mv "$JAR_DL" "$JAR" + elif [ $(command -v wget) ]; then + wget --quiet ${URL} -O "$JAR_DL" && mv "$JAR_DL" "$JAR" + else + printf "You do not have curl or wget installed, please install rat manually.\n" exit -1 fi - printf "Launching rat from ${JAR}\n" + fi + + unzip -tq "$JAR" &> /dev/null + if [ $? -ne 0 ]; then + # We failed to download + rm "$JAR" + printf "Our attempt to download rat locally to ${JAR} failed. Please install rat manually.\n" + exit -1 fi } @@ -71,6 +69,11 @@ mkdir -p "$FWDIR"/lib $java_cmd -jar "$rat_jar" -E "$FWDIR"/.rat-excludes -d "$FWDIR" > rat-results.txt +if [ $? -ne 0 ]; then + echo "RAT exited abnormally" + exit 1 +fi + ERRORS="$(cat rat-results.txt | grep -e "??")" if test ! -z "$ERRORS"; then diff --git a/dev/create-release/create-release.sh b/dev/create-release/create-release.sh index 607ce1c803507..b5a67dd783b93 100755 --- a/dev/create-release/create-release.sh +++ b/dev/create-release/create-release.sh @@ -34,6 +34,9 @@ ASF_PASSWORD=${ASF_PASSWORD:-XXX} GPG_PASSPHRASE=${GPG_PASSPHRASE:-XXX} GIT_BRANCH=${GIT_BRANCH:-branch-1.0} RELEASE_VERSION=${RELEASE_VERSION:-1.2.0} +# Allows publishing under a different version identifier than +# was present in the actual release sources (e.g. rc-X) +PUBLISH_VERSION=${PUBLISH_VERSION:-$RELEASE_VERSION} NEXT_VERSION=${NEXT_VERSION:-1.2.1} RC_NAME=${RC_NAME:-rc2} @@ -97,10 +100,16 @@ if [[ ! "$@" =~ --skip-publish ]]; then pushd spark git checkout --force $GIT_TAG + # Substitute in case published version is different than released + old="^\( \{2,4\}\)${RELEASE_VERSION}<\/version>$" + new="\1${PUBLISH_VERSION}<\/version>" + find . -name pom.xml | grep -v dev | xargs -I {} sed -i \ + -e "s/${old}/${new}/" {} + # Using Nexus API documented here: # https://support.sonatype.com/entries/39720203-Uploading-to-a-Staging-Repository-via-REST-API echo "Creating Nexus staging repository" - repo_request="Apache Spark $GIT_TAG" + repo_request="Apache Spark $GIT_TAG (published as $PUBLISH_VERSION)" out=$(curl -X POST -d "$repo_request" -u $ASF_USERNAME:$ASF_PASSWORD \ -H "Content-Type:application/xml" -v \ $NEXUS_ROOT/profiles/$NEXUS_PROFILE/start) @@ -109,13 +118,13 @@ if [[ ! "$@" =~ --skip-publish ]]; then rm -rf $SPARK_REPO - mvn -DskipTests -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 \ + build/mvn -DskipTests -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 \ -Pyarn -Phive -Phadoop-2.2 -Pspark-ganglia-lgpl -Pkinesis-asl \ clean install ./dev/change-version-to-2.11.sh - mvn -DskipTests -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 \ + build/mvn -DskipTests -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 \ -Dscala-2.11 -Pyarn -Phive -Phadoop-2.2 -Pspark-ganglia-lgpl -Pkinesis-asl \ clean install @@ -152,7 +161,7 @@ if [[ ! "$@" =~ --skip-publish ]]; then done echo "Closing nexus staging repository" - repo_request="$staged_repo_idApache Spark $GIT_TAG" + repo_request="$staged_repo_idApache Spark $GIT_TAG (published as $PUBLISH_VERSION)" out=$(curl -X POST -d "$repo_request" -u $ASF_USERNAME:$ASF_PASSWORD \ -H "Content-Type:application/xml" -v \ $NEXUS_ROOT/profiles/$NEXUS_PROFILE/finish) @@ -184,10 +193,12 @@ if [[ ! "$@" =~ --skip-package ]]; then echo $GPG_PASSPHRASE | gpg --passphrase-fd 0 --print-md SHA512 spark-$RELEASE_VERSION.tgz > \ spark-$RELEASE_VERSION.tgz.sha rm -rf spark-$RELEASE_VERSION - + + # Updated for each binary build make_binary_release() { NAME=$1 FLAGS=$2 + ZINC_PORT=$3 cp -r spark spark-$RELEASE_VERSION-bin-$NAME cd spark-$RELEASE_VERSION-bin-$NAME @@ -197,10 +208,12 @@ if [[ ! "$@" =~ --skip-package ]]; then ./dev/change-version-to-2.11.sh fi - ./make-distribution.sh --name $NAME --tgz $FLAGS 2>&1 | tee ../binary-release-$NAME.log + export ZINC_PORT=$ZINC_PORT + echo "Creating distribution: $NAME ($FLAGS)" + ./make-distribution.sh --name $NAME --tgz $FLAGS -DzincPort=$ZINC_PORT 2>&1 > \ + ../binary-release-$NAME.log cd .. cp spark-$RELEASE_VERSION-bin-$NAME/spark-$RELEASE_VERSION-bin-$NAME.tgz . - rm -rf spark-$RELEASE_VERSION-bin-$NAME echo $GPG_PASSPHRASE | gpg --passphrase-fd 0 --armour \ --output spark-$RELEASE_VERSION-bin-$NAME.tgz.asc \ @@ -213,16 +226,18 @@ if [[ ! "$@" =~ --skip-package ]]; then spark-$RELEASE_VERSION-bin-$NAME.tgz.sha } - - make_binary_release "hadoop1" "-Phive -Phive-thriftserver -Dhadoop.version=1.0.4" & - make_binary_release "hadoop1-scala2.11" "-Phive -Dscala-2.11" & - make_binary_release "cdh4" "-Phive -Phive-thriftserver -Dhadoop.version=2.0.0-mr1-cdh4.2.0" & - make_binary_release "hadoop2.3" "-Phadoop-2.3 -Phive -Phive-thriftserver -Pyarn" & - make_binary_release "hadoop2.4" "-Phadoop-2.4 -Phive -Phive-thriftserver -Pyarn" & - make_binary_release "mapr3" "-Pmapr3 -Phive -Phive-thriftserver" & - make_binary_release "mapr4" "-Pmapr4 -Pyarn -Phive -Phive-thriftserver" & - make_binary_release "hadoop2.4-without-hive" "-Phadoop-2.4 -Pyarn" & + # We increment the Zinc port each time to avoid OOM's and other craziness if multiple builds + # share the same Zinc server. + make_binary_release "hadoop1" "-Phive -Phive-thriftserver -Dhadoop.version=1.0.4" "3030" & + make_binary_release "hadoop1-scala2.11" "-Phive -Dscala-2.11" "3031" & + make_binary_release "cdh4" "-Phive -Phive-thriftserver -Dhadoop.version=2.0.0-mr1-cdh4.2.0" "3032" & + make_binary_release "hadoop2.3" "-Phadoop-2.3 -Phive -Phive-thriftserver -Pyarn" "3033" & + make_binary_release "hadoop2.4" "-Phadoop-2.4 -Phive -Phive-thriftserver -Pyarn" "3034" & + make_binary_release "mapr3" "-Pmapr3 -Phive -Phive-thriftserver" "3035" & + make_binary_release "mapr4" "-Pmapr4 -Pyarn -Phive -Phive-thriftserver" "3036" & + make_binary_release "hadoop2.4-without-hive" "-Phadoop-2.4 -Pyarn" "3037" & wait + rm -rf spark-$RELEASE_VERSION-bin-*/ # Copy data echo "Copying release tarballs" @@ -237,7 +252,7 @@ if [[ ! "$@" =~ --skip-package ]]; then sbt/sbt clean cd docs # Compile docs with Java 7 to use nicer format - JAVA_HOME=$JAVA_7_HOME PRODUCTION=1 jekyll build + JAVA_HOME="$JAVA_7_HOME" PRODUCTION=1 RELEASE_VERSION="$RELEASE_VERSION" jekyll build echo "Copying release documentation" rc_docs_folder=${rc_folder}-docs ssh $ASF_USERNAME@people.apache.org \ diff --git a/dev/create-release/known_translations b/dev/create-release/known_translations index b74e4ee8a330b..0a599b5a65549 100644 --- a/dev/create-release/known_translations +++ b/dev/create-release/known_translations @@ -57,3 +57,37 @@ watermen - Yadong Qi witgo - Guoqiang Li xinyunh - Xinyun Huang zsxwing - Shixiong Zhu +Bilna - Bilna P +DoingDone9 - Doing Done +Earne - Ernest +FlytxtRnD - Meethu Mathew +GenTang - Gen TANG +JoshRosen - Josh Rosen +MechCoder - Manoj Kumar +OopsOutOfMemory - Sheng Li +Peishen-Jia - Peishen Jia +SaintBacchus - Huang Zhaowei +azagrebin - Andrey Zagrebin +bzz - Alexander Bezzubov +fjiang6 - Fan Jiang +gasparms - Gaspar Munoz +guowei2 - Guo Wei +hhbyyh - Yuhao Yang +hseagle - Peng Xu +javadba - Stephen Boesch +jbencook - Ben Cook +kul - Kuldeep +ligangty - Gang Li +marsishandsome - Liangliang Gu +medale - Markus Dale +nemccarthy - Nathan McCarthy +nxwhite-str - Nate Crosswhite +seayi - Xiaohua Yi +tianyi - Yi Tian +uncleGen - Uncle Gen +viper-kun - Xu Kun +x1- - Yuri Saito +zapletal-martin - Martin Zapletal +zuxqoj - Shekhar Bansal +mingyukim - Mingyu Kim +sigmoidanalytics - Mayur Rustagi diff --git a/dev/lint-python b/dev/lint-python index 772f856154ae0..f50d149dc4d44 100755 --- a/dev/lint-python +++ b/dev/lint-python @@ -19,43 +19,54 @@ SCRIPT_DIR="$( cd "$( dirname "$0" )" && pwd )" SPARK_ROOT_DIR="$(dirname "$SCRIPT_DIR")" -PEP8_REPORT_PATH="$SPARK_ROOT_DIR/dev/pep8-report.txt" +PATHS_TO_CHECK="./python/pyspark/ ./ec2/spark_ec2.py ./examples/src/main/python/" +PYTHON_LINT_REPORT_PATH="$SPARK_ROOT_DIR/dev/python-lint-report.txt" cd "$SPARK_ROOT_DIR" +# compileall: https://docs.python.org/2/library/compileall.html +python -B -m compileall -q -l $PATHS_TO_CHECK > "$PYTHON_LINT_REPORT_PATH" +compile_status="${PIPESTATUS[0]}" + # Get pep8 at runtime so that we don't rely on it being installed on the build server. #+ See: https://github.com/apache/spark/pull/1744#issuecomment-50982162 #+ TODOs: -#+ - Dynamically determine latest release version of pep8 and use that. -#+ - Download this from a more reliable source. (GitHub raw can be flaky, apparently. (?)) -PEP8_SCRIPT_PATH="$SPARK_ROOT_DIR/dev/pep8.py" -PEP8_SCRIPT_REMOTE_PATH="https://raw.githubusercontent.com/jcrocholl/pep8/1.5.7/pep8.py" -PEP8_PATHS_TO_CHECK="./python/pyspark/ ./ec2/spark_ec2.py ./examples/src/main/python/" - -curl --silent -o "$PEP8_SCRIPT_PATH" "$PEP8_SCRIPT_REMOTE_PATH" -curl_status=$? - -if [ $curl_status -ne 0 ]; then - echo "Failed to download pep8.py from \"$PEP8_SCRIPT_REMOTE_PATH\"." - exit $curl_status -fi +#+ - Download pep8 from PyPI. It's more "official". +PEP8_VERSION="1.6.2" +PEP8_SCRIPT_PATH="$SPARK_ROOT_DIR/dev/pep8-$PEP8_VERSION.py" +PEP8_SCRIPT_REMOTE_PATH="https://raw.githubusercontent.com/jcrocholl/pep8/$PEP8_VERSION/pep8.py" +if [ ! -e "$PEP8_SCRIPT_PATH" ]; then + curl --silent -o "$PEP8_SCRIPT_PATH" "$PEP8_SCRIPT_REMOTE_PATH" + curl_status="$?" + + if [ "$curl_status" -ne 0 ]; then + echo "Failed to download pep8.py from \"$PEP8_SCRIPT_REMOTE_PATH\"." + exit "$curl_status" + fi +fi # There is no need to write this output to a file #+ first, but we do so so that the check status can #+ be output before the report, like with the #+ scalastyle and RAT checks. -python "$PEP8_SCRIPT_PATH" $PEP8_PATHS_TO_CHECK > "$PEP8_REPORT_PATH" -pep8_status=${PIPESTATUS[0]} #$? +python "$PEP8_SCRIPT_PATH" --ignore=E402,E731,E241,W503,E226 $PATHS_TO_CHECK >> "$PYTHON_LINT_REPORT_PATH" +pep8_status="${PIPESTATUS[0]}" + +if [ "$compile_status" -eq 0 -a "$pep8_status" -eq 0 ]; then + lint_status=0 +else + lint_status=1 +fi -if [ $pep8_status -ne 0 ]; then - echo "PEP 8 checks failed." - cat "$PEP8_REPORT_PATH" +if [ "$lint_status" -ne 0 ]; then + echo "Python lint checks failed." + cat "$PYTHON_LINT_REPORT_PATH" else - echo "PEP 8 checks passed." + echo "Python lint checks passed." fi -rm "$PEP8_REPORT_PATH" -rm "$PEP8_SCRIPT_PATH" +# rm "$PEP8_SCRIPT_PATH" +rm "$PYTHON_LINT_REPORT_PATH" -exit $pep8_status +exit "$lint_status" diff --git a/dev/merge_spark_pr.py b/dev/merge_spark_pr.py index dfa924d2aa0ba..3062e9c3c6651 100755 --- a/dev/merge_spark_pr.py +++ b/dev/merge_spark_pr.py @@ -244,6 +244,8 @@ def resolve_jira_issue(merge_branches, comment, default_jira_id=""): versions = asf_jira.project_versions("SPARK") versions = sorted(versions, key=lambda x: x.name, reverse=True) versions = filter(lambda x: x.raw['released'] is False, versions) + # Consider only x.y.z versions + versions = filter(lambda x: re.match('\d+\.\d+\.\d+', x.name), versions) default_fix_versions = map(lambda x: fix_version_from_branch(x, versions).name, merge_branches) for v in default_fix_versions: diff --git a/dev/run-tests b/dev/run-tests index 2257a566bb1bb..861d1671182c2 100755 --- a/dev/run-tests +++ b/dev/run-tests @@ -36,7 +36,7 @@ function handle_error () { } -# Build against the right verison of Hadoop. +# Build against the right version of Hadoop. { if [ -n "$AMPLAB_JENKINS_BUILD_PROFILE" ]; then if [ "$AMPLAB_JENKINS_BUILD_PROFILE" = "hadoop1.0" ]; then @@ -77,7 +77,7 @@ export SBT_MAVEN_PROFILES_ARGS="$SBT_MAVEN_PROFILES_ARGS -Pkinesis-asl" fi } -# Only run Hive tests if there are sql changes. +# Only run Hive tests if there are SQL changes. # Partial solution for SPARK-1455. if [ -n "$AMPLAB_JENKINS" ]; then git fetch origin master:master @@ -141,31 +141,52 @@ echo "=========================================================================" CURRENT_BLOCK=$BLOCK_BUILD { + HIVE_BUILD_ARGS="$SBT_MAVEN_PROFILES_ARGS -Phive -Phive-thriftserver" + HIVE_12_BUILD_ARGS="$HIVE_BUILD_ARGS -Phive-0.12.0" - # NOTE: echo "q" is needed because sbt on encountering a build file with failure - # (either resolution or compilation) prompts the user for input either q, r, etc - # to quit or retry. This echo is there to make it not block. - # NOTE: Do not quote $BUILD_MVN_PROFILE_ARGS or else it will be interpreted as a - # single argument! - # QUESTION: Why doesn't 'yes "q"' work? - # QUESTION: Why doesn't 'grep -v -e "^\[info\] Resolving"' work? # First build with Hive 0.12.0 to ensure patches do not break the Hive 0.12.0 build - HIVE_12_BUILD_ARGS="$SBT_MAVEN_PROFILES_ARGS -Phive -Phive-thriftserver -Phive-0.12.0" echo "[info] Compile with Hive 0.12.0" - echo -e "q\n" \ - | build/sbt $HIVE_12_BUILD_ARGS clean hive/compile hive-thriftserver/compile \ - | grep -v -e "info.*Resolving" -e "warn.*Merging" -e "info.*Including" + [ -d "lib_managed" ] && rm -rf lib_managed + echo "[info] Building Spark with these arguments: $HIVE_12_BUILD_ARGS" + + if [ "${AMPLAB_JENKINS_BUILD_TOOL}" == "maven" ]; then + build/mvn $HIVE_12_BUILD_ARGS clean package -DskipTests + else + # NOTE: echo "q" is needed because sbt on encountering a build file with failure + # (either resolution or compilation) prompts the user for input either q, r, etc + # to quit or retry. This echo is there to make it not block. + # NOTE: Do not quote $BUILD_MVN_PROFILE_ARGS or else it will be interpreted as a + # single argument! + # QUESTION: Why doesn't 'yes "q"' work? + # QUESTION: Why doesn't 'grep -v -e "^\[info\] Resolving"' work? + echo -e "q\n" \ + | build/sbt $HIVE_12_BUILD_ARGS clean hive/compile hive-thriftserver/compile \ + | grep -v -e "info.*Resolving" -e "warn.*Merging" -e "info.*Including" + fi # Then build with default Hive version (0.13.1) because tests are based on this version echo "[info] Compile with Hive 0.13.1" - rm -rf lib_managed - echo "[info] Building Spark with these arguments: $SBT_MAVEN_PROFILES_ARGS"\ - " -Phive -Phive-thriftserver" - echo -e "q\n" \ - | build/sbt $SBT_MAVEN_PROFILES_ARGS -Phive -Phive-thriftserver package assembly/assembly \ - | grep -v -e "info.*Resolving" -e "warn.*Merging" -e "info.*Including" + [ -d "lib_managed" ] && rm -rf lib_managed + echo "[info] Building Spark with these arguments: $HIVE_BUILD_ARGS" + + if [ "${AMPLAB_JENKINS_BUILD_TOOL}" == "maven" ]; then + build/mvn $HIVE_BUILD_ARGS clean package -DskipTests + else + echo -e "q\n" \ + | build/sbt $HIVE_BUILD_ARGS package assembly/assembly streaming-kafka-assembly/assembly \ + | grep -v -e "info.*Resolving" -e "warn.*Merging" -e "info.*Including" + fi } +echo "" +echo "=========================================================================" +echo "Detecting binary incompatibilities with MiMa" +echo "=========================================================================" + +CURRENT_BLOCK=$BLOCK_MIMA + +./dev/mima + echo "" echo "=========================================================================" echo "Running Spark unit tests" @@ -183,24 +204,28 @@ CURRENT_BLOCK=$BLOCK_SPARK_UNIT_TESTS if [ -n "$_SQL_TESTS_ONLY" ]; then # This must be an array of individual arguments. Otherwise, having one long string # will be interpreted as a single test, which doesn't work. - SBT_MAVEN_TEST_ARGS=("catalyst/test" "sql/test" "hive/test" "mllib/test") + SBT_MAVEN_TEST_ARGS=("catalyst/test" "sql/test" "hive/test" "hive-thriftserver/test" "mllib/test") else SBT_MAVEN_TEST_ARGS=("test") fi echo "[info] Running Spark tests with these arguments: $SBT_MAVEN_PROFILES_ARGS ${SBT_MAVEN_TEST_ARGS[@]}" - # NOTE: echo "q" is needed because sbt on encountering a build file with failure - # (either resolution or compilation) prompts the user for input either q, r, etc - # to quit or retry. This echo is there to make it not block. - # NOTE: Do not quote $SBT_MAVEN_PROFILES_ARGS or else it will be interpreted as a - # single argument! - # "${SBT_MAVEN_TEST_ARGS[@]}" is cool because it's an array. - # QUESTION: Why doesn't 'yes "q"' work? - # QUESTION: Why doesn't 'grep -v -e "^\[info\] Resolving"' work? - echo -e "q\n" \ - | build/sbt $SBT_MAVEN_PROFILES_ARGS "${SBT_MAVEN_TEST_ARGS[@]}" \ - | grep -v -e "info.*Resolving" -e "warn.*Merging" -e "info.*Including" + if [ "${AMPLAB_JENKINS_BUILD_TOOL}" == "maven" ]; then + build/mvn test $SBT_MAVEN_PROFILES_ARGS --fail-at-end + else + # NOTE: echo "q" is needed because sbt on encountering a build file with failure + # (either resolution or compilation) prompts the user for input either q, r, etc + # to quit or retry. This echo is there to make it not block. + # NOTE: Do not quote $SBT_MAVEN_PROFILES_ARGS or else it will be interpreted as a + # single argument! + # "${SBT_MAVEN_TEST_ARGS[@]}" is cool because it's an array. + # QUESTION: Why doesn't 'yes "q"' work? + # QUESTION: Why doesn't 'grep -v -e "^\[info\] Resolving"' work? + echo -e "q\n" \ + | build/sbt $SBT_MAVEN_PROFILES_ARGS "${SBT_MAVEN_TEST_ARGS[@]}" \ + | grep -v -e "info.*Resolving" -e "warn.*Merging" -e "info.*Including" + fi } echo "" @@ -210,13 +235,21 @@ echo "=========================================================================" CURRENT_BLOCK=$BLOCK_PYSPARK_UNIT_TESTS +# add path for python 3 in jenkins +export PATH="${PATH}:/home/anaonda/envs/py3k/bin" ./python/run-tests echo "" echo "=========================================================================" -echo "Detecting binary incompatibilities with MiMa" +echo "Running SparkR tests" echo "=========================================================================" -CURRENT_BLOCK=$BLOCK_MIMA +CURRENT_BLOCK=$BLOCK_SPARKR_UNIT_TESTS + +if [ $(command -v R) ]; then + ./R/install-dev.sh + ./R/run-tests.sh +else + echo "Ignoring SparkR tests as R was not found in PATH" +fi -./dev/mima diff --git a/dev/run-tests-codes.sh b/dev/run-tests-codes.sh index 1348e0609dda4..154e01255b2ef 100644 --- a/dev/run-tests-codes.sh +++ b/dev/run-tests-codes.sh @@ -22,6 +22,7 @@ readonly BLOCK_RAT=11 readonly BLOCK_SCALA_STYLE=12 readonly BLOCK_PYTHON_STYLE=13 readonly BLOCK_BUILD=14 -readonly BLOCK_SPARK_UNIT_TESTS=15 -readonly BLOCK_PYSPARK_UNIT_TESTS=16 -readonly BLOCK_MIMA=17 +readonly BLOCK_MIMA=15 +readonly BLOCK_SPARK_UNIT_TESTS=16 +readonly BLOCK_PYSPARK_UNIT_TESTS=17 +readonly BLOCK_SPARKR_UNIT_TESTS=18 diff --git a/dev/run-tests-jenkins b/dev/run-tests-jenkins index 6a849e4f77207..030f2cdddb350 100755 --- a/dev/run-tests-jenkins +++ b/dev/run-tests-jenkins @@ -47,7 +47,23 @@ COMMIT_URL="https://github.com/apache/spark/commit/${ghprbActualCommit}" # GitHub doesn't auto-link short hashes when submitted via the API, unfortunately. :( SHORT_COMMIT_HASH="${ghprbActualCommit:0:7}" -TESTS_TIMEOUT="120m" # format: http://linux.die.net/man/1/timeout +TESTS_TIMEOUT="150m" # format: http://linux.die.net/man/1/timeout + +# Array to capture all tests to run on the pull request. These tests are held under the +#+ dev/tests/ directory. +# +# To write a PR test: +#+ * the file must reside within the dev/tests directory +#+ * be an executable bash script +#+ * accept three arguments on the command line, the first being the Github PR long commit +#+ hash, the second the Github SHA1 hash, and the final the current PR hash +#+ * and, lastly, return string output to be included in the pr message output that will +#+ be posted to Github +PR_TESTS=( + "pr_merge_ability" + "pr_public_classes" + "pr_new_dependencies" +) function post_message () { local message=$1 @@ -131,61 +147,42 @@ function send_archived_logs () { fi } - -# We diff master...$ghprbActualCommit because that gets us changes introduced in the PR -#+ and not anything else added to master since the PR was branched. - -# check PR merge-ability and check for new public classes -{ - if [ "$sha1" == "$ghprbActualCommit" ]; then - merge_note=" * This patch **does not merge cleanly**." - else - merge_note=" * This patch merges cleanly." - fi - - source_files=$( - git diff master...$ghprbActualCommit --name-only `# diff patch against master from branch point` \ - | grep -v -e "\/test" `# ignore files in test directories` \ - | grep -e "\.py$" -e "\.java$" -e "\.scala$" `# include only code files` \ - | tr "\n" " " - ) - new_public_classes=$( - git diff master...$ghprbActualCommit ${source_files} `# diff patch against master from branch point` \ - | grep "^\+" `# filter in only added lines` \ - | sed -r -e "s/^\+//g" `# remove the leading +` \ - | grep -e "trait " -e "class " `# filter in lines with these key words` \ - | grep -e "{" -e "(" `# filter in lines with these key words, too` \ - | grep -v -e "\@\@" -e "private" `# exclude lines with these words` \ - | grep -v -e "^// " -e "^/\*" -e "^ \* " `# exclude comment lines` \ - | sed -r -e "s/\{.*//g" `# remove from the { onwards` \ - | sed -r -e "s/\}//g" `# just in case, remove }; they mess the JSON` \ - | sed -r -e "s/\"/\\\\\"/g" `# escape double quotes; they mess the JSON` \ - | sed -r -e "s/^(.*)$/\`\1\`/g" `# surround with backticks for style` \ - | sed -r -e "s/^/ \* /g" `# prepend ' *' to start of line` \ - | sed -r -e "s/$/\\\n/g" `# append newline to end of line` \ - | tr -d "\n" `# remove actual LF characters` - ) - - if [ -z "$new_public_classes" ]; then - public_classes_note=" * This patch adds no public classes." - else - public_classes_note=" * This patch adds the following public classes _(experimental)_:" - public_classes_note="${public_classes_note}\n${new_public_classes}" - fi -} - # post start message { start_message="\ [Test build ${BUILD_DISPLAY_NAME} has started](${BUILD_URL}consoleFull) for \ PR $ghprbPullId at commit [\`${SHORT_COMMIT_HASH}\`](${COMMIT_URL})." - start_message="${start_message}\n${merge_note}" - # start_message="${start_message}\n${public_classes_note}" - post_message "$start_message" } +# Environment variable to capture PR test output +pr_message="" +# Ensure we save off the current HEAD to revert to +current_pr_head="`git rev-parse HEAD`" + +echo "HEAD: `git rev-parse HEAD`" +echo "GHPRB: $ghprbActualCommit" +echo "SHA1: $sha1" + +# Run pull request tests +for t in "${PR_TESTS[@]}"; do + this_test="${FWDIR}/dev/tests/${t}.sh" + # Ensure the test can be found and is a file + if [ -f "${this_test}" ]; then + echo "Running test: $t" + this_mssg="$(bash "${this_test}" "${ghprbActualCommit}" "${sha1}" "${current_pr_head}")" + # Check if this is the merge test as we submit that note *before* and *after* + # the tests run + [ "$t" == "pr_merge_ability" ] && merge_note="${this_mssg}" + pr_message="${pr_message}\n${this_mssg}" + # Ensure, after each test, that we're back on the current PR + git checkout -f "${current_pr_head}" &>/dev/null + else + echo "Cannot find test ${this_test}." + fi +done + # run tests { timeout "${TESTS_TIMEOUT}" ./dev/run-tests @@ -211,12 +208,14 @@ function send_archived_logs () { failing_test="Python style tests" elif [ "$test_result" -eq "$BLOCK_BUILD" ]; then failing_test="to build" + elif [ "$test_result" -eq "$BLOCK_MIMA" ]; then + failing_test="MiMa tests" elif [ "$test_result" -eq "$BLOCK_SPARK_UNIT_TESTS" ]; then failing_test="Spark unit tests" elif [ "$test_result" -eq "$BLOCK_PYSPARK_UNIT_TESTS" ]; then failing_test="PySpark unit tests" - elif [ "$test_result" -eq "$BLOCK_MIMA" ]; then - failing_test="MiMa tests" + elif [ "$test_result" -eq "$BLOCK_SPARKR_UNIT_TESTS" ]; then + failing_test="SparkR unit tests" else failing_test="some tests" fi @@ -234,8 +233,7 @@ function send_archived_logs () { PR $ghprbPullId at commit [\`${SHORT_COMMIT_HASH}\`](${COMMIT_URL})." result_message="${result_message}\n${test_result_note}" - result_message="${result_message}\n${merge_note}" - result_message="${result_message}\n${public_classes_note}" + result_message="${result_message}${pr_message}" post_message "$result_message" } diff --git a/dev/scalastyle b/dev/scalastyle index 86919227ed1ab..4e03f89ed5d5d 100755 --- a/dev/scalastyle +++ b/dev/scalastyle @@ -18,9 +18,10 @@ # echo -e "q\n" | build/sbt -Phive -Phive-thriftserver scalastyle > scalastyle.txt +echo -e "q\n" | build/sbt -Phive -Phive-thriftserver test:scalastyle >> scalastyle.txt # Check style with YARN built too -echo -e "q\n" | build/sbt -Pyarn -Phadoop-2.2 -Dhadoop.version=2.2.0 scalastyle \ - >> scalastyle.txt +echo -e "q\n" | build/sbt -Pyarn -Phadoop-2.2 -Dhadoop.version=2.2.0 scalastyle >> scalastyle.txt +echo -e "q\n" | build/sbt -Pyarn -Phadoop-2.2 -Dhadoop.version=2.2.0 test:scalastyle >> scalastyle.txt ERRORS=$(cat scalastyle.txt | awk '{if($1~/error/)print}') rm scalastyle.txt diff --git a/dev/tests/pr_merge_ability.sh b/dev/tests/pr_merge_ability.sh new file mode 100755 index 0000000000000..d9a347fe24a8c --- /dev/null +++ b/dev/tests/pr_merge_ability.sh @@ -0,0 +1,39 @@ +#!/usr/bin/env bash + +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# +# This script follows the base format for testing pull requests against +# another branch and returning results to be published. More details can be +# found at dev/run-tests-jenkins. +# +# Arg1: The Github Pull Request Actual Commit +#+ known as `ghprbActualCommit` in `run-tests-jenkins` +# Arg2: The SHA1 hash +#+ known as `sha1` in `run-tests-jenkins` +# + +ghprbActualCommit="$1" +sha1="$2" + +# check PR merge-ability +if [ "${sha1}" == "${ghprbActualCommit}" ]; then + echo " * This patch **does not merge cleanly**." +else + echo " * This patch merges cleanly." +fi diff --git a/dev/tests/pr_new_dependencies.sh b/dev/tests/pr_new_dependencies.sh new file mode 100755 index 0000000000000..fdfb3c62aff58 --- /dev/null +++ b/dev/tests/pr_new_dependencies.sh @@ -0,0 +1,117 @@ +#!/usr/bin/env bash + +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# +# This script follows the base format for testing pull requests against +# another branch and returning results to be published. More details can be +# found at dev/run-tests-jenkins. +# +# Arg1: The Github Pull Request Actual Commit +#+ known as `ghprbActualCommit` in `run-tests-jenkins` +# Arg2: The SHA1 hash +#+ known as `sha1` in `run-tests-jenkins` +# Arg3: Current PR Commit Hash +#+ the PR hash for the current commit +# + +ghprbActualCommit="$1" +sha1="$2" +current_pr_head="$3" + +MVN_BIN="build/mvn" +CURR_CP_FILE="my-classpath.txt" +MASTER_CP_FILE="master-classpath.txt" + +# First switch over to the master branch +git checkout -f master +# Find and copy all pom.xml files into a *.gate file that we can check +# against through various `git` changes +find -name "pom.xml" -exec cp {} {}.gate \; +# Switch back to the current PR +git checkout -f "${current_pr_head}" + +# Check if any *.pom files from the current branch are different from the master +difference_q="" +for p in $(find -name "pom.xml"); do + [[ -f "${p}" && -f "${p}.gate" ]] && \ + difference_q="${difference_q}$(diff $p.gate $p)" +done + +# If no pom files were changed we can easily say no new dependencies were added +if [ -z "${difference_q}" ]; then + echo " * This patch does not change any dependencies." +else + # Else we need to manually build spark to determine what, if any, dependencies + # were added into the Spark assembly jar + ${MVN_BIN} clean package dependency:build-classpath -DskipTests 2>/dev/null | \ + sed -n -e '/Building Spark Project Assembly/,$p' | \ + grep --context=1 -m 2 "Dependencies classpath:" | \ + head -n 3 | \ + tail -n 1 | \ + tr ":" "\n" | \ + rev | \ + cut -d "/" -f 1 | \ + rev | \ + sort > ${CURR_CP_FILE} + + # Checkout the master branch to compare against + git checkout -f master + + ${MVN_BIN} clean package dependency:build-classpath -DskipTests 2>/dev/null | \ + sed -n -e '/Building Spark Project Assembly/,$p' | \ + grep --context=1 -m 2 "Dependencies classpath:" | \ + head -n 3 | \ + tail -n 1 | \ + tr ":" "\n" | \ + rev | \ + cut -d "/" -f 1 | \ + rev | \ + sort > ${MASTER_CP_FILE} + + DIFF_RESULTS="`diff ${CURR_CP_FILE} ${MASTER_CP_FILE}`" + + if [ -z "${DIFF_RESULTS}" ]; then + echo " * This patch does not change any dependencies." + else + # Pretty print the new dependencies + added_deps=$(echo "${DIFF_RESULTS}" | grep "<" | cut -d' ' -f2 | awk '{printf " * \`"$1"\`\\n"}') + removed_deps=$(echo "${DIFF_RESULTS}" | grep ">" | cut -d' ' -f2 | awk '{printf " * \`"$1"\`\\n"}') + added_deps_text=" * This patch **adds the following new dependencies:**\n${added_deps}" + removed_deps_text=" * This patch **removes the following dependencies:**\n${removed_deps}" + + # Construct the final returned message with proper + return_mssg="" + [ -n "${added_deps}" ] && return_mssg="${added_deps_text}" + if [ -n "${removed_deps}" ]; then + if [ -n "${return_mssg}" ]; then + return_mssg="${return_mssg}\n${removed_deps_text}" + else + return_mssg="${removed_deps_text}" + fi + fi + echo "${return_mssg}" + fi + + # Remove the files we've left over + [ -f "${CURR_CP_FILE}" ] && rm -f "${CURR_CP_FILE}" + [ -f "${MASTER_CP_FILE}" ] && rm -f "${MASTER_CP_FILE}" + + # Clean up our mess from the Maven builds just in case + ${MVN_BIN} clean &>/dev/null +fi diff --git a/dev/tests/pr_public_classes.sh b/dev/tests/pr_public_classes.sh new file mode 100755 index 0000000000000..927295b88c963 --- /dev/null +++ b/dev/tests/pr_public_classes.sh @@ -0,0 +1,65 @@ +#!/usr/bin/env bash + +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# +# This script follows the base format for testing pull requests against +# another branch and returning results to be published. More details can be +# found at dev/run-tests-jenkins. +# +# Arg1: The Github Pull Request Actual Commit +#+ known as `ghprbActualCommit` in `run-tests-jenkins` +# Arg2: The SHA1 hash +#+ known as `sha1` in `run-tests-jenkins` +# + +# We diff master...$ghprbActualCommit because that gets us changes introduced in the PR +#+ and not anything else added to master since the PR was branched. + +ghprbActualCommit="$1" +sha1="$2" + +source_files=$( + git diff master...$ghprbActualCommit --name-only `# diff patch against master from branch point` \ + | grep -v -e "\/test" `# ignore files in test directories` \ + | grep -e "\.py$" -e "\.java$" -e "\.scala$" `# include only code files` \ + | tr "\n" " " +) +new_public_classes=$( + git diff master...$ghprbActualCommit ${source_files} `# diff patch against master from branch point` \ + | grep "^\+" `# filter in only added lines` \ + | sed -r -e "s/^\+//g" `# remove the leading +` \ + | grep -e "trait " -e "class " `# filter in lines with these key words` \ + | grep -e "{" -e "(" `# filter in lines with these key words, too` \ + | grep -v -e "\@\@" -e "private" `# exclude lines with these words` \ + | grep -v -e "^// " -e "^/\*" -e "^ \* " `# exclude comment lines` \ + | sed -r -e "s/\{.*//g" `# remove from the { onwards` \ + | sed -r -e "s/\}//g" `# just in case, remove }; they mess the JSON` \ + | sed -r -e "s/\"/\\\\\"/g" `# escape double quotes; they mess the JSON` \ + | sed -r -e "s/^(.*)$/\`\1\`/g" `# surround with backticks for style` \ + | sed -r -e "s/^/ \* /g" `# prepend ' *' to start of line` \ + | sed -r -e "s/$/\\\n/g" `# append newline to end of line` \ + | tr -d "\n" `# remove actual LF characters` +) + +if [ -z "$new_public_classes" ]; then + echo " * This patch adds no public classes." +else + public_classes_note=" * This patch adds the following public classes _(experimental)_:" + echo "${public_classes_note}\n${new_public_classes}" +fi diff --git a/docs/README.md b/docs/README.md index 8a54724c4beae..5852f972a051d 100644 --- a/docs/README.md +++ b/docs/README.md @@ -58,19 +58,25 @@ phase, use the following sytax: We use Sphinx to generate Python API docs, so you will need to install it by running `sudo pip install sphinx`. -## API Docs (Scaladoc and Sphinx) +## knitr, devtools -You can build just the Spark scaladoc by running `build/sbt doc` from the SPARK_PROJECT_ROOT directory. +SparkR documentation is written using `roxygen2` and we use `knitr`, `devtools` to generate +documentation. To install these packages you can run `install.packages(c("knitr", "devtools"))` from a +R console. + +## API Docs (Scaladoc, Sphinx, roxygen2) + +You can build just the Spark scaladoc by running `build/sbt unidoc` from the SPARK_PROJECT_ROOT directory. Similarly, you can build just the PySpark docs by running `make html` from the SPARK_PROJECT_ROOT/python/docs directory. Documentation is only generated for classes that are listed as -public in `__init__.py`. +public in `__init__.py`. The SparkR docs can be built by running SPARK_PROJECT_ROOT/R/create-docs.sh. When you run `jekyll` in the `docs` directory, it will also copy over the scaladoc for the various Spark subprojects into the `docs` directory (and then also into the `_site` directory). We use a -jekyll plugin to run `build/sbt doc` before building the site so if you haven't run it (recently) it +jekyll plugin to run `build/sbt unidoc` before building the site so if you haven't run it (recently) it may take some time as it generates all of the scaladoc. The jekyll plugin also generates the PySpark docs [Sphinx](http://sphinx-doc.org/). -NOTE: To skip the step of building and copying over the Scala and Python API docs, run `SKIP_API=1 +NOTE: To skip the step of building and copying over the Scala, Python, R API docs, run `SKIP_API=1 jekyll`. diff --git a/docs/_config.yml b/docs/_config.yml index e2db274e1f619..b22b627f09007 100644 --- a/docs/_config.yml +++ b/docs/_config.yml @@ -10,11 +10,12 @@ kramdown: include: - _static + - _modules # These allow the documentation to be updated with newer releases # of Spark, Scala, and Mesos. -SPARK_VERSION: 1.3.0-SNAPSHOT -SPARK_VERSION_SHORT: 1.3.0 +SPARK_VERSION: 1.4.0-SNAPSHOT +SPARK_VERSION_SHORT: 1.4.0 SCALA_BINARY_VERSION: "2.10" SCALA_VERSION: "2.10.4" MESOS_VERSION: 0.21.0 diff --git a/docs/_layouts/global.html b/docs/_layouts/global.html index efc4c612937df..b92c75f90b11c 100755 --- a/docs/_layouts/global.html +++ b/docs/_layouts/global.html @@ -71,7 +71,7 @@
  • Spark Programming Guide
  • Spark Streaming
    • -
  • Spark SQL
    • +
  • DataFrames and SQL
  • MLlib (Machine Learning)
  • GraphX (Graph Processing)
  • Bagel (Pregel on Spark)
    • @@ -84,6 +84,7 @@
  • Scala
  • Java
  • Python
    • +
  • R
    • diff --git a/docs/_plugins/copy_api_dirs.rb b/docs/_plugins/copy_api_dirs.rb index 3c626a0b7f54b..0ea3f8eab461b 100644 --- a/docs/_plugins/copy_api_dirs.rb +++ b/docs/_plugins/copy_api_dirs.rb @@ -78,5 +78,18 @@ puts "cp -r python/docs/_build/html/. docs/api/python" cp_r("python/docs/_build/html/.", "docs/api/python") - cd("..") + # Build SparkR API docs + puts "Moving to R directory and building roxygen docs." + cd("R") + puts `./create-docs.sh` + + puts "Moving back into home dir." + cd("../") + + puts "Making directory api/R" + mkdir_p "docs/api/R" + + puts "cp -r R/pkg/html/. docs/api/R" + cp_r("R/pkg/html/.", "docs/api/R") + end diff --git a/docs/building-spark.md b/docs/building-spark.md index db69905813817..ea79c5bc276d3 100644 --- a/docs/building-spark.md +++ b/docs/building-spark.md @@ -9,6 +9,10 @@ redirect_from: "building-with-maven.html" Building Spark using Maven requires Maven 3.0.4 or newer and Java 6+. +**Note:** Building Spark with Java 7 or later can create JAR files that may not be +readable with early versions of Java 6, due to the large number of files in the JAR +archive. Build with Java 6 if this is an issue for your deployment. + # Building with `build/mvn` Spark now comes packaged with a self-contained Maven installation to ease building and deployment of Spark from source located under the `build/` directory. This script will automatically download and setup all necessary build requirements ([Maven](https://maven.apache.org/), [Scala](http://www.scala-lang.org/), and [Zinc](https://github.com/typesafehub/zinc)) locally within the `build/` directory itself. It honors any `mvn` binary if present already, however, will pull down its own copy of Scala and Zinc regardless to ensure proper version requirements are met. `build/mvn` execution acts as a pass through to the `mvn` call allowing easy transition from previous build methods. As an example, one can build a version of Spark as follows: @@ -19,6 +23,18 @@ build/mvn -Pyarn -Phadoop-2.4 -Dhadoop.version=2.4.0 -DskipTests clean package Other build examples can be found below. +**Note:** When building on an encrypted filesystem (if your home directory is encrypted, for example), then the Spark build might fail with a "Filename too long" error. As a workaround, add the following in the configuration args of the `scala-maven-plugin` in the project `pom.xml`: + + -Xmax-classfile-name + 128 + +and in `project/SparkBuild.scala` add: + + scalacOptions in Compile ++= Seq("-Xmax-classfile-name", "128"), + +to the `sharedSettings` val. See also [this PR](https://github.com/apache/spark/pull/2883/files) if you are unsure of where to add these lines. + + # Setting up Maven's Memory Usage You'll need to configure Maven to use more memory than usual by setting `MAVEN_OPTS`. We recommend the following settings: @@ -111,9 +127,9 @@ To produce a Spark package compiled with Scala 2.11, use the `-Dscala-2.11` prop dev/change-version-to-2.11.sh mvn -Pyarn -Phadoop-2.4 -Dscala-2.11 -DskipTests clean package -Scala 2.11 support in Spark is experimental and does not support a few features. -Specifically, Spark's external Kafka library and JDBC component are not yet -supported in Scala 2.11 builds. +Scala 2.11 support in Spark does not support a few features due to dependencies +which are themselves not Scala 2.11 ready. Specifically, Spark's external +Kafka library and JDBC component are not yet supported in Scala 2.11 builds. # Spark Tests in Maven @@ -159,14 +175,6 @@ Thus, the full flow for running continuous-compilation of the `core` submodule m For help in setting up IntelliJ IDEA or Eclipse for Spark development, and troubleshooting, refer to the [wiki page for IDE setup](https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark#ContributingtoSpark-IDESetup). -# Building Spark Debian Packages - -The Maven build includes support for building a Debian package containing the assembly 'fat-jar', PySpark, and the necessary scripts and configuration files. This can be created by specifying the following: - - mvn -Pdeb -DskipTests clean package - -The debian package can then be found under assembly/target. We added the short commit hash to the file name so that we can distinguish individual packages built for SNAPSHOT versions. - # Running Java 8 Test Suites Running only Java 8 tests and nothing else. diff --git a/docs/cluster-overview.md b/docs/cluster-overview.md index 6a75d5c457f02..7079de546e2f5 100644 --- a/docs/cluster-overview.md +++ b/docs/cluster-overview.md @@ -33,7 +33,11 @@ There are several useful things to note about this architecture: 2. Spark is agnostic to the underlying cluster manager. As long as it can acquire executor processes, and these communicate with each other, it is relatively easy to run it even on a cluster manager that also supports other applications (e.g. Mesos/YARN). -3. Because the driver schedules tasks on the cluster, it should be run close to the worker +3. The driver program must listen for and accept incoming connections from its executors throughout + its lifetime (e.g., see [spark.driver.port and spark.fileserver.port in the network config + section](configuration.html#networking)). As such, the driver program must be network + addressable from the worker nodes. +4. Because the driver schedules tasks on the cluster, it should be run close to the worker nodes, preferably on the same local area network. If you'd like to send requests to the cluster remotely, it's better to open an RPC to the driver and have it submit operations from nearby than to run a driver far away from the worker nodes. diff --git a/docs/configuration.md b/docs/configuration.md index 00e973c245005..d9e9e67026cbb 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -35,9 +35,19 @@ val conf = new SparkConf() val sc = new SparkContext(conf) {% endhighlight %} -Note that we can have more than 1 thread in local mode, and in cases like spark streaming, we may actually -require one to prevent any sort of starvation issues. +Note that we can have more than 1 thread in local mode, and in cases like Spark Streaming, we may +actually require one to prevent any sort of starvation issues. +Properties that specify some time duration should be configured with a unit of time. +The following format is accepted: + + 25ms (milliseconds) + 5s (seconds) + 10m or 10min (minutes) + 3h (hours) + 5d (days) + 1y (years) + ## Dynamically Loading Spark Properties In some cases, you may want to avoid hard-coding certain configurations in a `SparkConf`. For instance, if you'd like to run the same application with different masters or different @@ -70,7 +80,9 @@ each line consists of a key and a value separated by whitespace. For example: Any values specified as flags or in the properties file will be passed on to the application and merged with those specified through SparkConf. Properties set directly on the SparkConf take highest precedence, then flags passed to `spark-submit` or `spark-shell`, then options -in the `spark-defaults.conf` file. +in the `spark-defaults.conf` file. A few configuration keys have been renamed since earlier +versions of Spark; in such cases, the older key names are still accepted, but take lower +precedence than any instance of the newer key. ## Viewing Spark Properties @@ -94,14 +106,6 @@ of the most common options to set are: The name of your application. This will appear in the UI and in log data. - - spark.master - (none) - - The cluster manager to connect to. See the list of - allowed master URL's. - - spark.driver.cores 1 @@ -109,23 +113,6 @@ of the most common options to set are: Number of cores to use for the driver process, only in cluster mode. - - spark.driver.memory - 512m - - Amount of memory to use for the driver process, i.e. where SparkContext is initialized. - (e.g. 512m, 2g). - - - - spark.executor.memory - 512m - - Amount of memory to use per executor process, in the same format as JVM memory strings - (e.g. 512m, 2g). - - - spark.driver.maxResultSize 1g @@ -138,38 +125,35 @@ of the most common options to set are: - spark.serializer - org.apache.spark.serializer.
    JavaSerializer + spark.driver.memory + 512m - Class to use for serializing objects that will be sent over the network or need to be cached - in serialized form. The default of Java serialization works with any Serializable Java object - but is quite slow, so we recommend using - org.apache.spark.serializer.KryoSerializer and configuring Kryo serialization - when speed is necessary. Can be any subclass of - - org.apache.spark.Serializer. + Amount of memory to use for the driver process, i.e. where SparkContext is initialized. + (e.g. 512m, 2g). + +
    Note: In client mode, this config must not be set through the SparkConf + directly in your application, because the driver JVM has already started at that point. + Instead, please set this through the --driver-memory command line option + or in your default properties file. - spark.kryo.classesToRegister - (none) + spark.executor.memory + 512m - If you use Kryo serialization, give a comma-separated list of custom class names to register - with Kryo. - See the tuning guide for more details. + Amount of memory to use per executor process, in the same format as JVM memory strings + (e.g. 512m, 2g). - spark.kryo.registrator + spark.extraListeners (none) - If you use Kryo serialization, set this class to register your custom classes with Kryo. This - property is useful if you need to register your classes in a custom way, e.g. to specify a custom - field serializer. Otherwise spark.kryo.classesToRegister is simpler. It should be - set to a class that extends - - KryoRegistrator. - See the tuning guide for more details. + A comma-separated list of classes that implement SparkListener; when initializing + SparkContext, instances of these classes will be created and registered with Spark's listener + bus. If a class has a single-argument constructor that accepts a SparkConf, that constructor + will be called; otherwise, a zero-argument constructor will be called. If no valid constructor + can be found, the SparkContext creation will fail with an exception. @@ -192,14 +176,11 @@ of the most common options to set are: - spark.extraListeners + spark.master (none) - A comma-separated list of classes that implement SparkListener; when initializing - SparkContext, instances of these classes will be created and registered with Spark's listener - bus. If a class has a single-argument constructor that accepts a SparkConf, that constructor - will be called; otherwise, a zero-argument constructor will be called. If no valid constructor - can be found, the SparkContext creation will fail with an exception. + The cluster manager to connect to. See the list of + allowed master URL's. @@ -210,17 +191,27 @@ Apart from these, the following properties are also available, and may be useful - + - + @@ -228,54 +219,56 @@ Apart from these, the following properties are also available, and may be useful - - + + - + - + - - + + @@ -289,30 +282,40 @@ Apart from these, the following properties are also available, and may be useful - + - + + + + + + - - + + @@ -338,6 +341,15 @@ Apart from these, the following properties are also available, and may be useful automatically. + + + + + @@ -348,40 +360,38 @@ Apart from these, the following properties are also available, and may be useful from JVM to Python worker for every task. +
    Property NameDefaultMeaning
    spark.driver.extraJavaOptionsspark.driver.extraClassPath (none) - A string of extra JVM options to pass to the driver. For instance, GC settings or other logging. + Extra classpath entries to append to the classpath of the driver. + +
    Note: In client mode, this config must not be set through the SparkConf + directly in your application, because the driver JVM has already started at that point. + Instead, please set this through the --driver-class-path command line option or in + your default properties file.
    spark.driver.extraClassPathspark.driver.extraJavaOptions (none) - Extra classpath entries to append to the classpath of the driver. + A string of extra JVM options to pass to the driver. For instance, GC settings or other logging. + +
    Note: In client mode, this config must not be set through the SparkConf + directly in your application, because the driver JVM has already started at that point. + Instead, please set this through the --driver-java-options command line option or in + your default properties file.
    (none) Set a special library path to use when launching the driver JVM. + +
    Note: In client mode, this config must not be set through the SparkConf + directly in your application, because the driver JVM has already started at that point. + Instead, please set this through the --driver-library-path command line option or in + your default properties file.
    spark.executor.extraJavaOptions(none)spark.driver.userClassPathFirstfalse - A string of extra JVM options to pass to executors. For instance, GC settings or other - logging. Note that it is illegal to set Spark properties or heap size settings with this - option. Spark properties should be set using a SparkConf object or the - spark-defaults.conf file used with the spark-submit script. Heap size settings can be set - with spark.executor.memory. + (Experimental) Whether to give user-added jars precedence over Spark's own jars when loading + classes in the the driver. This feature can be used to mitigate conflicts between Spark's + dependencies and user dependencies. It is currently an experimental feature. + + This is used in cluster mode only.
    spark.executor.extraClassPath (none) - Extra classpath entries to append to the classpath of executors. This exists primarily - for backwards-compatibility with older versions of Spark. Users typically should not need - to set this option. + Extra classpath entries to append to the classpath of executors. This exists primarily for + backwards-compatibility with older versions of Spark. Users typically should not need to set + this option.
    spark.executor.extraLibraryPathspark.executor.extraJavaOptions (none) - Set a special library path to use when launching executor JVM's. + A string of extra JVM options to pass to executors. For instance, GC settings or other logging. + Note that it is illegal to set Spark properties or heap size settings with this option. Spark + properties should be set using a SparkConf object or the spark-defaults.conf file used with the + spark-submit script. Heap size settings can be set with spark.executor.memory.
    spark.executor.logs.rolling.strategyspark.executor.extraLibraryPath (none) - Set the strategy of rolling of executor logs. By default it is disabled. It can - be set to "time" (time-based rolling) or "size" (size-based rolling). For "time", - use spark.executor.logs.rolling.time.interval to set the rolling interval. - For "size", use spark.executor.logs.rolling.size.maxBytes to set - the maximum file size for rolling. + Set a special library path to use when launching executor JVM's.
    spark.executor.logs.rolling.time.intervaldailyspark.executor.logs.rolling.maxRetainedFiles(none) - Set the time interval by which the executor logs will be rolled over. - Rolling is disabled by default. Valid values are `daily`, `hourly`, `minutely` or - any interval in seconds. See spark.executor.logs.rolling.maxRetainedFiles - for automatic cleaning of old logs. + Sets the number of latest rolling log files that are going to be retained by the system. + Older log files will be deleted. Disabled by default.
    spark.executor.logs.rolling.maxRetainedFilesspark.executor.logs.rolling.strategy (none) - Sets the number of latest rolling log files that are going to be retained by the system. - Older log files will be deleted. Disabled by default. + Set the strategy of rolling of executor logs. By default it is disabled. It can + be set to "time" (time-based rolling) or "size" (size-based rolling). For "time", + use spark.executor.logs.rolling.time.interval to set the rolling interval. + For "size", use spark.executor.logs.rolling.size.maxBytes to set + the maximum file size for rolling.
    spark.files.userClassPathFirstspark.executor.logs.rolling.time.intervaldaily + Set the time interval by which the executor logs will be rolled over. + Rolling is disabled by default. Valid values are `daily`, `hourly`, `minutely` or + any interval in seconds. See spark.executor.logs.rolling.maxRetainedFiles + for automatic cleaning of old logs. +
    spark.executor.userClassPathFirst false - (Experimental) Whether to give user-added jars precedence over Spark's own jars when - loading classes in Executors. This feature can be used to mitigate conflicts between - Spark's dependencies and user dependencies. It is currently an experimental feature. - (Currently, this setting does not work for YARN, see SPARK-2996 for more details). + (Experimental) Same functionality as spark.driver.userClassPathFirst, but + applied to executor instances.
    spark.python.worker.memory512mspark.executorEnv.[EnvironmentVariableName](none) - Amount of memory to use per python worker process during aggregation, in the same - format as JVM memory strings (e.g. 512m, 2g). If the memory - used during aggregation goes above this amount, it will spill the data into disks. + Add the environment variable specified by EnvironmentVariableName to the Executor + process. The user can specify multiple of these to set multiple environment variables.
    spark.python.worker.memory512m + Amount of memory to use per python worker process during aggregation, in the same + format as JVM memory strings (e.g. 512m, 2g). If the memory + used during aggregation goes above this amount, it will spill the data into disks. +
    spark.python.worker.reuse true
    + +#### Shuffle Behavior + + - - + + - - + + - - + + -
    Property NameDefaultMeaning
    spark.executorEnv.[EnvironmentVariableName](none)spark.reducer.maxMbInFlight48 - Add the environment variable specified by EnvironmentVariableName to the Executor - process. The user can specify multiple of these to set multiple environment variables. + Maximum size (in megabytes) of map outputs to fetch simultaneously from each reduce task. Since + each output requires us to create a buffer to receive it, this represents a fixed memory + overhead per reduce task, so keep it small unless you have a large amount of memory.
    spark.mesos.executor.homedriver side SPARK_HOMEspark.shuffle.blockTransferServicenetty - Set the directory in which Spark is installed on the executors in Mesos. By default, the - executors will simply use the driver's Spark home directory, which may not be visible to - them. Note that this is only relevant if a Spark binary package is not specified through - spark.executor.uri. + Implementation to use for transferring shuffle and cached blocks between executors. There + are two implementations available: netty and nio. Netty-based + block transfer is intended to be simpler but equally efficient and is the default option + starting in 1.2.
    spark.mesos.executor.memoryOverheadexecutor memory * 0.07, with minimum of 384spark.shuffle.compresstrue - This value is an additive for spark.executor.memory, specified in MiB, - which is used to calculate the total Mesos task memory. A value of 384 - implies a 384MiB overhead. Additionally, there is a hard-coded 7% minimum - overhead. The final overhead will be the larger of either - `spark.mesos.executor.memoryOverhead` or 7% of `spark.executor.memory`. + Whether to compress map output files. Generally a good idea. Compression will use + spark.io.compression.codec.
    - -#### Shuffle Behavior - - @@ -393,55 +403,46 @@ Apart from these, the following properties are also available, and may be useful - - + + - - + + - - + + - + - - - - - - - + + @@ -453,6 +454,17 @@ Apart from these, the following properties are also available, and may be useful the default option starting in 1.2. + + + + + @@ -462,13 +474,19 @@ Apart from these, the following properties are also available, and may be useful - - + + + + + + +
    Property NameDefaultMeaning
    spark.shuffle.consolidateFiles false
    spark.shuffle.spilltruespark.shuffle.file.buffer.kb32 - If set to "true", limits the amount of memory used during reduces by spilling data out to disk. - This spilling threshold is specified by spark.shuffle.memoryFraction. + Size of the in-memory buffer for each shuffle file output stream, in kilobytes. These buffers + reduce the number of disk seeks and system calls made in creating intermediate shuffle files.
    spark.shuffle.spill.compresstruespark.shuffle.io.maxRetries3 - Whether to compress data spilled during shuffles. Compression will use - spark.io.compression.codec. + (Netty only) Fetches that fail due to IO-related exceptions are automatically retried if this is + set to a non-zero value. This retry logic helps stabilize large shuffles in the face of long GC + pauses or transient network connectivity issues.
    spark.shuffle.memoryFraction0.2spark.shuffle.io.numConnectionsPerPeer1 - Fraction of Java heap to use for aggregation and cogroups during shuffles, if - spark.shuffle.spill is true. At any given time, the collective size of - all in-memory maps used for shuffles is bounded by this limit, beyond which the contents will - begin to spill to disk. If spills are often, consider increasing this value at the expense of - spark.storage.memoryFraction. + (Netty only) Connections between hosts are reused in order to reduce connection buildup for + large clusters. For clusters with many hard disks and few hosts, this may result in insufficient + concurrency to saturate all disks, and so users may consider increasing this value.
    spark.shuffle.compressspark.shuffle.io.preferDirectBufs true - Whether to compress map output files. Generally a good idea. Compression will use - spark.io.compression.codec. -
    spark.shuffle.file.buffer.kb32 - Size of the in-memory buffer for each shuffle file output stream, in kilobytes. These buffers - reduce the number of disk seeks and system calls made in creating intermediate shuffle files. + (Netty only) Off-heap buffers are used to reduce garbage collection during shuffle and cache + block transfer. For environments where off-heap memory is tightly limited, users may wish to + turn this off to force all allocations from Netty to be on-heap.
    spark.reducer.maxMbInFlight48spark.shuffle.io.retryWait5s - Maximum size (in megabytes) of map outputs to fetch simultaneously from each reduce task. Since - each output requires us to create a buffer to receive it, this represents a fixed memory - overhead per reduce task, so keep it small unless you have a large amount of memory. + (Netty only) How long to wait between retries of fetches. The maximum delay caused by retrying + is 15 seconds by default, calculated as maxRetries * retryWait.
    spark.shuffle.memoryFraction0.2 + Fraction of Java heap to use for aggregation and cogroups during shuffles, if + spark.shuffle.spill is true. At any given time, the collective size of + all in-memory maps used for shuffles is bounded by this limit, beyond which the contents will + begin to spill to disk. If spills are often, consider increasing this value at the expense of + spark.storage.memoryFraction. +
    spark.shuffle.sort.bypassMergeThreshold 200
    spark.shuffle.blockTransferServicenettyspark.shuffle.spilltrue - Implementation to use for transferring shuffle and cached blocks between executors. There - are two implementations available: netty and nio. Netty-based - block transfer is intended to be simpler but equally efficient and is the default option - starting in 1.2. + If set to "true", limits the amount of memory used during reduces by spilling data out to disk. + This spilling threshold is specified by spark.shuffle.memoryFraction. +
    spark.shuffle.spill.compresstrue + Whether to compress data spilled during shuffles. Compression will use + spark.io.compression.codec.
    @@ -477,26 +495,28 @@ Apart from these, the following properties are also available, and may be useful - - + + - - + + - - + + @@ -507,28 +527,26 @@ Apart from these, the following properties are also available, and may be useful - - + + - - + + - - + +
    Property NameDefaultMeaning
    spark.ui.port4040spark.eventLog.compressfalse - Port for your application's dashboard, which shows memory and workload data. + Whether to compress logged events, if spark.eventLog.enabled is true.
    spark.ui.retainedStages1000spark.eventLog.dirfile:///tmp/spark-events - How many stages the Spark UI and status APIs remember before garbage - collecting. + Base directory in which Spark events are logged, if spark.eventLog.enabled is true. + Within this base directory, Spark creates a sub-directory for each application, and logs the + events specific to the application in this directory. Users may want to set this to + a unified location like an HDFS directory so history files can be read by the history server.
    spark.ui.retainedJobs1000spark.eventLog.enabledfalse - How many jobs the Spark UI and status APIs remember before garbage - collecting. + Whether to log Spark events, useful for reconstructing the Web UI after the application has + finished.
    spark.eventLog.enabledfalsespark.ui.port4040 - Whether to log Spark events, useful for reconstructing the Web UI after the application has - finished. + Port for your application's dashboard, which shows memory and workload data.
    spark.eventLog.compressfalsespark.ui.retainedJobs1000 - Whether to compress logged events, if spark.eventLog.enabled is true. + How many jobs the Spark UI and status APIs remember before garbage + collecting.
    spark.eventLog.dirfile:///tmp/spark-eventsspark.ui.retainedStages1000 - Base directory in which Spark events are logged, if spark.eventLog.enabled is true. - Within this base directory, Spark creates a sub-directory for each application, and logs the - events specific to the application in this directory. Users may want to set this to - a unified location like an HDFS directory so history files can be read by the history server. + How many stages the Spark UI and status APIs remember before garbage + collecting.
    @@ -544,12 +562,10 @@ Apart from these, the following properties are also available, and may be useful - spark.rdd.compress - false + spark.closure.serializer + org.apache.spark.serializer.
    JavaSerializer - Whether to compress serialized RDD partitions (e.g. for - StorageLevel.MEMORY_ONLY_SER). Can save substantial space at the cost of some - extra CPU time. + Serializer class to use for closures. Currently only the Java serializer is supported. @@ -565,14 +581,6 @@ Apart from these, the following properties are also available, and may be useful and org.apache.spark.io.SnappyCompressionCodec. - - spark.io.compression.snappy.block.size - 32768 - - Block size (in bytes) used in Snappy compression, in the case when Snappy compression codec - is used. Lowering this block size will also lower shuffle memory usage when Snappy is used. - - spark.io.compression.lz4.block.size 32768 @@ -582,21 +590,20 @@ Apart from these, the following properties are also available, and may be useful - spark.closure.serializer - org.apache.spark.serializer.
    JavaSerializer + spark.io.compression.snappy.block.size + 32768 - Serializer class to use for closures. Currently only the Java serializer is supported. + Block size (in bytes) used in Snappy compression, in the case when Snappy compression codec + is used. Lowering this block size will also lower shuffle memory usage when Snappy is used. - spark.serializer.objectStreamReset - 100 + spark.kryo.classesToRegister + (none) - When serializing using org.apache.spark.serializer.JavaSerializer, the serializer caches - objects to prevent writing redundant data, however that stops garbage collection of those - objects. By calling 'reset' you flush that info from the serializer, and allow old - objects to be collected. To turn off this periodic reset set it to -1. - By default it will reset the serializer every 100 objects. + If you use Kryo serialization, give a comma-separated list of custom class names to register + with Kryo. + See the tuning guide for more details. @@ -621,12 +628,16 @@ Apart from these, the following properties are also available, and may be useful - spark.kryoserializer.buffer.mb - 0.064 + spark.kryo.registrator + (none) - Initial size of Kryo's serialization buffer, in megabytes. Note that there will be one buffer - per core on each worker. This buffer will grow up to - spark.kryoserializer.buffer.max.mb if needed. + If you use Kryo serialization, set this class to register your custom classes with Kryo. This + property is useful if you need to register your classes in a custom way, e.g. to specify a custom + field serializer. Otherwise spark.kryo.classesToRegister is simpler. It should be + set to a class that extends + + KryoRegistrator. + See the tuning guide for more details. @@ -638,35 +649,53 @@ Apart from these, the following properties are also available, and may be useful inside Kryo. - - -#### Execution Behavior - - - + + + + + + - - + + + + + + + +
    Property NameDefaultMeaning
    spark.default.parallelismspark.kryoserializer.buffer.mb0.064 - For distributed shuffle operations like reduceByKey and join, the - largest number of partitions in a parent RDD. For operations like parallelize - with no parent RDDs, it depends on the cluster manager: -
      -
    • Local mode: number of cores on the local machine
      • -
    • Mesos fine grained mode: 8
      • -
    • Others: total number of cores on all executor nodes or 2, whichever is larger
      • -
    + Initial size of Kryo's serialization buffer, in megabytes. Note that there will be one buffer + per core on each worker. This buffer will grow up to + spark.kryoserializer.buffer.max.mb if needed.
    spark.rdd.compressfalse - Default number of partitions in RDDs returned by transformations like join, - reduceByKey, and parallelize when not set by user. + Whether to compress serialized RDD partitions (e.g. for + StorageLevel.MEMORY_ONLY_SER). Can save substantial space at the cost of some + extra CPU time.
    spark.broadcast.factoryorg.apache.spark.broadcast.
    TorrentBroadcastFactory    		spark.serializerorg.apache.spark.serializer.
    JavaSerializer    		 - Which broadcast implementation to use. + Class to use for serializing objects that will be sent over the network or need to be cached + in serialized form. The default of Java serialization works with any Serializable Java object + but is quite slow, so we recommend using + org.apache.spark.serializer.KryoSerializer and configuring Kryo serialization + when speed is necessary. Can be any subclass of + + org.apache.spark.Serializer.
    spark.serializer.objectStreamReset100 + When serializing using org.apache.spark.serializer.JavaSerializer, the serializer caches + objects to prevent writing redundant data, however that stops garbage collection of those + objects. By calling 'reset' you flush that info from the serializer, and allow old + objects to be collected. To turn off this periodic reset set it to -1. + By default it will reset the serializer every 100 objects. +
    + +#### Execution Behavior + + @@ -677,84 +706,85 @@ Apart from these, the following properties are also available, and may be useful - - + + - - + + - - + + - - + - - - - - - - + + + - - + + - - + + - - - + + + @@ -766,75 +796,71 @@ Apart from these, the following properties are also available, and may be useful are not affected by these issues. - - - - -
    Property NameDefaultMeaning
    spark.broadcast.blockSize 4096
    spark.files.overwritefalsespark.broadcast.factoryorg.apache.spark.broadcast.
    TorrentBroadcastFactory    		 - Whether to overwrite files added through SparkContext.addFile() when the target file exists and - its contents do not match those of the source. + Which broadcast implementation to use.
    spark.files.fetchTimeout60spark.cleaner.ttl(infinite) - Communication timeout to use when fetching files added through SparkContext.addFile() from - the driver, in seconds. + Duration (seconds) of how long Spark will remember any metadata (stages generated, tasks + generated, etc.). Periodic cleanups will ensure that metadata older than this duration will be + forgotten. This is useful for running Spark for many hours / days (for example, running 24/7 in + case of Spark Streaming applications). Note that any RDD that persists in memory for more than + this duration will be cleared as well.
    spark.storage.memoryFraction0.6spark.executor.cores1 in YARN mode, all the available cores on the worker in standalone mode. - Fraction of Java heap to use for Spark's memory cache. This should not be larger than the "old" - generation of objects in the JVM, which by default is given 0.6 of the heap, but you can - increase it if you configure your own old generation size. + The number of cores to use on each executor. For YARN and standalone mode only. + + In standalone mode, setting this parameter allows an application to run multiple executors on + the same worker, provided that there are enough cores on that worker. Otherwise, only one + executor per application will run on each worker.
    spark.storage.unrollFraction0.2spark.default.parallelism - Fraction of spark.storage.memoryFraction to use for unrolling blocks in memory. - This is dynamically allocated by dropping existing blocks when there is not enough free - storage space to unroll the new block in its entirety. + For distributed shuffle operations like reduceByKey and join, the + largest number of partitions in a parent RDD. For operations like parallelize + with no parent RDDs, it depends on the cluster manager: +
      +
    • Local mode: number of cores on the local machine
      • +
    • Mesos fine grained mode: 8
      • +
    • Others: total number of cores on all executor nodes or 2, whichever is larger
      • +
    spark.tachyonStore.baseDirSystem.getProperty("java.io.tmpdir") - Directories of the Tachyon File System that store RDDs. The Tachyon file system's URL is set by - spark.tachyonStore.url. It can also be a comma-separated list of multiple - directories on Tachyon file system. + Default number of partitions in RDDs returned by transformations like join, + reduceByKey, and parallelize when not set by user.
    spark.storage.memoryMapThreshold2097152 - Size of a block, in bytes, above which Spark memory maps when reading a block from disk. - This prevents Spark from memory mapping very small blocks. In general, memory - mapping has high overhead for blocks close to or below the page size of the operating system. - spark.executor.heartbeatInterval10sInterval between each executor's heartbeats to the driver. Heartbeats let + the driver know that the executor is still alive and update it with metrics for in-progress + tasks.
    spark.tachyonStore.urltachyon://localhost:19998spark.files.fetchTimeout60s - The URL of the underlying Tachyon file system in the TachyonStore. + Communication timeout to use when fetching files added through SparkContext.addFile() from + the driver.
    spark.cleaner.ttl(infinite)spark.files.useFetchCachetrue - Duration (seconds) of how long Spark will remember any metadata (stages generated, tasks - generated, etc.). Periodic cleanups will ensure that metadata older than this duration will be - forgotten. This is useful for running Spark for many hours / days (for example, running 24/7 in - case of Spark Streaming applications). Note that any RDD that persists in memory for more than - this duration will be cleared as well. + If set to true (default), file fetching will use a local cache that is shared by executors + that belong to the same application, which can improve task launching performance when + running many executors on the same host. If set to false, these caching optimizations will + be disabled and all executors will fetch their own copies of files. This optimization may be + disabled in order to use Spark local directories that reside on NFS filesystems (see + SPARK-6313 for more details).
    spark.hadoop.validateOutputSpecstrueIf set to true, validates the output specification (e.g. checking if the output directory already exists) - used in saveAsHadoopFile and other variants. This can be disabled to silence exceptions due to pre-existing - output directories. We recommend that users do not disable this except if trying to achieve compatibility with - previous versions of Spark. Simply use Hadoop's FileSystem API to delete output directories by hand. - This setting is ignored for jobs generated through Spark Streaming's StreamingContext, since - data may need to be rewritten to pre-existing output directories during checkpoint recovery.spark.files.overwritefalse + Whether to overwrite files added through SparkContext.addFile() when the target file exists and + its contents do not match those of the source. +
    spark.hadoop.cloneConf
    spark.executor.heartbeatInterval10000Interval (milliseconds) between each executor's heartbeats to the driver. Heartbeats let - the driver know that the executor is still alive and update it with metrics for in-progress - tasks.
    - -#### Networking - - - - - - - - - - - + + + - - + + - - + + - - + + - - + + - - + + +
    Property NameDefaultMeaning
    spark.driver.host(local hostname) - Hostname or IP address for the driver to listen on. - This is used for communicating with the executors and the standalone Master. -
    spark.driver.port(random) - Port for the driver to listen on. - This is used for communicating with the executors and the standalone Master. - spark.hadoop.validateOutputSpecstrueIf set to true, validates the output specification (e.g. checking if the output directory already exists) + used in saveAsHadoopFile and other variants. This can be disabled to silence exceptions due to pre-existing + output directories. We recommend that users do not disable this except if trying to achieve compatibility with + previous versions of Spark. Simply use Hadoop's FileSystem API to delete output directories by hand. + This setting is ignored for jobs generated through Spark Streaming's StreamingContext, since + data may need to be rewritten to pre-existing output directories during checkpoint recovery.
    spark.fileserver.port(random)spark.storage.memoryFraction0.6 - Port for the driver's HTTP file server to listen on. + Fraction of Java heap to use for Spark's memory cache. This should not be larger than the "old" + generation of objects in the JVM, which by default is given 0.6 of the heap, but you can + increase it if you configure your own old generation size.
    spark.broadcast.port(random)spark.storage.memoryMapThreshold2097152 - Port for the driver's HTTP broadcast server to listen on. - This is not relevant for torrent broadcast. + Size of a block, in bytes, above which Spark memory maps when reading a block from disk. + This prevents Spark from memory mapping very small blocks. In general, memory + mapping has high overhead for blocks close to or below the page size of the operating system.
    spark.replClassServer.port(random)spark.storage.unrollFraction0.2 - Port for the driver's HTTP class server to listen on. - This is only relevant for the Spark shell. + Fraction of spark.storage.memoryFraction to use for unrolling blocks in memory. + This is dynamically allocated by dropping existing blocks when there is not enough free + storage space to unroll the new block in its entirety.
    spark.blockManager.port(random)spark.tachyonStore.baseDirSystem.getProperty("java.io.tmpdir") - Port for all block managers to listen on. These exist on both the driver and the executors. + Directories of the Tachyon File System that store RDDs. The Tachyon file system's URL is set by + spark.tachyonStore.url. It can also be a comma-separated list of multiple + directories on Tachyon file system.
    spark.executor.port(random)spark.tachyonStore.urltachyon://localhost:19998 - Port for the executor to listen on. This is used for communicating with the driver. + The URL of the underlying Tachyon file system in the TachyonStore.
    + +#### Networking + + - - + + @@ -847,131 +873,120 @@ Apart from these, the following properties are also available, and may be useful - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + -
    Property NameDefaultMeaning
    spark.port.maxRetries16spark.akka.failure-detector.threshold300.0 - Default maximum number of retries when binding to a port before giving up. + This is set to a larger value to disable failure detector that comes inbuilt akka. It can be + enabled again, if you plan to use this feature (Not recommended). This maps to akka's + `akka.remote.transport-failure-detector.threshold`. Tune this in combination of + `spark.akka.heartbeat.pauses` and `spark.akka.heartbeat.interval` if you need to.
    spark.akka.threads4spark.akka.heartbeat.interval1000s - Number of actor threads to use for communication. Can be useful to increase on large clusters - when the driver has a lot of CPU cores. + This is set to a larger value to disable the transport failure detector that comes built in to + Akka. It can be enabled again, if you plan to use this feature (Not recommended). A larger + interval value reduces network overhead and a smaller value ( ~ 1 s) might be more + informative for Akka's failure detector. Tune this in combination of `spark.akka.heartbeat.pauses` + if you need to. A likely positive use case for using failure detector would be: a sensistive + failure detector can help evict rogue executors quickly. However this is usually not the case + as GC pauses and network lags are expected in a real Spark cluster. Apart from that enabling + this leads to a lot of exchanges of heart beats between nodes leading to flooding the network + with those.
    spark.akka.timeout100spark.akka.heartbeat.pauses6000s - Communication timeout between Spark nodes, in seconds. + This is set to a larger value to disable the transport failure detector that comes built in to Akka. + It can be enabled again, if you plan to use this feature (Not recommended). Acceptable heart + beat pause for Akka. This can be used to control sensitivity to GC pauses. Tune + this along with `spark.akka.heartbeat.interval` if you need to.
    spark.network.timeout120spark.akka.threads4 - Default timeout for all network interactions, in seconds. This config will be used in - place of spark.core.connection.ack.wait.timeout, spark.akka.timeout, - spark.storage.blockManagerSlaveTimeoutMs or - spark.shuffle.io.connectionTimeout, if they are not configured. + Number of actor threads to use for communication. Can be useful to increase on large clusters + when the driver has a lot of CPU cores.
    spark.akka.heartbeat.pauses6000spark.akka.timeout100s - This is set to a larger value to disable failure detector that comes inbuilt akka. It can be - enabled again, if you plan to use this feature (Not recommended). Acceptable heart beat pause - in seconds for akka. This can be used to control sensitivity to gc pauses. Tune this in - combination of `spark.akka.heartbeat.interval` and `spark.akka.failure-detector.threshold` - if you need to. + Communication timeout between Spark nodes.
    spark.akka.failure-detector.threshold300.0spark.blockManager.port(random) - This is set to a larger value to disable failure detector that comes inbuilt akka. It can be - enabled again, if you plan to use this feature (Not recommended). This maps to akka's - `akka.remote.transport-failure-detector.threshold`. Tune this in combination of - `spark.akka.heartbeat.pauses` and `spark.akka.heartbeat.interval` if you need to. + Port for all block managers to listen on. These exist on both the driver and the executors.
    spark.akka.heartbeat.interval1000spark.broadcast.port(random) - This is set to a larger value to disable failure detector that comes inbuilt akka. It can be - enabled again, if you plan to use this feature (Not recommended). A larger interval value in - seconds reduces network overhead and a smaller value ( ~ 1 s) might be more informative for - akka's failure detector. Tune this in combination of `spark.akka.heartbeat.pauses` and - `spark.akka.failure-detector.threshold` if you need to. Only positive use case for using - failure detector can be, a sensistive failure detector can help evict rogue executors really - quick. However this is usually not the case as gc pauses and network lags are expected in a - real Spark cluster. Apart from that enabling this leads to a lot of exchanges of heart beats - between nodes leading to flooding the network with those. + Port for the driver's HTTP broadcast server to listen on. + This is not relevant for torrent broadcast.
    spark.shuffle.io.preferDirectBufstruespark.driver.host(local hostname) - (Netty only) Off-heap buffers are used to reduce garbage collection during shuffle and cache - block transfer. For environments where off-heap memory is tightly limited, users may wish to - turn this off to force all allocations from Netty to be on-heap. + Hostname or IP address for the driver to listen on. + This is used for communicating with the executors and the standalone Master.
    spark.shuffle.io.numConnectionsPerPeer1spark.driver.port(random) - (Netty only) Connections between hosts are reused in order to reduce connection buildup for - large clusters. For clusters with many hard disks and few hosts, this may result in insufficient - concurrency to saturate all disks, and so users may consider increasing this value. + Port for the driver to listen on. + This is used for communicating with the executors and the standalone Master.
    spark.shuffle.io.maxRetries3spark.executor.port(random) - (Netty only) Fetches that fail due to IO-related exceptions are automatically retried if this is - set to a non-zero value. This retry logic helps stabilize large shuffles in the face of long GC - pauses or transient network connectivity issues. + Port for the executor to listen on. This is used for communicating with the driver.
    spark.shuffle.io.retryWait5spark.fileserver.port(random) - (Netty only) Seconds to wait between retries of fetches. The maximum delay caused by retrying - is simply maxRetries * retryWait, by default 15 seconds. + Port for the driver's HTTP file server to listen on.
    - -#### Scheduling - - - - + + - - + + - - + + +
    Property NameDefaultMeaning
    spark.task.cpus1spark.network.timeout120s - Number of cores to allocate for each task. + Default timeout for all network interactions. This config will be used in place of + spark.core.connection.ack.wait.timeout, spark.akka.timeout, + spark.storage.blockManagerSlaveTimeoutMs or + spark.shuffle.io.connectionTimeout, if they are not configured.
    spark.task.maxFailures4spark.port.maxRetries16 - Number of individual task failures before giving up on the job. - Should be greater than or equal to 1. Number of allowed retries = this value - 1. + Default maximum number of retries when binding to a port before giving up.
    spark.scheduler.modeFIFOspark.replClassServer.port(random) - The scheduling mode between - jobs submitted to the same SparkContext. Can be set to FAIR - to use fair sharing instead of queueing jobs one after another. Useful for - multi-user services. + Port for the driver's HTTP class server to listen on. + This is only relevant for the Spark shell.
    + +#### Scheduling + + @@ -985,50 +1000,19 @@ Apart from these, the following properties are also available, and may be useful - - - - - - + - - - - - - - - - - - - - - - - + - + - + @@ -1060,16 +1044,15 @@ Apart from these, the following properties are also available, and may be useful - - + + - + - + - - + + + + + + + - + + + + + + + + + + + + + + + + + + + + + + + + + +
    Property NameDefaultMeaning
    spark.cores.max (not set)
    spark.mesos.coarsefalse - If set to "true", runs over Mesos clusters in - "coarse-grained" sharing mode, - where Spark acquires one long-lived Mesos task on each machine instead of one Mesos task per - Spark task. This gives lower-latency scheduling for short queries, but leaves resources in use - for the whole duration of the Spark job. -
    spark.speculationspark.localExecution.enabled false - If set to "true", performs speculative execution of tasks. This means if one or more tasks are - running slowly in a stage, they will be re-launched. -
    spark.speculation.interval100 - How often Spark will check for tasks to speculate, in milliseconds. -
    spark.speculation.quantile0.75 - Percentage of tasks which must be complete before speculation is enabled for a particular stage. -
    spark.speculation.multiplier1.5 - How many times slower a task is than the median to be considered for speculation. + Enables Spark to run certain jobs, such as first() or take() on the driver, without sending + tasks to the cluster. This can make certain jobs execute very quickly, but may require + shipping a whole partition of data to the driver.
    spark.locality.wait30003s - Number of milliseconds to wait to launch a data-local task before giving up and launching it + How long to wait to launch a data-local task before giving up and launching it on a less-local node. The same wait will be used to step through multiple locality levels (process-local, node-local, rack-local and then any). It is also possible to customize the waiting time for each level by setting spark.locality.wait.node, etc. @@ -1037,19 +1021,19 @@ Apart from these, the following properties are also available, and may be useful
    spark.locality.wait.processspark.locality.wait.node spark.locality.wait - Customize the locality wait for process locality. This affects tasks that attempt to access - cached data in a particular executor process. + Customize the locality wait for node locality. For example, you can set this to 0 to skip + node locality and search immediately for rack locality (if your cluster has rack information).
    spark.locality.wait.nodespark.locality.wait.process spark.locality.wait - Customize the locality wait for node locality. For example, you can set this to 0 to skip - node locality and search immediately for rack locality (if your cluster has rack information). + Customize the locality wait for process locality. This affects tasks that attempt to access + cached data in a particular executor process.
    spark.scheduler.revive.interval1000spark.scheduler.maxRegisteredResourcesWaitingTime30s - The interval length for the scheduler to revive the worker resource offers to run tasks - (in milliseconds). + Maximum amount of time to wait for resources to register before scheduling begins.
    spark.scheduler.minRegisteredResourcesRatio0.0 for Mesos and Standalone mode, 0.8 for YARN0.8 for YARN mode; 0.0 otherwise The minimum ratio of registered resources (registered resources / total expected resources) (resources are executors in yarn mode, CPU cores in standalone mode) @@ -1080,25 +1063,69 @@ Apart from these, the following properties are also available, and may be useful
    spark.scheduler.maxRegisteredResourcesWaitingTime30000spark.scheduler.modeFIFO + The scheduling mode between + jobs submitted to the same SparkContext. Can be set to FAIR + to use fair sharing instead of queueing jobs one after another. Useful for + multi-user services. +
    spark.scheduler.revive.interval1s - Maximum amount of time to wait for resources to register before scheduling begins - (in milliseconds). + The interval length for the scheduler to revive the worker resource offers to run tasks.
    spark.localExecution.enabledspark.speculation false - Enables Spark to run certain jobs, such as first() or take() on the driver, without sending - tasks to the cluster. This can make certain jobs execute very quickly, but may require - shipping a whole partition of data to the driver. + If set to "true", performs speculative execution of tasks. This means if one or more tasks are + running slowly in a stage, they will be re-launched. +
    spark.speculation.interval100ms + How often Spark will check for tasks to speculate. +
    spark.speculation.multiplier1.5 + How many times slower a task is than the median to be considered for speculation. +
    spark.speculation.quantile0.75 + Percentage of tasks which must be complete before speculation is enabled for a particular stage. +
    spark.task.cpus1 + Number of cores to allocate for each task. +
    spark.task.maxFailures4 + Number of individual task failures before giving up on the job. + Should be greater than or equal to 1. Number of allowed retries = this value - 1.
    -#### Dynamic allocation +#### Dynamic Allocation @@ -1118,10 +1145,19 @@ Apart from these, the following properties are also available, and may be useful + + + + + + - @@ -1132,18 +1168,18 @@ Apart from these, the following properties are also available, and may be useful - + - + @@ -1156,20 +1192,30 @@ Apart from these, the following properties are also available, and may be useful description. - - - - -
    Property NameDefaultMeaning
    spark.dynamicAllocation.executorIdleTimeout600s + If dynamic allocation is enabled and an executor has been idle for more than this duration, + the executor will be removed. For more detail, see this + description. +
    spark.dynamicAllocation.initialExecutors spark.dynamicAllocation.minExecutors0 - Lower bound for the number of executors if dynamic allocation is enabled. + Initial number of executors to run if dynamic allocation is enabled.
    spark.dynamicAllocation.maxExecutors spark.dynamicAllocation.minExecutors0 - Initial number of executors to run if dynamic allocation is enabled. + Lower bound for the number of executors if dynamic allocation is enabled.
    spark.dynamicAllocation.schedulerBacklogTimeout55s If dynamic allocation is enabled and there have been pending tasks backlogged for more than - this duration (in seconds), new executors will be requested. For more detail, see this + this duration, new executors will be requested. For more detail, see this description.
    spark.dynamicAllocation.executorIdleTimeout600 - If dynamic allocation is enabled and an executor has been idle for more than this duration - (in seconds), the executor will be removed. For more detail, see this - description. -
    #### Security + + + + + + + + + + @@ -1186,21 +1232,29 @@ Apart from these, the following properties are also available, and may be useful not running on YARN and authentication is enabled. + + + + + - + - - + + @@ -1217,16 +1271,6 @@ Apart from these, the following properties are also available, and may be useful -Dspark.com.test.filter1.params='param1=foo,param2=testing' - - - - - @@ -1235,23 +1279,6 @@ Apart from these, the following properties are also available, and may be useful user that started the Spark job has view access. - - - - - - - - - -
    Property NameDefaultMeaning
    spark.acls.enablefalse + Whether Spark acls should are enabled. If enabled, this checks to see if the user has + access permissions to view or modify the job. Note this requires the user to be known, + so if the user comes across as null no checks are done. Filters can be used with the UI + to authenticate and set the user. +
    spark.admin.aclsEmpty + Comma separated list of users/administrators that have view and modify access to all Spark jobs. + This can be used if you run on a shared cluster and have a set of administrators or devs who + help debug when things work. +
    spark.authenticate false
    spark.core.connection.ack.wait.timeout60s + How long for the connection to wait for ack to occur before timing + out and giving up. To avoid unwilling timeout caused by long pause like GC, + you can set larger value. +
    spark.core.connection.auth.wait.timeout3030s - Number of seconds for the connection to wait for authentication to occur before timing + How long for the connection to wait for authentication to occur before timing out and giving up.
    spark.core.connection.ack.wait.timeout60spark.modify.aclsEmpty - Number of seconds for the connection to wait for ack to occur before timing - out and giving up. To avoid unwilling timeout caused by long pause like GC, - you can set larger value. + Comma separated list of users that have modify access to the Spark job. By default only the + user that started the Spark job has access to modify it (kill it for example).
    spark.acls.enablefalse - Whether Spark acls should are enabled. If enabled, this checks to see if the user has - access permissions to view or modify the job. Note this requires the user to be known, - so if the user comes across as null no checks are done. Filters can be used with the UI - to authenticate and set the user. -
    spark.ui.view.acls Empty
    spark.modify.aclsEmpty - Comma separated list of users that have modify access to the Spark job. By default only the - user that started the Spark job has access to modify it (kill it for example). -
    spark.admin.aclsEmpty - Comma separated list of users/administrators that have view and modify access to all Spark jobs. - This can be used if you run on a shared cluster and have a set of administrators or devs who - help debug when things work. -
    #### Encryption @@ -1275,6 +1302,23 @@ Apart from these, the following properties are also available, and may be useful file server.

    + + spark.ssl.enabledAlgorithms + Empty + + A comma separated list of ciphers. The specified ciphers must be supported by JVM. + The reference list of protocols one can find on + this + page. + + + + spark.ssl.keyPassword + None + + A password to the private key in key-store. + + spark.ssl.keyStore None @@ -1291,10 +1335,12 @@ Apart from these, the following properties are also available, and may be useful - spark.ssl.keyPassword + spark.ssl.protocol None - A password to the private key in key-store. + A protocol name. The protocol must be supported by JVM. The reference list of protocols + one can find on this + page. @@ -1312,25 +1358,6 @@ Apart from these, the following properties are also available, and may be useful A password to the trust-store. - - spark.ssl.protocol - None - - A protocol name. The protocol must be supported by JVM. The reference list of protocols - one can find on this - page. - - - - spark.ssl.enabledAlgorithms - Empty - - A comma separated list of ciphers. The specified ciphers must be supported by JVM. - The reference list of protocols one can find on - this - page. - - @@ -1339,9 +1366,9 @@ Apart from these, the following properties are also available, and may be useful Property NameDefaultMeaning spark.streaming.blockInterval - 200 + 200ms - Interval (milliseconds) at which data received by Spark Streaming receivers is chunked + Interval at which data received by Spark Streaming receivers is chunked into blocks of data before storing them in Spark. Minimum recommended - 50 ms. See the performance tuning section in the Spark Streaming programing guide for more details. @@ -1349,9 +1376,9 @@ Apart from these, the following properties are also available, and may be useful spark.streaming.receiver.maxRate - infinite + not set - Maximum number records per second at which each receiver will receive data. + Maximum rate (number of records per second) at which each receiver will receive data. Effectively, each stream will consume at most this number of records per second. Setting this configuration to 0 or a negative number will put no limit on the rate. See the deployment guide @@ -1379,15 +1406,27 @@ Apart from these, the following properties are also available, and may be useful higher memory usage in Spark. + + spark.streaming.kafka.maxRatePerPartition + not set + + Maximum rate (number of records per second) at which data will be read from each Kafka + partition when using the new Kafka direct stream API. See the + Kafka Integration guide + for more details. + + #### Cluster Managers Each cluster manager in Spark has additional configuration options. Configurations can be found on the pages for each mode: - * [YARN](running-on-yarn.html#configuration) - * [Mesos](running-on-mesos.html) - * [Standalone Mode](spark-standalone.html#cluster-launch-scripts) +##### [YARN](running-on-yarn.html#configuration) + +##### [Mesos](running-on-mesos.html#configuration) + +##### [Standalone Mode](spark-standalone.html#cluster-launch-scripts) # Environment Variables diff --git a/docs/ec2-scripts.md b/docs/ec2-scripts.md index 8c9a1e1262d8f..7f60f82b966fe 100644 --- a/docs/ec2-scripts.md +++ b/docs/ec2-scripts.md @@ -5,7 +5,7 @@ title: Running Spark on EC2 The `spark-ec2` script, located in Spark's `ec2` directory, allows you to launch, manage and shut down Spark clusters on Amazon EC2. It automatically -sets up Spark, Shark and HDFS on the cluster for you. This guide describes +sets up Spark and HDFS on the cluster for you. This guide describes how to use `spark-ec2` to launch clusters, how to run jobs on them, and how to shut them down. It assumes you've already signed up for an EC2 account on the [Amazon Web Services site](http://aws.amazon.com/). diff --git a/docs/graphx-programming-guide.md b/docs/graphx-programming-guide.md index 826f6d8f371c7..3f10cb2dc3d2a 100644 --- a/docs/graphx-programming-guide.md +++ b/docs/graphx-programming-guide.md @@ -538,7 +538,7 @@ val joinedGraph = graph.joinVertices(uniqueCosts, ## Neighborhood Aggregation -A key step in may graph analytics tasks is aggregating information about the neighborhood of each +A key step in many graph analytics tasks is aggregating information about the neighborhood of each vertex. For example, we might want to know the number of followers each user has or the average age of the the followers of each user. Many iterative graph algorithms (e.g., PageRank, Shortest Path, and @@ -634,7 +634,7 @@ avgAgeOfOlderFollowers.collect.foreach(println(_)) ### Map Reduce Triplets Transition Guide (Legacy) -In earlier versions of GraphX we neighborhood aggregation was accomplished using the +In earlier versions of GraphX neighborhood aggregation was accomplished using the [`mapReduceTriplets`][Graph.mapReduceTriplets] operator: {% highlight scala %} @@ -663,7 +663,7 @@ val graph: Graph[Int, Float] = ... def msgFun(triplet: Triplet[Int, Float]): Iterator[(Int, String)] = { Iterator((triplet.dstId, "Hi")) } -def reduceFun(a: Int, b: Int): Int = a + b +def reduceFun(a: String, b: String): String = a + " " + b val result = graph.mapReduceTriplets[String](msgFun, reduceFun) {% endhighlight %} @@ -674,7 +674,7 @@ val graph: Graph[Int, Float] = ... def msgFun(triplet: EdgeContext[Int, Float, String]) { triplet.sendToDst("Hi") } -def reduceFun(a: Int, b: Int): Int = a + b +def reduceFun(a: String, b: String): String = a + " " + b val result = graph.aggregateMessages[String](msgFun, reduceFun) {% endhighlight %} @@ -682,8 +682,8 @@ val result = graph.aggregateMessages[String](msgFun, reduceFun) ### Computing Degree Information A common aggregation task is computing the degree of each vertex: the number of edges adjacent to -each vertex. In the context of directed graphs it often necessary to know the in-degree, out- -degree, and the total degree of each vertex. The [`GraphOps`][GraphOps] class contains a +each vertex. In the context of directed graphs it is often necessary to know the in-degree, +out-degree, and the total degree of each vertex. The [`GraphOps`][GraphOps] class contains a collection of operators to compute the degrees of each vertex. For example in the following we compute the max in, out, and total degrees: @@ -899,6 +899,8 @@ class VertexRDD[VD] extends RDD[(VertexID, VD)] { // Transform the values without changing the ids (preserves the internal index) def mapValues[VD2](map: VD => VD2): VertexRDD[VD2] def mapValues[VD2](map: (VertexId, VD) => VD2): VertexRDD[VD2] + // Show only vertices unique to this set based on their VertexId's + def minus(other: RDD[(VertexId, VD)]) // Remove vertices from this set that appear in the other set def diff(other: VertexRDD[VD]): VertexRDD[VD] // Join operators that take advantage of the internal indexing to accelerate joins (substantially) diff --git a/docs/img/PIClusteringFiveCirclesInputsAndOutputs.png b/docs/img/PIClusteringFiveCirclesInputsAndOutputs.png deleted file mode 100644 index ed9adad11d03a..0000000000000 Binary files a/docs/img/PIClusteringFiveCirclesInputsAndOutputs.png and /dev/null differ diff --git a/docs/img/cluster-overview.png b/docs/img/cluster-overview.png index 368274068e754..317554c5f2a5b 100644 Binary files a/docs/img/cluster-overview.png and b/docs/img/cluster-overview.png differ diff --git a/docs/img/cluster-overview.pptx b/docs/img/cluster-overview.pptx index af3c462cd904d..1b90d7ec5a7ae 100644 Binary files a/docs/img/cluster-overview.pptx and b/docs/img/cluster-overview.pptx differ diff --git a/docs/index.md b/docs/index.md index e006be640e582..b5b016e34795e 100644 --- a/docs/index.md +++ b/docs/index.md @@ -74,7 +74,7 @@ options for deployment: in all supported languages (Scala, Java, Python) * Modules built on Spark: * [Spark Streaming](streaming-programming-guide.html): processing real-time data streams - * [Spark SQL](sql-programming-guide.html): support for structured data and relational queries + * [Spark SQL and DataFrames](sql-programming-guide.html): support for structured data and relational queries * [MLlib](mllib-guide.html): built-in machine learning library * [GraphX](graphx-programming-guide.html): Spark's new API for graph processing * [Bagel (Pregel on Spark)](bagel-programming-guide.html): older, simple graph processing model @@ -115,6 +115,8 @@ options for deployment: * [Spark Homepage](http://spark.apache.org) * [Spark Wiki](https://cwiki.apache.org/confluence/display/SPARK) +* [Spark Community](http://spark.apache.org/community.html) resources, including local meetups +* [StackOverflow tag `apache-spark`](http://stackoverflow.com/questions/tagged/apache-spark) * [Mailing Lists](http://spark.apache.org/mailing-lists.html): ask questions about Spark here * [AMP Camps](http://ampcamp.berkeley.edu/): a series of training camps at UC Berkeley that featured talks and exercises about Spark, Spark Streaming, Mesos, and more. [Videos](http://ampcamp.berkeley.edu/3/), @@ -123,11 +125,3 @@ options for deployment: * [Code Examples](http://spark.apache.org/examples.html): more are also available in the `examples` subfolder of Spark ([Scala]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/examples), [Java]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/java/org/apache/spark/examples), [Python]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/python)) - -# Community - -To get help using Spark or keep up with Spark development, sign up for the [user mailing list](http://spark.apache.org/mailing-lists.html). - -If you're in the San Francisco Bay Area, there's a regular [Spark meetup](http://www.meetup.com/spark-users/) every few weeks. Come by to meet the developers and other users. - -Finally, if you'd like to contribute code to Spark, read [how to contribute](contributing-to-spark.html). diff --git a/docs/job-scheduling.md b/docs/job-scheduling.md index 5295e351dd711..963e88a3e1d8f 100644 --- a/docs/job-scheduling.md +++ b/docs/job-scheduling.md @@ -14,8 +14,7 @@ runs an independent set of executor processes. The cluster managers that Spark r facilities for [scheduling across applications](#scheduling-across-applications). Second, _within_ each Spark application, multiple "jobs" (Spark actions) may be running concurrently if they were submitted by different threads. This is common if your application is serving requests -over the network; for example, the [Shark](http://shark.cs.berkeley.edu) server works this way. Spark -includes a [fair scheduler](#scheduling-within-an-application) to schedule resources within each SparkContext. +over the network. Spark includes a [fair scheduler](#scheduling-within-an-application) to schedule resources within each SparkContext. # Scheduling Across Applications @@ -52,8 +51,7 @@ an application to gain back cores on one node when it has work to do. To use thi Note that none of the modes currently provide memory sharing across applications. If you would like to share data this way, we recommend running a single server application that can serve multiple requests by querying -the same RDDs. For example, the [Shark](http://shark.cs.berkeley.edu) JDBC server works this way for SQL -queries. In future releases, in-memory storage systems such as [Tachyon](http://tachyon-project.org) will +the same RDDs. In future releases, in-memory storage systems such as [Tachyon](http://tachyon-project.org) will provide another approach to share RDDs. ## Dynamic Resource Allocation diff --git a/docs/ml-guide.md b/docs/ml-guide.md index be178d7689fdd..771a07183e26f 100644 --- a/docs/ml-guide.md +++ b/docs/ml-guide.md @@ -23,13 +23,13 @@ to `spark.ml`. Spark ML standardizes APIs for machine learning algorithms to make it easier to combine multiple algorithms into a single pipeline, or workflow. This section covers the key concepts introduced by the Spark ML API. -* **[ML Dataset](ml-guide.html#ml-dataset)**: Spark ML uses the [`SchemaRDD`](api/scala/index.html#org.apache.spark.sql.SchemaRDD) from Spark SQL as a dataset which can hold a variety of data types. +* **[ML Dataset](ml-guide.html#ml-dataset)**: Spark ML uses the [`DataFrame`](api/scala/index.html#org.apache.spark.sql.DataFrame) from Spark SQL as a dataset which can hold a variety of data types. E.g., a dataset could have different columns storing text, feature vectors, true labels, and predictions. -* **[`Transformer`](ml-guide.html#transformers)**: A `Transformer` is an algorithm which can transform one `SchemaRDD` into another `SchemaRDD`. +* **[`Transformer`](ml-guide.html#transformers)**: A `Transformer` is an algorithm which can transform one `DataFrame` into another `DataFrame`. E.g., an ML model is a `Transformer` which transforms an RDD with features into an RDD with predictions. -* **[`Estimator`](ml-guide.html#estimators)**: An `Estimator` is an algorithm which can be fit on a `SchemaRDD` to produce a `Transformer`. +* **[`Estimator`](ml-guide.html#estimators)**: An `Estimator` is an algorithm which can be fit on a `DataFrame` to produce a `Transformer`. E.g., a learning algorithm is an `Estimator` which trains on a dataset and produces a model. * **[`Pipeline`](ml-guide.html#pipeline)**: A `Pipeline` chains multiple `Transformer`s and `Estimator`s together to specify an ML workflow. @@ -39,20 +39,20 @@ E.g., a learning algorithm is an `Estimator` which trains on a dataset and produ ## ML Dataset Machine learning can be applied to a wide variety of data types, such as vectors, text, images, and structured data. -Spark ML adopts the [`SchemaRDD`](api/scala/index.html#org.apache.spark.sql.SchemaRDD) from Spark SQL in order to support a variety of data types under a unified Dataset concept. +Spark ML adopts the [`DataFrame`](api/scala/index.html#org.apache.spark.sql.DataFrame) from Spark SQL in order to support a variety of data types under a unified Dataset concept. -`SchemaRDD` supports many basic and structured types; see the [Spark SQL datatype reference](sql-programming-guide.html#spark-sql-datatype-reference) for a list of supported types. -In addition to the types listed in the Spark SQL guide, `SchemaRDD` can use ML [`Vector`](api/scala/index.html#org.apache.spark.mllib.linalg.Vector) types. +`DataFrame` supports many basic and structured types; see the [Spark SQL datatype reference](sql-programming-guide.html#spark-sql-datatype-reference) for a list of supported types. +In addition to the types listed in the Spark SQL guide, `DataFrame` can use ML [`Vector`](api/scala/index.html#org.apache.spark.mllib.linalg.Vector) types. -A `SchemaRDD` can be created either implicitly or explicitly from a regular `RDD`. See the code examples below and the [Spark SQL programming guide](sql-programming-guide.html) for examples. +A `DataFrame` can be created either implicitly or explicitly from a regular `RDD`. See the code examples below and the [Spark SQL programming guide](sql-programming-guide.html) for examples. -Columns in a `SchemaRDD` are named. The code examples below use names such as "text," "features," and "label." +Columns in a `DataFrame` are named. The code examples below use names such as "text," "features," and "label." ## ML Algorithms ### Transformers -A [`Transformer`](api/scala/index.html#org.apache.spark.ml.Transformer) is an abstraction which includes feature transformers and learned models. Technically, a `Transformer` implements a method `transform()` which converts one `SchemaRDD` into another, generally by appending one or more columns. +A [`Transformer`](api/scala/index.html#org.apache.spark.ml.Transformer) is an abstraction which includes feature transformers and learned models. Technically, a `Transformer` implements a method `transform()` which converts one `DataFrame` into another, generally by appending one or more columns. For example: * A feature transformer might take a dataset, read a column (e.g., text), convert it into a new column (e.g., feature vectors), append the new column to the dataset, and output the updated dataset. @@ -60,7 +60,7 @@ For example: ### Estimators -An [`Estimator`](api/scala/index.html#org.apache.spark.ml.Estimator) abstracts the concept of a learning algorithm or any algorithm which fits or trains on data. Technically, an `Estimator` implements a method `fit()` which accepts a `SchemaRDD` and produces a `Transformer`. +An [`Estimator`](api/scala/index.html#org.apache.spark.ml.Estimator) abstracts the concept of a learning algorithm or any algorithm which fits or trains on data. Technically, an `Estimator` implements a method `fit()` which accepts a `DataFrame` and produces a `Transformer`. For example, a learning algorithm such as `LogisticRegression` is an `Estimator`, and calling `fit()` trains a `LogisticRegressionModel`, which is a `Transformer`. ### Properties of ML Algorithms @@ -101,7 +101,7 @@ We illustrate this for the simple text document workflow. The figure below is f Above, the top row represents a `Pipeline` with three stages. The first two (`Tokenizer` and `HashingTF`) are `Transformer`s (blue), and the third (`LogisticRegression`) is an `Estimator` (red). -The bottom row represents data flowing through the pipeline, where cylinders indicate `SchemaRDD`s. +The bottom row represents data flowing through the pipeline, where cylinders indicate `DataFrame`s. The `Pipeline.fit()` method is called on the original dataset which has raw text documents and labels. The `Tokenizer.transform()` method splits the raw text documents into words, adding a new column with words into the dataset. The `HashingTF.transform()` method converts the words column into feature vectors, adding a new column with those vectors to the dataset. @@ -130,7 +130,7 @@ Each stage's `transform()` method updates the dataset and passes it to the next *DAG `Pipeline`s*: A `Pipeline`'s stages are specified as an ordered array. The examples given here are all for linear `Pipeline`s, i.e., `Pipeline`s in which each stage uses data produced by the previous stage. It is possible to create non-linear `Pipeline`s as long as the data flow graph forms a Directed Acyclic Graph (DAG). This graph is currently specified implicitly based on the input and output column names of each stage (generally specified as parameters). If the `Pipeline` forms a DAG, then the stages must be specified in topological order. -*Runtime checking*: Since `Pipeline`s can operate on datasets with varied types, they cannot use compile-time type checking. `Pipeline`s and `PipelineModel`s instead do runtime checking before actually running the `Pipeline`. This type checking is done using the dataset *schema*, a description of the data types of columns in the `SchemaRDD`. +*Runtime checking*: Since `Pipeline`s can operate on datasets with varied types, they cannot use compile-time type checking. `Pipeline`s and `PipelineModel`s instead do runtime checking before actually running the `Pipeline`. This type checking is done using the dataset *schema*, a description of the data types of columns in the `DataFrame`. ## Parameters @@ -171,12 +171,12 @@ import org.apache.spark.sql.{Row, SQLContext} val conf = new SparkConf().setAppName("SimpleParamsExample") val sc = new SparkContext(conf) val sqlContext = new SQLContext(sc) -import sqlContext._ +import sqlContext.implicits._ // Prepare training data. // We use LabeledPoint, which is a case class. Spark SQL can convert RDDs of case classes -// into SchemaRDDs, where it uses the case class metadata to infer the schema. -val training = sparkContext.parallelize(Seq( +// into DataFrames, where it uses the case class metadata to infer the schema. +val training = sc.parallelize(Seq( LabeledPoint(1.0, Vectors.dense(0.0, 1.1, 0.1)), LabeledPoint(0.0, Vectors.dense(2.0, 1.0, -1.0)), LabeledPoint(0.0, Vectors.dense(2.0, 1.3, 1.0)), @@ -192,7 +192,7 @@ lr.setMaxIter(10) .setRegParam(0.01) // Learn a LogisticRegression model. This uses the parameters stored in lr. -val model1 = lr.fit(training) +val model1 = lr.fit(training.toDF) // Since model1 is a Model (i.e., a Transformer produced by an Estimator), // we can view the parameters it used during fit(). // This prints the parameter (name: value) pairs, where names are unique IDs for this @@ -203,33 +203,35 @@ println("Model 1 was fit using parameters: " + model1.fittingParamMap) // which supports several methods for specifying parameters. val paramMap = ParamMap(lr.maxIter -> 20) paramMap.put(lr.maxIter, 30) // Specify 1 Param. This overwrites the original maxIter. -paramMap.put(lr.regParam -> 0.1, lr.threshold -> 0.5) // Specify multiple Params. +paramMap.put(lr.regParam -> 0.1, lr.threshold -> 0.55) // Specify multiple Params. // One can also combine ParamMaps. -val paramMap2 = ParamMap(lr.scoreCol -> "probability") // Changes output column name. +val paramMap2 = ParamMap(lr.probabilityCol -> "myProbability") // Change output column name val paramMapCombined = paramMap ++ paramMap2 // Now learn a new model using the paramMapCombined parameters. // paramMapCombined overrides all parameters set earlier via lr.set* methods. -val model2 = lr.fit(training, paramMapCombined) +val model2 = lr.fit(training.toDF, paramMapCombined) println("Model 2 was fit using parameters: " + model2.fittingParamMap) -// Prepare test documents. -val test = sparkContext.parallelize(Seq( +// Prepare test data. +val test = sc.parallelize(Seq( LabeledPoint(1.0, Vectors.dense(-1.0, 1.5, 1.3)), LabeledPoint(0.0, Vectors.dense(3.0, 2.0, -0.1)), LabeledPoint(1.0, Vectors.dense(0.0, 2.2, -1.5)))) -// Make predictions on test documents using the Transformer.transform() method. +// Make predictions on test data using the Transformer.transform() method. // LogisticRegression.transform will only use the 'features' column. -// Note that model2.transform() outputs a 'probability' column instead of the usual 'score' -// column since we renamed the lr.scoreCol parameter previously. -model2.transform(test) - .select('features, 'label, 'probability, 'prediction) +// Note that model2.transform() outputs a 'myProbability' column instead of the usual +// 'probability' column since we renamed the lr.probabilityCol parameter previously. +model2.transform(test.toDF) + .select("features", "label", "myProbability", "prediction") .collect() - .foreach { case Row(features: Vector, label: Double, prob: Double, prediction: Double) => - println("(" + features + ", " + label + ") -> prob=" + prob + ", prediction=" + prediction) + .foreach { case Row(features: Vector, label: Double, prob: Vector, prediction: Double) => + println("($features, $label) -> prob=$prob, prediction=$prediction") } + +sc.stop() {% endhighlight %}
    @@ -244,23 +246,23 @@ import org.apache.spark.ml.param.ParamMap; import org.apache.spark.ml.classification.LogisticRegression; import org.apache.spark.mllib.linalg.Vectors; import org.apache.spark.mllib.regression.LabeledPoint; -import org.apache.spark.sql.api.java.JavaSQLContext; -import org.apache.spark.sql.api.java.JavaSchemaRDD; -import org.apache.spark.sql.api.java.Row; +import org.apache.spark.sql.DataFrame; +import org.apache.spark.sql.SQLContext; +import org.apache.spark.sql.Row; SparkConf conf = new SparkConf().setAppName("JavaSimpleParamsExample"); JavaSparkContext jsc = new JavaSparkContext(conf); -JavaSQLContext jsql = new JavaSQLContext(jsc); +SQLContext jsql = new SQLContext(jsc); // Prepare training data. -// We use LabeledPoint, which is a case class. Spark SQL can convert RDDs of case classes -// into SchemaRDDs, where it uses the case class metadata to infer the schema. +// We use LabeledPoint, which is a JavaBean. Spark SQL can convert RDDs of JavaBeans +// into DataFrames, where it uses the bean metadata to infer the schema. List localTraining = Lists.newArrayList( new LabeledPoint(1.0, Vectors.dense(0.0, 1.1, 0.1)), new LabeledPoint(0.0, Vectors.dense(2.0, 1.0, -1.0)), new LabeledPoint(0.0, Vectors.dense(2.0, 1.3, 1.0)), new LabeledPoint(1.0, Vectors.dense(0.0, 1.2, -0.5))); -JavaSchemaRDD training = jsql.applySchema(jsc.parallelize(localTraining), LabeledPoint.class); +DataFrame training = jsql.createDataFrame(jsc.parallelize(localTraining), LabeledPoint.class); // Create a LogisticRegression instance. This instance is an Estimator. LogisticRegression lr = new LogisticRegression(); @@ -281,13 +283,13 @@ System.out.println("Model 1 was fit using parameters: " + model1.fittingParamMap // We may alternatively specify parameters using a ParamMap. ParamMap paramMap = new ParamMap(); -paramMap.put(lr.maxIter(), 20); // Specify 1 Param. +paramMap.put(lr.maxIter().w(20)); // Specify 1 Param. paramMap.put(lr.maxIter(), 30); // This overwrites the original maxIter. -paramMap.put(lr.regParam(), 0.1); +paramMap.put(lr.regParam().w(0.1), lr.threshold().w(0.55)); // Specify multiple Params. // One can also combine ParamMaps. ParamMap paramMap2 = new ParamMap(); -paramMap2.put(lr.scoreCol(), "probability"); // Changes output column name. +paramMap2.put(lr.probabilityCol().w("myProbability")); // Change output column name ParamMap paramMapCombined = paramMap.$plus$plus(paramMap2); // Now learn a new model using the paramMapCombined parameters. @@ -300,19 +302,19 @@ List localTest = Lists.newArrayList( new LabeledPoint(1.0, Vectors.dense(-1.0, 1.5, 1.3)), new LabeledPoint(0.0, Vectors.dense(3.0, 2.0, -0.1)), new LabeledPoint(1.0, Vectors.dense(0.0, 2.2, -1.5))); -JavaSchemaRDD test = jsql.applySchema(jsc.parallelize(localTest), LabeledPoint.class); +DataFrame test = jsql.createDataFrame(jsc.parallelize(localTest), LabeledPoint.class); // Make predictions on test documents using the Transformer.transform() method. // LogisticRegression.transform will only use the 'features' column. -// Note that model2.transform() outputs a 'probability' column instead of the usual 'score' -// column since we renamed the lr.scoreCol parameter previously. -model2.transform(test).registerAsTable("results"); -JavaSchemaRDD results = - jsql.sql("SELECT features, label, probability, prediction FROM results"); -for (Row r: results.collect()) { +// Note that model2.transform() outputs a 'myProbability' column instead of the usual +// 'probability' column since we renamed the lr.probabilityCol parameter previously. +DataFrame results = model2.transform(test); +for (Row r: results.select("features", "label", "myProbability", "prediction").collect()) { System.out.println("(" + r.get(0) + ", " + r.get(1) + ") -> prob=" + r.get(2) + ", prediction=" + r.get(3)); } + +jsc.stop(); {% endhighlight %} @@ -330,6 +332,7 @@ import org.apache.spark.{SparkConf, SparkContext} import org.apache.spark.ml.Pipeline import org.apache.spark.ml.classification.LogisticRegression import org.apache.spark.ml.feature.{HashingTF, Tokenizer} +import org.apache.spark.mllib.linalg.Vector import org.apache.spark.sql.{Row, SQLContext} // Labeled and unlabeled instance types. @@ -337,14 +340,14 @@ import org.apache.spark.sql.{Row, SQLContext} case class LabeledDocument(id: Long, text: String, label: Double) case class Document(id: Long, text: String) -// Set up contexts. Import implicit conversions to SchemaRDD from sqlContext. +// Set up contexts. Import implicit conversions to DataFrame from sqlContext. val conf = new SparkConf().setAppName("SimpleTextClassificationPipeline") val sc = new SparkContext(conf) val sqlContext = new SQLContext(sc) -import sqlContext._ +import sqlContext.implicits._ // Prepare training documents, which are labeled. -val training = sparkContext.parallelize(Seq( +val training = sc.parallelize(Seq( LabeledDocument(0L, "a b c d e spark", 1.0), LabeledDocument(1L, "b d", 0.0), LabeledDocument(2L, "spark f g h", 1.0), @@ -365,30 +368,32 @@ val pipeline = new Pipeline() .setStages(Array(tokenizer, hashingTF, lr)) // Fit the pipeline to training documents. -val model = pipeline.fit(training) +val model = pipeline.fit(training.toDF) // Prepare test documents, which are unlabeled. -val test = sparkContext.parallelize(Seq( +val test = sc.parallelize(Seq( Document(4L, "spark i j k"), Document(5L, "l m n"), Document(6L, "mapreduce spark"), Document(7L, "apache hadoop"))) // Make predictions on test documents. -model.transform(test) - .select('id, 'text, 'score, 'prediction) +model.transform(test.toDF) + .select("id", "text", "probability", "prediction") .collect() - .foreach { case Row(id: Long, text: String, score: Double, prediction: Double) => - println("(" + id + ", " + text + ") --> score=" + score + ", prediction=" + prediction) + .foreach { case Row(id: Long, text: String, prob: Vector, prediction: Double) => + println("($id, $text) --> prob=$prob, prediction=$prediction") } + +sc.stop() {% endhighlight %}
    {% highlight java %} -import java.io.Serializable; import java.util.List; import com.google.common.collect.Lists; +import org.apache.spark.SparkConf; import org.apache.spark.api.java.JavaSparkContext; import org.apache.spark.ml.Pipeline; import org.apache.spark.ml.PipelineModel; @@ -396,45 +401,44 @@ import org.apache.spark.ml.PipelineStage; import org.apache.spark.ml.classification.LogisticRegression; import org.apache.spark.ml.feature.HashingTF; import org.apache.spark.ml.feature.Tokenizer; -import org.apache.spark.sql.api.java.JavaSQLContext; -import org.apache.spark.sql.api.java.JavaSchemaRDD; -import org.apache.spark.sql.api.java.Row; -import org.apache.spark.SparkConf; +import org.apache.spark.sql.DataFrame; +import org.apache.spark.sql.Row; +import org.apache.spark.sql.SQLContext; // Labeled and unlabeled instance types. // Spark SQL can infer schema from Java Beans. public class Document implements Serializable { - private Long id; + private long id; private String text; - public Document(Long id, String text) { + public Document(long id, String text) { this.id = id; this.text = text; } - public Long getId() { return this.id; } - public void setId(Long id) { this.id = id; } + public long getId() { return this.id; } + public void setId(long id) { this.id = id; } public String getText() { return this.text; } public void setText(String text) { this.text = text; } } public class LabeledDocument extends Document implements Serializable { - private Double label; + private double label; - public LabeledDocument(Long id, String text, Double label) { + public LabeledDocument(long id, String text, double label) { super(id, text); this.label = label; } - public Double getLabel() { return this.label; } - public void setLabel(Double label) { this.label = label; } + public double getLabel() { return this.label; } + public void setLabel(double label) { this.label = label; } } // Set up contexts. SparkConf conf = new SparkConf().setAppName("JavaSimpleTextClassificationPipeline"); JavaSparkContext jsc = new JavaSparkContext(conf); -JavaSQLContext jsql = new JavaSQLContext(jsc); +SQLContext jsql = new SQLContext(jsc); // Prepare training documents, which are labeled. List localTraining = Lists.newArrayList( @@ -442,8 +446,7 @@ List localTraining = Lists.newArrayList( new LabeledDocument(1L, "b d", 0.0), new LabeledDocument(2L, "spark f g h", 1.0), new LabeledDocument(3L, "hadoop mapreduce", 0.0)); -JavaSchemaRDD training = - jsql.applySchema(jsc.parallelize(localTraining), LabeledDocument.class); +DataFrame training = jsql.createDataFrame(jsc.parallelize(localTraining), LabeledDocument.class); // Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr. Tokenizer tokenizer = new Tokenizer() @@ -468,16 +471,62 @@ List localTest = Lists.newArrayList( new Document(5L, "l m n"), new Document(6L, "mapreduce spark"), new Document(7L, "apache hadoop")); -JavaSchemaRDD test = - jsql.applySchema(jsc.parallelize(localTest), Document.class); +DataFrame test = jsql.createDataFrame(jsc.parallelize(localTest), Document.class); // Make predictions on test documents. -model.transform(test).registerAsTable("prediction"); -JavaSchemaRDD predictions = jsql.sql("SELECT id, text, score, prediction FROM prediction"); -for (Row r: predictions.collect()) { - System.out.println("(" + r.get(0) + ", " + r.get(1) + ") --> score=" + r.get(2) +DataFrame predictions = model.transform(test); +for (Row r: predictions.select("id", "text", "probability", "prediction").collect()) { + System.out.println("(" + r.get(0) + ", " + r.get(1) + ") --> prob=" + r.get(2) + ", prediction=" + r.get(3)); } + +jsc.stop(); +{% endhighlight %} +
    + +
    +{% highlight python %} +from pyspark import SparkContext +from pyspark.ml import Pipeline +from pyspark.ml.classification import LogisticRegression +from pyspark.ml.feature import HashingTF, Tokenizer +from pyspark.sql import Row, SQLContext + +sc = SparkContext(appName="SimpleTextClassificationPipeline") +sqlContext = SQLContext(sc) + +# Prepare training documents, which are labeled. +LabeledDocument = Row("id", "text", "label") +training = sc.parallelize([(0L, "a b c d e spark", 1.0), + (1L, "b d", 0.0), + (2L, "spark f g h", 1.0), + (3L, "hadoop mapreduce", 0.0)]) \ + .map(lambda x: LabeledDocument(*x)).toDF() + +# Configure an ML pipeline, which consists of tree stages: tokenizer, hashingTF, and lr. +tokenizer = Tokenizer(inputCol="text", outputCol="words") +hashingTF = HashingTF(inputCol=tokenizer.getOutputCol(), outputCol="features") +lr = LogisticRegression(maxIter=10, regParam=0.01) +pipeline = Pipeline(stages=[tokenizer, hashingTF, lr]) + +# Fit the pipeline to training documents. +model = pipeline.fit(training) + +# Prepare test documents, which are unlabeled. +Document = Row("id", "text") +test = sc.parallelize([(4L, "spark i j k"), + (5L, "l m n"), + (6L, "mapreduce spark"), + (7L, "apache hadoop")]) \ + .map(lambda x: Document(*x)).toDF() + +# Make predictions on test documents and print columns of interest. +prediction = model.transform(test) +selected = prediction.select("id", "text", "prediction") +for row in selected.collect(): + print row + +sc.stop() {% endhighlight %}
    @@ -508,21 +557,26 @@ However, it is also a well-established method for choosing parameters which is m
    {% highlight scala %} import org.apache.spark.{SparkConf, SparkContext} -import org.apache.spark.SparkContext._ import org.apache.spark.ml.Pipeline import org.apache.spark.ml.classification.LogisticRegression import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator import org.apache.spark.ml.feature.{HashingTF, Tokenizer} import org.apache.spark.ml.tuning.{ParamGridBuilder, CrossValidator} +import org.apache.spark.mllib.linalg.Vector import org.apache.spark.sql.{Row, SQLContext} +// Labeled and unlabeled instance types. +// Spark SQL can infer schema from case classes. +case class LabeledDocument(id: Long, text: String, label: Double) +case class Document(id: Long, text: String) + val conf = new SparkConf().setAppName("CrossValidatorExample") val sc = new SparkContext(conf) val sqlContext = new SQLContext(sc) -import sqlContext._ +import sqlContext.implicits._ // Prepare training documents, which are labeled. -val training = sparkContext.parallelize(Seq( +val training = sc.parallelize(Seq( LabeledDocument(0L, "a b c d e spark", 1.0), LabeledDocument(1L, "b d", 0.0), LabeledDocument(2L, "spark f g h", 1.0), @@ -565,24 +619,24 @@ crossval.setEstimatorParamMaps(paramGrid) crossval.setNumFolds(2) // Use 3+ in practice // Run cross-validation, and choose the best set of parameters. -val cvModel = crossval.fit(training) -// Get the best LogisticRegression model (with the best set of parameters from paramGrid). -val lrModel = cvModel.bestModel +val cvModel = crossval.fit(training.toDF) // Prepare test documents, which are unlabeled. -val test = sparkContext.parallelize(Seq( +val test = sc.parallelize(Seq( Document(4L, "spark i j k"), Document(5L, "l m n"), Document(6L, "mapreduce spark"), Document(7L, "apache hadoop"))) // Make predictions on test documents. cvModel uses the best model found (lrModel). -cvModel.transform(test) - .select('id, 'text, 'score, 'prediction) +cvModel.transform(test.toDF) + .select("id", "text", "probability", "prediction") .collect() - .foreach { case Row(id: Long, text: String, score: Double, prediction: Double) => - println("(" + id + ", " + text + ") --> score=" + score + ", prediction=" + prediction) + .foreach { case Row(id: Long, text: String, prob: Vector, prediction: Double) => + println(s"($id, $text) --> prob=$prob, prediction=$prediction") } + +sc.stop() {% endhighlight %}
    @@ -592,7 +646,6 @@ import java.util.List; import com.google.common.collect.Lists; import org.apache.spark.SparkConf; import org.apache.spark.api.java.JavaSparkContext; -import org.apache.spark.ml.Model; import org.apache.spark.ml.Pipeline; import org.apache.spark.ml.PipelineStage; import org.apache.spark.ml.classification.LogisticRegression; @@ -603,13 +656,43 @@ import org.apache.spark.ml.param.ParamMap; import org.apache.spark.ml.tuning.CrossValidator; import org.apache.spark.ml.tuning.CrossValidatorModel; import org.apache.spark.ml.tuning.ParamGridBuilder; -import org.apache.spark.sql.api.java.JavaSQLContext; -import org.apache.spark.sql.api.java.JavaSchemaRDD; -import org.apache.spark.sql.api.java.Row; +import org.apache.spark.sql.DataFrame; +import org.apache.spark.sql.Row; +import org.apache.spark.sql.SQLContext; + +// Labeled and unlabeled instance types. +// Spark SQL can infer schema from Java Beans. +public class Document implements Serializable { + private long id; + private String text; + + public Document(long id, String text) { + this.id = id; + this.text = text; + } + + public long getId() { return this.id; } + public void setId(long id) { this.id = id; } + + public String getText() { return this.text; } + public void setText(String text) { this.text = text; } +} + +public class LabeledDocument extends Document implements Serializable { + private double label; + + public LabeledDocument(long id, String text, double label) { + super(id, text); + this.label = label; + } + + public double getLabel() { return this.label; } + public void setLabel(double label) { this.label = label; } +} SparkConf conf = new SparkConf().setAppName("JavaCrossValidatorExample"); JavaSparkContext jsc = new JavaSparkContext(conf); -JavaSQLContext jsql = new JavaSQLContext(jsc); +SQLContext jsql = new SQLContext(jsc); // Prepare training documents, which are labeled. List localTraining = Lists.newArrayList( @@ -625,8 +708,7 @@ List localTraining = Lists.newArrayList( new LabeledDocument(9L, "a e c l", 0.0), new LabeledDocument(10L, "spark compile", 1.0), new LabeledDocument(11L, "hadoop software", 0.0)); -JavaSchemaRDD training = - jsql.applySchema(jsc.parallelize(localTraining), LabeledDocument.class); +DataFrame training = jsql.createDataFrame(jsc.parallelize(localTraining), LabeledDocument.class); // Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr. Tokenizer tokenizer = new Tokenizer() @@ -660,8 +742,6 @@ crossval.setNumFolds(2); // Use 3+ in practice // Run cross-validation, and choose the best set of parameters. CrossValidatorModel cvModel = crossval.fit(training); -// Get the best LogisticRegression model (with the best set of parameters from paramGrid). -Model lrModel = cvModel.bestModel(); // Prepare test documents, which are unlabeled. List localTest = Lists.newArrayList( @@ -669,15 +749,16 @@ List localTest = Lists.newArrayList( new Document(5L, "l m n"), new Document(6L, "mapreduce spark"), new Document(7L, "apache hadoop")); -JavaSchemaRDD test = jsql.applySchema(jsc.parallelize(localTest), Document.class); +DataFrame test = jsql.createDataFrame(jsc.parallelize(localTest), Document.class); // Make predictions on test documents. cvModel uses the best model found (lrModel). -cvModel.transform(test).registerAsTable("prediction"); -JavaSchemaRDD predictions = jsql.sql("SELECT id, text, score, prediction FROM prediction"); -for (Row r: predictions.collect()) { - System.out.println("(" + r.get(0) + ", " + r.get(1) + ") --> score=" + r.get(2) +DataFrame predictions = cvModel.transform(test); +for (Row r: predictions.select("id", "text", "probability", "prediction").collect()) { + System.out.println("(" + r.get(0) + ", " + r.get(1) + ") --> prob=" + r.get(2) + ", prediction=" + r.get(3)); } + +jsc.stop(); {% endhighlight %} @@ -686,6 +767,21 @@ for (Row r: predictions.collect()) { # Dependencies Spark ML currently depends on MLlib and has the same dependencies. -Please see the [MLlib Dependencies guide](mllib-guide.html#Dependencies) for more info. +Please see the [MLlib Dependencies guide](mllib-guide.html#dependencies) for more info. Spark ML also depends upon Spark SQL, but the relevant parts of Spark SQL do not bring additional dependencies. + +# Migration Guide + +## From 1.2 to 1.3 + +The main API changes are from Spark SQL. We list the most important changes here: + +* The old [SchemaRDD](http://spark.apache.org/docs/1.2.1/api/scala/index.html#org.apache.spark.sql.SchemaRDD) has been replaced with [DataFrame](api/scala/index.html#org.apache.spark.sql.DataFrame) with a somewhat modified API. All algorithms in Spark ML which used to use SchemaRDD now use DataFrame. +* In Spark 1.2, we used implicit conversions from `RDD`s of `LabeledPoint` into `SchemaRDD`s by calling `import sqlContext._` where `sqlContext` was an instance of `SQLContext`. These implicits have been moved, so we now call `import sqlContext.implicits._`. +* Java APIs for SQL have also changed accordingly. Please see the examples above and the [Spark SQL Programming Guide](sql-programming-guide.html) for details. + +Other changes were in `LogisticRegression`: + +* The `scoreCol` output column (with default value "score") was renamed to be `probabilityCol` (with default value "probability"). The type was originally `Double` (for the probability of class 1.0), but it is now `Vector` (for the probability of each class, to support multiclass classification in the future). +* In Spark 1.2, `LogisticRegressionModel` did not include an intercept. In Spark 1.3, it includes an intercept; however, it will always be 0.0 since it uses the default settings for [spark.mllib.LogisticRegressionWithLBFGS](api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS). The option to use an intercept will be added in the future. diff --git a/docs/mllib-classification-regression.md b/docs/mllib-classification-regression.md index 719cc95767b00..8e91d62f4a907 100644 --- a/docs/mllib-classification-regression.md +++ b/docs/mllib-classification-regression.md @@ -17,13 +17,13 @@ the supported algorithms for each type of problem. - Binary Classificationlinear SVMs, logistic regression, decision trees, naive Bayes + Binary Classificationlinear SVMs, logistic regression, decision trees, random forests, gradient-boosted trees, naive Bayes - Multiclass Classificationdecision trees, naive Bayes + Multiclass Classificationdecision trees, random forests, naive Bayes - Regressionlinear least squares, Lasso, ridge regression, decision trees + Regressionlinear least squares, Lasso, ridge regression, decision trees, random forests, gradient-boosted trees, isotonic regression @@ -34,4 +34,8 @@ More details for these methods can be found here: * [binary classification (SVMs, logistic regression)](mllib-linear-methods.html#binary-classification) * [linear regression (least squares, Lasso, ridge)](mllib-linear-methods.html#linear-least-squares-lasso-and-ridge-regression) * [Decision trees](mllib-decision-tree.html) +* [Ensembles of decision trees](mllib-ensembles.html) + * [random forests](mllib-ensembles.html#random-forests) + * [gradient-boosted trees](mllib-ensembles.html#gradient-boosted-trees-gbts) * [Naive Bayes](mllib-naive-bayes.html) +* [Isotonic regression](mllib-isotonic-regression.html) diff --git a/docs/mllib-clustering.md b/docs/mllib-clustering.md index 1e9ef345b7435..f5aa15b7d9b79 100644 --- a/docs/mllib-clustering.md +++ b/docs/mllib-clustering.md @@ -4,28 +4,25 @@ title: Clustering - MLlib displayTitle: MLlib - Clustering --- -* Table of contents -{:toc} - - -## Clustering - Clustering is an unsupervised learning problem whereby we aim to group subsets of entities with one another based on some notion of similarity. Clustering is often used for exploratory analysis and/or as a component of a hierarchical supervised learning pipeline (in which distinct classifiers or regression -models are trained for each cluster). +models are trained for each cluster). MLlib supports the following models: -### k-means +* Table of contents +{:toc} + +## K-means [k-means](http://en.wikipedia.org/wiki/K-means_clustering) is one of the most commonly used clustering algorithms that clusters the data points into a predefined number of clusters. The MLlib implementation includes a parallelized variant of the [k-means++](http://en.wikipedia.org/wiki/K-means%2B%2B) method called [kmeans||](http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf). -The implementation in MLlib has the following parameters: +The implementation in MLlib has the following parameters: * *k* is the number of desired clusters. * *maxIterations* is the maximum number of iterations to run. @@ -35,45 +32,9 @@ initialization via k-means\|\|. guaranteed to find a globally optimal solution, and when run multiple times on a given dataset, the algorithm returns the best clustering result). * *initializationSteps* determines the number of steps in the k-means\|\| algorithm. -* *epsilon* determines the distance threshold within which we consider k-means to have converged. +* *epsilon* determines the distance threshold within which we consider k-means to have converged. -### Gaussian mixture - -A [Gaussian Mixture Model](http://en.wikipedia.org/wiki/Mixture_model#Multivariate_Gaussian_mixture_model) -represents a composite distribution whereby points are drawn from one of *k* Gaussian sub-distributions, -each with its own probability. The MLlib implementation uses the -[expectation-maximization](http://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm) - algorithm to induce the maximum-likelihood model given a set of samples. The implementation -has the following parameters: - -* *k* is the number of desired clusters. -* *convergenceTol* is the maximum change in log-likelihood at which we consider convergence achieved. -* *maxIterations* is the maximum number of iterations to perform without reaching convergence. -* *initialModel* is an optional starting point from which to start the EM algorithm. If this parameter is omitted, a random starting point will be constructed from the data. - -### Power Iteration Clustering - -Power iteration clustering is a scalable and efficient algorithm for clustering points given pointwise mutual affinity values. Internally the algorithm: - -* accepts a [Graph](https://spark.apache.org/docs/0.9.2/api/graphx/index.html#org.apache.spark.graphx.Graph) that represents a normalized pairwise affinity between all input points. -* calculates the principal eigenvalue and eigenvector -* Clusters each of the input points according to their principal eigenvector component value - -Details of this algorithm are found within [Power Iteration Clustering, Lin and Cohen]{www.icml2010.org/papers/387.pdf} - -Example outputs for a dataset inspired by the paper - but with five clusters instead of three- have he following output from our implementation: - -

    - The Property Graph - -

    - -### Examples - -#### k-means +**Examples**
    @@ -187,17 +148,32 @@ print("Within Set Sum of Squared Error = " + str(WSSSE))
    -#### GaussianMixture +## Gaussian mixture + +A [Gaussian Mixture Model](http://en.wikipedia.org/wiki/Mixture_model#Multivariate_Gaussian_mixture_model) +represents a composite distribution whereby points are drawn from one of *k* Gaussian sub-distributions, +each with its own probability. The MLlib implementation uses the +[expectation-maximization](http://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm) + algorithm to induce the maximum-likelihood model given a set of samples. The implementation +has the following parameters: + +* *k* is the number of desired clusters. +* *convergenceTol* is the maximum change in log-likelihood at which we consider convergence achieved. +* *maxIterations* is the maximum number of iterations to perform without reaching convergence. +* *initialModel* is an optional starting point from which to start the EM algorithm. If this parameter is omitted, a random starting point will be constructed from the data. + +**Examples**
    In the following example after loading and parsing data, we use a -[GaussianMixture](api/scala/index.html#org.apache.spark.mllib.clustering.GaussianMixture) -object to cluster the data into two clusters. The number of desired clusters is passed +[GaussianMixture](api/scala/index.html#org.apache.spark.mllib.clustering.GaussianMixture) +object to cluster the data into two clusters. The number of desired clusters is passed to the algorithm. We then output the parameters of the mixture model. {% highlight scala %} import org.apache.spark.mllib.clustering.GaussianMixture +import org.apache.spark.mllib.clustering.GaussianMixtureModel import org.apache.spark.mllib.linalg.Vectors // Load and parse the data @@ -207,9 +183,13 @@ val parsedData = data.map(s => Vectors.dense(s.trim.split(' ').map(_.toDouble))) // Cluster the data into two classes using GaussianMixture val gmm = new GaussianMixture().setK(2).run(parsedData) +// Save and load model +gmm.save(sc, "myGMMModel") +val sameModel = GaussianMixtureModel.load(sc, "myGMMModel") + // output parameters of max-likelihood model for (i <- 0 until gmm.k) { - println("weight=%f\nmu=%s\nsigma=\n%s\n" format + println("weight=%f\nmu=%s\nsigma=\n%s\n" format (gmm.weights(i), gmm.gaussians(i).mu, gmm.gaussians(i).sigma)) } @@ -256,6 +236,9 @@ public class GaussianMixtureExample { // Cluster the data into two classes using GaussianMixture GaussianMixtureModel gmm = new GaussianMixture().setK(2).run(parsedData.rdd()); + // Save and load GaussianMixtureModel + gmm.save(sc, "myGMMModel") + GaussianMixtureModel sameModel = GaussianMixtureModel.load(sc, "myGMMModel") // Output the parameters of the mixture model for(int j=0; j In the following example after loading and parsing data, we use a [GaussianMixture](api/python/pyspark.mllib.html#pyspark.mllib.clustering.GaussianMixture) -object to cluster the data into two clusters. The number of desired clusters is passed +object to cluster the data into two clusters. The number of desired clusters is passed to the algorithm. We then output the parameters of the mixture model. {% highlight python %} @@ -293,41 +276,250 @@ for i in range(2):
    -In order to run the above application, follow the instructions -provided in the [Self-Contained Applications](quick-start.html#self-contained-applications) -section of the Spark -Quick Start guide. Be sure to also include *spark-mllib* to your build file as -a dependency. +## Power iteration clustering (PIC) + +Power iteration clustering (PIC) is a scalable and efficient algorithm for clustering vertices of a +graph given pairwise similarties as edge properties, +described in [Lin and Cohen, Power Iteration Clustering](http://www.icml2010.org/papers/387.pdf). +It computes a pseudo-eigenvector of the normalized affinity matrix of the graph via +[power iteration](http://en.wikipedia.org/wiki/Power_iteration) and uses it to cluster vertices. +MLlib includes an implementation of PIC using GraphX as its backend. +It takes an `RDD` of `(srcId, dstId, similarity)` tuples and outputs a model with the clustering assignments. +The similarities must be nonnegative. +PIC assumes that the similarity measure is symmetric. +A pair `(srcId, dstId)` regardless of the ordering should appear at most once in the input data. +If a pair is missing from input, their similarity is treated as zero. +MLlib's PIC implementation takes the following (hyper-)parameters: + +* `k`: number of clusters +* `maxIterations`: maximum number of power iterations +* `initializationMode`: initialization model. This can be either "random", which is the default, + to use a random vector as vertex properties, or "degree" to use normalized sum similarities. + +**Examples** + +In the following, we show code snippets to demonstrate how to use PIC in MLlib. + +
    +
    + +[`PowerIterationClustering`](api/scala/index.html#org.apache.spark.mllib.clustering.PowerIterationClustering) +implements the PIC algorithm. +It takes an `RDD` of `(srcId: Long, dstId: Long, similarity: Double)` tuples representing the +affinity matrix. +Calling `PowerIterationClustering.run` returns a +[`PowerIterationClusteringModel`](api/scala/index.html#org.apache.spark.mllib.clustering.PowerIterationClusteringModel), +which contains the computed clustering assignments. + +{% highlight scala %} +import org.apache.spark.mllib.clustering.PowerIterationClustering +import org.apache.spark.mllib.linalg.Vectors + +val similarities: RDD[(Long, Long, Double)] = ... + +val pic = new PowerIteartionClustering() + .setK(3) + .setMaxIterations(20) +val model = pic.run(similarities) + +model.assignments.foreach { a => + println(s"${a.id} -> ${a.cluster}") +} +{% endhighlight %} + +A full example that produces the experiment described in the PIC paper can be found under +[`examples/`](https://github.com/apache/spark/blob/master/examples/src/main/scala/org/apache/spark/examples/mllib/PowerIterationClusteringExample.scala). + +
    + +
    -## Streaming clustering +[`PowerIterationClustering`](api/java/org/apache/spark/mllib/clustering/PowerIterationClustering.html) +implements the PIC algorithm. +It takes an `JavaRDD` of `(srcId: Long, dstId: Long, similarity: Double)` tuples representing the +affinity matrix. +Calling `PowerIterationClustering.run` returns a +[`PowerIterationClusteringModel`](api/java/org/apache/spark/mllib/clustering/PowerIterationClusteringModel.html) +which contains the computed clustering assignments. -When data arrive in a stream, we may want to estimate clusters dynamically, -updating them as new data arrive. MLlib provides support for streaming k-means clustering, -with parameters to control the decay (or "forgetfulness") of the estimates. The algorithm -uses a generalization of the mini-batch k-means update rule. For each batch of data, we assign +{% highlight java %} +import scala.Tuple2; +import scala.Tuple3; + +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.mllib.clustering.PowerIterationClustering; +import org.apache.spark.mllib.clustering.PowerIterationClusteringModel; + +JavaRDD> similarities = ... + +PowerIterationClustering pic = new PowerIterationClustering() + .setK(2) + .setMaxIterations(10); +PowerIterationClusteringModel model = pic.run(similarities); + +for (PowerIterationClustering.Assignment a: model.assignments().toJavaRDD().collect()) { + System.out.println(a.id() + " -> " + a.cluster()); +} +{% endhighlight %} +
    + +
    + +## Latent Dirichlet allocation (LDA) + +[Latent Dirichlet allocation (LDA)](http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation) +is a topic model which infers topics from a collection of text documents. +LDA can be thought of as a clustering algorithm as follows: + +* Topics correspond to cluster centers, and documents correspond to examples (rows) in a dataset. +* Topics and documents both exist in a feature space, where feature vectors are vectors of word counts. +* Rather than estimating a clustering using a traditional distance, LDA uses a function based + on a statistical model of how text documents are generated. + +LDA takes in a collection of documents as vectors of word counts. +It learns clustering using [expectation-maximization](http://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm) +on the likelihood function. After fitting on the documents, LDA provides: + +* Topics: Inferred topics, each of which is a probability distribution over terms (words). +* Topic distributions for documents: For each document in the training set, LDA gives a probability distribution over topics. + +LDA takes the following parameters: + +* `k`: Number of topics (i.e., cluster centers) +* `maxIterations`: Limit on the number of iterations of EM used for learning +* `docConcentration`: Hyperparameter for prior over documents' distributions over topics. Currently must be > 1, where larger values encourage smoother inferred distributions. +* `topicConcentration`: Hyperparameter for prior over topics' distributions over terms (words). Currently must be > 1, where larger values encourage smoother inferred distributions. +* `checkpointInterval`: If using checkpointing (set in the Spark configuration), this parameter specifies the frequency with which checkpoints will be created. If `maxIterations` is large, using checkpointing can help reduce shuffle file sizes on disk and help with failure recovery. + +*Note*: LDA is a new feature with some missing functionality. In particular, it does not yet +support prediction on new documents, and it does not have a Python API. These will be added in the future. + +**Examples** + +In the following example, we load word count vectors representing a corpus of documents. +We then use [LDA](api/scala/index.html#org.apache.spark.mllib.clustering.LDA) +to infer three topics from the documents. The number of desired clusters is passed +to the algorithm. We then output the topics, represented as probability distributions over words. + +
    +
    + +{% highlight scala %} +import org.apache.spark.mllib.clustering.LDA +import org.apache.spark.mllib.linalg.Vectors + +// Load and parse the data +val data = sc.textFile("data/mllib/sample_lda_data.txt") +val parsedData = data.map(s => Vectors.dense(s.trim.split(' ').map(_.toDouble))) +// Index documents with unique IDs +val corpus = parsedData.zipWithIndex.map(_.swap).cache() + +// Cluster the documents into three topics using LDA +val ldaModel = new LDA().setK(3).run(corpus) + +// Output topics. Each is a distribution over words (matching word count vectors) +println("Learned topics (as distributions over vocab of " + ldaModel.vocabSize + " words):") +val topics = ldaModel.topicsMatrix +for (topic <- Range(0, 3)) { + print("Topic " + topic + ":") + for (word <- Range(0, ldaModel.vocabSize)) { print(" " + topics(word, topic)); } + println() +} +{% endhighlight %} +
    + +
    +{% highlight java %} +import scala.Tuple2; + +import org.apache.spark.api.java.*; +import org.apache.spark.api.java.function.Function; +import org.apache.spark.mllib.clustering.DistributedLDAModel; +import org.apache.spark.mllib.clustering.LDA; +import org.apache.spark.mllib.linalg.Matrix; +import org.apache.spark.mllib.linalg.Vector; +import org.apache.spark.mllib.linalg.Vectors; +import org.apache.spark.SparkConf; + +public class JavaLDAExample { + public static void main(String[] args) { + SparkConf conf = new SparkConf().setAppName("LDA Example"); + JavaSparkContext sc = new JavaSparkContext(conf); + + // Load and parse the data + String path = "data/mllib/sample_lda_data.txt"; + JavaRDD data = sc.textFile(path); + JavaRDD parsedData = data.map( + new Function() { + public Vector call(String s) { + String[] sarray = s.trim().split(" "); + double[] values = new double[sarray.length]; + for (int i = 0; i < sarray.length; i++) + values[i] = Double.parseDouble(sarray[i]); + return Vectors.dense(values); + } + } + ); + // Index documents with unique IDs + JavaPairRDD corpus = JavaPairRDD.fromJavaRDD(parsedData.zipWithIndex().map( + new Function, Tuple2>() { + public Tuple2 call(Tuple2 doc_id) { + return doc_id.swap(); + } + } + )); + corpus.cache(); + + // Cluster the documents into three topics using LDA + DistributedLDAModel ldaModel = new LDA().setK(3).run(corpus); + + // Output topics. Each is a distribution over words (matching word count vectors) + System.out.println("Learned topics (as distributions over vocab of " + ldaModel.vocabSize() + + " words):"); + Matrix topics = ldaModel.topicsMatrix(); + for (int topic = 0; topic < 3; topic++) { + System.out.print("Topic " + topic + ":"); + for (int word = 0; word < ldaModel.vocabSize(); word++) { + System.out.print(" " + topics.apply(word, topic)); + } + System.out.println(); + } + } +} +{% endhighlight %} +
    + +
    + +## Streaming k-means + +When data arrive in a stream, we may want to estimate clusters dynamically, +updating them as new data arrive. MLlib provides support for streaming k-means clustering, +with parameters to control the decay (or "forgetfulness") of the estimates. The algorithm +uses a generalization of the mini-batch k-means update rule. For each batch of data, we assign all points to their nearest cluster, compute new cluster centers, then update each cluster using: `\begin{equation} c_{t+1} = \frac{c_tn_t\alpha + x_tm_t}{n_t\alpha+m_t} \end{equation}` `\begin{equation} - n_{t+1} = n_t + m_t + n_{t+1} = n_t + m_t \end{equation}` -Where `$c_t$` is the previous center for the cluster, `$n_t$` is the number of points assigned -to the cluster thus far, `$x_t$` is the new cluster center from the current batch, and `$m_t$` -is the number of points added to the cluster in the current batch. The decay factor `$\alpha$` -can be used to ignore the past: with `$\alpha$=1` all data will be used from the beginning; -with `$\alpha$=0` only the most recent data will be used. This is analogous to an -exponentially-weighted moving average. +Where `$c_t$` is the previous center for the cluster, `$n_t$` is the number of points assigned +to the cluster thus far, `$x_t$` is the new cluster center from the current batch, and `$m_t$` +is the number of points added to the cluster in the current batch. The decay factor `$\alpha$` +can be used to ignore the past: with `$\alpha$=1` all data will be used from the beginning; +with `$\alpha$=0` only the most recent data will be used. This is analogous to an +exponentially-weighted moving average. -The decay can be specified using a `halfLife` parameter, which determines the +The decay can be specified using a `halfLife` parameter, which determines the correct decay factor `a` such that, for data acquired at time `t`, its contribution by time `t + halfLife` will have dropped to 0.5. The unit of time can be specified either as `batches` or `points` and the update rule will be adjusted accordingly. -### Examples +**Examples** This example shows how to estimate clusters on streaming data. @@ -345,9 +537,9 @@ import org.apache.spark.mllib.clustering.StreamingKMeans {% endhighlight %} -Then we make an input stream of vectors for training, as well as a stream of labeled data -points for testing. We assume a StreamingContext `ssc` has been created, see -[Spark Streaming Programming Guide](streaming-programming-guide.html#initializing) for more info. +Then we make an input stream of vectors for training, as well as a stream of labeled data +points for testing. We assume a StreamingContext `ssc` has been created, see +[Spark Streaming Programming Guide](streaming-programming-guide.html#initializing) for more info. {% highlight scala %} @@ -369,24 +561,24 @@ val model = new StreamingKMeans() {% endhighlight %} -Now register the streams for training and testing and start the job, printing +Now register the streams for training and testing and start the job, printing the predicted cluster assignments on new data points as they arrive. {% highlight scala %} model.trainOn(trainingData) -model.predictOnValues(testData).print() +model.predictOnValues(testData.map(lp => (lp.label, lp.features))).print() ssc.start() ssc.awaitTermination() - + {% endhighlight %} -As you add new text files with data the cluster centers will update. Each training +As you add new text files with data the cluster centers will update. Each training point should be formatted as `[x1, x2, x3]`, and each test data point -should be formatted as `(y, [x1, x2, x3])`, where `y` is some useful label or identifier -(e.g. a true category assignment). Anytime a text file is placed in `/training/data/dir` -the model will update. Anytime a text file is placed in `/testing/data/dir` +should be formatted as `(y, [x1, x2, x3])`, where `y` is some useful label or identifier +(e.g. a true category assignment). Anytime a text file is placed in `/training/data/dir` +the model will update. Anytime a text file is placed in `/testing/data/dir` you will see predictions. With new data, the cluster centers will change!
    diff --git a/docs/mllib-collaborative-filtering.md b/docs/mllib-collaborative-filtering.md index ef18cec9371d6..76140282a2dd0 100644 --- a/docs/mllib-collaborative-filtering.md +++ b/docs/mllib-collaborative-filtering.md @@ -66,6 +66,7 @@ recommendation model by measuring the Mean Squared Error of rating prediction. {% highlight scala %} import org.apache.spark.mllib.recommendation.ALS +import org.apache.spark.mllib.recommendation.MatrixFactorizationModel import org.apache.spark.mllib.recommendation.Rating // Load and parse the data @@ -95,6 +96,10 @@ val MSE = ratesAndPreds.map { case ((user, product), (r1, r2)) => err * err }.mean() println("Mean Squared Error = " + MSE) + +// Save and load model +model.save(sc, "myModelPath") +val sameModel = MatrixFactorizationModel.load(sc, "myModelPath") {% endhighlight %} If the rating matrix is derived from another source of information (e.g., it is inferred from @@ -181,6 +186,10 @@ public class CollaborativeFiltering { } ).rdd()).mean(); System.out.println("Mean Squared Error = " + MSE); + + // Save and load model + model.save(sc.sc(), "myModelPath"); + MatrixFactorizationModel sameModel = MatrixFactorizationModel.load(sc.sc(), "myModelPath"); } } {% endhighlight %} @@ -192,7 +201,7 @@ We use the default ALS.train() method which assumes ratings are explicit. We eva recommendation by measuring the Mean Squared Error of rating prediction. {% highlight python %} -from pyspark.mllib.recommendation import ALS, Rating +from pyspark.mllib.recommendation import ALS, MatrixFactorizationModel, Rating # Load and parse the data data = sc.textFile("data/mllib/als/test.data") @@ -209,6 +218,10 @@ predictions = model.predictAll(testdata).map(lambda r: ((r[0], r[1]), r[2])) ratesAndPreds = ratings.map(lambda r: ((r[0], r[1]), r[2])).join(predictions) MSE = ratesAndPreds.map(lambda r: (r[1][0] - r[1][1])**2).reduce(lambda x, y: x + y) / ratesAndPreds.count() print("Mean Squared Error = " + str(MSE)) + +# Save and load model +model.save(sc, "myModelPath") +sameModel = MatrixFactorizationModel.load(sc, "myModelPath") {% endhighlight %} If the rating matrix is derived from other source of information (i.e., it is inferred from other diff --git a/docs/mllib-data-types.md b/docs/mllib-data-types.md index 101dc2f8695f3..4f2a2f71048f7 100644 --- a/docs/mllib-data-types.md +++ b/docs/mllib-data-types.md @@ -78,13 +78,13 @@ MLlib recognizes the following types as dense vectors: and the following as sparse vectors: -* MLlib's [`SparseVector`](api/python/pyspark.mllib.linalg.SparseVector-class.html). +* MLlib's [`SparseVector`](api/python/pyspark.mllib.html#pyspark.mllib.linalg.SparseVector). * SciPy's [`csc_matrix`](http://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csc_matrix.html#scipy.sparse.csc_matrix) with a single column We recommend using NumPy arrays over lists for efficiency, and using the factory methods implemented -in [`Vectors`](api/python/pyspark.mllib.linalg.Vectors-class.html) to create sparse vectors. +in [`Vectors`](api/python/pyspark.mllib.html#pyspark.mllib.linalg.Vector) to create sparse vectors. {% highlight python %} import numpy as np @@ -151,7 +151,7 @@ LabeledPoint neg = new LabeledPoint(1.0, Vectors.sparse(3, new int[] {0, 2}, new
    A labeled point is represented by -[`LabeledPoint`](api/python/pyspark.mllib.regression.LabeledPoint-class.html). +[`LabeledPoint`](api/python/pyspark.mllib.html#pyspark.mllib.regression.LabeledPoint). {% highlight python %} from pyspark.mllib.linalg import SparseVector @@ -211,7 +211,7 @@ JavaRDD examples =
    -[`MLUtils.loadLibSVMFile`](api/python/pyspark.mllib.util.MLUtils-class.html) reads training +[`MLUtils.loadLibSVMFile`](api/python/pyspark.mllib.html#pyspark.mllib.util.MLUtils) reads training examples stored in LIBSVM format. {% highlight python %} @@ -296,6 +296,70 @@ backed by an RDD of its entries. The underlying RDDs of a distributed matrix must be deterministic, because we cache the matrix size. In general the use of non-deterministic RDDs can lead to errors. +### BlockMatrix + +A `BlockMatrix` is a distributed matrix backed by an RDD of `MatrixBlock`s, where a `MatrixBlock` is +a tuple of `((Int, Int), Matrix)`, where the `(Int, Int)` is the index of the block, and `Matrix` is +the sub-matrix at the given index with size `rowsPerBlock` x `colsPerBlock`. +`BlockMatrix` supports methods such as `add` and `multiply` with another `BlockMatrix`. +`BlockMatrix` also has a helper function `validate` which can be used to check whether the +`BlockMatrix` is set up properly. + +
    +
    + +A [`BlockMatrix`](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.BlockMatrix) can be +most easily created from an `IndexedRowMatrix` or `CoordinateMatrix` by calling `toBlockMatrix`. +`toBlockMatrix` creates blocks of size 1024 x 1024 by default. +Users may change the block size by supplying the values through `toBlockMatrix(rowsPerBlock, colsPerBlock)`. + +{% highlight scala %} +import org.apache.spark.mllib.linalg.distributed.{BlockMatrix, CoordinateMatrix, MatrixEntry} + +val entries: RDD[MatrixEntry] = ... // an RDD of (i, j, v) matrix entries +// Create a CoordinateMatrix from an RDD[MatrixEntry]. +val coordMat: CoordinateMatrix = new CoordinateMatrix(entries) +// Transform the CoordinateMatrix to a BlockMatrix +val matA: BlockMatrix = coordMat.toBlockMatrix().cache() + +// Validate whether the BlockMatrix is set up properly. Throws an Exception when it is not valid. +// Nothing happens if it is valid. +matA.validate() + +// Calculate A^T A. +val ata = matA.transpose.multiply(matA) +{% endhighlight %} +
    + +
    + +A [`BlockMatrix`](api/java/org/apache/spark/mllib/linalg/distributed/BlockMatrix.html) can be +most easily created from an `IndexedRowMatrix` or `CoordinateMatrix` by calling `toBlockMatrix`. +`toBlockMatrix` creates blocks of size 1024 x 1024 by default. +Users may change the block size by supplying the values through `toBlockMatrix(rowsPerBlock, colsPerBlock)`. + +{% highlight java %} +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.mllib.linalg.distributed.BlockMatrix; +import org.apache.spark.mllib.linalg.distributed.CoordinateMatrix; +import org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix; + +JavaRDD entries = ... // a JavaRDD of (i, j, v) Matrix Entries +// Create a CoordinateMatrix from a JavaRDD. +CoordinateMatrix coordMat = new CoordinateMatrix(entries.rdd()); +// Transform the CoordinateMatrix to a BlockMatrix +BlockMatrix matA = coordMat.toBlockMatrix().cache(); + +// Validate whether the BlockMatrix is set up properly. Throws an Exception when it is not valid. +// Nothing happens if it is valid. +matA.validate(); + +// Calculate A^T A. +BlockMatrix ata = matA.transpose().multiply(matA); +{% endhighlight %} +
    +
    + ### RowMatrix A `RowMatrix` is a row-oriented distributed matrix without meaningful row indices, backed by an RDD diff --git a/docs/mllib-decision-tree.md b/docs/mllib-decision-tree.md index d1537def851e7..c1d0f8a6b1cd8 100644 --- a/docs/mllib-decision-tree.md +++ b/docs/mllib-decision-tree.md @@ -54,8 +54,8 @@ impurity measure for regression (variance). Variance Regression - $\frac{1}{N} \sum_{i=1}^{N} (x_i - \mu)^2$$y_i$ is label for an instance, - $N$ is the number of instances and $\mu$ is the mean given by $\frac{1}{N} \sum_{i=1}^N x_i$. + $\frac{1}{N} \sum_{i=1}^{N} (y_i - \mu)^2$$y_i$ is label for an instance, + $N$ is the number of instances and $\mu$ is the mean given by $\frac{1}{N} \sum_{i=1}^N y_i$. @@ -194,6 +194,7 @@ maximum tree depth of 5. The test error is calculated to measure the algorithm a
    {% highlight scala %} import org.apache.spark.mllib.tree.DecisionTree +import org.apache.spark.mllib.tree.model.DecisionTreeModel import org.apache.spark.mllib.util.MLUtils // Load and parse the data file. @@ -221,6 +222,10 @@ val labelAndPreds = testData.map { point => val testErr = labelAndPreds.filter(r => r._1 != r._2).count.toDouble / testData.count() println("Test Error = " + testErr) println("Learned classification tree model:\n" + model.toDebugString) + +// Save and load model +model.save(sc, "myModelPath") +val sameModel = DecisionTreeModel.load(sc, "myModelPath") {% endhighlight %}
    @@ -279,13 +284,18 @@ Double testErr = }).count() / testData.count(); System.out.println("Test Error: " + testErr); System.out.println("Learned classification tree model:\n" + model.toDebugString()); + +// Save and load model +model.save(sc.sc(), "myModelPath"); +DecisionTreeModel sameModel = DecisionTreeModel.load(sc.sc(), "myModelPath"); {% endhighlight %}
    + {% highlight python %} from pyspark.mllib.regression import LabeledPoint -from pyspark.mllib.tree import DecisionTree +from pyspark.mllib.tree import DecisionTree, DecisionTreeModel from pyspark.mllib.util import MLUtils # Load and parse the data file into an RDD of LabeledPoint. @@ -305,6 +315,10 @@ testErr = labelsAndPredictions.filter(lambda (v, p): v != p).count() / float(tes print('Test Error = ' + str(testErr)) print('Learned classification tree model:') print(model.toDebugString()) + +# Save and load model +model.save(sc, "myModelPath") +sameModel = DecisionTreeModel.load(sc, "myModelPath") {% endhighlight %}
    @@ -324,6 +338,7 @@ depth of 5. The Mean Squared Error (MSE) is computed at the end to evaluate
    {% highlight scala %} import org.apache.spark.mllib.tree.DecisionTree +import org.apache.spark.mllib.tree.model.DecisionTreeModel import org.apache.spark.mllib.util.MLUtils // Load and parse the data file. @@ -350,6 +365,10 @@ val labelsAndPredictions = testData.map { point => val testMSE = labelsAndPredictions.map{ case(v, p) => math.pow((v - p), 2)}.mean() println("Test Mean Squared Error = " + testMSE) println("Learned regression tree model:\n" + model.toDebugString) + +// Save and load model +model.save(sc, "myModelPath") +val sameModel = DecisionTreeModel.load(sc, "myModelPath") {% endhighlight %}
    @@ -414,13 +433,18 @@ Double testMSE = }) / data.count(); System.out.println("Test Mean Squared Error: " + testMSE); System.out.println("Learned regression tree model:\n" + model.toDebugString()); + +// Save and load model +model.save(sc.sc(), "myModelPath"); +DecisionTreeModel sameModel = DecisionTreeModel.load(sc.sc(), "myModelPath"); {% endhighlight %}
    + {% highlight python %} from pyspark.mllib.regression import LabeledPoint -from pyspark.mllib.tree import DecisionTree +from pyspark.mllib.tree import DecisionTree, DecisionTreeModel from pyspark.mllib.util import MLUtils # Load and parse the data file into an RDD of LabeledPoint. @@ -440,6 +464,10 @@ testMSE = labelsAndPredictions.map(lambda (v, p): (v - p) * (v - p)).sum() / flo print('Test Mean Squared Error = ' + str(testMSE)) print('Learned regression tree model:') print(model.toDebugString()) + +# Save and load model +model.save(sc, "myModelPath") +sameModel = DecisionTreeModel.load(sc, "myModelPath") {% endhighlight %}
    diff --git a/docs/mllib-ensembles.md b/docs/mllib-ensembles.md index 23ede04b62d5b..7521fb14a7bd6 100644 --- a/docs/mllib-ensembles.md +++ b/docs/mllib-ensembles.md @@ -98,6 +98,7 @@ The test error is calculated to measure the algorithm accuracy.
    {% highlight scala %} import org.apache.spark.mllib.tree.RandomForest +import org.apache.spark.mllib.tree.model.RandomForestModel import org.apache.spark.mllib.util.MLUtils // Load and parse the data file. @@ -127,6 +128,10 @@ val labelAndPreds = testData.map { point => val testErr = labelAndPreds.filter(r => r._1 != r._2).count.toDouble / testData.count() println("Test Error = " + testErr) println("Learned classification forest model:\n" + model.toDebugString) + +// Save and load model +model.save(sc, "myModelPath") +val sameModel = RandomForestModel.load(sc, "myModelPath") {% endhighlight %}
    @@ -188,12 +193,17 @@ Double testErr = }).count() / testData.count(); System.out.println("Test Error: " + testErr); System.out.println("Learned classification forest model:\n" + model.toDebugString()); + +// Save and load model +model.save(sc.sc(), "myModelPath"); +RandomForestModel sameModel = RandomForestModel.load(sc.sc(), "myModelPath"); {% endhighlight %}
    + {% highlight python %} -from pyspark.mllib.tree import RandomForest +from pyspark.mllib.tree import RandomForest, RandomForestModel from pyspark.mllib.util import MLUtils # Load and parse the data file into an RDD of LabeledPoint. @@ -216,6 +226,10 @@ testErr = labelsAndPredictions.filter(lambda (v, p): v != p).count() / float(tes print('Test Error = ' + str(testErr)) print('Learned classification forest model:') print(model.toDebugString()) + +# Save and load model +model.save(sc, "myModelPath") +sameModel = RandomForestModel.load(sc, "myModelPath") {% endhighlight %}
    @@ -235,6 +249,7 @@ The Mean Squared Error (MSE) is computed at the end to evaluate
    {% highlight scala %} import org.apache.spark.mllib.tree.RandomForest +import org.apache.spark.mllib.tree.model.RandomForestModel import org.apache.spark.mllib.util.MLUtils // Load and parse the data file. @@ -264,6 +279,10 @@ val labelsAndPredictions = testData.map { point => val testMSE = labelsAndPredictions.map{ case(v, p) => math.pow((v - p), 2)}.mean() println("Test Mean Squared Error = " + testMSE) println("Learned regression forest model:\n" + model.toDebugString) + +// Save and load model +model.save(sc, "myModelPath") +val sameModel = RandomForestModel.load(sc, "myModelPath") {% endhighlight %}
    @@ -328,12 +347,17 @@ Double testMSE = }) / testData.count(); System.out.println("Test Mean Squared Error: " + testMSE); System.out.println("Learned regression forest model:\n" + model.toDebugString()); + +// Save and load model +model.save(sc.sc(), "myModelPath"); +RandomForestModel sameModel = RandomForestModel.load(sc.sc(), "myModelPath"); {% endhighlight %}
    + {% highlight python %} -from pyspark.mllib.tree import RandomForest +from pyspark.mllib.tree import RandomForest, RandomForestModel from pyspark.mllib.util import MLUtils # Load and parse the data file into an RDD of LabeledPoint. @@ -356,6 +380,10 @@ testMSE = labelsAndPredictions.map(lambda (v, p): (v - p) * (v - p)).sum() / flo print('Test Mean Squared Error = ' + str(testMSE)) print('Learned regression forest model:') print(model.toDebugString()) + +# Save and load model +model.save(sc, "myModelPath") +sameModel = RandomForestModel.load(sc, "myModelPath") {% endhighlight %}
    @@ -427,10 +455,19 @@ We omit some decision tree parameters since those are covered in the [decision t * **`algo`**: The algorithm or task (classification vs. regression) is set using the tree [Strategy] parameter. +#### Validation while training -### Examples +Gradient boosting can overfit when trained with more trees. In order to prevent overfitting, it is useful to validate while +training. The method runWithValidation has been provided to make use of this option. It takes a pair of RDD's as arguments, the +first one being the training dataset and the second being the validation dataset. -GBTs currently have APIs in Scala and Java. Examples in both languages are shown below. +The training is stopped when the improvement in the validation error is not more than a certain tolerance +(supplied by the `validationTol` argument in `BoostingStrategy`). In practice, the validation error +decreases initially and later increases. There might be cases in which the validation error does not change monotonically, +and the user is advised to set a large enough negative tolerance and examine the validation curve using `evaluateEachIteration` +(which gives the error or loss per iteration) to tune the number of iterations. + +### Examples #### Classification @@ -446,6 +483,7 @@ The test error is calculated to measure the algorithm accuracy. {% highlight scala %} import org.apache.spark.mllib.tree.GradientBoostedTrees import org.apache.spark.mllib.tree.configuration.BoostingStrategy +import org.apache.spark.mllib.tree.model.GradientBoostedTreesModel import org.apache.spark.mllib.util.MLUtils // Load and parse the data file. @@ -458,7 +496,7 @@ val (trainingData, testData) = (splits(0), splits(1)) // The defaultParams for Classification use LogLoss by default. val boostingStrategy = BoostingStrategy.defaultParams("Classification") boostingStrategy.numIterations = 3 // Note: Use more iterations in practice. -boostingStrategy.treeStrategy.numClassesForClassification = 2 +boostingStrategy.treeStrategy.numClasses = 2 boostingStrategy.treeStrategy.maxDepth = 5 // Empty categoricalFeaturesInfo indicates all features are continuous. boostingStrategy.treeStrategy.categoricalFeaturesInfo = Map[Int, Int]() @@ -473,6 +511,10 @@ val labelAndPreds = testData.map { point => val testErr = labelAndPreds.filter(r => r._1 != r._2).count.toDouble / testData.count() println("Test Error = " + testErr) println("Learned classification GBT model:\n" + model.toDebugString) + +// Save and load model +model.save(sc, "myModelPath") +val sameModel = GradientBoostedTreesModel.load(sc, "myModelPath") {% endhighlight %} @@ -534,6 +576,41 @@ Double testErr = }).count() / testData.count(); System.out.println("Test Error: " + testErr); System.out.println("Learned classification GBT model:\n" + model.toDebugString()); + +// Save and load model +model.save(sc.sc(), "myModelPath"); +GradientBoostedTreesModel sameModel = GradientBoostedTreesModel.load(sc.sc(), "myModelPath"); +{% endhighlight %} + + +
    + +{% highlight python %} +from pyspark.mllib.tree import GradientBoostedTrees, GradientBoostedTreesModel +from pyspark.mllib.util import MLUtils + +# Load and parse the data file. +data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt") +# Split the data into training and test sets (30% held out for testing) +(trainingData, testData) = data.randomSplit([0.7, 0.3]) + +# Train a GradientBoostedTrees model. +# Notes: (a) Empty categoricalFeaturesInfo indicates all features are continuous. +# (b) Use more iterations in practice. +model = GradientBoostedTrees.trainClassifier(trainingData, + categoricalFeaturesInfo={}, numIterations=3) + +# Evaluate model on test instances and compute test error +predictions = model.predict(testData.map(lambda x: x.features)) +labelsAndPredictions = testData.map(lambda lp: lp.label).zip(predictions) +testErr = labelsAndPredictions.filter(lambda (v, p): v != p).count() / float(testData.count()) +print('Test Error = ' + str(testErr)) +print('Learned classification GBT model:') +print(model.toDebugString()) + +# Save and load model +model.save(sc, "myModelPath") +sameModel = GradientBoostedTreesModel.load(sc, "myModelPath") {% endhighlight %}
    @@ -554,6 +631,7 @@ The Mean Squared Error (MSE) is computed at the end to evaluate {% highlight scala %} import org.apache.spark.mllib.tree.GradientBoostedTrees import org.apache.spark.mllib.tree.configuration.BoostingStrategy +import org.apache.spark.mllib.tree.model.GradientBoostedTreesModel import org.apache.spark.mllib.util.MLUtils // Load and parse the data file. @@ -580,6 +658,10 @@ val labelsAndPredictions = testData.map { point => val testMSE = labelsAndPredictions.map{ case(v, p) => math.pow((v - p), 2)}.mean() println("Test Mean Squared Error = " + testMSE) println("Learned regression GBT model:\n" + model.toDebugString) + +// Save and load model +model.save(sc, "myModelPath") +val sameModel = GradientBoostedTreesModel.load(sc, "myModelPath") {% endhighlight %} @@ -647,6 +729,41 @@ Double testMSE = }) / data.count(); System.out.println("Test Mean Squared Error: " + testMSE); System.out.println("Learned regression GBT model:\n" + model.toDebugString()); + +// Save and load model +model.save(sc.sc(), "myModelPath"); +GradientBoostedTreesModel sameModel = GradientBoostedTreesModel.load(sc.sc(), "myModelPath"); +{% endhighlight %} + + +
    + +{% highlight python %} +from pyspark.mllib.tree import GradientBoostedTrees, GradientBoostedTreesModel +from pyspark.mllib.util import MLUtils + +# Load and parse the data file. +data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt") +# Split the data into training and test sets (30% held out for testing) +(trainingData, testData) = data.randomSplit([0.7, 0.3]) + +# Train a GradientBoostedTrees model. +# Notes: (a) Empty categoricalFeaturesInfo indicates all features are continuous. +# (b) Use more iterations in practice. +model = GradientBoostedTrees.trainRegressor(trainingData, + categoricalFeaturesInfo={}, numIterations=3) + +# Evaluate model on test instances and compute test error +predictions = model.predict(testData.map(lambda x: x.features)) +labelsAndPredictions = testData.map(lambda lp: lp.label).zip(predictions) +testMSE = labelsAndPredictions.map(lambda (v, p): (v - p) * (v - p)).sum() / float(testData.count()) +print('Test Mean Squared Error = ' + str(testMSE)) +print('Learned regression GBT model:') +print(model.toDebugString()) + +# Save and load model +model.save(sc, "myModelPath") +sameModel = GradientBoostedTreesModel.load(sc, "myModelPath") {% endhighlight %}
    diff --git a/docs/mllib-feature-extraction.md b/docs/mllib-feature-extraction.md index d4a61a7fbf3d7..80842b27effd8 100644 --- a/docs/mllib-feature-extraction.md +++ b/docs/mllib-feature-extraction.md @@ -375,3 +375,105 @@ data2 = labels.zip(normalizer2.transform(features)) {% endhighlight %} + +## Feature selection +[Feature selection](http://en.wikipedia.org/wiki/Feature_selection) allows selecting the most relevant features for use in model construction. Feature selection reduces the size of the vector space and, in turn, the complexity of any subsequent operation with vectors. The number of features to select can be tuned using a held-out validation set. + +### ChiSqSelector +[`ChiSqSelector`](api/scala/index.html#org.apache.spark.mllib.feature.ChiSqSelector) stands for Chi-Squared feature selection. It operates on labeled data with categorical features. `ChiSqSelector` orders features based on a Chi-Squared test of independence from the class, and then filters (selects) the top features which are most closely related to the label. + +#### Model Fitting + +[`ChiSqSelector`](api/scala/index.html#org.apache.spark.mllib.feature.ChiSqSelector) has the +following parameters in the constructor: + +* `numTopFeatures` number of top features that the selector will select (filter). + +We provide a [`fit`](api/scala/index.html#org.apache.spark.mllib.feature.ChiSqSelector) method in +`ChiSqSelector` which can take an input of `RDD[LabeledPoint]` with categorical features, learn the summary statistics, and then +return a `ChiSqSelectorModel` which can transform an input dataset into the reduced feature space. + +This model implements [`VectorTransformer`](api/scala/index.html#org.apache.spark.mllib.feature.VectorTransformer) +which can apply the Chi-Squared feature selection on a `Vector` to produce a reduced `Vector` or on +an `RDD[Vector]` to produce a reduced `RDD[Vector]`. + +Note that the user can also construct a `ChiSqSelectorModel` by hand by providing an array of selected feature indices (which must be sorted in ascending order). + +#### Example + +The following example shows the basic use of ChiSqSelector. + +
    +
    +{% highlight scala %} +import org.apache.spark.SparkContext._ +import org.apache.spark.mllib.linalg.Vectors +import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.mllib.util.MLUtils + +// Load some data in libsvm format +val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt") +// Discretize data in 16 equal bins since ChiSqSelector requires categorical features +val discretizedData = data.map { lp => + LabeledPoint(lp.label, Vectors.dense(lp.features.toArray.map { x => x / 16 } ) ) +} +// Create ChiSqSelector that will select 50 features +val selector = new ChiSqSelector(50) +// Create ChiSqSelector model (selecting features) +val transformer = selector.fit(discretizedData) +// Filter the top 50 features from each feature vector +val filteredData = discretizedData.map { lp => + LabeledPoint(lp.label, transformer.transform(lp.features)) +} +{% endhighlight %} +
    + +
    +{% highlight java %} +import org.apache.spark.SparkConf; +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.api.java.function.Function; +import org.apache.spark.mllib.feature.ChiSqSelector; +import org.apache.spark.mllib.feature.ChiSqSelectorModel; +import org.apache.spark.mllib.linalg.Vectors; +import org.apache.spark.mllib.regression.LabeledPoint; +import org.apache.spark.mllib.util.MLUtils; + +SparkConf sparkConf = new SparkConf().setAppName("JavaChiSqSelector"); +JavaSparkContext sc = new JavaSparkContext(sparkConf); +JavaRDD points = MLUtils.loadLibSVMFile(sc.sc(), + "data/mllib/sample_libsvm_data.txt").toJavaRDD().cache(); + +// Discretize data in 16 equal bins since ChiSqSelector requires categorical features +JavaRDD discretizedData = points.map( + new Function() { + @Override + public LabeledPoint call(LabeledPoint lp) { + final double[] discretizedFeatures = new double[lp.features().size()]; + for (int i = 0; i < lp.features().size(); ++i) { + discretizedFeatures[i] = lp.features().apply(i) / 16; + } + return new LabeledPoint(lp.label(), Vectors.dense(discretizedFeatures)); + } + }); + +// Create ChiSqSelector that will select 50 features +ChiSqSelector selector = new ChiSqSelector(50); +// Create ChiSqSelector model (selecting features) +final ChiSqSelectorModel transformer = selector.fit(discretizedData.rdd()); +// Filter the top 50 features from each feature vector +JavaRDD filteredData = discretizedData.map( + new Function() { + @Override + public LabeledPoint call(LabeledPoint lp) { + return new LabeledPoint(lp.label(), transformer.transform(lp.features())); + } + } +); + +sc.stop(); +{% endhighlight %} +
    +
    + diff --git a/docs/mllib-frequent-pattern-mining.md b/docs/mllib-frequent-pattern-mining.md new file mode 100644 index 0000000000000..9fd9be0dd01b1 --- /dev/null +++ b/docs/mllib-frequent-pattern-mining.md @@ -0,0 +1,98 @@ +--- +layout: global +title: Frequent Pattern Mining - MLlib +displayTitle: MLlib - Frequent Pattern Mining +--- + +Mining frequent items, itemsets, subsequences, or other substructures is usually among the +first steps to analyze a large-scale dataset, which has been an active research topic in +data mining for years. +We refer users to Wikipedia's [association rule learning](http://en.wikipedia.org/wiki/Association_rule_learning) +for more information. +MLlib provides a parallel implementation of FP-growth, +a popular algorithm to mining frequent itemsets. + +## FP-growth + +The FP-growth algorithm is described in the paper +[Han et al., Mining frequent patterns without candidate generation](http://dx.doi.org/10.1145/335191.335372), +where "FP" stands for frequent pattern. +Given a dataset of transactions, the first step of FP-growth is to calculate item frequencies and identify frequent items. +Different from [Apriori-like](http://en.wikipedia.org/wiki/Apriori_algorithm) algorithms designed for the same purpose, +the second step of FP-growth uses a suffix tree (FP-tree) structure to encode transactions without generating candidate sets +explicitly, which are usually expensive to generate. +After the second step, the frequent itemsets can be extracted from the FP-tree. +In MLlib, we implemented a parallel version of FP-growth called PFP, +as described in [Li et al., PFP: Parallel FP-growth for query recommendation](http://dx.doi.org/10.1145/1454008.1454027). +PFP distributes the work of growing FP-trees based on the suffices of transactions, +and hence more scalable than a single-machine implementation. +We refer users to the papers for more details. + +MLlib's FP-growth implementation takes the following (hyper-)parameters: + +* `minSupport`: the minimum support for an itemset to be identified as frequent. + For example, if an item appears 3 out of 5 transactions, it has a support of 3/5=0.6. +* `numPartitions`: the number of partitions used to distribute the work. + +**Examples** + +
    +
    + +[`FPGrowth`](api/java/org/apache/spark/mllib/fpm/FPGrowth.html) implements the +FP-growth algorithm. +It take a `JavaRDD` of transactions, where each transaction is an `Iterable` of items of a generic type. +Calling `FPGrowth.run` with transactions returns an +[`FPGrowthModel`](api/java/org/apache/spark/mllib/fpm/FPGrowthModel.html) +that stores the frequent itemsets with their frequencies. + +{% highlight scala %} +import org.apache.spark.rdd.RDD +import org.apache.spark.mllib.fpm.{FPGrowth, FPGrowthModel} + +val transactions: RDD[Array[String]] = ... + +val fpg = new FPGrowth() + .setMinSupport(0.2) + .setNumPartitions(10) +val model = fpg.run(transactions) + +model.freqItemsets.collect().foreach { itemset => + println(itemset.items.mkString("[", ",", "]") + ", " + itemset.freq) +} +{% endhighlight %} + +
    + +
    + +[`FPGrowth`](api/java/org/apache/spark/mllib/fpm/FPGrowth.html) implements the +FP-growth algorithm. +It take an `RDD` of transactions, where each transaction is an `Array` of items of a generic type. +Calling `FPGrowth.run` with transactions returns an +[`FPGrowthModel`](api/java/org/apache/spark/mllib/fpm/FPGrowthModel.html) +that stores the frequent itemsets with their frequencies. + +{% highlight java %} +import java.util.List; + +import com.google.common.base.Joiner; + +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.mllib.fpm.FPGrowth; +import org.apache.spark.mllib.fpm.FPGrowthModel; + +JavaRDD> transactions = ... + +FPGrowth fpg = new FPGrowth() + .setMinSupport(0.2) + .setNumPartitions(10); +FPGrowthModel model = fpg.run(transactions); + +for (FPGrowth.FreqItemset itemset: model.freqItemsets().toJavaRDD().collect()) { + System.out.println("[" + Joiner.on(",").join(s.javaItems()) + "], " + s.freq()); +} +{% endhighlight %} + +
    +
    diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md index 7779fbc9c49e4..f8e879496c135 100644 --- a/docs/mllib-guide.md +++ b/docs/mllib-guide.md @@ -21,16 +21,21 @@ filtering, dimensionality reduction, as well as underlying optimization primitiv * [naive Bayes](mllib-naive-bayes.html) * [decision trees](mllib-decision-tree.html) * [ensembles of trees](mllib-ensembles.html) (Random Forests and Gradient-Boosted Trees) + * [isotonic regression](mllib-isotonic-regression.html) * [Collaborative filtering](mllib-collaborative-filtering.html) * alternating least squares (ALS) * [Clustering](mllib-clustering.html) - * k-means - * Gaussian mixture - * power iteration + * [k-means](mllib-clustering.html#k-means) + * [Gaussian mixture](mllib-clustering.html#gaussian-mixture) + * [power iteration clustering (PIC)](mllib-clustering.html#power-iteration-clustering-pic) + * [latent Dirichlet allocation (LDA)](mllib-clustering.html#latent-dirichlet-allocation-lda) + * [streaming k-means](mllib-clustering.html#streaming-k-means) * [Dimensionality reduction](mllib-dimensionality-reduction.html) * singular value decomposition (SVD) * principal component analysis (PCA) * [Feature extraction and transformation](mllib-feature-extraction.html) +* [Frequent pattern mining](mllib-frequent-pattern-mining.html) + * FP-growth * [Optimization (developer)](mllib-optimization.html) * stochastic gradient descent * limited-memory BFGS (L-BFGS) @@ -41,7 +46,7 @@ and the migration guide below will explain all changes between releases. # spark.ml: high-level APIs for ML pipelines -Spark 1.2 includes a new package called `spark.ml`, which aims to provide a uniform set of +Spark 1.2 introduced a new package called `spark.ml`, which aims to provide a uniform set of high-level APIs that help users create and tune practical machine learning pipelines. It is currently an alpha component, and we would like to hear back from the community about how it fits real-world use cases and how it could be improved. @@ -56,149 +61,50 @@ See the **[spark.ml programming guide](ml-guide.html)** for more information on # Dependencies -MLlib uses the linear algebra package [Breeze](http://www.scalanlp.org/), -which depends on [netlib-java](https://github.com/fommil/netlib-java), -and [jblas](https://github.com/mikiobraun/jblas). -`netlib-java` and `jblas` depend on native Fortran routines. -You need to install the -[gfortran runtime library](https://github.com/mikiobraun/jblas/wiki/Missing-Libraries) -if it is not already present on your nodes. -MLlib will throw a linking error if it cannot detect these libraries automatically. -Due to license issues, we do not include `netlib-java`'s native libraries in MLlib's -dependency set under default settings. -If no native library is available at runtime, you will see a warning message. -To use native libraries from `netlib-java`, please build Spark with `-Pnetlib-lgpl` or -include `com.github.fommil.netlib:all:1.1.2` as a dependency of your project. -If you want to use optimized BLAS/LAPACK libraries such as -[OpenBLAS](http://www.openblas.net/), please link its shared libraries to -`/usr/lib/libblas.so.3` and `/usr/lib/liblapack.so.3`, respectively. -BLAS/LAPACK libraries on worker nodes should be built without multithreading. - -To use MLlib in Python, you will need [NumPy](http://www.numpy.org) version 1.4 or newer. +MLlib uses the linear algebra package +[Breeze](http://www.scalanlp.org/), which depends on +[netlib-java](https://github.com/fommil/netlib-java) for optimised +numerical processing. If natives are not available at runtime, you +will see a warning message and a pure JVM implementation will be used +instead. ---- - -# Migration Guide - -## From 1.1 to 1.2 - -The only API changes in MLlib v1.2 are in -[`DecisionTree`](api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree), -which continues to be an experimental API in MLlib 1.2: - -1. *(Breaking change)* The Scala API for classification takes a named argument specifying the number -of classes. In MLlib v1.1, this argument was called `numClasses` in Python and -`numClassesForClassification` in Scala. In MLlib v1.2, the names are both set to `numClasses`. -This `numClasses` parameter is specified either via -[`Strategy`](api/scala/index.html#org.apache.spark.mllib.tree.configuration.Strategy) -or via [`DecisionTree`](api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree) -static `trainClassifier` and `trainRegressor` methods. - -2. *(Breaking change)* The API for -[`Node`](api/scala/index.html#org.apache.spark.mllib.tree.model.Node) has changed. -This should generally not affect user code, unless the user manually constructs decision trees -(instead of using the `trainClassifier` or `trainRegressor` methods). -The tree `Node` now includes more information, including the probability of the predicted label -(for classification). - -3. Printing methods' output has changed. The `toString` (Scala/Java) and `__repr__` (Python) methods used to print the full model; they now print a summary. For the full model, use `toDebugString`. - -Examples in the Spark distribution and examples in the -[Decision Trees Guide](mllib-decision-tree.html#examples) have been updated accordingly. - -## From 1.0 to 1.1 - -The only API changes in MLlib v1.1 are in -[`DecisionTree`](api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree), -which continues to be an experimental API in MLlib 1.1: - -1. *(Breaking change)* The meaning of tree depth has been changed by 1 in order to match -the implementations of trees in -[scikit-learn](http://scikit-learn.org/stable/modules/classes.html#module-sklearn.tree) -and in [rpart](http://cran.r-project.org/web/packages/rpart/index.html). -In MLlib v1.0, a depth-1 tree had 1 leaf node, and a depth-2 tree had 1 root node and 2 leaf nodes. -In MLlib v1.1, a depth-0 tree has 1 leaf node, and a depth-1 tree has 1 root node and 2 leaf nodes. -This depth is specified by the `maxDepth` parameter in -[`Strategy`](api/scala/index.html#org.apache.spark.mllib.tree.configuration.Strategy) -or via [`DecisionTree`](api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree) -static `trainClassifier` and `trainRegressor` methods. - -2. *(Non-breaking change)* We recommend using the newly added `trainClassifier` and `trainRegressor` -methods to build a [`DecisionTree`](api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree), -rather than using the old parameter class `Strategy`. These new training methods explicitly -separate classification and regression, and they replace specialized parameter types with -simple `String` types. +To learn more about the benefits and background of system optimised +natives, you may wish to watch Sam Halliday's ScalaX talk on +[High Performance Linear Algebra in Scala](http://fommil.github.io/scalax14/#/)). -Examples of the new, recommended `trainClassifier` and `trainRegressor` are given in the -[Decision Trees Guide](mllib-decision-tree.html#examples). +Due to licensing issues with runtime proprietary binaries, we do not +include `netlib-java`'s native proxies by default. To configure +`netlib-java` / Breeze to use system optimised binaries, include +`com.github.fommil.netlib:all:1.1.2` (or build Spark with +`-Pnetlib-lgpl`) as a dependency of your project and read the +[netlib-java](https://github.com/fommil/netlib-java) documentation for +your platform's additional installation instructions. -## From 0.9 to 1.0 +To use MLlib in Python, you will need [NumPy](http://www.numpy.org) +version 1.4 or newer. -In MLlib v1.0, we support both dense and sparse input in a unified way, which introduces a few -breaking changes. If your data is sparse, please store it in a sparse format instead of dense to -take advantage of sparsity in both storage and computation. Details are described below. - -
    -
    - -We used to represent a feature vector by `Array[Double]`, which is replaced by -[`Vector`](api/scala/index.html#org.apache.spark.mllib.linalg.Vector) in v1.0. Algorithms that used -to accept `RDD[Array[Double]]` now take -`RDD[Vector]`. [`LabeledPoint`](api/scala/index.html#org.apache.spark.mllib.regression.LabeledPoint) -is now a wrapper of `(Double, Vector)` instead of `(Double, Array[Double])`. Converting -`Array[Double]` to `Vector` is straightforward: - -{% highlight scala %} -import org.apache.spark.mllib.linalg.{Vector, Vectors} - -val array: Array[Double] = ... // a double array -val vector: Vector = Vectors.dense(array) // a dense vector -{% endhighlight %} - -[`Vectors`](api/scala/index.html#org.apache.spark.mllib.linalg.Vectors$) provides factory methods to create sparse vectors. - -*Note*: Scala imports `scala.collection.immutable.Vector` by default, so you have to import `org.apache.spark.mllib.linalg.Vector` explicitly to use MLlib's `Vector`. - -
    - -
    - -We used to represent a feature vector by `double[]`, which is replaced by -[`Vector`](api/java/index.html?org/apache/spark/mllib/linalg/Vector.html) in v1.0. Algorithms that used -to accept `RDD` now take -`RDD`. [`LabeledPoint`](api/java/index.html?org/apache/spark/mllib/regression/LabeledPoint.html) -is now a wrapper of `(double, Vector)` instead of `(double, double[])`. Converting `double[]` to -`Vector` is straightforward: - -{% highlight java %} -import org.apache.spark.mllib.linalg.Vector; -import org.apache.spark.mllib.linalg.Vectors; +--- -double[] array = ... // a double array -Vector vector = Vectors.dense(array); // a dense vector -{% endhighlight %} +# Migration Guide -[`Vectors`](api/scala/index.html#org.apache.spark.mllib.linalg.Vectors$) provides factory methods to -create sparse vectors. +For the `spark.ml` package, please see the [spark.ml Migration Guide](ml-guide.html#migration-guide). -
    - -
    +## From 1.2 to 1.3 -We used to represent a labeled feature vector in a NumPy array, where the first entry corresponds to -the label and the rest are features. This representation is replaced by class -[`LabeledPoint`](api/python/pyspark.mllib.regression.LabeledPoint-class.html), which takes both -dense and sparse feature vectors. +In the `spark.mllib` package, there were several breaking changes. The first change (in `ALS`) is the only one in a component not marked as Alpha or Experimental. -{% highlight python %} -from pyspark.mllib.linalg import SparseVector -from pyspark.mllib.regression import LabeledPoint +* *(Breaking change)* In [`ALS`](api/scala/index.html#org.apache.spark.mllib.recommendation.ALS), the extraneous method `solveLeastSquares` has been removed. The `DeveloperApi` method `analyzeBlocks` was also removed. +* *(Breaking change)* [`StandardScalerModel`](api/scala/index.html#org.apache.spark.mllib.feature.StandardScalerModel) remains an Alpha component. In it, the `variance` method has been replaced with the `std` method. To compute the column variance values returned by the original `variance` method, simply square the standard deviation values returned by `std`. +* *(Breaking change)* [`StreamingLinearRegressionWithSGD`](api/scala/index.html#org.apache.spark.mllib.regression.StreamingLinearRegressionWithSGD) remains an Experimental component. In it, there were two changes: + * The constructor taking arguments was removed in favor of a builder patten using the default constructor plus parameter setter methods. + * Variable `model` is no longer public. +* *(Breaking change)* [`DecisionTree`](api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree) remains an Experimental component. In it and its associated classes, there were several changes: + * In `DecisionTree`, the deprecated class method `train` has been removed. (The object/static `train` methods remain.) + * In `Strategy`, the `checkpointDir` parameter has been removed. Checkpointing is still supported, but the checkpoint directory must be set before calling tree and tree ensemble training. +* `PythonMLlibAPI` (the interface between Scala/Java and Python for MLlib) was a public API but is now private, declared `private[python]`. This was never meant for external use. +* In linear regression (including Lasso and ridge regression), the squared loss is now divided by 2. + So in order to produce the same result as in 1.2, the regularization parameter needs to be divided by 2 and the step size needs to be multiplied by 2. -# Create a labeled point with a positive label and a dense feature vector. -pos = LabeledPoint(1.0, [1.0, 0.0, 3.0]) +## Previous Spark Versions -# Create a labeled point with a negative label and a sparse feature vector. -neg = LabeledPoint(0.0, SparseVector(3, [0, 2], [1.0, 3.0])) -{% endhighlight %} -
    -
    +Earlier migration guides are archived [on this page](mllib-migration-guides.html). diff --git a/docs/mllib-isotonic-regression.md b/docs/mllib-isotonic-regression.md new file mode 100644 index 0000000000000..12fb29d426741 --- /dev/null +++ b/docs/mllib-isotonic-regression.md @@ -0,0 +1,155 @@ +--- +layout: global +title: Naive Bayes - MLlib +displayTitle: MLlib - Regression +--- + +## Isotonic regression +[Isotonic regression](http://en.wikipedia.org/wiki/Isotonic_regression) +belongs to the family of regression algorithms. Formally isotonic regression is a problem where +given a finite set of real numbers `$Y = {y_1, y_2, ..., y_n}$` representing observed responses +and `$X = {x_1, x_2, ..., x_n}$` the unknown response values to be fitted +finding a function that minimises + +`\begin{equation} + f(x) = \sum_{i=1}^n w_i (y_i - x_i)^2 +\end{equation}` + +with respect to complete order subject to +`$x_1\le x_2\le ...\le x_n$` where `$w_i$` are positive weights. +The resulting function is called isotonic regression and it is unique. +It can be viewed as least squares problem under order restriction. +Essentially isotonic regression is a +[monotonic function](http://en.wikipedia.org/wiki/Monotonic_function) +best fitting the original data points. + +MLlib supports a +[pool adjacent violators algorithm](http://doi.org/10.1198/TECH.2010.10111) +which uses an approach to +[parallelizing isotonic regression](http://doi.org/10.1007/978-3-642-99789-1_10). +The training input is a RDD of tuples of three double values that represent +label, feature and weight in this order. Additionally IsotonicRegression algorithm has one +optional parameter called $isotonic$ defaulting to true. +This argument specifies if the isotonic regression is +isotonic (monotonically increasing) or antitonic (monotonically decreasing). + +Training returns an IsotonicRegressionModel that can be used to predict +labels for both known and unknown features. The result of isotonic regression +is treated as piecewise linear function. The rules for prediction therefore are: + +* If the prediction input exactly matches a training feature + then associated prediction is returned. In case there are multiple predictions with the same + feature then one of them is returned. Which one is undefined + (same as java.util.Arrays.binarySearch). +* If the prediction input is lower or higher than all training features + then prediction with lowest or highest feature is returned respectively. + In case there are multiple predictions with the same feature + then the lowest or highest is returned respectively. +* If the prediction input falls between two training features then prediction is treated + as piecewise linear function and interpolated value is calculated from the + predictions of the two closest features. In case there are multiple values + with the same feature then the same rules as in previous point are used. + +### Examples + +
    +
    +Data are read from a file where each line has a format label,feature +i.e. 4710.28,500.00. The data are split to training and testing set. +Model is created using the training set and a mean squared error is calculated from the predicted +labels and real labels in the test set. + +{% highlight scala %} +import org.apache.spark.mllib.regression.IsotonicRegression + +val data = sc.textFile("data/mllib/sample_isotonic_regression_data.txt") + +// Create label, feature, weight tuples from input data with weight set to default value 1.0. +val parsedData = data.map { line => + val parts = line.split(',').map(_.toDouble) + (parts(0), parts(1), 1.0) +} + +// Split data into training (60%) and test (40%) sets. +val splits = parsedData.randomSplit(Array(0.6, 0.4), seed = 11L) +val training = splits(0) +val test = splits(1) + +// Create isotonic regression model from training data. +// Isotonic parameter defaults to true so it is only shown for demonstration +val model = new IsotonicRegression().setIsotonic(true).run(training) + +// Create tuples of predicted and real labels. +val predictionAndLabel = test.map { point => + val predictedLabel = model.predict(point._2) + (predictedLabel, point._1) +} + +// Calculate mean squared error between predicted and real labels. +val meanSquaredError = predictionAndLabel.map{case(p, l) => math.pow((p - l), 2)}.mean() +println("Mean Squared Error = " + meanSquaredError) +{% endhighlight %} +
    + +
    +Data are read from a file where each line has a format label,feature +i.e. 4710.28,500.00. The data are split to training and testing set. +Model is created using the training set and a mean squared error is calculated from the predicted +labels and real labels in the test set. + +{% highlight java %} +import org.apache.spark.SparkConf; +import org.apache.spark.api.java.JavaDoubleRDD; +import org.apache.spark.api.java.JavaPairRDD; +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.api.java.function.Function; +import org.apache.spark.api.java.function.PairFunction; +import org.apache.spark.mllib.regression.IsotonicRegressionModel; +import scala.Tuple2; +import scala.Tuple3; + +JavaRDD data = sc.textFile("data/mllib/sample_isotonic_regression_data.txt"); + +// Create label, feature, weight tuples from input data with weight set to default value 1.0. +JavaRDD> parsedData = data.map( + new Function>() { + public Tuple3 call(String line) { + String[] parts = line.split(","); + return new Tuple3<>(new Double(parts[0]), new Double(parts[1]), 1.0); + } + } +); + +// Split data into training (60%) and test (40%) sets. +JavaRDD>[] splits = parsedData.randomSplit(new double[] {0.6, 0.4}, 11L); +JavaRDD> training = splits[0]; +JavaRDD> test = splits[1]; + +// Create isotonic regression model from training data. +// Isotonic parameter defaults to true so it is only shown for demonstration +IsotonicRegressionModel model = new IsotonicRegression().setIsotonic(true).run(training); + +// Create tuples of predicted and real labels. +JavaPairRDD predictionAndLabel = test.mapToPair( + new PairFunction, Double, Double>() { + @Override public Tuple2 call(Tuple3 point) { + Double predictedLabel = model.predict(point._2()); + return new Tuple2(predictedLabel, point._1()); + } + } +); + +// Calculate mean squared error between predicted and real labels. +Double meanSquaredError = new JavaDoubleRDD(predictionAndLabel.map( + new Function, Object>() { + @Override public Object call(Tuple2 pl) { + return Math.pow(pl._1() - pl._2(), 2); + } + } +).rdd()).mean(); + +System.out.println("Mean Squared Error = " + meanSquaredError); +{% endhighlight %} +
    +
    \ No newline at end of file diff --git a/docs/mllib-linear-methods.md b/docs/mllib-linear-methods.md index 44b7f67c57734..2b2be4d9d0273 100644 --- a/docs/mllib-linear-methods.md +++ b/docs/mllib-linear-methods.md @@ -17,7 +17,7 @@ displayTitle: MLlib - Linear Methods \newcommand{\av}{\mathbf{\alpha}} \newcommand{\bv}{\mathbf{b}} \newcommand{\N}{\mathbb{N}} -\newcommand{\id}{\mathbf{I}} +\newcommand{\id}{\mathbf{I}} \newcommand{\ind}{\mathbf{1}} \newcommand{\0}{\mathbf{0}} \newcommand{\unit}{\mathbf{e}} @@ -114,18 +114,26 @@ especially when the number of training examples is small. Under the hood, linear methods use convex optimization methods to optimize the objective functions. MLlib uses two methods, SGD and L-BFGS, described in the [optimization section](mllib-optimization.html). Currently, most algorithm APIs support Stochastic Gradient Descent (SGD), and a few support L-BFGS. Refer to [this optimization section](mllib-optimization.html#Choosing-an-Optimization-Method) for guidelines on choosing between optimization methods. -## Binary classification - -[Binary classification](http://en.wikipedia.org/wiki/Binary_classification) -aims to divide items into two categories: positive and negative. MLlib -supports two linear methods for binary classification: linear Support Vector -Machines (SVMs) and logistic regression. For both methods, MLlib supports -L1 and L2 regularized variants. The training data set is represented by an RDD -of [LabeledPoint](mllib-data-types.html) in MLlib. Note that, in the -mathematical formulation in this guide, a training label $y$ is denoted as -either $+1$ (positive) or $-1$ (negative), which is convenient for the -formulation. *However*, the negative label is represented by $0$ in MLlib -instead of $-1$, to be consistent with multiclass labeling. +## Classification + +[Classification](http://en.wikipedia.org/wiki/Statistical_classification) aims to divide items into +categories. +The most common classification type is +[binary classificaion](http://en.wikipedia.org/wiki/Binary_classification), where there are two +categories, usually named positive and negative. +If there are more than two categories, it is called +[multiclass classification](http://en.wikipedia.org/wiki/Multiclass_classification). +MLlib supports two linear methods for classification: linear Support Vector Machines (SVMs) +and logistic regression. +Linear SVMs supports only binary classification, while logistic regression supports both binary and +multiclass classification problems. +For both methods, MLlib supports L1 and L2 regularized variants. +The training data set is represented by an RDD of [LabeledPoint](mllib-data-types.html) in MLlib, +where labels are class indices starting from zero: $0, 1, 2, \ldots$. +Note that, in the mathematical formulation in this guide, a binary label $y$ is denoted as either +$+1$ (positive) or $-1$ (negative), which is convenient for the formulation. +*However*, the negative label is represented by $0$ in MLlib instead of $-1$, to be consistent with +multiclass labeling. ### Linear Support Vector Machines (SVMs) @@ -144,41 +152,7 @@ denoted by $\x$, the model makes predictions based on the value of $\wv^T \x$. By the default, if $\wv^T \x \geq 0$ then the outcome is positive, and negative otherwise. -### Logistic regression - -[Logistic regression](http://en.wikipedia.org/wiki/Logistic_regression) is widely used to predict a -binary response. -It is a linear method as described above in equation `$\eqref{eq:regPrimal}$`, with the loss -function in the formulation given by the logistic loss: -`\[ -L(\wv;\x,y) := \log(1+\exp( -y \wv^T \x)). -\]` - -The logistic regression algorithm outputs a logistic regression model. Given a -new data point, denoted by $\x$, the model makes predictions by -applying the logistic function -`\[ -\mathrm{f}(z) = \frac{1}{1 + e^{-z}} -\]` -where $z = \wv^T \x$. -By default, if $\mathrm{f}(\wv^T x) > 0.5$, the outcome is positive, or -negative otherwise, though unlike linear SVMs, the raw output of the logistic regression -model, $\mathrm{f}(z)$, has a probabilistic interpretation (i.e., the probability -that $\x$ is positive). - -### Evaluation metrics - -MLlib supports common evaluation metrics for binary classification (not available in PySpark). -This -includes precision, recall, [F-measure](http://en.wikipedia.org/wiki/F1_score), -[receiver operating characteristic (ROC)](http://en.wikipedia.org/wiki/Receiver_operating_characteristic), -precision-recall curve, and -[area under the curves (AUC)](http://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_the_curve). -AUC is commonly used to compare the performance of various models while -precision/recall/F-measure can help determine the appropriate threshold to use -for prediction purposes. - -### Examples +**Examples**
    @@ -190,7 +164,7 @@ error. {% highlight scala %} import org.apache.spark.SparkContext -import org.apache.spark.mllib.classification.SVMWithSGD +import org.apache.spark.mllib.classification.{SVMModel, SVMWithSGD} import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.linalg.Vectors @@ -211,7 +185,7 @@ val model = SVMWithSGD.train(training, numIterations) // Clear the default threshold. model.clearThreshold() -// Compute raw scores on the test set. +// Compute raw scores on the test set. val scoreAndLabels = test.map { point => val score = model.predict(point.features) (score, point.label) @@ -222,6 +196,10 @@ val metrics = new BinaryClassificationMetrics(scoreAndLabels) val auROC = metrics.areaUnderROC() println("Area under ROC = " + auROC) + +// Save and load model +model.save(sc, "myModelPath") +val sameModel = SVMModel.load(sc, "myModelPath") {% endhighlight %} The `SVMWithSGD.train()` method by default performs L2 regularization with the @@ -243,8 +221,6 @@ svmAlg.optimizer. val modelL1 = svmAlg.run(training) {% endhighlight %} -[`LogisticRegressionWithSGD`](api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithSGD) can be used in a similar fashion as `SVMWithSGD`. -
    @@ -280,11 +256,11 @@ public class SVMClassifier { JavaRDD training = data.sample(false, 0.6, 11L); training.cache(); JavaRDD test = data.subtract(training); - + // Run training algorithm to build the model. int numIterations = 100; final SVMModel model = SVMWithSGD.train(training.rdd(), numIterations); - + // Clear the default threshold. model.clearThreshold(); @@ -297,13 +273,17 @@ public class SVMClassifier { } } ); - + // Get evaluation metrics. - BinaryClassificationMetrics metrics = + BinaryClassificationMetrics metrics = new BinaryClassificationMetrics(JavaRDD.toRDD(scoreAndLabels)); double auROC = metrics.areaUnderROC(); - + System.out.println("Area under ROC = " + auROC); + + // Save and load model + model.save(sc.sc(), "myModelPath"); + SVMModel sameModel = SVMModel.load(sc.sc(), "myModelPath"); } } {% endhighlight %} @@ -338,6 +318,8 @@ a dependency. The following example shows how to load a sample dataset, build Logistic Regression model, and make predictions with the resulting model to compute the training error. +Note that the Python API does not yet support model save/load but will in the future. + {% highlight python %} from pyspark.mllib.classification import LogisticRegressionWithSGD from pyspark.mllib.regression import LabeledPoint @@ -362,7 +344,191 @@ print("Training Error = " + str(trainErr))
    -## Linear least squares, Lasso, and ridge regression +### Logistic regression + +[Logistic regression](http://en.wikipedia.org/wiki/Logistic_regression) is widely used to predict a +binary response. It is a linear method as described above in equation `$\eqref{eq:regPrimal}$`, +with the loss function in the formulation given by the logistic loss: +`\[ +L(\wv;\x,y) := \log(1+\exp( -y \wv^T \x)). +\]` + +For binary classification problems, the algorithm outputs a binary logistic regression model. +Given a new data point, denoted by $\x$, the model makes predictions by +applying the logistic function +`\[ +\mathrm{f}(z) = \frac{1}{1 + e^{-z}} +\]` +where $z = \wv^T \x$. +By default, if $\mathrm{f}(\wv^T x) > 0.5$, the outcome is positive, or +negative otherwise, though unlike linear SVMs, the raw output of the logistic regression +model, $\mathrm{f}(z)$, has a probabilistic interpretation (i.e., the probability +that $\x$ is positive). + +Binary logistic regression can be generalized into +[multinomial logistic regression](http://en.wikipedia.org/wiki/Multinomial_logistic_regression) to +train and predict multiclass classification problems. +For example, for $K$ possible outcomes, one of the outcomes can be chosen as a "pivot", and the +other $K - 1$ outcomes can be separately regressed against the pivot outcome. +In MLlib, the first class $0$ is chosen as the "pivot" class. +See Section 4.4 of +[The Elements of Statistical Learning](http://statweb.stanford.edu/~tibs/ElemStatLearn/) for +references. +Here is an +[detailed mathematical derivation](http://www.slideshare.net/dbtsai/2014-0620-mlor-36132297). + +For multiclass classification problems, the algorithm will output a multinomial logistic regression +model, which contains $K - 1$ binary logistic regression models regressed against the first class. +Given a new data points, $K - 1$ models will be run, and the class with largest probability will be +chosen as the predicted class. + +We implemented two algorithms to solve logistic regression: mini-batch gradient descent and L-BFGS. +We recommend L-BFGS over mini-batch gradient descent for faster convergence. + +**Examples** + +
    + +
    +The following code illustrates how to load a sample multiclass dataset, split it into train and +test, and use +[LogisticRegressionWithLBFGS](api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS) +to fit a logistic regression model. +Then the model is evaluated against the test dataset and saved to disk. + +{% highlight scala %} +import org.apache.spark.SparkContext +import org.apache.spark.mllib.classification.{LogisticRegressionWithLBFGS, LogisticRegressionModel} +import org.apache.spark.mllib.evaluation.MulticlassMetrics +import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.mllib.linalg.Vectors +import org.apache.spark.mllib.util.MLUtils + +// Load training data in LIBSVM format. +val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt") + +// Split data into training (60%) and test (40%). +val splits = data.randomSplit(Array(0.6, 0.4), seed = 11L) +val training = splits(0).cache() +val test = splits(1) + +// Run training algorithm to build the model +val model = new LogisticRegressionWithLBFGS() + .setNumClasses(10) + .run(training) + +// Compute raw scores on the test set. +val predictionAndLabels = test.map { case LabeledPoint(label, features) => + val prediction = model.predict(features) + (prediction, label) +} + +// Get evaluation metrics. +val metrics = new MulticlassMetrics(predictionAndLabels) +val precision = metrics.precision +println("Precision = " + precision) + +// Save and load model +model.save(sc, "myModelPath") +val sameModel = LogisticRegressionModel.load(sc, "myModelPath") +{% endhighlight %} + +
    + +
    +The following code illustrates how to load a sample multiclass dataset, split it into train and +test, and use +[LogisticRegressionWithLBFGS](api/java/org/apache/spark/mllib/classification/LogisticRegressionWithLBFGS.html) +to fit a logistic regression model. +Then the model is evaluated against the test dataset and saved to disk. + +{% highlight java %} +import scala.Tuple2; + +import org.apache.spark.api.java.*; +import org.apache.spark.api.java.function.Function; +import org.apache.spark.mllib.classification.LogisticRegressionModel; +import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS; +import org.apache.spark.mllib.evaluation.MulticlassMetrics; +import org.apache.spark.mllib.regression.LabeledPoint; +import org.apache.spark.mllib.util.MLUtils; +import org.apache.spark.SparkConf; +import org.apache.spark.SparkContext; + +public class MultinomialLogisticRegressionExample { + public static void main(String[] args) { + SparkConf conf = new SparkConf().setAppName("SVM Classifier Example"); + SparkContext sc = new SparkContext(conf); + String path = "data/mllib/sample_libsvm_data.txt"; + JavaRDD data = MLUtils.loadLibSVMFile(sc, path).toJavaRDD(); + + // Split initial RDD into two... [60% training data, 40% testing data]. + JavaRDD[] splits = data.randomSplit(new double[] {0.6, 0.4}, 11L); + JavaRDD training = splits[0].cache(); + JavaRDD test = splits[1]; + + // Run training algorithm to build the model. + final LogisticRegressionModel model = new LogisticRegressionWithLBFGS() + .setNumClasses(10) + .run(training.rdd()); + + // Compute raw scores on the test set. + JavaRDD> predictionAndLabels = test.map( + new Function>() { + public Tuple2 call(LabeledPoint p) { + Double prediction = model.predict(p.features()); + return new Tuple2(prediction, p.label()); + } + } + ); + + // Get evaluation metrics. + MulticlassMetrics metrics = new MulticlassMetrics(predictionAndLabels.rdd()); + double precision = metrics.precision(); + System.out.println("Precision = " + precision); + + // Save and load model + model.save(sc, "myModelPath"); + LogisticRegressionModel sameModel = LogisticRegressionModel.load(sc, "myModelPath"); + } +} +{% endhighlight %} +
    + +
    +The following example shows how to load a sample dataset, build Logistic Regression model, +and make predictions with the resulting model to compute the training error. + +Note that the Python API does not yet support multiclass classification and model save/load but +will in the future. + +{% highlight python %} +from pyspark.mllib.classification import LogisticRegressionWithLBFGS +from pyspark.mllib.regression import LabeledPoint +from numpy import array + +# Load and parse the data +def parsePoint(line): + values = [float(x) for x in line.split(' ')] + return LabeledPoint(values[0], values[1:]) + +data = sc.textFile("data/mllib/sample_svm_data.txt") +parsedData = data.map(parsePoint) + +# Build the model +model = LogisticRegressionWithLBFGS.train(parsedData) + +# Evaluating the model on training data +labelsAndPreds = parsedData.map(lambda p: (p.label, model.predict(p.features))) +trainErr = labelsAndPreds.filter(lambda (v, p): v != p).count() / float(parsedData.count()) +print("Training Error = " + str(trainErr)) +{% endhighlight %} +
    +
    + +# Regression + +### Linear least squares, Lasso, and ridge regression Linear least squares is the most common formulation for regression problems. @@ -380,7 +546,7 @@ regularization; and [*Lasso*](http://en.wikipedia.org/wiki/Lasso_(statistics)) u regularization. For all of these models, the average loss or training error, $\frac{1}{n} \sum_{i=1}^n (\wv^T x_i - y_i)^2$, is known as the [mean squared error](http://en.wikipedia.org/wiki/Mean_squared_error). -### Examples +**Examples**
    @@ -391,8 +557,9 @@ values. We compute the mean squared error at the end to evaluate [goodness of fit](http://en.wikipedia.org/wiki/Goodness_of_fit). {% highlight scala %} -import org.apache.spark.mllib.regression.LinearRegressionWithSGD import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.mllib.regression.LinearRegressionModel +import org.apache.spark.mllib.regression.LinearRegressionWithSGD import org.apache.spark.mllib.linalg.Vectors // Load and parse the data @@ -413,6 +580,10 @@ val valuesAndPreds = parsedData.map { point => } val MSE = valuesAndPreds.map{case(v, p) => math.pow((v - p), 2)}.mean() println("training Mean Squared Error = " + MSE) + +// Save and load model +model.save(sc, "myModelPath") +val sameModel = LinearRegressionModel.load(sc, "myModelPath") {% endhighlight %} [`RidgeRegressionWithSGD`](api/scala/index.html#org.apache.spark.mllib.regression.RidgeRegressionWithSGD) @@ -483,6 +654,10 @@ public class LinearRegression { } ).rdd()).mean(); System.out.println("training Mean Squared Error = " + MSE); + + // Save and load model + model.save(sc.sc(), "myModelPath"); + LinearRegressionModel sameModel = LinearRegressionModel.load(sc.sc(), "myModelPath"); } } {% endhighlight %} @@ -494,6 +669,8 @@ The example then uses LinearRegressionWithSGD to build a simple linear model to values. We compute the mean squared error at the end to evaluate [goodness of fit](http://en.wikipedia.org/wiki/Goodness_of_fit). +Note that the Python API does not yet support model save/load but will in the future. + {% highlight python %} from pyspark.mllib.regression import LabeledPoint, LinearRegressionWithSGD from numpy import array @@ -523,7 +700,7 @@ section of the Spark quick-start guide. Be sure to also include *spark-mllib* to your build file as a dependency. -## Streaming linear regression +###Streaming linear regression When data arrive in a streaming fashion, it is useful to fit regression models online, updating the parameters of the model as new data arrives. MLlib currently supports @@ -531,7 +708,7 @@ streaming linear regression using ordinary least squares. The fitting is similar to that performed offline, except fitting occurs on each batch of data, so that the model continually updates to reflect the data from the stream. -### Examples +**Examples** The following example demonstrates how to load training and testing data from two different input streams of text files, parse the streams as labeled points, fit a linear regression model @@ -598,7 +775,7 @@ will get better!
    -## Implementation (developer) +# Implementation (developer) Behind the scene, MLlib implements a simple distributed version of stochastic gradient descent (SGD), building on the underlying gradient descent primitive (as described in the MLlib - Old Migration Guides +description: MLlib migration guides from before Spark SPARK_VERSION_SHORT +--- + +The migration guide for the current Spark version is kept on the [MLlib Programming Guide main page](mllib-guide.html#migration-guide). + +## From 1.1 to 1.2 + +The only API changes in MLlib v1.2 are in +[`DecisionTree`](api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree), +which continues to be an experimental API in MLlib 1.2: + +1. *(Breaking change)* The Scala API for classification takes a named argument specifying the number +of classes. In MLlib v1.1, this argument was called `numClasses` in Python and +`numClassesForClassification` in Scala. In MLlib v1.2, the names are both set to `numClasses`. +This `numClasses` parameter is specified either via +[`Strategy`](api/scala/index.html#org.apache.spark.mllib.tree.configuration.Strategy) +or via [`DecisionTree`](api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree) +static `trainClassifier` and `trainRegressor` methods. + +2. *(Breaking change)* The API for +[`Node`](api/scala/index.html#org.apache.spark.mllib.tree.model.Node) has changed. +This should generally not affect user code, unless the user manually constructs decision trees +(instead of using the `trainClassifier` or `trainRegressor` methods). +The tree `Node` now includes more information, including the probability of the predicted label +(for classification). + +3. Printing methods' output has changed. The `toString` (Scala/Java) and `__repr__` (Python) methods used to print the full model; they now print a summary. For the full model, use `toDebugString`. + +Examples in the Spark distribution and examples in the +[Decision Trees Guide](mllib-decision-tree.html#examples) have been updated accordingly. + +## From 1.0 to 1.1 + +The only API changes in MLlib v1.1 are in +[`DecisionTree`](api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree), +which continues to be an experimental API in MLlib 1.1: + +1. *(Breaking change)* The meaning of tree depth has been changed by 1 in order to match +the implementations of trees in +[scikit-learn](http://scikit-learn.org/stable/modules/classes.html#module-sklearn.tree) +and in [rpart](http://cran.r-project.org/web/packages/rpart/index.html). +In MLlib v1.0, a depth-1 tree had 1 leaf node, and a depth-2 tree had 1 root node and 2 leaf nodes. +In MLlib v1.1, a depth-0 tree has 1 leaf node, and a depth-1 tree has 1 root node and 2 leaf nodes. +This depth is specified by the `maxDepth` parameter in +[`Strategy`](api/scala/index.html#org.apache.spark.mllib.tree.configuration.Strategy) +or via [`DecisionTree`](api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree) +static `trainClassifier` and `trainRegressor` methods. + +2. *(Non-breaking change)* We recommend using the newly added `trainClassifier` and `trainRegressor` +methods to build a [`DecisionTree`](api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree), +rather than using the old parameter class `Strategy`. These new training methods explicitly +separate classification and regression, and they replace specialized parameter types with +simple `String` types. + +Examples of the new, recommended `trainClassifier` and `trainRegressor` are given in the +[Decision Trees Guide](mllib-decision-tree.html#examples). + +## From 0.9 to 1.0 + +In MLlib v1.0, we support both dense and sparse input in a unified way, which introduces a few +breaking changes. If your data is sparse, please store it in a sparse format instead of dense to +take advantage of sparsity in both storage and computation. Details are described below. + diff --git a/docs/mllib-naive-bayes.md b/docs/mllib-naive-bayes.md index d5b044d94fdd7..9780ea52c4994 100644 --- a/docs/mllib-naive-bayes.md +++ b/docs/mllib-naive-bayes.md @@ -13,12 +13,15 @@ compute the conditional probability distribution of label given an observation and use it for prediction. MLlib supports [multinomial naive -Bayes](http://en.wikipedia.org/wiki/Naive_Bayes_classifier#Multinomial_naive_Bayes), -which is typically used for [document -classification](http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html). +Bayes](http://en.wikipedia.org/wiki/Naive_Bayes_classifier#Multinomial_naive_Bayes) +and [Bernoulli naive Bayes] (http://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html). +These models are typically used for [document classification] +(http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html). Within that context, each observation is a document and each -feature represents a term whose value is the frequency of the term. -Feature values must be nonnegative to represent term frequencies. +feature represents a term whose value is the frequency of the term (in multinomial naive Bayes) or +a zero or one indicating whether the term was found in the document (in Bernoulli naive Bayes). +Feature values must be nonnegative. The model type is selected with an optional parameter +"Multinomial" or "Bernoulli" with "Multinomial" as the default. [Additive smoothing](http://en.wikipedia.org/wiki/Lidstone_smoothing) can be used by setting the parameter $\lambda$ (default to $1.0$). For document classification, the input feature vectors are usually sparse, and sparse vectors should be supplied as input to take advantage of @@ -32,12 +35,12 @@ sparsity. Since the training data is only used once, it is not necessary to cach [NaiveBayes](api/scala/index.html#org.apache.spark.mllib.classification.NaiveBayes$) implements multinomial naive Bayes. It takes an RDD of [LabeledPoint](api/scala/index.html#org.apache.spark.mllib.regression.LabeledPoint) and an optional -smoothing parameter `lambda` as input, and output a +smoothing parameter `lambda` as input, an optional model type parameter (default is Multinomial), and outputs a [NaiveBayesModel](api/scala/index.html#org.apache.spark.mllib.classification.NaiveBayesModel), which can be used for evaluation and prediction. {% highlight scala %} -import org.apache.spark.mllib.classification.NaiveBayes +import org.apache.spark.mllib.classification.{NaiveBayes, NaiveBayesModel} import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.regression.LabeledPoint @@ -51,10 +54,14 @@ val splits = parsedData.randomSplit(Array(0.6, 0.4), seed = 11L) val training = splits(0) val test = splits(1) -val model = NaiveBayes.train(training, lambda = 1.0) +val model = NaiveBayes.train(training, lambda = 1.0, model = "Multinomial") val predictionAndLabel = test.map(p => (model.predict(p.features), p.label)) val accuracy = 1.0 * predictionAndLabel.filter(x => x._1 == x._2).count() / test.count() + +// Save and load model +model.save(sc, "myModelPath") +val sameModel = NaiveBayesModel.load(sc, "myModelPath") {% endhighlight %} @@ -93,34 +100,46 @@ double accuracy = predictionAndLabel.filter(new Function, return pl._1().equals(pl._2()); } }).count() / (double) test.count(); + +// Save and load model +model.save(sc.sc(), "myModelPath"); +NaiveBayesModel sameModel = NaiveBayesModel.load(sc.sc(), "myModelPath"); {% endhighlight %}
    -[NaiveBayes](api/python/pyspark.mllib.classification.NaiveBayes-class.html) implements multinomial +[NaiveBayes](api/python/pyspark.mllib.html#pyspark.mllib.classification.NaiveBayes) implements multinomial naive Bayes. It takes an RDD of -[LabeledPoint](api/python/pyspark.mllib.regression.LabeledPoint-class.html) and an optionally +[LabeledPoint](api/python/pyspark.mllib.html#pyspark.mllib.regression.LabeledPoint) and an optionally smoothing parameter `lambda` as input, and output a -[NaiveBayesModel](api/python/pyspark.mllib.classification.NaiveBayesModel-class.html), which can be +[NaiveBayesModel](api/python/pyspark.mllib.html#pyspark.mllib.classification.NaiveBayesModel), which can be used for evaluation and prediction. - +Note that the Python API does not yet support model save/load but will in the future. + {% highlight python %} -from pyspark.mllib.regression import LabeledPoint from pyspark.mllib.classification import NaiveBayes +from pyspark.mllib.linalg import Vectors +from pyspark.mllib.regression import LabeledPoint + +def parseLine(line): + parts = line.split(',') + label = float(parts[0]) + features = Vectors.dense([float(x) for x in parts[1].split(' ')]) + return LabeledPoint(label, features) + +data = sc.textFile('data/mllib/sample_naive_bayes_data.txt').map(parseLine) -# an RDD of LabeledPoint -data = sc.parallelize([ - LabeledPoint(0.0, [0.0, 0.0]) - ... # more labeled points -]) +# Split data aproximately into training (60%) and test (40%) +training, test = data.randomSplit([0.6, 0.4], seed = 0) # Train a naive Bayes model. -model = NaiveBayes.train(data, 1.0) +model = NaiveBayes.train(training, 1.0) -# Make prediction. -prediction = model.predict([0.0, 0.0]) +# Make prediction and test accuracy. +predictionAndLabel = test.map(lambda p : (model.predict(p.features), p.label)) +accuracy = 1.0 * predictionAndLabel.filter(lambda (x, v): x == v).count() / test.count() {% endhighlight %}
    diff --git a/docs/mllib-optimization.md b/docs/mllib-optimization.md index 4d101afca2c97..6cabc1610a151 100644 --- a/docs/mllib-optimization.md +++ b/docs/mllib-optimization.md @@ -203,6 +203,10 @@ regularization, as well as L2 regularizer. recommended. * `maxNumIterations` is the maximal number of iterations that L-BFGS can be run. * `regParam` is the regularization parameter when using regularization. +* `convergenceTol` controls how much relative change is still allowed when L-BFGS +is considered to converge. This must be nonnegative. Lower values are less tolerant and +therefore generally cause more iterations to be run. This value looks at both average +improvement and the norm of gradient inside [Breeze LBFGS](https://github.com/scalanlp/breeze/blob/master/math/src/main/scala/breeze/optimize/LBFGS.scala). The `return` is a tuple containing two elements. The first element is a column matrix containing weights for every feature, and the second element is an array containing diff --git a/docs/mllib-statistics.md b/docs/mllib-statistics.md index ca8c29218f52d..887eae7f4f07b 100644 --- a/docs/mllib-statistics.md +++ b/docs/mllib-statistics.md @@ -81,8 +81,8 @@ System.out.println(summary.numNonzeros()); // number of nonzeros in each column
    -[`colStats()`](api/python/pyspark.mllib.stat.Statistics-class.html#colStats) returns an instance of -[`MultivariateStatisticalSummary`](api/python/pyspark.mllib.stat.MultivariateStatisticalSummary-class.html), +[`colStats()`](api/python/pyspark.mllib.html#pyspark.mllib.stat.Statistics.colStats) returns an instance of +[`MultivariateStatisticalSummary`](api/python/pyspark.mllib.html#pyspark.mllib.stat.MultivariateStatisticalSummary), which contains the column-wise max, min, mean, variance, and number of nonzeros, as well as the total count. @@ -169,7 +169,7 @@ Matrix correlMatrix = Statistics.corr(data.rdd(), "pearson");
    -[`Statistics`](api/python/pyspark.mllib.stat.Statistics-class.html) provides methods to +[`Statistics`](api/python/pyspark.mllib.html#pyspark.mllib.stat.Statistics) provides methods to calculate correlations between series. Depending on the type of input, two `RDD[Double]`s or an `RDD[Vector]`, the output will be a `Double` or the correlation `Matrix` respectively. @@ -258,7 +258,7 @@ JavaPairRDD exactSample = data.sampleByKeyExact(false, fractions); {% endhighlight %}
    -[`sampleByKey()`](api/python/pyspark.rdd.RDD-class.html#sampleByKey) allows users to +[`sampleByKey()`](api/python/pyspark.html#pyspark.RDD.sampleByKey) allows users to sample approximately $\lceil f_k \cdot n_k \rceil \, \forall k \in K$ items, where $f_k$ is the desired fraction for key $k$, $n_k$ is the number of key-value pairs for key $k$, and $K$ is the set of keys. @@ -476,7 +476,7 @@ JavaDoubleRDD v = u.map(
    -[`RandomRDDs`](api/python/pyspark.mllib.random.RandomRDDs-class.html) provides factory +[`RandomRDDs`](api/python/pyspark.mllib.html#pyspark.mllib.random.RandomRDDs) provides factory methods to generate random double RDDs or vector RDDs. The following example generates a random double RDD, whose values follows the standard normal distribution `N(0, 1)`, and then map it to `N(1, 4)`. diff --git a/docs/monitoring.md b/docs/monitoring.md index 7a5cadc171d6d..8a85928d6d44d 100644 --- a/docs/monitoring.md +++ b/docs/monitoring.md @@ -86,10 +86,10 @@ follows: - spark.history.fs.updateInterval - 10 + spark.history.fs.update.interval + 10s - The period, in seconds, at which information displayed by this history server is updated. + The period at which information displayed by this history server is updated. Each update checks for any changes made to the event logs in persisted storage. @@ -145,11 +145,35 @@ follows: If disabled, no access control checks are made. + + spark.history.fs.cleaner.enabled + false + + Specifies whether the History Server should periodically clean up event logs from storage. + + + + spark.history.fs.cleaner.interval + 1d + + How often the job history cleaner checks for files to delete. + Files are only deleted if they are older than spark.history.fs.cleaner.maxAge. + + + + spark.history.fs.cleaner.maxAge + 7d + + Job history files older than this will be deleted when the history cleaner runs. + + Note that in all of these UIs, the tables are sortable by clicking their headers, making it easy to identify slow tasks, data skew, etc. +Note that the history server only displays completed Spark jobs. One way to signal the completion of a Spark job is to stop the Spark Context explicitly (`sc.stop()`), or in Python using the `with SparkContext() as sc:` to handle the Spark Context setup and tear down, and still show the job history on the UI. + # Metrics Spark has a configurable metrics system based on the @@ -176,6 +200,7 @@ Each instance can report to zero or more _sinks_. Sinks are contained in the * `JmxSink`: Registers metrics for viewing in a JMX console. * `MetricsServlet`: Adds a servlet within the existing Spark UI to serve metrics data as JSON data. * `GraphiteSink`: Sends metrics to a Graphite node. +* `Slf4jSink`: Sends metrics to slf4j as log entries. Spark also supports a Ganglia sink which is not included in the default build due to licensing restrictions: diff --git a/docs/programming-guide.md b/docs/programming-guide.md index 6b365e83fb56d..f4fabb0927b66 100644 --- a/docs/programming-guide.md +++ b/docs/programming-guide.md @@ -142,8 +142,8 @@ JavaSparkContext sc = new JavaSparkContext(conf);
    -The first thing a Spark program must do is to create a [SparkContext](api/python/pyspark.context.SparkContext-class.html) object, which tells Spark -how to access a cluster. To create a `SparkContext` you first need to build a [SparkConf](api/python/pyspark.conf.SparkConf-class.html) object +The first thing a Spark program must do is to create a [SparkContext](api/python/pyspark.html#pyspark.SparkContext) object, which tells Spark +how to access a cluster. To create a `SparkContext` you first need to build a [SparkConf](api/python/pyspark.html#pyspark.SparkConf) object that contains information about your application. {% highlight python %} @@ -173,8 +173,11 @@ in-process. In the Spark shell, a special interpreter-aware SparkContext is already created for you, in the variable called `sc`. Making your own SparkContext will not work. You can set which master the context connects to using the `--master` argument, and you can add JARs to the classpath -by passing a comma-separated list to the `--jars` argument. -For example, to run `bin/spark-shell` on exactly four cores, use: +by passing a comma-separated list to the `--jars` argument. You can also add dependencies +(e.g. Spark Packages) to your shell session by supplying a comma-separated list of maven coordinates +to the `--packages` argument. Any additional repositories where dependencies might exist (e.g. SonaType) +can be passed to the `--repositories` argument. For example, to run `bin/spark-shell` on exactly +four cores, use: {% highlight bash %} $ ./bin/spark-shell --master local[4] @@ -186,6 +189,12 @@ Or, to also add `code.jar` to its classpath, use: $ ./bin/spark-shell --master local[4] --jars code.jar {% endhighlight %} +To include a dependency using maven coordinates: + +{% highlight bash %} +$ ./bin/spark-shell --master local[4] --packages "org.example:example:0.1" +{% endhighlight %} + For a complete list of options, run `spark-shell --help`. Behind the scenes, `spark-shell` invokes the more general [`spark-submit` script](submitting-applications.html). @@ -196,7 +205,11 @@ For a complete list of options, run `spark-shell --help`. Behind the scenes, In the PySpark shell, a special interpreter-aware SparkContext is already created for you, in the variable called `sc`. Making your own SparkContext will not work. You can set which master the context connects to using the `--master` argument, and you can add Python .zip, .egg or .py files -to the runtime path by passing a comma-separated list to `--py-files`. +to the runtime path by passing a comma-separated list to `--py-files`. You can also add dependencies +(e.g. Spark Packages) to your shell session by supplying a comma-separated list of maven coordinates +to the `--packages` argument. Any additional repositories where dependencies might exist (e.g. SonaType) +can be passed to the `--repositories` argument. Any python dependencies a Spark Package has (listed in +the requirements.txt of that package) must be manually installed using pip when necessary. For example, to run `bin/pyspark` on exactly four cores, use: {% highlight bash %} @@ -224,9 +237,13 @@ You can customize the `ipython` command by setting `PYSPARK_DRIVER_PYTHON_OPTS`. the [IPython Notebook](http://ipython.org/notebook.html) with PyLab plot support: {% highlight bash %} -$ PYSPARK_DRIVER_PYTHON=ipython PYSPARK_DRIVER_PYTHON_OPTS="notebook --pylab inline" ./bin/pyspark +$ PYSPARK_DRIVER_PYTHON=ipython PYSPARK_DRIVER_PYTHON_OPTS="notebook" ./bin/pyspark {% endhighlight %} +After the IPython Notebook server is launched, you can create a new "Python 2" notebook from +the "Files" tab. Inside the notebook, you can input the command `%pylab inline` as part of +your notebook before you start to try Spark from the IPython notebook. +
    @@ -322,7 +339,7 @@ Apart from text files, Spark's Scala API also supports several other data format * For [SequenceFiles](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/mapred/SequenceFileInputFormat.html), use SparkContext's `sequenceFile[K, V]` method where `K` and `V` are the types of key and values in the file. These should be subclasses of Hadoop's [Writable](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/io/Writable.html) interface, like [IntWritable](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/io/IntWritable.html) and [Text](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/io/Text.html). In addition, Spark allows you to specify native types for a few common Writables; for example, `sequenceFile[Int, String]` will automatically read IntWritables and Texts. -* For other Hadoop InputFormats, you can use the `SparkContext.hadoopRDD` method, which takes an arbitrary `JobConf` and input format class, key class and value class. Set these the same way you would for a Hadoop job with your input source. You can also use `SparkContext.newHadoopRDD` for InputFormats based on the "new" MapReduce API (`org.apache.hadoop.mapreduce`). +* For other Hadoop InputFormats, you can use the `SparkContext.hadoopRDD` method, which takes an arbitrary `JobConf` and input format class, key class and value class. Set these the same way you would for a Hadoop job with your input source. You can also use `SparkContext.newAPIHadoopRDD` for InputFormats based on the "new" MapReduce API (`org.apache.hadoop.mapreduce`). * `RDD.saveAsObjectFile` and `SparkContext.objectFile` support saving an RDD in a simple format consisting of serialized Java objects. While this is not as efficient as specialized formats like Avro, it offers an easy way to save any RDD. @@ -354,7 +371,7 @@ Apart from text files, Spark's Java API also supports several other data formats * For [SequenceFiles](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/mapred/SequenceFileInputFormat.html), use SparkContext's `sequenceFile[K, V]` method where `K` and `V` are the types of key and values in the file. These should be subclasses of Hadoop's [Writable](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/io/Writable.html) interface, like [IntWritable](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/io/IntWritable.html) and [Text](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/io/Text.html). -* For other Hadoop InputFormats, you can use the `JavaSparkContext.hadoopRDD` method, which takes an arbitrary `JobConf` and input format class, key class and value class. Set these the same way you would for a Hadoop job with your input source. You can also use `JavaSparkContext.newHadoopRDD` for InputFormats based on the "new" MapReduce API (`org.apache.hadoop.mapreduce`). +* For other Hadoop InputFormats, you can use the `JavaSparkContext.hadoopRDD` method, which takes an arbitrary `JobConf` and input format class, key class and value class. Set these the same way you would for a Hadoop job with your input source. You can also use `JavaSparkContext.newAPIHadoopRDD` for InputFormats based on the "new" MapReduce API (`org.apache.hadoop.mapreduce`). * `JavaRDD.saveAsObjectFile` and `JavaSparkContext.objectFile` support saving an RDD in a simple format consisting of serialized Java objects. While this is not as efficient as specialized formats like Avro, it offers an easy way to save any RDD. @@ -712,7 +729,7 @@ class MyClass(object): def __init__(self): self.field = "Hello" def doStuff(self, rdd): - return rdd.map(lambda s: self.field + x) + return rdd.map(lambda s: self.field + s) {% endhighlight %} To avoid this issue, the simplest way is to copy `field` into a local variable instead @@ -721,13 +738,76 @@ of accessing it externally: {% highlight python %} def doStuff(self, rdd): field = self.field - return rdd.map(lambda s: field + x) + return rdd.map(lambda s: field + s) +{% endhighlight %} + + + + + +### Understanding closures +One of the harder things about Spark is understanding the scope and life cycle of variables and methods when executing code across a cluster. RDD operations that modify variables outside of their scope can be a frequent source of confusion. In the example below we'll look at code that uses `foreach()` to increment a counter, but similar issues can occur for other operations as well. + +#### Example + +Consider the naive RDD element sum below, which behaves completely differently depending on whether execution is happening within the same JVM. A common example of this is when running Spark in `local` mode (`--master = local[n]`) versus deploying a Spark application to a cluster (e.g. via spark-submit to YARN): + +
    + +
    +{% highlight scala %} +var counter = 0 +var rdd = sc.parallelize(data) + +// Wrong: Don't do this!! +rdd.foreach(x => counter += x) + +println("Counter value: " + counter) +{% endhighlight %} +
    + +
    +{% highlight java %} +int counter = 0; +JavaRDD rdd = sc.parallelize(data); + +// Wrong: Don't do this!! +rdd.foreach(x -> counter += x); + +println("Counter value: " + counter); {% endhighlight %} +
    + +
    +{% highlight python %} +counter = 0 +rdd = sc.parallelize(data) + +# Wrong: Don't do this!! +rdd.foreach(lambda x: counter += x) + +print("Counter value: " + counter) +{% endhighlight %}
    +#### Local vs. cluster modes + +The primary challenge is that the behavior of the above code is undefined. In local mode with a single JVM, the above code will sum the values within the RDD and store it in **counter**. This is because both the RDD and the variable **counter** are in the same memory space on the driver node. + +However, in `cluster` mode, what happens is more complicated, and the above may not work as intended. To execute jobs, Spark breaks up the processing of RDD operations into tasks - each of which is operated on by an executor. Prior to execution, Spark computes the **closure**. The closure is those variables and methods which must be visible for the executor to perform its computations on the RDD (in this case `foreach()`). This closure is serialized and sent to each executor. In `local` mode, there is only the one executors so everything shares the same closure. In other modes however, this is not the case and the executors running on seperate worker nodes each have their own copy of the closure. + +What is happening here is that the variables within the closure sent to each executor are now copies and thus, when **counter** is referenced within the `foreach` function, it's no longer the **counter** on the driver node. There is still a **counter** in the memory of the driver node but this is no longer visible to the executors! The executors only sees the copy from the serialized closure. Thus, the final value of **counter** will still be zero since all operations on **counter** were referencing the value within the serialized closure. + +To ensure well-defined behavior in these sorts of scenarios one should use an [`Accumulator`](#AccumLink). Accumulators in Spark are used specifically to provide a mechanism for safely updating a variable when execution is split up across worker nodes in a cluster. The Accumulators section of this guide discusses these in more detail. + +In general, closures - constructs like loops or locally defined methods, should not be used to mutate some global state. Spark does not define or guarantee the behavior of mutations to objects referenced from outside of closures. Some code that does this may work in local mode, but that's just by accident and such code will not behave as expected in distributed mode. Use an Accumulator instead if some global aggregation is needed. + +#### Printing elements of an RDD +Another common idiom is attempting to print out the elements of an RDD using `rdd.foreach(println)` or `rdd.map(println)`. On a single machine, this will generate the expected output and print all the RDD's elements. However, in `cluster` mode, the output to `stdout` being called by the executors is now writing to the executor's `stdout` instead, not the one on the driver, so `stdout` on the driver won't show these! To print all elements on the driver, one can use the `collect()` method to first bring the RDD to the driver node thus: `rdd.collect().foreach(println)`. This can cause the driver to run out of memory, though, because `collect()` fetches the entire RDD to a single machine; if you only need to print a few elements of the RDD, a safer approach is to use the `take()`: `rdd.take(100).foreach(println)`. + ### Working with Key-Value Pairs
    @@ -836,7 +916,7 @@ The following table lists some of the common transformations supported by Spark. RDD API doc ([Scala](api/scala/index.html#org.apache.spark.rdd.RDD), [Java](api/java/index.html?org/apache/spark/api/java/JavaRDD.html), - [Python](api/python/pyspark.rdd.RDD-class.html)) + [Python](api/python/pyspark.html#pyspark.RDD)) and pair RDD functions doc ([Scala](api/scala/index.html#org.apache.spark.rdd.PairRDDFunctions), [Java](api/java/index.html?org/apache/spark/api/java/JavaPairRDD.html)) @@ -857,7 +937,7 @@ for details. Similar to map, but each input item can be mapped to 0 or more output items (so func should return a Seq rather than a single item). - mapPartitions(func) + mapPartitions(func) Similar to map, but runs separately on each partition (block) of the RDD, so func must be of type Iterator<T> => Iterator<U> when running on an RDD of type T. @@ -884,7 +964,7 @@ for details. Return a new dataset that contains the distinct elements of the source dataset. - groupByKey([numTasks]) + groupByKey([numTasks]) When called on a dataset of (K, V) pairs, returns a dataset of (K, Iterable<V>) pairs.
    Note: If you are grouping in order to perform an aggregation (such as a sum or average) over each key, using reduceByKey or aggregateByKey will yield much better @@ -895,25 +975,25 @@ for details. - reduceByKey(func, [numTasks]) + reduceByKey(func, [numTasks]) When called on a dataset of (K, V) pairs, returns a dataset of (K, V) pairs where the values for each key are aggregated using the given reduce function func, which must be of type (V,V) => V. Like in groupByKey, the number of reduce tasks is configurable through an optional second argument. - aggregateByKey(zeroValue)(seqOp, combOp, [numTasks]) + aggregateByKey(zeroValue)(seqOp, combOp, [numTasks]) When called on a dataset of (K, V) pairs, returns a dataset of (K, U) pairs where the values for each key are aggregated using the given combine functions and a neutral "zero" value. Allows an aggregated value type that is different than the input value type, while avoiding unnecessary allocations. Like in groupByKey, the number of reduce tasks is configurable through an optional second argument. - sortByKey([ascending], [numTasks]) + sortByKey([ascending], [numTasks]) When called on a dataset of (K, V) pairs where K implements Ordered, returns a dataset of (K, V) pairs sorted by keys in ascending or descending order, as specified in the boolean ascending argument. - join(otherDataset, [numTasks]) + join(otherDataset, [numTasks]) When called on datasets of type (K, V) and (K, W), returns a dataset of (K, (V, W)) pairs with all pairs of elements for each key. Outer joins are supported through leftOuterJoin, rightOuterJoin, and fullOuterJoin. - cogroup(otherDataset, [numTasks]) + cogroup(otherDataset, [numTasks]) When called on datasets of type (K, V) and (K, W), returns a dataset of (K, (Iterable<V>, Iterable<W>)) tuples. This operation is also called groupWith. @@ -926,17 +1006,17 @@ for details. process's stdin and lines output to its stdout are returned as an RDD of strings. - coalesce(numPartitions) + coalesce(numPartitions) Decrease the number of partitions in the RDD to numPartitions. Useful for running operations more efficiently after filtering down a large dataset. repartition(numPartitions) Reshuffle the data in the RDD randomly to create either more or fewer partitions and balance it across them. - This always shuffles all data over the network. + This always shuffles all data over the network. - repartitionAndSortWithinPartitions(partitioner) + repartitionAndSortWithinPartitions(partitioner) Repartition the RDD according to the given partitioner and, within each resulting partition, sort records by their keys. This is more efficient than calling repartition and then sorting within each partition because it can push the sorting down into the shuffle machinery. @@ -949,7 +1029,7 @@ The following table lists some of the common actions supported by Spark. Refer t RDD API doc ([Scala](api/scala/index.html#org.apache.spark.rdd.RDD), [Java](api/java/index.html?org/apache/spark/api/java/JavaRDD.html), - [Python](api/python/pyspark.rdd.RDD-class.html)) + [Python](api/python/pyspark.html#pyspark.RDD)) and pair RDD functions doc ([Scala](api/scala/index.html#org.apache.spark.rdd.PairRDDFunctions), [Java](api/java/index.html?org/apache/spark/api/java/JavaPairRDD.html)) @@ -975,7 +1055,7 @@ for details. take(n) - Return an array with the first n elements of the dataset. Note that this is currently not executed in parallel. Instead, the driver program computes all the elements. + Return an array with the first n elements of the dataset. takeSample(withReplacement, num, [seed]) @@ -1000,15 +1080,77 @@ for details. SparkContext.objectFile(). - countByKey() + countByKey() Only available on RDDs of type (K, V). Returns a hashmap of (K, Int) pairs with the count of each key. foreach(func) - Run a function func on each element of the dataset. This is usually done for side effects such as updating an accumulator variable (see below) or interacting with external storage systems. + Run a function func on each element of the dataset. This is usually done for side effects such as updating an Accumulator or interacting with external storage systems. +
    Note: modifying variables other than Accumulators outside of the foreach() may result in undefined behavior. See Understanding closures for more details. +### Shuffle operations + +Certain operations within Spark trigger an event known as the shuffle. The shuffle is Spark's +mechanism for re-distributing data so that is grouped differently across partitions. This typically +involves copying data across executors and machines, making the shuffle a complex and +costly operation. + +#### Background + +To understand what happens during the shuffle we can consider the example of the +[`reduceByKey`](#ReduceByLink) operation. The `reduceByKey` operation generates a new RDD where all +values for a single key are combined into a tuple - the key and the result of executing a reduce +function against all values associated with that key. The challenge is that not all values for a +single key necessarily reside on the same partition, or even the same machine, but they must be +co-located to compute the result. + +In Spark, data is generally not distributed across partitions to be in the necessary place for a +specific operation. During computations, a single task will operate on a single partition - thus, to +organize all the data for a single `reduceByKey` reduce task to execute, Spark needs to perform an +all-to-all operation. It must read from all partitions to find all the values for all keys, +and then bring together values across partitions to compute the final result for each key - +this is called the **shuffle**. + +Although the set of elements in each partition of newly shuffled data will be deterministic, and so +is the ordering of partitions themselves, the ordering of these elements is not. If one desires predictably +ordered data following shuffle then it's possible to use: + +* `mapPartitions` to sort each partition using, for example, `.sorted` +* `repartitionAndSortWithinPartitions` to efficiently sort partitions while simultaneously repartitioning +* `sortBy` to make a globally ordered RDD + +Operations which can cause a shuffle include **repartition** operations like +[`repartition`](#RepartitionLink), and [`coalesce`](#CoalesceLink), **'ByKey** operations +(except for counting) like [`groupByKey`](#GroupByLink) and [`reduceByKey`](#ReduceByLink), and +**join** operations like [`cogroup`](#CogroupLink) and [`join`](#JoinLink). + +#### Performance Impact +The **Shuffle** is an expensive operation since it involves disk I/O, data serialization, and +network I/O. To organize data for the shuffle, Spark generates sets of tasks - *map* tasks to +organize the data, and a set of *reduce* tasks to aggregate it. This nomenclature comes from +MapReduce and does not directly relate to Spark's `map` and `reduce` operations. + +Internally, results from individual map tasks are kept in memory until they can't fit. Then, these +are sorted based on the target partition and written to a single file. On the reduce side, tasks +read the relevant sorted blocks. + +Certain shuffle operations can consume significant amounts of heap memory since they employ +in-memory data structures to organize records before or after transferring them. Specifically, +`reduceByKey` and `aggregateByKey` create these structures on the map side and `'ByKey` operations +generate these on the reduce side. When data does not fit in memory Spark will spill these tables +to disk, incurring the additional overhead of disk I/O and increased garbage collection. + +Shuffle also generates a large number of intermediate files on disk. As of Spark 1.3, these files +are not cleaned up from Spark's temporary storage until Spark is stopped, which means that +long-running Spark jobs may consume available disk space. This is done so the shuffle doesn't need +to be re-computed if the lineage is re-computed. The temporary storage directory is specified by the +`spark.local.dir` configuration parameter when configuring the Spark context. + +Shuffle behavior can be tuned by adjusting a variety of configuration parameters. See the +'Shuffle Behavior' section within the [Spark Configuration Guide](configuration.html). + ## RDD Persistence One of the most important capabilities in Spark is *persisting* (or *caching*) a dataset in memory @@ -1028,7 +1170,7 @@ replicate it across nodes, or store it off-heap in [Tachyon](http://tachyon-proj These levels are set by passing a `StorageLevel` object ([Scala](api/scala/index.html#org.apache.spark.storage.StorageLevel), [Java](api/java/index.html?org/apache/spark/storage/StorageLevel.html), -[Python](api/python/pyspark.storagelevel.StorageLevel-class.html)) +[Python](api/python/pyspark.html#pyspark.StorageLevel)) to `persist()`. The `cache()` method is a shorthand for using the default storage level, which is `StorageLevel.MEMORY_ONLY` (store deserialized objects in memory). The full set of storage levels is: @@ -1130,6 +1272,12 @@ than shipping a copy of it with tasks. They can be used, for example, to give ev large input dataset in an efficient manner. Spark also attempts to distribute broadcast variables using efficient broadcast algorithms to reduce communication cost. +Spark actions are executed through a set of stages, separated by distributed "shuffle" operations. +Spark automatically broadcasts the common data needed by tasks within each stage. The data +broadcasted this way is cached in serialized form and deserialized before running each task. This +means that explicitly creating broadcast variables is only useful when tasks across multiple stages +need the same data or when caching the data in deserialized form is important. + Broadcast variables are created from a variable `v` by calling `SparkContext.broadcast(v)`. The broadcast variable is a wrapper around `v`, and its value can be accessed by calling the `value` method. The code below shows this: @@ -1178,7 +1326,7 @@ run on the cluster so that `v` is not shipped to the nodes more than once. In ad `v` should not be modified after it is broadcast in order to ensure that all nodes get the same value of the broadcast variable (e.g. if the variable is shipped to a new node later). -## Accumulators +## Accumulators Accumulators are variables that are only "added" to through an associative operation and can therefore be efficiently supported in parallel. They can be used to implement counters (as in @@ -1291,7 +1439,7 @@ scala> accum.value {% endhighlight %} While this code used the built-in support for accumulators of type Int, programmers can also -create their own types by subclassing [AccumulatorParam](api/python/pyspark.accumulators.AccumulatorParam-class.html). +create their own types by subclassing [AccumulatorParam](api/python/pyspark.html#pyspark.AccumulatorParam). The AccumulatorParam interface has two methods: `zero` for providing a "zero value" for your data type, and `addInPlace` for adding two values together. For example, supposing we had a `Vector` class representing mathematical vectors, we could write: @@ -1323,25 +1471,28 @@ Accumulators do not change the lazy evaluation model of Spark. If they are being
    {% highlight scala %} -val acc = sc.accumulator(0) -data.map(x => acc += x; f(x)) -// Here, acc is still 0 because no actions have cause the `map` to be computed. +val accum = sc.accumulator(0) +data.map { x => accum += x; f(x) } +// Here, accum is still 0 because no actions have caused the `map` to be computed. {% endhighlight %}
    {% highlight java %} Accumulator accum = sc.accumulator(0); -data.map(x -> accum.add(x); f(x);); -// Here, accum is still 0 because no actions have cause the `map` to be computed. +data.map(x -> { accum.add(x); return f(x); }); +// Here, accum is still 0 because no actions have caused the `map` to be computed. {% endhighlight %}
    {% highlight python %} accum = sc.accumulator(0) -data.map(lambda x => acc.add(x); f(x)) -# Here, acc is still 0 because no actions have cause the `map` to be computed. +def g(x): + accum.add(x) + return f(x) +data.map(g) +# Here, accum is still 0 because no actions have caused the `map` to be computed. {% endhighlight %}
    @@ -1353,6 +1504,11 @@ The [application submission guide](submitting-applications.html) describes how t In short, once you package your application into a JAR (for Java/Scala) or a set of `.py` or `.zip` files (for Python), the `bin/spark-submit` script lets you submit it to any supported cluster manager. +# Launching Spark jobs from Java / Scala + +The [org.apache.spark.launcher](api/java/index.html?org/apache/spark/launcher/package-summary.html) +package provides classes for launching Spark jobs as child processes using a simple Java API. + # Unit Testing Spark is friendly to unit testing with any popular unit test framework. diff --git a/docs/running-on-mesos.md b/docs/running-on-mesos.md index 78358499fd01f..594bf78b67713 100644 --- a/docs/running-on-mesos.md +++ b/docs/running-on-mesos.md @@ -110,7 +110,7 @@ cluster, or `mesos://zk://host:2181` for a multi-master Mesos cluster using ZooK The driver also needs some configuration in `spark-env.sh` to interact properly with Mesos: 1. In `spark-env.sh` set some environment variables: - * `export MESOS_NATIVE_LIBRARY=`. This path is typically + * `export MESOS_NATIVE_JAVA_LIBRARY=`. This path is typically `/lib/libmesos.so` where the prefix is `/usr/local` by default. See Mesos installation instructions above. On Mac OS X, the library is called `libmesos.dylib` instead of `libmesos.so`. @@ -167,9 +167,6 @@ acquire. By default, it will acquire *all* cores in the cluster (that get offere only makes sense if you run just one application at a time. You can cap the maximum number of cores using `conf.set("spark.cores.max", "10")` (for example). -# Known issues -- When using the "fine-grained" mode, make sure that your executors always leave 32 MB free on the slaves. Otherwise it can happen that your Spark job does not proceed anymore. Currently, Apache Mesos only offers resources if there are at least 32 MB memory allocatable. But as Spark allocates memory only for the executor and cpu only for tasks, it can happen on high slave memory usage that no new tasks will be started anymore. More details can be found in [MESOS-1688](https://issues.apache.org/jira/browse/MESOS-1688). Alternatively use the "coarse-gained" mode, which is not affected by this issue. - # Running Alongside Hadoop You can run Spark and Mesos alongside your existing Hadoop cluster by just launching them as a @@ -197,7 +194,11 @@ See the [configuration page](configuration.html) for information on Spark config spark.mesos.coarse false - Set the run mode for Spark on Mesos. For more information about the run mode, refer to #Mesos Run Mode section above. + If set to "true", runs over Mesos clusters in + "coarse-grained" sharing mode, + where Spark acquires one long-lived Mesos task on each machine instead of one Mesos task per + Spark task. This gives lower-latency scheduling for short queries, but leaves resources in use + for the whole duration of the Spark job. @@ -209,21 +210,33 @@ See the [configuration page](configuration.html) for information on Spark config Note that total amount of cores the executor will request in total will not exceed the spark.cores.max setting. + + spark.mesos.mesosExecutor.cores + 1.0 + + (Fine-grained mode only) Number of cores to give each Mesos executor. This does not + include the cores used to run the Spark tasks. In other words, even if no Spark task + is being run, each Mesos executor will occupy the number of cores configured here. + The value can be a floating point number. + + spark.mesos.executor.home - SPARK_HOME + driver side SPARK_HOME - The location where the mesos executor will look for Spark binaries to execute, and uses the SPARK_HOME setting on default. - This variable is only used when no spark.executor.uri is provided, and assumes Spark is installed on the specified location - on each slave. + Set the directory in which Spark is installed on the executors in Mesos. By default, the + executors will simply use the driver's Spark home directory, which may not be visible to + them. Note that this is only relevant if a Spark binary package is not specified through + spark.executor.uri. spark.mesos.executor.memoryOverhead - 384 + executor memory * 0.10, with minimum of 384 - The amount of memory that Mesos executor will request for the task to account for the overhead of running the executor itself. - The final total amount of memory allocated is the maximum value between executor memory plus memoryOverhead, and overhead fraction (1.07) plus the executor memory. + The amount of additional memory, specified in MB, to be allocated per executor. By default, + the overhead will be larger of either 384 or 10% of `spark.executor.memory`. If it's set, + the final overhead will be this value. diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md index 2b93eef6c26ed..0968fc5ad632b 100644 --- a/docs/running-on-yarn.md +++ b/docs/running-on-yarn.md @@ -48,9 +48,9 @@ Most of the configs are the same for Spark on YARN as for other deployment modes spark.yarn.am.waitTime - 100000 + 100s - In yarn-cluster mode, time in milliseconds for the application master to wait for the + In yarn-cluster mode, time for the application master to wait for the SparkContext to be initialized. In yarn-client mode, time for the application master to wait for the driver to connect to it. @@ -87,7 +87,8 @@ Most of the configs are the same for Spark on YARN as for other deployment modes spark.yarn.historyServer.address (none) - The address of the Spark history server (i.e. host.com:18080). The address should not contain a scheme (http://). Defaults to not being set since the history server is an optional service. This address is given to the YARN ResourceManager when the Spark application finishes to link the application from the ResourceManager UI to the Spark history server UI. + The address of the Spark history server (i.e. host.com:18080). The address should not contain a scheme (http://). Defaults to not being set since the history server is an optional service. This address is given to the YARN ResourceManager when the Spark application finishes to link the application from the ResourceManager UI to the Spark history server UI. + For this property, YARN properties can be used as variables, and these are substituted by Spark at runtime. For eg, if the Spark history server runs on the same node as the YARN ResourceManager, it can be set to `${hadoopconf-yarn.resourcemanager.hostname}:18080`. @@ -113,7 +114,7 @@ Most of the configs are the same for Spark on YARN as for other deployment modes spark.yarn.executor.memoryOverhead - executorMemory * 0.07, with minimum of 384 + executorMemory * 0.10, with minimum of 384 The amount of off heap memory (in megabytes) to be allocated per executor. This is memory that accounts for things like VM overheads, interned strings, other native overheads, etc. This tends to grow with the executor size (typically 6-10%). @@ -196,12 +197,25 @@ Most of the configs are the same for Spark on YARN as for other deployment modes It should be no larger than the global number of max attempts in the YARN configuration. + + spark.yarn.submit.waitAppCompletion + true + + In YARN cluster mode, controls whether the client waits to exit until the application completes. + If set to true, the client process will stay alive reporting the application's status. + Otherwise, the client process will exit after submission. + + # Launching Spark on YARN Ensure that `HADOOP_CONF_DIR` or `YARN_CONF_DIR` points to the directory which contains the (client side) configuration files for the Hadoop cluster. -These configs are used to write to the dfs and connect to the YARN ResourceManager. +These configs are used to write to the dfs and connect to the YARN ResourceManager. The +configuration contained in this directory will be distributed to the YARN cluster so that all +containers used by the application use the same configuration. If the configuration references +Java system properties or environment variables not managed by YARN, they should also be set in the +Spark application's configuration (driver, executors, and the AM when running in client mode). There are two deploy modes that can be used to launch Spark applications on YARN. In yarn-cluster mode, the Spark driver runs inside an application master process which is managed by YARN on the cluster, and the client can go away after initiating the application. In yarn-client mode, the driver runs in the client process, and the application master is only used for requesting resources from YARN. @@ -274,6 +288,6 @@ If you need a reference to the proper location to put log files in the YARN so t # Important notes - Whether core requests are honored in scheduling decisions depends on which scheduler is in use and how it is configured. -- The local directories used by Spark executors will be the local directories configured for YARN (Hadoop YARN config `yarn.nodemanager.local-dirs`). If the user specifies `spark.local.dir`, it will be ignored. +- In `yarn-cluster` mode, the local directories used by the Spark executors and the Spark driver will be the local directories configured for YARN (Hadoop YARN config `yarn.nodemanager.local-dirs`). If the user specifies `spark.local.dir`, it will be ignored. In `yarn-client` mode, the Spark executors will use the local directories configured for YARN while the Spark driver will use those defined in `spark.local.dir`. This is because the Spark driver does not run on the YARN cluster in `yarn-client` mode, only the Spark executors do. - The `--files` and `--archives` options support specifying file names with the # similar to Hadoop. For example you can specify: `--files localtest.txt#appSees.txt` and this will upload the file you have locally named localtest.txt into HDFS but this will be linked to by the name `appSees.txt`, and your application should use the name as `appSees.txt` to reference it when running on YARN. - The `--jars` option allows the `SparkContext.addJar` function to work if you are using it with local files and running in `yarn-cluster` mode. It does not need to be used if you are using it with HDFS, HTTP, HTTPS, or FTP files. diff --git a/docs/spark-standalone.md b/docs/spark-standalone.md index 5c6084fb46255..0eed9adacf123 100644 --- a/docs/spark-standalone.md +++ b/docs/spark-standalone.md @@ -24,7 +24,7 @@ the master's web UI, which is [http://localhost:8080](http://localhost:8080) by Similarly, you can start one or more workers and connect them to the master via: - ./bin/spark-class org.apache.spark.deploy.worker.Worker spark://IP:PORT + ./sbin/start-slave.sh Once you have started a worker, look at the master's web UI ([http://localhost:8080](http://localhost:8080) by default). You should see the new node listed there, along with its number of CPUs and memory (minus one gigabyte left for the OS). @@ -81,6 +81,7 @@ Once you've set up this file, you can launch or stop your cluster with the follo - `sbin/start-master.sh` - Starts a master instance on the machine the script is executed on. - `sbin/start-slaves.sh` - Starts a slave instance on each machine specified in the `conf/slaves` file. +- `sbin/start-slave.sh` - Starts a slave instance on the machine the script is executed on. - `sbin/start-all.sh` - Starts both a master and a number of slaves as described above. - `sbin/stop-master.sh` - Stops the master that was started via the `bin/start-master.sh` script. - `sbin/stop-slaves.sh` - Stops all slave instances on the machines specified in the `conf/slaves` file. @@ -222,8 +223,7 @@ SPARK_WORKER_OPTS supports the following system properties: false Enable periodic cleanup of worker / application directories. Note that this only affects standalone - mode, as YARN works differently. Applications directories are cleaned up regardless of whether - the application is still running. + mode, as YARN works differently. Only the directories of stopped applications are cleaned up. diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md index 38f617d0c836c..b2022546268a7 100644 --- a/docs/sql-programming-guide.md +++ b/docs/sql-programming-guide.md @@ -1,7 +1,7 @@ --- layout: global -displayTitle: Spark SQL Programming Guide -title: Spark SQL +displayTitle: Spark SQL and DataFrame Guide +title: Spark SQL and DataFrames --- * This will become a table of contents (this text will be scraped). @@ -9,168 +9,348 @@ title: Spark SQL # Overview +Spark SQL is a Spark module for structured data processing. It provides a programming abstraction called DataFrames and can also act as distributed SQL query engine. + + +# DataFrames + +A DataFrame is a distributed collection of data organized into named columns. It is conceptually equivalent to a table in a relational database or a data frame in R/Python, but with richer optimizations under the hood. DataFrames can be constructed from a wide array of sources such as: structured data files, tables in Hive, external databases, or existing RDDs. + +The DataFrame API is available in [Scala](api/scala/index.html#org.apache.spark.sql.DataFrame), [Java](api/java/index.html?org/apache/spark/sql/DataFrame.html), and [Python](api/python/pyspark.sql.html#pyspark.sql.DataFrame). + +All of the examples on this page use sample data included in the Spark distribution and can be run in the `spark-shell` or the `pyspark` shell. + + +## Starting Point: `SQLContext` +
    -Spark SQL allows relational queries expressed in SQL, HiveQL, or Scala to be executed using -Spark. At the core of this component is a new type of RDD, -[SchemaRDD](api/scala/index.html#org.apache.spark.sql.SchemaRDD). SchemaRDDs are composed of -[Row](api/scala/index.html#org.apache.spark.sql.package@Row:org.apache.spark.sql.catalyst.expressions.Row.type) objects, along with -a schema that describes the data types of each column in the row. A SchemaRDD is similar to a table -in a traditional relational database. A SchemaRDD can be created from an existing RDD, a [Parquet](http://parquet.io) -file, a JSON dataset, or by running HiveQL against data stored in [Apache Hive](http://hive.apache.org/). +The entry point into all functionality in Spark SQL is the +[`SQLContext`](api/scala/index.html#org.apache.spark.sql.`SQLContext`) class, or one of its +descendants. To create a basic `SQLContext`, all you need is a SparkContext. + +{% highlight scala %} +val sc: SparkContext // An existing SparkContext. +val sqlContext = new org.apache.spark.sql.SQLContext(sc) -All of the examples on this page use sample data included in the Spark distribution and can be run in the `spark-shell`. +// this is used to implicitly convert an RDD to a DataFrame. +import sqlContext.implicits._ +{% endhighlight %}
    -
    -Spark SQL allows relational queries expressed in SQL or HiveQL to be executed using -Spark. At the core of this component is a new type of RDD, -[JavaSchemaRDD](api/scala/index.html#org.apache.spark.sql.api.java.JavaSchemaRDD). JavaSchemaRDDs are composed of -[Row](api/scala/index.html#org.apache.spark.sql.api.java.Row) objects, along with -a schema that describes the data types of each column in the row. A JavaSchemaRDD is similar to a table -in a traditional relational database. A JavaSchemaRDD can be created from an existing RDD, a [Parquet](http://parquet.io) -file, a JSON dataset, or by running HiveQL against data stored in [Apache Hive](http://hive.apache.org/). +
    + +The entry point into all functionality in Spark SQL is the +[`SQLContext`](api/java/index.html#org.apache.spark.sql.SQLContext) class, or one of its +descendants. To create a basic `SQLContext`, all you need is a SparkContext. + +{% highlight java %} +JavaSparkContext sc = ...; // An existing JavaSparkContext. +SQLContext sqlContext = new org.apache.spark.sql.SQLContext(sc); +{% endhighlight %} +
    -Spark SQL allows relational queries expressed in SQL or HiveQL to be executed using -Spark. At the core of this component is a new type of RDD, -[SchemaRDD](api/python/pyspark.sql.SchemaRDD-class.html). SchemaRDDs are composed of -[Row](api/python/pyspark.sql.Row-class.html) objects, along with -a schema that describes the data types of each column in the row. A SchemaRDD is similar to a table -in a traditional relational database. A SchemaRDD can be created from an existing RDD, a [Parquet](http://parquet.io) -file, a JSON dataset, or by running HiveQL against data stored in [Apache Hive](http://hive.apache.org/). +The entry point into all relational functionality in Spark is the +[`SQLContext`](api/python/pyspark.sql.html#pyspark.sql.SQLContext) class, or one +of its decedents. To create a basic `SQLContext`, all you need is a SparkContext. + +{% highlight python %} +from pyspark.sql import SQLContext +sqlContext = SQLContext(sc) +{% endhighlight %} -All of the examples on this page use sample data included in the Spark distribution and can be run in the `pyspark` shell.
    -**Spark SQL is currently an alpha component. While we will minimize API changes, some APIs may change in future releases.** +In addition to the basic `SQLContext`, you can also create a `HiveContext`, which provides a +superset of the functionality provided by the basic `SQLContext`. Additional features include +the ability to write queries using the more complete HiveQL parser, access to Hive UDFs, and the +ability to read data from Hive tables. To use a `HiveContext`, you do not need to have an +existing Hive setup, and all of the data sources available to a `SQLContext` are still available. +`HiveContext` is only packaged separately to avoid including all of Hive's dependencies in the default +Spark build. If these dependencies are not a problem for your application then using `HiveContext` +is recommended for the 1.3 release of Spark. Future releases will focus on bringing `SQLContext` up +to feature parity with a `HiveContext`. + +The specific variant of SQL that is used to parse queries can also be selected using the +`spark.sql.dialect` option. This parameter can be changed using either the `setConf` method on +a `SQLContext` or by using a `SET key=value` command in SQL. For a `SQLContext`, the only dialect +available is "sql" which uses a simple SQL parser provided by Spark SQL. In a `HiveContext`, the +default is "hiveql", though "sql" is also available. Since the HiveQL parser is much more complete, +this is recommended for most use cases. + + +## Creating DataFrames -*************************************************************************************************** +With a `SQLContext`, applications can create `DataFrame`s from an existing `RDD`, from a Hive table, or from data sources. -# Getting Started +As an example, the following creates a `DataFrame` based on the content of a JSON file:
    - -The entry point into all relational functionality in Spark is the -[SQLContext](api/scala/index.html#org.apache.spark.sql.SQLContext) class, or one of its -descendants. To create a basic SQLContext, all you need is a SparkContext. - {% highlight scala %} val sc: SparkContext // An existing SparkContext. val sqlContext = new org.apache.spark.sql.SQLContext(sc) -// createSchemaRDD is used to implicitly convert an RDD to a SchemaRDD. -import sqlContext.createSchemaRDD -{% endhighlight %} +val df = sqlContext.jsonFile("examples/src/main/resources/people.json") -In addition to the basic SQLContext, you can also create a HiveContext, which provides a -superset of the functionality provided by the basic SQLContext. Additional features include -the ability to write queries using the more complete HiveQL parser, access to HiveUDFs, and the -ability to read data from Hive tables. To use a HiveContext, you do not need to have an -existing Hive setup, and all of the data sources available to a SQLContext are still available. -HiveContext is only packaged separately to avoid including all of Hive's dependencies in the default -Spark build. If these dependencies are not a problem for your application then using HiveContext -is recommended for the 1.2 release of Spark. Future releases will focus on bringing SQLContext up to -feature parity with a HiveContext. +// Displays the content of the DataFrame to stdout +df.show() +{% endhighlight %}
    - -The entry point into all relational functionality in Spark is the -[JavaSQLContext](api/scala/index.html#org.apache.spark.sql.api.java.JavaSQLContext) class, or one -of its descendants. To create a basic JavaSQLContext, all you need is a JavaSparkContext. - {% highlight java %} JavaSparkContext sc = ...; // An existing JavaSparkContext. -JavaSQLContext sqlContext = new org.apache.spark.sql.api.java.JavaSQLContext(sc); -{% endhighlight %} +SQLContext sqlContext = new org.apache.spark.sql.SQLContext(sc); -In addition to the basic SQLContext, you can also create a HiveContext, which provides a strict -super set of the functionality provided by the basic SQLContext. Additional features include -the ability to write queries using the more complete HiveQL parser, access to HiveUDFs, and the -ability to read data from Hive tables. To use a HiveContext, you do not need to have an -existing Hive setup, and all of the data sources available to a SQLContext are still available. -HiveContext is only packaged separately to avoid including all of Hive's dependencies in the default -Spark build. If these dependencies are not a problem for your application then using HiveContext -is recommended for the 1.2 release of Spark. Future releases will focus on bringing SQLContext up to -feature parity with a HiveContext. +DataFrame df = sqlContext.jsonFile("examples/src/main/resources/people.json"); + +// Displays the content of the DataFrame to stdout +df.show(); +{% endhighlight %}
    +{% highlight python %} +from pyspark.sql import SQLContext +sqlContext = SQLContext(sc) -The entry point into all relational functionality in Spark is the -[SQLContext](api/python/pyspark.sql.SQLContext-class.html) class, or one -of its decedents. To create a basic SQLContext, all you need is a SparkContext. +df = sqlContext.jsonFile("examples/src/main/resources/people.json") + +# Displays the content of the DataFrame to stdout +df.show() +{% endhighlight %} + +
    +
    + + +## DataFrame Operations + +DataFrames provide a domain-specific language for structured data manipulation in [Scala](api/scala/index.html#org.apache.spark.sql.DataFrame), [Java](api/java/index.html?org/apache/spark/sql/DataFrame.html), and [Python](api/python/pyspark.sql.html#pyspark.sql.DataFrame). + +Here we include some basic examples of structured data processing using DataFrames: + + +
    +
    +{% highlight scala %} +val sc: SparkContext // An existing SparkContext. +val sqlContext = new org.apache.spark.sql.SQLContext(sc) + +// Create the DataFrame +val df = sqlContext.jsonFile("examples/src/main/resources/people.json") + +// Show the content of the DataFrame +df.show() +// age name +// null Michael +// 30 Andy +// 19 Justin + +// Print the schema in a tree format +df.printSchema() +// root +// |-- age: long (nullable = true) +// |-- name: string (nullable = true) + +// Select only the "name" column +df.select("name").show() +// name +// Michael +// Andy +// Justin + +// Select everybody, but increment the age by 1 +df.select(df("name"), df("age") + 1).show() +// name (age + 1) +// Michael null +// Andy 31 +// Justin 20 + +// Select people older than 21 +df.filter(df("age") > 21).show() +// age name +// 30 Andy + +// Count people by age +df.groupBy("age").count().show() +// age count +// null 1 +// 19 1 +// 30 1 +{% endhighlight %} + +
    + +
    +{% highlight java %} +JavaSparkContext sc // An existing SparkContext. +SQLContext sqlContext = new org.apache.spark.sql.SQLContext(sc) + +// Create the DataFrame +DataFrame df = sqlContext.jsonFile("examples/src/main/resources/people.json"); + +// Show the content of the DataFrame +df.show(); +// age name +// null Michael +// 30 Andy +// 19 Justin +// Print the schema in a tree format +df.printSchema(); +// root +// |-- age: long (nullable = true) +// |-- name: string (nullable = true) + +// Select only the "name" column +df.select("name").show(); +// name +// Michael +// Andy +// Justin + +// Select everybody, but increment the age by 1 +df.select(df.col("name"), df.col("age").plus(1)).show(); +// name (age + 1) +// Michael null +// Andy 31 +// Justin 20 + +// Select people older than 21 +df.filter(df.col("age").gt(21)).show(); +// age name +// 30 Andy + +// Count people by age +df.groupBy("age").count().show(); +// age count +// null 1 +// 19 1 +// 30 1 +{% endhighlight %} + +
    + +
    {% highlight python %} from pyspark.sql import SQLContext sqlContext = SQLContext(sc) -{% endhighlight %} -In addition to the basic SQLContext, you can also create a HiveContext, which provides a strict -super set of the functionality provided by the basic SQLContext. Additional features include -the ability to write queries using the more complete HiveQL parser, access to HiveUDFs, and the -ability to read data from Hive tables. To use a HiveContext, you do not need to have an -existing Hive setup, and all of the data sources available to a SQLContext are still available. -HiveContext is only packaged separately to avoid including all of Hive's dependencies in the default -Spark build. If these dependencies are not a problem for your application then using HiveContext -is recommended for the 1.2 release of Spark. Future releases will focus on bringing SQLContext up to -feature parity with a HiveContext. +# Create the DataFrame +df = sqlContext.jsonFile("examples/src/main/resources/people.json") + +# Show the content of the DataFrame +df.show() +## age name +## null Michael +## 30 Andy +## 19 Justin + +# Print the schema in a tree format +df.printSchema() +## root +## |-- age: long (nullable = true) +## |-- name: string (nullable = true) + +# Select only the "name" column +df.select("name").show() +## name +## Michael +## Andy +## Justin + +# Select everybody, but increment the age by 1 +df.select(df.name, df.age + 1).show() +## name (age + 1) +## Michael null +## Andy 31 +## Justin 20 + +# Select people older than 21 +df.filter(df.age > 21).show() +## age name +## 30 Andy + +# Count people by age +df.groupBy("age").count().show() +## age count +## null 1 +## 19 1 +## 30 1 + +{% endhighlight %}
    +
    + + +## Running SQL Queries Programmatically +The `sql` function on a `SQLContext` enables applications to run SQL queries programmatically and returns the result as a `DataFrame`. + +
    +
    +{% highlight scala %} +val sqlContext = ... // An existing SQLContext +val df = sqlContext.sql("SELECT * FROM table") +{% endhighlight %}
    -The specific variant of SQL that is used to parse queries can also be selected using the -`spark.sql.dialect` option. This parameter can be changed using either the `setConf` method on -a SQLContext or by using a `SET key=value` command in SQL. For a SQLContext, the only dialect -available is "sql" which uses a simple SQL parser provided by Spark SQL. In a HiveContext, the -default is "hiveql", though "sql" is also available. Since the HiveQL parser is much more complete, - this is recommended for most use cases. +
    +{% highlight java %} +SQLContext sqlContext = ... // An existing SQLContext +DataFrame df = sqlContext.sql("SELECT * FROM table") +{% endhighlight %} +
    -# Data Sources +
    +{% highlight python %} +from pyspark.sql import SQLContext +sqlContext = SQLContext(sc) +df = sqlContext.sql("SELECT * FROM table") +{% endhighlight %} +
    +
    -Spark SQL supports operating on a variety of data sources through the `SchemaRDD` interface. -A SchemaRDD can be operated on as normal RDDs and can also be registered as a temporary table. -Registering a SchemaRDD as a table allows you to run SQL queries over its data. This section -describes the various methods for loading data into a SchemaRDD. -## RDDs +## Interoperating with RDDs -Spark SQL supports two different methods for converting existing RDDs into SchemaRDDs. The first +Spark SQL supports two different methods for converting existing RDDs into DataFrames. The first method uses reflection to infer the schema of an RDD that contains specific types of objects. This reflection based approach leads to more concise code and works well when you already know the schema while writing your Spark application. -The second method for creating SchemaRDDs is through a programmatic interface that allows you to +The second method for creating DataFrames is through a programmatic interface that allows you to construct a schema and then apply it to an existing RDD. While this method is more verbose, it allows -you to construct SchemaRDDs when the columns and their types are not known until runtime. +you to construct DataFrames when the columns and their types are not known until runtime. ### Inferring the Schema Using Reflection
    -The Scala interaface for Spark SQL supports automatically converting an RDD containing case classes -to a SchemaRDD. The case class +The Scala interface for Spark SQL supports automatically converting an RDD containing case classes +to a DataFrame. The case class defines the schema of the table. The names of the arguments to the case class are read using reflection and become the names of the columns. Case classes can also be nested or contain complex -types such as Sequences or Arrays. This RDD can be implicitly converted to a SchemaRDD and then be +types such as Sequences or Arrays. This RDD can be implicitly converted to a DataFrame and then be registered as a table. Tables can be used in subsequent SQL statements. {% highlight scala %} // sc is an existing SparkContext. val sqlContext = new org.apache.spark.sql.SQLContext(sc) -// createSchemaRDD is used to implicitly convert an RDD to a SchemaRDD. -import sqlContext.createSchemaRDD +// this is used to implicitly convert an RDD to a DataFrame. +import sqlContext.implicits._ // Define the schema using a case class. // Note: Case classes in Scala 2.10 can support only up to 22 fields. To work around this limit, @@ -178,13 +358,13 @@ import sqlContext.createSchemaRDD case class Person(name: String, age: Int) // Create an RDD of Person objects and register it as a table. -val people = sc.textFile("examples/src/main/resources/people.txt").map(_.split(",")).map(p => Person(p(0), p(1).trim.toInt)) +val people = sc.textFile("examples/src/main/resources/people.txt").map(_.split(",")).map(p => Person(p(0), p(1).trim.toInt)).toDF() people.registerTempTable("people") // SQL statements can be run by using the sql methods provided by sqlContext. val teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19") -// The results of SQL queries are SchemaRDDs and support all the normal RDD operations. +// The results of SQL queries are DataFrames and support all the normal RDD operations. // The columns of a row in the result can be accessed by ordinal. teenagers.map(t => "Name: " + t(0)).collect().foreach(println) {% endhighlight %} @@ -194,7 +374,7 @@ teenagers.map(t => "Name: " + t(0)).collect().foreach(println)
    Spark SQL supports automatically converting an RDD of [JavaBeans](http://stackoverflow.com/questions/3295496/what-is-a-javabean-exactly) -into a Schema RDD. The BeanInfo, obtained using reflection, defines the schema of the table. +into a DataFrame. The BeanInfo, obtained using reflection, defines the schema of the table. Currently, Spark SQL does not support JavaBeans that contain nested or contain complex types such as Lists or Arrays. You can create a JavaBean by creating a class that implements Serializable and has getters and setters for all of its fields. @@ -225,12 +405,12 @@ public static class Person implements Serializable { {% endhighlight %} -A schema can be applied to an existing RDD by calling `applySchema` and providing the Class object +A schema can be applied to an existing RDD by calling `createDataFrame` and providing the Class object for the JavaBean. {% highlight java %} // sc is an existing JavaSparkContext. -JavaSQLContext sqlContext = new org.apache.spark.sql.api.java.JavaSQLContext(sc); +SQLContext sqlContext = new org.apache.spark.sql.SQLContext(sc); // Load a text file and convert each line to a JavaBean. JavaRDD people = sc.textFile("examples/src/main/resources/people.txt").map( @@ -247,15 +427,15 @@ JavaRDD people = sc.textFile("examples/src/main/resources/people.txt").m }); // Apply a schema to an RDD of JavaBeans and register it as a table. -JavaSchemaRDD schemaPeople = sqlContext.applySchema(people, Person.class); +DataFrame schemaPeople = sqlContext.createDataFrame(people, Person.class); schemaPeople.registerTempTable("people"); // SQL can be run over RDDs that have been registered as tables. -JavaSchemaRDD teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19") +DataFrame teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19") -// The results of SQL queries are SchemaRDDs and support all the normal RDD operations. +// The results of SQL queries are DataFrames and support all the normal RDD operations. // The columns of a row in the result can be accessed by ordinal. -List teenagerNames = teenagers.map(new Function() { +List teenagerNames = teenagers.javaRDD().map(new Function() { public String call(Row row) { return "Name: " + row.getString(0); } @@ -267,7 +447,7 @@ List teenagerNames = teenagers.map(new Function() {
    -Spark SQL can convert an RDD of Row objects to a SchemaRDD, inferring the datatypes. Rows are constructed by passing a list of +Spark SQL can convert an RDD of Row objects to a DataFrame, inferring the datatypes. Rows are constructed by passing a list of key/value pairs as kwargs to the Row class. The keys of this list define the column names of the table, and the types are inferred by looking at the first row. Since we currently only look at the first row, it is important that there is no missing data in the first row of the RDD. In future versions we @@ -284,11 +464,11 @@ lines = sc.textFile("examples/src/main/resources/people.txt") parts = lines.map(lambda l: l.split(",")) people = parts.map(lambda p: Row(name=p[0], age=int(p[1]))) -# Infer the schema, and register the SchemaRDD as a table. +# Infer the schema, and register the DataFrame as a table. schemaPeople = sqlContext.inferSchema(people) schemaPeople.registerTempTable("people") -# SQL can be run over SchemaRDDs that have been registered as a table. +# SQL can be run over DataFrames that have been registered as a table. teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19") # The results of SQL queries are RDDs and support all the normal RDD operations. @@ -310,12 +490,12 @@ for teenName in teenNames.collect(): When case classes cannot be defined ahead of time (for example, the structure of records is encoded in a string, or a text dataset will be parsed and fields will be projected differently for different users), -a `SchemaRDD` can be created programmatically with three steps. +a `DataFrame` can be created programmatically with three steps. 1. Create an RDD of `Row`s from the original RDD; 2. Create the schema represented by a `StructType` matching the structure of `Row`s in the RDD created in Step 1. -3. Apply the schema to the RDD of `Row`s via `applySchema` method provided +3. Apply the schema to the RDD of `Row`s via `createDataFrame` method provided by `SQLContext`. For example: @@ -329,8 +509,11 @@ val people = sc.textFile("examples/src/main/resources/people.txt") // The schema is encoded in a string val schemaString = "name age" -// Import Spark SQL data types and Row. -import org.apache.spark.sql._ +// Import Row. +import org.apache.spark.sql.Row; + +// Import Spark SQL data types +import org.apache.spark.sql.types.{StructType,StructField,StringType}; // Generate the schema based on the string of schema val schema = @@ -341,15 +524,15 @@ val schema = val rowRDD = people.map(_.split(",")).map(p => Row(p(0), p(1).trim)) // Apply the schema to the RDD. -val peopleSchemaRDD = sqlContext.applySchema(rowRDD, schema) +val peopleDataFrame = sqlContext.createDataFrame(rowRDD, schema) -// Register the SchemaRDD as a table. -peopleSchemaRDD.registerTempTable("people") +// Register the DataFrames as a table. +peopleDataFrame.registerTempTable("people") // SQL statements can be run by using the sql methods provided by sqlContext. val results = sqlContext.sql("SELECT name FROM people") -// The results of SQL queries are SchemaRDDs and support all the normal RDD operations. +// The results of SQL queries are DataFrames and support all the normal RDD operations. // The columns of a row in the result can be accessed by ordinal. results.map(t => "Name: " + t(0)).collect().foreach(println) {% endhighlight %} @@ -362,26 +545,29 @@ results.map(t => "Name: " + t(0)).collect().foreach(println) When JavaBean classes cannot be defined ahead of time (for example, the structure of records is encoded in a string, or a text dataset will be parsed and fields will be projected differently for different users), -a `SchemaRDD` can be created programmatically with three steps. +a `DataFrame` can be created programmatically with three steps. 1. Create an RDD of `Row`s from the original RDD; 2. Create the schema represented by a `StructType` matching the structure of `Row`s in the RDD created in Step 1. -3. Apply the schema to the RDD of `Row`s via `applySchema` method provided -by `JavaSQLContext`. +3. Apply the schema to the RDD of `Row`s via `createDataFrame` method provided +by `SQLContext`. For example: {% highlight java %} -// Import factory methods provided by DataType. -import org.apache.spark.sql.api.java.DataType +import org.apache.spark.api.java.function.Function; +// Import factory methods provided by DataTypes. +import org.apache.spark.sql.types.DataTypes; // Import StructType and StructField -import org.apache.spark.sql.api.java.StructType -import org.apache.spark.sql.api.java.StructField +import org.apache.spark.sql.types.StructType; +import org.apache.spark.sql.types.StructField; // Import Row. -import org.apache.spark.sql.api.java.Row +import org.apache.spark.sql.Row; +// Import RowFactory. +import org.apache.spark.sql.RowFactory; // sc is an existing JavaSparkContext. -JavaSQLContext sqlContext = new org.apache.spark.sql.api.java.JavaSQLContext(sc); +SQLContext sqlContext = new org.apache.spark.sql.SQLContext(sc); // Load a text file and convert each line to a JavaBean. JavaRDD people = sc.textFile("examples/src/main/resources/people.txt"); @@ -392,31 +578,31 @@ String schemaString = "name age"; // Generate the schema based on the string of schema List fields = new ArrayList(); for (String fieldName: schemaString.split(" ")) { - fields.add(DataType.createStructField(fieldName, DataType.StringType, true)); + fields.add(DataTypes.createStructField(fieldName, DataTypes.StringType, true)); } -StructType schema = DataType.createStructType(fields); +StructType schema = DataTypes.createStructType(fields); // Convert records of the RDD (people) to Rows. JavaRDD rowRDD = people.map( new Function() { public Row call(String record) throws Exception { String[] fields = record.split(","); - return Row.create(fields[0], fields[1].trim()); + return RowFactory.create(fields[0], fields[1].trim()); } }); // Apply the schema to the RDD. -JavaSchemaRDD peopleSchemaRDD = sqlContext.applySchema(rowRDD, schema); +DataFrame peopleDataFrame = sqlContext.createDataFrame(rowRDD, schema); -// Register the SchemaRDD as a table. -peopleSchemaRDD.registerTempTable("people"); +// Register the DataFrame as a table. +peopleDataFrame.registerTempTable("people"); // SQL can be run over RDDs that have been registered as tables. -JavaSchemaRDD results = sqlContext.sql("SELECT name FROM people"); +DataFrame results = sqlContext.sql("SELECT name FROM people"); -// The results of SQL queries are SchemaRDDs and support all the normal RDD operations. +// The results of SQL queries are DataFrames and support all the normal RDD operations. // The columns of a row in the result can be accessed by ordinal. -List names = results.map(new Function() { +List names = results.javaRDD().map(new Function() { public String call(Row row) { return "Name: " + row.getString(0); } @@ -431,17 +617,18 @@ List names = results.map(new Function() { When a dictionary of kwargs cannot be defined ahead of time (for example, the structure of records is encoded in a string, or a text dataset will be parsed and fields will be projected differently for different users), -a `SchemaRDD` can be created programmatically with three steps. +a `DataFrame` can be created programmatically with three steps. 1. Create an RDD of tuples or lists from the original RDD; 2. Create the schema represented by a `StructType` matching the structure of tuples or lists in the RDD created in the step 1. -3. Apply the schema to the RDD via `applySchema` method provided by `SQLContext`. +3. Apply the schema to the RDD via `createDataFrame` method provided by `SQLContext`. For example: {% highlight python %} # Import SQLContext and data types -from pyspark.sql import * +from pyspark.sql import SQLContext +from pyspark.sql.types import * # sc is an existing SparkContext. sqlContext = SQLContext(sc) @@ -458,12 +645,12 @@ fields = [StructField(field_name, StringType(), True) for field_name in schemaSt schema = StructType(fields) # Apply the schema to the RDD. -schemaPeople = sqlContext.applySchema(people, schema) +schemaPeople = sqlContext.createDataFrame(people, schema) -# Register the SchemaRDD as a table. +# Register the DataFrame as a table. schemaPeople.registerTempTable("people") -# SQL can be run over SchemaRDDs that have been registered as a table. +# SQL can be run over DataFrames that have been registered as a table. results = sqlContext.sql("SELECT name FROM people") # The results of SQL queries are RDDs and support all the normal RDD operations. @@ -472,11 +659,157 @@ for name in names.collect(): print name {% endhighlight %} +
    + +# Data Sources + +Spark SQL supports operating on a variety of data sources through the `DataFrame` interface. +A DataFrame can be operated on as normal RDDs and can also be registered as a temporary table. +Registering a DataFrame as a table allows you to run SQL queries over its data. This section +describes the general methods for loading and saving data using the Spark Data Sources and then +goes into specific options that are available for the built-in data sources. + +## Generic Load/Save Functions + +In the simplest form, the default data source (`parquet` unless otherwise configured by +`spark.sql.sources.default`) will be used for all operations. + +
    +
    + +{% highlight scala %} +val df = sqlContext.load("people.parquet") +df.select("name", "age").save("namesAndAges.parquet") +{% endhighlight %} +
    +
    + +{% highlight java %} + +DataFrame df = sqlContext.load("people.parquet"); +df.select("name", "age").save("namesAndAges.parquet"); + +{% endhighlight %} + +
    + +
    + +{% highlight python %} + +df = sqlContext.load("people.parquet") +df.select("name", "age").save("namesAndAges.parquet") + +{% endhighlight %} + +
    +
    + +### Manually Specifying Options + +You can also manually specify the data source that will be used along with any extra options +that you would like to pass to the data source. Data sources are specified by their fully qualified +name (i.e., `org.apache.spark.sql.parquet`), but for built-in sources you can also use the shorted +name (`json`, `parquet`, `jdbc`). DataFrames of any type can be converted into other types +using this syntax. + +
    +
    + +{% highlight scala %} +val df = sqlContext.load("people.json", "json") +df.select("name", "age").save("namesAndAges.parquet", "parquet") +{% endhighlight %} + +
    + +
    + +{% highlight java %} + +DataFrame df = sqlContext.load("people.json", "json"); +df.select("name", "age").save("namesAndAges.parquet", "parquet"); + +{% endhighlight %} + +
    + +
    + +{% highlight python %} + +df = sqlContext.load("people.json", "json") +df.select("name", "age").save("namesAndAges.parquet", "parquet") + +{% endhighlight %} + +
    +
    + +### Save Modes + +Save operations can optionally take a `SaveMode`, that specifies how to handle existing data if +present. It is important to realize that these save modes do not utilize any locking and are not +atomic. Thus, it is not safe to have multiple writers attempting to write to the same location. +Additionally, when performing a `Overwrite`, the data will be deleted before writing out the +new data. + + + + + + + + + + + + + + + + + + + + + + + +
    Scala/JavaPythonMeaning
    SaveMode.ErrorIfExists (default)"error" (default) + When saving a DataFrame to a data source, if data already exists, + an exception is expected to be thrown. +
    SaveMode.Append"append" + When saving a DataFrame to a data source, if data/table already exists, + contents of the DataFrame are expected to be appended to existing data. +
    SaveMode.Overwrite"overwrite" + Overwrite mode means that when saving a DataFrame to a data source, + if data/table already exists, existing data is expected to be overwritten by the contents of + the DataFrame. +
    SaveMode.Ignore"ignore" + Ignore mode means that when saving a DataFrame to a data source, if data already exists, + the save operation is expected to not save the contents of the DataFrame and to not + change the existing data. This is similar to a `CREATE TABLE IF NOT EXISTS` in SQL. +
    + +### Saving to Persistent Tables + +When working with a `HiveContext`, `DataFrames` can also be saved as persistent tables using the +`saveAsTable` command. Unlike the `registerTempTable` command, `saveAsTable` will materialize the +contents of the dataframe and create a pointer to the data in the HiveMetastore. Persistent tables +will still exist even after your Spark program has restarted, as long as you maintain your connection +to the same metastore. A DataFrame for a persistent table can be created by calling the `table` +method on a `SQLContext` with the name of the table. + +By default `saveAsTable` will create a "managed table", meaning that the location of the data will +be controlled by the metastore. Managed tables will also have their data deleted automatically +when a table is dropped. + ## Parquet Files [Parquet](http://parquet.io) is a columnar format that is supported by many other data processing systems. @@ -493,16 +826,16 @@ Using the data from the above example: {% highlight scala %} // sqlContext from the previous example is used in this example. -// createSchemaRDD is used to implicitly convert an RDD to a SchemaRDD. -import sqlContext.createSchemaRDD +// This is used to implicitly convert an RDD to a DataFrame. +import sqlContext.implicits._ val people: RDD[Person] = ... // An RDD of case class objects, from the previous example. -// The RDD is implicitly converted to a SchemaRDD by createSchemaRDD, allowing it to be stored using Parquet. +// The RDD is implicitly converted to a DataFrame by implicits, allowing it to be stored using Parquet. people.saveAsParquetFile("people.parquet") // Read in the parquet file created above. Parquet files are self-describing so the schema is preserved. -// The result of loading a Parquet file is also a SchemaRDD. +// The result of loading a Parquet file is also a DataFrame. val parquetFile = sqlContext.parquetFile("people.parquet") //Parquet files can also be registered as tables and then used in SQL statements. @@ -518,19 +851,19 @@ teenagers.map(t => "Name: " + t(0)).collect().foreach(println) {% highlight java %} // sqlContext from the previous example is used in this example. -JavaSchemaRDD schemaPeople = ... // The JavaSchemaRDD from the previous example. +DataFrame schemaPeople = ... // The DataFrame from the previous example. -// JavaSchemaRDDs can be saved as Parquet files, maintaining the schema information. +// DataFrames can be saved as Parquet files, maintaining the schema information. schemaPeople.saveAsParquetFile("people.parquet"); // Read in the Parquet file created above. Parquet files are self-describing so the schema is preserved. -// The result of loading a parquet file is also a JavaSchemaRDD. -JavaSchemaRDD parquetFile = sqlContext.parquetFile("people.parquet"); +// The result of loading a parquet file is also a DataFrame. +DataFrame parquetFile = sqlContext.parquetFile("people.parquet"); //Parquet files can also be registered as tables and then used in SQL statements. parquetFile.registerTempTable("parquetFile"); -JavaSchemaRDD teenagers = sqlContext.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19"); -List teenagerNames = teenagers.map(new Function() { +DataFrame teenagers = sqlContext.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19"); +List teenagerNames = teenagers.javaRDD().map(new Function() { public String call(Row row) { return "Name: " + row.getString(0); } @@ -544,13 +877,13 @@ List teenagerNames = teenagers.map(new Function() { {% highlight python %} # sqlContext from the previous example is used in this example. -schemaPeople # The SchemaRDD from the previous example. +schemaPeople # The DataFrame from the previous example. -# SchemaRDDs can be saved as Parquet files, maintaining the schema information. +# DataFrames can be saved as Parquet files, maintaining the schema information. schemaPeople.saveAsParquetFile("people.parquet") # Read in the Parquet file created above. Parquet files are self-describing so the schema is preserved. -# The result of loading a parquet file is also a SchemaRDD. +# The result of loading a parquet file is also a DataFrame. parquetFile = sqlContext.parquetFile("people.parquet") # Parquet files can also be registered as tables and then used in SQL statements. @@ -563,11 +896,150 @@ for teenName in teenNames.collect():
    +
    + +{% highlight sql %} + +CREATE TEMPORARY TABLE parquetTable +USING org.apache.spark.sql.parquet +OPTIONS ( + path "examples/src/main/resources/people.parquet" +) + +SELECT * FROM parquetTable + +{% endhighlight %} + +
    + +
    + +### Partition discovery + +Table partitioning is a common optimization approach used in systems like Hive. In a partitioned +table, data are usually stored in different directories, with partitioning column values encoded in +the path of each partition directory. The Parquet data source is now able to discover and infer +partitioning information automatically. For exmaple, we can store all our previously used +population data into a partitioned table using the following directory structure, with two extra +columns, `gender` and `country` as partitioning columns: + +{% highlight text %} + +path +└── to + └── table + ├── gender=male + │   ├── ... + │   │ + │   ├── country=US + │   │   └── data.parquet + │   ├── country=CN + │   │   └── data.parquet + │   └── ... + └── gender=female +    ├── ... +    │ +    ├── country=US +    │   └── data.parquet +    ├── country=CN +    │   └── data.parquet +    └── ... + +{% endhighlight %} + +By passing `path/to/table` to either `SQLContext.parquetFile` or `SQLContext.load`, Spark SQL will +automatically extract the partitioning information from the paths. Now the schema of the returned +DataFrame becomes: + +{% highlight text %} + +root +|-- name: string (nullable = true) +|-- age: long (nullable = true) +|-- gender: string (nullable = true) +|-- country: string (nullable = true) + +{% endhighlight %} + +Notice that the data types of the partitioning columns are automatically inferred. Currently, +numeric data types and string type are supported. + +### Schema merging + +Like ProtocolBuffer, Avro, and Thrift, Parquet also supports schema evolution. Users can start with +a simple schema, and gradually add more columns to the schema as needed. In this way, users may end +up with multiple Parquet files with different but mutually compatible schemas. The Parquet data +source is now able to automatically detect this case and merge schemas of all these files. + +
    + +
    + +{% highlight scala %} +// sqlContext from the previous example is used in this example. +// This is used to implicitly convert an RDD to a DataFrame. +import sqlContext.implicits._ + +// Create a simple DataFrame, stored into a partition directory +val df1 = sparkContext.makeRDD(1 to 5).map(i => (i, i * 2)).toDF("single", "double") +df1.saveAsParquetFile("data/test_table/key=1") + +// Create another DataFrame in a new partition directory, +// adding a new column and dropping an existing column +val df2 = sparkContext.makeRDD(6 to 10).map(i => (i, i * 3)).toDF("single", "triple") +df2.saveAsParquetFile("data/test_table/key=2") + +// Read the partitioned table +val df3 = sqlContext.parquetFile("data/test_table") +df3.printSchema() + +// The final schema consists of all 3 columns in the Parquet files together +// with the partiioning column appeared in the partition directory paths. +// root +// |-- single: int (nullable = true) +// |-- double: int (nullable = true) +// |-- triple: int (nullable = true) +// |-- key : int (nullable = true) +{% endhighlight %} + +
    + +
    + +{% highlight python %} +# sqlContext from the previous example is used in this example. + +# Create a simple DataFrame, stored into a partition directory +df1 = sqlContext.createDataFrame(sc.parallelize(range(1, 6))\ + .map(lambda i: Row(single=i, double=i * 2))) +df1.save("data/test_table/key=1", "parquet") + +# Create another DataFrame in a new partition directory, +# adding a new column and dropping an existing column +df2 = sqlContext.createDataFrame(sc.parallelize(range(6, 11)) + .map(lambda i: Row(single=i, triple=i * 3))) +df2.save("data/test_table/key=2", "parquet") + +# Read the partitioned table +df3 = sqlContext.parquetFile("data/test_table") +df3.printSchema() + +# The final schema consists of all 3 columns in the Parquet files together +# with the partiioning column appeared in the partition directory paths. +# root +# |-- single: int (nullable = true) +# |-- double: int (nullable = true) +# |-- triple: int (nullable = true) +# |-- key : int (nullable = true) +{% endhighlight %} + +
    +
    ### Configuration -Configuration of Parquet can be done using the `setConf` method on SQLContext or by running +Configuration of Parquet can be done using the `setConf` method on `SQLContext` or by running `SET key=value` commands using SQL. @@ -629,8 +1101,8 @@ Configuration of Parquet can be done using the `setConf` method on SQLContext or
    -Spark SQL can automatically infer the schema of a JSON dataset and load it as a SchemaRDD. -This conversion can be done using one of two methods in a SQLContext: +Spark SQL can automatically infer the schema of a JSON dataset and load it as a DataFrame. +This conversion can be done using one of two methods in a `SQLContext`: * `jsonFile` - loads data from a directory of JSON files where each line of the files is a JSON object. * `jsonRDD` - loads data from an existing RDD where each element of the RDD is a string containing a JSON object. @@ -646,7 +1118,7 @@ val sqlContext = new org.apache.spark.sql.SQLContext(sc) // A JSON dataset is pointed to by path. // The path can be either a single text file or a directory storing text files. val path = "examples/src/main/resources/people.json" -// Create a SchemaRDD from the file(s) pointed to by path +// Create a DataFrame from the file(s) pointed to by path val people = sqlContext.jsonFile(path) // The inferred schema can be visualized using the printSchema() method. @@ -655,13 +1127,13 @@ people.printSchema() // |-- age: integer (nullable = true) // |-- name: string (nullable = true) -// Register this SchemaRDD as a table. +// Register this DataFrame as a table. people.registerTempTable("people") // SQL statements can be run by using the sql methods provided by sqlContext. val teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19") -// Alternatively, a SchemaRDD can be created for a JSON dataset represented by +// Alternatively, a DataFrame can be created for a JSON dataset represented by // an RDD[String] storing one JSON object per string. val anotherPeopleRDD = sc.parallelize( """{"name":"Yin","address":{"city":"Columbus","state":"Ohio"}}""" :: Nil) @@ -671,8 +1143,8 @@ val anotherPeople = sqlContext.jsonRDD(anotherPeopleRDD)
    -Spark SQL can automatically infer the schema of a JSON dataset and load it as a JavaSchemaRDD. -This conversion can be done using one of two methods in a JavaSQLContext : +Spark SQL can automatically infer the schema of a JSON dataset and load it as a DataFrame. +This conversion can be done using one of two methods in a `SQLContext` : * `jsonFile` - loads data from a directory of JSON files where each line of the files is a JSON object. * `jsonRDD` - loads data from an existing RDD where each element of the RDD is a string containing a JSON object. @@ -683,13 +1155,13 @@ a regular multi-line JSON file will most often fail. {% highlight java %} // sc is an existing JavaSparkContext. -JavaSQLContext sqlContext = new org.apache.spark.sql.api.java.JavaSQLContext(sc); +SQLContext sqlContext = new org.apache.spark.sql.SQLContext(sc); // A JSON dataset is pointed to by path. // The path can be either a single text file or a directory storing text files. String path = "examples/src/main/resources/people.json"; -// Create a JavaSchemaRDD from the file(s) pointed to by path -JavaSchemaRDD people = sqlContext.jsonFile(path); +// Create a DataFrame from the file(s) pointed to by path +DataFrame people = sqlContext.jsonFile(path); // The inferred schema can be visualized using the printSchema() method. people.printSchema(); @@ -697,24 +1169,24 @@ people.printSchema(); // |-- age: integer (nullable = true) // |-- name: string (nullable = true) -// Register this JavaSchemaRDD as a table. +// Register this DataFrame as a table. people.registerTempTable("people"); // SQL statements can be run by using the sql methods provided by sqlContext. -JavaSchemaRDD teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19"); +DataFrame teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19"); -// Alternatively, a JavaSchemaRDD can be created for a JSON dataset represented by +// Alternatively, a DataFrame can be created for a JSON dataset represented by // an RDD[String] storing one JSON object per string. List jsonData = Arrays.asList( "{\"name\":\"Yin\",\"address\":{\"city\":\"Columbus\",\"state\":\"Ohio\"}}"); JavaRDD anotherPeopleRDD = sc.parallelize(jsonData); -JavaSchemaRDD anotherPeople = sqlContext.jsonRDD(anotherPeopleRDD); +DataFrame anotherPeople = sqlContext.jsonRDD(anotherPeopleRDD); {% endhighlight %}
    -Spark SQL can automatically infer the schema of a JSON dataset and load it as a SchemaRDD. -This conversion can be done using one of two methods in a SQLContext: +Spark SQL can automatically infer the schema of a JSON dataset and load it as a DataFrame. +This conversion can be done using one of two methods in a `SQLContext`: * `jsonFile` - loads data from a directory of JSON files where each line of the files is a JSON object. * `jsonRDD` - loads data from an existing RDD where each element of the RDD is a string containing a JSON object. @@ -731,7 +1203,7 @@ sqlContext = SQLContext(sc) # A JSON dataset is pointed to by path. # The path can be either a single text file or a directory storing text files. path = "examples/src/main/resources/people.json" -# Create a SchemaRDD from the file(s) pointed to by path +# Create a DataFrame from the file(s) pointed to by path people = sqlContext.jsonFile(path) # The inferred schema can be visualized using the printSchema() method. @@ -740,13 +1212,13 @@ people.printSchema() # |-- age: integer (nullable = true) # |-- name: string (nullable = true) -# Register this SchemaRDD as a table. +# Register this DataFrame as a table. people.registerTempTable("people") -# SQL statements can be run by using the sql methods provided by sqlContext. +# SQL statements can be run by using the sql methods provided by `sqlContext`. teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19") -# Alternatively, a SchemaRDD can be created for a JSON dataset represented by +# Alternatively, a DataFrame can be created for a JSON dataset represented by # an RDD[String] storing one JSON object per string. anotherPeopleRDD = sc.parallelize([ '{"name":"Yin","address":{"city":"Columbus","state":"Ohio"}}']) @@ -754,6 +1226,22 @@ anotherPeople = sqlContext.jsonRDD(anotherPeopleRDD) {% endhighlight %}
    +
    + +{% highlight sql %} + +CREATE TEMPORARY TABLE jsonTable +USING org.apache.spark.sql.json +OPTIONS ( + path "examples/src/main/resources/people.json" +) + +SELECT * FROM jsonTable + +{% endhighlight %} + +
    +
    ## Hive Tables @@ -773,7 +1261,7 @@ Configuration of Hive is done by placing your `hive-site.xml` file in `conf/`. When working with Hive one must construct a `HiveContext`, which inherits from `SQLContext`, and adds support for finding tables in the MetaStore and writing queries using HiveQL. Users who do -not have an existing Hive deployment can still create a HiveContext. When not configured by the +not have an existing Hive deployment can still create a `HiveContext`. When not configured by the hive-site.xml, the context automatically creates `metastore_db` and `warehouse` in the current directory. @@ -792,14 +1280,14 @@ sqlContext.sql("FROM src SELECT key, value").collect().foreach(println)
    -When working with Hive one must construct a `JavaHiveContext`, which inherits from `JavaSQLContext`, and +When working with Hive one must construct a `HiveContext`, which inherits from `SQLContext`, and adds support for finding tables in the MetaStore and writing queries using HiveQL. In addition to -the `sql` method a `JavaHiveContext` also provides an `hql` methods, which allows queries to be +the `sql` method a `HiveContext` also provides an `hql` methods, which allows queries to be expressed in HiveQL. {% highlight java %} // sc is an existing JavaSparkContext. -JavaHiveContext sqlContext = new org.apache.spark.sql.hive.api.java.HiveContext(sc); +HiveContext sqlContext = new org.apache.spark.sql.hive.HiveContext(sc); sqlContext.sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)"); sqlContext.sql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src"); @@ -834,6 +1322,124 @@ results = sqlContext.sql("FROM src SELECT key, value").collect()
    +## JDBC To Other Databases + +Spark SQL also includes a data source that can read data from other databases using JDBC. This +functionality should be preferred over using [JdbcRDD](api/scala/index.html#org.apache.spark.rdd.JdbcRDD). +This is because the results are returned +as a DataFrame and they can easily be processed in Spark SQL or joined with other data sources. +The JDBC data source is also easier to use from Java or Python as it does not require the user to +provide a ClassTag. +(Note that this is different than the Spark SQL JDBC server, which allows other applications to +run queries using Spark SQL). + +To get started you will need to include the JDBC driver for you particular database on the +spark classpath. For example, to connect to postgres from the Spark Shell you would run the +following command: + +{% highlight bash %} +SPARK_CLASSPATH=postgresql-9.3-1102-jdbc41.jar bin/spark-shell +{% endhighlight %} + +Tables from the remote database can be loaded as a DataFrame or Spark SQL Temporary table using +the Data Sources API. The following options are supported: + +
    + + + + + + + + + + + + + + + + + + +
    Property NameMeaning
    url + The JDBC URL to connect to. +
    dbtable + The JDBC table that should be read. Note that anything that is valid in a `FROM` clause of + a SQL query can be used. For example, instead of a full table you could also use a + subquery in parentheses. +
    driver + The class name of the JDBC driver needed to connect to this URL. This class with be loaded + on the master and workers before running an JDBC commands to allow the driver to + register itself with the JDBC subsystem. +
    partitionColumn, lowerBound, upperBound, numPartitions + These options must all be specified if any of them is specified. They describe how to + partition the table when reading in parallel from multiple workers. + partitionColumn must be a numeric column from the table in question. Notice + that lowerBound and upperBound are just used to decide the + partition stride, not for filtering the rows in table. So all rows in the table will be + partitioned and returned. +
    + +
    + +
    + +{% highlight scala %} +val jdbcDF = sqlContext.load("jdbc", Map( + "url" -> "jdbc:postgresql:dbserver", + "dbtable" -> "schema.tablename")) +{% endhighlight %} + +
    + +
    + +{% highlight java %} + +Map options = new HashMap(); +options.put("url", "jdbc:postgresql:dbserver"); +options.put("dbtable", "schema.tablename"); + +DataFrame jdbcDF = sqlContext.load("jdbc", options) +{% endhighlight %} + + +
    + +
    + +{% highlight python %} + +df = sqlContext.load(source="jdbc", url="jdbc:postgresql:dbserver", dbtable="schema.tablename") + +{% endhighlight %} + +
    + +
    + +{% highlight sql %} + +CREATE TEMPORARY TABLE jdbcTable +USING org.apache.spark.sql.jdbc +OPTIONS ( + url "jdbc:postgresql:dbserver", + dbtable "schema.tablename" +) + +{% endhighlight %} + +
    +
    + +## Troubleshooting + + * The JDBC driver class must be visible to the primordial class loader on the client session and on all executors. This is because Java's DriverManager class does a security check that results in it ignoring all drivers not visible to the primordial class loader when one goes to open a connection. One convenient way to do this is to modify compute_classpath.sh on all worker nodes to include your driver JARs. + * Some databases, such as H2, convert all names to upper case. You'll need to use upper case to refer to those names in Spark SQL. + + # Performance Tuning For some workloads it is possible to improve performance by either caching data in memory, or by @@ -841,11 +1447,11 @@ turning on some experimental options. ## Caching Data In Memory -Spark SQL can cache tables using an in-memory columnar format by calling `sqlContext.cacheTable("tableName")` or `schemaRDD.cache()`. +Spark SQL can cache tables using an in-memory columnar format by calling `sqlContext.cacheTable("tableName")` or `dataFrame.cache()`. Then Spark SQL will scan only required columns and will automatically tune compression to minimize memory usage and GC pressure. You can call `sqlContext.uncacheTable("tableName")` to remove the table from memory. -Configuration of in-memory caching can be done using the `setConf` method on SQLContext or by running +Configuration of in-memory caching can be done using the `setConf` method on `SQLContext` or by running `SET key=value` commands using SQL. @@ -904,15 +1510,14 @@ that these options will be deprecated in future release as more optimizations ar
    -# Other SQL Interfaces +# Distributed SQL Engine -Spark SQL also supports interfaces for running SQL queries directly without the need to write any -code. +Spark SQL can also act as a distributed query engine using its JDBC/ODBC or command-line interface. In this mode, end-users or applications can interact with Spark SQL directly to run SQL queries, without the need to write any code. ## Running the Thrift JDBC/ODBC server The Thrift JDBC/ODBC server implemented here corresponds to the [`HiveServer2`](https://cwiki.apache.org/confluence/display/Hive/Setting+Up+HiveServer2) -in Hive 0.12. You can test the JDBC server with the beeline script that comes with either Spark or Hive 0.12. +in Hive 0.13. You can test the JDBC server with the beeline script that comes with either Spark or Hive 0.13. To start the JDBC/ODBC server, run the following in the Spark directory: @@ -957,10 +1562,10 @@ Configuration of Hive is done by placing your `hive-site.xml` file in `conf/`. You may also use the beeline script that comes with Hive. -Thrift JDBC server also supports sending thrift RPC messages over HTTP transport. -Use the following setting to enable HTTP mode as system property or in `hive-site.xml` file in `conf/`: +Thrift JDBC server also supports sending thrift RPC messages over HTTP transport. +Use the following setting to enable HTTP mode as system property or in `hive-site.xml` file in `conf/`: - hive.server2.transport.mode - Set this to value: http + hive.server2.transport.mode - Set this to value: http hive.server2.thrift.http.port - HTTP port number fo listen on; default is 10001 hive.server2.http.endpoint - HTTP endpoint; default is cliservice @@ -982,7 +1587,88 @@ Configuration of Hive is done by placing your `hive-site.xml` file in `conf/`. You may run `./bin/spark-sql --help` for a complete list of all available options. -# Compatibility with Other Systems +# Migration Guide + +## Upgrading from Spark SQL 1.0-1.2 to 1.3 + +In Spark 1.3 we removed the "Alpha" label from Spark SQL and as part of this did a cleanup of the +available APIs. From Spark 1.3 onwards, Spark SQL will provide binary compatibility with other +releases in the 1.X series. This compatibility guarantee excludes APIs that are explicitly marked +as unstable (i.e., DeveloperAPI or Experimental). + +#### Rename of SchemaRDD to DataFrame + +The largest change that users will notice when upgrading to Spark SQL 1.3 is that `SchemaRDD` has +been renamed to `DataFrame`. This is primarily because DataFrames no longer inherit from RDD +directly, but instead provide most of the functionality that RDDs provide though their own +implementation. DataFrames can still be converted to RDDs by calling the `.rdd` method. + +In Scala there is a type alias from `SchemaRDD` to `DataFrame` to provide source compatibility for +some use cases. It is still recommended that users update their code to use `DataFrame` instead. +Java and Python users will need to update their code. + +#### Unification of the Java and Scala APIs + +Prior to Spark 1.3 there were separate Java compatible classes (`JavaSQLContext` and `JavaSchemaRDD`) +that mirrored the Scala API. In Spark 1.3 the Java API and Scala API have been unified. Users +of either language should use `SQLContext` and `DataFrame`. In general theses classes try to +use types that are usable from both languages (i.e. `Array` instead of language specific collections). +In some cases where no common type exists (e.g., for passing in closures or Maps) function overloading +is used instead. + +Additionally the Java specific types API has been removed. Users of both Scala and Java should +use the classes present in `org.apache.spark.sql.types` to describe schema programmatically. + + +#### Isolation of Implicit Conversions and Removal of dsl Package (Scala-only) + +Many of the code examples prior to Spark 1.3 started with `import sqlContext._`, which brought +all of the functions from sqlContext into scope. In Spark 1.3 we have isolated the implicit +conversions for converting `RDD`s into `DataFrame`s into an object inside of the `SQLContext`. +Users should now write `import sqlContext.implicits._`. + +Additionally, the implicit conversions now only augment RDDs that are composed of `Product`s (i.e., +case classes or tuples) with a method `toDF`, instead of applying automatically. + +When using function inside of the DSL (now replaced with the `DataFrame` API) users used to import +`org.apache.spark.sql.catalyst.dsl`. Instead the public dataframe functions API should be used: +`import org.apache.spark.sql.functions._`. + +#### Removal of the type aliases in org.apache.spark.sql for DataType (Scala-only) + +Spark 1.3 removes the type aliases that were present in the base sql package for `DataType`. Users +should instead import the classes in `org.apache.spark.sql.types` + +#### UDF Registration Moved to `sqlContext.udf` (Java & Scala) + +Functions that are used to register UDFs, either for use in the DataFrame DSL or SQL, have been +moved into the udf object in `SQLContext`. + +
    +
    +{% highlight java %} + +sqlContext.udf.register("strLen", (s: String) => s.length()) + +{% endhighlight %} +
    + +
    +{% highlight java %} + +sqlContext.udf().register("strLen", (String s) -> { s.length(); }); + +{% endhighlight %} +
    + +
    + +Python UDF registration is unchanged. + +#### Python DataTypes No Longer Singletons + +When using DataTypes in Python you will need to construct them (i.e. `StringType()`) instead of +referencing a singleton. ## Migration Guide for Shark User @@ -1102,15 +1788,11 @@ in Hive deployments. * Tables with buckets: bucket is the hash partitioning within a Hive table partition. Spark SQL doesn't support buckets yet. + **Esoteric Hive Features** -* Tables with partitions using different input formats: In Spark SQL, all table partitions need to - have the same input format. -* Non-equi outer join: For the uncommon use case of using outer joins with non-equi join conditions - (e.g. condition "`key < 10`"), Spark SQL will output wrong result for the `NULL` tuple. * `UNION` type * Unique join -* Single query multi insert * Column statistics collecting: Spark SQL does not piggyback scans to collect column statistics at the moment and only supports populating the sizeInBytes field of the hive metastore. @@ -1126,9 +1808,6 @@ less important due to Spark SQL's in-memory computational model. Others are slot releases of Spark SQL. * Block level bitmap indexes and virtual columns (used to build indexes) -* Automatically convert a join to map join: For joining a large table with multiple small tables, - Hive automatically converts the join into a map join. We are adding this auto conversion in the - next release. * Automatically determine the number of reducers for joins and groupbys: Currently in Spark SQL, you need to control the degree of parallelism post-shuffle using "`SET spark.sql.shuffle.partitions=[num_tasks];`". * Meta-data only query: For queries that can be answered by using only meta data, Spark SQL still @@ -1139,33 +1818,10 @@ releases of Spark SQL. Hive can optionally merge the small files into fewer large files to avoid overflowing the HDFS metadata. Spark SQL does not support that. -# Writing Language-Integrated Relational Queries -**Language-Integrated queries are experimental and currently only supported in Scala.** +# Data Types -Spark SQL also supports a domain specific language for writing queries. Once again, -using the data from the above examples: - -{% highlight scala %} -// sc is an existing SparkContext. -val sqlContext = new org.apache.spark.sql.SQLContext(sc) -// Importing the SQL context gives access to all the public SQL functions and implicit conversions. -import sqlContext._ -val people: RDD[Person] = ... // An RDD of case class objects, from the first example. - -// The following is the same as 'SELECT name FROM people WHERE age >= 10 AND age <= 19' -val teenagers = people.where('age >= 10).where('age <= 19).select('name) -teenagers.map(t => "Name: " + t(0)).collect().foreach(println) -{% endhighlight %} - -The DSL uses Scala symbols to represent columns in the underlying table, which are identifiers -prefixed with a tick (`'`). Implicit conversions turn these symbols into expressions that are -evaluated by the SQL execution engine. A full list of the functions supported can be found in the -[ScalaDoc](api/scala/index.html#org.apache.spark.sql.SchemaRDD). - - - -# Spark SQL DataType Reference +Spark SQL and DataFrames support the following data types: * Numeric types - `ByteType`: Represents 1-byte signed integer numbers. @@ -1208,10 +1864,10 @@ evaluated by the SQL execution engine. A full list of the functions supported c
    -All data types of Spark SQL are located in the package `org.apache.spark.sql`. +All data types of Spark SQL are located in the package `org.apache.spark.sql.types`. You can access them by doing {% highlight scala %} -import org.apache.spark.sql._ +import org.apache.spark.sql.types._ {% endhighlight %} @@ -1263,7 +1919,7 @@ import org.apache.spark.sql._ - + @@ -1457,7 +2113,7 @@ please use factory methods provided in - +
    DecimalType scala.math.BigDecimal java.math.BigDecimal DecimalType
    StructType org.apache.spark.sql.api.java.Row org.apache.spark.sql.Row DataTypes.createStructType(fields)
    Note: fields is a List or an array of StructFields. @@ -1478,10 +2134,10 @@ please use factory methods provided in
    -All data types of Spark SQL are located in the package of `pyspark.sql`. +All data types of Spark SQL are located in the package of `pyspark.sql.types`. You can access them by doing {% highlight python %} -from pyspark.sql import * +from pyspark.sql.types import * {% endhighlight %} diff --git a/docs/streaming-flume-integration.md b/docs/streaming-flume-integration.md index ac01dd3d8019a..c8ab146bcae0a 100644 --- a/docs/streaming-flume-integration.md +++ b/docs/streaming-flume-integration.md @@ -5,6 +5,8 @@ title: Spark Streaming + Flume Integration Guide [Apache Flume](https://flume.apache.org/) is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. Here we explain how to configure Flume and Spark Streaming to receive data from Flume. There are two approaches to this. +Python API Flume is not yet available in the Python API. + ## Approach 1: Flume-style Push-based Approach Flume is designed to push data between Flume agents. In this approach, Spark Streaming essentially sets up a receiver that acts an Avro agent for Flume, to which Flume can push the data. Here are the configuration steps. @@ -64,7 +66,7 @@ configuring Flume agents. 3. **Deploying:** Package `spark-streaming-flume_{{site.SCALA_BINARY_VERSION}}` and its dependencies (except `spark-core_{{site.SCALA_BINARY_VERSION}}` and `spark-streaming_{{site.SCALA_BINARY_VERSION}}` which are provided by `spark-submit`) into the application JAR. Then use `spark-submit` to launch your application (see [Deploying section](streaming-programming-guide.html#deploying-applications) in the main programming guide). -## Approach 2 (Experimental): Pull-based Approach using a Custom Sink +## Approach 2: Pull-based Approach using a Custom Sink Instead of Flume pushing data directly to Spark Streaming, this approach runs a custom Flume sink that allows the following. - Flume pushes data into the sink, and the data stays buffered. diff --git a/docs/streaming-kafka-integration.md b/docs/streaming-kafka-integration.md index 77c0abbbacbd0..64714f0b799fc 100644 --- a/docs/streaming-kafka-integration.md +++ b/docs/streaming-kafka-integration.md @@ -2,58 +2,155 @@ layout: global title: Spark Streaming + Kafka Integration Guide --- -[Apache Kafka](http://kafka.apache.org/) is publish-subscribe messaging rethought as a distributed, partitioned, replicated commit log service. Here we explain how to configure Spark Streaming to receive data from Kafka. +[Apache Kafka](http://kafka.apache.org/) is publish-subscribe messaging rethought as a distributed, partitioned, replicated commit log service. Here we explain how to configure Spark Streaming to receive data from Kafka. There are two approaches to this - the old approach using Receivers and Kafka's high-level API, and a new experimental approach (introduced in Spark 1.3) without using Receivers. They have different programming models, performance characteristics, and semantics guarantees, so read on for more details. -1. **Linking:** In your SBT/Maven project definition, link your streaming application against the following artifact (see [Linking section](streaming-programming-guide.html#linking) in the main programming guide for further information). +## Approach 1: Receiver-based Approach +This approach uses a Receiver to receive the data. The Received is implemented using the Kafka high-level consumer API. As with all receivers, the data received from Kafka through a Receiver is stored in Spark executors, and then jobs launched by Spark Streaming processes the data. + +However, under default configuration, this approach can lose data under failures (see [receiver reliability](streaming-programming-guide.html#receiver-reliability). To ensure zero-data loss, you have to additionally enable Write Ahead Logs in Spark Streaming. To ensure zero data loss, enable the Write Ahead Logs (introduced in Spark 1.2). This synchronously saves all the received Kafka data into write ahead logs on a distributed file system (e.g HDFS), so that all the data can be recovered on failure. See [Deploying section](streaming-programming-guide.html#deploying-applications) in the streaming programming guide for more details on Write Ahead Logs. + +Next, we discuss how to use this approach in your streaming application. + +1. **Linking:** For Scala/Java applications using SBT/Maven project definitions, link your streaming application with the following artifact (see [Linking section](streaming-programming-guide.html#linking) in the main programming guide for further information). groupId = org.apache.spark artifactId = spark-streaming-kafka_{{site.SCALA_BINARY_VERSION}} version = {{site.SPARK_VERSION_SHORT}} -2. **Programming:** In the streaming application code, import `KafkaUtils` and create input DStream as follows. + For Python applications, you will have to add this above library and its dependencies when deploying your application. See the *Deploying* subsection below. + +2. **Programming:** In the streaming application code, import `KafkaUtils` and create an input DStream as follows.
    import org.apache.spark.streaming.kafka._ - val kafkaStream = KafkaUtils.createStream( - streamingContext, [zookeeperQuorum], [group id of the consumer], [per-topic number of Kafka partitions to consume]) + val kafkaStream = KafkaUtils.createStream(streamingContext, + [ZK quorum], [consumer group id], [per-topic number of Kafka partitions to consume]) - See the [API docs](api/scala/index.html#org.apache.spark.streaming.kafka.KafkaUtils$) + You can also specify the key and value classes and their corresponding decoder classes using variations of `createStream`. See the [API docs](api/scala/index.html#org.apache.spark.streaming.kafka.KafkaUtils$) and the [example]({{site.SPARK_GITHUB_URL}}/blob/master/examples/scala-2.10/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala).
    import org.apache.spark.streaming.kafka.*; - JavaPairReceiverInputDStream kafkaStream = KafkaUtils.createStream( - streamingContext, [zookeeperQuorum], [group id of the consumer], [per-topic number of Kafka partitions to consume]); + JavaPairReceiverInputDStream kafkaStream = + KafkaUtils.createStream(streamingContext, + [ZK quorum], [consumer group id], [per-topic number of Kafka partitions to consume]); - See the [API docs](api/java/index.html?org/apache/spark/streaming/kafka/KafkaUtils.html) + You can also specify the key and value classes and their corresponding decoder classes using variations of `createStream`. See the [API docs](api/java/index.html?org/apache/spark/streaming/kafka/KafkaUtils.html) and the [example]({{site.SPARK_GITHUB_URL}}/blob/master/examples/scala-2.10/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java). + +
    +
    + from pyspark.streaming.kafka import KafkaUtils + + kafkaStream = KafkaUtils.createStream(streamingContext, \ + [ZK quorum], [consumer group id], [per-topic number of Kafka partitions to consume]) + + By default, the Python API will decode Kafka data as UTF8 encoded strings. You can specify your custom decoding function to decode the byte arrays in Kafka records to any arbitrary data type. See the [API docs](api/python/pyspark.streaming.html#pyspark.streaming.kafka.KafkaUtils) + and the [example]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/python/streaming/kafka_wordcount.py).
    - *Points to remember:* + **Points to remember:** - Topic partitions in Kafka does not correlate to partitions of RDDs generated in Spark Streaming. So increasing the number of topic-specific partitions in the `KafkaUtils.createStream()` only increases the number of threads using which topics that are consumed within a single receiver. It does not increase the parallelism of Spark in processing the data. Refer to the main document for more information on that. - Multiple Kafka input DStreams can be created with different groups and topics for parallel receiving of data using multiple receivers. -3. **Deploying:** Package `spark-streaming-kafka_{{site.SCALA_BINARY_VERSION}}` and its dependencies (except `spark-core_{{site.SCALA_BINARY_VERSION}}` and `spark-streaming_{{site.SCALA_BINARY_VERSION}}` which are provided by `spark-submit`) into the application JAR. Then use `spark-submit` to launch your application (see [Deploying section](streaming-programming-guide.html#deploying-applications) in the main programming guide). - -Note that the Kafka receiver used by default is an -[*unreliable* receiver](streaming-programming-guide.html#receiver-reliability) section in the -programming guide). In Spark 1.2, we have added an experimental *reliable* Kafka receiver that -provides stronger -[fault-tolerance guarantees](streaming-programming-guide.html#fault-tolerance-semantics) of zero -data loss on failures. This receiver is automatically used when the write ahead log -(also introduced in Spark 1.2) is enabled -(see [Deployment](#deploying-applications.html) section in the programming guide). This -may reduce the receiving throughput of individual Kafka receivers compared to the unreliable -receivers, but this can be corrected by running -[more receivers in parallel](streaming-programming-guide.html#level-of-parallelism-in-data-receiving) -to increase aggregate throughput. Additionally, it is recommended that the replication of the -received data within Spark be disabled when the write ahead log is enabled as the log is already stored -in a replicated storage system. This can be done by setting the storage level for the input -stream to `StorageLevel.MEMORY_AND_DISK_SER` (that is, use + - If you have enabled Write Ahead Logs with a replicated file system like HDFS, the received data is already being replicated in the log. Hence, the storage level in storage level for the input stream to `StorageLevel.MEMORY_AND_DISK_SER` (that is, use `KafkaUtils.createStream(..., StorageLevel.MEMORY_AND_DISK_SER)`). + +3. **Deploying:** As with any Spark applications, `spark-submit` is used to launch your application. However, the details are slightly different for Scala/Java applications and Python applications. + + For Scala and Java applications, if you are using SBT or Maven for project management, then package `spark-streaming-kafka_{{site.SCALA_BINARY_VERSION}}` and its dependencies into the application JAR. Make sure `spark-core_{{site.SCALA_BINARY_VERSION}}` and `spark-streaming_{{site.SCALA_BINARY_VERSION}}` are marked as `provided` dependencies as those are already present in a Spark installation. Then use `spark-submit` to launch your application (see [Deploying section](streaming-programming-guide.html#deploying-applications) in the main programming guide). + + For Python applications which lack SBT/Maven project management, `spark-streaming-kafka_{{site.SCALA_BINARY_VERSION}}` and its dependencies can be directly added to `spark-submit` using `--packages` (see [Application Submission Guide](submitting-applications.html)). That is, + + ./bin/spark-submit --packages org.apache.spark:spark-streaming-kafka_{{site.SCALA_BINARY_VERSION}}:{{site.SPARK_VERSION_SHORT}} ... + + Alternatively, you can also download the JAR of the Maven artifact `spark-streaming-kafka-assembly` from the + [Maven repository](http://search.maven.org/#search|ga|1|a%3A%22spark-streaming-kafka-assembly_2.10%22%20AND%20v%3A%22{{site.SPARK_VERSION_SHORT}}%22) and add it to `spark-submit` with `--jars`. + +## Approach 2: Direct Approach (No Receivers) +This is a new receiver-less "direct" approach has been introduced in Spark 1.3 to ensure stronger end-to-end guarantees. Instead of using receivers to receive data, this approach periodically queries Kafka for the latest offsets in each topic+partition, and accordingly defines the offset ranges to process in each batch. When the jobs to process the data are launched, Kafka's simple consumer API is used to read the defined ranges of offsets from Kafka (similar to read files from a file system). Note that this is an experimental feature in Spark 1.3 and is only available in the Scala and Java API. + +This approach has the following advantages over the received-based approach (i.e. Approach 1). + +- *Simplified Parallelism:* No need to create multiple input Kafka streams and union-ing them. With `directStream`, Spark Streaming will create as many RDD partitions as there is Kafka partitions to consume, which will all read data from Kafka in parallel. So there is one-to-one mapping between Kafka and RDD partitions, which is easier to understand and tune. + +- *Efficiency:* Achieving zero-data loss in the first approach required the data to be stored in a Write Ahead Log, which further replicated the data. This is actually inefficient as the data effectively gets replicated twice - once by Kafka, and a second time by the Write Ahead Log. This second approach eliminate the problem as there is no receiver, and hence no need for Write Ahead Logs. + +- *Exactly-once semantics:* The first approach uses Kafka's high level API to store consumed offsets in Zookeeper. This is traditionally the way to consume data from Kafka. While this approach (in combination with write ahead logs) can ensure zero data loss (i.e. at-least once semantics), there is a small chance some records may get consumed twice under some failures. This occurs because of inconsistencies between data reliably received by Spark Streaming and offsets tracked by Zookeeper. Hence, in this second approach, we use simple Kafka API that does not use Zookeeper and offsets tracked only by Spark Streaming within its checkpoints. This eliminates inconsistencies between Spark Streaming and Zookeeper/Kafka, and so each record is received by Spark Streaming effectively exactly once despite failures. + +Note that one disadvantage of this approach is that it does not update offsets in Zookeeper, hence Zookeeper-based Kafka monitoring tools will not show progress. However, you can access the offsets processed by this approach in each batch and update Zookeeper yourself (see below). + +Next, we discuss how to use this approach in your streaming application. + +1. **Linking:** This approach is supported only in Scala/Java application. Link your SBT/Maven project with the following artifact (see [Linking section](streaming-programming-guide.html#linking) in the main programming guide for further information). + + groupId = org.apache.spark + artifactId = spark-streaming-kafka_{{site.SCALA_BINARY_VERSION}} + version = {{site.SPARK_VERSION_SHORT}} + +2. **Programming:** In the streaming application code, import `KafkaUtils` and create an input DStream as follows. + +
    +
    + import org.apache.spark.streaming.kafka._ + + val directKafkaStream = KafkaUtils.createDirectStream[ + [key class], [value class], [key decoder class], [value decoder class] ]( + streamingContext, [map of Kafka parameters], [set of topics to consume]) + + See the [API docs](api/scala/index.html#org.apache.spark.streaming.kafka.KafkaUtils$) + and the [example]({{site.SPARK_GITHUB_URL}}/blob/master/examples/scala-2.10/src/main/scala/org/apache/spark/examples/streaming/DirectKafkaWordCount.scala). +
    +
    + import org.apache.spark.streaming.kafka.*; + + JavaPairReceiverInputDStream directKafkaStream = + KafkaUtils.createDirectStream(streamingContext, + [key class], [value class], [key decoder class], [value decoder class], + [map of Kafka parameters], [set of topics to consume]); + + See the [API docs](api/java/index.html?org/apache/spark/streaming/kafka/KafkaUtils.html) + and the [example]({{site.SPARK_GITHUB_URL}}/blob/master/examples/scala-2.10/src/main/java/org/apache/spark/examples/streaming/JavaDirectKafkaWordCount.java). + +
    +
    + + In the Kafka parameters, you must specify either `metadata.broker.list` or `bootstrap.servers`. + By default, it will start consuming from the latest offset of each Kafka partition. If you set configuration `auto.offset.reset` in Kafka parameters to `smallest`, then it will start consuming from the smallest offset. + + You can also start consuming from any arbitrary offset using other variations of `KafkaUtils.createDirectStream`. Furthermore, if you want to access the Kafka offsets consumed in each batch, you can do the following. + +
    +
    + directKafkaStream.foreachRDD { rdd => + val offsetRanges = rdd.asInstanceOf[HasOffsetRanges] + // offsetRanges.length = # of Kafka partitions being consumed + ... + } +
    +
    + directKafkaStream.foreachRDD( + new Function, Void>() { + @Override + public Void call(JavaPairRDD rdd) throws IOException { + OffsetRange[] offsetRanges = ((HasOffsetRanges)rdd).offsetRanges + // offsetRanges.length = # of Kafka partitions being consumed + ... + return null; + } + } + ); +
    +
    + + You can use this to update Zookeeper yourself if you want Zookeeper-based Kafka monitoring tools to show progress of the streaming application. + + Another thing to note is that since this approach does not use Receivers, the standard receiver-related (that is, [configurations](configuration.html) of the form `spark.streaming.receiver.*` ) will not apply to the input DStreams created by this approach (will apply to other input DStreams though). Instead, use the [configurations](configuration.html) `spark.streaming.kafka.*`. An important one is `spark.streaming.kafka.maxRatePerPartition` which is the maximum rate at which each Kafka partition will be read by this direct API. + +3. **Deploying:** Similar to the first approach, you can package `spark-streaming-kafka_{{site.SCALA_BINARY_VERSION}}` and its dependencies into the application JAR and the launch the application using `spark-submit`. Make sure `spark-core_{{site.SCALA_BINARY_VERSION}}` and `spark-streaming_{{site.SCALA_BINARY_VERSION}}` are marked as `provided` dependencies as those are already present in a Spark installation. \ No newline at end of file diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md index 96fb12ce5e0b9..262512a639046 100644 --- a/docs/streaming-programming-guide.md +++ b/docs/streaming-programming-guide.md @@ -189,15 +189,15 @@ Next, we want to count these words. {% highlight java %} // Count each word in each batch -JavaPairDStream pairs = words.map( +JavaPairDStream pairs = words.mapToPair( new PairFunction() { - @Override public Tuple2 call(String s) throws Exception { + @Override public Tuple2 call(String s) { return new Tuple2(s, 1); } }); JavaPairDStream wordCounts = pairs.reduceByKey( new Function2() { - @Override public Integer call(Integer i1, Integer i2) throws Exception { + @Override public Integer call(Integer i1, Integer i2) { return i1 + i2; } }); @@ -432,7 +432,7 @@ some of the common ones are as follows.
    For an up-to-date list, please refer to the -[Apache repository](http://search.maven.org/#search%7Cga%7C1%7Cg%3A%22org.apache.spark%22%20AND%20v%3A%22{{site.SPARK_VERSION_SHORT}}%22) +[Maven repository](http://search.maven.org/#search%7Cga%7C1%7Cg%3A%22org.apache.spark%22%20AND%20v%3A%22{{site.SPARK_VERSION_SHORT}}%22) for the full list of supported sources and artifacts. *** @@ -662,8 +662,7 @@ methods for creating DStreams from files and Akka actors as input sources. For simple text files, there is an easier method `streamingContext.textFileStream(dataDirectory)`. And file streams do not require running a receiver, hence does not require allocating cores. - Python API As of Spark 1.2, - `fileStream` is not available in the Python API, only `textFileStream` is available. + Python API `fileStream` is not available in the Python API, only `textFileStream` is available. - **Streams based on Custom Actors:** DStreams can be created with data streams received through Akka actors by using `streamingContext.actorStream(actorProps, actor-name)`. See the [Custom Receiver @@ -682,8 +681,9 @@ for Java, and [StreamingContext](api/python/pyspark.streaming.html#pyspark.strea ### Advanced Sources {:.no_toc} -Python API As of Spark 1.2, -these sources are not available in the Python API. + +Python API As of Spark 1.3, +out of these sources, *only* Kafka is available in the Python API. We will add more advanced sources in the Python API in future. This category of sources require interfacing with external non-Spark libraries, some of them with complex dependencies (e.g., Kafka and Flume). Hence, to minimize issues related to version conflicts @@ -704,7 +704,7 @@ create a DStream using data from Twitter's stream of tweets, you have to do the {% highlight scala %} import org.apache.spark.streaming.twitter._ -TwitterUtils.createStream(ssc) +TwitterUtils.createStream(ssc, None) {% endhighlight %}
    @@ -723,6 +723,12 @@ and it in the classpath. Some of these advanced sources are as follows. +- **Kafka:** Spark Streaming {{site.SPARK_VERSION_SHORT}} is compatible with Kafka 0.8.1.1. See the [Kafka Integration Guide](streaming-kafka-integration.html) for more details. + +- **Flume:** Spark Streaming {{site.SPARK_VERSION_SHORT}} is compatible with Flume 1.4.0. See the [Flume Integration Guide](streaming-flume-integration.html) for more details. + +- **Kinesis:** See the [Kinesis Integration Guide](streaming-kinesis-integration.html) for more details. + - **Twitter:** Spark Streaming's TwitterUtils uses Twitter4j 3.0.3 to get the public stream of tweets using [Twitter's Streaming API](https://dev.twitter.com/docs/streaming-apis). Authentication information can be provided by any of the [methods](http://twitter4j.org/en/configuration.html) supported by @@ -732,17 +738,10 @@ Some of these advanced sources are as follows. ([TwitterPopularTags]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterPopularTags.scala) and [TwitterAlgebirdCMS]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdCMS.scala)). -- **Flume:** Spark Streaming {{site.SPARK_VERSION_SHORT}} can received data from Flume 1.4.0. See the [Flume Integration Guide](streaming-flume-integration.html) for more details. - -- **Kafka:** Spark Streaming {{site.SPARK_VERSION_SHORT}} can receive data from Kafka 0.8.0. See the [Kafka Integration Guide](streaming-kafka-integration.html) for more details. - -- **Kinesis:** See the [Kinesis Integration Guide](streaming-kinesis-integration.html) for more details. - ### Custom Sources {:.no_toc} -Python API As of Spark 1.2, -these sources are not available in the Python API. +Python API This is not yet supported in Python. Input DStreams can also be created out of custom data sources. All you have to do is implement an user-defined **receiver** (see next section to understand what that is) that can receive data from @@ -846,7 +845,7 @@ Some of the common ones are as follows.
    -The last two transformations are worth highlighting again. +A few of these transformations are worth discussing in more detail. #### UpdateStateByKey Operation {:.no_toc} @@ -878,6 +877,12 @@ This is applied on a DStream containing words (say, the `pairs` DStream containi val runningCounts = pairs.updateStateByKey[Int](updateFunction _) {% endhighlight %} +The update function will be called for each word, with `newValues` having a sequence of 1's (from +the `(word, 1)` pairs) and the `runningCount` having the previous count. For the complete +Scala code, take a look at the example +[StatefulNetworkWordCount.scala]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/scala/org/apache +/spark/examples/streaming/StatefulNetworkWordCount.scala). +
    @@ -899,6 +904,12 @@ This is applied on a DStream containing words (say, the `pairs` DStream containi JavaPairDStream runningCounts = pairs.updateStateByKey(updateFunction); {% endhighlight %} +The update function will be called for each word, with `newValues` having a sequence of 1's (from +the `(word, 1)` pairs) and the `runningCount` having the previous count. For the complete +Java code, take a look at the example +[JavaStatefulNetworkWordCount.java]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/java/org/apache/spark/examples/streaming +/JavaStatefulNetworkWordCount.java). +
    @@ -916,14 +927,14 @@ This is applied on a DStream containing words (say, the `pairs` DStream containi runningCounts = pairs.updateStateByKey(updateFunction) {% endhighlight %} -
    -
    - The update function will be called for each word, with `newValues` having a sequence of 1's (from the `(word, 1)` pairs) and the `runningCount` having the previous count. For the complete -Scala code, take a look at the example +Python code, take a look at the example [stateful_network_wordcount.py]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/python/streaming/stateful_network_wordcount.py). +
    +
    + Note that using `updateStateByKey` requires the checkpoint directory to be configured, which is discussed in detail in the [checkpointing](#checkpointing) section. @@ -985,7 +996,7 @@ In fact, you can also use [machine learning](mllib-guide.html) and #### Window Operations {:.no_toc} -Finally, Spark Streaming also provides *windowed computations*, which allow you to apply +Spark Streaming also provides *windowed computations*, which allow you to apply transformations over a sliding window of data. This following figure illustrates this sliding window. @@ -1029,7 +1040,7 @@ val windowedWordCounts = pairs.reduceByKeyAndWindow((a:Int,b:Int) => (a + b), Se {% highlight java %} // Reduce function adding two integers, defined separately for clarity Function2 reduceFunc = new Function2() { - @Override public Integer call(Integer i1, Integer i2) throws Exception { + @Override public Integer call(Integer i1, Integer i2) { return i1 + i2; } }; @@ -1108,6 +1119,100 @@ said two parameters - windowLength and slideInterval. +#### Join Operations +{:.no_toc} +Finally, its worth highlighting how easily you can perform different kinds of joins in Spark Streaming. + + +##### Stream-stream joins +{:.no_toc} +Streams can be very easily joined with other streams. + +
    +
    +{% highlight scala %} +val stream1: DStream[String, String] = ... +val stream2: DStream[String, String] = ... +val joinedStream = stream1.join(stream2) +{% endhighlight %} +
    +
    +{% highlight java %} +JavaPairDStream stream1 = ... +JavaPairDStream stream2 = ... +JavaPairDStream joinedStream = stream1.join(stream2); +{% endhighlight %} +
    +
    +{% highlight python %} +stream1 = ... +stream2 = ... +joinedStream = stream1.join(stream2) +{% endhighlight %} +
    +
    +Here, in each batch interval, the RDD generated by `stream1` will be joined with the RDD generated by `stream2`. You can also do `leftOuterJoin`, `rightOuterJoin`, `fullOuterJoin`. Furthermore, it is often very useful to do joins over windows of the streams. That is pretty easy as well. + +
    +
    +{% highlight scala %} +val windowedStream1 = stream1.window(Seconds(20)) +val windowedStream2 = stream2.window(Minutes(1)) +val joinedStream = windowedStream1.join(windowedStream2) +{% endhighlight %} +
    +
    +{% highlight java %} +JavaPairDStream windowedStream1 = stream1.window(Durations.seconds(20)); +JavaPairDStream windowedStream2 = stream2.window(Durations.minutes(1)); +JavaPairDStream joinedStream = windowedStream1.join(windowedStream2); +{% endhighlight %} +
    +
    +{% highlight python %} +windowedStream1 = stream1.window(20) +windowedStream2 = stream2.window(60) +joinedStream = windowedStream1.join(windowedStream2) +{% endhighlight %} +
    +
    + +##### Stream-dataset joins +{:.no_toc} +This has already been shown earlier while explain `DStream.transform` operation. Here is yet another example of joining a windowed stream with a dataset. + +
    +
    +{% highlight scala %} +val dataset: RDD[String, String] = ... +val windowedStream = stream.window(Seconds(20))... +val joinedStream = windowedStream.transform { rdd => rdd.join(dataset) } +{% endhighlight %} +
    +
    +{% highlight java %} +JavaPairRDD dataset = ... +JavaPairDStream windowedStream = stream.window(Durations.seconds(20)); +JavaPairDStream joinedStream = windowedStream.transform( + new Function>, JavaRDD>>() { + @Override + public JavaRDD> call(JavaRDD> rdd) { + return rdd.join(dataset); + } + } +); +{% endhighlight %} +
    +
    +{% highlight python %} +dataset = ... # some RDD +windowedStream = stream.window(20) +joinedStream = windowedStream.transform(lambda rdd: rdd.join(dataset)) +{% endhighlight %} +
    +
    + +In fact, you can also dynamically change the dataset you want to join against. The function provided to `transform` is evaluated every batch interval and therefore will use the current dataset that `dataset` reference points to. The complete list of DStream transformations is available in the API documentation. For the Scala API, see [DStream](api/scala/index.html#org.apache.spark.streaming.dstream.DStream) @@ -1315,6 +1420,178 @@ Note that the connections in the pool should be lazily created on demand and tim *** +## DataFrame and SQL Operations +You can easily use [DataFrames and SQL](sql-programming-guide.html) operations on streaming data. You have to create a SQLContext using the SparkContext that the StreamingContext is using. Furthermore this has to done such that it can be restarted on driver failures. This is done by creating a lazily instantiated singleton instance of SQLContext. This is shown in the following example. It modifies the earlier [word count example](#a-quick-example) to generate word counts using DataFrames and SQL. Each RDD is converted to a DataFrame, registered as a temporary table and then queried using SQL. + +
    +
    +{% highlight scala %} + +/** Lazily instantiated singleton instance of SQLContext */ +object SQLContextSingleton { + @transient private var instance: SQLContext = null + + // Instantiate SQLContext on demand + def getInstance(sparkContext: SparkContext): SQLContext = synchronized { + if (instance == null) { + instance = new SQLContext(sparkContext) + } + instance + } +} + +... + +/** Case class for converting RDD to DataFrame */ +case class Row(word: String) + +... + +/** DataFrame operations inside your streaming program */ + +val words: DStream[String] = ... + +words.foreachRDD { rdd => + + // Get the singleton instance of SQLContext + val sqlContext = SQLContextSingleton.getInstance(rdd.sparkContext) + import sqlContext.implicits._ + + // Convert RDD[String] to RDD[case class] to DataFrame + val wordsDataFrame = rdd.map(w => Row(w)).toDF() + + // Register as table + wordsDataFrame.registerTempTable("words") + + // Do word count on DataFrame using SQL and print it + val wordCountsDataFrame = + sqlContext.sql("select word, count(*) as total from words group by word") + wordCountsDataFrame.show() +} + +{% endhighlight %} + +See the full [source code]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/scala/org/apache/spark/examples/streaming/SqlNetworkWordCount.scala). +
    +
    +{% highlight java %} + +/** Lazily instantiated singleton instance of SQLContext */ +class JavaSQLContextSingleton { + static private transient SQLContext instance = null; + static public SQLContext getInstance(SparkContext sparkContext) { + if (instance == null) { + instance = new SQLContext(sparkContext); + } + return instance; + } +} + +... + +/** Java Bean class for converting RDD to DataFrame */ +public class JavaRow implements java.io.Serializable { + private String word; + + public String getWord() { + return word; + } + + public void setWord(String word) { + this.word = word; + } +} + +... + +/** DataFrame operations inside your streaming program */ + +JavaDStream words = ... + +words.foreachRDD( + new Function2, Time, Void>() { + @Override + public Void call(JavaRDD rdd, Time time) { + SQLContext sqlContext = JavaSQLContextSingleton.getInstance(rdd.context()); + + // Convert RDD[String] to RDD[case class] to DataFrame + JavaRDD rowRDD = rdd.map(new Function() { + public JavaRow call(String word) { + JavaRow record = new JavaRow(); + record.setWord(word); + return record; + } + }); + DataFrame wordsDataFrame = sqlContext.createDataFrame(rowRDD, JavaRow.class); + + // Register as table + wordsDataFrame.registerTempTable("words"); + + // Do word count on table using SQL and print it + DataFrame wordCountsDataFrame = + sqlContext.sql("select word, count(*) as total from words group by word"); + wordCountsDataFrame.show(); + return null; + } + } +); +{% endhighlight %} + +See the full [source code]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/java/org/apache/spark/examples/streaming/JavaSqlNetworkWordCount.java). +
    +
    +{% highlight python %} + +# Lazily instantiated global instance of SQLContext +def getSqlContextInstance(sparkContext): + if ('sqlContextSingletonInstance' not in globals()): + globals()['sqlContextSingletonInstance'] = SQLContext(sparkContext) + return globals()['sqlContextSingletonInstance'] + +... + +# DataFrame operations inside your streaming program + +words = ... # DStream of strings + +def process(time, rdd): + print "========= %s =========" % str(time) + try: + # Get the singleton instance of SQLContext + sqlContext = getSqlContextInstance(rdd.context) + + # Convert RDD[String] to RDD[Row] to DataFrame + rowRdd = rdd.map(lambda w: Row(word=w)) + wordsDataFrame = sqlContext.createDataFrame(rowRdd) + + # Register as table + wordsDataFrame.registerTempTable("words") + + # Do word count on table using SQL and print it + wordCountsDataFrame = sqlContext.sql("select word, count(*) as total from words group by word") + wordCountsDataFrame.show() + except: + pass + +words.foreachRDD(process) +{% endhighlight %} + +See the full [source code]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/python/streaming/sql_network_wordcount.py). + +
    +
    + +You can also run SQL queries on tables defined on streaming data from a different thread (that is, asynchronous to the running StreamingContext). Just make sure that you set the StreamingContext to remember sufficient amount of streaming data such that query can run. Otherwise the StreamingContext, which is unaware of the any asynchronous SQL queries, will delete off old streaming data before the query can complete. For example, if you want to query the last batch, but your query can take 5 minutes to run, then call `streamingContext.remember(Minutes(5))` (in Scala, or equivalent in other languages). + +See the [DataFrames and SQL](sql-programming-guide.html) guide to learn more about DataFrames. + +*** + +## MLlib Operations +You can also easily use machine learning algorithms provided by [MLlib](mllib-guide.html). First of all, there are streaming machine learning algorithms (e.g. (Streaming Linear Regression](mllib-linear-methods.html#streaming-linear-regression), [Streaming KMeans](mllib-clustering.html#streaming-k-means), etc.) which can simultaneously learn from the streaming data as well as apply the model on the streaming data. Beyond these, for a much larger class of machine learning algorithms, you can learn a learning model offline (i.e. using historical data) and then apply the model online on streaming data. See the [MLlib](mllib-guide.html) guide for more details. + +*** + ## Caching / Persistence Similar to RDDs, DStreams also allow developers to persist the stream's data in memory. That is, using `persist()` method on a DStream will automatically persist every RDD of that DStream in @@ -1568,9 +1845,8 @@ To run a Spark Streaming applications, you need to have the following. + *Mesos* - [Marathon](https://github.com/mesosphere/marathon) has been used to achieve this with Mesos. - -- *[Experimental in Spark 1.2] Configuring write ahead logs* - In Spark 1.2, - we have introduced a new experimental feature of write ahead logs for achieving strong +- *[Since Spark 1.2] Configuring write ahead logs* - Since Spark 1.2, + we have introduced _write ahead logs_ for achieving strong fault-tolerance guarantees. If enabled, all the data received from a receiver gets written into a write ahead log in the configuration checkpoint directory. This prevents data loss on driver recovery, thus ensuring zero data loss (discussed in detail in the @@ -1656,7 +1932,7 @@ improve the performance of you application. At a high level, you need to conside 2. Setting the right batch size such that the batches of data can be processed as fast as they are received (that is, data processing keeps up with the data ingestion). -## Reducing the Processing Time of each Batch +## Reducing the Batch Processing Times There are a number of optimizations that can be done in Spark to minimize the processing time of each batch. These have been discussed in detail in [Tuning Guide](tuning.html). This section highlights some of the most important ones. @@ -1728,16 +2004,15 @@ documentation), or set the `spark.default.parallelism` ### Data Serialization {:.no_toc} -The overhead of data serialization can be significant, especially when sub-second batch sizes are - to be achieved. There are two aspects to it. +The overheads of data serialization can be reduce by tuning the serialization formats. In case of streaming, there are two types of data that are being serialized. -* **Serialization of RDD data in Spark**: Please refer to the detailed discussion on data - serialization in the [Tuning Guide](tuning.html). However, note that unlike Spark, by default - RDDs are persisted as serialized byte arrays to minimize pauses related to GC. +* **Input data**: By default, the input data received through Receivers is stored in the executors' memory with [StorageLevel.MEMORY_AND_DISK_SER_2](api/scala/index.html#org.apache.spark.storage.StorageLevel$). That is, the data is serialized into bytes to reduce GC overheads, and replicated for tolerating executor failures. Also, the data is kept first in memory, and spilled over to disk only if the memory is unsufficient to hold all the input data necessary for the streaming computation. This serialization obviously has overheads -- the receiver must deserialize the received data and re-serialize it using Spark's serialization format. -* **Serialization of input data**: To ingest external data into Spark, data received as bytes - (say, from the network) needs to deserialized from bytes and re-serialized into Spark's - serialization format. Hence, the deserialization overhead of input data may be a bottleneck. +* **Persisted RDDs generated by Streaming Operations**: RDDs generated by streaming computations may be persisted in memory. For example, window operation persist data in memory as they would be processed multiple times. However, unlike Spark, by default RDDs are persisted with [StorageLevel.MEMORY_ONLY_SER](api/scala/index.html#org.apache.spark.storage.StorageLevel$) (i.e. serialized) to minimize GC overheads. + +In both cases, using Kryo serialization can reduce both CPU and memory overheads. See the [Spark Tuning Guide](tuning.html#data-serialization)) for more details. Consider registering custom classes, and disabling object reference tracking for Kryo (see Kryo-related configurations in the [Configuration Guide](configuration.html#compression-and-serialization)). + +In specific cases where the amount of data that needs to be retained for the streaming application is not large, it may be feasible to persist data (both types) as deserialized objects without incurring excessive GC overheads. For example, if you are using batch intervals of few seconds and no window operations, then you can try disabling serialization in persisted data by explicitly setting the storage level accordingly. This would reduce the CPU overheads due to serialization, potentially improving performance without too much GC overheads. ### Task Launching Overheads {:.no_toc} @@ -1757,7 +2032,7 @@ thus allowing sub-second batch size to be viable. *** -## Setting the Right Batch Size +## Setting the Right Batch Interval For a Spark Streaming application running on a cluster to be stable, the system should be able to process data as fast as it is being received. In other words, batches of data should be processed as fast as they are being generated. Whether this is true for an application can be found by @@ -1789,40 +2064,40 @@ temporary data rate increases maybe fine as long as the delay reduces back to a ## Memory Tuning Tuning the memory usage and GC behavior of Spark applications have been discussed in great detail -in the [Tuning Guide](tuning.html). It is recommended that you read that. In this section, -we highlight a few customizations that are strongly recommended to minimize GC related pauses -in Spark Streaming applications and achieving more consistent batch processing times. - -* **Default persistence level of DStreams**: Unlike RDDs, the default persistence level of DStreams -serializes the data in memory (that is, -[StorageLevel.MEMORY_ONLY_SER](api/scala/index.html#org.apache.spark.storage.StorageLevel$) for -DStream compared to -[StorageLevel.MEMORY_ONLY](api/scala/index.html#org.apache.spark.storage.StorageLevel$) for RDDs). -Even though keeping the data serialized incurs higher serialization/deserialization overheads, -it significantly reduces GC pauses. - -* **Clearing persistent RDDs**: By default, all persistent RDDs generated by Spark Streaming will - be cleared from memory based on Spark's built-in policy (LRU). If `spark.cleaner.ttl` is set, - then persistent RDDs that are older than that value are periodically cleared. As mentioned - [earlier](#operation), this needs to be careful set based on operations used in the Spark - Streaming program. However, a smarter unpersisting of RDDs can be enabled by setting the - [configuration property](configuration.html#spark-properties) `spark.streaming.unpersist` to - `true`. This makes the system to figure out which RDDs are not necessary to be kept around and - unpersists them. This is likely to reduce - the RDD memory usage of Spark, potentially improving GC behavior as well. - -* **Concurrent garbage collector**: Using the concurrent mark-and-sweep GC further -minimizes the variability of GC pauses. Even though concurrent GC is known to reduce the +in the [Tuning Guide](tuning.html#memory-tuning). It is strongly recommended that you read that. In this section, we discuss a few tuning parameters specifically in the context of Spark Streaming applications. + +The amount of cluster memory required by a Spark Streaming application depends heavily on the type of transformations used. For example, if you want to use a window operation on last 10 minutes of data, then your cluster should have sufficient memory to hold 10 minutes of worth of data in memory. Or if you want to use `updateStateByKey` with a large number of keys, then the necessary memory will be high. On the contrary, if you want to do a simple map-filter-store operation, then necessary memory will be low. + +In general, since the data received through receivers are stored with StorageLevel.MEMORY_AND_DISK_SER_2, the data that does not fit in memory will spill over to the disk. This may reduce the performance of the streaming application, and hence it is advised to provide sufficient memory as required by your streaming application. Its best to try and see the memory usage on a small scale and estimate accordingly. + +Another aspect of memory tuning is garbage collection. For a streaming application that require low latency, it is undesirable to have large pauses caused by JVM Garbage Collection. + +There are a few parameters that can help you tune the memory usage and GC overheads. + +* **Persistence Level of DStreams**: As mentioned earlier in the [Data Serialization](#data-serialization) section, the input data and RDDs are by default persisted as serialized bytes. This reduces both, the memory usage and GC overheads, compared to deserialized persistence. Enabling Kryo serialization further reduces serialized sizes and memory usage. Further reduction in memory usage can be achieved with compression (see the Spark configuration `spark.rdd.compress`), at the cost of CPU time. + +* **Clearing old data**: By default, all input data and persisted RDDs generated by DStream transformations are automatically cleared. Spark Streaming decides when to clear the data based on the transformations that are used. For example, if you are using window operation of 10 minutes, then Spark Streaming will keep around last 10 minutes of data, and actively throw away older data. +Data can be retained for longer duration (e.g. interactively querying older data) by setting `streamingContext.remember`. + +* **CMS Garbage Collector**: Use of the concurrent mark-and-sweep GC is strongly recommended for keeping GC-related pauses consistently low. Even though concurrent GC is known to reduce the overall processing throughput of the system, its use is still recommended to achieve more -consistent batch processing times. +consistent batch processing times. Make sure you set the CMS GC on both the driver (using `--driver-java-options` in `spark-submit`) and the executors (using [Spark configuration](configuration.html#runtime-environment) `spark.executor.extraJavaOptions`). + +* **Other tips**: To further reduce GC overheads, here are some more tips to try. + - Use Tachyon for off-heap storage of persisted RDDs. See more detail in the [Spark Programming Guide](programming-guide.html#rdd-persistence). + - Use more executors with smaller heap sizes. This will reduce the GC pressure within each JVM heap. + *************************************************************************************************** *************************************************************************************************** # Fault-tolerance Semantics In this section, we will discuss the behavior of Spark Streaming applications in the event -of node failures. To understand this, let us remember the basic fault-tolerance semantics of -Spark's RDDs. +of failures. + +## Background +{:.no_toc} +To understand the semantics provided by Spark Streaming, let us remember the basic fault-tolerance semantics of Spark's RDDs. 1. An RDD is an immutable, deterministically re-computable, distributed dataset. Each RDD remembers the lineage of deterministic operations that were used on a fault-tolerant input @@ -1856,13 +2131,43 @@ Furthermore, there are two kinds of failures that we should be concerned about: With this basic knowledge, let us understand the fault-tolerance semantics of Spark Streaming. -## Semantics with files as input source +## Definitions +{:.no_toc} +The semantics of streaming systems are often captured in terms of how many times each record can be processed by the system. There are three types of guarantees that a system can provide under all possible operating conditions (despite failures, etc.) + +1. *At most once*: Each record will be either processed once or not processed at all. +2. *At least once*: Each record will be processed one or more times. This is stronger than *at-most once* as it ensure that no data will be lost. But there may be duplicates. +3. *Exactly once*: Each record will be processed exactly once - no data will be lost and no data will be processed multiple times. This is obviously the strongest guarantee of the three. + +## Basic Semantics +{:.no_toc} +In any stream processing system, broadly speaking, there are three steps in processing the data. + +1. *Receiving the data*: The data is received from sources using Receivers or otherwise. + +1. *Transforming the data*: The data received data is transformed using DStream and RDD transformations. + +1. *Pushing out the data*: The final transformed data is pushed out to external systems like file systems, databases, dashboards, etc. + +If a streaming application has to achieve end-to-end exactly-once guarantees, then each step has to provide exactly-once guarantee. That is, each record must be received exactly once, transformed exactly once, and pushed to downstream systems exactly once. Let's understand the semantics of these steps in the context of Spark Streaming. + +1. *Receiving the data*: Different input sources provided different guarantees. This is discussed in detail in the next subsection. + +1. *Transforming the data*: All data that has been received will be processed _exactly once_, thanks to the guarantees that RDDs provide. Even if there are failures, as long as the received input data is accessible, the final transformed RDDs will always have the same contents. + +1. *Pushing out the data*: Output operations by default ensure _at-least once_ semantics because it depends on the type of output operation (idempotent, or not) and the semantics of the downstream system (supports transactions or not). But users can implement their own transaction mechanisms to achieve _exactly-once_ semantics. This is discussed in more details later in the section. + +## Semantics of Received Data +{:.no_toc} +Different input sources provide different guarantees, ranging from _at-least once_ to _exactly once_. Read for more details. + +### With Files {:.no_toc} If all of the input data is already present in a fault-tolerant files system like HDFS, Spark Streaming can always recover from any failure and process all the data. This gives *exactly-once* semantics, that all the data will be processed exactly once no matter what fails. -## Semantics with input sources based on receivers +### With Receiver-based Sources {:.no_toc} For input sources based on receivers, the fault-tolerance semantics depend on both the failure scenario and the type of receiver. @@ -1881,10 +2186,9 @@ receivers, data received but not replicated can get lost. If the driver node fai then besides these losses, all the past data that was received and replicated in memory will be lost. This will affect the results of the stateful transformations. -To avoid this loss of past received data, Spark 1.2 introduces an experimental feature of _write +To avoid this loss of past received data, Spark 1.2 introduced _write ahead logs_ which saves the received data to fault-tolerant storage. With the [write ahead logs -enabled](#deploying-applications) and reliable receivers, there is zero data loss and -exactly-once semantics. +enabled](#deploying-applications) and reliable receivers, there is zero data loss. In terms of semantics, it provides at-least once guarantee. The following table summarizes the semantics under failures: @@ -1896,23 +2200,30 @@ The following table summarizes the semantics under failures: - Spark 1.1 or earlier, or
    - Spark 1.2 without write ahead log + Spark 1.1 or earlier, OR
    + Spark 1.2 or later without write ahead logs Buffered data lost with unreliable receivers
    - Zero data loss with reliable receivers and files
    + Zero data loss with reliable receivers
    + At-least once semantics Buffered data lost with unreliable receivers
    Past data lost with all receivers
    - Zero data loss with files - + Undefined semantics + - Spark 1.2 with write ahead log - Zero data loss with reliable receivers and files - Zero data loss with reliable receivers and files + Spark 1.2 or later with write ahead logs + + Zero data loss with reliable receivers
    + At-least once semantics + + + Zero data loss with reliable receivers and files
    + At-least once semantics + @@ -1921,17 +2232,24 @@ The following table summarizes the semantics under failures: +### With Kafka Direct API +{:.no_toc} +In Spark 1.3, we have introduced a new Kafka Direct API, which can ensure that all the Kafka data is received by Spark Streaming exactly once. Along with this, if you implement exactly-once output operation, you can achieve end-to-end exactly-once guarantees. This approach (experimental as of Spark 1.3) is further discussed in the [Kafka Integration Guide](streaming-kafka-integration.html). + ## Semantics of output operations {:.no_toc} -Since all data is modeled as RDDs with their lineage of deterministic operations, any recomputation - always leads to the same result. As a result, all DStream transformations are guaranteed to have - _exactly-once_ semantics. That is, the final transformed result will be same even if there were - was a worker node failure. However, output operations (like `foreachRDD`) have _at-least once_ - semantics, that is, the transformed data may get written to an external entity more than once in - the event of a worker failure. While this is acceptable for saving to HDFS using the - `saveAs***Files` operations (as the file will simply get over-written by the same data), - additional transactions-like mechanisms may be necessary to achieve exactly-once semantics - for output operations. +Output operations (like `foreachRDD`) have _at-least once_ semantics, that is, +the transformed data may get written to an external entity more than once in +the event of a worker failure. While this is acceptable for saving to file systems using the +`saveAs***Files` operations (as the file will simply get overwritten with the same data), +additional effort may be necessary to achieve exactly-once semantics. There are two approaches. + +- *Idempotent updates*: Multiple attempts always write the same data. For example, `saveAs***Files` always writes the same data to the generated files. + +- *Transactional updates*: All updates are made transactionally so that updates are made exactly once atomically. One way to do this would be the following. + + - Use the batch time (available in `foreachRDD`) and the partition index of the transformed RDD to create an identifier. This identifier uniquely identifies a blob data in the streaming application. + - Update external system with this blob transactionally (that is, exactly once, atomically) using the identifier. That is, if the identifier is not already committed, commit the partition data and the identifier atomically. Else if this was already committed, skip the update. *************************************************************************************************** @@ -1989,7 +2307,11 @@ package and renamed for better clarity. *************************************************************************************************** # Where to Go from Here - +* Additional guides + - [Kafka Integration Guide](streaming-kafka-integration.html) + - [Flume Integration Guide](streaming-flume-integration.html) + - [Kinesis Integration Guide](streaming-kinesis-integration.html) + - [Custom Receiver Guide](streaming-custom-receivers.html) * API documentation - Scala docs * [StreamingContext](api/scala/index.html#org.apache.spark.streaming.StreamingContext) and @@ -2011,8 +2333,8 @@ package and renamed for better clarity. [ZeroMQUtils](api/java/index.html?org/apache/spark/streaming/zeromq/ZeroMQUtils.html), and [MQTTUtils](api/java/index.html?org/apache/spark/streaming/mqtt/MQTTUtils.html) - Python docs - * [StreamingContext](api/python/pyspark.streaming.html#pyspark.streaming.StreamingContext) - * [DStream](api/python/pyspark.streaming.html#pyspark.streaming.DStream) + * [StreamingContext](api/python/pyspark.streaming.html#pyspark.streaming.StreamingContext) and [DStream](api/python/pyspark.streaming.html#pyspark.streaming.DStream) + * [KafkaUtils](api/python/pyspark.streaming.html#pyspark.streaming.kafka.KafkaUtils) * More examples in [Scala]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/examples/streaming) and [Java]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/java/org/apache/spark/examples/streaming) diff --git a/docs/submitting-applications.md b/docs/submitting-applications.md index 14a87f8436984..3ecbf2308cd44 100644 --- a/docs/submitting-applications.md +++ b/docs/submitting-applications.md @@ -133,10 +133,10 @@ The master URL passed to Spark can be in one of the following formats: Or, for a Mesos cluster using ZooKeeper, use mesos://zk://.... yarn-client Connect to a YARN cluster in -client mode. The cluster location will be found based on the HADOOP_CONF_DIR variable. +client mode. The cluster location will be found based on the HADOOP_CONF_DIR or YARN_CONF_DIR variable. yarn-cluster Connect to a YARN cluster in -cluster mode. The cluster location will be found based on HADOOP_CONF_DIR. +cluster mode. The cluster location will be found based on the HADOOP_CONF_DIR or YARN_CONF_DIR variable. @@ -174,6 +174,11 @@ This can use up a significant amount of space over time and will need to be clea is handled automatically, and with Spark standalone, automatic cleanup can be configured with the `spark.worker.cleanup.appDataTtl` property. +Users may also include any other dependencies by supplying a comma-delimited list of maven coordinates +with `--packages`. All transitive dependencies will be handled when using this command. Additional +repositories (or resolvers in SBT) can be added in a comma-delimited fashion with the flag `--repositories`. +These commands can be used with `pyspark`, `spark-shell`, and `spark-submit` to include Spark Packages. + For Python, the equivalent `--py-files` option can be used to distribute `.egg`, `.zip` and `.py` libraries to executors. diff --git a/ec2/deploy.generic/root/spark-ec2/ec2-variables.sh b/ec2/deploy.generic/root/spark-ec2/ec2-variables.sh index 740c267fd9866..4f3e8da809f7f 100644 --- a/ec2/deploy.generic/root/spark-ec2/ec2-variables.sh +++ b/ec2/deploy.generic/root/spark-ec2/ec2-variables.sh @@ -25,10 +25,10 @@ export MAPRED_LOCAL_DIRS="{{mapred_local_dirs}}" export SPARK_LOCAL_DIRS="{{spark_local_dirs}}" export MODULES="{{modules}}" export SPARK_VERSION="{{spark_version}}" -export SHARK_VERSION="{{shark_version}}" +export TACHYON_VERSION="{{tachyon_version}}" export HADOOP_MAJOR_VERSION="{{hadoop_major_version}}" export SWAP_MB="{{swap}}" export SPARK_WORKER_INSTANCES="{{spark_worker_instances}}" export SPARK_MASTER_OPTS="{{spark_master_opts}}" export AWS_ACCESS_KEY_ID="{{aws_access_key_id}}" -export AWS_SECRET_ACCESS_KEY="{{aws_secret_access_key}}" \ No newline at end of file +export AWS_SECRET_ACCESS_KEY="{{aws_secret_access_key}}" diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py index 725b1e47e0cea..87c0818279713 100755 --- a/ec2/spark_ec2.py +++ b/ec2/spark_ec2.py @@ -19,9 +19,10 @@ # limitations under the License. # -from __future__ import with_statement +from __future__ import with_statement, print_function import hashlib +import itertools import logging import os import os.path @@ -34,14 +35,20 @@ import sys import tarfile import tempfile +import textwrap import time -import urllib2 import warnings from datetime import datetime from optparse import OptionParser from sys import stderr -SPARK_EC2_VERSION = "1.2.0" +if sys.version < "3": + from urllib2 import urlopen, Request, HTTPError +else: + from urllib.request import urlopen, Request + from urllib.error import HTTPError + +SPARK_EC2_VERSION = "1.2.1" SPARK_EC2_DIR = os.path.dirname(os.path.realpath(__file__)) VALID_SPARK_VERSIONS = set([ @@ -57,44 +64,81 @@ "1.1.0", "1.1.1", "1.2.0", + "1.2.1", ]) +SPARK_TACHYON_MAP = { + "1.0.0": "0.4.1", + "1.0.1": "0.4.1", + "1.0.2": "0.4.1", + "1.1.0": "0.5.0", + "1.1.1": "0.5.0", + "1.2.0": "0.5.0", + "1.2.1": "0.5.0", +} + DEFAULT_SPARK_VERSION = SPARK_EC2_VERSION DEFAULT_SPARK_GITHUB_REPO = "https://github.com/apache/spark" -MESOS_SPARK_EC2_BRANCH = "branch-1.3" - -# A URL prefix from which to fetch AMI information -AMI_PREFIX = "https://raw.github.com/mesos/spark-ec2/{b}/ami-list".format(b=MESOS_SPARK_EC2_BRANCH) - - -def setup_boto(): - # Download Boto if it's not already present in the SPARK_EC2_DIR/lib folder: - version = "boto-2.34.0" - md5 = "5556223d2d0cc4d06dd4829e671dcecd" - url = "https://pypi.python.org/packages/source/b/boto/%s.tar.gz" % version - lib_dir = os.path.join(SPARK_EC2_DIR, "lib") - if not os.path.exists(lib_dir): - os.mkdir(lib_dir) - boto_lib_dir = os.path.join(lib_dir, version) - if not os.path.isdir(boto_lib_dir): - tgz_file_path = os.path.join(lib_dir, "%s.tar.gz" % version) - print "Downloading Boto from PyPi" - download_stream = urllib2.urlopen(url) - with open(tgz_file_path, "wb") as tgz_file: - tgz_file.write(download_stream.read()) - with open(tgz_file_path) as tar: - if hashlib.md5(tar.read()).hexdigest() != md5: - print >> stderr, "ERROR: Got wrong md5sum for Boto" - sys.exit(1) - tar = tarfile.open(tgz_file_path) - tar.extractall(path=lib_dir) - tar.close() - os.remove(tgz_file_path) - print "Finished downloading Boto" - sys.path.insert(0, boto_lib_dir) +# Default location to get the spark-ec2 scripts (and ami-list) from +DEFAULT_SPARK_EC2_GITHUB_REPO = "https://github.com/mesos/spark-ec2" +DEFAULT_SPARK_EC2_BRANCH = "branch-1.3" + + +def setup_external_libs(libs): + """ + Download external libraries from PyPI to SPARK_EC2_DIR/lib/ and prepend them to our PATH. + """ + PYPI_URL_PREFIX = "https://pypi.python.org/packages/source" + SPARK_EC2_LIB_DIR = os.path.join(SPARK_EC2_DIR, "lib") + + if not os.path.exists(SPARK_EC2_LIB_DIR): + print("Downloading external libraries that spark-ec2 needs from PyPI to {path}...".format( + path=SPARK_EC2_LIB_DIR + )) + print("This should be a one-time operation.") + os.mkdir(SPARK_EC2_LIB_DIR) + + for lib in libs: + versioned_lib_name = "{n}-{v}".format(n=lib["name"], v=lib["version"]) + lib_dir = os.path.join(SPARK_EC2_LIB_DIR, versioned_lib_name) + + if not os.path.isdir(lib_dir): + tgz_file_path = os.path.join(SPARK_EC2_LIB_DIR, versioned_lib_name + ".tar.gz") + print(" - Downloading {lib}...".format(lib=lib["name"])) + download_stream = urlopen( + "{prefix}/{first_letter}/{lib_name}/{lib_name}-{lib_version}.tar.gz".format( + prefix=PYPI_URL_PREFIX, + first_letter=lib["name"][:1], + lib_name=lib["name"], + lib_version=lib["version"] + ) + ) + with open(tgz_file_path, "wb") as tgz_file: + tgz_file.write(download_stream.read()) + with open(tgz_file_path) as tar: + if hashlib.md5(tar.read()).hexdigest() != lib["md5"]: + print("ERROR: Got wrong md5sum for {lib}.".format(lib=lib["name"]), file=stderr) + sys.exit(1) + tar = tarfile.open(tgz_file_path) + tar.extractall(path=SPARK_EC2_LIB_DIR) + tar.close() + os.remove(tgz_file_path) + print(" - Finished downloading {lib}.".format(lib=lib["name"])) + sys.path.insert(1, lib_dir) + + +# Only PyPI libraries are supported. +external_libs = [ + { + "name": "boto", + "version": "2.34.0", + "md5": "5556223d2d0cc4d06dd4829e671dcecd" + } +] + +setup_external_libs(external_libs) -setup_boto() import boto from boto.ec2.blockdevicemapping import BlockDeviceMapping, BlockDeviceType, EBSBlockDeviceType from boto import ec2 @@ -111,6 +155,7 @@ def parse_args(): version="%prog {v}".format(v=SPARK_EC2_VERSION), usage="%prog [options] \n\n" + " can be: launch, destroy, login, stop, start, get-master, reboot-slaves") + parser.add_option( "-s", "--slaves", type="int", default=1, help="Number of slaves to launch (default: %default)") @@ -132,13 +177,15 @@ def parse_args(): help="Master instance type (leave empty for same as instance-type)") parser.add_option( "-r", "--region", default="us-east-1", - help="EC2 region zone to launch instances in") + help="EC2 region used to launch instances in, or to find them in (default: %default)") parser.add_option( "-z", "--zone", default="", help="Availability zone to launch instances in, or 'all' to spread " + "slaves across multiple (an additional $0.01/Gb for bandwidth" + "between zones applies) (default: a single zone chosen at random)") - parser.add_option("-a", "--ami", help="Amazon Machine Image ID to use") + parser.add_option( + "-a", "--ami", + help="Amazon Machine Image ID to use") parser.add_option( "-v", "--spark-version", default=DEFAULT_SPARK_VERSION, help="Version of Spark to use: 'X.Y.Z' or a specific git hash (default: %default)") @@ -146,6 +193,23 @@ def parse_args(): "--spark-git-repo", default=DEFAULT_SPARK_GITHUB_REPO, help="Github repo from which to checkout supplied commit hash (default: %default)") + parser.add_option( + "--spark-ec2-git-repo", + default=DEFAULT_SPARK_EC2_GITHUB_REPO, + help="Github repo from which to checkout spark-ec2 (default: %default)") + parser.add_option( + "--spark-ec2-git-branch", + default=DEFAULT_SPARK_EC2_BRANCH, + help="Github repo branch of spark-ec2 to use (default: %default)") + parser.add_option( + "--deploy-root-dir", + default=None, + help="A directory to copy into / on the first master. " + + "Must be absolute. Note that a trailing slash is handled as per rsync: " + + "If you omit it, the last directory of the --deploy-root-dir path will be created " + + "in / before copying its contents. If you append the trailing slash, " + + "the directory is not created and its contents are copied directly into /. " + + "(default: %default).") parser.add_option( "--hadoop-major-version", default="1", help="Major version of Hadoop (default: %default)") @@ -170,10 +234,11 @@ def parse_args(): "Only possible on EBS-backed AMIs. " + "EBS volumes are only attached if --ebs-vol-size > 0." + "Only support up to 8 EBS volumes.") - parser.add_option("--placement-group", type="string", default=None, - help="Which placement group to try and launch " + - "instances into. Assumes placement group is already " + - "created.") + parser.add_option( + "--placement-group", type="string", default=None, + help="Which placement group to try and launch " + + "instances into. Assumes placement group is already " + + "created.") parser.add_option( "--swap", metavar="SWAP", type="int", default=1024, help="Swap space to set up per node, in MB (default: %default)") @@ -206,7 +271,7 @@ def parse_args(): "(e.g -Dspark.worker.timeout=180)") parser.add_option( "--user-data", type="string", default="", - help="Path to a user-data file (most AMI's interpret this as an initialization script)") + help="Path to a user-data file (most AMIs interpret this as an initialization script)") parser.add_option( "--authorized-address", type="string", default="0.0.0.0/0", help="Address to authorize on created security groups (default: %default)") @@ -217,9 +282,15 @@ def parse_args(): "--copy-aws-credentials", action="store_true", default=False, help="Add AWS credentials to hadoop configuration to allow Spark to access S3") parser.add_option( - "--subnet-id", default=None, help="VPC subnet to launch instances in") + "--subnet-id", default=None, + help="VPC subnet to launch instances in") + parser.add_option( + "--vpc-id", default=None, + help="VPC to launch instances in") parser.add_option( - "--vpc-id", default=None, help="VPC to launch instances in") + "--private-ips", action="store_true", default=False, + help="Use private IPs for instances rather than public if VPC/subnet " + + "requires that.") (opts, args) = parser.parse_args() if len(args) != 2: @@ -233,12 +304,12 @@ def parse_args(): if home_dir is None or not os.path.isfile(home_dir + '/.boto'): if not os.path.isfile('/etc/boto.cfg'): if os.getenv('AWS_ACCESS_KEY_ID') is None: - print >> stderr, ("ERROR: The environment variable AWS_ACCESS_KEY_ID " + - "must be set") + print("ERROR: The environment variable AWS_ACCESS_KEY_ID must be set", + file=stderr) sys.exit(1) if os.getenv('AWS_SECRET_ACCESS_KEY') is None: - print >> stderr, ("ERROR: The environment variable AWS_SECRET_ACCESS_KEY " + - "must be set") + print("ERROR: The environment variable AWS_SECRET_ACCESS_KEY must be set", + file=stderr) sys.exit(1) return (opts, action, cluster_name) @@ -250,7 +321,7 @@ def get_or_make_group(conn, name, vpc_id): if len(group) > 0: return group[0] else: - print "Creating security group " + name + print("Creating security group " + name) return conn.create_security_group(name, "Spark EC2 group", vpc_id) @@ -258,86 +329,90 @@ def get_validate_spark_version(version, repo): if "." in version: version = version.replace("v", "") if version not in VALID_SPARK_VERSIONS: - print >> stderr, "Don't know about Spark version: {v}".format(v=version) + print("Don't know about Spark version: {v}".format(v=version), file=stderr) sys.exit(1) return version else: github_commit_url = "{repo}/commit/{commit_hash}".format(repo=repo, commit_hash=version) - request = urllib2.Request(github_commit_url) + request = Request(github_commit_url) request.get_method = lambda: 'HEAD' try: - response = urllib2.urlopen(request) - except urllib2.HTTPError, e: - print >> stderr, "Couldn't validate Spark commit: {url}".format(url=github_commit_url) - print >> stderr, "Received HTTP response code of {code}.".format(code=e.code) + response = urlopen(request) + except HTTPError as e: + print("Couldn't validate Spark commit: {url}".format(url=github_commit_url), + file=stderr) + print("Received HTTP response code of {code}.".format(code=e.code), file=stderr) sys.exit(1) return version -# Check whether a given EC2 instance object is in a state we consider active, -# i.e. not terminating or terminated. We count both stopping and stopped as -# active since we can restart stopped clusters. -def is_active(instance): - return (instance.state in ['pending', 'running', 'stopping', 'stopped']) - - -# Attempt to resolve an appropriate AMI given the architecture and region of the request. # Source: http://aws.amazon.com/amazon-linux-ami/instance-type-matrix/ # Last Updated: 2014-06-20 # For easy maintainability, please keep this manually-inputted dictionary sorted by key. +EC2_INSTANCE_TYPES = { + "c1.medium": "pvm", + "c1.xlarge": "pvm", + "c3.2xlarge": "pvm", + "c3.4xlarge": "pvm", + "c3.8xlarge": "pvm", + "c3.large": "pvm", + "c3.xlarge": "pvm", + "cc1.4xlarge": "hvm", + "cc2.8xlarge": "hvm", + "cg1.4xlarge": "hvm", + "cr1.8xlarge": "hvm", + "hi1.4xlarge": "pvm", + "hs1.8xlarge": "pvm", + "i2.2xlarge": "hvm", + "i2.4xlarge": "hvm", + "i2.8xlarge": "hvm", + "i2.xlarge": "hvm", + "m1.large": "pvm", + "m1.medium": "pvm", + "m1.small": "pvm", + "m1.xlarge": "pvm", + "m2.2xlarge": "pvm", + "m2.4xlarge": "pvm", + "m2.xlarge": "pvm", + "m3.2xlarge": "hvm", + "m3.large": "hvm", + "m3.medium": "hvm", + "m3.xlarge": "hvm", + "r3.2xlarge": "hvm", + "r3.4xlarge": "hvm", + "r3.8xlarge": "hvm", + "r3.large": "hvm", + "r3.xlarge": "hvm", + "t1.micro": "pvm", + "t2.medium": "hvm", + "t2.micro": "hvm", + "t2.small": "hvm", +} + + +def get_tachyon_version(spark_version): + return SPARK_TACHYON_MAP.get(spark_version, "") + + +# Attempt to resolve an appropriate AMI given the architecture and region of the request. def get_spark_ami(opts): - instance_types = { - "c1.medium": "pvm", - "c1.xlarge": "pvm", - "c3.2xlarge": "pvm", - "c3.4xlarge": "pvm", - "c3.8xlarge": "pvm", - "c3.large": "pvm", - "c3.xlarge": "pvm", - "cc1.4xlarge": "hvm", - "cc2.8xlarge": "hvm", - "cg1.4xlarge": "hvm", - "cr1.8xlarge": "hvm", - "hi1.4xlarge": "pvm", - "hs1.8xlarge": "pvm", - "i2.2xlarge": "hvm", - "i2.4xlarge": "hvm", - "i2.8xlarge": "hvm", - "i2.xlarge": "hvm", - "m1.large": "pvm", - "m1.medium": "pvm", - "m1.small": "pvm", - "m1.xlarge": "pvm", - "m2.2xlarge": "pvm", - "m2.4xlarge": "pvm", - "m2.xlarge": "pvm", - "m3.2xlarge": "hvm", - "m3.large": "hvm", - "m3.medium": "hvm", - "m3.xlarge": "hvm", - "r3.2xlarge": "hvm", - "r3.4xlarge": "hvm", - "r3.8xlarge": "hvm", - "r3.large": "hvm", - "r3.xlarge": "hvm", - "t1.micro": "pvm", - "t2.medium": "hvm", - "t2.micro": "hvm", - "t2.small": "hvm", - } - if opts.instance_type in instance_types: - instance_type = instance_types[opts.instance_type] + if opts.instance_type in EC2_INSTANCE_TYPES: + instance_type = EC2_INSTANCE_TYPES[opts.instance_type] else: instance_type = "pvm" - print >> stderr,\ - "Don't recognize %s, assuming type is pvm" % opts.instance_type + print("Don't recognize %s, assuming type is pvm" % opts.instance_type, file=stderr) - ami_path = "%s/%s/%s" % (AMI_PREFIX, opts.region, instance_type) + # URL prefix from which to fetch AMI information + ami_prefix = "{r}/{b}/ami-list".format( + r=opts.spark_ec2_git_repo.replace("https://github.com", "https://raw.github.com", 1), + b=opts.spark_ec2_git_branch) + + ami_path = "%s/%s/%s" % (ami_prefix, opts.region, instance_type) try: - ami = urllib2.urlopen(ami_path).read().strip() - print "Spark AMI: " + ami + ami = urlopen(ami_path).read().strip() + print("Spark AMI: " + ami) except: - print >> stderr, "Could not resolve AMI at: " + ami_path + print("Could not resolve AMI at: " + ami_path, file=stderr) sys.exit(1) return ami @@ -349,11 +424,11 @@ def get_spark_ami(opts): # Fails if there already instances running in the cluster's groups. def launch_cluster(conn, opts, cluster_name): if opts.identity_file is None: - print >> stderr, "ERROR: Must provide an identity file (-i) for ssh connections." + print("ERROR: Must provide an identity file (-i) for ssh connections.", file=stderr) sys.exit(1) if opts.key_pair is None: - print >> stderr, "ERROR: Must provide a key pair name (-k) to use on instances." + print("ERROR: Must provide a key pair name (-k) to use on instances.", file=stderr) sys.exit(1) user_data_content = None @@ -361,7 +436,7 @@ def launch_cluster(conn, opts, cluster_name): with open(opts.user_data) as user_data_file: user_data_content = user_data_file.read() - print "Setting up security groups..." + print("Setting up security groups...") master_group = get_or_make_group(conn, cluster_name + "-master", opts.vpc_id) slave_group = get_or_make_group(conn, cluster_name + "-slaves", opts.vpc_id) authorized_address = opts.authorized_address @@ -390,6 +465,13 @@ def launch_cluster(conn, opts, cluster_name): master_group.authorize('tcp', 50070, 50070, authorized_address) master_group.authorize('tcp', 60070, 60070, authorized_address) master_group.authorize('tcp', 4040, 4045, authorized_address) + # HDFS NFS gateway requires 111,2049,4242 for tcp & udp + master_group.authorize('tcp', 111, 111, authorized_address) + master_group.authorize('udp', 111, 111, authorized_address) + master_group.authorize('tcp', 2049, 2049, authorized_address) + master_group.authorize('udp', 2049, 2049, authorized_address) + master_group.authorize('tcp', 4242, 4242, authorized_address) + master_group.authorize('udp', 4242, 4242, authorized_address) if opts.ganglia: master_group.authorize('tcp', 5080, 5080, authorized_address) if slave_group.rules == []: # Group was just now created @@ -420,8 +502,8 @@ def launch_cluster(conn, opts, cluster_name): existing_masters, existing_slaves = get_existing_cluster(conn, opts, cluster_name, die_on_error=False) if existing_slaves or (existing_masters and not opts.use_existing_master): - print >> stderr, ("ERROR: There are already instances running in " + - "group %s or %s" % (master_group.name, slave_group.name)) + print("ERROR: There are already instances running in group %s or %s" % + (master_group.name, slave_group.name), file=stderr) sys.exit(1) # Figure out Spark AMI @@ -434,12 +516,12 @@ def launch_cluster(conn, opts, cluster_name): additional_group_ids = [sg.id for sg in conn.get_all_security_groups() if opts.additional_security_group in (sg.name, sg.id)] - print "Launching instances..." + print("Launching instances...") try: image = conn.get_all_images(image_ids=[opts.ami])[0] except: - print >> stderr, "Could not find AMI " + opts.ami + print("Could not find AMI " + opts.ami, file=stderr) sys.exit(1) # Create block device mapping so that we can add EBS volumes if asked to. @@ -465,8 +547,8 @@ def launch_cluster(conn, opts, cluster_name): # Launch slaves if opts.spot_price is not None: # Launch spot instances with the requested price - print ("Requesting %d slaves as spot instances with price $%.3f" % - (opts.slaves, opts.spot_price)) + print("Requesting %d slaves as spot instances with price $%.3f" % + (opts.slaves, opts.spot_price)) zones = get_zones(conn, opts) num_zones = len(zones) i = 0 @@ -489,7 +571,7 @@ def launch_cluster(conn, opts, cluster_name): my_req_ids += [req.id for req in slave_reqs] i += 1 - print "Waiting for spot instances to be granted..." + print("Waiting for spot instances to be granted...") try: while True: time.sleep(10) @@ -502,24 +584,24 @@ def launch_cluster(conn, opts, cluster_name): if i in id_to_req and id_to_req[i].state == "active": active_instance_ids.append(id_to_req[i].instance_id) if len(active_instance_ids) == opts.slaves: - print "All %d slaves granted" % opts.slaves + print("All %d slaves granted" % opts.slaves) reservations = conn.get_all_reservations(active_instance_ids) slave_nodes = [] for r in reservations: slave_nodes += r.instances break else: - print "%d of %d slaves granted, waiting longer" % ( - len(active_instance_ids), opts.slaves) + print("%d of %d slaves granted, waiting longer" % ( + len(active_instance_ids), opts.slaves)) except: - print "Canceling spot instance requests" + print("Canceling spot instance requests") conn.cancel_spot_instance_requests(my_req_ids) # Log a warning if any of these requests actually launched instances: (master_nodes, slave_nodes) = get_existing_cluster( conn, opts, cluster_name, die_on_error=False) running = len(master_nodes) + len(slave_nodes) if running: - print >> stderr, ("WARNING: %d instances are still running" % running) + print(("WARNING: %d instances are still running" % running), file=stderr) sys.exit(0) else: # Launch non-spot instances @@ -541,13 +623,16 @@ def launch_cluster(conn, opts, cluster_name): placement_group=opts.placement_group, user_data=user_data_content) slave_nodes += slave_res.instances - print "Launched %d slaves in %s, regid = %s" % (num_slaves_this_zone, - zone, slave_res.id) + print("Launched {s} slave{plural_s} in {z}, regid = {r}".format( + s=num_slaves_this_zone, + plural_s=('' if num_slaves_this_zone == 1 else 's'), + z=zone, + r=slave_res.id)) i += 1 # Launch or resume masters if existing_masters: - print "Starting master..." + print("Starting master...") for inst in existing_masters: if inst.state not in ["shutting-down", "terminated"]: inst.start() @@ -570,10 +655,10 @@ def launch_cluster(conn, opts, cluster_name): user_data=user_data_content) master_nodes = master_res.instances - print "Launched master in %s, regid = %s" % (zone, master_res.id) + print("Launched master in %s, regid = %s" % (zone, master_res.id)) # This wait time corresponds to SPARK-4983 - print "Waiting for AWS to propagate instance metadata..." + print("Waiting for AWS to propagate instance metadata...") time.sleep(5) # Give the instances descriptive names for master in master_nodes: @@ -589,43 +674,50 @@ def launch_cluster(conn, opts, cluster_name): return (master_nodes, slave_nodes) -# Get the EC2 instances in an existing cluster if available. -# Returns a tuple of lists of EC2 instance objects for the masters and slaves +def get_existing_cluster(conn, opts, cluster_name, die_on_error=True): + """ + Get the EC2 instances in an existing cluster if available. + Returns a tuple of lists of EC2 instance objects for the masters and slaves. + """ + print("Searching for existing cluster {c} in region {r}...".format( + c=cluster_name, r=opts.region)) + def get_instances(group_names): + """ + Get all non-terminated instances that belong to any of the provided security groups. -def get_existing_cluster(conn, opts, cluster_name, die_on_error=True): - print "Searching for existing cluster " + cluster_name + "..." - reservations = conn.get_all_reservations() - master_nodes = [] - slave_nodes = [] - for res in reservations: - active = [i for i in res.instances if is_active(i)] - for inst in active: - group_names = [g.name for g in inst.groups] - if (cluster_name + "-master") in group_names: - master_nodes.append(inst) - elif (cluster_name + "-slaves") in group_names: - slave_nodes.append(inst) - if any((master_nodes, slave_nodes)): - print "Found %d master(s), %d slaves" % (len(master_nodes), len(slave_nodes)) - if master_nodes != [] or not die_on_error: - return (master_nodes, slave_nodes) - else: - if master_nodes == [] and slave_nodes != []: - print >> sys.stderr, "ERROR: Could not find master in group " + cluster_name + "-master" - else: - print >> sys.stderr, "ERROR: Could not find any existing cluster" + EC2 reservation filters and instance states are documented here: + http://docs.aws.amazon.com/cli/latest/reference/ec2/describe-instances.html#options + """ + reservations = conn.get_all_reservations( + filters={"instance.group-name": group_names}) + instances = itertools.chain.from_iterable(r.instances for r in reservations) + return [i for i in instances if i.state not in ["shutting-down", "terminated"]] + + master_instances = get_instances([cluster_name + "-master"]) + slave_instances = get_instances([cluster_name + "-slaves"]) + + if any((master_instances, slave_instances)): + print("Found {m} master{plural_m}, {s} slave{plural_s}.".format( + m=len(master_instances), + plural_m=('' if len(master_instances) == 1 else 's'), + s=len(slave_instances), + plural_s=('' if len(slave_instances) == 1 else 's'))) + + if not master_instances and die_on_error: + print("ERROR: Could not find a master for cluster {c} in region {r}.".format( + c=cluster_name, r=opts.region), file=sys.stderr) sys.exit(1) + return (master_instances, slave_instances) + # Deploy configuration files and run setup scripts on a newly launched # or started EC2 cluster. - - def setup_cluster(conn, master_nodes, slave_nodes, opts, deploy_ssh_key): - master = master_nodes[0].public_dns_name + master = get_dns_name(master_nodes[0], opts.private_ips) if deploy_ssh_key: - print "Generating cluster's SSH key on master..." + print("Generating cluster's SSH key on master...") key_setup = """ [ -f ~/.ssh/id_rsa ] || (ssh-keygen -q -t rsa -N '' -f ~/.ssh/id_rsa && @@ -633,10 +725,11 @@ def setup_cluster(conn, master_nodes, slave_nodes, opts, deploy_ssh_key): """ ssh(master, opts, key_setup) dot_ssh_tar = ssh_read(master, opts, ['tar', 'c', '.ssh']) - print "Transferring cluster's SSH key to slaves..." + print("Transferring cluster's SSH key to slaves...") for slave in slave_nodes: - print slave.public_dns_name - ssh_write(slave.public_dns_name, opts, ['tar', 'x'], dot_ssh_tar) + slave_address = get_dns_name(slave, opts.private_ips) + print(slave_address) + ssh_write(slave_address, opts, ['tar', 'x'], dot_ssh_tar) modules = ['spark', 'ephemeral-hdfs', 'persistent-hdfs', 'mapreduce', 'spark-standalone', 'tachyon'] @@ -649,15 +742,18 @@ def setup_cluster(conn, master_nodes, slave_nodes, opts, deploy_ssh_key): # NOTE: We should clone the repository before running deploy_files to # prevent ec2-variables.sh from being overwritten + print("Cloning spark-ec2 scripts from {r}/tree/{b} on master...".format( + r=opts.spark_ec2_git_repo, b=opts.spark_ec2_git_branch)) ssh( host=master, opts=opts, command="rm -rf spark-ec2" + " && " - + "git clone https://github.com/mesos/spark-ec2.git -b {b}".format(b=MESOS_SPARK_EC2_BRANCH) + + "git clone {r} -b {b} spark-ec2".format(r=opts.spark_ec2_git_repo, + b=opts.spark_ec2_git_branch) ) - print "Deploying files to master..." + print("Deploying files to master...") deploy_files( conn=conn, root_dir=SPARK_EC2_DIR + "/" + "deploy.generic", @@ -667,35 +763,54 @@ def setup_cluster(conn, master_nodes, slave_nodes, opts, deploy_ssh_key): modules=modules ) - print "Running setup on master..." + if opts.deploy_root_dir is not None: + print("Deploying {s} to master...".format(s=opts.deploy_root_dir)) + deploy_user_files( + root_dir=opts.deploy_root_dir, + opts=opts, + master_nodes=master_nodes + ) + + print("Running setup on master...") setup_spark_cluster(master, opts) - print "Done!" + print("Done!") def setup_spark_cluster(master, opts): ssh(master, opts, "chmod u+x spark-ec2/setup.sh") ssh(master, opts, "spark-ec2/setup.sh") - print "Spark standalone cluster started at http://%s:8080" % master + print("Spark standalone cluster started at http://%s:8080" % master) if opts.ganglia: - print "Ganglia started at http://%s:5080/ganglia" % master + print("Ganglia started at http://%s:5080/ganglia" % master) -def is_ssh_available(host, opts): +def is_ssh_available(host, opts, print_ssh_output=True): """ Check if SSH is available on a host. """ - try: - with open(os.devnull, 'w') as devnull: - ret = subprocess.check_call( - ssh_command(opts) + ['-t', '-t', '-o', 'ConnectTimeout=3', - '%s@%s' % (opts.user, host), stringify_command('true')], - stdout=devnull, - stderr=devnull - ) - return ret == 0 - except subprocess.CalledProcessError as e: - return False + s = subprocess.Popen( + ssh_command(opts) + ['-t', '-t', '-o', 'ConnectTimeout=3', + '%s@%s' % (opts.user, host), stringify_command('true')], + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT # we pipe stderr through stdout to preserve output order + ) + cmd_output = s.communicate()[0] # [1] is stderr, which we redirected to stdout + + if s.returncode != 0 and print_ssh_output: + # extra leading newline is for spacing in wait_for_cluster_state() + print(textwrap.dedent("""\n + Warning: SSH connection error. (This could be temporary.) + Host: {h} + SSH return code: {r} + SSH output: {o} + """).format( + h=host, + r=s.returncode, + o=cmd_output.strip() + )) + + return s.returncode == 0 def is_cluster_ssh_available(cluster_instances, opts): @@ -703,7 +818,8 @@ def is_cluster_ssh_available(cluster_instances, opts): Check if SSH is available on all the instances in a cluster. """ for i in cluster_instances: - if not is_ssh_available(host=i.ip_address, opts=opts): + dns_name = get_dns_name(i, opts.private_ips) + if not is_ssh_available(host=dns_name, opts=opts): return False else: return True @@ -753,10 +869,10 @@ def wait_for_cluster_state(conn, opts, cluster_instances, cluster_state): sys.stdout.write("\n") end_time = datetime.now() - print "Cluster is now in '{s}' state. Waited {t} seconds.".format( + print("Cluster is now in '{s}' state. Waited {t} seconds.".format( s=cluster_state, t=(end_time - start_time).seconds - ) + )) # Get number of local disks available for a given EC2 instance type. @@ -804,8 +920,8 @@ def get_num_disks(instance_type): if instance_type in disks_by_instance: return disks_by_instance[instance_type] else: - print >> stderr, ("WARNING: Don't know number of disks on instance type %s; assuming 1" - % instance_type) + print("WARNING: Don't know number of disks on instance type %s; assuming 1" + % instance_type, file=stderr) return 1 @@ -817,7 +933,7 @@ def get_num_disks(instance_type): # # root_dir should be an absolute path to the directory with the files we want to deploy. def deploy_files(conn, root_dir, opts, master_nodes, slave_nodes, modules): - active_master = master_nodes[0].public_dns_name + active_master = get_dns_name(master_nodes[0], opts.private_ips) num_disks = get_num_disks(opts.instance_type) hdfs_data_dirs = "/mnt/ephemeral-hdfs/data" @@ -834,14 +950,20 @@ def deploy_files(conn, root_dir, opts, master_nodes, slave_nodes, modules): if "." in opts.spark_version: # Pre-built Spark deploy spark_v = get_validate_spark_version(opts.spark_version, opts.spark_git_repo) + tachyon_v = get_tachyon_version(spark_v) else: # Spark-only custom deploy spark_v = "%s|%s" % (opts.spark_git_repo, opts.spark_version) + tachyon_v = "" + print("Deploying Spark via git hash; Tachyon won't be set up") + modules = filter(lambda x: x != "tachyon", modules) + master_addresses = [get_dns_name(i, opts.private_ips) for i in master_nodes] + slave_addresses = [get_dns_name(i, opts.private_ips) for i in slave_nodes] template_vars = { - "master_list": '\n'.join([i.public_dns_name for i in master_nodes]), + "master_list": '\n'.join(master_addresses), "active_master": active_master, - "slave_list": '\n'.join([i.public_dns_name for i in slave_nodes]), + "slave_list": '\n'.join(slave_addresses), "cluster_url": cluster_url, "hdfs_data_dirs": hdfs_data_dirs, "mapred_local_dirs": mapred_local_dirs, @@ -849,6 +971,7 @@ def deploy_files(conn, root_dir, opts, master_nodes, slave_nodes, modules): "swap": str(opts.swap), "modules": '\n'.join(modules), "spark_version": spark_v, + "tachyon_version": tachyon_v, "hadoop_major_version": opts.hadoop_major_version, "spark_worker_instances": "%d" % opts.worker_instances, "spark_master_opts": opts.master_opts @@ -893,6 +1016,23 @@ def deploy_files(conn, root_dir, opts, master_nodes, slave_nodes, modules): shutil.rmtree(tmp_dir) +# Deploy a given local directory to a cluster, WITHOUT parameter substitution. +# Note that unlike deploy_files, this works for binary files. +# Also, it is up to the user to add (or not) the trailing slash in root_dir. +# Files are only deployed to the first master instance in the cluster. +# +# root_dir should be an absolute path. +def deploy_user_files(root_dir, opts, master_nodes): + active_master = get_dns_name(master_nodes[0], opts.private_ips) + command = [ + 'rsync', '-rv', + '-e', stringify_command(ssh_command(opts)), + "%s" % root_dir, + "%s@%s:/" % (opts.user, active_master) + ] + subprocess.check_call(command) + + def stringify_command(parts): if isinstance(parts, str): return parts @@ -931,8 +1071,8 @@ def ssh(host, opts, command): "--key-pair parameters and try again.".format(host)) else: raise e - print >> stderr, \ - "Error executing remote command, retrying after 30 seconds: {0}".format(e) + print("Error executing remote command, retrying after 30 seconds: {0}".format(e), + file=stderr) time.sleep(30) tries = tries + 1 @@ -971,8 +1111,8 @@ def ssh_write(host, opts, command, arguments): elif tries > 5: raise RuntimeError("ssh_write failed with error %s" % proc.returncode) else: - print >> stderr, \ - "Error {0} while executing remote command, retrying after 30 seconds".format(status) + print("Error {0} while executing remote command, retrying after 30 seconds". + format(status), file=stderr) time.sleep(30) tries = tries + 1 @@ -994,6 +1134,20 @@ def get_partition(total, num_partitions, current_partitions): return num_slaves_this_zone +# Gets the IP address, taking into account the --private-ips flag +def get_ip_address(instance, private_ips=False): + ip = instance.ip_address if not private_ips else \ + instance.private_ip_address + return ip + + +# Gets the DNS name, taking into account the --private-ips flag +def get_dns_name(instance, private_ips=False): + dns = instance.public_dns_name if not private_ips else \ + instance.private_ip_address + return dns + + def real_main(): (opts, action, cluster_name) = parse_args() @@ -1012,24 +1166,65 @@ def real_main(): if opts.identity_file is not None: if not os.path.exists(opts.identity_file): - print >> stderr,\ - "ERROR: The identity file '{f}' doesn't exist.".format(f=opts.identity_file) + print("ERROR: The identity file '{f}' doesn't exist.".format(f=opts.identity_file), + file=stderr) sys.exit(1) file_mode = os.stat(opts.identity_file).st_mode if not (file_mode & S_IRUSR) or not oct(file_mode)[-2:] == '00': - print >> stderr, "ERROR: The identity file must be accessible only by you." - print >> stderr, 'You can fix this with: chmod 400 "{f}"'.format(f=opts.identity_file) + print("ERROR: The identity file must be accessible only by you.", file=stderr) + print('You can fix this with: chmod 400 "{f}"'.format(f=opts.identity_file), + file=stderr) sys.exit(1) + if opts.instance_type not in EC2_INSTANCE_TYPES: + print("Warning: Unrecognized EC2 instance type for instance-type: {t}".format( + t=opts.instance_type), file=stderr) + + if opts.master_instance_type != "": + if opts.master_instance_type not in EC2_INSTANCE_TYPES: + print("Warning: Unrecognized EC2 instance type for master-instance-type: {t}".format( + t=opts.master_instance_type), file=stderr) + # Since we try instance types even if we can't resolve them, we check if they resolve first + # and, if they do, see if they resolve to the same virtualization type. + if opts.instance_type in EC2_INSTANCE_TYPES and \ + opts.master_instance_type in EC2_INSTANCE_TYPES: + if EC2_INSTANCE_TYPES[opts.instance_type] != \ + EC2_INSTANCE_TYPES[opts.master_instance_type]: + print("Error: spark-ec2 currently does not support having a master and slaves " + "with different AMI virtualization types.", file=stderr) + print("master instance virtualization type: {t}".format( + t=EC2_INSTANCE_TYPES[opts.master_instance_type]), file=stderr) + print("slave instance virtualization type: {t}".format( + t=EC2_INSTANCE_TYPES[opts.instance_type]), file=stderr) + sys.exit(1) + if opts.ebs_vol_num > 8: - print >> stderr, "ebs-vol-num cannot be greater than 8" + print("ebs-vol-num cannot be greater than 8", file=stderr) + sys.exit(1) + + # Prevent breaking ami_prefix (/, .git and startswith checks) + # Prevent forks with non spark-ec2 names for now. + if opts.spark_ec2_git_repo.endswith("/") or \ + opts.spark_ec2_git_repo.endswith(".git") or \ + not opts.spark_ec2_git_repo.startswith("https://github.com") or \ + not opts.spark_ec2_git_repo.endswith("spark-ec2"): + print("spark-ec2-git-repo must be a github repo and it must not have a trailing / or .git. " + "Furthermore, we currently only support forks named spark-ec2.", file=stderr) + sys.exit(1) + + if not (opts.deploy_root_dir is None or + (os.path.isabs(opts.deploy_root_dir) and + os.path.isdir(opts.deploy_root_dir) and + os.path.exists(opts.deploy_root_dir))): + print("--deploy-root-dir must be an absolute path to a directory that exists " + "on the local file system", file=stderr) sys.exit(1) try: conn = ec2.connect_to_region(opts.region) except Exception as e: - print >> stderr, (e) + print((e), file=stderr) sys.exit(1) # Select an AZ at random if it was not specified. @@ -1038,7 +1233,7 @@ def real_main(): if action == "launch": if opts.slaves <= 0: - print >> sys.stderr, "ERROR: You have to start at least 1 slave" + print("ERROR: You have to start at least 1 slave", file=sys.stderr) sys.exit(1) if opts.resume: (master_nodes, slave_nodes) = get_existing_cluster(conn, opts, cluster_name) @@ -1053,26 +1248,27 @@ def real_main(): setup_cluster(conn, master_nodes, slave_nodes, opts, True) elif action == "destroy": - print "Are you sure you want to destroy the cluster %s?" % cluster_name - print "The following instances will be terminated:" (master_nodes, slave_nodes) = get_existing_cluster( conn, opts, cluster_name, die_on_error=False) - for inst in master_nodes + slave_nodes: - print "> %s" % inst.public_dns_name - msg = "ALL DATA ON ALL NODES WILL BE LOST!!\nDestroy cluster %s (y/N): " % cluster_name + if any(master_nodes + slave_nodes): + print("The following instances will be terminated:") + for inst in master_nodes + slave_nodes: + print("> %s" % get_dns_name(inst, opts.private_ips)) + print("ALL DATA ON ALL NODES WILL BE LOST!!") + + msg = "Are you sure you want to destroy the cluster {c}? (y/N) ".format(c=cluster_name) response = raw_input(msg) if response == "y": - print "Terminating master..." + print("Terminating master...") for inst in master_nodes: inst.terminate() - print "Terminating slaves..." + print("Terminating slaves...") for inst in slave_nodes: inst.terminate() # Delete security groups as well if opts.delete_groups: - print "Deleting security groups (this will take some time)..." group_names = [cluster_name + "-master", cluster_name + "-slaves"] wait_for_cluster_state( conn=conn, @@ -1080,15 +1276,16 @@ def real_main(): cluster_instances=(master_nodes + slave_nodes), cluster_state='terminated' ) + print("Deleting security groups (this will take some time)...") attempt = 1 while attempt <= 3: - print "Attempt %d" % attempt + print("Attempt %d" % attempt) groups = [g for g in conn.get_all_security_groups() if g.name in group_names] success = True # Delete individual rules in all groups before deleting groups to # remove dependencies between them for group in groups: - print "Deleting rules in security group " + group.name + print("Deleting rules in security group " + group.name) for rule in group.rules: for grant in rule.grants: success &= group.revoke(ip_protocol=rule.ip_protocol, @@ -1101,11 +1298,12 @@ def real_main(): time.sleep(30) # Yes, it does have to be this long :-( for group in groups: try: - conn.delete_security_group(group.name) - print "Deleted security group " + group.name + # It is needed to use group_id to make it work with VPC + conn.delete_security_group(group_id=group.id) + print("Deleted security group %s" % group.name) except boto.exception.EC2ResponseError: success = False - print "Failed to delete security group " + group.name + print("Failed to delete security group %s" % group.name) # Unfortunately, group.revoke() returns True even if a rule was not # deleted, so this needs to be rerun if something fails @@ -1115,18 +1313,21 @@ def real_main(): attempt += 1 if not success: - print "Failed to delete all security groups after 3 tries." - print "Try re-running in a few minutes." + print("Failed to delete all security groups after 3 tries.") + print("Try re-running in a few minutes.") elif action == "login": (master_nodes, slave_nodes) = get_existing_cluster(conn, opts, cluster_name) - master = master_nodes[0].public_dns_name - print "Logging into master " + master + "..." - proxy_opt = [] - if opts.proxy_port is not None: - proxy_opt = ['-D', opts.proxy_port] - subprocess.check_call( - ssh_command(opts) + proxy_opt + ['-t', '-t', "%s@%s" % (opts.user, master)]) + if not master_nodes[0].public_dns_name and not opts.private_ips: + print("Master has no public DNS name. Maybe you meant to specify --private-ips?") + else: + master = get_dns_name(master_nodes[0], opts.private_ips) + print("Logging into master " + master + "...") + proxy_opt = [] + if opts.proxy_port is not None: + proxy_opt = ['-D', opts.proxy_port] + subprocess.check_call( + ssh_command(opts) + proxy_opt + ['-t', '-t', "%s@%s" % (opts.user, master)]) elif action == "reboot-slaves": response = raw_input( @@ -1136,15 +1337,18 @@ def real_main(): if response == "y": (master_nodes, slave_nodes) = get_existing_cluster( conn, opts, cluster_name, die_on_error=False) - print "Rebooting slaves..." + print("Rebooting slaves...") for inst in slave_nodes: if inst.state not in ["shutting-down", "terminated"]: - print "Rebooting " + inst.id + print("Rebooting " + inst.id) inst.reboot() elif action == "get-master": (master_nodes, slave_nodes) = get_existing_cluster(conn, opts, cluster_name) - print master_nodes[0].public_dns_name + if not master_nodes[0].public_dns_name and not opts.private_ips: + print("Master has no public DNS name. Maybe you meant to specify --private-ips?") + else: + print(get_dns_name(master_nodes[0], opts.private_ips)) elif action == "stop": response = raw_input( @@ -1157,11 +1361,11 @@ def real_main(): if response == "y": (master_nodes, slave_nodes) = get_existing_cluster( conn, opts, cluster_name, die_on_error=False) - print "Stopping master..." + print("Stopping master...") for inst in master_nodes: if inst.state not in ["shutting-down", "terminated"]: inst.stop() - print "Stopping slaves..." + print("Stopping slaves...") for inst in slave_nodes: if inst.state not in ["shutting-down", "terminated"]: if inst.spot_instance_request_id: @@ -1171,11 +1375,11 @@ def real_main(): elif action == "start": (master_nodes, slave_nodes) = get_existing_cluster(conn, opts, cluster_name) - print "Starting slaves..." + print("Starting slaves...") for inst in slave_nodes: if inst.state not in ["shutting-down", "terminated"]: inst.start() - print "Starting master..." + print("Starting master...") for inst in master_nodes: if inst.state not in ["shutting-down", "terminated"]: inst.start() @@ -1185,18 +1389,29 @@ def real_main(): cluster_instances=(master_nodes + slave_nodes), cluster_state='ssh-ready' ) + + # Determine types of running instances + existing_master_type = master_nodes[0].instance_type + existing_slave_type = slave_nodes[0].instance_type + # Setting opts.master_instance_type to the empty string indicates we + # have the same instance type for the master and the slaves + if existing_master_type == existing_slave_type: + existing_master_type = "" + opts.master_instance_type = existing_master_type + opts.instance_type = existing_slave_type + setup_cluster(conn, master_nodes, slave_nodes, opts, False) else: - print >> stderr, "Invalid action: %s" % action + print("Invalid action: %s" % action, file=stderr) sys.exit(1) def main(): try: real_main() - except UsageError, e: - print >> stderr, "\nError:\n", e + except UsageError as e: + print("\nError:\n", e, file=stderr) sys.exit(1) diff --git a/examples/pom.xml b/examples/pom.xml index 8caad2bc2e27a..afd7c6d52f0dd 100644 --- a/examples/pom.xml +++ b/examples/pom.xml @@ -20,8 +20,8 @@ 4.0.0 org.apache.spark - spark-parent - 1.3.0-SNAPSHOT + spark-parent_2.10 + 1.4.0-SNAPSHOT ../pom.xml @@ -90,6 +90,12 @@ org.apache.spark spark-streaming-zeromq_${scala.binary.version} ${project.version} + + + org.spark-project.protobuf + protobuf-java + + org.apache.hbase @@ -234,6 +240,7 @@ org.apache.commons commons-math3 + provided com.twitter @@ -262,6 +269,22 @@ com.ning compress-lzf + + commons-cli + commons-cli + + + commons-codec + commons-codec + + + commons-lang + commons-lang + + + commons-logging + commons-logging + io.netty netty @@ -270,10 +293,22 @@ jline jline + + net.jpountz.lz4 + lz4 + org.apache.cassandra.deps avro + + org.apache.commons + commons-math3 + + + org.apache.thrift + libthrift + @@ -281,6 +316,17 @@ scopt_${scala.binary.version} 3.2.0 + + + + org.scala-lang + scala-library + provided + + @@ -322,12 +368,6 @@ - - - org.apache.commons.math3 - org.spark-project.commons.math3 - - diff --git a/examples/scala-2.10/src/main/java/org/apache/spark/examples/streaming/JavaDirectKafkaWordCount.java b/examples/scala-2.10/src/main/java/org/apache/spark/examples/streaming/JavaDirectKafkaWordCount.java new file mode 100644 index 0000000000000..bab9f2478e779 --- /dev/null +++ b/examples/scala-2.10/src/main/java/org/apache/spark/examples/streaming/JavaDirectKafkaWordCount.java @@ -0,0 +1,113 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.examples.streaming; + +import java.util.HashMap; +import java.util.HashSet; +import java.util.Arrays; +import java.util.regex.Pattern; + +import scala.Tuple2; + +import com.google.common.collect.Lists; +import kafka.serializer.StringDecoder; + +import org.apache.spark.SparkConf; +import org.apache.spark.api.java.function.*; +import org.apache.spark.streaming.api.java.*; +import org.apache.spark.streaming.kafka.KafkaUtils; +import org.apache.spark.streaming.Durations; + +/** + * Consumes messages from one or more topics in Kafka and does wordcount. + * Usage: DirectKafkaWordCount + * is a list of one or more Kafka brokers + * is a list of one or more kafka topics to consume from + * + * Example: + * $ bin/run-example streaming.KafkaWordCount broker1-host:port,broker2-host:port topic1,topic2 + */ + +public final class JavaDirectKafkaWordCount { + private static final Pattern SPACE = Pattern.compile(" "); + + public static void main(String[] args) { + if (args.length < 2) { + System.err.println("Usage: DirectKafkaWordCount \n" + + " is a list of one or more Kafka brokers\n" + + " is a list of one or more kafka topics to consume from\n\n"); + System.exit(1); + } + + StreamingExamples.setStreamingLogLevels(); + + String brokers = args[0]; + String topics = args[1]; + + // Create context with 2 second batch interval + SparkConf sparkConf = new SparkConf().setAppName("JavaDirectKafkaWordCount"); + JavaStreamingContext jssc = new JavaStreamingContext(sparkConf, Durations.seconds(2)); + + HashSet topicsSet = new HashSet(Arrays.asList(topics.split(","))); + HashMap kafkaParams = new HashMap(); + kafkaParams.put("metadata.broker.list", brokers); + + // Create direct kafka stream with brokers and topics + JavaPairInputDStream messages = KafkaUtils.createDirectStream( + jssc, + String.class, + String.class, + StringDecoder.class, + StringDecoder.class, + kafkaParams, + topicsSet + ); + + // Get the lines, split them into words, count the words and print + JavaDStream lines = messages.map(new Function, String>() { + @Override + public String call(Tuple2 tuple2) { + return tuple2._2(); + } + }); + JavaDStream words = lines.flatMap(new FlatMapFunction() { + @Override + public Iterable call(String x) { + return Lists.newArrayList(SPACE.split(x)); + } + }); + JavaPairDStream wordCounts = words.mapToPair( + new PairFunction() { + @Override + public Tuple2 call(String s) { + return new Tuple2(s, 1); + } + }).reduceByKey( + new Function2() { + @Override + public Integer call(Integer i1, Integer i2) { + return i1 + i2; + } + }); + wordCounts.print(); + + // Start the computation + jssc.start(); + jssc.awaitTermination(); + } +} diff --git a/examples/scala-2.10/src/main/scala/org/apache/spark/examples/streaming/DirectKafkaWordCount.scala b/examples/scala-2.10/src/main/scala/org/apache/spark/examples/streaming/DirectKafkaWordCount.scala new file mode 100644 index 0000000000000..11a8cf09533ce --- /dev/null +++ b/examples/scala-2.10/src/main/scala/org/apache/spark/examples/streaming/DirectKafkaWordCount.scala @@ -0,0 +1,72 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.examples.streaming + +import kafka.serializer.StringDecoder + +import org.apache.spark.streaming._ +import org.apache.spark.streaming.kafka._ +import org.apache.spark.SparkConf + +/** + * Consumes messages from one or more topics in Kafka and does wordcount. + * Usage: DirectKafkaWordCount + * is a list of one or more Kafka brokers + * is a list of one or more kafka topics to consume from + * + * Example: + * $ bin/run-example streaming.DirectKafkaWordCount broker1-host:port,broker2-host:port \ + * topic1,topic2 + */ +object DirectKafkaWordCount { + def main(args: Array[String]) { + if (args.length < 2) { + System.err.println(s""" + |Usage: DirectKafkaWordCount + | is a list of one or more Kafka brokers + | is a list of one or more kafka topics to consume from + | + """.stripMargin) + System.exit(1) + } + + StreamingExamples.setStreamingLogLevels() + + val Array(brokers, topics) = args + + // Create context with 2 second batch interval + val sparkConf = new SparkConf().setAppName("DirectKafkaWordCount") + val ssc = new StreamingContext(sparkConf, Seconds(2)) + + // Create direct kafka stream with brokers and topics + val topicsSet = topics.split(",").toSet + val kafkaParams = Map[String, String]("metadata.broker.list" -> brokers) + val messages = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder]( + ssc, kafkaParams, topicsSet) + + // Get the lines, split them into words, count the words and print + val lines = messages.map(_._2) + val words = lines.flatMap(_.split(" ")) + val wordCounts = words.map(x => (x, 1L)).reduceByKey(_ + _) + wordCounts.print() + + // Start the computation + ssc.start() + ssc.awaitTermination() + } +} diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaCrossValidatorExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaCrossValidatorExample.java index 5041e0b6d34b0..9bbc14ea40875 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaCrossValidatorExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaCrossValidatorExample.java @@ -34,8 +34,8 @@ import org.apache.spark.ml.tuning.CrossValidatorModel; import org.apache.spark.ml.tuning.ParamGridBuilder; import org.apache.spark.sql.DataFrame; -import org.apache.spark.sql.SQLContext; import org.apache.spark.sql.Row; +import org.apache.spark.sql.SQLContext; /** * A simple example demonstrating model selection using CrossValidator. @@ -71,7 +71,7 @@ public static void main(String[] args) { new LabeledDocument(9L, "a e c l", 0.0), new LabeledDocument(10L, "spark compile", 1.0), new LabeledDocument(11L, "hadoop software", 0.0)); - DataFrame training = jsql.applySchema(jsc.parallelize(localTraining), LabeledDocument.class); + DataFrame training = jsql.createDataFrame(jsc.parallelize(localTraining), LabeledDocument.class); // Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr. Tokenizer tokenizer = new Tokenizer() @@ -112,12 +112,11 @@ public static void main(String[] args) { new Document(5L, "l m n"), new Document(6L, "mapreduce spark"), new Document(7L, "apache hadoop")); - DataFrame test = jsql.applySchema(jsc.parallelize(localTest), Document.class); + DataFrame test = jsql.createDataFrame(jsc.parallelize(localTest), Document.class); // Make predictions on test documents. cvModel uses the best model found (lrModel). - cvModel.transform(test).registerTempTable("prediction"); - DataFrame predictions = jsql.sql("SELECT id, text, probability, prediction FROM prediction"); - for (Row r: predictions.collect()) { + DataFrame predictions = cvModel.transform(test); + for (Row r: predictions.select("id", "text", "probability", "prediction").collect()) { System.out.println("(" + r.get(0) + ", " + r.get(1) + ") --> prob=" + r.get(2) + ", prediction=" + r.get(3)); } diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java index 4d9dad9f23038..eaf00d09f550d 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java @@ -62,7 +62,7 @@ public static void main(String[] args) throws Exception { new LabeledPoint(0.0, Vectors.dense(2.0, 1.0, -1.0)), new LabeledPoint(0.0, Vectors.dense(2.0, 1.3, 1.0)), new LabeledPoint(1.0, Vectors.dense(0.0, 1.2, -0.5))); - DataFrame training = jsql.applySchema(jsc.parallelize(localTraining), LabeledPoint.class); + DataFrame training = jsql.createDataFrame(jsc.parallelize(localTraining), LabeledPoint.class); // Create a LogisticRegression instance. This instance is an Estimator. MyJavaLogisticRegression lr = new MyJavaLogisticRegression(); @@ -80,7 +80,7 @@ public static void main(String[] args) throws Exception { new LabeledPoint(1.0, Vectors.dense(-1.0, 1.5, 1.3)), new LabeledPoint(0.0, Vectors.dense(3.0, 2.0, -0.1)), new LabeledPoint(1.0, Vectors.dense(0.0, 2.2, -1.5))); - DataFrame test = jsql.applySchema(jsc.parallelize(localTest), LabeledPoint.class); + DataFrame test = jsql.createDataFrame(jsc.parallelize(localTest), LabeledPoint.class); // Make predictions on test documents. cvModel uses the best model found (lrModel). DataFrame results = model.transform(test); @@ -116,7 +116,7 @@ class MyJavaLogisticRegression */ IntParam maxIter = new IntParam(this, "maxIter", "max number of iterations"); - int getMaxIter() { return (Integer) get(maxIter); } + int getMaxIter() { return (Integer) getOrDefault(maxIter); } public MyJavaLogisticRegression() { setMaxIter(100); @@ -211,7 +211,7 @@ public Vector predictRaw(Vector features) { public MyJavaLogisticRegressionModel copy() { MyJavaLogisticRegressionModel m = new MyJavaLogisticRegressionModel(parent_, fittingParamMap_, weights_); - Params$.MODULE$.inheritValues(this.paramMap(), this, m); + Params$.MODULE$.inheritValues(this.extractParamMap(), this, m); return m; } } diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java index cc69e6315fdda..4e02acce696e6 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java @@ -29,8 +29,8 @@ import org.apache.spark.mllib.linalg.Vectors; import org.apache.spark.mllib.regression.LabeledPoint; import org.apache.spark.sql.DataFrame; -import org.apache.spark.sql.SQLContext; import org.apache.spark.sql.Row; +import org.apache.spark.sql.SQLContext; /** * A simple example demonstrating ways to specify parameters for Estimators and Transformers. @@ -54,7 +54,7 @@ public static void main(String[] args) { new LabeledPoint(0.0, Vectors.dense(2.0, 1.0, -1.0)), new LabeledPoint(0.0, Vectors.dense(2.0, 1.3, 1.0)), new LabeledPoint(1.0, Vectors.dense(0.0, 1.2, -0.5))); - DataFrame training = jsql.applySchema(jsc.parallelize(localTraining), LabeledPoint.class); + DataFrame training = jsql.createDataFrame(jsc.parallelize(localTraining), LabeledPoint.class); // Create a LogisticRegression instance. This instance is an Estimator. LogisticRegression lr = new LogisticRegression(); @@ -94,16 +94,14 @@ public static void main(String[] args) { new LabeledPoint(1.0, Vectors.dense(-1.0, 1.5, 1.3)), new LabeledPoint(0.0, Vectors.dense(3.0, 2.0, -0.1)), new LabeledPoint(1.0, Vectors.dense(0.0, 2.2, -1.5))); - DataFrame test = jsql.applySchema(jsc.parallelize(localTest), LabeledPoint.class); + DataFrame test = jsql.createDataFrame(jsc.parallelize(localTest), LabeledPoint.class); // Make predictions on test documents using the Transformer.transform() method. // LogisticRegression.transform will only use the 'features' column. // Note that model2.transform() outputs a 'myProbability' column instead of the usual // 'probability' column since we renamed the lr.probabilityCol parameter previously. - model2.transform(test).registerTempTable("results"); - DataFrame results = - jsql.sql("SELECT features, label, myProbability, prediction FROM results"); - for (Row r: results.collect()) { + DataFrame results = model2.transform(test); + for (Row r: results.select("features", "label", "myProbability", "prediction").collect()) { System.out.println("(" + r.get(0) + ", " + r.get(1) + ") -> prob=" + r.get(2) + ", prediction=" + r.get(3)); } diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleTextClassificationPipeline.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleTextClassificationPipeline.java index d929f1ad2014a..ef1ec103a879f 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleTextClassificationPipeline.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleTextClassificationPipeline.java @@ -30,8 +30,8 @@ import org.apache.spark.ml.feature.HashingTF; import org.apache.spark.ml.feature.Tokenizer; import org.apache.spark.sql.DataFrame; -import org.apache.spark.sql.SQLContext; import org.apache.spark.sql.Row; +import org.apache.spark.sql.SQLContext; /** * A simple text classification pipeline that recognizes "spark" from input text. It uses the Java @@ -54,7 +54,7 @@ public static void main(String[] args) { new LabeledDocument(1L, "b d", 0.0), new LabeledDocument(2L, "spark f g h", 1.0), new LabeledDocument(3L, "hadoop mapreduce", 0.0)); - DataFrame training = jsql.applySchema(jsc.parallelize(localTraining), LabeledDocument.class); + DataFrame training = jsql.createDataFrame(jsc.parallelize(localTraining), LabeledDocument.class); // Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr. Tokenizer tokenizer = new Tokenizer() @@ -79,12 +79,11 @@ public static void main(String[] args) { new Document(5L, "l m n"), new Document(6L, "mapreduce spark"), new Document(7L, "apache hadoop")); - DataFrame test = jsql.applySchema(jsc.parallelize(localTest), Document.class); + DataFrame test = jsql.createDataFrame(jsc.parallelize(localTest), Document.class); // Make predictions on test documents. - model.transform(test).registerTempTable("prediction"); - DataFrame predictions = jsql.sql("SELECT id, text, score, prediction FROM prediction"); - for (Row r: predictions.collect()) { + DataFrame predictions = model.transform(test); + for (Row r: predictions.select("id", "text", "probability", "prediction").collect()) { System.out.println("(" + r.get(0) + ", " + r.get(1) + ") --> prob=" + r.get(2) + ", prediction=" + r.get(3)); } diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaFPGrowthExample.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaFPGrowthExample.java new file mode 100644 index 0000000000000..36baf5868736c --- /dev/null +++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaFPGrowthExample.java @@ -0,0 +1,78 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.examples.mllib; + +import java.util.ArrayList; + +import com.google.common.base.Joiner; +import com.google.common.collect.Lists; + +import org.apache.spark.SparkConf; +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.api.java.function.Function; +import org.apache.spark.mllib.fpm.FPGrowth; +import org.apache.spark.mllib.fpm.FPGrowthModel; + +/** + * Java example for mining frequent itemsets using FP-growth. + * Example usage: ./bin/run-example mllib.JavaFPGrowthExample ./data/mllib/sample_fpgrowth.txt + */ +public class JavaFPGrowthExample { + + public static void main(String[] args) { + String inputFile; + double minSupport = 0.3; + int numPartition = -1; + if (args.length < 1) { + System.err.println( + "Usage: JavaFPGrowth [minSupport] [numPartition]"); + System.exit(1); + } + inputFile = args[0]; + if (args.length >= 2) { + minSupport = Double.parseDouble(args[1]); + } + if (args.length >= 3) { + numPartition = Integer.parseInt(args[2]); + } + + SparkConf sparkConf = new SparkConf().setAppName("JavaFPGrowthExample"); + JavaSparkContext sc = new JavaSparkContext(sparkConf); + + JavaRDD> transactions = sc.textFile(inputFile).map( + new Function>() { + @Override + public ArrayList call(String s) { + return Lists.newArrayList(s.split(" ")); + } + } + ); + + FPGrowthModel model = new FPGrowth() + .setMinSupport(minSupport) + .setNumPartitions(numPartition) + .run(transactions); + + for (FPGrowth.FreqItemset s: model.freqItemsets().toJavaRDD().collect()) { + System.out.println("[" + Joiner.on(",").join(s.javaItems()) + "], " + s.freq()); + } + + sc.stop(); + } +} diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaLDAExample.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaLDAExample.java new file mode 100644 index 0000000000000..36207ae38d9a9 --- /dev/null +++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaLDAExample.java @@ -0,0 +1,76 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.examples.mllib; + +import scala.Tuple2; + +import org.apache.spark.api.java.*; +import org.apache.spark.api.java.function.Function; +import org.apache.spark.mllib.clustering.DistributedLDAModel; +import org.apache.spark.mllib.clustering.LDA; +import org.apache.spark.mllib.linalg.Matrix; +import org.apache.spark.mllib.linalg.Vector; +import org.apache.spark.mllib.linalg.Vectors; +import org.apache.spark.SparkConf; + +public class JavaLDAExample { + public static void main(String[] args) { + SparkConf conf = new SparkConf().setAppName("LDA Example"); + JavaSparkContext sc = new JavaSparkContext(conf); + + // Load and parse the data + String path = "data/mllib/sample_lda_data.txt"; + JavaRDD data = sc.textFile(path); + JavaRDD parsedData = data.map( + new Function() { + public Vector call(String s) { + String[] sarray = s.trim().split(" "); + double[] values = new double[sarray.length]; + for (int i = 0; i < sarray.length; i++) + values[i] = Double.parseDouble(sarray[i]); + return Vectors.dense(values); + } + } + ); + // Index documents with unique IDs + JavaPairRDD corpus = JavaPairRDD.fromJavaRDD(parsedData.zipWithIndex().map( + new Function, Tuple2>() { + public Tuple2 call(Tuple2 doc_id) { + return doc_id.swap(); + } + } + )); + corpus.cache(); + + // Cluster the documents into three topics using LDA + DistributedLDAModel ldaModel = new LDA().setK(3).run(corpus); + + // Output topics. Each is a distribution over words (matching word count vectors) + System.out.println("Learned topics (as distributions over vocab of " + ldaModel.vocabSize() + + " words):"); + Matrix topics = ldaModel.topicsMatrix(); + for (int topic = 0; topic < 3; topic++) { + System.out.print("Topic " + topic + ":"); + for (int word = 0; word < ldaModel.vocabSize(); word++) { + System.out.print(" " + topics.apply(word, topic)); + } + System.out.println(); + } + sc.stop(); + } +} diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaPowerIterationClusteringExample.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaPowerIterationClusteringExample.java new file mode 100644 index 0000000000000..6c6f9768f015e --- /dev/null +++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaPowerIterationClusteringExample.java @@ -0,0 +1,57 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.examples.mllib; + +import scala.Tuple3; + +import com.google.common.collect.Lists; + +import org.apache.spark.SparkConf; +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.mllib.clustering.PowerIterationClustering; +import org.apache.spark.mllib.clustering.PowerIterationClusteringModel; + +/** + * Java example for graph clustering using power iteration clustering (PIC). + */ +public class JavaPowerIterationClusteringExample { + public static void main(String[] args) { + SparkConf sparkConf = new SparkConf().setAppName("JavaPowerIterationClusteringExample"); + JavaSparkContext sc = new JavaSparkContext(sparkConf); + + @SuppressWarnings("unchecked") + JavaRDD> similarities = sc.parallelize(Lists.newArrayList( + new Tuple3(0L, 1L, 0.9), + new Tuple3(1L, 2L, 0.9), + new Tuple3(2L, 3L, 0.9), + new Tuple3(3L, 4L, 0.1), + new Tuple3(4L, 5L, 0.9))); + + PowerIterationClustering pic = new PowerIterationClustering() + .setK(2) + .setMaxIterations(10); + PowerIterationClusteringModel model = pic.run(similarities); + + for (PowerIterationClustering.Assignment a: model.assignments().toJavaRDD().collect()) { + System.out.println(a.id() + " -> " + a.cluster()); + } + + sc.stop(); + } +} diff --git a/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java b/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java index 8defb769ffaaf..8159ffbe2d269 100644 --- a/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java +++ b/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java @@ -55,7 +55,7 @@ public void setAge(int age) { public static void main(String[] args) throws Exception { SparkConf sparkConf = new SparkConf().setAppName("JavaSparkSQL"); JavaSparkContext ctx = new JavaSparkContext(sparkConf); - SQLContext sqlCtx = new SQLContext(ctx); + SQLContext sqlContext = new SQLContext(ctx); System.out.println("=== Data source: RDD ==="); // Load a text file and convert each line to a Java Bean. @@ -74,11 +74,11 @@ public Person call(String line) { }); // Apply a schema to an RDD of Java Beans and register it as a table. - DataFrame schemaPeople = sqlCtx.applySchema(people, Person.class); + DataFrame schemaPeople = sqlContext.createDataFrame(people, Person.class); schemaPeople.registerTempTable("people"); // SQL can be run over RDDs that have been registered as tables. - DataFrame teenagers = sqlCtx.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19"); + DataFrame teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19"); // The results of SQL queries are DataFrames and support all the normal RDD operations. // The columns of a row in the result can be accessed by ordinal. @@ -99,12 +99,12 @@ public String call(Row row) { // Read in the parquet file created above. // Parquet files are self-describing so the schema is preserved. // The result of loading a parquet file is also a DataFrame. - DataFrame parquetFile = sqlCtx.parquetFile("people.parquet"); + DataFrame parquetFile = sqlContext.parquetFile("people.parquet"); //Parquet files can also be registered as tables and then used in SQL statements. parquetFile.registerTempTable("parquetFile"); DataFrame teenagers2 = - sqlCtx.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19"); + sqlContext.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19"); teenagerNames = teenagers2.toJavaRDD().map(new Function() { @Override public String call(Row row) { @@ -120,7 +120,7 @@ public String call(Row row) { // The path can be either a single text file or a directory storing text files. String path = "examples/src/main/resources/people.json"; // Create a DataFrame from the file(s) pointed by path - DataFrame peopleFromJsonFile = sqlCtx.jsonFile(path); + DataFrame peopleFromJsonFile = sqlContext.jsonFile(path); // Because the schema of a JSON dataset is automatically inferred, to write queries, // it is better to take a look at what is the schema. @@ -133,8 +133,8 @@ public String call(Row row) { // Register this DataFrame as a table. peopleFromJsonFile.registerTempTable("people"); - // SQL statements can be run by using the sql methods provided by sqlCtx. - DataFrame teenagers3 = sqlCtx.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19"); + // SQL statements can be run by using the sql methods provided by sqlContext. + DataFrame teenagers3 = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19"); // The results of SQL queries are DataFrame and support all the normal RDD operations. // The columns of a row in the result can be accessed by ordinal. @@ -151,7 +151,7 @@ public String call(Row row) { List jsonData = Arrays.asList( "{\"name\":\"Yin\",\"address\":{\"city\":\"Columbus\",\"state\":\"Ohio\"}}"); JavaRDD anotherPeopleRDD = ctx.parallelize(jsonData); - DataFrame peopleFromJsonRDD = sqlCtx.jsonRDD(anotherPeopleRDD.rdd()); + DataFrame peopleFromJsonRDD = sqlContext.jsonRDD(anotherPeopleRDD.rdd()); // Take a look at the schema of this new DataFrame. peopleFromJsonRDD.printSchema(); @@ -164,7 +164,7 @@ public String call(Row row) { peopleFromJsonRDD.registerTempTable("people2"); - DataFrame peopleWithCity = sqlCtx.sql("SELECT name, address.city FROM people2"); + DataFrame peopleWithCity = sqlContext.sql("SELECT name, address.city FROM people2"); List nameAndCity = peopleWithCity.toJavaRDD().map(new Function() { @Override public String call(Row row) { diff --git a/core/src/test/scala/org/apache/spark/util/FakeClock.scala b/examples/src/main/java/org/apache/spark/examples/streaming/JavaRecord.java similarity index 71% rename from core/src/test/scala/org/apache/spark/util/FakeClock.scala rename to examples/src/main/java/org/apache/spark/examples/streaming/JavaRecord.java index 0a45917b08dd2..e63697a79f23a 100644 --- a/core/src/test/scala/org/apache/spark/util/FakeClock.scala +++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaRecord.java @@ -15,12 +15,17 @@ * limitations under the License. */ -package org.apache.spark.util +package org.apache.spark.examples.streaming; -class FakeClock extends Clock { - private var time = 0L +/** Java Bean class to be used with the example JavaSqlNetworkWordCount. */ +public class JavaRecord implements java.io.Serializable { + private String word; - def advance(millis: Long): Unit = time += millis + public String getWord() { + return word; + } - def getTime(): Long = time + public void setWord(String word) { + this.word = word; + } } diff --git a/examples/src/main/java/org/apache/spark/examples/streaming/JavaSqlNetworkWordCount.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaSqlNetworkWordCount.java new file mode 100644 index 0000000000000..46562ddbbcb57 --- /dev/null +++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaSqlNetworkWordCount.java @@ -0,0 +1,122 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.examples.streaming; + +import java.util.regex.Pattern; + +import com.google.common.collect.Lists; + +import org.apache.spark.SparkConf; +import org.apache.spark.SparkContext; +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.function.FlatMapFunction; +import org.apache.spark.api.java.function.Function; +import org.apache.spark.api.java.function.Function2; +import org.apache.spark.sql.SQLContext; +import org.apache.spark.sql.DataFrame; +import org.apache.spark.api.java.StorageLevels; +import org.apache.spark.streaming.Durations; +import org.apache.spark.streaming.Time; +import org.apache.spark.streaming.api.java.JavaDStream; +import org.apache.spark.streaming.api.java.JavaReceiverInputDStream; +import org.apache.spark.streaming.api.java.JavaStreamingContext; + +/** + * Use DataFrames and SQL to count words in UTF8 encoded, '\n' delimited text received from the + * network every second. + * + * Usage: JavaSqlNetworkWordCount + * and describe the TCP server that Spark Streaming would connect to receive data. + * + * To run this on your local machine, you need to first run a Netcat server + * `$ nc -lk 9999` + * and then run the example + * `$ bin/run-example org.apache.spark.examples.streaming.JavaSqlNetworkWordCount localhost 9999` + */ + +public final class JavaSqlNetworkWordCount { + private static final Pattern SPACE = Pattern.compile(" "); + + public static void main(String[] args) { + if (args.length < 2) { + System.err.println("Usage: JavaNetworkWordCount "); + System.exit(1); + } + + StreamingExamples.setStreamingLogLevels(); + + // Create the context with a 1 second batch size + SparkConf sparkConf = new SparkConf().setAppName("JavaSqlNetworkWordCount"); + JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, Durations.seconds(1)); + + // Create a JavaReceiverInputDStream on target ip:port and count the + // words in input stream of \n delimited text (eg. generated by 'nc') + // Note that no duplication in storage level only for running locally. + // Replication necessary in distributed scenario for fault tolerance. + JavaReceiverInputDStream lines = ssc.socketTextStream( + args[0], Integer.parseInt(args[1]), StorageLevels.MEMORY_AND_DISK_SER); + JavaDStream words = lines.flatMap(new FlatMapFunction() { + @Override + public Iterable call(String x) { + return Lists.newArrayList(SPACE.split(x)); + } + }); + + // Convert RDDs of the words DStream to DataFrame and run SQL query + words.foreachRDD(new Function2, Time, Void>() { + @Override + public Void call(JavaRDD rdd, Time time) { + SQLContext sqlContext = JavaSQLContextSingleton.getInstance(rdd.context()); + + // Convert JavaRDD[String] to JavaRDD[bean class] to DataFrame + JavaRDD rowRDD = rdd.map(new Function() { + public JavaRecord call(String word) { + JavaRecord record = new JavaRecord(); + record.setWord(word); + return record; + } + }); + DataFrame wordsDataFrame = sqlContext.createDataFrame(rowRDD, JavaRecord.class); + + // Register as table + wordsDataFrame.registerTempTable("words"); + + // Do word count on table using SQL and print it + DataFrame wordCountsDataFrame = + sqlContext.sql("select word, count(*) as total from words group by word"); + System.out.println("========= " + time + "========="); + wordCountsDataFrame.show(); + return null; + } + }); + + ssc.start(); + ssc.awaitTermination(); + } +} + +/** Lazily instantiated singleton instance of SQLContext */ +class JavaSQLContextSingleton { + static private transient SQLContext instance = null; + static public SQLContext getInstance(SparkContext sparkContext) { + if (instance == null) { + instance = new SQLContext(sparkContext); + } + return instance; + } +} diff --git a/examples/src/main/java/org/apache/spark/examples/streaming/JavaStatefulNetworkWordCount.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaStatefulNetworkWordCount.java new file mode 100644 index 0000000000000..dbf2ef02d7b76 --- /dev/null +++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaStatefulNetworkWordCount.java @@ -0,0 +1,116 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.examples.streaming; + +import java.util.Arrays; +import java.util.List; +import java.util.regex.Pattern; + +import scala.Tuple2; + +import com.google.common.base.Optional; +import com.google.common.collect.Lists; + +import org.apache.spark.HashPartitioner; +import org.apache.spark.SparkConf; +import org.apache.spark.api.java.JavaPairRDD; +import org.apache.spark.api.java.StorageLevels; +import org.apache.spark.api.java.function.FlatMapFunction; +import org.apache.spark.api.java.function.Function2; +import org.apache.spark.api.java.function.PairFunction; +import org.apache.spark.streaming.Durations; +import org.apache.spark.streaming.api.java.JavaDStream; +import org.apache.spark.streaming.api.java.JavaPairDStream; +import org.apache.spark.streaming.api.java.JavaReceiverInputDStream; +import org.apache.spark.streaming.api.java.JavaStreamingContext; + +/** + * Counts words cumulatively in UTF8 encoded, '\n' delimited text received from the network every + * second starting with initial value of word count. + * Usage: JavaStatefulNetworkWordCount + * and describe the TCP server that Spark Streaming would connect to receive + * data. + *

    + * To run this on your local machine, you need to first run a Netcat server + * `$ nc -lk 9999` + * and then run the example + * `$ bin/run-example + * org.apache.spark.examples.streaming.JavaStatefulNetworkWordCount localhost 9999` + */ +public class JavaStatefulNetworkWordCount { + private static final Pattern SPACE = Pattern.compile(" "); + + public static void main(String[] args) { + if (args.length < 2) { + System.err.println("Usage: JavaStatefulNetworkWordCount "); + System.exit(1); + } + + StreamingExamples.setStreamingLogLevels(); + + // Update the cumulative count function + final Function2, Optional, Optional> updateFunction = + new Function2, Optional, Optional>() { + @Override + public Optional call(List values, Optional state) { + Integer newSum = state.or(0); + for (Integer value : values) { + newSum += value; + } + return Optional.of(newSum); + } + }; + + // Create the context with a 1 second batch size + SparkConf sparkConf = new SparkConf().setAppName("JavaStatefulNetworkWordCount"); + JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, Durations.seconds(1)); + ssc.checkpoint("."); + + // Initial RDD input to updateStateByKey + @SuppressWarnings("unchecked") + List> tuples = Arrays.asList(new Tuple2("hello", 1), + new Tuple2("world", 1)); + JavaPairRDD initialRDD = ssc.sc().parallelizePairs(tuples); + + JavaReceiverInputDStream lines = ssc.socketTextStream( + args[0], Integer.parseInt(args[1]), StorageLevels.MEMORY_AND_DISK_SER_2); + + JavaDStream words = lines.flatMap(new FlatMapFunction() { + @Override + public Iterable call(String x) { + return Lists.newArrayList(SPACE.split(x)); + } + }); + + JavaPairDStream wordsDstream = words.mapToPair( + new PairFunction() { + @Override + public Tuple2 call(String s) { + return new Tuple2(s, 1); + } + }); + + // This will give a Dstream made of state (which is the cumulative count of the words) + JavaPairDStream stateDstream = wordsDstream.updateStateByKey(updateFunction, + new HashPartitioner(ssc.sc().defaultParallelism()), initialRDD); + + stateDstream.print(); + ssc.start(); + ssc.awaitTermination(); + } +} diff --git a/examples/src/main/python/als.py b/examples/src/main/python/als.py index 70b6146e39a87..1c3a787bd0e94 100755 --- a/examples/src/main/python/als.py +++ b/examples/src/main/python/als.py @@ -21,7 +21,8 @@ This example requires numpy (http://www.numpy.org/) """ -from os.path import realpath +from __future__ import print_function + import sys import numpy as np @@ -57,9 +58,9 @@ def update(i, vec, mat, ratings): Usage: als [M] [U] [F] [iterations] [partitions]" """ - print >> sys.stderr, """WARN: This is a naive implementation of ALS and is given as an + print("""WARN: This is a naive implementation of ALS and is given as an example. Please use the ALS method found in pyspark.mllib.recommendation for more - conventional use.""" + conventional use.""", file=sys.stderr) sc = SparkContext(appName="PythonALS") M = int(sys.argv[1]) if len(sys.argv) > 1 else 100 @@ -68,8 +69,8 @@ def update(i, vec, mat, ratings): ITERATIONS = int(sys.argv[4]) if len(sys.argv) > 4 else 5 partitions = int(sys.argv[5]) if len(sys.argv) > 5 else 2 - print "Running ALS with M=%d, U=%d, F=%d, iters=%d, partitions=%d\n" % \ - (M, U, F, ITERATIONS, partitions) + print("Running ALS with M=%d, U=%d, F=%d, iters=%d, partitions=%d\n" % + (M, U, F, ITERATIONS, partitions)) R = matrix(rand(M, F)) * matrix(rand(U, F).T) ms = matrix(rand(M, F)) @@ -95,7 +96,7 @@ def update(i, vec, mat, ratings): usb = sc.broadcast(us) error = rmse(R, ms, us) - print "Iteration %d:" % i - print "\nRMSE: %5.4f\n" % error + print("Iteration %d:" % i) + print("\nRMSE: %5.4f\n" % error) sc.stop() diff --git a/examples/src/main/python/avro_inputformat.py b/examples/src/main/python/avro_inputformat.py index 4626bbb7e3b02..da368ac628a49 100644 --- a/examples/src/main/python/avro_inputformat.py +++ b/examples/src/main/python/avro_inputformat.py @@ -15,9 +15,12 @@ # limitations under the License. # +from __future__ import print_function + import sys from pyspark import SparkContext +from functools import reduce """ Read data file users.avro in local Spark distro: @@ -49,7 +52,7 @@ """ if __name__ == "__main__": if len(sys.argv) != 2 and len(sys.argv) != 3: - print >> sys.stderr, """ + print(""" Usage: avro_inputformat [reader_schema_file] Run with example jar: @@ -57,7 +60,7 @@ /path/to/examples/avro_inputformat.py [reader_schema_file] Assumes you have Avro data stored in . Reader schema can be optionally specified in [reader_schema_file]. - """ + """, file=sys.stderr) exit(-1) path = sys.argv[1] @@ -77,6 +80,6 @@ conf=conf) output = avro_rdd.map(lambda x: x[0]).collect() for k in output: - print k + print(k) sc.stop() diff --git a/examples/src/main/python/cassandra_inputformat.py b/examples/src/main/python/cassandra_inputformat.py index 05f34b74df45a..93ca0cfcc9302 100644 --- a/examples/src/main/python/cassandra_inputformat.py +++ b/examples/src/main/python/cassandra_inputformat.py @@ -15,6 +15,8 @@ # limitations under the License. # +from __future__ import print_function + import sys from pyspark import SparkContext @@ -47,14 +49,14 @@ """ if __name__ == "__main__": if len(sys.argv) != 4: - print >> sys.stderr, """ + print(""" Usage: cassandra_inputformat Run with example jar: ./bin/spark-submit --driver-class-path /path/to/example/jar \ /path/to/examples/cassandra_inputformat.py Assumes you have some data in Cassandra already, running on , in and - """ + """, file=sys.stderr) exit(-1) host = sys.argv[1] @@ -77,6 +79,6 @@ conf=conf) output = cass_rdd.collect() for (k, v) in output: - print (k, v) + print((k, v)) sc.stop() diff --git a/examples/src/main/python/cassandra_outputformat.py b/examples/src/main/python/cassandra_outputformat.py index d144539e58b8f..5d643eac92f94 100644 --- a/examples/src/main/python/cassandra_outputformat.py +++ b/examples/src/main/python/cassandra_outputformat.py @@ -15,6 +15,8 @@ # limitations under the License. # +from __future__ import print_function + import sys from pyspark import SparkContext @@ -46,7 +48,7 @@ """ if __name__ == "__main__": if len(sys.argv) != 7: - print >> sys.stderr, """ + print(""" Usage: cassandra_outputformat Run with example jar: @@ -60,7 +62,7 @@ ... fname text, ... lname text ... ); - """ + """, file=sys.stderr) exit(-1) host = sys.argv[1] diff --git a/examples/src/main/python/hbase_inputformat.py b/examples/src/main/python/hbase_inputformat.py index 3b16010f1cb97..e17819d5feb76 100644 --- a/examples/src/main/python/hbase_inputformat.py +++ b/examples/src/main/python/hbase_inputformat.py @@ -15,6 +15,8 @@ # limitations under the License. # +from __future__ import print_function + import sys from pyspark import SparkContext @@ -47,14 +49,14 @@ """ if __name__ == "__main__": if len(sys.argv) != 3: - print >> sys.stderr, """ + print(""" Usage: hbase_inputformat Run with example jar: ./bin/spark-submit --driver-class-path /path/to/example/jar \ /path/to/examples/hbase_inputformat.py
    Assumes you have some data in HBase already, running on , in
    - """ + """, file=sys.stderr) exit(-1) host = sys.argv[1] @@ -74,6 +76,6 @@ conf=conf) output = hbase_rdd.collect() for (k, v) in output: - print (k, v) + print((k, v)) sc.stop() diff --git a/examples/src/main/python/hbase_outputformat.py b/examples/src/main/python/hbase_outputformat.py index abb425b1f886a..9e5641789a976 100644 --- a/examples/src/main/python/hbase_outputformat.py +++ b/examples/src/main/python/hbase_outputformat.py @@ -15,6 +15,8 @@ # limitations under the License. # +from __future__ import print_function + import sys from pyspark import SparkContext @@ -40,7 +42,7 @@ """ if __name__ == "__main__": if len(sys.argv) != 7: - print >> sys.stderr, """ + print(""" Usage: hbase_outputformat
    Run with example jar: @@ -48,7 +50,7 @@ /path/to/examples/hbase_outputformat.py Assumes you have created
    with column family in HBase running on already - """ + """, file=sys.stderr) exit(-1) host = sys.argv[1] diff --git a/examples/src/main/python/kmeans.py b/examples/src/main/python/kmeans.py index 86ef6f32c84e8..19391506463f0 100755 --- a/examples/src/main/python/kmeans.py +++ b/examples/src/main/python/kmeans.py @@ -22,6 +22,7 @@ This example requires NumPy (http://www.numpy.org/). """ +from __future__ import print_function import sys @@ -47,12 +48,12 @@ def closestPoint(p, centers): if __name__ == "__main__": if len(sys.argv) != 4: - print >> sys.stderr, "Usage: kmeans " + print("Usage: kmeans ", file=sys.stderr) exit(-1) - print >> sys.stderr, """WARN: This is a naive implementation of KMeans Clustering and is given + print("""WARN: This is a naive implementation of KMeans Clustering and is given as an example! Please refer to examples/src/main/python/mllib/kmeans.py for an example on - how to use MLlib's KMeans implementation.""" + how to use MLlib's KMeans implementation.""", file=sys.stderr) sc = SparkContext(appName="PythonKMeans") lines = sc.textFile(sys.argv[1]) @@ -69,13 +70,13 @@ def closestPoint(p, centers): pointStats = closest.reduceByKey( lambda (x1, y1), (x2, y2): (x1 + x2, y1 + y2)) newPoints = pointStats.map( - lambda (x, (y, z)): (x, y / z)).collect() + lambda xy: (xy[0], xy[1][0] / xy[1][1])).collect() tempDist = sum(np.sum((kPoints[x] - y) ** 2) for (x, y) in newPoints) for (x, y) in newPoints: kPoints[x] = y - print "Final centers: " + str(kPoints) + print("Final centers: " + str(kPoints)) sc.stop() diff --git a/examples/src/main/python/logistic_regression.py b/examples/src/main/python/logistic_regression.py index 3aa56b0528168..b318b7d87bfdc 100755 --- a/examples/src/main/python/logistic_regression.py +++ b/examples/src/main/python/logistic_regression.py @@ -22,10 +22,8 @@ In practice, one may prefer to use the LogisticRegression algorithm in MLlib, as shown in examples/src/main/python/mllib/logistic_regression.py. """ +from __future__ import print_function -from collections import namedtuple -from math import exp -from os.path import realpath import sys import numpy as np @@ -42,19 +40,19 @@ def readPointBatch(iterator): strs = list(iterator) matrix = np.zeros((len(strs), D + 1)) - for i in xrange(len(strs)): - matrix[i] = np.fromstring(strs[i].replace(',', ' '), dtype=np.float32, sep=' ') + for i, s in enumerate(strs): + matrix[i] = np.fromstring(s.replace(',', ' '), dtype=np.float32, sep=' ') return [matrix] if __name__ == "__main__": if len(sys.argv) != 3: - print >> sys.stderr, "Usage: logistic_regression " + print("Usage: logistic_regression ", file=sys.stderr) exit(-1) - print >> sys.stderr, """WARN: This is a naive implementation of Logistic Regression and is + print("""WARN: This is a naive implementation of Logistic Regression and is given as an example! Please refer to examples/src/main/python/mllib/logistic_regression.py - to see how MLlib's implementation is used.""" + to see how MLlib's implementation is used.""", file=sys.stderr) sc = SparkContext(appName="PythonLR") points = sc.textFile(sys.argv[1]).mapPartitions(readPointBatch).cache() @@ -62,7 +60,7 @@ def readPointBatch(iterator): # Initialize w to a random value w = 2 * np.random.ranf(size=D) - 1 - print "Initial w: " + str(w) + print("Initial w: " + str(w)) # Compute logistic regression gradient for a matrix of data points def gradient(matrix, w): @@ -76,9 +74,9 @@ def add(x, y): return x for i in range(iterations): - print "On iteration %i" % (i + 1) + print("On iteration %i" % (i + 1)) w -= points.map(lambda m: gradient(m, w)).reduce(add) - print "Final w: " + str(w) + print("Final w: " + str(w)) sc.stop() diff --git a/examples/src/main/python/ml/simple_text_classification_pipeline.py b/examples/src/main/python/ml/simple_text_classification_pipeline.py index c7df3d7b74767..fab21f003b233 100644 --- a/examples/src/main/python/ml/simple_text_classification_pipeline.py +++ b/examples/src/main/python/ml/simple_text_classification_pipeline.py @@ -15,11 +15,13 @@ # limitations under the License. # +from __future__ import print_function + from pyspark import SparkContext -from pyspark.sql import SQLContext, Row from pyspark.ml import Pipeline -from pyspark.ml.feature import HashingTF, Tokenizer from pyspark.ml.classification import LogisticRegression +from pyspark.ml.feature import HashingTF, Tokenizer +from pyspark.sql import Row, SQLContext """ @@ -33,47 +35,37 @@ if __name__ == "__main__": sc = SparkContext(appName="SimpleTextClassificationPipeline") - sqlCtx = SQLContext(sc) + sqlContext = SQLContext(sc) # Prepare training documents, which are labeled. - LabeledDocument = Row('id', 'text', 'label') - training = sqlCtx.inferSchema( - sc.parallelize([(0L, "a b c d e spark", 1.0), - (1L, "b d", 0.0), - (2L, "spark f g h", 1.0), - (3L, "hadoop mapreduce", 0.0)]) - .map(lambda x: LabeledDocument(*x))) + LabeledDocument = Row("id", "text", "label") + training = sc.parallelize([(0, "a b c d e spark", 1.0), + (1, "b d", 0.0), + (2, "spark f g h", 1.0), + (3, "hadoop mapreduce", 0.0)]) \ + .map(lambda x: LabeledDocument(*x)).toDF() # Configure an ML pipeline, which consists of tree stages: tokenizer, hashingTF, and lr. - tokenizer = Tokenizer() \ - .setInputCol("text") \ - .setOutputCol("words") - hashingTF = HashingTF() \ - .setInputCol(tokenizer.getOutputCol()) \ - .setOutputCol("features") - lr = LogisticRegression() \ - .setMaxIter(10) \ - .setRegParam(0.01) - pipeline = Pipeline() \ - .setStages([tokenizer, hashingTF, lr]) + tokenizer = Tokenizer(inputCol="text", outputCol="words") + hashingTF = HashingTF(inputCol=tokenizer.getOutputCol(), outputCol="features") + lr = LogisticRegression(maxIter=10, regParam=0.01) + pipeline = Pipeline(stages=[tokenizer, hashingTF, lr]) # Fit the pipeline to training documents. model = pipeline.fit(training) # Prepare test documents, which are unlabeled. - Document = Row('id', 'text') - test = sqlCtx.inferSchema( - sc.parallelize([(4L, "spark i j k"), - (5L, "l m n"), - (6L, "mapreduce spark"), - (7L, "apache hadoop")]) - .map(lambda x: Document(*x))) + Document = Row("id", "text") + test = sc.parallelize([(4, "spark i j k"), + (5, "l m n"), + (6, "mapreduce spark"), + (7, "apache hadoop")]) \ + .map(lambda x: Document(*x)).toDF() # Make predictions on test documents and print columns of interest. prediction = model.transform(test) - prediction.registerTempTable("prediction") - selected = sqlCtx.sql("SELECT id, text, prediction from prediction") + selected = prediction.select("id", "text", "prediction") for row in selected.collect(): - print row + print(row) sc.stop() diff --git a/examples/src/main/python/mllib/correlations.py b/examples/src/main/python/mllib/correlations.py index 4218eca822a99..0e13546b88e67 100755 --- a/examples/src/main/python/mllib/correlations.py +++ b/examples/src/main/python/mllib/correlations.py @@ -18,6 +18,7 @@ """ Correlations using MLlib. """ +from __future__ import print_function import sys @@ -29,7 +30,7 @@ if __name__ == "__main__": if len(sys.argv) not in [1, 2]: - print >> sys.stderr, "Usage: correlations ()" + print("Usage: correlations ()", file=sys.stderr) exit(-1) sc = SparkContext(appName="PythonCorrelations") if len(sys.argv) == 2: @@ -41,20 +42,20 @@ points = MLUtils.loadLibSVMFile(sc, filepath)\ .map(lambda lp: LabeledPoint(lp.label, lp.features.toArray())) - print - print 'Summary of data file: ' + filepath - print '%d data points' % points.count() + print() + print('Summary of data file: ' + filepath) + print('%d data points' % points.count()) # Statistics (correlations) - print - print 'Correlation (%s) between label and each feature' % corrType - print 'Feature\tCorrelation' + print() + print('Correlation (%s) between label and each feature' % corrType) + print('Feature\tCorrelation') numFeatures = points.take(1)[0].features.size labelRDD = points.map(lambda lp: lp.label) for i in range(numFeatures): featureRDD = points.map(lambda lp: lp.features[i]) corr = Statistics.corr(labelRDD, featureRDD, corrType) - print '%d\t%g' % (i, corr) - print + print('%d\t%g' % (i, corr)) + print() sc.stop() diff --git a/examples/src/main/python/mllib/dataset_example.py b/examples/src/main/python/mllib/dataset_example.py index b5a70db2b9a3c..e23ecc0c5d302 100644 --- a/examples/src/main/python/mllib/dataset_example.py +++ b/examples/src/main/python/mllib/dataset_example.py @@ -19,6 +19,7 @@ An example of how to use DataFrame as a dataset for ML. Run with:: bin/spark-submit examples/src/main/python/mllib/dataset_example.py """ +from __future__ import print_function import os import sys @@ -32,31 +33,31 @@ def summarize(dataset): - print "schema: %s" % dataset.schema().json() + print("schema: %s" % dataset.schema().json()) labels = dataset.map(lambda r: r.label) - print "label average: %f" % labels.mean() + print("label average: %f" % labels.mean()) features = dataset.map(lambda r: r.features) summary = Statistics.colStats(features) - print "features average: %r" % summary.mean() + print("features average: %r" % summary.mean()) if __name__ == "__main__": if len(sys.argv) > 2: - print >> sys.stderr, "Usage: dataset_example.py " + print("Usage: dataset_example.py ", file=sys.stderr) exit(-1) sc = SparkContext(appName="DatasetExample") - sqlCtx = SQLContext(sc) + sqlContext = SQLContext(sc) if len(sys.argv) == 2: input = sys.argv[1] else: input = "data/mllib/sample_libsvm_data.txt" points = MLUtils.loadLibSVMFile(sc, input) - dataset0 = sqlCtx.inferSchema(points).setName("dataset0").cache() + dataset0 = sqlContext.inferSchema(points).setName("dataset0").cache() summarize(dataset0) tempdir = tempfile.NamedTemporaryFile(delete=False).name os.unlink(tempdir) - print "Save dataset as a Parquet file to %s." % tempdir + print("Save dataset as a Parquet file to %s." % tempdir) dataset0.saveAsParquetFile(tempdir) - print "Load it back and summarize it again." - dataset1 = sqlCtx.parquetFile(tempdir).setName("dataset1").cache() + print("Load it back and summarize it again.") + dataset1 = sqlContext.parquetFile(tempdir).setName("dataset1").cache() summarize(dataset1) shutil.rmtree(tempdir) diff --git a/examples/src/main/python/mllib/decision_tree_runner.py b/examples/src/main/python/mllib/decision_tree_runner.py index fccabd841b139..513ed8fd51450 100755 --- a/examples/src/main/python/mllib/decision_tree_runner.py +++ b/examples/src/main/python/mllib/decision_tree_runner.py @@ -20,6 +20,7 @@ This example requires NumPy (http://www.numpy.org/). """ +from __future__ import print_function import numpy import os @@ -83,18 +84,17 @@ def reindexClassLabels(data): numClasses = len(classCounts) # origToNewLabels: class --> index in 0,...,numClasses-1 if (numClasses < 2): - print >> sys.stderr, \ - "Dataset for classification should have at least 2 classes." + \ - " The given dataset had only %d classes." % numClasses + print("Dataset for classification should have at least 2 classes." + " The given dataset had only %d classes." % numClasses, file=sys.stderr) exit(1) origToNewLabels = dict([(sortedClasses[i], i) for i in range(0, numClasses)]) - print "numClasses = %d" % numClasses - print "Per-class example fractions, counts:" - print "Class\tFrac\tCount" + print("numClasses = %d" % numClasses) + print("Per-class example fractions, counts:") + print("Class\tFrac\tCount") for c in sortedClasses: frac = classCounts[c] / (numExamples + 0.0) - print "%g\t%g\t%d" % (c, frac, classCounts[c]) + print("%g\t%g\t%d" % (c, frac, classCounts[c])) if (sortedClasses[0] == 0 and sortedClasses[-1] == numClasses - 1): return (data, origToNewLabels) @@ -105,8 +105,7 @@ def reindexClassLabels(data): def usage(): - print >> sys.stderr, \ - "Usage: decision_tree_runner [libsvm format data filepath]" + print("Usage: decision_tree_runner [libsvm format data filepath]", file=sys.stderr) exit(1) @@ -133,13 +132,13 @@ def usage(): model = DecisionTree.trainClassifier(reindexedData, numClasses=numClasses, categoricalFeaturesInfo=categoricalFeaturesInfo) # Print learned tree and stats. - print "Trained DecisionTree for classification:" - print " Model numNodes: %d" % model.numNodes() - print " Model depth: %d" % model.depth() - print " Training accuracy: %g" % getAccuracy(model, reindexedData) + print("Trained DecisionTree for classification:") + print(" Model numNodes: %d" % model.numNodes()) + print(" Model depth: %d" % model.depth()) + print(" Training accuracy: %g" % getAccuracy(model, reindexedData)) if model.numNodes() < 20: - print model.toDebugString() + print(model.toDebugString()) else: - print model + print(model) sc.stop() diff --git a/examples/src/main/python/mllib/gaussian_mixture_model.py b/examples/src/main/python/mllib/gaussian_mixture_model.py index a2cd626c9f19d..2cb8010cdc07f 100644 --- a/examples/src/main/python/mllib/gaussian_mixture_model.py +++ b/examples/src/main/python/mllib/gaussian_mixture_model.py @@ -18,7 +18,8 @@ """ A Gaussian Mixture Model clustering program using MLlib. """ -import sys +from __future__ import print_function + import random import argparse import numpy as np @@ -59,7 +60,7 @@ def parseVector(line): model = GaussianMixture.train(data, args.k, args.convergenceTol, args.maxIterations, args.seed) for i in range(args.k): - print ("weight = ", model.weights[i], "mu = ", model.gaussians[i].mu, - "sigma = ", model.gaussians[i].sigma.toArray()) - print ("Cluster labels (first 100): ", model.predict(data).take(100)) + print(("weight = ", model.weights[i], "mu = ", model.gaussians[i].mu, + "sigma = ", model.gaussians[i].sigma.toArray())) + print(("Cluster labels (first 100): ", model.predict(data).take(100))) sc.stop() diff --git a/examples/src/main/python/mllib/gradient_boosted_trees.py b/examples/src/main/python/mllib/gradient_boosted_trees.py index e647773ad9060..781bd61c9d2b5 100644 --- a/examples/src/main/python/mllib/gradient_boosted_trees.py +++ b/examples/src/main/python/mllib/gradient_boosted_trees.py @@ -18,6 +18,7 @@ """ Gradient boosted Trees classification and regression using MLlib. """ +from __future__ import print_function import sys @@ -34,7 +35,7 @@ def testClassification(trainingData, testData): # Evaluate model on test instances and compute test error predictions = model.predict(testData.map(lambda x: x.features)) labelsAndPredictions = testData.map(lambda lp: lp.label).zip(predictions) - testErr = labelsAndPredictions.filter(lambda (v, p): v != p).count() \ + testErr = labelsAndPredictions.filter(lambda v_p: v_p[0] != v_p[1]).count() \ / float(testData.count()) print('Test Error = ' + str(testErr)) print('Learned classification ensemble model:') @@ -49,7 +50,7 @@ def testRegression(trainingData, testData): # Evaluate model on test instances and compute test error predictions = model.predict(testData.map(lambda x: x.features)) labelsAndPredictions = testData.map(lambda lp: lp.label).zip(predictions) - testMSE = labelsAndPredictions.map(lambda (v, p): (v - p) * (v - p)).sum() \ + testMSE = labelsAndPredictions.map(lambda vp: (vp[0] - vp[1]) * (vp[0] - vp[1])).sum() \ / float(testData.count()) print('Test Mean Squared Error = ' + str(testMSE)) print('Learned regression ensemble model:') @@ -58,7 +59,7 @@ def testRegression(trainingData, testData): if __name__ == "__main__": if len(sys.argv) > 1: - print >> sys.stderr, "Usage: gradient_boosted_trees" + print("Usage: gradient_boosted_trees", file=sys.stderr) exit(1) sc = SparkContext(appName="PythonGradientBoostedTrees") diff --git a/examples/src/main/python/mllib/kmeans.py b/examples/src/main/python/mllib/kmeans.py index 2eeb1abeeb12b..f901a87fa63ac 100755 --- a/examples/src/main/python/mllib/kmeans.py +++ b/examples/src/main/python/mllib/kmeans.py @@ -20,6 +20,7 @@ This example requires NumPy (http://www.numpy.org/). """ +from __future__ import print_function import sys @@ -34,12 +35,12 @@ def parseVector(line): if __name__ == "__main__": if len(sys.argv) != 3: - print >> sys.stderr, "Usage: kmeans " + print("Usage: kmeans ", file=sys.stderr) exit(-1) sc = SparkContext(appName="KMeans") lines = sc.textFile(sys.argv[1]) data = lines.map(parseVector) k = int(sys.argv[2]) model = KMeans.train(data, k) - print "Final centers: " + str(model.clusterCenters) + print("Final centers: " + str(model.clusterCenters)) sc.stop() diff --git a/examples/src/main/python/mllib/logistic_regression.py b/examples/src/main/python/mllib/logistic_regression.py index 8cae27fc4a52d..d4f1d34e2d8cf 100755 --- a/examples/src/main/python/mllib/logistic_regression.py +++ b/examples/src/main/python/mllib/logistic_regression.py @@ -20,11 +20,10 @@ This example requires NumPy (http://www.numpy.org/). """ +from __future__ import print_function -from math import exp import sys -import numpy as np from pyspark import SparkContext from pyspark.mllib.regression import LabeledPoint from pyspark.mllib.classification import LogisticRegressionWithSGD @@ -42,12 +41,12 @@ def parsePoint(line): if __name__ == "__main__": if len(sys.argv) != 3: - print >> sys.stderr, "Usage: logistic_regression " + print("Usage: logistic_regression ", file=sys.stderr) exit(-1) sc = SparkContext(appName="PythonLR") points = sc.textFile(sys.argv[1]).map(parsePoint) iterations = int(sys.argv[2]) model = LogisticRegressionWithSGD.train(points, iterations) - print "Final weights: " + str(model.weights) - print "Final intercept: " + str(model.intercept) + print("Final weights: " + str(model.weights)) + print("Final intercept: " + str(model.intercept)) sc.stop() diff --git a/examples/src/main/python/mllib/random_forest_example.py b/examples/src/main/python/mllib/random_forest_example.py index d3c24f7664329..4cfdad868c66e 100755 --- a/examples/src/main/python/mllib/random_forest_example.py +++ b/examples/src/main/python/mllib/random_forest_example.py @@ -22,6 +22,7 @@ For information on multiclass classification, please refer to the decision_tree_runner.py example. """ +from __future__ import print_function import sys @@ -43,7 +44,7 @@ def testClassification(trainingData, testData): # Evaluate model on test instances and compute test error predictions = model.predict(testData.map(lambda x: x.features)) labelsAndPredictions = testData.map(lambda lp: lp.label).zip(predictions) - testErr = labelsAndPredictions.filter(lambda (v, p): v != p).count()\ + testErr = labelsAndPredictions.filter(lambda v_p: v_p[0] != v_p[1]).count()\ / float(testData.count()) print('Test Error = ' + str(testErr)) print('Learned classification forest model:') @@ -62,8 +63,8 @@ def testRegression(trainingData, testData): # Evaluate model on test instances and compute test error predictions = model.predict(testData.map(lambda x: x.features)) labelsAndPredictions = testData.map(lambda lp: lp.label).zip(predictions) - testMSE = labelsAndPredictions.map(lambda (v, p): (v - p) * (v - p)).sum()\ - / float(testData.count()) + testMSE = labelsAndPredictions.map(lambda v_p1: (v_p1[0] - v_p1[1]) * (v_p1[0] - v_p1[1]))\ + .sum() / float(testData.count()) print('Test Mean Squared Error = ' + str(testMSE)) print('Learned regression forest model:') print(model.toDebugString()) @@ -71,7 +72,7 @@ def testRegression(trainingData, testData): if __name__ == "__main__": if len(sys.argv) > 1: - print >> sys.stderr, "Usage: random_forest_example" + print("Usage: random_forest_example", file=sys.stderr) exit(1) sc = SparkContext(appName="PythonRandomForestExample") diff --git a/examples/src/main/python/mllib/random_rdd_generation.py b/examples/src/main/python/mllib/random_rdd_generation.py index 1e8892741e714..729bae30b152c 100755 --- a/examples/src/main/python/mllib/random_rdd_generation.py +++ b/examples/src/main/python/mllib/random_rdd_generation.py @@ -18,6 +18,7 @@ """ Randomly generated RDDs. """ +from __future__ import print_function import sys @@ -27,7 +28,7 @@ if __name__ == "__main__": if len(sys.argv) not in [1, 2]: - print >> sys.stderr, "Usage: random_rdd_generation" + print("Usage: random_rdd_generation", file=sys.stderr) exit(-1) sc = SparkContext(appName="PythonRandomRDDGeneration") @@ -37,19 +38,19 @@ # Example: RandomRDDs.normalRDD normalRDD = RandomRDDs.normalRDD(sc, numExamples) - print 'Generated RDD of %d examples sampled from the standard normal distribution'\ - % normalRDD.count() - print ' First 5 samples:' + print('Generated RDD of %d examples sampled from the standard normal distribution' + % normalRDD.count()) + print(' First 5 samples:') for sample in normalRDD.take(5): - print ' ' + str(sample) - print + print(' ' + str(sample)) + print() # Example: RandomRDDs.normalVectorRDD normalVectorRDD = RandomRDDs.normalVectorRDD(sc, numRows=numExamples, numCols=2) - print 'Generated RDD of %d examples of length-2 vectors.' % normalVectorRDD.count() - print ' First 5 samples:' + print('Generated RDD of %d examples of length-2 vectors.' % normalVectorRDD.count()) + print(' First 5 samples:') for sample in normalVectorRDD.take(5): - print ' ' + str(sample) - print + print(' ' + str(sample)) + print() sc.stop() diff --git a/examples/src/main/python/mllib/sampled_rdds.py b/examples/src/main/python/mllib/sampled_rdds.py index 92af3af5ebd1e..b7033ab7daeb3 100755 --- a/examples/src/main/python/mllib/sampled_rdds.py +++ b/examples/src/main/python/mllib/sampled_rdds.py @@ -18,6 +18,7 @@ """ Randomly sampled RDDs. """ +from __future__ import print_function import sys @@ -27,7 +28,7 @@ if __name__ == "__main__": if len(sys.argv) not in [1, 2]: - print >> sys.stderr, "Usage: sampled_rdds " + print("Usage: sampled_rdds ", file=sys.stderr) exit(-1) if len(sys.argv) == 2: datapath = sys.argv[1] @@ -41,24 +42,24 @@ examples = MLUtils.loadLibSVMFile(sc, datapath) numExamples = examples.count() if numExamples == 0: - print >> sys.stderr, "Error: Data file had no samples to load." + print("Error: Data file had no samples to load.", file=sys.stderr) exit(1) - print 'Loaded data with %d examples from file: %s' % (numExamples, datapath) + print('Loaded data with %d examples from file: %s' % (numExamples, datapath)) # Example: RDD.sample() and RDD.takeSample() expectedSampleSize = int(numExamples * fraction) - print 'Sampling RDD using fraction %g. Expected sample size = %d.' \ - % (fraction, expectedSampleSize) + print('Sampling RDD using fraction %g. Expected sample size = %d.' + % (fraction, expectedSampleSize)) sampledRDD = examples.sample(withReplacement=True, fraction=fraction) - print ' RDD.sample(): sample has %d examples' % sampledRDD.count() + print(' RDD.sample(): sample has %d examples' % sampledRDD.count()) sampledArray = examples.takeSample(withReplacement=True, num=expectedSampleSize) - print ' RDD.takeSample(): sample has %d examples' % len(sampledArray) + print(' RDD.takeSample(): sample has %d examples' % len(sampledArray)) - print + print() # Example: RDD.sampleByKey() keyedRDD = examples.map(lambda lp: (int(lp.label), lp.features)) - print ' Keyed data using label (Int) as key ==> Orig' + print(' Keyed data using label (Int) as key ==> Orig') # Count examples per label in original data. keyCountsA = keyedRDD.countByKey() @@ -69,18 +70,18 @@ sampledByKeyRDD = keyedRDD.sampleByKey(withReplacement=True, fractions=fractions) keyCountsB = sampledByKeyRDD.countByKey() sizeB = sum(keyCountsB.values()) - print ' Sampled %d examples using approximate stratified sampling (by label). ==> Sample' \ - % sizeB + print(' Sampled %d examples using approximate stratified sampling (by label). ==> Sample' + % sizeB) # Compare samples - print ' \tFractions of examples with key' - print 'Key\tOrig\tSample' + print(' \tFractions of examples with key') + print('Key\tOrig\tSample') for k in sorted(keyCountsA.keys()): fracA = keyCountsA[k] / float(numExamples) if sizeB != 0: fracB = keyCountsB.get(k, 0) / float(sizeB) else: fracB = 0 - print '%d\t%g\t%g' % (k, fracA, fracB) + print('%d\t%g\t%g' % (k, fracA, fracB)) sc.stop() diff --git a/examples/src/main/python/mllib/word2vec.py b/examples/src/main/python/mllib/word2vec.py index 99fef4276a369..40d1b887927e0 100644 --- a/examples/src/main/python/mllib/word2vec.py +++ b/examples/src/main/python/mllib/word2vec.py @@ -23,6 +23,7 @@ # grep -o -E '\w+(\W+\w+){0,15}' text8 > text8_lines # This was done so that the example can be run in local mode +from __future__ import print_function import sys @@ -34,7 +35,7 @@ if __name__ == "__main__": if len(sys.argv) < 2: - print USAGE + print(USAGE) sys.exit("Argument for file not provided") file_path = sys.argv[1] sc = SparkContext(appName='Word2Vec') @@ -46,5 +47,5 @@ synonyms = model.findSynonyms('china', 40) for word, cosine_distance in synonyms: - print "{}: {}".format(word, cosine_distance) + print("{}: {}".format(word, cosine_distance)) sc.stop() diff --git a/examples/src/main/python/pagerank.py b/examples/src/main/python/pagerank.py index a5f25d78c1146..2fdc9773d4eb1 100755 --- a/examples/src/main/python/pagerank.py +++ b/examples/src/main/python/pagerank.py @@ -19,6 +19,7 @@ This is an example implementation of PageRank. For more conventional use, Please refer to PageRank implementation provided by graphx """ +from __future__ import print_function import re import sys @@ -42,11 +43,12 @@ def parseNeighbors(urls): if __name__ == "__main__": if len(sys.argv) != 3: - print >> sys.stderr, "Usage: pagerank " + print("Usage: pagerank ", file=sys.stderr) exit(-1) - print >> sys.stderr, """WARN: This is a naive implementation of PageRank and is - given as an example! Please refer to PageRank implementation provided by graphx""" + print("""WARN: This is a naive implementation of PageRank and is + given as an example! Please refer to PageRank implementation provided by graphx""", + file=sys.stderr) # Initialize the spark context. sc = SparkContext(appName="PythonPageRank") @@ -62,19 +64,19 @@ def parseNeighbors(urls): links = lines.map(lambda urls: parseNeighbors(urls)).distinct().groupByKey().cache() # Loads all URLs with other URL(s) link to from input file and initialize ranks of them to one. - ranks = links.map(lambda (url, neighbors): (url, 1.0)) + ranks = links.map(lambda url_neighbors: (url_neighbors[0], 1.0)) # Calculates and updates URL ranks continuously using PageRank algorithm. - for iteration in xrange(int(sys.argv[2])): + for iteration in range(int(sys.argv[2])): # Calculates URL contributions to the rank of other URLs. contribs = links.join(ranks).flatMap( - lambda (url, (urls, rank)): computeContribs(urls, rank)) + lambda url_urls_rank: computeContribs(url_urls_rank[1][0], url_urls_rank[1][1])) # Re-calculates URL ranks based on neighbor contributions. ranks = contribs.reduceByKey(add).mapValues(lambda rank: rank * 0.85 + 0.15) # Collects all URL ranks and dump them to console. for (link, rank) in ranks.collect(): - print "%s has rank: %s." % (link, rank) + print("%s has rank: %s." % (link, rank)) sc.stop() diff --git a/examples/src/main/python/parquet_inputformat.py b/examples/src/main/python/parquet_inputformat.py index fa4c20ab20281..96ddac761d698 100644 --- a/examples/src/main/python/parquet_inputformat.py +++ b/examples/src/main/python/parquet_inputformat.py @@ -1,3 +1,4 @@ +from __future__ import print_function # # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with @@ -35,14 +36,14 @@ """ if __name__ == "__main__": if len(sys.argv) != 2: - print >> sys.stderr, """ + print(""" Usage: parquet_inputformat.py Run with example jar: ./bin/spark-submit --driver-class-path /path/to/example/jar \\ /path/to/examples/parquet_inputformat.py Assumes you have Parquet data stored in . - """ + """, file=sys.stderr) exit(-1) path = sys.argv[1] @@ -56,6 +57,6 @@ valueConverter='org.apache.spark.examples.pythonconverters.IndexedRecordToJavaConverter') output = parquet_rdd.map(lambda x: x[1]).collect() for k in output: - print k + print(k) sc.stop() diff --git a/examples/src/main/python/pi.py b/examples/src/main/python/pi.py index a7c74e969cdb9..92e5cf45abc8b 100755 --- a/examples/src/main/python/pi.py +++ b/examples/src/main/python/pi.py @@ -1,3 +1,4 @@ +from __future__ import print_function # # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with @@ -35,7 +36,7 @@ def f(_): y = random() * 2 - 1 return 1 if x ** 2 + y ** 2 < 1 else 0 - count = sc.parallelize(xrange(1, n + 1), partitions).map(f).reduce(add) - print "Pi is roughly %f" % (4.0 * count / n) + count = sc.parallelize(range(1, n + 1), partitions).map(f).reduce(add) + print("Pi is roughly %f" % (4.0 * count / n)) sc.stop() diff --git a/examples/src/main/python/sort.py b/examples/src/main/python/sort.py index bb686f17518a0..f6b0ecb02c100 100755 --- a/examples/src/main/python/sort.py +++ b/examples/src/main/python/sort.py @@ -15,6 +15,8 @@ # limitations under the License. # +from __future__ import print_function + import sys from pyspark import SparkContext @@ -22,7 +24,7 @@ if __name__ == "__main__": if len(sys.argv) != 2: - print >> sys.stderr, "Usage: sort " + print("Usage: sort ", file=sys.stderr) exit(-1) sc = SparkContext(appName="PythonSort") lines = sc.textFile(sys.argv[1], 1) @@ -33,6 +35,6 @@ # In reality, we wouldn't want to collect all the data to the driver node. output = sortedCount.collect() for (num, unitcount) in output: - print num + print(num) sc.stop() diff --git a/examples/src/main/python/sql.py b/examples/src/main/python/sql.py index 7f5c68e3d0fe2..87d7b088f077b 100644 --- a/examples/src/main/python/sql.py +++ b/examples/src/main/python/sql.py @@ -15,11 +15,13 @@ # limitations under the License. # +from __future__ import print_function + import os from pyspark import SparkContext from pyspark.sql import SQLContext -from pyspark.sql import Row, StructField, StructType, StringType, IntegerType +from pyspark.sql.types import Row, StructField, StructType, StringType, IntegerType if __name__ == "__main__": @@ -31,7 +33,7 @@ Row(name="Smith", age=23), Row(name="Sarah", age=18)]) # Infer schema from the first row, create a DataFrame and print the schema - some_df = sqlContext.inferSchema(some_rdd) + some_df = sqlContext.createDataFrame(some_rdd) some_df.printSchema() # Another RDD is created from a list of tuples @@ -40,7 +42,7 @@ schema = StructType([StructField("person_name", StringType(), False), StructField("person_age", IntegerType(), False)]) # Create a DataFrame by applying the schema to the RDD and print the schema - another_df = sqlContext.applySchema(another_rdd, schema) + another_df = sqlContext.createDataFrame(another_rdd, schema) another_df.printSchema() # root # |-- age: integer (nullable = true) @@ -68,6 +70,6 @@ teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19") for each in teenagers.collect(): - print each[0] + print(each[0]) sc.stop() diff --git a/examples/src/main/python/status_api_demo.py b/examples/src/main/python/status_api_demo.py new file mode 100644 index 0000000000000..49b7902185aaa --- /dev/null +++ b/examples/src/main/python/status_api_demo.py @@ -0,0 +1,69 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from __future__ import print_function + +import time +import threading +import Queue + +from pyspark import SparkConf, SparkContext + + +def delayed(seconds): + def f(x): + time.sleep(seconds) + return x + return f + + +def call_in_background(f, *args): + result = Queue.Queue(1) + t = threading.Thread(target=lambda: result.put(f(*args))) + t.daemon = True + t.start() + return result + + +def main(): + conf = SparkConf().set("spark.ui.showConsoleProgress", "false") + sc = SparkContext(appName="PythonStatusAPIDemo", conf=conf) + + def run(): + rdd = sc.parallelize(range(10), 10).map(delayed(2)) + reduced = rdd.map(lambda x: (x, 1)).reduceByKey(lambda x, y: x + y) + return reduced.map(delayed(2)).collect() + + result = call_in_background(run) + status = sc.statusTracker() + while result.empty(): + ids = status.getJobIdsForGroup() + for id in ids: + job = status.getJobInfo(id) + print("Job", id, "status: ", job.status) + for sid in job.stageIds: + info = status.getStageInfo(sid) + if info: + print("Stage %d: %d tasks total (%d active, %d complete)" % + (sid, info.numTasks, info.numActiveTasks, info.numCompletedTasks)) + time.sleep(1) + + print("Job results are:", result.get()) + sc.stop() + +if __name__ == "__main__": + main() diff --git a/examples/src/main/python/streaming/hdfs_wordcount.py b/examples/src/main/python/streaming/hdfs_wordcount.py index f7ffb5379681e..f815dd26823d1 100644 --- a/examples/src/main/python/streaming/hdfs_wordcount.py +++ b/examples/src/main/python/streaming/hdfs_wordcount.py @@ -25,6 +25,7 @@ Then create a text file in `localdir` and the words in the file will get counted. """ +from __future__ import print_function import sys @@ -33,7 +34,7 @@ if __name__ == "__main__": if len(sys.argv) != 2: - print >> sys.stderr, "Usage: hdfs_wordcount.py " + print("Usage: hdfs_wordcount.py ", file=sys.stderr) exit(-1) sc = SparkContext(appName="PythonStreamingHDFSWordCount") diff --git a/examples/src/main/python/streaming/kafka_wordcount.py b/examples/src/main/python/streaming/kafka_wordcount.py index ed398a82b8bb0..b178e7899b5e1 100644 --- a/examples/src/main/python/streaming/kafka_wordcount.py +++ b/examples/src/main/python/streaming/kafka_wordcount.py @@ -17,16 +17,17 @@ """ Counts words in UTF8 encoded, '\n' delimited text received from the network every second. - Usage: network_wordcount.py + Usage: kafka_wordcount.py To run this on your local machine, you need to setup Kafka and create a producer first, see http://kafka.apache.org/documentation.html#quickstart and then run the example - `$ bin/spark-submit --driver-class-path external/kafka-assembly/target/scala-*/\ + `$ bin/spark-submit --jars external/kafka-assembly/target/scala-*/\ spark-streaming-kafka-assembly-*.jar examples/src/main/python/streaming/kafka_wordcount.py \ localhost:2181 test` """ +from __future__ import print_function import sys @@ -36,7 +37,7 @@ if __name__ == "__main__": if len(sys.argv) != 3: - print >> sys.stderr, "Usage: kafka_wordcount.py " + print("Usage: kafka_wordcount.py ", file=sys.stderr) exit(-1) sc = SparkContext(appName="PythonStreamingKafkaWordCount") diff --git a/examples/src/main/python/streaming/network_wordcount.py b/examples/src/main/python/streaming/network_wordcount.py index cfa9c1ff5bfbc..2b48bcfd55db0 100644 --- a/examples/src/main/python/streaming/network_wordcount.py +++ b/examples/src/main/python/streaming/network_wordcount.py @@ -25,6 +25,7 @@ and then run the example `$ bin/spark-submit examples/src/main/python/streaming/network_wordcount.py localhost 9999` """ +from __future__ import print_function import sys @@ -33,7 +34,7 @@ if __name__ == "__main__": if len(sys.argv) != 3: - print >> sys.stderr, "Usage: network_wordcount.py " + print("Usage: network_wordcount.py ", file=sys.stderr) exit(-1) sc = SparkContext(appName="PythonStreamingNetworkWordCount") ssc = StreamingContext(sc, 1) diff --git a/examples/src/main/python/streaming/recoverable_network_wordcount.py b/examples/src/main/python/streaming/recoverable_network_wordcount.py index fc6827c82bf9b..ac91f0a06b172 100644 --- a/examples/src/main/python/streaming/recoverable_network_wordcount.py +++ b/examples/src/main/python/streaming/recoverable_network_wordcount.py @@ -35,6 +35,7 @@ checkpoint data exists in ~/checkpoint/, then it will create StreamingContext from the checkpoint data. """ +from __future__ import print_function import os import sys @@ -46,7 +47,7 @@ def createContext(host, port, outputPath): # If you do not see this printed, that means the StreamingContext has been loaded # from the new checkpoint - print "Creating new context" + print("Creating new context") if os.path.exists(outputPath): os.remove(outputPath) sc = SparkContext(appName="PythonStreamingRecoverableNetworkWordCount") @@ -60,8 +61,8 @@ def createContext(host, port, outputPath): def echo(time, rdd): counts = "Counts at time %s %s" % (time, rdd.collect()) - print counts - print "Appending to " + os.path.abspath(outputPath) + print(counts) + print("Appending to " + os.path.abspath(outputPath)) with open(outputPath, 'a') as f: f.write(counts + "\n") @@ -70,8 +71,8 @@ def echo(time, rdd): if __name__ == "__main__": if len(sys.argv) != 5: - print >> sys.stderr, "Usage: recoverable_network_wordcount.py "\ - " " + print("Usage: recoverable_network_wordcount.py " + " ", file=sys.stderr) exit(-1) host, port, checkpoint, output = sys.argv[1:] ssc = StreamingContext.getOrCreate(checkpoint, diff --git a/examples/src/main/python/streaming/sql_network_wordcount.py b/examples/src/main/python/streaming/sql_network_wordcount.py new file mode 100644 index 0000000000000..da90c07dbd82f --- /dev/null +++ b/examples/src/main/python/streaming/sql_network_wordcount.py @@ -0,0 +1,83 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +""" + Use DataFrames and SQL to count words in UTF8 encoded, '\n' delimited text received from the + network every second. + + Usage: sql_network_wordcount.py + and describe the TCP server that Spark Streaming would connect to receive data. + + To run this on your local machine, you need to first run a Netcat server + `$ nc -lk 9999` + and then run the example + `$ bin/spark-submit examples/src/main/python/streaming/sql_network_wordcount.py localhost 9999` +""" +from __future__ import print_function + +import os +import sys + +from pyspark import SparkContext +from pyspark.streaming import StreamingContext +from pyspark.sql import SQLContext, Row + + +def getSqlContextInstance(sparkContext): + if ('sqlContextSingletonInstance' not in globals()): + globals()['sqlContextSingletonInstance'] = SQLContext(sparkContext) + return globals()['sqlContextSingletonInstance'] + + +if __name__ == "__main__": + if len(sys.argv) != 3: + print("Usage: sql_network_wordcount.py ", file=sys.stderr) + exit(-1) + host, port = sys.argv[1:] + sc = SparkContext(appName="PythonSqlNetworkWordCount") + ssc = StreamingContext(sc, 1) + + # Create a socket stream on target ip:port and count the + # words in input stream of \n delimited text (eg. generated by 'nc') + lines = ssc.socketTextStream(host, int(port)) + words = lines.flatMap(lambda line: line.split(" ")) + + # Convert RDDs of the words DStream to DataFrame and run SQL query + def process(time, rdd): + print("========= %s =========" % str(time)) + + try: + # Get the singleton instance of SQLContext + sqlContext = getSqlContextInstance(rdd.context) + + # Convert RDD[String] to RDD[Row] to DataFrame + rowRdd = rdd.map(lambda w: Row(word=w)) + wordsDataFrame = sqlContext.createDataFrame(rowRdd) + + # Register as table + wordsDataFrame.registerTempTable("words") + + # Do word count on table using SQL and print it + wordCountsDataFrame = \ + sqlContext.sql("select word, count(*) as total from words group by word") + wordCountsDataFrame.show() + except: + pass + + words.foreachRDD(process) + ssc.start() + ssc.awaitTermination() diff --git a/examples/src/main/python/streaming/stateful_network_wordcount.py b/examples/src/main/python/streaming/stateful_network_wordcount.py index 18a9a5a452ffb..16ef646b7c42e 100644 --- a/examples/src/main/python/streaming/stateful_network_wordcount.py +++ b/examples/src/main/python/streaming/stateful_network_wordcount.py @@ -29,6 +29,7 @@ `$ bin/spark-submit examples/src/main/python/streaming/stateful_network_wordcount.py \ localhost 9999` """ +from __future__ import print_function import sys @@ -37,7 +38,7 @@ if __name__ == "__main__": if len(sys.argv) != 3: - print >> sys.stderr, "Usage: stateful_network_wordcount.py " + print("Usage: stateful_network_wordcount.py ", file=sys.stderr) exit(-1) sc = SparkContext(appName="PythonStreamingStatefulNetworkWordCount") ssc = StreamingContext(sc, 1) diff --git a/examples/src/main/python/transitive_closure.py b/examples/src/main/python/transitive_closure.py index 00a281bfb6506..7bf5fb6ddfe29 100755 --- a/examples/src/main/python/transitive_closure.py +++ b/examples/src/main/python/transitive_closure.py @@ -15,6 +15,8 @@ # limitations under the License. # +from __future__ import print_function + import sys from random import Random @@ -49,20 +51,20 @@ def generateGraph(): # the graph to obtain the path (x, z). # Because join() joins on keys, the edges are stored in reversed order. - edges = tc.map(lambda (x, y): (y, x)) + edges = tc.map(lambda x_y: (x_y[1], x_y[0])) - oldCount = 0L + oldCount = 0 nextCount = tc.count() while True: oldCount = nextCount # Perform the join, obtaining an RDD of (y, (z, x)) pairs, # then project the result to obtain the new (x, z) paths. - new_edges = tc.join(edges).map(lambda (_, (a, b)): (b, a)) + new_edges = tc.join(edges).map(lambda __a_b: (__a_b[1][1], __a_b[1][0])) tc = tc.union(new_edges).distinct().cache() nextCount = tc.count() if nextCount == oldCount: break - print "TC has %i edges" % tc.count() + print("TC has %i edges" % tc.count()) sc.stop() diff --git a/examples/src/main/python/wordcount.py b/examples/src/main/python/wordcount.py index ae6cd13b83d92..7c0143607b61d 100755 --- a/examples/src/main/python/wordcount.py +++ b/examples/src/main/python/wordcount.py @@ -15,6 +15,8 @@ # limitations under the License. # +from __future__ import print_function + import sys from operator import add @@ -23,7 +25,7 @@ if __name__ == "__main__": if len(sys.argv) != 2: - print >> sys.stderr, "Usage: wordcount " + print("Usage: wordcount ", file=sys.stderr) exit(-1) sc = SparkContext(appName="PythonWordCount") lines = sc.textFile(sys.argv[1], 1) @@ -32,6 +34,6 @@ .reduceByKey(add) output = counts.collect() for (word, count) in output: - print "%s: %i" % (word, count) + print("%s: %i" % (word, count)) sc.stop() diff --git a/examples/src/main/r/kmeans.R b/examples/src/main/r/kmeans.R new file mode 100644 index 0000000000000..6e6b5cb93789c --- /dev/null +++ b/examples/src/main/r/kmeans.R @@ -0,0 +1,93 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +library(SparkR) + +# Logistic regression in Spark. +# Note: unlike the example in Scala, a point here is represented as a vector of +# doubles. + +parseVectors <- function(lines) { + lines <- strsplit(as.character(lines) , " ", fixed = TRUE) + list(matrix(as.numeric(unlist(lines)), ncol = length(lines[[1]]))) +} + +dist.fun <- function(P, C) { + apply( + C, + 1, + function(x) { + colSums((t(P) - x)^2) + } + ) +} + +closestPoint <- function(P, C) { + max.col(-dist.fun(P, C)) +} +# Main program + +args <- commandArgs(trailing = TRUE) + +if (length(args) != 3) { + print("Usage: kmeans ") + q("no") +} + +sc <- sparkR.init(appName = "RKMeans") +K <- as.integer(args[[2]]) +convergeDist <- as.double(args[[3]]) + +lines <- textFile(sc, args[[1]]) +points <- cache(lapplyPartition(lines, parseVectors)) +# kPoints <- take(points, K) +kPoints <- do.call(rbind, takeSample(points, FALSE, K, 16189L)) +tempDist <- 1.0 + +while (tempDist > convergeDist) { + closest <- lapplyPartition( + lapply(points, + function(p) { + cp <- closestPoint(p, kPoints); + mapply(list, unique(cp), split.data.frame(cbind(1, p), cp), SIMPLIFY=FALSE) + }), + function(x) {do.call(c, x) + }) + + pointStats <- reduceByKey(closest, + function(p1, p2) { + t(colSums(rbind(p1, p2))) + }, + 2L) + + newPoints <- do.call( + rbind, + collect(lapply(pointStats, + function(tup) { + point.sum <- tup[[2]][, -1] + point.count <- tup[[2]][, 1] + point.sum/point.count + }))) + + D <- dist.fun(kPoints, newPoints) + tempDist <- sum(D[cbind(1:3, max.col(-D))]) + kPoints <- newPoints + cat("Finished iteration (delta = ", tempDist, ")\n") +} + +cat("Final centers:\n") +writeLines(unlist(lapply(kPoints, paste, collapse = " "))) diff --git a/examples/src/main/r/linear_solver_mnist.R b/examples/src/main/r/linear_solver_mnist.R new file mode 100644 index 0000000000000..c864a4232d010 --- /dev/null +++ b/examples/src/main/r/linear_solver_mnist.R @@ -0,0 +1,107 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# Instructions: https://github.com/amplab-extras/SparkR-pkg/wiki/SparkR-Example:-Digit-Recognition-on-EC2 + +library(SparkR) +library(Matrix) + +args <- commandArgs(trailing = TRUE) + +# number of random features; default to 1100 +D <- ifelse(length(args) > 0, as.integer(args[[1]]), 1100) +# number of partitions for training dataset +trainParts <- 12 +# dimension of digits +d <- 784 +# number of test examples +NTrain <- 60000 +# number of training examples +NTest <- 10000 +# scale of features +gamma <- 4e-4 + +sc <- sparkR.init(appName = "SparkR-LinearSolver") + +# You can also use HDFS path to speed things up: +# hdfs:///train-mnist-dense-with-labels.data +file <- textFile(sc, "/data/train-mnist-dense-with-labels.data", trainParts) + +W <- gamma * matrix(nrow=D, ncol=d, data=rnorm(D*d)) +b <- 2 * pi * matrix(nrow=D, ncol=1, data=runif(D)) +broadcastW <- broadcast(sc, W) +broadcastB <- broadcast(sc, b) + +includePackage(sc, Matrix) +numericLines <- lapplyPartitionsWithIndex(file, + function(split, part) { + matList <- sapply(part, function(line) { + as.numeric(strsplit(line, ",", fixed=TRUE)[[1]]) + }, simplify=FALSE) + mat <- Matrix(ncol=d+1, data=unlist(matList, F, F), + sparse=T, byrow=T) + mat + }) + +featureLabels <- cache(lapplyPartition( + numericLines, + function(part) { + label <- part[,1] + mat <- part[,-1] + ones <- rep(1, nrow(mat)) + features <- cos( + mat %*% t(value(broadcastW)) + (matrix(ncol=1, data=ones) %*% t(value(broadcastB)))) + onesMat <- Matrix(ones) + featuresPlus <- cBind(features, onesMat) + labels <- matrix(nrow=nrow(mat), ncol=10, data=-1) + for (i in 1:nrow(mat)) { + labels[i, label[i]] <- 1 + } + list(label=labels, features=featuresPlus) + })) + +FTF <- Reduce("+", collect(lapplyPartition(featureLabels, + function(part) { + t(part$features) %*% part$features + }), flatten=F)) + +FTY <- Reduce("+", collect(lapplyPartition(featureLabels, + function(part) { + t(part$features) %*% part$label + }), flatten=F)) + +# solve for the coefficient matrix +C <- solve(FTF, FTY) + +test <- Matrix(as.matrix(read.csv("/data/test-mnist-dense-with-labels.data", + header=F), sparse=T)) +testData <- test[,-1] +testLabels <- matrix(ncol=1, test[,1]) + +err <- 0 + +# contstruct the feature maps for all examples from this digit +featuresTest <- cos(testData %*% t(value(broadcastW)) + + (matrix(ncol=1, data=rep(1, NTest)) %*% t(value(broadcastB)))) +featuresTest <- cBind(featuresTest, Matrix(rep(1, NTest))) + +# extract the one vs. all assignment +results <- featuresTest %*% C +labelsGot <- apply(results, 1, which.max) +err <- sum(testLabels != labelsGot) / nrow(testLabels) + +cat("\nFinished running. The error rate is: ", err, ".\n") diff --git a/examples/src/main/r/logistic_regression.R b/examples/src/main/r/logistic_regression.R new file mode 100644 index 0000000000000..2a86aa98160d3 --- /dev/null +++ b/examples/src/main/r/logistic_regression.R @@ -0,0 +1,62 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +library(SparkR) + +args <- commandArgs(trailing = TRUE) + +if (length(args) != 3) { + print("Usage: logistic_regression ") + q("no") +} + +# Initialize Spark context +sc <- sparkR.init(appName = "LogisticRegressionR") +iterations <- as.integer(args[[2]]) +D <- as.integer(args[[3]]) + +readPartition <- function(part){ + part = strsplit(part, " ", fixed = T) + list(matrix(as.numeric(unlist(part)), ncol = length(part[[1]]))) +} + +# Read data points and convert each partition to a matrix +points <- cache(lapplyPartition(textFile(sc, args[[1]]), readPartition)) + +# Initialize w to a random value +w <- runif(n=D, min = -1, max = 1) +cat("Initial w: ", w, "\n") + +# Compute logistic regression gradient for a matrix of data points +gradient <- function(partition) { + partition = partition[[1]] + Y <- partition[, 1] # point labels (first column of input file) + X <- partition[, -1] # point coordinates + + # For each point (x, y), compute gradient function + dot <- X %*% w + logit <- 1 / (1 + exp(-Y * dot)) + grad <- t(X) %*% ((logit - 1) * Y) + list(grad) +} + +for (i in 1:iterations) { + cat("On iteration ", i, "\n") + w <- w - reduce(lapplyPartition(points, gradient), "+") +} + +cat("Final w: ", w, "\n") diff --git a/examples/src/main/r/pi.R b/examples/src/main/r/pi.R new file mode 100644 index 0000000000000..aa7a833e147a0 --- /dev/null +++ b/examples/src/main/r/pi.R @@ -0,0 +1,46 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +library(SparkR) + +args <- commandArgs(trailing = TRUE) + +sc <- sparkR.init(appName = "PiR") + +slices <- ifelse(length(args) > 1, as.integer(args[[2]]), 2) + +n <- 100000 * slices + +piFunc <- function(elem) { + rands <- runif(n = 2, min = -1, max = 1) + val <- ifelse((rands[1]^2 + rands[2]^2) < 1, 1.0, 0.0) + val +} + + +piFuncVec <- function(elems) { + message(length(elems)) + rands1 <- runif(n = length(elems), min = -1, max = 1) + rands2 <- runif(n = length(elems), min = -1, max = 1) + val <- ifelse((rands1^2 + rands2^2) < 1, 1.0, 0.0) + sum(val) +} + +rdd <- parallelize(sc, 1:n, slices) +count <- reduce(lapplyPartition(rdd, piFuncVec), sum) +cat("Pi is roughly", 4.0 * count / n, "\n") +cat("Num elements in RDD ", count(rdd), "\n") diff --git a/examples/src/main/r/wordcount.R b/examples/src/main/r/wordcount.R new file mode 100644 index 0000000000000..b734cb0ecf55b --- /dev/null +++ b/examples/src/main/r/wordcount.R @@ -0,0 +1,42 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +library(SparkR) + +args <- commandArgs(trailing = TRUE) + +if (length(args) != 1) { + print("Usage: wordcount ") + q("no") +} + +# Initialize Spark context +sc <- sparkR.init(appName = "RwordCount") +lines <- textFile(sc, args[[1]]) + +words <- flatMap(lines, + function(line) { + strsplit(line, " ")[[1]] + }) +wordCount <- lapply(words, function(word) { list(word, 1L) }) + +counts <- reduceByKey(wordCount, "+", 2L) +output <- collect(counts) + +for (wordcount in output) { + cat(wordcount[[1]], ": ", wordcount[[2]], "\n") +} diff --git a/examples/src/main/scala/org/apache/spark/examples/BroadcastTest.scala b/examples/src/main/scala/org/apache/spark/examples/BroadcastTest.scala index 1b53f3edbe92e..4c129dbe2d12d 100644 --- a/examples/src/main/scala/org/apache/spark/examples/BroadcastTest.scala +++ b/examples/src/main/scala/org/apache/spark/examples/BroadcastTest.scala @@ -29,7 +29,7 @@ object BroadcastTest { val blockSize = if (args.length > 3) args(3) else "4096" val sparkConf = new SparkConf().setAppName("Broadcast Test") - .set("spark.broadcast.factory", s"org.apache.spark.broadcast.${bcName}BroaddcastFactory") + .set("spark.broadcast.factory", s"org.apache.spark.broadcast.${bcName}BroadcastFactory") .set("spark.broadcast.blockSize", blockSize) val sc = new SparkContext(sparkConf) diff --git a/examples/src/main/scala/org/apache/spark/examples/HBaseTest.scala b/examples/src/main/scala/org/apache/spark/examples/HBaseTest.scala index 822673347bdce..f4684b42b5d41 100644 --- a/examples/src/main/scala/org/apache/spark/examples/HBaseTest.scala +++ b/examples/src/main/scala/org/apache/spark/examples/HBaseTest.scala @@ -18,7 +18,7 @@ package org.apache.spark.examples import org.apache.hadoop.hbase.client.HBaseAdmin -import org.apache.hadoop.hbase.{HBaseConfiguration, HTableDescriptor} +import org.apache.hadoop.hbase.{HBaseConfiguration, HTableDescriptor, TableName} import org.apache.hadoop.hbase.mapreduce.TableInputFormat import org.apache.spark._ @@ -36,7 +36,7 @@ object HBaseTest { // Initialize hBase table if necessary val admin = new HBaseAdmin(conf) if (!admin.isTableAvailable(args(0))) { - val tableDesc = new HTableDescriptor(args(0)) + val tableDesc = new HTableDescriptor(TableName.valueOf(args(0))) admin.createTable(tableDesc) } diff --git a/examples/src/main/scala/org/apache/spark/examples/LocalKMeans.scala b/examples/src/main/scala/org/apache/spark/examples/LocalKMeans.scala index 17624c20cff3d..f73eac1e2b906 100644 --- a/examples/src/main/scala/org/apache/spark/examples/LocalKMeans.scala +++ b/examples/src/main/scala/org/apache/spark/examples/LocalKMeans.scala @@ -40,8 +40,8 @@ object LocalKMeans { val convergeDist = 0.001 val rand = new Random(42) - def generateData = { - def generatePoint(i: Int) = { + def generateData: Array[DenseVector[Double]] = { + def generatePoint(i: Int): DenseVector[Double] = { DenseVector.fill(D){rand.nextDouble * R} } Array.tabulate(N)(generatePoint) diff --git a/examples/src/main/scala/org/apache/spark/examples/LocalLR.scala b/examples/src/main/scala/org/apache/spark/examples/LocalLR.scala index 92a683ad57ea1..a55e0dc8d36c2 100644 --- a/examples/src/main/scala/org/apache/spark/examples/LocalLR.scala +++ b/examples/src/main/scala/org/apache/spark/examples/LocalLR.scala @@ -37,8 +37,8 @@ object LocalLR { case class DataPoint(x: Vector[Double], y: Double) - def generateData = { - def generatePoint(i: Int) = { + def generateData: Array[DataPoint] = { + def generatePoint(i: Int): DataPoint = { val y = if(i % 2 == 0) -1 else 1 val x = DenseVector.fill(D){rand.nextGaussian + y * R} DataPoint(x, y) diff --git a/examples/src/main/scala/org/apache/spark/examples/LogQuery.scala b/examples/src/main/scala/org/apache/spark/examples/LogQuery.scala index 74620ad007d83..32e02eab8b031 100644 --- a/examples/src/main/scala/org/apache/spark/examples/LogQuery.scala +++ b/examples/src/main/scala/org/apache/spark/examples/LogQuery.scala @@ -54,8 +54,8 @@ object LogQuery { // scalastyle:on /** Tracks the total query count and number of aggregate bytes for a particular group. */ class Stats(val count: Int, val numBytes: Int) extends Serializable { - def merge(other: Stats) = new Stats(count + other.count, numBytes + other.numBytes) - override def toString = "bytes=%s\tn=%s".format(numBytes, count) + def merge(other: Stats): Stats = new Stats(count + other.count, numBytes + other.numBytes) + override def toString: String = "bytes=%s\tn=%s".format(numBytes, count) } def extractKey(line: String): (String, String, String) = { diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala b/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala index 257a7d29f922a..8c01a60844620 100644 --- a/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala +++ b/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala @@ -42,8 +42,8 @@ object SparkLR { case class DataPoint(x: Vector[Double], y: Double) - def generateData = { - def generatePoint(i: Int) = { + def generateData: Array[DataPoint] = { + def generatePoint(i: Int): DataPoint = { val y = if(i % 2 == 0) -1 else 1 val x = DenseVector.fill(D){rand.nextGaussian + y * R} DataPoint(x, y) diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkTC.scala b/examples/src/main/scala/org/apache/spark/examples/SparkTC.scala index f7f83086df3db..772cd897f5140 100644 --- a/examples/src/main/scala/org/apache/spark/examples/SparkTC.scala +++ b/examples/src/main/scala/org/apache/spark/examples/SparkTC.scala @@ -31,7 +31,7 @@ object SparkTC { val numVertices = 100 val rand = new Random(42) - def generateGraph = { + def generateGraph: Seq[(Int, Int)] = { val edges: mutable.Set[(Int, Int)] = mutable.Set.empty while (edges.size < numEdges) { val from = rand.nextInt(numVertices) diff --git a/examples/src/main/scala/org/apache/spark/examples/bagel/PageRankUtils.scala b/examples/src/main/scala/org/apache/spark/examples/bagel/PageRankUtils.scala index e322d4ce5a745..ab6e63deb3c95 100644 --- a/examples/src/main/scala/org/apache/spark/examples/bagel/PageRankUtils.scala +++ b/examples/src/main/scala/org/apache/spark/examples/bagel/PageRankUtils.scala @@ -90,7 +90,7 @@ class PRMessage() extends Message[String] with Serializable { } class CustomPartitioner(partitions: Int) extends Partitioner { - def numPartitions = partitions + def numPartitions: Int = partitions def getPartition(key: Any): Int = { val hash = key match { diff --git a/examples/src/main/scala/org/apache/spark/examples/graphx/LiveJournalPageRank.scala b/examples/src/main/scala/org/apache/spark/examples/graphx/LiveJournalPageRank.scala index e809a65b79975..f6f8d9f90c275 100644 --- a/examples/src/main/scala/org/apache/spark/examples/graphx/LiveJournalPageRank.scala +++ b/examples/src/main/scala/org/apache/spark/examples/graphx/LiveJournalPageRank.scala @@ -17,11 +17,6 @@ package org.apache.spark.examples.graphx -import org.apache.spark.SparkContext._ -import org.apache.spark._ -import org.apache.spark.graphx._ - - /** * Uses GraphX to run PageRank on a LiveJournal social network graph. Download the dataset from * http://snap.stanford.edu/data/soc-LiveJournal1.html. @@ -31,13 +26,13 @@ object LiveJournalPageRank { if (args.length < 1) { System.err.println( "Usage: LiveJournalPageRank \n" + + " --numEPart=\n" + + " The number of partitions for the graph's edge RDD.\n" + " [--tol=]\n" + " The tolerance allowed at convergence (smaller => more accurate). Default is " + "0.001.\n" + " [--output=]\n" + " If specified, the file to write the ranks to.\n" + - " [--numEPart=]\n" + - " The number of partitions for the graph's edge RDD. Default is 4.\n" + " [--partStrategy=RandomVertexCut | EdgePartition1D | EdgePartition2D | " + "CanonicalRandomVertexCut]\n" + " The way edges are assigned to edge partitions. Default is RandomVertexCut.") diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/CrossValidatorExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/CrossValidatorExample.scala index a2893f78e0fec..6c0af20461d3b 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/CrossValidatorExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/CrossValidatorExample.scala @@ -90,7 +90,7 @@ object CrossValidatorExample { crossval.setNumFolds(2) // Use 3+ in practice // Run cross-validation, and choose the best set of parameters. - val cvModel = crossval.fit(training) + val cvModel = crossval.fit(training.toDF()) // Prepare test documents, which are unlabeled. val test = sc.parallelize(Seq( @@ -100,7 +100,7 @@ object CrossValidatorExample { Document(7L, "apache hadoop"))) // Make predictions on test documents. cvModel uses the best model found (lrModel). - cvModel.transform(test) + cvModel.transform(test.toDF()) .select("id", "text", "probability", "prediction") .collect() .foreach { case Row(id: Long, text: String, prob: Vector, prediction: Double) => diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala new file mode 100644 index 0000000000000..d4cc8dede07ef --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala @@ -0,0 +1,322 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.examples.ml + +import scala.collection.mutable +import scala.language.reflectiveCalls + +import scopt.OptionParser + +import org.apache.spark.ml.tree.DecisionTreeModel +import org.apache.spark.{SparkConf, SparkContext} +import org.apache.spark.examples.mllib.AbstractParams +import org.apache.spark.ml.{Pipeline, PipelineStage} +import org.apache.spark.ml.classification.{DecisionTreeClassificationModel, DecisionTreeClassifier} +import org.apache.spark.ml.feature.{VectorIndexer, StringIndexer} +import org.apache.spark.ml.regression.{DecisionTreeRegressionModel, DecisionTreeRegressor} +import org.apache.spark.ml.util.MetadataUtils +import org.apache.spark.mllib.evaluation.{RegressionMetrics, MulticlassMetrics} +import org.apache.spark.mllib.linalg.Vector +import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.mllib.util.MLUtils +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.types.StringType +import org.apache.spark.sql.{SQLContext, DataFrame} + + +/** + * An example runner for decision trees. Run with + * {{{ + * ./bin/run-example ml.DecisionTreeExample [options] + * }}} + * If you use it as a template to create your own app, please use `spark-submit` to submit your app. + */ +object DecisionTreeExample { + + case class Params( + input: String = null, + testInput: String = "", + dataFormat: String = "libsvm", + algo: String = "Classification", + maxDepth: Int = 5, + maxBins: Int = 32, + minInstancesPerNode: Int = 1, + minInfoGain: Double = 0.0, + numTrees: Int = 1, + featureSubsetStrategy: String = "auto", + fracTest: Double = 0.2, + cacheNodeIds: Boolean = false, + checkpointDir: Option[String] = None, + checkpointInterval: Int = 10) extends AbstractParams[Params] + + def main(args: Array[String]) { + val defaultParams = Params() + + val parser = new OptionParser[Params]("DecisionTreeExample") { + head("DecisionTreeExample: an example decision tree app.") + opt[String]("algo") + .text(s"algorithm (Classification, Regression), default: ${defaultParams.algo}") + .action((x, c) => c.copy(algo = x)) + opt[Int]("maxDepth") + .text(s"max depth of the tree, default: ${defaultParams.maxDepth}") + .action((x, c) => c.copy(maxDepth = x)) + opt[Int]("maxBins") + .text(s"max number of bins, default: ${defaultParams.maxBins}") + .action((x, c) => c.copy(maxBins = x)) + opt[Int]("minInstancesPerNode") + .text(s"min number of instances required at child nodes to create the parent split," + + s" default: ${defaultParams.minInstancesPerNode}") + .action((x, c) => c.copy(minInstancesPerNode = x)) + opt[Double]("minInfoGain") + .text(s"min info gain required to create a split, default: ${defaultParams.minInfoGain}") + .action((x, c) => c.copy(minInfoGain = x)) + opt[Double]("fracTest") + .text(s"fraction of data to hold out for testing. If given option testInput, " + + s"this option is ignored. default: ${defaultParams.fracTest}") + .action((x, c) => c.copy(fracTest = x)) + opt[Boolean]("cacheNodeIds") + .text(s"whether to use node Id cache during training, " + + s"default: ${defaultParams.cacheNodeIds}") + .action((x, c) => c.copy(cacheNodeIds = x)) + opt[String]("checkpointDir") + .text(s"checkpoint directory where intermediate node Id caches will be stored, " + + s"default: ${defaultParams.checkpointDir match { + case Some(strVal) => strVal + case None => "None" + }}") + .action((x, c) => c.copy(checkpointDir = Some(x))) + opt[Int]("checkpointInterval") + .text(s"how often to checkpoint the node Id cache, " + + s"default: ${defaultParams.checkpointInterval}") + .action((x, c) => c.copy(checkpointInterval = x)) + opt[String]("testInput") + .text(s"input path to test dataset. If given, option fracTest is ignored." + + s" default: ${defaultParams.testInput}") + .action((x, c) => c.copy(testInput = x)) + opt[String]("") + .text("data format: libsvm (default), dense (deprecated in Spark v1.1)") + .action((x, c) => c.copy(dataFormat = x)) + arg[String]("") + .text("input path to labeled examples") + .required() + .action((x, c) => c.copy(input = x)) + checkConfig { params => + if (params.fracTest < 0 || params.fracTest > 1) { + failure(s"fracTest ${params.fracTest} value incorrect; should be in [0,1].") + } else { + success + } + } + } + + parser.parse(args, defaultParams).map { params => + run(params) + }.getOrElse { + sys.exit(1) + } + } + + /** Load a dataset from the given path, using the given format */ + private[ml] def loadData( + sc: SparkContext, + path: String, + format: String, + expectedNumFeatures: Option[Int] = None): RDD[LabeledPoint] = { + format match { + case "dense" => MLUtils.loadLabeledPoints(sc, path) + case "libsvm" => expectedNumFeatures match { + case Some(numFeatures) => MLUtils.loadLibSVMFile(sc, path, numFeatures) + case None => MLUtils.loadLibSVMFile(sc, path) + } + case _ => throw new IllegalArgumentException(s"Bad data format: $format") + } + } + + /** + * Load training and test data from files. + * @param input Path to input dataset. + * @param dataFormat "libsvm" or "dense" + * @param testInput Path to test dataset. + * @param algo Classification or Regression + * @param fracTest Fraction of input data to hold out for testing. Ignored if testInput given. + * @return (training dataset, test dataset) + */ + private[ml] def loadDatasets( + sc: SparkContext, + input: String, + dataFormat: String, + testInput: String, + algo: String, + fracTest: Double): (DataFrame, DataFrame) = { + val sqlContext = new SQLContext(sc) + import sqlContext.implicits._ + + // Load training data + val origExamples: RDD[LabeledPoint] = loadData(sc, input, dataFormat) + + // Load or create test set + val splits: Array[RDD[LabeledPoint]] = if (testInput != "") { + // Load testInput. + val numFeatures = origExamples.take(1)(0).features.size + val origTestExamples: RDD[LabeledPoint] = loadData(sc, input, dataFormat, Some(numFeatures)) + Array(origExamples, origTestExamples) + } else { + // Split input into training, test. + origExamples.randomSplit(Array(1.0 - fracTest, fracTest), seed = 12345) + } + + // For classification, convert labels to Strings since we will index them later with + // StringIndexer. + def labelsToStrings(data: DataFrame): DataFrame = { + algo.toLowerCase match { + case "classification" => + data.withColumn("labelString", data("label").cast(StringType)) + case "regression" => + data + case _ => + throw new IllegalArgumentException("Algo ${params.algo} not supported.") + } + } + val dataframes = splits.map(_.toDF()).map(labelsToStrings).map(_.cache()) + + (dataframes(0), dataframes(1)) + } + + def run(params: Params) { + val conf = new SparkConf().setAppName(s"DecisionTreeExample with $params") + val sc = new SparkContext(conf) + params.checkpointDir.foreach(sc.setCheckpointDir) + val algo = params.algo.toLowerCase + + println(s"DecisionTreeExample with parameters:\n$params") + + // Load training and test data and cache it. + val (training: DataFrame, test: DataFrame) = + loadDatasets(sc, params.input, params.dataFormat, params.testInput, algo, params.fracTest) + + val numTraining = training.count() + val numTest = test.count() + val numFeatures = training.select("features").first().getAs[Vector](0).size + println("Loaded data:") + println(s" numTraining = $numTraining, numTest = $numTest") + println(s" numFeatures = $numFeatures") + + // Set up Pipeline + val stages = new mutable.ArrayBuffer[PipelineStage]() + // (1) For classification, re-index classes. + val labelColName = if (algo == "classification") "indexedLabel" else "label" + if (algo == "classification") { + val labelIndexer = new StringIndexer().setInputCol("labelString").setOutputCol(labelColName) + stages += labelIndexer + } + // (2) Identify categorical features using VectorIndexer. + // Features with more than maxCategories values will be treated as continuous. + val featuresIndexer = new VectorIndexer().setInputCol("features") + .setOutputCol("indexedFeatures").setMaxCategories(10) + stages += featuresIndexer + // (3) Learn DecisionTree + val dt = algo match { + case "classification" => + new DecisionTreeClassifier().setFeaturesCol("indexedFeatures") + .setLabelCol(labelColName) + .setMaxDepth(params.maxDepth) + .setMaxBins(params.maxBins) + .setMinInstancesPerNode(params.minInstancesPerNode) + .setMinInfoGain(params.minInfoGain) + .setCacheNodeIds(params.cacheNodeIds) + .setCheckpointInterval(params.checkpointInterval) + case "regression" => + new DecisionTreeRegressor().setFeaturesCol("indexedFeatures") + .setLabelCol(labelColName) + .setMaxDepth(params.maxDepth) + .setMaxBins(params.maxBins) + .setMinInstancesPerNode(params.minInstancesPerNode) + .setMinInfoGain(params.minInfoGain) + .setCacheNodeIds(params.cacheNodeIds) + .setCheckpointInterval(params.checkpointInterval) + case _ => throw new IllegalArgumentException("Algo ${params.algo} not supported.") + } + stages += dt + val pipeline = new Pipeline().setStages(stages.toArray) + + // Fit the Pipeline + val startTime = System.nanoTime() + val pipelineModel = pipeline.fit(training) + val elapsedTime = (System.nanoTime() - startTime) / 1e9 + println(s"Training time: $elapsedTime seconds") + + // Get the trained Decision Tree from the fitted PipelineModel + val treeModel: DecisionTreeModel = algo match { + case "classification" => + pipelineModel.getModel[DecisionTreeClassificationModel]( + dt.asInstanceOf[DecisionTreeClassifier]) + case "regression" => + pipelineModel.getModel[DecisionTreeRegressionModel](dt.asInstanceOf[DecisionTreeRegressor]) + case _ => throw new IllegalArgumentException("Algo ${params.algo} not supported.") + } + if (treeModel.numNodes < 20) { + println(treeModel.toDebugString) // Print full model. + } else { + println(treeModel) // Print model summary. + } + + // Predict on training + val trainingFullPredictions = pipelineModel.transform(training).cache() + val trainingPredictions = trainingFullPredictions.select("prediction") + .map(_.getDouble(0)) + val trainingLabels = trainingFullPredictions.select(labelColName).map(_.getDouble(0)) + // Predict on test data + val testFullPredictions = pipelineModel.transform(test).cache() + val testPredictions = testFullPredictions.select("prediction") + .map(_.getDouble(0)) + val testLabels = testFullPredictions.select(labelColName).map(_.getDouble(0)) + + // For classification, print number of classes for reference. + if (algo == "classification") { + val numClasses = + MetadataUtils.getNumClasses(trainingFullPredictions.schema(labelColName)) match { + case Some(n) => n + case None => throw new RuntimeException( + "DecisionTreeExample had unknown failure when indexing labels for classification.") + } + println(s"numClasses = $numClasses.") + } + + // Evaluate model on training, test data + algo match { + case "classification" => + val trainingAccuracy = + new MulticlassMetrics(trainingPredictions.zip(trainingLabels)).precision + println(s"Train accuracy = $trainingAccuracy") + val testAccuracy = + new MulticlassMetrics(testPredictions.zip(testLabels)).precision + println(s"Test accuracy = $testAccuracy") + case "regression" => + val trainingRMSE = + new RegressionMetrics(trainingPredictions.zip(trainingLabels)).rootMeanSquaredError + println(s"Training root mean squared error (RMSE) = $trainingRMSE") + val testRMSE = + new RegressionMetrics(testPredictions.zip(testLabels)).rootMeanSquaredError + println(s"Test root mean squared error (RMSE) = $testRMSE") + case _ => + throw new IllegalArgumentException("Algo ${params.algo} not supported.") + } + + sc.stop() + } +} diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala index aed44238939c7..2245fa429fda3 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala @@ -58,7 +58,7 @@ object DeveloperApiExample { lr.setMaxIter(10) // Learn a LogisticRegression model. This uses the parameters stored in lr. - val model = lr.fit(training) + val model = lr.fit(training.toDF()) // Prepare test data. val test = sc.parallelize(Seq( @@ -67,7 +67,7 @@ object DeveloperApiExample { LabeledPoint(1.0, Vectors.dense(0.0, 2.2, -1.5)))) // Make predictions on test data. - val sumPredictions: Double = model.transform(test) + val sumPredictions: Double = model.transform(test.toDF()) .select("features", "label", "prediction") .collect() .map { case Row(features: Vector, label: Double, prediction: Double) => @@ -99,7 +99,7 @@ private trait MyLogisticRegressionParams extends ClassifierParams { * class since the maxIter parameter is only used during training (not in the Model). */ val maxIter: IntParam = new IntParam(this, "maxIter", "max number of iterations") - def getMaxIter: Int = get(maxIter) + def getMaxIter: Int = getOrDefault(maxIter) } /** @@ -174,11 +174,11 @@ private class MyLogisticRegressionModel( * Create a copy of the model. * The copy is shallow, except for the embedded paramMap, which gets a deep copy. * - * This is used for the defaul implementation of [[transform()]]. + * This is used for the default implementation of [[transform()]]. */ override protected def copy(): MyLogisticRegressionModel = { val m = new MyLogisticRegressionModel(parent, fittingParamMap, weights) - Params.inheritValues(this.paramMap, this, m) + Params.inheritValues(extractParamMap(), this, m) m } } diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/MovieLensALS.scala b/examples/src/main/scala/org/apache/spark/examples/ml/MovieLensALS.scala index 836ea2e01201e..25f21113bf622 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/MovieLensALS.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/MovieLensALS.scala @@ -93,8 +93,8 @@ object MovieLensALS { | bin/spark-submit --class org.apache.spark.examples.ml.MovieLensALS \ | examples/target/scala-*/spark-examples-*.jar \ | --rank 10 --maxIter 15 --regParam 0.1 \ - | --movies path/to/movielens/movies.dat \ - | --ratings path/to/movielens/ratings.dat + | --movies data/mllib/als/sample_movielens_movies.txt \ + | --ratings data/mllib/als/sample_movielens_ratings.txt """.stripMargin) } @@ -137,9 +137,9 @@ object MovieLensALS { .setRegParam(params.regParam) .setNumBlocks(params.numBlocks) - val model = als.fit(training) + val model = als.fit(training.toDF()) - val predictions = model.transform(test).cache() + val predictions = model.transform(test.toDF()).cache() // Evaluate the model. // TODO: Create an evaluator to compute RMSE. @@ -157,17 +157,23 @@ object MovieLensALS { println(s"Test RMSE = $rmse.") // Inspect false positives. - predictions.registerTempTable("prediction") - sc.textFile(params.movies).map(Movie.parseMovie).registerTempTable("movie") - sqlContext.sql( - """ - |SELECT userId, prediction.movieId, title, rating, prediction - | FROM prediction JOIN movie ON prediction.movieId = movie.movieId - | WHERE rating <= 1 AND prediction >= 4 - | LIMIT 100 - """.stripMargin) - .collect() - .foreach(println) + // Note: We reference columns in 2 ways: + // (1) predictions("movieId") lets us specify the movieId column in the predictions + // DataFrame, rather than the movieId column in the movies DataFrame. + // (2) $"userId" specifies the userId column in the predictions DataFrame. + // We could also write predictions("userId") but do not have to since + // the movies DataFrame does not have a column "userId." + val movies = sc.textFile(params.movies).map(Movie.parseMovie).toDF() + val falsePositives = predictions.join(movies) + .where((predictions("movieId") === movies("movieId")) + && ($"rating" <= 1) && ($"prediction" >= 4)) + .select($"userId", predictions("movieId"), $"title", $"rating", $"prediction") + val numFalsePositives = falsePositives.count() + println(s"Found $numFalsePositives false positives") + if (numFalsePositives > 0) { + println(s"Example false positives:") + falsePositives.limit(100).collect().foreach(println) + } sc.stop() } diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala index 80c9f5ff5781e..bf805149d0af6 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala @@ -40,8 +40,8 @@ object SimpleParamsExample { import sqlContext.implicits._ // Prepare training data. - // We use LabeledPoint, which is a case class. Spark SQL can convert RDDs of Java Beans - // into DataFrames, where it uses the bean metadata to infer the schema. + // We use LabeledPoint, which is a case class. Spark SQL can convert RDDs of case classes + // into DataFrames, where it uses the case class metadata to infer the schema. val training = sc.parallelize(Seq( LabeledPoint(1.0, Vectors.dense(0.0, 1.1, 0.1)), LabeledPoint(0.0, Vectors.dense(2.0, 1.0, -1.0)), @@ -58,7 +58,7 @@ object SimpleParamsExample { .setRegParam(0.01) // Learn a LogisticRegression model. This uses the parameters stored in lr. - val model1 = lr.fit(training) + val model1 = lr.fit(training.toDF()) // Since model1 is a Model (i.e., a Transformer produced by an Estimator), // we can view the parameters it used during fit(). // This prints the parameter (name: value) pairs, where names are unique IDs for this @@ -77,7 +77,7 @@ object SimpleParamsExample { // Now learn a new model using the paramMapCombined parameters. // paramMapCombined overrides all parameters set earlier via lr.set* methods. - val model2 = lr.fit(training, paramMapCombined) + val model2 = lr.fit(training.toDF(), paramMapCombined) println("Model 2 was fit using parameters: " + model2.fittingParamMap) // Prepare test data. @@ -90,11 +90,11 @@ object SimpleParamsExample { // LogisticRegression.transform will only use the 'features' column. // Note that model2.transform() outputs a 'myProbability' column instead of the usual // 'probability' column since we renamed the lr.probabilityCol parameter previously. - model2.transform(test) + model2.transform(test.toDF()) .select("features", "label", "myProbability", "prediction") .collect() .foreach { case Row(features: Vector, label: Double, prob: Vector, prediction: Double) => - println("($features, $label) -> prob=$prob, prediction=$prediction") + println(s"($features, $label) -> prob=$prob, prediction=$prediction") } sc.stop() diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleTextClassificationPipeline.scala b/examples/src/main/scala/org/apache/spark/examples/ml/SimpleTextClassificationPipeline.scala index 968cb292120d8..6772efd2c581c 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleTextClassificationPipeline.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/SimpleTextClassificationPipeline.scala @@ -69,7 +69,7 @@ object SimpleTextClassificationPipeline { .setStages(Array(tokenizer, hashingTF, lr)) // Fit the pipeline to training documents. - val model = pipeline.fit(training) + val model = pipeline.fit(training.toDF()) // Prepare test documents, which are unlabeled. val test = sc.parallelize(Seq( @@ -79,11 +79,11 @@ object SimpleTextClassificationPipeline { Document(7L, "apache hadoop"))) // Make predictions on test documents. - model.transform(test) + model.transform(test.toDF()) .select("id", "text", "probability", "prediction") .collect() .foreach { case Row(id: Long, text: String, prob: Vector, prediction: Double) => - println("($id, $text) --> prob=$prob, prediction=$prediction") + println(s"($id, $text) --> prob=$prob, prediction=$prediction") } sc.stop() diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/DatasetExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/DatasetExample.scala index 89b6255991a38..e943d6c889fab 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/DatasetExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/DatasetExample.scala @@ -81,18 +81,18 @@ object DatasetExample { println(s"Loaded ${origData.count()} instances from file: ${params.input}") // Convert input data to DataFrame explicitly. - val df: DataFrame = origData.toDataFrame + val df: DataFrame = origData.toDF() println(s"Inferred schema:\n${df.schema.prettyJson}") println(s"Converted to DataFrame with ${df.count()} records") - // Select columns, using implicit conversion to DataFrames. - val labelsDf: DataFrame = origData.select("label") + // Select columns + val labelsDf: DataFrame = df.select("label") val labels: RDD[Double] = labelsDf.map { case Row(v: Double) => v } val numLabels = labels.count() val meanLabel = labels.fold(0.0)(_ + _) / numLabels println(s"Selected label column with average value $meanLabel") - val featuresDf: DataFrame = origData.select("features") + val featuresDf: DataFrame = df.select("features") val features: RDD[Vector] = featuresDf.map { case Row(v: Vector) => v } val featureSummary = features.aggregate(new MultivariateOnlineSummarizer())( (summary, feat) => summary.add(feat), diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/DenseKMeans.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/DenseKMeans.scala index 11e35598baf50..14cc5cbb679c5 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/DenseKMeans.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/DenseKMeans.scala @@ -56,7 +56,7 @@ object DenseKMeans { .text(s"number of clusters, required") .action((x, c) => c.copy(k = x)) opt[Int]("numIterations") - .text(s"number of iterations, default; ${defaultParams.numIterations}") + .text(s"number of iterations, default: ${defaultParams.numIterations}") .action((x, c) => c.copy(numIterations = x)) opt[String]("initMode") .text(s"initialization mode (${InitializationMode.values.mkString(",")}), " + diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/FPGrowthExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/FPGrowthExample.scala new file mode 100644 index 0000000000000..13f24a1e59610 --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/FPGrowthExample.scala @@ -0,0 +1,82 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.examples.mllib + +import scopt.OptionParser + +import org.apache.spark.mllib.fpm.FPGrowth +import org.apache.spark.{SparkConf, SparkContext} + +/** + * Example for mining frequent itemsets using FP-growth. + * Example usage: ./bin/run-example mllib.FPGrowthExample \ + * --minSupport 0.8 --numPartition 2 ./data/mllib/sample_fpgrowth.txt + */ +object FPGrowthExample { + + case class Params( + input: String = null, + minSupport: Double = 0.3, + numPartition: Int = -1) extends AbstractParams[Params] + + def main(args: Array[String]) { + val defaultParams = Params() + + val parser = new OptionParser[Params]("FPGrowthExample") { + head("FPGrowth: an example FP-growth app.") + opt[Double]("minSupport") + .text(s"minimal support level, default: ${defaultParams.minSupport}") + .action((x, c) => c.copy(minSupport = x)) + opt[Int]("numPartition") + .text(s"number of partition, default: ${defaultParams.numPartition}") + .action((x, c) => c.copy(numPartition = x)) + arg[String]("") + .text("input paths to input data set, whose file format is that each line " + + "contains a transaction with each item in String and separated by a space") + .required() + .action((x, c) => c.copy(input = x)) + } + + parser.parse(args, defaultParams).map { params => + run(params) + }.getOrElse { + sys.exit(1) + } + } + + def run(params: Params) { + val conf = new SparkConf().setAppName(s"FPGrowthExample with $params") + val sc = new SparkContext(conf) + val transactions = sc.textFile(params.input).map(_.split(" ")).cache() + + println(s"Number of transactions: ${transactions.count()}") + + val model = new FPGrowth() + .setMinSupport(params.minSupport) + .setNumPartitions(params.numPartition) + .run(transactions) + + println(s"Number of frequent itemsets: ${model.freqItemsets.count()}") + + model.freqItemsets.collect().foreach { itemset => + println(itemset.items.mkString("[", ",", "]") + ", " + itemset.freq) + } + + sc.stop() + } +} diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/LDAExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/LDAExample.scala index 0e1b27a8bd2ee..08a93595a2e17 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/LDAExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/LDAExample.scala @@ -159,7 +159,7 @@ object LDAExample { } println() } - + sc.stop() } /** @@ -173,7 +173,9 @@ object LDAExample { stopwordFile: String): (RDD[(Long, Vector)], Array[String], Long) = { // Get dataset of document texts - // One document per line in each text file. + // One document per line in each text file. If the input consists of many small files, + // this can result in a large number of small partitions, which can degrade performance. + // In this case, consider using coalesce() to create fewer, larger partitions. val textRDD: RDD[String] = sc.textFile(paths.mkString(",")) // Split text into words diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala index 91a0a860d6c71..0bc36ea65e1ab 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala @@ -175,9 +175,12 @@ object MovieLensALS { } /** Compute RMSE (Root Mean Squared Error). */ - def computeRmse(model: MatrixFactorizationModel, data: RDD[Rating], implicitPrefs: Boolean) = { + def computeRmse(model: MatrixFactorizationModel, data: RDD[Rating], implicitPrefs: Boolean) + : Double = { - def mapPredictedRating(r: Double) = if (implicitPrefs) math.max(math.min(r, 1.0), 0.0) else r + def mapPredictedRating(r: Double): Double = { + if (implicitPrefs) math.max(math.min(r, 1.0), 0.0) else r + } val predictions: RDD[Rating] = model.predict(data.map(x => (x.user, x.product))) val predictionsAndRatings = predictions.map{ x => diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/PowerIterationClusteringExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/PowerIterationClusteringExample.scala new file mode 100644 index 0000000000000..6d8b806569dfd --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/PowerIterationClusteringExample.scala @@ -0,0 +1,157 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.examples.mllib + +import org.apache.log4j.{Level, Logger} +import scopt.OptionParser + +import org.apache.spark.mllib.clustering.PowerIterationClustering +import org.apache.spark.rdd.RDD +import org.apache.spark.{SparkConf, SparkContext} + +/** + * An example Power Iteration Clustering http://www.icml2010.org/papers/387.pdf app. + * Takes an input of K concentric circles and the number of points in the innermost circle. + * The output should be K clusters - each cluster containing precisely the points associated + * with each of the input circles. + * + * Run with + * {{{ + * ./bin/run-example mllib.PowerIterationClusteringExample [options] + * + * Where options include: + * k: Number of circles/clusters + * n: Number of sampled points on innermost circle.. There are proportionally more points + * within the outer/larger circles + * maxIterations: Number of Power Iterations + * outerRadius: radius of the outermost of the concentric circles + * }}} + * + * Here is a sample run and output: + * + * ./bin/run-example mllib.PowerIterationClusteringExample -k 3 --n 30 --maxIterations 15 + * + * Cluster assignments: 1 -> [0,1,2,3,4],2 -> [5,6,7,8,9,10,11,12,13,14], + * 0 -> [15,16,17,18,19,20,21,22,23,24,25,26,27,28,29] + * + * + * If you use it as a template to create your own app, please use `spark-submit` to submit your app. + */ +object PowerIterationClusteringExample { + + case class Params( + input: String = null, + k: Int = 3, + numPoints: Int = 5, + maxIterations: Int = 10, + outerRadius: Double = 3.0 + ) extends AbstractParams[Params] + + def main(args: Array[String]) { + val defaultParams = Params() + + val parser = new OptionParser[Params]("PowerIterationClusteringExample") { + head("PowerIterationClusteringExample: an example PIC app using concentric circles.") + opt[Int]('k', "k") + .text(s"number of circles (/clusters), default: ${defaultParams.k}") + .action((x, c) => c.copy(k = x)) + opt[Int]('n', "n") + .text(s"number of points in smallest circle, default: ${defaultParams.numPoints}") + .action((x, c) => c.copy(numPoints = x)) + opt[Int]("maxIterations") + .text(s"number of iterations, default: ${defaultParams.maxIterations}") + .action((x, c) => c.copy(maxIterations = x)) + opt[Double]('r', "r") + .text(s"radius of outermost circle, default: ${defaultParams.outerRadius}") + .action((x, c) => c.copy(outerRadius = x)) + } + + parser.parse(args, defaultParams).map { params => + run(params) + }.getOrElse { + sys.exit(1) + } + } + + def run(params: Params) { + val conf = new SparkConf() + .setMaster("local") + .setAppName(s"PowerIterationClustering with $params") + val sc = new SparkContext(conf) + + Logger.getRootLogger.setLevel(Level.WARN) + + val circlesRdd = generateCirclesRdd(sc, params.k, params.numPoints, params.outerRadius) + val model = new PowerIterationClustering() + .setK(params.k) + .setMaxIterations(params.maxIterations) + .run(circlesRdd) + + val clusters = model.assignments.collect().groupBy(_.cluster).mapValues(_.map(_.id)) + val assignments = clusters.toList.sortBy { case (k, v) => v.length} + val assignmentsStr = assignments + .map { case (k, v) => + s"$k -> ${v.sorted.mkString("[", ",", "]")}" + }.mkString(",") + val sizesStr = assignments.map { + _._2.size + }.sorted.mkString("(", ",", ")") + println(s"Cluster assignments: $assignmentsStr\ncluster sizes: $sizesStr") + + sc.stop() + } + + def generateCircle(radius: Double, n: Int): Seq[(Double, Double)] = { + Seq.tabulate(n) { i => + val theta = 2.0 * math.Pi * i / n + (radius * math.cos(theta), radius * math.sin(theta)) + } + } + + def generateCirclesRdd(sc: SparkContext, + nCircles: Int = 3, + nPoints: Int = 30, + outerRadius: Double): RDD[(Long, Long, Double)] = { + + val radii = Array.tabulate(nCircles) { cx => outerRadius / (nCircles - cx)} + val groupSizes = Array.tabulate(nCircles) { cx => (cx + 1) * nPoints} + val points = (0 until nCircles).flatMap { cx => + generateCircle(radii(cx), groupSizes(cx)) + }.zipWithIndex + val rdd = sc.parallelize(points) + val distancesRdd = rdd.cartesian(rdd).flatMap { case (((x0, y0), i0), ((x1, y1), i1)) => + if (i0 < i1) { + Some((i0.toLong, i1.toLong, gaussianSimilarity((x0, y0), (x1, y1), 1.0))) + } else { + None + } + } + distancesRdd + } + + /** + * Gaussian Similarity: http://en.wikipedia.org/wiki/Radial_basis_function_kernel + */ + def gaussianSimilarity(p1: (Double, Double), p2: (Double, Double), sigma: Double): Double = { + val coeff = 1.0 / (math.sqrt(2.0 * math.Pi) * sigma) + val expCoeff = -1.0 / 2.0 * math.pow(sigma, 2.0) + val ssquares = (p1._1 - p2._1) * (p1._1 - p2._1) + (p1._2 - p2._2) * (p1._2 - p2._2) + coeff * math.exp(expCoeff * ssquares) + } +} + diff --git a/examples/src/main/scala/org/apache/spark/examples/sql/RDDRelation.scala b/examples/src/main/scala/org/apache/spark/examples/sql/RDDRelation.scala index 1eac3c8d03e39..6331d1c0060f8 100644 --- a/examples/src/main/scala/org/apache/spark/examples/sql/RDDRelation.scala +++ b/examples/src/main/scala/org/apache/spark/examples/sql/RDDRelation.scala @@ -19,7 +19,7 @@ package org.apache.spark.examples.sql import org.apache.spark.{SparkConf, SparkContext} import org.apache.spark.sql.SQLContext -import org.apache.spark.sql.Dsl._ +import org.apache.spark.sql.functions._ // One method for defining the schema of an RDD is to make a case class with the desired column // names and types. @@ -34,10 +34,10 @@ object RDDRelation { // Importing the SQL context gives access to all the SQL functions and implicit conversions. import sqlContext.implicits._ - val rdd = sc.parallelize((1 to 100).map(i => Record(i, s"val_$i"))) + val df = sc.parallelize((1 to 100).map(i => Record(i, s"val_$i"))).toDF() // Any RDD containing case classes can be registered as a table. The schema of the table is // automatically inferred using scala reflection. - rdd.registerTempTable("records") + df.registerTempTable("records") // Once tables have been registered, you can run SQL queries over them. println("Result of SELECT *:") @@ -55,10 +55,10 @@ object RDDRelation { rddFromSql.map(row => s"Key: ${row(0)}, Value: ${row(1)}").collect().foreach(println) // Queries can also be written using a LINQ-like Scala DSL. - rdd.where($"key" === 1).orderBy($"value".asc).select($"key").collect().foreach(println) + df.where($"key" === 1).orderBy($"value".asc).select($"key").collect().foreach(println) // Write out an RDD as a parquet file. - rdd.saveAsParquetFile("pair.parquet") + df.saveAsParquetFile("pair.parquet") // Read in parquet file. Parquet files are self-describing so the schmema is preserved. val parquetFile = sqlContext.parquetFile("pair.parquet") diff --git a/examples/src/main/scala/org/apache/spark/examples/sql/hive/HiveFromSpark.scala b/examples/src/main/scala/org/apache/spark/examples/sql/hive/HiveFromSpark.scala index 15754cdfcc35e..b7ba60ec28155 100644 --- a/examples/src/main/scala/org/apache/spark/examples/sql/hive/HiveFromSpark.scala +++ b/examples/src/main/scala/org/apache/spark/examples/sql/hive/HiveFromSpark.scala @@ -68,7 +68,7 @@ object HiveFromSpark { // You can also register RDDs as temporary tables within a HiveContext. val rdd = sc.parallelize((1 to 100).map(i => Record(i, s"val_$i"))) - rdd.registerTempTable("records") + rdd.toDF().registerTempTable("records") // Queries can then join RDD data with data stored in Hive. println("Result of SELECT *:") diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/ActorWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/ActorWordCount.scala index b433082dce1a2..92867b44be138 100644 --- a/examples/src/main/scala/org/apache/spark/examples/streaming/ActorWordCount.scala +++ b/examples/src/main/scala/org/apache/spark/examples/streaming/ActorWordCount.scala @@ -85,13 +85,13 @@ extends Actor with ActorHelper { lazy private val remotePublisher = context.actorSelection(urlOfPublisher) - override def preStart = remotePublisher ! SubscribeReceiver(context.self) + override def preStart(): Unit = remotePublisher ! SubscribeReceiver(context.self) - def receive = { + def receive: PartialFunction[Any, Unit] = { case msg => store(msg.asInstanceOf[T]) } - override def postStop() = remotePublisher ! UnsubscribeReceiver(context.self) + override def postStop(): Unit = remotePublisher ! UnsubscribeReceiver(context.self) } diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/MQTTWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/MQTTWordCount.scala index 6ff0c47793a25..f40caad322f59 100644 --- a/examples/src/main/scala/org/apache/spark/examples/streaming/MQTTWordCount.scala +++ b/examples/src/main/scala/org/apache/spark/examples/streaming/MQTTWordCount.scala @@ -17,8 +17,8 @@ package org.apache.spark.examples.streaming -import org.eclipse.paho.client.mqttv3.{MqttClient, MqttClientPersistence, MqttException, MqttMessage, MqttTopic} -import org.eclipse.paho.client.mqttv3.persist.MqttDefaultFilePersistence +import org.eclipse.paho.client.mqttv3._ +import org.eclipse.paho.client.mqttv3.persist.MemoryPersistence import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming.{Seconds, StreamingContext} @@ -31,8 +31,6 @@ import org.apache.spark.SparkConf */ object MQTTPublisher { - var client: MqttClient = _ - def main(args: Array[String]) { if (args.length < 2) { System.err.println("Usage: MQTTPublisher ") @@ -42,25 +40,36 @@ object MQTTPublisher { StreamingExamples.setStreamingLogLevels() val Seq(brokerUrl, topic) = args.toSeq + + var client: MqttClient = null try { - var peristance:MqttClientPersistence =new MqttDefaultFilePersistence("/tmp") - client = new MqttClient(brokerUrl, MqttClient.generateClientId(), peristance) + val persistence = new MemoryPersistence() + client = new MqttClient(brokerUrl, MqttClient.generateClientId(), persistence) + + client.connect() + + val msgtopic = client.getTopic(topic) + val msgContent = "hello mqtt demo for spark streaming" + val message = new MqttMessage(msgContent.getBytes("utf-8")) + + while (true) { + try { + msgtopic.publish(message) + println(s"Published data. topic: {msgtopic.getName()}; Message: {message}") + } catch { + case e: MqttException if e.getReasonCode == MqttException.REASON_CODE_MAX_INFLIGHT => + Thread.sleep(10) + println("Queue is full, wait for to consume data from the message queue") + } + } } catch { case e: MqttException => println("Exception Caught: " + e) + } finally { + if (client != null) { + client.disconnect() + } } - - client.connect() - - val msgtopic: MqttTopic = client.getTopic(topic) - val msg: String = "hello mqtt demo for spark streaming" - - while (true) { - val message: MqttMessage = new MqttMessage(String.valueOf(msg).getBytes("utf-8")) - msgtopic.publish(message) - println("Published data. topic: " + msgtopic.getName() + " Message: " + message) - } - client.disconnect() } } @@ -96,9 +105,9 @@ object MQTTWordCount { val sparkConf = new SparkConf().setAppName("MQTTWordCount") val ssc = new StreamingContext(sparkConf, Seconds(2)) val lines = MQTTUtils.createStream(ssc, brokerUrl, topic, StorageLevel.MEMORY_ONLY_SER_2) - - val words = lines.flatMap(x => x.toString.split(" ")) + val words = lines.flatMap(x => x.split(" ")) val wordCounts = words.map(x => (x, 1)).reduceByKey(_ + _) + wordCounts.print() ssc.start() ssc.awaitTermination() diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala index c3a05c89d817e..751b30ea15782 100644 --- a/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala +++ b/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala @@ -55,7 +55,8 @@ import org.apache.spark.util.IntParam */ object RecoverableNetworkWordCount { - def createContext(ip: String, port: Int, outputPath: String, checkpointDirectory: String) = { + def createContext(ip: String, port: Int, outputPath: String, checkpointDirectory: String) + : StreamingContext = { // If you do not see this printed, that means the StreamingContext has been loaded // from the new checkpoint diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/SqlNetworkWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/SqlNetworkWordCount.scala new file mode 100644 index 0000000000000..5a6b9216a3fbc --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/streaming/SqlNetworkWordCount.scala @@ -0,0 +1,101 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.examples.streaming + +import org.apache.spark.SparkConf +import org.apache.spark.SparkContext +import org.apache.spark.rdd.RDD +import org.apache.spark.streaming.{Time, Seconds, StreamingContext} +import org.apache.spark.util.IntParam +import org.apache.spark.sql.SQLContext +import org.apache.spark.storage.StorageLevel + +/** + * Use DataFrames and SQL to count words in UTF8 encoded, '\n' delimited text received from the + * network every second. + * + * Usage: SqlNetworkWordCount + * and describe the TCP server that Spark Streaming would connect to receive data. + * + * To run this on your local machine, you need to first run a Netcat server + * `$ nc -lk 9999` + * and then run the example + * `$ bin/run-example org.apache.spark.examples.streaming.SqlNetworkWordCount localhost 9999` + */ + +object SqlNetworkWordCount { + def main(args: Array[String]) { + if (args.length < 2) { + System.err.println("Usage: NetworkWordCount ") + System.exit(1) + } + + StreamingExamples.setStreamingLogLevels() + + // Create the context with a 2 second batch size + val sparkConf = new SparkConf().setAppName("SqlNetworkWordCount") + val ssc = new StreamingContext(sparkConf, Seconds(2)) + + // Create a socket stream on target ip:port and count the + // words in input stream of \n delimited text (eg. generated by 'nc') + // Note that no duplication in storage level only for running locally. + // Replication necessary in distributed scenario for fault tolerance. + val lines = ssc.socketTextStream(args(0), args(1).toInt, StorageLevel.MEMORY_AND_DISK_SER) + val words = lines.flatMap(_.split(" ")) + + // Convert RDDs of the words DStream to DataFrame and run SQL query + words.foreachRDD((rdd: RDD[String], time: Time) => { + // Get the singleton instance of SQLContext + val sqlContext = SQLContextSingleton.getInstance(rdd.sparkContext) + import sqlContext.implicits._ + + // Convert RDD[String] to RDD[case class] to DataFrame + val wordsDataFrame = rdd.map(w => Record(w)).toDF() + + // Register as table + wordsDataFrame.registerTempTable("words") + + // Do word count on table using SQL and print it + val wordCountsDataFrame = + sqlContext.sql("select word, count(*) as total from words group by word") + println(s"========= $time =========") + wordCountsDataFrame.show() + }) + + ssc.start() + ssc.awaitTermination() + } +} + + +/** Case class for converting RDD to DataFrame */ +case class Record(word: String) + + +/** Lazily instantiated singleton instance of SQLContext */ +object SQLContextSingleton { + + @transient private var instance: SQLContext = _ + + def getInstance(sparkContext: SparkContext): SQLContext = { + if (instance == null) { + instance = new SQLContext(sparkContext) + } + instance + } +} diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/ZeroMQWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/ZeroMQWordCount.scala index 6510c70bd1866..e99d1baa72b9f 100644 --- a/examples/src/main/scala/org/apache/spark/examples/streaming/ZeroMQWordCount.scala +++ b/examples/src/main/scala/org/apache/spark/examples/streaming/ZeroMQWordCount.scala @@ -35,7 +35,7 @@ import org.apache.spark.SparkConf */ object SimpleZeroMQPublisher { - def main(args: Array[String]) = { + def main(args: Array[String]): Unit = { if (args.length < 2) { System.err.println("Usage: SimpleZeroMQPublisher ") System.exit(1) @@ -45,7 +45,7 @@ object SimpleZeroMQPublisher { val acs: ActorSystem = ActorSystem() val pubSocket = ZeroMQExtension(acs).newSocket(SocketType.Pub, Bind(url)) - implicit def stringToByteString(x: String) = ByteString(x) + implicit def stringToByteString(x: String): ByteString = ByteString(x) val messages: List[ByteString] = List("words ", "may ", "count ") while (true) { Thread.sleep(1000) @@ -86,7 +86,7 @@ object ZeroMQWordCount { // Create the context and set the batch size val ssc = new StreamingContext(sparkConf, Seconds(2)) - def bytesToStringIterator(x: Seq[ByteString]) = (x.map(_.utf8String)).iterator + def bytesToStringIterator(x: Seq[ByteString]): Iterator[String] = x.map(_.utf8String).iterator // For this stream, a zeroMQ publisher should be running. val lines = ZeroMQUtils.createStream(ssc, url, Subscribe(topic), bytesToStringIterator _) diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewGenerator.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewGenerator.scala index 8402491b62671..54d996b8ac990 100644 --- a/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewGenerator.scala +++ b/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewGenerator.scala @@ -94,7 +94,7 @@ object PageViewGenerator { while (true) { val socket = listener.accept() new Thread() { - override def run = { + override def run(): Unit = { println("Got client connected from: " + socket.getInetAddress) val out = new PrintWriter(socket.getOutputStream(), true) diff --git a/external/flume-sink/pom.xml b/external/flume-sink/pom.xml index 0706f1ebf66e2..67907bbfb6d1b 100644 --- a/external/flume-sink/pom.xml +++ b/external/flume-sink/pom.xml @@ -20,8 +20,8 @@ 4.0.0 org.apache.spark - spark-parent - 1.3.0-SNAPSHOT + spark-parent_2.10 + 1.4.0-SNAPSHOT ../../pom.xml diff --git a/external/flume-sink/src/test/resources/log4j.properties b/external/flume-sink/src/test/resources/log4j.properties index 2a58e99817224..42df8792f147f 100644 --- a/external/flume-sink/src/test/resources/log4j.properties +++ b/external/flume-sink/src/test/resources/log4j.properties @@ -24,5 +24,5 @@ log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n # Ignore messages below warning level from Jetty, because it's a bit verbose -log4j.logger.org.eclipse.jetty=WARN +log4j.logger.org.spark-project.jetty=WARN diff --git a/external/flume/pom.xml b/external/flume/pom.xml index 1f2681394c583..8df7edbdcad33 100644 --- a/external/flume/pom.xml +++ b/external/flume/pom.xml @@ -20,8 +20,8 @@ 4.0.0 org.apache.spark - spark-parent - 1.3.0-SNAPSHOT + spark-parent_2.10 + 1.4.0-SNAPSHOT ../../pom.xml diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala index 2de2a7926bfd1..60e2994431b38 100644 --- a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala +++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala @@ -37,8 +37,7 @@ import org.apache.spark.streaming.dstream._ import org.apache.spark.streaming.StreamingContext import org.apache.spark.streaming.receiver.Receiver -import org.jboss.netty.channel.ChannelPipelineFactory -import org.jboss.netty.channel.Channels +import org.jboss.netty.channel.{ChannelPipeline, ChannelPipelineFactory, Channels} import org.jboss.netty.channel.socket.nio.NioServerSocketChannelFactory import org.jboss.netty.handler.codec.compression._ @@ -187,8 +186,8 @@ class FlumeReceiver( logInfo("Flume receiver stopped") } - override def preferredLocation = Some(host) - + override def preferredLocation: Option[String] = Option(host) + /** A Netty Pipeline factory that will decompress incoming data from * and the Netty client and compress data going back to the client. * @@ -198,13 +197,12 @@ class FlumeReceiver( */ private[streaming] class CompressionChannelPipelineFactory extends ChannelPipelineFactory { - - def getPipeline() = { + def getPipeline(): ChannelPipeline = { val pipeline = Channels.pipeline() val encoder = new ZlibEncoder(6) pipeline.addFirst("deflater", encoder) pipeline.addFirst("inflater", new ZlibDecoder()) pipeline + } } } -} diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeUtils.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeUtils.scala index 4b732c1592ab2..44dec45c227ca 100644 --- a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeUtils.scala +++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeUtils.scala @@ -19,7 +19,6 @@ package org.apache.spark.streaming.flume import java.net.InetSocketAddress -import org.apache.spark.annotation.Experimental import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming.StreamingContext import org.apache.spark.streaming.api.java.{JavaReceiverInputDStream, JavaStreamingContext} @@ -121,7 +120,6 @@ object FlumeUtils { * @param port Port of the host at which the Spark Sink is listening * @param storageLevel Storage level to use for storing the received objects */ - @Experimental def createPollingStream( ssc: StreamingContext, hostname: String, @@ -138,7 +136,6 @@ object FlumeUtils { * @param addresses List of InetSocketAddresses representing the hosts to connect to. * @param storageLevel Storage level to use for storing the received objects */ - @Experimental def createPollingStream( ssc: StreamingContext, addresses: Seq[InetSocketAddress], @@ -159,7 +156,6 @@ object FlumeUtils { * result in this stream using more threads * @param storageLevel Storage level to use for storing the received objects */ - @Experimental def createPollingStream( ssc: StreamingContext, addresses: Seq[InetSocketAddress], @@ -178,7 +174,6 @@ object FlumeUtils { * @param hostname Hostname of the host on which the Spark Sink is running * @param port Port of the host at which the Spark Sink is listening */ - @Experimental def createPollingStream( jssc: JavaStreamingContext, hostname: String, @@ -195,7 +190,6 @@ object FlumeUtils { * @param port Port of the host at which the Spark Sink is listening * @param storageLevel Storage level to use for storing the received objects */ - @Experimental def createPollingStream( jssc: JavaStreamingContext, hostname: String, @@ -212,7 +206,6 @@ object FlumeUtils { * @param addresses List of InetSocketAddresses on which the Spark Sink is running. * @param storageLevel Storage level to use for storing the received objects */ - @Experimental def createPollingStream( jssc: JavaStreamingContext, addresses: Array[InetSocketAddress], @@ -233,7 +226,6 @@ object FlumeUtils { * result in this stream using more threads * @param storageLevel Storage level to use for storing the received objects */ - @Experimental def createPollingStream( jssc: JavaStreamingContext, addresses: Array[InetSocketAddress], diff --git a/external/flume/src/test/java/org/apache/spark/streaming/LocalJavaStreamingContext.java b/external/flume/src/test/java/org/apache/spark/streaming/LocalJavaStreamingContext.java index 1e24da7f5f60c..cfedb5a042a35 100644 --- a/external/flume/src/test/java/org/apache/spark/streaming/LocalJavaStreamingContext.java +++ b/external/flume/src/test/java/org/apache/spark/streaming/LocalJavaStreamingContext.java @@ -31,7 +31,7 @@ public void setUp() { SparkConf conf = new SparkConf() .setMaster("local[2]") .setAppName("test") - .set("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock"); + .set("spark.streaming.clock", "org.apache.spark.util.ManualClock"); ssc = new JavaStreamingContext(conf, new Duration(1000)); ssc.checkpoint("checkpoint"); } diff --git a/external/flume/src/test/resources/log4j.properties b/external/flume/src/test/resources/log4j.properties index 9697237bfa1a3..75e3b53a093f6 100644 --- a/external/flume/src/test/resources/log4j.properties +++ b/external/flume/src/test/resources/log4j.properties @@ -24,5 +24,5 @@ log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n # Ignore messages below warning level from Jetty, because it's a bit verbose -log4j.logger.org.eclipse.jetty=WARN +log4j.logger.org.spark-project.jetty=WARN diff --git a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala index b57a1c71e35b9..2edea9b5b69ba 100644 --- a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala +++ b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala @@ -1,21 +1,20 @@ /* - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at * - * http://www.apache.org/licenses/LICENSE-2.0 + * http://www.apache.org/licenses/LICENSE-2.0 * - * Unless required by applicable law or agreed to in writing, - * software distributed under the License is distributed on an - * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY - * KIND, either express or implied. See the License for the - * specific language governing permissions and limitations - * under the License. + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. */ + package org.apache.spark.streaming.flume import java.net.InetSocketAddress @@ -34,10 +33,9 @@ import org.scalatest.{BeforeAndAfter, FunSuite} import org.apache.spark.{SparkConf, Logging} import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming.dstream.ReceiverInputDStream -import org.apache.spark.streaming.util.ManualClock import org.apache.spark.streaming.{Seconds, TestOutputStream, StreamingContext} import org.apache.spark.streaming.flume.sink._ -import org.apache.spark.util.Utils +import org.apache.spark.util.{ManualClock, Utils} class FlumePollingStreamSuite extends FunSuite with BeforeAndAfter with Logging { @@ -54,7 +52,7 @@ class FlumePollingStreamSuite extends FunSuite with BeforeAndAfter with Logging def beforeFunction() { logInfo("Using manual clock") - conf.set("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock") + conf.set("spark.streaming.clock", "org.apache.spark.util.ManualClock") } before(beforeFunction()) @@ -214,7 +212,7 @@ class FlumePollingStreamSuite extends FunSuite with BeforeAndAfter with Logging assert(counter === totalEventsPerChannel * channels.size) } - def assertChannelIsEmpty(channel: MemoryChannel) = { + def assertChannelIsEmpty(channel: MemoryChannel): Unit = { val queueRemaining = channel.getClass.getDeclaredField("queueRemaining") queueRemaining.setAccessible(true) val m = queueRemaining.get(channel).getClass.getDeclaredMethod("availablePermits") @@ -236,7 +234,7 @@ class FlumePollingStreamSuite extends FunSuite with BeforeAndAfter with Logging tx.commit() tx.close() Thread.sleep(500) // Allow some time for the events to reach - clock.addToTime(batchDuration.milliseconds) + clock.advance(batchDuration.milliseconds) } null } diff --git a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala index f333e3891b5f0..39e6754c81dbf 100644 --- a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala +++ b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala @@ -19,15 +19,16 @@ package org.apache.spark.streaming.flume import java.net.{InetSocketAddress, ServerSocket} import java.nio.ByteBuffer -import java.nio.charset.Charset import scala.collection.JavaConversions._ import scala.collection.mutable.{ArrayBuffer, SynchronizedBuffer} import scala.concurrent.duration._ import scala.language.postfixOps +import com.google.common.base.Charsets import org.apache.avro.ipc.NettyTransceiver import org.apache.avro.ipc.specific.SpecificRequestor +import org.apache.commons.lang3.RandomUtils import org.apache.flume.source.avro import org.apache.flume.source.avro.{AvroFlumeEvent, AvroSourceProtocol} import org.jboss.netty.channel.ChannelPipeline @@ -40,7 +41,6 @@ import org.scalatest.concurrent.Eventually._ import org.apache.spark.{Logging, SparkConf} import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming.{Milliseconds, StreamingContext, TestOutputStream} -import org.apache.spark.streaming.scheduler.{StreamingListener, StreamingListenerReceiverStarted} import org.apache.spark.util.Utils class FlumeStreamSuite extends FunSuite with BeforeAndAfter with Matchers with Logging { @@ -76,7 +76,8 @@ class FlumeStreamSuite extends FunSuite with BeforeAndAfter with Matchers with L /** Find a free port */ private def findFreePort(): Int = { - Utils.startServiceOnPort(23456, (trialPort: Int) => { + val candidatePort = RandomUtils.nextInt(1024, 65536) + Utils.startServiceOnPort(candidatePort, (trialPort: Int) => { val socket = new ServerSocket(trialPort) socket.close() (null, trialPort) @@ -108,7 +109,7 @@ class FlumeStreamSuite extends FunSuite with BeforeAndAfter with Matchers with L val inputEvents = input.map { item => val event = new AvroFlumeEvent - event.setBody(ByteBuffer.wrap(item.getBytes("UTF-8"))) + event.setBody(ByteBuffer.wrap(item.getBytes(Charsets.UTF_8))) event.setHeaders(Map[CharSequence, CharSequence]("test" -> "header")) event } @@ -138,20 +139,21 @@ class FlumeStreamSuite extends FunSuite with BeforeAndAfter with Matchers with L status should be (avro.Status.OK) } - val decoder = Charset.forName("UTF-8").newDecoder() eventually(timeout(10 seconds), interval(100 milliseconds)) { val outputEvents = outputBuffer.flatten.map { _.event } outputEvents.foreach { event => event.getHeaders.get("test") should be("header") } - val output = outputEvents.map(event => decoder.decode(event.getBody()).toString) + val output = outputEvents.map(event => new String(event.getBody.array(), Charsets.UTF_8)) output should be (input) } } /** Class to create socket channel with compression */ - private class CompressionChannelFactory(compressionLevel: Int) extends NioClientSocketChannelFactory { + private class CompressionChannelFactory(compressionLevel: Int) + extends NioClientSocketChannelFactory { + override def newChannel(pipeline: ChannelPipeline): SocketChannel = { val encoder = new ZlibEncoder(compressionLevel) pipeline.addFirst("deflater", encoder) diff --git a/external/kafka-assembly/pom.xml b/external/kafka-assembly/pom.xml index 503fc129dc4f2..0b79f47647f6b 100644 --- a/external/kafka-assembly/pom.xml +++ b/external/kafka-assembly/pom.xml @@ -20,8 +20,8 @@ 4.0.0 org.apache.spark - spark-parent - 1.3.0-SNAPSHOT + spark-parent_2.10 + 1.4.0-SNAPSHOT ../../pom.xml @@ -33,9 +33,6 @@ streaming-kafka-assembly - scala-${scala.binary.version} - spark-streaming-kafka-assembly-${project.version}.jar - ${project.build.directory}/${spark.jar.dir}/${spark.jar.basename} @@ -61,7 +58,6 @@ maven-shade-plugin false - ${spark.jar} *:* diff --git a/external/kafka/pom.xml b/external/kafka/pom.xml index af96138d79405..f695cff410a18 100644 --- a/external/kafka/pom.xml +++ b/external/kafka/pom.xml @@ -20,8 +20,8 @@ 4.0.0 org.apache.spark - spark-parent - 1.3.0-SNAPSHOT + spark-parent_2.10 + 1.4.0-SNAPSHOT ../../pom.xml diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/Broker.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/Broker.scala new file mode 100644 index 0000000000000..5a74febb4bd46 --- /dev/null +++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/Broker.scala @@ -0,0 +1,68 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming.kafka + +import org.apache.spark.annotation.Experimental + +/** + * :: Experimental :: + * Represent the host and port info for a Kafka broker. + * Differs from the Kafka project's internal kafka.cluster.Broker, which contains a server ID + */ +@Experimental +final class Broker private( + /** Broker's hostname */ + val host: String, + /** Broker's port */ + val port: Int) extends Serializable { + override def equals(obj: Any): Boolean = obj match { + case that: Broker => + this.host == that.host && + this.port == that.port + case _ => false + } + + override def hashCode: Int = { + 41 * (41 + host.hashCode) + port + } + + override def toString(): String = { + s"Broker($host, $port)" + } +} + +/** + * :: Experimental :: + * Companion object that provides methods to create instances of [[Broker]]. + */ +@Experimental +object Broker { + def create(host: String, port: Int): Broker = + new Broker(host, port) + + def apply(host: String, port: Int): Broker = + new Broker(host, port) + + def unapply(broker: Broker): Option[(String, Int)] = { + if (broker == null) { + None + } else { + Some((broker.host, broker.port)) + } + } +} diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala index c7bca43eb889d..1b1fc8051d052 100644 --- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala +++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala @@ -50,14 +50,13 @@ import org.apache.spark.streaming.dstream._ * @param fromOffsets per-topic/partition Kafka offsets defining the (inclusive) * starting point of the stream * @param messageHandler function for translating each message into the desired type - * @param maxRetries maximum number of times in a row to retry getting leaders' offsets */ private[streaming] class DirectKafkaInputDStream[ K: ClassTag, V: ClassTag, - U <: Decoder[_]: ClassTag, - T <: Decoder[_]: ClassTag, + U <: Decoder[K]: ClassTag, + T <: Decoder[V]: ClassTag, R: ClassTag]( @transient ssc_ : StreamingContext, val kafkaParams: Map[String, String], @@ -130,8 +129,9 @@ class DirectKafkaInputDStream[ private[streaming] class DirectKafkaInputDStreamCheckpointData extends DStreamCheckpointData(this) { - def batchForTime = data.asInstanceOf[mutable.HashMap[ - Time, Array[OffsetRange.OffsetRangeTuple]]] + def batchForTime: mutable.HashMap[Time, Array[(String, Int, Long, Long)]] = { + data.asInstanceOf[mutable.HashMap[Time, Array[OffsetRange.OffsetRangeTuple]]] + } override def update(time: Time) { batchForTime.clear() diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaCluster.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaCluster.scala index ccc62bfe8f057..bd767031c1849 100644 --- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaCluster.scala +++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaCluster.scala @@ -123,9 +123,17 @@ class KafkaCluster(val kafkaParams: Map[String, String]) extends Serializable { val errs = new Err withBrokers(Random.shuffle(config.seedBrokers), errs) { consumer => val resp: TopicMetadataResponse = consumer.send(req) - // error codes here indicate missing / just created topic, - // repeating on a different broker wont be useful - return Right(resp.topicsMetadata.toSet) + val respErrs = resp.topicsMetadata.filter(m => m.errorCode != ErrorMapping.NoError) + + if (respErrs.isEmpty) { + return Right(resp.topicsMetadata.toSet) + } else { + respErrs.foreach { m => + val cause = ErrorMapping.exceptionFor(m.errorCode) + val msg = s"Error getting partition metadata for '${m.topic}'. Does the topic exist?" + errs.append(new SparkException(msg, cause)) + } + } } Left(errs) } @@ -332,6 +340,9 @@ object KafkaCluster { extends ConsumerConfig(originalProps) { val seedBrokers: Array[(String, Int)] = brokers.split(",").map { hp => val hpa = hp.split(":") + if (hpa.size == 1) { + throw new SparkException(s"Broker not the in correct format of : [$brokers]") + } (hpa(0), hpa(1).toInt) } } diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDD.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDD.scala index 50bf7cbdb8dbf..a1b4a12e5d6a0 100644 --- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDD.scala +++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDD.scala @@ -23,10 +23,9 @@ import org.apache.spark.{Logging, Partition, SparkContext, SparkException, TaskC import org.apache.spark.rdd.RDD import org.apache.spark.util.NextIterator -import java.util.Properties import kafka.api.{FetchRequestBuilder, FetchResponse} import kafka.common.{ErrorMapping, TopicAndPartition} -import kafka.consumer.{ConsumerConfig, SimpleConsumer} +import kafka.consumer.SimpleConsumer import kafka.message.{MessageAndMetadata, MessageAndOffset} import kafka.serializer.Decoder import kafka.utils.VerifiableProperties @@ -36,14 +35,12 @@ import kafka.utils.VerifiableProperties * Starting and ending offsets are specified in advance, * so that you can control exactly-once semantics. * @param kafkaParams Kafka - * configuration parameters. - * Requires "metadata.broker.list" or "bootstrap.servers" to be set with Kafka broker(s), - * NOT zookeeper servers, specified in host1:port1,host2:port2 form. - * @param batch Each KafkaRDDPartition in the batch corresponds to a - * range of offsets for a given Kafka topic/partition + * configuration parameters. Requires "metadata.broker.list" or "bootstrap.servers" to be set + * with Kafka broker(s) specified in host1:port1,host2:port2 form. + * @param offsetRanges offset ranges that define the Kafka data belonging to this RDD * @param messageHandler function for translating each message into the desired type */ -private[spark] +private[kafka] class KafkaRDD[ K: ClassTag, V: ClassTag, @@ -88,7 +85,7 @@ class KafkaRDD[ val part = thePart.asInstanceOf[KafkaRDDPartition] assert(part.fromOffset <= part.untilOffset, errBeginAfterEnd(part)) if (part.fromOffset == part.untilOffset) { - log.warn("Beginning offset ${part.fromOffset} is the same as ending offset " + + log.info(s"Beginning offset ${part.fromOffset} is the same as ending offset " + s"skipping ${part.topic} ${part.partition}") Iterator.empty } else { @@ -157,7 +154,7 @@ class KafkaRDD[ .dropWhile(_.offset < requestOffset) } - override def close() = consumer.close() + override def close(): Unit = consumer.close() override def getNext(): R = { if (iter == null || !iter.hasNext) { @@ -183,7 +180,7 @@ class KafkaRDD[ } } -private[spark] +private[kafka] object KafkaRDD { import KafkaCluster.LeaderOffset @@ -209,7 +206,7 @@ object KafkaRDD { fromOffsets: Map[TopicAndPartition, Long], untilOffsets: Map[TopicAndPartition, LeaderOffset], messageHandler: MessageAndMetadata[K, V] => R - ): KafkaRDD[K, V, U, T, R] = { + ): KafkaRDD[K, V, U, T, R] = { val leaders = untilOffsets.map { case (tp, lo) => tp -> (lo.host, lo.port) }.toMap diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDDPartition.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDDPartition.scala index 36372e08f65f6..a842a6f17766f 100644 --- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDDPartition.scala +++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDDPartition.scala @@ -26,7 +26,7 @@ import org.apache.spark.Partition * @param host preferred kafka host, i.e. the leader at the time the rdd was created * @param port preferred kafka host's port */ -private[spark] +private[kafka] class KafkaRDDPartition( val index: Int, val topic: String, @@ -36,24 +36,3 @@ class KafkaRDDPartition( val host: String, val port: Int ) extends Partition - -private[spark] -object KafkaRDDPartition { - def apply( - index: Int, - topic: String, - partition: Int, - fromOffset: Long, - untilOffset: Long, - host: String, - port: Int - ): KafkaRDDPartition = new KafkaRDDPartition( - index, - topic, - partition, - fromOffset, - untilOffset, - host, - port - ) -} diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaTestUtils.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaTestUtils.scala new file mode 100644 index 0000000000000..13e9475065979 --- /dev/null +++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaTestUtils.scala @@ -0,0 +1,261 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming.kafka + +import java.io.File +import java.lang.{Integer => JInt} +import java.net.InetSocketAddress +import java.util.{Map => JMap} +import java.util.Properties +import java.util.concurrent.TimeoutException + +import scala.annotation.tailrec +import scala.language.postfixOps +import scala.util.control.NonFatal + +import kafka.admin.AdminUtils +import kafka.producer.{KeyedMessage, Producer, ProducerConfig} +import kafka.serializer.StringEncoder +import kafka.server.{KafkaConfig, KafkaServer} +import kafka.utils.ZKStringSerializer +import org.apache.zookeeper.server.{NIOServerCnxnFactory, ZooKeeperServer} +import org.I0Itec.zkclient.ZkClient + +import org.apache.spark.{Logging, SparkConf} +import org.apache.spark.streaming.Time +import org.apache.spark.util.Utils + +/** + * This is a helper class for Kafka test suites. This has the functionality to set up + * and tear down local Kafka servers, and to push data using Kafka producers. + * + * The reason to put Kafka test utility class in src is to test Python related Kafka APIs. + */ +private class KafkaTestUtils extends Logging { + + // Zookeeper related configurations + private val zkHost = "localhost" + private var zkPort: Int = 0 + private val zkConnectionTimeout = 6000 + private val zkSessionTimeout = 6000 + + private var zookeeper: EmbeddedZookeeper = _ + + private var zkClient: ZkClient = _ + + // Kafka broker related configurations + private val brokerHost = "localhost" + private var brokerPort = 9092 + private var brokerConf: KafkaConfig = _ + + // Kafka broker server + private var server: KafkaServer = _ + + // Kafka producer + private var producer: Producer[String, String] = _ + + // Flag to test whether the system is correctly started + private var zkReady = false + private var brokerReady = false + + def zkAddress: String = { + assert(zkReady, "Zookeeper not setup yet or already torn down, cannot get zookeeper address") + s"$zkHost:$zkPort" + } + + def brokerAddress: String = { + assert(brokerReady, "Kafka not setup yet or already torn down, cannot get broker address") + s"$brokerHost:$brokerPort" + } + + def zookeeperClient: ZkClient = { + assert(zkReady, "Zookeeper not setup yet or already torn down, cannot get zookeeper client") + Option(zkClient).getOrElse( + throw new IllegalStateException("Zookeeper client is not yet initialized")) + } + + // Set up the Embedded Zookeeper server and get the proper Zookeeper port + private def setupEmbeddedZookeeper(): Unit = { + // Zookeeper server startup + zookeeper = new EmbeddedZookeeper(s"$zkHost:$zkPort") + // Get the actual zookeeper binding port + zkPort = zookeeper.actualPort + zkClient = new ZkClient(s"$zkHost:$zkPort", zkSessionTimeout, zkConnectionTimeout, + ZKStringSerializer) + zkReady = true + } + + // Set up the Embedded Kafka server + private def setupEmbeddedKafkaServer(): Unit = { + assert(zkReady, "Zookeeper should be set up beforehand") + + // Kafka broker startup + Utils.startServiceOnPort(brokerPort, port => { + brokerPort = port + brokerConf = new KafkaConfig(brokerConfiguration) + server = new KafkaServer(brokerConf) + server.startup() + (server, port) + }, new SparkConf(), "KafkaBroker") + + brokerReady = true + } + + /** setup the whole embedded servers, including Zookeeper and Kafka brokers */ + def setup(): Unit = { + setupEmbeddedZookeeper() + setupEmbeddedKafkaServer() + } + + /** Teardown the whole servers, including Kafka broker and Zookeeper */ + def teardown(): Unit = { + brokerReady = false + zkReady = false + + if (producer != null) { + producer.close() + producer = null + } + + if (server != null) { + server.shutdown() + server = null + } + + brokerConf.logDirs.foreach { f => Utils.deleteRecursively(new File(f)) } + + if (zkClient != null) { + zkClient.close() + zkClient = null + } + + if (zookeeper != null) { + zookeeper.shutdown() + zookeeper = null + } + } + + /** Create a Kafka topic and wait until it propagated to the whole cluster */ + def createTopic(topic: String): Unit = { + AdminUtils.createTopic(zkClient, topic, 1, 1) + // wait until metadata is propagated + waitUntilMetadataIsPropagated(topic, 0) + } + + /** Java-friendly function for sending messages to the Kafka broker */ + def sendMessages(topic: String, messageToFreq: JMap[String, JInt]): Unit = { + import scala.collection.JavaConversions._ + sendMessages(topic, Map(messageToFreq.mapValues(_.intValue()).toSeq: _*)) + } + + /** Send the messages to the Kafka broker */ + def sendMessages(topic: String, messageToFreq: Map[String, Int]): Unit = { + val messages = messageToFreq.flatMap { case (s, freq) => Seq.fill(freq)(s) }.toArray + sendMessages(topic, messages) + } + + /** Send the array of messages to the Kafka broker */ + def sendMessages(topic: String, messages: Array[String]): Unit = { + producer = new Producer[String, String](new ProducerConfig(producerConfiguration)) + producer.send(messages.map { new KeyedMessage[String, String](topic, _ ) }: _*) + producer.close() + producer = null + } + + private def brokerConfiguration: Properties = { + val props = new Properties() + props.put("broker.id", "0") + props.put("host.name", "localhost") + props.put("port", brokerPort.toString) + props.put("log.dir", Utils.createTempDir().getAbsolutePath) + props.put("zookeeper.connect", zkAddress) + props.put("log.flush.interval.messages", "1") + props.put("replica.socket.timeout.ms", "1500") + props + } + + private def producerConfiguration: Properties = { + val props = new Properties() + props.put("metadata.broker.list", brokerAddress) + props.put("serializer.class", classOf[StringEncoder].getName) + props + } + + // A simplified version of scalatest eventually, rewritten here to avoid adding extra test + // dependency + def eventually[T](timeout: Time, interval: Time)(func: => T): T = { + def makeAttempt(): Either[Throwable, T] = { + try { + Right(func) + } catch { + case e if NonFatal(e) => Left(e) + } + } + + val startTime = System.currentTimeMillis() + @tailrec + def tryAgain(attempt: Int): T = { + makeAttempt() match { + case Right(result) => result + case Left(e) => + val duration = System.currentTimeMillis() - startTime + if (duration < timeout.milliseconds) { + Thread.sleep(interval.milliseconds) + } else { + throw new TimeoutException(e.getMessage) + } + + tryAgain(attempt + 1) + } + } + + tryAgain(1) + } + + private def waitUntilMetadataIsPropagated(topic: String, partition: Int): Unit = { + eventually(Time(10000), Time(100)) { + assert( + server.apis.metadataCache.containsTopicAndPartition(topic, partition), + s"Partition [$topic, $partition] metadata not propagated after timeout" + ) + } + } + + private class EmbeddedZookeeper(val zkConnect: String) { + val snapshotDir = Utils.createTempDir() + val logDir = Utils.createTempDir() + + val zookeeper = new ZooKeeperServer(snapshotDir, logDir, 500) + val (ip, port) = { + val splits = zkConnect.split(":") + (splits(0), splits(1).toInt) + } + val factory = new NIOServerCnxnFactory() + factory.configure(new InetSocketAddress(ip, port), 16) + factory.startup(zookeeper) + + val actualPort = factory.getLocalPort + + def shutdown() { + factory.shutdown() + Utils.deleteRecursively(snapshotDir) + Utils.deleteRecursively(logDir) + } + } +} + diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala index f8aa6c5c6263c..5a9bd4214cf51 100644 --- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala +++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala @@ -18,27 +18,30 @@ package org.apache.spark.streaming.kafka import java.lang.{Integer => JInt} +import java.lang.{Long => JLong} import java.util.{Map => JMap} +import java.util.{Set => JSet} import scala.reflect.ClassTag import scala.collection.JavaConversions._ import kafka.common.TopicAndPartition import kafka.message.MessageAndMetadata -import kafka.serializer.{Decoder, StringDecoder} - +import kafka.serializer.{DefaultDecoder, Decoder, StringDecoder} +import org.apache.spark.api.java.function.{Function => JFunction} import org.apache.spark.{SparkContext, SparkException} import org.apache.spark.annotation.Experimental import org.apache.spark.rdd.RDD import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming.StreamingContext -import org.apache.spark.streaming.api.java.{JavaPairReceiverInputDStream, JavaStreamingContext} +import org.apache.spark.streaming.api.java.{JavaPairInputDStream, JavaInputDStream, JavaPairReceiverInputDStream, JavaStreamingContext} import org.apache.spark.streaming.dstream.{InputDStream, ReceiverInputDStream} +import org.apache.spark.api.java.{JavaSparkContext, JavaPairRDD, JavaRDD} object KafkaUtils { /** - * Create an input stream that pulls messages from a Kafka Broker. + * Create an input stream that pulls messages from Kafka Brokers. * @param ssc StreamingContext object * @param zkQuorum Zookeeper quorum (hostname:port,hostname:port,..) * @param groupId The group id for this consumer @@ -62,7 +65,7 @@ object KafkaUtils { } /** - * Create an input stream that pulls messages from a Kafka Broker. + * Create an input stream that pulls messages from Kafka Brokers. * @param ssc StreamingContext object * @param kafkaParams Map of kafka configuration parameters, * see http://kafka.apache.org/08/configuration.html @@ -81,7 +84,7 @@ object KafkaUtils { } /** - * Create an input stream that pulls messages from a Kafka Broker. + * Create an input stream that pulls messages from Kafka Brokers. * Storage level of the data will be the default StorageLevel.MEMORY_AND_DISK_SER_2. * @param jssc JavaStreamingContext object * @param zkQuorum Zookeeper quorum (hostname:port,hostname:port,..) @@ -99,7 +102,7 @@ object KafkaUtils { } /** - * Create an input stream that pulls messages from a Kafka Broker. + * Create an input stream that pulls messages from Kafka Brokers. * @param jssc JavaStreamingContext object * @param zkQuorum Zookeeper quorum (hostname:port,hostname:port,..). * @param groupId The group id for this consumer. @@ -119,10 +122,10 @@ object KafkaUtils { } /** - * Create an input stream that pulls messages from a Kafka Broker. + * Create an input stream that pulls messages from Kafka Brokers. * @param jssc JavaStreamingContext object - * @param keyTypeClass Key type of RDD - * @param valueTypeClass value type of RDD + * @param keyTypeClass Key type of DStream + * @param valueTypeClass value type of Dstream * @param keyDecoderClass Type of kafka key decoder * @param valueDecoderClass Type of kafka value decoder * @param kafkaParams Map of kafka configuration parameters, @@ -151,14 +154,27 @@ object KafkaUtils { jssc.ssc, kafkaParams.toMap, Map(topics.mapValues(_.intValue()).toSeq: _*), storageLevel) } - /** A batch-oriented interface for consuming from Kafka. - * Starting and ending offsets are specified in advance, - * so that you can control exactly-once semantics. + /** get leaders for the given offset ranges, or throw an exception */ + private def leadersForRanges( + kafkaParams: Map[String, String], + offsetRanges: Array[OffsetRange]): Map[TopicAndPartition, (String, Int)] = { + val kc = new KafkaCluster(kafkaParams) + val topics = offsetRanges.map(o => TopicAndPartition(o.topic, o.partition)).toSet + val leaders = kc.findLeaders(topics).fold( + errs => throw new SparkException(errs.mkString("\n")), + ok => ok + ) + leaders + } + + /** + * Create a RDD from Kafka using offset ranges for each topic and partition. + * * @param sc SparkContext object * @param kafkaParams Kafka - * configuration parameters. - * Requires "metadata.broker.list" or "bootstrap.servers" to be set with Kafka broker(s), - * NOT zookeeper servers, specified in host1:port1,host2:port2 form. + * configuration parameters. Requires "metadata.broker.list" or "bootstrap.servers" + * to be set with Kafka broker(s) (NOT zookeeper servers) specified in + * host1:port1,host2:port2 form. * @param offsetRanges Each OffsetRange in the batch corresponds to a * range of offsets for a given Kafka topic/partition */ @@ -166,134 +182,212 @@ object KafkaUtils { def createRDD[ K: ClassTag, V: ClassTag, - U <: Decoder[_]: ClassTag, - T <: Decoder[_]: ClassTag] ( + KD <: Decoder[K]: ClassTag, + VD <: Decoder[V]: ClassTag]( sc: SparkContext, kafkaParams: Map[String, String], offsetRanges: Array[OffsetRange] - ): RDD[(K, V)] = { + ): RDD[(K, V)] = { val messageHandler = (mmd: MessageAndMetadata[K, V]) => (mmd.key, mmd.message) - val kc = new KafkaCluster(kafkaParams) - val topics = offsetRanges.map(o => TopicAndPartition(o.topic, o.partition)).toSet - val leaders = kc.findLeaders(topics).fold( - errs => throw new SparkException(errs.mkString("\n")), - ok => ok - ) - new KafkaRDD[K, V, U, T, (K, V)](sc, kafkaParams, offsetRanges, leaders, messageHandler) + val leaders = leadersForRanges(kafkaParams, offsetRanges) + new KafkaRDD[K, V, KD, VD, (K, V)](sc, kafkaParams, offsetRanges, leaders, messageHandler) } - /** A batch-oriented interface for consuming from Kafka. - * Starting and ending offsets are specified in advance, - * so that you can control exactly-once semantics. + /** + * :: Experimental :: + * Create a RDD from Kafka using offset ranges for each topic and partition. This allows you + * specify the Kafka leader to connect to (to optimize fetching) and access the message as well + * as the metadata. + * * @param sc SparkContext object * @param kafkaParams Kafka - * configuration parameters. - * Requires "metadata.broker.list" or "bootstrap.servers" to be set with Kafka broker(s), - * NOT zookeeper servers, specified in host1:port1,host2:port2 form. + * configuration parameters. Requires "metadata.broker.list" or "bootstrap.servers" + * to be set with Kafka broker(s) (NOT zookeeper servers) specified in + * host1:port1,host2:port2 form. * @param offsetRanges Each OffsetRange in the batch corresponds to a * range of offsets for a given Kafka topic/partition - * @param leaders Kafka leaders for each offset range in batch - * @param messageHandler function for translating each message into the desired type + * @param leaders Kafka brokers for each TopicAndPartition in offsetRanges. May be an empty map, + * in which case leaders will be looked up on the driver. + * @param messageHandler Function for translating each message and metadata into the desired type */ @Experimental def createRDD[ K: ClassTag, V: ClassTag, - U <: Decoder[_]: ClassTag, - T <: Decoder[_]: ClassTag, - R: ClassTag] ( + KD <: Decoder[K]: ClassTag, + VD <: Decoder[V]: ClassTag, + R: ClassTag]( sc: SparkContext, kafkaParams: Map[String, String], offsetRanges: Array[OffsetRange], - leaders: Array[Leader], + leaders: Map[TopicAndPartition, Broker], messageHandler: MessageAndMetadata[K, V] => R - ): RDD[R] = { - - val leaderMap = leaders - .map(l => TopicAndPartition(l.topic, l.partition) -> (l.host, l.port)) - .toMap - new KafkaRDD[K, V, U, T, R](sc, kafkaParams, offsetRanges, leaderMap, messageHandler) + ): RDD[R] = { + val leaderMap = if (leaders.isEmpty) { + leadersForRanges(kafkaParams, offsetRanges) + } else { + // This could be avoided by refactoring KafkaRDD.leaders and KafkaCluster to use Broker + leaders.map { + case (tp: TopicAndPartition, Broker(host, port)) => (tp, (host, port)) + }.toMap + } + new KafkaRDD[K, V, KD, VD, R](sc, kafkaParams, offsetRanges, leaderMap, messageHandler) } + /** - * This stream can guarantee that each message from Kafka is included in transformations - * (as opposed to output actions) exactly once, even in most failure situations. - * - * Points to note: + * Create a RDD from Kafka using offset ranges for each topic and partition. * - * Failure Recovery - You must checkpoint this stream, or save offsets yourself and provide them - * as the fromOffsets parameter on restart. - * Kafka must have sufficient log retention to obtain messages after failure. - * - * Getting offsets from the stream - see programming guide + * @param jsc JavaSparkContext object + * @param kafkaParams Kafka + * configuration parameters. Requires "metadata.broker.list" or "bootstrap.servers" + * to be set with Kafka broker(s) (NOT zookeeper servers) specified in + * host1:port1,host2:port2 form. + * @param offsetRanges Each OffsetRange in the batch corresponds to a + * range of offsets for a given Kafka topic/partition + */ + @Experimental + def createRDD[K, V, KD <: Decoder[K], VD <: Decoder[V]]( + jsc: JavaSparkContext, + keyClass: Class[K], + valueClass: Class[V], + keyDecoderClass: Class[KD], + valueDecoderClass: Class[VD], + kafkaParams: JMap[String, String], + offsetRanges: Array[OffsetRange] + ): JavaPairRDD[K, V] = { + implicit val keyCmt: ClassTag[K] = ClassTag(keyClass) + implicit val valueCmt: ClassTag[V] = ClassTag(valueClass) + implicit val keyDecoderCmt: ClassTag[KD] = ClassTag(keyDecoderClass) + implicit val valueDecoderCmt: ClassTag[VD] = ClassTag(valueDecoderClass) + new JavaPairRDD(createRDD[K, V, KD, VD]( + jsc.sc, Map(kafkaParams.toSeq: _*), offsetRanges)) + } + + /** + * :: Experimental :: + * Create a RDD from Kafka using offset ranges for each topic and partition. This allows you + * specify the Kafka leader to connect to (to optimize fetching) and access the message as well + * as the metadata. * -. * Zookeeper - This does not use Zookeeper to store offsets. For interop with Kafka monitors - * that depend on Zookeeper, you must store offsets in ZK yourself. + * @param jsc JavaSparkContext object + * @param kafkaParams Kafka + * configuration parameters. Requires "metadata.broker.list" or "bootstrap.servers" + * to be set with Kafka broker(s) (NOT zookeeper servers) specified in + * host1:port1,host2:port2 form. + * @param offsetRanges Each OffsetRange in the batch corresponds to a + * range of offsets for a given Kafka topic/partition + * @param leaders Kafka brokers for each TopicAndPartition in offsetRanges. May be an empty map, + * in which case leaders will be looked up on the driver. + * @param messageHandler Function for translating each message and metadata into the desired type + */ + @Experimental + def createRDD[K, V, KD <: Decoder[K], VD <: Decoder[V], R]( + jsc: JavaSparkContext, + keyClass: Class[K], + valueClass: Class[V], + keyDecoderClass: Class[KD], + valueDecoderClass: Class[VD], + recordClass: Class[R], + kafkaParams: JMap[String, String], + offsetRanges: Array[OffsetRange], + leaders: JMap[TopicAndPartition, Broker], + messageHandler: JFunction[MessageAndMetadata[K, V], R] + ): JavaRDD[R] = { + implicit val keyCmt: ClassTag[K] = ClassTag(keyClass) + implicit val valueCmt: ClassTag[V] = ClassTag(valueClass) + implicit val keyDecoderCmt: ClassTag[KD] = ClassTag(keyDecoderClass) + implicit val valueDecoderCmt: ClassTag[VD] = ClassTag(valueDecoderClass) + implicit val recordCmt: ClassTag[R] = ClassTag(recordClass) + val leaderMap = Map(leaders.toSeq: _*) + createRDD[K, V, KD, VD, R]( + jsc.sc, Map(kafkaParams.toSeq: _*), offsetRanges, leaderMap, messageHandler.call _) + } + + /** + * :: Experimental :: + * Create an input stream that directly pulls messages from Kafka Brokers + * without using any receiver. This stream can guarantee that each message + * from Kafka is included in transformations exactly once (see points below). * - * End-to-end semantics - This does not guarantee that any output operation will push each record - * exactly once. To ensure end-to-end exactly-once semantics (that is, receiving exactly once and - * outputting exactly once), you have to either ensure that the output operation is - * idempotent, or transactionally store offsets with the output. See the programming guide for - * more details. + * Points to note: + * - No receivers: This stream does not use any receiver. It directly queries Kafka + * - Offsets: This does not use Zookeeper to store offsets. The consumed offsets are tracked + * by the stream itself. For interoperability with Kafka monitoring tools that depend on + * Zookeeper, you have to update Kafka/Zookeeper yourself from the streaming application. + * You can access the offsets used in each batch from the generated RDDs (see + * [[org.apache.spark.streaming.kafka.HasOffsetRanges]]). + * - Failure Recovery: To recover from driver failures, you have to enable checkpointing + * in the [[StreamingContext]]. The information on consumed offset can be + * recovered from the checkpoint. See the programming guide for details (constraints, etc.). + * - End-to-end semantics: This stream ensures that every records is effectively received and + * transformed exactly once, but gives no guarantees on whether the transformed data are + * outputted exactly once. For end-to-end exactly-once semantics, you have to either ensure + * that the output operation is idempotent, or use transactions to output records atomically. + * See the programming guide for more details. * * @param ssc StreamingContext object * @param kafkaParams Kafka - * configuration parameters. - * Requires "metadata.broker.list" or "bootstrap.servers" to be set with Kafka broker(s), - * NOT zookeeper servers, specified in host1:port1,host2:port2 form. - * @param messageHandler function for translating each message into the desired type - * @param fromOffsets per-topic/partition Kafka offsets defining the (inclusive) - * starting point of the stream + * configuration parameters. Requires "metadata.broker.list" or "bootstrap.servers" + * to be set with Kafka broker(s) (NOT zookeeper servers) specified in + * host1:port1,host2:port2 form. + * @param fromOffsets Per-topic/partition Kafka offsets defining the (inclusive) + * starting point of the stream + * @param messageHandler Function for translating each message and metadata into the desired type */ @Experimental def createDirectStream[ K: ClassTag, V: ClassTag, - U <: Decoder[_]: ClassTag, - T <: Decoder[_]: ClassTag, + KD <: Decoder[K]: ClassTag, + VD <: Decoder[V]: ClassTag, R: ClassTag] ( ssc: StreamingContext, kafkaParams: Map[String, String], fromOffsets: Map[TopicAndPartition, Long], messageHandler: MessageAndMetadata[K, V] => R ): InputDStream[R] = { - new DirectKafkaInputDStream[K, V, U, T, R]( + new DirectKafkaInputDStream[K, V, KD, VD, R]( ssc, kafkaParams, fromOffsets, messageHandler) } /** - * This stream can guarantee that each message from Kafka is included in transformations - * (as opposed to output actions) exactly once, even in most failure situations. + * :: Experimental :: + * Create an input stream that directly pulls messages from Kafka Brokers + * without using any receiver. This stream can guarantee that each message + * from Kafka is included in transformations exactly once (see points below). * * Points to note: - * - * Failure Recovery - You must checkpoint this stream. - * Kafka must have sufficient log retention to obtain messages after failure. - * - * Getting offsets from the stream - see programming guide - * -. * Zookeeper - This does not use Zookeeper to store offsets. For interop with Kafka monitors - * that depend on Zookeeper, you must store offsets in ZK yourself. - * - * End-to-end semantics - This does not guarantee that any output operation will push each record - * exactly once. To ensure end-to-end exactly-once semantics (that is, receiving exactly once and - * outputting exactly once), you have to ensure that the output operation is idempotent. + * - No receivers: This stream does not use any receiver. It directly queries Kafka + * - Offsets: This does not use Zookeeper to store offsets. The consumed offsets are tracked + * by the stream itself. For interoperability with Kafka monitoring tools that depend on + * Zookeeper, you have to update Kafka/Zookeeper yourself from the streaming application. + * You can access the offsets used in each batch from the generated RDDs (see + * [[org.apache.spark.streaming.kafka.HasOffsetRanges]]). + * - Failure Recovery: To recover from driver failures, you have to enable checkpointing + * in the [[StreamingContext]]. The information on consumed offset can be + * recovered from the checkpoint. See the programming guide for details (constraints, etc.). + * - End-to-end semantics: This stream ensures that every records is effectively received and + * transformed exactly once, but gives no guarantees on whether the transformed data are + * outputted exactly once. For end-to-end exactly-once semantics, you have to either ensure + * that the output operation is idempotent, or use transactions to output records atomically. + * See the programming guide for more details. * * @param ssc StreamingContext object * @param kafkaParams Kafka - * configuration parameters. - * Requires "metadata.broker.list" or "bootstrap.servers" to be set with Kafka broker(s), - * NOT zookeeper servers, specified in host1:port1,host2:port2 form. - * If starting without a checkpoint, "auto.offset.reset" may be set to "largest" or "smallest" + * configuration parameters. Requires "metadata.broker.list" or "bootstrap.servers" + * to be set with Kafka broker(s) (NOT zookeeper servers), specified in + * host1:port1,host2:port2 form. + * If not starting from a checkpoint, "auto.offset.reset" may be set to "largest" or "smallest" * to determine where the stream starts (defaults to "largest") - * @param topics names of the topics to consume + * @param topics Names of the topics to consume */ @Experimental def createDirectStream[ K: ClassTag, V: ClassTag, - U <: Decoder[_]: ClassTag, - T <: Decoder[_]: ClassTag] ( + KD <: Decoder[K]: ClassTag, + VD <: Decoder[V]: ClassTag] ( ssc: StreamingContext, kafkaParams: Map[String, String], topics: Set[String] @@ -313,11 +407,155 @@ object KafkaUtils { val fromOffsets = leaderOffsets.map { case (tp, lo) => (tp, lo.offset) } - new DirectKafkaInputDStream[K, V, U, T, (K, V)]( + new DirectKafkaInputDStream[K, V, KD, VD, (K, V)]( ssc, kafkaParams, fromOffsets, messageHandler) }).fold( errs => throw new SparkException(errs.mkString("\n")), ok => ok ) } + + /** + * :: Experimental :: + * Create an input stream that directly pulls messages from Kafka Brokers + * without using any receiver. This stream can guarantee that each message + * from Kafka is included in transformations exactly once (see points below). + * + * Points to note: + * - No receivers: This stream does not use any receiver. It directly queries Kafka + * - Offsets: This does not use Zookeeper to store offsets. The consumed offsets are tracked + * by the stream itself. For interoperability with Kafka monitoring tools that depend on + * Zookeeper, you have to update Kafka/Zookeeper yourself from the streaming application. + * You can access the offsets used in each batch from the generated RDDs (see + * [[org.apache.spark.streaming.kafka.HasOffsetRanges]]). + * - Failure Recovery: To recover from driver failures, you have to enable checkpointing + * in the [[StreamingContext]]. The information on consumed offset can be + * recovered from the checkpoint. See the programming guide for details (constraints, etc.). + * - End-to-end semantics: This stream ensures that every records is effectively received and + * transformed exactly once, but gives no guarantees on whether the transformed data are + * outputted exactly once. For end-to-end exactly-once semantics, you have to either ensure + * that the output operation is idempotent, or use transactions to output records atomically. + * See the programming guide for more details. + * + * @param jssc JavaStreamingContext object + * @param keyClass Class of the keys in the Kafka records + * @param valueClass Class of the values in the Kafka records + * @param keyDecoderClass Class of the key decoder + * @param valueDecoderClass Class of the value decoder + * @param recordClass Class of the records in DStream + * @param kafkaParams Kafka + * configuration parameters. Requires "metadata.broker.list" or "bootstrap.servers" + * to be set with Kafka broker(s) (NOT zookeeper servers), specified in + * host1:port1,host2:port2 form. + * @param fromOffsets Per-topic/partition Kafka offsets defining the (inclusive) + * starting point of the stream + * @param messageHandler Function for translating each message and metadata into the desired type + */ + @Experimental + def createDirectStream[K, V, KD <: Decoder[K], VD <: Decoder[V], R]( + jssc: JavaStreamingContext, + keyClass: Class[K], + valueClass: Class[V], + keyDecoderClass: Class[KD], + valueDecoderClass: Class[VD], + recordClass: Class[R], + kafkaParams: JMap[String, String], + fromOffsets: JMap[TopicAndPartition, JLong], + messageHandler: JFunction[MessageAndMetadata[K, V], R] + ): JavaInputDStream[R] = { + implicit val keyCmt: ClassTag[K] = ClassTag(keyClass) + implicit val valueCmt: ClassTag[V] = ClassTag(valueClass) + implicit val keyDecoderCmt: ClassTag[KD] = ClassTag(keyDecoderClass) + implicit val valueDecoderCmt: ClassTag[VD] = ClassTag(valueDecoderClass) + implicit val recordCmt: ClassTag[R] = ClassTag(recordClass) + createDirectStream[K, V, KD, VD, R]( + jssc.ssc, + Map(kafkaParams.toSeq: _*), + Map(fromOffsets.mapValues { _.longValue() }.toSeq: _*), + messageHandler.call _ + ) + } + + /** + * :: Experimental :: + * Create an input stream that directly pulls messages from Kafka Brokers + * without using any receiver. This stream can guarantee that each message + * from Kafka is included in transformations exactly once (see points below). + * + * Points to note: + * - No receivers: This stream does not use any receiver. It directly queries Kafka + * - Offsets: This does not use Zookeeper to store offsets. The consumed offsets are tracked + * by the stream itself. For interoperability with Kafka monitoring tools that depend on + * Zookeeper, you have to update Kafka/Zookeeper yourself from the streaming application. + * You can access the offsets used in each batch from the generated RDDs (see + * [[org.apache.spark.streaming.kafka.HasOffsetRanges]]). + * - Failure Recovery: To recover from driver failures, you have to enable checkpointing + * in the [[StreamingContext]]. The information on consumed offset can be + * recovered from the checkpoint. See the programming guide for details (constraints, etc.). + * - End-to-end semantics: This stream ensures that every records is effectively received and + * transformed exactly once, but gives no guarantees on whether the transformed data are + * outputted exactly once. For end-to-end exactly-once semantics, you have to either ensure + * that the output operation is idempotent, or use transactions to output records atomically. + * See the programming guide for more details. + * + * @param jssc JavaStreamingContext object + * @param keyClass Class of the keys in the Kafka records + * @param valueClass Class of the values in the Kafka records + * @param keyDecoderClass Class of the key decoder + * @param valueDecoderClass Class type of the value decoder + * @param kafkaParams Kafka + * configuration parameters. Requires "metadata.broker.list" or "bootstrap.servers" + * to be set with Kafka broker(s) (NOT zookeeper servers), specified in + * host1:port1,host2:port2 form. + * If not starting from a checkpoint, "auto.offset.reset" may be set to "largest" or "smallest" + * to determine where the stream starts (defaults to "largest") + * @param topics Names of the topics to consume + */ + @Experimental + def createDirectStream[K, V, KD <: Decoder[K], VD <: Decoder[V]]( + jssc: JavaStreamingContext, + keyClass: Class[K], + valueClass: Class[V], + keyDecoderClass: Class[KD], + valueDecoderClass: Class[VD], + kafkaParams: JMap[String, String], + topics: JSet[String] + ): JavaPairInputDStream[K, V] = { + implicit val keyCmt: ClassTag[K] = ClassTag(keyClass) + implicit val valueCmt: ClassTag[V] = ClassTag(valueClass) + implicit val keyDecoderCmt: ClassTag[KD] = ClassTag(keyDecoderClass) + implicit val valueDecoderCmt: ClassTag[VD] = ClassTag(valueDecoderClass) + createDirectStream[K, V, KD, VD]( + jssc.ssc, + Map(kafkaParams.toSeq: _*), + Set(topics.toSeq: _*) + ) + } +} + +/** + * This is a helper class that wraps the KafkaUtils.createStream() into more + * Python-friendly class and function so that it can be easily + * instantiated and called from Python's KafkaUtils (see SPARK-6027). + * + * The zero-arg constructor helps instantiate this class from the Class object + * classOf[KafkaUtilsPythonHelper].newInstance(), and the createStream() + * takes care of known parameters instead of passing them from Python + */ +private class KafkaUtilsPythonHelper { + def createStream( + jssc: JavaStreamingContext, + kafkaParams: JMap[String, String], + topics: JMap[String, JInt], + storageLevel: StorageLevel): JavaPairReceiverInputDStream[Array[Byte], Array[Byte]] = { + KafkaUtils.createStream[Array[Byte], Array[Byte], DefaultDecoder, DefaultDecoder]( + jssc, + classOf[Array[Byte]], + classOf[Array[Byte]], + classOf[DefaultDecoder], + classOf[DefaultDecoder], + kafkaParams, + topics, + storageLevel) + } } diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/Leader.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/Leader.scala deleted file mode 100644 index 3454d92e72b47..0000000000000 --- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/Leader.scala +++ /dev/null @@ -1,46 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.streaming.kafka - -import kafka.common.TopicAndPartition - -/** Host info for the leader of a Kafka TopicAndPartition */ -final class Leader private( - /** kafka topic name */ - val topic: String, - /** kafka partition id */ - val partition: Int, - /** kafka hostname */ - val host: String, - /** kafka host's port */ - val port: Int) extends Serializable - -object Leader { - def create(topic: String, partition: Int, host: String, port: Int): Leader = - new Leader(topic, partition, host, port) - - def create(topicAndPartition: TopicAndPartition, host: String, port: Int): Leader = - new Leader(topicAndPartition.topic, topicAndPartition.partition, host, port) - - def apply(topic: String, partition: Int, host: String, port: Int): Leader = - new Leader(topic, partition, host, port) - - def apply(topicAndPartition: TopicAndPartition, host: String, port: Int): Leader = - new Leader(topicAndPartition.topic, topicAndPartition.partition, host, port) - -} diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/OffsetRange.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/OffsetRange.scala index 334c12e4627b4..9c3dfeb8f5928 100644 --- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/OffsetRange.scala +++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/OffsetRange.scala @@ -19,16 +19,35 @@ package org.apache.spark.streaming.kafka import kafka.common.TopicAndPartition -/** Something that has a collection of OffsetRanges */ +import org.apache.spark.annotation.Experimental + +/** + * :: Experimental :: + * Represents any object that has a collection of [[OffsetRange]]s. This can be used access the + * offset ranges in RDDs generated by the direct Kafka DStream (see + * [[KafkaUtils.createDirectStream()]]). + * {{{ + * KafkaUtils.createDirectStream(...).foreachRDD { rdd => + * val offsetRanges = rdd.asInstanceOf[HasOffsetRanges].offsetRanges + * ... + * } + * }}} + */ +@Experimental trait HasOffsetRanges { def offsetRanges: Array[OffsetRange] } -/** Represents a range of offsets from a single Kafka TopicAndPartition */ +/** + * :: Experimental :: + * Represents a range of offsets from a single Kafka TopicAndPartition. Instances of this class + * can be created with `OffsetRange.create()`. + */ +@Experimental final class OffsetRange private( - /** kafka topic name */ + /** Kafka topic name */ val topic: String, - /** kafka partition id */ + /** Kafka partition id */ val partition: Int, /** inclusive starting offset */ val fromOffset: Long, @@ -36,11 +55,33 @@ final class OffsetRange private( val untilOffset: Long) extends Serializable { import OffsetRange.OffsetRangeTuple + override def equals(obj: Any): Boolean = obj match { + case that: OffsetRange => + this.topic == that.topic && + this.partition == that.partition && + this.fromOffset == that.fromOffset && + this.untilOffset == that.untilOffset + case _ => false + } + + override def hashCode(): Int = { + toTuple.hashCode() + } + + override def toString(): String = { + s"OffsetRange(topic: '$topic', partition: $partition, range: [$fromOffset -> $untilOffset]" + } + /** this is to avoid ClassNotFoundException during checkpoint restore */ private[streaming] def toTuple: OffsetRangeTuple = (topic, partition, fromOffset, untilOffset) } +/** + * :: Experimental :: + * Companion object the provides methods to create instances of [[OffsetRange]]. + */ +@Experimental object OffsetRange { def create(topic: String, partition: Int, fromOffset: Long, untilOffset: Long): OffsetRange = new OffsetRange(topic, partition, fromOffset, untilOffset) @@ -61,10 +102,10 @@ object OffsetRange { new OffsetRange(topicAndPartition.topic, topicAndPartition.partition, fromOffset, untilOffset) /** this is to avoid ClassNotFoundException during checkpoint restore */ - private[spark] + private[kafka] type OffsetRangeTuple = (String, Int, Long, Long) - private[streaming] + private[kafka] def apply(t: OffsetRangeTuple) = new OffsetRange(t._1, t._2, t._3, t._4) } diff --git a/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaDirectKafkaStreamSuite.java b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaDirectKafkaStreamSuite.java new file mode 100644 index 0000000000000..4c1d6a03eb2b8 --- /dev/null +++ b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaDirectKafkaStreamSuite.java @@ -0,0 +1,158 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming.kafka; + +import java.io.Serializable; +import java.util.HashMap; +import java.util.HashSet; +import java.util.Arrays; + +import scala.Tuple2; + +import kafka.common.TopicAndPartition; +import kafka.message.MessageAndMetadata; +import kafka.serializer.StringDecoder; +import org.junit.After; +import org.junit.Assert; +import org.junit.Before; +import org.junit.Test; + +import org.apache.spark.SparkConf; +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.function.Function; +import org.apache.spark.streaming.Durations; +import org.apache.spark.streaming.api.java.JavaDStream; +import org.apache.spark.streaming.api.java.JavaStreamingContext; + +public class JavaDirectKafkaStreamSuite implements Serializable { + private transient JavaStreamingContext ssc = null; + private transient KafkaTestUtils kafkaTestUtils = null; + + @Before + public void setUp() { + kafkaTestUtils = new KafkaTestUtils(); + kafkaTestUtils.setup(); + SparkConf sparkConf = new SparkConf() + .setMaster("local[4]").setAppName(this.getClass().getSimpleName()); + ssc = new JavaStreamingContext(sparkConf, Durations.milliseconds(200)); + } + + @After + public void tearDown() { + if (ssc != null) { + ssc.stop(); + ssc = null; + } + + if (kafkaTestUtils != null) { + kafkaTestUtils.teardown(); + kafkaTestUtils = null; + } + } + + @Test + public void testKafkaStream() throws InterruptedException { + String topic1 = "topic1"; + String topic2 = "topic2"; + + String[] topic1data = createTopicAndSendData(topic1); + String[] topic2data = createTopicAndSendData(topic2); + + HashSet sent = new HashSet(); + sent.addAll(Arrays.asList(topic1data)); + sent.addAll(Arrays.asList(topic2data)); + + HashMap kafkaParams = new HashMap(); + kafkaParams.put("metadata.broker.list", kafkaTestUtils.brokerAddress()); + kafkaParams.put("auto.offset.reset", "smallest"); + + JavaDStream stream1 = KafkaUtils.createDirectStream( + ssc, + String.class, + String.class, + StringDecoder.class, + StringDecoder.class, + kafkaParams, + topicToSet(topic1) + ).map( + new Function, String>() { + @Override + public String call(Tuple2 kv) throws Exception { + return kv._2(); + } + } + ); + + JavaDStream stream2 = KafkaUtils.createDirectStream( + ssc, + String.class, + String.class, + StringDecoder.class, + StringDecoder.class, + String.class, + kafkaParams, + topicOffsetToMap(topic2, (long) 0), + new Function, String>() { + @Override + public String call(MessageAndMetadata msgAndMd) throws Exception { + return msgAndMd.message(); + } + } + ); + JavaDStream unifiedStream = stream1.union(stream2); + + final HashSet result = new HashSet(); + unifiedStream.foreachRDD( + new Function, Void>() { + @Override + public Void call(JavaRDD rdd) throws Exception { + result.addAll(rdd.collect()); + return null; + } + } + ); + ssc.start(); + long startTime = System.currentTimeMillis(); + boolean matches = false; + while (!matches && System.currentTimeMillis() - startTime < 20000) { + matches = sent.size() == result.size(); + Thread.sleep(50); + } + Assert.assertEquals(sent, result); + ssc.stop(); + } + + private HashSet topicToSet(String topic) { + HashSet topicSet = new HashSet(); + topicSet.add(topic); + return topicSet; + } + + private HashMap topicOffsetToMap(String topic, Long offsetToStart) { + HashMap topicMap = new HashMap(); + topicMap.put(new TopicAndPartition(topic, 0), offsetToStart); + return topicMap; + } + + private String[] createTopicAndSendData(String topic) { + String[] data = { topic + "-1", topic + "-2", topic + "-3"}; + kafkaTestUtils.createTopic(topic); + kafkaTestUtils.sendMessages(topic, data); + return data; + } +} diff --git a/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaRDDSuite.java b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaRDDSuite.java new file mode 100644 index 0000000000000..a9dc6e50613ca --- /dev/null +++ b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaRDDSuite.java @@ -0,0 +1,155 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming.kafka; + +import java.io.Serializable; +import java.util.HashMap; + +import scala.Tuple2; + +import kafka.common.TopicAndPartition; +import kafka.message.MessageAndMetadata; +import kafka.serializer.StringDecoder; +import org.junit.After; +import org.junit.Assert; +import org.junit.Before; +import org.junit.Test; + +import org.apache.spark.SparkConf; +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.api.java.function.Function; + +public class JavaKafkaRDDSuite implements Serializable { + private transient JavaSparkContext sc = null; + private transient KafkaTestUtils kafkaTestUtils = null; + + @Before + public void setUp() { + kafkaTestUtils = new KafkaTestUtils(); + kafkaTestUtils.setup(); + SparkConf sparkConf = new SparkConf() + .setMaster("local[4]").setAppName(this.getClass().getSimpleName()); + sc = new JavaSparkContext(sparkConf); + } + + @After + public void tearDown() { + if (sc != null) { + sc.stop(); + sc = null; + } + + if (kafkaTestUtils != null) { + kafkaTestUtils.teardown(); + kafkaTestUtils = null; + } + } + + @Test + public void testKafkaRDD() throws InterruptedException { + String topic1 = "topic1"; + String topic2 = "topic2"; + + String[] topic1data = createTopicAndSendData(topic1); + String[] topic2data = createTopicAndSendData(topic2); + + HashMap kafkaParams = new HashMap(); + kafkaParams.put("metadata.broker.list", kafkaTestUtils.brokerAddress()); + + OffsetRange[] offsetRanges = { + OffsetRange.create(topic1, 0, 0, 1), + OffsetRange.create(topic2, 0, 0, 1) + }; + + HashMap emptyLeaders = new HashMap(); + HashMap leaders = new HashMap(); + String[] hostAndPort = kafkaTestUtils.brokerAddress().split(":"); + Broker broker = Broker.create(hostAndPort[0], Integer.parseInt(hostAndPort[1])); + leaders.put(new TopicAndPartition(topic1, 0), broker); + leaders.put(new TopicAndPartition(topic2, 0), broker); + + JavaRDD rdd1 = KafkaUtils.createRDD( + sc, + String.class, + String.class, + StringDecoder.class, + StringDecoder.class, + kafkaParams, + offsetRanges + ).map( + new Function, String>() { + @Override + public String call(Tuple2 kv) throws Exception { + return kv._2(); + } + } + ); + + JavaRDD rdd2 = KafkaUtils.createRDD( + sc, + String.class, + String.class, + StringDecoder.class, + StringDecoder.class, + String.class, + kafkaParams, + offsetRanges, + emptyLeaders, + new Function, String>() { + @Override + public String call(MessageAndMetadata msgAndMd) throws Exception { + return msgAndMd.message(); + } + } + ); + + JavaRDD rdd3 = KafkaUtils.createRDD( + sc, + String.class, + String.class, + StringDecoder.class, + StringDecoder.class, + String.class, + kafkaParams, + offsetRanges, + leaders, + new Function, String>() { + @Override + public String call(MessageAndMetadata msgAndMd) throws Exception { + return msgAndMd.message(); + } + } + ); + + // just making sure the java user apis work; the scala tests handle logic corner cases + long count1 = rdd1.count(); + long count2 = rdd2.count(); + long count3 = rdd3.count(); + Assert.assertTrue(count1 > 0); + Assert.assertEquals(count1, count2); + Assert.assertEquals(count1, count3); + } + + private String[] createTopicAndSendData(String topic) { + String[] data = { topic + "-1", topic + "-2", topic + "-3"}; + kafkaTestUtils.createTopic(topic); + kafkaTestUtils.sendMessages(topic, data); + return data; + } +} diff --git a/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaStreamSuite.java b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaStreamSuite.java index 6e1abf3f385ee..540f4ceabab47 100644 --- a/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaStreamSuite.java +++ b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaStreamSuite.java @@ -22,37 +22,32 @@ import java.util.List; import java.util.Random; -import org.apache.spark.SparkConf; -import org.apache.spark.streaming.Duration; -import scala.Predef; import scala.Tuple2; -import scala.collection.JavaConverters; - -import junit.framework.Assert; import kafka.serializer.StringDecoder; +import org.junit.After; +import org.junit.Assert; +import org.junit.Before; +import org.junit.Test; +import org.apache.spark.SparkConf; import org.apache.spark.api.java.JavaPairRDD; import org.apache.spark.api.java.function.Function; import org.apache.spark.storage.StorageLevel; +import org.apache.spark.streaming.Duration; import org.apache.spark.streaming.api.java.JavaDStream; import org.apache.spark.streaming.api.java.JavaPairDStream; import org.apache.spark.streaming.api.java.JavaStreamingContext; -import org.junit.Test; -import org.junit.After; -import org.junit.Before; - public class JavaKafkaStreamSuite implements Serializable { private transient JavaStreamingContext ssc = null; private transient Random random = new Random(); - private transient KafkaStreamSuiteBase suiteBase = null; + private transient KafkaTestUtils kafkaTestUtils = null; @Before public void setUp() { - suiteBase = new KafkaStreamSuiteBase() { }; - suiteBase.setupKafka(); - System.clearProperty("spark.driver.port"); + kafkaTestUtils = new KafkaTestUtils(); + kafkaTestUtils.setup(); SparkConf sparkConf = new SparkConf() .setMaster("local[4]").setAppName(this.getClass().getSimpleName()); ssc = new JavaStreamingContext(sparkConf, new Duration(500)); @@ -60,10 +55,15 @@ public void setUp() { @After public void tearDown() { - ssc.stop(); - ssc = null; - System.clearProperty("spark.driver.port"); - suiteBase.tearDownKafka(); + if (ssc != null) { + ssc.stop(); + ssc = null; + } + + if (kafkaTestUtils != null) { + kafkaTestUtils.teardown(); + kafkaTestUtils = null; + } } @Test @@ -77,14 +77,11 @@ public void testKafkaStream() throws InterruptedException { sent.put("b", 3); sent.put("c", 10); - suiteBase.createTopic(topic); - HashMap tmp = new HashMap(sent); - suiteBase.produceAndSendMessage(topic, - JavaConverters.mapAsScalaMapConverter(tmp).asScala().toMap( - Predef.>conforms())); + kafkaTestUtils.createTopic(topic); + kafkaTestUtils.sendMessages(topic, sent); HashMap kafkaParams = new HashMap(); - kafkaParams.put("zookeeper.connect", suiteBase.zkAddress()); + kafkaParams.put("zookeeper.connect", kafkaTestUtils.zkAddress()); kafkaParams.put("group.id", "test-consumer-" + random.nextInt(10000)); kafkaParams.put("auto.offset.reset", "smallest"); @@ -127,6 +124,7 @@ public Void call(JavaPairRDD rdd) throws Exception { ); ssc.start(); + long startTime = System.currentTimeMillis(); boolean sizeMatches = false; while (!sizeMatches && System.currentTimeMillis() - startTime < 20000) { @@ -137,6 +135,5 @@ public Void call(JavaPairRDD rdd) throws Exception { for (String k : sent.keySet()) { Assert.assertEquals(sent.get(k).intValue(), result.get(k).intValue()); } - ssc.stop(); } } diff --git a/external/kafka/src/test/resources/log4j.properties b/external/kafka/src/test/resources/log4j.properties index 9697237bfa1a3..75e3b53a093f6 100644 --- a/external/kafka/src/test/resources/log4j.properties +++ b/external/kafka/src/test/resources/log4j.properties @@ -24,5 +24,5 @@ log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n # Ignore messages below warning level from Jetty, because it's a bit verbose -log4j.logger.org.eclipse.jetty=WARN +log4j.logger.org.spark-project.jetty=WARN diff --git a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/DirectKafkaStreamSuite.scala b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/DirectKafkaStreamSuite.scala new file mode 100644 index 0000000000000..415730f5559c5 --- /dev/null +++ b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/DirectKafkaStreamSuite.scala @@ -0,0 +1,316 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming.kafka + +import java.io.File + +import scala.collection.mutable +import scala.collection.mutable.ArrayBuffer +import scala.concurrent.duration._ +import scala.language.postfixOps + +import kafka.common.TopicAndPartition +import kafka.message.MessageAndMetadata +import kafka.serializer.StringDecoder +import org.scalatest.{BeforeAndAfter, BeforeAndAfterAll, FunSuite} +import org.scalatest.concurrent.Eventually + +import org.apache.spark.{Logging, SparkConf, SparkContext} +import org.apache.spark.rdd.RDD +import org.apache.spark.streaming.{Milliseconds, StreamingContext, Time} +import org.apache.spark.streaming.dstream.DStream +import org.apache.spark.util.Utils + +class DirectKafkaStreamSuite + extends FunSuite + with BeforeAndAfter + with BeforeAndAfterAll + with Eventually + with Logging { + val sparkConf = new SparkConf() + .setMaster("local[4]") + .setAppName(this.getClass.getSimpleName) + + private var sc: SparkContext = _ + private var ssc: StreamingContext = _ + private var testDir: File = _ + + private var kafkaTestUtils: KafkaTestUtils = _ + + override def beforeAll { + kafkaTestUtils = new KafkaTestUtils + kafkaTestUtils.setup() + } + + override def afterAll { + if (kafkaTestUtils != null) { + kafkaTestUtils.teardown() + kafkaTestUtils = null + } + } + + after { + if (ssc != null) { + ssc.stop() + sc = null + } + if (sc != null) { + sc.stop() + } + if (testDir != null) { + Utils.deleteRecursively(testDir) + } + } + + + test("basic stream receiving with multiple topics and smallest starting offset") { + val topics = Set("basic1", "basic2", "basic3") + val data = Map("a" -> 7, "b" -> 9) + topics.foreach { t => + kafkaTestUtils.createTopic(t) + kafkaTestUtils.sendMessages(t, data) + } + val totalSent = data.values.sum * topics.size + val kafkaParams = Map( + "metadata.broker.list" -> kafkaTestUtils.brokerAddress, + "auto.offset.reset" -> "smallest" + ) + + ssc = new StreamingContext(sparkConf, Milliseconds(200)) + val stream = withClue("Error creating direct stream") { + KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder]( + ssc, kafkaParams, topics) + } + + val allReceived = new ArrayBuffer[(String, String)] + + stream.foreachRDD { rdd => + // Get the offset ranges in the RDD + val offsets = rdd.asInstanceOf[HasOffsetRanges].offsetRanges + val collected = rdd.mapPartitionsWithIndex { (i, iter) => + // For each partition, get size of the range in the partition, + // and the number of items in the partition + val off = offsets(i) + val all = iter.toSeq + val partSize = all.size + val rangeSize = off.untilOffset - off.fromOffset + Iterator((partSize, rangeSize)) + }.collect + + // Verify whether number of elements in each partition + // matches with the corresponding offset range + collected.foreach { case (partSize, rangeSize) => + assert(partSize === rangeSize, "offset ranges are wrong") + } + } + stream.foreachRDD { rdd => allReceived ++= rdd.collect() } + ssc.start() + eventually(timeout(20000.milliseconds), interval(200.milliseconds)) { + assert(allReceived.size === totalSent, + "didn't get expected number of messages, messages:\n" + allReceived.mkString("\n")) + } + ssc.stop() + } + + test("receiving from largest starting offset") { + val topic = "largest" + val topicPartition = TopicAndPartition(topic, 0) + val data = Map("a" -> 10) + kafkaTestUtils.createTopic(topic) + val kafkaParams = Map( + "metadata.broker.list" -> kafkaTestUtils.brokerAddress, + "auto.offset.reset" -> "largest" + ) + val kc = new KafkaCluster(kafkaParams) + def getLatestOffset(): Long = { + kc.getLatestLeaderOffsets(Set(topicPartition)).right.get(topicPartition).offset + } + + // Send some initial messages before starting context + kafkaTestUtils.sendMessages(topic, data) + eventually(timeout(10 seconds), interval(20 milliseconds)) { + assert(getLatestOffset() > 3) + } + val offsetBeforeStart = getLatestOffset() + + // Setup context and kafka stream with largest offset + ssc = new StreamingContext(sparkConf, Milliseconds(200)) + val stream = withClue("Error creating direct stream") { + KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder]( + ssc, kafkaParams, Set(topic)) + } + assert( + stream.asInstanceOf[DirectKafkaInputDStream[_, _, _, _, _]] + .fromOffsets(topicPartition) >= offsetBeforeStart, + "Start offset not from latest" + ) + + val collectedData = new mutable.ArrayBuffer[String]() + stream.map { _._2 }.foreachRDD { rdd => collectedData ++= rdd.collect() } + ssc.start() + val newData = Map("b" -> 10) + kafkaTestUtils.sendMessages(topic, newData) + eventually(timeout(10 seconds), interval(50 milliseconds)) { + collectedData.contains("b") + } + assert(!collectedData.contains("a")) + } + + + test("creating stream by offset") { + val topic = "offset" + val topicPartition = TopicAndPartition(topic, 0) + val data = Map("a" -> 10) + kafkaTestUtils.createTopic(topic) + val kafkaParams = Map( + "metadata.broker.list" -> kafkaTestUtils.brokerAddress, + "auto.offset.reset" -> "largest" + ) + val kc = new KafkaCluster(kafkaParams) + def getLatestOffset(): Long = { + kc.getLatestLeaderOffsets(Set(topicPartition)).right.get(topicPartition).offset + } + + // Send some initial messages before starting context + kafkaTestUtils.sendMessages(topic, data) + eventually(timeout(10 seconds), interval(20 milliseconds)) { + assert(getLatestOffset() >= 10) + } + val offsetBeforeStart = getLatestOffset() + + // Setup context and kafka stream with largest offset + ssc = new StreamingContext(sparkConf, Milliseconds(200)) + val stream = withClue("Error creating direct stream") { + KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder, String]( + ssc, kafkaParams, Map(topicPartition -> 11L), + (m: MessageAndMetadata[String, String]) => m.message()) + } + assert( + stream.asInstanceOf[DirectKafkaInputDStream[_, _, _, _, _]] + .fromOffsets(topicPartition) >= offsetBeforeStart, + "Start offset not from latest" + ) + + val collectedData = new mutable.ArrayBuffer[String]() + stream.foreachRDD { rdd => collectedData ++= rdd.collect() } + ssc.start() + val newData = Map("b" -> 10) + kafkaTestUtils.sendMessages(topic, newData) + eventually(timeout(10 seconds), interval(50 milliseconds)) { + collectedData.contains("b") + } + assert(!collectedData.contains("a")) + } + + // Test to verify the offset ranges can be recovered from the checkpoints + test("offset recovery") { + val topic = "recovery" + kafkaTestUtils.createTopic(topic) + testDir = Utils.createTempDir() + + val kafkaParams = Map( + "metadata.broker.list" -> kafkaTestUtils.brokerAddress, + "auto.offset.reset" -> "smallest" + ) + + // Send data to Kafka and wait for it to be received + def sendDataAndWaitForReceive(data: Seq[Int]) { + val strings = data.map { _.toString} + kafkaTestUtils.sendMessages(topic, strings.map { _ -> 1}.toMap) + eventually(timeout(10 seconds), interval(50 milliseconds)) { + assert(strings.forall { DirectKafkaStreamSuite.collectedData.contains }) + } + } + + // Setup the streaming context + ssc = new StreamingContext(sparkConf, Milliseconds(100)) + val kafkaStream = withClue("Error creating direct stream") { + KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder]( + ssc, kafkaParams, Set(topic)) + } + val keyedStream = kafkaStream.map { v => "key" -> v._2.toInt } + val stateStream = keyedStream.updateStateByKey { (values: Seq[Int], state: Option[Int]) => + Some(values.sum + state.getOrElse(0)) + } + ssc.checkpoint(testDir.getAbsolutePath) + + // This is to collect the raw data received from Kafka + kafkaStream.foreachRDD { (rdd: RDD[(String, String)], time: Time) => + val data = rdd.map { _._2 }.collect() + DirectKafkaStreamSuite.collectedData.appendAll(data) + } + + // This is ensure all the data is eventually receiving only once + stateStream.foreachRDD { (rdd: RDD[(String, Int)]) => + rdd.collect().headOption.foreach { x => DirectKafkaStreamSuite.total = x._2 } + } + ssc.start() + + // Send some data and wait for them to be received + for (i <- (1 to 10).grouped(4)) { + sendDataAndWaitForReceive(i) + } + + // Verify that offset ranges were generated + val offsetRangesBeforeStop = getOffsetRanges(kafkaStream) + assert(offsetRangesBeforeStop.size >= 1, "No offset ranges generated") + assert( + offsetRangesBeforeStop.head._2.forall { _.fromOffset === 0 }, + "starting offset not zero" + ) + ssc.stop() + logInfo("====== RESTARTING ========") + + // Recover context from checkpoints + ssc = new StreamingContext(testDir.getAbsolutePath) + val recoveredStream = ssc.graph.getInputStreams().head.asInstanceOf[DStream[(String, String)]] + + // Verify offset ranges have been recovered + val recoveredOffsetRanges = getOffsetRanges(recoveredStream) + assert(recoveredOffsetRanges.size > 0, "No offset ranges recovered") + val earlierOffsetRangesAsSets = offsetRangesBeforeStop.map { x => (x._1, x._2.toSet) } + assert( + recoveredOffsetRanges.forall { or => + earlierOffsetRangesAsSets.contains((or._1, or._2.toSet)) + }, + "Recovered ranges are not the same as the ones generated" + ) + + // Restart context, give more data and verify the total at the end + // If the total is write that means each records has been received only once + ssc.start() + sendDataAndWaitForReceive(11 to 20) + eventually(timeout(10 seconds), interval(50 milliseconds)) { + assert(DirectKafkaStreamSuite.total === (1 to 20).sum) + } + ssc.stop() + } + + /** Get the generated offset ranges from the DirectKafkaStream */ + private def getOffsetRanges[K, V]( + kafkaStream: DStream[(K, V)]): Seq[(Time, Array[OffsetRange])] = { + kafkaStream.generatedRDDs.mapValues { rdd => + rdd.asInstanceOf[KafkaRDD[K, V, _, _, (K, V)]].offsetRanges + }.toSeq.sortBy { _._1 } + } +} + +object DirectKafkaStreamSuite { + val collectedData = new mutable.ArrayBuffer[String]() + var total = -1L +} diff --git a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaClusterSuite.scala b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaClusterSuite.scala index e57c8f6987fdc..7fb841b79cb65 100644 --- a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaClusterSuite.scala +++ b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaClusterSuite.scala @@ -19,36 +19,42 @@ package org.apache.spark.streaming.kafka import scala.util.Random -import org.scalatest.BeforeAndAfter import kafka.common.TopicAndPartition +import org.scalatest.{BeforeAndAfterAll, FunSuite} -class KafkaClusterSuite extends KafkaStreamSuiteBase with BeforeAndAfter { - val brokerHost = "localhost" +class KafkaClusterSuite extends FunSuite with BeforeAndAfterAll { + private val topic = "kcsuitetopic" + Random.nextInt(10000) + private val topicAndPartition = TopicAndPartition(topic, 0) + private var kc: KafkaCluster = null - val kafkaParams = Map("metadata.broker.list" -> s"$brokerHost:$brokerPort") + private var kafkaTestUtils: KafkaTestUtils = _ - val kc = new KafkaCluster(kafkaParams) + override def beforeAll() { + kafkaTestUtils = new KafkaTestUtils + kafkaTestUtils.setup() - val topic = "kcsuitetopic" + Random.nextInt(10000) - - val topicAndPartition = TopicAndPartition(topic, 0) - - before { - setupKafka() - createTopic(topic) - produceAndSendMessage(topic, Map("a" -> 1)) + kafkaTestUtils.createTopic(topic) + kafkaTestUtils.sendMessages(topic, Map("a" -> 1)) + kc = new KafkaCluster(Map("metadata.broker.list" -> kafkaTestUtils.brokerAddress)) } - after { - tearDownKafka() + override def afterAll() { + if (kafkaTestUtils != null) { + kafkaTestUtils.teardown() + kafkaTestUtils = null + } } test("metadata apis") { - val leader = kc.findLeaders(Set(topicAndPartition)).right.get - assert(leader(topicAndPartition) === (brokerHost, brokerPort), "didn't get leader") + val leader = kc.findLeaders(Set(topicAndPartition)).right.get(topicAndPartition) + val leaderAddress = s"${leader._1}:${leader._2}" + assert(leaderAddress === kafkaTestUtils.brokerAddress, "didn't get leader") val parts = kc.getPartitions(Set(topic)).right.get assert(parts(topicAndPartition), "didn't get partitions") + + val err = kc.getPartitions(Set(topic + "BAD")) + assert(err.isLeft, "getPartitions for a nonexistant topic should be an error") } test("leader offset apis") { diff --git a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaDirectStreamSuite.scala b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaDirectStreamSuite.scala deleted file mode 100644 index 0891ce344f16a..0000000000000 --- a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaDirectStreamSuite.scala +++ /dev/null @@ -1,92 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.streaming.kafka - -import scala.util.Random -import scala.concurrent.duration._ - -import org.scalatest.BeforeAndAfter -import org.scalatest.concurrent.Eventually - -import kafka.serializer.StringDecoder - -import org.apache.spark.SparkConf -import org.apache.spark.storage.StorageLevel -import org.apache.spark.streaming.{Milliseconds, StreamingContext} - -class KafkaDirectStreamSuite extends KafkaStreamSuiteBase with BeforeAndAfter with Eventually { - val sparkConf = new SparkConf() - .setMaster("local[4]") - .setAppName(this.getClass.getSimpleName) - - val brokerHost = "localhost" - - val kafkaParams = Map( - "metadata.broker.list" -> s"$brokerHost:$brokerPort", - "auto.offset.reset" -> "smallest" - ) - - var ssc: StreamingContext = _ - - before { - setupKafka() - - ssc = new StreamingContext(sparkConf, Milliseconds(500)) - } - - after { - if (ssc != null) { - ssc.stop() - } - tearDownKafka() - } - - test("multi topic stream") { - val topics = Set("newA", "newB") - val data = Map("a" -> 7, "b" -> 9) - topics.foreach { t => - createTopic(t) - produceAndSendMessage(t, data) - } - val stream = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder]( - ssc, kafkaParams, topics) - var total = 0L; - - stream.foreachRDD { rdd => - val offsets = rdd.asInstanceOf[HasOffsetRanges].offsetRanges - val collected = rdd.mapPartitionsWithIndex { (i, iter) => - val off = offsets(i) - val all = iter.toSeq - val partSize = all.size - val rangeSize = off.untilOffset - off.fromOffset - all.map { _ => - (partSize, rangeSize) - }.toIterator - }.collect - collected.foreach { case (partSize, rangeSize) => - assert(partSize === rangeSize, "offset ranges are wrong") - } - total += collected.size - } - ssc.start() - eventually(timeout(20000.milliseconds), interval(200.milliseconds)) { - assert(total === data.values.sum * topics.size, "didn't get all messages") - } - ssc.stop() - } -} diff --git a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaRDDSuite.scala b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaRDDSuite.scala index 9b9e3f5fce8bd..7d26ce50875b3 100644 --- a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaRDDSuite.scala +++ b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaRDDSuite.scala @@ -21,79 +21,125 @@ import scala.util.Random import kafka.serializer.StringDecoder import kafka.common.TopicAndPartition -import org.scalatest.BeforeAndAfter +import kafka.message.MessageAndMetadata +import org.scalatest.{BeforeAndAfterAll, FunSuite} import org.apache.spark._ -import org.apache.spark.SparkContext._ -class KafkaRDDSuite extends KafkaStreamSuiteBase with BeforeAndAfter { - var sc: SparkContext = _ - before { - setupKafka() +class KafkaRDDSuite extends FunSuite with BeforeAndAfterAll { + + private var kafkaTestUtils: KafkaTestUtils = _ + + private val sparkConf = new SparkConf().setMaster("local[4]") + .setAppName(this.getClass.getSimpleName) + private var sc: SparkContext = _ + + override def beforeAll { + sc = new SparkContext(sparkConf) + kafkaTestUtils = new KafkaTestUtils + kafkaTestUtils.setup() } - after { + override def afterAll { if (sc != null) { sc.stop sc = null } - tearDownKafka() + + if (kafkaTestUtils != null) { + kafkaTestUtils.teardown() + kafkaTestUtils = null + } } - test("Kafka RDD") { - val sparkConf = new SparkConf().setMaster("local[4]").setAppName(this.getClass.getSimpleName) - sc = new SparkContext(sparkConf) + test("basic usage") { + val topic = "topicbasic" + kafkaTestUtils.createTopic(topic) + val messages = Set("the", "quick", "brown", "fox") + kafkaTestUtils.sendMessages(topic, messages.toArray) + + + val kafkaParams = Map("metadata.broker.list" -> kafkaTestUtils.brokerAddress, + "group.id" -> s"test-consumer-${Random.nextInt(10000)}") + + val offsetRanges = Array(OffsetRange(topic, 0, 0, messages.size)) + + val rdd = KafkaUtils.createRDD[String, String, StringDecoder, StringDecoder]( + sc, kafkaParams, offsetRanges) + + val received = rdd.map(_._2).collect.toSet + assert(received === messages) + } + + test("iterator boundary conditions") { + // the idea is to find e.g. off-by-one errors between what kafka has available and the rdd val topic = "topic1" val sent = Map("a" -> 5, "b" -> 3, "c" -> 10) - createTopic(topic) - produceAndSendMessage(topic, sent) + kafkaTestUtils.createTopic(topic) - val kafkaParams = Map("metadata.broker.list" -> s"localhost:$brokerPort", + val kafkaParams = Map("metadata.broker.list" -> kafkaTestUtils.brokerAddress, "group.id" -> s"test-consumer-${Random.nextInt(10000)}") val kc = new KafkaCluster(kafkaParams) - val rdd = getRdd(kc, Set(topic)) // this is the "lots of messages" case - // make sure we get all of them + kafkaTestUtils.sendMessages(topic, sent) + // rdd defined from leaders after sending messages, should get the number sent + val rdd = getRdd(kc, Set(topic)) + assert(rdd.isDefined) - assert(rdd.get.count === sent.values.sum) + assert(rdd.get.count === sent.values.sum, "didn't get all sent messages") - kc.setConsumerOffsets( - kafkaParams("group.id"), - rdd.get.offsetRanges.map(o => TopicAndPartition(o.topic, o.partition) -> o.untilOffset).toMap) + val ranges = rdd.get.asInstanceOf[HasOffsetRanges] + .offsetRanges.map(o => TopicAndPartition(o.topic, o.partition) -> o.untilOffset).toMap + + kc.setConsumerOffsets(kafkaParams("group.id"), ranges) - val rdd2 = getRdd(kc, Set(topic)) - val sent2 = Map("d" -> 1) - produceAndSendMessage(topic, sent2) // this is the "0 messages" case - // make sure we dont get anything, since messages were sent after rdd was defined + val rdd2 = getRdd(kc, Set(topic)) + // shouldn't get anything, since message is sent after rdd was defined + val sentOnlyOne = Map("d" -> 1) + + kafkaTestUtils.sendMessages(topic, sentOnlyOne) assert(rdd2.isDefined) - assert(rdd2.get.count === 0) + assert(rdd2.get.count === 0, "got messages when there shouldn't be any") + // this is the "exactly 1 message" case, namely the single message from sentOnlyOne above val rdd3 = getRdd(kc, Set(topic)) - produceAndSendMessage(topic, Map("extra" -> 22)) - // this is the "exactly 1 message" case - // make sure we get exactly one message, despite there being lots more available + // send lots of messages after rdd was defined, they shouldn't show up + kafkaTestUtils.sendMessages(topic, Map("extra" -> 22)) + assert(rdd3.isDefined) - assert(rdd3.get.count === sent2.values.sum) + assert(rdd3.get.count === sentOnlyOne.values.sum, "didn't get exactly one message") } // get an rdd from the committed consumer offsets until the latest leader offsets, private def getRdd(kc: KafkaCluster, topics: Set[String]) = { val groupId = kc.kafkaParams("group.id") - for { - topicPartitions <- kc.getPartitions(topics).right.toOption - from <- kc.getConsumerOffsets(groupId, topicPartitions).right.toOption.orElse( + def consumerOffsets(topicPartitions: Set[TopicAndPartition]) = { + kc.getConsumerOffsets(groupId, topicPartitions).right.toOption.orElse( kc.getEarliestLeaderOffsets(topicPartitions).right.toOption.map { offs => offs.map(kv => kv._1 -> kv._2.offset) } ) - until <- kc.getLatestLeaderOffsets(topicPartitions).right.toOption - } yield { - KafkaRDD[String, String, StringDecoder, StringDecoder, String]( - sc, kc.kafkaParams, from, until, mmd => s"${mmd.offset} ${mmd.message}") + } + kc.getPartitions(topics).right.toOption.flatMap { topicPartitions => + consumerOffsets(topicPartitions).flatMap { from => + kc.getLatestLeaderOffsets(topicPartitions).right.toOption.map { until => + val offsetRanges = from.map { case (tp: TopicAndPartition, fromOffset: Long) => + OffsetRange(tp.topic, tp.partition, fromOffset, until(tp).offset) + }.toArray + + val leaders = until.map { case (tp: TopicAndPartition, lo: KafkaCluster.LeaderOffset) => + tp -> Broker(lo.host, lo.port) + }.toMap + + KafkaUtils.createRDD[String, String, StringDecoder, StringDecoder, String]( + sc, kc.kafkaParams, offsetRanges, leaders, + (mmd: MessageAndMetadata[String, String]) => s"${mmd.offset} ${mmd.message}") + } + } } } } diff --git a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaStreamSuite.scala b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaStreamSuite.scala index f207dc6d4fa04..24699dfc33adb 100644 --- a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaStreamSuite.scala +++ b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaStreamSuite.scala @@ -17,199 +17,38 @@ package org.apache.spark.streaming.kafka -import java.io.File -import java.net.InetSocketAddress -import java.util.Properties - import scala.collection.mutable import scala.concurrent.duration._ import scala.language.postfixOps import scala.util.Random -import kafka.admin.AdminUtils -import kafka.common.{KafkaException, TopicAndPartition} -import kafka.producer.{KeyedMessage, Producer, ProducerConfig} -import kafka.serializer.{StringDecoder, StringEncoder} -import kafka.server.{KafkaConfig, KafkaServer} -import kafka.utils.ZKStringSerializer -import org.I0Itec.zkclient.ZkClient -import org.apache.zookeeper.server.{NIOServerCnxnFactory, ZooKeeperServer} -import org.scalatest.{BeforeAndAfter, FunSuite} +import kafka.serializer.StringDecoder +import org.scalatest.{BeforeAndAfterAll, FunSuite} import org.scalatest.concurrent.Eventually -import org.apache.spark.{Logging, SparkConf} +import org.apache.spark.SparkConf import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming.{Milliseconds, StreamingContext} -import org.apache.spark.util.Utils - -/** - * This is an abstract base class for Kafka testsuites. This has the functionality to set up - * and tear down local Kafka servers, and to push data using Kafka producers. - */ -abstract class KafkaStreamSuiteBase extends FunSuite with Eventually with Logging { - - var zkAddress: String = _ - var zkClient: ZkClient = _ - - private val zkHost = "localhost" - private val zkConnectionTimeout = 6000 - private val zkSessionTimeout = 6000 - private var zookeeper: EmbeddedZookeeper = _ - private var zkPort: Int = 0 - protected var brokerPort = 9092 - private var brokerConf: KafkaConfig = _ - private var server: KafkaServer = _ - private var producer: Producer[String, String] = _ - - def setupKafka() { - // Zookeeper server startup - zookeeper = new EmbeddedZookeeper(s"$zkHost:$zkPort") - // Get the actual zookeeper binding port - zkPort = zookeeper.actualPort - zkAddress = s"$zkHost:$zkPort" - logInfo("==================== 0 ====================") - - zkClient = new ZkClient(zkAddress, zkSessionTimeout, zkConnectionTimeout, - ZKStringSerializer) - logInfo("==================== 1 ====================") - - // Kafka broker startup - var bindSuccess: Boolean = false - while(!bindSuccess) { - try { - val brokerProps = getBrokerConfig() - brokerConf = new KafkaConfig(brokerProps) - server = new KafkaServer(brokerConf) - logInfo("==================== 2 ====================") - server.startup() - logInfo("==================== 3 ====================") - bindSuccess = true - } catch { - case e: KafkaException => - if (e.getMessage != null && e.getMessage.contains("Socket server failed to bind to")) { - brokerPort += 1 - } - case e: Exception => throw new Exception("Kafka server create failed", e) - } - } - - Thread.sleep(2000) - logInfo("==================== 4 ====================") - } - - def tearDownKafka() { - if (producer != null) { - producer.close() - producer = null - } - - if (server != null) { - server.shutdown() - server = null - } - - brokerConf.logDirs.foreach { f => Utils.deleteRecursively(new File(f)) } - if (zkClient != null) { - zkClient.close() - zkClient = null - } - - if (zookeeper != null) { - zookeeper.shutdown() - zookeeper = null - } - } - - private def createTestMessage(topic: String, sent: Map[String, Int]) - : Seq[KeyedMessage[String, String]] = { - val messages = for ((s, freq) <- sent; i <- 0 until freq) yield { - new KeyedMessage[String, String](topic, s) - } - messages.toSeq - } - - def createTopic(topic: String) { - AdminUtils.createTopic(zkClient, topic, 1, 1) - logInfo("==================== 5 ====================") - // wait until metadata is propagated - waitUntilMetadataIsPropagated(topic, 0) - } +class KafkaStreamSuite extends FunSuite with Eventually with BeforeAndAfterAll { + private var ssc: StreamingContext = _ + private var kafkaTestUtils: KafkaTestUtils = _ - def produceAndSendMessage(topic: String, sent: Map[String, Int]) { - producer = new Producer[String, String](new ProducerConfig(getProducerConfig())) - producer.send(createTestMessage(topic, sent): _*) - producer.close() - logInfo("==================== 6 ====================") + override def beforeAll(): Unit = { + kafkaTestUtils = new KafkaTestUtils + kafkaTestUtils.setup() } - private def getBrokerConfig(): Properties = { - val props = new Properties() - props.put("broker.id", "0") - props.put("host.name", "localhost") - props.put("port", brokerPort.toString) - props.put("log.dir", Utils.createTempDir().getAbsolutePath) - props.put("zookeeper.connect", zkAddress) - props.put("log.flush.interval.messages", "1") - props.put("replica.socket.timeout.ms", "1500") - props - } - - private def getProducerConfig(): Properties = { - val brokerAddr = brokerConf.hostName + ":" + brokerConf.port - val props = new Properties() - props.put("metadata.broker.list", brokerAddr) - props.put("serializer.class", classOf[StringEncoder].getName) - props - } - - private def waitUntilMetadataIsPropagated(topic: String, partition: Int) { - eventually(timeout(10000 milliseconds), interval(100 milliseconds)) { - assert( - server.apis.metadataCache.containsTopicAndPartition(topic, partition), - s"Partition [$topic, $partition] metadata not propagated after timeout" - ) - } - } - - class EmbeddedZookeeper(val zkConnect: String) { - val random = new Random() - val snapshotDir = Utils.createTempDir() - val logDir = Utils.createTempDir() - - val zookeeper = new ZooKeeperServer(snapshotDir, logDir, 500) - val (ip, port) = { - val splits = zkConnect.split(":") - (splits(0), splits(1).toInt) - } - val factory = new NIOServerCnxnFactory() - factory.configure(new InetSocketAddress(ip, port), 16) - factory.startup(zookeeper) - - val actualPort = factory.getLocalPort - - def shutdown() { - factory.shutdown() - Utils.deleteRecursively(snapshotDir) - Utils.deleteRecursively(logDir) - } - } -} - - -class KafkaStreamSuite extends KafkaStreamSuiteBase with BeforeAndAfter { - var ssc: StreamingContext = _ - - before { - setupKafka() - } - - after { + override def afterAll(): Unit = { if (ssc != null) { ssc.stop() ssc = null } - tearDownKafka() + + if (kafkaTestUtils != null) { + kafkaTestUtils.teardown() + kafkaTestUtils = null + } } test("Kafka input stream") { @@ -217,10 +56,10 @@ class KafkaStreamSuite extends KafkaStreamSuiteBase with BeforeAndAfter { ssc = new StreamingContext(sparkConf, Milliseconds(500)) val topic = "topic1" val sent = Map("a" -> 5, "b" -> 3, "c" -> 10) - createTopic(topic) - produceAndSendMessage(topic, sent) + kafkaTestUtils.createTopic(topic) + kafkaTestUtils.sendMessages(topic, sent) - val kafkaParams = Map("zookeeper.connect" -> zkAddress, + val kafkaParams = Map("zookeeper.connect" -> kafkaTestUtils.zkAddress, "group.id" -> s"test-consumer-${Random.nextInt(10000)}", "auto.offset.reset" -> "smallest") @@ -234,14 +73,14 @@ class KafkaStreamSuite extends KafkaStreamSuiteBase with BeforeAndAfter { result.put(kv._1, count) } } + ssc.start() + eventually(timeout(10000 milliseconds), interval(100 milliseconds)) { assert(sent.size === result.size) sent.keys.foreach { k => assert(sent(k) === result(k).toInt) } } - ssc.stop() } } - diff --git a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/ReliableKafkaStreamSuite.scala b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/ReliableKafkaStreamSuite.scala index 64ccc92c81fa9..38548dd73b82c 100644 --- a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/ReliableKafkaStreamSuite.scala +++ b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/ReliableKafkaStreamSuite.scala @@ -17,7 +17,6 @@ package org.apache.spark.streaming.kafka - import java.io.File import scala.collection.mutable @@ -25,61 +24,71 @@ import scala.concurrent.duration._ import scala.language.postfixOps import scala.util.Random -import com.google.common.io.Files import kafka.serializer.StringDecoder import kafka.utils.{ZKGroupTopicDirs, ZkUtils} -import org.apache.commons.io.FileUtils -import org.scalatest.BeforeAndAfter +import org.scalatest.{BeforeAndAfter, BeforeAndAfterAll, FunSuite} import org.scalatest.concurrent.Eventually import org.apache.spark.SparkConf import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming.{Milliseconds, StreamingContext} +import org.apache.spark.util.Utils -class ReliableKafkaStreamSuite extends KafkaStreamSuiteBase with BeforeAndAfter with Eventually { +class ReliableKafkaStreamSuite extends FunSuite + with BeforeAndAfterAll with BeforeAndAfter with Eventually { - val sparkConf = new SparkConf() + private val sparkConf = new SparkConf() .setMaster("local[4]") .setAppName(this.getClass.getSimpleName) .set("spark.streaming.receiver.writeAheadLog.enable", "true") - val data = Map("a" -> 10, "b" -> 10, "c" -> 10) + private val data = Map("a" -> 10, "b" -> 10, "c" -> 10) + private var kafkaTestUtils: KafkaTestUtils = _ - var groupId: String = _ - var kafkaParams: Map[String, String] = _ - var ssc: StreamingContext = _ - var tempDirectory: File = null + private var groupId: String = _ + private var kafkaParams: Map[String, String] = _ + private var ssc: StreamingContext = _ + private var tempDirectory: File = null + + override def beforeAll() : Unit = { + kafkaTestUtils = new KafkaTestUtils + kafkaTestUtils.setup() - before { - setupKafka() groupId = s"test-consumer-${Random.nextInt(10000)}" kafkaParams = Map( - "zookeeper.connect" -> zkAddress, + "zookeeper.connect" -> kafkaTestUtils.zkAddress, "group.id" -> groupId, "auto.offset.reset" -> "smallest" ) + tempDirectory = Utils.createTempDir() + } + + override def afterAll(): Unit = { + Utils.deleteRecursively(tempDirectory) + + if (kafkaTestUtils != null) { + kafkaTestUtils.teardown() + kafkaTestUtils = null + } + } + + before { ssc = new StreamingContext(sparkConf, Milliseconds(500)) - tempDirectory = Files.createTempDir() ssc.checkpoint(tempDirectory.getAbsolutePath) } after { if (ssc != null) { ssc.stop() + ssc = null } - if (tempDirectory != null && tempDirectory.exists()) { - FileUtils.deleteDirectory(tempDirectory) - tempDirectory = null - } - tearDownKafka() } - test("Reliable Kafka input stream with single topic") { - var topic = "test-topic" - createTopic(topic) - produceAndSendMessage(topic, data) + val topic = "test-topic" + kafkaTestUtils.createTopic(topic) + kafkaTestUtils.sendMessages(topic, data) // Verify whether the offset of this group/topic/partition is 0 before starting. assert(getCommitOffset(groupId, topic, 0) === None) @@ -95,6 +104,7 @@ class ReliableKafkaStreamSuite extends KafkaStreamSuiteBase with BeforeAndAfter } } ssc.start() + eventually(timeout(20000 milliseconds), interval(200 milliseconds)) { // A basic process verification for ReliableKafkaReceiver. // Verify whether received message number is equal to the sent message number. @@ -104,14 +114,13 @@ class ReliableKafkaStreamSuite extends KafkaStreamSuiteBase with BeforeAndAfter // Verify the offset number whether it is equal to the total message number. assert(getCommitOffset(groupId, topic, 0) === Some(29L)) } - ssc.stop() } test("Reliable Kafka input stream with multiple topics") { val topics = Map("topic1" -> 1, "topic2" -> 1, "topic3" -> 1) topics.foreach { case (t, _) => - createTopic(t) - produceAndSendMessage(t, data) + kafkaTestUtils.createTopic(t) + kafkaTestUtils.sendMessages(t, data) } // Before started, verify all the group/topic/partition offsets are 0. @@ -122,19 +131,18 @@ class ReliableKafkaStreamSuite extends KafkaStreamSuiteBase with BeforeAndAfter ssc, kafkaParams, topics, StorageLevel.MEMORY_ONLY) stream.foreachRDD(_ => Unit) ssc.start() + eventually(timeout(20000 milliseconds), interval(100 milliseconds)) { // Verify the offset for each group/topic to see whether they are equal to the expected one. topics.foreach { case (t, _) => assert(getCommitOffset(groupId, t, 0) === Some(29L)) } } - ssc.stop() } /** Getting partition offset from Zookeeper. */ private def getCommitOffset(groupId: String, topic: String, partition: Int): Option[Long] = { - assert(zkClient != null, "Zookeeper client is not initialized") val topicDirs = new ZKGroupTopicDirs(groupId, topic) val zkPath = s"${topicDirs.consumerOffsetDir}/$partition" - ZkUtils.readDataMaybeNull(zkClient, zkPath)._1.map(_.toLong) + ZkUtils.readDataMaybeNull(kafkaTestUtils.zookeeperClient, zkPath)._1.map(_.toLong) } } diff --git a/external/mqtt/pom.xml b/external/mqtt/pom.xml index 560c8b9d18276..98f95a9a64fa0 100644 --- a/external/mqtt/pom.xml +++ b/external/mqtt/pom.xml @@ -20,8 +20,8 @@ 4.0.0 org.apache.spark - spark-parent - 1.3.0-SNAPSHOT + spark-parent_2.10 + 1.4.0-SNAPSHOT ../../pom.xml diff --git a/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTInputDStream.scala b/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTInputDStream.scala index 1ef91dd49284f..3c0ef94cb0fab 100644 --- a/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTInputDStream.scala +++ b/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTInputDStream.scala @@ -17,23 +17,23 @@ package org.apache.spark.streaming.mqtt +import java.io.IOException +import java.util.concurrent.Executors +import java.util.Properties + +import scala.collection.JavaConversions._ import scala.collection.Map import scala.collection.mutable.HashMap -import scala.collection.JavaConversions._ import scala.reflect.ClassTag -import java.util.Properties -import java.util.concurrent.Executors -import java.io.IOException - +import org.eclipse.paho.client.mqttv3.IMqttDeliveryToken import org.eclipse.paho.client.mqttv3.MqttCallback import org.eclipse.paho.client.mqttv3.MqttClient import org.eclipse.paho.client.mqttv3.MqttClientPersistence -import org.eclipse.paho.client.mqttv3.persist.MemoryPersistence -import org.eclipse.paho.client.mqttv3.IMqttDeliveryToken import org.eclipse.paho.client.mqttv3.MqttException import org.eclipse.paho.client.mqttv3.MqttMessage import org.eclipse.paho.client.mqttv3.MqttTopic +import org.eclipse.paho.client.mqttv3.persist.MemoryPersistence import org.apache.spark.Logging import org.apache.spark.storage.StorageLevel @@ -82,18 +82,18 @@ class MQTTReceiver( val client = new MqttClient(brokerUrl, MqttClient.generateClientId(), persistence) // Callback automatically triggers as and when new message arrives on specified topic - val callback: MqttCallback = new MqttCallback() { + val callback = new MqttCallback() { // Handles Mqtt message - override def messageArrived(arg0: String, arg1: MqttMessage) { - store(new String(arg1.getPayload(),"utf-8")) + override def messageArrived(topic: String, message: MqttMessage) { + store(new String(message.getPayload(),"utf-8")) } - override def deliveryComplete(arg0: IMqttDeliveryToken) { + override def deliveryComplete(token: IMqttDeliveryToken) { } - override def connectionLost(arg0: Throwable) { - restart("Connection lost ", arg0) + override def connectionLost(cause: Throwable) { + restart("Connection lost ", cause) } } diff --git a/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTUtils.scala b/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTUtils.scala index c5ffe51f9986c..1142d0f56ba34 100644 --- a/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTUtils.scala +++ b/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTUtils.scala @@ -17,10 +17,11 @@ package org.apache.spark.streaming.mqtt +import scala.reflect.ClassTag + import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming.StreamingContext import org.apache.spark.streaming.api.java.{JavaReceiverInputDStream, JavaStreamingContext, JavaDStream} -import scala.reflect.ClassTag import org.apache.spark.streaming.dstream.{ReceiverInputDStream, DStream} object MQTTUtils { diff --git a/external/mqtt/src/test/java/org/apache/spark/streaming/LocalJavaStreamingContext.java b/external/mqtt/src/test/java/org/apache/spark/streaming/LocalJavaStreamingContext.java index 1e24da7f5f60c..cfedb5a042a35 100644 --- a/external/mqtt/src/test/java/org/apache/spark/streaming/LocalJavaStreamingContext.java +++ b/external/mqtt/src/test/java/org/apache/spark/streaming/LocalJavaStreamingContext.java @@ -31,7 +31,7 @@ public void setUp() { SparkConf conf = new SparkConf() .setMaster("local[2]") .setAppName("test") - .set("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock"); + .set("spark.streaming.clock", "org.apache.spark.util.ManualClock"); ssc = new JavaStreamingContext(conf, new Duration(1000)); ssc.checkpoint("checkpoint"); } diff --git a/external/mqtt/src/test/resources/log4j.properties b/external/mqtt/src/test/resources/log4j.properties index 9697237bfa1a3..75e3b53a093f6 100644 --- a/external/mqtt/src/test/resources/log4j.properties +++ b/external/mqtt/src/test/resources/log4j.properties @@ -24,5 +24,5 @@ log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n # Ignore messages below warning level from Jetty, because it's a bit verbose -log4j.logger.org.eclipse.jetty=WARN +log4j.logger.org.spark-project.jetty=WARN diff --git a/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTStreamSuite.scala b/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTStreamSuite.scala index e84adc088a680..a19a72c58a705 100644 --- a/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTStreamSuite.scala +++ b/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTStreamSuite.scala @@ -25,6 +25,7 @@ import scala.concurrent.duration._ import scala.language.postfixOps import org.apache.activemq.broker.{TransportConnector, BrokerService} +import org.apache.commons.lang3.RandomUtils import org.eclipse.paho.client.mqttv3._ import org.eclipse.paho.client.mqttv3.persist.MqttDefaultFilePersistence @@ -42,8 +43,8 @@ import org.apache.spark.util.Utils class MQTTStreamSuite extends FunSuite with Eventually with BeforeAndAfter { private val batchDuration = Milliseconds(500) - private val master: String = "local[2]" - private val framework: String = this.getClass.getSimpleName + private val master = "local[2]" + private val framework = this.getClass.getSimpleName private val freePort = findFreePort() private val brokerUri = "//localhost:" + freePort private val topic = "def" @@ -69,7 +70,7 @@ class MQTTStreamSuite extends FunSuite with Eventually with BeforeAndAfter { test("mqtt input stream") { val sendMessage = "MQTT demo for spark streaming" - val receiveStream: ReceiverInputDStream[String] = + val receiveStream = MQTTUtils.createStream(ssc, "tcp:" + brokerUri, topic, StorageLevel.MEMORY_ONLY) @volatile var receiveMessage: List[String] = List() receiveStream.foreachRDD { rdd => @@ -93,6 +94,7 @@ class MQTTStreamSuite extends FunSuite with Eventually with BeforeAndAfter { private def setupMQTT() { broker = new BrokerService() + broker.setDataDirectoryFile(Utils.createTempDir()) connector = new TransportConnector() connector.setName("mqtt") connector.setUri(new URI("mqtt:" + brokerUri)) @@ -112,7 +114,8 @@ class MQTTStreamSuite extends FunSuite with Eventually with BeforeAndAfter { } private def findFreePort(): Int = { - Utils.startServiceOnPort(23456, (trialPort: Int) => { + val candidatePort = RandomUtils.nextInt(1024, 65536) + Utils.startServiceOnPort(candidatePort, (trialPort: Int) => { val socket = new ServerSocket(trialPort) socket.close() (null, trialPort) @@ -122,12 +125,12 @@ class MQTTStreamSuite extends FunSuite with Eventually with BeforeAndAfter { def publishData(data: String): Unit = { var client: MqttClient = null try { - val persistence: MqttClientPersistence = new MqttDefaultFilePersistence(persistenceDir.getAbsolutePath) + val persistence = new MqttDefaultFilePersistence(persistenceDir.getAbsolutePath) client = new MqttClient("tcp:" + brokerUri, MqttClient.generateClientId(), persistence) client.connect() if (client.isConnected) { - val msgTopic: MqttTopic = client.getTopic(topic) - val message: MqttMessage = new MqttMessage(data.getBytes("utf-8")) + val msgTopic = client.getTopic(topic) + val message = new MqttMessage(data.getBytes("utf-8")) message.setQos(1) message.setRetained(true) @@ -136,7 +139,8 @@ class MQTTStreamSuite extends FunSuite with Eventually with BeforeAndAfter { msgTopic.publish(message) } catch { case e: MqttException if e.getReasonCode == MqttException.REASON_CODE_MAX_INFLIGHT => - Thread.sleep(50) // wait for Spark streaming to consume something from the message queue + // wait for Spark streaming to consume something from the message queue + Thread.sleep(50) } } } diff --git a/external/twitter/pom.xml b/external/twitter/pom.xml index da6ffe7662f63..8b6a8959ac4cf 100644 --- a/external/twitter/pom.xml +++ b/external/twitter/pom.xml @@ -20,8 +20,8 @@ 4.0.0 org.apache.spark - spark-parent - 1.3.0-SNAPSHOT + spark-parent_2.10 + 1.4.0-SNAPSHOT ../../pom.xml diff --git a/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/TwitterInputDStream.scala b/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/TwitterInputDStream.scala index 4eacc47da5699..7cf02d85d73d3 100644 --- a/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/TwitterInputDStream.scala +++ b/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/TwitterInputDStream.scala @@ -70,7 +70,7 @@ class TwitterReceiver( try { val newTwitterStream = new TwitterStreamFactory().getInstance(twitterAuth) newTwitterStream.addListener(new StatusListener { - def onStatus(status: Status) = { + def onStatus(status: Status): Unit = { store(status) } // Unimplemented diff --git a/external/twitter/src/test/java/org/apache/spark/streaming/LocalJavaStreamingContext.java b/external/twitter/src/test/java/org/apache/spark/streaming/LocalJavaStreamingContext.java index 1e24da7f5f60c..cfedb5a042a35 100644 --- a/external/twitter/src/test/java/org/apache/spark/streaming/LocalJavaStreamingContext.java +++ b/external/twitter/src/test/java/org/apache/spark/streaming/LocalJavaStreamingContext.java @@ -31,7 +31,7 @@ public void setUp() { SparkConf conf = new SparkConf() .setMaster("local[2]") .setAppName("test") - .set("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock"); + .set("spark.streaming.clock", "org.apache.spark.util.ManualClock"); ssc = new JavaStreamingContext(conf, new Duration(1000)); ssc.checkpoint("checkpoint"); } diff --git a/external/twitter/src/test/resources/log4j.properties b/external/twitter/src/test/resources/log4j.properties index 64bfc5745088f..9a3569789d2e0 100644 --- a/external/twitter/src/test/resources/log4j.properties +++ b/external/twitter/src/test/resources/log4j.properties @@ -24,5 +24,5 @@ log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n # Ignore messages below warning level from Jetty, because it's a bit verbose -log4j.logger.org.eclipse.jetty=WARN +log4j.logger.org.spark-project.jetty=WARN diff --git a/external/zeromq/pom.xml b/external/zeromq/pom.xml index e919c2c9b19ea..a50d378b34335 100644 --- a/external/zeromq/pom.xml +++ b/external/zeromq/pom.xml @@ -20,8 +20,8 @@ 4.0.0 org.apache.spark - spark-parent - 1.3.0-SNAPSHOT + spark-parent_2.10 + 1.4.0-SNAPSHOT ../../pom.xml diff --git a/external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/ZeroMQReceiver.scala b/external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/ZeroMQReceiver.scala index 554705878ee78..588e6bac7b14a 100644 --- a/external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/ZeroMQReceiver.scala +++ b/external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/ZeroMQReceiver.scala @@ -29,13 +29,16 @@ import org.apache.spark.streaming.receiver.ActorHelper /** * A receiver to subscribe to ZeroMQ stream. */ -private[streaming] class ZeroMQReceiver[T: ClassTag](publisherUrl: String, - subscribe: Subscribe, - bytesToObjects: Seq[ByteString] => Iterator[T]) +private[streaming] class ZeroMQReceiver[T: ClassTag]( + publisherUrl: String, + subscribe: Subscribe, + bytesToObjects: Seq[ByteString] => Iterator[T]) extends Actor with ActorHelper with Logging { - override def preStart() = ZeroMQExtension(context.system) - .newSocket(SocketType.Sub, Listener(self), Connect(publisherUrl), subscribe) + override def preStart(): Unit = { + ZeroMQExtension(context.system) + .newSocket(SocketType.Sub, Listener(self), Connect(publisherUrl), subscribe) + } def receive: Receive = { diff --git a/external/zeromq/src/test/java/org/apache/spark/streaming/LocalJavaStreamingContext.java b/external/zeromq/src/test/java/org/apache/spark/streaming/LocalJavaStreamingContext.java index 1e24da7f5f60c..cfedb5a042a35 100644 --- a/external/zeromq/src/test/java/org/apache/spark/streaming/LocalJavaStreamingContext.java +++ b/external/zeromq/src/test/java/org/apache/spark/streaming/LocalJavaStreamingContext.java @@ -31,7 +31,7 @@ public void setUp() { SparkConf conf = new SparkConf() .setMaster("local[2]") .setAppName("test") - .set("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock"); + .set("spark.streaming.clock", "org.apache.spark.util.ManualClock"); ssc = new JavaStreamingContext(conf, new Duration(1000)); ssc.checkpoint("checkpoint"); } diff --git a/external/zeromq/src/test/resources/log4j.properties b/external/zeromq/src/test/resources/log4j.properties index 9697237bfa1a3..75e3b53a093f6 100644 --- a/external/zeromq/src/test/resources/log4j.properties +++ b/external/zeromq/src/test/resources/log4j.properties @@ -24,5 +24,5 @@ log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n # Ignore messages below warning level from Jetty, because it's a bit verbose -log4j.logger.org.eclipse.jetty=WARN +log4j.logger.org.spark-project.jetty=WARN diff --git a/extras/java8-tests/pom.xml b/extras/java8-tests/pom.xml index 0fb431808bacd..4351a8a12fe21 100644 --- a/extras/java8-tests/pom.xml +++ b/extras/java8-tests/pom.xml @@ -19,8 +19,8 @@ 4.0.0 org.apache.spark - spark-parent - 1.3.0-SNAPSHOT + spark-parent_2.10 + 1.4.0-SNAPSHOT ../../pom.xml diff --git a/extras/java8-tests/src/test/resources/log4j.properties b/extras/java8-tests/src/test/resources/log4j.properties index 287c8e3563503..eb3b1999eb996 100644 --- a/extras/java8-tests/src/test/resources/log4j.properties +++ b/extras/java8-tests/src/test/resources/log4j.properties @@ -24,5 +24,5 @@ log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n # Ignore messages below warning level from Jetty, because it's a bit verbose -log4j.logger.org.eclipse.jetty=WARN -org.eclipse.jetty.LEVEL=WARN +log4j.logger.org.spark-project.jetty=WARN +org.spark-project.jetty.LEVEL=WARN diff --git a/extras/kinesis-asl/pom.xml b/extras/kinesis-asl/pom.xml index c815eda52bda7..25847a1b33d9c 100644 --- a/extras/kinesis-asl/pom.xml +++ b/extras/kinesis-asl/pom.xml @@ -19,8 +19,8 @@ 4.0.0 org.apache.spark - spark-parent - 1.3.0-SNAPSHOT + spark-parent_2.10 + 1.4.0-SNAPSHOT ../../pom.xml @@ -67,11 +67,6 @@ scalacheck_${scala.binary.version} test - - org.easymock - easymockclassextension - test - com.novocode junit-interface diff --git a/extras/kinesis-asl/src/main/resources/log4j.properties b/extras/kinesis-asl/src/main/resources/log4j.properties index 97348fb5b6123..6cdc9286c5d76 100644 --- a/extras/kinesis-asl/src/main/resources/log4j.properties +++ b/extras/kinesis-asl/src/main/resources/log4j.properties @@ -31,7 +31,7 @@ log4j.appender.console.layout=org.apache.log4j.PatternLayout log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n # Settings to quiet third party logs that are too verbose -log4j.logger.org.eclipse.jetty=WARN -log4j.logger.org.eclipse.jetty.util.component.AbstractLifeCycle=ERROR +log4j.logger.org.spark-project.jetty=WARN +log4j.logger.org.spark-project.jetty.util.component.AbstractLifeCycle=ERROR log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO \ No newline at end of file diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisCheckpointState.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisCheckpointState.scala index 0b80b611cdce7..588e86a1887ec 100644 --- a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisCheckpointState.scala +++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisCheckpointState.scala @@ -18,9 +18,7 @@ package org.apache.spark.streaming.kinesis import org.apache.spark.Logging import org.apache.spark.streaming.Duration -import org.apache.spark.streaming.util.Clock -import org.apache.spark.streaming.util.ManualClock -import org.apache.spark.streaming.util.SystemClock +import org.apache.spark.util.{Clock, ManualClock, SystemClock} /** * This is a helper class for managing checkpoint clocks. @@ -35,7 +33,7 @@ private[kinesis] class KinesisCheckpointState( /* Initialize the checkpoint clock using the given currentClock + checkpointInterval millis */ val checkpointClock = new ManualClock() - checkpointClock.setTime(currentClock.currentTime() + checkpointInterval.milliseconds) + checkpointClock.setTime(currentClock.getTimeMillis() + checkpointInterval.milliseconds) /** * Check if it's time to checkpoint based on the current time and the derived time @@ -44,13 +42,13 @@ private[kinesis] class KinesisCheckpointState( * @return true if it's time to checkpoint */ def shouldCheckpoint(): Boolean = { - new SystemClock().currentTime() > checkpointClock.currentTime() + new SystemClock().getTimeMillis() > checkpointClock.getTimeMillis() } /** * Advance the checkpoint clock by the checkpoint interval. */ def advanceCheckpoint() = { - checkpointClock.addToTime(checkpointInterval.milliseconds) + checkpointClock.advance(checkpointInterval.milliseconds) } } diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisReceiver.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisReceiver.scala index 1bd1f324298e7..a7fe4476cacb8 100644 --- a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisReceiver.scala +++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisReceiver.scala @@ -23,6 +23,7 @@ import org.apache.spark.Logging import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming.Duration import org.apache.spark.streaming.receiver.Receiver +import org.apache.spark.util.Utils import com.amazonaws.auth.AWSCredentialsProvider import com.amazonaws.auth.DefaultAWSCredentialsProviderChain @@ -118,7 +119,7 @@ private[kinesis] class KinesisReceiver( * method. */ override def onStart() { - workerId = InetAddress.getLocalHost.getHostAddress() + ":" + UUID.randomUUID() + workerId = Utils.localHostName() + ":" + UUID.randomUUID() credentialsProvider = new DefaultAWSCredentialsProviderChain() kinesisClientLibConfiguration = new KinesisClientLibConfiguration(appName, streamName, credentialsProvider, workerId).withKinesisEndpoint(endpointUrl) diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisRecordProcessor.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisRecordProcessor.scala index 8ecc2d90160b1..af8cd875b4541 100644 --- a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisRecordProcessor.scala +++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisRecordProcessor.scala @@ -104,7 +104,7 @@ private[kinesis] class KinesisRecordProcessor( logDebug(s"Checkpoint: WorkerId $workerId completed checkpoint of ${batch.size}" + s" records for shardId $shardId") logDebug(s"Checkpoint: Next checkpoint is at " + - s" ${checkpointState.checkpointClock.currentTime()} for shardId $shardId") + s" ${checkpointState.checkpointClock.getTimeMillis()} for shardId $shardId") } } catch { case e: Throwable => { diff --git a/extras/kinesis-asl/src/test/resources/log4j.properties b/extras/kinesis-asl/src/test/resources/log4j.properties index 853ef0ed2986f..edbecdae92096 100644 --- a/extras/kinesis-asl/src/test/resources/log4j.properties +++ b/extras/kinesis-asl/src/test/resources/log4j.properties @@ -24,4 +24,4 @@ log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n # Ignore messages below warning level from Jetty, because it's a bit verbose -log4j.logger.org.eclipse.jetty=WARN +log4j.logger.org.spark-project.jetty=WARN diff --git a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisReceiverSuite.scala b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisReceiverSuite.scala index 41dbd64c2b1fa..255fe65819608 100644 --- a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisReceiverSuite.scala +++ b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisReceiverSuite.scala @@ -20,17 +20,17 @@ import java.nio.ByteBuffer import scala.collection.JavaConversions.seqAsJavaList -import org.apache.spark.annotation.Experimental import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming.Milliseconds import org.apache.spark.streaming.Seconds import org.apache.spark.streaming.StreamingContext import org.apache.spark.streaming.TestSuiteBase -import org.apache.spark.streaming.util.Clock -import org.apache.spark.streaming.util.ManualClock +import org.apache.spark.util.{ManualClock, Clock} + +import org.mockito.Mockito._ import org.scalatest.BeforeAndAfter import org.scalatest.Matchers -import org.scalatest.mock.EasyMockSugar +import org.scalatest.mock.MockitoSugar import com.amazonaws.services.kinesis.clientlibrary.exceptions.InvalidStateException import com.amazonaws.services.kinesis.clientlibrary.exceptions.KinesisClientLibDependencyException @@ -42,10 +42,10 @@ import com.amazonaws.services.kinesis.clientlibrary.types.ShutdownReason import com.amazonaws.services.kinesis.model.Record /** - * Suite of Kinesis streaming receiver tests focusing mostly on the KinesisRecordProcessor + * Suite of Kinesis streaming receiver tests focusing mostly on the KinesisRecordProcessor */ class KinesisReceiverSuite extends TestSuiteBase with Matchers with BeforeAndAfter - with EasyMockSugar { + with MockitoSugar { val app = "TestKinesisReceiver" val stream = "mySparkStream" @@ -73,6 +73,14 @@ class KinesisReceiverSuite extends TestSuiteBase with Matchers with BeforeAndAft currentClockMock = mock[Clock] } + override def afterFunction(): Unit = { + super.afterFunction() + // Since this suite was originally written using EasyMock, add this to preserve the old + // mocking semantics (see SPARK-5735 for more details) + verifyNoMoreInteractions(receiverMock, checkpointerMock, checkpointClockMock, + checkpointStateMock, currentClockMock) + } + test("kinesis utils api") { val ssc = new StreamingContext(master, framework, batchDuration) // Tests the API, does not actually test data receiving @@ -83,193 +91,175 @@ class KinesisReceiverSuite extends TestSuiteBase with Matchers with BeforeAndAft } test("process records including store and checkpoint") { - val expectedCheckpointIntervalMillis = 10 - expecting { - receiverMock.isStopped().andReturn(false).once() - receiverMock.store(record1.getData().array()).once() - receiverMock.store(record2.getData().array()).once() - checkpointStateMock.shouldCheckpoint().andReturn(true).once() - checkpointerMock.checkpoint().once() - checkpointStateMock.advanceCheckpoint().once() - } - whenExecuting(receiverMock, checkpointerMock, checkpointStateMock) { - val recordProcessor = new KinesisRecordProcessor(receiverMock, workerId, - checkpointStateMock) - recordProcessor.processRecords(batch, checkpointerMock) - } + when(receiverMock.isStopped()).thenReturn(false) + when(checkpointStateMock.shouldCheckpoint()).thenReturn(true) + + val recordProcessor = new KinesisRecordProcessor(receiverMock, workerId, checkpointStateMock) + recordProcessor.processRecords(batch, checkpointerMock) + + verify(receiverMock, times(1)).isStopped() + verify(receiverMock, times(1)).store(record1.getData().array()) + verify(receiverMock, times(1)).store(record2.getData().array()) + verify(checkpointStateMock, times(1)).shouldCheckpoint() + verify(checkpointerMock, times(1)).checkpoint() + verify(checkpointStateMock, times(1)).advanceCheckpoint() } test("shouldn't store and checkpoint when receiver is stopped") { - expecting { - receiverMock.isStopped().andReturn(true).once() - } - whenExecuting(receiverMock, checkpointerMock, checkpointStateMock) { - val recordProcessor = new KinesisRecordProcessor(receiverMock, workerId, - checkpointStateMock) - recordProcessor.processRecords(batch, checkpointerMock) - } + when(receiverMock.isStopped()).thenReturn(true) + + val recordProcessor = new KinesisRecordProcessor(receiverMock, workerId, checkpointStateMock) + recordProcessor.processRecords(batch, checkpointerMock) + + verify(receiverMock, times(1)).isStopped() } test("shouldn't checkpoint when exception occurs during store") { - expecting { - receiverMock.isStopped().andReturn(false).once() - receiverMock.store(record1.getData().array()).andThrow(new RuntimeException()).once() - } - whenExecuting(receiverMock, checkpointerMock, checkpointStateMock) { - intercept[RuntimeException] { - val recordProcessor = new KinesisRecordProcessor(receiverMock, workerId, - checkpointStateMock) - recordProcessor.processRecords(batch, checkpointerMock) - } + when(receiverMock.isStopped()).thenReturn(false) + when(receiverMock.store(record1.getData().array())).thenThrow(new RuntimeException()) + + intercept[RuntimeException] { + val recordProcessor = new KinesisRecordProcessor(receiverMock, workerId, checkpointStateMock) + recordProcessor.processRecords(batch, checkpointerMock) } + + verify(receiverMock, times(1)).isStopped() + verify(receiverMock, times(1)).store(record1.getData().array()) } test("should set checkpoint time to currentTime + checkpoint interval upon instantiation") { - expecting { - currentClockMock.currentTime().andReturn(0).once() - } - whenExecuting(currentClockMock) { + when(currentClockMock.getTimeMillis()).thenReturn(0) + val checkpointIntervalMillis = 10 - val checkpointState = new KinesisCheckpointState(Milliseconds(checkpointIntervalMillis), currentClockMock) - assert(checkpointState.checkpointClock.currentTime() == checkpointIntervalMillis) - } + val checkpointState = + new KinesisCheckpointState(Milliseconds(checkpointIntervalMillis), currentClockMock) + assert(checkpointState.checkpointClock.getTimeMillis() == checkpointIntervalMillis) + + verify(currentClockMock, times(1)).getTimeMillis() } test("should checkpoint if we have exceeded the checkpoint interval") { - expecting { - currentClockMock.currentTime().andReturn(0).once() - } - whenExecuting(currentClockMock) { - val checkpointState = new KinesisCheckpointState(Milliseconds(Long.MinValue), currentClockMock) - assert(checkpointState.shouldCheckpoint()) - } + when(currentClockMock.getTimeMillis()).thenReturn(0) + + val checkpointState = new KinesisCheckpointState(Milliseconds(Long.MinValue), currentClockMock) + assert(checkpointState.shouldCheckpoint()) + + verify(currentClockMock, times(1)).getTimeMillis() } test("shouldn't checkpoint if we have not exceeded the checkpoint interval") { - expecting { - currentClockMock.currentTime().andReturn(0).once() - } - whenExecuting(currentClockMock) { - val checkpointState = new KinesisCheckpointState(Milliseconds(Long.MaxValue), currentClockMock) - assert(!checkpointState.shouldCheckpoint()) - } + when(currentClockMock.getTimeMillis()).thenReturn(0) + + val checkpointState = new KinesisCheckpointState(Milliseconds(Long.MaxValue), currentClockMock) + assert(!checkpointState.shouldCheckpoint()) + + verify(currentClockMock, times(1)).getTimeMillis() } test("should add to time when advancing checkpoint") { - expecting { - currentClockMock.currentTime().andReturn(0).once() - } - whenExecuting(currentClockMock) { - val checkpointIntervalMillis = 10 - val checkpointState = new KinesisCheckpointState(Milliseconds(checkpointIntervalMillis), currentClockMock) - assert(checkpointState.checkpointClock.currentTime() == checkpointIntervalMillis) - checkpointState.advanceCheckpoint() - assert(checkpointState.checkpointClock.currentTime() == (2 * checkpointIntervalMillis)) - } + when(currentClockMock.getTimeMillis()).thenReturn(0) + + val checkpointIntervalMillis = 10 + val checkpointState = + new KinesisCheckpointState(Milliseconds(checkpointIntervalMillis), currentClockMock) + assert(checkpointState.checkpointClock.getTimeMillis() == checkpointIntervalMillis) + checkpointState.advanceCheckpoint() + assert(checkpointState.checkpointClock.getTimeMillis() == (2 * checkpointIntervalMillis)) + + verify(currentClockMock, times(1)).getTimeMillis() } test("shutdown should checkpoint if the reason is TERMINATE") { - expecting { - checkpointerMock.checkpoint().once() - } - whenExecuting(checkpointerMock, checkpointStateMock) { - val recordProcessor = new KinesisRecordProcessor(receiverMock, workerId, - checkpointStateMock) - val reason = ShutdownReason.TERMINATE - recordProcessor.shutdown(checkpointerMock, reason) - } + val recordProcessor = new KinesisRecordProcessor(receiverMock, workerId, checkpointStateMock) + val reason = ShutdownReason.TERMINATE + recordProcessor.shutdown(checkpointerMock, reason) + + verify(checkpointerMock, times(1)).checkpoint() } test("shutdown should not checkpoint if the reason is something other than TERMINATE") { - expecting { - } - whenExecuting(checkpointerMock, checkpointStateMock) { - val recordProcessor = new KinesisRecordProcessor(receiverMock, workerId, - checkpointStateMock) - recordProcessor.shutdown(checkpointerMock, ShutdownReason.ZOMBIE) - recordProcessor.shutdown(checkpointerMock, null) - } + val recordProcessor = new KinesisRecordProcessor(receiverMock, workerId, checkpointStateMock) + recordProcessor.shutdown(checkpointerMock, ShutdownReason.ZOMBIE) + recordProcessor.shutdown(checkpointerMock, null) + + verify(checkpointerMock, never()).checkpoint() } test("retry success on first attempt") { val expectedIsStopped = false - expecting { - receiverMock.isStopped().andReturn(expectedIsStopped).once() - } - whenExecuting(receiverMock) { - val actualVal = KinesisRecordProcessor.retryRandom(receiverMock.isStopped(), 2, 100) - assert(actualVal == expectedIsStopped) - } + when(receiverMock.isStopped()).thenReturn(expectedIsStopped) + + val actualVal = KinesisRecordProcessor.retryRandom(receiverMock.isStopped(), 2, 100) + assert(actualVal == expectedIsStopped) + + verify(receiverMock, times(1)).isStopped() } test("retry success on second attempt after a Kinesis throttling exception") { val expectedIsStopped = false - expecting { - receiverMock.isStopped().andThrow(new ThrottlingException("error message")) - .andReturn(expectedIsStopped).once() - } - whenExecuting(receiverMock) { - val actualVal = KinesisRecordProcessor.retryRandom(receiverMock.isStopped(), 2, 100) - assert(actualVal == expectedIsStopped) - } + when(receiverMock.isStopped()) + .thenThrow(new ThrottlingException("error message")) + .thenReturn(expectedIsStopped) + + val actualVal = KinesisRecordProcessor.retryRandom(receiverMock.isStopped(), 2, 100) + assert(actualVal == expectedIsStopped) + + verify(receiverMock, times(2)).isStopped() } test("retry success on second attempt after a Kinesis dependency exception") { val expectedIsStopped = false - expecting { - receiverMock.isStopped().andThrow(new KinesisClientLibDependencyException("error message")) - .andReturn(expectedIsStopped).once() - } - whenExecuting(receiverMock) { - val actualVal = KinesisRecordProcessor.retryRandom(receiverMock.isStopped(), 2, 100) - assert(actualVal == expectedIsStopped) - } + when(receiverMock.isStopped()) + .thenThrow(new KinesisClientLibDependencyException("error message")) + .thenReturn(expectedIsStopped) + + val actualVal = KinesisRecordProcessor.retryRandom(receiverMock.isStopped(), 2, 100) + assert(actualVal == expectedIsStopped) + + verify(receiverMock, times(2)).isStopped() } test("retry failed after a shutdown exception") { - expecting { - checkpointerMock.checkpoint().andThrow(new ShutdownException("error message")).once() - } - whenExecuting(checkpointerMock) { - intercept[ShutdownException] { - KinesisRecordProcessor.retryRandom(checkpointerMock.checkpoint(), 2, 100) - } + when(checkpointerMock.checkpoint()).thenThrow(new ShutdownException("error message")) + + intercept[ShutdownException] { + KinesisRecordProcessor.retryRandom(checkpointerMock.checkpoint(), 2, 100) } + + verify(checkpointerMock, times(1)).checkpoint() } test("retry failed after an invalid state exception") { - expecting { - checkpointerMock.checkpoint().andThrow(new InvalidStateException("error message")).once() - } - whenExecuting(checkpointerMock) { - intercept[InvalidStateException] { - KinesisRecordProcessor.retryRandom(checkpointerMock.checkpoint(), 2, 100) - } + when(checkpointerMock.checkpoint()).thenThrow(new InvalidStateException("error message")) + + intercept[InvalidStateException] { + KinesisRecordProcessor.retryRandom(checkpointerMock.checkpoint(), 2, 100) } + + verify(checkpointerMock, times(1)).checkpoint() } test("retry failed after unexpected exception") { - expecting { - checkpointerMock.checkpoint().andThrow(new RuntimeException("error message")).once() - } - whenExecuting(checkpointerMock) { - intercept[RuntimeException] { - KinesisRecordProcessor.retryRandom(checkpointerMock.checkpoint(), 2, 100) - } + when(checkpointerMock.checkpoint()).thenThrow(new RuntimeException("error message")) + + intercept[RuntimeException] { + KinesisRecordProcessor.retryRandom(checkpointerMock.checkpoint(), 2, 100) } + + verify(checkpointerMock, times(1)).checkpoint() } test("retry failed after exhausing all retries") { val expectedErrorMessage = "final try error message" - expecting { - checkpointerMock.checkpoint().andThrow(new ThrottlingException("error message")) - .andThrow(new ThrottlingException(expectedErrorMessage)).once() - } - whenExecuting(checkpointerMock) { - val exception = intercept[RuntimeException] { - KinesisRecordProcessor.retryRandom(checkpointerMock.checkpoint(), 2, 100) - } - exception.getMessage().shouldBe(expectedErrorMessage) + when(checkpointerMock.checkpoint()) + .thenThrow(new ThrottlingException("error message")) + .thenThrow(new ThrottlingException(expectedErrorMessage)) + + val exception = intercept[RuntimeException] { + KinesisRecordProcessor.retryRandom(checkpointerMock.checkpoint(), 2, 100) } + exception.getMessage().shouldBe(expectedErrorMessage) + + verify(checkpointerMock, times(2)).checkpoint() } } diff --git a/extras/spark-ganglia-lgpl/pom.xml b/extras/spark-ganglia-lgpl/pom.xml index f2f0aa78b0a4b..e14bbae4a9b6e 100644 --- a/extras/spark-ganglia-lgpl/pom.xml +++ b/extras/spark-ganglia-lgpl/pom.xml @@ -19,8 +19,8 @@ 4.0.0 org.apache.spark - spark-parent - 1.3.0-SNAPSHOT + spark-parent_2.10 + 1.4.0-SNAPSHOT ../../pom.xml diff --git a/graphx/pom.xml b/graphx/pom.xml index 8fac24b6ed86d..d38a3aa8256b7 100644 --- a/graphx/pom.xml +++ b/graphx/pom.xml @@ -20,8 +20,8 @@ 4.0.0 org.apache.spark - spark-parent - 1.3.0-SNAPSHOT + spark-parent_2.10 + 1.4.0-SNAPSHOT ../pom.xml @@ -45,9 +45,14 @@ guava - org.jblas - jblas - ${jblas.version} + com.github.fommil.netlib + core + ${netlib.java.version} + + + net.sourceforge.f2j + arpack_combined_all + 0.1 org.scalacheck diff --git a/graphx/src/main/scala/org/apache/spark/graphx/EdgeContext.scala b/graphx/src/main/scala/org/apache/spark/graphx/EdgeContext.scala index f70715fca6eea..23430179f12ec 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/EdgeContext.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/EdgeContext.scala @@ -49,3 +49,19 @@ abstract class EdgeContext[VD, ED, A] { et } } + +object EdgeContext { + + /** + * Extractor mainly used for Graph#aggregateMessages*. + * Example: + * {{{ + * val messages = graph.aggregateMessages( + * case ctx @ EdgeContext(_, _, _, _, attr) => + * ctx.sendToDst(attr) + * , _ + _) + * }}} + */ + def unapply[VD, ED, A](edge: EdgeContext[VD, ED, A]): Some[(VertexId, VertexId, VD, VD, ED)] = + Some(edge.srcId, edge.dstId, edge.srcAttr, edge.dstAttr, edge.attr) +} diff --git a/graphx/src/main/scala/org/apache/spark/graphx/EdgeDirection.scala b/graphx/src/main/scala/org/apache/spark/graphx/EdgeDirection.scala index 6f03eb1439773..058c8c8aa1b24 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/EdgeDirection.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/EdgeDirection.scala @@ -34,12 +34,12 @@ class EdgeDirection private (private val name: String) extends Serializable { override def toString: String = "EdgeDirection." + name - override def equals(o: Any) = o match { + override def equals(o: Any): Boolean = o match { case other: EdgeDirection => other.name == name case _ => false } - override def hashCode = name.hashCode + override def hashCode: Int = name.hashCode } @@ -48,14 +48,14 @@ class EdgeDirection private (private val name: String) extends Serializable { */ object EdgeDirection { /** Edges arriving at a vertex. */ - final val In = new EdgeDirection("In") + final val In: EdgeDirection = new EdgeDirection("In") /** Edges originating from a vertex. */ - final val Out = new EdgeDirection("Out") + final val Out: EdgeDirection = new EdgeDirection("Out") /** Edges originating from *or* arriving at a vertex of interest. */ - final val Either = new EdgeDirection("Either") + final val Either: EdgeDirection = new EdgeDirection("Either") /** Edges originating from *and* arriving at a vertex of interest. */ - final val Both = new EdgeDirection("Both") + final val Both: EdgeDirection = new EdgeDirection("Both") } diff --git a/graphx/src/main/scala/org/apache/spark/graphx/EdgeTriplet.scala b/graphx/src/main/scala/org/apache/spark/graphx/EdgeTriplet.scala index 9d473d5ebda44..c8790cac3d8a0 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/EdgeTriplet.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/EdgeTriplet.scala @@ -62,7 +62,7 @@ class EdgeTriplet[VD, ED] extends Edge[ED] { def vertexAttr(vid: VertexId): VD = if (srcId == vid) srcAttr else { assert(dstId == vid); dstAttr } - override def toString = ((srcId, srcAttr), (dstId, dstAttr), attr).toString() + override def toString: String = ((srcId, srcAttr), (dstId, dstAttr), attr).toString() def toTuple: ((VertexId, VD), (VertexId, VD), ED) = ((srcId, srcAttr), (dstId, dstAttr), attr) } diff --git a/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala b/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala index 8494d06b1cdb7..36dc7b0f86c89 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala @@ -409,7 +409,7 @@ abstract class Graph[VD: ClassTag, ED: ClassTag] protected () extends Serializab * {{{ * val rawGraph: Graph[_, _] = Graph.textFile("twittergraph") * val inDeg: RDD[(VertexId, Int)] = - * aggregateMessages[Int](ctx => ctx.sendToDst(1), _ + _) + * rawGraph.aggregateMessages[Int](ctx => ctx.sendToDst(1), _ + _) * }}} * * @note By expressing computation at the edge level we achieve diff --git a/graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala b/graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala index dc8b4789c4b61..86f611d55aa8a 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala @@ -113,7 +113,7 @@ class GraphOps[VD: ClassTag, ED: ClassTag](graph: Graph[VD, ED]) extends Seriali * Collect the neighbor vertex attributes for each vertex. * * @note This function could be highly inefficient on power-law - * graphs where high degree vertices may force a large ammount of + * graphs where high degree vertices may force a large amount of * information to be collected to a single location. * * @param edgeDirection the direction along which to collect @@ -187,7 +187,7 @@ class GraphOps[VD: ClassTag, ED: ClassTag](graph: Graph[VD, ED]) extends Seriali /** * Join the vertices with an RDD and then apply a function from the - * the vertex and RDD entry to a new vertex value. The input table + * vertex and RDD entry to a new vertex value. The input table * should contain at most one entry for each vertex. If no entry is * provided the map function is skipped and the old value is used. * diff --git a/graphx/src/main/scala/org/apache/spark/graphx/Pregel.scala b/graphx/src/main/scala/org/apache/spark/graphx/Pregel.scala index 5e55620147df8..01b013ff716fc 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/Pregel.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/Pregel.scala @@ -78,8 +78,8 @@ object Pregel extends Logging { * * @param graph the input graph. * - * @param initialMsg the message each vertex will receive at the on - * the first iteration + * @param initialMsg the message each vertex will receive at the first + * iteration * * @param maxIterations the maximum number of iterations to run for * diff --git a/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala b/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala index 09ae3f9f6c09b..a9f04b559c3d1 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala @@ -122,8 +122,36 @@ abstract class VertexRDD[VD]( def mapValues[VD2: ClassTag](f: (VertexId, VD) => VD2): VertexRDD[VD2] /** - * Hides vertices that are the same between `this` and `other`; for vertices that are different, - * keeps the values from `other`. + * For each VertexId present in both `this` and `other`, minus will act as a set difference + * operation returning only those unique VertexId's present in `this`. + * + * @param other an RDD to run the set operation against + */ + def minus(other: RDD[(VertexId, VD)]): VertexRDD[VD] + + /** + * For each VertexId present in both `this` and `other`, minus will act as a set difference + * operation returning only those unique VertexId's present in `this`. + * + * @param other a VertexRDD to run the set operation against + */ + def minus(other: VertexRDD[VD]): VertexRDD[VD] + + /** + * For each vertex present in both `this` and `other`, `diff` returns only those vertices with + * differing values; for values that are different, keeps the values from `other`. This is + * only guaranteed to work if the VertexRDDs share a common ancestor. + * + * @param other the other RDD[(VertexId, VD)] with which to diff against. + */ + def diff(other: RDD[(VertexId, VD)]): VertexRDD[VD] + + /** + * For each vertex present in both `this` and `other`, `diff` returns only those vertices with + * differing values; for values that are different, keeps the values from `other`. This is + * only guaranteed to work if the VertexRDDs share a common ancestor. + * + * @param other the other VertexRDD with which to diff against. */ def diff(other: VertexRDD[VD]): VertexRDD[VD] diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartition.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartition.scala index 373af75448374..c561570809253 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartition.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartition.scala @@ -324,7 +324,7 @@ class EdgePartition[ * * @return an iterator over edges in the partition */ - def iterator = new Iterator[Edge[ED]] { + def iterator: Iterator[Edge[ED]] = new Iterator[Edge[ED]] { private[this] val edge = new Edge[ED] private[this] var pos = 0 @@ -351,7 +351,7 @@ class EdgePartition[ override def hasNext: Boolean = pos < EdgePartition.this.size - override def next() = { + override def next(): EdgeTriplet[VD, ED] = { val triplet = new EdgeTriplet[VD, ED] val localSrcId = localSrcIds(pos) val localDstId = localDstIds(pos) @@ -518,11 +518,11 @@ private class AggregatingEdgeContext[VD, ED, A]( _attr = attr } - override def srcId = _srcId - override def dstId = _dstId - override def srcAttr = _srcAttr - override def dstAttr = _dstAttr - override def attr = _attr + override def srcId: VertexId = _srcId + override def dstId: VertexId = _dstId + override def srcAttr: VD = _srcAttr + override def dstAttr: VD = _dstAttr + override def attr: ED = _attr override def sendToSrc(msg: A) { send(_localSrcId, msg) diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgeRDDImpl.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgeRDDImpl.scala index 56cb41661e300..c88b2f65a86cd 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgeRDDImpl.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgeRDDImpl.scala @@ -19,7 +19,7 @@ package org.apache.spark.graphx.impl import scala.reflect.{classTag, ClassTag} -import org.apache.spark.{OneToOneDependency, HashPartitioner, TaskContext} +import org.apache.spark.{OneToOneDependency, HashPartitioner} import org.apache.spark.rdd.RDD import org.apache.spark.storage.StorageLevel @@ -70,9 +70,9 @@ class EdgeRDDImpl[ED: ClassTag, VD: ClassTag] private[graphx] ( this } - override def getStorageLevel = partitionsRDD.getStorageLevel + override def getStorageLevel: StorageLevel = partitionsRDD.getStorageLevel - override def checkpoint() = { + override def checkpoint(): Unit = { partitionsRDD.checkpoint() } diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/ReplicatedVertexView.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/ReplicatedVertexView.scala index 8ab255bd4038c..1df86449fa0c2 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/impl/ReplicatedVertexView.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/ReplicatedVertexView.scala @@ -50,7 +50,7 @@ class ReplicatedVertexView[VD: ClassTag, ED: ClassTag]( * Return a new `ReplicatedVertexView` where edges are reversed and shipping levels are swapped to * match. */ - def reverse() = { + def reverse(): ReplicatedVertexView[VD, ED] = { val newEdges = edges.mapEdgePartitions((pid, part) => part.reverse) new ReplicatedVertexView(newEdges, hasDstId, hasSrcId) } diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBaseOps.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBaseOps.scala index 4fd2548b7faf6..b90f9fa327052 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBaseOps.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBaseOps.scala @@ -88,6 +88,21 @@ private[graphx] abstract class VertexPartitionBaseOps this.withMask(newMask) } + /** Hides the VertexId's that are the same between `this` and `other`. */ + def minus(other: Self[VD]): Self[VD] = { + if (self.index != other.index) { + logWarning("Minus operations on two VertexPartitions with different indexes is slow.") + minus(createUsingIndex(other.iterator)) + } else { + self.withMask(self.mask.andNot(other.mask)) + } + } + + /** Hides the VertexId's that are the same between `this` and `other`. */ + def minus(other: Iterator[(VertexId, VD)]): Self[VD] = { + minus(createUsingIndex(other)) + } + /** * Hides vertices that are the same between this and other. For vertices that are different, keeps * the values from `other`. The indices of `this` and `other` must be the same. diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexRDDImpl.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexRDDImpl.scala index 6dad167fa7411..33ac7b0ed6095 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexRDDImpl.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexRDDImpl.scala @@ -71,9 +71,9 @@ class VertexRDDImpl[VD] private[graphx] ( this } - override def getStorageLevel = partitionsRDD.getStorageLevel + override def getStorageLevel: StorageLevel = partitionsRDD.getStorageLevel - override def checkpoint() = { + override def checkpoint(): Unit = { partitionsRDD.checkpoint() } @@ -103,9 +103,44 @@ class VertexRDDImpl[VD] private[graphx] ( override def mapValues[VD2: ClassTag](f: (VertexId, VD) => VD2): VertexRDD[VD2] = this.mapVertexPartitions(_.map(f)) + override def minus(other: RDD[(VertexId, VD)]): VertexRDD[VD] = { + minus(this.aggregateUsingIndex(other, (a: VD, b: VD) => a)) + } + + override def minus (other: VertexRDD[VD]): VertexRDD[VD] = { + other match { + case other: VertexRDD[_] if this.partitioner == other.partitioner => + this.withPartitionsRDD[VD]( + partitionsRDD.zipPartitions( + other.partitionsRDD, preservesPartitioning = true) { + (thisIter, otherIter) => + val thisPart = thisIter.next() + val otherPart = otherIter.next() + Iterator(thisPart.minus(otherPart)) + }) + case _ => + this.withPartitionsRDD[VD]( + partitionsRDD.zipPartitions( + other.partitionBy(this.partitioner.get), preservesPartitioning = true) { + (partIter, msgs) => partIter.map(_.minus(msgs)) + } + ) + } + } + + override def diff(other: RDD[(VertexId, VD)]): VertexRDD[VD] = { + diff(this.aggregateUsingIndex(other, (a: VD, b: VD) => a)) + } + override def diff(other: VertexRDD[VD]): VertexRDD[VD] = { + val otherPartition = other match { + case other: VertexRDD[_] if this.partitioner == other.partitioner => + other.partitionsRDD + case _ => + VertexRDD(other.partitionBy(this.partitioner.get)).partitionsRDD + } val newPartitionsRDD = partitionsRDD.zipPartitions( - other.partitionsRDD, preservesPartitioning = true + otherPartition, preservesPartitioning = true ) { (thisIter, otherIter) => val thisPart = thisIter.next() val otherPart = otherIter.next() @@ -133,7 +168,7 @@ class VertexRDDImpl[VD] private[graphx] ( // Test if the other vertex is a VertexRDD to choose the optimal join strategy. // If the other set is a VertexRDD then we use the much more efficient leftZipJoin other match { - case other: VertexRDD[_] => + case other: VertexRDD[_] if this.partitioner == other.partitioner => leftZipJoin(other)(f) case _ => this.withPartitionsRDD[VD3]( @@ -162,7 +197,7 @@ class VertexRDDImpl[VD] private[graphx] ( // Test if the other vertex is a VertexRDD to choose the optimal join strategy. // If the other set is a VertexRDD then we use the much more efficient innerZipJoin other match { - case other: VertexRDD[_] => + case other: VertexRDD[_] if this.partitioner == other.partitioner => innerZipJoin(other)(f) case _ => this.withPartitionsRDD( diff --git a/graphx/src/main/scala/org/apache/spark/graphx/lib/ConnectedComponents.scala b/graphx/src/main/scala/org/apache/spark/graphx/lib/ConnectedComponents.scala index e2f6cc138958e..859f896039047 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/lib/ConnectedComponents.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/lib/ConnectedComponents.scala @@ -37,7 +37,7 @@ object ConnectedComponents { */ def run[VD: ClassTag, ED: ClassTag](graph: Graph[VD, ED]): Graph[VertexId, ED] = { val ccGraph = graph.mapVertices { case (vid, _) => vid } - def sendMessage(edge: EdgeTriplet[VertexId, ED]) = { + def sendMessage(edge: EdgeTriplet[VertexId, ED]): Iterator[(VertexId, VertexId)] = { if (edge.srcAttr < edge.dstAttr) { Iterator((edge.dstId, edge.srcAttr)) } else if (edge.srcAttr > edge.dstAttr) { diff --git a/graphx/src/main/scala/org/apache/spark/graphx/lib/LabelPropagation.scala b/graphx/src/main/scala/org/apache/spark/graphx/lib/LabelPropagation.scala index 82e9e06515179..2bcf8684b8b8e 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/lib/LabelPropagation.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/lib/LabelPropagation.scala @@ -43,7 +43,7 @@ object LabelPropagation { */ def run[VD, ED: ClassTag](graph: Graph[VD, ED], maxSteps: Int): Graph[VertexId, ED] = { val lpaGraph = graph.mapVertices { case (vid, _) => vid } - def sendMessage(e: EdgeTriplet[VertexId, ED]) = { + def sendMessage(e: EdgeTriplet[VertexId, ED]): Iterator[(VertexId, Map[VertexId, VertexId])] = { Iterator((e.srcId, Map(e.dstAttr -> 1L)), (e.dstId, Map(e.srcAttr -> 1L))) } def mergeMessage(count1: Map[VertexId, Long], count2: Map[VertexId, Long]) @@ -54,7 +54,7 @@ object LabelPropagation { i -> (count1Val + count2Val) }.toMap } - def vertexProgram(vid: VertexId, attr: Long, message: Map[VertexId, Long]) = { + def vertexProgram(vid: VertexId, attr: Long, message: Map[VertexId, Long]): VertexId = { if (message.isEmpty) attr else message.maxBy(_._2)._1 } val initialMessage = Map[VertexId, Long]() diff --git a/graphx/src/main/scala/org/apache/spark/graphx/lib/PageRank.scala b/graphx/src/main/scala/org/apache/spark/graphx/lib/PageRank.scala index e139959c3f5c1..042e366a29f58 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/lib/PageRank.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/lib/PageRank.scala @@ -25,8 +25,8 @@ import org.apache.spark.graphx._ /** * PageRank algorithm implementation. There are two implementations of PageRank implemented. * - * The first implementation uses the [[Pregel]] interface and runs PageRank for a fixed number - * of iterations: + * The first implementation uses the standalone [[Graph]] interface and runs PageRank + * for a fixed number of iterations: * {{{ * var PR = Array.fill(n)( 1.0 ) * val oldPR = Array.fill(n)( 1.0 ) @@ -38,7 +38,7 @@ import org.apache.spark.graphx._ * } * }}} * - * The second implementation uses the standalone [[Graph]] interface and runs PageRank until + * The second implementation uses the [[Pregel]] interface and runs PageRank until * convergence: * * {{{ @@ -156,7 +156,7 @@ object PageRank extends Logging { (newPR, newPR - oldPR) } - def sendMessage(edge: EdgeTriplet[(Double, Double), Double]) = { + def sendMessage(edge: EdgeTriplet[(Double, Double), Double]): Iterator[(VertexId, Double)] = { if (edge.srcAttr._2 > tol) { Iterator((edge.dstId, edge.srcAttr._2 * edge.attr)) } else { diff --git a/graphx/src/main/scala/org/apache/spark/graphx/lib/SVDPlusPlus.scala b/graphx/src/main/scala/org/apache/spark/graphx/lib/SVDPlusPlus.scala index f58587e10a820..3b0e1628d86b5 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/lib/SVDPlusPlus.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/lib/SVDPlusPlus.scala @@ -18,7 +18,9 @@ package org.apache.spark.graphx.lib import scala.util.Random -import org.jblas.DoubleMatrix + +import com.github.fommil.netlib.BLAS.{getInstance => blas} + import org.apache.spark.rdd._ import org.apache.spark.graphx._ @@ -37,12 +39,23 @@ object SVDPlusPlus { var gamma7: Double) extends Serializable + /** + * This method is now replaced by the updated version of `run()` and returns exactly + * the same result. + */ + @deprecated("Call run()", "1.4.0") + def runSVDPlusPlus(edges: RDD[Edge[Double]], conf: Conf) + : (Graph[(Array[Double], Array[Double], Double, Double), Double], Double) = + { + run(edges, conf) + } + /** * Implement SVD++ based on "Factorization Meets the Neighborhood: * a Multifaceted Collaborative Filtering Model", * available at [[http://public.research.att.com/~volinsky/netflix/kdd08koren.pdf]]. * - * The prediction rule is rui = u + bu + bi + qi*(pu + |N(u)|^(-0.5)*sum(y)), + * The prediction rule is rui = u + bu + bi + qi*(pu + |N(u)|^^-0.5^^*sum(y)), * see the details on page 6. * * @param edges edges for constructing the graph @@ -52,16 +65,13 @@ object SVDPlusPlus { * @return a graph with vertex attributes containing the trained model */ def run(edges: RDD[Edge[Double]], conf: Conf) - : (Graph[(DoubleMatrix, DoubleMatrix, Double, Double), Double], Double) = + : (Graph[(Array[Double], Array[Double], Double, Double), Double], Double) = { // Generate default vertex attribute - def defaultF(rank: Int): (DoubleMatrix, DoubleMatrix, Double, Double) = { - val v1 = new DoubleMatrix(rank) - val v2 = new DoubleMatrix(rank) - for (i <- 0 until rank) { - v1.put(i, Random.nextDouble()) - v2.put(i, Random.nextDouble()) - } + def defaultF(rank: Int): (Array[Double], Array[Double], Double, Double) = { + // TODO: use a fixed random seed + val v1 = Array.fill(rank)(Random.nextDouble()) + val v2 = Array.fill(rank)(Random.nextDouble()) (v1, v2, 0.0, 0.0) } @@ -72,38 +82,47 @@ object SVDPlusPlus { // construct graph var g = Graph.fromEdges(edges, defaultF(conf.rank)).cache() + materialize(g) + edges.unpersist() // Calculate initial bias and norm val t0 = g.aggregateMessages[(Long, Double)]( ctx => { ctx.sendToSrc((1L, ctx.attr)); ctx.sendToDst((1L, ctx.attr)) }, (g1, g2) => (g1._1 + g2._1, g1._2 + g2._2)) - g = g.outerJoinVertices(t0) { - (vid: VertexId, vd: (DoubleMatrix, DoubleMatrix, Double, Double), + val gJoinT0 = g.outerJoinVertices(t0) { + (vid: VertexId, vd: (Array[Double], Array[Double], Double, Double), msg: Option[(Long, Double)]) => - (vd._1, vd._2, msg.get._2 / msg.get._1, 1.0 / scala.math.sqrt(msg.get._1)) - } + (vd._1, vd._2, msg.get._2 / msg.get._1 - u, 1.0 / scala.math.sqrt(msg.get._1)) + }.cache() + materialize(gJoinT0) + g.unpersist() + g = gJoinT0 def sendMsgTrainF(conf: Conf, u: Double) (ctx: EdgeContext[ - (DoubleMatrix, DoubleMatrix, Double, Double), + (Array[Double], Array[Double], Double, Double), Double, - (DoubleMatrix, DoubleMatrix, Double)]) { + (Array[Double], Array[Double], Double)]) { val (usr, itm) = (ctx.srcAttr, ctx.dstAttr) val (p, q) = (usr._1, itm._1) - var pred = u + usr._3 + itm._3 + q.dot(usr._2) + val rank = p.length + var pred = u + usr._3 + itm._3 + blas.ddot(rank, q, 1, usr._2, 1) pred = math.max(pred, conf.minVal) pred = math.min(pred, conf.maxVal) val err = ctx.attr - pred - val updateP = q.mul(err) - .subColumnVector(p.mul(conf.gamma7)) - .mul(conf.gamma2) - val updateQ = usr._2.mul(err) - .subColumnVector(q.mul(conf.gamma7)) - .mul(conf.gamma2) - val updateY = q.mul(err * usr._4) - .subColumnVector(itm._2.mul(conf.gamma7)) - .mul(conf.gamma2) + // updateP = (err * q - conf.gamma7 * p) * conf.gamma2 + val updateP = q.clone() + blas.dscal(rank, err * conf.gamma2, updateP, 1) + blas.daxpy(rank, -conf.gamma7 * conf.gamma2, p, 1, updateP, 1) + // updateQ = (err * usr._2 - conf.gamma7 * q) * conf.gamma2 + val updateQ = usr._2.clone() + blas.dscal(rank, err * conf.gamma2, updateQ, 1) + blas.daxpy(rank, -conf.gamma7 * conf.gamma2, q, 1, updateQ, 1) + // updateY = (err * usr._4 * q - conf.gamma7 * itm._2) * conf.gamma2 + val updateY = q.clone() + blas.dscal(rank, err * usr._4 * conf.gamma2, updateY, 1) + blas.daxpy(rank, -conf.gamma7 * conf.gamma2, itm._2, 1, updateY, 1) ctx.sendToSrc((updateP, updateY, (err - conf.gamma6 * usr._3) * conf.gamma1)) ctx.sendToDst((updateQ, updateY, (err - conf.gamma6 * itm._3) * conf.gamma1)) } @@ -111,49 +130,89 @@ object SVDPlusPlus { for (i <- 0 until conf.maxIters) { // Phase 1, calculate pu + |N(u)|^(-0.5)*sum(y) for user nodes g.cache() - val t1 = g.aggregateMessages[DoubleMatrix]( + val t1 = g.aggregateMessages[Array[Double]]( ctx => ctx.sendToSrc(ctx.dstAttr._2), - (g1, g2) => g1.addColumnVector(g2)) - g = g.outerJoinVertices(t1) { - (vid: VertexId, vd: (DoubleMatrix, DoubleMatrix, Double, Double), - msg: Option[DoubleMatrix]) => - if (msg.isDefined) (vd._1, vd._1 - .addColumnVector(msg.get.mul(vd._4)), vd._3, vd._4) else vd - } + (g1, g2) => { + val out = g1.clone() + blas.daxpy(out.length, 1.0, g2, 1, out, 1) + out + }) + val gJoinT1 = g.outerJoinVertices(t1) { + (vid: VertexId, vd: (Array[Double], Array[Double], Double, Double), + msg: Option[Array[Double]]) => + if (msg.isDefined) { + val out = vd._1.clone() + blas.daxpy(out.length, vd._4, msg.get, 1, out, 1) + (vd._1, out, vd._3, vd._4) + } else { + vd + } + }.cache() + materialize(gJoinT1) + g.unpersist() + g = gJoinT1 // Phase 2, update p for user nodes and q, y for item nodes g.cache() val t2 = g.aggregateMessages( sendMsgTrainF(conf, u), - (g1: (DoubleMatrix, DoubleMatrix, Double), g2: (DoubleMatrix, DoubleMatrix, Double)) => - (g1._1.addColumnVector(g2._1), g1._2.addColumnVector(g2._2), g1._3 + g2._3)) - g = g.outerJoinVertices(t2) { + (g1: (Array[Double], Array[Double], Double), g2: (Array[Double], Array[Double], Double)) => + { + val out1 = g1._1.clone() + blas.daxpy(out1.length, 1.0, g2._1, 1, out1, 1) + val out2 = g2._2.clone() + blas.daxpy(out2.length, 1.0, g2._2, 1, out2, 1) + (out1, out2, g1._3 + g2._3) + }) + val gJoinT2 = g.outerJoinVertices(t2) { (vid: VertexId, - vd: (DoubleMatrix, DoubleMatrix, Double, Double), - msg: Option[(DoubleMatrix, DoubleMatrix, Double)]) => - (vd._1.addColumnVector(msg.get._1), vd._2.addColumnVector(msg.get._2), - vd._3 + msg.get._3, vd._4) - } + vd: (Array[Double], Array[Double], Double, Double), + msg: Option[(Array[Double], Array[Double], Double)]) => { + val out1 = vd._1.clone() + blas.daxpy(out1.length, 1.0, msg.get._1, 1, out1, 1) + val out2 = vd._2.clone() + blas.daxpy(out2.length, 1.0, msg.get._2, 1, out2, 1) + (out1, out2, vd._3 + msg.get._3, vd._4) + } + }.cache() + materialize(gJoinT2) + g.unpersist() + g = gJoinT2 } // calculate error on training set def sendMsgTestF(conf: Conf, u: Double) - (ctx: EdgeContext[(DoubleMatrix, DoubleMatrix, Double, Double), Double, Double]) { + (ctx: EdgeContext[(Array[Double], Array[Double], Double, Double), Double, Double]) { val (usr, itm) = (ctx.srcAttr, ctx.dstAttr) val (p, q) = (usr._1, itm._1) - var pred = u + usr._3 + itm._3 + q.dot(usr._2) + var pred = u + usr._3 + itm._3 + blas.ddot(q.length, q, 1, usr._2, 1) pred = math.max(pred, conf.minVal) pred = math.min(pred, conf.maxVal) val err = (ctx.attr - pred) * (ctx.attr - pred) ctx.sendToDst(err) } + g.cache() val t3 = g.aggregateMessages[Double](sendMsgTestF(conf, u), _ + _) - g = g.outerJoinVertices(t3) { - (vid: VertexId, vd: (DoubleMatrix, DoubleMatrix, Double, Double), msg: Option[Double]) => + val gJoinT3 = g.outerJoinVertices(t3) { + (vid: VertexId, vd: (Array[Double], Array[Double], Double, Double), msg: Option[Double]) => if (msg.isDefined) (vd._1, vd._2, vd._3, msg.get) else vd - } + }.cache() + materialize(gJoinT3) + g.unpersist() + g = gJoinT3 - (g, u) + // Convert DoubleMatrix to Array[Double]: + val newVertices = g.vertices.mapValues(v => (v._1.toArray, v._2.toArray, v._3, v._4)) + (Graph(newVertices, g.edges), u) } + + /** + * Forces materialization of a Graph by count()ing its RDDs. + */ + private def materialize(g: Graph[_,_]): Unit = { + g.vertices.count() + g.edges.count() + } + } diff --git a/graphx/src/main/scala/org/apache/spark/graphx/lib/ShortestPaths.scala b/graphx/src/main/scala/org/apache/spark/graphx/lib/ShortestPaths.scala index 590f0474957dd..179f2843818e0 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/lib/ShortestPaths.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/lib/ShortestPaths.scala @@ -61,8 +61,8 @@ object ShortestPaths { } def sendMessage(edge: EdgeTriplet[SPMap, _]): Iterator[(VertexId, SPMap)] = { - val newAttr = incrementMap(edge.srcAttr) - if (edge.dstAttr != addMaps(newAttr, edge.dstAttr)) Iterator((edge.dstId, newAttr)) + val newAttr = incrementMap(edge.dstAttr) + if (edge.srcAttr != addMaps(newAttr, edge.srcAttr)) Iterator((edge.srcId, newAttr)) else Iterator.empty } diff --git a/graphx/src/main/scala/org/apache/spark/graphx/util/collection/GraphXPrimitiveKeyOpenHashMap.scala b/graphx/src/main/scala/org/apache/spark/graphx/util/collection/GraphXPrimitiveKeyOpenHashMap.scala index 57b01b6f2e1fb..e2754ea699da9 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/util/collection/GraphXPrimitiveKeyOpenHashMap.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/util/collection/GraphXPrimitiveKeyOpenHashMap.scala @@ -56,7 +56,7 @@ class GraphXPrimitiveKeyOpenHashMap[@specialized(Long, Int) K: ClassTag, private var _oldValues: Array[V] = null - override def size = keySet.size + override def size: Int = keySet.size /** Get the value for a given key */ def apply(k: K): V = { @@ -112,7 +112,7 @@ class GraphXPrimitiveKeyOpenHashMap[@specialized(Long, Int) K: ClassTag, } } - override def iterator = new Iterator[(K, V)] { + override def iterator: Iterator[(K, V)] = new Iterator[(K, V)] { var pos = 0 var nextPair: (K, V) = computeNextPair() @@ -128,9 +128,9 @@ class GraphXPrimitiveKeyOpenHashMap[@specialized(Long, Int) K: ClassTag, } } - def hasNext = nextPair != null + def hasNext: Boolean = nextPair != null - def next() = { + def next(): (K, V) = { val pair = nextPair nextPair = computeNextPair() pair diff --git a/graphx/src/test/resources/log4j.properties b/graphx/src/test/resources/log4j.properties index 287c8e3563503..eb3b1999eb996 100644 --- a/graphx/src/test/resources/log4j.properties +++ b/graphx/src/test/resources/log4j.properties @@ -24,5 +24,5 @@ log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n # Ignore messages below warning level from Jetty, because it's a bit verbose -log4j.logger.org.eclipse.jetty=WARN -org.eclipse.jetty.LEVEL=WARN +log4j.logger.org.spark-project.jetty=WARN +org.spark-project.jetty.LEVEL=WARN diff --git a/graphx/src/test/scala/org/apache/spark/graphx/GraphSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/GraphSuite.scala index b61d9f0fbe5e4..a570e4ed75fc3 100644 --- a/graphx/src/test/scala/org/apache/spark/graphx/GraphSuite.scala +++ b/graphx/src/test/scala/org/apache/spark/graphx/GraphSuite.scala @@ -19,13 +19,12 @@ package org.apache.spark.graphx import org.scalatest.FunSuite -import com.google.common.io.Files - import org.apache.spark.SparkContext import org.apache.spark.graphx.Graph._ import org.apache.spark.graphx.PartitionStrategy._ import org.apache.spark.rdd._ import org.apache.spark.storage.StorageLevel +import org.apache.spark.util.Utils class GraphSuite extends FunSuite with LocalSparkContext { @@ -39,12 +38,12 @@ class GraphSuite extends FunSuite with LocalSparkContext { val doubleRing = ring ++ ring val graph = Graph.fromEdgeTuples(sc.parallelize(doubleRing), 1) assert(graph.edges.count() === doubleRing.size) - assert(graph.edges.collect.forall(e => e.attr == 1)) + assert(graph.edges.collect().forall(e => e.attr == 1)) // uniqueEdges option should uniquify edges and store duplicate count in edge attributes val uniqueGraph = Graph.fromEdgeTuples(sc.parallelize(doubleRing), 1, Some(RandomVertexCut)) assert(uniqueGraph.edges.count() === ring.size) - assert(uniqueGraph.edges.collect.forall(e => e.attr == 2)) + assert(uniqueGraph.edges.collect().forall(e => e.attr == 2)) } } @@ -65,7 +64,7 @@ class GraphSuite extends FunSuite with LocalSparkContext { assert( graph.edges.count() === rawEdges.size ) // Vertices not explicitly provided but referenced by edges should be created automatically assert( graph.vertices.count() === 100) - graph.triplets.collect.map { et => + graph.triplets.collect().map { et => assert((et.srcId < 10 && et.srcAttr) || (et.srcId >= 10 && !et.srcAttr)) assert((et.dstId < 10 && et.dstAttr) || (et.dstId >= 10 && !et.dstAttr)) } @@ -76,15 +75,17 @@ class GraphSuite extends FunSuite with LocalSparkContext { withSpark { sc => val n = 5 val star = starGraph(sc, n) - assert(star.triplets.map(et => (et.srcId, et.dstId, et.srcAttr, et.dstAttr)).collect.toSet === - (1 to n).map(x => (0: VertexId, x: VertexId, "v", "v")).toSet) + assert(star.triplets.map(et => (et.srcId, et.dstId, et.srcAttr, et.dstAttr)).collect().toSet + === (1 to n).map(x => (0: VertexId, x: VertexId, "v", "v")).toSet) } } test("partitionBy") { withSpark { sc => - def mkGraph(edges: List[(Long, Long)]) = Graph.fromEdgeTuples(sc.parallelize(edges, 2), 0) - def nonemptyParts(graph: Graph[Int, Int]) = { + def mkGraph(edges: List[(Long, Long)]): Graph[Int, Int] = { + Graph.fromEdgeTuples(sc.parallelize(edges, 2), 0) + } + def nonemptyParts(graph: Graph[Int, Int]): RDD[List[Edge[Int]]] = { graph.edges.partitionsRDD.mapPartitions { iter => Iterator(iter.next()._2.iterator.toList) }.filter(_.nonEmpty) @@ -103,7 +104,8 @@ class GraphSuite extends FunSuite with LocalSparkContext { assert(nonemptyParts(mkGraph(sameSrcEdges).partitionBy(EdgePartition1D)).count === 1) // partitionBy(CanonicalRandomVertexCut) puts edges that are identical modulo direction into // the same partition - assert(nonemptyParts(mkGraph(canonicalEdges).partitionBy(CanonicalRandomVertexCut)).count === 1) + assert( + nonemptyParts(mkGraph(canonicalEdges).partitionBy(CanonicalRandomVertexCut)).count === 1) // partitionBy(EdgePartition2D) puts identical edges in the same partition assert(nonemptyParts(mkGraph(identicalEdges).partitionBy(EdgePartition2D)).count === 1) @@ -141,10 +143,10 @@ class GraphSuite extends FunSuite with LocalSparkContext { val g = Graph( sc.parallelize(List((0L, "a"), (1L, "b"), (2L, "c"))), sc.parallelize(List(Edge(0L, 1L, 1), Edge(0L, 2L, 1)), 2)) - assert(g.triplets.collect.map(_.toTuple).toSet === + assert(g.triplets.collect().map(_.toTuple).toSet === Set(((0L, "a"), (1L, "b"), 1), ((0L, "a"), (2L, "c"), 1))) val gPart = g.partitionBy(EdgePartition2D) - assert(gPart.triplets.collect.map(_.toTuple).toSet === + assert(gPart.triplets.collect().map(_.toTuple).toSet === Set(((0L, "a"), (1L, "b"), 1), ((0L, "a"), (2L, "c"), 1))) } } @@ -155,10 +157,10 @@ class GraphSuite extends FunSuite with LocalSparkContext { val star = starGraph(sc, n) // mapVertices preserving type val mappedVAttrs = star.mapVertices((vid, attr) => attr + "2") - assert(mappedVAttrs.vertices.collect.toSet === (0 to n).map(x => (x: VertexId, "v2")).toSet) + assert(mappedVAttrs.vertices.collect().toSet === (0 to n).map(x => (x: VertexId, "v2")).toSet) // mapVertices changing type val mappedVAttrs2 = star.mapVertices((vid, attr) => attr.length) - assert(mappedVAttrs2.vertices.collect.toSet === (0 to n).map(x => (x: VertexId, 1)).toSet) + assert(mappedVAttrs2.vertices.collect().toSet === (0 to n).map(x => (x: VertexId, 1)).toSet) } } @@ -178,12 +180,12 @@ class GraphSuite extends FunSuite with LocalSparkContext { // Trigger initial vertex replication graph0.triplets.foreach(x => {}) // Change type of replicated vertices, but preserve erased type - val graph1 = graph0.mapVertices { - case (vid, integerOpt) => integerOpt.map((x: java.lang.Integer) => (x.toDouble): java.lang.Double) + val graph1 = graph0.mapVertices { case (vid, integerOpt) => + integerOpt.map((x: java.lang.Integer) => x.toDouble: java.lang.Double) } // Access replicated vertices, exposing the erased type val graph2 = graph1.mapTriplets(t => t.srcAttr.get) - assert(graph2.edges.map(_.attr).collect.toSet === Set[java.lang.Double](1.0, 2.0, 3.0)) + assert(graph2.edges.map(_.attr).collect().toSet === Set[java.lang.Double](1.0, 2.0, 3.0)) } } @@ -203,7 +205,7 @@ class GraphSuite extends FunSuite with LocalSparkContext { withSpark { sc => val n = 5 val star = starGraph(sc, n) - assert(star.mapTriplets(et => et.srcAttr + et.dstAttr).edges.collect.toSet === + assert(star.mapTriplets(et => et.srcAttr + et.dstAttr).edges.collect().toSet === (1L to n).map(x => Edge(0, x, "vv")).toSet) } } @@ -212,7 +214,7 @@ class GraphSuite extends FunSuite with LocalSparkContext { withSpark { sc => val n = 5 val star = starGraph(sc, n) - assert(star.reverse.outDegrees.collect.toSet === (1 to n).map(x => (x: VertexId, 1)).toSet) + assert(star.reverse.outDegrees.collect().toSet === (1 to n).map(x => (x: VertexId, 1)).toSet) } } @@ -222,7 +224,7 @@ class GraphSuite extends FunSuite with LocalSparkContext { val edges: RDD[Edge[Int]] = sc.parallelize(Array(Edge(1L, 2L, 0))) val graph = Graph(vertices, edges).reverse val result = graph.mapReduceTriplets[Int](et => Iterator((et.dstId, et.srcAttr)), _ + _) - assert(result.collect.toSet === Set((1L, 2))) + assert(result.collect().toSet === Set((1L, 2))) } } @@ -238,7 +240,8 @@ class GraphSuite extends FunSuite with LocalSparkContext { assert(subgraph.vertices.collect().toSet === (0 to n by 2).map(x => (x, "v")).toSet) // And 4 edges. - assert(subgraph.edges.map(_.copy()).collect().toSet === (2 to n by 2).map(x => Edge(0, x, 1)).toSet) + assert(subgraph.edges.map(_.copy()).collect().toSet === + (2 to n by 2).map(x => Edge(0, x, 1)).toSet) } } @@ -274,9 +277,9 @@ class GraphSuite extends FunSuite with LocalSparkContext { sc.parallelize((1 to n).flatMap(x => List((0: VertexId, x: VertexId), (0: VertexId, x: VertexId))), 1), "v") val star2 = doubleStar.groupEdges { (a, b) => a} - assert(star2.edges.collect.toArray.sorted(Edge.lexicographicOrdering[Int]) === - star.edges.collect.toArray.sorted(Edge.lexicographicOrdering[Int])) - assert(star2.vertices.collect.toSet === star.vertices.collect.toSet) + assert(star2.edges.collect().toArray.sorted(Edge.lexicographicOrdering[Int]) === + star.edges.collect().toArray.sorted(Edge.lexicographicOrdering[Int])) + assert(star2.vertices.collect().toSet === star.vertices.collect().toSet) } } @@ -301,21 +304,23 @@ class GraphSuite extends FunSuite with LocalSparkContext { throw new Exception("map ran on edge with dst vid %d, which is odd".format(et.dstId)) } Iterator((et.srcId, 1)) - }, (a: Int, b: Int) => a + b, Some((active, EdgeDirection.In))).collect.toSet + }, (a: Int, b: Int) => a + b, Some((active, EdgeDirection.In))).collect().toSet assert(numEvenNeighbors === (1 to n).map(x => (x: VertexId, n / 2)).toSet) // outerJoinVertices followed by mapReduceTriplets(activeSetOpt) - val ringEdges = sc.parallelize((0 until n).map(x => (x: VertexId, (x+1) % n: VertexId)), 3) + val ringEdges = sc.parallelize((0 until n).map(x => (x: VertexId, (x + 1) % n: VertexId)), 3) val ring = Graph.fromEdgeTuples(ringEdges, 0) .mapVertices((vid, attr) => vid).cache() val changed = ring.vertices.filter { case (vid, attr) => attr % 2 == 1 }.mapValues(-_).cache() - val changedGraph = ring.outerJoinVertices(changed) { (vid, old, newOpt) => newOpt.getOrElse(old) } + val changedGraph = ring.outerJoinVertices(changed) { (vid, old, newOpt) => + newOpt.getOrElse(old) + } val numOddNeighbors = changedGraph.mapReduceTriplets(et => { // Map function should only run on edges with source in the active set if (et.srcId % 2 != 1) { throw new Exception("map ran on edge with src vid %d, which is even".format(et.dstId)) } Iterator((et.dstId, 1)) - }, (a: Int, b: Int) => a + b, Some(changed, EdgeDirection.Out)).collect.toSet + }, (a: Int, b: Int) => a + b, Some(changed, EdgeDirection.Out)).collect().toSet assert(numOddNeighbors === (2 to n by 2).map(x => (x: VertexId, 1)).toSet) } @@ -341,17 +346,18 @@ class GraphSuite extends FunSuite with LocalSparkContext { val n = 5 val reverseStar = starGraph(sc, n).reverse.cache() // outerJoinVertices changing type - val reverseStarDegrees = - reverseStar.outerJoinVertices(reverseStar.outDegrees) { (vid, a, bOpt) => bOpt.getOrElse(0) } + val reverseStarDegrees = reverseStar.outerJoinVertices(reverseStar.outDegrees) { + (vid, a, bOpt) => bOpt.getOrElse(0) + } val neighborDegreeSums = reverseStarDegrees.mapReduceTriplets( et => Iterator((et.srcId, et.dstAttr), (et.dstId, et.srcAttr)), - (a: Int, b: Int) => a + b).collect.toSet + (a: Int, b: Int) => a + b).collect().toSet assert(neighborDegreeSums === Set((0: VertexId, n)) ++ (1 to n).map(x => (x: VertexId, 0))) // outerJoinVertices preserving type val messages = reverseStar.vertices.mapValues { (vid, attr) => vid.toString } val newReverseStar = reverseStar.outerJoinVertices(messages) { (vid, a, bOpt) => a + bOpt.getOrElse("") } - assert(newReverseStar.vertices.map(_._2).collect.toSet === + assert(newReverseStar.vertices.map(_._2).collect().toSet === (0 to n).map(x => "v%d".format(x)).toSet) } } @@ -362,15 +368,14 @@ class GraphSuite extends FunSuite with LocalSparkContext { val edges = sc.parallelize(List(Edge(1, 2, 0), Edge(2, 1, 0)), 2) val graph = Graph(verts, edges) val triplets = graph.triplets.map(et => (et.srcId, et.dstId, et.srcAttr, et.dstAttr)) - .collect.toSet + .collect().toSet assert(triplets === Set((1: VertexId, 2: VertexId, "a", "b"), (2: VertexId, 1: VertexId, "b", "a"))) } } test("checkpoint") { - val checkpointDir = Files.createTempDir() - checkpointDir.deleteOnExit() + val checkpointDir = Utils.createTempDir() withSpark { sc => sc.setCheckpointDir(checkpointDir.getAbsolutePath) val ring = (0L to 100L).zip((1L to 99L) :+ 0L).map { case (a, b) => Edge(a, b, 1)} @@ -419,7 +424,7 @@ class GraphSuite extends FunSuite with LocalSparkContext { val graph = Graph.fromEdgeTuples(edges, 1) val neighborAttrSums = graph.mapReduceTriplets[Int]( et => Iterator((et.dstId, et.srcAttr)), _ + _) - assert(neighborAttrSums.collect.toSet === Set((0: VertexId, n))) + assert(neighborAttrSums.collect().toSet === Set((0: VertexId, n))) } finally { sc.stop() } diff --git a/graphx/src/test/scala/org/apache/spark/graphx/LocalSparkContext.scala b/graphx/src/test/scala/org/apache/spark/graphx/LocalSparkContext.scala index a3e28efc75a98..d2ad9be555770 100644 --- a/graphx/src/test/scala/org/apache/spark/graphx/LocalSparkContext.scala +++ b/graphx/src/test/scala/org/apache/spark/graphx/LocalSparkContext.scala @@ -26,7 +26,7 @@ import org.apache.spark.SparkContext */ trait LocalSparkContext { /** Runs `f` on a new SparkContext and ensures that it is stopped afterwards. */ - def withSpark[T](f: SparkContext => T) = { + def withSpark[T](f: SparkContext => T): T = { val conf = new SparkConf() GraphXUtils.registerKryoClasses(conf) val sc = new SparkContext("local", "test", conf) diff --git a/graphx/src/test/scala/org/apache/spark/graphx/VertexRDDSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/VertexRDDSuite.scala index 131959cea3ef7..d0a7198d691d7 100644 --- a/graphx/src/test/scala/org/apache/spark/graphx/VertexRDDSuite.scala +++ b/graphx/src/test/scala/org/apache/spark/graphx/VertexRDDSuite.scala @@ -19,12 +19,13 @@ package org.apache.spark.graphx import org.scalatest.FunSuite -import org.apache.spark.SparkContext +import org.apache.spark.{HashPartitioner, SparkContext} +import org.apache.spark.rdd.RDD import org.apache.spark.storage.StorageLevel class VertexRDDSuite extends FunSuite with LocalSparkContext { - def vertices(sc: SparkContext, n: Int) = { + private def vertices(sc: SparkContext, n: Int) = { VertexRDD(sc.parallelize((0 to n).map(x => (x.toLong, x)), 5)) } @@ -46,6 +47,35 @@ class VertexRDDSuite extends FunSuite with LocalSparkContext { } } + test("minus") { + withSpark { sc => + val vertexA = VertexRDD(sc.parallelize(0 until 75, 2).map(i => (i.toLong, 0))).cache() + val vertexB = VertexRDD(sc.parallelize(25 until 100, 2).map(i => (i.toLong, 1))).cache() + val vertexC = vertexA.minus(vertexB) + assert(vertexC.map(_._1).collect().toSet === (0 until 25).toSet) + } + } + + test("minus with RDD[(VertexId, VD)]") { + withSpark { sc => + val vertexA = VertexRDD(sc.parallelize(0 until 75, 2).map(i => (i.toLong, 0))).cache() + val vertexB: RDD[(VertexId, Int)] = + sc.parallelize(25 until 100, 2).map(i => (i.toLong, 1)).cache() + val vertexC = vertexA.minus(vertexB) + assert(vertexC.map(_._1).collect().toSet === (0 until 25).toSet) + } + } + + test("minus with non-equal number of partitions") { + withSpark { sc => + val vertexA = VertexRDD(sc.parallelize(0 until 75, 5).map(i => (i.toLong, 0))) + val vertexB = VertexRDD(sc.parallelize(50 until 100, 2).map(i => (i.toLong, 1))) + assert(vertexA.partitions.size != vertexB.partitions.size) + val vertexC = vertexA.minus(vertexB) + assert(vertexC.map(_._1).collect().toSet === (0 until 50).toSet) + } + } + test("diff") { withSpark { sc => val n = 100 @@ -58,42 +88,90 @@ class VertexRDDSuite extends FunSuite with LocalSparkContext { } } + test("diff with RDD[(VertexId, VD)]") { + withSpark { sc => + val n = 100 + val verts = vertices(sc, n).cache() + val flipEvens: RDD[(VertexId, Int)] = + sc.parallelize(0L to 100L) + .map(id => if (id % 2 == 0) (id, -id.toInt) else (id, id.toInt)).cache() + // diff should keep only the changed vertices + assert(verts.diff(flipEvens).map(_._2).collect().toSet === (2 to n by 2).map(-_).toSet) + } + } + + test("diff vertices with non-equal number of partitions") { + withSpark { sc => + val vertexA = VertexRDD(sc.parallelize(0 until 24, 3).map(i => (i.toLong, 0))) + val vertexB = VertexRDD(sc.parallelize(8 until 16, 2).map(i => (i.toLong, 1))) + assert(vertexA.partitions.size != vertexB.partitions.size) + val vertexC = vertexA.diff(vertexB) + assert(vertexC.map(_._1).collect().toSet === (8 until 16).toSet) + } + } + test("leftJoin") { withSpark { sc => val n = 100 val verts = vertices(sc, n).cache() val evens = verts.filter(q => ((q._2 % 2) == 0)).cache() // leftJoin with another VertexRDD - assert(verts.leftJoin(evens) { (id, a, bOpt) => a - bOpt.getOrElse(0) }.collect.toSet === + assert(verts.leftJoin(evens) { (id, a, bOpt) => a - bOpt.getOrElse(0) }.collect().toSet === (0 to n by 2).map(x => (x.toLong, 0)).toSet ++ (1 to n by 2).map(x => (x.toLong, x)).toSet) // leftJoin with an RDD val evensRDD = evens.map(identity) - assert(verts.leftJoin(evensRDD) { (id, a, bOpt) => a - bOpt.getOrElse(0) }.collect.toSet === + assert(verts.leftJoin(evensRDD) { (id, a, bOpt) => a - bOpt.getOrElse(0) }.collect().toSet === (0 to n by 2).map(x => (x.toLong, 0)).toSet ++ (1 to n by 2).map(x => (x.toLong, x)).toSet) } } + test("leftJoin vertices with non-equal number of partitions") { + withSpark { sc => + val vertexA = VertexRDD(sc.parallelize(0 until 100, 2).map(i => (i.toLong, 1))) + val vertexB = VertexRDD( + vertexA.filter(v => v._1 % 2 == 0).partitionBy(new HashPartitioner(3))) + assert(vertexA.partitions.size != vertexB.partitions.size) + val vertexC = vertexA.leftJoin(vertexB) { (vid, old, newOpt) => + old - newOpt.getOrElse(0) + } + assert(vertexC.filter(v => v._2 != 0).map(_._1).collect().toSet == (1 to 99 by 2).toSet) + } + } + test("innerJoin") { withSpark { sc => val n = 100 val verts = vertices(sc, n).cache() val evens = verts.filter(q => ((q._2 % 2) == 0)).cache() // innerJoin with another VertexRDD - assert(verts.innerJoin(evens) { (id, a, b) => a - b }.collect.toSet === + assert(verts.innerJoin(evens) { (id, a, b) => a - b }.collect().toSet === (0 to n by 2).map(x => (x.toLong, 0)).toSet) // innerJoin with an RDD val evensRDD = evens.map(identity) - assert(verts.innerJoin(evensRDD) { (id, a, b) => a - b }.collect.toSet === + assert(verts.innerJoin(evensRDD) { (id, a, b) => a - b }.collect().toSet === (0 to n by 2).map(x => (x.toLong, 0)).toSet) } } + test("innerJoin vertices with the non-equal number of partitions") { + withSpark { sc => + val vertexA = VertexRDD(sc.parallelize(0 until 100, 2).map(i => (i.toLong, 1))) + val vertexB = VertexRDD( + vertexA.filter(v => v._1 % 2 == 0).partitionBy(new HashPartitioner(3))) + assert(vertexA.partitions.size != vertexB.partitions.size) + val vertexC = vertexA.innerJoin(vertexB) { (vid, old, newVal) => + old - newVal + } + assert(vertexC.filter(v => v._2 == 0).map(_._1).collect().toSet == (0 to 98 by 2).toSet) + } + } + test("aggregateUsingIndex") { withSpark { sc => val n = 100 val verts = vertices(sc, n) val messageTargets = (0 to n) ++ (0 to n by 2) val messages = sc.parallelize(messageTargets.map(x => (x.toLong, 1))) - assert(verts.aggregateUsingIndex[Int](messages, _ + _).collect.toSet === + assert(verts.aggregateUsingIndex[Int](messages, _ + _).collect().toSet === (0 to n).map(x => (x.toLong, if (x % 2 == 0) 2 else 1)).toSet) } } @@ -105,7 +183,7 @@ class VertexRDDSuite extends FunSuite with LocalSparkContext { val edges = EdgeRDD.fromEdges(sc.parallelize(List.empty[Edge[Int]])) val rdd = VertexRDD(verts, edges, 0, (a: Int, b: Int) => a + b) // test merge function - assert(rdd.collect.toSet == Set((0L, 0), (1L, 3), (2L, 9))) + assert(rdd.collect().toSet == Set((0L, 0), (1L, 3), (2L, 9))) } } diff --git a/graphx/src/test/scala/org/apache/spark/graphx/lib/ConnectedComponentsSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/lib/ConnectedComponentsSuite.scala index 3915be15b3434..4cc30a96408f8 100644 --- a/graphx/src/test/scala/org/apache/spark/graphx/lib/ConnectedComponentsSuite.scala +++ b/graphx/src/test/scala/org/apache/spark/graphx/lib/ConnectedComponentsSuite.scala @@ -32,7 +32,7 @@ class ConnectedComponentsSuite extends FunSuite with LocalSparkContext { withSpark { sc => val gridGraph = GraphGenerators.gridGraph(sc, 10, 10) val ccGraph = gridGraph.connectedComponents() - val maxCCid = ccGraph.vertices.map { case (vid, ccId) => ccId }.sum + val maxCCid = ccGraph.vertices.map { case (vid, ccId) => ccId }.sum() assert(maxCCid === 0) } } // end of Grid connected components @@ -42,7 +42,7 @@ class ConnectedComponentsSuite extends FunSuite with LocalSparkContext { withSpark { sc => val gridGraph = GraphGenerators.gridGraph(sc, 10, 10).reverse val ccGraph = gridGraph.connectedComponents() - val maxCCid = ccGraph.vertices.map { case (vid, ccId) => ccId }.sum + val maxCCid = ccGraph.vertices.map { case (vid, ccId) => ccId }.sum() assert(maxCCid === 0) } } // end of Grid connected components @@ -50,8 +50,8 @@ class ConnectedComponentsSuite extends FunSuite with LocalSparkContext { test("Chain Connected Components") { withSpark { sc => - val chain1 = (0 until 9).map(x => (x, x+1) ) - val chain2 = (10 until 20).map(x => (x, x+1) ) + val chain1 = (0 until 9).map(x => (x, x + 1)) + val chain2 = (10 until 20).map(x => (x, x + 1)) val rawEdges = sc.parallelize(chain1 ++ chain2, 3).map { case (s,d) => (s.toLong, d.toLong) } val twoChains = Graph.fromEdgeTuples(rawEdges, 1.0) val ccGraph = twoChains.connectedComponents() @@ -73,12 +73,12 @@ class ConnectedComponentsSuite extends FunSuite with LocalSparkContext { test("Reverse Chain Connected Components") { withSpark { sc => - val chain1 = (0 until 9).map(x => (x, x+1) ) - val chain2 = (10 until 20).map(x => (x, x+1) ) + val chain1 = (0 until 9).map(x => (x, x + 1)) + val chain2 = (10 until 20).map(x => (x, x + 1)) val rawEdges = sc.parallelize(chain1 ++ chain2, 3).map { case (s,d) => (s.toLong, d.toLong) } val twoChains = Graph.fromEdgeTuples(rawEdges, true).reverse val ccGraph = twoChains.connectedComponents() - val vertices = ccGraph.vertices.collect + val vertices = ccGraph.vertices.collect() for ( (id, cc) <- vertices ) { if (id < 10) { assert(cc === 0) @@ -120,9 +120,9 @@ class ConnectedComponentsSuite extends FunSuite with LocalSparkContext { // Build the initial Graph val graph = Graph(users, relationships, defaultUser) val ccGraph = graph.connectedComponents() - val vertices = ccGraph.vertices.collect + val vertices = ccGraph.vertices.collect() for ( (id, cc) <- vertices ) { - assert(cc == 0) + assert(cc === 0) } } } // end of toy connected components diff --git a/graphx/src/test/scala/org/apache/spark/graphx/lib/PageRankSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/lib/PageRankSuite.scala index fc491ae327c2a..95804b07b1db0 100644 --- a/graphx/src/test/scala/org/apache/spark/graphx/lib/PageRankSuite.scala +++ b/graphx/src/test/scala/org/apache/spark/graphx/lib/PageRankSuite.scala @@ -19,15 +19,12 @@ package org.apache.spark.graphx.lib import org.scalatest.FunSuite -import org.apache.spark.SparkContext -import org.apache.spark.SparkContext._ import org.apache.spark.graphx._ -import org.apache.spark.graphx.lib._ import org.apache.spark.graphx.util.GraphGenerators -import org.apache.spark.rdd._ + object GridPageRank { - def apply(nRows: Int, nCols: Int, nIter: Int, resetProb: Double) = { + def apply(nRows: Int, nCols: Int, nIter: Int, resetProb: Double): Seq[(VertexId, Double)] = { val inNbrs = Array.fill(nRows * nCols)(collection.mutable.MutableList.empty[Int]) val outDegree = Array.fill(nRows * nCols)(0) // Convert row column address into vertex ids (row major order) @@ -35,13 +32,13 @@ object GridPageRank { // Make the grid graph for (r <- 0 until nRows; c <- 0 until nCols) { val ind = sub2ind(r,c) - if (r+1 < nRows) { + if (r + 1 < nRows) { outDegree(ind) += 1 - inNbrs(sub2ind(r+1,c)) += ind + inNbrs(sub2ind(r + 1,c)) += ind } - if (c+1 < nCols) { + if (c + 1 < nCols) { outDegree(ind) += 1 - inNbrs(sub2ind(r,c+1)) += ind + inNbrs(sub2ind(r,c + 1)) += ind } } // compute the pagerank @@ -64,7 +61,7 @@ class PageRankSuite extends FunSuite with LocalSparkContext { def compareRanks(a: VertexRDD[Double], b: VertexRDD[Double]): Double = { a.leftJoin(b) { case (id, a, bOpt) => (a - bOpt.getOrElse(0.0)) * (a - bOpt.getOrElse(0.0)) } - .map { case (id, error) => error }.sum + .map { case (id, error) => error }.sum() } test("Star PageRank") { @@ -80,12 +77,12 @@ class PageRankSuite extends FunSuite with LocalSparkContext { // Static PageRank should only take 2 iterations to converge val notMatching = staticRanks1.innerZipJoin(staticRanks2) { (vid, pr1, pr2) => if (pr1 != pr2) 1 else 0 - }.map { case (vid, test) => test }.sum + }.map { case (vid, test) => test }.sum() assert(notMatching === 0) val staticErrors = staticRanks2.map { case (vid, pr) => - val correct = (vid > 0 && pr == resetProb) || - (vid == 0 && math.abs(pr - (resetProb + (1.0 - resetProb) * (resetProb * (nVertices - 1)) )) < 1.0E-5) + val p = math.abs(pr - (resetProb + (1.0 - resetProb) * (resetProb * (nVertices - 1)) )) + val correct = (vid > 0 && pr == resetProb) || (vid == 0L && p < 1.0E-5) if (!correct) 1 else 0 } assert(staticErrors.sum === 0) @@ -95,8 +92,6 @@ class PageRankSuite extends FunSuite with LocalSparkContext { } } // end of test Star PageRank - - test("Grid PageRank") { withSpark { sc => val rows = 10 @@ -109,18 +104,18 @@ class PageRankSuite extends FunSuite with LocalSparkContext { val staticRanks = gridGraph.staticPageRank(numIter, resetProb).vertices.cache() val dynamicRanks = gridGraph.pageRank(tol, resetProb).vertices.cache() - val referenceRanks = VertexRDD(sc.parallelize(GridPageRank(rows, cols, numIter, resetProb))).cache() + val referenceRanks = VertexRDD( + sc.parallelize(GridPageRank(rows, cols, numIter, resetProb))).cache() assert(compareRanks(staticRanks, referenceRanks) < errorTol) assert(compareRanks(dynamicRanks, referenceRanks) < errorTol) } } // end of Grid PageRank - test("Chain PageRank") { withSpark { sc => - val chain1 = (0 until 9).map(x => (x, x+1) ) - val rawEdges = sc.parallelize(chain1, 1).map { case (s,d) => (s.toLong, d.toLong) } + val chain1 = (0 until 9).map(x => (x, x + 1)) + val rawEdges = sc.parallelize(chain1, 1).map { case (s, d) => (s.toLong, d.toLong) } val chain = Graph.fromEdgeTuples(rawEdges, 1.0).cache() val resetProb = 0.15 val tol = 0.0001 diff --git a/graphx/src/test/scala/org/apache/spark/graphx/lib/SVDPlusPlusSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/lib/SVDPlusPlusSuite.scala index e01df56e94de9..7bd6b7f3c4ab2 100644 --- a/graphx/src/test/scala/org/apache/spark/graphx/lib/SVDPlusPlusSuite.scala +++ b/graphx/src/test/scala/org/apache/spark/graphx/lib/SVDPlusPlusSuite.scala @@ -32,11 +32,11 @@ class SVDPlusPlusSuite extends FunSuite with LocalSparkContext { Edge(fields(0).toLong * 2, fields(1).toLong * 2 + 1, fields(2).toDouble) } val conf = new SVDPlusPlus.Conf(10, 2, 0.0, 5.0, 0.007, 0.007, 0.005, 0.015) // 2 iterations - var (graph, u) = SVDPlusPlus.run(edges, conf) + val (graph, _) = SVDPlusPlus.run(edges, conf) graph.cache() - val err = graph.vertices.collect().map{ case (vid, vd) => + val err = graph.vertices.map { case (vid, vd) => if (vid % 2 == 1) vd._4 else 0.0 - }.reduce(_ + _) / graph.triplets.collect().size + }.reduce(_ + _) / graph.numEdges assert(err <= svdppErr) } } diff --git a/graphx/src/test/scala/org/apache/spark/graphx/lib/StronglyConnectedComponentsSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/lib/StronglyConnectedComponentsSuite.scala index df54aa37cad68..1f658c371ffcf 100644 --- a/graphx/src/test/scala/org/apache/spark/graphx/lib/StronglyConnectedComponentsSuite.scala +++ b/graphx/src/test/scala/org/apache/spark/graphx/lib/StronglyConnectedComponentsSuite.scala @@ -34,8 +34,8 @@ class StronglyConnectedComponentsSuite extends FunSuite with LocalSparkContext { val edges = sc.parallelize(Seq.empty[Edge[Int]]) val graph = Graph(vertices, edges) val sccGraph = graph.stronglyConnectedComponents(5) - for ((id, scc) <- sccGraph.vertices.collect) { - assert(id == scc) + for ((id, scc) <- sccGraph.vertices.collect()) { + assert(id === scc) } } } @@ -45,8 +45,8 @@ class StronglyConnectedComponentsSuite extends FunSuite with LocalSparkContext { val rawEdges = sc.parallelize((0L to 6L).map(x => (x, (x + 1) % 7))) val graph = Graph.fromEdgeTuples(rawEdges, -1) val sccGraph = graph.stronglyConnectedComponents(20) - for ((id, scc) <- sccGraph.vertices.collect) { - assert(0L == scc) + for ((id, scc) <- sccGraph.vertices.collect()) { + assert(0L === scc) } } } @@ -60,13 +60,14 @@ class StronglyConnectedComponentsSuite extends FunSuite with LocalSparkContext { val rawEdges = sc.parallelize(edges) val graph = Graph.fromEdgeTuples(rawEdges, -1) val sccGraph = graph.stronglyConnectedComponents(20) - for ((id, scc) <- sccGraph.vertices.collect) { - if (id < 3) - assert(0L == scc) - else if (id < 6) - assert(3L == scc) - else - assert(id == scc) + for ((id, scc) <- sccGraph.vertices.collect()) { + if (id < 3) { + assert(0L === scc) + } else if (id < 6) { + assert(3L === scc) + } else { + assert(id === scc) + } } } } diff --git a/launcher/pom.xml b/launcher/pom.xml new file mode 100644 index 0000000000000..182e5f60218db --- /dev/null +++ b/launcher/pom.xml @@ -0,0 +1,78 @@ + + + + + 4.0.0 + + org.apache.spark + spark-parent_2.10 + 1.4.0-SNAPSHOT + ../pom.xml + + + org.apache.spark + spark-launcher_2.10 + jar + Spark Launcher Project + http://spark.apache.org/ + + launcher + + + + + + log4j + log4j + test + + + junit + junit + test + + + org.mockito + mockito-all + test + + + org.slf4j + slf4j-api + test + + + org.slf4j + slf4j-log4j12 + test + + + + + org.apache.hadoop + hadoop-client + test + + + + + target/scala-${scala.binary.version}/classes + target/scala-${scala.binary.version}/test-classes + + diff --git a/launcher/src/main/java/org/apache/spark/launcher/AbstractCommandBuilder.java b/launcher/src/main/java/org/apache/spark/launcher/AbstractCommandBuilder.java new file mode 100644 index 0000000000000..b8f02b961113d --- /dev/null +++ b/launcher/src/main/java/org/apache/spark/launcher/AbstractCommandBuilder.java @@ -0,0 +1,343 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.launcher; + +import java.io.BufferedReader; +import java.io.File; +import java.io.FileFilter; +import java.io.FileInputStream; +import java.io.InputStreamReader; +import java.io.IOException; +import java.util.ArrayList; +import java.util.Arrays; +import java.util.Collections; +import java.util.HashMap; +import java.util.List; +import java.util.Map; +import java.util.Properties; +import java.util.jar.JarFile; +import java.util.regex.Pattern; + +import static org.apache.spark.launcher.CommandBuilderUtils.*; + +/** + * Abstract Spark command builder that defines common functionality. + */ +abstract class AbstractCommandBuilder { + + boolean verbose; + String appName; + String appResource; + String deployMode; + String javaHome; + String mainClass; + String master; + String propertiesFile; + final List appArgs; + final List jars; + final List files; + final List pyFiles; + final Map childEnv; + final Map conf; + + public AbstractCommandBuilder() { + this.appArgs = new ArrayList(); + this.childEnv = new HashMap(); + this.conf = new HashMap(); + this.files = new ArrayList(); + this.jars = new ArrayList(); + this.pyFiles = new ArrayList(); + } + + /** + * Builds the command to execute. + * + * @param env A map containing environment variables for the child process. It may already contain + * entries defined by the user (such as SPARK_HOME, or those defined by the + * SparkLauncher constructor that takes an environment), and may be modified to + * include other variables needed by the process to be executed. + */ + abstract List buildCommand(Map env) throws IOException; + + /** + * Builds a list of arguments to run java. + * + * This method finds the java executable to use and appends JVM-specific options for running a + * class with Spark in the classpath. It also loads options from the "java-opts" file in the + * configuration directory being used. + * + * Callers should still add at least the class to run, as well as any arguments to pass to the + * class. + */ + List buildJavaCommand(String extraClassPath) throws IOException { + List cmd = new ArrayList(); + String envJavaHome; + + if (javaHome != null) { + cmd.add(join(File.separator, javaHome, "bin", "java")); + } else if ((envJavaHome = System.getenv("JAVA_HOME")) != null) { + cmd.add(join(File.separator, envJavaHome, "bin", "java")); + } else { + cmd.add(join(File.separator, System.getProperty("java.home"), "bin", "java")); + } + + // Load extra JAVA_OPTS from conf/java-opts, if it exists. + File javaOpts = new File(join(File.separator, getConfDir(), "java-opts")); + if (javaOpts.isFile()) { + BufferedReader br = new BufferedReader(new InputStreamReader( + new FileInputStream(javaOpts), "UTF-8")); + try { + String line; + while ((line = br.readLine()) != null) { + addOptionString(cmd, line); + } + } finally { + br.close(); + } + } + + cmd.add("-cp"); + cmd.add(join(File.pathSeparator, buildClassPath(extraClassPath))); + return cmd; + } + + /** + * Adds the default perm gen size option for Spark if the VM requires it and the user hasn't + * set it. + */ + void addPermGenSizeOpt(List cmd) { + // Don't set MaxPermSize for Java 8 and later. + String[] version = System.getProperty("java.version").split("\\."); + if (Integer.parseInt(version[0]) > 1 || Integer.parseInt(version[1]) > 7) { + return; + } + + for (String arg : cmd) { + if (arg.startsWith("-XX:MaxPermSize=")) { + return; + } + } + + cmd.add("-XX:MaxPermSize=128m"); + } + + void addOptionString(List cmd, String options) { + if (!isEmpty(options)) { + for (String opt : parseOptionString(options)) { + cmd.add(opt); + } + } + } + + /** + * Builds the classpath for the application. Returns a list with one classpath entry per element; + * each entry is formatted in the way expected by java.net.URLClassLoader (more + * specifically, with trailing slashes for directories). + */ + List buildClassPath(String appClassPath) throws IOException { + String sparkHome = getSparkHome(); + + List cp = new ArrayList(); + addToClassPath(cp, getenv("SPARK_CLASSPATH")); + addToClassPath(cp, appClassPath); + + addToClassPath(cp, getConfDir()); + + boolean prependClasses = !isEmpty(getenv("SPARK_PREPEND_CLASSES")); + boolean isTesting = "1".equals(getenv("SPARK_TESTING")); + if (prependClasses || isTesting) { + String scala = getScalaVersion(); + List projects = Arrays.asList("core", "repl", "mllib", "bagel", "graphx", + "streaming", "tools", "sql/catalyst", "sql/core", "sql/hive", "sql/hive-thriftserver", + "yarn", "launcher"); + if (prependClasses) { + System.err.println( + "NOTE: SPARK_PREPEND_CLASSES is set, placing locally compiled Spark classes ahead of " + + "assembly."); + for (String project : projects) { + addToClassPath(cp, String.format("%s/%s/target/scala-%s/classes", sparkHome, project, + scala)); + } + } + if (isTesting) { + for (String project : projects) { + addToClassPath(cp, String.format("%s/%s/target/scala-%s/test-classes", sparkHome, + project, scala)); + } + } + + // Add this path to include jars that are shaded in the final deliverable created during + // the maven build. These jars are copied to this directory during the build. + addToClassPath(cp, String.format("%s/core/target/jars/*", sparkHome)); + } + + // We can't rely on the ENV_SPARK_ASSEMBLY variable to be set. Certain situations, such as + // when running unit tests, or user code that embeds Spark and creates a SparkContext + // with a local or local-cluster master, will cause this code to be called from an + // environment where that env variable is not guaranteed to exist. + // + // For the testing case, we rely on the test code to set and propagate the test classpath + // appropriately. + // + // For the user code case, we fall back to looking for the Spark assembly under SPARK_HOME. + // That duplicates some of the code in the shell scripts that look for the assembly, though. + String assembly = getenv(ENV_SPARK_ASSEMBLY); + if (assembly == null && isEmpty(getenv("SPARK_TESTING"))) { + assembly = findAssembly(); + } + addToClassPath(cp, assembly); + + // Datanucleus jars must be included on the classpath. Datanucleus jars do not work if only + // included in the uber jar as plugin.xml metadata is lost. Both sbt and maven will populate + // "lib_managed/jars/" with the datanucleus jars when Spark is built with Hive + File libdir; + if (new File(sparkHome, "RELEASE").isFile()) { + libdir = new File(sparkHome, "lib"); + } else { + libdir = new File(sparkHome, "lib_managed/jars"); + } + + checkState(libdir.isDirectory(), "Library directory '%s' does not exist.", + libdir.getAbsolutePath()); + for (File jar : libdir.listFiles()) { + if (jar.getName().startsWith("datanucleus-")) { + addToClassPath(cp, jar.getAbsolutePath()); + } + } + + addToClassPath(cp, getenv("HADOOP_CONF_DIR")); + addToClassPath(cp, getenv("YARN_CONF_DIR")); + addToClassPath(cp, getenv("SPARK_DIST_CLASSPATH")); + return cp; + } + + /** + * Adds entries to the classpath. + * + * @param cp List to which the new entries are appended. + * @param entries New classpath entries (separated by File.pathSeparator). + */ + private void addToClassPath(List cp, String entries) { + if (isEmpty(entries)) { + return; + } + String[] split = entries.split(Pattern.quote(File.pathSeparator)); + for (String entry : split) { + if (!isEmpty(entry)) { + if (new File(entry).isDirectory() && !entry.endsWith(File.separator)) { + entry += File.separator; + } + cp.add(entry); + } + } + } + + String getScalaVersion() { + String scala = getenv("SPARK_SCALA_VERSION"); + if (scala != null) { + return scala; + } + String sparkHome = getSparkHome(); + File scala210 = new File(sparkHome, "assembly/target/scala-2.10"); + File scala211 = new File(sparkHome, "assembly/target/scala-2.11"); + checkState(!scala210.isDirectory() || !scala211.isDirectory(), + "Presence of build for both scala versions (2.10 and 2.11) detected.\n" + + "Either clean one of them or set SPARK_SCALA_VERSION in your environment."); + if (scala210.isDirectory()) { + return "2.10"; + } else { + checkState(scala211.isDirectory(), "Cannot find any assembly build directories."); + return "2.11"; + } + } + + String getSparkHome() { + String path = getenv(ENV_SPARK_HOME); + checkState(path != null, + "Spark home not found; set it explicitly or use the SPARK_HOME environment variable."); + return path; + } + + /** + * Loads the configuration file for the application, if it exists. This is either the + * user-specified properties file, or the spark-defaults.conf file under the Spark configuration + * directory. + */ + Properties loadPropertiesFile() throws IOException { + Properties props = new Properties(); + File propsFile; + if (propertiesFile != null) { + propsFile = new File(propertiesFile); + checkArgument(propsFile.isFile(), "Invalid properties file '%s'.", propertiesFile); + } else { + propsFile = new File(getConfDir(), DEFAULT_PROPERTIES_FILE); + } + + if (propsFile.isFile()) { + FileInputStream fd = null; + try { + fd = new FileInputStream(propsFile); + props.load(new InputStreamReader(fd, "UTF-8")); + } finally { + if (fd != null) { + try { + fd.close(); + } catch (IOException e) { + // Ignore. + } + } + } + } + + return props; + } + + String getenv(String key) { + return firstNonEmpty(childEnv.get(key), System.getenv(key)); + } + + private String findAssembly() { + String sparkHome = getSparkHome(); + File libdir; + if (new File(sparkHome, "RELEASE").isFile()) { + libdir = new File(sparkHome, "lib"); + checkState(libdir.isDirectory(), "Library directory '%s' does not exist.", + libdir.getAbsolutePath()); + } else { + libdir = new File(sparkHome, String.format("assembly/target/scala-%s", getScalaVersion())); + } + + final Pattern re = Pattern.compile("spark-assembly.*hadoop.*\\.jar"); + FileFilter filter = new FileFilter() { + @Override + public boolean accept(File file) { + return file.isFile() && re.matcher(file.getName()).matches(); + } + }; + File[] assemblies = libdir.listFiles(filter); + checkState(assemblies != null && assemblies.length > 0, "No assemblies found in '%s'.", libdir); + checkState(assemblies.length == 1, "Multiple assemblies found in '%s'.", libdir); + return assemblies[0].getAbsolutePath(); + } + + private String getConfDir() { + String confDir = getenv("SPARK_CONF_DIR"); + return confDir != null ? confDir : join(File.separator, getSparkHome(), "conf"); + } + +} diff --git a/launcher/src/main/java/org/apache/spark/launcher/CommandBuilderUtils.java b/launcher/src/main/java/org/apache/spark/launcher/CommandBuilderUtils.java new file mode 100644 index 0000000000000..8028e42ffb483 --- /dev/null +++ b/launcher/src/main/java/org/apache/spark/launcher/CommandBuilderUtils.java @@ -0,0 +1,297 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.launcher; + +import java.io.File; +import java.util.ArrayList; +import java.util.List; +import java.util.Map; + +/** + * Helper methods for command builders. + */ +class CommandBuilderUtils { + + static final String DEFAULT_MEM = "512m"; + static final String DEFAULT_PROPERTIES_FILE = "spark-defaults.conf"; + static final String ENV_SPARK_HOME = "SPARK_HOME"; + static final String ENV_SPARK_ASSEMBLY = "_SPARK_ASSEMBLY"; + + /** Returns whether the given string is null or empty. */ + static boolean isEmpty(String s) { + return s == null || s.isEmpty(); + } + + /** Joins a list of strings using the given separator. */ + static String join(String sep, String... elements) { + StringBuilder sb = new StringBuilder(); + for (String e : elements) { + if (e != null) { + if (sb.length() > 0) { + sb.append(sep); + } + sb.append(e); + } + } + return sb.toString(); + } + + /** Joins a list of strings using the given separator. */ + static String join(String sep, Iterable elements) { + StringBuilder sb = new StringBuilder(); + for (String e : elements) { + if (e != null) { + if (sb.length() > 0) { + sb.append(sep); + } + sb.append(e); + } + } + return sb.toString(); + } + + /** + * Returns the first non-empty value mapped to the given key in the given maps, or null otherwise. + */ + static String firstNonEmptyValue(String key, Map... maps) { + for (Map map : maps) { + String value = (String) map.get(key); + if (!isEmpty(value)) { + return value; + } + } + return null; + } + + /** Returns the first non-empty, non-null string in the given list, or null otherwise. */ + static String firstNonEmpty(String... candidates) { + for (String s : candidates) { + if (!isEmpty(s)) { + return s; + } + } + return null; + } + + /** Returns the name of the env variable that holds the native library path. */ + static String getLibPathEnvName() { + if (isWindows()) { + return "PATH"; + } + + String os = System.getProperty("os.name"); + if (os.startsWith("Mac OS X")) { + return "DYLD_LIBRARY_PATH"; + } else { + return "LD_LIBRARY_PATH"; + } + } + + /** Returns whether the OS is Windows. */ + static boolean isWindows() { + String os = System.getProperty("os.name"); + return os.startsWith("Windows"); + } + + /** + * Updates the user environment, appending the given pathList to the existing value of the given + * environment variable (or setting it if it hasn't yet been set). + */ + static void mergeEnvPathList(Map userEnv, String envKey, String pathList) { + if (!isEmpty(pathList)) { + String current = firstNonEmpty(userEnv.get(envKey), System.getenv(envKey)); + userEnv.put(envKey, join(File.pathSeparator, current, pathList)); + } + } + + /** + * Parse a string as if it were a list of arguments, following bash semantics. + * For example: + * + * Input: "\"ab cd\" efgh 'i \" j'" + * Output: [ "ab cd", "efgh", "i \" j" ] + */ + static List parseOptionString(String s) { + List opts = new ArrayList(); + StringBuilder opt = new StringBuilder(); + boolean inOpt = false; + boolean inSingleQuote = false; + boolean inDoubleQuote = false; + boolean escapeNext = false; + + // This is needed to detect when a quoted empty string is used as an argument ("" or ''). + boolean hasData = false; + + for (int i = 0; i < s.length(); i++) { + int c = s.codePointAt(i); + if (escapeNext) { + opt.appendCodePoint(c); + escapeNext = false; + } else if (inOpt) { + switch (c) { + case '\\': + if (inSingleQuote) { + opt.appendCodePoint(c); + } else { + escapeNext = true; + } + break; + case '\'': + if (inDoubleQuote) { + opt.appendCodePoint(c); + } else { + inSingleQuote = !inSingleQuote; + } + break; + case '"': + if (inSingleQuote) { + opt.appendCodePoint(c); + } else { + inDoubleQuote = !inDoubleQuote; + } + break; + default: + if (!Character.isWhitespace(c) || inSingleQuote || inDoubleQuote) { + opt.appendCodePoint(c); + } else { + opts.add(opt.toString()); + opt.setLength(0); + inOpt = false; + hasData = false; + } + } + } else { + switch (c) { + case '\'': + inSingleQuote = true; + inOpt = true; + hasData = true; + break; + case '"': + inDoubleQuote = true; + inOpt = true; + hasData = true; + break; + case '\\': + escapeNext = true; + inOpt = true; + hasData = true; + break; + default: + if (!Character.isWhitespace(c)) { + inOpt = true; + hasData = true; + opt.appendCodePoint(c); + } + } + } + } + + checkArgument(!inSingleQuote && !inDoubleQuote && !escapeNext, "Invalid option string: %s", s); + if (hasData) { + opts.add(opt.toString()); + } + return opts; + } + + /** Throws IllegalArgumentException if the given object is null. */ + static void checkNotNull(Object o, String arg) { + if (o == null) { + throw new IllegalArgumentException(String.format("'%s' must not be null.", arg)); + } + } + + /** Throws IllegalArgumentException with the given message if the check is false. */ + static void checkArgument(boolean check, String msg, Object... args) { + if (!check) { + throw new IllegalArgumentException(String.format(msg, args)); + } + } + + /** Throws IllegalStateException with the given message if the check is false. */ + static void checkState(boolean check, String msg, Object... args) { + if (!check) { + throw new IllegalStateException(String.format(msg, args)); + } + } + + /** + * Quote a command argument for a command to be run by a Windows batch script, if the argument + * needs quoting. Arguments only seem to need quotes in batch scripts if they have certain + * special characters, some of which need extra (and different) escaping. + * + * For example: + * original single argument: ab="cde fgh" + * quoted: "ab^=""cde fgh""" + */ + static String quoteForBatchScript(String arg) { + + boolean needsQuotes = false; + for (int i = 0; i < arg.length(); i++) { + int c = arg.codePointAt(i); + if (Character.isWhitespace(c) || c == '"' || c == '=') { + needsQuotes = true; + break; + } + } + if (!needsQuotes) { + return arg; + } + StringBuilder quoted = new StringBuilder(); + quoted.append("\""); + for (int i = 0; i < arg.length(); i++) { + int cp = arg.codePointAt(i); + switch (cp) { + case '"': + quoted.append('"'); + break; + + case '=': + quoted.append('^'); + break; + + default: + break; + } + quoted.appendCodePoint(cp); + } + quoted.append("\""); + return quoted.toString(); + } + + /** + * Quotes a string so that it can be used in a command string. + * Basically, just add simple escapes. E.g.: + * original single argument : ab "cd" ef + * after: "ab \"cd\" ef" + * + * This can be parsed back into a single argument by python's "shlex.split()" function. + */ + static String quoteForCommandString(String s) { + StringBuilder quoted = new StringBuilder().append('"'); + for (int i = 0; i < s.length(); i++) { + int cp = s.codePointAt(i); + if (cp == '"' || cp == '\\') { + quoted.appendCodePoint('\\'); + } + quoted.appendCodePoint(cp); + } + return quoted.append('"').toString(); + } + +} diff --git a/launcher/src/main/java/org/apache/spark/launcher/Main.java b/launcher/src/main/java/org/apache/spark/launcher/Main.java new file mode 100644 index 0000000000000..206acfb514d86 --- /dev/null +++ b/launcher/src/main/java/org/apache/spark/launcher/Main.java @@ -0,0 +1,173 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.launcher; + +import java.util.ArrayList; +import java.util.Arrays; +import java.util.HashMap; +import java.util.List; +import java.util.Map; + +import static org.apache.spark.launcher.CommandBuilderUtils.*; + +/** + * Command line interface for the Spark launcher. Used internally by Spark scripts. + */ +class Main { + + /** + * Usage: Main [class] [class args] + *

    + * This CLI works in two different modes: + *

      + *
    • "spark-submit": if class is "org.apache.spark.deploy.SparkSubmit", the + * {@link SparkLauncher} class is used to launch a Spark application.
      • + *
    • "spark-class": if another class is provided, an internal Spark class is run.
      • + *
    + * + * This class works in tandem with the "bin/spark-class" script on Unix-like systems, and + * "bin/spark-class2.cmd" batch script on Windows to execute the final command. + *

    + * On Unix-like systems, the output is a list of command arguments, separated by the NULL + * character. On Windows, the output is a command line suitable for direct execution from the + * script. + */ + public static void main(String[] argsArray) throws Exception { + checkArgument(argsArray.length > 0, "Not enough arguments: missing class name."); + + List args = new ArrayList(Arrays.asList(argsArray)); + String className = args.remove(0); + + boolean printLaunchCommand; + boolean printUsage; + AbstractCommandBuilder builder; + try { + if (className.equals("org.apache.spark.deploy.SparkSubmit")) { + builder = new SparkSubmitCommandBuilder(args); + } else { + builder = new SparkClassCommandBuilder(className, args); + } + printLaunchCommand = !isEmpty(System.getenv("SPARK_PRINT_LAUNCH_COMMAND")); + printUsage = false; + } catch (IllegalArgumentException e) { + builder = new UsageCommandBuilder(e.getMessage()); + printLaunchCommand = false; + printUsage = true; + } + + Map env = new HashMap(); + List cmd = builder.buildCommand(env); + if (printLaunchCommand) { + System.err.println("Spark Command: " + join(" ", cmd)); + System.err.println("========================================"); + } + + if (isWindows()) { + // When printing the usage message, we can't use "cmd /v" since that prevents the env + // variable from being seen in the caller script. So do not call prepareWindowsCommand(). + if (printUsage) { + System.out.println(join(" ", cmd)); + } else { + System.out.println(prepareWindowsCommand(cmd, env)); + } + } else { + // In bash, use NULL as the arg separator since it cannot be used in an argument. + List bashCmd = prepareBashCommand(cmd, env); + for (String c : bashCmd) { + System.out.print(c); + System.out.print('\0'); + } + } + } + + /** + * Prepare a command line for execution from a Windows batch script. + * + * The method quotes all arguments so that spaces are handled as expected. Quotes within arguments + * are "double quoted" (which is batch for escaping a quote). This page has more details about + * quoting and other batch script fun stuff: http://ss64.com/nt/syntax-esc.html + * + * The command is executed using "cmd /c" and formatted in single line, since that's the + * easiest way to consume this from a batch script (see spark-class2.cmd). + */ + private static String prepareWindowsCommand(List cmd, Map childEnv) { + StringBuilder cmdline = new StringBuilder("cmd /c \""); + for (Map.Entry e : childEnv.entrySet()) { + cmdline.append(String.format("set %s=%s", e.getKey(), e.getValue())); + cmdline.append(" && "); + } + for (String arg : cmd) { + cmdline.append(quoteForBatchScript(arg)); + cmdline.append(" "); + } + cmdline.append("\""); + return cmdline.toString(); + } + + /** + * Prepare the command for execution from a bash script. The final command will have commands to + * set up any needed environment variables needed by the child process. + */ + private static List prepareBashCommand(List cmd, Map childEnv) { + if (childEnv.isEmpty()) { + return cmd; + } + + List newCmd = new ArrayList(); + newCmd.add("env"); + + for (Map.Entry e : childEnv.entrySet()) { + newCmd.add(String.format("%s=%s", e.getKey(), e.getValue())); + } + newCmd.addAll(cmd); + return newCmd; + } + + /** + * Internal builder used when command line parsing fails. This will behave differently depending + * on the platform: + * + * - On Unix-like systems, it will print a call to the "usage" function with two arguments: the + * the error string, and the exit code to use. The function is expected to print the command's + * usage and exit with the provided exit code. The script should use "export -f usage" after + * declaring a function called "usage", so that the function is available to downstream scripts. + * + * - On Windows it will set the variable "SPARK_LAUNCHER_USAGE_ERROR" to the usage error message. + * The batch script should check for this variable and print its usage, since batch scripts + * don't really support the "export -f" functionality used in bash. + */ + private static class UsageCommandBuilder extends AbstractCommandBuilder { + + private final String message; + + UsageCommandBuilder(String message) { + this.message = message; + } + + @Override + public List buildCommand(Map env) { + if (isWindows()) { + return Arrays.asList("set", "SPARK_LAUNCHER_USAGE_ERROR=" + message); + } else { + return Arrays.asList("usage", message, "1"); + } + } + + } + +} diff --git a/launcher/src/main/java/org/apache/spark/launcher/SparkClassCommandBuilder.java b/launcher/src/main/java/org/apache/spark/launcher/SparkClassCommandBuilder.java new file mode 100644 index 0000000000000..e601a0a19f368 --- /dev/null +++ b/launcher/src/main/java/org/apache/spark/launcher/SparkClassCommandBuilder.java @@ -0,0 +1,108 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.launcher; + +import java.io.File; +import java.io.IOException; +import java.util.ArrayList; +import java.util.List; +import java.util.Map; +import java.util.regex.Pattern; + +import static org.apache.spark.launcher.CommandBuilderUtils.*; + +/** + * Command builder for internal Spark classes. + *

    + * This class handles building the command to launch all internal Spark classes except for + * SparkSubmit (which is handled by {@link SparkSubmitCommandBuilder} class. + */ +class SparkClassCommandBuilder extends AbstractCommandBuilder { + + private final String className; + private final List classArgs; + + SparkClassCommandBuilder(String className, List classArgs) { + this.className = className; + this.classArgs = classArgs; + } + + @Override + public List buildCommand(Map env) throws IOException { + List javaOptsKeys = new ArrayList(); + String memKey = null; + String extraClassPath = null; + + // Master, Worker, and HistoryServer use SPARK_DAEMON_JAVA_OPTS (and specific opts) + + // SPARK_DAEMON_MEMORY. + if (className.equals("org.apache.spark.deploy.master.Master")) { + javaOptsKeys.add("SPARK_DAEMON_JAVA_OPTS"); + javaOptsKeys.add("SPARK_MASTER_OPTS"); + memKey = "SPARK_DAEMON_MEMORY"; + } else if (className.equals("org.apache.spark.deploy.worker.Worker")) { + javaOptsKeys.add("SPARK_DAEMON_JAVA_OPTS"); + javaOptsKeys.add("SPARK_WORKER_OPTS"); + memKey = "SPARK_DAEMON_MEMORY"; + } else if (className.equals("org.apache.spark.deploy.history.HistoryServer")) { + javaOptsKeys.add("SPARK_DAEMON_JAVA_OPTS"); + javaOptsKeys.add("SPARK_HISTORY_OPTS"); + memKey = "SPARK_DAEMON_MEMORY"; + } else if (className.equals("org.apache.spark.executor.CoarseGrainedExecutorBackend")) { + javaOptsKeys.add("SPARK_JAVA_OPTS"); + javaOptsKeys.add("SPARK_EXECUTOR_OPTS"); + memKey = "SPARK_EXECUTOR_MEMORY"; + } else if (className.equals("org.apache.spark.executor.MesosExecutorBackend")) { + javaOptsKeys.add("SPARK_EXECUTOR_OPTS"); + memKey = "SPARK_EXECUTOR_MEMORY"; + } else if (className.startsWith("org.apache.spark.tools.")) { + String sparkHome = getSparkHome(); + File toolsDir = new File(join(File.separator, sparkHome, "tools", "target", + "scala-" + getScalaVersion())); + checkState(toolsDir.isDirectory(), "Cannot find tools build directory."); + + Pattern re = Pattern.compile("spark-tools_.*\\.jar"); + for (File f : toolsDir.listFiles()) { + if (re.matcher(f.getName()).matches()) { + extraClassPath = f.getAbsolutePath(); + break; + } + } + + checkState(extraClassPath != null, + "Failed to find Spark Tools Jar in %s.\n" + + "You need to run \"build/sbt tools/package\" before running %s.", + toolsDir.getAbsolutePath(), className); + + javaOptsKeys.add("SPARK_JAVA_OPTS"); + } + + List cmd = buildJavaCommand(extraClassPath); + for (String key : javaOptsKeys) { + addOptionString(cmd, System.getenv(key)); + } + + String mem = firstNonEmpty(memKey != null ? System.getenv(memKey) : null, DEFAULT_MEM); + cmd.add("-Xms" + mem); + cmd.add("-Xmx" + mem); + addPermGenSizeOpt(cmd); + cmd.add(className); + cmd.addAll(classArgs); + return cmd; + } + +} diff --git a/launcher/src/main/java/org/apache/spark/launcher/SparkLauncher.java b/launcher/src/main/java/org/apache/spark/launcher/SparkLauncher.java new file mode 100644 index 0000000000000..d4cfeacb6ef18 --- /dev/null +++ b/launcher/src/main/java/org/apache/spark/launcher/SparkLauncher.java @@ -0,0 +1,279 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.launcher; + +import java.io.File; +import java.io.IOException; +import java.util.ArrayList; +import java.util.List; +import java.util.Map; + +import static org.apache.spark.launcher.CommandBuilderUtils.*; + +/** + * Launcher for Spark applications. + *

    + * Use this class to start Spark applications programmatically. The class uses a builder pattern + * to allow clients to configure the Spark application and launch it as a child process. + */ +public class SparkLauncher { + + /** The Spark master. */ + public static final String SPARK_MASTER = "spark.master"; + + /** Configuration key for the driver memory. */ + public static final String DRIVER_MEMORY = "spark.driver.memory"; + /** Configuration key for the driver class path. */ + public static final String DRIVER_EXTRA_CLASSPATH = "spark.driver.extraClassPath"; + /** Configuration key for the driver VM options. */ + public static final String DRIVER_EXTRA_JAVA_OPTIONS = "spark.driver.extraJavaOptions"; + /** Configuration key for the driver native library path. */ + public static final String DRIVER_EXTRA_LIBRARY_PATH = "spark.driver.extraLibraryPath"; + + /** Configuration key for the executor memory. */ + public static final String EXECUTOR_MEMORY = "spark.executor.memory"; + /** Configuration key for the executor class path. */ + public static final String EXECUTOR_EXTRA_CLASSPATH = "spark.executor.extraClassPath"; + /** Configuration key for the executor VM options. */ + public static final String EXECUTOR_EXTRA_JAVA_OPTIONS = "spark.executor.extraJavaOptions"; + /** Configuration key for the executor native library path. */ + public static final String EXECUTOR_EXTRA_LIBRARY_PATH = "spark.executor.extraLibraryPath"; + /** Configuration key for the number of executor CPU cores. */ + public static final String EXECUTOR_CORES = "spark.executor.cores"; + + private final SparkSubmitCommandBuilder builder; + + public SparkLauncher() { + this(null); + } + + /** + * Creates a launcher that will set the given environment variables in the child. + * + * @param env Environment variables to set. + */ + public SparkLauncher(Map env) { + this.builder = new SparkSubmitCommandBuilder(); + if (env != null) { + this.builder.childEnv.putAll(env); + } + } + + /** + * Set a custom JAVA_HOME for launching the Spark application. + * + * @param javaHome Path to the JAVA_HOME to use. + * @return This launcher. + */ + public SparkLauncher setJavaHome(String javaHome) { + checkNotNull(javaHome, "javaHome"); + builder.javaHome = javaHome; + return this; + } + + /** + * Set a custom Spark installation location for the application. + * + * @param sparkHome Path to the Spark installation to use. + * @return This launcher. + */ + public SparkLauncher setSparkHome(String sparkHome) { + checkNotNull(sparkHome, "sparkHome"); + builder.childEnv.put(ENV_SPARK_HOME, sparkHome); + return this; + } + + /** + * Set a custom properties file with Spark configuration for the application. + * + * @param path Path to custom properties file to use. + * @return This launcher. + */ + public SparkLauncher setPropertiesFile(String path) { + checkNotNull(path, "path"); + builder.propertiesFile = path; + return this; + } + + /** + * Set a single configuration value for the application. + * + * @param key Configuration key. + * @param value The value to use. + * @return This launcher. + */ + public SparkLauncher setConf(String key, String value) { + checkNotNull(key, "key"); + checkNotNull(value, "value"); + checkArgument(key.startsWith("spark."), "'key' must start with 'spark.'"); + builder.conf.put(key, value); + return this; + } + + /** + * Set the application name. + * + * @param appName Application name. + * @return This launcher. + */ + public SparkLauncher setAppName(String appName) { + checkNotNull(appName, "appName"); + builder.appName = appName; + return this; + } + + /** + * Set the Spark master for the application. + * + * @param master Spark master. + * @return This launcher. + */ + public SparkLauncher setMaster(String master) { + checkNotNull(master, "master"); + builder.master = master; + return this; + } + + /** + * Set the deploy mode for the application. + * + * @param mode Deploy mode. + * @return This launcher. + */ + public SparkLauncher setDeployMode(String mode) { + checkNotNull(mode, "mode"); + builder.deployMode = mode; + return this; + } + + /** + * Set the main application resource. This should be the location of a jar file for Scala/Java + * applications, or a python script for PySpark applications. + * + * @param resource Path to the main application resource. + * @return This launcher. + */ + public SparkLauncher setAppResource(String resource) { + checkNotNull(resource, "resource"); + builder.appResource = resource; + return this; + } + + /** + * Sets the application class name for Java/Scala applications. + * + * @param mainClass Application's main class. + * @return This launcher. + */ + public SparkLauncher setMainClass(String mainClass) { + checkNotNull(mainClass, "mainClass"); + builder.mainClass = mainClass; + return this; + } + + /** + * Adds command line arguments for the application. + * + * @param args Arguments to pass to the application's main class. + * @return This launcher. + */ + public SparkLauncher addAppArgs(String... args) { + for (String arg : args) { + checkNotNull(arg, "arg"); + builder.appArgs.add(arg); + } + return this; + } + + /** + * Adds a jar file to be submitted with the application. + * + * @param jar Path to the jar file. + * @return This launcher. + */ + public SparkLauncher addJar(String jar) { + checkNotNull(jar, "jar"); + builder.jars.add(jar); + return this; + } + + /** + * Adds a file to be submitted with the application. + * + * @param file Path to the file. + * @return This launcher. + */ + public SparkLauncher addFile(String file) { + checkNotNull(file, "file"); + builder.files.add(file); + return this; + } + + /** + * Adds a python file / zip / egg to be submitted with the application. + * + * @param file Path to the file. + * @return This launcher. + */ + public SparkLauncher addPyFile(String file) { + checkNotNull(file, "file"); + builder.pyFiles.add(file); + return this; + } + + /** + * Enables verbose reporting for SparkSubmit. + * + * @param verbose Whether to enable verbose output. + * @return This launcher. + */ + public SparkLauncher setVerbose(boolean verbose) { + builder.verbose = verbose; + return this; + } + + /** + * Launches a sub-process that will start the configured Spark application. + * + * @return A process handle for the Spark app. + */ + public Process launch() throws IOException { + List cmd = new ArrayList(); + String script = isWindows() ? "spark-submit.cmd" : "spark-submit"; + cmd.add(join(File.separator, builder.getSparkHome(), "bin", script)); + cmd.addAll(builder.buildSparkSubmitArgs()); + + // Since the child process is a batch script, let's quote things so that special characters are + // preserved, otherwise the batch interpreter will mess up the arguments. Batch scripts are + // weird. + if (isWindows()) { + List winCmd = new ArrayList(); + for (String arg : cmd) { + winCmd.add(quoteForBatchScript(arg)); + } + cmd = winCmd; + } + + ProcessBuilder pb = new ProcessBuilder(cmd.toArray(new String[cmd.size()])); + for (Map.Entry e : builder.childEnv.entrySet()) { + pb.environment().put(e.getKey(), e.getValue()); + } + return pb.start(); + } + +} diff --git a/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java new file mode 100644 index 0000000000000..a73c9c87e3126 --- /dev/null +++ b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java @@ -0,0 +1,360 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.launcher; + +import java.io.File; +import java.io.IOException; +import java.util.*; + +import static org.apache.spark.launcher.CommandBuilderUtils.*; + +/** + * Special command builder for handling a CLI invocation of SparkSubmit. + *

    + * This builder adds command line parsing compatible with SparkSubmit. It handles setting + * driver-side options and special parsing behavior needed for the special-casing certain internal + * Spark applications. + *

    + * This class has also some special features to aid launching pyspark. + */ +class SparkSubmitCommandBuilder extends AbstractCommandBuilder { + + /** + * Name of the app resource used to identify the PySpark shell. The command line parser expects + * the resource name to be the very first argument to spark-submit in this case. + * + * NOTE: this cannot be "pyspark-shell" since that identifies the PySpark shell to SparkSubmit + * (see java_gateway.py), and can cause this code to enter into an infinite loop. + */ + static final String PYSPARK_SHELL = "pyspark-shell-main"; + + /** + * This is the actual resource name that identifies the PySpark shell to SparkSubmit. + */ + static final String PYSPARK_SHELL_RESOURCE = "pyspark-shell"; + + /** + * Name of the app resource used to identify the SparkR shell. The command line parser expects + * the resource name to be the very first argument to spark-submit in this case. + * + * NOTE: this cannot be "sparkr-shell" since that identifies the SparkR shell to SparkSubmit + * (see sparkR.R), and can cause this code to enter into an infinite loop. + */ + static final String SPARKR_SHELL = "sparkr-shell-main"; + + /** + * This is the actual resource name that identifies the SparkR shell to SparkSubmit. + */ + static final String SPARKR_SHELL_RESOURCE = "sparkr-shell"; + + /** + * This map must match the class names for available special classes, since this modifies the way + * command line parsing works. This maps the class name to the resource to use when calling + * spark-submit. + */ + private static final Map specialClasses = new HashMap(); + static { + specialClasses.put("org.apache.spark.repl.Main", "spark-shell"); + specialClasses.put("org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver", + "spark-internal"); + specialClasses.put("org.apache.spark.sql.hive.thriftserver.HiveThriftServer2", + "spark-internal"); + } + + private final List sparkArgs; + + /** + * Controls whether mixing spark-submit arguments with app arguments is allowed. This is needed + * to parse the command lines for things like bin/spark-shell, which allows users to mix and + * match arguments (e.g. "bin/spark-shell SparkShellArg --master foo"). + */ + private boolean allowsMixedArguments; + + SparkSubmitCommandBuilder() { + this.sparkArgs = new ArrayList(); + } + + SparkSubmitCommandBuilder(List args) { + this(); + List submitArgs = args; + if (args.size() > 0 && args.get(0).equals(PYSPARK_SHELL)) { + this.allowsMixedArguments = true; + appResource = PYSPARK_SHELL_RESOURCE; + submitArgs = args.subList(1, args.size()); + } else if (args.size() > 0 && args.get(0).equals(SPARKR_SHELL)) { + this.allowsMixedArguments = true; + appResource = SPARKR_SHELL_RESOURCE; + submitArgs = args.subList(1, args.size()); + } else { + this.allowsMixedArguments = false; + } + + new OptionParser().parse(submitArgs); + } + + @Override + public List buildCommand(Map env) throws IOException { + if (PYSPARK_SHELL_RESOURCE.equals(appResource)) { + return buildPySparkShellCommand(env); + } else if (SPARKR_SHELL_RESOURCE.equals(appResource)) { + return buildSparkRCommand(env); + } else { + return buildSparkSubmitCommand(env); + } + } + + List buildSparkSubmitArgs() { + List args = new ArrayList(); + SparkSubmitOptionParser parser = new SparkSubmitOptionParser(); + + if (verbose) { + args.add(parser.VERBOSE); + } + + if (master != null) { + args.add(parser.MASTER); + args.add(master); + } + + if (deployMode != null) { + args.add(parser.DEPLOY_MODE); + args.add(deployMode); + } + + if (appName != null) { + args.add(parser.NAME); + args.add(appName); + } + + for (Map.Entry e : conf.entrySet()) { + args.add(parser.CONF); + args.add(String.format("%s=%s", e.getKey(), e.getValue())); + } + + if (propertiesFile != null) { + args.add(parser.PROPERTIES_FILE); + args.add(propertiesFile); + } + + if (!jars.isEmpty()) { + args.add(parser.JARS); + args.add(join(",", jars)); + } + + if (!files.isEmpty()) { + args.add(parser.FILES); + args.add(join(",", files)); + } + + if (!pyFiles.isEmpty()) { + args.add(parser.PY_FILES); + args.add(join(",", pyFiles)); + } + + if (mainClass != null) { + args.add(parser.CLASS); + args.add(mainClass); + } + + args.addAll(sparkArgs); + if (appResource != null) { + args.add(appResource); + } + args.addAll(appArgs); + + return args; + } + + private List buildSparkSubmitCommand(Map env) throws IOException { + // Load the properties file and check whether spark-submit will be running the app's driver + // or just launching a cluster app. When running the driver, the JVM's argument will be + // modified to cover the driver's configuration. + Properties props = loadPropertiesFile(); + boolean isClientMode = isClientMode(props); + String extraClassPath = isClientMode ? + firstNonEmptyValue(SparkLauncher.DRIVER_EXTRA_CLASSPATH, conf, props) : null; + + List cmd = buildJavaCommand(extraClassPath); + addOptionString(cmd, System.getenv("SPARK_SUBMIT_OPTS")); + addOptionString(cmd, System.getenv("SPARK_JAVA_OPTS")); + + if (isClientMode) { + // Figuring out where the memory value come from is a little tricky due to precedence. + // Precedence is observed in the following order: + // - explicit configuration (setConf()), which also covers --driver-memory cli argument. + // - properties file. + // - SPARK_DRIVER_MEMORY env variable + // - SPARK_MEM env variable + // - default value (512m) + String memory = firstNonEmpty(firstNonEmptyValue(SparkLauncher.DRIVER_MEMORY, conf, props), + System.getenv("SPARK_DRIVER_MEMORY"), System.getenv("SPARK_MEM"), DEFAULT_MEM); + cmd.add("-Xms" + memory); + cmd.add("-Xmx" + memory); + addOptionString(cmd, firstNonEmptyValue(SparkLauncher.DRIVER_EXTRA_JAVA_OPTIONS, conf, props)); + mergeEnvPathList(env, getLibPathEnvName(), + firstNonEmptyValue(SparkLauncher.DRIVER_EXTRA_LIBRARY_PATH, conf, props)); + } + + addPermGenSizeOpt(cmd); + cmd.add("org.apache.spark.deploy.SparkSubmit"); + cmd.addAll(buildSparkSubmitArgs()); + return cmd; + } + + private List buildPySparkShellCommand(Map env) throws IOException { + // For backwards compatibility, if a script is specified in + // the pyspark command line, then run it using spark-submit. + if (!appArgs.isEmpty() && appArgs.get(0).endsWith(".py")) { + System.err.println( + "WARNING: Running python applications through 'pyspark' is deprecated as of Spark 1.0.\n" + + "Use ./bin/spark-submit "); + appResource = appArgs.get(0); + appArgs.remove(0); + return buildCommand(env); + } + + checkArgument(appArgs.isEmpty(), "pyspark does not support any application options."); + + // When launching the pyspark shell, the spark-submit arguments should be stored in the + // PYSPARK_SUBMIT_ARGS env variable. + constructEnvVarArgs(env, "PYSPARK_SUBMIT_ARGS"); + + // The executable is the PYSPARK_DRIVER_PYTHON env variable set by the pyspark script, + // followed by PYSPARK_DRIVER_PYTHON_OPTS. + List pyargs = new ArrayList(); + pyargs.add(firstNonEmpty(System.getenv("PYSPARK_DRIVER_PYTHON"), "python")); + String pyOpts = System.getenv("PYSPARK_DRIVER_PYTHON_OPTS"); + if (!isEmpty(pyOpts)) { + pyargs.addAll(parseOptionString(pyOpts)); + } + + return pyargs; + } + + private List buildSparkRCommand(Map env) throws IOException { + if (!appArgs.isEmpty() && appArgs.get(0).endsWith(".R")) { + appResource = appArgs.get(0); + appArgs.remove(0); + return buildCommand(env); + } + // When launching the SparkR shell, store the spark-submit arguments in the SPARKR_SUBMIT_ARGS + // env variable. + constructEnvVarArgs(env, "SPARKR_SUBMIT_ARGS"); + + // Set shell.R as R_PROFILE_USER to load the SparkR package when the shell comes up. + String sparkHome = System.getenv("SPARK_HOME"); + env.put("R_PROFILE_USER", + join(File.separator, sparkHome, "R", "lib", "SparkR", "profile", "shell.R")); + + List args = new ArrayList(); + args.add(firstNonEmpty(System.getenv("SPARKR_DRIVER_R"), "R")); + return args; + } + + private void constructEnvVarArgs( + Map env, + String submitArgsEnvVariable) throws IOException { + Properties props = loadPropertiesFile(); + mergeEnvPathList(env, getLibPathEnvName(), + firstNonEmptyValue(SparkLauncher.DRIVER_EXTRA_LIBRARY_PATH, conf, props)); + + StringBuilder submitArgs = new StringBuilder(); + for (String arg : buildSparkSubmitArgs()) { + if (submitArgs.length() > 0) { + submitArgs.append(" "); + } + submitArgs.append(quoteForCommandString(arg)); + } + env.put(submitArgsEnvVariable, submitArgs.toString()); + } + + + private boolean isClientMode(Properties userProps) { + String userMaster = firstNonEmpty(master, (String) userProps.get(SparkLauncher.SPARK_MASTER)); + // Default master is "local[*]", so assume client mode in that case. + return userMaster == null || + "client".equals(deployMode) || + (!userMaster.equals("yarn-cluster") && deployMode == null); + } + + private class OptionParser extends SparkSubmitOptionParser { + + @Override + protected boolean handle(String opt, String value) { + if (opt.equals(MASTER)) { + master = value; + } else if (opt.equals(DEPLOY_MODE)) { + deployMode = value; + } else if (opt.equals(PROPERTIES_FILE)) { + propertiesFile = value; + } else if (opt.equals(DRIVER_MEMORY)) { + conf.put(SparkLauncher.DRIVER_MEMORY, value); + } else if (opt.equals(DRIVER_JAVA_OPTIONS)) { + conf.put(SparkLauncher.DRIVER_EXTRA_JAVA_OPTIONS, value); + } else if (opt.equals(DRIVER_LIBRARY_PATH)) { + conf.put(SparkLauncher.DRIVER_EXTRA_LIBRARY_PATH, value); + } else if (opt.equals(DRIVER_CLASS_PATH)) { + conf.put(SparkLauncher.DRIVER_EXTRA_CLASSPATH, value); + } else if (opt.equals(CONF)) { + String[] setConf = value.split("=", 2); + checkArgument(setConf.length == 2, "Invalid argument to %s: %s", CONF, value); + conf.put(setConf[0], setConf[1]); + } else if (opt.equals(CLASS)) { + // The special classes require some special command line handling, since they allow + // mixing spark-submit arguments with arguments that should be propagated to the shell + // itself. Note that for this to work, the "--class" argument must come before any + // non-spark-submit arguments. + mainClass = value; + if (specialClasses.containsKey(value)) { + allowsMixedArguments = true; + appResource = specialClasses.get(value); + } + } else { + sparkArgs.add(opt); + if (value != null) { + sparkArgs.add(value); + } + } + return true; + } + + @Override + protected boolean handleUnknown(String opt) { + // When mixing arguments, add unrecognized parameters directly to the user arguments list. In + // normal mode, any unrecognized parameter triggers the end of command line parsing, and the + // parameter itself will be interpreted by SparkSubmit as the application resource. The + // remaining params will be appended to the list of SparkSubmit arguments. + if (allowsMixedArguments) { + appArgs.add(opt); + return true; + } else { + sparkArgs.add(opt); + return false; + } + } + + @Override + protected void handleExtraArgs(List extra) { + for (String arg : extra) { + sparkArgs.add(arg); + } + } + + } + +} diff --git a/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitOptionParser.java b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitOptionParser.java new file mode 100644 index 0000000000000..8526d2e7cfa3f --- /dev/null +++ b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitOptionParser.java @@ -0,0 +1,224 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.launcher; + +import java.util.List; +import java.util.regex.Matcher; +import java.util.regex.Pattern; + +/** + * Parser for spark-submit command line options. + *

    + * This class encapsulates the parsing code for spark-submit command line options, so that there + * is a single list of options that needs to be maintained (well, sort of, but it makes it harder + * to break things). + */ +class SparkSubmitOptionParser { + + // The following constants define the "main" name for the available options. They're defined + // to avoid copy & paste of the raw strings where they're needed. + // + // The fields are not static so that they're exposed to Scala code that uses this class. See + // SparkSubmitArguments.scala. That is also why this class is not abstract - to allow code to + // easily use these constants without having to create dummy implementations of this class. + protected final String CLASS = "--class"; + protected final String CONF = "--conf"; + protected final String DEPLOY_MODE = "--deploy-mode"; + protected final String DRIVER_CLASS_PATH = "--driver-class-path"; + protected final String DRIVER_CORES = "--driver-cores"; + protected final String DRIVER_JAVA_OPTIONS = "--driver-java-options"; + protected final String DRIVER_LIBRARY_PATH = "--driver-library-path"; + protected final String DRIVER_MEMORY = "--driver-memory"; + protected final String EXECUTOR_MEMORY = "--executor-memory"; + protected final String FILES = "--files"; + protected final String JARS = "--jars"; + protected final String KILL_SUBMISSION = "--kill"; + protected final String MASTER = "--master"; + protected final String NAME = "--name"; + protected final String PACKAGES = "--packages"; + protected final String PROPERTIES_FILE = "--properties-file"; + protected final String PROXY_USER = "--proxy-user"; + protected final String PY_FILES = "--py-files"; + protected final String REPOSITORIES = "--repositories"; + protected final String STATUS = "--status"; + protected final String TOTAL_EXECUTOR_CORES = "--total-executor-cores"; + + // Options that do not take arguments. + protected final String HELP = "--help"; + protected final String SUPERVISE = "--supervise"; + protected final String VERBOSE = "--verbose"; + protected final String VERSION = "--version"; + + // Standalone-only options. + + // YARN-only options. + protected final String ARCHIVES = "--archives"; + protected final String EXECUTOR_CORES = "--executor-cores"; + protected final String QUEUE = "--queue"; + protected final String NUM_EXECUTORS = "--num-executors"; + + /** + * This is the canonical list of spark-submit options. Each entry in the array contains the + * different aliases for the same option; the first element of each entry is the "official" + * name of the option, passed to {@link #handle(String, String)}. + *

    + * Options not listed here nor in the "switch" list below will result in a call to + * {@link $#handleUnknown(String)}. + *

    + * These two arrays are visible for tests. + */ + final String[][] opts = { + { ARCHIVES }, + { CLASS }, + { CONF, "-c" }, + { DEPLOY_MODE }, + { DRIVER_CLASS_PATH }, + { DRIVER_CORES }, + { DRIVER_JAVA_OPTIONS }, + { DRIVER_LIBRARY_PATH }, + { DRIVER_MEMORY }, + { EXECUTOR_CORES }, + { EXECUTOR_MEMORY }, + { FILES }, + { JARS }, + { KILL_SUBMISSION }, + { MASTER }, + { NAME }, + { NUM_EXECUTORS }, + { PACKAGES }, + { PROPERTIES_FILE }, + { PROXY_USER }, + { PY_FILES }, + { QUEUE }, + { REPOSITORIES }, + { STATUS }, + { TOTAL_EXECUTOR_CORES }, + }; + + /** + * List of switches (command line options that do not take parameters) recognized by spark-submit. + */ + final String[][] switches = { + { HELP, "-h" }, + { SUPERVISE }, + { VERBOSE, "-v" }, + { VERSION }, + }; + + /** + * Parse a list of spark-submit command line options. + *

    + * See SparkSubmitArguments.scala for a more formal description of available options. + * + * @throws IllegalArgumentException If an error is found during parsing. + */ + protected final void parse(List args) { + Pattern eqSeparatedOpt = Pattern.compile("(--[^=]+)=(.+)"); + + int idx = 0; + for (idx = 0; idx < args.size(); idx++) { + String arg = args.get(idx); + String value = null; + + Matcher m = eqSeparatedOpt.matcher(arg); + if (m.matches()) { + arg = m.group(1); + value = m.group(2); + } + + // Look for options with a value. + String name = findCliOption(arg, opts); + if (name != null) { + if (value == null) { + if (idx == args.size() - 1) { + throw new IllegalArgumentException( + String.format("Missing argument for option '%s'.", arg)); + } + idx++; + value = args.get(idx); + } + if (!handle(name, value)) { + break; + } + continue; + } + + // Look for a switch. + name = findCliOption(arg, switches); + if (name != null) { + if (!handle(name, null)) { + break; + } + continue; + } + + if (!handleUnknown(arg)) { + break; + } + } + + if (idx < args.size()) { + idx++; + } + handleExtraArgs(args.subList(idx, args.size())); + } + + /** + * Callback for when an option with an argument is parsed. + * + * @param opt The long name of the cli option (might differ from actual command line). + * @param value The value. This will be null if the option does not take a value. + * @return Whether to continue parsing the argument list. + */ + protected boolean handle(String opt, String value) { + throw new UnsupportedOperationException(); + } + + /** + * Callback for when an unrecognized option is parsed. + * + * @param opt Unrecognized option from the command line. + * @return Whether to continue parsing the argument list. + */ + protected boolean handleUnknown(String opt) { + throw new UnsupportedOperationException(); + } + + /** + * Callback for remaining command line arguments after either {@link #handle(String, String)} or + * {@link #handleUnknown(String)} return "false". This will be called at the end of parsing even + * when there are no remaining arguments. + * + * @param extra List of remaining arguments. + */ + protected void handleExtraArgs(List extra) { + throw new UnsupportedOperationException(); + } + + private String findCliOption(String name, String[][] available) { + for (String[] candidates : available) { + for (String candidate : candidates) { + if (candidate.equals(name)) { + return candidates[0]; + } + } + } + return null; + } + +} diff --git a/launcher/src/main/java/org/apache/spark/launcher/package-info.java b/launcher/src/main/java/org/apache/spark/launcher/package-info.java new file mode 100644 index 0000000000000..7ed756f4b8591 --- /dev/null +++ b/launcher/src/main/java/org/apache/spark/launcher/package-info.java @@ -0,0 +1,45 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * Library for launching Spark applications. + *

    + * This library allows applications to launch Spark programmatically. There's only one entry + * point to the library - the {@link org.apache.spark.launcher.SparkLauncher} class. + *

    + * To launch a Spark application, just instantiate a {@link org.apache.spark.launcher.SparkLauncher} + * and configure the application to run. For example: + * + * 

    + * {@code
+ *   import org.apache.spark.launcher.SparkLauncher;
+ *
+ *   public class MyLauncher {
+ *     public static void main(String[] args) throws Exception {
+ *       Process spark = new SparkLauncher()
+ *         .setAppResource("/my/app.jar")
+ *         .setMainClass("my.spark.app.Main")
+ *         .setMaster("local")
+ *         .setConf(SparkLauncher.DRIVER_MEMORY, "2g")
+ *         .launch();
+ *       spark.waitFor();
+ *     }
+ *   }
+ * }
+ *
    + */ +package org.apache.spark.launcher; diff --git a/launcher/src/test/java/org/apache/spark/launcher/CommandBuilderUtilsSuite.java b/launcher/src/test/java/org/apache/spark/launcher/CommandBuilderUtilsSuite.java new file mode 100644 index 0000000000000..1ae42eed8a3af --- /dev/null +++ b/launcher/src/test/java/org/apache/spark/launcher/CommandBuilderUtilsSuite.java @@ -0,0 +1,101 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.launcher; + +import java.util.ArrayList; +import java.util.Arrays; +import java.util.List; + +import org.junit.Test; +import static org.junit.Assert.*; + +import static org.apache.spark.launcher.CommandBuilderUtils.*; + +public class CommandBuilderUtilsSuite { + + @Test + public void testValidOptionStrings() { + testOpt("a b c d e", Arrays.asList("a", "b", "c", "d", "e")); + testOpt("a 'b c' \"d\" e", Arrays.asList("a", "b c", "d", "e")); + testOpt("a 'b\\\"c' \"'d'\" e", Arrays.asList("a", "b\\\"c", "'d'", "e")); + testOpt("a 'b\"c' \"\\\"d\\\"\" e", Arrays.asList("a", "b\"c", "\"d\"", "e")); + testOpt(" a b c \\\\ ", Arrays.asList("a", "b", "c", "\\")); + + // Following tests ported from UtilsSuite.scala. + testOpt("", new ArrayList()); + testOpt("a", Arrays.asList("a")); + testOpt("aaa", Arrays.asList("aaa")); + testOpt("a b c", Arrays.asList("a", "b", "c")); + testOpt(" a b\t c ", Arrays.asList("a", "b", "c")); + testOpt("a 'b c'", Arrays.asList("a", "b c")); + testOpt("a 'b c' d", Arrays.asList("a", "b c", "d")); + testOpt("'b c'", Arrays.asList("b c")); + testOpt("a \"b c\"", Arrays.asList("a", "b c")); + testOpt("a \"b c\" d", Arrays.asList("a", "b c", "d")); + testOpt("\"b c\"", Arrays.asList("b c")); + testOpt("a 'b\" c' \"d' e\"", Arrays.asList("a", "b\" c", "d' e")); + testOpt("a\t'b\nc'\nd", Arrays.asList("a", "b\nc", "d")); + testOpt("a \"b\\\\c\"", Arrays.asList("a", "b\\c")); + testOpt("a \"b\\\"c\"", Arrays.asList("a", "b\"c")); + testOpt("a 'b\\\"c'", Arrays.asList("a", "b\\\"c")); + testOpt("'a'b", Arrays.asList("ab")); + testOpt("'a''b'", Arrays.asList("ab")); + testOpt("\"a\"b", Arrays.asList("ab")); + testOpt("\"a\"\"b\"", Arrays.asList("ab")); + testOpt("''", Arrays.asList("")); + testOpt("\"\"", Arrays.asList("")); + } + + @Test + public void testInvalidOptionStrings() { + testInvalidOpt("\\"); + testInvalidOpt("\"abcde"); + testInvalidOpt("'abcde"); + } + + @Test + public void testWindowsBatchQuoting() { + assertEquals("abc", quoteForBatchScript("abc")); + assertEquals("\"a b c\"", quoteForBatchScript("a b c")); + assertEquals("\"a \"\"b\"\" c\"", quoteForBatchScript("a \"b\" c")); + assertEquals("\"a\"\"b\"\"c\"", quoteForBatchScript("a\"b\"c")); + assertEquals("\"ab^=\"\"cd\"\"\"", quoteForBatchScript("ab=\"cd\"")); + } + + @Test + public void testPythonArgQuoting() { + assertEquals("\"abc\"", quoteForCommandString("abc")); + assertEquals("\"a b c\"", quoteForCommandString("a b c")); + assertEquals("\"a \\\"b\\\" c\"", quoteForCommandString("a \"b\" c")); + } + + private void testOpt(String opts, List expected) { + assertEquals(String.format("test string failed to parse: [[ %s ]]", opts), + expected, parseOptionString(opts)); + } + + private void testInvalidOpt(String opts) { + try { + parseOptionString(opts); + fail("Expected exception for invalid option string."); + } catch (IllegalArgumentException e) { + // pass. + } + } + +} diff --git a/launcher/src/test/java/org/apache/spark/launcher/SparkLauncherSuite.java b/launcher/src/test/java/org/apache/spark/launcher/SparkLauncherSuite.java new file mode 100644 index 0000000000000..252d5abae1ca3 --- /dev/null +++ b/launcher/src/test/java/org/apache/spark/launcher/SparkLauncherSuite.java @@ -0,0 +1,94 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.launcher; + +import java.io.BufferedReader; +import java.io.InputStream; +import java.io.InputStreamReader; +import java.util.HashMap; +import java.util.Map; + +import org.junit.Test; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; +import static org.junit.Assert.*; + +/** + * These tests require the Spark assembly to be built before they can be run. + */ +public class SparkLauncherSuite { + + private static final Logger LOG = LoggerFactory.getLogger(SparkLauncherSuite.class); + + @Test + public void testChildProcLauncher() throws Exception { + Map env = new HashMap(); + env.put("SPARK_PRINT_LAUNCH_COMMAND", "1"); + + SparkLauncher launcher = new SparkLauncher(env) + .setSparkHome(System.getProperty("spark.test.home")) + .setMaster("local") + .setAppResource("spark-internal") + .setConf(SparkLauncher.DRIVER_EXTRA_JAVA_OPTIONS, + "-Dfoo=bar -Dtest.name=-testChildProcLauncher") + .setConf(SparkLauncher.DRIVER_EXTRA_CLASSPATH, System.getProperty("java.class.path")) + .setMainClass(SparkLauncherTestApp.class.getName()) + .addAppArgs("proc"); + final Process app = launcher.launch(); + new Redirector("stdout", app.getInputStream()).start(); + new Redirector("stderr", app.getErrorStream()).start(); + assertEquals(0, app.waitFor()); + } + + public static class SparkLauncherTestApp { + + public static void main(String[] args) throws Exception { + assertEquals(1, args.length); + assertEquals("proc", args[0]); + assertEquals("bar", System.getProperty("foo")); + assertEquals("local", System.getProperty(SparkLauncher.SPARK_MASTER)); + } + + } + + private static class Redirector extends Thread { + + private final InputStream in; + + Redirector(String name, InputStream in) { + this.in = in; + setName(name); + setDaemon(true); + } + + @Override + public void run() { + try { + BufferedReader reader = new BufferedReader(new InputStreamReader(in, "UTF-8")); + String line; + while ((line = reader.readLine()) != null) { + LOG.warn(line); + } + } catch (Exception e) { + LOG.error("Error reading process output.", e); + } + } + + } + +} diff --git a/launcher/src/test/java/org/apache/spark/launcher/SparkSubmitCommandBuilderSuite.java b/launcher/src/test/java/org/apache/spark/launcher/SparkSubmitCommandBuilderSuite.java new file mode 100644 index 0000000000000..97043a76cc612 --- /dev/null +++ b/launcher/src/test/java/org/apache/spark/launcher/SparkSubmitCommandBuilderSuite.java @@ -0,0 +1,287 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.launcher; + +import java.io.File; +import java.util.Arrays; +import java.util.Collections; +import java.util.HashMap; +import java.util.List; +import java.util.Map; +import java.util.regex.Pattern; + +import org.junit.AfterClass; +import org.junit.BeforeClass; +import org.junit.Test; +import static org.junit.Assert.*; + +public class SparkSubmitCommandBuilderSuite { + + private static File dummyPropsFile; + private static SparkSubmitOptionParser parser; + + @BeforeClass + public static void setUp() throws Exception { + dummyPropsFile = File.createTempFile("spark", "properties"); + parser = new SparkSubmitOptionParser(); + } + + @AfterClass + public static void cleanUp() throws Exception { + dummyPropsFile.delete(); + } + + @Test + public void testDriverCmdBuilder() throws Exception { + testCmdBuilder(true); + } + + @Test + public void testClusterCmdBuilder() throws Exception { + testCmdBuilder(false); + } + + @Test + public void testCliParser() throws Exception { + List sparkSubmitArgs = Arrays.asList( + parser.MASTER, + "local", + parser.DRIVER_MEMORY, + "42g", + parser.DRIVER_CLASS_PATH, + "/driverCp", + parser.DRIVER_JAVA_OPTIONS, + "extraJavaOpt", + parser.CONF, + "spark.randomOption=foo", + parser.CONF, + SparkLauncher.DRIVER_EXTRA_LIBRARY_PATH + "=/driverLibPath"); + Map env = new HashMap(); + List cmd = buildCommand(sparkSubmitArgs, env); + + assertTrue(findInStringList(env.get(CommandBuilderUtils.getLibPathEnvName()), + File.pathSeparator, "/driverLibPath")); + assertTrue(findInStringList(findArgValue(cmd, "-cp"), File.pathSeparator, "/driverCp")); + assertTrue("Driver -Xms should be configured.", cmd.contains("-Xms42g")); + assertTrue("Driver -Xmx should be configured.", cmd.contains("-Xmx42g")); + assertTrue("Command should contain user-defined conf.", + Collections.indexOfSubList(cmd, Arrays.asList(parser.CONF, "spark.randomOption=foo")) > 0); + } + + @Test + public void testShellCliParser() throws Exception { + List sparkSubmitArgs = Arrays.asList( + parser.CLASS, + "org.apache.spark.repl.Main", + parser.MASTER, + "foo", + "--app-arg", + "bar", + "--app-switch", + parser.FILES, + "baz", + parser.NAME, + "appName"); + + List args = newCommandBuilder(sparkSubmitArgs).buildSparkSubmitArgs(); + List expected = Arrays.asList("spark-shell", "--app-arg", "bar", "--app-switch"); + assertEquals(expected, args.subList(args.size() - expected.size(), args.size())); + } + + @Test + public void testAlternateSyntaxParsing() throws Exception { + List sparkSubmitArgs = Arrays.asList( + parser.CLASS + "=org.my.Class", + parser.MASTER + "=foo", + parser.DEPLOY_MODE + "=bar"); + + List cmd = newCommandBuilder(sparkSubmitArgs).buildSparkSubmitArgs(); + assertEquals("org.my.Class", findArgValue(cmd, parser.CLASS)); + assertEquals("foo", findArgValue(cmd, parser.MASTER)); + assertEquals("bar", findArgValue(cmd, parser.DEPLOY_MODE)); + } + + @Test + public void testPySparkLauncher() throws Exception { + List sparkSubmitArgs = Arrays.asList( + SparkSubmitCommandBuilder.PYSPARK_SHELL, + "--master=foo", + "--deploy-mode=bar"); + + Map env = new HashMap(); + List cmd = buildCommand(sparkSubmitArgs, env); + assertEquals("python", cmd.get(cmd.size() - 1)); + assertEquals( + String.format("\"%s\" \"foo\" \"%s\" \"bar\" \"%s\"", + parser.MASTER, parser.DEPLOY_MODE, SparkSubmitCommandBuilder.PYSPARK_SHELL_RESOURCE), + env.get("PYSPARK_SUBMIT_ARGS")); + } + + @Test + public void testPySparkFallback() throws Exception { + List sparkSubmitArgs = Arrays.asList( + "--master=foo", + "--deploy-mode=bar", + "script.py", + "arg1"); + + Map env = new HashMap(); + List cmd = buildCommand(sparkSubmitArgs, env); + + assertEquals("foo", findArgValue(cmd, "--master")); + assertEquals("bar", findArgValue(cmd, "--deploy-mode")); + assertEquals("script.py", cmd.get(cmd.size() - 2)); + assertEquals("arg1", cmd.get(cmd.size() - 1)); + } + + private void testCmdBuilder(boolean isDriver) throws Exception { + String deployMode = isDriver ? "client" : "cluster"; + + SparkSubmitCommandBuilder launcher = + newCommandBuilder(Collections.emptyList()); + launcher.childEnv.put(CommandBuilderUtils.ENV_SPARK_HOME, + System.getProperty("spark.test.home")); + launcher.master = "yarn"; + launcher.deployMode = deployMode; + launcher.appResource = "/foo"; + launcher.appName = "MyApp"; + launcher.mainClass = "my.Class"; + launcher.propertiesFile = dummyPropsFile.getAbsolutePath(); + launcher.appArgs.add("foo"); + launcher.appArgs.add("bar"); + launcher.conf.put(SparkLauncher.DRIVER_MEMORY, "1g"); + launcher.conf.put(SparkLauncher.DRIVER_EXTRA_CLASSPATH, "/driver"); + launcher.conf.put(SparkLauncher.DRIVER_EXTRA_JAVA_OPTIONS, "-Ddriver -XX:MaxPermSize=256m"); + launcher.conf.put(SparkLauncher.DRIVER_EXTRA_LIBRARY_PATH, "/native"); + launcher.conf.put("spark.foo", "foo"); + + Map env = new HashMap(); + List cmd = launcher.buildCommand(env); + + // Checks below are different for driver and non-driver mode. + + if (isDriver) { + assertTrue("Driver -Xms should be configured.", cmd.contains("-Xms1g")); + assertTrue("Driver -Xmx should be configured.", cmd.contains("-Xmx1g")); + } else { + boolean found = false; + for (String arg : cmd) { + if (arg.startsWith("-Xms") || arg.startsWith("-Xmx")) { + found = true; + break; + } + } + assertFalse("Memory arguments should not be set.", found); + } + + for (String arg : cmd) { + if (arg.startsWith("-XX:MaxPermSize=")) { + if (isDriver) { + assertEquals("-XX:MaxPermSize=256m", arg); + } else { + assertEquals("-XX:MaxPermSize=128m", arg); + } + } + } + + String[] cp = findArgValue(cmd, "-cp").split(Pattern.quote(File.pathSeparator)); + if (isDriver) { + assertTrue("Driver classpath should contain provided entry.", contains("/driver", cp)); + } else { + assertFalse("Driver classpath should not be in command.", contains("/driver", cp)); + } + + String libPath = env.get(CommandBuilderUtils.getLibPathEnvName()); + if (isDriver) { + assertNotNull("Native library path should be set.", libPath); + assertTrue("Native library path should contain provided entry.", + contains("/native", libPath.split(Pattern.quote(File.pathSeparator)))); + } else { + assertNull("Native library should not be set.", libPath); + } + + // Checks below are the same for both driver and non-driver mode. + assertEquals(dummyPropsFile.getAbsolutePath(), findArgValue(cmd, parser.PROPERTIES_FILE)); + assertEquals("yarn", findArgValue(cmd, parser.MASTER)); + assertEquals(deployMode, findArgValue(cmd, parser.DEPLOY_MODE)); + assertEquals("my.Class", findArgValue(cmd, parser.CLASS)); + assertEquals("MyApp", findArgValue(cmd, parser.NAME)); + + boolean appArgsOk = false; + for (int i = 0; i < cmd.size(); i++) { + if (cmd.get(i).equals("/foo")) { + assertEquals("foo", cmd.get(i + 1)); + assertEquals("bar", cmd.get(i + 2)); + assertEquals(cmd.size(), i + 3); + appArgsOk = true; + break; + } + } + assertTrue("App resource and args should be added to command.", appArgsOk); + + Map conf = parseConf(cmd, parser); + assertEquals("foo", conf.get("spark.foo")); + } + + private boolean contains(String needle, String[] haystack) { + for (String entry : haystack) { + if (entry.equals(needle)) { + return true; + } + } + return false; + } + + private Map parseConf(List cmd, SparkSubmitOptionParser parser) { + Map conf = new HashMap(); + for (int i = 0; i < cmd.size(); i++) { + if (cmd.get(i).equals(parser.CONF)) { + String[] val = cmd.get(i + 1).split("=", 2); + conf.put(val[0], val[1]); + i += 1; + } + } + return conf; + } + + private String findArgValue(List cmd, String name) { + for (int i = 0; i < cmd.size(); i++) { + if (cmd.get(i).equals(name)) { + return cmd.get(i + 1); + } + } + fail(String.format("arg '%s' not found", name)); + return null; + } + + private boolean findInStringList(String list, String sep, String needle) { + return contains(needle, list.split(sep)); + } + + private SparkSubmitCommandBuilder newCommandBuilder(List args) { + SparkSubmitCommandBuilder builder = new SparkSubmitCommandBuilder(args); + builder.childEnv.put(CommandBuilderUtils.ENV_SPARK_HOME, System.getProperty("spark.test.home")); + builder.childEnv.put(CommandBuilderUtils.ENV_SPARK_ASSEMBLY, "dummy"); + return builder; + } + + private List buildCommand(List args, Map env) throws Exception { + return newCommandBuilder(args).buildCommand(env); + } + +} diff --git a/launcher/src/test/java/org/apache/spark/launcher/SparkSubmitOptionParserSuite.java b/launcher/src/test/java/org/apache/spark/launcher/SparkSubmitOptionParserSuite.java new file mode 100644 index 0000000000000..f3d2109917056 --- /dev/null +++ b/launcher/src/test/java/org/apache/spark/launcher/SparkSubmitOptionParserSuite.java @@ -0,0 +1,108 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.launcher; + +import java.util.Arrays; +import java.util.Collections; +import java.util.List; + +import org.junit.Before; +import org.junit.Test; +import static org.junit.Assert.*; +import static org.mockito.Mockito.*; + +import static org.apache.spark.launcher.SparkSubmitOptionParser.*; + +public class SparkSubmitOptionParserSuite { + + private SparkSubmitOptionParser parser; + + @Before + public void setUp() { + parser = spy(new DummyParser()); + } + + @Test + public void testAllOptions() { + int count = 0; + for (String[] optNames : parser.opts) { + for (String optName : optNames) { + String value = optName + "-value"; + parser.parse(Arrays.asList(optName, value)); + count++; + verify(parser).handle(eq(optNames[0]), eq(value)); + verify(parser, times(count)).handle(anyString(), anyString()); + verify(parser, times(count)).handleExtraArgs(eq(Collections.emptyList())); + } + } + + for (String[] switchNames : parser.switches) { + int switchCount = 0; + for (String name : switchNames) { + parser.parse(Arrays.asList(name)); + count++; + switchCount++; + verify(parser, times(switchCount)).handle(eq(switchNames[0]), same((String) null)); + verify(parser, times(count)).handle(anyString(), any(String.class)); + verify(parser, times(count)).handleExtraArgs(eq(Collections.emptyList())); + } + } + } + + @Test + public void testExtraOptions() { + List args = Arrays.asList(parser.MASTER, parser.MASTER, "foo", "bar"); + parser.parse(args); + verify(parser).handle(eq(parser.MASTER), eq(parser.MASTER)); + verify(parser).handleUnknown(eq("foo")); + verify(parser).handleExtraArgs(eq(Arrays.asList("bar"))); + } + + @Test(expected=IllegalArgumentException.class) + public void testMissingArg() { + parser.parse(Arrays.asList(parser.MASTER)); + } + + @Test + public void testEqualSeparatedOption() { + List args = Arrays.asList(parser.MASTER + "=" + parser.MASTER); + parser.parse(args); + verify(parser).handle(eq(parser.MASTER), eq(parser.MASTER)); + verify(parser).handleExtraArgs(eq(Collections.emptyList())); + } + + private static class DummyParser extends SparkSubmitOptionParser { + + @Override + protected boolean handle(String opt, String value) { + return true; + } + + @Override + protected boolean handleUnknown(String opt) { + return false; + } + + @Override + protected void handleExtraArgs(List extra) { + + } + + } + +} diff --git a/launcher/src/test/resources/log4j.properties b/launcher/src/test/resources/log4j.properties new file mode 100644 index 0000000000000..67a6a98217118 --- /dev/null +++ b/launcher/src/test/resources/log4j.properties @@ -0,0 +1,31 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# Set everything to be logged to the file core/target/unit-tests.log +log4j.rootCategory=INFO, file +log4j.appender.file=org.apache.log4j.FileAppender +log4j.appender.file.append=false + +# Some tests will set "test.name" to avoid overwriting the main log file. +log4j.appender.file.file=target/unit-tests${test.name}.log + +log4j.appender.file.layout=org.apache.log4j.PatternLayout +log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n + +# Ignore messages below warning level from Jetty, because it's a bit verbose +log4j.logger.org.spark-project.jetty=WARN +org.spark-project.jetty.LEVEL=WARN diff --git a/make-distribution.sh b/make-distribution.sh index 051c87c0894ae..738a9c4d69601 100755 --- a/make-distribution.sh +++ b/make-distribution.sh @@ -98,9 +98,9 @@ done if [ -z "$JAVA_HOME" ]; then # Fall back on JAVA_HOME from rpm, if found if [ $(command -v rpm) ]; then - RPM_JAVA_HOME=$(rpm -E %java_home 2>/dev/null) + RPM_JAVA_HOME="$(rpm -E %java_home 2>/dev/null)" if [ "$RPM_JAVA_HOME" != "%java_home" ]; then - JAVA_HOME=$RPM_JAVA_HOME + JAVA_HOME="$RPM_JAVA_HOME" echo "No JAVA_HOME set, proceeding with '$JAVA_HOME' learned from rpm" fi fi @@ -113,24 +113,27 @@ fi if [ $(command -v git) ]; then GITREV=$(git rev-parse --short HEAD 2>/dev/null || :) - if [ ! -z $GITREV ]; then + if [ ! -z "$GITREV" ]; then GITREVSTRING=" (git revision $GITREV)" fi unset GITREV fi -if [ ! $(command -v $MVN) ] ; then +if [ ! $(command -v "$MVN") ] ; then echo -e "Could not locate Maven command: '$MVN'." echo -e "Specify the Maven command with the --mvn flag" exit -1; fi -VERSION=$($MVN help:evaluate -Dexpression=project.version 2>/dev/null | grep -v "INFO" | tail -n 1) -SPARK_HADOOP_VERSION=$($MVN help:evaluate -Dexpression=hadoop.version $@ 2>/dev/null\ +VERSION=$("$MVN" help:evaluate -Dexpression=project.version 2>/dev/null | grep -v "INFO" | tail -n 1) +SCALA_VERSION=$("$MVN" help:evaluate -Dexpression=scala.binary.version $@ 2>/dev/null\ | grep -v "INFO"\ | tail -n 1) -SPARK_HIVE=$($MVN help:evaluate -Dexpression=project.activeProfiles -pl sql/hive $@ 2>/dev/null\ +SPARK_HADOOP_VERSION=$("$MVN" help:evaluate -Dexpression=hadoop.version $@ 2>/dev/null\ + | grep -v "INFO"\ + | tail -n 1) +SPARK_HIVE=$("$MVN" help:evaluate -Dexpression=project.activeProfiles -pl sql/hive $@ 2>/dev/null\ | grep -v "INFO"\ | fgrep --count "hive";\ # Reset exit status to 0, otherwise the script stops here if the last grep finds nothing\ @@ -147,7 +150,7 @@ if [[ ! "$JAVA_VERSION" =~ "1.6" && -z "$SKIP_JAVA_TEST" ]]; then echo "Output from 'java -version' was:" echo "$JAVA_VERSION" read -p "Would you like to continue anyways? [y,n]: " -r - if [[ ! $REPLY =~ ^[Yy]$ ]]; then + if [[ ! "$REPLY" =~ ^[Yy]$ ]]; then echo "Okay, exiting." exit 1 fi @@ -232,7 +235,7 @@ cp -r "$SPARK_HOME/ec2" "$DISTDIR" if [ "$SPARK_TACHYON" == "true" ]; then TMPD=`mktemp -d 2>/dev/null || mktemp -d -t 'disttmp'` - pushd $TMPD > /dev/null + pushd "$TMPD" > /dev/null echo "Fetching tachyon tgz" TACHYON_DL="${TACHYON_TGZ}.part" @@ -259,7 +262,7 @@ if [ "$SPARK_TACHYON" == "true" ]; then fi popd > /dev/null - rm -rf $TMPD + rm -rf "$TMPD" fi if [ "$MAKE_TGZ" == "true" ]; then diff --git a/mllib/pom.xml b/mllib/pom.xml index e40a509a70e61..640a7aea8fa37 100644 --- a/mllib/pom.xml +++ b/mllib/pom.xml @@ -20,8 +20,8 @@ 4.0.0 org.apache.spark - spark-parent - 1.3.0-SNAPSHOT + spark-parent_2.10 + 1.4.0-SNAPSHOT ../pom.xml @@ -59,11 +59,12 @@ org.jblas jblas ${jblas.version} + test     org.scalanlp breeze_${scala.binary.version} - 0.10 + 0.11.2 @@ -121,7 +122,7 @@ com.github.fommil.netlib all - 1.1.2 + ${netlib.java.version} pom     diff --git a/mllib/src/main/scala/org/apache/spark/ml/Estimator.scala b/mllib/src/main/scala/org/apache/spark/ml/Estimator.scala index eff7ef925dfbd..d6b3503ebdd9a 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Estimator.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Estimator.scala @@ -40,7 +40,7 @@ abstract class Estimator[M <: Model[M]] extends PipelineStage with Params { */ @varargs def fit(dataset: DataFrame, paramPairs: ParamPair[_]*): M = { - val map = new ParamMap().put(paramPairs: _*) + val map = ParamMap(paramPairs: _*) fit(dataset, map) } diff --git a/mllib/src/main/scala/org/apache/spark/ml/Identifiable.scala b/mllib/src/main/scala/org/apache/spark/ml/Identifiable.scala index cd84b05bfb496..a1d49095c24ac 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Identifiable.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Identifiable.scala @@ -25,9 +25,9 @@ import java.util.UUID private[ml] trait Identifiable extends Serializable { /** - * A unique id for the object. The default implementation concatenates the class name, "-", and 8 + * A unique id for the object. The default implementation concatenates the class name, "_", and 8 * random hex chars. */ private[ml] val uid: String = - this.getClass.getSimpleName + "-" + UUID.randomUUID().toString.take(8) + this.getClass.getSimpleName + "_" + UUID.randomUUID().toString.take(8) } diff --git a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala index bb291e6e1fd7d..8eddf79cdfe28 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala @@ -20,7 +20,7 @@ package org.apache.spark.ml import scala.collection.mutable.ListBuffer import org.apache.spark.Logging -import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.annotation.{AlphaComponent, DeveloperApi} import org.apache.spark.ml.param.{Param, ParamMap} import org.apache.spark.sql.DataFrame import org.apache.spark.sql.types.StructType @@ -33,12 +33,23 @@ import org.apache.spark.sql.types.StructType abstract class PipelineStage extends Serializable with Logging { /** + * :: DeveloperApi :: + * * Derives the output schema from the input schema and parameters. + * The schema describes the columns and types of the data. + * + * @param schema Input schema to this stage + * @param paramMap Parameters passed to this stage + * @return Output schema from this stage */ - private[ml] def transformSchema(schema: StructType, paramMap: ParamMap): StructType + @DeveloperApi + def transformSchema(schema: StructType, paramMap: ParamMap): StructType /** * Derives the output schema from the input schema and parameters, optionally with logging. + * + * This should be optimistic. If it is unclear whether the schema will be valid, then it should + * be assumed valid until proven otherwise. */ protected def transformSchema( schema: StructType, @@ -73,7 +84,7 @@ class Pipeline extends Estimator[PipelineModel] { /** param for pipeline stages */ val stages: Param[Array[PipelineStage]] = new Param(this, "stages", "stages of the pipeline") def setStages(value: Array[PipelineStage]): this.type = { set(stages, value); this } - def getStages: Array[PipelineStage] = get(stages) + def getStages: Array[PipelineStage] = getOrDefault(stages) /** * Fits the pipeline to the input dataset with additional parameters. If a stage is an @@ -90,7 +101,7 @@ class Pipeline extends Estimator[PipelineModel] { */ override def fit(dataset: DataFrame, paramMap: ParamMap): PipelineModel = { transformSchema(dataset.schema, paramMap, logging = true) - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) val theStages = map(stages) // Search for the last estimator. var indexOfLastEstimator = -1 @@ -114,7 +125,9 @@ class Pipeline extends Estimator[PipelineModel] { throw new IllegalArgumentException( s"Do not support stage $stage of type ${stage.getClass}") } - curDataset = transformer.transform(curDataset, paramMap) + if (index < indexOfLastEstimator) { + curDataset = transformer.transform(curDataset, paramMap) + } transformers += transformer } else { transformers += stage.asInstanceOf[Transformer] @@ -124,8 +137,8 @@ class Pipeline extends Estimator[PipelineModel] { new PipelineModel(this, map, transformers.toArray) } - private[ml] override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = this.paramMap ++ paramMap + override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { + val map = extractParamMap(paramMap) val theStages = map(stages) require(theStages.toSet.size == theStages.size, "Cannot have duplicate components in a pipeline.") @@ -164,14 +177,14 @@ class PipelineModel private[ml] ( override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { // Precedence of ParamMaps: paramMap > this.paramMap > fittingParamMap - val map = (fittingParamMap ++ this.paramMap) ++ paramMap + val map = fittingParamMap ++ extractParamMap(paramMap) transformSchema(dataset.schema, map, logging = true) stages.foldLeft(dataset)((cur, transformer) => transformer.transform(cur, map)) } - private[ml] override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { + override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { // Precedence of ParamMaps: paramMap > this.paramMap > fittingParamMap - val map = (fittingParamMap ++ this.paramMap) ++ paramMap + val map = fittingParamMap ++ extractParamMap(paramMap) stages.foldLeft(schema)((cur, transformer) => transformer.transformSchema(cur, map)) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala b/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala index cd95c16aa768d..7fb87fe452ee6 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala @@ -22,8 +22,9 @@ import scala.annotation.varargs import org.apache.spark.Logging import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.param._ +import org.apache.spark.ml.param.shared._ import org.apache.spark.sql.DataFrame -import org.apache.spark.sql.Dsl._ +import org.apache.spark.sql.functions._ import org.apache.spark.sql.types._ /** @@ -62,7 +63,10 @@ abstract class Transformer extends PipelineStage with Params { private[ml] abstract class UnaryTransformer[IN, OUT, T <: UnaryTransformer[IN, OUT, T]] extends Transformer with HasInputCol with HasOutputCol with Logging { + /** @group setParam */ def setInputCol(value: String): T = set(inputCol, value).asInstanceOf[T] + + /** @group setParam */ def setOutputCol(value: String): T = set(outputCol, value).asInstanceOf[T] /** @@ -83,7 +87,7 @@ private[ml] abstract class UnaryTransformer[IN, OUT, T <: UnaryTransformer[IN, O protected def validateInputType(inputType: DataType): Unit = {} override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) val inputType = schema(map(inputCol)).dataType validateInputType(inputType) if (schema.fieldNames.contains(map(outputCol))) { @@ -96,8 +100,8 @@ private[ml] abstract class UnaryTransformer[IN, OUT, T <: UnaryTransformer[IN, O override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { transformSchema(dataset.schema, paramMap, logging = true) - val map = this.paramMap ++ paramMap - dataset.select($"*", callUDF( - this.createTransformFunc(map), outputDataType, dataset(map(inputCol))).as(map(outputCol))) + val map = extractParamMap(paramMap) + dataset.withColumn(map(outputCol), + callUDF(this.createTransformFunc(map), outputDataType, dataset(map(inputCol)))) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeGroup.scala b/mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeGroup.scala new file mode 100644 index 0000000000000..d7dee8fed2a55 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeGroup.scala @@ -0,0 +1,241 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.attribute + +import scala.collection.mutable.ArrayBuffer + +import org.apache.spark.mllib.linalg.VectorUDT +import org.apache.spark.sql.types.{Metadata, MetadataBuilder, StructField} + +/** + * Attributes that describe a vector ML column. + * + * @param name name of the attribute group (the ML column name) + * @param numAttributes optional number of attributes. At most one of `numAttributes` and `attrs` + * can be defined. + * @param attrs optional array of attributes. Attribute will be copied with their corresponding + * indices in the array. + */ +class AttributeGroup private ( + val name: String, + val numAttributes: Option[Int], + attrs: Option[Array[Attribute]]) extends Serializable { + + require(name.nonEmpty, "Cannot have an empty string for name.") + require(!(numAttributes.isDefined && attrs.isDefined), + "Cannot have both numAttributes and attrs defined.") + + /** + * Creates an attribute group without attribute info. + * @param name name of the attribute group + */ + def this(name: String) = this(name, None, None) + + /** + * Creates an attribute group knowing only the number of attributes. + * @param name name of the attribute group + * @param numAttributes number of attributes + */ + def this(name: String, numAttributes: Int) = this(name, Some(numAttributes), None) + + /** + * Creates an attribute group with attributes. + * @param name name of the attribute group + * @param attrs array of attributes. Attributes will be copied with their corresponding indices in + * the array. + */ + def this(name: String, attrs: Array[Attribute]) = this(name, None, Some(attrs)) + + /** + * Optional array of attributes. At most one of `numAttributes` and `attributes` can be defined. + */ + val attributes: Option[Array[Attribute]] = attrs.map(_.view.zipWithIndex.map { case (attr, i) => + attr.withIndex(i) + }.toArray) + + private lazy val nameToIndex: Map[String, Int] = { + attributes.map(_.view.flatMap { attr => + attr.name.map(_ -> attr.index.get) + }.toMap).getOrElse(Map.empty) + } + + /** Size of the attribute group. Returns -1 if the size is unknown. */ + def size: Int = { + if (numAttributes.isDefined) { + numAttributes.get + } else if (attributes.isDefined) { + attributes.get.length + } else { + -1 + } + } + + /** Test whether this attribute group contains a specific attribute. */ + def hasAttr(attrName: String): Boolean = nameToIndex.contains(attrName) + + /** Index of an attribute specified by name. */ + def indexOf(attrName: String): Int = nameToIndex(attrName) + + /** Gets an attribute by its name. */ + def apply(attrName: String): Attribute = { + attributes.get(indexOf(attrName)) + } + + /** Gets an attribute by its name. */ + def getAttr(attrName: String): Attribute = this(attrName) + + /** Gets an attribute by its index. */ + def apply(attrIndex: Int): Attribute = attributes.get(attrIndex) + + /** Gets an attribute by its index. */ + def getAttr(attrIndex: Int): Attribute = this(attrIndex) + + /** Converts to metadata without name. */ + private[attribute] def toMetadataImpl: Metadata = { + import AttributeKeys._ + val bldr = new MetadataBuilder() + if (attributes.isDefined) { + val numericMetadata = ArrayBuffer.empty[Metadata] + val nominalMetadata = ArrayBuffer.empty[Metadata] + val binaryMetadata = ArrayBuffer.empty[Metadata] + attributes.get.foreach { + case numeric: NumericAttribute => + // Skip default numeric attributes. + if (numeric.withoutIndex != NumericAttribute.defaultAttr) { + numericMetadata += numeric.toMetadataImpl(withType = false) + } + case nominal: NominalAttribute => + nominalMetadata += nominal.toMetadataImpl(withType = false) + case binary: BinaryAttribute => + binaryMetadata += binary.toMetadataImpl(withType = false) + } + val attrBldr = new MetadataBuilder + if (numericMetadata.nonEmpty) { + attrBldr.putMetadataArray(AttributeType.Numeric.name, numericMetadata.toArray) + } + if (nominalMetadata.nonEmpty) { + attrBldr.putMetadataArray(AttributeType.Nominal.name, nominalMetadata.toArray) + } + if (binaryMetadata.nonEmpty) { + attrBldr.putMetadataArray(AttributeType.Binary.name, binaryMetadata.toArray) + } + bldr.putMetadata(ATTRIBUTES, attrBldr.build()) + bldr.putLong(NUM_ATTRIBUTES, attributes.get.length) + } else if (numAttributes.isDefined) { + bldr.putLong(NUM_ATTRIBUTES, numAttributes.get) + } + bldr.build() + } + + /** Converts to ML metadata with some existing metadata. */ + def toMetadata(existingMetadata: Metadata): Metadata = { + new MetadataBuilder() + .withMetadata(existingMetadata) + .putMetadata(AttributeKeys.ML_ATTR, toMetadataImpl) + .build() + } + + /** Converts to ML metadata */ + def toMetadata(): Metadata = toMetadata(Metadata.empty) + + /** Converts to a StructField with some existing metadata. */ + def toStructField(existingMetadata: Metadata): StructField = { + StructField(name, new VectorUDT, nullable = false, toMetadata(existingMetadata)) + } + + /** Converts to a StructField. */ + def toStructField(): StructField = toStructField(Metadata.empty) + + override def equals(other: Any): Boolean = { + other match { + case o: AttributeGroup => + (name == o.name) && + (numAttributes == o.numAttributes) && + (attributes.map(_.toSeq) == o.attributes.map(_.toSeq)) + case _ => + false + } + } + + override def hashCode: Int = { + var sum = 17 + sum = 37 * sum + name.hashCode + sum = 37 * sum + numAttributes.hashCode + sum = 37 * sum + attributes.map(_.toSeq).hashCode + sum + } +} + +/** Factory methods to create attribute groups. */ +object AttributeGroup { + + import AttributeKeys._ + + /** Creates an attribute group from a [[Metadata]] instance with name. */ + private[attribute] def fromMetadata(metadata: Metadata, name: String): AttributeGroup = { + import org.apache.spark.ml.attribute.AttributeType._ + if (metadata.contains(ATTRIBUTES)) { + val numAttrs = metadata.getLong(NUM_ATTRIBUTES).toInt + val attributes = new Array[Attribute](numAttrs) + val attrMetadata = metadata.getMetadata(ATTRIBUTES) + if (attrMetadata.contains(Numeric.name)) { + attrMetadata.getMetadataArray(Numeric.name) + .map(NumericAttribute.fromMetadata) + .foreach { attr => + attributes(attr.index.get) = attr + } + } + if (attrMetadata.contains(Nominal.name)) { + attrMetadata.getMetadataArray(Nominal.name) + .map(NominalAttribute.fromMetadata) + .foreach { attr => + attributes(attr.index.get) = attr + } + } + if (attrMetadata.contains(Binary.name)) { + attrMetadata.getMetadataArray(Binary.name) + .map(BinaryAttribute.fromMetadata) + .foreach { attr => + attributes(attr.index.get) = attr + } + } + var i = 0 + while (i < numAttrs) { + if (attributes(i) == null) { + attributes(i) = NumericAttribute.defaultAttr + } + i += 1 + } + new AttributeGroup(name, attributes) + } else if (metadata.contains(NUM_ATTRIBUTES)) { + new AttributeGroup(name, metadata.getLong(NUM_ATTRIBUTES).toInt) + } else { + new AttributeGroup(name) + } + } + + /** Creates an attribute group from a [[StructField]] instance. */ + def fromStructField(field: StructField): AttributeGroup = { + require(field.dataType == new VectorUDT) + if (field.metadata.contains(ML_ATTR)) { + fromMetadata(field.metadata.getMetadata(ML_ATTR), field.name) + } else { + new AttributeGroup(field.name) + } + } +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeKeys.scala b/mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeKeys.scala new file mode 100644 index 0000000000000..f714f7becc7e6 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeKeys.scala @@ -0,0 +1,37 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.attribute + +/** + * Keys used to store attributes. + */ +private[attribute] object AttributeKeys { + val ML_ATTR: String = "ml_attr" + val TYPE: String = "type" + val NAME: String = "name" + val INDEX: String = "idx" + val MIN: String = "min" + val MAX: String = "max" + val STD: String = "std" + val SPARSITY: String = "sparsity" + val ORDINAL: String = "ord" + val VALUES: String = "vals" + val NUM_VALUES: String = "num_vals" + val ATTRIBUTES: String = "attrs" + val NUM_ATTRIBUTES: String = "num_attrs" +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeType.scala b/mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeType.scala new file mode 100644 index 0000000000000..65e7e43d5a5b0 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeType.scala @@ -0,0 +1,61 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.attribute + +/** + * An enum-like type for attribute types: [[AttributeType$#Numeric]], [[AttributeType$#Nominal]], + * and [[AttributeType$#Binary]]. + */ +sealed abstract class AttributeType(val name: String) + +object AttributeType { + + /** Numeric type. */ + val Numeric: AttributeType = { + case object Numeric extends AttributeType("numeric") + Numeric + } + + /** Nominal type. */ + val Nominal: AttributeType = { + case object Nominal extends AttributeType("nominal") + Nominal + } + + /** Binary type. */ + val Binary: AttributeType = { + case object Binary extends AttributeType("binary") + Binary + } + + /** + * Gets the [[AttributeType]] object from its name. + * @param name attribute type name: "numeric", "nominal", or "binary" + */ + def fromName(name: String): AttributeType = { + if (name == Numeric.name) { + Numeric + } else if (name == Nominal.name) { + Nominal + } else if (name == Binary.name) { + Binary + } else { + throw new IllegalArgumentException(s"Cannot recognize type $name.") + } + } +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/attribute/attributes.scala b/mllib/src/main/scala/org/apache/spark/ml/attribute/attributes.scala new file mode 100644 index 0000000000000..5717d6ec2eaec --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/attribute/attributes.scala @@ -0,0 +1,537 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.attribute + +import scala.annotation.varargs + +import org.apache.spark.sql.types.{DoubleType, Metadata, MetadataBuilder, StructField} + +/** + * Abstract class for ML attributes. + */ +sealed abstract class Attribute extends Serializable { + + name.foreach { n => + require(n.nonEmpty, "Cannot have an empty string for name.") + } + index.foreach { i => + require(i >= 0, s"Index cannot be negative but got $i") + } + + /** Attribute type. */ + def attrType: AttributeType + + /** Name of the attribute. None if it is not set. */ + def name: Option[String] + + /** Copy with a new name. */ + def withName(name: String): Attribute + + /** Copy without the name. */ + def withoutName: Attribute + + /** Index of the attribute. None if it is not set. */ + def index: Option[Int] + + /** Copy with a new index. */ + def withIndex(index: Int): Attribute + + /** Copy without the index. */ + def withoutIndex: Attribute + + /** + * Tests whether this attribute is numeric, true for [[NumericAttribute]] and [[BinaryAttribute]]. + */ + def isNumeric: Boolean + + /** + * Tests whether this attribute is nominal, true for [[NominalAttribute]] and [[BinaryAttribute]]. + */ + def isNominal: Boolean + + /** + * Converts this attribute to [[Metadata]]. + * @param withType whether to include the type info + */ + private[attribute] def toMetadataImpl(withType: Boolean): Metadata + + /** + * Converts this attribute to [[Metadata]]. For numeric attributes, the type info is excluded to + * save space, because numeric type is the default attribute type. For nominal and binary + * attributes, the type info is included. + */ + private[attribute] def toMetadataImpl(): Metadata = { + if (attrType == AttributeType.Numeric) { + toMetadataImpl(withType = false) + } else { + toMetadataImpl(withType = true) + } + } + + /** Converts to ML metadata with some existing metadata. */ + def toMetadata(existingMetadata: Metadata): Metadata = { + new MetadataBuilder() + .withMetadata(existingMetadata) + .putMetadata(AttributeKeys.ML_ATTR, toMetadataImpl()) + .build() + } + + /** Converts to ML metadata */ + def toMetadata(): Metadata = toMetadata(Metadata.empty) + + /** + * Converts to a [[StructField]] with some existing metadata. + * @param existingMetadata existing metadata to carry over + */ + def toStructField(existingMetadata: Metadata): StructField = { + val newMetadata = new MetadataBuilder() + .withMetadata(existingMetadata) + .putMetadata(AttributeKeys.ML_ATTR, withoutName.withoutIndex.toMetadataImpl()) + .build() + StructField(name.get, DoubleType, nullable = false, newMetadata) + } + + /** Converts to a [[StructField]]. */ + def toStructField(): StructField = toStructField(Metadata.empty) + + override def toString: String = toMetadataImpl(withType = true).toString +} + +/** Trait for ML attribute factories. */ +private[attribute] trait AttributeFactory { + + /** + * Creates an [[Attribute]] from a [[Metadata]] instance. + */ + private[attribute] def fromMetadata(metadata: Metadata): Attribute + + /** + * Creates an [[Attribute]] from a [[StructField]] instance. + */ + def fromStructField(field: StructField): Attribute = { + require(field.dataType == DoubleType) + fromMetadata(field.metadata.getMetadata(AttributeKeys.ML_ATTR)).withName(field.name) + } +} + +object Attribute extends AttributeFactory { + + private[attribute] override def fromMetadata(metadata: Metadata): Attribute = { + import org.apache.spark.ml.attribute.AttributeKeys._ + val attrType = if (metadata.contains(TYPE)) { + metadata.getString(TYPE) + } else { + AttributeType.Numeric.name + } + getFactory(attrType).fromMetadata(metadata) + } + + /** Gets the attribute factory given the attribute type name. */ + private def getFactory(attrType: String): AttributeFactory = { + if (attrType == AttributeType.Numeric.name) { + NumericAttribute + } else if (attrType == AttributeType.Nominal.name) { + NominalAttribute + } else if (attrType == AttributeType.Binary.name) { + BinaryAttribute + } else { + throw new IllegalArgumentException(s"Cannot recognize type $attrType.") + } + } +} + + +/** + * A numeric attribute with optional summary statistics. + * @param name optional name + * @param index optional index + * @param min optional min value + * @param max optional max value + * @param std optional standard deviation + * @param sparsity optional sparsity (ratio of zeros) + */ +class NumericAttribute private[ml] ( + override val name: Option[String] = None, + override val index: Option[Int] = None, + val min: Option[Double] = None, + val max: Option[Double] = None, + val std: Option[Double] = None, + val sparsity: Option[Double] = None) extends Attribute { + + std.foreach { s => + require(s >= 0.0, s"Standard deviation cannot be negative but got $s.") + } + sparsity.foreach { s => + require(s >= 0.0 && s <= 1.0, s"Sparsity must be in [0, 1] but got $s.") + } + + override def attrType: AttributeType = AttributeType.Numeric + + override def withName(name: String): NumericAttribute = copy(name = Some(name)) + override def withoutName: NumericAttribute = copy(name = None) + + override def withIndex(index: Int): NumericAttribute = copy(index = Some(index)) + override def withoutIndex: NumericAttribute = copy(index = None) + + /** Copy with a new min value. */ + def withMin(min: Double): NumericAttribute = copy(min = Some(min)) + + /** Copy without the min value. */ + def withoutMin: NumericAttribute = copy(min = None) + + + /** Copy with a new max value. */ + def withMax(max: Double): NumericAttribute = copy(max = Some(max)) + + /** Copy without the max value. */ + def withoutMax: NumericAttribute = copy(max = None) + + /** Copy with a new standard deviation. */ + def withStd(std: Double): NumericAttribute = copy(std = Some(std)) + + /** Copy without the standard deviation. */ + def withoutStd: NumericAttribute = copy(std = None) + + /** Copy with a new sparsity. */ + def withSparsity(sparsity: Double): NumericAttribute = copy(sparsity = Some(sparsity)) + + /** Copy without the sparsity. */ + def withoutSparsity: NumericAttribute = copy(sparsity = None) + + /** Copy without summary statistics. */ + def withoutSummary: NumericAttribute = copy(min = None, max = None, std = None, sparsity = None) + + override def isNumeric: Boolean = true + + override def isNominal: Boolean = false + + /** Convert this attribute to metadata. */ + override private[attribute] def toMetadataImpl(withType: Boolean): Metadata = { + import org.apache.spark.ml.attribute.AttributeKeys._ + val bldr = new MetadataBuilder() + if (withType) bldr.putString(TYPE, attrType.name) + name.foreach(bldr.putString(NAME, _)) + index.foreach(bldr.putLong(INDEX, _)) + min.foreach(bldr.putDouble(MIN, _)) + max.foreach(bldr.putDouble(MAX, _)) + std.foreach(bldr.putDouble(STD, _)) + sparsity.foreach(bldr.putDouble(SPARSITY, _)) + bldr.build() + } + + /** Creates a copy of this attribute with optional changes. */ + private def copy( + name: Option[String] = name, + index: Option[Int] = index, + min: Option[Double] = min, + max: Option[Double] = max, + std: Option[Double] = std, + sparsity: Option[Double] = sparsity): NumericAttribute = { + new NumericAttribute(name, index, min, max, std, sparsity) + } + + override def equals(other: Any): Boolean = { + other match { + case o: NumericAttribute => + (name == o.name) && + (index == o.index) && + (min == o.min) && + (max == o.max) && + (std == o.std) && + (sparsity == o.sparsity) + case _ => + false + } + } + + override def hashCode: Int = { + var sum = 17 + sum = 37 * sum + name.hashCode + sum = 37 * sum + index.hashCode + sum = 37 * sum + min.hashCode + sum = 37 * sum + max.hashCode + sum = 37 * sum + std.hashCode + sum = 37 * sum + sparsity.hashCode + sum + } +} + +/** + * Factory methods for numeric attributes. + */ +object NumericAttribute extends AttributeFactory { + + /** The default numeric attribute. */ + val defaultAttr: NumericAttribute = new NumericAttribute + + private[attribute] override def fromMetadata(metadata: Metadata): NumericAttribute = { + import org.apache.spark.ml.attribute.AttributeKeys._ + val name = if (metadata.contains(NAME)) Some(metadata.getString(NAME)) else None + val index = if (metadata.contains(INDEX)) Some(metadata.getLong(INDEX).toInt) else None + val min = if (metadata.contains(MIN)) Some(metadata.getDouble(MIN)) else None + val max = if (metadata.contains(MAX)) Some(metadata.getDouble(MAX)) else None + val std = if (metadata.contains(STD)) Some(metadata.getDouble(STD)) else None + val sparsity = if (metadata.contains(SPARSITY)) Some(metadata.getDouble(SPARSITY)) else None + new NumericAttribute(name, index, min, max, std, sparsity) + } +} + +/** + * A nominal attribute. + * @param name optional name + * @param index optional index + * @param isOrdinal whether this attribute is ordinal (optional) + * @param numValues optional number of values. At most one of `numValues` and `values` can be + * defined. + * @param values optional values. At most one of `numValues` and `values` can be defined. + */ +class NominalAttribute private[ml] ( + override val name: Option[String] = None, + override val index: Option[Int] = None, + val isOrdinal: Option[Boolean] = None, + val numValues: Option[Int] = None, + val values: Option[Array[String]] = None) extends Attribute { + + numValues.foreach { n => + require(n >= 0, s"numValues cannot be negative but got $n.") + } + require(!(numValues.isDefined && values.isDefined), + "Cannot have both numValues and values defined.") + + override def attrType: AttributeType = AttributeType.Nominal + + override def isNumeric: Boolean = false + + override def isNominal: Boolean = true + + private lazy val valueToIndex: Map[String, Int] = { + values.map(_.zipWithIndex.toMap).getOrElse(Map.empty) + } + + /** Index of a specific value. */ + def indexOf(value: String): Int = { + valueToIndex(value) + } + + /** Tests whether this attribute contains a specific value. */ + def hasValue(value: String): Boolean = valueToIndex.contains(value) + + /** Gets a value given its index. */ + def getValue(index: Int): String = values.get(index) + + override def withName(name: String): NominalAttribute = copy(name = Some(name)) + override def withoutName: NominalAttribute = copy(name = None) + + override def withIndex(index: Int): NominalAttribute = copy(index = Some(index)) + override def withoutIndex: NominalAttribute = copy(index = None) + + /** Copy with new values and empty `numValues`. */ + def withValues(values: Array[String]): NominalAttribute = { + copy(numValues = None, values = Some(values)) + } + + /** Copy with new values and empty `numValues`. */ + @varargs + def withValues(first: String, others: String*): NominalAttribute = { + copy(numValues = None, values = Some((first +: others).toArray)) + } + + /** Copy without the values. */ + def withoutValues: NominalAttribute = { + copy(values = None) + } + + /** Copy with a new `numValues` and empty `values`. */ + def withNumValues(numValues: Int): NominalAttribute = { + copy(numValues = Some(numValues), values = None) + } + + /** Copy without the `numValues`. */ + def withoutNumValues: NominalAttribute = copy(numValues = None) + + /** + * Get the number of values, either from `numValues` or from `values`. + * Return None if unknown. + */ + def getNumValues: Option[Int] = { + if (numValues.nonEmpty) { + numValues + } else if (values.nonEmpty) { + Some(values.get.length) + } else { + None + } + } + + /** Creates a copy of this attribute with optional changes. */ + private def copy( + name: Option[String] = name, + index: Option[Int] = index, + isOrdinal: Option[Boolean] = isOrdinal, + numValues: Option[Int] = numValues, + values: Option[Array[String]] = values): NominalAttribute = { + new NominalAttribute(name, index, isOrdinal, numValues, values) + } + + override private[attribute] def toMetadataImpl(withType: Boolean): Metadata = { + import org.apache.spark.ml.attribute.AttributeKeys._ + val bldr = new MetadataBuilder() + if (withType) bldr.putString(TYPE, attrType.name) + name.foreach(bldr.putString(NAME, _)) + index.foreach(bldr.putLong(INDEX, _)) + isOrdinal.foreach(bldr.putBoolean(ORDINAL, _)) + numValues.foreach(bldr.putLong(NUM_VALUES, _)) + values.foreach(v => bldr.putStringArray(VALUES, v)) + bldr.build() + } + + override def equals(other: Any): Boolean = { + other match { + case o: NominalAttribute => + (name == o.name) && + (index == o.index) && + (isOrdinal == o.isOrdinal) && + (numValues == o.numValues) && + (values.map(_.toSeq) == o.values.map(_.toSeq)) + case _ => + false + } + } + + override def hashCode: Int = { + var sum = 17 + sum = 37 * sum + name.hashCode + sum = 37 * sum + index.hashCode + sum = 37 * sum + isOrdinal.hashCode + sum = 37 * sum + numValues.hashCode + sum = 37 * sum + values.map(_.toSeq).hashCode + sum + } +} + +/** Factory methods for nominal attributes. */ +object NominalAttribute extends AttributeFactory { + + /** The default nominal attribute. */ + final val defaultAttr: NominalAttribute = new NominalAttribute + + private[attribute] override def fromMetadata(metadata: Metadata): NominalAttribute = { + import org.apache.spark.ml.attribute.AttributeKeys._ + val name = if (metadata.contains(NAME)) Some(metadata.getString(NAME)) else None + val index = if (metadata.contains(INDEX)) Some(metadata.getLong(INDEX).toInt) else None + val isOrdinal = if (metadata.contains(ORDINAL)) Some(metadata.getBoolean(ORDINAL)) else None + val numValues = + if (metadata.contains(NUM_VALUES)) Some(metadata.getLong(NUM_VALUES).toInt) else None + val values = + if (metadata.contains(VALUES)) Some(metadata.getStringArray(VALUES)) else None + new NominalAttribute(name, index, isOrdinal, numValues, values) + } +} + +/** + * A binary attribute. + * @param name optional name + * @param index optional index + * @param values optionla values. If set, its size must be 2. + */ +class BinaryAttribute private[ml] ( + override val name: Option[String] = None, + override val index: Option[Int] = None, + val values: Option[Array[String]] = None) + extends Attribute { + + values.foreach { v => + require(v.length == 2, s"Number of values must be 2 for a binary attribute but got ${v.toSeq}.") + } + + override def attrType: AttributeType = AttributeType.Binary + + override def isNumeric: Boolean = true + + override def isNominal: Boolean = true + + override def withName(name: String): BinaryAttribute = copy(name = Some(name)) + override def withoutName: BinaryAttribute = copy(name = None) + + override def withIndex(index: Int): BinaryAttribute = copy(index = Some(index)) + override def withoutIndex: BinaryAttribute = copy(index = None) + + /** + * Copy with new values. + * @param negative name for negative + * @param positive name for positive + */ + def withValues(negative: String, positive: String): BinaryAttribute = + copy(values = Some(Array(negative, positive))) + + /** Copy without the values. */ + def withoutValues: BinaryAttribute = copy(values = None) + + /** Creates a copy of this attribute with optional changes. */ + private def copy( + name: Option[String] = name, + index: Option[Int] = index, + values: Option[Array[String]] = values): BinaryAttribute = { + new BinaryAttribute(name, index, values) + } + + override private[attribute] def toMetadataImpl(withType: Boolean): Metadata = { + import org.apache.spark.ml.attribute.AttributeKeys._ + val bldr = new MetadataBuilder + if (withType) bldr.putString(TYPE, attrType.name) + name.foreach(bldr.putString(NAME, _)) + index.foreach(bldr.putLong(INDEX, _)) + values.foreach(v => bldr.putStringArray(VALUES, v)) + bldr.build() + } + + override def equals(other: Any): Boolean = { + other match { + case o: BinaryAttribute => + (name == o.name) && + (index == o.index) && + (values.map(_.toSeq) == o.values.map(_.toSeq)) + case _ => + false + } + } + + override def hashCode: Int = { + var sum = 17 + sum = 37 * sum + name.hashCode + sum = 37 * sum + index.hashCode + sum = 37 * sum + values.map(_.toSeq).hashCode + sum + } +} + +/** Factory methods for binary attributes. */ +object BinaryAttribute extends AttributeFactory { + + /** The default binary attribute. */ + final val defaultAttr: BinaryAttribute = new BinaryAttribute + + private[attribute] override def fromMetadata(metadata: Metadata): BinaryAttribute = { + import org.apache.spark.ml.attribute.AttributeKeys._ + val name = if (metadata.contains(NAME)) Some(metadata.getString(NAME)) else None + val index = if (metadata.contains(INDEX)) Some(metadata.getLong(INDEX).toInt) else None + val values = + if (metadata.contains(VALUES)) Some(metadata.getStringArray(VALUES)) else None + new BinaryAttribute(name, index, values) + } +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/attribute/package-info.java b/mllib/src/main/scala/org/apache/spark/ml/attribute/package-info.java new file mode 100644 index 0000000000000..e3474f3c1d3ff --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/attribute/package-info.java @@ -0,0 +1,41 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +// The content here should be in sync with `package.scala`. + +/** + *

    ML attributes

    + * + * The ML pipeline API uses {@link org.apache.spark.sql.DataFrame}s as ML datasets. + * Each dataset consists of typed columns, e.g., string, double, vector, etc. + * However, knowing only the column type may not be sufficient to handle the data properly. + * For instance, a double column with values 0.0, 1.0, 2.0, ... may represent some label indices, + * which cannot be treated as numeric values in ML algorithms, and, for another instance, we may + * want to know the names and types of features stored in a vector column. + * ML attributes are used to provide additional information to describe columns in a dataset. + * + *

    ML columns

    + * + * A column with ML attributes attached is called an ML column. + * The data in ML columns are stored as double values, i.e., an ML column is either a scalar column + * of double values or a vector column. + * Columns of other types must be encoded into ML columns using transformers. + * We use {@link org.apache.spark.ml.attribute.Attribute} to describe a scalar ML column, and + * {@link org.apache.spark.ml.attribute.AttributeGroup} to describe a vector ML column. + * ML attributes are stored in the metadata field of the column schema. + */ +package org.apache.spark.ml.attribute; diff --git a/mllib/src/main/scala/org/apache/spark/ml/attribute/package.scala b/mllib/src/main/scala/org/apache/spark/ml/attribute/package.scala new file mode 100644 index 0000000000000..7ac21d7d563f2 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/attribute/package.scala @@ -0,0 +1,44 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml + +import org.apache.spark.sql.DataFrame +import org.apache.spark.ml.attribute.{Attribute, AttributeGroup} + +/** + * ==ML attributes== + * + * The ML pipeline API uses [[DataFrame]]s as ML datasets. + * Each dataset consists of typed columns, e.g., string, double, vector, etc. + * However, knowing only the column type may not be sufficient to handle the data properly. + * For instance, a double column with values 0.0, 1.0, 2.0, ... may represent some label indices, + * which cannot be treated as numeric values in ML algorithms, and, for another instance, we may + * want to know the names and types of features stored in a vector column. + * ML attributes are used to provide additional information to describe columns in a dataset. + * + * ===ML columns=== + * + * A column with ML attributes attached is called an ML column. + * The data in ML columns are stored as double values, i.e., an ML column is either a scalar column + * of double values or a vector column. + * Columns of other types must be encoded into ML columns using transformers. + * We use [[Attribute]] to describe a scalar ML column, and [[AttributeGroup]] to describe a vector + * ML column. + * ML attributes are stored in the metadata field of the column schema. + */ +package object attribute diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala index 1bf8eb4640d11..29339c98f51cf 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala @@ -17,12 +17,14 @@ package org.apache.spark.ml.classification -import org.apache.spark.annotation.{DeveloperApi, AlphaComponent} +import org.apache.spark.annotation.{AlphaComponent, DeveloperApi} import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor, PredictorParams} -import org.apache.spark.ml.param.{Params, ParamMap, HasRawPredictionCol} +import org.apache.spark.ml.param.{ParamMap, Params} +import org.apache.spark.ml.param.shared.HasRawPredictionCol +import org.apache.spark.ml.util.SchemaUtils import org.apache.spark.mllib.linalg.{Vector, VectorUDT} -import org.apache.spark.sql.Dsl._ import org.apache.spark.sql.DataFrame +import org.apache.spark.sql.functions._ import org.apache.spark.sql.types.{DataType, DoubleType, StructType} @@ -42,8 +44,8 @@ private[spark] trait ClassifierParams extends PredictorParams fitting: Boolean, featuresDataType: DataType): StructType = { val parentSchema = super.validateAndTransformSchema(schema, paramMap, fitting, featuresDataType) - val map = this.paramMap ++ paramMap - addOutputColumn(parentSchema, map(rawPredictionCol), new VectorUDT) + val map = extractParamMap(paramMap) + SchemaUtils.appendColumn(parentSchema, map(rawPredictionCol), new VectorUDT) } } @@ -66,8 +68,8 @@ private[spark] abstract class Classifier[ extends Predictor[FeaturesType, E, M] with ClassifierParams { - def setRawPredictionCol(value: String): E = - set(rawPredictionCol, value).asInstanceOf[E] + /** @group setParam */ + def setRawPredictionCol(value: String): E = set(rawPredictionCol, value).asInstanceOf[E] // TODO: defaultEvaluator (follow-up PR) } @@ -87,6 +89,7 @@ private[spark] abstract class ClassificationModel[FeaturesType, M <: ClassificationModel[FeaturesType, M]] extends PredictionModel[FeaturesType, M] with ClassifierParams { + /** @group setParam */ def setRawPredictionCol(value: String): M = set(rawPredictionCol, value).asInstanceOf[M] /** Number of classes (values which the label can take). */ @@ -107,7 +110,7 @@ abstract class ClassificationModel[FeaturesType, M <: ClassificationModel[Featur // Check schema transformSchema(dataset.schema, paramMap, logging = true) - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) // Prepare model val tmpModel = if (paramMap.size != 0) { @@ -180,24 +183,22 @@ private[ml] object ClassificationModel { if (map(model.rawPredictionCol) != "") { // output raw prediction val features2raw: FeaturesType => Vector = model.predictRaw - tmpData = tmpData.select($"*", - callUDF(features2raw, new VectorUDT, - col(map(model.featuresCol))).as(map(model.rawPredictionCol))) + tmpData = tmpData.withColumn(map(model.rawPredictionCol), + callUDF(features2raw, new VectorUDT, col(map(model.featuresCol)))) numColsOutput += 1 if (map(model.predictionCol) != "") { val raw2pred: Vector => Double = (rawPred) => { rawPred.toArray.zipWithIndex.maxBy(_._1)._2 } - tmpData = tmpData.select($"*", callUDF(raw2pred, DoubleType, - col(map(model.rawPredictionCol))).as(map(model.predictionCol))) + tmpData = tmpData.withColumn(map(model.predictionCol), + callUDF(raw2pred, DoubleType, col(map(model.rawPredictionCol)))) numColsOutput += 1 } } else if (map(model.predictionCol) != "") { // output prediction val features2pred: FeaturesType => Double = model.predict - tmpData = tmpData.select($"*", - callUDF(features2pred, DoubleType, - col(map(model.featuresCol))).as(map(model.predictionCol))) + tmpData = tmpData.withColumn(map(model.predictionCol), + callUDF(features2pred, DoubleType, col(map(model.featuresCol)))) numColsOutput += 1 } (numColsOutput, tmpData) diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala new file mode 100644 index 0000000000000..3855e396b5534 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala @@ -0,0 +1,155 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.classification + +import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.ml.impl.estimator.{Predictor, PredictionModel} +import org.apache.spark.ml.impl.tree._ +import org.apache.spark.ml.param.{Params, ParamMap} +import org.apache.spark.ml.tree.{DecisionTreeModel, Node} +import org.apache.spark.ml.util.MetadataUtils +import org.apache.spark.mllib.linalg.Vector +import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.mllib.tree.{DecisionTree => OldDecisionTree} +import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, Strategy => OldStrategy} +import org.apache.spark.mllib.tree.model.{DecisionTreeModel => OldDecisionTreeModel} +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.DataFrame + + +/** + * :: AlphaComponent :: + * + * [[http://en.wikipedia.org/wiki/Decision_tree_learning Decision tree]] learning algorithm + * for classification. + * It supports both binary and multiclass labels, as well as both continuous and categorical + * features. + */ +@AlphaComponent +final class DecisionTreeClassifier + extends Predictor[Vector, DecisionTreeClassifier, DecisionTreeClassificationModel] + with DecisionTreeParams + with TreeClassifierParams { + + // Override parameter setters from parent trait for Java API compatibility. + + override def setMaxDepth(value: Int): this.type = super.setMaxDepth(value) + + override def setMaxBins(value: Int): this.type = super.setMaxBins(value) + + override def setMinInstancesPerNode(value: Int): this.type = + super.setMinInstancesPerNode(value) + + override def setMinInfoGain(value: Double): this.type = super.setMinInfoGain(value) + + override def setMaxMemoryInMB(value: Int): this.type = super.setMaxMemoryInMB(value) + + override def setCacheNodeIds(value: Boolean): this.type = + super.setCacheNodeIds(value) + + override def setCheckpointInterval(value: Int): this.type = + super.setCheckpointInterval(value) + + override def setImpurity(value: String): this.type = super.setImpurity(value) + + override protected def train( + dataset: DataFrame, + paramMap: ParamMap): DecisionTreeClassificationModel = { + val categoricalFeatures: Map[Int, Int] = + MetadataUtils.getCategoricalFeatures(dataset.schema(paramMap(featuresCol))) + val numClasses: Int = MetadataUtils.getNumClasses(dataset.schema(paramMap(labelCol))) match { + case Some(n: Int) => n + case None => throw new IllegalArgumentException("DecisionTreeClassifier was given input" + + s" with invalid label column, without the number of classes specified.") + // TODO: Automatically index labels. + } + val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, paramMap) + val strategy = getOldStrategy(categoricalFeatures, numClasses) + val oldModel = OldDecisionTree.train(oldDataset, strategy) + DecisionTreeClassificationModel.fromOld(oldModel, this, paramMap, categoricalFeatures) + } + + /** (private[ml]) Create a Strategy instance to use with the old API. */ + override private[ml] def getOldStrategy( + categoricalFeatures: Map[Int, Int], + numClasses: Int): OldStrategy = { + val strategy = super.getOldStrategy(categoricalFeatures, numClasses) + strategy.algo = OldAlgo.Classification + strategy.setImpurity(getOldImpurity) + strategy + } +} + +object DecisionTreeClassifier { + /** Accessor for supported impurities */ + final val supportedImpurities: Array[String] = TreeClassifierParams.supportedImpurities +} + +/** + * :: AlphaComponent :: + * + * [[http://en.wikipedia.org/wiki/Decision_tree_learning Decision tree]] model for classification. + * It supports both binary and multiclass labels, as well as both continuous and categorical + * features. + */ +@AlphaComponent +final class DecisionTreeClassificationModel private[ml] ( + override val parent: DecisionTreeClassifier, + override val fittingParamMap: ParamMap, + override val rootNode: Node) + extends PredictionModel[Vector, DecisionTreeClassificationModel] + with DecisionTreeModel with Serializable { + + require(rootNode != null, + "DecisionTreeClassificationModel given null rootNode, but it requires a non-null rootNode.") + + override protected def predict(features: Vector): Double = { + rootNode.predict(features) + } + + override protected def copy(): DecisionTreeClassificationModel = { + val m = new DecisionTreeClassificationModel(parent, fittingParamMap, rootNode) + Params.inheritValues(this.extractParamMap(), this, m) + m + } + + override def toString: String = { + s"DecisionTreeClassificationModel of depth $depth with $numNodes nodes" + } + + /** (private[ml]) Convert to a model in the old API */ + private[ml] def toOld: OldDecisionTreeModel = { + new OldDecisionTreeModel(rootNode.toOld(1), OldAlgo.Classification) + } +} + +private[ml] object DecisionTreeClassificationModel { + + /** (private[ml]) Convert a model from the old API */ + def fromOld( + oldModel: OldDecisionTreeModel, + parent: DecisionTreeClassifier, + fittingParamMap: ParamMap, + categoricalFeatures: Map[Int, Int]): DecisionTreeClassificationModel = { + require(oldModel.algo == OldAlgo.Classification, + s"Cannot convert non-classification DecisionTreeModel (old API) to" + + s" DecisionTreeClassificationModel (new API). Algo is: ${oldModel.algo}") + val rootNode = Node.fromOld(oldModel.topNode, categoricalFeatures) + new DecisionTreeClassificationModel(parent, fittingParamMap, rootNode) + } +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala index c146fe244c66e..cc8b0721cf2b6 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala @@ -19,11 +19,11 @@ package org.apache.spark.ml.classification import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.param._ +import org.apache.spark.ml.param.shared._ import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS import org.apache.spark.mllib.linalg.{VectorUDT, BLAS, Vector, Vectors} import org.apache.spark.sql.DataFrame -import org.apache.spark.sql.Dsl._ -import org.apache.spark.sql.types.DoubleType +import org.apache.spark.sql.functions._ import org.apache.spark.storage.StorageLevel @@ -31,8 +31,10 @@ import org.apache.spark.storage.StorageLevel * Params for logistic regression. */ private[classification] trait LogisticRegressionParams extends ProbabilisticClassifierParams - with HasRegParam with HasMaxIter with HasThreshold + with HasRegParam with HasMaxIter with HasFitIntercept with HasThreshold { + setDefault(regParam -> 0.1, maxIter -> 100, threshold -> 0.5) +} /** * :: AlphaComponent :: @@ -45,12 +47,16 @@ class LogisticRegression extends ProbabilisticClassifier[Vector, LogisticRegression, LogisticRegressionModel] with LogisticRegressionParams { - setRegParam(0.1) - setMaxIter(100) - setThreshold(0.5) - + /** @group setParam */ def setRegParam(value: Double): this.type = set(regParam, value) + + /** @group setParam */ def setMaxIter(value: Int): this.type = set(maxIter, value) + + /** @group setParam */ + def setFitIntercept(value: Boolean): this.type = set(fitIntercept, value) + + /** @group setParam */ def setThreshold(value: Double): this.type = set(threshold, value) override protected def train(dataset: DataFrame, paramMap: ParamMap): LogisticRegressionModel = { @@ -62,7 +68,8 @@ class LogisticRegression } // Train model - val lr = new LogisticRegressionWithLBFGS + val lr = new LogisticRegressionWithLBFGS() + .setIntercept(paramMap(fitIntercept)) lr.optimizer .setRegParam(paramMap(regParam)) .setNumIterations(paramMap(maxIter)) @@ -91,8 +98,7 @@ class LogisticRegressionModel private[ml] ( extends ProbabilisticClassificationModel[Vector, LogisticRegressionModel] with LogisticRegressionParams { - setThreshold(0.5) - + /** @group setParam */ def setThreshold(value: Double): this.type = set(threshold, value) private val margin: Vector => Double = (features) => { @@ -113,7 +119,7 @@ class LogisticRegressionModel private[ml] ( // Check schema transformSchema(dataset.schema, paramMap, logging = true) - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) // Output selected columns only. // This is a bit complicated since it tries to avoid repeated computation. @@ -124,44 +130,39 @@ class LogisticRegressionModel private[ml] ( var numColsOutput = 0 if (map(rawPredictionCol) != "") { val features2raw: Vector => Vector = (features) => predictRaw(features) - tmpData = tmpData.select($"*", - callUDF(features2raw, new VectorUDT, col(map(featuresCol))).as(map(rawPredictionCol))) + tmpData = tmpData.withColumn(map(rawPredictionCol), + callUDF(features2raw, new VectorUDT, col(map(featuresCol)))) numColsOutput += 1 } if (map(probabilityCol) != "") { if (map(rawPredictionCol) != "") { - val raw2prob: Vector => Vector = { (rawPreds: Vector) => + val raw2prob = udf { (rawPreds: Vector) => val prob1 = 1.0 / (1.0 + math.exp(-rawPreds(1))) - Vectors.dense(1.0 - prob1, prob1) + Vectors.dense(1.0 - prob1, prob1): Vector } - tmpData = tmpData.select($"*", - callUDF(raw2prob, new VectorUDT, col(map(rawPredictionCol))).as(map(probabilityCol))) + tmpData = tmpData.withColumn(map(probabilityCol), raw2prob(col(map(rawPredictionCol)))) } else { - val features2prob: Vector => Vector = (features: Vector) => predictProbabilities(features) - tmpData = tmpData.select($"*", - callUDF(features2prob, new VectorUDT, col(map(featuresCol))).as(map(probabilityCol))) + val features2prob = udf { (features: Vector) => predictProbabilities(features) : Vector } + tmpData = tmpData.withColumn(map(probabilityCol), features2prob(col(map(featuresCol)))) } numColsOutput += 1 } if (map(predictionCol) != "") { val t = map(threshold) if (map(probabilityCol) != "") { - val predict: Vector => Double = { probs: Vector => + val predict = udf { probs: Vector => if (probs(1) > t) 1.0 else 0.0 } - tmpData = tmpData.select($"*", - callUDF(predict, DoubleType, col(map(probabilityCol))).as(map(predictionCol))) + tmpData = tmpData.withColumn(map(predictionCol), predict(col(map(probabilityCol)))) } else if (map(rawPredictionCol) != "") { - val predict: Vector => Double = { rawPreds: Vector => + val predict = udf { rawPreds: Vector => val prob1 = 1.0 / (1.0 + math.exp(-rawPreds(1))) if (prob1 > t) 1.0 else 0.0 } - tmpData = tmpData.select($"*", - callUDF(predict, DoubleType, col(map(rawPredictionCol))).as(map(predictionCol))) + tmpData = tmpData.withColumn(map(predictionCol), predict(col(map(rawPredictionCol)))) } else { - val predict: Vector => Double = (features: Vector) => this.predict(features) - tmpData = tmpData.select($"*", - callUDF(predict, DoubleType, col(map(featuresCol))).as(map(predictionCol))) + val predict = udf { features: Vector => this.predict(features) } + tmpData = tmpData.withColumn(map(predictionCol), predict(col(map(featuresCol)))) } numColsOutput += 1 } @@ -179,8 +180,7 @@ class LogisticRegressionModel private[ml] ( * The behavior of this can be adjusted using [[threshold]]. */ override protected def predict(features: Vector): Double = { - println(s"LR.predict with threshold: ${paramMap(threshold)}") - if (score(features) > paramMap(threshold)) 1 else 0 + if (score(features) > getThreshold) 1 else 0 } override protected def predictProbabilities(features: Vector): Vector = { @@ -195,7 +195,7 @@ class LogisticRegressionModel private[ml] ( override protected def copy(): LogisticRegressionModel = { val m = new LogisticRegressionModel(parent, fittingParamMap, weights, intercept) - Params.inheritValues(this.paramMap, this, m) + Params.inheritValues(this.extractParamMap(), this, m) m } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala index 1202528ca654e..10404548ccfde 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala @@ -18,13 +18,14 @@ package org.apache.spark.ml.classification import org.apache.spark.annotation.{AlphaComponent, DeveloperApi} -import org.apache.spark.ml.param.{HasProbabilityCol, ParamMap, Params} +import org.apache.spark.ml.param.{ParamMap, Params} +import org.apache.spark.ml.param.shared._ +import org.apache.spark.ml.util.SchemaUtils import org.apache.spark.mllib.linalg.{Vector, VectorUDT} import org.apache.spark.sql.DataFrame -import org.apache.spark.sql.Dsl._ +import org.apache.spark.sql.functions._ import org.apache.spark.sql.types.{DataType, StructType} - /** * Params for probabilistic classification. */ @@ -37,8 +38,8 @@ private[classification] trait ProbabilisticClassifierParams fitting: Boolean, featuresDataType: DataType): StructType = { val parentSchema = super.validateAndTransformSchema(schema, paramMap, fitting, featuresDataType) - val map = this.paramMap ++ paramMap - addOutputColumn(parentSchema, map(probabilityCol), new VectorUDT) + val map = extractParamMap(paramMap) + SchemaUtils.appendColumn(parentSchema, map(probabilityCol), new VectorUDT) } } @@ -61,6 +62,7 @@ private[spark] abstract class ProbabilisticClassifier[ M <: ProbabilisticClassificationModel[FeaturesType, M]] extends Classifier[FeaturesType, E, M] with ProbabilisticClassifierParams { + /** @group setParam */ def setProbabilityCol(value: String): E = set(probabilityCol, value).asInstanceOf[E] } @@ -82,6 +84,7 @@ private[spark] abstract class ProbabilisticClassificationModel[ M <: ProbabilisticClassificationModel[FeaturesType, M]] extends ClassificationModel[FeaturesType, M] with ProbabilisticClassifierParams { + /** @group setParam */ def setProbabilityCol(value: String): M = set(probabilityCol, value).asInstanceOf[M] /** @@ -100,7 +103,7 @@ private[spark] abstract class ProbabilisticClassificationModel[ // Check schema transformSchema(dataset.schema, paramMap, logging = true) - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) // Prepare model val tmpModel = if (paramMap.size != 0) { @@ -120,8 +123,8 @@ private[spark] abstract class ProbabilisticClassificationModel[ val features2probs: FeaturesType => Vector = (features) => { tmpModel.predictProbabilities(features) } - outputData.select($"*", - callUDF(features2probs, new VectorUDT, col(map(featuresCol))).as(map(probabilityCol))) + outputData.withColumn(map(probabilityCol), + callUDF(features2probs, new VectorUDT, col(map(featuresCol)))) } else { if (numColsOutput == 0) { this.logWarning(s"$uid: ProbabilisticClassificationModel.transform() was called as NOOP" + diff --git a/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala index f21a30627e540..c865eb9fe092d 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala @@ -20,12 +20,13 @@ package org.apache.spark.ml.evaluation import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.Evaluator import org.apache.spark.ml.param._ +import org.apache.spark.ml.param.shared._ +import org.apache.spark.ml.util.SchemaUtils import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics import org.apache.spark.mllib.linalg.{Vector, VectorUDT} import org.apache.spark.sql.{DataFrame, Row} import org.apache.spark.sql.types.DoubleType - /** * :: AlphaComponent :: * @@ -35,21 +36,33 @@ import org.apache.spark.sql.types.DoubleType class BinaryClassificationEvaluator extends Evaluator with Params with HasRawPredictionCol with HasLabelCol { - /** param for metric name in evaluation */ + /** + * param for metric name in evaluation + * @group param + */ val metricName: Param[String] = new Param(this, "metricName", - "metric name in evaluation (areaUnderROC|areaUnderPR)", Some("areaUnderROC")) - def getMetricName: String = get(metricName) + "metric name in evaluation (areaUnderROC|areaUnderPR)") + + /** @group getParam */ + def getMetricName: String = getOrDefault(metricName) + + /** @group setParam */ def setMetricName(value: String): this.type = set(metricName, value) + /** @group setParam */ def setScoreCol(value: String): this.type = set(rawPredictionCol, value) + + /** @group setParam */ def setLabelCol(value: String): this.type = set(labelCol, value) + setDefault(metricName -> "areaUnderROC") + override def evaluate(dataset: DataFrame, paramMap: ParamMap): Double = { - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) val schema = dataset.schema - checkInputColumn(schema, map(rawPredictionCol), new VectorUDT) - checkInputColumn(schema, map(labelCol), DoubleType) + SchemaUtils.checkColumnType(schema, map(rawPredictionCol), new VectorUDT) + SchemaUtils.checkColumnType(schema, map(labelCol), DoubleType) // TODO: When dataset metadata has been implemented, check rawPredictionCol vector length = 2. val scoreAndLabels = dataset.select(map(rawPredictionCol), map(labelCol)) diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala index 0956062643f23..b20f2fc49a8f6 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala @@ -31,10 +31,19 @@ import org.apache.spark.sql.types.DataType @AlphaComponent class HashingTF extends UnaryTransformer[Iterable[_], Vector, HashingTF] { - /** number of features */ - val numFeatures = new IntParam(this, "numFeatures", "number of features", Some(1 << 18)) - def setNumFeatures(value: Int) = set(numFeatures, value) - def getNumFeatures: Int = get(numFeatures) + /** + * number of features + * @group param + */ + val numFeatures = new IntParam(this, "numFeatures", "number of features") + + /** @group getParam */ + def getNumFeatures: Int = getOrDefault(numFeatures) + + /** @group setParam */ + def setNumFeatures(value: Int): this.type = set(numFeatures, value) + + setDefault(numFeatures -> (1 << 18)) override protected def createTransformFunc(paramMap: ParamMap): Iterable[_] => Vector = { val hashingTF = new feature.HashingTF(paramMap(numFeatures)) diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala new file mode 100644 index 0000000000000..decaeb0da6246 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala @@ -0,0 +1,54 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature + +import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.ml.UnaryTransformer +import org.apache.spark.ml.param.{DoubleParam, ParamMap} +import org.apache.spark.mllib.feature +import org.apache.spark.mllib.linalg.{VectorUDT, Vector} +import org.apache.spark.sql.types.DataType + +/** + * :: AlphaComponent :: + * Normalize a vector to have unit norm using the given p-norm. + */ +@AlphaComponent +class Normalizer extends UnaryTransformer[Vector, Vector, Normalizer] { + + /** + * Normalization in L^p^ space, p = 2 by default. + * @group param + */ + val p = new DoubleParam(this, "p", "the p norm value") + + /** @group getParam */ + def getP: Double = getOrDefault(p) + + /** @group setParam */ + def setP(value: Double): this.type = set(p, value) + + setDefault(p -> 2.0) + + override protected def createTransformFunc(paramMap: ParamMap): Vector => Vector = { + val normalizer = new feature.Normalizer(paramMap(p)) + normalizer.transform + } + + override protected def outputDataType: DataType = new VectorUDT() +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala index 4745a7ae95679..1b102619b3524 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala @@ -20,10 +20,11 @@ package org.apache.spark.ml.feature import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml._ import org.apache.spark.ml.param._ +import org.apache.spark.ml.param.shared._ import org.apache.spark.mllib.feature import org.apache.spark.mllib.linalg.{Vector, VectorUDT} import org.apache.spark.sql._ -import org.apache.spark.sql.Dsl._ +import org.apache.spark.sql.functions._ import org.apache.spark.sql.types.{StructField, StructType} /** @@ -39,12 +40,15 @@ private[feature] trait StandardScalerParams extends Params with HasInputCol with @AlphaComponent class StandardScaler extends Estimator[StandardScalerModel] with StandardScalerParams { + /** @group setParam */ def setInputCol(value: String): this.type = set(inputCol, value) + + /** @group setParam */ def setOutputCol(value: String): this.type = set(outputCol, value) override def fit(dataset: DataFrame, paramMap: ParamMap): StandardScalerModel = { transformSchema(dataset.schema, paramMap, logging = true) - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) val input = dataset.select(map(inputCol)).map { case Row(v: Vector) => v } val scaler = new feature.StandardScaler().fit(input) val model = new StandardScalerModel(this, map, scaler) @@ -52,8 +56,8 @@ class StandardScaler extends Estimator[StandardScalerModel] with StandardScalerP model } - private[ml] override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = this.paramMap ++ paramMap + override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { + val map = extractParamMap(paramMap) val inputType = schema(map(inputCol)).dataType require(inputType.isInstanceOf[VectorUDT], s"Input column ${map(inputCol)} must be a vector column") @@ -75,18 +79,21 @@ class StandardScalerModel private[ml] ( scaler: feature.StandardScalerModel) extends Model[StandardScalerModel] with StandardScalerParams { + /** @group setParam */ def setInputCol(value: String): this.type = set(inputCol, value) + + /** @group setParam */ def setOutputCol(value: String): this.type = set(outputCol, value) override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { transformSchema(dataset.schema, paramMap, logging = true) - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) val scale = udf((v: Vector) => { scaler.transform(v) } : Vector) - dataset.select($"*", scale(col(map(inputCol))).as(map(outputCol))) + dataset.withColumn(map(outputCol), scale(col(map(inputCol)))) } - private[ml] override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = this.paramMap ++ paramMap + override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { + val map = extractParamMap(paramMap) val inputType = schema(map(inputCol)).dataType require(inputType.isInstanceOf[VectorUDT], s"Input column ${map(inputCol)} must be a vector column") diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala new file mode 100644 index 0000000000000..23956c512c8a6 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala @@ -0,0 +1,128 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature + +import org.apache.spark.SparkException +import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.ml.{Estimator, Model} +import org.apache.spark.ml.attribute.NominalAttribute +import org.apache.spark.ml.param._ +import org.apache.spark.ml.param.shared._ +import org.apache.spark.ml.util.SchemaUtils +import org.apache.spark.sql.DataFrame +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.types.{StringType, StructType} +import org.apache.spark.util.collection.OpenHashMap + +/** + * Base trait for [[StringIndexer]] and [[StringIndexerModel]]. + */ +private[feature] trait StringIndexerBase extends Params with HasInputCol with HasOutputCol { + + /** Validates and transforms the input schema. */ + protected def validateAndTransformSchema(schema: StructType, paramMap: ParamMap): StructType = { + val map = extractParamMap(paramMap) + SchemaUtils.checkColumnType(schema, map(inputCol), StringType) + val inputFields = schema.fields + val outputColName = map(outputCol) + require(inputFields.forall(_.name != outputColName), + s"Output column $outputColName already exists.") + val attr = NominalAttribute.defaultAttr.withName(map(outputCol)) + val outputFields = inputFields :+ attr.toStructField() + StructType(outputFields) + } +} + +/** + * :: AlphaComponent :: + * A label indexer that maps a string column of labels to an ML column of label indices. + * The indices are in [0, numLabels), ordered by label frequencies. + * So the most frequent label gets index 0. + */ +@AlphaComponent +class StringIndexer extends Estimator[StringIndexerModel] with StringIndexerBase { + + /** @group setParam */ + def setInputCol(value: String): this.type = set(inputCol, value) + + /** @group setParam */ + def setOutputCol(value: String): this.type = set(outputCol, value) + + // TODO: handle unseen labels + + override def fit(dataset: DataFrame, paramMap: ParamMap): StringIndexerModel = { + val map = extractParamMap(paramMap) + val counts = dataset.select(map(inputCol)).map(_.getString(0)).countByValue() + val labels = counts.toSeq.sortBy(-_._2).map(_._1).toArray + val model = new StringIndexerModel(this, map, labels) + Params.inheritValues(map, this, model) + model + } + + override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { + validateAndTransformSchema(schema, paramMap) + } +} + +/** + * :: AlphaComponent :: + * Model fitted by [[StringIndexer]]. + */ +@AlphaComponent +class StringIndexerModel private[ml] ( + override val parent: StringIndexer, + override val fittingParamMap: ParamMap, + labels: Array[String]) extends Model[StringIndexerModel] with StringIndexerBase { + + private val labelToIndex: OpenHashMap[String, Double] = { + val n = labels.length + val map = new OpenHashMap[String, Double](n) + var i = 0 + while (i < n) { + map.update(labels(i), i) + i += 1 + } + map + } + + /** @group setParam */ + def setInputCol(value: String): this.type = set(inputCol, value) + + /** @group setParam */ + def setOutputCol(value: String): this.type = set(outputCol, value) + + override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { + val map = extractParamMap(paramMap) + val indexer = udf { label: String => + if (labelToIndex.contains(label)) { + labelToIndex(label) + } else { + // TODO: handle unseen labels + throw new SparkException(s"Unseen label: $label.") + } + } + val outputColName = map(outputCol) + val metadata = NominalAttribute.defaultAttr + .withName(outputColName).withValues(labels).toMetadata() + dataset.select(col("*"), indexer(dataset(map(inputCol))).as(outputColName, metadata)) + } + + override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { + validateAndTransformSchema(schema, paramMap) + } +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala index 0b1f90daa7d8e..376a004858b4c 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala @@ -19,7 +19,7 @@ package org.apache.spark.ml.feature import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.UnaryTransformer -import org.apache.spark.ml.param.ParamMap +import org.apache.spark.ml.param.{ParamMap, IntParam, BooleanParam, Param} import org.apache.spark.sql.types.{DataType, StringType, ArrayType} /** @@ -39,3 +39,67 @@ class Tokenizer extends UnaryTransformer[String, Seq[String], Tokenizer] { override protected def outputDataType: DataType = new ArrayType(StringType, false) } + +/** + * :: AlphaComponent :: + * A regex based tokenizer that extracts tokens either by repeatedly matching the regex(default) + * or using it to split the text (set matching to false). Optional parameters also allow to fold + * the text to lowercase prior to it being tokenized and to filer tokens using a minimal length. + * It returns an array of strings that can be empty. + * The default parameters are regex = "\\p{L}+|[^\\p{L}\\s]+", matching = true, + * lowercase = false, minTokenLength = 1 + */ +@AlphaComponent +class RegexTokenizer extends UnaryTransformer[String, Seq[String], RegexTokenizer] { + + /** + * param for minimum token length, default is one to avoid returning empty strings + * @group param + */ + val minTokenLength: IntParam = new IntParam(this, "minLength", "minimum token length") + + /** @group setParam */ + def setMinTokenLength(value: Int): this.type = set(minTokenLength, value) + + /** @group getParam */ + def getMinTokenLength: Int = getOrDefault(minTokenLength) + + /** + * param sets regex as splitting on gaps (true) or matching tokens (false) + * @group param + */ + val gaps: BooleanParam = new BooleanParam(this, "gaps", "Set regex to match gaps or tokens") + + /** @group setParam */ + def setGaps(value: Boolean): this.type = set(gaps, value) + + /** @group getParam */ + def getGaps: Boolean = getOrDefault(gaps) + + /** + * param sets regex pattern used by tokenizer + * @group param + */ + val pattern: Param[String] = new Param(this, "pattern", "regex pattern used for tokenizing") + + /** @group setParam */ + def setPattern(value: String): this.type = set(pattern, value) + + /** @group getParam */ + def getPattern: String = getOrDefault(pattern) + + setDefault(minTokenLength -> 1, gaps -> false, pattern -> "\\p{L}+|[^\\p{L}\\s]+") + + override protected def createTransformFunc(paramMap: ParamMap): String => Seq[String] = { str => + val re = paramMap(pattern).r + val tokens = if (paramMap(gaps)) re.split(str).toSeq else re.findAllIn(str).toSeq + val minLength = paramMap(minTokenLength) + tokens.filter(_.length >= minLength) + } + + override protected def validateInputType(inputType: DataType): Unit = { + require(inputType == StringType, s"Input type must be string type but got $inputType.") + } + + override protected def outputDataType: DataType = new ArrayType(StringType, false) +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala new file mode 100644 index 0000000000000..e567e069e7c0b --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala @@ -0,0 +1,112 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature + +import scala.collection.mutable.ArrayBuilder + +import org.apache.spark.SparkException +import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.ml.Transformer +import org.apache.spark.ml.param.ParamMap +import org.apache.spark.ml.param.shared._ +import org.apache.spark.mllib.linalg.{Vector, VectorUDT, Vectors} +import org.apache.spark.sql.{Column, DataFrame, Row} +import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute +import org.apache.spark.sql.catalyst.expressions.{Alias, Cast, CreateStruct} +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.types._ + +/** + * :: AlphaComponent :: + * A feature transformer than merge multiple columns into a vector column. + */ +@AlphaComponent +class VectorAssembler extends Transformer with HasInputCols with HasOutputCol { + + /** @group setParam */ + def setInputCols(value: Array[String]): this.type = set(inputCols, value) + + /** @group setParam */ + def setOutputCol(value: String): this.type = set(outputCol, value) + + override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { + val map = extractParamMap(paramMap) + val assembleFunc = udf { r: Row => + VectorAssembler.assemble(r.toSeq: _*) + } + val schema = dataset.schema + val inputColNames = map(inputCols) + val args = inputColNames.map { c => + schema(c).dataType match { + case DoubleType => UnresolvedAttribute(c) + case t if t.isInstanceOf[VectorUDT] => UnresolvedAttribute(c) + case _: NativeType => Alias(Cast(UnresolvedAttribute(c), DoubleType), s"${c}_double_$uid")() + } + } + dataset.select(col("*"), assembleFunc(new Column(CreateStruct(args))).as(map(outputCol))) + } + + override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { + val map = extractParamMap(paramMap) + val inputColNames = map(inputCols) + val outputColName = map(outputCol) + val inputDataTypes = inputColNames.map(name => schema(name).dataType) + inputDataTypes.foreach { + case _: NativeType => + case t if t.isInstanceOf[VectorUDT] => + case other => + throw new IllegalArgumentException(s"Data type $other is not supported.") + } + if (schema.fieldNames.contains(outputColName)) { + throw new IllegalArgumentException(s"Output column $outputColName already exists.") + } + StructType(schema.fields :+ new StructField(outputColName, new VectorUDT, false)) + } +} + +@AlphaComponent +object VectorAssembler { + + private[feature] def assemble(vv: Any*): Vector = { + val indices = ArrayBuilder.make[Int] + val values = ArrayBuilder.make[Double] + var cur = 0 + vv.foreach { + case v: Double => + if (v != 0.0) { + indices += cur + values += v + } + cur += 1 + case vec: Vector => + vec.foreachActive { case (i, v) => + if (v != 0.0) { + indices += cur + i + values += v + } + } + cur += vec.size + case null => + // TODO: output Double.NaN? + throw new SparkException("Values to assemble cannot be null.") + case o => + throw new SparkException(s"$o of type ${o.getClass.getName} is not supported.") + } + Vectors.sparse(cur, indices.result(), values.result()) + } +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala new file mode 100644 index 0000000000000..452faa06e2021 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala @@ -0,0 +1,396 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature + +import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.ml.util.SchemaUtils +import org.apache.spark.ml.{Estimator, Model} +import org.apache.spark.ml.attribute.{BinaryAttribute, NumericAttribute, NominalAttribute, + Attribute, AttributeGroup} +import org.apache.spark.ml.param.{IntParam, ParamMap, Params} +import org.apache.spark.ml.param.shared._ +import org.apache.spark.mllib.linalg.{SparseVector, DenseVector, Vector, VectorUDT} +import org.apache.spark.sql.{Row, DataFrame} +import org.apache.spark.sql.functions.callUDF +import org.apache.spark.sql.types.{StructField, StructType} +import org.apache.spark.util.collection.OpenHashSet + + +/** Private trait for params for VectorIndexer and VectorIndexerModel */ +private[ml] trait VectorIndexerParams extends Params with HasInputCol with HasOutputCol { + + /** + * Threshold for the number of values a categorical feature can take. + * If a feature is found to have > maxCategories values, then it is declared continuous. + * + * (default = 20) + */ + val maxCategories = new IntParam(this, "maxCategories", + "Threshold for the number of values a categorical feature can take." + + " If a feature is found to have > maxCategories values, then it is declared continuous.") + + /** @group getParam */ + def getMaxCategories: Int = getOrDefault(maxCategories) + + setDefault(maxCategories -> 20) +} + +/** + * :: AlphaComponent :: + * + * Class for indexing categorical feature columns in a dataset of [[Vector]]. + * + * This has 2 usage modes: + * - Automatically identify categorical features (default behavior) + * - This helps process a dataset of unknown vectors into a dataset with some continuous + * features and some categorical features. The choice between continuous and categorical + * is based upon a maxCategories parameter. + * - Set maxCategories to the maximum number of categorical any categorical feature should have. + * - E.g.: Feature 0 has unique values {-1.0, 0.0}, and feature 1 values {1.0, 3.0, 5.0}. + * If maxCategories = 2, then feature 0 will be declared categorical and use indices {0, 1}, + * and feature 1 will be declared continuous. + * - Index all features, if all features are categorical + * - If maxCategories is set to be very large, then this will build an index of unique + * values for all features. + * - Warning: This can cause problems if features are continuous since this will collect ALL + * unique values to the driver. + * - E.g.: Feature 0 has unique values {-1.0, 0.0}, and feature 1 values {1.0, 3.0, 5.0}. + * If maxCategories >= 3, then both features will be declared categorical. + * + * This returns a model which can transform categorical features to use 0-based indices. + * + * Index stability: + * - This is not guaranteed to choose the same category index across multiple runs. + * - If a categorical feature includes value 0, then this is guaranteed to map value 0 to index 0. + * This maintains vector sparsity. + * - More stability may be added in the future. + * + * TODO: Future extensions: The following functionality is planned for the future: + * - Preserve metadata in transform; if a feature's metadata is already present, do not recompute. + * - Specify certain features to not index, either via a parameter or via existing metadata. + * - Add warning if a categorical feature has only 1 category. + * - Add option for allowing unknown categories. + */ +@AlphaComponent +class VectorIndexer extends Estimator[VectorIndexerModel] with VectorIndexerParams { + + /** @group setParam */ + def setMaxCategories(value: Int): this.type = { + require(value > 1, + s"DatasetIndexer given maxCategories = value, but requires maxCategories > 1.") + set(maxCategories, value) + } + + /** @group setParam */ + def setInputCol(value: String): this.type = set(inputCol, value) + + /** @group setParam */ + def setOutputCol(value: String): this.type = set(outputCol, value) + + override def fit(dataset: DataFrame, paramMap: ParamMap): VectorIndexerModel = { + transformSchema(dataset.schema, paramMap, logging = true) + val map = extractParamMap(paramMap) + val firstRow = dataset.select(map(inputCol)).take(1) + require(firstRow.length == 1, s"VectorIndexer cannot be fit on an empty dataset.") + val numFeatures = firstRow(0).getAs[Vector](0).size + val vectorDataset = dataset.select(map(inputCol)).map { case Row(v: Vector) => v } + val maxCats = map(maxCategories) + val categoryStats: VectorIndexer.CategoryStats = vectorDataset.mapPartitions { iter => + val localCatStats = new VectorIndexer.CategoryStats(numFeatures, maxCats) + iter.foreach(localCatStats.addVector) + Iterator(localCatStats) + }.reduce((stats1, stats2) => stats1.merge(stats2)) + val model = new VectorIndexerModel(this, map, numFeatures, categoryStats.getCategoryMaps) + Params.inheritValues(map, this, model) + model + } + + override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { + // We do not transfer feature metadata since we do not know what types of features we will + // produce in transform(). + val map = extractParamMap(paramMap) + val dataType = new VectorUDT + require(map.contains(inputCol), s"VectorIndexer requires input column parameter: $inputCol") + require(map.contains(outputCol), s"VectorIndexer requires output column parameter: $outputCol") + SchemaUtils.checkColumnType(schema, map(inputCol), dataType) + SchemaUtils.appendColumn(schema, map(outputCol), dataType) + } +} + +private object VectorIndexer { + + /** + * Helper class for tracking unique values for each feature. + * + * TODO: Track which features are known to be continuous already; do not update counts for them. + * + * @param numFeatures This class fails if it encounters a Vector whose length is not numFeatures. + * @param maxCategories This class caps the number of unique values collected at maxCategories. + */ + class CategoryStats(private val numFeatures: Int, private val maxCategories: Int) + extends Serializable { + + /** featureValueSets[feature index] = set of unique values */ + private val featureValueSets = + Array.fill[OpenHashSet[Double]](numFeatures)(new OpenHashSet[Double]()) + + /** Merge with another instance, modifying this instance. */ + def merge(other: CategoryStats): CategoryStats = { + featureValueSets.zip(other.featureValueSets).foreach { case (thisValSet, otherValSet) => + otherValSet.iterator.foreach { x => + // Once we have found > maxCategories values, we know the feature is continuous + // and do not need to collect more values for it. + if (thisValSet.size <= maxCategories) thisValSet.add(x) + } + } + this + } + + /** Add a new vector to this index, updating sets of unique feature values */ + def addVector(v: Vector): Unit = { + require(v.size == numFeatures, s"VectorIndexer expected $numFeatures features but" + + s" found vector of size ${v.size}.") + v match { + case dv: DenseVector => addDenseVector(dv) + case sv: SparseVector => addSparseVector(sv) + } + } + + /** + * Based on stats collected, decide which features are categorical, + * and choose indices for categories. + * + * Sparsity: This tries to maintain sparsity by treating value 0.0 specially. + * If a categorical feature takes value 0.0, then value 0.0 is given index 0. + * + * @return Feature value index. Keys are categorical feature indices (column indices). + * Values are mappings from original features values to 0-based category indices. + */ + def getCategoryMaps: Map[Int, Map[Double, Int]] = { + // Filter out features which are declared continuous. + featureValueSets.zipWithIndex.filter(_._1.size <= maxCategories).map { + case (featureValues: OpenHashSet[Double], featureIndex: Int) => + var sortedFeatureValues = featureValues.iterator.filter(_ != 0.0).toArray.sorted + val zeroExists = sortedFeatureValues.length + 1 == featureValues.size + if (zeroExists) { + sortedFeatureValues = 0.0 +: sortedFeatureValues + } + val categoryMap: Map[Double, Int] = sortedFeatureValues.zipWithIndex.toMap + (featureIndex, categoryMap) + }.toMap + } + + private def addDenseVector(dv: DenseVector): Unit = { + var i = 0 + while (i < dv.size) { + if (featureValueSets(i).size <= maxCategories) { + featureValueSets(i).add(dv(i)) + } + i += 1 + } + } + + private def addSparseVector(sv: SparseVector): Unit = { + // TODO: This might be able to handle 0's more efficiently. + var vecIndex = 0 // index into vector + var k = 0 // index into non-zero elements + while (vecIndex < sv.size) { + val featureValue = if (k < sv.indices.length && vecIndex == sv.indices(k)) { + k += 1 + sv.values(k - 1) + } else { + 0.0 + } + if (featureValueSets(vecIndex).size <= maxCategories) { + featureValueSets(vecIndex).add(featureValue) + } + vecIndex += 1 + } + } + } +} + +/** + * :: AlphaComponent :: + * + * Transform categorical features to use 0-based indices instead of their original values. + * - Categorical features are mapped to indices. + * - Continuous features (columns) are left unchanged. + * This also appends metadata to the output column, marking features as Numeric (continuous), + * Nominal (categorical), or Binary (either continuous or categorical). + * + * This maintains vector sparsity. + * + * @param numFeatures Number of features, i.e., length of Vectors which this transforms + * @param categoryMaps Feature value index. Keys are categorical feature indices (column indices). + * Values are maps from original features values to 0-based category indices. + * If a feature is not in this map, it is treated as continuous. + */ +@AlphaComponent +class VectorIndexerModel private[ml] ( + override val parent: VectorIndexer, + override val fittingParamMap: ParamMap, + val numFeatures: Int, + val categoryMaps: Map[Int, Map[Double, Int]]) + extends Model[VectorIndexerModel] with VectorIndexerParams { + + /** + * Pre-computed feature attributes, with some missing info. + * In transform(), set attribute name and other info, if available. + */ + private val partialFeatureAttributes: Array[Attribute] = { + val attrs = new Array[Attribute](numFeatures) + var categoricalFeatureCount = 0 // validity check for numFeatures, categoryMaps + var featureIndex = 0 + while (featureIndex < numFeatures) { + if (categoryMaps.contains(featureIndex)) { + // categorical feature + val featureValues: Array[String] = + categoryMaps(featureIndex).toArray.sortBy(_._1).map(_._1).map(_.toString) + if (featureValues.length == 2) { + attrs(featureIndex) = new BinaryAttribute(index = Some(featureIndex), + values = Some(featureValues)) + } else { + attrs(featureIndex) = new NominalAttribute(index = Some(featureIndex), + isOrdinal = Some(false), values = Some(featureValues)) + } + categoricalFeatureCount += 1 + } else { + // continuous feature + attrs(featureIndex) = new NumericAttribute(index = Some(featureIndex)) + } + featureIndex += 1 + } + require(categoricalFeatureCount == categoryMaps.size, "VectorIndexerModel given categoryMaps" + + s" with keys outside expected range [0,...,numFeatures), where numFeatures=$numFeatures") + attrs + } + + // TODO: Check more carefully about whether this whole class will be included in a closure. + + private val transformFunc: Vector => Vector = { + val sortedCategoricalFeatureIndices = categoryMaps.keys.toArray.sorted + val localVectorMap = categoryMaps + val f: Vector => Vector = { + case dv: DenseVector => + val tmpv = dv.copy + localVectorMap.foreach { case (featureIndex: Int, categoryMap: Map[Double, Int]) => + tmpv.values(featureIndex) = categoryMap(tmpv(featureIndex)) + } + tmpv + case sv: SparseVector => + // We use the fact that categorical value 0 is always mapped to index 0. + val tmpv = sv.copy + var catFeatureIdx = 0 // index into sortedCategoricalFeatureIndices + var k = 0 // index into non-zero elements of sparse vector + while (catFeatureIdx < sortedCategoricalFeatureIndices.length && k < tmpv.indices.length) { + val featureIndex = sortedCategoricalFeatureIndices(catFeatureIdx) + if (featureIndex < tmpv.indices(k)) { + catFeatureIdx += 1 + } else if (featureIndex > tmpv.indices(k)) { + k += 1 + } else { + tmpv.values(k) = localVectorMap(featureIndex)(tmpv.values(k)) + catFeatureIdx += 1 + k += 1 + } + } + tmpv + } + f + } + + /** @group setParam */ + def setInputCol(value: String): this.type = set(inputCol, value) + + /** @group setParam */ + def setOutputCol(value: String): this.type = set(outputCol, value) + + override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { + transformSchema(dataset.schema, paramMap, logging = true) + val map = extractParamMap(paramMap) + val newField = prepOutputField(dataset.schema, map) + val newCol = callUDF(transformFunc, new VectorUDT, dataset(map(inputCol))) + // For now, just check the first row of inputCol for vector length. + val firstRow = dataset.select(map(inputCol)).take(1) + if (firstRow.length != 0) { + val actualNumFeatures = firstRow(0).getAs[Vector](0).size + require(numFeatures == actualNumFeatures, "VectorIndexerModel expected vector of length" + + s" $numFeatures but found length $actualNumFeatures") + } + dataset.withColumn(map(outputCol), newCol.as(map(outputCol), newField.metadata)) + } + + override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { + val map = extractParamMap(paramMap) + val dataType = new VectorUDT + require(map.contains(inputCol), + s"VectorIndexerModel requires input column parameter: $inputCol") + require(map.contains(outputCol), + s"VectorIndexerModel requires output column parameter: $outputCol") + SchemaUtils.checkColumnType(schema, map(inputCol), dataType) + + val origAttrGroup = AttributeGroup.fromStructField(schema(map(inputCol))) + val origNumFeatures: Option[Int] = if (origAttrGroup.attributes.nonEmpty) { + Some(origAttrGroup.attributes.get.length) + } else { + origAttrGroup.numAttributes + } + require(origNumFeatures.forall(_ == numFeatures), "VectorIndexerModel expected" + + s" $numFeatures features, but input column ${map(inputCol)} had metadata specifying" + + s" ${origAttrGroup.numAttributes.get} features.") + + val newField = prepOutputField(schema, map) + val outputFields = schema.fields :+ newField + StructType(outputFields) + } + + /** + * Prepare the output column field, including per-feature metadata. + * @param schema Input schema + * @param map Parameter map (with this class' embedded parameter map folded in) + * @return Output column field + */ + private def prepOutputField(schema: StructType, map: ParamMap): StructField = { + val origAttrGroup = AttributeGroup.fromStructField(schema(map(inputCol))) + val featureAttributes: Array[Attribute] = if (origAttrGroup.attributes.nonEmpty) { + // Convert original attributes to modified attributes + val origAttrs: Array[Attribute] = origAttrGroup.attributes.get + origAttrs.zip(partialFeatureAttributes).map { + case (origAttr: Attribute, featAttr: BinaryAttribute) => + if (origAttr.name.nonEmpty) { + featAttr.withName(origAttr.name.get) + } else { + featAttr + } + case (origAttr: Attribute, featAttr: NominalAttribute) => + if (origAttr.name.nonEmpty) { + featAttr.withName(origAttr.name.get) + } else { + featAttr + } + case (origAttr: Attribute, featAttr: NumericAttribute) => + origAttr.withIndex(featAttr.index.get) + } + } else { + partialFeatureAttributes + } + val newAttributeGroup = new AttributeGroup(map(outputCol), featureAttributes) + newAttributeGroup.toStructField(schema(map(inputCol)).metadata) + } +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/impl/estimator/Predictor.scala b/mllib/src/main/scala/org/apache/spark/ml/impl/estimator/Predictor.scala index 89b53f3890ea3..195333a5cc47f 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/impl/estimator/Predictor.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/impl/estimator/Predictor.scala @@ -18,13 +18,15 @@ package org.apache.spark.ml.impl.estimator import org.apache.spark.annotation.{AlphaComponent, DeveloperApi} +import org.apache.spark.ml.util.SchemaUtils import org.apache.spark.ml.{Estimator, Model} import org.apache.spark.ml.param._ +import org.apache.spark.ml.param.shared._ import org.apache.spark.mllib.linalg.{VectorUDT, Vector} import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.rdd.RDD import org.apache.spark.sql.{DataFrame, Row} -import org.apache.spark.sql.Dsl._ +import org.apache.spark.sql.functions._ import org.apache.spark.sql.types.{DataType, DoubleType, StructType} @@ -53,14 +55,14 @@ private[spark] trait PredictorParams extends Params paramMap: ParamMap, fitting: Boolean, featuresDataType: DataType): StructType = { - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) // TODO: Support casting Array[Double] and Array[Float] to Vector when FeaturesType = Vector - checkInputColumn(schema, map(featuresCol), featuresDataType) + SchemaUtils.checkColumnType(schema, map(featuresCol), featuresDataType) if (fitting) { // TODO: Allow other numeric types - checkInputColumn(schema, map(labelCol), DoubleType) + SchemaUtils.checkColumnType(schema, map(labelCol), DoubleType) } - addOutputColumn(schema, map(predictionCol), DoubleType) + SchemaUtils.appendColumn(schema, map(predictionCol), DoubleType) } } @@ -85,15 +87,20 @@ private[spark] abstract class Predictor[ M <: PredictionModel[FeaturesType, M]] extends Estimator[M] with PredictorParams { + /** @group setParam */ def setLabelCol(value: String): Learner = set(labelCol, value).asInstanceOf[Learner] + + /** @group setParam */ def setFeaturesCol(value: String): Learner = set(featuresCol, value).asInstanceOf[Learner] + + /** @group setParam */ def setPredictionCol(value: String): Learner = set(predictionCol, value).asInstanceOf[Learner] override def fit(dataset: DataFrame, paramMap: ParamMap): M = { // This handles a few items such as schema validation. // Developers only need to implement train(). transformSchema(dataset.schema, paramMap, logging = true) - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) val model = train(dataset, map) Params.inheritValues(map, this, model) // copy params to model model @@ -127,7 +134,7 @@ private[spark] abstract class Predictor[ @DeveloperApi protected def featuresDataType: DataType = new VectorUDT - private[ml] override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { + override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { validateAndTransformSchema(schema, paramMap, fitting = true, featuresDataType) } @@ -136,7 +143,7 @@ private[spark] abstract class Predictor[ * and put it in an RDD with strong types. */ protected def extractLabeledPoints(dataset: DataFrame, paramMap: ParamMap): RDD[LabeledPoint] = { - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) dataset.select(map(labelCol), map(featuresCol)) .map { case Row(label: Double, features: Vector) => LabeledPoint(label, features) @@ -160,8 +167,10 @@ private[spark] abstract class Predictor[ private[spark] abstract class PredictionModel[FeaturesType, M <: PredictionModel[FeaturesType, M]] extends Model[M] with PredictorParams { + /** @group setParam */ def setFeaturesCol(value: String): M = set(featuresCol, value).asInstanceOf[M] + /** @group setParam */ def setPredictionCol(value: String): M = set(predictionCol, value).asInstanceOf[M] /** @@ -177,7 +186,7 @@ private[spark] abstract class PredictionModel[FeaturesType, M <: PredictionModel @DeveloperApi protected def featuresDataType: DataType = new VectorUDT - private[ml] override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { + override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { validateAndTransformSchema(schema, paramMap, fitting = false, featuresDataType) } @@ -194,7 +203,7 @@ private[spark] abstract class PredictionModel[FeaturesType, M <: PredictionModel // Check schema transformSchema(dataset.schema, paramMap, logging = true) - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) // Prepare model val tmpModel = if (paramMap.size != 0) { @@ -209,7 +218,7 @@ private[spark] abstract class PredictionModel[FeaturesType, M <: PredictionModel val pred: FeaturesType => Double = (features) => { tmpModel.predict(features) } - dataset.select($"*", callUDF(pred, DoubleType, col(map(featuresCol))).as(map(predictionCol))) + dataset.withColumn(map(predictionCol), callUDF(pred, DoubleType, col(map(featuresCol)))) } else { this.logWarning(s"$uid: Predictor.transform() was called as NOOP" + " since no output columns were set.") diff --git a/mllib/src/main/scala/org/apache/spark/ml/impl/tree/treeParams.scala b/mllib/src/main/scala/org/apache/spark/ml/impl/tree/treeParams.scala new file mode 100644 index 0000000000000..6f4509f03d033 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/impl/tree/treeParams.scala @@ -0,0 +1,300 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.impl.tree + +import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.ml.impl.estimator.PredictorParams +import org.apache.spark.ml.param._ +import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, Strategy => OldStrategy} +import org.apache.spark.mllib.tree.impurity.{Gini => OldGini, Entropy => OldEntropy, + Impurity => OldImpurity, Variance => OldVariance} + + +/** + * :: DeveloperApi :: + * Parameters for Decision Tree-based algorithms. + * + * Note: Marked as private and DeveloperApi since this may be made public in the future. + */ +@DeveloperApi +private[ml] trait DecisionTreeParams extends PredictorParams { + + /** + * Maximum depth of the tree. + * E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes. + * (default = 5) + * @group param + */ + final val maxDepth: IntParam = + new IntParam(this, "maxDepth", "Maximum depth of the tree." + + " E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.") + + /** + * Maximum number of bins used for discretizing continuous features and for choosing how to split + * on features at each node. More bins give higher granularity. + * Must be >= 2 and >= number of categories in any categorical feature. + * (default = 32) + * @group param + */ + final val maxBins: IntParam = new IntParam(this, "maxBins", "Max number of bins for" + + " discretizing continuous features. Must be >=2 and >= number of categories for any" + + " categorical feature.") + + /** + * Minimum number of instances each child must have after split. + * If a split causes the left or right child to have fewer than minInstancesPerNode, + * the split will be discarded as invalid. + * Should be >= 1. + * (default = 1) + * @group param + */ + final val minInstancesPerNode: IntParam = new IntParam(this, "minInstancesPerNode", "Minimum" + + " number of instances each child must have after split. If a split causes the left or right" + + " child to have fewer than minInstancesPerNode, the split will be discarded as invalid." + + " Should be >= 1.") + + /** + * Minimum information gain for a split to be considered at a tree node. + * (default = 0.0) + * @group param + */ + final val minInfoGain: DoubleParam = new DoubleParam(this, "minInfoGain", + "Minimum information gain for a split to be considered at a tree node.") + + /** + * Maximum memory in MB allocated to histogram aggregation. + * (default = 256 MB) + * @group expertParam + */ + final val maxMemoryInMB: IntParam = new IntParam(this, "maxMemoryInMB", + "Maximum memory in MB allocated to histogram aggregation.") + + /** + * If false, the algorithm will pass trees to executors to match instances with nodes. + * If true, the algorithm will cache node IDs for each instance. + * Caching can speed up training of deeper trees. + * (default = false) + * @group expertParam + */ + final val cacheNodeIds: BooleanParam = new BooleanParam(this, "cacheNodeIds", "If false, the" + + " algorithm will pass trees to executors to match instances with nodes. If true, the" + + " algorithm will cache node IDs for each instance. Caching can speed up training of deeper" + + " trees.") + + /** + * Specifies how often to checkpoint the cached node IDs. + * E.g. 10 means that the cache will get checkpointed every 10 iterations. + * This is only used if cacheNodeIds is true and if the checkpoint directory is set in + * [[org.apache.spark.SparkContext]]. + * Must be >= 1. + * (default = 10) + * @group expertParam + */ + final val checkpointInterval: IntParam = new IntParam(this, "checkpointInterval", "Specifies" + + " how often to checkpoint the cached node IDs. E.g. 10 means that the cache will get" + + " checkpointed every 10 iterations. This is only used if cacheNodeIds is true and if the" + + " checkpoint directory is set in the SparkContext. Must be >= 1.") + + setDefault(maxDepth -> 5, maxBins -> 32, minInstancesPerNode -> 1, minInfoGain -> 0.0, + maxMemoryInMB -> 256, cacheNodeIds -> false, checkpointInterval -> 10) + + /** @group setParam */ + def setMaxDepth(value: Int): this.type = { + require(value >= 0, s"maxDepth parameter must be >= 0. Given bad value: $value") + set(maxDepth, value) + this.asInstanceOf[this.type] + } + + /** @group getParam */ + def getMaxDepth: Int = getOrDefault(maxDepth) + + /** @group setParam */ + def setMaxBins(value: Int): this.type = { + require(value >= 2, s"maxBins parameter must be >= 2. Given bad value: $value") + set(maxBins, value) + this + } + + /** @group getParam */ + def getMaxBins: Int = getOrDefault(maxBins) + + /** @group setParam */ + def setMinInstancesPerNode(value: Int): this.type = { + require(value >= 1, s"minInstancesPerNode parameter must be >= 1. Given bad value: $value") + set(minInstancesPerNode, value) + this + } + + /** @group getParam */ + def getMinInstancesPerNode: Int = getOrDefault(minInstancesPerNode) + + /** @group setParam */ + def setMinInfoGain(value: Double): this.type = { + set(minInfoGain, value) + this + } + + /** @group getParam */ + def getMinInfoGain: Double = getOrDefault(minInfoGain) + + /** @group expertSetParam */ + def setMaxMemoryInMB(value: Int): this.type = { + require(value > 0, s"maxMemoryInMB parameter must be > 0. Given bad value: $value") + set(maxMemoryInMB, value) + this + } + + /** @group expertGetParam */ + def getMaxMemoryInMB: Int = getOrDefault(maxMemoryInMB) + + /** @group expertSetParam */ + def setCacheNodeIds(value: Boolean): this.type = { + set(cacheNodeIds, value) + this + } + + /** @group expertGetParam */ + def getCacheNodeIds: Boolean = getOrDefault(cacheNodeIds) + + /** @group expertSetParam */ + def setCheckpointInterval(value: Int): this.type = { + require(value >= 1, s"checkpointInterval parameter must be >= 1. Given bad value: $value") + set(checkpointInterval, value) + this + } + + /** @group expertGetParam */ + def getCheckpointInterval: Int = getOrDefault(checkpointInterval) + + /** + * Create a Strategy instance to use with the old API. + * NOTE: The caller should set impurity and subsamplingRate (which is set to 1.0, + * the default for single trees). + */ + private[ml] def getOldStrategy( + categoricalFeatures: Map[Int, Int], + numClasses: Int): OldStrategy = { + val strategy = OldStrategy.defaultStategy(OldAlgo.Classification) + strategy.checkpointInterval = getCheckpointInterval + strategy.maxBins = getMaxBins + strategy.maxDepth = getMaxDepth + strategy.maxMemoryInMB = getMaxMemoryInMB + strategy.minInfoGain = getMinInfoGain + strategy.minInstancesPerNode = getMinInstancesPerNode + strategy.useNodeIdCache = getCacheNodeIds + strategy.numClasses = numClasses + strategy.categoricalFeaturesInfo = categoricalFeatures + strategy.subsamplingRate = 1.0 // default for individual trees + strategy + } +} + +/** + * (private trait) Parameters for Decision Tree-based classification algorithms. + */ +private[ml] trait TreeClassifierParams extends Params { + + /** + * Criterion used for information gain calculation (case-insensitive). + * Supported: "entropy" and "gini". + * (default = gini) + * @group param + */ + val impurity: Param[String] = new Param[String](this, "impurity", "Criterion used for" + + " information gain calculation (case-insensitive). Supported options:" + + s" ${TreeClassifierParams.supportedImpurities.mkString(", ")}") + + setDefault(impurity -> "gini") + + /** @group setParam */ + def setImpurity(value: String): this.type = { + val impurityStr = value.toLowerCase + require(TreeClassifierParams.supportedImpurities.contains(impurityStr), + s"Tree-based classifier was given unrecognized impurity: $value." + + s" Supported options: ${TreeClassifierParams.supportedImpurities.mkString(", ")}") + set(impurity, impurityStr) + this + } + + /** @group getParam */ + def getImpurity: String = getOrDefault(impurity) + + /** Convert new impurity to old impurity. */ + private[ml] def getOldImpurity: OldImpurity = { + getImpurity match { + case "entropy" => OldEntropy + case "gini" => OldGini + case _ => + // Should never happen because of check in setter method. + throw new RuntimeException( + s"TreeClassifierParams was given unrecognized impurity: $impurity.") + } + } +} + +private[ml] object TreeClassifierParams { + // These options should be lowercase. + val supportedImpurities: Array[String] = Array("entropy", "gini").map(_.toLowerCase) +} + +/** + * (private trait) Parameters for Decision Tree-based regression algorithms. + */ +private[ml] trait TreeRegressorParams extends Params { + + /** + * Criterion used for information gain calculation (case-insensitive). + * Supported: "variance". + * (default = variance) + * @group param + */ + val impurity: Param[String] = new Param[String](this, "impurity", "Criterion used for" + + " information gain calculation (case-insensitive). Supported options:" + + s" ${TreeRegressorParams.supportedImpurities.mkString(", ")}") + + setDefault(impurity -> "variance") + + /** @group setParam */ + def setImpurity(value: String): this.type = { + val impurityStr = value.toLowerCase + require(TreeRegressorParams.supportedImpurities.contains(impurityStr), + s"Tree-based regressor was given unrecognized impurity: $value." + + s" Supported options: ${TreeRegressorParams.supportedImpurities.mkString(", ")}") + set(impurity, impurityStr) + this + } + + /** @group getParam */ + def getImpurity: String = getOrDefault(impurity) + + /** Convert new impurity to old impurity. */ + protected def getOldImpurity: OldImpurity = { + getImpurity match { + case "variance" => OldVariance + case _ => + // Should never happen because of check in setter method. + throw new RuntimeException( + s"TreeRegressorParams was given unrecognized impurity: $impurity") + } + } +} + +private[ml] object TreeRegressorParams { + // These options should be lowercase. + val supportedImpurities: Array[String] = Array("variance").map(_.toLowerCase) +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/package.scala b/mllib/src/main/scala/org/apache/spark/ml/package.scala index 51cd48c90432a..ac75e9de1a8f2 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/package.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/package.scala @@ -20,5 +20,31 @@ package org.apache.spark /** * Spark ML is an ALPHA component that adds a new set of machine learning APIs to let users quickly * assemble and configure practical machine learning pipelines. + * + * @groupname param Parameters + * @groupdesc param A list of (hyper-)parameter keys this algorithm can take. Users can set and get + * the parameter values through setters and getters, respectively. + * @groupprio param -5 + * + * @groupname setParam Parameter setters + * @groupprio setParam 5 + * + * @groupname getParam Parameter getters + * @groupprio getParam 6 + * + * @groupname expertParam (expert-only) Parameters + * @groupdesc expertParam A list of advanced, expert-only (hyper-)parameter keys this algorithm can + * take. Users can set and get the parameter values through setters and getters, + * respectively. + * @groupprio expertParam 7 + * + * @groupname expertSetParam (expert-only) Parameter setters + * @groupprio expertSetParam 8 + * + * @groupname expertGetParam (expert-only) Parameter getters + * @groupprio expertGetParam 9 + * + * @groupname Ungrouped Members + * @groupprio Ungrouped 0 */ package object ml diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala index 17ece897a6c55..ddc5907e7facd 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala @@ -17,15 +17,14 @@ package org.apache.spark.ml.param +import java.lang.reflect.Modifier +import java.util.NoSuchElementException + import scala.annotation.varargs import scala.collection.mutable -import java.lang.reflect.Modifier - import org.apache.spark.annotation.{AlphaComponent, DeveloperApi} import org.apache.spark.ml.Identifiable -import org.apache.spark.sql.types.{DataType, StructField, StructType} - /** * :: AlphaComponent :: @@ -38,12 +37,7 @@ import org.apache.spark.sql.types.{DataType, StructField, StructType} * @tparam T param value type */ @AlphaComponent -class Param[T] ( - val parent: Params, - val name: String, - val doc: String, - val defaultValue: Option[T] = None) - extends Serializable { +class Param[T] (val parent: Params, val name: String, val doc: String) extends Serializable { /** * Creates a param pair with the given value (for Java). @@ -55,58 +49,55 @@ class Param[T] ( */ def ->(value: T): ParamPair[T] = ParamPair(this, value) + /** + * Converts this param's name, doc, and optionally its default value and the user-supplied + * value in its parent to string. + */ override def toString: String = { - if (defaultValue.isDefined) { - s"$name: $doc (default: ${defaultValue.get})" + val valueStr = if (parent.isDefined(this)) { + val defaultValueStr = parent.getDefault(this).map("default: " + _) + val currentValueStr = parent.get(this).map("current: " + _) + (defaultValueStr ++ currentValueStr).mkString("(", ", ", ")") } else { - s"$name: $doc" + "(undefined)" } + s"$name: $doc $valueStr" } } // specialize primitive-typed params because Java doesn't recognize scala.Double, scala.Int, ... /** Specialized version of [[Param[Double]]] for Java. */ -class DoubleParam(parent: Params, name: String, doc: String, defaultValue: Option[Double]) - extends Param[Double](parent, name, doc, defaultValue) { - - def this(parent: Params, name: String, doc: String) = this(parent, name, doc, None) +class DoubleParam(parent: Params, name: String, doc: String) + extends Param[Double](parent, name, doc) { override def w(value: Double): ParamPair[Double] = super.w(value) } /** Specialized version of [[Param[Int]]] for Java. */ -class IntParam(parent: Params, name: String, doc: String, defaultValue: Option[Int]) - extends Param[Int](parent, name, doc, defaultValue) { - - def this(parent: Params, name: String, doc: String) = this(parent, name, doc, None) +class IntParam(parent: Params, name: String, doc: String) + extends Param[Int](parent, name, doc) { override def w(value: Int): ParamPair[Int] = super.w(value) } /** Specialized version of [[Param[Float]]] for Java. */ -class FloatParam(parent: Params, name: String, doc: String, defaultValue: Option[Float]) - extends Param[Float](parent, name, doc, defaultValue) { - - def this(parent: Params, name: String, doc: String) = this(parent, name, doc, None) +class FloatParam(parent: Params, name: String, doc: String) + extends Param[Float](parent, name, doc) { override def w(value: Float): ParamPair[Float] = super.w(value) } /** Specialized version of [[Param[Long]]] for Java. */ -class LongParam(parent: Params, name: String, doc: String, defaultValue: Option[Long]) - extends Param[Long](parent, name, doc, defaultValue) { - - def this(parent: Params, name: String, doc: String) = this(parent, name, doc, None) +class LongParam(parent: Params, name: String, doc: String) + extends Param[Long](parent, name, doc) { override def w(value: Long): ParamPair[Long] = super.w(value) } /** Specialized version of [[Param[Boolean]]] for Java. */ -class BooleanParam(parent: Params, name: String, doc: String, defaultValue: Option[Boolean]) - extends Param[Boolean](parent, name, doc, defaultValue) { - - def this(parent: Params, name: String, doc: String) = this(parent, name, doc, None) +class BooleanParam(parent: Params, name: String, doc: String) + extends Param[Boolean](parent, name, doc) { override def w(value: Boolean): ParamPair[Boolean] = super.w(value) } @@ -124,8 +115,11 @@ case class ParamPair[T](param: Param[T], value: T) @AlphaComponent trait Params extends Identifiable with Serializable { - /** Returns all params. */ - def params: Array[Param[_]] = { + /** + * Returns all params sorted by their names. The default implementation uses Java reflection to + * list all public methods that have no arguments and return [[Param]]. + */ + lazy val params: Array[Param[_]] = { val methods = this.getClass.getMethods methods.filter { m => Modifier.isPublic(m.getModifiers) && @@ -153,25 +147,29 @@ trait Params extends Identifiable with Serializable { def explainParams(): String = params.mkString("\n") /** Checks whether a param is explicitly set. */ - def isSet(param: Param[_]): Boolean = { - require(param.parent.eq(this)) + final def isSet(param: Param[_]): Boolean = { + shouldOwn(param) paramMap.contains(param) } + /** Checks whether a param is explicitly set or has a default value. */ + final def isDefined(param: Param[_]): Boolean = { + shouldOwn(param) + defaultParamMap.contains(param) || paramMap.contains(param) + } + /** Gets a param by its name. */ - private[ml] def getParam(paramName: String): Param[Any] = { - val m = this.getClass.getMethod(paramName) - assert(Modifier.isPublic(m.getModifiers) && - classOf[Param[_]].isAssignableFrom(m.getReturnType) && - m.getParameterTypes.isEmpty) - m.invoke(this).asInstanceOf[Param[Any]] + def getParam(paramName: String): Param[Any] = { + params.find(_.name == paramName).getOrElse { + throw new NoSuchElementException(s"Param $paramName does not exist.") + }.asInstanceOf[Param[Any]] } /** * Sets a parameter in the embedded param map. */ - protected def set[T](param: Param[T], value: T): this.type = { - require(param.parent.eq(this)) + protected final def set[T](param: Param[T], value: T): this.type = { + shouldOwn(param) paramMap.put(param.asInstanceOf[Param[Any]], value) this } @@ -179,44 +177,102 @@ trait Params extends Identifiable with Serializable { /** * Sets a parameter (by name) in the embedded param map. */ - private[ml] def set(param: String, value: Any): this.type = { + protected final def set(param: String, value: Any): this.type = { set(getParam(param), value) } /** - * Gets the value of a parameter in the embedded param map. + * Optionally returns the user-supplied value of a param. */ - protected def get[T](param: Param[T]): T = { - require(param.parent.eq(this)) - paramMap(param) + final def get[T](param: Param[T]): Option[T] = { + shouldOwn(param) + paramMap.get(param) } /** - * Internal param map. + * Clears the user-supplied value for the input param. */ - protected val paramMap: ParamMap = ParamMap.empty + protected final def clear(param: Param[_]): this.type = { + shouldOwn(param) + paramMap.remove(param) + this + } /** - * Check whether the given schema contains an input column. - * @param colName Parameter name for the input column. - * @param dataType SQL DataType of the input column. + * Gets the value of a param in the embedded param map or its default value. Throws an exception + * if neither is set. */ - protected def checkInputColumn(schema: StructType, colName: String, dataType: DataType): Unit = { - val actualDataType = schema(colName).dataType - require(actualDataType.equals(dataType), - s"Input column $colName must be of type $dataType" + - s" but was actually $actualDataType. Column param description: ${getParam(colName)}") + final def getOrDefault[T](param: Param[T]): T = { + shouldOwn(param) + get(param).orElse(getDefault(param)).get } - protected def addOutputColumn( - schema: StructType, - colName: String, - dataType: DataType): StructType = { - if (colName.length == 0) return schema - val fieldNames = schema.fieldNames - require(!fieldNames.contains(colName), s"Prediction column $colName already exists.") - val outputFields = schema.fields ++ Seq(StructField(colName, dataType, nullable = false)) - StructType(outputFields) + /** + * Sets a default value for a param. + * @param param param to set the default value. Make sure that this param is initialized before + * this method gets called. + * @param value the default value + */ + protected final def setDefault[T](param: Param[T], value: T): this.type = { + shouldOwn(param) + defaultParamMap.put(param, value) + this + } + + /** + * Sets default values for a list of params. + * @param paramPairs a list of param pairs that specify params and their default values to set + * respectively. Make sure that the params are initialized before this method + * gets called. + */ + protected final def setDefault(paramPairs: ParamPair[_]*): this.type = { + paramPairs.foreach { p => + setDefault(p.param.asInstanceOf[Param[Any]], p.value) + } + this + } + + /** + * Gets the default value of a parameter. + */ + final def getDefault[T](param: Param[T]): Option[T] = { + shouldOwn(param) + defaultParamMap.get(param) + } + + /** + * Tests whether the input param has a default value set. + */ + final def hasDefault[T](param: Param[T]): Boolean = { + shouldOwn(param) + defaultParamMap.contains(param) + } + + /** + * Extracts the embedded default param values and user-supplied values, and then merges them with + * extra values from input into a flat param map, where the latter value is used if there exist + * conflicts, i.e., with ordering: default param values < user-supplied values < extraParamMap. + */ + protected final def extractParamMap(extraParamMap: ParamMap): ParamMap = { + defaultParamMap ++ paramMap ++ extraParamMap + } + + /** + * [[extractParamMap]] with no extra values. + */ + protected final def extractParamMap(): ParamMap = { + extractParamMap(ParamMap.empty) + } + + /** Internal param map for user-supplied values. */ + private val paramMap: ParamMap = ParamMap.empty + + /** Internal param map for default values. */ + private val defaultParamMap: ParamMap = ParamMap.empty + + /** Validates that the input param belongs to this instance. */ + private def shouldOwn(param: Param[_]): Unit = { + require(param.parent.eq(this), s"Param $param does not belong to $this.") } } @@ -240,8 +296,9 @@ private[spark] object Params { paramMap: ParamMap, parent: E, child: M): Unit = { + val childParams = child.params.map(_.name).toSet parent.params.foreach { param => - if (paramMap.contains(param)) { + if (paramMap.contains(param) && childParams.contains(param.name)) { child.set(child.getParam(param.name), paramMap(param)) } } @@ -253,12 +310,13 @@ private[spark] object Params { * A param to value map. */ @AlphaComponent -class ParamMap private[ml] (private val map: mutable.Map[Param[Any], Any]) extends Serializable { +final class ParamMap private[ml] (private val map: mutable.Map[Param[Any], Any]) + extends Serializable { /** * Creates an empty param map. */ - def this() = this(mutable.Map.empty[Param[Any], Any]) + def this() = this(mutable.Map.empty) /** * Puts a (param, value) pair (overwrites if the input param exists). @@ -280,12 +338,17 @@ class ParamMap private[ml] (private val map: mutable.Map[Param[Any], Any]) exten } /** - * Optionally returns the value associated with a param or its default. + * Optionally returns the value associated with a param. */ def get[T](param: Param[T]): Option[T] = { - map.get(param.asInstanceOf[Param[Any]]) - .orElse(param.defaultValue) - .asInstanceOf[Option[T]] + map.get(param.asInstanceOf[Param[Any]]).asInstanceOf[Option[T]] + } + + /** + * Returns the value associated with a param or a default value. + */ + def getOrElse[T](param: Param[T], default: T): T = { + get(param).getOrElse(default) } /** @@ -293,10 +356,7 @@ class ParamMap private[ml] (private val map: mutable.Map[Param[Any], Any]) exten * Raises a NoSuchElementException if there is no value associated with the input param. */ def apply[T](param: Param[T]): T = { - val value = get(param) - if (value.isDefined) { - value.get - } else { + get(param).getOrElse { throw new NoSuchElementException(s"Cannot find param ${param.name}.") } } @@ -308,6 +368,13 @@ class ParamMap private[ml] (private val map: mutable.Map[Param[Any], Any]) exten map.contains(param.asInstanceOf[Param[Any]]) } + /** + * Removes a key from this map and returns its value associated previously as an option. + */ + def remove[T](param: Param[T]): Option[T] = { + map.remove(param.asInstanceOf[Param[Any]]).asInstanceOf[Option[T]] + } + /** * Filters this param map for the given parent. */ @@ -317,7 +384,7 @@ class ParamMap private[ml] (private val map: mutable.Map[Param[Any], Any]) exten } /** - * Make a copy of this param map. + * Creates a copy of this param map. */ def copy: ParamMap = new ParamMap(map.clone()) @@ -329,7 +396,7 @@ class ParamMap private[ml] (private val map: mutable.Map[Param[Any], Any]) exten /** * Returns a new param map that contains parameters in this map and the given map, - * where the latter overwrites this if there exists conflicts. + * where the latter overwrites this if there exist conflicts. */ def ++(other: ParamMap): ParamMap = { // TODO: Provide a better method name for Java users. @@ -355,7 +422,7 @@ class ParamMap private[ml] (private val map: mutable.Map[Param[Any], Any]) exten } /** - * Number of param pairs in this set. + * Number of param pairs in this map. */ def size: Int = map.size } diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala new file mode 100644 index 0000000000000..95d7e64790c79 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala @@ -0,0 +1,169 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.param.shared + +import java.io.PrintWriter + +import scala.reflect.ClassTag + +/** + * Code generator for shared params (sharedParams.scala). Run under the Spark folder with + * {{{ + * build/sbt "mllib/runMain org.apache.spark.ml.param.shared.SharedParamsCodeGen" + * }}} + */ +private[shared] object SharedParamsCodeGen { + + def main(args: Array[String]): Unit = { + val params = Seq( + ParamDesc[Double]("regParam", "regularization parameter"), + ParamDesc[Int]("maxIter", "max number of iterations"), + ParamDesc[String]("featuresCol", "features column name", Some("\"features\"")), + ParamDesc[String]("labelCol", "label column name", Some("\"label\"")), + ParamDesc[String]("predictionCol", "prediction column name", Some("\"prediction\"")), + ParamDesc[String]("rawPredictionCol", "raw prediction (a.k.a. confidence) column name", + Some("\"rawPrediction\"")), + ParamDesc[String]("probabilityCol", + "column name for predicted class conditional probabilities", Some("\"probability\"")), + ParamDesc[Double]("threshold", "threshold in binary classification prediction"), + ParamDesc[String]("inputCol", "input column name"), + ParamDesc[Array[String]]("inputCols", "input column names"), + ParamDesc[String]("outputCol", "output column name"), + ParamDesc[Int]("checkpointInterval", "checkpoint interval"), + ParamDesc[Boolean]("fitIntercept", "whether to fit an intercept term", Some("true"))) + + val code = genSharedParams(params) + val file = "src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala" + val writer = new PrintWriter(file) + writer.write(code) + writer.close() + } + + /** Description of a param. */ + private case class ParamDesc[T: ClassTag]( + name: String, + doc: String, + defaultValueStr: Option[String] = None) { + + require(name.matches("[a-z][a-zA-Z0-9]*"), s"Param name $name is invalid.") + require(doc.nonEmpty) // TODO: more rigorous on doc + + def paramTypeName: String = { + val c = implicitly[ClassTag[T]].runtimeClass + c match { + case _ if c == classOf[Int] => "IntParam" + case _ if c == classOf[Long] => "LongParam" + case _ if c == classOf[Float] => "FloatParam" + case _ if c == classOf[Double] => "DoubleParam" + case _ if c == classOf[Boolean] => "BooleanParam" + case _ => s"Param[${getTypeString(c)}]" + } + } + + def valueTypeName: String = { + val c = implicitly[ClassTag[T]].runtimeClass + getTypeString(c) + } + + private def getTypeString(c: Class[_]): String = { + c match { + case _ if c == classOf[Int] => "Int" + case _ if c == classOf[Long] => "Long" + case _ if c == classOf[Float] => "Float" + case _ if c == classOf[Double] => "Double" + case _ if c == classOf[Boolean] => "Boolean" + case _ if c == classOf[String] => "String" + case _ if c.isArray => s"Array[${getTypeString(c.getComponentType)}]" + } + } + } + + /** Generates the HasParam trait code for the input param. */ + private def genHasParamTrait(param: ParamDesc[_]): String = { + val name = param.name + val Name = name(0).toUpper +: name.substring(1) + val Param = param.paramTypeName + val T = param.valueTypeName + val doc = param.doc + val defaultValue = param.defaultValueStr + val defaultValueDoc = defaultValue.map { v => + s" (default: $v)" + }.getOrElse("") + val setDefault = defaultValue.map { v => + s""" + | setDefault($name, $v) + |""".stripMargin + }.getOrElse("") + + s""" + |/** + | * :: DeveloperApi :: + | * Trait for shared param $name$defaultValueDoc. + | */ + |@DeveloperApi + |trait Has$Name extends Params { + | + | /** + | * Param for $doc. + | * @group param + | */ + | final val $name: $Param = new $Param(this, "$name", "$doc") + |$setDefault + | /** @group getParam */ + | final def get$Name: $T = getOrDefault($name) + |} + |""".stripMargin + } + + /** Generates Scala source code for the input params with header. */ + private def genSharedParams(params: Seq[ParamDesc[_]]): String = { + val header = + """/* + | * Licensed to the Apache Software Foundation (ASF) under one or more + | * contributor license agreements. See the NOTICE file distributed with + | * this work for additional information regarding copyright ownership. + | * The ASF licenses this file to You under the Apache License, Version 2.0 + | * (the "License"); you may not use this file except in compliance with + | * the License. You may obtain a copy of the License at + | * + | * http://www.apache.org/licenses/LICENSE-2.0 + | * + | * Unless required by applicable law or agreed to in writing, software + | * distributed under the License is distributed on an "AS IS" BASIS, + | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + | * See the License for the specific language governing permissions and + | * limitations under the License. + | */ + | + |package org.apache.spark.ml.param.shared + | + |import org.apache.spark.annotation.DeveloperApi + |import org.apache.spark.ml.param._ + | + |// DO NOT MODIFY THIS FILE! It was generated by SharedParamsCodeGen. + | + |// scalastyle:off + |""".stripMargin + + val footer = "// scalastyle:on\n" + + val traits = params.map(genHasParamTrait).mkString + + header + traits + footer + } +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala new file mode 100644 index 0000000000000..72b08bf276483 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala @@ -0,0 +1,259 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.param.shared + +import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.ml.param._ + +// DO NOT MODIFY THIS FILE! It was generated by SharedParamsCodeGen. + +// scalastyle:off + +/** + * :: DeveloperApi :: + * Trait for shared param regParam. + */ +@DeveloperApi +trait HasRegParam extends Params { + + /** + * Param for regularization parameter. + * @group param + */ + final val regParam: DoubleParam = new DoubleParam(this, "regParam", "regularization parameter") + + /** @group getParam */ + final def getRegParam: Double = getOrDefault(regParam) +} + +/** + * :: DeveloperApi :: + * Trait for shared param maxIter. + */ +@DeveloperApi +trait HasMaxIter extends Params { + + /** + * Param for max number of iterations. + * @group param + */ + final val maxIter: IntParam = new IntParam(this, "maxIter", "max number of iterations") + + /** @group getParam */ + final def getMaxIter: Int = getOrDefault(maxIter) +} + +/** + * :: DeveloperApi :: + * Trait for shared param featuresCol (default: "features"). + */ +@DeveloperApi +trait HasFeaturesCol extends Params { + + /** + * Param for features column name. + * @group param + */ + final val featuresCol: Param[String] = new Param[String](this, "featuresCol", "features column name") + + setDefault(featuresCol, "features") + + /** @group getParam */ + final def getFeaturesCol: String = getOrDefault(featuresCol) +} + +/** + * :: DeveloperApi :: + * Trait for shared param labelCol (default: "label"). + */ +@DeveloperApi +trait HasLabelCol extends Params { + + /** + * Param for label column name. + * @group param + */ + final val labelCol: Param[String] = new Param[String](this, "labelCol", "label column name") + + setDefault(labelCol, "label") + + /** @group getParam */ + final def getLabelCol: String = getOrDefault(labelCol) +} + +/** + * :: DeveloperApi :: + * Trait for shared param predictionCol (default: "prediction"). + */ +@DeveloperApi +trait HasPredictionCol extends Params { + + /** + * Param for prediction column name. + * @group param + */ + final val predictionCol: Param[String] = new Param[String](this, "predictionCol", "prediction column name") + + setDefault(predictionCol, "prediction") + + /** @group getParam */ + final def getPredictionCol: String = getOrDefault(predictionCol) +} + +/** + * :: DeveloperApi :: + * Trait for shared param rawPredictionCol (default: "rawPrediction"). + */ +@DeveloperApi +trait HasRawPredictionCol extends Params { + + /** + * Param for raw prediction (a.k.a. confidence) column name. + * @group param + */ + final val rawPredictionCol: Param[String] = new Param[String](this, "rawPredictionCol", "raw prediction (a.k.a. confidence) column name") + + setDefault(rawPredictionCol, "rawPrediction") + + /** @group getParam */ + final def getRawPredictionCol: String = getOrDefault(rawPredictionCol) +} + +/** + * :: DeveloperApi :: + * Trait for shared param probabilityCol (default: "probability"). + */ +@DeveloperApi +trait HasProbabilityCol extends Params { + + /** + * Param for column name for predicted class conditional probabilities. + * @group param + */ + final val probabilityCol: Param[String] = new Param[String](this, "probabilityCol", "column name for predicted class conditional probabilities") + + setDefault(probabilityCol, "probability") + + /** @group getParam */ + final def getProbabilityCol: String = getOrDefault(probabilityCol) +} + +/** + * :: DeveloperApi :: + * Trait for shared param threshold. + */ +@DeveloperApi +trait HasThreshold extends Params { + + /** + * Param for threshold in binary classification prediction. + * @group param + */ + final val threshold: DoubleParam = new DoubleParam(this, "threshold", "threshold in binary classification prediction") + + /** @group getParam */ + final def getThreshold: Double = getOrDefault(threshold) +} + +/** + * :: DeveloperApi :: + * Trait for shared param inputCol. + */ +@DeveloperApi +trait HasInputCol extends Params { + + /** + * Param for input column name. + * @group param + */ + final val inputCol: Param[String] = new Param[String](this, "inputCol", "input column name") + + /** @group getParam */ + final def getInputCol: String = getOrDefault(inputCol) +} + +/** + * :: DeveloperApi :: + * Trait for shared param inputCols. + */ +@DeveloperApi +trait HasInputCols extends Params { + + /** + * Param for input column names. + * @group param + */ + final val inputCols: Param[Array[String]] = new Param[Array[String]](this, "inputCols", "input column names") + + /** @group getParam */ + final def getInputCols: Array[String] = getOrDefault(inputCols) +} + +/** + * :: DeveloperApi :: + * Trait for shared param outputCol. + */ +@DeveloperApi +trait HasOutputCol extends Params { + + /** + * Param for output column name. + * @group param + */ + final val outputCol: Param[String] = new Param[String](this, "outputCol", "output column name") + + /** @group getParam */ + final def getOutputCol: String = getOrDefault(outputCol) +} + +/** + * :: DeveloperApi :: + * Trait for shared param checkpointInterval. + */ +@DeveloperApi +trait HasCheckpointInterval extends Params { + + /** + * Param for checkpoint interval. + * @group param + */ + final val checkpointInterval: IntParam = new IntParam(this, "checkpointInterval", "checkpoint interval") + + /** @group getParam */ + final def getCheckpointInterval: Int = getOrDefault(checkpointInterval) +} + +/** + * :: DeveloperApi :: + * Trait for shared param fitIntercept (default: true). + */ +@DeveloperApi +trait HasFitIntercept extends Params { + + /** + * Param for whether to fit an intercept term. + * @group param + */ + final val fitIntercept: BooleanParam = new BooleanParam(this, "fitIntercept", "whether to fit an intercept term") + + setDefault(fitIntercept, true) + + /** @group getParam */ + final def getFitIntercept: Boolean = getOrDefault(fitIntercept) +} +// scalastyle:on diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/sharedParams.scala b/mllib/src/main/scala/org/apache/spark/ml/param/sharedParams.scala deleted file mode 100644 index 32fc74462ef4a..0000000000000 --- a/mllib/src/main/scala/org/apache/spark/ml/param/sharedParams.scala +++ /dev/null @@ -1,90 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.ml.param - -/* NOTE TO DEVELOPERS: - * If you mix these parameter traits into your algorithm, please add a setter method as well - * so that users may use a builder pattern: - * val myLearner = new MyLearner().setParam1(x).setParam2(y)... - */ - -private[ml] trait HasRegParam extends Params { - /** param for regularization parameter */ - val regParam: DoubleParam = new DoubleParam(this, "regParam", "regularization parameter") - def getRegParam: Double = get(regParam) -} - -private[ml] trait HasMaxIter extends Params { - /** param for max number of iterations */ - val maxIter: IntParam = new IntParam(this, "maxIter", "max number of iterations") - def getMaxIter: Int = get(maxIter) -} - -private[ml] trait HasFeaturesCol extends Params { - /** param for features column name */ - val featuresCol: Param[String] = - new Param(this, "featuresCol", "features column name", Some("features")) - def getFeaturesCol: String = get(featuresCol) -} - -private[ml] trait HasLabelCol extends Params { - /** param for label column name */ - val labelCol: Param[String] = new Param(this, "labelCol", "label column name", Some("label")) - def getLabelCol: String = get(labelCol) -} - -private[ml] trait HasPredictionCol extends Params { - /** param for prediction column name */ - val predictionCol: Param[String] = - new Param(this, "predictionCol", "prediction column name", Some("prediction")) - def getPredictionCol: String = get(predictionCol) -} - -private[ml] trait HasRawPredictionCol extends Params { - /** param for raw prediction column name */ - val rawPredictionCol: Param[String] = - new Param(this, "rawPredictionCol", "raw prediction (a.k.a. confidence) column name", - Some("rawPrediction")) - def getRawPredictionCol: String = get(rawPredictionCol) -} - -private[ml] trait HasProbabilityCol extends Params { - /** param for predicted class conditional probabilities column name */ - val probabilityCol: Param[String] = - new Param(this, "probabilityCol", "column name for predicted class conditional probabilities", - Some("probability")) - def getProbabilityCol: String = get(probabilityCol) -} - -private[ml] trait HasThreshold extends Params { - /** param for threshold in (binary) prediction */ - val threshold: DoubleParam = new DoubleParam(this, "threshold", "threshold in prediction") - def getThreshold: Double = get(threshold) -} - -private[ml] trait HasInputCol extends Params { - /** param for input column name */ - val inputCol: Param[String] = new Param(this, "inputCol", "input column name") - def getInputCol: String = get(inputCol) -} - -private[ml] trait HasOutputCol extends Params { - /** param for output column name */ - val outputCol: Param[String] = new Param(this, "outputCol", "output column name") - def getOutputCol: String = get(outputCol) -} diff --git a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala index bf5737177ceee..bd793beba35b6 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala @@ -18,6 +18,7 @@ package org.apache.spark.ml.recommendation import java.{util => ju} +import java.io.IOException import scala.collection.mutable import scala.reflect.ClassTag @@ -26,17 +27,18 @@ import scala.util.hashing.byteswap64 import com.github.fommil.netlib.BLAS.{getInstance => blas} import com.github.fommil.netlib.LAPACK.{getInstance => lapack} -import org.jblas.DoubleMatrix +import org.apache.hadoop.fs.{FileSystem, Path} import org.netlib.util.intW -import org.apache.spark.{HashPartitioner, Logging, Partitioner} +import org.apache.spark.{Logging, Partitioner} import org.apache.spark.annotation.DeveloperApi import org.apache.spark.ml.{Estimator, Model} import org.apache.spark.ml.param._ +import org.apache.spark.ml.param.shared._ import org.apache.spark.mllib.optimization.NNLS import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame -import org.apache.spark.sql.Dsl._ +import org.apache.spark.sql.functions._ import org.apache.spark.sql.types.{DoubleType, FloatType, IntegerType, StructField, StructType} import org.apache.spark.storage.StorageLevel import org.apache.spark.util.Utils @@ -47,46 +49,94 @@ import org.apache.spark.util.random.XORShiftRandom * Common params for ALS. */ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasRegParam - with HasPredictionCol { + with HasPredictionCol with HasCheckpointInterval { - /** Param for rank of the matrix factorization. */ - val rank = new IntParam(this, "rank", "rank of the factorization", Some(10)) - def getRank: Int = get(rank) + /** + * Param for rank of the matrix factorization. + * @group param + */ + val rank = new IntParam(this, "rank", "rank of the factorization") + + /** @group getParam */ + def getRank: Int = getOrDefault(rank) - /** Param for number of user blocks. */ - val numUserBlocks = new IntParam(this, "numUserBlocks", "number of user blocks", Some(10)) - def getNumUserBlocks: Int = get(numUserBlocks) + /** + * Param for number of user blocks. + * @group param + */ + val numUserBlocks = new IntParam(this, "numUserBlocks", "number of user blocks") - /** Param for number of item blocks. */ + /** @group getParam */ + def getNumUserBlocks: Int = getOrDefault(numUserBlocks) + + /** + * Param for number of item blocks. + * @group param + */ val numItemBlocks = - new IntParam(this, "numItemBlocks", "number of item blocks", Some(10)) - def getNumItemBlocks: Int = get(numItemBlocks) + new IntParam(this, "numItemBlocks", "number of item blocks") + + /** @group getParam */ + def getNumItemBlocks: Int = getOrDefault(numItemBlocks) + + /** + * Param to decide whether to use implicit preference. + * @group param + */ + val implicitPrefs = new BooleanParam(this, "implicitPrefs", "whether to use implicit preference") + + /** @group getParam */ + def getImplicitPrefs: Boolean = getOrDefault(implicitPrefs) + + /** + * Param for the alpha parameter in the implicit preference formulation. + * @group param + */ + val alpha = new DoubleParam(this, "alpha", "alpha for implicit preference") + + /** @group getParam */ + def getAlpha: Double = getOrDefault(alpha) + + /** + * Param for the column name for user ids. + * @group param + */ + val userCol = new Param[String](this, "userCol", "column name for user ids") - /** Param to decide whether to use implicit preference. */ - val implicitPrefs = - new BooleanParam(this, "implicitPrefs", "whether to use implicit preference", Some(false)) - def getImplicitPrefs: Boolean = get(implicitPrefs) + /** @group getParam */ + def getUserCol: String = getOrDefault(userCol) - /** Param for the alpha parameter in the implicit preference formulation. */ - val alpha = new DoubleParam(this, "alpha", "alpha for implicit preference", Some(1.0)) - def getAlpha: Double = get(alpha) + /** + * Param for the column name for item ids. + * @group param + */ + val itemCol = new Param[String](this, "itemCol", "column name for item ids") - /** Param for the column name for user ids. */ - val userCol = new Param[String](this, "userCol", "column name for user ids", Some("user")) - def getUserCol: String = get(userCol) + /** @group getParam */ + def getItemCol: String = getOrDefault(itemCol) - /** Param for the column name for item ids. */ - val itemCol = - new Param[String](this, "itemCol", "column name for item ids", Some("item")) - def getItemCol: String = get(itemCol) + /** + * Param for the column name for ratings. + * @group param + */ + val ratingCol = new Param[String](this, "ratingCol", "column name for ratings") - /** Param for the column name for ratings. */ - val ratingCol = new Param[String](this, "ratingCol", "column name for ratings", Some("rating")) - def getRatingCol: String = get(ratingCol) + /** @group getParam */ + def getRatingCol: String = getOrDefault(ratingCol) + /** + * Param for whether to apply nonnegativity constraints. + * @group param + */ val nonnegative = new BooleanParam( - this, "nonnegative", "whether to use nonnegative constraint for least squares", Some(false)) - val getNonnegative: Boolean = get(nonnegative) + this, "nonnegative", "whether to use nonnegative constraint for least squares") + + /** @group getParam */ + def getNonnegative: Boolean = getOrDefault(nonnegative) + + setDefault(rank -> 10, maxIter -> 10, regParam -> 0.1, numUserBlocks -> 10, numItemBlocks -> 10, + implicitPrefs -> false, alpha -> 1.0, userCol -> "user", itemCol -> "item", + ratingCol -> "rating", nonnegative -> false) /** * Validates and transforms the input schema. @@ -95,7 +145,7 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR * @return output schema */ protected def validateAndTransformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) assert(schema(map(userCol)).dataType == IntegerType) assert(schema(map(itemCol)).dataType== IntegerType) val ratingType = schema(map(ratingCol)).dataType @@ -119,13 +169,14 @@ class ALSModel private[ml] ( itemFactors: RDD[(Int, Array[Float])]) extends Model[ALSModel] with ALSParams { + /** @group setParam */ def setPredictionCol(value: String): this.type = set(predictionCol, value) override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { import dataset.sqlContext.implicits._ - val map = this.paramMap ++ paramMap - val users = userFactors.toDataFrame("id", "features") - val items = itemFactors.toDataFrame("id", "features") + val map = extractParamMap(paramMap) + val users = userFactors.toDF("id", "features") + val items = itemFactors.toDF("id", "features") // Register a UDF for DataFrame, and then // create a new column named map(predictionCol) by running the predict UDF. @@ -142,7 +193,7 @@ class ALSModel private[ml] ( .select(dataset("*"), predict(users("features"), items("features")).as(map(predictionCol))) } - override private[ml] def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { + override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { validateAndTransformSchema(schema, paramMap) } } @@ -181,20 +232,49 @@ class ALS extends Estimator[ALSModel] with ALSParams { import org.apache.spark.ml.recommendation.ALS.Rating + /** @group setParam */ def setRank(value: Int): this.type = set(rank, value) + + /** @group setParam */ def setNumUserBlocks(value: Int): this.type = set(numUserBlocks, value) + + /** @group setParam */ def setNumItemBlocks(value: Int): this.type = set(numItemBlocks, value) + + /** @group setParam */ def setImplicitPrefs(value: Boolean): this.type = set(implicitPrefs, value) + + /** @group setParam */ def setAlpha(value: Double): this.type = set(alpha, value) + + /** @group setParam */ def setUserCol(value: String): this.type = set(userCol, value) + + /** @group setParam */ def setItemCol(value: String): this.type = set(itemCol, value) + + /** @group setParam */ def setRatingCol(value: String): this.type = set(ratingCol, value) + + /** @group setParam */ def setPredictionCol(value: String): this.type = set(predictionCol, value) + + /** @group setParam */ def setMaxIter(value: Int): this.type = set(maxIter, value) + + /** @group setParam */ def setRegParam(value: Double): this.type = set(regParam, value) + + /** @group setParam */ def setNonnegative(value: Boolean): this.type = set(nonnegative, value) - /** Sets both numUserBlocks and numItemBlocks to the specific value. */ + /** @group setParam */ + def setCheckpointInterval(value: Int): this.type = set(checkpointInterval, value) + + /** + * Sets both numUserBlocks and numItemBlocks to the specific value. + * @group setParam + */ def setNumBlocks(value: Int): this.type = { setNumUserBlocks(value) setNumItemBlocks(value) @@ -203,9 +283,10 @@ class ALS extends Estimator[ALSModel] with ALSParams { setMaxIter(20) setRegParam(1.0) + setCheckpointInterval(10) override def fit(dataset: DataFrame, paramMap: ParamMap): ALSModel = { - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) val ratings = dataset .select(col(map(userCol)), col(map(itemCol)), col(map(ratingCol)).cast(FloatType)) .map { row => @@ -214,13 +295,14 @@ class ALS extends Estimator[ALSModel] with ALSParams { val (userFactors, itemFactors) = ALS.train(ratings, rank = map(rank), numUserBlocks = map(numUserBlocks), numItemBlocks = map(numItemBlocks), maxIter = map(maxIter), regParam = map(regParam), implicitPrefs = map(implicitPrefs), - alpha = map(alpha), nonnegative = map(nonnegative)) + alpha = map(alpha), nonnegative = map(nonnegative), + checkpointInterval = map(checkpointInterval)) val model = new ALSModel(this, map, map(rank), userFactors, itemFactors) Params.inheritValues(map, this, model) model } - override private[ml] def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { + override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { validateAndTransformSchema(schema, paramMap) } } @@ -241,7 +323,7 @@ object ALS extends Logging { /** Trait for least squares solvers applied to the normal equation. */ private[recommendation] trait LeastSquaresNESolver extends Serializable { - /** Solves a least squares problem (possibly with other constraints). */ + /** Solves a least squares problem with regularization (possibly with other constraints). */ def solve(ne: NormalEquation, lambda: Double): Array[Float] } @@ -253,20 +335,19 @@ object ALS extends Logging { /** * Solves a least squares problem with L2 regularization: * - * min norm(A x - b)^2^ + lambda * n * norm(x)^2^ + * min norm(A x - b)^2^ + lambda * norm(x)^2^ * * @param ne a [[NormalEquation]] instance that contains AtA, Atb, and n (number of instances) - * @param lambda regularization constant, which will be scaled by n + * @param lambda regularization constant * @return the solution x */ override def solve(ne: NormalEquation, lambda: Double): Array[Float] = { val k = ne.k // Add scaled lambda to the diagonals of AtA. - val scaledlambda = lambda * ne.n var i = 0 var j = 2 while (i < ne.triK) { - ne.ata(i) += scaledlambda + ne.ata(i) += lambda i += j j += 1 } @@ -289,14 +370,14 @@ object ALS extends Logging { private[recommendation] class NNLSSolver extends LeastSquaresNESolver { private var rank: Int = -1 private var workspace: NNLS.Workspace = _ - private var ata: DoubleMatrix = _ + private var ata: Array[Double] = _ private var initialized: Boolean = false private def initialize(rank: Int): Unit = { if (!initialized) { this.rank = rank workspace = NNLS.createWorkspace(rank) - ata = new DoubleMatrix(rank, rank) + ata = new Array[Double](rank * rank) initialized = true } else { require(this.rank == rank) @@ -312,8 +393,8 @@ object ALS extends Logging { override def solve(ne: NormalEquation, lambda: Double): Array[Float] = { val rank = ne.k initialize(rank) - fillAtA(ne.ata, lambda * ne.n) - val x = NNLS.solve(ata, new DoubleMatrix(rank, 1, ne.atb: _*), workspace) + fillAtA(ne.ata, lambda) + val x = NNLS.solve(ata, ne.atb, workspace) ne.reset() x.map(x => x.toFloat) } @@ -326,23 +407,30 @@ object ALS extends Logging { var i = 0 var pos = 0 var a = 0.0 - val data = ata.data while (i < rank) { var j = 0 while (j <= i) { a = triAtA(pos) - data(i * rank + j) = a - data(j * rank + i) = a + ata(i * rank + j) = a + ata(j * rank + i) = a pos += 1 j += 1 } - data(i * rank + i) += lambda + ata(i * rank + i) += lambda i += 1 } } } - /** Representing a normal equation (ALS' subproblem). */ + /** + * Representing a normal equation to solve the following weighted least squares problem: + * + * minimize \sum,,i,, c,,i,, (a,,i,,^T^ x - b,,i,,)^2^ + lambda * x^T^ x. + * + * Its normal equation is given by + * + * \sum,,i,, c,,i,, (a,,i,, a,,i,,^T^ x - b,,i,, a,,i,,) + lambda * x = 0. + */ private[recommendation] class NormalEquation(val k: Int) extends Serializable { /** Number of entries in the upper triangular part of a k-by-k matrix. */ @@ -351,8 +439,6 @@ object ALS extends Logging { val ata = new Array[Double](triK) /** A^T^ * b */ val atb = new Array[Double](k) - /** Number of observations. */ - var n = 0 private val da = new Array[Double](k) private val upper = "U" @@ -366,28 +452,13 @@ object ALS extends Logging { } /** Adds an observation. */ - def add(a: Array[Float], b: Float): this.type = { - require(a.length == k) - copyToDouble(a) - blas.dspr(upper, k, 1.0, da, 1, ata) - blas.daxpy(k, b.toDouble, da, 1, atb, 1) - n += 1 - this - } - - /** - * Adds an observation with implicit feedback. Note that this does not increment the counter. - */ - def addImplicit(a: Array[Float], b: Float, alpha: Double): this.type = { + def add(a: Array[Float], b: Double, c: Double = 1.0): this.type = { + require(c >= 0.0) require(a.length == k) - // Extension to the original paper to handle b < 0. confidence is a function of |b| instead - // so that it is never negative. - val confidence = 1.0 + alpha * math.abs(b) copyToDouble(a) - blas.dspr(upper, k, confidence - 1.0, da, 1, ata) - // For b <= 0, the corresponding preference is 0. So the term below is only added for b > 0. - if (b > 0) { - blas.daxpy(k, confidence, da, 1, atb, 1) + blas.dspr(upper, k, c, da, 1, ata) + if (b != 0.0) { + blas.daxpy(k, c * b, da, 1, atb, 1) } this } @@ -397,7 +468,6 @@ object ALS extends Logging { require(other.k == k) blas.daxpy(ata.length, 1.0, other.ata, 1, ata, 1) blas.daxpy(atb.length, 1.0, other.atb, 1, atb, 1) - n += other.n this } @@ -405,7 +475,6 @@ object ALS extends Logging { def reset(): Unit = { ju.Arrays.fill(ata, 0.0) ju.Arrays.fill(atb, 0.0) - n = 0 } } @@ -424,13 +493,14 @@ object ALS extends Logging { nonnegative: Boolean = false, intermediateRDDStorageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK, finalRDDStorageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK, + checkpointInterval: Int = 10, seed: Long = 0L)( implicit ord: Ordering[ID]): (RDD[(ID, Array[Float])], RDD[(ID, Array[Float])]) = { require(intermediateRDDStorageLevel != StorageLevel.NONE, "ALS is not designed to run without persisting intermediate RDDs.") val sc = ratings.sparkContext - val userPart = new HashPartitioner(numUserBlocks) - val itemPart = new HashPartitioner(numItemBlocks) + val userPart = new ALSPartitioner(numUserBlocks) + val itemPart = new ALSPartitioner(numItemBlocks) val userLocalIndexEncoder = new LocalIndexEncoder(userPart.numPartitions) val itemLocalIndexEncoder = new LocalIndexEncoder(itemPart.numPartitions) val solver = if (nonnegative) new NNLSSolver else new CholeskySolver @@ -451,6 +521,18 @@ object ALS extends Logging { val seedGen = new XORShiftRandom(seed) var userFactors = initialize(userInBlocks, rank, seedGen.nextLong()) var itemFactors = initialize(itemInBlocks, rank, seedGen.nextLong()) + var previousCheckpointFile: Option[String] = None + val shouldCheckpoint: Int => Boolean = (iter) => + sc.checkpointDir.isDefined && (iter % checkpointInterval == 0) + val deletePreviousCheckpointFile: () => Unit = () => + previousCheckpointFile.foreach { file => + try { + FileSystem.get(sc.hadoopConfiguration).delete(new Path(file), true) + } catch { + case e: IOException => + logWarning(s"Cannot delete checkpoint file $file:", e) + } + } if (implicitPrefs) { for (iter <- 1 to maxIter) { userFactors.setName(s"userFactors-$iter").persist(intermediateRDDStorageLevel) @@ -458,19 +540,30 @@ object ALS extends Logging { itemFactors = computeFactors(userFactors, userOutBlocks, itemInBlocks, rank, regParam, userLocalIndexEncoder, implicitPrefs, alpha, solver) previousItemFactors.unpersist() - if (sc.checkpointDir.isDefined && (iter % 3 == 0)) { - itemFactors.checkpoint() - } itemFactors.setName(s"itemFactors-$iter").persist(intermediateRDDStorageLevel) + // TODO: Generalize PeriodicGraphCheckpointer and use it here. + if (shouldCheckpoint(iter)) { + itemFactors.checkpoint() // itemFactors gets materialized in computeFactors. + } val previousUserFactors = userFactors userFactors = computeFactors(itemFactors, itemOutBlocks, userInBlocks, rank, regParam, itemLocalIndexEncoder, implicitPrefs, alpha, solver) + if (shouldCheckpoint(iter)) { + deletePreviousCheckpointFile() + previousCheckpointFile = itemFactors.getCheckpointFile + } previousUserFactors.unpersist() } } else { for (iter <- 0 until maxIter) { itemFactors = computeFactors(userFactors, userOutBlocks, itemInBlocks, rank, regParam, userLocalIndexEncoder, solver = solver) + if (shouldCheckpoint(iter)) { + itemFactors.checkpoint() + itemFactors.count() // checkpoint item factors and cut lineage + deletePreviousCheckpointFile() + previousCheckpointFile = itemFactors.getCheckpointFile + } userFactors = computeFactors(itemFactors, itemOutBlocks, userInBlocks, rank, regParam, itemLocalIndexEncoder, solver = solver) } @@ -478,13 +571,23 @@ object ALS extends Logging { val userIdAndFactors = userInBlocks .mapValues(_.srcIds) .join(userFactors) - .values + .mapPartitions({ items => + items.flatMap { case (_, (ids, factors)) => + ids.view.zip(factors) + } + // Preserve the partitioning because IDs are consistent with the partitioners in userInBlocks + // and userFactors. + }, preservesPartitioning = true) .setName("userFactors") .persist(finalRDDStorageLevel) val itemIdAndFactors = itemInBlocks .mapValues(_.srcIds) .join(itemFactors) - .values + .mapPartitions({ items => + items.flatMap { case (_, (ids, factors)) => + ids.view.zip(factors) + } + }, preservesPartitioning = true) .setName("itemFactors") .persist(finalRDDStorageLevel) if (finalRDDStorageLevel != StorageLevel.NONE) { @@ -497,13 +600,7 @@ object ALS extends Logging { itemOutBlocks.unpersist() blockRatings.unpersist() } - val userOutput = userIdAndFactors.flatMap { case (ids, factors) => - ids.view.zip(factors) - } - val itemOutput = itemIdAndFactors.flatMap { case (ids, factors) => - ids.view.zip(factors) - } - (userOutput, itemOutput) + (userIdAndFactors, itemIdAndFactors) } /** @@ -923,15 +1020,15 @@ object ALS extends Logging { "Converting to local indices took " + (System.nanoTime() - start) / 1e9 + " seconds.") val dstLocalIndices = dstIds.map(dstIdToLocalIndex.apply) (srcBlockId, (dstBlockId, srcIds, dstLocalIndices, ratings)) - }.groupByKey(new HashPartitioner(srcPart.numPartitions)) - .mapValues { iter => - val builder = - new UncompressedInBlockBuilder[ID](new LocalIndexEncoder(dstPart.numPartitions)) - iter.foreach { case (dstBlockId, srcIds, dstLocalIndices, ratings) => - builder.add(dstBlockId, srcIds, dstLocalIndices, ratings) - } - builder.build().compress() - }.setName(prefix + "InBlocks") + }.groupByKey(new ALSPartitioner(srcPart.numPartitions)) + .mapValues { iter => + val builder = + new UncompressedInBlockBuilder[ID](new LocalIndexEncoder(dstPart.numPartitions)) + iter.foreach { case (dstBlockId, srcIds, dstLocalIndices, ratings) => + builder.add(dstBlockId, srcIds, dstLocalIndices, ratings) + } + builder.build().compress() + }.setName(prefix + "InBlocks") .persist(storageLevel) val outBlocks = inBlocks.mapValues { case InBlock(srcIds, dstPtrs, dstEncodedIndices, _) => val encoder = new LocalIndexEncoder(dstPart.numPartitions) @@ -992,7 +1089,7 @@ object ALS extends Logging { (dstBlockId, (srcBlockId, activeIndices.map(idx => srcFactors(idx)))) } } - val merged = srcOut.groupByKey(new HashPartitioner(dstInBlocks.partitions.length)) + val merged = srcOut.groupByKey(new ALSPartitioner(dstInBlocks.partitions.length)) dstInBlocks.join(merged).mapValues { case (InBlock(dstIds, srcPtrs, srcEncodedIndices, ratings), srcFactors) => val sortedSrcFactors = new Array[FactorBlock](numSrcBlocks) @@ -1008,6 +1105,7 @@ object ALS extends Logging { ls.merge(YtY.get) } var i = srcPtrs(j) + var numExplicits = 0 while (i < srcPtrs(j + 1)) { val encoded = srcEncodedIndices(i) val blockId = srcEncoder.blockId(encoded) @@ -1015,13 +1113,23 @@ object ALS extends Logging { val srcFactor = sortedSrcFactors(blockId)(localIndex) val rating = ratings(i) if (implicitPrefs) { - ls.addImplicit(srcFactor, rating, alpha) + // Extension to the original paper to handle b < 0. confidence is a function of |b| + // instead so that it is never negative. c1 is confidence - 1.0. + val c1 = alpha * math.abs(rating) + // For rating <= 0, the corresponding preference is 0. So the term below is only added + // for rating > 0. Because YtY is already added, we need to adjust the scaling here. + if (rating > 0) { + numExplicits += 1 + ls.add(srcFactor, (c1 + 1.0) / c1, c1) + } } else { ls.add(srcFactor, rating) + numExplicits += 1 } i += 1 } - dstFactors(j) = solver.solve(ls, regParam) + // Weight lambda by the number of explicit ratings based on the ALS-WR paper. + dstFactors(j) = solver.solve(ls, numExplicits * regParam) j += 1 } dstFactors @@ -1035,7 +1143,7 @@ object ALS extends Logging { private def computeYtY(factorBlocks: RDD[(Int, FactorBlock)], rank: Int): NormalEquation = { factorBlocks.values.aggregate(new NormalEquation(rank))( seqOp = (ne, factors) => { - factors.foreach(ne.add(_, 0.0f)) + factors.foreach(ne.add(_, 0.0)) ne }, combOp = (ne1, ne2) => ne1.merge(ne2)) @@ -1077,4 +1185,11 @@ object ALS extends Logging { encoded & localIndexMask } } + + /** + * Partitioner used by ALS. We requires that getPartition is a projection. That is, for any key k, + * we have getPartition(getPartition(k)) = getPartition(k). Since the the default HashPartitioner + * satisfies this requirement, we simply use a type alias here. + */ + private[recommendation] type ALSPartitioner = org.apache.spark.HashPartitioner } diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala new file mode 100644 index 0000000000000..49a8b77acf960 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala @@ -0,0 +1,145 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.regression + +import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor} +import org.apache.spark.ml.impl.tree._ +import org.apache.spark.ml.param.{Params, ParamMap} +import org.apache.spark.ml.tree.{DecisionTreeModel, Node} +import org.apache.spark.ml.util.MetadataUtils +import org.apache.spark.mllib.linalg.Vector +import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.mllib.tree.{DecisionTree => OldDecisionTree} +import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, Strategy => OldStrategy} +import org.apache.spark.mllib.tree.model.{DecisionTreeModel => OldDecisionTreeModel} +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.DataFrame + + +/** + * :: AlphaComponent :: + * + * [[http://en.wikipedia.org/wiki/Decision_tree_learning Decision tree]] learning algorithm + * for regression. + * It supports both continuous and categorical features. + */ +@AlphaComponent +final class DecisionTreeRegressor + extends Predictor[Vector, DecisionTreeRegressor, DecisionTreeRegressionModel] + with DecisionTreeParams + with TreeRegressorParams { + + // Override parameter setters from parent trait for Java API compatibility. + + override def setMaxDepth(value: Int): this.type = super.setMaxDepth(value) + + override def setMaxBins(value: Int): this.type = super.setMaxBins(value) + + override def setMinInstancesPerNode(value: Int): this.type = + super.setMinInstancesPerNode(value) + + override def setMinInfoGain(value: Double): this.type = super.setMinInfoGain(value) + + override def setMaxMemoryInMB(value: Int): this.type = super.setMaxMemoryInMB(value) + + override def setCacheNodeIds(value: Boolean): this.type = super.setCacheNodeIds(value) + + override def setCheckpointInterval(value: Int): this.type = + super.setCheckpointInterval(value) + + override def setImpurity(value: String): this.type = super.setImpurity(value) + + override protected def train( + dataset: DataFrame, + paramMap: ParamMap): DecisionTreeRegressionModel = { + val categoricalFeatures: Map[Int, Int] = + MetadataUtils.getCategoricalFeatures(dataset.schema(paramMap(featuresCol))) + val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, paramMap) + val strategy = getOldStrategy(categoricalFeatures) + val oldModel = OldDecisionTree.train(oldDataset, strategy) + DecisionTreeRegressionModel.fromOld(oldModel, this, paramMap, categoricalFeatures) + } + + /** (private[ml]) Create a Strategy instance to use with the old API. */ + private[ml] def getOldStrategy(categoricalFeatures: Map[Int, Int]): OldStrategy = { + val strategy = super.getOldStrategy(categoricalFeatures, numClasses = 0) + strategy.algo = OldAlgo.Regression + strategy.setImpurity(getOldImpurity) + strategy + } +} + +object DecisionTreeRegressor { + /** Accessor for supported impurities */ + final val supportedImpurities: Array[String] = TreeRegressorParams.supportedImpurities +} + +/** + * :: AlphaComponent :: + * + * [[http://en.wikipedia.org/wiki/Decision_tree_learning Decision tree]] model for regression. + * It supports both continuous and categorical features. + * @param rootNode Root of the decision tree + */ +@AlphaComponent +final class DecisionTreeRegressionModel private[ml] ( + override val parent: DecisionTreeRegressor, + override val fittingParamMap: ParamMap, + override val rootNode: Node) + extends PredictionModel[Vector, DecisionTreeRegressionModel] + with DecisionTreeModel with Serializable { + + require(rootNode != null, + "DecisionTreeClassificationModel given null rootNode, but it requires a non-null rootNode.") + + override protected def predict(features: Vector): Double = { + rootNode.predict(features) + } + + override protected def copy(): DecisionTreeRegressionModel = { + val m = new DecisionTreeRegressionModel(parent, fittingParamMap, rootNode) + Params.inheritValues(this.extractParamMap(), this, m) + m + } + + override def toString: String = { + s"DecisionTreeRegressionModel of depth $depth with $numNodes nodes" + } + + /** Convert to a model in the old API */ + private[ml] def toOld: OldDecisionTreeModel = { + new OldDecisionTreeModel(rootNode.toOld(1), OldAlgo.Regression) + } +} + +private[ml] object DecisionTreeRegressionModel { + + /** (private[ml]) Convert a model from the old API */ + def fromOld( + oldModel: OldDecisionTreeModel, + parent: DecisionTreeRegressor, + fittingParamMap: ParamMap, + categoricalFeatures: Map[Int, Int]): DecisionTreeRegressionModel = { + require(oldModel.algo == OldAlgo.Regression, + s"Cannot convert non-regression DecisionTreeModel (old API) to" + + s" DecisionTreeRegressionModel (new API). Algo is: ${oldModel.algo}") + val rootNode = Node.fromOld(oldModel.topNode, categoricalFeatures) + new DecisionTreeRegressionModel(parent, fittingParamMap, rootNode) + } +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala index d5a7bdafcb623..26ca7459c4fdf 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala @@ -18,7 +18,8 @@ package org.apache.spark.ml.regression import org.apache.spark.annotation.AlphaComponent -import org.apache.spark.ml.param.{Params, ParamMap, HasMaxIter, HasRegParam} +import org.apache.spark.ml.param.{Params, ParamMap} +import org.apache.spark.ml.param.shared._ import org.apache.spark.mllib.linalg.{BLAS, Vector} import org.apache.spark.mllib.regression.LinearRegressionWithSGD import org.apache.spark.sql.DataFrame @@ -41,10 +42,12 @@ private[regression] trait LinearRegressionParams extends RegressorParams class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegressionModel] with LinearRegressionParams { - setRegParam(0.1) - setMaxIter(100) + setDefault(regParam -> 0.1, maxIter -> 100) + /** @group setParam */ def setRegParam(value: Double): this.type = set(regParam, value) + + /** @group setParam */ def setMaxIter(value: Int): this.type = set(maxIter, value) override protected def train(dataset: DataFrame, paramMap: ParamMap): LinearRegressionModel = { @@ -90,7 +93,7 @@ class LinearRegressionModel private[ml] ( override protected def copy(): LinearRegressionModel = { val m = new LinearRegressionModel(parent, fittingParamMap, weights, intercept) - Params.inheritValues(this.paramMap, this, m) + Params.inheritValues(extractParamMap(), this, m) m } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala new file mode 100644 index 0000000000000..d6e2203d9f937 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala @@ -0,0 +1,205 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.tree + +import org.apache.spark.mllib.linalg.Vector +import org.apache.spark.mllib.tree.model.{InformationGainStats => OldInformationGainStats, + Node => OldNode, Predict => OldPredict} + + +/** + * Decision tree node interface. + */ +sealed abstract class Node extends Serializable { + + // TODO: Add aggregate stats (once available). This will happen after we move the DecisionTree + // code into the new API and deprecate the old API. + + /** Prediction this node makes (or would make, if it is an internal node) */ + def prediction: Double + + /** Impurity measure at this node (for training data) */ + def impurity: Double + + /** Recursive prediction helper method */ + private[ml] def predict(features: Vector): Double = prediction + + /** + * Get the number of nodes in tree below this node, including leaf nodes. + * E.g., if this is a leaf, returns 0. If both children are leaves, returns 2. + */ + private[tree] def numDescendants: Int + + /** + * Recursive print function. + * @param indentFactor The number of spaces to add to each level of indentation. + */ + private[tree] def subtreeToString(indentFactor: Int = 0): String + + /** + * Get depth of tree from this node. + * E.g.: Depth 0 means this is a leaf node. Depth 1 means 1 internal and 2 leaf nodes. + */ + private[tree] def subtreeDepth: Int + + /** + * Create a copy of this node in the old Node format, recursively creating child nodes as needed. + * @param id Node ID using old format IDs + */ + private[ml] def toOld(id: Int): OldNode +} + +private[ml] object Node { + + /** + * Create a new Node from the old Node format, recursively creating child nodes as needed. + */ + def fromOld(oldNode: OldNode, categoricalFeatures: Map[Int, Int]): Node = { + if (oldNode.isLeaf) { + // TODO: Once the implementation has been moved to this API, then include sufficient + // statistics here. + new LeafNode(prediction = oldNode.predict.predict, impurity = oldNode.impurity) + } else { + val gain = if (oldNode.stats.nonEmpty) { + oldNode.stats.get.gain + } else { + 0.0 + } + new InternalNode(prediction = oldNode.predict.predict, impurity = oldNode.impurity, + gain = gain, leftChild = fromOld(oldNode.leftNode.get, categoricalFeatures), + rightChild = fromOld(oldNode.rightNode.get, categoricalFeatures), + split = Split.fromOld(oldNode.split.get, categoricalFeatures)) + } + } +} + +/** + * Decision tree leaf node. + * @param prediction Prediction this node makes + * @param impurity Impurity measure at this node (for training data) + */ +final class LeafNode private[ml] ( + override val prediction: Double, + override val impurity: Double) extends Node { + + override def toString: String = s"LeafNode(prediction = $prediction, impurity = $impurity)" + + override private[ml] def predict(features: Vector): Double = prediction + + override private[tree] def numDescendants: Int = 0 + + override private[tree] def subtreeToString(indentFactor: Int = 0): String = { + val prefix: String = " " * indentFactor + prefix + s"Predict: $prediction\n" + } + + override private[tree] def subtreeDepth: Int = 0 + + override private[ml] def toOld(id: Int): OldNode = { + // NOTE: We do NOT store 'prob' in the new API currently. + new OldNode(id, new OldPredict(prediction, prob = 0.0), impurity, isLeaf = true, + None, None, None, None) + } +} + +/** + * Internal Decision Tree node. + * @param prediction Prediction this node would make if it were a leaf node + * @param impurity Impurity measure at this node (for training data) + * @param gain Information gain value. + * Values < 0 indicate missing values; this quirk will be removed with future updates. + * @param leftChild Left-hand child node + * @param rightChild Right-hand child node + * @param split Information about the test used to split to the left or right child. + */ +final class InternalNode private[ml] ( + override val prediction: Double, + override val impurity: Double, + val gain: Double, + val leftChild: Node, + val rightChild: Node, + val split: Split) extends Node { + + override def toString: String = { + s"InternalNode(prediction = $prediction, impurity = $impurity, split = $split)" + } + + override private[ml] def predict(features: Vector): Double = { + if (split.shouldGoLeft(features)) { + leftChild.predict(features) + } else { + rightChild.predict(features) + } + } + + override private[tree] def numDescendants: Int = { + 2 + leftChild.numDescendants + rightChild.numDescendants + } + + override private[tree] def subtreeToString(indentFactor: Int = 0): String = { + val prefix: String = " " * indentFactor + prefix + s"If (${InternalNode.splitToString(split, left=true)})\n" + + leftChild.subtreeToString(indentFactor + 1) + + prefix + s"Else (${InternalNode.splitToString(split, left=false)})\n" + + rightChild.subtreeToString(indentFactor + 1) + } + + override private[tree] def subtreeDepth: Int = { + 1 + math.max(leftChild.subtreeDepth, rightChild.subtreeDepth) + } + + override private[ml] def toOld(id: Int): OldNode = { + assert(id.toLong * 2 < Int.MaxValue, "Decision Tree could not be converted from new to old API" + + " since the old API does not support deep trees.") + // NOTE: We do NOT store 'prob' in the new API currently. + new OldNode(id, new OldPredict(prediction, prob = 0.0), impurity, isLeaf = false, + Some(split.toOld), Some(leftChild.toOld(OldNode.leftChildIndex(id))), + Some(rightChild.toOld(OldNode.rightChildIndex(id))), + Some(new OldInformationGainStats(gain, impurity, leftChild.impurity, rightChild.impurity, + new OldPredict(leftChild.prediction, prob = 0.0), + new OldPredict(rightChild.prediction, prob = 0.0)))) + } +} + +private object InternalNode { + + /** + * Helper method for [[Node.subtreeToString()]]. + * @param split Split to print + * @param left Indicates whether this is the part of the split going to the left, + * or that going to the right. + */ + private def splitToString(split: Split, left: Boolean): String = { + val featureStr = s"feature ${split.featureIndex}" + split match { + case contSplit: ContinuousSplit => + if (left) { + s"$featureStr <= ${contSplit.threshold}" + } else { + s"$featureStr > ${contSplit.threshold}" + } + case catSplit: CategoricalSplit => + val categoriesStr = catSplit.getLeftCategories.mkString("{", ",", "}") + if (left) { + s"$featureStr in $categoriesStr" + } else { + s"$featureStr not in $categoriesStr" + } + } + } +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/Split.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/Split.scala new file mode 100644 index 0000000000000..cb940f62990ed --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/tree/Split.scala @@ -0,0 +1,151 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.tree + +import org.apache.spark.mllib.linalg.Vector +import org.apache.spark.mllib.tree.configuration.{FeatureType => OldFeatureType} +import org.apache.spark.mllib.tree.model.{Split => OldSplit} + + +/** + * Interface for a "Split," which specifies a test made at a decision tree node + * to choose the left or right path. + */ +sealed trait Split extends Serializable { + + /** Index of feature which this split tests */ + def featureIndex: Int + + /** Return true (split to left) or false (split to right) */ + private[ml] def shouldGoLeft(features: Vector): Boolean + + /** Convert to old Split format */ + private[tree] def toOld: OldSplit +} + +private[ml] object Split { + + def fromOld(oldSplit: OldSplit, categoricalFeatures: Map[Int, Int]): Split = { + oldSplit.featureType match { + case OldFeatureType.Categorical => + new CategoricalSplit(featureIndex = oldSplit.feature, + leftCategories = oldSplit.categories.toArray, categoricalFeatures(oldSplit.feature)) + case OldFeatureType.Continuous => + new ContinuousSplit(featureIndex = oldSplit.feature, threshold = oldSplit.threshold) + } + } +} + +/** + * Split which tests a categorical feature. + * @param featureIndex Index of the feature to test + * @param leftCategories If the feature value is in this set of categories, then the split goes + * left. Otherwise, it goes right. + * @param numCategories Number of categories for this feature. + */ +final class CategoricalSplit( + override val featureIndex: Int, + leftCategories: Array[Double], + private val numCategories: Int) + extends Split { + + require(leftCategories.forall(cat => 0 <= cat && cat < numCategories), "Invalid leftCategories" + + s" (should be in range [0, $numCategories)): ${leftCategories.mkString(",")}") + + /** + * If true, then "categories" is the set of categories for splitting to the left, and vice versa. + */ + private val isLeft: Boolean = leftCategories.length <= numCategories / 2 + + /** Set of categories determining the splitting rule, along with [[isLeft]]. */ + private val categories: Set[Double] = { + if (isLeft) { + leftCategories.toSet + } else { + setComplement(leftCategories.toSet) + } + } + + override private[ml] def shouldGoLeft(features: Vector): Boolean = { + if (isLeft) { + categories.contains(features(featureIndex)) + } else { + !categories.contains(features(featureIndex)) + } + } + + override def equals(o: Any): Boolean = { + o match { + case other: CategoricalSplit => featureIndex == other.featureIndex && + isLeft == other.isLeft && categories == other.categories + case _ => false + } + } + + override private[tree] def toOld: OldSplit = { + val oldCats = if (isLeft) { + categories + } else { + setComplement(categories) + } + OldSplit(featureIndex, threshold = 0.0, OldFeatureType.Categorical, oldCats.toList) + } + + /** Get sorted categories which split to the left */ + def getLeftCategories: Array[Double] = { + val cats = if (isLeft) categories else setComplement(categories) + cats.toArray.sorted + } + + /** Get sorted categories which split to the right */ + def getRightCategories: Array[Double] = { + val cats = if (isLeft) setComplement(categories) else categories + cats.toArray.sorted + } + + /** [0, numCategories) \ cats */ + private def setComplement(cats: Set[Double]): Set[Double] = { + Range(0, numCategories).map(_.toDouble).filter(cat => !cats.contains(cat)).toSet + } +} + +/** + * Split which tests a continuous feature. + * @param featureIndex Index of the feature to test + * @param threshold If the feature value is <= this threshold, then the split goes left. + * Otherwise, it goes right. + */ +final class ContinuousSplit(override val featureIndex: Int, val threshold: Double) extends Split { + + override private[ml] def shouldGoLeft(features: Vector): Boolean = { + features(featureIndex) <= threshold + } + + override def equals(o: Any): Boolean = { + o match { + case other: ContinuousSplit => + featureIndex == other.featureIndex && threshold == other.threshold + case _ => + false + } + } + + override private[tree] def toOld: OldSplit = { + OldSplit(featureIndex, threshold, OldFeatureType.Continuous, List.empty[Double]) + } +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala new file mode 100644 index 0000000000000..8e3bc3849dcf0 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala @@ -0,0 +1,60 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.tree + +import org.apache.spark.annotation.AlphaComponent + + +/** + * :: AlphaComponent :: + * + * Abstraction for Decision Tree models. + * + * TODO: Add support for predicting probabilities and raw predictions + */ +@AlphaComponent +trait DecisionTreeModel { + + /** Root of the decision tree */ + def rootNode: Node + + /** Number of nodes in tree, including leaf nodes. */ + def numNodes: Int = { + 1 + rootNode.numDescendants + } + + /** + * Depth of the tree. + * E.g.: Depth 0 means 1 leaf node. Depth 1 means 1 internal node and 2 leaf nodes. + */ + lazy val depth: Int = { + rootNode.subtreeDepth + } + + /** Summary of the model */ + override def toString: String = { + // Implementing classes should generally override this method to be more descriptive. + s"DecisionTreeModel of depth $depth with $numNodes nodes" + } + + /** Full description of model */ + def toDebugString: String = { + val header = toString + "\n" + header + rootNode.subtreeToString(2) + } +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala index 5d51c51346665..4bb4ed813c006 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala @@ -31,23 +31,45 @@ import org.apache.spark.sql.types.StructType * Params for [[CrossValidator]] and [[CrossValidatorModel]]. */ private[ml] trait CrossValidatorParams extends Params { - /** param for the estimator to be cross-validated */ + + /** + * param for the estimator to be cross-validated + * @group param + */ val estimator: Param[Estimator[_]] = new Param(this, "estimator", "estimator for selection") - def getEstimator: Estimator[_] = get(estimator) - /** param for estimator param maps */ + /** @group getParam */ + def getEstimator: Estimator[_] = getOrDefault(estimator) + + /** + * param for estimator param maps + * @group param + */ val estimatorParamMaps: Param[Array[ParamMap]] = new Param(this, "estimatorParamMaps", "param maps for the estimator") - def getEstimatorParamMaps: Array[ParamMap] = get(estimatorParamMaps) - /** param for the evaluator for selection */ + /** @group getParam */ + def getEstimatorParamMaps: Array[ParamMap] = getOrDefault(estimatorParamMaps) + + /** + * param for the evaluator for selection + * @group param + */ val evaluator: Param[Evaluator] = new Param(this, "evaluator", "evaluator for selection") - def getEvaluator: Evaluator = get(evaluator) - /** param for number of folds for cross validation */ - val numFolds: IntParam = - new IntParam(this, "numFolds", "number of folds for cross validation", Some(3)) - def getNumFolds: Int = get(numFolds) + /** @group getParam */ + def getEvaluator: Evaluator = getOrDefault(evaluator) + + /** + * param for number of folds for cross validation + * @group param + */ + val numFolds: IntParam = new IntParam(this, "numFolds", "number of folds for cross validation") + + /** @group getParam */ + def getNumFolds: Int = getOrDefault(numFolds) + + setDefault(numFolds -> 3) } /** @@ -59,13 +81,20 @@ class CrossValidator extends Estimator[CrossValidatorModel] with CrossValidatorP private val f2jBLAS = new F2jBLAS + /** @group setParam */ def setEstimator(value: Estimator[_]): this.type = set(estimator, value) + + /** @group setParam */ def setEstimatorParamMaps(value: Array[ParamMap]): this.type = set(estimatorParamMaps, value) + + /** @group setParam */ def setEvaluator(value: Evaluator): this.type = set(evaluator, value) + + /** @group setParam */ def setNumFolds(value: Int): this.type = set(numFolds, value) override def fit(dataset: DataFrame, paramMap: ParamMap): CrossValidatorModel = { - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) val schema = dataset.schema transformSchema(dataset.schema, paramMap, logging = true) val sqlCtx = dataset.sqlContext @@ -76,11 +105,12 @@ class CrossValidator extends Estimator[CrossValidatorModel] with CrossValidatorP val metrics = new Array[Double](epm.size) val splits = MLUtils.kFold(dataset.rdd, map(numFolds), 0) splits.zipWithIndex.foreach { case ((training, validation), splitIndex) => - val trainingDataset = sqlCtx.applySchema(training, schema).cache() - val validationDataset = sqlCtx.applySchema(validation, schema).cache() + val trainingDataset = sqlCtx.createDataFrame(training, schema).cache() + val validationDataset = sqlCtx.createDataFrame(validation, schema).cache() // multi-model training logDebug(s"Train split $splitIndex with multiple sets of parameters.") val models = est.fit(trainingDataset, epm).asInstanceOf[Seq[Model[_]]] + trainingDataset.unpersist() var i = 0 while (i < numModels) { val metric = eval.evaluate(models(i).transform(validationDataset, epm(i)), map) @@ -88,6 +118,7 @@ class CrossValidator extends Estimator[CrossValidatorModel] with CrossValidatorP metrics(i) += metric i += 1 } + validationDataset.unpersist() } f2jBLAS.dscal(numModels, 1.0 / map(numFolds), metrics, 1) logInfo(s"Average cross-validation metrics: ${metrics.toSeq}") @@ -100,8 +131,8 @@ class CrossValidator extends Estimator[CrossValidatorModel] with CrossValidatorP cvModel } - private[ml] override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = this.paramMap ++ paramMap + override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { + val map = extractParamMap(paramMap) map(estimator).transformSchema(schema, paramMap) } } @@ -121,7 +152,7 @@ class CrossValidatorModel private[ml] ( bestModel.transform(dataset, paramMap) } - private[ml] override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { + override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { bestModel.transformSchema(schema, paramMap) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/util/MetadataUtils.scala b/mllib/src/main/scala/org/apache/spark/ml/util/MetadataUtils.scala new file mode 100644 index 0000000000000..c84c8b4eb744f --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/util/MetadataUtils.scala @@ -0,0 +1,82 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.util + +import scala.collection.immutable.HashMap + +import org.apache.spark.annotation.Experimental +import org.apache.spark.ml.attribute.{Attribute, AttributeGroup, BinaryAttribute, NominalAttribute, + NumericAttribute} +import org.apache.spark.sql.types.StructField + + +/** + * :: Experimental :: + * + * Helper utilities for tree-based algorithms + */ +@Experimental +object MetadataUtils { + + /** + * Examine a schema to identify the number of classes in a label column. + * Returns None if the number of labels is not specified, or if the label column is continuous. + */ + def getNumClasses(labelSchema: StructField): Option[Int] = { + Attribute.fromStructField(labelSchema) match { + case numAttr: NumericAttribute => None + case binAttr: BinaryAttribute => Some(2) + case nomAttr: NominalAttribute => nomAttr.getNumValues + } + } + + /** + * Examine a schema to identify categorical (Binary and Nominal) features. + * + * @param featuresSchema Schema of the features column. + * If a feature does not have metadata, it is assumed to be continuous. + * If a feature is Nominal, then it must have the number of values + * specified. + * @return Map: feature index --> number of categories. + * The map's set of keys will be the set of categorical feature indices. + */ + def getCategoricalFeatures(featuresSchema: StructField): Map[Int, Int] = { + val metadata = AttributeGroup.fromStructField(featuresSchema) + if (metadata.attributes.isEmpty) { + HashMap.empty[Int, Int] + } else { + metadata.attributes.get.zipWithIndex.flatMap { case (attr, idx) => + if (attr == null) { + Iterator() + } else { + attr match { + case numAttr: NumericAttribute => Iterator() + case binAttr: BinaryAttribute => Iterator(idx -> 2) + case nomAttr: NominalAttribute => + nomAttr.getNumValues match { + case Some(numValues: Int) => Iterator(idx -> numValues) + case None => throw new IllegalArgumentException(s"Feature $idx is marked as" + + " Nominal (categorical), but it does not have the number of values specified.") + } + } + } + }.toMap + } + } + +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/util/SchemaUtils.scala b/mllib/src/main/scala/org/apache/spark/ml/util/SchemaUtils.scala new file mode 100644 index 0000000000000..0383bf0b382b7 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/util/SchemaUtils.scala @@ -0,0 +1,61 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.util + +import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.sql.types.{DataType, StructField, StructType} + +/** + * :: DeveloperApi :: + * Utils for handling schemas. + */ +@DeveloperApi +object SchemaUtils { + + // TODO: Move the utility methods to SQL. + + /** + * Check whether the given schema contains a column of the required data type. + * @param colName column name + * @param dataType required column data type + */ + def checkColumnType(schema: StructType, colName: String, dataType: DataType): Unit = { + val actualDataType = schema(colName).dataType + require(actualDataType.equals(dataType), + s"Column $colName must be of type $dataType but was actually $actualDataType.") + } + + /** + * Appends a new column to the input schema. This fails if the given output column already exists. + * @param schema input schema + * @param colName new column name. If this column name is an empty string "", this method returns + * the input schema unchanged. This allows users to disable output columns. + * @param dataType new column data type + * @return new schema with the input column appended + */ + def appendColumn( + schema: StructType, + colName: String, + dataType: DataType): StructType = { + if (colName.isEmpty) return schema + val fieldNames = schema.fieldNames + require(!fieldNames.contains(colName), s"Column $colName already exists.") + val outputFields = schema.fields :+ StructField(colName, dataType, nullable = false) + StructType(outputFields) + } +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/FPGrowthModelWrapper.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/FPGrowthModelWrapper.scala new file mode 100644 index 0000000000000..ee933f4cfcafd --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/FPGrowthModelWrapper.scala @@ -0,0 +1,33 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.mllib.api.python + +import org.apache.spark.api.java.JavaRDD +import org.apache.spark.mllib.fpm.{FPGrowth, FPGrowthModel} +import org.apache.spark.rdd.RDD + +/** + * A Wrapper of FPGrowthModel to provide helper method for Python + */ +private[python] class FPGrowthModelWrapper(model: FPGrowthModel[Any]) + extends FPGrowthModel(model.freqItemsets) { + + def getFreqItemsets: RDD[Array[Any]] = { + SerDe.fromTuple2RDD(model.freqItemsets.map(x => (x.javaItems, x.freq))) + } +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/MatrixFactorizationModelWrapper.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/MatrixFactorizationModelWrapper.scala new file mode 100644 index 0000000000000..534edac56bc5a --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/MatrixFactorizationModelWrapper.scala @@ -0,0 +1,45 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.mllib.api.python + +import org.apache.spark.api.java.JavaRDD +import org.apache.spark.mllib.linalg.Vectors +import org.apache.spark.mllib.recommendation.{MatrixFactorizationModel, Rating} +import org.apache.spark.rdd.RDD + +/** + * A Wrapper of MatrixFactorizationModel to provide helper method for Python. + */ +private[python] class MatrixFactorizationModelWrapper(model: MatrixFactorizationModel) + extends MatrixFactorizationModel(model.rank, model.userFeatures, model.productFeatures) { + + def predict(userAndProducts: JavaRDD[Array[Any]]): RDD[Rating] = + predict(SerDe.asTupleRDD(userAndProducts.rdd)) + + def getUserFeatures: RDD[Array[Any]] = { + SerDe.fromTuple2RDD(userFeatures.map { + case (user, feature) => (user, Vectors.dense(feature)) + }.asInstanceOf[RDD[(Any, Any)]]) + } + + def getProductFeatures: RDD[Array[Any]] = { + SerDe.fromTuple2RDD(productFeatures.map { + case (product, feature) => (product, Vectors.dense(feature)) + }.asInstanceOf[RDD[(Any, Any)]]) + } +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala index cbd87ea8aeb37..f976d2f97b043 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala @@ -28,26 +28,26 @@ import scala.reflect.ClassTag import net.razorvine.pickle._ -import org.apache.spark.annotation.DeveloperApi import org.apache.spark.api.java.{JavaRDD, JavaSparkContext} import org.apache.spark.api.python.SerDeUtil import org.apache.spark.mllib.classification._ import org.apache.spark.mllib.clustering._ import org.apache.spark.mllib.feature._ +import org.apache.spark.mllib.fpm.{FPGrowth, FPGrowthModel} import org.apache.spark.mllib.linalg._ import org.apache.spark.mllib.optimization._ import org.apache.spark.mllib.random.{RandomRDDs => RG} import org.apache.spark.mllib.recommendation._ import org.apache.spark.mllib.regression._ -import org.apache.spark.mllib.stat.{MultivariateStatisticalSummary, Statistics} import org.apache.spark.mllib.stat.correlation.CorrelationNames import org.apache.spark.mllib.stat.distribution.MultivariateGaussian import org.apache.spark.mllib.stat.test.ChiSqTestResult -import org.apache.spark.mllib.tree.{GradientBoostedTrees, RandomForest, DecisionTree} -import org.apache.spark.mllib.tree.configuration.{BoostingStrategy, Algo, Strategy} +import org.apache.spark.mllib.stat.{MultivariateStatisticalSummary, Statistics} +import org.apache.spark.mllib.tree.configuration.{Algo, BoostingStrategy, Strategy} import org.apache.spark.mllib.tree.impurity._ import org.apache.spark.mllib.tree.loss.Losses -import org.apache.spark.mllib.tree.model.{GradientBoostedTreesModel, RandomForestModel, DecisionTreeModel} +import org.apache.spark.mllib.tree.model.{DecisionTreeModel, GradientBoostedTreesModel, RandomForestModel} +import org.apache.spark.mllib.tree.{DecisionTree, GradientBoostedTrees, RandomForest} import org.apache.spark.mllib.util.MLUtils import org.apache.spark.rdd.RDD import org.apache.spark.storage.StorageLevel @@ -58,7 +58,6 @@ import org.apache.spark.util.Utils */ private[python] class PythonMLLibAPI extends Serializable { - /** * Loads and serializes labeled points saved with `RDD#saveAsTextFile`. * @param jsc Java SparkContext @@ -78,7 +77,13 @@ private[python] class PythonMLLibAPI extends Serializable { initialWeights: Vector): JList[Object] = { try { val model = learner.run(data.rdd.persist(StorageLevel.MEMORY_AND_DISK), initialWeights) - List(model.weights, model.intercept).map(_.asInstanceOf[Object]).asJava + if (model.isInstanceOf[LogisticRegressionModel]) { + val lrModel = model.asInstanceOf[LogisticRegressionModel] + List(lrModel.weights, lrModel.intercept, lrModel.numFeatures, lrModel.numClasses) + .map(_.asInstanceOf[Object]).asJava + } else { + List(model.weights, model.intercept).map(_.asInstanceOf[Object]).asJava + } } finally { data.rdd.unpersist(blocking = false) } @@ -111,9 +116,11 @@ private[python] class PythonMLLibAPI extends Serializable { initialWeights: Vector, regParam: Double, regType: String, - intercept: Boolean): JList[Object] = { + intercept: Boolean, + validateData: Boolean): JList[Object] = { val lrAlg = new LinearRegressionWithSGD() lrAlg.setIntercept(intercept) + .setValidateData(validateData) lrAlg.optimizer .setNumIterations(numIterations) .setRegParam(regParam) @@ -135,8 +142,12 @@ private[python] class PythonMLLibAPI extends Serializable { stepSize: Double, regParam: Double, miniBatchFraction: Double, - initialWeights: Vector): JList[Object] = { + initialWeights: Vector, + intercept: Boolean, + validateData: Boolean): JList[Object] = { val lassoAlg = new LassoWithSGD() + lassoAlg.setIntercept(intercept) + .setValidateData(validateData) lassoAlg.optimizer .setNumIterations(numIterations) .setRegParam(regParam) @@ -157,8 +168,12 @@ private[python] class PythonMLLibAPI extends Serializable { stepSize: Double, regParam: Double, miniBatchFraction: Double, - initialWeights: Vector): JList[Object] = { + initialWeights: Vector, + intercept: Boolean, + validateData: Boolean): JList[Object] = { val ridgeAlg = new RidgeRegressionWithSGD() + ridgeAlg.setIntercept(intercept) + .setValidateData(validateData) ridgeAlg.optimizer .setNumIterations(numIterations) .setRegParam(regParam) @@ -181,9 +196,11 @@ private[python] class PythonMLLibAPI extends Serializable { miniBatchFraction: Double, initialWeights: Vector, regType: String, - intercept: Boolean): JList[Object] = { + intercept: Boolean, + validateData: Boolean): JList[Object] = { val SVMAlg = new SVMWithSGD() SVMAlg.setIntercept(intercept) + .setValidateData(validateData) SVMAlg.optimizer .setNumIterations(numIterations) .setRegParam(regParam) @@ -207,9 +224,11 @@ private[python] class PythonMLLibAPI extends Serializable { initialWeights: Vector, regParam: Double, regType: String, - intercept: Boolean): JList[Object] = { + intercept: Boolean, + validateData: Boolean): JList[Object] = { val LogRegAlg = new LogisticRegressionWithSGD() LogRegAlg.setIntercept(intercept) + .setValidateData(validateData) LogRegAlg.optimizer .setNumIterations(numIterations) .setRegParam(regParam) @@ -233,9 +252,13 @@ private[python] class PythonMLLibAPI extends Serializable { regType: String, intercept: Boolean, corrections: Int, - tolerance: Double): JList[Object] = { + tolerance: Double, + validateData: Boolean, + numClasses: Int): JList[Object] = { val LogRegAlg = new LogisticRegressionWithLBFGS() LogRegAlg.setIntercept(intercept) + .setValidateData(validateData) + .setNumClasses(numClasses) LogRegAlg.optimizer .setNumIterations(numIterations) .setRegParam(regParam) @@ -255,7 +278,7 @@ private[python] class PythonMLLibAPI extends Serializable { data: JavaRDD[LabeledPoint], lambda: Double): JList[Object] = { val model = NaiveBayes.train(data.rdd, lambda) - List(Vectors.dense(model.labels), Vectors.dense(model.pi), model.theta). + List(Vectors.dense(model.labels), Vectors.dense(model.pi), model.theta.map(Vectors.dense)). map(_.asInstanceOf[Object]).asJava } @@ -311,7 +334,7 @@ private[python] class PythonMLLibAPI extends Serializable { mu += model.gaussians(i).mu sigma += model.gaussians(i).sigma } - List(wt.toArray, mu.toArray, sigma.toArray).map(_.asInstanceOf[Object]).asJava + List(Vectors.dense(wt.toArray), mu.toArray, sigma.toArray).map(_.asInstanceOf[Object]).asJava } finally { data.rdd.unpersist(blocking = false) } @@ -322,33 +345,18 @@ private[python] class PythonMLLibAPI extends Serializable { */ def predictSoftGMM( data: JavaRDD[Vector], - wt: Object, + wt: Vector, mu: Array[Object], - si: Array[Object]): RDD[Array[Double]] = { + si: Array[Object]): RDD[Vector] = { - val weight = wt.asInstanceOf[Array[Double]] + val weight = wt.toArray val mean = mu.map(_.asInstanceOf[DenseVector]) val sigma = si.map(_.asInstanceOf[DenseMatrix]) val gaussians = Array.tabulate(weight.length){ i => new MultivariateGaussian(mean(i), sigma(i)) } val model = new GaussianMixtureModel(weight, gaussians) - model.predictSoft(data) - } - - /** - * A Wrapper of MatrixFactorizationModel to provide helpfer method for Python - */ - private[python] class MatrixFactorizationModelWrapper(model: MatrixFactorizationModel) - extends MatrixFactorizationModel(model.rank, model.userFeatures, model.productFeatures) { - - def predict(userAndProducts: JavaRDD[Array[Any]]): RDD[Rating] = - predict(SerDe.asTupleRDD(userAndProducts.rdd)) - - def getUserFeatures = SerDe.fromTuple2RDD(userFeatures.asInstanceOf[RDD[(Any, Any)]]) - - def getProductFeatures = SerDe.fromTuple2RDD(productFeatures.asInstanceOf[RDD[(Any, Any)]]) - + model.predictSoft(data).map(Vectors.dense) } /** @@ -410,6 +418,24 @@ private[python] class PythonMLLibAPI extends Serializable { new MatrixFactorizationModelWrapper(model) } + /** + * Java stub for Python mllib FPGrowth.train(). This stub returns a handle + * to the Java object instead of the content of the Java object. Extra care + * needs to be taken in the Python code to ensure it gets freed on exit; see + * the Py4J documentation. + */ + def trainFPGrowthModel( + data: JavaRDD[java.lang.Iterable[Any]], + minSupport: Double, + numPartitions: Int): FPGrowthModel[Any] = { + val fpg = new FPGrowth() + .setMinSupport(minSupport) + .setNumPartitions(numPartitions) + + val model = fpg.run(data.rdd.map(_.asScala.toArray)) + new FPGrowthModelWrapper(model) + } + /** * Java stub for Normalizer.transform() */ @@ -423,9 +449,9 @@ private[python] class PythonMLLibAPI extends Serializable { def normalizeVector(p: Double, rdd: JavaRDD[Vector]): JavaRDD[Vector] = { new Normalizer(p).transform(rdd) } - + /** - * Java stub for IDF.fit(). This stub returns a + * Java stub for StandardScaler.fit(). This stub returns a * handle to the Java object instead of the content of the Java object. * Extra care needs to be taken in the Python code to ensure it gets freed on * exit; see the Py4J documentation. @@ -466,13 +492,15 @@ private[python] class PythonMLLibAPI extends Serializable { learningRate: Double, numPartitions: Int, numIterations: Int, - seed: Long): Word2VecModelWrapper = { + seed: Long, + minCount: Int): Word2VecModelWrapper = { val word2vec = new Word2Vec() .setVectorSize(vectorSize) .setLearningRate(learningRate) .setNumPartitions(numPartitions) .setNumIterations(numIterations) .setSeed(seed) + .setMinCount(minCount) try { val model = word2vec.fit(dataJRDD.rdd.persist(StorageLevel.MEMORY_AND_DISK_SER)) new Word2VecModelWrapper(model) @@ -506,6 +534,10 @@ private[python] class PythonMLLibAPI extends Serializable { val words = result.map(_._1) List(words, similarity).map(_.asInstanceOf[Object]).asJava } + + def getVectors: JMap[String, JList[Float]] = { + model.getVectors.map({case (k, v) => (k, v.toList.asJava)}).asJava + } } /** @@ -903,13 +935,21 @@ private[spark] object SerDe extends Serializable { out.write(code) } + protected def getBytes(obj: Object): Array[Byte] = { + if (obj.getClass.isArray) { + obj.asInstanceOf[Array[Byte]] + } else { + obj.asInstanceOf[String].getBytes(LATIN1) + } + } + private[python] def saveState(obj: Object, out: OutputStream, pickler: Pickler) } // Pickler for DenseVector private[python] class DenseVectorPickler extends BasePickler[DenseVector] { - def saveState(obj: Object, out: OutputStream, pickler: Pickler) = { + def saveState(obj: Object, out: OutputStream, pickler: Pickler): Unit = { val vector: DenseVector = obj.asInstanceOf[DenseVector] val bytes = new Array[Byte](8 * vector.size) val bb = ByteBuffer.wrap(bytes) @@ -928,7 +968,7 @@ private[spark] object SerDe extends Serializable { if (args.length != 1) { throw new PickleException("should be 1") } - val bytes = args(0).asInstanceOf[String].getBytes(LATIN1) + val bytes = getBytes(args(0)) val bb = ByteBuffer.wrap(bytes, 0, bytes.length) bb.order(ByteOrder.nativeOrder()) val db = bb.asDoubleBuffer() @@ -941,7 +981,7 @@ private[spark] object SerDe extends Serializable { // Pickler for DenseMatrix private[python] class DenseMatrixPickler extends BasePickler[DenseMatrix] { - def saveState(obj: Object, out: OutputStream, pickler: Pickler) = { + def saveState(obj: Object, out: OutputStream, pickler: Pickler): Unit = { val m: DenseMatrix = obj.asInstanceOf[DenseMatrix] val bytes = new Array[Byte](8 * m.values.size) val order = ByteOrder.nativeOrder() @@ -961,7 +1001,7 @@ private[spark] object SerDe extends Serializable { if (args.length != 3) { throw new PickleException("should be 3") } - val bytes = args(2).asInstanceOf[String].getBytes(LATIN1) + val bytes = getBytes(args(2)) val n = bytes.length / 8 val values = new Array[Double](n) val order = ByteOrder.nativeOrder() @@ -973,7 +1013,7 @@ private[spark] object SerDe extends Serializable { // Pickler for SparseVector private[python] class SparseVectorPickler extends BasePickler[SparseVector] { - def saveState(obj: Object, out: OutputStream, pickler: Pickler) = { + def saveState(obj: Object, out: OutputStream, pickler: Pickler): Unit = { val v: SparseVector = obj.asInstanceOf[SparseVector] val n = v.indices.size val indiceBytes = new Array[Byte](4 * n) @@ -998,8 +1038,8 @@ private[spark] object SerDe extends Serializable { throw new PickleException("should be 3") } val size = args(0).asInstanceOf[Int] - val indiceBytes = args(1).asInstanceOf[String].getBytes(LATIN1) - val valueBytes = args(2).asInstanceOf[String].getBytes(LATIN1) + val indiceBytes = getBytes(args(1)) + val valueBytes = getBytes(args(2)) val n = indiceBytes.length / 4 val indices = new Array[Int](n) val values = new Array[Double](n) @@ -1015,7 +1055,7 @@ private[spark] object SerDe extends Serializable { // Pickler for LabeledPoint private[python] class LabeledPointPickler extends BasePickler[LabeledPoint] { - def saveState(obj: Object, out: OutputStream, pickler: Pickler) = { + def saveState(obj: Object, out: OutputStream, pickler: Pickler): Unit = { val point: LabeledPoint = obj.asInstanceOf[LabeledPoint] saveObjects(out, pickler, point.label, point.features) } @@ -1031,7 +1071,7 @@ private[spark] object SerDe extends Serializable { // Pickler for Rating private[python] class RatingPickler extends BasePickler[Rating] { - def saveState(obj: Object, out: OutputStream, pickler: Pickler) = { + def saveState(obj: Object, out: OutputStream, pickler: Pickler): Unit = { val rating: Rating = obj.asInstanceOf[Rating] saveObjects(out, pickler, rating.user, rating.product, rating.rating) } @@ -1103,7 +1143,10 @@ private[spark] object SerDe extends Serializable { iter.flatMap { row => val obj = unpickle.loads(row) if (batched) { - obj.asInstanceOf[JArrayList[_]].asScala + obj match { + case list: JArrayList[_] => list.asScala + case arr: Array[_] => arr + } } else { Seq(obj) } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala index 348c1e8760a66..35a0db76f3a8c 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala @@ -17,12 +17,12 @@ package org.apache.spark.mllib.classification +import org.json4s.{DefaultFormats, JValue} + import org.apache.spark.annotation.Experimental import org.apache.spark.api.java.JavaRDD import org.apache.spark.mllib.linalg.Vector -import org.apache.spark.mllib.util.Loader import org.apache.spark.rdd.RDD -import org.apache.spark.sql.{DataFrame, Row} /** * :: Experimental :: @@ -60,16 +60,10 @@ private[mllib] object ClassificationModel { /** * Helper method for loading GLM classification model metadata. - * - * @param modelClass String name for model class (used for error messages) * @return (numFeatures, numClasses) */ - def getNumFeaturesClasses(metadata: DataFrame, modelClass: String, path: String): (Int, Int) = { - metadata.select("numFeatures", "numClasses").take(1)(0) match { - case Row(nFeatures: Int, nClasses: Int) => (nFeatures, nClasses) - case _ => throw new Exception(s"$modelClass unable to load" + - s" numFeatures, numClasses from metadata: ${Loader.metadataPath(path)}") - } + def getNumFeaturesClasses(metadata: JValue): (Int, Int) = { + implicit val formats = DefaultFormats + ((metadata \ "numFeatures").extract[Int], (metadata \ "numClasses").extract[Int]) } - } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala index 43dc93c353e44..bd2e9079ce1ae 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala @@ -63,6 +63,15 @@ class LogisticRegressionModel ( s" but was given weights of length ${weights.size}") } + private val dataWithBiasSize: Int = weights.size / (numClasses - 1) + + private val weightsArray: Array[Double] = weights match { + case dv: DenseVector => dv.values + case _ => + throw new IllegalArgumentException( + s"weights only supports dense vector but got type ${weights.getClass}.") + } + /** * Constructs a [[LogisticRegressionModel]] with weights and intercept for binary classification. */ @@ -75,6 +84,7 @@ class LogisticRegressionModel ( * Sets the threshold that separates positive predictions from negative predictions * in Binary Logistic Regression. An example with prediction score greater than or equal to * this threshold is identified as an positive, and negative otherwise. The default value is 0.5. + * It is only used for binary classification. */ @Experimental def setThreshold(threshold: Double): this.type = { @@ -85,6 +95,7 @@ class LogisticRegressionModel ( /** * :: Experimental :: * Returns the threshold (if any) used for converting raw prediction scores into 0/1 predictions. + * It is only used for binary classification. */ @Experimental def getThreshold: Option[Double] = threshold @@ -92,6 +103,7 @@ class LogisticRegressionModel ( /** * :: Experimental :: * Clears the threshold so that `predict` will output raw prediction scores. + * It is only used for binary classification. */ @Experimental def clearThreshold(): this.type = { @@ -107,7 +119,6 @@ class LogisticRegressionModel ( // If dataMatrix and weightMatrix have the same dimension, it's binary logistic regression. if (numClasses == 2) { - require(numFeatures == weightMatrix.size) val margin = dot(weightMatrix, dataMatrix) + intercept val score = 1.0 / (1.0 + math.exp(-margin)) threshold match { @@ -115,30 +126,9 @@ class LogisticRegressionModel ( case None => score } } else { - val dataWithBiasSize = weightMatrix.size / (numClasses - 1) - - val weightsArray = weightMatrix match { - case dv: DenseVector => dv.values - case _ => - throw new IllegalArgumentException( - s"weights only supports dense vector but got type ${weightMatrix.getClass}.") - } - - val margins = (0 until numClasses - 1).map { i => - var margin = 0.0 - dataMatrix.foreachActive { (index, value) => - if (value != 0.0) margin += value * weightsArray((i * dataWithBiasSize) + index) - } - // Intercept is required to be added into margin. - if (dataMatrix.size + 1 == dataWithBiasSize) { - margin += weightsArray((i * dataWithBiasSize) + dataMatrix.size) - } - margin - } - /** - * Find the one with maximum margins. If the maxMargin is negative, then the prediction - * result will be the first class. + * Compute and find the one with maximum margins. If the maxMargin is negative, then the + * prediction result will be the first class. * * PS, if you want to compute the probabilities for each outcome instead of the outcome * with maximum probability, remember to subtract the maxMargin from margins if maxMargin @@ -146,13 +136,20 @@ class LogisticRegressionModel ( */ var bestClass = 0 var maxMargin = 0.0 - var i = 0 - while(i < margins.size) { - if (margins(i) > maxMargin) { - maxMargin = margins(i) + val withBias = dataMatrix.size + 1 == dataWithBiasSize + (0 until numClasses - 1).foreach { i => + var margin = 0.0 + dataMatrix.foreachActive { (index, value) => + if (value != 0.0) margin += value * weightsArray((i * dataWithBiasSize) + index) + } + // Intercept is required to be added into margin. + if (withBias) { + margin += weightsArray((i * dataWithBiasSize) + dataMatrix.size) + } + if (margin > maxMargin) { + maxMargin = margin bestClass = i + 1 } - i += 1 } bestClass.toDouble } @@ -164,6 +161,10 @@ class LogisticRegressionModel ( } override protected def formatVersion: String = "1.0" + + override def toString: String = { + s"${super.toString}, numClasses = ${numClasses}, threshold = ${threshold.get}" + } } object LogisticRegressionModel extends Loader[LogisticRegressionModel] { @@ -174,8 +175,7 @@ object LogisticRegressionModel extends Loader[LogisticRegressionModel] { val classNameV1_0 = "org.apache.spark.mllib.classification.LogisticRegressionModel" (loadedClassName, version) match { case (className, "1.0") if className == classNameV1_0 => - val (numFeatures, numClasses) = - ClassificationModel.getNumFeaturesClasses(metadata, classNameV1_0, path) + val (numFeatures, numClasses) = ClassificationModel.getNumFeaturesClasses(metadata) val data = GLMClassificationModel.SaveLoadV1_0.loadData(sc, path, classNameV1_0) // numFeatures, numClasses, weights are checked in model initialization val model = @@ -357,6 +357,10 @@ class LogisticRegressionWithLBFGS } override protected def createModel(weights: Vector, intercept: Double) = { - new LogisticRegressionModel(weights, intercept, numFeatures, numOfLinearPredictor + 1) + if (numOfLinearPredictor == 1) { + new LogisticRegressionModel(weights, intercept) + } else { + new LogisticRegressionModel(weights, intercept, numFeatures, numOfLinearPredictor + 1) + } } } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala index d9ce2822dd391..c9b3ff0172e2e 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala @@ -17,9 +17,18 @@ package org.apache.spark.mllib.classification -import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, argmax => brzArgmax, sum => brzSum} +import java.lang.{Iterable => JIterable} -import org.apache.spark.{SparkContext, SparkException, Logging} +import scala.collection.JavaConverters._ + +import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, argmax => brzArgmax, sum => brzSum, Axis} +import breeze.numerics.{exp => brzExp, log => brzLog} + +import org.json4s.JsonDSL._ +import org.json4s.jackson.JsonMethods._ +import org.json4s.{DefaultFormats, JValue} + +import org.apache.spark.{Logging, SparkContext, SparkException} import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector} import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.util.{Loader, Saveable} @@ -34,26 +43,39 @@ import org.apache.spark.sql.{DataFrame, SQLContext} * @param pi log of class priors, whose dimension is C, number of labels * @param theta log of class conditional probabilities, whose dimension is C-by-D, * where D is number of features + * @param modelType The type of NB model to fit can be "Multinomial" or "Bernoulli" */ class NaiveBayesModel private[mllib] ( val labels: Array[Double], val pi: Array[Double], - val theta: Array[Array[Double]]) extends ClassificationModel with Serializable with Saveable { + val theta: Array[Array[Double]], + val modelType: String) + extends ClassificationModel with Serializable with Saveable { + + private[mllib] def this(labels: Array[Double], pi: Array[Double], theta: Array[Array[Double]]) = + this(labels, pi, theta, "Multinomial") + + /** A Java-friendly constructor that takes three Iterable parameters. */ + private[mllib] def this( + labels: JIterable[Double], + pi: JIterable[Double], + theta: JIterable[JIterable[Double]]) = + this(labels.asScala.toArray, pi.asScala.toArray, theta.asScala.toArray.map(_.asScala.toArray)) private val brzPi = new BDV[Double](pi) - private val brzTheta = new BDM[Double](theta.length, theta(0).length) + private val brzTheta = new BDM(theta(0).length, theta.length, theta.flatten).t - { - // Need to put an extra pair of braces to prevent Scala treating `i` as a member. - var i = 0 - while (i < theta.length) { - var j = 0 - while (j < theta(i).length) { - brzTheta(i, j) = theta(i)(j) - j += 1 - } - i += 1 - } + // Bernoulli scoring requires log(condprob) if 1, log(1-condprob) if 0. + // This precomputes log(1.0 - exp(theta)) and its sum which are used for the linear algebra + // application of this condition (in predict function). + private val (brzNegTheta, brzNegThetaSum) = modelType match { + case "Multinomial" => (None, None) + case "Bernoulli" => + val negTheta = brzLog((brzExp(brzTheta.copy) :*= (-1.0)) :+= 1.0) // log(1.0 - exp(x)) + (Option(negTheta), Option(brzSum(negTheta, Axis._1))) + case _ => + // This should never happen. + throw new UnknownError(s"NaiveBayesModel was created with an unknown ModelType: $modelType") } override def predict(testData: RDD[Vector]): RDD[Double] = { @@ -65,43 +87,102 @@ class NaiveBayesModel private[mllib] ( } override def predict(testData: Vector): Double = { - labels(brzArgmax(brzPi + brzTheta * testData.toBreeze)) + modelType match { + case "Multinomial" => + labels (brzArgmax (brzPi + brzTheta * testData.toBreeze) ) + case "Bernoulli" => + labels (brzArgmax (brzPi + + (brzTheta - brzNegTheta.get) * testData.toBreeze + brzNegThetaSum.get)) + case _ => + // This should never happen. + throw new UnknownError(s"NaiveBayesModel was created with an unknown ModelType: $modelType") + } } override def save(sc: SparkContext, path: String): Unit = { - val data = NaiveBayesModel.SaveLoadV1_0.Data(labels, pi, theta) - NaiveBayesModel.SaveLoadV1_0.save(sc, path, data) + val data = NaiveBayesModel.SaveLoadV2_0.Data(labels, pi, theta, modelType) + NaiveBayesModel.SaveLoadV2_0.save(sc, path, data) } - override protected def formatVersion: String = "1.0" + override protected def formatVersion: String = "2.0" } object NaiveBayesModel extends Loader[NaiveBayesModel] { - import Loader._ + import org.apache.spark.mllib.util.Loader._ + + private[mllib] object SaveLoadV2_0 { + + def thisFormatVersion: String = "2.0" + + /** Hard-code class name string in case it changes in the future */ + def thisClassName: String = "org.apache.spark.mllib.classification.NaiveBayesModel" + + /** Model data for model import/export */ + case class Data( + labels: Array[Double], + pi: Array[Double], + theta: Array[Array[Double]], + modelType: String) + + def save(sc: SparkContext, path: String, data: Data): Unit = { + val sqlContext = new SQLContext(sc) + import sqlContext.implicits._ + + // Create JSON metadata. + val metadata = compact(render( + ("class" -> thisClassName) ~ ("version" -> thisFormatVersion) ~ + ("numFeatures" -> data.theta(0).length) ~ ("numClasses" -> data.pi.length))) + sc.parallelize(Seq(metadata), 1).saveAsTextFile(metadataPath(path)) + + // Create Parquet data. + val dataRDD: DataFrame = sc.parallelize(Seq(data), 1).toDF() + dataRDD.saveAsParquetFile(dataPath(path)) + } + + def load(sc: SparkContext, path: String): NaiveBayesModel = { + val sqlContext = new SQLContext(sc) + // Load Parquet data. + val dataRDD = sqlContext.parquetFile(dataPath(path)) + // Check schema explicitly since erasure makes it hard to use match-case for checking. + checkSchema[Data](dataRDD.schema) + val dataArray = dataRDD.select("labels", "pi", "theta", "modelType").take(1) + assert(dataArray.size == 1, s"Unable to load NaiveBayesModel data from: ${dataPath(path)}") + val data = dataArray(0) + val labels = data.getAs[Seq[Double]](0).toArray + val pi = data.getAs[Seq[Double]](1).toArray + val theta = data.getAs[Seq[Seq[Double]]](2).map(_.toArray).toArray + val modelType = data.getString(3) + new NaiveBayesModel(labels, pi, theta, modelType) + } + + } - private object SaveLoadV1_0 { + private[mllib] object SaveLoadV1_0 { - def thisFormatVersion = "1.0" + def thisFormatVersion: String = "1.0" /** Hard-code class name string in case it changes in the future */ - def thisClassName = "org.apache.spark.mllib.classification.NaiveBayesModel" + def thisClassName: String = "org.apache.spark.mllib.classification.NaiveBayesModel" /** Model data for model import/export */ - case class Data(labels: Array[Double], pi: Array[Double], theta: Array[Array[Double]]) + case class Data( + labels: Array[Double], + pi: Array[Double], + theta: Array[Array[Double]]) def save(sc: SparkContext, path: String, data: Data): Unit = { val sqlContext = new SQLContext(sc) import sqlContext.implicits._ // Create JSON metadata. - val metadataRDD = - sc.parallelize(Seq((thisClassName, thisFormatVersion, data.theta(0).size, data.pi.size)), 1) - .toDataFrame("class", "version", "numFeatures", "numClasses") - metadataRDD.toJSON.saveAsTextFile(metadataPath(path)) + val metadata = compact(render( + ("class" -> thisClassName) ~ ("version" -> thisFormatVersion) ~ + ("numFeatures" -> data.theta(0).length) ~ ("numClasses" -> data.pi.length))) + sc.parallelize(Seq(metadata), 1).saveAsTextFile(metadataPath(path)) // Create Parquet data. - val dataRDD: DataFrame = sc.parallelize(Seq(data), 1) + val dataRDD: DataFrame = sc.parallelize(Seq(data), 1).toDF() dataRDD.saveAsParquetFile(dataPath(path)) } @@ -124,27 +205,32 @@ object NaiveBayesModel extends Loader[NaiveBayesModel] { override def load(sc: SparkContext, path: String): NaiveBayesModel = { val (loadedClassName, version, metadata) = loadMetadata(sc, path) val classNameV1_0 = SaveLoadV1_0.thisClassName - (loadedClassName, version) match { + val classNameV2_0 = SaveLoadV2_0.thisClassName + val (model, numFeatures, numClasses) = (loadedClassName, version) match { case (className, "1.0") if className == classNameV1_0 => - val (numFeatures, numClasses) = - ClassificationModel.getNumFeaturesClasses(metadata, classNameV1_0, path) + val (numFeatures, numClasses) = ClassificationModel.getNumFeaturesClasses(metadata) val model = SaveLoadV1_0.load(sc, path) - assert(model.pi.size == numClasses, - s"NaiveBayesModel.load expected $numClasses classes," + - s" but class priors vector pi had ${model.pi.size} elements") - assert(model.theta.size == numClasses, - s"NaiveBayesModel.load expected $numClasses classes," + - s" but class conditionals array theta had ${model.theta.size} elements") - assert(model.theta.forall(_.size == numFeatures), - s"NaiveBayesModel.load expected $numFeatures features," + - s" but class conditionals array theta had elements of size:" + - s" ${model.theta.map(_.size).mkString(",")}") - model + (model, numFeatures, numClasses) + case (className, "2.0") if className == classNameV2_0 => + val (numFeatures, numClasses) = ClassificationModel.getNumFeaturesClasses(metadata) + val model = SaveLoadV2_0.load(sc, path) + (model, numFeatures, numClasses) case _ => throw new Exception( s"NaiveBayesModel.load did not recognize model with (className, format version):" + s"($loadedClassName, $version). Supported:\n" + s" ($classNameV1_0, 1.0)") } + assert(model.pi.size == numClasses, + s"NaiveBayesModel.load expected $numClasses classes," + + s" but class priors vector pi had ${model.pi.size} elements") + assert(model.theta.size == numClasses, + s"NaiveBayesModel.load expected $numClasses classes," + + s" but class conditionals array theta had ${model.theta.size} elements") + assert(model.theta.forall(_.size == numFeatures), + s"NaiveBayesModel.load expected $numFeatures features," + + s" but class conditionals array theta had elements of size:" + + s" ${model.theta.map(_.size).mkString(",")}") + model } } @@ -156,9 +242,14 @@ object NaiveBayesModel extends Loader[NaiveBayesModel] { * document classification. By making every vector a 0-1 vector, it can also be used as * Bernoulli NB ([[http://tinyurl.com/p7c96j6]]). The input feature values must be nonnegative. */ -class NaiveBayes private (private var lambda: Double) extends Serializable with Logging { - def this() = this(1.0) +class NaiveBayes private ( + private var lambda: Double, + private var modelType: String) extends Serializable with Logging { + + def this(lambda: Double) = this(lambda, "Multinomial") + + def this() = this(1.0, "Multinomial") /** Set the smoothing parameter. Default: 1.0. */ def setLambda(lambda: Double): NaiveBayes = { @@ -166,12 +257,30 @@ class NaiveBayes private (private var lambda: Double) extends Serializable with this } + /** Get the smoothing parameter. */ + def getLambda: Double = lambda + + /** + * Set the model type using a string (case-sensitive). + * Supported options: "Multinomial" and "Bernoulli". + * (default: Multinomial) + */ + def setModelType(modelType:String): NaiveBayes = { + require(NaiveBayes.supportedModelTypes.contains(modelType), + s"NaiveBayes was created with an unknown ModelType: $modelType") + this.modelType = modelType + this + } + + /** Get the model type. */ + def getModelType: String = this.modelType + /** * Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries. * * @param data RDD of [[org.apache.spark.mllib.regression.LabeledPoint]]. */ - def run(data: RDD[LabeledPoint]) = { + def run(data: RDD[LabeledPoint]): NaiveBayesModel = { val requireNonnegativeValues: Vector => Unit = (v: Vector) => { val values = v match { case SparseVector(size, indices, values) => @@ -199,21 +308,30 @@ class NaiveBayes private (private var lambda: Double) extends Serializable with mergeCombiners = (c1: (Long, BDV[Double]), c2: (Long, BDV[Double])) => (c1._1 + c2._1, c1._2 += c2._2) ).collect() + val numLabels = aggregated.length var numDocuments = 0L aggregated.foreach { case (_, (n, _)) => numDocuments += n } val numFeatures = aggregated.head match { case (_, (_, v)) => v.size } + val labels = new Array[Double](numLabels) val pi = new Array[Double](numLabels) val theta = Array.fill(numLabels)(new Array[Double](numFeatures)) + val piLogDenom = math.log(numDocuments + numLabels * lambda) var i = 0 aggregated.foreach { case (label, (n, sumTermFreqs)) => labels(i) = label - val thetaLogDenom = math.log(brzSum(sumTermFreqs) + numFeatures * lambda) pi(i) = math.log(n + lambda) - piLogDenom + val thetaLogDenom = modelType match { + case "Multinomial" => math.log(brzSum(sumTermFreqs) + numFeatures * lambda) + case "Bernoulli" => math.log(n + 2.0 * lambda) + case _ => + // This should never happen. + throw new UnknownError(s"NaiveBayes was created with an unknown ModelType: $modelType") + } var j = 0 while (j < numFeatures) { theta(i)(j) = math.log(sumTermFreqs(j) + lambda) - thetaLogDenom @@ -222,7 +340,7 @@ class NaiveBayes private (private var lambda: Double) extends Serializable with i += 1 } - new NaiveBayesModel(labels, pi, theta) + new NaiveBayesModel(labels, pi, theta, modelType) } } @@ -230,13 +348,16 @@ class NaiveBayes private (private var lambda: Double) extends Serializable with * Top-level methods for calling naive Bayes. */ object NaiveBayes { + + /* Set of modelTypes that NaiveBayes supports */ + private[mllib] val supportedModelTypes = Set("Multinomial", "Bernoulli") + /** * Trains a Naive Bayes model given an RDD of `(label, features)` pairs. * - * This is the Multinomial NB ([[http://tinyurl.com/lsdw6p]]) which can handle all kinds of - * discrete data. For example, by converting documents into TF-IDF vectors, it can be used for - * document classification. By making every vector a 0-1 vector, it can also be used as - * Bernoulli NB ([[http://tinyurl.com/p7c96j6]]). + * This is the default Multinomial NB ([[http://tinyurl.com/lsdw6p]]) which can handle all + * kinds of discrete data. For example, by converting documents into TF-IDF vectors, it + * can be used for document classification. * * This version of the method uses a default smoothing parameter of 1.0. * @@ -250,16 +371,40 @@ object NaiveBayes { /** * Trains a Naive Bayes model given an RDD of `(label, features)` pairs. * - * This is the Multinomial NB ([[http://tinyurl.com/lsdw6p]]) which can handle all kinds of - * discrete data. For example, by converting documents into TF-IDF vectors, it can be used for - * document classification. By making every vector a 0-1 vector, it can also be used as - * Bernoulli NB ([[http://tinyurl.com/p7c96j6]]). + * This is the default Multinomial NB ([[http://tinyurl.com/lsdw6p]]) which can handle all + * kinds of discrete data. For example, by converting documents into TF-IDF vectors, it + * can be used for document classification. * * @param input RDD of `(label, array of features)` pairs. Every vector should be a frequency * vector or a count vector. * @param lambda The smoothing parameter */ def train(input: RDD[LabeledPoint], lambda: Double): NaiveBayesModel = { - new NaiveBayes(lambda).run(input) + new NaiveBayes(lambda, "Multinomial").run(input) } + + /** + * Trains a Naive Bayes model given an RDD of `(label, features)` pairs. + * + * The model type can be set to either Multinomial NB ([[http://tinyurl.com/lsdw6p]]) + * or Bernoulli NB ([[http://tinyurl.com/p7c96j6]]). The Multinomial NB can handle + * discrete count data and can be called by setting the model type to "multinomial". + * For example, it can be used with word counts or TF_IDF vectors of documents. + * The Bernoulli model fits presence or absence (0-1) counts. By making every vector a + * 0-1 vector and setting the model type to "bernoulli", the fits and predicts as + * Bernoulli NB. + * + * @param input RDD of `(label, array of features)` pairs. Every vector should be a frequency + * vector or a count vector. + * @param lambda The smoothing parameter + * + * @param modelType The type of NB model to fit from the enumeration NaiveBayesModels, can be + * multinomial or bernoulli + */ + def train(input: RDD[LabeledPoint], lambda: Double, modelType: String): NaiveBayesModel = { + require(supportedModelTypes.contains(modelType), + s"NaiveBayes was created with an unknown ModelType: $modelType") + new NaiveBayes(lambda, modelType).run(input) + } + } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala index 5cc83ae4ab5ab..33104cf06c6ea 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala @@ -24,10 +24,9 @@ import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.optimization._ import org.apache.spark.mllib.pmml.PMMLExportable import org.apache.spark.mllib.regression._ -import org.apache.spark.mllib.util.{DataValidators, Saveable, Loader} +import org.apache.spark.mllib.util.{DataValidators, Loader, Saveable} import org.apache.spark.rdd.RDD - /** * Model for Support Vector Machines (SVMs). * @@ -88,6 +87,10 @@ class SVMModel ( } override protected def formatVersion: String = "1.0" + + override def toString: String = { + s"${super.toString}, numClasses = 2, threshold = ${threshold.get}" + } } object SVMModel extends Loader[SVMModel] { @@ -98,8 +101,7 @@ object SVMModel extends Loader[SVMModel] { val classNameV1_0 = "org.apache.spark.mllib.classification.SVMModel" (loadedClassName, version) match { case (className, "1.0") if className == classNameV1_0 => - val (numFeatures, numClasses) = - ClassificationModel.getNumFeaturesClasses(metadata, classNameV1_0, path) + val (numFeatures, numClasses) = ClassificationModel.getNumFeaturesClasses(metadata) val data = GLMClassificationModel.SaveLoadV1_0.loadData(sc, path, classNameV1_0) val model = new SVMModel(data.weights, data.intercept) assert(model.weights.size == numFeatures, s"SVMModel.load with numFeatures=$numFeatures" + diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.scala index b89f38cf5aba4..7d33df3221fbf 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.scala @@ -63,6 +63,8 @@ class StreamingLogisticRegressionWithSGD private[mllib] ( protected val algorithm = new LogisticRegressionWithSGD( stepSize, numIterations, regParam, miniBatchFraction) + protected var model: Option[LogisticRegressionModel] = None + /** Set the step size for gradient descent. Default: 0.1. */ def setStepSize(stepSize: Double): this.type = { this.algorithm.optimizer.setStepSize(stepSize) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/impl/GLMClassificationModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/impl/GLMClassificationModel.scala index 8d600572ed7f3..3b6790cce47c6 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/classification/impl/GLMClassificationModel.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/impl/GLMClassificationModel.scala @@ -17,10 +17,13 @@ package org.apache.spark.mllib.classification.impl +import org.json4s.JsonDSL._ +import org.json4s.jackson.JsonMethods._ + import org.apache.spark.SparkContext import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.util.Loader -import org.apache.spark.sql.{DataFrame, Row, SQLContext} +import org.apache.spark.sql.{Row, SQLContext} /** * Helper class for import/export of GLM classification models. @@ -29,7 +32,7 @@ private[classification] object GLMClassificationModel { object SaveLoadV1_0 { - def thisFormatVersion = "1.0" + def thisFormatVersion: String = "1.0" /** Model data for import/export */ case class Data(weights: Vector, intercept: Double, threshold: Option[Double]) @@ -52,16 +55,14 @@ private[classification] object GLMClassificationModel { import sqlContext.implicits._ // Create JSON metadata. - val metadataRDD = - sc.parallelize(Seq((modelClass, thisFormatVersion, numFeatures, numClasses)), 1) - .toDataFrame("class", "version", "numFeatures", "numClasses") - metadataRDD.toJSON.saveAsTextFile(Loader.metadataPath(path)) + val metadata = compact(render( + ("class" -> modelClass) ~ ("version" -> thisFormatVersion) ~ + ("numFeatures" -> numFeatures) ~ ("numClasses" -> numClasses))) + sc.parallelize(Seq(metadata), 1).saveAsTextFile(Loader.metadataPath(path)) // Create Parquet data. val data = Data(weights, intercept, threshold) - val dataRDD: DataFrame = sc.parallelize(Seq(data), 1) - // TODO: repartition with 1 partition after SPARK-5532 gets fixed - dataRDD.saveAsParquetFile(Loader.dataPath(path)) + sc.parallelize(Seq(data), 1).toDF().saveAsParquetFile(Loader.dataPath(path)) } /** diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala index 0be3014de862e..568b65305649f 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala @@ -19,10 +19,10 @@ package org.apache.spark.mllib.clustering import scala.collection.mutable.IndexedSeq -import breeze.linalg.{DenseMatrix => BreezeMatrix, DenseVector => BreezeVector, Transpose, diag} +import breeze.linalg.{diag, DenseMatrix => BreezeMatrix, DenseVector => BDV, Vector => BV} import org.apache.spark.annotation.Experimental -import org.apache.spark.mllib.linalg.{BLAS, DenseMatrix, DenseVector, Matrices, Vector, Vectors} +import org.apache.spark.mllib.linalg.{BLAS, DenseMatrix, Matrices, Vector, Vectors} import org.apache.spark.mllib.stat.distribution.MultivariateGaussian import org.apache.spark.mllib.util.MLUtils import org.apache.spark.rdd.RDD @@ -41,7 +41,11 @@ import org.apache.spark.util.Utils * less than convergenceTol, or until it has reached the max number of iterations. * While this process is generally guaranteed to converge, it is not guaranteed * to find a global optimum. - * + * + * Note: For high-dimensional data (with many features), this algorithm may perform poorly. + * This is due to high-dimensional data (a) making it difficult to cluster at all (based + * on statistical/theoretical arguments) and (b) numerical issues with Gaussian distributions. + * * @param k The number of independent Gaussians in the mixture model * @param convergenceTol The maximum change in log-likelihood at which convergence * is considered to have occurred. @@ -130,7 +134,7 @@ class GaussianMixture private ( val sc = data.sparkContext // we will operate on the data as breeze data - val breezeData = data.map(u => u.toBreeze.toDenseVector).cache() + val breezeData = data.map(_.toBreeze).cache() // Get length of the input vectors val d = breezeData.first().length @@ -148,7 +152,7 @@ class GaussianMixture private ( (Array.fill(k)(1.0 / k), Array.tabulate(k) { i => val slice = samples.view(i * nSamples, (i + 1) * nSamples) new MultivariateGaussian(vectorMean(slice), initCovariance(slice)) - }) + }) } } @@ -169,7 +173,7 @@ class GaussianMixture private ( var i = 0 while (i < k) { val mu = sums.means(i) / sums.weights(i) - BLAS.syr(-sums.weights(i), Vectors.fromBreeze(mu).asInstanceOf[DenseVector], + BLAS.syr(-sums.weights(i), Vectors.fromBreeze(mu), Matrices.fromBreeze(sums.sigmas(i)).asInstanceOf[DenseMatrix]) weights(i) = sums.weights(i) / sumWeights gaussians(i) = new MultivariateGaussian(mu, sums.sigmas(i) / sums.weights(i)) @@ -185,8 +189,8 @@ class GaussianMixture private ( } /** Average of dense breeze vectors */ - private def vectorMean(x: IndexedSeq[BreezeVector[Double]]): BreezeVector[Double] = { - val v = BreezeVector.zeros[Double](x(0).length) + private def vectorMean(x: IndexedSeq[BV[Double]]): BDV[Double] = { + val v = BDV.zeros[Double](x(0).length) x.foreach(xi => v += xi) v / x.length.toDouble } @@ -195,10 +199,10 @@ class GaussianMixture private ( * Construct matrix where diagonal entries are element-wise * variance of input vectors (computes biased variance) */ - private def initCovariance(x: IndexedSeq[BreezeVector[Double]]): BreezeMatrix[Double] = { + private def initCovariance(x: IndexedSeq[BV[Double]]): BreezeMatrix[Double] = { val mu = vectorMean(x) - val ss = BreezeVector.zeros[Double](x(0).length) - x.map(xi => (xi - mu) :^ 2.0).foreach(u => ss += u) + val ss = BDV.zeros[Double](x(0).length) + x.foreach(xi => ss += (xi - mu) :^ 2.0) diag(ss / x.length.toDouble) } } @@ -207,7 +211,7 @@ class GaussianMixture private ( private object ExpectationSum { def zero(k: Int, d: Int): ExpectationSum = { new ExpectationSum(0.0, Array.fill(k)(0.0), - Array.fill(k)(BreezeVector.zeros(d)), Array.fill(k)(BreezeMatrix.zeros(d,d))) + Array.fill(k)(BDV.zeros(d)), Array.fill(k)(BreezeMatrix.zeros(d,d))) } // compute cluster contributions for each input point @@ -215,19 +219,18 @@ private object ExpectationSum { def add( weights: Array[Double], dists: Array[MultivariateGaussian]) - (sums: ExpectationSum, x: BreezeVector[Double]): ExpectationSum = { + (sums: ExpectationSum, x: BV[Double]): ExpectationSum = { val p = weights.zip(dists).map { case (weight, dist) => MLUtils.EPSILON + weight * dist.pdf(x) } val pSum = p.sum sums.logLikelihood += math.log(pSum) - val xxt = x * new Transpose(x) var i = 0 while (i < sums.k) { p(i) /= pSum sums.weights(i) += p(i) sums.means(i) += x * p(i) - BLAS.syr(p(i), Vectors.fromBreeze(x).asInstanceOf[DenseVector], + BLAS.syr(p(i), Vectors.fromBreeze(x), Matrices.fromBreeze(sums.sigmas(i)).asInstanceOf[DenseMatrix]) i = i + 1 } @@ -239,7 +242,7 @@ private object ExpectationSum { private class ExpectationSum( var logLikelihood: Double, val weights: Array[Double], - val means: Array[BreezeVector[Double]], + val means: Array[BDV[Double]], val sigmas: Array[BreezeMatrix[Double]]) extends Serializable { val k = weights.length diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala index af6f83c74bb40..ec65a3da689de 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala @@ -19,11 +19,17 @@ package org.apache.spark.mllib.clustering import breeze.linalg.{DenseVector => BreezeVector} +import org.json4s.DefaultFormats +import org.json4s.JsonDSL._ +import org.json4s.jackson.JsonMethods._ + +import org.apache.spark.SparkContext import org.apache.spark.annotation.Experimental -import org.apache.spark.mllib.linalg.Vector +import org.apache.spark.mllib.linalg.{Vector, Matrices, Matrix} import org.apache.spark.mllib.stat.distribution.MultivariateGaussian -import org.apache.spark.mllib.util.MLUtils +import org.apache.spark.mllib.util.{MLUtils, Loader, Saveable} import org.apache.spark.rdd.RDD +import org.apache.spark.sql.{SQLContext, Row} /** * :: Experimental :: @@ -41,10 +47,16 @@ import org.apache.spark.rdd.RDD @Experimental class GaussianMixtureModel( val weights: Array[Double], - val gaussians: Array[MultivariateGaussian]) extends Serializable { + val gaussians: Array[MultivariateGaussian]) extends Serializable with Saveable{ require(weights.length == gaussians.length, "Length of weight and Gaussian arrays must match") - + + override protected def formatVersion = "1.0" + + override def save(sc: SparkContext, path: String): Unit = { + GaussianMixtureModel.SaveLoadV1_0.save(sc, path, weights, gaussians) + } + /** Number of gaussians in mixture */ def k: Int = weights.length @@ -83,5 +95,79 @@ class GaussianMixtureModel( p(i) /= pSum } p - } + } +} + +@Experimental +object GaussianMixtureModel extends Loader[GaussianMixtureModel] { + + private object SaveLoadV1_0 { + + case class Data(weight: Double, mu: Vector, sigma: Matrix) + + val formatVersionV1_0 = "1.0" + + val classNameV1_0 = "org.apache.spark.mllib.clustering.GaussianMixtureModel" + + def save( + sc: SparkContext, + path: String, + weights: Array[Double], + gaussians: Array[MultivariateGaussian]): Unit = { + + val sqlContext = new SQLContext(sc) + import sqlContext.implicits._ + + // Create JSON metadata. + val metadata = compact(render + (("class" -> classNameV1_0) ~ ("version" -> formatVersionV1_0) ~ ("k" -> weights.length))) + sc.parallelize(Seq(metadata), 1).saveAsTextFile(Loader.metadataPath(path)) + + // Create Parquet data. + val dataArray = Array.tabulate(weights.length) { i => + Data(weights(i), gaussians(i).mu, gaussians(i).sigma) + } + sc.parallelize(dataArray, 1).toDF().saveAsParquetFile(Loader.dataPath(path)) + } + + def load(sc: SparkContext, path: String): GaussianMixtureModel = { + val dataPath = Loader.dataPath(path) + val sqlContext = new SQLContext(sc) + val dataFrame = sqlContext.parquetFile(dataPath) + val dataArray = dataFrame.select("weight", "mu", "sigma").collect() + + // Check schema explicitly since erasure makes it hard to use match-case for checking. + Loader.checkSchema[Data](dataFrame.schema) + + val (weights, gaussians) = dataArray.map { + case Row(weight: Double, mu: Vector, sigma: Matrix) => + (weight, new MultivariateGaussian(mu, sigma)) + }.unzip + + return new GaussianMixtureModel(weights.toArray, gaussians.toArray) + } + } + + override def load(sc: SparkContext, path: String) : GaussianMixtureModel = { + val (loadedClassName, version, metadata) = Loader.loadMetadata(sc, path) + implicit val formats = DefaultFormats + val k = (metadata \ "k").extract[Int] + val classNameV1_0 = SaveLoadV1_0.classNameV1_0 + (loadedClassName, version) match { + case (classNameV1_0, "1.0") => { + val model = SaveLoadV1_0.load(sc, path) + require(model.weights.length == k, + s"GaussianMixtureModel requires weights of length $k " + + s"got weights of length ${model.weights.length}") + require(model.gaussians.length == k, + s"GaussianMixtureModel requires gaussians of length $k" + + s"got gaussians of length ${model.gaussians.length}") + model + } + case _ => throw new Exception( + s"GaussianMixtureModel.load did not recognize model with (className, format version):" + + s"($loadedClassName, $version). Supported:\n" + + s" ($classNameV1_0, 1.0)") + } + } } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala index 11633e8242313..0f8d6a399682d 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala @@ -52,18 +52,33 @@ class KMeans private ( */ def this() = this(2, 20, 1, KMeans.K_MEANS_PARALLEL, 5, 1e-4, Utils.random.nextLong()) + /** + * Number of clusters to create (k). + */ + def getK: Int = k + /** Set the number of clusters to create (k). Default: 2. */ def setK(k: Int): this.type = { this.k = k this } + /** + * Maximum number of iterations to run. + */ + def getMaxIterations: Int = maxIterations + /** Set maximum number of iterations to run. Default: 20. */ def setMaxIterations(maxIterations: Int): this.type = { this.maxIterations = maxIterations this } + /** + * The initialization algorithm. This can be either "random" or "k-means||". + */ + def getInitializationMode: String = initializationMode + /** * Set the initialization algorithm. This can be either "random" to choose random points as * initial cluster centers, or "k-means||" to use a parallel variant of k-means++ @@ -77,6 +92,13 @@ class KMeans private ( this } + /** + * :: Experimental :: + * Number of runs of the algorithm to execute in parallel. + */ + @Experimental + def getRuns: Int = runs + /** * :: Experimental :: * Set the number of runs of the algorithm to execute in parallel. We initialize the algorithm @@ -92,6 +114,11 @@ class KMeans private ( this } + /** + * Number of steps for the k-means|| initialization mode + */ + def getInitializationSteps: Int = initializationSteps + /** * Set the number of steps for the k-means|| initialization mode. This is an advanced * setting -- the default of 5 is almost always enough. Default: 5. @@ -104,6 +131,11 @@ class KMeans private ( this } + /** + * The distance threshold within which we've consider centers to have converged. + */ + def getEpsilon: Double = epsilon + /** * Set the distance threshold within which we've consider centers to have converged. * If all centers move less than this Euclidean distance, we stop iterating one run. @@ -113,6 +145,11 @@ class KMeans private ( this } + /** + * The random seed for cluster initialization. + */ + def getSeed: Long = seed + /** Set the random seed for cluster initialization. */ def setSeed(seed: Long): this.type = { this.seed = seed @@ -499,5 +536,5 @@ class VectorWithNorm(val vector: Vector, val norm: Double) extends Serializable def this(array: Array[Double]) = this(Vectors.dense(array)) /** Converts the vector to a dense vector. */ - def toDense = new VectorWithNorm(Vectors.dense(vector.toArray), norm) + def toDense: VectorWithNorm = new VectorWithNorm(Vectors.dense(vector.toArray), norm) } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala index 644db66f9c5d0..1dcc04f17444b 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala @@ -17,16 +17,28 @@ package org.apache.spark.mllib.clustering +import scala.collection.JavaConverters._ + +import org.json4s._ +import org.json4s.JsonDSL._ +import org.json4s.jackson.JsonMethods._ + import org.apache.spark.api.java.JavaRDD -import org.apache.spark.rdd.RDD -import org.apache.spark.SparkContext._ import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.pmml.PMMLExportable +import org.apache.spark.mllib.util.{Loader, Saveable} +import org.apache.spark.rdd.RDD +import org.apache.spark.SparkContext +import org.apache.spark.sql.SQLContext +import org.apache.spark.sql.Row /** * A clustering model for K-means. Each point belongs to the cluster with the closest center. */ -class KMeansModel (val clusterCenters: Array[Vector]) extends Serializable with PMMLExportable { +class KMeansModel (val clusterCenters: Array[Vector]) extends Saveable with Serializable with PMMLExportable { + + /** A Java-friendly constructor that takes an Iterable of Vectors. */ + def this(centers: java.lang.Iterable[Vector]) = this(centers.asScala.toArray) /** Total number of clusters. */ def k: Int = clusterCenters.length @@ -59,4 +71,59 @@ class KMeansModel (val clusterCenters: Array[Vector]) extends Serializable with private def clusterCentersWithNorm: Iterable[VectorWithNorm] = clusterCenters.map(new VectorWithNorm(_)) + + override def save(sc: SparkContext, path: String): Unit = { + KMeansModel.SaveLoadV1_0.save(sc, this, path) + } + + override protected def formatVersion: String = "1.0" +} + +object KMeansModel extends Loader[KMeansModel] { + override def load(sc: SparkContext, path: String): KMeansModel = { + KMeansModel.SaveLoadV1_0.load(sc, path) + } + + private case class Cluster(id: Int, point: Vector) + + private object Cluster { + def apply(r: Row): Cluster = { + Cluster(r.getInt(0), r.getAs[Vector](1)) + } + } + + private[clustering] + object SaveLoadV1_0 { + + private val thisFormatVersion = "1.0" + + private[clustering] + val thisClassName = "org.apache.spark.mllib.clustering.KMeansModel" + + def save(sc: SparkContext, model: KMeansModel, path: String): Unit = { + val sqlContext = new SQLContext(sc) + import sqlContext.implicits._ + val metadata = compact(render( + ("class" -> thisClassName) ~ ("version" -> thisFormatVersion) ~ ("k" -> model.k))) + sc.parallelize(Seq(metadata), 1).saveAsTextFile(Loader.metadataPath(path)) + val dataRDD = sc.parallelize(model.clusterCenters.zipWithIndex).map { case (point, id) => + Cluster(id, point) + }.toDF() + dataRDD.saveAsParquetFile(Loader.dataPath(path)) + } + + def load(sc: SparkContext, path: String): KMeansModel = { + implicit val formats = DefaultFormats + val sqlContext = new SQLContext(sc) + val (className, formatVersion, metadata) = Loader.loadMetadata(sc, path) + assert(className == thisClassName) + assert(formatVersion == thisFormatVersion) + val k = (metadata \ "k").extract[Int] + val centriods = sqlContext.parquetFile(Loader.dataPath(path)) + Loader.checkSchema[Cluster](centriods.schema) + val localCentriods = centriods.map(Cluster.apply).collect() + assert(k == localCentriods.size) + new KMeansModel(localCentriods.sortBy(_.id).map(_.point)) + } + } } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala index a1d3df03a1140..9d63a08e211bc 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala @@ -19,7 +19,7 @@ package org.apache.spark.mllib.clustering import java.util.Random -import breeze.linalg.{DenseVector => BDV, normalize, axpy => brzAxpy} +import breeze.linalg.{DenseVector => BDV, normalize} import org.apache.spark.Logging import org.apache.spark.annotation.Experimental @@ -450,34 +450,23 @@ private[clustering] object LDA { // Create vertices. // Initially, we use random soft assignments of tokens to topics (random gamma). - val edgesWithGamma: RDD[(Edge[TokenCount], TopicCounts)] = - edges.mapPartitionsWithIndex { case (partIndex, partEdges) => - val random = new Random(partIndex + randomSeed) - partEdges.map { edge => - // Create a random gamma_{wjk} - (edge, normalize(BDV.fill[Double](k)(random.nextDouble()), 1.0)) + def createVertices(): RDD[(VertexId, TopicCounts)] = { + val verticesTMP: RDD[(VertexId, TopicCounts)] = + edges.mapPartitionsWithIndex { case (partIndex, partEdges) => + val random = new Random(partIndex + randomSeed) + partEdges.flatMap { edge => + val gamma = normalize(BDV.fill[Double](k)(random.nextDouble()), 1.0) + val sum = gamma * edge.attr + Seq((edge.srcId, sum), (edge.dstId, sum)) + } } - } - def createVertices(sendToWhere: Edge[TokenCount] => VertexId): RDD[(VertexId, TopicCounts)] = { - val verticesTMP: RDD[(VertexId, (TokenCount, TopicCounts))] = - edgesWithGamma.map { case (edge, gamma: TopicCounts) => - (sendToWhere(edge), (edge.attr, gamma)) - } - verticesTMP.aggregateByKey(BDV.zeros[Double](k))( - (sum, t) => { - brzAxpy(t._1, t._2, sum) - sum - }, - (sum0, sum1) => { - sum0 += sum1 - } - ) + verticesTMP.reduceByKey(_ + _) } - val docVertices = createVertices(_.srcId) - val termVertices = createVertices(_.dstId) + + val docTermVertices = createVertices() // Partition such that edges are grouped by document - val graph = Graph(docVertices ++ termVertices, edges) + val graph = Graph(docTermVertices, edges) .partitionBy(PartitionStrategy.EdgePartition1D) new EMOptimizer(graph, k, vocabSize, docConcentration, topicConcentration, checkpointInterval) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala index b0e991d2f2344..0a3f21ecee0dc 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala @@ -130,7 +130,7 @@ abstract class LDAModel private[clustering] { /* TODO * Compute the estimated topic distribution for each document. - * This is often called “theta” in the literature. + * This is often called 'theta' in the literature. * * @param documents RDD of documents, which are term (word) count vectors paired with IDs. * The term count vectors are "bags of words" with a fixed-size vocabulary diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala index 3b1caf0c679ef..aa53e88d59856 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala @@ -17,14 +17,20 @@ package org.apache.spark.mllib.clustering -import org.apache.spark.{Logging, SparkException} +import org.json4s.JsonDSL._ +import org.json4s._ +import org.json4s.jackson.JsonMethods._ + import org.apache.spark.annotation.Experimental +import org.apache.spark.api.java.JavaRDD import org.apache.spark.graphx._ import org.apache.spark.graphx.impl.GraphImpl import org.apache.spark.mllib.linalg.Vectors -import org.apache.spark.mllib.util.MLUtils +import org.apache.spark.mllib.util.{Loader, MLUtils, Saveable} import org.apache.spark.rdd.RDD +import org.apache.spark.sql.{Row, SQLContext} import org.apache.spark.util.random.XORShiftRandom +import org.apache.spark.{Logging, SparkContext, SparkException} /** * :: Experimental :: @@ -32,12 +38,65 @@ import org.apache.spark.util.random.XORShiftRandom * Model produced by [[PowerIterationClustering]]. * * @param k number of clusters - * @param assignments an RDD of (vertexID, clusterID) pairs + * @param assignments an RDD of clustering [[PowerIterationClustering#Assignment]]s */ @Experimental class PowerIterationClusteringModel( val k: Int, - val assignments: RDD[(Long, Int)]) extends Serializable + val assignments: RDD[PowerIterationClustering.Assignment]) extends Saveable with Serializable { + + override def save(sc: SparkContext, path: String): Unit = { + PowerIterationClusteringModel.SaveLoadV1_0.save(sc, this, path) + } + + override protected def formatVersion: String = "1.0" +} + +object PowerIterationClusteringModel extends Loader[PowerIterationClusteringModel] { + override def load(sc: SparkContext, path: String): PowerIterationClusteringModel = { + PowerIterationClusteringModel.SaveLoadV1_0.load(sc, path) + } + + private[clustering] + object SaveLoadV1_0 { + + private val thisFormatVersion = "1.0" + + private[clustering] + val thisClassName = "org.apache.spark.mllib.clustering.PowerIterationClusteringModel" + + def save(sc: SparkContext, model: PowerIterationClusteringModel, path: String): Unit = { + val sqlContext = new SQLContext(sc) + import sqlContext.implicits._ + + val metadata = compact(render( + ("class" -> thisClassName) ~ ("version" -> thisFormatVersion) ~ ("k" -> model.k))) + sc.parallelize(Seq(metadata), 1).saveAsTextFile(Loader.metadataPath(path)) + + val dataRDD = model.assignments.toDF() + dataRDD.saveAsParquetFile(Loader.dataPath(path)) + } + + def load(sc: SparkContext, path: String): PowerIterationClusteringModel = { + implicit val formats = DefaultFormats + val sqlContext = new SQLContext(sc) + + val (className, formatVersion, metadata) = Loader.loadMetadata(sc, path) + assert(className == thisClassName) + assert(formatVersion == thisFormatVersion) + + val k = (metadata \ "k").extract[Int] + val assignments = sqlContext.parquetFile(Loader.dataPath(path)) + Loader.checkSchema[PowerIterationClustering.Assignment](assignments.schema) + + val assignmentsRDD = assignments.map { + case Row(id: Long, cluster: Int) => PowerIterationClustering.Assignment(id, cluster) + } + + new PowerIterationClusteringModel(k, assignmentsRDD) + } + } +} /** * :: Experimental :: @@ -115,6 +174,14 @@ class PowerIterationClustering private[clustering] ( pic(w0) } + /** + * A Java-friendly version of [[PowerIterationClustering.run]]. + */ + def run(similarities: JavaRDD[(java.lang.Long, java.lang.Long, java.lang.Double)]) + : PowerIterationClusteringModel = { + run(similarities.rdd.asInstanceOf[RDD[(Long, Long, Double)]]) + } + /** * Runs the PIC algorithm. * @@ -124,16 +191,33 @@ class PowerIterationClustering private[clustering] ( */ private def pic(w: Graph[Double, Double]): PowerIterationClusteringModel = { val v = powerIter(w, maxIterations) - val assignments = kMeans(v, k) + val assignments = kMeans(v, k).mapPartitions({ iter => + iter.map { case (id, cluster) => + Assignment(id, cluster) + } + }, preservesPartitioning = true) new PowerIterationClusteringModel(k, assignments) } } -private[clustering] object PowerIterationClustering extends Logging { +@Experimental +object PowerIterationClustering extends Logging { + + /** + * :: Experimental :: + * Cluster assignment. + * @param id node id + * @param cluster assigned cluster id + */ + @Experimental + case class Assignment(id: Long, cluster: Int) + /** * Normalizes the affinity matrix (A) by row sums and returns the normalized affinity matrix (W). */ - def normalize(similarities: RDD[(Long, Long, Double)]): Graph[Double, Double] = { + private[clustering] + def normalize(similarities: RDD[(Long, Long, Double)]) + : Graph[Double, Double] = { val edges = similarities.flatMap { case (i, j, s) => if (s < 0.0) { throw new SparkException("Similarity must be nonnegative but found s($i, $j) = $s.") @@ -164,6 +248,7 @@ private[clustering] object PowerIterationClustering extends Logging { * @return a graph with edges representing W and vertices representing a random vector * with unit 1-norm */ + private[clustering] def randomInit(g: Graph[Double, Double]): Graph[Double, Double] = { val r = g.vertices.mapPartitionsWithIndex( (part, iter) => { @@ -185,6 +270,7 @@ private[clustering] object PowerIterationClustering extends Logging { * @param g a graph representing the normalized affinity matrix (W) * @return a graph with edges representing W and vertices representing the degree vector */ + private[clustering] def initDegreeVector(g: Graph[Double, Double]): Graph[Double, Double] = { val sum = g.vertices.values.sum() val v0 = g.vertices.mapValues(_ / sum) @@ -198,6 +284,7 @@ private[clustering] object PowerIterationClustering extends Logging { * @param maxIterations maximum number of iterations * @return a [[VertexRDD]] representing the pseudo-eigenvector */ + private[clustering] def powerIter( g: Graph[Double, Double], maxIterations: Int): VertexRDD[Double] = { @@ -237,6 +324,7 @@ private[clustering] object PowerIterationClustering extends Logging { * @param k number of clusters * @return a [[VertexRDD]] representing the clustering assignments */ + private[clustering] def kMeans(v: VertexRDD[Double], k: Int): VertexRDD[Int] = { val points = v.mapValues(x => Vectors.dense(x)).cache() val model = new KMeans() diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala index ced042e2f96ca..c1d1a224817e8 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala @@ -22,6 +22,7 @@ import org.apache.spark.Logging import org.apache.spark.SparkContext._ import org.apache.spark.mllib.evaluation.binary._ import org.apache.spark.rdd.{RDD, UnionRDD} +import org.apache.spark.sql.DataFrame /** * :: Experimental :: @@ -53,6 +54,13 @@ class BinaryClassificationMetrics( */ def this(scoreAndLabels: RDD[(Double, Double)]) = this(scoreAndLabels, 0) + /** + * An auxiliary constructor taking a DataFrame. + * @param scoreAndLabels a DataFrame with two double columns: score and label + */ + private[mllib] def this(scoreAndLabels: DataFrame) = + this(scoreAndLabels.map(r => (r.getDouble(0), r.getDouble(1)))) + /** Unpersist intermediate RDDs used in the computation. */ def unpersist() { cumulativeCounts.unpersist() diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala index ea10bde5fa252..a8378a76d20ae 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala @@ -96,30 +96,30 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])] * Returns precision for a given label (category) * @param label the label. */ - def precision(label: Double) = { + def precision(label: Double): Double = { val tp = tpPerClass(label) val fp = fpPerClass.getOrElse(label, 0L) - if (tp + fp == 0) 0 else tp.toDouble / (tp + fp) + if (tp + fp == 0) 0.0 else tp.toDouble / (tp + fp) } /** * Returns recall for a given label (category) * @param label the label. */ - def recall(label: Double) = { + def recall(label: Double): Double = { val tp = tpPerClass(label) val fn = fnPerClass.getOrElse(label, 0L) - if (tp + fn == 0) 0 else tp.toDouble / (tp + fn) + if (tp + fn == 0) 0.0 else tp.toDouble / (tp + fn) } /** * Returns f1-measure for a given label (category) * @param label the label. */ - def f1Measure(label: Double) = { + def f1Measure(label: Double): Double = { val p = precision(label) val r = recall(label) - if((p + r) == 0) 0 else 2 * p * r / (p + r) + if((p + r) == 0) 0.0 else 2 * p * r / (p + r) } private lazy val sumTp = tpPerClass.foldLeft(0L) { case (sum, (_, tp)) => sum + tp } @@ -130,7 +130,7 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])] * Returns micro-averaged label-based precision * (equals to micro-averaged document-based precision) */ - lazy val microPrecision = { + lazy val microPrecision: Double = { val sumFp = fpPerClass.foldLeft(0L){ case(cum, (_, fp)) => cum + fp} sumTp.toDouble / (sumTp + sumFp) } @@ -139,7 +139,7 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])] * Returns micro-averaged label-based recall * (equals to micro-averaged document-based recall) */ - lazy val microRecall = { + lazy val microRecall: Double = { val sumFn = fnPerClass.foldLeft(0.0){ case(cum, (_, fn)) => cum + fn} sumTp.toDouble / (sumTp + sumFn) } @@ -148,7 +148,7 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])] * Returns micro-averaged label-based f1-measure * (equals to micro-averaged document-based f1-measure) */ - lazy val microF1Measure = 2.0 * sumTp / (2 * sumTp + sumFnClass + sumFpClass) + lazy val microF1Measure: Double = 2.0 * sumTp / (2 * sumTp + sumFnClass + sumFpClass) /** * Returns the sequence of labels in ascending order diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala index a3e40200bc063..b2d9053f70145 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala @@ -21,18 +21,25 @@ import java.lang.{Iterable => JavaIterable} import scala.collection.JavaConverters._ import scala.collection.mutable -import scala.collection.mutable.ArrayBuffer +import scala.collection.mutable.ArrayBuilder import com.github.fommil.netlib.BLAS.{getInstance => blas} +import org.json4s.DefaultFormats +import org.json4s.JsonDSL._ +import org.json4s.jackson.JsonMethods._ + import org.apache.spark.Logging +import org.apache.spark.SparkContext import org.apache.spark.SparkContext._ import org.apache.spark.annotation.Experimental import org.apache.spark.api.java.JavaRDD import org.apache.spark.mllib.linalg.{Vector, Vectors} +import org.apache.spark.mllib.util.{Loader, Saveable} import org.apache.spark.rdd._ import org.apache.spark.util.Utils import org.apache.spark.util.random.XORShiftRandom +import org.apache.spark.sql.{SQLContext, Row} /** * Entry in vocabulary @@ -272,7 +279,7 @@ class Word2Vec extends Serializable with Logging { def hasNext: Boolean = iter.hasNext def next(): Array[Int] = { - var sentence = new ArrayBuffer[Int] + val sentence = ArrayBuilder.make[Int] var sentenceLength = 0 while (iter.hasNext && sentenceLength < MAX_SENTENCE_LENGTH) { val word = bcVocabHash.value.get(iter.next()) @@ -283,7 +290,7 @@ class Word2Vec extends Serializable with Logging { case None => } } - sentence.toArray + sentence.result() } } } @@ -422,7 +429,7 @@ class Word2Vec extends Serializable with Logging { */ @Experimental class Word2VecModel private[mllib] ( - private val model: Map[String, Array[Float]]) extends Serializable { + private val model: Map[String, Array[Float]]) extends Serializable with Saveable { private def cosineSimilarity(v1: Array[Float], v2: Array[Float]): Double = { require(v1.length == v2.length, "Vectors should have the same length") @@ -432,7 +439,13 @@ class Word2VecModel private[mllib] ( if (norm1 == 0 || norm2 == 0) return 0.0 blas.sdot(n, v1, 1, v2,1) / norm1 / norm2 } - + + override protected def formatVersion = "1.0" + + def save(sc: SparkContext, path: String): Unit = { + Word2VecModel.SaveLoadV1_0.save(sc, path, model) + } + /** * Transforms a word to its vector representation * @param word a word @@ -475,7 +488,7 @@ class Word2VecModel private[mllib] ( .tail .toArray } - + /** * Returns a map of words to their vector representations. */ @@ -483,3 +496,71 @@ class Word2VecModel private[mllib] ( model } } + +@Experimental +object Word2VecModel extends Loader[Word2VecModel] { + + private object SaveLoadV1_0 { + + val formatVersionV1_0 = "1.0" + + val classNameV1_0 = "org.apache.spark.mllib.feature.Word2VecModel" + + case class Data(word: String, vector: Array[Float]) + + def load(sc: SparkContext, path: String): Word2VecModel = { + val dataPath = Loader.dataPath(path) + val sqlContext = new SQLContext(sc) + val dataFrame = sqlContext.parquetFile(dataPath) + + val dataArray = dataFrame.select("word", "vector").collect() + + // Check schema explicitly since erasure makes it hard to use match-case for checking. + Loader.checkSchema[Data](dataFrame.schema) + + val word2VecMap = dataArray.map(i => (i.getString(0), i.getSeq[Float](1).toArray)).toMap + new Word2VecModel(word2VecMap) + } + + def save(sc: SparkContext, path: String, model: Map[String, Array[Float]]): Unit = { + + val sqlContext = new SQLContext(sc) + import sqlContext.implicits._ + + val vectorSize = model.values.head.size + val numWords = model.size + val metadata = compact(render + (("class" -> classNameV1_0) ~ ("version" -> formatVersionV1_0) ~ + ("vectorSize" -> vectorSize) ~ ("numWords" -> numWords))) + sc.parallelize(Seq(metadata), 1).saveAsTextFile(Loader.metadataPath(path)) + + val dataArray = model.toSeq.map { case (w, v) => Data(w, v) } + sc.parallelize(dataArray.toSeq, 1).toDF().saveAsParquetFile(Loader.dataPath(path)) + } + } + + override def load(sc: SparkContext, path: String): Word2VecModel = { + + val (loadedClassName, loadedVersion, metadata) = Loader.loadMetadata(sc, path) + implicit val formats = DefaultFormats + val expectedVectorSize = (metadata \ "vectorSize").extract[Int] + val expectedNumWords = (metadata \ "numWords").extract[Int] + val classNameV1_0 = SaveLoadV1_0.classNameV1_0 + (loadedClassName, loadedVersion) match { + case (classNameV1_0, "1.0") => + val model = SaveLoadV1_0.load(sc, path) + val vectorSize = model.getVectors.values.head.size + val numWords = model.getVectors.size + require(expectedVectorSize == vectorSize, + s"Word2VecModel requires each word to be mapped to a vector of size " + + s"$expectedVectorSize, got vector of size $vectorSize") + require(expectedNumWords == numWords, + s"Word2VecModel requires $expectedNumWords words, but got $numWords") + model + case _ => throw new Exception( + s"Word2VecModel.load did not recognize model with (className, format version):" + + s"($loadedClassName, $loadedVersion). Supported:\n" + + s" ($classNameV1_0, 1.0)") + } + } +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala index 3168d608c9556..efa8459d3cdba 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala @@ -26,8 +26,9 @@ import scala.reflect.ClassTag import org.apache.spark.{HashPartitioner, Logging, Partitioner, SparkException} import org.apache.spark.annotation.Experimental -import org.apache.spark.api.java.{JavaPairRDD, JavaRDD} +import org.apache.spark.api.java.JavaRDD import org.apache.spark.api.java.JavaSparkContext.fakeClassTag +import org.apache.spark.mllib.fpm.FPGrowth.FreqItemset import org.apache.spark.rdd.RDD import org.apache.spark.storage.StorageLevel @@ -35,18 +36,11 @@ import org.apache.spark.storage.StorageLevel * :: Experimental :: * * Model trained by [[FPGrowth]], which holds frequent itemsets. - * @param freqItemsets frequent itemset, which is an RDD of (itemset, frequency) pairs + * @param freqItemsets frequent itemset, which is an RDD of [[FreqItemset]] * @tparam Item item type */ @Experimental -class FPGrowthModel[Item: ClassTag]( - val freqItemsets: RDD[(Array[Item], Long)]) extends Serializable { - - /** Returns frequent itemsets as a [[org.apache.spark.api.java.JavaPairRDD]]. */ - def javaFreqItemsets(): JavaPairRDD[Array[Item], java.lang.Long] = { - JavaPairRDD.fromRDD(freqItemsets).asInstanceOf[JavaPairRDD[Array[Item], java.lang.Long]] - } -} +class FPGrowthModel[Item: ClassTag](val freqItemsets: RDD[FreqItemset[Item]]) extends Serializable /** * :: Experimental :: @@ -151,7 +145,7 @@ class FPGrowth private ( data: RDD[Array[Item]], minCount: Long, freqItems: Array[Item], - partitioner: Partitioner): RDD[(Array[Item], Long)] = { + partitioner: Partitioner): RDD[FreqItemset[Item]] = { val itemToRank = freqItems.zipWithIndex.toMap data.flatMap { transaction => genCondTransactions(transaction, itemToRank, partitioner) @@ -161,7 +155,7 @@ class FPGrowth private ( .flatMap { case (part, tree) => tree.extract(minCount, x => partitioner.getPartition(x) == part) }.map { case (ranks, count) => - (ranks.map(i => freqItems(i)).toArray, count) + new FreqItemset(ranks.map(i => freqItems(i)).toArray, count) } } @@ -193,3 +187,26 @@ class FPGrowth private ( output } } + +/** + * :: Experimental :: + */ +@Experimental +object FPGrowth { + + /** + * Frequent itemset. + * @param items items in this itemset. Java users should call [[FreqItemset#javaItems]] instead. + * @param freq frequency + * @tparam Item item type + */ + class FreqItemset[Item](val items: Array[Item], val freq: Long) extends Serializable { + + /** + * Returns items in a Java List. + */ + def javaItems: java.util.List[Item] = { + items.toList.asJava + } + } +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala index 079f7ca564a92..87052e1ba8539 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala @@ -235,12 +235,24 @@ private[spark] object BLAS extends Serializable with Logging { * @param x the vector x that contains the n elements. * @param A the symmetric matrix A. Size of n x n. */ - def syr(alpha: Double, x: DenseVector, A: DenseMatrix) { + def syr(alpha: Double, x: Vector, A: DenseMatrix) { val mA = A.numRows val nA = A.numCols - require(mA == nA, s"A is not a symmetric matrix. A: $mA x $nA") + require(mA == nA, s"A is not a square matrix (and hence is not symmetric). A: $mA x $nA") require(mA == x.size, s"The size of x doesn't match the rank of A. A: $mA x $nA, x: ${x.size}") + x match { + case dv: DenseVector => syr(alpha, dv, A) + case sv: SparseVector => syr(alpha, sv, A) + case _ => + throw new IllegalArgumentException(s"syr doesn't support vector type ${x.getClass}.") + } + } + + private def syr(alpha: Double, x: DenseVector, A: DenseMatrix) { + val nA = A.numRows + val mA = A.numCols + nativeBLAS.dsyr("U", x.size, alpha, x.values, 1, A.values, nA) // Fill lower triangular part of A @@ -255,6 +267,26 @@ private[spark] object BLAS extends Serializable with Logging { } } + private def syr(alpha: Double, x: SparseVector, A: DenseMatrix) { + val mA = A.numCols + val xIndices = x.indices + val xValues = x.values + val nnz = xValues.length + val Avalues = A.values + + var i = 0 + while (i < nnz) { + val multiplier = alpha * xValues(i) + val offset = xIndices(i) * mA + var j = 0 + while (j < nnz) { + Avalues(xIndices(j) + offset) += multiplier * xValues(j) + j += 1 + } + i += 1 + } + } + /** * C := alpha * A * B + beta * C * @param alpha a scalar to scale the multiplication A * B. diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/EigenValueDecomposition.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/EigenValueDecomposition.scala index 9d6f97528148e..866936aa4f118 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/EigenValueDecomposition.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/EigenValueDecomposition.scala @@ -117,7 +117,7 @@ private[mllib] object EigenValueDecomposition { info.`val` match { case 1 => throw new IllegalStateException("ARPACK returns non-zero info = " + info.`val` + " Maximum number of iterations taken. (Refer ARPACK user guide for details)") - case 2 => throw new IllegalStateException("ARPACK returns non-zero info = " + info.`val` + + case 3 => throw new IllegalStateException("ARPACK returns non-zero info = " + info.`val` + " No shifts could be applied. Try to increase NCV. " + "(Refer ARPACK user guide for details)") case _ => throw new IllegalStateException("ARPACK returns non-zero info = " + info.`val` + diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala index 84f8ac2e0d9cd..3fa5e068d16d4 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala @@ -23,9 +23,15 @@ import scala.collection.mutable.{ArrayBuilder => MArrayBuilder, HashSet => MHash import breeze.linalg.{CSCMatrix => BSM, DenseMatrix => BDM, Matrix => BM} +import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.sql.Row +import org.apache.spark.sql.types._ +import org.apache.spark.sql.catalyst.expressions.GenericMutableRow + /** * Trait for a local matrix. */ +@SQLUserDefinedType(udt = classOf[MatrixUDT]) sealed trait Matrix extends Serializable { /** Number of rows. */ @@ -50,7 +56,7 @@ sealed trait Matrix extends Serializable { private[mllib] def toBreeze: BM[Double] /** Gets the (i, j)-th element. */ - private[mllib] def apply(i: Int, j: Int): Double + def apply(i: Int, j: Int): Double /** Return the index for the (i, j)-th element in the backing array. */ private[mllib] def index(i: Int, j: Int): Int @@ -81,6 +87,9 @@ sealed trait Matrix extends Serializable { /** A human readable representation of the matrix */ override def toString: String = toBreeze.toString() + /** A human readable representation of the matrix with maximum lines and width */ + def toString(maxLines: Int, maxLineWidth: Int): String = toBreeze.toString(maxLines, maxLineWidth) + /** Map the values of this matrix using a function. Generates a new matrix. Performs the * function on only the backing array. For example, an operation such as addition or * subtraction will only be performed on the non-zero values in a `SparseMatrix`. */ @@ -102,6 +111,90 @@ sealed trait Matrix extends Serializable { private[spark] def foreachActive(f: (Int, Int, Double) => Unit) } +@DeveloperApi +private[spark] class MatrixUDT extends UserDefinedType[Matrix] { + + override def sqlType: StructType = { + // type: 0 = sparse, 1 = dense + // the dense matrix is built by numRows, numCols, values and isTransposed, all of which are + // set as not nullable, except values since in the future, support for binary matrices might + // be added for which values are not needed. + // the sparse matrix needs colPtrs and rowIndices, which are set as + // null, while building the dense matrix. + StructType(Seq( + StructField("type", ByteType, nullable = false), + StructField("numRows", IntegerType, nullable = false), + StructField("numCols", IntegerType, nullable = false), + StructField("colPtrs", ArrayType(IntegerType, containsNull = false), nullable = true), + StructField("rowIndices", ArrayType(IntegerType, containsNull = false), nullable = true), + StructField("values", ArrayType(DoubleType, containsNull = false), nullable = true), + StructField("isTransposed", BooleanType, nullable = false) + )) + } + + override def serialize(obj: Any): Row = { + val row = new GenericMutableRow(7) + obj match { + case sm: SparseMatrix => + row.setByte(0, 0) + row.setInt(1, sm.numRows) + row.setInt(2, sm.numCols) + row.update(3, sm.colPtrs.toSeq) + row.update(4, sm.rowIndices.toSeq) + row.update(5, sm.values.toSeq) + row.setBoolean(6, sm.isTransposed) + + case dm: DenseMatrix => + row.setByte(0, 1) + row.setInt(1, dm.numRows) + row.setInt(2, dm.numCols) + row.setNullAt(3) + row.setNullAt(4) + row.update(5, dm.values.toSeq) + row.setBoolean(6, dm.isTransposed) + } + row + } + + override def deserialize(datum: Any): Matrix = { + datum match { + // TODO: something wrong with UDT serialization, should never happen. + case m: Matrix => m + case row: Row => + require(row.length == 7, + s"MatrixUDT.deserialize given row with length ${row.length} but requires length == 7") + val tpe = row.getByte(0) + val numRows = row.getInt(1) + val numCols = row.getInt(2) + val values = row.getAs[Iterable[Double]](5).toArray + val isTransposed = row.getBoolean(6) + tpe match { + case 0 => + val colPtrs = row.getAs[Iterable[Int]](3).toArray + val rowIndices = row.getAs[Iterable[Int]](4).toArray + new SparseMatrix(numRows, numCols, colPtrs, rowIndices, values, isTransposed) + case 1 => + new DenseMatrix(numRows, numCols, values, isTransposed) + } + } + } + + override def userClass: Class[Matrix] = classOf[Matrix] + + override def equals(o: Any): Boolean = { + o match { + case v: MatrixUDT => true + case _ => false + } + } + + override def hashCode(): Int = 1994 + + override def typeName: String = "matrix" + + private[spark] override def asNullable: MatrixUDT = this +} + /** * Column-major dense matrix. * The entry values are stored in a single array of doubles with columns listed in sequence. @@ -119,6 +212,7 @@ sealed trait Matrix extends Serializable { * @param isTransposed whether the matrix is transposed. If true, `values` stores the matrix in * row major. */ +@SQLUserDefinedType(udt = classOf[MatrixUDT]) class DenseMatrix( val numRows: Int, val numCols: Int, @@ -146,12 +240,16 @@ class DenseMatrix( def this(numRows: Int, numCols: Int, values: Array[Double]) = this(numRows, numCols, values, false) - override def equals(o: Any) = o match { + override def equals(o: Any): Boolean = o match { case m: DenseMatrix => m.numRows == numRows && m.numCols == numCols && Arrays.equals(toArray, m.toArray) case _ => false } + override def hashCode: Int = { + com.google.common.base.Objects.hashCode(numRows : Integer, numCols: Integer, toArray) + } + private[mllib] def toBreeze: BM[Double] = { if (!isTransposed) { new BDM[Double](numRows, numCols, values) @@ -163,7 +261,7 @@ class DenseMatrix( private[mllib] def apply(i: Int): Double = values(i) - private[mllib] def apply(i: Int, j: Int): Double = values(index(i, j)) + override def apply(i: Int, j: Int): Double = values(index(i, j)) private[mllib] def index(i: Int, j: Int): Int = { if (!isTransposed) i + numRows * j else j + numCols * i @@ -173,7 +271,7 @@ class DenseMatrix( values(index(i, j)) = v } - override def copy = new DenseMatrix(numRows, numCols, values.clone()) + override def copy: DenseMatrix = new DenseMatrix(numRows, numCols, values.clone()) private[mllib] def map(f: Double => Double) = new DenseMatrix(numRows, numCols, values.map(f)) @@ -256,8 +354,11 @@ object DenseMatrix { * @param numCols number of columns of the matrix * @return `DenseMatrix` with size `numRows` x `numCols` and values of zeros */ - def zeros(numRows: Int, numCols: Int): DenseMatrix = + def zeros(numRows: Int, numCols: Int): DenseMatrix = { + require(numRows.toLong * numCols <= Int.MaxValue, + s"$numRows x $numCols dense matrix is too large to allocate") new DenseMatrix(numRows, numCols, new Array[Double](numRows * numCols)) + } /** * Generate a `DenseMatrix` consisting of ones. @@ -265,8 +366,11 @@ object DenseMatrix { * @param numCols number of columns of the matrix * @return `DenseMatrix` with size `numRows` x `numCols` and values of ones */ - def ones(numRows: Int, numCols: Int): DenseMatrix = + def ones(numRows: Int, numCols: Int): DenseMatrix = { + require(numRows.toLong * numCols <= Int.MaxValue, + s"$numRows x $numCols dense matrix is too large to allocate") new DenseMatrix(numRows, numCols, Array.fill(numRows * numCols)(1.0)) + } /** * Generate an Identity Matrix in `DenseMatrix` format. @@ -291,6 +395,8 @@ object DenseMatrix { * @return `DenseMatrix` with size `numRows` x `numCols` and values in U(0, 1) */ def rand(numRows: Int, numCols: Int, rng: Random): DenseMatrix = { + require(numRows.toLong * numCols <= Int.MaxValue, + s"$numRows x $numCols dense matrix is too large to allocate") new DenseMatrix(numRows, numCols, Array.fill(numRows * numCols)(rng.nextDouble())) } @@ -302,6 +408,8 @@ object DenseMatrix { * @return `DenseMatrix` with size `numRows` x `numCols` and values in N(0, 1) */ def randn(numRows: Int, numCols: Int, rng: Random): DenseMatrix = { + require(numRows.toLong * numCols <= Int.MaxValue, + s"$numRows x $numCols dense matrix is too large to allocate") new DenseMatrix(numRows, numCols, Array.fill(numRows * numCols)(rng.nextGaussian())) } @@ -346,6 +454,7 @@ object DenseMatrix { * Compressed Sparse Row (CSR) format, where `colPtrs` behaves as rowPtrs, * and `rowIndices` behave as colIndices, and `values` are stored in row major. */ +@SQLUserDefinedType(udt = classOf[MatrixUDT]) class SparseMatrix( val numRows: Int, val numCols: Int, @@ -398,7 +507,7 @@ class SparseMatrix( } } - private[mllib] def apply(i: Int, j: Int): Double = { + override def apply(i: Int, j: Int): Double = { val ind = index(i, j) if (ind < 0) 0.0 else values(ind) } @@ -421,7 +530,9 @@ class SparseMatrix( } } - override def copy = new SparseMatrix(numRows, numCols, colPtrs, rowIndices, values.clone()) + override def copy: SparseMatrix = { + new SparseMatrix(numRows, numCols, colPtrs, rowIndices, values.clone()) + } private[mllib] def map(f: Double => Double) = new SparseMatrix(numRows, numCols, colPtrs, rowIndices, values.map(f)) @@ -696,7 +807,7 @@ object Matrices { } /** - * Generate a `DenseMatrix` consisting of zeros. + * Generate a `Matrix` consisting of zeros. * @param numRows number of rows of the matrix * @param numCols number of columns of the matrix * @return `Matrix` with size `numRows` x `numCols` and values of zeros @@ -768,8 +879,8 @@ object Matrices { SparseMatrix.sprandn(numRows, numCols, density, rng) /** - * Generate a diagonal matrix in `DenseMatrix` format from the supplied values. - * @param vector a `Vector` tat will form the values on the diagonal of the matrix + * Generate a diagonal matrix in `Matrix` format from the supplied values. + * @param vector a `Vector` that will form the values on the diagonal of the matrix * @return Square `Matrix` with size `values.length` x `values.length` and `values` * on the diagonal */ diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala index 480bbfb5fe94a..4ef171f4f0419 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala @@ -182,6 +182,12 @@ private[spark] class VectorUDT extends UserDefinedType[Vector] { case _ => false } } + + override def hashCode: Int = 7919 + + override def typeName: String = "vector" + + private[spark] override def asNullable: VectorUDT = this } /** @@ -221,7 +227,7 @@ object Vectors { * @param elements vector elements in (index, value) pairs. */ def sparse(size: Int, elements: Seq[(Int, Double)]): Vector = { - require(size > 0) + require(size > 0, "The size of the requested sparse vector must be greater than 0.") val (indices, values) = elements.sortBy(_._1).unzip var prev = -1 @@ -229,7 +235,8 @@ object Vectors { require(prev < i, s"Found duplicate indices: $i.") prev = i } - require(prev < size) + require(prev < size, s"You may not write an element to index $prev because the declared " + + s"size of your vector is $size") new SparseVector(size, indices.toArray, values.toArray) } @@ -247,7 +254,7 @@ object Vectors { } /** - * Creates a dense vector of all zeros. + * Creates a vector of all zeros. * * @param size vector size * @return a zero vector @@ -303,7 +310,8 @@ object Vectors { * @return norm in L^p^ space. */ def norm(vector: Vector, p: Double): Double = { - require(p >= 1.0) + require(p >= 1.0, "To compute the p-norm of the vector, we require that you specify a p>=1. " + + s"You specified p=$p.") val values = vector match { case DenseVector(vs) => vs case SparseVector(n, ids, vs) => vs @@ -354,7 +362,8 @@ object Vectors { * @return squared distance between two Vectors. */ def sqdist(v1: Vector, v2: Vector): Double = { - require(v1.size == v2.size, "vector dimension mismatch") + require(v1.size == v2.size, s"Vector dimensions do not match: Dim(v1)=${v1.size} and Dim(v2)" + + s"=${v2.size}.") var squaredDistance = 0.0 (v1, v2) match { case (v1: SparseVector, v2: SparseVector) => @@ -476,7 +485,7 @@ class DenseVector(val values: Array[Double]) extends Vector { private[mllib] override def toBreeze: BV[Double] = new BDV[Double](values) - override def apply(i: Int) = values(i) + override def apply(i: Int): Double = values(i) override def copy: DenseVector = { new DenseVector(values.clone()) @@ -512,7 +521,9 @@ class SparseVector( val indices: Array[Int], val values: Array[Double]) extends Vector { - require(indices.length == values.length) + require(indices.length == values.length, "Sparse vectors require that the dimension of the" + + s" indices match the dimension of the values. You provided ${indices.size} indices and " + + s" ${values.size} values.") override def toString: String = "(%s,%s,%s)".format(size, indices.mkString("[", ",", "]"), values.mkString("[", ",", "]")) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala index 1d253963130f1..3323ae7b1fba0 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala @@ -49,7 +49,7 @@ private[mllib] class GridPartitioner( private val rowPartitions = math.ceil(rows * 1.0 / rowsPerPart).toInt private val colPartitions = math.ceil(cols * 1.0 / colsPerPart).toInt - override val numPartitions = rowPartitions * colPartitions + override val numPartitions: Int = rowPartitions * colPartitions /** * Returns the index of the partition the input coordinate belongs to. @@ -85,6 +85,14 @@ private[mllib] class GridPartitioner( false } } + + override def hashCode: Int = { + com.google.common.base.Objects.hashCode( + rows: java.lang.Integer, + cols: java.lang.Integer, + rowsPerPart: java.lang.Integer, + colsPerPart: java.lang.Integer) + } } private[mllib] object GridPartitioner { diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala index 961111507f2c2..9a89a6f3a515f 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala @@ -531,7 +531,6 @@ class RowMatrix( val rand = new XORShiftRandom(indx) val scaled = new Array[Double](p.size) iter.flatMap { row => - val buf = new ListBuffer[((Int, Int), Double)]() row match { case SparseVector(size, indices, values) => val nnz = indices.size @@ -540,8 +539,9 @@ class RowMatrix( scaled(k) = values(k) / q(indices(k)) k += 1 } - k = 0 - while (k < nnz) { + + Iterator.tabulate (nnz) { k => + val buf = new ListBuffer[((Int, Int), Double)]() val i = indices(k) val iVal = scaled(k) if (iVal != 0 && rand.nextDouble() < p(i)) { @@ -555,8 +555,8 @@ class RowMatrix( l += 1 } } - k += 1 - } + buf + }.flatten case DenseVector(values) => val n = values.size var i = 0 @@ -564,8 +564,8 @@ class RowMatrix( scaled(i) = values(i) / q(i) i += 1 } - i = 0 - while (i < n) { + Iterator.tabulate (n) { i => + val buf = new ListBuffer[((Int, Int), Double)]() val iVal = scaled(i) if (iVal != 0 && rand.nextDouble() < p(i)) { var j = i + 1 @@ -577,10 +577,9 @@ class RowMatrix( j += 1 } } - i += 1 - } + buf + }.flatten } - buf } }.reduceByKey(_ + _).map { case ((i, j), sim) => MatrixEntry(i.toLong, j.toLong, sim) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala index 0acdab797e8f3..8bfa0d2b64995 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala @@ -63,10 +63,12 @@ abstract class Gradient extends Serializable { * http://statweb.stanford.edu/~tibs/ElemStatLearn/ , Eq. (4.17) on page 119 gives the formula of * multinomial logistic regression model. A simple calculation shows that * + * {{{ * P(y=0|x, w) = 1 / (1 + \sum_i^{K-1} \exp(x w_i)) * P(y=1|x, w) = exp(x w_1) / (1 + \sum_i^{K-1} \exp(x w_i)) * ... * P(y=K-1|x, w) = exp(x w_{K-1}) / (1 + \sum_i^{K-1} \exp(x w_i)) + * }}} * * for K classes multiclass classification problem. * @@ -75,9 +77,11 @@ abstract class Gradient extends Serializable { * will be (K-1) * N. * * As a result, the loss of objective function for a single instance of data can be written as + * {{{ * l(w, x) = -log P(y|x, w) = -\alpha(y) log P(y=0|x, w) - (1-\alpha(y)) log P(y|x, w) * = log(1 + \sum_i^{K-1}\exp(x w_i)) - (1-\alpha(y)) x w_{y-1} * = log(1 + \sum_i^{K-1}\exp(margins_i)) - (1-\alpha(y)) margins_{y-1} + * }}} * * where \alpha(i) = 1 if i != 0, and * \alpha(i) = 0 if i == 0, @@ -86,14 +90,16 @@ abstract class Gradient extends Serializable { * For optimization, we have to calculate the first derivative of the loss function, and * a simple calculation shows that * + * {{{ * \frac{\partial l(w, x)}{\partial w_{ij}} * = (\exp(x w_i) / (1 + \sum_k^{K-1} \exp(x w_k)) - (1-\alpha(y)\delta_{y, i+1})) * x_j * = multiplier_i * x_j + * }}} * * where \delta_{i, j} = 1 if i == j, * \delta_{i, j} = 0 if i != j, and - * multiplier - * = \exp(margins_i) / (1 + \sum_k^{K-1} \exp(margins_i)) - (1-\alpha(y)\delta_{y, i+1}) + * multiplier = + * \exp(margins_i) / (1 + \sum_k^{K-1} \exp(margins_i)) - (1-\alpha(y)\delta_{y, i+1}) * * If any of margins is larger than 709.78, the numerical computation of multiplier and loss * function will be suffered from arithmetic overflow. This issue occurs when there are outliers @@ -103,10 +109,12 @@ abstract class Gradient extends Serializable { * Fortunately, when max(margins) = maxMargin > 0, the loss function and the multiplier can be * easily rewritten into the following equivalent numerically stable formula. * + * {{{ * l(w, x) = log(1 + \sum_i^{K-1}\exp(margins_i)) - (1-\alpha(y)) margins_{y-1} * = log(\exp(-maxMargin) + \sum_i^{K-1}\exp(margins_i - maxMargin)) + maxMargin * - (1-\alpha(y)) margins_{y-1} * = log(1 + sum) + maxMargin - (1-\alpha(y)) margins_{y-1} + * }}} * * where sum = \exp(-maxMargin) + \sum_i^{K-1}\exp(margins_i - maxMargin) - 1. * @@ -115,8 +123,10 @@ abstract class Gradient extends Serializable { * * For multiplier, similar trick can be applied as the following, * + * {{{ * multiplier = \exp(margins_i) / (1 + \sum_k^{K-1} \exp(margins_i)) - (1-\alpha(y)\delta_{y, i+1}) * = \exp(margins_i - maxMargin) / (1 + sum) - (1-\alpha(y)\delta_{y, i+1}) + * }}} * * where each term in \exp is also smaller than zero, so overflow is not a concern. * diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala index d5e4f4ccbff10..ef6eccd90711a 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala @@ -60,6 +60,8 @@ class LBFGS(private var gradient: Gradient, private var updater: Updater) /** * Set the convergence tolerance of iterations for L-BFGS. Default 1E-4. * Smaller value will lead to higher accuracy with the cost of more iterations. + * This value must be nonnegative. Lower convergence values are less tolerant + * and therefore generally cause more iterations to be run. */ def setConvergenceTol(tolerance: Double): this.type = { this.convergenceTol = tolerance @@ -142,7 +144,9 @@ object LBFGS extends Logging { * one single data example) * @param updater - Updater function to actually perform a gradient step in a given direction. * @param numCorrections - The number of corrections used in the L-BFGS update. - * @param convergenceTol - The convergence tolerance of iterations for L-BFGS + * @param convergenceTol - The convergence tolerance of iterations for L-BFGS which is must be + * nonnegative. Lower values are less tolerant and therefore generally + * cause more iterations to be run. * @param maxNumIterations - Maximal number of iterations that L-BFGS can be run. * @param regParam - Regularization parameter * diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/NNLS.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/NNLS.scala index ccd93b318bc23..4766f7708295d 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/optimization/NNLS.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/NNLS.scala @@ -17,7 +17,9 @@ package org.apache.spark.mllib.optimization -import org.jblas.{DoubleMatrix, SimpleBlas} +import java.{util => ju} + +import com.github.fommil.netlib.BLAS.{getInstance => blas} /** * Object used to solve nonnegative least squares problems using a modified @@ -25,20 +27,20 @@ import org.jblas.{DoubleMatrix, SimpleBlas} */ private[spark] object NNLS { class Workspace(val n: Int) { - val scratch = new DoubleMatrix(n, 1) - val grad = new DoubleMatrix(n, 1) - val x = new DoubleMatrix(n, 1) - val dir = new DoubleMatrix(n, 1) - val lastDir = new DoubleMatrix(n, 1) - val res = new DoubleMatrix(n, 1) - - def wipe() { - scratch.fill(0.0) - grad.fill(0.0) - x.fill(0.0) - dir.fill(0.0) - lastDir.fill(0.0) - res.fill(0.0) + val scratch = new Array[Double](n) + val grad = new Array[Double](n) + val x = new Array[Double](n) + val dir = new Array[Double](n) + val lastDir = new Array[Double](n) + val res = new Array[Double](n) + + def wipe(): Unit = { + ju.Arrays.fill(scratch, 0.0) + ju.Arrays.fill(grad, 0.0) + ju.Arrays.fill(x, 0.0) + ju.Arrays.fill(dir, 0.0) + ju.Arrays.fill(lastDir, 0.0) + ju.Arrays.fill(res, 0.0) } } @@ -60,18 +62,18 @@ private[spark] object NNLS { * direction, however, while this method only uses a conjugate gradient direction if the last * iteration did not cause a previously-inactive constraint to become active. */ - def solve(ata: DoubleMatrix, atb: DoubleMatrix, ws: Workspace): Array[Double] = { + def solve(ata: Array[Double], atb: Array[Double], ws: Workspace): Array[Double] = { ws.wipe() - val n = atb.rows + val n = atb.length val scratch = ws.scratch // find the optimal unconstrained step - def steplen(dir: DoubleMatrix, res: DoubleMatrix): Double = { - val top = SimpleBlas.dot(dir, res) - SimpleBlas.gemv(1.0, ata, dir, 0.0, scratch) + def steplen(dir: Array[Double], res: Array[Double]): Double = { + val top = blas.ddot(n, dir, 1, res, 1) + blas.dgemv("N", n, n, 1.0, ata, n, dir, 1, 0.0, scratch, 1) // Push the denominator upward very slightly to avoid infinities and silliness - top / (SimpleBlas.dot(scratch, dir) + 1e-20) + top / (blas.ddot(n, scratch, 1, dir, 1) + 1e-20) } // stopping condition @@ -96,52 +98,52 @@ private[spark] object NNLS { var i = 0 while (iterno < iterMax) { // find the residual - SimpleBlas.gemv(1.0, ata, x, 0.0, res) - SimpleBlas.axpy(-1.0, atb, res) - SimpleBlas.copy(res, grad) + blas.dgemv("N", n, n, 1.0, ata, n, x, 1, 0.0, res, 1) + blas.daxpy(n, -1.0, atb, 1, res, 1) + blas.dcopy(n, res, 1, grad, 1) // project the gradient i = 0 while (i < n) { - if (grad.data(i) > 0.0 && x.data(i) == 0.0) { - grad.data(i) = 0.0 + if (grad(i) > 0.0 && x(i) == 0.0) { + grad(i) = 0.0 } i = i + 1 } - val ngrad = SimpleBlas.dot(grad, grad) + val ngrad = blas.ddot(n, grad, 1, grad, 1) - SimpleBlas.copy(grad, dir) + blas.dcopy(n, grad, 1, dir, 1) // use a CG direction under certain conditions var step = steplen(grad, res) var ndir = 0.0 - val nx = SimpleBlas.dot(x, x) + val nx = blas.ddot(n, x, 1, x, 1) if (iterno > lastWall + 1) { val alpha = ngrad / lastNorm - SimpleBlas.axpy(alpha, lastDir, dir) + blas.daxpy(n, alpha, lastDir, 1, dir, 1) val dstep = steplen(dir, res) - ndir = SimpleBlas.dot(dir, dir) + ndir = blas.ddot(n, dir, 1, dir, 1) if (stop(dstep, ndir, nx)) { // reject the CG step if it could lead to premature termination - SimpleBlas.copy(grad, dir) - ndir = SimpleBlas.dot(dir, dir) + blas.dcopy(n, grad, 1, dir, 1) + ndir = blas.ddot(n, dir, 1, dir, 1) } else { step = dstep } } else { - ndir = SimpleBlas.dot(dir, dir) + ndir = blas.ddot(n, dir, 1, dir, 1) } // terminate? if (stop(step, ndir, nx)) { - return x.data.clone + return x.clone } // don't run through the walls i = 0 while (i < n) { - if (step * dir.data(i) > x.data(i)) { - step = x.data(i) / dir.data(i) + if (step * dir(i) > x(i)) { + step = x(i) / dir(i) } i = i + 1 } @@ -149,19 +151,19 @@ private[spark] object NNLS { // take the step i = 0 while (i < n) { - if (step * dir.data(i) > x.data(i) * (1 - 1e-14)) { - x.data(i) = 0 + if (step * dir(i) > x(i) * (1 - 1e-14)) { + x(i) = 0 lastWall = iterno } else { - x.data(i) -= step * dir.data(i) + x(i) -= step * dir(i) } i = i + 1 } iterno = iterno + 1 - SimpleBlas.copy(dir, lastDir) + blas.dcopy(n, dir, 1, lastDir, 1) lastNorm = ngrad } - x.data.clone + x.clone } } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/random/RandomDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/random/RandomDataGenerator.scala index 405bae62ee8b6..9349ecaa13f56 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/random/RandomDataGenerator.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/random/RandomDataGenerator.scala @@ -56,7 +56,7 @@ class UniformGenerator extends RandomDataGenerator[Double] { random.nextDouble() } - override def setSeed(seed: Long) = random.setSeed(seed) + override def setSeed(seed: Long): Unit = random.setSeed(seed) override def copy(): UniformGenerator = new UniformGenerator() } @@ -75,7 +75,7 @@ class StandardNormalGenerator extends RandomDataGenerator[Double] { random.nextGaussian() } - override def setSeed(seed: Long) = random.setSeed(seed) + override def setSeed(seed: Long): Unit = random.setSeed(seed) override def copy(): StandardNormalGenerator = new StandardNormalGenerator() } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctions.scala b/mllib/src/main/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctions.scala new file mode 100644 index 0000000000000..9213fd3f595c3 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctions.scala @@ -0,0 +1,60 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.mllib.rdd + +import scala.language.implicitConversions +import scala.reflect.ClassTag + +import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.rdd.RDD +import org.apache.spark.util.BoundedPriorityQueue + +/** + * Machine learning specific Pair RDD functions. + */ +@DeveloperApi +class MLPairRDDFunctions[K: ClassTag, V: ClassTag](self: RDD[(K, V)]) extends Serializable { + /** + * Returns the top k (largest) elements for each key from this RDD as defined by the specified + * implicit Ordering[T]. + * If the number of elements for a certain key is less than k, all of them will be returned. + * + * @param num k, the number of top elements to return + * @param ord the implicit ordering for T + * @return an RDD that contains the top k values for each key + */ + def topByKey(num: Int)(implicit ord: Ordering[V]): RDD[(K, Array[V])] = { + self.aggregateByKey(new BoundedPriorityQueue[V](num)(ord))( + seqOp = (queue, item) => { + queue += item + queue + }, + combOp = (queue1, queue2) => { + queue1 ++= queue2 + queue1 + } + ).mapValues(_.toArray.sorted(ord.reverse)) + } +} + +@DeveloperApi +object MLPairRDDFunctions { + /** Implicit conversion from a pair RDD to MLPairRDDFunctions. */ + implicit def fromPairRDD[K: ClassTag, V: ClassTag](rdd: RDD[(K, V)]): MLPairRDDFunctions[K, V] = + new MLPairRDDFunctions[K, V](rdd) +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala index 4bb28d1b1e071..dddefe1944e9d 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala @@ -18,7 +18,7 @@ package org.apache.spark.mllib.recommendation import org.apache.spark.Logging -import org.apache.spark.annotation.{DeveloperApi, Experimental} +import org.apache.spark.annotation.DeveloperApi import org.apache.spark.api.java.JavaRDD import org.apache.spark.ml.recommendation.{ALS => NewALS} import org.apache.spark.rdd.RDD @@ -82,6 +82,9 @@ class ALS private ( private var intermediateRDDStorageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK private var finalRDDStorageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK + /** checkpoint interval */ + private var checkpointInterval: Int = 10 + /** * Set the number of blocks for both user blocks and product blocks to parallelize the computation * into; pass -1 for an auto-configured number of blocks. Default: -1. @@ -182,6 +185,19 @@ class ALS private ( this } + /** + * Set period (in iterations) between checkpoints (default = 10). Checkpointing helps with + * recovery (when nodes fail) and StackOverflow exceptions caused by long lineage. It also helps + * with eliminating temporary shuffle files on disk, which can be important when there are many + * ALS iterations. If the checkpoint directory is not set in [[org.apache.spark.SparkContext]], + * this setting is ignored. + */ + @DeveloperApi + def setCheckpointInterval(checkpointInterval: Int): this.type = { + this.checkpointInterval = checkpointInterval + this + } + /** * Run ALS with the configured parameters on an input RDD of (user, product, rating) triples. * Returns a MatrixFactorizationModel with feature vectors for each user and product. @@ -212,6 +228,7 @@ class ALS private ( nonnegative = nonnegative, intermediateRDDStorageLevel = intermediateRDDStorageLevel, finalRDDStorageLevel = StorageLevel.NONE, + checkpointInterval = checkpointInterval, seed = seed) val userFactors = floatUserFactors diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala index ed2f8b41bcae5..36cbf060d9998 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala @@ -17,13 +17,20 @@ package org.apache.spark.mllib.recommendation +import java.io.IOException import java.lang.{Integer => JavaInteger} -import org.jblas.DoubleMatrix +import org.apache.hadoop.fs.Path +import org.json4s._ +import org.json4s.JsonDSL._ +import org.json4s.jackson.JsonMethods._ +import com.github.fommil.netlib.BLAS.{getInstance => blas} -import org.apache.spark.Logging +import org.apache.spark.{Logging, SparkContext} import org.apache.spark.api.java.{JavaPairRDD, JavaRDD} +import org.apache.spark.mllib.util.{Loader, Saveable} import org.apache.spark.rdd.RDD +import org.apache.spark.sql.{Row, SQLContext} import org.apache.spark.storage.StorageLevel /** @@ -41,7 +48,8 @@ import org.apache.spark.storage.StorageLevel class MatrixFactorizationModel( val rank: Int, val userFeatures: RDD[(Int, Array[Double])], - val productFeatures: RDD[(Int, Array[Double])]) extends Serializable with Logging { + val productFeatures: RDD[(Int, Array[Double])]) + extends Saveable with Serializable with Logging { require(rank > 0) validateFeatures("User", userFeatures) @@ -62,9 +70,9 @@ class MatrixFactorizationModel( /** Predict the rating of one user for one product. */ def predict(user: Int, product: Int): Double = { - val userVector = new DoubleMatrix(userFeatures.lookup(user).head) - val productVector = new DoubleMatrix(productFeatures.lookup(product).head) - userVector.dot(productVector) + val userVector = userFeatures.lookup(user).head + val productVector = productFeatures.lookup(product).head + blas.ddot(userVector.length, userVector, 1, productVector, 1) } /** @@ -81,9 +89,7 @@ class MatrixFactorizationModel( } users.join(productFeatures).map { case (product, ((user, uFeatures), pFeatures)) => - val userVector = new DoubleMatrix(uFeatures) - val productVector = new DoubleMatrix(pFeatures) - Rating(user, product, userVector.dot(productVector)) + Rating(user, product, blas.ddot(uFeatures.length, uFeatures, 1, pFeatures, 1)) } } @@ -125,14 +131,87 @@ class MatrixFactorizationModel( recommend(productFeatures.lookup(product).head, userFeatures, num) .map(t => Rating(t._1, product, t._2)) + protected override val formatVersion: String = "1.0" + + override def save(sc: SparkContext, path: String): Unit = { + MatrixFactorizationModel.SaveLoadV1_0.save(this, path) + } + private def recommend( recommendToFeatures: Array[Double], recommendableFeatures: RDD[(Int, Array[Double])], num: Int): Array[(Int, Double)] = { - val recommendToVector = new DoubleMatrix(recommendToFeatures) val scored = recommendableFeatures.map { case (id,features) => - (id, recommendToVector.dot(new DoubleMatrix(features))) + (id, blas.ddot(features.length, recommendToFeatures, 1, features, 1)) } scored.top(num)(Ordering.by(_._2)) } } + +object MatrixFactorizationModel extends Loader[MatrixFactorizationModel] { + + import org.apache.spark.mllib.util.Loader._ + + override def load(sc: SparkContext, path: String): MatrixFactorizationModel = { + val (loadedClassName, formatVersion, _) = loadMetadata(sc, path) + val classNameV1_0 = SaveLoadV1_0.thisClassName + (loadedClassName, formatVersion) match { + case (className, "1.0") if className == classNameV1_0 => + SaveLoadV1_0.load(sc, path) + case _ => + throw new IOException("MatrixFactorizationModel.load did not recognize model with" + + s"(class: $loadedClassName, version: $formatVersion). Supported:\n" + + s" ($classNameV1_0, 1.0)") + } + } + + private[recommendation] + object SaveLoadV1_0 { + + private val thisFormatVersion = "1.0" + + private[recommendation] + val thisClassName = "org.apache.spark.mllib.recommendation.MatrixFactorizationModel" + + /** + * Saves a [[MatrixFactorizationModel]], where user features are saved under `data/users` and + * product features are saved under `data/products`. + */ + def save(model: MatrixFactorizationModel, path: String): Unit = { + val sc = model.userFeatures.sparkContext + val sqlContext = new SQLContext(sc) + import sqlContext.implicits._ + val metadata = compact(render( + ("class" -> thisClassName) ~ ("version" -> thisFormatVersion) ~ ("rank" -> model.rank))) + sc.parallelize(Seq(metadata), 1).saveAsTextFile(metadataPath(path)) + model.userFeatures.toDF("id", "features").saveAsParquetFile(userPath(path)) + model.productFeatures.toDF("id", "features").saveAsParquetFile(productPath(path)) + } + + def load(sc: SparkContext, path: String): MatrixFactorizationModel = { + implicit val formats = DefaultFormats + val sqlContext = new SQLContext(sc) + val (className, formatVersion, metadata) = loadMetadata(sc, path) + assert(className == thisClassName) + assert(formatVersion == thisFormatVersion) + val rank = (metadata \ "rank").extract[Int] + val userFeatures = sqlContext.parquetFile(userPath(path)) + .map { case Row(id: Int, features: Seq[_]) => + (id, features.asInstanceOf[Seq[Double]].toArray) + } + val productFeatures = sqlContext.parquetFile(productPath(path)) + .map { case Row(id: Int, features: Seq[_]) => + (id, features.asInstanceOf[Seq[Double]].toArray) + } + new MatrixFactorizationModel(rank, userFeatures, productFeatures) + } + + private def userPath(path: String): String = { + new Path(dataPath(path), "user").toUri.toString + } + + private def productPath(path: String): String = { + new Path(dataPath(path), "product").toUri.toString + } + } +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala index 17de215b97f9d..9fd60ff7a0c79 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala @@ -76,7 +76,12 @@ abstract class GeneralizedLinearModel(val weights: Vector, val intercept: Double predictPoint(testData, weights, intercept) } - override def toString() = "(weights=%s, intercept=%s)".format(weights, intercept) + /** + * Print a summary of the model. + */ + override def toString: String = { + s"${this.getClass.getName}: intercept = ${intercept}, numFeatures = ${weights.size}" + } } /** @@ -126,7 +131,12 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel] /** * The dimension of training features. */ - protected var numFeatures: Int = 0 + def getNumFeatures: Int = this.numFeatures + + /** + * The dimension of training features. + */ + protected var numFeatures: Int = -1 /** * Set if the algorithm should use feature scaling to improve the convergence during optimization. @@ -141,6 +151,11 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel] */ protected def createModel(weights: Vector, intercept: Double): M + /** + * Get if the algorithm uses addIntercept + */ + def isAddIntercept: Boolean = this.addIntercept + /** * Set if the algorithm should add an intercept. Default false. * We set the default to false because adding the intercept will cause memory allocation. @@ -163,7 +178,9 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel] * RDD of LabeledPoint entries. */ def run(input: RDD[LabeledPoint]): M = { - numFeatures = input.first().features.size + if (numFeatures < 0) { + numFeatures = input.map(_.features.size).first() + } /** * When `numOfLinearPredictor > 1`, the intercepts are encapsulated into weights, @@ -193,7 +210,10 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel] * of LabeledPoint entries starting from the initial weights provided. */ def run(input: RDD[LabeledPoint], initialWeights: Vector): M = { - numFeatures = input.first().features.size + + if (numFeatures < 0) { + numFeatures = input.map(_.features.size).first() + } if (input.getStorageLevel == StorageLevel.NONE) { logWarning("The input data is not directly cached, which may hurt performance if its" @@ -205,7 +225,7 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel] throw new SparkException("Input validation failed.") } - /** + /* * Scaling columns to unit variance as a heuristic to reduce the condition number: * * During the optimization process, the convergence (rate) depends on the condition number of @@ -225,26 +245,27 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel] * Currently, it's only enabled in LogisticRegressionWithLBFGS */ val scaler = if (useFeatureScaling) { - (new StandardScaler(withStd = true, withMean = false)).fit(input.map(x => x.features)) + new StandardScaler(withStd = true, withMean = false).fit(input.map(_.features)) } else { null } // Prepend an extra variable consisting of all 1.0's for the intercept. - val data = if (addIntercept) { - if (useFeatureScaling) { - input.map(labeledPoint => - (labeledPoint.label, appendBias(scaler.transform(labeledPoint.features)))) - } else { - input.map(labeledPoint => (labeledPoint.label, appendBias(labeledPoint.features))) - } - } else { - if (useFeatureScaling) { - input.map(labeledPoint => (labeledPoint.label, scaler.transform(labeledPoint.features))) + // TODO: Apply feature scaling to the weight vector instead of input data. + val data = + if (addIntercept) { + if (useFeatureScaling) { + input.map(lp => (lp.label, appendBias(scaler.transform(lp.features)))).cache() + } else { + input.map(lp => (lp.label, appendBias(lp.features))).cache() + } } else { - input.map(labeledPoint => (labeledPoint.label, labeledPoint.features)) + if (useFeatureScaling) { + input.map(lp => (lp.label, scaler.transform(lp.features))).cache() + } else { + input.map(lp => (lp.label, lp.features)) + } } - } /** * TODO: For better convergence, in logistic regression, the intercepts should be computed diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala index 8a9f60eea3c01..4f482384f0f38 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala @@ -59,7 +59,7 @@ object LassoModel extends Loader[LassoModel] { val classNameV1_0 = "org.apache.spark.mllib.regression.LassoModel" (loadedClassName, version) match { case (className, "1.0") if className == classNameV1_0 => - val numFeatures = RegressionModel.getNumFeatures(metadata, classNameV1_0, path) + val numFeatures = RegressionModel.getNumFeatures(metadata) val data = GLMRegressionModel.SaveLoadV1_0.loadData(sc, path, classNameV1_0, numFeatures) new LassoModel(data.weights, data.intercept) case _ => throw new Exception( diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala index b212bb7d55fe2..9453c4f66c216 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala @@ -59,7 +59,7 @@ object LinearRegressionModel extends Loader[LinearRegressionModel] { val classNameV1_0 = "org.apache.spark.mllib.regression.LinearRegressionModel" (loadedClassName, version) match { case (className, "1.0") if className == classNameV1_0 => - val numFeatures = RegressionModel.getNumFeatures(metadata, classNameV1_0, path) + val numFeatures = RegressionModel.getNumFeatures(metadata) val data = GLMRegressionModel.SaveLoadV1_0.loadData(sc, path, classNameV1_0, numFeatures) new LinearRegressionModel(data.weights, data.intercept) case _ => throw new Exception( diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala index 843e59bdfbdd2..214ac4d0ed7dd 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala @@ -17,12 +17,12 @@ package org.apache.spark.mllib.regression +import org.json4s.{DefaultFormats, JValue} + import org.apache.spark.annotation.Experimental import org.apache.spark.api.java.JavaRDD import org.apache.spark.mllib.linalg.Vector -import org.apache.spark.mllib.util.Loader import org.apache.spark.rdd.RDD -import org.apache.spark.sql.{DataFrame, Row} @Experimental trait RegressionModel extends Serializable { @@ -55,16 +55,10 @@ private[mllib] object RegressionModel { /** * Helper method for loading GLM regression model metadata. - * - * @param modelClass String name for model class (used for error messages) * @return numFeatures */ - def getNumFeatures(metadata: DataFrame, modelClass: String, path: String): Int = { - metadata.select("numFeatures").take(1)(0) match { - case Row(nFeatures: Int) => nFeatures - case _ => throw new Exception(s"$modelClass unable to load" + - s" numFeatures from metadata: ${Loader.metadataPath(path)}") - } + def getNumFeatures(metadata: JValue): Int = { + implicit val formats = DefaultFormats + (metadata \ "numFeatures").extract[Int] } - } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala index 8700846f8c679..2f15d1036ef05 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala @@ -60,7 +60,7 @@ object RidgeRegressionModel extends Loader[RidgeRegressionModel] { val classNameV1_0 = "org.apache.spark.mllib.regression.RidgeRegressionModel" (loadedClassName, version) match { case (className, "1.0") if className == classNameV1_0 => - val numFeatures = RegressionModel.getNumFeatures(metadata, classNameV1_0, path) + val numFeatures = RegressionModel.getNumFeatures(metadata) val data = GLMRegressionModel.SaveLoadV1_0.loadData(sc, path, classNameV1_0, numFeatures) new RidgeRegressionModel(data.weights, data.intercept) case _ => throw new Exception( diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala index ce95c063db970..cea8f3f47307b 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala @@ -60,7 +60,7 @@ abstract class StreamingLinearAlgorithm[ A <: GeneralizedLinearAlgorithm[M]] extends Logging { /** The model to be updated and used for prediction. */ - protected var model: Option[M] = None + protected var model: Option[M] /** The algorithm to use for updating. */ protected val algorithm: A @@ -114,7 +114,7 @@ abstract class StreamingLinearAlgorithm[ if (model.isEmpty) { throw new IllegalArgumentException("Model must be initialized before starting prediction.") } - data.map(model.get.predict) + data.map{x => model.get.predict(x)} } /** Java-friendly version of `predictOn`. */ @@ -132,7 +132,7 @@ abstract class StreamingLinearAlgorithm[ if (model.isEmpty) { throw new IllegalArgumentException("Model must be initialized before starting prediction") } - data.mapValues(model.get.predict) + data.mapValues{x => model.get.predict(x)} } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala index e5e6301127a28..a49153bf73c0d 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala @@ -59,6 +59,8 @@ class StreamingLinearRegressionWithSGD private[mllib] ( val algorithm = new LinearRegressionWithSGD(stepSize, numIterations, miniBatchFraction) + protected var model: Option[LinearRegressionModel] = None + /** Set the step size for gradient descent. Default: 0.1. */ def setStepSize(stepSize: Double): this.type = { this.algorithm.optimizer.setStepSize(stepSize) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/impl/GLMRegressionModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/impl/GLMRegressionModel.scala index 838100e949ec2..b55944f74f623 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/impl/GLMRegressionModel.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/impl/GLMRegressionModel.scala @@ -17,6 +17,9 @@ package org.apache.spark.mllib.regression.impl +import org.json4s.JsonDSL._ +import org.json4s.jackson.JsonMethods._ + import org.apache.spark.SparkContext import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.util.Loader @@ -29,7 +32,7 @@ private[regression] object GLMRegressionModel { object SaveLoadV1_0 { - def thisFormatVersion = "1.0" + def thisFormatVersion: String = "1.0" /** Model data for model import/export */ case class Data(weights: Vector, intercept: Double) @@ -48,14 +51,14 @@ private[regression] object GLMRegressionModel { import sqlContext.implicits._ // Create JSON metadata. - val metadataRDD = - sc.parallelize(Seq((modelClass, thisFormatVersion, weights.size)), 1) - .toDataFrame("class", "version", "numFeatures") - metadataRDD.toJSON.saveAsTextFile(Loader.metadataPath(path)) + val metadata = compact(render( + ("class" -> modelClass) ~ ("version" -> thisFormatVersion) ~ + ("numFeatures" -> weights.size))) + sc.parallelize(Seq(metadata), 1).saveAsTextFile(Loader.metadataPath(path)) // Create Parquet data. val data = Data(weights, intercept) - val dataRDD: DataFrame = sc.parallelize(Seq(data), 1) + val dataRDD: DataFrame = sc.parallelize(Seq(data), 1).toDF() // TODO: repartition with 1 partition after SPARK-5532 gets fixed dataRDD.saveAsParquetFile(Loader.dataPath(path)) } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/KernelDensity.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/KernelDensity.scala new file mode 100644 index 0000000000000..0deef11b4511a --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/KernelDensity.scala @@ -0,0 +1,71 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.mllib.stat + +import org.apache.spark.rdd.RDD + +private[stat] object KernelDensity { + /** + * Given a set of samples from a distribution, estimates its density at the set of given points. + * Uses a Gaussian kernel with the given standard deviation. + */ + def estimate(samples: RDD[Double], standardDeviation: Double, + evaluationPoints: Array[Double]): Array[Double] = { + if (standardDeviation <= 0.0) { + throw new IllegalArgumentException("Standard deviation must be positive") + } + + // This gets used in each Gaussian PDF computation, so compute it up front + val logStandardDeviationPlusHalfLog2Pi = + Math.log(standardDeviation) + 0.5 * Math.log(2 * Math.PI) + + val (points, count) = samples.aggregate((new Array[Double](evaluationPoints.length), 0))( + (x, y) => { + var i = 0 + while (i < evaluationPoints.length) { + x._1(i) += normPdf(y, standardDeviation, logStandardDeviationPlusHalfLog2Pi, + evaluationPoints(i)) + i += 1 + } + (x._1, i) + }, + (x, y) => { + var i = 0 + while (i < evaluationPoints.length) { + x._1(i) += y._1(i) + i += 1 + } + (x._1, x._2 + y._2) + }) + + var i = 0 + while (i < points.length) { + points(i) /= count + i += 1 + } + points + } + + private def normPdf(mean: Double, standardDeviation: Double, + logStandardDeviationPlusHalfLog2Pi: Double, x: Double): Double = { + val x0 = x - mean + val x1 = x0 / standardDeviation + val logDensity = -0.5 * x1 * x1 - logStandardDeviationPlusHalfLog2Pi + Math.exp(logDensity) + } +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala index b3fad0c52d655..32561620ac914 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala @@ -149,4 +149,18 @@ object Statistics { def chiSqTest(data: RDD[LabeledPoint]): Array[ChiSqTestResult] = { ChiSqTest.chiSquaredFeatures(data) } + + /** + * Given an empirical distribution defined by the input RDD of samples, estimate its density at + * each of the given evaluation points using a Gaussian kernel. + * + * @param samples The samples RDD used to define the empirical distribution. + * @param standardDeviation The standard deviation of the kernel Gaussians. + * @param evaluationPoints The points at which to estimate densities. + * @return An array the same size as evaluationPoints with the density at each point. + */ + def kernelDensity(samples: RDD[Double], standardDeviation: Double, + evaluationPoints: Iterable[Double]): Array[Double] = { + KernelDensity.estimate(samples, standardDeviation, evaluationPoints.toArray) + } } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala index fd186b5ee6f72..cd6add9d60b0d 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala @@ -17,7 +17,7 @@ package org.apache.spark.mllib.stat.distribution -import breeze.linalg.{DenseVector => DBV, DenseMatrix => DBM, diag, max, eigSym} +import breeze.linalg.{DenseVector => DBV, DenseMatrix => DBM, diag, max, eigSym, Vector => BV} import org.apache.spark.annotation.DeveloperApi; import org.apache.spark.mllib.linalg.{Vectors, Vector, Matrices, Matrix} @@ -62,21 +62,21 @@ class MultivariateGaussian ( /** Returns density of this multivariate Gaussian at given point, x */ def pdf(x: Vector): Double = { - pdf(x.toBreeze.toDenseVector) + pdf(x.toBreeze) } /** Returns the log-density of this multivariate Gaussian at given point, x */ def logpdf(x: Vector): Double = { - logpdf(x.toBreeze.toDenseVector) + logpdf(x.toBreeze) } /** Returns density of this multivariate Gaussian at given point, x */ - private[mllib] def pdf(x: DBV[Double]): Double = { + private[mllib] def pdf(x: BV[Double]): Double = { math.exp(logpdf(x)) } /** Returns the log-density of this multivariate Gaussian at given point, x */ - private[mllib] def logpdf(x: DBV[Double]): Double = { + private[mllib] def logpdf(x: BV[Double]): Double = { val delta = x - breezeMu val v = rootSigmaInv * delta u + v.t * v * -0.5 diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala index b3e8ed9af8c51..dfe3a0b6913ef 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala @@ -19,12 +19,11 @@ package org.apache.spark.mllib.tree import scala.collection.JavaConverters._ import scala.collection.mutable -import scala.collection.mutable.ArrayBuffer - +import scala.collection.mutable.ArrayBuilder +import org.apache.spark.Logging import org.apache.spark.annotation.Experimental import org.apache.spark.api.java.JavaRDD -import org.apache.spark.Logging import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.tree.RandomForest.NodeIndexInfo import org.apache.spark.mllib.tree.configuration.Strategy @@ -32,13 +31,10 @@ import org.apache.spark.mllib.tree.configuration.Algo._ import org.apache.spark.mllib.tree.configuration.FeatureType._ import org.apache.spark.mllib.tree.configuration.QuantileStrategy._ import org.apache.spark.mllib.tree.impl._ -import org.apache.spark.mllib.tree.impurity.{Impurities, Impurity} import org.apache.spark.mllib.tree.impurity._ import org.apache.spark.mllib.tree.model._ import org.apache.spark.rdd.RDD import org.apache.spark.util.random.XORShiftRandom -import org.apache.spark.SparkContext._ - /** * :: Experimental :: @@ -331,14 +327,14 @@ object DecisionTree extends Serializable with Logging { * @param agg Array storing aggregate calculation, with a set of sufficient statistics for * each (feature, bin). * @param treePoint Data point being aggregated. - * @param bins possible bins for all features, indexed (numFeatures)(numBins) + * @param splits possible splits indexed (numFeatures)(numSplits) * @param unorderedFeatures Set of indices of unordered features. * @param instanceWeight Weight (importance) of instance in dataset. */ private def mixedBinSeqOp( agg: DTStatsAggregator, treePoint: TreePoint, - bins: Array[Array[Bin]], + splits: Array[Array[Split]], unorderedFeatures: Set[Int], instanceWeight: Double, featuresForNode: Option[Array[Int]]): Unit = { @@ -366,7 +362,7 @@ object DecisionTree extends Serializable with Logging { val numSplits = agg.metadata.numSplits(featureIndex) var splitIndex = 0 while (splitIndex < numSplits) { - if (bins(featureIndex)(splitIndex).highSplit.categories.contains(featureValue)) { + if (splits(featureIndex)(splitIndex).categories.contains(featureValue)) { agg.featureUpdate(leftNodeFeatureOffset, splitIndex, treePoint.label, instanceWeight) } else { @@ -510,8 +506,8 @@ object DecisionTree extends Serializable with Logging { if (metadata.unorderedFeatures.isEmpty) { orderedBinSeqOp(agg(aggNodeIndex), baggedPoint.datum, instanceWeight, featuresForNode) } else { - mixedBinSeqOp(agg(aggNodeIndex), baggedPoint.datum, bins, metadata.unorderedFeatures, - instanceWeight, featuresForNode) + mixedBinSeqOp(agg(aggNodeIndex), baggedPoint.datum, splits, + metadata.unorderedFeatures, instanceWeight, featuresForNode) } } } @@ -1028,35 +1024,15 @@ object DecisionTree extends Serializable with Logging { // Categorical feature val featureArity = metadata.featureArity(featureIndex) if (metadata.isUnordered(featureIndex)) { - // TODO: The second half of the bins are unused. Actually, we could just use - // splits and not build bins for unordered features. That should be part of - // a later PR since it will require changing other code (using splits instead - // of bins in a few places). // Unordered features - // 2^(maxFeatureValue - 1) - 1 combinations + // 2^(maxFeatureValue - 1) - 1 combinations splits(featureIndex) = new Array[Split](numSplits) - bins(featureIndex) = new Array[Bin](numBins) var splitIndex = 0 while (splitIndex < numSplits) { val categories: List[Double] = extractMultiClassCategories(splitIndex + 1, featureArity) splits(featureIndex)(splitIndex) = new Split(featureIndex, Double.MinValue, Categorical, categories) - bins(featureIndex)(splitIndex) = { - if (splitIndex == 0) { - new Bin( - new DummyCategoricalSplit(featureIndex, Categorical), - splits(featureIndex)(0), - Categorical, - Double.MinValue) - } else { - new Bin( - splits(featureIndex)(splitIndex - 1), - splits(featureIndex)(splitIndex), - Categorical, - Double.MinValue) - } - } splitIndex += 1 } } else { @@ -1064,8 +1040,11 @@ object DecisionTree extends Serializable with Logging { // Bins correspond to feature values, so we do not need to compute splits or bins // beforehand. Splits are constructed as needed during training. splits(featureIndex) = new Array[Split](0) - bins(featureIndex) = new Array[Bin](0) } + // For ordered features, bins correspond to feature values. + // For unordered categorical features, there is no need to construct the bins. + // since there is a one-to-one correspondence between the splits and the bins. + bins(featureIndex) = new Array[Bin](0) } featureIndex += 1 } @@ -1140,7 +1119,7 @@ object DecisionTree extends Serializable with Logging { logDebug("stride = " + stride) // iterate `valueCount` to find splits - val splits = new ArrayBuffer[Double] + val splitsBuilder = ArrayBuilder.make[Double] var index = 1 // currentCount: sum of counts of values that have been visited var currentCount = valueCounts(0)._2 @@ -1158,17 +1137,20 @@ object DecisionTree extends Serializable with Logging { // makes the gap between currentCount and targetCount smaller, // previous value is a split threshold. if (previousGap < currentGap) { - splits.append(valueCounts(index - 1)._1) + splitsBuilder += valueCounts(index - 1)._1 targetCount += stride } index += 1 } - splits.toArray + splitsBuilder.result() } } - assert(splits.length > 0) + // TODO: Do not fail; just ignore the useless feature. + assert(splits.length > 0, + s"DecisionTree could not handle feature $featureIndex since it had only 1 unique value." + + " Please remove this feature and then try again.") // set number of splits accordingly metadata.setNumSplits(featureIndex, splits.length) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala index 61f6b1313f82e..0e31c7ed58df8 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala @@ -60,11 +60,12 @@ class GradientBoostedTrees(private val boostingStrategy: BoostingStrategy) def run(input: RDD[LabeledPoint]): GradientBoostedTreesModel = { val algo = boostingStrategy.treeStrategy.algo algo match { - case Regression => GradientBoostedTrees.boost(input, boostingStrategy) + case Regression => GradientBoostedTrees.boost(input, input, boostingStrategy, validate=false) case Classification => // Map labels to -1, +1 so binary classification can be treated as regression. val remappedInput = input.map(x => new LabeledPoint((x.label * 2) - 1, x.features)) - GradientBoostedTrees.boost(remappedInput, boostingStrategy) + GradientBoostedTrees.boost(remappedInput, + remappedInput, boostingStrategy, validate=false) case _ => throw new IllegalArgumentException(s"$algo is not supported by the gradient boosting.") } @@ -76,8 +77,46 @@ class GradientBoostedTrees(private val boostingStrategy: BoostingStrategy) def run(input: JavaRDD[LabeledPoint]): GradientBoostedTreesModel = { run(input.rdd) } -} + /** + * Method to validate a gradient boosting model + * @param input Training dataset: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]]. + * @param validationInput Validation dataset. + * This dataset should be different from the training dataset, + * but it should follow the same distribution. + * E.g., these two datasets could be created from an original dataset + * by using [[org.apache.spark.rdd.RDD.randomSplit()]] + * @return a gradient boosted trees model that can be used for prediction + */ + def runWithValidation( + input: RDD[LabeledPoint], + validationInput: RDD[LabeledPoint]): GradientBoostedTreesModel = { + val algo = boostingStrategy.treeStrategy.algo + algo match { + case Regression => GradientBoostedTrees.boost( + input, validationInput, boostingStrategy, validate=true) + case Classification => + // Map labels to -1, +1 so binary classification can be treated as regression. + val remappedInput = input.map( + x => new LabeledPoint((x.label * 2) - 1, x.features)) + val remappedValidationInput = validationInput.map( + x => new LabeledPoint((x.label * 2) - 1, x.features)) + GradientBoostedTrees.boost(remappedInput, remappedValidationInput, boostingStrategy, + validate=true) + case _ => + throw new IllegalArgumentException(s"$algo is not supported by the gradient boosting.") + } + } + + /** + * Java-friendly API for [[org.apache.spark.mllib.tree.GradientBoostedTrees!#runWithValidation]]. + */ + def runWithValidation( + input: JavaRDD[LabeledPoint], + validationInput: JavaRDD[LabeledPoint]): GradientBoostedTreesModel = { + runWithValidation(input.rdd, validationInput.rdd) + } +} object GradientBoostedTrees extends Logging { @@ -108,13 +147,16 @@ object GradientBoostedTrees extends Logging { /** * Internal method for performing regression using trees as base learners. * @param input training dataset + * @param validationInput validation dataset, ignored if validate is set to false. * @param boostingStrategy boosting parameters + * @param validate whether or not to use the validation dataset. * @return a gradient boosted trees model that can be used for prediction */ private def boost( input: RDD[LabeledPoint], - boostingStrategy: BoostingStrategy): GradientBoostedTreesModel = { - + validationInput: RDD[LabeledPoint], + boostingStrategy: BoostingStrategy, + validate: Boolean): GradientBoostedTreesModel = { val timer = new TimeTracker() timer.start("total") timer.start("init") @@ -129,6 +171,7 @@ object GradientBoostedTrees extends Logging { val learningRate = boostingStrategy.learningRate // Prepare strategy for individual trees, which use regression with variance impurity. val treeStrategy = boostingStrategy.treeStrategy.copy + val validationTol = boostingStrategy.validationTol treeStrategy.algo = Regression treeStrategy.impurity = Variance treeStrategy.assertValid() @@ -148,16 +191,26 @@ object GradientBoostedTrees extends Logging { // Initialize tree timer.start("building tree 0") val firstTreeModel = new DecisionTree(treeStrategy).run(data) + val firstTreeWeight = 1.0 baseLearners(0) = firstTreeModel - baseLearnerWeights(0) = 1.0 - val startingModel = new GradientBoostedTreesModel(Regression, Array(firstTreeModel), Array(1.0)) - logDebug("error of gbt = " + loss.computeError(startingModel, input)) + baseLearnerWeights(0) = firstTreeWeight + + var predError: RDD[(Double, Double)] = GradientBoostedTreesModel. + computeInitialPredictionAndError(input, firstTreeWeight, firstTreeModel, loss) + logDebug("error of gbt = " + predError.values.mean()) + // Note: A model of type regression is used since we require raw prediction timer.stop("building tree 0") - // psuedo-residual for second iteration - data = input.map(point => LabeledPoint(loss.gradient(startingModel, point), - point.features)) + var validatePredError: RDD[(Double, Double)] = GradientBoostedTreesModel. + computeInitialPredictionAndError(validationInput, firstTreeWeight, firstTreeModel, loss) + var bestValidateError = if (validate) validatePredError.values.mean() else 0.0 + var bestM = 1 + + // pseudo-residual for second iteration + data = predError.zip(input).map { case ((pred, _), point) => + LabeledPoint(-loss.gradient(pred, point.label), point.features) + } var m = 1 while (m < numIterations) { @@ -175,11 +228,36 @@ object GradientBoostedTrees extends Logging { baseLearnerWeights(m) = learningRate // Note: A model of type regression is used since we require raw prediction val partialModel = new GradientBoostedTreesModel( - Regression, baseLearners.slice(0, m + 1), baseLearnerWeights.slice(0, m + 1)) - logDebug("error of gbt = " + loss.computeError(partialModel, input)) + Regression, baseLearners.slice(0, m + 1), + baseLearnerWeights.slice(0, m + 1)) + + predError = GradientBoostedTreesModel.updatePredictionError( + input, predError, baseLearnerWeights(m), baseLearners(m), loss) + logDebug("error of gbt = " + predError.values.mean()) + + if (validate) { + // Stop training early if + // 1. Reduction in error is less than the validationTol or + // 2. If the error increases, that is if the model is overfit. + // We want the model returned corresponding to the best validation error. + + validatePredError = GradientBoostedTreesModel.updatePredictionError( + validationInput, validatePredError, baseLearnerWeights(m), baseLearners(m), loss) + val currentValidateError = validatePredError.values.mean() + if (bestValidateError - currentValidateError < validationTol) { + return new GradientBoostedTreesModel( + boostingStrategy.treeStrategy.algo, + baseLearners.slice(0, bestM), + baseLearnerWeights.slice(0, bestM)) + } else if (currentValidateError < bestValidateError) { + bestValidateError = currentValidateError + bestM = m + 1 + } + } // Update data with pseudo-residuals - data = input.map(point => LabeledPoint(-loss.gradient(partialModel, point), - point.features)) + data = predError.zip(input).map { case ((pred, _), point) => + LabeledPoint(-loss.gradient(pred, point.label), point.features) + } m += 1 } @@ -187,8 +265,15 @@ object GradientBoostedTrees extends Logging { logInfo("Internal timing for DecisionTree:") logInfo(s"$timer") - - new GradientBoostedTreesModel( - boostingStrategy.treeStrategy.algo, baseLearners, baseLearnerWeights) + if (validate) { + new GradientBoostedTreesModel( + boostingStrategy.treeStrategy.algo, + baseLearners.slice(0, bestM), + baseLearnerWeights.slice(0, bestM)) + } else { + new GradientBoostedTreesModel( + boostingStrategy.treeStrategy.algo, baseLearners, baseLearnerWeights) + } } + } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala index db01f2e229e5a..055e60c7d9c95 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala @@ -249,7 +249,7 @@ private class RandomForest ( nodeIdCache.get.deleteAllCheckpoints() } catch { case e:IOException => - logWarning(s"delete all chackpoints failed. Error reason: ${e.getMessage}") + logWarning(s"delete all checkpoints failed. Error reason: ${e.getMessage}") } } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala index ed8e6a796f8c4..2d6b01524ff3d 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala @@ -34,6 +34,9 @@ import org.apache.spark.mllib.tree.loss.{LogLoss, SquaredError, Loss} * weak hypotheses used in the final model. * @param learningRate Learning rate for shrinking the contribution of each estimator. The * learning rate should be between in the interval (0, 1] + * @param validationTol Useful when runWithValidation is used. If the error rate on the + * validation input between two iterations is less than the validationTol + * then stop. Ignored when [[run]] is used. */ @Experimental case class BoostingStrategy( @@ -42,7 +45,8 @@ case class BoostingStrategy( @BeanProperty var loss: Loss, // Optional boosting parameters @BeanProperty var numIterations: Int = 100, - @BeanProperty var learningRate: Double = 0.1) extends Serializable { + @BeanProperty var learningRate: Double = 0.1, + @BeanProperty var validationTol: Double = 1e-5) extends Serializable { /** * Check validity of parameters. @@ -85,14 +89,14 @@ object BoostingStrategy { * @return Configuration for boosting algorithm */ def defaultParams(algo: Algo): BoostingStrategy = { - val treeStragtegy = Strategy.defaultStategy(algo) - treeStragtegy.maxDepth = 3 + val treeStrategy = Strategy.defaultStategy(algo) + treeStrategy.maxDepth = 3 algo match { case Algo.Classification => - treeStragtegy.numClasses = 2 - new BoostingStrategy(treeStragtegy, LogLoss) + treeStrategy.numClasses = 2 + new BoostingStrategy(treeStrategy, LogLoss) case Algo.Regression => - new BoostingStrategy(treeStragtegy, SquaredError) + new BoostingStrategy(treeStrategy, SquaredError) case _ => throw new IllegalArgumentException(s"$algo is not supported by boosting.") } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala index 8d5c36da32bdb..ada227c200a79 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala @@ -83,10 +83,13 @@ class Strategy ( @BeanProperty var useNodeIdCache: Boolean = false, @BeanProperty var checkpointInterval: Int = 10) extends Serializable { - def isMulticlassClassification = + def isMulticlassClassification: Boolean = { algo == Classification && numClasses > 2 - def isMulticlassWithCategoricalFeatures - = isMulticlassClassification && (categoricalFeaturesInfo.size > 0) + } + + def isMulticlassWithCategoricalFeatures: Boolean = { + isMulticlassClassification && (categoricalFeaturesInfo.size > 0) + } /** * Java-friendly constructor for [[org.apache.spark.mllib.tree.configuration.Strategy]] diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/TreePoint.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/TreePoint.scala index 35e361ae309cc..50b292e71b067 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/TreePoint.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/TreePoint.scala @@ -55,17 +55,15 @@ private[tree] object TreePoint { input: RDD[LabeledPoint], bins: Array[Array[Bin]], metadata: DecisionTreeMetadata): RDD[TreePoint] = { - // Construct arrays for featureArity and isUnordered for efficiency in the inner loop. + // Construct arrays for featureArity for efficiency in the inner loop. val featureArity: Array[Int] = new Array[Int](metadata.numFeatures) - val isUnordered: Array[Boolean] = new Array[Boolean](metadata.numFeatures) var featureIndex = 0 while (featureIndex < metadata.numFeatures) { featureArity(featureIndex) = metadata.featureArity.getOrElse(featureIndex, 0) - isUnordered(featureIndex) = metadata.isUnordered(featureIndex) featureIndex += 1 } input.map { x => - TreePoint.labeledPointToTreePoint(x, bins, featureArity, isUnordered) + TreePoint.labeledPointToTreePoint(x, bins, featureArity) } } @@ -74,19 +72,17 @@ private[tree] object TreePoint { * @param bins Bins for features, of size (numFeatures, numBins). * @param featureArity Array indexed by feature, with value 0 for continuous and numCategories * for categorical features. - * @param isUnordered Array index by feature, with value true for unordered categorical features. */ private def labeledPointToTreePoint( labeledPoint: LabeledPoint, bins: Array[Array[Bin]], - featureArity: Array[Int], - isUnordered: Array[Boolean]): TreePoint = { + featureArity: Array[Int]): TreePoint = { val numFeatures = labeledPoint.features.size val arr = new Array[Int](numFeatures) var featureIndex = 0 while (featureIndex < numFeatures) { arr(featureIndex) = findBin(featureIndex, labeledPoint, featureArity(featureIndex), - isUnordered(featureIndex), bins) + bins) featureIndex += 1 } new TreePoint(labeledPoint.label, arr) @@ -96,14 +92,12 @@ private[tree] object TreePoint { * Find bin for one (labeledPoint, feature). * * @param featureArity 0 for continuous features; number of categories for categorical features. - * @param isUnorderedFeature (only applies if feature is categorical) * @param bins Bins for features, of size (numFeatures, numBins). */ private def findBin( featureIndex: Int, labeledPoint: LabeledPoint, featureArity: Int, - isUnorderedFeature: Boolean, bins: Array[Array[Bin]]): Int = { /** diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala index b7950e00786ab..5ac10f3fd32dd 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala @@ -71,7 +71,7 @@ object Entropy extends Impurity { * Get this impurity instance. * This is useful for passing impurity parameters to a Strategy in Java. */ - def instance = this + def instance: this.type = this } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala index c946db9c0d1c8..19d318203c344 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala @@ -67,7 +67,7 @@ object Gini extends Impurity { * Get this impurity instance. * This is useful for passing impurity parameters to a Strategy in Java. */ - def instance = this + def instance: this.type = this } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala index df9eafa5da16a..7104a7fa4dd4c 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala @@ -58,7 +58,7 @@ object Variance extends Impurity { * Get this impurity instance. * This is useful for passing impurity parameters to a Strategy in Java. */ - def instance = this + def instance: this.type = this } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/AbsoluteError.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/AbsoluteError.scala index d1bde15e6b150..2bdef73c4a8f1 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/AbsoluteError.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/AbsoluteError.scala @@ -20,7 +20,7 @@ package org.apache.spark.mllib.tree.loss import org.apache.spark.annotation.DeveloperApi import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.tree.model.TreeEnsembleModel -import org.apache.spark.rdd.RDD + /** * :: DeveloperApi :: @@ -37,28 +37,16 @@ object AbsoluteError extends Loss { * Method to calculate the gradients for the gradient boosting calculation for least * absolute error calculation. * The gradient with respect to F(x) is: sign(F(x) - y) - * @param model Ensemble model - * @param point Instance of the training dataset + * @param prediction Predicted label. + * @param label True label. * @return Loss gradient */ - override def gradient( - model: TreeEnsembleModel, - point: LabeledPoint): Double = { - if ((point.label - model.predict(point.features)) < 0) 1.0 else -1.0 + override def gradient(prediction: Double, label: Double): Double = { + if (label - prediction < 0) 1.0 else -1.0 } - /** - * Method to calculate loss of the base learner for the gradient boosting calculation. - * Note: This method is not used by the gradient boosting algorithm but is useful for debugging - * purposes. - * @param model Ensemble model - * @param data Training dataset: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]]. - * @return Mean absolute error of model on data - */ - override def computeError(model: TreeEnsembleModel, data: RDD[LabeledPoint]): Double = { - data.map { y => - val err = model.predict(y.features) - y.label - math.abs(err) - }.mean() + override private[mllib] def computeError(prediction: Double, label: Double): Double = { + val err = label - prediction + math.abs(err) } } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/LogLoss.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/LogLoss.scala index 55213e695638c..778c24526de70 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/LogLoss.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/LogLoss.scala @@ -21,7 +21,7 @@ import org.apache.spark.annotation.DeveloperApi import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.tree.model.TreeEnsembleModel import org.apache.spark.mllib.util.MLUtils -import org.apache.spark.rdd.RDD + /** * :: DeveloperApi :: @@ -39,31 +39,17 @@ object LogLoss extends Loss { * Method to calculate the loss gradients for the gradient boosting calculation for binary * classification * The gradient with respect to F(x) is: - 4 y / (1 + exp(2 y F(x))) - * @param model Ensemble model - * @param point Instance of the training dataset + * @param prediction Predicted label. + * @param label True label. * @return Loss gradient */ - override def gradient( - model: TreeEnsembleModel, - point: LabeledPoint): Double = { - val prediction = model.predict(point.features) - - 4.0 * point.label / (1.0 + math.exp(2.0 * point.label * prediction)) + override def gradient(prediction: Double, label: Double): Double = { + - 4.0 * label / (1.0 + math.exp(2.0 * label * prediction)) } - /** - * Method to calculate loss of the base learner for the gradient boosting calculation. - * Note: This method is not used by the gradient boosting algorithm but is useful for debugging - * purposes. - * @param model Ensemble model - * @param data Training dataset: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]]. - * @return Mean log loss of model on data - */ - override def computeError(model: TreeEnsembleModel, data: RDD[LabeledPoint]): Double = { - data.map { case point => - val prediction = model.predict(point.features) - val margin = 2.0 * point.label * prediction - // The following is equivalent to 2.0 * log(1 + exp(-margin)) but more numerically stable. - 2.0 * MLUtils.log1pExp(-margin) - }.mean() + override private[mllib] def computeError(prediction: Double, label: Double): Double = { + val margin = 2.0 * label * prediction + // The following is equivalent to 2.0 * log(1 + exp(-margin)) but more numerically stable. + 2.0 * MLUtils.log1pExp(-margin) } } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Loss.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Loss.scala index e1169d9f66ea4..64ffccbce073f 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Loss.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Loss.scala @@ -22,6 +22,7 @@ import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.tree.model.TreeEnsembleModel import org.apache.spark.rdd.RDD + /** * :: DeveloperApi :: * Trait for adding "pluggable" loss functions for the gradient boosting algorithm. @@ -31,13 +32,11 @@ trait Loss extends Serializable { /** * Method to calculate the gradients for the gradient boosting calculation. - * @param model Model of the weak learner. - * @param point Instance of the training dataset. + * @param prediction Predicted feature + * @param label true label. * @return Loss gradient. */ - def gradient( - model: TreeEnsembleModel, - point: LabeledPoint): Double + def gradient(prediction: Double, label: Double): Double /** * Method to calculate error of the base learner for the gradient boosting calculation. @@ -47,6 +46,17 @@ trait Loss extends Serializable { * @param data Training dataset: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]]. * @return Measure of model error on data */ - def computeError(model: TreeEnsembleModel, data: RDD[LabeledPoint]): Double + def computeError(model: TreeEnsembleModel, data: RDD[LabeledPoint]): Double = { + data.map(point => computeError(model.predict(point.features), point.label)).mean() + } + /** + * Method to calculate loss when the predictions are already known. + * Note: This method is used in the method evaluateEachIteration to avoid recomputing the + * predicted values from previously fit trees. + * @param prediction Predicted label. + * @param label True label. + * @return Measure of model error on datapoint. + */ + private[mllib] def computeError(prediction: Double, label: Double): Double } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/SquaredError.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/SquaredError.scala index 50ecaa2f86f35..a5582d3ef3324 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/SquaredError.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/SquaredError.scala @@ -20,7 +20,7 @@ package org.apache.spark.mllib.tree.loss import org.apache.spark.annotation.DeveloperApi import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.tree.model.TreeEnsembleModel -import org.apache.spark.rdd.RDD + /** * :: DeveloperApi :: @@ -37,28 +37,16 @@ object SquaredError extends Loss { * Method to calculate the gradients for the gradient boosting calculation for least * squares error calculation. * The gradient with respect to F(x) is: - 2 (y - F(x)) - * @param model Ensemble model - * @param point Instance of the training dataset + * @param prediction Predicted label. + * @param label True label. * @return Loss gradient */ - override def gradient( - model: TreeEnsembleModel, - point: LabeledPoint): Double = { - 2.0 * (model.predict(point.features) - point.label) + override def gradient(prediction: Double, label: Double): Double = { + 2.0 * (prediction - label) } - /** - * Method to calculate loss of the base learner for the gradient boosting calculation. - * Note: This method is not used by the gradient boosting algorithm but is useful for debugging - * purposes. - * @param model Ensemble model - * @param data Training dataset: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]]. - * @return Mean squared error of model on data - */ - override def computeError(model: TreeEnsembleModel, data: RDD[LabeledPoint]): Double = { - data.map { y => - val err = model.predict(y.features) - y.label - err * err - }.mean() + override private[mllib] def computeError(prediction: Double, label: Double): Double = { + val err = prediction - label + err * err } } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala index a25e625a4017a..331af428533de 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala @@ -17,11 +17,22 @@ package org.apache.spark.mllib.tree.model +import scala.collection.mutable + +import org.json4s._ +import org.json4s.JsonDSL._ +import org.json4s.jackson.JsonMethods._ + +import org.apache.spark.{Logging, SparkContext} import org.apache.spark.annotation.Experimental import org.apache.spark.api.java.JavaRDD import org.apache.spark.mllib.linalg.Vector +import org.apache.spark.mllib.tree.configuration.{Algo, FeatureType} import org.apache.spark.mllib.tree.configuration.Algo._ +import org.apache.spark.mllib.util.{Loader, Saveable} import org.apache.spark.rdd.RDD +import org.apache.spark.sql.{DataFrame, Row, SQLContext} +import org.apache.spark.util.Utils /** * :: Experimental :: @@ -31,7 +42,7 @@ import org.apache.spark.rdd.RDD * @param algo algorithm type -- classification or regression */ @Experimental -class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable { +class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable with Saveable { /** * Predict values for a single data point using the model trained. @@ -98,4 +109,207 @@ class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable header + topNode.subtreeToString(2) } + override def save(sc: SparkContext, path: String): Unit = { + DecisionTreeModel.SaveLoadV1_0.save(sc, path, this) + } + + override protected def formatVersion: String = DecisionTreeModel.formatVersion +} + +object DecisionTreeModel extends Loader[DecisionTreeModel] with Logging { + + private[spark] def formatVersion: String = "1.0" + + private[tree] object SaveLoadV1_0 { + + def thisFormatVersion: String = "1.0" + + // Hard-code class name string in case it changes in the future + def thisClassName: String = "org.apache.spark.mllib.tree.DecisionTreeModel" + + case class PredictData(predict: Double, prob: Double) { + def toPredict: Predict = new Predict(predict, prob) + } + + object PredictData { + def apply(p: Predict): PredictData = PredictData(p.predict, p.prob) + + def apply(r: Row): PredictData = PredictData(r.getDouble(0), r.getDouble(1)) + } + + case class SplitData( + feature: Int, + threshold: Double, + featureType: Int, + categories: Seq[Double]) { // TODO: Change to List once SPARK-3365 is fixed + def toSplit: Split = { + new Split(feature, threshold, FeatureType(featureType), categories.toList) + } + } + + object SplitData { + def apply(s: Split): SplitData = { + SplitData(s.feature, s.threshold, s.featureType.id, s.categories) + } + + def apply(r: Row): SplitData = { + SplitData(r.getInt(0), r.getDouble(1), r.getInt(2), r.getAs[Seq[Double]](3)) + } + } + + /** Model data for model import/export */ + case class NodeData( + treeId: Int, + nodeId: Int, + predict: PredictData, + impurity: Double, + isLeaf: Boolean, + split: Option[SplitData], + leftNodeId: Option[Int], + rightNodeId: Option[Int], + infoGain: Option[Double]) + + object NodeData { + def apply(treeId: Int, n: Node): NodeData = { + NodeData(treeId, n.id, PredictData(n.predict), n.impurity, n.isLeaf, + n.split.map(SplitData.apply), n.leftNode.map(_.id), n.rightNode.map(_.id), + n.stats.map(_.gain)) + } + + def apply(r: Row): NodeData = { + val split = if (r.isNullAt(5)) None else Some(SplitData(r.getStruct(5))) + val leftNodeId = if (r.isNullAt(6)) None else Some(r.getInt(6)) + val rightNodeId = if (r.isNullAt(7)) None else Some(r.getInt(7)) + val infoGain = if (r.isNullAt(8)) None else Some(r.getDouble(8)) + NodeData(r.getInt(0), r.getInt(1), PredictData(r.getStruct(2)), r.getDouble(3), + r.getBoolean(4), split, leftNodeId, rightNodeId, infoGain) + } + } + + def save(sc: SparkContext, path: String, model: DecisionTreeModel): Unit = { + val sqlContext = new SQLContext(sc) + import sqlContext.implicits._ + + // SPARK-6120: We do a hacky check here so users understand why save() is failing + // when they run the ML guide example. + // TODO: Fix this issue for real. + val memThreshold = 768 + if (sc.isLocal) { + val driverMemory = sc.getConf.getOption("spark.driver.memory") + .orElse(Option(System.getenv("SPARK_DRIVER_MEMORY"))) + .map(Utils.memoryStringToMb) + .getOrElse(512) + if (driverMemory <= memThreshold) { + logWarning(s"$thisClassName.save() was called, but it may fail because of too little" + + s" driver memory (${driverMemory}m)." + + s" If failure occurs, try setting driver-memory ${memThreshold}m (or larger).") + } + } else { + if (sc.executorMemory <= memThreshold) { + logWarning(s"$thisClassName.save() was called, but it may fail because of too little" + + s" executor memory (${sc.executorMemory}m)." + + s" If failure occurs try setting executor-memory ${memThreshold}m (or larger).") + } + } + + // Create JSON metadata. + val metadata = compact(render( + ("class" -> thisClassName) ~ ("version" -> thisFormatVersion) ~ + ("algo" -> model.algo.toString) ~ ("numNodes" -> model.numNodes))) + sc.parallelize(Seq(metadata), 1).saveAsTextFile(Loader.metadataPath(path)) + + // Create Parquet data. + val nodes = model.topNode.subtreeIterator.toSeq + val dataRDD: DataFrame = sc.parallelize(nodes) + .map(NodeData.apply(0, _)) + .toDF() + dataRDD.saveAsParquetFile(Loader.dataPath(path)) + } + + def load(sc: SparkContext, path: String, algo: String, numNodes: Int): DecisionTreeModel = { + val datapath = Loader.dataPath(path) + val sqlContext = new SQLContext(sc) + // Load Parquet data. + val dataRDD = sqlContext.parquetFile(datapath) + // Check schema explicitly since erasure makes it hard to use match-case for checking. + Loader.checkSchema[NodeData](dataRDD.schema) + val nodes = dataRDD.map(NodeData.apply) + // Build node data into a tree. + val trees = constructTrees(nodes) + assert(trees.size == 1, + "Decision tree should contain exactly one tree but got ${trees.size} trees.") + val model = new DecisionTreeModel(trees(0), Algo.fromString(algo)) + assert(model.numNodes == numNodes, s"Unable to load DecisionTreeModel data from: $datapath." + + s" Expected $numNodes nodes but found ${model.numNodes}") + model + } + + def constructTrees(nodes: RDD[NodeData]): Array[Node] = { + val trees = nodes + .groupBy(_.treeId) + .mapValues(_.toArray) + .collect() + .map { case (treeId, data) => + (treeId, constructTree(data)) + }.sortBy(_._1) + val numTrees = trees.size + val treeIndices = trees.map(_._1).toSeq + assert(treeIndices == (0 until numTrees), + s"Tree indices must start from 0 and increment by 1, but we found $treeIndices.") + trees.map(_._2) + } + + /** + * Given a list of nodes from a tree, construct the tree. + * @param data array of all node data in a tree. + */ + def constructTree(data: Array[NodeData]): Node = { + val dataMap: Map[Int, NodeData] = data.map(n => n.nodeId -> n).toMap + assert(dataMap.contains(1), + s"DecisionTree missing root node (id = 1).") + constructNode(1, dataMap, mutable.Map.empty) + } + + /** + * Builds a node from the node data map and adds new nodes to the input nodes map. + */ + private def constructNode( + id: Int, + dataMap: Map[Int, NodeData], + nodes: mutable.Map[Int, Node]): Node = { + if (nodes.contains(id)) { + return nodes(id) + } + val data = dataMap(id) + val node = + if (data.isLeaf) { + Node(data.nodeId, data.predict.toPredict, data.impurity, data.isLeaf) + } else { + val leftNode = constructNode(data.leftNodeId.get, dataMap, nodes) + val rightNode = constructNode(data.rightNodeId.get, dataMap, nodes) + val stats = new InformationGainStats(data.infoGain.get, data.impurity, leftNode.impurity, + rightNode.impurity, leftNode.predict, rightNode.predict) + new Node(data.nodeId, data.predict.toPredict, data.impurity, data.isLeaf, + data.split.map(_.toSplit), Some(leftNode), Some(rightNode), Some(stats)) + } + nodes += node.id -> node + node + } + } + + override def load(sc: SparkContext, path: String): DecisionTreeModel = { + implicit val formats = DefaultFormats + val (loadedClassName, version, metadata) = Loader.loadMetadata(sc, path) + val algo = (metadata \ "algo").extract[String] + val numNodes = (metadata \ "numNodes").extract[Int] + val classNameV1_0 = SaveLoadV1_0.thisClassName + (loadedClassName, version) match { + case (className, "1.0") if className == classNameV1_0 => + SaveLoadV1_0.load(sc, path, algo, numNodes) + case _ => throw new Exception( + s"DecisionTreeModel.load did not recognize model with (className, format version):" + + s"($loadedClassName, $version). Supported:\n" + + s" ($classNameV1_0, 1.0)") + } + } } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala index 9a50ecb550c38..f209fdafd3653 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala @@ -38,21 +38,32 @@ class InformationGainStats( val leftPredict: Predict, val rightPredict: Predict) extends Serializable { - override def toString = { + override def toString: String = { "gain = %f, impurity = %f, left impurity = %f, right impurity = %f" .format(gain, impurity, leftImpurity, rightImpurity) } - override def equals(o: Any) = - o match { - case other: InformationGainStats => { - gain == other.gain && - impurity == other.impurity && - leftImpurity == other.leftImpurity && - rightImpurity == other.rightImpurity - } - case _ => false - } + override def equals(o: Any): Boolean = o match { + case other: InformationGainStats => + gain == other.gain && + impurity == other.impurity && + leftImpurity == other.leftImpurity && + rightImpurity == other.rightImpurity && + leftPredict == other.leftPredict && + rightPredict == other.rightPredict + + case _ => false + } + + override def hashCode: Int = { + com.google.common.base.Objects.hashCode( + gain: java.lang.Double, + impurity: java.lang.Double, + leftImpurity: java.lang.Double, + rightImpurity: java.lang.Double, + leftPredict, + rightPredict) + } } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala index 2179da8dbe03e..708ba04b567d3 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala @@ -50,8 +50,10 @@ class Node ( var rightNode: Option[Node], var stats: Option[InformationGainStats]) extends Serializable with Logging { - override def toString = "id = " + id + ", isLeaf = " + isLeaf + ", predict = " + predict + ", " + - "impurity = " + impurity + "split = " + split + ", stats = " + stats + override def toString: String = { + "id = " + id + ", isLeaf = " + isLeaf + ", predict = " + predict + ", " + + "impurity = " + impurity + "split = " + split + ", stats = " + stats + } /** * build the left node and right nodes if not leaf @@ -166,9 +168,14 @@ class Node ( } } + /** Returns an iterator that traverses (DFS, left to right) the subtree of this node. */ + private[tree] def subtreeIterator: Iterator[Node] = { + Iterator.single(this) ++ leftNode.map(_.subtreeIterator).getOrElse(Iterator.empty) ++ + rightNode.map(_.subtreeIterator).getOrElse(Iterator.empty) + } } -private[tree] object Node { +private[spark] object Node { /** * Return a node with the given node id (but nothing else set). diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala index 004838ee5ba0e..25990af7c6cf7 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala @@ -29,7 +29,18 @@ class Predict( val predict: Double, val prob: Double = 0.0) extends Serializable { - override def toString = { + override def toString: String = { "predict = %f, prob = %f".format(predict, prob) } + + override def equals(other: Any): Boolean = { + other match { + case p: Predict => predict == p.predict && prob == p.prob + case _ => false + } + } + + override def hashCode: Int = { + com.google.common.base.Objects.hashCode(predict: java.lang.Double, prob: java.lang.Double) + } } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala index b7a85f58544a3..fb35e70a8d077 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala @@ -38,9 +38,10 @@ case class Split( featureType: FeatureType, categories: List[Double]) { - override def toString = + override def toString: String = { "Feature = " + feature + ", threshold = " + threshold + ", featureType = " + featureType + ", categories = " + categories + } } /** diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala index 22997110de8dd..8341219bfa71c 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala @@ -20,13 +20,24 @@ package org.apache.spark.mllib.tree.model import scala.collection.mutable import com.github.fommil.netlib.BLAS.{getInstance => blas} +import org.json4s._ +import org.json4s.JsonDSL._ +import org.json4s.jackson.JsonMethods._ +import org.apache.spark.{Logging, SparkContext} import org.apache.spark.annotation.Experimental import org.apache.spark.api.java.JavaRDD import org.apache.spark.mllib.linalg.Vector +import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.mllib.tree.configuration.Algo import org.apache.spark.mllib.tree.configuration.Algo._ import org.apache.spark.mllib.tree.configuration.EnsembleCombiningStrategy._ +import org.apache.spark.mllib.tree.loss.Loss +import org.apache.spark.mllib.util.{Loader, Saveable} import org.apache.spark.rdd.RDD +import org.apache.spark.sql.SQLContext +import org.apache.spark.util.Utils + /** * :: Experimental :: @@ -37,10 +48,45 @@ import org.apache.spark.rdd.RDD */ @Experimental class RandomForestModel(override val algo: Algo, override val trees: Array[DecisionTreeModel]) - extends TreeEnsembleModel(algo, trees, Array.fill(trees.size)(1.0), - combiningStrategy = if (algo == Classification) Vote else Average) { + extends TreeEnsembleModel(algo, trees, Array.fill(trees.length)(1.0), + combiningStrategy = if (algo == Classification) Vote else Average) + with Saveable { require(trees.forall(_.algo == algo)) + + override def save(sc: SparkContext, path: String): Unit = { + TreeEnsembleModel.SaveLoadV1_0.save(sc, path, this, + RandomForestModel.SaveLoadV1_0.thisClassName) + } + + override protected def formatVersion: String = RandomForestModel.formatVersion +} + +object RandomForestModel extends Loader[RandomForestModel] { + + private[mllib] def formatVersion: String = TreeEnsembleModel.SaveLoadV1_0.thisFormatVersion + + override def load(sc: SparkContext, path: String): RandomForestModel = { + val (loadedClassName, version, jsonMetadata) = Loader.loadMetadata(sc, path) + val classNameV1_0 = SaveLoadV1_0.thisClassName + (loadedClassName, version) match { + case (className, "1.0") if className == classNameV1_0 => + val metadata = TreeEnsembleModel.SaveLoadV1_0.readMetadata(jsonMetadata) + assert(metadata.treeWeights.forall(_ == 1.0)) + val trees = + TreeEnsembleModel.SaveLoadV1_0.loadTrees(sc, path, metadata.treeAlgo) + new RandomForestModel(Algo.fromString(metadata.algo), trees) + case _ => throw new Exception(s"RandomForestModel.load did not recognize model" + + s" with (className, format version): ($loadedClassName, $version). Supported:\n" + + s" ($classNameV1_0, 1.0)") + } + } + + private object SaveLoadV1_0 { + // Hard-code class name string in case it changes in the future + def thisClassName: String = "org.apache.spark.mllib.tree.model.RandomForestModel" + } + } /** @@ -56,9 +102,138 @@ class GradientBoostedTreesModel( override val algo: Algo, override val trees: Array[DecisionTreeModel], override val treeWeights: Array[Double]) - extends TreeEnsembleModel(algo, trees, treeWeights, combiningStrategy = Sum) { + extends TreeEnsembleModel(algo, trees, treeWeights, combiningStrategy = Sum) + with Saveable { + + require(trees.length == treeWeights.length) + + override def save(sc: SparkContext, path: String): Unit = { + TreeEnsembleModel.SaveLoadV1_0.save(sc, path, this, + GradientBoostedTreesModel.SaveLoadV1_0.thisClassName) + } + + /** + * Method to compute error or loss for every iteration of gradient boosting. + * @param data RDD of [[org.apache.spark.mllib.regression.LabeledPoint]] + * @param loss evaluation metric. + * @return an array with index i having the losses or errors for the ensemble + * containing the first i+1 trees + */ + def evaluateEachIteration( + data: RDD[LabeledPoint], + loss: Loss): Array[Double] = { + + val sc = data.sparkContext + val remappedData = algo match { + case Classification => data.map(x => new LabeledPoint((x.label * 2) - 1, x.features)) + case _ => data + } + + val numIterations = trees.length + val evaluationArray = Array.fill(numIterations)(0.0) + val localTreeWeights = treeWeights + + var predictionAndError = GradientBoostedTreesModel.computeInitialPredictionAndError( + remappedData, localTreeWeights(0), trees(0), loss) + + evaluationArray(0) = predictionAndError.values.mean() + + val broadcastTrees = sc.broadcast(trees) + (1 until numIterations).foreach { nTree => + predictionAndError = remappedData.zip(predictionAndError).mapPartitions { iter => + val currentTree = broadcastTrees.value(nTree) + val currentTreeWeight = localTreeWeights(nTree) + iter.map { case (point, (pred, error)) => + val newPred = pred + currentTree.predict(point.features) * currentTreeWeight + val newError = loss.computeError(newPred, point.label) + (newPred, newError) + } + } + evaluationArray(nTree) = predictionAndError.values.mean() + } + + broadcastTrees.unpersist() + evaluationArray + } + + override protected def formatVersion: String = GradientBoostedTreesModel.formatVersion +} + +object GradientBoostedTreesModel extends Loader[GradientBoostedTreesModel] { + + /** + * Compute the initial predictions and errors for a dataset for the first + * iteration of gradient boosting. + * @param data: training data. + * @param initTreeWeight: learning rate assigned to the first tree. + * @param initTree: first DecisionTreeModel. + * @param loss: evaluation metric. + * @return a RDD with each element being a zip of the prediction and error + * corresponding to every sample. + */ + def computeInitialPredictionAndError( + data: RDD[LabeledPoint], + initTreeWeight: Double, + initTree: DecisionTreeModel, + loss: Loss): RDD[(Double, Double)] = { + data.map { lp => + val pred = initTreeWeight * initTree.predict(lp.features) + val error = loss.computeError(pred, lp.label) + (pred, error) + } + } + + /** + * Update a zipped predictionError RDD + * (as obtained with computeInitialPredictionAndError) + * @param data: training data. + * @param predictionAndError: predictionError RDD + * @param treeWeight: Learning rate. + * @param tree: Tree using which the prediction and error should be updated. + * @param loss: evaluation metric. + * @return a RDD with each element being a zip of the prediction and error + * corresponding to each sample. + */ + def updatePredictionError( + data: RDD[LabeledPoint], + predictionAndError: RDD[(Double, Double)], + treeWeight: Double, + tree: DecisionTreeModel, + loss: Loss): RDD[(Double, Double)] = { + + val newPredError = data.zip(predictionAndError).mapPartitions { iter => + iter.map { case (lp, (pred, error)) => + val newPred = pred + tree.predict(lp.features) * treeWeight + val newError = loss.computeError(newPred, lp.label) + (newPred, newError) + } + } + newPredError + } + + private[mllib] def formatVersion: String = TreeEnsembleModel.SaveLoadV1_0.thisFormatVersion + + override def load(sc: SparkContext, path: String): GradientBoostedTreesModel = { + val (loadedClassName, version, jsonMetadata) = Loader.loadMetadata(sc, path) + val classNameV1_0 = SaveLoadV1_0.thisClassName + (loadedClassName, version) match { + case (className, "1.0") if className == classNameV1_0 => + val metadata = TreeEnsembleModel.SaveLoadV1_0.readMetadata(jsonMetadata) + assert(metadata.combiningStrategy == Sum.toString) + val trees = + TreeEnsembleModel.SaveLoadV1_0.loadTrees(sc, path, metadata.treeAlgo) + new GradientBoostedTreesModel(Algo.fromString(metadata.algo), trees, metadata.treeWeights) + case _ => throw new Exception(s"GradientBoostedTreesModel.load did not recognize model" + + s" with (className, format version): ($loadedClassName, $version). Supported:\n" + + s" ($classNameV1_0, 1.0)") + } + } + + private object SaveLoadV1_0 { + // Hard-code class name string in case it changes in the future + def thisClassName: String = "org.apache.spark.mllib.tree.model.GradientBoostedTreesModel" + } - require(trees.size == treeWeights.size) } /** @@ -167,12 +342,105 @@ private[tree] sealed class TreeEnsembleModel( } /** - * Get number of trees in forest. + * Get number of trees in ensemble. */ - def numTrees: Int = trees.size + def numTrees: Int = trees.length /** - * Get total number of nodes, summed over all trees in the forest. + * Get total number of nodes, summed over all trees in the ensemble. */ def totalNumNodes: Int = trees.map(_.numNodes).sum } + +private[tree] object TreeEnsembleModel extends Logging { + + object SaveLoadV1_0 { + + import org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0.{NodeData, constructTrees} + + def thisFormatVersion: String = "1.0" + + case class Metadata( + algo: String, + treeAlgo: String, + combiningStrategy: String, + treeWeights: Array[Double]) + + /** + * Model data for model import/export. + * We have to duplicate NodeData here since Spark SQL does not yet support extracting subfields + * of nested fields; once that is possible, we can use something like: + * case class EnsembleNodeData(treeId: Int, node: NodeData), + * where NodeData is from DecisionTreeModel. + */ + case class EnsembleNodeData(treeId: Int, node: NodeData) + + def save(sc: SparkContext, path: String, model: TreeEnsembleModel, className: String): Unit = { + val sqlContext = new SQLContext(sc) + import sqlContext.implicits._ + + // SPARK-6120: We do a hacky check here so users understand why save() is failing + // when they run the ML guide example. + // TODO: Fix this issue for real. + val memThreshold = 768 + if (sc.isLocal) { + val driverMemory = sc.getConf.getOption("spark.driver.memory") + .orElse(Option(System.getenv("SPARK_DRIVER_MEMORY"))) + .map(Utils.memoryStringToMb) + .getOrElse(512) + if (driverMemory <= memThreshold) { + logWarning(s"$className.save() was called, but it may fail because of too little" + + s" driver memory (${driverMemory}m)." + + s" If failure occurs, try setting driver-memory ${memThreshold}m (or larger).") + } + } else { + if (sc.executorMemory <= memThreshold) { + logWarning(s"$className.save() was called, but it may fail because of too little" + + s" executor memory (${sc.executorMemory}m)." + + s" If failure occurs try setting executor-memory ${memThreshold}m (or larger).") + } + } + + // Create JSON metadata. + implicit val format = DefaultFormats + val ensembleMetadata = Metadata(model.algo.toString, model.trees(0).algo.toString, + model.combiningStrategy.toString, model.treeWeights) + val metadata = compact(render( + ("class" -> className) ~ ("version" -> thisFormatVersion) ~ + ("metadata" -> Extraction.decompose(ensembleMetadata)))) + sc.parallelize(Seq(metadata), 1).saveAsTextFile(Loader.metadataPath(path)) + + // Create Parquet data. + val dataRDD = sc.parallelize(model.trees.zipWithIndex).flatMap { case (tree, treeId) => + tree.topNode.subtreeIterator.toSeq.map(node => NodeData(treeId, node)) + }.toDF() + dataRDD.saveAsParquetFile(Loader.dataPath(path)) + } + + /** + * Read metadata from the loaded JSON metadata. + */ + def readMetadata(metadata: JValue): Metadata = { + implicit val formats = DefaultFormats + (metadata \ "metadata").extract[Metadata] + } + + /** + * Load trees for an ensemble, and return them in order. + * @param path path to load the model from + * @param treeAlgo Algorithm for individual trees (which may differ from the ensemble's + * algorithm). + */ + def loadTrees( + sc: SparkContext, + path: String, + treeAlgo: String): Array[DecisionTreeModel] = { + val datapath = Loader.dataPath(path) + val sqlContext = new SQLContext(sc) + val nodes = sqlContext.parquetFile(datapath).map(NodeData.apply) + val trees = constructTrees(nodes) + trees.map(new DecisionTreeModel(_, Algo.fromString(treeAlgo))) + } + } + +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala index 97f54aa62d31c..c9d33787b0bb5 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala @@ -20,7 +20,7 @@ package org.apache.spark.mllib.util import scala.collection.JavaConversions._ import scala.util.Random -import org.jblas.DoubleMatrix +import com.github.fommil.netlib.BLAS.{getInstance => blas} import org.apache.spark.annotation.DeveloperApi import org.apache.spark.SparkContext @@ -72,11 +72,10 @@ object LinearDataGenerator { eps: Double = 0.1): Seq[LabeledPoint] = { val rnd = new Random(seed) - val weightsMat = new DoubleMatrix(1, weights.length, weights:_*) val x = Array.fill[Array[Double]](nPoints)( Array.fill[Double](weights.length)(2 * rnd.nextDouble - 1.0)) val y = x.map { xi => - new DoubleMatrix(1, xi.length, xi: _*).dot(weightsMat) + intercept + eps * rnd.nextGaussian() + blas.ddot(weights.length, xi, 1, weights, 1) + intercept + eps * rnd.nextGaussian() } y.zip(x).map(p => LabeledPoint(p._1, Vectors.dense(p._2))) } @@ -100,9 +99,9 @@ object LinearDataGenerator { eps: Double, nparts: Int = 2, intercept: Double = 0.0) : RDD[LabeledPoint] = { - org.jblas.util.Random.seed(42) + val random = new Random(42) // Random values distributed uniformly in [-0.5, 0.5] - val w = DoubleMatrix.rand(nfeatures, 1).subi(0.5) + val w = Array.fill(nfeatures)(random.nextDouble() - 0.5) val data: RDD[LabeledPoint] = sc.parallelize(0 until nparts, nparts).flatMap { p => val seed = 42 + p diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/MFDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/MFDataGenerator.scala index b76fbe89c3681..0c5b4f9d04a74 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/util/MFDataGenerator.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/util/MFDataGenerator.scala @@ -17,13 +17,14 @@ package org.apache.spark.mllib.util +import java.{util => ju} + import scala.language.postfixOps import scala.util.Random -import org.jblas.DoubleMatrix - -import org.apache.spark.annotation.DeveloperApi import org.apache.spark.SparkContext +import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.mllib.linalg.{BLAS, DenseMatrix} import org.apache.spark.rdd.RDD /** @@ -72,24 +73,25 @@ object MFDataGenerator { val sc = new SparkContext(sparkMaster, "MFDataGenerator") - val A = DoubleMatrix.randn(m, rank) - val B = DoubleMatrix.randn(rank, n) - val z = 1 / scala.math.sqrt(scala.math.sqrt(rank)) - A.mmuli(z) - B.mmuli(z) - val fullData = A.mmul(B) + val random = new ju.Random(42L) + + val A = DenseMatrix.randn(m, rank, random) + val B = DenseMatrix.randn(rank, n, random) + val z = 1 / math.sqrt(rank) + val fullData = DenseMatrix.zeros(m, n) + BLAS.gemm(z, A, B, 1.0, fullData) val df = rank * (m + n - rank) val sampSize = scala.math.min(scala.math.round(trainSampFact * df), scala.math.round(.99 * m * n)).toInt val rand = new Random() val mn = m * n - val shuffled = rand.shuffle(1 to mn toList) + val shuffled = rand.shuffle((0 until mn).toList) val omega = shuffled.slice(0, sampSize) val ordered = omega.sortWith(_ < _).toArray val trainData: RDD[(Int, Int, Double)] = sc.parallelize(ordered) - .map(x => (fullData.indexRows(x - 1), fullData.indexColumns(x - 1), fullData.get(x - 1))) + .map(x => (x % m, x / m, fullData.values(x))) // optionally add gaussian noise if (noise) { @@ -105,7 +107,7 @@ object MFDataGenerator { val testOmega = shuffled.slice(sampSize, sampSize + testSampSize) val testOrdered = testOmega.sortWith(_ < _).toArray val testData: RDD[(Int, Int, Double)] = sc.parallelize(testOrdered) - .map(x => (fullData.indexRows(x - 1), fullData.indexColumns(x - 1), fullData.get(x - 1))) + .map(x => (x % m, x / m, fullData.values(x))) testData.map(x => x._1 + "," + x._2 + "," + x._3).saveAsTextFile(outputPath) } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/NumericParser.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/NumericParser.scala index f7cba6c6cb628..308f7f3578e21 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/util/NumericParser.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/util/NumericParser.scala @@ -19,7 +19,7 @@ package org.apache.spark.mllib.util import java.util.StringTokenizer -import scala.collection.mutable.{ArrayBuffer, ListBuffer} +import scala.collection.mutable.{ArrayBuilder, ListBuffer} import org.apache.spark.SparkException @@ -51,7 +51,7 @@ private[mllib] object NumericParser { } private def parseArray(tokenizer: StringTokenizer): Array[Double] = { - val values = ArrayBuffer.empty[Double] + val values = ArrayBuilder.make[Double] var parsing = true var allowComma = false var token: String = null @@ -67,14 +67,14 @@ private[mllib] object NumericParser { } } else { // expecting a number - values.append(parseDouble(token)) + values += parseDouble(token) allowComma = true } } if (parsing) { throw new SparkException(s"An array must end with ']'.") } - values.toArray + values.result() } private def parseTuple(tokenizer: StringTokenizer): Seq[_] = { @@ -114,7 +114,7 @@ private[mllib] object NumericParser { try { java.lang.Double.parseDouble(s) } catch { - case e: Throwable => + case e: NumberFormatException => throw new SparkException(s"Cannot parse a double from: $s", e) } } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/SVMDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/SVMDataGenerator.scala index 7db97e6bac688..a8e30cc9d730c 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/util/SVMDataGenerator.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/util/SVMDataGenerator.scala @@ -19,7 +19,7 @@ package org.apache.spark.mllib.util import scala.util.Random -import org.jblas.DoubleMatrix +import com.github.fommil.netlib.BLAS.{getInstance => blas} import org.apache.spark.annotation.DeveloperApi import org.apache.spark.SparkContext @@ -51,8 +51,7 @@ object SVMDataGenerator { val sc = new SparkContext(sparkMaster, "SVMGenerator") val globalRnd = new Random(94720) - val trueWeights = new DoubleMatrix(1, nfeatures + 1, - Array.fill[Double](nfeatures + 1)(globalRnd.nextGaussian()):_*) + val trueWeights = Array.fill[Double](nfeatures + 1)(globalRnd.nextGaussian()) val data: RDD[LabeledPoint] = sc.parallelize(0 until nexamples, parts).map { idx => val rnd = new Random(42 + idx) @@ -60,7 +59,7 @@ object SVMDataGenerator { val x = Array.fill[Double](nfeatures) { rnd.nextDouble() * 2.0 - 1.0 } - val yD = new DoubleMatrix(1, x.length, x: _*).dot(trueWeights) + rnd.nextGaussian() * 0.1 + val yD = blas.ddot(trueWeights.length, x, 1, trueWeights, 1) + rnd.nextGaussian() * 0.1 val y = if (yD < 0) 0.0 else 1.0 LabeledPoint(y, Vectors.dense(x)) } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/modelSaveLoad.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/modelSaveLoad.scala index 56b77a7d12e83..30d642c754b7c 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/util/modelSaveLoad.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/util/modelSaveLoad.scala @@ -20,13 +20,13 @@ package org.apache.spark.mllib.util import scala.reflect.runtime.universe.TypeTag import org.apache.hadoop.fs.Path +import org.json4s._ +import org.json4s.jackson.JsonMethods._ import org.apache.spark.SparkContext import org.apache.spark.annotation.DeveloperApi -import org.apache.spark.sql.{DataFrame, Row, SQLContext} import org.apache.spark.sql.catalyst.ScalaReflection -import org.apache.spark.sql.types.{DataType, StructType, StructField} - +import org.apache.spark.sql.types.{DataType, StructField, StructType} /** * :: DeveloperApi :: @@ -48,7 +48,7 @@ trait Saveable { * * @param sc Spark context used to save model data. * @param path Path specifying the directory in which to save this model. - * This directory and any intermediate directory will be created if needed. + * If the directory already exists, this method throws an exception. */ def save(sc: SparkContext, path: String): Unit @@ -110,7 +110,7 @@ private[mllib] object Loader { assert(loadedFields.contains(field.name), s"Unable to parse model data." + s" Expected field with name ${field.name} was missing in loaded schema:" + s" ${loadedFields.mkString(", ")}") - assert(loadedFields(field.name) == field.dataType, + assert(loadedFields(field.name).sameType(field.dataType), s"Unable to parse model data. Expected field $field but found field" + s" with different type: ${loadedFields(field.name)}") } @@ -120,20 +120,11 @@ private[mllib] object Loader { * Load metadata from the given path. * @return (class name, version, metadata) */ - def loadMetadata(sc: SparkContext, path: String): (String, String, DataFrame) = { - val sqlContext = new SQLContext(sc) - val metadata = sqlContext.jsonFile(metadataPath(path)) - val (clazz, version) = try { - val metadataArray = metadata.select("class", "version").take(1) - assert(metadataArray.size == 1) - metadataArray(0) match { - case Row(clazz: String, version: String) => (clazz, version) - } - } catch { - case e: Exception => - throw new Exception(s"Unable to load model metadata from: ${metadataPath(path)}") - } + def loadMetadata(sc: SparkContext, path: String): (String, String, JValue) = { + implicit val formats = DefaultFormats + val metadata = parse(sc.textFile(metadataPath(path)).first()) + val clazz = (metadata \ "class").extract[String] + val version = (metadata \ "version").extract[String] (clazz, version, metadata) } - } diff --git a/mllib/src/test/java/org/apache/spark/ml/JavaPipelineSuite.java b/mllib/src/test/java/org/apache/spark/ml/JavaPipelineSuite.java index 50995ffef9ad5..0a8c9e5954676 100644 --- a/mllib/src/test/java/org/apache/spark/ml/JavaPipelineSuite.java +++ b/mllib/src/test/java/org/apache/spark/ml/JavaPipelineSuite.java @@ -45,7 +45,7 @@ public void setUp() { jsql = new SQLContext(jsc); JavaRDD points = jsc.parallelize(generateLogisticInputAsList(1.0, 1.0, 100, 42), 2); - dataset = jsql.applySchema(points, LabeledPoint.class); + dataset = jsql.createDataFrame(points, LabeledPoint.class); } @After diff --git a/mllib/src/test/java/org/apache/spark/ml/attribute/JavaAttributeGroupSuite.java b/mllib/src/test/java/org/apache/spark/ml/attribute/JavaAttributeGroupSuite.java new file mode 100644 index 0000000000000..38eb58673ad53 --- /dev/null +++ b/mllib/src/test/java/org/apache/spark/ml/attribute/JavaAttributeGroupSuite.java @@ -0,0 +1,45 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.attribute; + +import org.junit.Assert; +import org.junit.Test; + +public class JavaAttributeGroupSuite { + + @Test + public void testAttributeGroup() { + Attribute[] attrs = new Attribute[]{ + NumericAttribute.defaultAttr(), + NominalAttribute.defaultAttr(), + BinaryAttribute.defaultAttr().withIndex(0), + NumericAttribute.defaultAttr().withName("age").withSparsity(0.8), + NominalAttribute.defaultAttr().withName("size").withValues("small", "medium", "large"), + BinaryAttribute.defaultAttr().withName("clicked").withValues("no", "yes"), + NumericAttribute.defaultAttr(), + NumericAttribute.defaultAttr() + }; + AttributeGroup group = new AttributeGroup("user", attrs); + Assert.assertEquals(8, group.size()); + Assert.assertEquals("user", group.name()); + Assert.assertEquals(NumericAttribute.defaultAttr().withIndex(0), group.getAttr(0)); + Assert.assertEquals(3, group.indexOf("age")); + Assert.assertFalse(group.hasAttr("abc")); + Assert.assertEquals(group, AttributeGroup.fromStructField(group.toStructField())); + } +} diff --git a/mllib/src/test/java/org/apache/spark/ml/attribute/JavaAttributeSuite.java b/mllib/src/test/java/org/apache/spark/ml/attribute/JavaAttributeSuite.java new file mode 100644 index 0000000000000..b74bbed231434 --- /dev/null +++ b/mllib/src/test/java/org/apache/spark/ml/attribute/JavaAttributeSuite.java @@ -0,0 +1,55 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.attribute; + +import org.junit.Test; +import org.junit.Assert; + +public class JavaAttributeSuite { + + @Test + public void testAttributeType() { + AttributeType numericType = AttributeType.Numeric(); + AttributeType nominalType = AttributeType.Nominal(); + AttributeType binaryType = AttributeType.Binary(); + Assert.assertEquals(numericType, NumericAttribute.defaultAttr().attrType()); + Assert.assertEquals(nominalType, NominalAttribute.defaultAttr().attrType()); + Assert.assertEquals(binaryType, BinaryAttribute.defaultAttr().attrType()); + } + + @Test + public void testNumericAttribute() { + NumericAttribute attr = NumericAttribute.defaultAttr() + .withName("age").withIndex(0).withMin(0.0).withMax(1.0).withStd(0.5).withSparsity(0.4); + Assert.assertEquals(attr.withoutIndex(), Attribute.fromStructField(attr.toStructField())); + } + + @Test + public void testNominalAttribute() { + NominalAttribute attr = NominalAttribute.defaultAttr() + .withName("size").withIndex(1).withValues("small", "medium", "large"); + Assert.assertEquals(attr.withoutIndex(), Attribute.fromStructField(attr.toStructField())); + } + + @Test + public void testBinaryAttribute() { + BinaryAttribute attr = BinaryAttribute.defaultAttr() + .withName("clicked").withIndex(2).withValues("no", "yes"); + Assert.assertEquals(attr.withoutIndex(), Attribute.fromStructField(attr.toStructField())); + } +} diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaDecisionTreeClassifierSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaDecisionTreeClassifierSuite.java new file mode 100644 index 0000000000000..43b8787f9dd7e --- /dev/null +++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaDecisionTreeClassifierSuite.java @@ -0,0 +1,98 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.classification; + +import java.io.File; +import java.io.Serializable; +import java.util.HashMap; +import java.util.Map; + +import org.junit.After; +import org.junit.Before; +import org.junit.Test; + +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.ml.impl.TreeTests; +import org.apache.spark.mllib.classification.LogisticRegressionSuite; +import org.apache.spark.mllib.regression.LabeledPoint; +import org.apache.spark.sql.DataFrame; +import org.apache.spark.util.Utils; + + +public class JavaDecisionTreeClassifierSuite implements Serializable { + + private transient JavaSparkContext sc; + + @Before + public void setUp() { + sc = new JavaSparkContext("local", "JavaDecisionTreeClassifierSuite"); + } + + @After + public void tearDown() { + sc.stop(); + sc = null; + } + + @Test + public void runDT() { + int nPoints = 20; + double A = 2.0; + double B = -1.5; + + JavaRDD data = sc.parallelize( + LogisticRegressionSuite.generateLogisticInputAsList(A, B, nPoints, 42), 2).cache(); + Map categoricalFeatures = new HashMap(); + DataFrame dataFrame = TreeTests.setMetadata(data, categoricalFeatures, 2); + + // This tests setters. Training with various options is tested in Scala. + DecisionTreeClassifier dt = new DecisionTreeClassifier() + .setMaxDepth(2) + .setMaxBins(10) + .setMinInstancesPerNode(5) + .setMinInfoGain(0.0) + .setMaxMemoryInMB(256) + .setCacheNodeIds(false) + .setCheckpointInterval(10) + .setMaxDepth(2); // duplicate setMaxDepth to check builder pattern + for (int i = 0; i < DecisionTreeClassifier.supportedImpurities().length; ++i) { + dt.setImpurity(DecisionTreeClassifier.supportedImpurities()[i]); + } + DecisionTreeClassificationModel model = dt.fit(dataFrame); + + model.transform(dataFrame); + model.numNodes(); + model.depth(); + model.toDebugString(); + + /* + // TODO: Add test once save/load are implemented. + File tempDir = Utils.createTempDir(System.getProperty("java.io.tmpdir"), "spark"); + String path = tempDir.toURI().toString(); + try { + model3.save(sc.sc(), path); + DecisionTreeClassificationModel sameModel = + DecisionTreeClassificationModel.load(sc.sc(), path); + TreeTests.checkEqual(model3, sameModel); + } finally { + Utils.deleteRecursively(tempDir); + } + */ + } +} diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java index d4b664479255d..3f8e59de0f05c 100644 --- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java +++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java @@ -50,7 +50,7 @@ public void setUp() { jsql = new SQLContext(jsc); List points = generateLogisticInputAsList(1.0, 1.0, 100, 42); datasetRDD = jsc.parallelize(points, 2); - dataset = jsql.applySchema(datasetRDD, LabeledPoint.class); + dataset = jsql.createDataFrame(datasetRDD, LabeledPoint.class); dataset.registerTempTable("dataset"); } diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaStreamingLogisticRegressionSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaStreamingLogisticRegressionSuite.java index ac945ba6f23c1..640d2ec55e4e7 100644 --- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaStreamingLogisticRegressionSuite.java +++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaStreamingLogisticRegressionSuite.java @@ -47,7 +47,7 @@ public void setUp() { SparkConf conf = new SparkConf() .setMaster("local[2]") .setAppName("test") - .set("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock"); + .set("spark.streaming.clock", "org.apache.spark.util.ManualClock"); ssc = new JavaStreamingContext(conf, new Duration(1000)); ssc.checkpoint("checkpoint"); } diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaTokenizerSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaTokenizerSuite.java new file mode 100644 index 0000000000000..3806f650025b2 --- /dev/null +++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaTokenizerSuite.java @@ -0,0 +1,71 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature; + +import com.google.common.collect.Lists; +import org.junit.After; +import org.junit.Assert; +import org.junit.Before; +import org.junit.Test; + +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.sql.DataFrame; +import org.apache.spark.sql.Row; +import org.apache.spark.sql.SQLContext; + +public class JavaTokenizerSuite { + private transient JavaSparkContext jsc; + private transient SQLContext jsql; + + @Before + public void setUp() { + jsc = new JavaSparkContext("local", "JavaTokenizerSuite"); + jsql = new SQLContext(jsc); + } + + @After + public void tearDown() { + jsc.stop(); + jsc = null; + } + + @Test + public void regexTokenizer() { + RegexTokenizer myRegExTokenizer = new RegexTokenizer() + .setInputCol("rawText") + .setOutputCol("tokens") + .setPattern("\\s") + .setGaps(true) + .setMinTokenLength(3); + + JavaRDD rdd = jsc.parallelize(Lists.newArrayList( + new TokenizerTestData("Test of tok.", new String[] {"Test", "tok."}), + new TokenizerTestData("Te,st. punct", new String[] {"Te,st.", "punct"}) + )); + DataFrame dataset = jsql.createDataFrame(rdd, TokenizerTestData.class); + + Row[] pairs = myRegExTokenizer.transform(dataset) + .select("tokens", "wantedTokens") + .collect(); + + for (Row r : pairs) { + Assert.assertEquals(r.get(0), r.get(1)); + } + } +} diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorIndexerSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorIndexerSuite.java new file mode 100644 index 0000000000000..161100134c92d --- /dev/null +++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorIndexerSuite.java @@ -0,0 +1,70 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature; + +import java.io.Serializable; +import java.util.List; + +import org.junit.After; +import org.junit.Assert; +import org.junit.Before; +import org.junit.Test; + +import com.google.common.collect.Lists; + +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.ml.feature.VectorIndexerSuite.FeatureData; +import org.apache.spark.mllib.linalg.Vectors; +import org.apache.spark.sql.DataFrame; +import org.apache.spark.sql.SQLContext; + + +public class JavaVectorIndexerSuite implements Serializable { + private transient JavaSparkContext sc; + + @Before + public void setUp() { + sc = new JavaSparkContext("local", "JavaVectorIndexerSuite"); + } + + @After + public void tearDown() { + sc.stop(); + sc = null; + } + + @Test + public void vectorIndexerAPI() { + // The tests are to check Java compatibility. + List points = Lists.newArrayList( + new FeatureData(Vectors.dense(0.0, -2.0)), + new FeatureData(Vectors.dense(1.0, 3.0)), + new FeatureData(Vectors.dense(1.0, 4.0)) + ); + SQLContext sqlContext = new SQLContext(sc); + DataFrame data = sqlContext.createDataFrame(sc.parallelize(points, 2), FeatureData.class); + VectorIndexer indexer = new VectorIndexer() + .setInputCol("features") + .setOutputCol("indexed") + .setMaxCategories(2); + VectorIndexerModel model = indexer.fit(data); + Assert.assertEquals(model.numFeatures(), 2); + Assert.assertEquals(model.categoryMaps().size(), 1); + DataFrame indexedData = model.transform(data); + } +} diff --git a/mllib/src/test/java/org/apache/spark/ml/regression/JavaDecisionTreeRegressorSuite.java b/mllib/src/test/java/org/apache/spark/ml/regression/JavaDecisionTreeRegressorSuite.java new file mode 100644 index 0000000000000..a3a339004f31c --- /dev/null +++ b/mllib/src/test/java/org/apache/spark/ml/regression/JavaDecisionTreeRegressorSuite.java @@ -0,0 +1,97 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.regression; + +import java.io.File; +import java.io.Serializable; +import java.util.HashMap; +import java.util.Map; + +import org.junit.After; +import org.junit.Before; +import org.junit.Test; + +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.ml.impl.TreeTests; +import org.apache.spark.mllib.classification.LogisticRegressionSuite; +import org.apache.spark.mllib.regression.LabeledPoint; +import org.apache.spark.sql.DataFrame; +import org.apache.spark.util.Utils; + + +public class JavaDecisionTreeRegressorSuite implements Serializable { + + private transient JavaSparkContext sc; + + @Before + public void setUp() { + sc = new JavaSparkContext("local", "JavaDecisionTreeRegressorSuite"); + } + + @After + public void tearDown() { + sc.stop(); + sc = null; + } + + @Test + public void runDT() { + int nPoints = 20; + double A = 2.0; + double B = -1.5; + + JavaRDD data = sc.parallelize( + LogisticRegressionSuite.generateLogisticInputAsList(A, B, nPoints, 42), 2).cache(); + Map categoricalFeatures = new HashMap(); + DataFrame dataFrame = TreeTests.setMetadata(data, categoricalFeatures, 2); + + // This tests setters. Training with various options is tested in Scala. + DecisionTreeRegressor dt = new DecisionTreeRegressor() + .setMaxDepth(2) + .setMaxBins(10) + .setMinInstancesPerNode(5) + .setMinInfoGain(0.0) + .setMaxMemoryInMB(256) + .setCacheNodeIds(false) + .setCheckpointInterval(10) + .setMaxDepth(2); // duplicate setMaxDepth to check builder pattern + for (int i = 0; i < DecisionTreeRegressor.supportedImpurities().length; ++i) { + dt.setImpurity(DecisionTreeRegressor.supportedImpurities()[i]); + } + DecisionTreeRegressionModel model = dt.fit(dataFrame); + + model.transform(dataFrame); + model.numNodes(); + model.depth(); + model.toDebugString(); + + /* + // TODO: Add test once save/load are implemented. + File tempDir = Utils.createTempDir(System.getProperty("java.io.tmpdir"), "spark"); + String path = tempDir.toURI().toString(); + try { + model2.save(sc.sc(), path); + DecisionTreeRegressionModel sameModel = DecisionTreeRegressionModel.load(sc.sc(), path); + TreeTests.checkEqual(model2, sameModel); + } finally { + Utils.deleteRecursively(tempDir); + } + */ + } +} diff --git a/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java b/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java index 40d5a92bb32af..0cc36c8d56d70 100644 --- a/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java +++ b/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java @@ -46,7 +46,7 @@ public void setUp() { jsql = new SQLContext(jsc); List points = generateLogisticInputAsList(1.0, 1.0, 100, 42); datasetRDD = jsc.parallelize(points, 2); - dataset = jsql.applySchema(datasetRDD, LabeledPoint.class); + dataset = jsql.createDataFrame(datasetRDD, LabeledPoint.class); dataset.registerTempTable("dataset"); } diff --git a/mllib/src/test/java/org/apache/spark/ml/tuning/JavaCrossValidatorSuite.java b/mllib/src/test/java/org/apache/spark/ml/tuning/JavaCrossValidatorSuite.java index 074b58c07df7a..0bb6b489f2757 100644 --- a/mllib/src/test/java/org/apache/spark/ml/tuning/JavaCrossValidatorSuite.java +++ b/mllib/src/test/java/org/apache/spark/ml/tuning/JavaCrossValidatorSuite.java @@ -45,7 +45,7 @@ public void setUp() { jsc = new JavaSparkContext("local", "JavaCrossValidatorSuite"); jsql = new SQLContext(jsc); List points = generateLogisticInputAsList(1.0, 1.0, 100, 42); - dataset = jsql.applySchema(jsc.parallelize(points, 2), LabeledPoint.class); + dataset = jsql.createDataFrame(jsc.parallelize(points, 2), LabeledPoint.class); } @After diff --git a/mllib/src/test/java/org/apache/spark/mllib/classification/JavaNaiveBayesSuite.java b/mllib/src/test/java/org/apache/spark/mllib/classification/JavaNaiveBayesSuite.java index 1c90522a0714a..71fb7f13c39c2 100644 --- a/mllib/src/test/java/org/apache/spark/mllib/classification/JavaNaiveBayesSuite.java +++ b/mllib/src/test/java/org/apache/spark/mllib/classification/JavaNaiveBayesSuite.java @@ -17,20 +17,22 @@ package org.apache.spark.mllib.classification; +import java.io.Serializable; +import java.util.Arrays; +import java.util.List; + +import org.junit.After; +import org.junit.Assert; +import org.junit.Before; +import org.junit.Test; + import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; import org.apache.spark.api.java.function.Function; import org.apache.spark.mllib.linalg.Vector; import org.apache.spark.mllib.linalg.Vectors; import org.apache.spark.mllib.regression.LabeledPoint; -import org.junit.After; -import org.junit.Assert; -import org.junit.Before; -import org.junit.Test; -import java.io.Serializable; -import java.util.Arrays; -import java.util.List; public class JavaNaiveBayesSuite implements Serializable { private transient JavaSparkContext sc; @@ -102,4 +104,11 @@ public Vector call(LabeledPoint v) throws Exception { // Should be able to get the first prediction. predictions.first(); } + + @Test + public void testModelTypeSetters() { + NaiveBayes nb = new NaiveBayes() + .setModelType("Bernoulli") + .setModelType("Multinomial"); + } } diff --git a/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaFPGrowthSuite.java b/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaFPGrowthSuite.java index 851707c8a19c4..bd0edf2b9ea62 100644 --- a/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaFPGrowthSuite.java +++ b/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaFPGrowthSuite.java @@ -19,6 +19,7 @@ import java.io.Serializable; import java.util.ArrayList; +import java.util.List; import org.junit.After; import org.junit.Before; @@ -28,6 +29,7 @@ import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.mllib.fpm.FPGrowth.FreqItemset; public class JavaFPGrowthSuite implements Serializable { private transient JavaSparkContext sc; @@ -55,30 +57,18 @@ public void runFPGrowth() { Lists.newArrayList("z".split(" ")), Lists.newArrayList("x z y r q t p".split(" "))), 2); - FPGrowth fpg = new FPGrowth(); - - FPGrowthModel model6 = fpg - .setMinSupport(0.9) - .setNumPartitions(1) - .run(rdd); - assertEquals(0, model6.javaFreqItemsets().count()); - - FPGrowthModel model3 = fpg + FPGrowthModel model = new FPGrowth() .setMinSupport(0.5) .setNumPartitions(2) .run(rdd); - assertEquals(18, model3.javaFreqItemsets().count()); - FPGrowthModel model2 = fpg - .setMinSupport(0.3) - .setNumPartitions(4) - .run(rdd); - assertEquals(54, model2.javaFreqItemsets().count()); + List> freqItemsets = model.freqItemsets().toJavaRDD().collect(); + assertEquals(18, freqItemsets.size()); - FPGrowthModel model1 = fpg - .setMinSupport(0.1) - .setNumPartitions(8) - .run(rdd); - assertEquals(625, model1.javaFreqItemsets().count()); + for (FreqItemset itemset: freqItemsets) { + // Test return types. + List items = itemset.javaItems(); + long freq = itemset.freq(); + } } } diff --git a/mllib/src/test/java/org/apache/spark/mllib/regression/JavaStreamingLinearRegressionSuite.java b/mllib/src/test/java/org/apache/spark/mllib/regression/JavaStreamingLinearRegressionSuite.java index a4dd1ac39a3c8..899c4ea607869 100644 --- a/mllib/src/test/java/org/apache/spark/mllib/regression/JavaStreamingLinearRegressionSuite.java +++ b/mllib/src/test/java/org/apache/spark/mllib/regression/JavaStreamingLinearRegressionSuite.java @@ -45,7 +45,7 @@ public void setUp() { SparkConf conf = new SparkConf() .setMaster("local[2]") .setAppName("test") - .set("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock"); + .set("spark.streaming.clock", "org.apache.spark.util.ManualClock"); ssc = new JavaStreamingContext(conf, new Duration(1000)); ssc.checkpoint("checkpoint"); } diff --git a/mllib/src/test/resources/log4j.properties b/mllib/src/test/resources/log4j.properties index 9697237bfa1a3..75e3b53a093f6 100644 --- a/mllib/src/test/resources/log4j.properties +++ b/mllib/src/test/resources/log4j.properties @@ -24,5 +24,5 @@ log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n # Ignore messages below warning level from Jetty, because it's a bit verbose -log4j.logger.org.eclipse.jetty=WARN +log4j.logger.org.spark-project.jetty=WARN diff --git a/mllib/src/test/scala/org/apache/spark/ml/attribute/AttributeGroupSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/attribute/AttributeGroupSuite.scala new file mode 100644 index 0000000000000..17ddd335deb6d --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/ml/attribute/AttributeGroupSuite.scala @@ -0,0 +1,65 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.attribute + +import org.scalatest.FunSuite + +class AttributeGroupSuite extends FunSuite { + + test("attribute group") { + val attrs = Array( + NumericAttribute.defaultAttr, + NominalAttribute.defaultAttr, + BinaryAttribute.defaultAttr.withIndex(0), + NumericAttribute.defaultAttr.withName("age").withSparsity(0.8), + NominalAttribute.defaultAttr.withName("size").withValues("small", "medium", "large"), + BinaryAttribute.defaultAttr.withName("clicked").withValues("no", "yes"), + NumericAttribute.defaultAttr, + NumericAttribute.defaultAttr) + val group = new AttributeGroup("user", attrs) + assert(group.size === 8) + assert(group.name === "user") + assert(group(0) === NumericAttribute.defaultAttr.withIndex(0)) + assert(group(2) === BinaryAttribute.defaultAttr.withIndex(2)) + assert(group.indexOf("age") === 3) + assert(group.indexOf("size") === 4) + assert(group.indexOf("clicked") === 5) + assert(!group.hasAttr("abc")) + intercept[NoSuchElementException] { + group("abc") + } + assert(group === AttributeGroup.fromMetadata(group.toMetadataImpl, group.name)) + assert(group === AttributeGroup.fromStructField(group.toStructField())) + } + + test("attribute group without attributes") { + val group0 = new AttributeGroup("user", 10) + assert(group0.name === "user") + assert(group0.numAttributes === Some(10)) + assert(group0.size === 10) + assert(group0.attributes.isEmpty) + assert(group0 === AttributeGroup.fromMetadata(group0.toMetadataImpl, group0.name)) + assert(group0 === AttributeGroup.fromStructField(group0.toStructField())) + + val group1 = new AttributeGroup("item") + assert(group1.name === "item") + assert(group1.numAttributes.isEmpty) + assert(group1.attributes.isEmpty) + assert(group1.size === -1) + } +} diff --git a/mllib/src/test/scala/org/apache/spark/ml/attribute/AttributeSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/attribute/AttributeSuite.scala new file mode 100644 index 0000000000000..3e1a7196e37cb --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/ml/attribute/AttributeSuite.scala @@ -0,0 +1,212 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.attribute + +import org.scalatest.FunSuite + +import org.apache.spark.sql.types.{DoubleType, MetadataBuilder, Metadata} + +class AttributeSuite extends FunSuite { + + test("default numeric attribute") { + val attr: NumericAttribute = NumericAttribute.defaultAttr + val metadata = Metadata.fromJson("{}") + val metadataWithType = Metadata.fromJson("""{"type":"numeric"}""") + assert(attr.attrType === AttributeType.Numeric) + assert(attr.isNumeric) + assert(!attr.isNominal) + assert(attr.name.isEmpty) + assert(attr.index.isEmpty) + assert(attr.min.isEmpty) + assert(attr.max.isEmpty) + assert(attr.std.isEmpty) + assert(attr.sparsity.isEmpty) + assert(attr.toMetadataImpl() === metadata) + assert(attr.toMetadataImpl(withType = false) === metadata) + assert(attr.toMetadataImpl(withType = true) === metadataWithType) + assert(attr === Attribute.fromMetadata(metadata)) + assert(attr === Attribute.fromMetadata(metadataWithType)) + intercept[NoSuchElementException] { + attr.toStructField() + } + } + + test("customized numeric attribute") { + val name = "age" + val index = 0 + val metadata = Metadata.fromJson("""{"name":"age","idx":0}""") + val metadataWithType = Metadata.fromJson("""{"type":"numeric","name":"age","idx":0}""") + val attr: NumericAttribute = NumericAttribute.defaultAttr + .withName(name) + .withIndex(index) + assert(attr.attrType == AttributeType.Numeric) + assert(attr.isNumeric) + assert(!attr.isNominal) + assert(attr.name === Some(name)) + assert(attr.index === Some(index)) + assert(attr.toMetadataImpl() === metadata) + assert(attr.toMetadataImpl(withType = false) === metadata) + assert(attr.toMetadataImpl(withType = true) === metadataWithType) + assert(attr === Attribute.fromMetadata(metadata)) + assert(attr === Attribute.fromMetadata(metadataWithType)) + val field = attr.toStructField() + assert(field.dataType === DoubleType) + assert(!field.nullable) + assert(attr.withoutIndex === Attribute.fromStructField(field)) + val existingMetadata = new MetadataBuilder() + .putString("name", "test") + .build() + assert(attr.toStructField(existingMetadata).metadata.getString("name") === "test") + + val attr2 = + attr.withoutName.withoutIndex.withMin(0.0).withMax(1.0).withStd(0.5).withSparsity(0.3) + assert(attr2.name.isEmpty) + assert(attr2.index.isEmpty) + assert(attr2.min === Some(0.0)) + assert(attr2.max === Some(1.0)) + assert(attr2.std === Some(0.5)) + assert(attr2.sparsity === Some(0.3)) + assert(attr2 === Attribute.fromMetadata(attr2.toMetadataImpl())) + } + + test("bad numeric attributes") { + val attr = NumericAttribute.defaultAttr + intercept[IllegalArgumentException](attr.withName("")) + intercept[IllegalArgumentException](attr.withIndex(-1)) + intercept[IllegalArgumentException](attr.withStd(-0.1)) + intercept[IllegalArgumentException](attr.withSparsity(-0.5)) + intercept[IllegalArgumentException](attr.withSparsity(1.5)) + } + + test("default nominal attribute") { + val attr: NominalAttribute = NominalAttribute.defaultAttr + val metadata = Metadata.fromJson("""{"type":"nominal"}""") + val metadataWithoutType = Metadata.fromJson("{}") + assert(attr.attrType === AttributeType.Nominal) + assert(!attr.isNumeric) + assert(attr.isNominal) + assert(attr.name.isEmpty) + assert(attr.index.isEmpty) + assert(attr.values.isEmpty) + assert(attr.numValues.isEmpty) + assert(attr.isOrdinal.isEmpty) + assert(attr.toMetadataImpl() === metadata) + assert(attr.toMetadataImpl(withType = true) === metadata) + assert(attr.toMetadataImpl(withType = false) === metadataWithoutType) + assert(attr === Attribute.fromMetadata(metadata)) + assert(attr === NominalAttribute.fromMetadata(metadataWithoutType)) + intercept[NoSuchElementException] { + attr.toStructField() + } + } + + test("customized nominal attribute") { + val name = "size" + val index = 1 + val values = Array("small", "medium", "large") + val metadata = Metadata.fromJson( + """{"type":"nominal","name":"size","idx":1,"vals":["small","medium","large"]}""") + val metadataWithoutType = Metadata.fromJson( + """{"name":"size","idx":1,"vals":["small","medium","large"]}""") + val attr: NominalAttribute = NominalAttribute.defaultAttr + .withName(name) + .withIndex(index) + .withValues(values) + assert(attr.attrType === AttributeType.Nominal) + assert(!attr.isNumeric) + assert(attr.isNominal) + assert(attr.name === Some(name)) + assert(attr.index === Some(index)) + assert(attr.values === Some(values)) + assert(attr.indexOf("medium") === 1) + assert(attr.getValue(1) === "medium") + assert(attr.toMetadataImpl() === metadata) + assert(attr.toMetadataImpl(withType = true) === metadata) + assert(attr.toMetadataImpl(withType = false) === metadataWithoutType) + assert(attr === Attribute.fromMetadata(metadata)) + assert(attr === NominalAttribute.fromMetadata(metadataWithoutType)) + assert(attr.withoutIndex === Attribute.fromStructField(attr.toStructField())) + + val attr2 = attr.withoutName.withoutIndex.withValues(attr.values.get :+ "x-large") + assert(attr2.name.isEmpty) + assert(attr2.index.isEmpty) + assert(attr2.values.get === Array("small", "medium", "large", "x-large")) + assert(attr2.indexOf("x-large") === 3) + assert(attr2 === Attribute.fromMetadata(attr2.toMetadataImpl())) + assert(attr2 === NominalAttribute.fromMetadata(attr2.toMetadataImpl(withType = false))) + } + + test("bad nominal attributes") { + val attr = NominalAttribute.defaultAttr + intercept[IllegalArgumentException](attr.withName("")) + intercept[IllegalArgumentException](attr.withIndex(-1)) + intercept[IllegalArgumentException](attr.withNumValues(-1)) + } + + test("default binary attribute") { + val attr = BinaryAttribute.defaultAttr + val metadata = Metadata.fromJson("""{"type":"binary"}""") + val metadataWithoutType = Metadata.fromJson("{}") + assert(attr.attrType === AttributeType.Binary) + assert(attr.isNumeric) + assert(attr.isNominal) + assert(attr.name.isEmpty) + assert(attr.index.isEmpty) + assert(attr.values.isEmpty) + assert(attr.toMetadataImpl() === metadata) + assert(attr.toMetadataImpl(withType = true) === metadata) + assert(attr.toMetadataImpl(withType = false) === metadataWithoutType) + assert(attr === Attribute.fromMetadata(metadata)) + assert(attr === BinaryAttribute.fromMetadata(metadataWithoutType)) + intercept[NoSuchElementException] { + attr.toStructField() + } + } + + test("customized binary attribute") { + val name = "clicked" + val index = 2 + val values = Array("no", "yes") + val metadata = Metadata.fromJson( + """{"type":"binary","name":"clicked","idx":2,"vals":["no","yes"]}""") + val metadataWithoutType = Metadata.fromJson( + """{"name":"clicked","idx":2,"vals":["no","yes"]}""") + val attr = BinaryAttribute.defaultAttr + .withName(name) + .withIndex(index) + .withValues(values(0), values(1)) + assert(attr.attrType === AttributeType.Binary) + assert(attr.isNumeric) + assert(attr.isNominal) + assert(attr.name === Some(name)) + assert(attr.index === Some(index)) + assert(attr.values.get === values) + assert(attr.toMetadataImpl() === metadata) + assert(attr.toMetadataImpl(withType = true) === metadata) + assert(attr.toMetadataImpl(withType = false) === metadataWithoutType) + assert(attr === Attribute.fromMetadata(metadata)) + assert(attr === BinaryAttribute.fromMetadata(metadataWithoutType)) + assert(attr.withoutIndex === Attribute.fromStructField(attr.toStructField())) + } + + test("bad binary attributes") { + val attr = BinaryAttribute.defaultAttr + intercept[IllegalArgumentException](attr.withName("")) + intercept[IllegalArgumentException](attr.withIndex(-1)) + } +} diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala new file mode 100644 index 0000000000000..af88595df5245 --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala @@ -0,0 +1,274 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.classification + +import org.scalatest.FunSuite + +import org.apache.spark.ml.impl.TreeTests +import org.apache.spark.mllib.linalg.Vectors +import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.mllib.tree.{DecisionTree => OldDecisionTree, + DecisionTreeSuite => OldDecisionTreeSuite} +import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.DataFrame + + +class DecisionTreeClassifierSuite extends FunSuite with MLlibTestSparkContext { + + import DecisionTreeClassifierSuite.compareAPIs + + private var categoricalDataPointsRDD: RDD[LabeledPoint] = _ + private var orderedLabeledPointsWithLabel0RDD: RDD[LabeledPoint] = _ + private var orderedLabeledPointsWithLabel1RDD: RDD[LabeledPoint] = _ + private var categoricalDataPointsForMulticlassRDD: RDD[LabeledPoint] = _ + private var continuousDataPointsForMulticlassRDD: RDD[LabeledPoint] = _ + private var categoricalDataPointsForMulticlassForOrderedFeaturesRDD: RDD[LabeledPoint] = _ + + override def beforeAll() { + super.beforeAll() + categoricalDataPointsRDD = + sc.parallelize(OldDecisionTreeSuite.generateCategoricalDataPoints()) + orderedLabeledPointsWithLabel0RDD = + sc.parallelize(OldDecisionTreeSuite.generateOrderedLabeledPointsWithLabel0()) + orderedLabeledPointsWithLabel1RDD = + sc.parallelize(OldDecisionTreeSuite.generateOrderedLabeledPointsWithLabel1()) + categoricalDataPointsForMulticlassRDD = + sc.parallelize(OldDecisionTreeSuite.generateCategoricalDataPointsForMulticlass()) + continuousDataPointsForMulticlassRDD = + sc.parallelize(OldDecisionTreeSuite.generateContinuousDataPointsForMulticlass()) + categoricalDataPointsForMulticlassForOrderedFeaturesRDD = sc.parallelize( + OldDecisionTreeSuite.generateCategoricalDataPointsForMulticlassForOrderedFeatures()) + } + + ///////////////////////////////////////////////////////////////////////////// + // Tests calling train() + ///////////////////////////////////////////////////////////////////////////// + + test("Binary classification stump with ordered categorical features") { + val dt = new DecisionTreeClassifier() + .setImpurity("gini") + .setMaxDepth(2) + .setMaxBins(100) + val categoricalFeatures = Map(0 -> 3, 1-> 3) + val numClasses = 2 + compareAPIs(categoricalDataPointsRDD, dt, categoricalFeatures, numClasses) + } + + test("Binary classification stump with fixed labels 0,1 for Entropy,Gini") { + val dt = new DecisionTreeClassifier() + .setMaxDepth(3) + .setMaxBins(100) + val numClasses = 2 + Array(orderedLabeledPointsWithLabel0RDD, orderedLabeledPointsWithLabel1RDD).foreach { rdd => + DecisionTreeClassifier.supportedImpurities.foreach { impurity => + dt.setImpurity(impurity) + compareAPIs(rdd, dt, categoricalFeatures = Map.empty[Int, Int], numClasses) + } + } + } + + test("Multiclass classification stump with 3-ary (unordered) categorical features") { + val rdd = categoricalDataPointsForMulticlassRDD + val dt = new DecisionTreeClassifier() + .setImpurity("Gini") + .setMaxDepth(4) + val numClasses = 3 + val categoricalFeatures = Map(0 -> 3, 1 -> 3) + compareAPIs(rdd, dt, categoricalFeatures, numClasses) + } + + test("Binary classification stump with 1 continuous feature, to check off-by-1 error") { + val arr = Array( + LabeledPoint(0.0, Vectors.dense(0.0)), + LabeledPoint(1.0, Vectors.dense(1.0)), + LabeledPoint(1.0, Vectors.dense(2.0)), + LabeledPoint(1.0, Vectors.dense(3.0))) + val rdd = sc.parallelize(arr) + val dt = new DecisionTreeClassifier() + .setImpurity("Gini") + .setMaxDepth(4) + val numClasses = 2 + compareAPIs(rdd, dt, categoricalFeatures = Map.empty[Int, Int], numClasses) + } + + test("Binary classification stump with 2 continuous features") { + val arr = Array( + LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 0.0)))), + LabeledPoint(1.0, Vectors.sparse(2, Seq((1, 1.0)))), + LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 0.0)))), + LabeledPoint(1.0, Vectors.sparse(2, Seq((1, 2.0))))) + val rdd = sc.parallelize(arr) + val dt = new DecisionTreeClassifier() + .setImpurity("Gini") + .setMaxDepth(4) + val numClasses = 2 + compareAPIs(rdd, dt, categoricalFeatures = Map.empty[Int, Int], numClasses) + } + + test("Multiclass classification stump with unordered categorical features," + + " with just enough bins") { + val maxBins = 2 * (math.pow(2, 3 - 1).toInt - 1) // just enough bins to allow unordered features + val rdd = categoricalDataPointsForMulticlassRDD + val dt = new DecisionTreeClassifier() + .setImpurity("Gini") + .setMaxDepth(4) + .setMaxBins(maxBins) + val categoricalFeatures = Map(0 -> 3, 1 -> 3) + val numClasses = 3 + compareAPIs(rdd, dt, categoricalFeatures, numClasses) + } + + test("Multiclass classification stump with continuous features") { + val rdd = continuousDataPointsForMulticlassRDD + val dt = new DecisionTreeClassifier() + .setImpurity("Gini") + .setMaxDepth(4) + .setMaxBins(100) + val numClasses = 3 + compareAPIs(rdd, dt, categoricalFeatures = Map.empty[Int, Int], numClasses) + } + + test("Multiclass classification stump with continuous + unordered categorical features") { + val rdd = continuousDataPointsForMulticlassRDD + val dt = new DecisionTreeClassifier() + .setImpurity("Gini") + .setMaxDepth(4) + .setMaxBins(100) + val categoricalFeatures = Map(0 -> 3) + val numClasses = 3 + compareAPIs(rdd, dt, categoricalFeatures, numClasses) + } + + test("Multiclass classification stump with 10-ary (ordered) categorical features") { + val rdd = categoricalDataPointsForMulticlassForOrderedFeaturesRDD + val dt = new DecisionTreeClassifier() + .setImpurity("Gini") + .setMaxDepth(4) + .setMaxBins(100) + val categoricalFeatures = Map(0 -> 10, 1 -> 10) + val numClasses = 3 + compareAPIs(rdd, dt, categoricalFeatures, numClasses) + } + + test("Multiclass classification tree with 10-ary (ordered) categorical features," + + " with just enough bins") { + val rdd = categoricalDataPointsForMulticlassForOrderedFeaturesRDD + val dt = new DecisionTreeClassifier() + .setImpurity("Gini") + .setMaxDepth(4) + .setMaxBins(10) + val categoricalFeatures = Map(0 -> 10, 1 -> 10) + val numClasses = 3 + compareAPIs(rdd, dt, categoricalFeatures, numClasses) + } + + test("split must satisfy min instances per node requirements") { + val arr = Array( + LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 0.0)))), + LabeledPoint(1.0, Vectors.sparse(2, Seq((1, 1.0)))), + LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 1.0))))) + val rdd = sc.parallelize(arr) + val dt = new DecisionTreeClassifier() + .setImpurity("Gini") + .setMaxDepth(2) + .setMinInstancesPerNode(2) + val numClasses = 2 + compareAPIs(rdd, dt, categoricalFeatures = Map.empty[Int, Int], numClasses) + } + + test("do not choose split that does not satisfy min instance per node requirements") { + // if a split does not satisfy min instances per node requirements, + // this split is invalid, even though the information gain of split is large. + val arr = Array( + LabeledPoint(0.0, Vectors.dense(0.0, 1.0)), + LabeledPoint(1.0, Vectors.dense(1.0, 1.0)), + LabeledPoint(0.0, Vectors.dense(0.0, 0.0)), + LabeledPoint(0.0, Vectors.dense(0.0, 0.0))) + val rdd = sc.parallelize(arr) + val dt = new DecisionTreeClassifier() + .setImpurity("Gini") + .setMaxBins(2) + .setMaxDepth(2) + .setMinInstancesPerNode(2) + val categoricalFeatures = Map(0 -> 2, 1-> 2) + val numClasses = 2 + compareAPIs(rdd, dt, categoricalFeatures, numClasses) + } + + test("split must satisfy min info gain requirements") { + val arr = Array( + LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 0.0)))), + LabeledPoint(1.0, Vectors.sparse(2, Seq((1, 1.0)))), + LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 1.0))))) + val rdd = sc.parallelize(arr) + + val dt = new DecisionTreeClassifier() + .setImpurity("Gini") + .setMaxDepth(2) + .setMinInfoGain(1.0) + val numClasses = 2 + compareAPIs(rdd, dt, categoricalFeatures = Map.empty[Int, Int], numClasses) + } + + ///////////////////////////////////////////////////////////////////////////// + // Tests of model save/load + ///////////////////////////////////////////////////////////////////////////// + + // TODO: Reinstate test once save/load are implemented + /* + test("model save/load") { + val tempDir = Utils.createTempDir() + val path = tempDir.toURI.toString + + val oldModel = OldDecisionTreeSuite.createModel(OldAlgo.Classification) + val newModel = DecisionTreeClassificationModel.fromOld(oldModel) + + // Save model, load it back, and compare. + try { + newModel.save(sc, path) + val sameNewModel = DecisionTreeClassificationModel.load(sc, path) + TreeTests.checkEqual(newModel, sameNewModel) + } finally { + Utils.deleteRecursively(tempDir) + } + } + */ +} + +private[ml] object DecisionTreeClassifierSuite extends FunSuite { + + /** + * Train 2 decision trees on the given dataset, one using the old API and one using the new API. + * Convert the old tree to the new format, compare them, and fail if they are not exactly equal. + */ + def compareAPIs( + data: RDD[LabeledPoint], + dt: DecisionTreeClassifier, + categoricalFeatures: Map[Int, Int], + numClasses: Int): Unit = { + val oldStrategy = dt.getOldStrategy(categoricalFeatures, numClasses) + val oldTree = OldDecisionTree.train(data, oldStrategy) + val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses) + val newTree = dt.fit(newData) + // Use parent, fittingParamMap from newTree since these are not checked anyways. + val oldTreeAsNew = DecisionTreeClassificationModel.fromOld(oldTree, newTree.parent, + newTree.fittingParamMap, categoricalFeatures) + TreeTests.checkEqual(oldTreeAsNew, newTree) + } +} diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala index b3d1bfcfbee0f..35d8c2e16c6cd 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala @@ -46,6 +46,7 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext { assert(lr.getPredictionCol == "prediction") assert(lr.getRawPredictionCol == "rawPrediction") assert(lr.getProbabilityCol == "probability") + assert(lr.getFitIntercept == true) val model = lr.fit(dataset) model.transform(dataset) .select("label", "probability", "prediction", "rawPrediction") @@ -55,6 +56,14 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext { assert(model.getPredictionCol == "prediction") assert(model.getRawPredictionCol == "rawPrediction") assert(model.getProbabilityCol == "probability") + assert(model.intercept !== 0.0) + } + + test("logistic regression doesn't fit intercept when fitIntercept is off") { + val lr = new LogisticRegression + lr.setFitIntercept(false) + val model = lr.fit(dataset) + assert(model.intercept === 0.0) } test("logistic regression with setters") { diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/NormalizerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/NormalizerSuite.scala new file mode 100644 index 0000000000000..9d09f24709e23 --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/NormalizerSuite.scala @@ -0,0 +1,112 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature + +import org.scalatest.FunSuite + +import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors} +import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.mllib.util.TestingUtils._ +import org.apache.spark.sql.{DataFrame, Row, SQLContext} + + +class NormalizerSuite extends FunSuite with MLlibTestSparkContext { + + @transient var data: Array[Vector] = _ + @transient var dataFrame: DataFrame = _ + @transient var normalizer: Normalizer = _ + @transient var l1Normalized: Array[Vector] = _ + @transient var l2Normalized: Array[Vector] = _ + + override def beforeAll(): Unit = { + super.beforeAll() + + data = Array( + Vectors.sparse(3, Seq((0, -2.0), (1, 2.3))), + Vectors.dense(0.0, 0.0, 0.0), + Vectors.dense(0.6, -1.1, -3.0), + Vectors.sparse(3, Seq((1, 0.91), (2, 3.2))), + Vectors.sparse(3, Seq((0, 5.7), (1, 0.72), (2, 2.7))), + Vectors.sparse(3, Seq()) + ) + l1Normalized = Array( + Vectors.sparse(3, Seq((0, -0.465116279), (1, 0.53488372))), + Vectors.dense(0.0, 0.0, 0.0), + Vectors.dense(0.12765957, -0.23404255, -0.63829787), + Vectors.sparse(3, Seq((1, 0.22141119), (2, 0.7785888))), + Vectors.dense(0.625, 0.07894737, 0.29605263), + Vectors.sparse(3, Seq()) + ) + l2Normalized = Array( + Vectors.sparse(3, Seq((0, -0.65617871), (1, 0.75460552))), + Vectors.dense(0.0, 0.0, 0.0), + Vectors.dense(0.184549876, -0.3383414, -0.922749378), + Vectors.sparse(3, Seq((1, 0.27352993), (2, 0.96186349))), + Vectors.dense(0.897906166, 0.113419726, 0.42532397), + Vectors.sparse(3, Seq()) + ) + + val sqlContext = new SQLContext(sc) + dataFrame = sqlContext.createDataFrame(sc.parallelize(data, 2).map(NormalizerSuite.FeatureData)) + normalizer = new Normalizer() + .setInputCol("features") + .setOutputCol("normalized_features") + } + + def collectResult(result: DataFrame): Array[Vector] = { + result.select("normalized_features").collect().map { + case Row(features: Vector) => features + } + } + + def assertTypeOfVector(lhs: Array[Vector], rhs: Array[Vector]): Unit = { + assert((lhs, rhs).zipped.forall { + case (v1: DenseVector, v2: DenseVector) => true + case (v1: SparseVector, v2: SparseVector) => true + case _ => false + }, "The vector type should be preserved after normalization.") + } + + def assertValues(lhs: Array[Vector], rhs: Array[Vector]): Unit = { + assert((lhs, rhs).zipped.forall { (vector1, vector2) => + vector1 ~== vector2 absTol 1E-5 + }, "The vector value is not correct after normalization.") + } + + test("Normalization with default parameter") { + val result = collectResult(normalizer.transform(dataFrame)) + + assertTypeOfVector(data, result) + + assertValues(result, l2Normalized) + } + + test("Normalization with setter") { + normalizer.setP(1) + + val result = collectResult(normalizer.transform(dataFrame)) + + assertTypeOfVector(data, result) + + assertValues(result, l1Normalized) + } +} + +private object NormalizerSuite { + case class FeatureData(features: Vector) +} diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala new file mode 100644 index 0000000000000..00b5d094d82f1 --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala @@ -0,0 +1,52 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature + +import org.scalatest.FunSuite + +import org.apache.spark.ml.attribute.{Attribute, NominalAttribute} +import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.sql.SQLContext + +class StringIndexerSuite extends FunSuite with MLlibTestSparkContext { + private var sqlContext: SQLContext = _ + + override def beforeAll(): Unit = { + super.beforeAll() + sqlContext = new SQLContext(sc) + } + + test("StringIndexer") { + val data = sc.parallelize(Seq((0, "a"), (1, "b"), (2, "c"), (3, "a"), (4, "a"), (5, "c")), 2) + val df = sqlContext.createDataFrame(data).toDF("id", "label") + val indexer = new StringIndexer() + .setInputCol("label") + .setOutputCol("labelIndex") + .fit(df) + val transformed = indexer.transform(df) + val attr = Attribute.fromStructField(transformed.schema("labelIndex")) + .asInstanceOf[NominalAttribute] + assert(attr.values.get === Array("a", "c", "b")) + val output = transformed.select("id", "labelIndex").map { r => + (r.getInt(0), r.getDouble(1)) + }.collect().toSet + // a -> 0, b -> 2, c -> 1 + val expected = Set((0, 0.0), (1, 2.0), (2, 1.0), (3, 0.0), (4, 0.0), (5, 1.0)) + assert(output === expected) + } +} diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/TokenizerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/TokenizerSuite.scala new file mode 100644 index 0000000000000..d186ead8f542f --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/TokenizerSuite.scala @@ -0,0 +1,82 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature + +import scala.beans.BeanInfo + +import org.scalatest.FunSuite + +import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.sql.{DataFrame, Row, SQLContext} + +@BeanInfo +case class TokenizerTestData(rawText: String, wantedTokens: Array[String]) + +class RegexTokenizerSuite extends FunSuite with MLlibTestSparkContext { + import org.apache.spark.ml.feature.RegexTokenizerSuite._ + + @transient var sqlContext: SQLContext = _ + + override def beforeAll(): Unit = { + super.beforeAll() + sqlContext = new SQLContext(sc) + } + + test("RegexTokenizer") { + val tokenizer = new RegexTokenizer() + .setInputCol("rawText") + .setOutputCol("tokens") + + val dataset0 = sqlContext.createDataFrame(Seq( + TokenizerTestData("Test for tokenization.", Array("Test", "for", "tokenization", ".")), + TokenizerTestData("Te,st. punct", Array("Te", ",", "st", ".", "punct")) + )) + testRegexTokenizer(tokenizer, dataset0) + + val dataset1 = sqlContext.createDataFrame(Seq( + TokenizerTestData("Test for tokenization.", Array("Test", "for", "tokenization")), + TokenizerTestData("Te,st. punct", Array("punct")) + )) + + tokenizer.setMinTokenLength(3) + testRegexTokenizer(tokenizer, dataset1) + + tokenizer + .setPattern("\\s") + .setGaps(true) + .setMinTokenLength(0) + val dataset2 = sqlContext.createDataFrame(Seq( + TokenizerTestData("Test for tokenization.", Array("Test", "for", "tokenization.")), + TokenizerTestData("Te,st. punct", Array("Te,st.", "", "punct")) + )) + testRegexTokenizer(tokenizer, dataset2) + } +} + +object RegexTokenizerSuite extends FunSuite { + + def testRegexTokenizer(t: RegexTokenizer, dataset: DataFrame): Unit = { + t.transform(dataset) + .select("tokens", "wantedTokens") + .collect() + .foreach { + case Row(tokens, wantedTokens) => + assert(tokens === wantedTokens) + } + } +} diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorAssemblerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorAssemblerSuite.scala new file mode 100644 index 0000000000000..57d0278e03639 --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorAssemblerSuite.scala @@ -0,0 +1,63 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature + +import org.scalatest.FunSuite + +import org.apache.spark.SparkException +import org.apache.spark.mllib.linalg.{Vector, Vectors} +import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.sql.{Row, SQLContext} + +class VectorAssemblerSuite extends FunSuite with MLlibTestSparkContext { + + @transient var sqlContext: SQLContext = _ + + override def beforeAll(): Unit = { + super.beforeAll() + sqlContext = new SQLContext(sc) + } + + test("assemble") { + import org.apache.spark.ml.feature.VectorAssembler.assemble + assert(assemble(0.0) === Vectors.sparse(1, Array.empty, Array.empty)) + assert(assemble(0.0, 1.0) === Vectors.sparse(2, Array(1), Array(1.0))) + val dv = Vectors.dense(2.0, 0.0) + assert(assemble(0.0, dv, 1.0) === Vectors.sparse(4, Array(1, 3), Array(2.0, 1.0))) + val sv = Vectors.sparse(2, Array(0, 1), Array(3.0, 4.0)) + assert(assemble(0.0, dv, 1.0, sv) === + Vectors.sparse(6, Array(1, 3, 4, 5), Array(2.0, 1.0, 3.0, 4.0))) + for (v <- Seq(1, "a", null)) { + intercept[SparkException](assemble(v)) + intercept[SparkException](assemble(1.0, v)) + } + } + + test("VectorAssembler") { + val df = sqlContext.createDataFrame(Seq( + (0, 0.0, Vectors.dense(1.0, 2.0), "a", Vectors.sparse(2, Array(1), Array(3.0)), 10L) + )).toDF("id", "x", "y", "name", "z", "n") + val assembler = new VectorAssembler() + .setInputCols(Array("x", "y", "z", "n")) + .setOutputCol("features") + assembler.transform(df).select("features").collect().foreach { + case Row(v: Vector) => + assert(v === Vectors.sparse(6, Array(1, 2, 4, 5), Array(1.0, 2.0, 3.0, 10.0))) + } + } +} diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala new file mode 100644 index 0000000000000..1b261b2643854 --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala @@ -0,0 +1,256 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature + +import scala.beans.{BeanInfo, BeanProperty} + +import org.scalatest.FunSuite + +import org.apache.spark.SparkException +import org.apache.spark.ml.attribute._ +import org.apache.spark.ml.util.TestingUtils +import org.apache.spark.mllib.linalg.{SparseVector, Vector, Vectors} +import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.{DataFrame, SQLContext} + + +class VectorIndexerSuite extends FunSuite with MLlibTestSparkContext { + + import VectorIndexerSuite.FeatureData + + @transient var sqlContext: SQLContext = _ + + // identical, of length 3 + @transient var densePoints1: DataFrame = _ + @transient var sparsePoints1: DataFrame = _ + @transient var point1maxes: Array[Double] = _ + + // identical, of length 2 + @transient var densePoints2: DataFrame = _ + @transient var sparsePoints2: DataFrame = _ + + // different lengths + @transient var badPoints: DataFrame = _ + + override def beforeAll(): Unit = { + super.beforeAll() + + val densePoints1Seq = Seq( + Vectors.dense(1.0, 2.0, 0.0), + Vectors.dense(0.0, 1.0, 2.0), + Vectors.dense(0.0, 0.0, -1.0), + Vectors.dense(1.0, 3.0, 2.0)) + val sparsePoints1Seq = Seq( + Vectors.sparse(3, Array(0, 1), Array(1.0, 2.0)), + Vectors.sparse(3, Array(1, 2), Array(1.0, 2.0)), + Vectors.sparse(3, Array(2), Array(-1.0)), + Vectors.sparse(3, Array(0, 1, 2), Array(1.0, 3.0, 2.0))) + point1maxes = Array(1.0, 3.0, 2.0) + + val densePoints2Seq = Seq( + Vectors.dense(1.0, 1.0, 0.0, 1.0), + Vectors.dense(0.0, 1.0, 1.0, 1.0), + Vectors.dense(-1.0, 1.0, 2.0, 0.0)) + val sparsePoints2Seq = Seq( + Vectors.sparse(4, Array(0, 1, 3), Array(1.0, 1.0, 1.0)), + Vectors.sparse(4, Array(1, 2, 3), Array(1.0, 1.0, 1.0)), + Vectors.sparse(4, Array(0, 1, 2), Array(-1.0, 1.0, 2.0))) + + val badPointsSeq = Seq( + Vectors.sparse(2, Array(0, 1), Array(1.0, 1.0)), + Vectors.sparse(3, Array(2), Array(-1.0))) + + // Sanity checks for assumptions made in tests + assert(densePoints1Seq.head.size == sparsePoints1Seq.head.size) + assert(densePoints2Seq.head.size == sparsePoints2Seq.head.size) + assert(densePoints1Seq.head.size != densePoints2Seq.head.size) + def checkPair(dvSeq: Seq[Vector], svSeq: Seq[Vector]): Unit = { + assert(dvSeq.zip(svSeq).forall { case (dv, sv) => dv.toArray === sv.toArray }, + "typo in unit test") + } + checkPair(densePoints1Seq, sparsePoints1Seq) + checkPair(densePoints2Seq, sparsePoints2Seq) + + sqlContext = new SQLContext(sc) + densePoints1 = sqlContext.createDataFrame(sc.parallelize(densePoints1Seq, 2).map(FeatureData)) + sparsePoints1 = sqlContext.createDataFrame(sc.parallelize(sparsePoints1Seq, 2).map(FeatureData)) + densePoints2 = sqlContext.createDataFrame(sc.parallelize(densePoints2Seq, 2).map(FeatureData)) + sparsePoints2 = sqlContext.createDataFrame(sc.parallelize(sparsePoints2Seq, 2).map(FeatureData)) + badPoints = sqlContext.createDataFrame(sc.parallelize(badPointsSeq, 2).map(FeatureData)) + } + + private def getIndexer: VectorIndexer = + new VectorIndexer().setInputCol("features").setOutputCol("indexed") + + test("Cannot fit an empty DataFrame") { + val rdd = sqlContext.createDataFrame(sc.parallelize(Array.empty[Vector], 2).map(FeatureData)) + val vectorIndexer = getIndexer + intercept[IllegalArgumentException] { + vectorIndexer.fit(rdd) + } + } + + test("Throws error when given RDDs with different size vectors") { + val vectorIndexer = getIndexer + val model = vectorIndexer.fit(densePoints1) // vectors of length 3 + model.transform(densePoints1) // should work + model.transform(sparsePoints1) // should work + intercept[IllegalArgumentException] { + model.transform(densePoints2) + println("Did not throw error when fit, transform were called on vectors of different lengths") + } + intercept[SparkException] { + vectorIndexer.fit(badPoints) + println("Did not throw error when fitting vectors of different lengths in same RDD.") + } + } + + test("Same result with dense and sparse vectors") { + def testDenseSparse(densePoints: DataFrame, sparsePoints: DataFrame): Unit = { + val denseVectorIndexer = getIndexer.setMaxCategories(2) + val sparseVectorIndexer = getIndexer.setMaxCategories(2) + val denseModel = denseVectorIndexer.fit(densePoints) + val sparseModel = sparseVectorIndexer.fit(sparsePoints) + val denseMap = denseModel.categoryMaps + val sparseMap = sparseModel.categoryMaps + assert(denseMap.keys.toSet == sparseMap.keys.toSet, + "Categorical features chosen from dense vs. sparse vectors did not match.") + assert(denseMap == sparseMap, + "Categorical feature value indexes chosen from dense vs. sparse vectors did not match.") + } + testDenseSparse(densePoints1, sparsePoints1) + testDenseSparse(densePoints2, sparsePoints2) + } + + test("Builds valid categorical feature value index, transform correctly, check metadata") { + def checkCategoryMaps( + data: DataFrame, + maxCategories: Int, + categoricalFeatures: Set[Int]): Unit = { + val collectedData = data.collect().map(_.getAs[Vector](0)) + val errMsg = s"checkCategoryMaps failed for input with maxCategories=$maxCategories," + + s" categoricalFeatures=${categoricalFeatures.mkString(", ")}" + try { + val vectorIndexer = getIndexer.setMaxCategories(maxCategories) + val model = vectorIndexer.fit(data) + val categoryMaps = model.categoryMaps + // Chose correct categorical features + assert(categoryMaps.keys.toSet === categoricalFeatures) + val transformed = model.transform(data).select("indexed") + val indexedRDD: RDD[Vector] = transformed.map(_.getAs[Vector](0)) + val featureAttrs = AttributeGroup.fromStructField(transformed.schema("indexed")) + assert(featureAttrs.name === "indexed") + assert(featureAttrs.attributes.get.length === model.numFeatures) + categoricalFeatures.foreach { feature: Int => + val origValueSet = collectedData.map(_(feature)).toSet + val targetValueIndexSet = Range(0, origValueSet.size).toSet + val catMap = categoryMaps(feature) + assert(catMap.keys.toSet === origValueSet) // Correct categories + assert(catMap.values.toSet === targetValueIndexSet) // Correct category indices + if (origValueSet.contains(0.0)) { + assert(catMap(0.0) === 0) // value 0 gets index 0 + } + // Check transformed data + assert(indexedRDD.map(_(feature)).collect().toSet === targetValueIndexSet) + // Check metadata + val featureAttr = featureAttrs(feature) + assert(featureAttr.index.get === feature) + featureAttr match { + case attr: BinaryAttribute => + assert(attr.values.get === origValueSet.toArray.sorted.map(_.toString)) + case attr: NominalAttribute => + assert(attr.values.get === origValueSet.toArray.sorted.map(_.toString)) + assert(attr.isOrdinal.get === false) + case _ => + throw new RuntimeException(errMsg + s". Categorical feature $feature failed" + + s" metadata check. Found feature attribute: $featureAttr.") + } + } + // Check numerical feature metadata. + Range(0, model.numFeatures).filter(feature => !categoricalFeatures.contains(feature)) + .foreach { feature: Int => + val featureAttr = featureAttrs(feature) + featureAttr match { + case attr: NumericAttribute => + assert(featureAttr.index.get === feature) + case _ => + throw new RuntimeException(errMsg + s". Numerical feature $feature failed" + + s" metadata check. Found feature attribute: $featureAttr.") + } + } + } catch { + case e: org.scalatest.exceptions.TestFailedException => + println(errMsg) + throw e + } + } + checkCategoryMaps(densePoints1, maxCategories = 2, categoricalFeatures = Set(0)) + checkCategoryMaps(densePoints1, maxCategories = 3, categoricalFeatures = Set(0, 2)) + checkCategoryMaps(densePoints2, maxCategories = 2, categoricalFeatures = Set(1, 3)) + } + + test("Maintain sparsity for sparse vectors") { + def checkSparsity(data: DataFrame, maxCategories: Int): Unit = { + val points = data.collect().map(_.getAs[Vector](0)) + val vectorIndexer = getIndexer.setMaxCategories(maxCategories) + val model = vectorIndexer.fit(data) + val indexedPoints = model.transform(data).select("indexed").map(_.getAs[Vector](0)).collect() + points.zip(indexedPoints).foreach { + case (orig: SparseVector, indexed: SparseVector) => + assert(orig.indices.length == indexed.indices.length) + case _ => throw new UnknownError("Unit test has a bug in it.") // should never happen + } + } + checkSparsity(sparsePoints1, maxCategories = 2) + checkSparsity(sparsePoints2, maxCategories = 2) + } + + test("Preserve metadata") { + // For continuous features, preserve name and stats. + val featureAttributes: Array[Attribute] = point1maxes.zipWithIndex.map { case (maxVal, i) => + NumericAttribute.defaultAttr.withName(i.toString).withMax(maxVal) + } + val attrGroup = new AttributeGroup("features", featureAttributes) + val densePoints1WithMeta = + densePoints1.select(densePoints1("features").as("features", attrGroup.toMetadata())) + val vectorIndexer = getIndexer.setMaxCategories(2) + val model = vectorIndexer.fit(densePoints1WithMeta) + // Check that ML metadata are preserved. + val indexedPoints = model.transform(densePoints1WithMeta) + val transAttributes: Array[Attribute] = + AttributeGroup.fromStructField(indexedPoints.schema("indexed")).attributes.get + featureAttributes.zip(transAttributes).foreach { case (orig, trans) => + assert(orig.name === trans.name) + (orig, trans) match { + case (orig: NumericAttribute, trans: NumericAttribute) => + assert(orig.max.nonEmpty && orig.max === trans.max) + case _ => + // do nothing + // TODO: Once input features marked as categorical are handled correctly, check that here. + } + } + // Check that non-ML metadata are preserved. + TestingUtils.testPreserveMetadata(densePoints1WithMeta, model, "features", "indexed") + } +} + +private[feature] object VectorIndexerSuite { + @BeanInfo + case class FeatureData(@BeanProperty features: Vector) +} diff --git a/mllib/src/test/scala/org/apache/spark/ml/impl/TreeTests.scala b/mllib/src/test/scala/org/apache/spark/ml/impl/TreeTests.scala new file mode 100644 index 0000000000000..2e57d4ce37f1d --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/ml/impl/TreeTests.scala @@ -0,0 +1,132 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.impl + +import scala.collection.JavaConverters._ + +import org.scalatest.FunSuite + +import org.apache.spark.api.java.JavaRDD +import org.apache.spark.ml.attribute.{AttributeGroup, NominalAttribute, NumericAttribute} +import org.apache.spark.ml.impl.tree._ +import org.apache.spark.ml.tree.{DecisionTreeModel, InternalNode, LeafNode, Node} +import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.{SQLContext, DataFrame} + + +private[ml] object TreeTests extends FunSuite { + + /** + * Convert the given data to a DataFrame, and set the features and label metadata. + * @param data Dataset. Categorical features and labels must already have 0-based indices. + * This must be non-empty. + * @param categoricalFeatures Map: categorical feature index -> number of distinct values + * @param numClasses Number of classes label can take. If 0, mark as continuous. + * @return DataFrame with metadata + */ + def setMetadata( + data: RDD[LabeledPoint], + categoricalFeatures: Map[Int, Int], + numClasses: Int): DataFrame = { + val sqlContext = new SQLContext(data.sparkContext) + import sqlContext.implicits._ + val df = data.toDF() + val numFeatures = data.first().features.size + val featuresAttributes = Range(0, numFeatures).map { feature => + if (categoricalFeatures.contains(feature)) { + NominalAttribute.defaultAttr.withIndex(feature).withNumValues(categoricalFeatures(feature)) + } else { + NumericAttribute.defaultAttr.withIndex(feature) + } + }.toArray + val featuresMetadata = new AttributeGroup("features", featuresAttributes).toMetadata() + val labelAttribute = if (numClasses == 0) { + NumericAttribute.defaultAttr.withName("label") + } else { + NominalAttribute.defaultAttr.withName("label").withNumValues(numClasses) + } + val labelMetadata = labelAttribute.toMetadata() + df.select(df("features").as("features", featuresMetadata), + df("label").as("label", labelMetadata)) + } + + /** Java-friendly version of [[setMetadata()]] */ + def setMetadata( + data: JavaRDD[LabeledPoint], + categoricalFeatures: java.util.Map[java.lang.Integer, java.lang.Integer], + numClasses: Int): DataFrame = { + setMetadata(data.rdd, categoricalFeatures.asInstanceOf[java.util.Map[Int, Int]].asScala.toMap, + numClasses) + } + + /** + * Check if the two trees are exactly the same. + * Note: I hesitate to override Node.equals since it could cause problems if users + * make mistakes such as creating loops of Nodes. + * If the trees are not equal, this prints the two trees and throws an exception. + */ + def checkEqual(a: DecisionTreeModel, b: DecisionTreeModel): Unit = { + try { + checkEqual(a.rootNode, b.rootNode) + } catch { + case ex: Exception => + throw new AssertionError("checkEqual failed since the two trees were not identical.\n" + + "TREE A:\n" + a.toDebugString + "\n" + + "TREE B:\n" + b.toDebugString + "\n", ex) + } + } + + /** + * Return true iff the two nodes and their descendants are exactly the same. + * Note: I hesitate to override Node.equals since it could cause problems if users + * make mistakes such as creating loops of Nodes. + */ + private def checkEqual(a: Node, b: Node): Unit = { + assert(a.prediction === b.prediction) + assert(a.impurity === b.impurity) + (a, b) match { + case (aye: InternalNode, bee: InternalNode) => + assert(aye.split === bee.split) + checkEqual(aye.leftChild, bee.leftChild) + checkEqual(aye.rightChild, bee.rightChild) + case (aye: LeafNode, bee: LeafNode) => // do nothing + case _ => + throw new AssertionError("Found mismatched nodes") + } + } + + // TODO: Reinstate after adding ensembles + /** + * Check if the two models are exactly the same. + * If the models are not equal, this throws an exception. + */ + /* + def checkEqual(a: TreeEnsembleModel, b: TreeEnsembleModel): Unit = { + try { + a.getTrees.zip(b.getTrees).foreach { case (treeA, treeB) => + TreeTests.checkEqual(treeA, treeB) + } + assert(a.getTreeWeights === b.getTreeWeights) + } catch { + case ex: Exception => throw new AssertionError( + "checkEqual failed since the two tree ensembles were not identical") + } + } + */ +} diff --git a/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala index 1ce2987612378..88ea679eeaad5 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala @@ -21,19 +21,25 @@ import org.scalatest.FunSuite class ParamsSuite extends FunSuite { - val solver = new TestParams() - import solver.{inputCol, maxIter} - test("param") { + val solver = new TestParams() + import solver.{maxIter, inputCol} + assert(maxIter.name === "maxIter") assert(maxIter.doc === "max number of iterations") - assert(maxIter.defaultValue.get === 100) assert(maxIter.parent.eq(solver)) - assert(maxIter.toString === "maxIter: max number of iterations (default: 100)") - assert(inputCol.defaultValue === None) + assert(maxIter.toString === "maxIter: max number of iterations (default: 10)") + + solver.setMaxIter(5) + assert(maxIter.toString === "maxIter: max number of iterations (default: 10, current: 5)") + + assert(inputCol.toString === "inputCol: input column name (undefined)") } test("param pair") { + val solver = new TestParams() + import solver.maxIter + val pair0 = maxIter -> 5 val pair1 = maxIter.w(5) val pair2 = ParamPair(maxIter, 5) @@ -44,10 +50,12 @@ class ParamsSuite extends FunSuite { } test("param map") { + val solver = new TestParams() + import solver.{maxIter, inputCol} + val map0 = ParamMap.empty assert(!map0.contains(maxIter)) - assert(map0(maxIter) === maxIter.defaultValue.get) map0.put(maxIter, 10) assert(map0.contains(maxIter)) assert(map0(maxIter) === 10) @@ -78,23 +86,39 @@ class ParamsSuite extends FunSuite { } test("params") { + val solver = new TestParams() + import solver.{maxIter, inputCol} + val params = solver.params - assert(params.size === 2) + assert(params.length === 2) assert(params(0).eq(inputCol), "params must be ordered by name") assert(params(1).eq(maxIter)) + + assert(!solver.isSet(maxIter)) + assert(solver.isDefined(maxIter)) + assert(solver.getMaxIter === 10) + solver.setMaxIter(100) + assert(solver.isSet(maxIter)) + assert(solver.getMaxIter === 100) + assert(!solver.isSet(inputCol)) + assert(!solver.isDefined(inputCol)) + intercept[NoSuchElementException](solver.getInputCol) + assert(solver.explainParams() === Seq(inputCol, maxIter).mkString("\n")) + assert(solver.getParam("inputCol").eq(inputCol)) assert(solver.getParam("maxIter").eq(maxIter)) - intercept[NoSuchMethodException] { + intercept[NoSuchElementException] { solver.getParam("abc") } - assert(!solver.isSet(inputCol)) + intercept[IllegalArgumentException] { solver.validate() } solver.validate(ParamMap(inputCol -> "input")) solver.setInputCol("input") assert(solver.isSet(inputCol)) + assert(solver.isDefined(inputCol)) assert(solver.getInputCol === "input") solver.validate() intercept[IllegalArgumentException] { @@ -104,5 +128,8 @@ class ParamsSuite extends FunSuite { intercept[IllegalArgumentException] { solver.validate() } + + solver.clearMaxIter() + assert(!solver.isSet(maxIter)) } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala b/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala index 1a65883d78a71..641b64b42a5e7 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala @@ -17,20 +17,21 @@ package org.apache.spark.ml.param +import org.apache.spark.ml.param.shared.{HasInputCol, HasMaxIter} + /** A subclass of Params for testing. */ -class TestParams extends Params { +class TestParams extends Params with HasMaxIter with HasInputCol { - val maxIter = new IntParam(this, "maxIter", "max number of iterations", Some(100)) def setMaxIter(value: Int): this.type = { set(maxIter, value); this } - def getMaxIter: Int = get(maxIter) - - val inputCol = new Param[String](this, "inputCol", "input column name") def setInputCol(value: String): this.type = { set(inputCol, value); this } - def getInputCol: String = get(inputCol) - override def validate(paramMap: ParamMap) = { - val m = this.paramMap ++ paramMap + setDefault(maxIter -> 10) + + override def validate(paramMap: ParamMap): Unit = { + val m = extractParamMap(paramMap) require(m(maxIter) >= 0) require(m.contains(inputCol)) } + + def clearMaxIter(): this.type = clear(maxIter) } diff --git a/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala index cb7d57de35c34..fc7349330cf86 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala @@ -17,31 +17,42 @@ package org.apache.spark.ml.recommendation +import java.io.File import java.util.Random import scala.collection.mutable import scala.collection.mutable.ArrayBuffer +import scala.language.existentials import com.github.fommil.netlib.BLAS.{getInstance => blas} import org.scalatest.FunSuite -import org.apache.spark.Logging +import org.apache.spark.{Logging, SparkException} import org.apache.spark.ml.recommendation.ALS._ import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.util.TestingUtils._ import org.apache.spark.rdd.RDD import org.apache.spark.sql.{Row, SQLContext} +import org.apache.spark.util.Utils class ALSSuite extends FunSuite with MLlibTestSparkContext with Logging { private var sqlContext: SQLContext = _ + private var tempDir: File = _ override def beforeAll(): Unit = { super.beforeAll() + tempDir = Utils.createTempDir() + sc.setCheckpointDir(tempDir.getAbsolutePath) sqlContext = new SQLContext(sc) } + override def afterAll(): Unit = { + Utils.deleteRecursively(tempDir) + super.afterAll() + } + test("LocalIndexEncoder") { val random = new Random for (numBlocks <- Seq(1, 2, 5, 10, 20, 50, 100)) { @@ -58,39 +69,42 @@ class ALSSuite extends FunSuite with MLlibTestSparkContext with Logging { } } - test("normal equation construction with explict feedback") { + test("normal equation construction") { val k = 2 val ne0 = new NormalEquation(k) - .add(Array(1.0f, 2.0f), 3.0f) - .add(Array(4.0f, 5.0f), 6.0f) + .add(Array(1.0f, 2.0f), 3.0) + .add(Array(4.0f, 5.0f), 6.0, 2.0) // weighted assert(ne0.k === k) assert(ne0.triK === k * (k + 1) / 2) - assert(ne0.n === 2) // NumPy code that computes the expected values: // A = np.matrix("1 2; 4 5") // b = np.matrix("3; 6") - // ata = A.transpose() * A - // atb = A.transpose() * b - assert(Vectors.dense(ne0.ata) ~== Vectors.dense(17.0, 22.0, 29.0) relTol 1e-8) - assert(Vectors.dense(ne0.atb) ~== Vectors.dense(27.0, 36.0) relTol 1e-8) + // C = np.matrix(np.diag([1, 2])) + // ata = A.transpose() * C * A + // atb = A.transpose() * C * b + assert(Vectors.dense(ne0.ata) ~== Vectors.dense(33.0, 42.0, 54.0) relTol 1e-8) + assert(Vectors.dense(ne0.atb) ~== Vectors.dense(51.0, 66.0) relTol 1e-8) val ne1 = new NormalEquation(2) - .add(Array(7.0f, 8.0f), 9.0f) + .add(Array(7.0f, 8.0f), 9.0) ne0.merge(ne1) - assert(ne0.n === 3) // NumPy code that computes the expected values: // A = np.matrix("1 2; 4 5; 7 8") // b = np.matrix("3; 6; 9") - // ata = A.transpose() * A - // atb = A.transpose() * b - assert(Vectors.dense(ne0.ata) ~== Vectors.dense(66.0, 78.0, 93.0) relTol 1e-8) - assert(Vectors.dense(ne0.atb) ~== Vectors.dense(90.0, 108.0) relTol 1e-8) + // C = np.matrix(np.diag([1, 2, 1])) + // ata = A.transpose() * C * A + // atb = A.transpose() * C * b + assert(Vectors.dense(ne0.ata) ~== Vectors.dense(82.0, 98.0, 118.0) relTol 1e-8) + assert(Vectors.dense(ne0.atb) ~== Vectors.dense(114.0, 138.0) relTol 1e-8) intercept[IllegalArgumentException] { - ne0.add(Array(1.0f), 2.0f) + ne0.add(Array(1.0f), 2.0) + } + intercept[IllegalArgumentException] { + ne0.add(Array(1.0f, 2.0f, 3.0f), 4.0) } intercept[IllegalArgumentException] { - ne0.add(Array(1.0f, 2.0f, 3.0f), 4.0f) + ne0.add(Array(1.0f, 2.0f), 0.0, -1.0) } intercept[IllegalArgumentException] { val ne2 = new NormalEquation(3) @@ -98,41 +112,16 @@ class ALSSuite extends FunSuite with MLlibTestSparkContext with Logging { } ne0.reset() - assert(ne0.n === 0) assert(ne0.ata.forall(_ == 0.0)) assert(ne0.atb.forall(_ == 0.0)) } - test("normal equation construction with implicit feedback") { - val k = 2 - val alpha = 0.5 - val ne0 = new NormalEquation(k) - .addImplicit(Array(-5.0f, -4.0f), -3.0f, alpha) - .addImplicit(Array(-2.0f, -1.0f), 0.0f, alpha) - .addImplicit(Array(1.0f, 2.0f), 3.0f, alpha) - assert(ne0.k === k) - assert(ne0.triK === k * (k + 1) / 2) - assert(ne0.n === 0) // addImplicit doesn't increase the count. - // NumPy code that computes the expected values: - // alpha = 0.5 - // A = np.matrix("-5 -4; -2 -1; 1 2") - // b = np.matrix("-3; 0; 3") - // b1 = b > 0 - // c = 1.0 + alpha * np.abs(b) - // C = np.diag(c.A1) - // I = np.eye(3) - // ata = A.transpose() * (C - I) * A - // atb = A.transpose() * C * b1 - assert(Vectors.dense(ne0.ata) ~== Vectors.dense(39.0, 33.0, 30.0) relTol 1e-8) - assert(Vectors.dense(ne0.atb) ~== Vectors.dense(2.5, 5.0) relTol 1e-8) - } - test("CholeskySolver") { val k = 2 val ne0 = new NormalEquation(k) - .add(Array(1.0f, 2.0f), 4.0f) - .add(Array(1.0f, 3.0f), 9.0f) - .add(Array(1.0f, 4.0f), 16.0f) + .add(Array(1.0f, 2.0f), 4.0) + .add(Array(1.0f, 3.0f), 9.0) + .add(Array(1.0f, 4.0f), 16.0) val ne1 = new NormalEquation(k) .merge(ne0) @@ -144,13 +133,12 @@ class ALSSuite extends FunSuite with MLlibTestSparkContext with Logging { // x0 = np.linalg.lstsq(A, b)[0] assert(Vectors.dense(x0) ~== Vectors.dense(-8.333333, 6.0) relTol 1e-6) - assert(ne0.n === 0) assert(ne0.ata.forall(_ == 0.0)) assert(ne0.atb.forall(_ == 0.0)) - val x1 = chol.solve(ne1, 0.5).map(_.toDouble) + val x1 = chol.solve(ne1, 1.5).map(_.toDouble) // NumPy code that computes the expected solution, where lambda is scaled by n: - // x0 = np.linalg.solve(A.transpose() * A + 0.5 * 3 * np.eye(2), A.transpose() * b) + // x0 = np.linalg.solve(A.transpose() * A + 1.5 * np.eye(2), A.transpose() * b) assert(Vectors.dense(x1) ~== Vectors.dense(-0.1155556, 3.28) relTol 1e-6) } @@ -358,8 +346,8 @@ class ALSSuite extends FunSuite with MLlibTestSparkContext with Logging { .setNumUserBlocks(numUserBlocks) .setNumItemBlocks(numItemBlocks) val alpha = als.getAlpha - val model = als.fit(training) - val predictions = model.transform(test) + val model = als.fit(training.toDF()) + val predictions = model.transform(test.toDF()) .select("rating", "prediction") .map { case Row(rating: Float, prediction: Float) => (rating.toDouble, prediction.toDouble) @@ -455,4 +443,41 @@ class ALSSuite extends FunSuite with MLlibTestSparkContext with Logging { assert(isNonnegative(itemFactors)) // TODO: Validate the solution. } + + test("als partitioner is a projection") { + for (p <- Seq(1, 10, 100, 1000)) { + val part = new ALSPartitioner(p) + var k = 0 + while (k < p) { + assert(k === part.getPartition(k)) + assert(k === part.getPartition(k.toLong)) + k += 1 + } + } + } + + test("partitioner in returned factors") { + val (ratings, _) = genImplicitTestData(numUsers = 20, numItems = 40, rank = 2, noiseStd = 0.01) + val (userFactors, itemFactors) = ALS.train( + ratings, rank = 2, maxIter = 4, numUserBlocks = 3, numItemBlocks = 4) + for ((tpe, factors) <- Seq(("User", userFactors), ("Item", itemFactors))) { + assert(userFactors.partitioner.isDefined, s"$tpe factors should have partitioner.") + val part = userFactors.partitioner.get + userFactors.mapPartitionsWithIndex { (idx, items) => + items.foreach { case (id, _) => + if (part.getPartition(id) != idx) { + throw new SparkException(s"$tpe with ID $id should not be in partition $idx.") + } + } + Iterator.empty + }.count() + } + } + + test("als with large number of iterations") { + val (ratings, _) = genExplicitTestData(numUsers = 4, numItems = 4, rank = 1) + ALS.train(ratings, rank = 1, maxIter = 50, numUserBlocks = 2, numItemBlocks = 2) + ALS.train( + ratings, rank = 1, maxIter = 50, numUserBlocks = 2, numItemBlocks = 2, implicitPrefs = true) + } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala new file mode 100644 index 0000000000000..0b40fe33fae9d --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala @@ -0,0 +1,91 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.regression + +import org.scalatest.FunSuite + +import org.apache.spark.ml.impl.TreeTests +import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.mllib.tree.{DecisionTree => OldDecisionTree, + DecisionTreeSuite => OldDecisionTreeSuite} +import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.DataFrame + + +class DecisionTreeRegressorSuite extends FunSuite with MLlibTestSparkContext { + + import DecisionTreeRegressorSuite.compareAPIs + + private var categoricalDataPointsRDD: RDD[LabeledPoint] = _ + + override def beforeAll() { + super.beforeAll() + categoricalDataPointsRDD = + sc.parallelize(OldDecisionTreeSuite.generateCategoricalDataPoints()) + } + + ///////////////////////////////////////////////////////////////////////////// + // Tests calling train() + ///////////////////////////////////////////////////////////////////////////// + + test("Regression stump with 3-ary (ordered) categorical features") { + val dt = new DecisionTreeRegressor() + .setImpurity("variance") + .setMaxDepth(2) + .setMaxBins(100) + val categoricalFeatures = Map(0 -> 3, 1-> 3) + compareAPIs(categoricalDataPointsRDD, dt, categoricalFeatures) + } + + test("Regression stump with binary (ordered) categorical features") { + val dt = new DecisionTreeRegressor() + .setImpurity("variance") + .setMaxDepth(2) + .setMaxBins(100) + val categoricalFeatures = Map(0 -> 2, 1-> 2) + compareAPIs(categoricalDataPointsRDD, dt, categoricalFeatures) + } + + ///////////////////////////////////////////////////////////////////////////// + // Tests of model save/load + ///////////////////////////////////////////////////////////////////////////// + + // TODO: test("model save/load") +} + +private[ml] object DecisionTreeRegressorSuite extends FunSuite { + + /** + * Train 2 decision trees on the given dataset, one using the old API and one using the new API. + * Convert the old tree to the new format, compare them, and fail if they are not exactly equal. + */ + def compareAPIs( + data: RDD[LabeledPoint], + dt: DecisionTreeRegressor, + categoricalFeatures: Map[Int, Int]): Unit = { + val oldStrategy = dt.getOldStrategy(categoricalFeatures) + val oldTree = OldDecisionTree.train(data, oldStrategy) + val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses = 0) + val newTree = dt.fit(newData) + // Use parent, fittingParamMap from newTree since these are not checked anyways. + val oldTreeAsNew = DecisionTreeRegressionModel.fromOld(oldTree, newTree.parent, + newTree.fittingParamMap, categoricalFeatures) + TreeTests.checkEqual(oldTreeAsNew, newTree) + } +} diff --git a/mllib/src/test/scala/org/apache/spark/ml/util/TestingUtils.scala b/mllib/src/test/scala/org/apache/spark/ml/util/TestingUtils.scala new file mode 100644 index 0000000000000..c44cb61b34171 --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/ml/util/TestingUtils.scala @@ -0,0 +1,60 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.util + +import org.apache.spark.ml.Transformer +import org.apache.spark.sql.DataFrame +import org.apache.spark.sql.types.MetadataBuilder +import org.scalatest.FunSuite + +private[ml] object TestingUtils extends FunSuite { + + /** + * Test whether unrelated metadata are preserved for this transformer. + * This attaches extra metadata to a column, transforms the column, and check to ensure the + * extra metadata have not changed. + * @param data Input dataset + * @param transformer Transformer to test + * @param inputCol Unique input column for Transformer. This must be the ONLY input column. + * @param outputCol Output column to test for metadata presence. + */ + def testPreserveMetadata( + data: DataFrame, + transformer: Transformer, + inputCol: String, + outputCol: String): Unit = { + // Create some fake metadata + val origMetadata = data.schema(inputCol).metadata + val metaKey = "__testPreserveMetadata__fake_key" + val metaValue = 12345 + assert(!origMetadata.contains(metaKey), + s"Unit test with testPreserveMetadata will fail since metadata key was present: $metaKey") + val newMetadata = + new MetadataBuilder().withMetadata(origMetadata).putLong(metaKey, metaValue).build() + // Add metadata to the inputCol + val withMetadata = data.select(data(inputCol).as(inputCol, newMetadata)) + // Transform, and ensure extra metadata was not affected + val transformed = transformer.transform(withMetadata) + val transMetadata = transformed.schema(outputCol).metadata + assert(transMetadata.contains(metaKey), + "Unit test with testPreserveMetadata failed; extra metadata key was not present.") + assert(transMetadata.getLong(metaKey) === metaValue, + "Unit test with testPreserveMetadata failed; extra metadata value was wrong." + + s" Expected $metaValue but found ${transMetadata.getLong(metaKey)}") + } +} diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala index d2b40f2cae020..a26c52852c4d7 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala @@ -372,8 +372,12 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext with M testRDD2.cache() testRDD3.cache() + val numIteration = 10 + val lrA = new LogisticRegressionWithLBFGS().setIntercept(true) + lrA.optimizer.setNumIterations(numIteration) val lrB = new LogisticRegressionWithLBFGS().setIntercept(true).setFeatureScaling(false) + lrB.optimizer.setNumIterations(numIteration) val modelA1 = lrA.run(testRDD1, initialWeights) val modelA2 = lrA.run(testRDD2, initialWeights) @@ -421,6 +425,12 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext with M val model = lr.run(testRDD) + val numFeatures = testRDD.map(_.features.size).first() + val initialWeights = Vectors.dense(new Array[Double]((numFeatures + 1) * 2)) + val model2 = lr.run(testRDD, initialWeights) + + LogisticRegressionSuite.checkModelsEqual(model, model2) + /** * The following is the instruction to reproduce the model using R's glmnet package. * diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala index 64dcc0fb9f82c..ea89b17b7c08f 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala @@ -19,6 +19,9 @@ package org.apache.spark.mllib.classification import scala.util.Random +import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, argmax => brzArgmax, sum => brzSum, Axis} +import breeze.stats.distributions.{Multinomial => BrzMultinomial} + import org.scalatest.FunSuite import org.apache.spark.SparkException @@ -41,37 +44,48 @@ object NaiveBayesSuite { // Generate input of the form Y = (theta * x).argmax() def generateNaiveBayesInput( - pi: Array[Double], // 1XC - theta: Array[Array[Double]], // CXD - nPoints: Int, - seed: Int): Seq[LabeledPoint] = { + pi: Array[Double], // 1XC + theta: Array[Array[Double]], // CXD + nPoints: Int, + seed: Int, + modelType: String = "Multinomial", + sample: Int = 10): Seq[LabeledPoint] = { val D = theta(0).length val rnd = new Random(seed) - val _pi = pi.map(math.pow(math.E, _)) val _theta = theta.map(row => row.map(math.pow(math.E, _))) for (i <- 0 until nPoints) yield { val y = calcLabel(rnd.nextDouble(), _pi) - val xi = Array.tabulate[Double](D) { j => - if (rnd.nextDouble() < _theta(y)(j)) 1 else 0 + val xi = modelType match { + case "Bernoulli" => Array.tabulate[Double] (D) { j => + if (rnd.nextDouble () < _theta(y)(j) ) 1 else 0 + } + case "Multinomial" => + val mult = BrzMultinomial(BDV(_theta(y))) + val emptyMap = (0 until D).map(x => (x, 0.0)).toMap + val counts = emptyMap ++ mult.sample(sample).groupBy(x => x).map { + case (index, reps) => (index, reps.size.toDouble) + } + counts.toArray.sortBy(_._1).map(_._2) + case _ => + // This should never happen. + throw new UnknownError(s"NaiveBayesSuite found unknown ModelType: $modelType") } LabeledPoint(y, Vectors.dense(xi)) } } - private val smallPi = Array(0.5, 0.3, 0.2).map(math.log) + /** Bernoulli NaiveBayes with binary labels, 3 features */ + private val binaryBernoulliModel = new NaiveBayesModel(labels = Array(0.0, 1.0), + pi = Array(0.2, 0.8), theta = Array(Array(0.1, 0.3, 0.6), Array(0.2, 0.4, 0.4)), + "Bernoulli") - private val smallTheta = Array( - Array(0.91, 0.03, 0.03, 0.03), // label 0 - Array(0.03, 0.91, 0.03, 0.03), // label 1 - Array(0.03, 0.03, 0.91, 0.03) // label 2 - ).map(_.map(math.log)) - - /** Binary labels, 3 features */ - private val binaryModel = new NaiveBayesModel(labels = Array(0.0, 1.0), pi = Array(0.2, 0.8), - theta = Array(Array(0.1, 0.3, 0.6), Array(0.2, 0.4, 0.4))) + /** Multinomial NaiveBayes with binary labels, 3 features */ + private val binaryMultinomialModel = new NaiveBayesModel(labels = Array(0.0, 1.0), + pi = Array(0.2, 0.8), theta = Array(Array(0.1, 0.3, 0.6), Array(0.2, 0.4, 0.4)), + "Multinomial") } class NaiveBayesSuite extends FunSuite with MLlibTestSparkContext { @@ -85,19 +99,79 @@ class NaiveBayesSuite extends FunSuite with MLlibTestSparkContext { assert(numOfPredictions < input.length / 5) } - test("Naive Bayes") { - val nPoints = 10000 + def validateModelFit( + piData: Array[Double], + thetaData: Array[Array[Double]], + model: NaiveBayesModel): Unit = { + def closeFit(d1: Double, d2: Double, precision: Double): Boolean = { + (d1 - d2).abs <= precision + } + val modelIndex = (0 until piData.length).zip(model.labels.map(_.toInt)) + for (i <- modelIndex) { + assert(closeFit(math.exp(piData(i._2)), math.exp(model.pi(i._1)), 0.05)) + } + for (i <- modelIndex) { + for (j <- 0 until thetaData(i._2).length) { + assert(closeFit(math.exp(thetaData(i._2)(j)), math.exp(model.theta(i._1)(j)), 0.05)) + } + } + } + + test("get, set params") { + val nb = new NaiveBayes() + nb.setLambda(2.0) + assert(nb.getLambda === 2.0) + nb.setLambda(3.0) + assert(nb.getLambda === 3.0) + } + + test("Naive Bayes Multinomial") { + val nPoints = 1000 + val pi = Array(0.5, 0.1, 0.4).map(math.log) + val theta = Array( + Array(0.70, 0.10, 0.10, 0.10), // label 0 + Array(0.10, 0.70, 0.10, 0.10), // label 1 + Array(0.10, 0.10, 0.70, 0.10) // label 2 + ).map(_.map(math.log)) + + val testData = NaiveBayesSuite.generateNaiveBayesInput( + pi, theta, nPoints, 42, "Multinomial") + val testRDD = sc.parallelize(testData, 2) + testRDD.cache() + + val model = NaiveBayes.train(testRDD, 1.0, "Multinomial") + validateModelFit(pi, theta, model) - val pi = NaiveBayesSuite.smallPi - val theta = NaiveBayesSuite.smallTheta + val validationData = NaiveBayesSuite.generateNaiveBayesInput( + pi, theta, nPoints, 17, "Multinomial") + val validationRDD = sc.parallelize(validationData, 2) + + // Test prediction on RDD. + validatePrediction(model.predict(validationRDD.map(_.features)).collect(), validationData) + + // Test prediction on Array. + validatePrediction(validationData.map(row => model.predict(row.features)), validationData) + } - val testData = NaiveBayesSuite.generateNaiveBayesInput(pi, theta, nPoints, 42) + test("Naive Bayes Bernoulli") { + val nPoints = 10000 + val pi = Array(0.5, 0.3, 0.2).map(math.log) + val theta = Array( + Array(0.50, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.40), // label 0 + Array(0.02, 0.70, 0.10, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02), // label 1 + Array(0.02, 0.02, 0.60, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.30) // label 2 + ).map(_.map(math.log)) + + val testData = NaiveBayesSuite.generateNaiveBayesInput( + pi, theta, nPoints, 45, "Bernoulli") val testRDD = sc.parallelize(testData, 2) testRDD.cache() - val model = NaiveBayes.train(testRDD) + val model = NaiveBayes.train(testRDD, 1.0, "Bernoulli") + validateModelFit(pi, theta, model) - val validationData = NaiveBayesSuite.generateNaiveBayesInput(pi, theta, nPoints, 17) + val validationData = NaiveBayesSuite.generateNaiveBayesInput( + pi, theta, nPoints, 20, "Bernoulli") val validationRDD = sc.parallelize(validationData, 2) // Test prediction on RDD. @@ -134,19 +208,41 @@ class NaiveBayesSuite extends FunSuite with MLlibTestSparkContext { } } - test("model save/load") { - val model = NaiveBayesSuite.binaryModel + test("model save/load: 2.0 to 2.0") { + val tempDir = Utils.createTempDir() + val path = tempDir.toURI.toString + + Seq(NaiveBayesSuite.binaryBernoulliModel, NaiveBayesSuite.binaryMultinomialModel).map { + model => + // Save model, load it back, and compare. + try { + model.save(sc, path) + val sameModel = NaiveBayesModel.load(sc, path) + assert(model.labels === sameModel.labels) + assert(model.pi === sameModel.pi) + assert(model.theta === sameModel.theta) + assert(model.modelType === sameModel.modelType) + } finally { + Utils.deleteRecursively(tempDir) + } + } + } + + test("model save/load: 1.0 to 2.0") { + val model = NaiveBayesSuite.binaryMultinomialModel val tempDir = Utils.createTempDir() val path = tempDir.toURI.toString - // Save model, load it back, and compare. + // Save model as version 1.0, load it back, and compare. try { - model.save(sc, path) + val data = NaiveBayesModel.SaveLoadV1_0.Data(model.labels, model.pi, model.theta) + NaiveBayesModel.SaveLoadV1_0.save(sc, path, data) val sameModel = NaiveBayesModel.load(sc, path) assert(model.labels === sameModel.labels) assert(model.pi === sameModel.pi) assert(model.theta === sameModel.theta) + assert(model.modelType === "Multinomial") } finally { Utils.deleteRecursively(tempDir) } @@ -164,8 +260,8 @@ class NaiveBayesClusterSuite extends FunSuite with LocalClusterSparkContext { LabeledPoint(random.nextInt(2), Vectors.dense(Array.fill(n)(random.nextDouble()))) } } - // If we serialize data directly in the task closure, the size of the serialized task would be - // greater than 1MB and hence Spark would throw an error. + // If we serialize data directly in the task closure, the size of the serialized task + // would be greater than 1MB and hence Spark would throw an error. val model = NaiveBayes.train(examples) val predictions = model.predict(examples.map(_.features)) } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionSuite.scala index 8b3e6e5ce9249..5683b55e8500a 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionSuite.scala @@ -30,7 +30,7 @@ import org.apache.spark.streaming.TestSuiteBase class StreamingLogisticRegressionSuite extends FunSuite with TestSuiteBase { // use longer wait time to ensure job completion - override def maxWaitTimeMillis = 30000 + override def maxWaitTimeMillis: Int = 30000 // Test if we can accurately learn B for Y = logistic(BX) on streaming data test("parameter accuracy") { @@ -132,4 +132,31 @@ class StreamingLogisticRegressionSuite extends FunSuite with TestSuiteBase { assert(errors.forall(x => x <= 0.4)) } + // Test training combined with prediction + test("training and prediction") { + // create model initialized with zero weights + val model = new StreamingLogisticRegressionWithSGD() + .setInitialWeights(Vectors.dense(-0.1)) + .setStepSize(0.01) + .setNumIterations(10) + + // generate sequence of simulated data for testing + val numBatches = 10 + val nPoints = 100 + val testInput = (0 until numBatches).map { i => + LogisticRegressionSuite.generateLogisticInput(0.0, 5.0, nPoints, 42 * (i + 1)) + } + + // train and predict + val ssc = setupStreams(testInput, (inputDStream: DStream[LabeledPoint]) => { + model.trainOn(inputDStream) + model.predictOnValues(inputDStream.map(x => (x.label, x.features))) + }) + + val output: Seq[Seq[(Double, Double)]] = runStreams(ssc, numBatches, numBatches) + + // assert that prediction error improves, ensuring that the updated model is being used + val error = output.map(batch => batch.map(p => math.abs(p._1 - p._2)).sum / nPoints).toList + assert(error.head > 0.8 & error.last < 0.2) + } } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/GaussianMixtureSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/GaussianMixtureSuite.scala index c2cd56ea40adc..f356ffa3e3a26 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/GaussianMixtureSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/GaussianMixtureSuite.scala @@ -23,6 +23,7 @@ import org.apache.spark.mllib.linalg.{Vectors, Matrices} import org.apache.spark.mllib.stat.distribution.MultivariateGaussian import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.util.TestingUtils._ +import org.apache.spark.util.Utils class GaussianMixtureSuite extends FunSuite with MLlibTestSparkContext { test("single cluster") { @@ -31,7 +32,7 @@ class GaussianMixtureSuite extends FunSuite with MLlibTestSparkContext { Vectors.dense(5.0, 10.0), Vectors.dense(4.0, 11.0) )) - + // expectations val Ew = 1.0 val Emu = Vectors.dense(5.0, 10.0) @@ -44,17 +45,12 @@ class GaussianMixtureSuite extends FunSuite with MLlibTestSparkContext { assert(gmm.gaussians(0).mu ~== Emu absTol 1E-5) assert(gmm.gaussians(0).sigma ~== Esigma absTol 1E-5) } + } test("two clusters") { - val data = sc.parallelize(Array( - Vectors.dense(-5.1971), Vectors.dense(-2.5359), Vectors.dense(-3.8220), - Vectors.dense(-5.2211), Vectors.dense(-5.0602), Vectors.dense( 4.7118), - Vectors.dense( 6.8989), Vectors.dense( 3.4592), Vectors.dense( 4.6322), - Vectors.dense( 5.7048), Vectors.dense( 4.6567), Vectors.dense( 5.5026), - Vectors.dense( 4.5605), Vectors.dense( 5.2043), Vectors.dense( 6.2734) - )) - + val data = sc.parallelize(GaussianTestData.data) + // we set an initial gaussian to induce expected results val initialGmm = new GaussianMixtureModel( Array(0.5, 0.5), @@ -63,7 +59,7 @@ class GaussianMixtureSuite extends FunSuite with MLlibTestSparkContext { new MultivariateGaussian(Vectors.dense(1.0), Matrices.dense(1, 1, Array(1.0))) ) ) - + val Ew = Array(1.0 / 3.0, 2.0 / 3.0) val Emu = Array(Vectors.dense(-4.3673), Vectors.dense(5.1604)) val Esigma = Array(Matrices.dense(1, 1, Array(1.1098)), Matrices.dense(1, 1, Array(0.86644))) @@ -72,7 +68,7 @@ class GaussianMixtureSuite extends FunSuite with MLlibTestSparkContext { .setK(2) .setInitialModel(initialGmm) .run(data) - + assert(gmm.weights(0) ~== Ew(0) absTol 1E-3) assert(gmm.weights(1) ~== Ew(1) absTol 1E-3) assert(gmm.gaussians(0).mu ~== Emu(0) absTol 1E-3) @@ -80,4 +76,88 @@ class GaussianMixtureSuite extends FunSuite with MLlibTestSparkContext { assert(gmm.gaussians(0).sigma ~== Esigma(0) absTol 1E-3) assert(gmm.gaussians(1).sigma ~== Esigma(1) absTol 1E-3) } + + test("single cluster with sparse data") { + val data = sc.parallelize(Array( + Vectors.sparse(3, Array(0, 2), Array(4.0, 2.0)), + Vectors.sparse(3, Array(0, 2), Array(2.0, 4.0)), + Vectors.sparse(3, Array(1), Array(6.0)) + )) + + val Ew = 1.0 + val Emu = Vectors.dense(2.0, 2.0, 2.0) + val Esigma = Matrices.dense(3, 3, + Array(8.0 / 3.0, -4.0, 4.0 / 3.0, -4.0, 8.0, -4.0, 4.0 / 3.0, -4.0, 8.0 / 3.0) + ) + + val seeds = Array(42, 1994, 27, 11, 0) + seeds.foreach { seed => + val gmm = new GaussianMixture().setK(1).setSeed(seed).run(data) + assert(gmm.weights(0) ~== Ew absTol 1E-5) + assert(gmm.gaussians(0).mu ~== Emu absTol 1E-5) + assert(gmm.gaussians(0).sigma ~== Esigma absTol 1E-5) + } + } + + test("two clusters with sparse data") { + val data = sc.parallelize(GaussianTestData.data) + val sparseData = data.map(point => Vectors.sparse(1, Array(0), point.toArray)) + // we set an initial gaussian to induce expected results + val initialGmm = new GaussianMixtureModel( + Array(0.5, 0.5), + Array( + new MultivariateGaussian(Vectors.dense(-1.0), Matrices.dense(1, 1, Array(1.0))), + new MultivariateGaussian(Vectors.dense(1.0), Matrices.dense(1, 1, Array(1.0))) + ) + ) + val Ew = Array(1.0 / 3.0, 2.0 / 3.0) + val Emu = Array(Vectors.dense(-4.3673), Vectors.dense(5.1604)) + val Esigma = Array(Matrices.dense(1, 1, Array(1.1098)), Matrices.dense(1, 1, Array(0.86644))) + + val sparseGMM = new GaussianMixture() + .setK(2) + .setInitialModel(initialGmm) + .run(data) + + assert(sparseGMM.weights(0) ~== Ew(0) absTol 1E-3) + assert(sparseGMM.weights(1) ~== Ew(1) absTol 1E-3) + assert(sparseGMM.gaussians(0).mu ~== Emu(0) absTol 1E-3) + assert(sparseGMM.gaussians(1).mu ~== Emu(1) absTol 1E-3) + assert(sparseGMM.gaussians(0).sigma ~== Esigma(0) absTol 1E-3) + assert(sparseGMM.gaussians(1).sigma ~== Esigma(1) absTol 1E-3) + } + + test("model save / load") { + val data = sc.parallelize(GaussianTestData.data) + + val gmm = new GaussianMixture().setK(2).setSeed(0).run(data) + val tempDir = Utils.createTempDir() + val path = tempDir.toURI.toString + + try { + gmm.save(sc, path) + + // TODO: GaussianMixtureModel should implement equals/hashcode directly. + val sameModel = GaussianMixtureModel.load(sc, path) + assert(sameModel.k === gmm.k) + (0 until sameModel.k).foreach { i => + assert(sameModel.gaussians(i).mu === gmm.gaussians(i).mu) + assert(sameModel.gaussians(i).sigma === gmm.gaussians(i).sigma) + } + } finally { + Utils.deleteRecursively(tempDir) + } + } + + object GaussianTestData { + + val data = Array( + Vectors.dense(-5.1971), Vectors.dense(-2.5359), Vectors.dense(-3.8220), + Vectors.dense(-5.2211), Vectors.dense(-5.0602), Vectors.dense( 4.7118), + Vectors.dense( 6.8989), Vectors.dense( 3.4592), Vectors.dense( 4.6322), + Vectors.dense( 5.7048), Vectors.dense( 4.6567), Vectors.dense( 5.5026), + Vectors.dense( 4.5605), Vectors.dense( 5.2043), Vectors.dense( 6.2734) + ) + + } } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala index caee5917000aa..0f2b26d462ad2 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala @@ -21,9 +21,10 @@ import scala.util.Random import org.scalatest.FunSuite -import org.apache.spark.mllib.linalg.{Vector, Vectors} +import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors} import org.apache.spark.mllib.util.{LocalClusterSparkContext, MLlibTestSparkContext} import org.apache.spark.mllib.util.TestingUtils._ +import org.apache.spark.util.Utils class KMeansSuite extends FunSuite with MLlibTestSparkContext { @@ -198,9 +199,13 @@ class KMeansSuite extends FunSuite with MLlibTestSparkContext { test("k-means|| initialization") { case class VectorWithCompare(x: Vector) extends Ordered[VectorWithCompare] { - @Override def compare(that: VectorWithCompare): Int = { - if(this.x.toArray.foldLeft[Double](0.0)((acc, x) => acc + x * x) > - that.x.toArray.foldLeft[Double](0.0)((acc, x) => acc + x * x)) -1 else 1 + override def compare(that: VectorWithCompare): Int = { + if (this.x.toArray.foldLeft[Double](0.0)((acc, x) => acc + x * x) > + that.x.toArray.foldLeft[Double](0.0)((acc, x) => acc + x * x)) { + -1 + } else { + 1 + } } } @@ -257,6 +262,47 @@ class KMeansSuite extends FunSuite with MLlibTestSparkContext { assert(predicts(0) != predicts(3)) } } + + test("model save/load") { + val tempDir = Utils.createTempDir() + val path = tempDir.toURI.toString + + Array(true, false).foreach { case selector => + val model = KMeansSuite.createModel(10, 3, selector) + // Save model, load it back, and compare. + try { + model.save(sc, path) + val sameModel = KMeansModel.load(sc, path) + KMeansSuite.checkEqual(model, sameModel) + } finally { + Utils.deleteRecursively(tempDir) + } + } + } +} + +object KMeansSuite extends FunSuite { + def createModel(dim: Int, k: Int, isSparse: Boolean): KMeansModel = { + val singlePoint = isSparse match { + case true => + Vectors.sparse(dim, Array.empty[Int], Array.empty[Double]) + case _ => + Vectors.dense(Array.fill[Double](dim)(0.0)) + } + new KMeansModel(Array.fill[Vector](k)(singlePoint)) + } + + def checkEqual(a: KMeansModel, b: KMeansModel): Unit = { + assert(a.k === b.k) + a.clusterCenters.zip(b.clusterCenters).foreach { + case (ca: SparseVector, cb: SparseVector) => + assert(ca === cb) + case (ca: DenseVector, cb: DenseVector) => + assert(ca === cb) + case _ => + throw new AssertionError("checkEqual failed since the two clusters were not identical.\n") + } + } } class KMeansClusterSuite extends FunSuite with LocalClusterSparkContext { diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala index 302d751eb8a94..15de10fd13a19 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala @@ -19,7 +19,7 @@ package org.apache.spark.mllib.clustering import org.scalatest.FunSuite -import org.apache.spark.mllib.linalg.{DenseMatrix, Matrix, Vectors} +import org.apache.spark.mllib.linalg.{Vector, DenseMatrix, Matrix, Vectors} import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.util.TestingUtils._ @@ -141,7 +141,7 @@ private[clustering] object LDASuite { (terms.toArray, termWeights.toArray) } - def tinyCorpus = Array( + def tinyCorpus: Array[(Long, Vector)] = Array( Vectors.dense(1, 3, 0, 2, 8), Vectors.dense(0, 2, 1, 0, 4), Vectors.dense(2, 3, 12, 3, 1), diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/PowerIterationClusteringSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/PowerIterationClusteringSuite.scala index 03ecd9ca730be..6d6fe6fe46bab 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/PowerIterationClusteringSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/PowerIterationClusteringSuite.scala @@ -18,12 +18,15 @@ package org.apache.spark.mllib.clustering import scala.collection.mutable +import scala.util.Random import org.scalatest.FunSuite +import org.apache.spark.SparkContext import org.apache.spark.graphx.{Edge, Graph} import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.util.TestingUtils._ +import org.apache.spark.util.Utils class PowerIterationClusteringSuite extends FunSuite with MLlibTestSparkContext { @@ -51,8 +54,8 @@ class PowerIterationClusteringSuite extends FunSuite with MLlibTestSparkContext .setK(2) .run(sc.parallelize(similarities, 2)) val predictions = Array.fill(2)(mutable.Set.empty[Long]) - model.assignments.collect().foreach { case (i, c) => - predictions(c) += i + model.assignments.collect().foreach { a => + predictions(a.cluster) += a.id } assert(predictions.toSet == Set((0 to 3).toSet, (4 to 15).toSet)) @@ -61,8 +64,8 @@ class PowerIterationClusteringSuite extends FunSuite with MLlibTestSparkContext .setInitializationMode("degree") .run(sc.parallelize(similarities, 2)) val predictions2 = Array.fill(2)(mutable.Set.empty[Long]) - model2.assignments.collect().foreach { case (i, c) => - predictions2(c) += i + model2.assignments.collect().foreach { a => + predictions2(a.cluster) += a.id } assert(predictions2.toSet == Set((0 to 3).toSet, (4 to 15).toSet)) } @@ -110,4 +113,35 @@ class PowerIterationClusteringSuite extends FunSuite with MLlibTestSparkContext assert(x ~== u1(i.toInt) absTol 1e-14) } } + + test("model save/load") { + val tempDir = Utils.createTempDir() + val path = tempDir.toURI.toString + val model = PowerIterationClusteringSuite.createModel(sc, 3, 10) + try { + model.save(sc, path) + val sameModel = PowerIterationClusteringModel.load(sc, path) + PowerIterationClusteringSuite.checkEqual(model, sameModel) + } finally { + Utils.deleteRecursively(tempDir) + } + } +} + +object PowerIterationClusteringSuite extends FunSuite { + def createModel(sc: SparkContext, k: Int, nPoints: Int): PowerIterationClusteringModel = { + val assignments = sc.parallelize( + (0 until nPoints).map(p => PowerIterationClustering.Assignment(p, Random.nextInt(k)))) + new PowerIterationClusteringModel(k, assignments) + } + + def checkEqual(a: PowerIterationClusteringModel, b: PowerIterationClusteringModel): Unit = { + assert(a.k === b.k) + + val aAssignments = a.assignments.map(x => (x.id, x.cluster)) + val bAssignments = b.assignments.map(x => (x.id, x.cluster)) + val unequalElements = aAssignments.join(bAssignments).filter { + case (id, (c1, c2)) => c1 != c2 }.count() + assert(unequalElements === 0L) + } } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/StreamingKMeansSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/StreamingKMeansSuite.scala index 850c9fce507cd..f90025d535e45 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/StreamingKMeansSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/StreamingKMeansSuite.scala @@ -27,7 +27,7 @@ import org.apache.spark.util.random.XORShiftRandom class StreamingKMeansSuite extends FunSuite with TestSuiteBase { - override def maxWaitTimeMillis = 30000 + override def maxWaitTimeMillis: Int = 30000 test("accuracy for single center and equivalence to grand average") { // set parameters @@ -59,7 +59,7 @@ class StreamingKMeansSuite extends FunSuite with TestSuiteBase { // estimated center from streaming should exactly match the arithmetic mean of all data points // because the decay factor is set to 1.0 val grandMean = - input.flatten.map(x => x.toBreeze).reduce(_+_) / (numBatches * numPoints).toDouble + input.flatten.map(x => x.toBreeze).reduce(_ + _) / (numBatches * numPoints).toDouble assert(model.latestModel().clusterCenters(0) ~== Vectors.dense(grandMean.toArray) absTol 1E-5) } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/feature/Word2VecSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/feature/Word2VecSuite.scala index 52278690dbd89..98a98a7599bcb 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/feature/Word2VecSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/feature/Word2VecSuite.scala @@ -21,6 +21,9 @@ import org.scalatest.FunSuite import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.mllib.util.TestingUtils._ +import org.apache.spark.util.Utils + class Word2VecSuite extends FunSuite with MLlibTestSparkContext { // TODO: add more tests @@ -51,4 +54,27 @@ class Word2VecSuite extends FunSuite with MLlibTestSparkContext { assert(syms(0)._1 == "taiwan") assert(syms(1)._1 == "japan") } + + test("model load / save") { + + val word2VecMap = Map( + ("china", Array(0.50f, 0.50f, 0.50f, 0.50f)), + ("japan", Array(0.40f, 0.50f, 0.50f, 0.50f)), + ("taiwan", Array(0.60f, 0.50f, 0.50f, 0.50f)), + ("korea", Array(0.45f, 0.60f, 0.60f, 0.60f)) + ) + val model = new Word2VecModel(word2VecMap) + + val tempDir = Utils.createTempDir() + val path = tempDir.toURI.toString + + try { + model.save(sc, path) + val sameModel = Word2VecModel.load(sc, path) + assert(sameModel.getVectors.mapValues(_.toSeq) === model.getVectors.mapValues(_.toSeq)) + } finally { + Utils.deleteRecursively(tempDir) + } + + } } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/fpm/FPGrowthSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/fpm/FPGrowthSuite.scala index 68128284b8608..bd5b9cc3afa10 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/fpm/FPGrowthSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/fpm/FPGrowthSuite.scala @@ -46,8 +46,8 @@ class FPGrowthSuite extends FunSuite with MLlibTestSparkContext { .setMinSupport(0.5) .setNumPartitions(2) .run(rdd) - val freqItemsets3 = model3.freqItemsets.collect().map { case (items, count) => - (items.toSet, count) + val freqItemsets3 = model3.freqItemsets.collect().map { itemset => + (itemset.items.toSet, itemset.freq) } val expected = Set( (Set("s"), 3L), (Set("z"), 5L), (Set("x"), 4L), (Set("t"), 3L), (Set("y"), 3L), @@ -96,10 +96,10 @@ class FPGrowthSuite extends FunSuite with MLlibTestSparkContext { .setMinSupport(0.5) .setNumPartitions(2) .run(rdd) - assert(model3.freqItemsets.first()._1.getClass === Array(1).getClass, + assert(model3.freqItemsets.first().items.getClass === Array(1).getClass, "frequent itemsets should use primitive arrays") - val freqItemsets3 = model3.freqItemsets.collect().map { case (items, count) => - (items.toSet, count) + val freqItemsets3 = model3.freqItemsets.collect().map { itemset => + (itemset.items.toSet, itemset.freq) } val expected = Set( (Set(1), 6L), (Set(2), 5L), (Set(3), 5L), (Set(4), 4L), diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala index b0b78acd6df16..002cb253862b5 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala @@ -166,6 +166,14 @@ class BLASSuite extends FunSuite { syr(alpha, y, dA) } } + + val xSparse = new SparseVector(4, Array(0, 2, 3), Array(1.0, 3.0, 4.0)) + val dD = new DenseMatrix(4, 4, + Array(0.0, 1.2, 2.2, 3.1, 1.2, 3.2, 5.3, 4.6, 2.2, 5.3, 1.8, 3.0, 3.1, 4.6, 3.0, 0.8)) + syr(0.1, xSparse, dD) + val expectedSparse = new DenseMatrix(4, 4, + Array(0.1, 1.2, 2.5, 3.5, 1.2, 3.2, 5.3, 4.6, 2.5, 5.3, 2.7, 4.2, 3.5, 4.6, 4.2, 2.4)) + assert(dD ~== expectedSparse absTol 1e-15) } test("gemm") { diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala index c098b5458fe6b..86119ec38101e 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala @@ -424,4 +424,35 @@ class MatricesSuite extends FunSuite { assert(mat.rowIndices.toSeq === Seq(3, 0, 2, 1)) assert(mat.values.toSeq === Seq(1.0, 2.0, 3.0, 4.0)) } + + test("MatrixUDT") { + val dm1 = new DenseMatrix(2, 2, Array(0.9, 1.2, 2.3, 9.8)) + val dm2 = new DenseMatrix(3, 2, Array(0.0, 1.21, 2.3, 9.8, 9.0, 0.0)) + val dm3 = new DenseMatrix(0, 0, Array()) + val sm1 = dm1.toSparse + val sm2 = dm2.toSparse + val sm3 = dm3.toSparse + val mUDT = new MatrixUDT() + Seq(dm1, dm2, dm3, sm1, sm2, sm3).foreach { + mat => assert(mat.toArray === mUDT.deserialize(mUDT.serialize(mat)).toArray) + } + assert(mUDT.typeName == "matrix") + assert(mUDT.simpleString == "matrix") + } + + test("toString") { + val empty = Matrices.ones(0, 0) + empty.toString(0, 0) + + val mat = Matrices.rand(5, 10, new Random()) + mat.toString(-1, -5) + mat.toString(0, 0) + mat.toString(Int.MinValue, Int.MinValue) + mat.toString(Int.MaxValue, Int.MaxValue) + var lines = mat.toString(6, 50).lines.toArray + assert(lines.size == 5 && lines.forall(_.size <= 50)) + + lines = mat.toString(5, 100).lines.toArray + assert(lines.size == 5 && lines.forall(_.size <= 100)) + } } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala index 5def899cea117..2839c4c289b2d 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala @@ -187,6 +187,8 @@ class VectorsSuite extends FunSuite { for (v <- Seq(dv0, dv1, sv0, sv1)) { assert(v === udt.deserialize(udt.serialize(v))) } + assert(udt.typeName == "vector") + assert(udt.simpleString == "vector") } test("fromBreeze") { diff --git a/mllib/src/test/scala/org/apache/spark/mllib/optimization/NNLSSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/optimization/NNLSSuite.scala index 82c327bd49fcd..22855e4e8f247 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/optimization/NNLSSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/optimization/NNLSSuite.scala @@ -55,7 +55,7 @@ class NNLSSuite extends FunSuite { for (k <- 0 until 100) { val (ata, atb) = genOnesData(n, rand) - val x = new DoubleMatrix(NNLS.solve(ata, atb, ws)) + val x = new DoubleMatrix(NNLS.solve(ata.data, atb.data, ws)) assert(x.length === n) val answer = DoubleMatrix.ones(n, 1) SimpleBlas.axpy(-1.0, answer, x) @@ -79,7 +79,7 @@ class NNLSSuite extends FunSuite { val goodx = Array(0.13025, 0.54506, 0.2874, 0.0, 0.028628) val ws = NNLS.createWorkspace(n) - val x = NNLS.solve(ata, atb, ws) + val x = NNLS.solve(ata.data, atb.data, ws) for (i <- 0 until n) { assert(x(i) ~== goodx(i) absTol 1E-3) assert(x(i) >= 0) @@ -104,7 +104,7 @@ class NNLSSuite extends FunSuite { val ws = NNLS.createWorkspace(n) - val x = new DoubleMatrix(NNLS.solve(ata, atb, ws)) + val x = new DoubleMatrix(NNLS.solve(ata.data, atb.data, ws)) val obj = computeObjectiveValue(ata, atb, x) assert(obj < refObj + 1E-5) diff --git a/mllib/src/test/scala/org/apache/spark/mllib/random/RandomRDDsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/random/RandomRDDsSuite.scala index 6395188a0842a..63f2ea916d457 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/random/RandomRDDsSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/random/RandomRDDsSuite.scala @@ -181,7 +181,8 @@ class RandomRDDsSuite extends FunSuite with MLlibTestSparkContext with Serializa val poisson = RandomRDDs.poissonVectorRDD(sc, poissonMean, rows, cols, parts, seed) testGeneratedVectorRDD(poisson, rows, cols, parts, poissonMean, math.sqrt(poissonMean), 0.1) - val exponential = RandomRDDs.exponentialVectorRDD(sc, exponentialMean, rows, cols, parts, seed) + val exponential = + RandomRDDs.exponentialVectorRDD(sc, exponentialMean, rows, cols, parts, seed) testGeneratedVectorRDD(exponential, rows, cols, parts, exponentialMean, exponentialMean, 0.1) val gamma = RandomRDDs.gammaVectorRDD(sc, gammaShape, gammaScale, rows, cols, parts, seed) @@ -197,7 +198,7 @@ private[random] class MockDistro extends RandomDataGenerator[Double] { // This allows us to check that each partition has a different seed override def nextValue(): Double = seed.toDouble - override def setSeed(seed: Long) = this.seed = seed + override def setSeed(seed: Long): Unit = this.seed = seed override def copy(): MockDistro = new MockDistro } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctionsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctionsSuite.scala new file mode 100644 index 0000000000000..1ac7c12c4e8e6 --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctionsSuite.scala @@ -0,0 +1,36 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.mllib.rdd + +import org.scalatest.FunSuite + +import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.mllib.rdd.MLPairRDDFunctions._ + +class MLPairRDDFunctionsSuite extends FunSuite with MLlibTestSparkContext { + test("topByKey") { + val topMap = sc.parallelize(Array((1, 1), (1, 2), (3, 2), (3, 7), (3, 5), (5, 1), (5, 3)), 2) + .topByKey(2) + .collectAsMap() + + assert(topMap.size === 3) + assert(topMap(1) === Array(2, 1)) + assert(topMap(3) === Array(7, 5)) + assert(topMap(5) === Array(3, 1)) + } +} diff --git a/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala index 8775c0ca9df84..b3798940ddc38 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala @@ -203,6 +203,7 @@ class ALSSuite extends FunSuite with MLlibTestSparkContext { * @param numProductBlocks number of product blocks to partition products into * @param negativeFactors whether the generated user/product factors can have negative entries */ + // scalastyle:off def testALS( users: Int, products: Int, @@ -216,6 +217,8 @@ class ALSSuite extends FunSuite with MLlibTestSparkContext { numUserBlocks: Int = -1, numProductBlocks: Int = -1, negativeFactors: Boolean = true) { + // scalastyle:on + val (sampledRatings, trueRatings, truePrefs) = ALSSuite.generateRatings(users, products, features, samplingRate, implicitPrefs, negativeWeights, negativeFactors) diff --git a/mllib/src/test/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModelSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModelSuite.scala index b9caecc904a23..9801e87576744 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModelSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModelSuite.scala @@ -22,6 +22,7 @@ import org.scalatest.FunSuite import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.util.TestingUtils._ import org.apache.spark.rdd.RDD +import org.apache.spark.util.Utils class MatrixFactorizationModelSuite extends FunSuite with MLlibTestSparkContext { @@ -53,4 +54,22 @@ class MatrixFactorizationModelSuite extends FunSuite with MLlibTestSparkContext new MatrixFactorizationModel(rank, userFeatures, prodFeatures1) } } + + test("save/load") { + val model = new MatrixFactorizationModel(rank, userFeatures, prodFeatures) + val tempDir = Utils.createTempDir() + val path = tempDir.toURI.toString + def collect(features: RDD[(Int, Array[Double])]): Set[(Int, Seq[Double])] = { + features.mapValues(_.toSeq).collect().toSet + } + try { + model.save(sc, path) + val newModel = MatrixFactorizationModel.load(sc, path) + assert(newModel.rank === rank) + assert(collect(newModel.userFeatures) === collect(userFeatures)) + assert(collect(newModel.productFeatures) === collect(prodFeatures)) + } finally { + Utils.deleteRecursively(tempDir) + } + } } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/RidgeRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/RidgeRegressionSuite.scala index 43d61151e2471..d6c93cc0e49cd 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/regression/RidgeRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/RidgeRegressionSuite.scala @@ -35,7 +35,7 @@ private object RidgeRegressionSuite { class RidgeRegressionSuite extends FunSuite with MLlibTestSparkContext { - def predictionError(predictions: Seq[Double], input: Seq[LabeledPoint]) = { + def predictionError(predictions: Seq[Double], input: Seq[LabeledPoint]): Double = { predictions.zip(input).map { case (prediction, expected) => (prediction - expected.label) * (prediction - expected.label) }.reduceLeft(_ + _) / predictions.size diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala index 70b43ddb7daf5..26604dbe6c1ef 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala @@ -29,7 +29,7 @@ import org.apache.spark.streaming.TestSuiteBase class StreamingLinearRegressionSuite extends FunSuite with TestSuiteBase { // use longer wait time to ensure job completion - override def maxWaitTimeMillis = 20000 + override def maxWaitTimeMillis: Int = 20000 // Assert that two values are equal within tolerance epsilon def assertEqual(v1: Double, v2: Double, epsilon: Double) { @@ -139,4 +139,32 @@ class StreamingLinearRegressionSuite extends FunSuite with TestSuiteBase { val errors = output.map(batch => batch.map(p => math.abs(p._1 - p._2)).sum / nPoints) assert(errors.forall(x => x <= 0.1)) } + + // Test training combined with prediction + test("training and prediction") { + // create model initialized with zero weights + val model = new StreamingLinearRegressionWithSGD() + .setInitialWeights(Vectors.dense(0.0, 0.0)) + .setStepSize(0.2) + .setNumIterations(25) + + // generate sequence of simulated data for testing + val numBatches = 10 + val nPoints = 100 + val testInput = (0 until numBatches).map { i => + LinearDataGenerator.generateLinearInput(0.0, Array(10.0, 10.0), nPoints, 42 * (i + 1)) + } + + // train and predict + val ssc = setupStreams(testInput, (inputDStream: DStream[LabeledPoint]) => { + model.trainOn(inputDStream) + model.predictOnValues(inputDStream.map(x => (x.label, x.features))) + }) + + val output: Seq[Seq[(Double, Double)]] = runStreams(ssc, numBatches, numBatches) + + // assert that prediction error improves, ensuring that the updated model is being used + val error = output.map(batch => batch.map(p => math.abs(p._1 - p._2)).sum / nPoints).toList + assert((error.head - error.last) > 2) + } } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/stat/KernelDensitySuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/stat/KernelDensitySuite.scala new file mode 100644 index 0000000000000..16ecae23dd9d4 --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/mllib/stat/KernelDensitySuite.scala @@ -0,0 +1,47 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.mllib.stat + +import org.scalatest.FunSuite + +import org.apache.commons.math3.distribution.NormalDistribution + +import org.apache.spark.mllib.util.MLlibTestSparkContext + +class KernelDensitySuite extends FunSuite with MLlibTestSparkContext { + test("kernel density single sample") { + val rdd = sc.parallelize(Array(5.0)) + val evaluationPoints = Array(5.0, 6.0) + val densities = KernelDensity.estimate(rdd, 3.0, evaluationPoints) + val normal = new NormalDistribution(5.0, 3.0) + val acceptableErr = 1e-6 + assert(densities(0) - normal.density(5.0) < acceptableErr) + assert(densities(0) - normal.density(6.0) < acceptableErr) + } + + test("kernel density multiple samples") { + val rdd = sc.parallelize(Array(5.0, 10.0)) + val evaluationPoints = Array(5.0, 6.0) + val densities = KernelDensity.estimate(rdd, 3.0, evaluationPoints) + val normal1 = new NormalDistribution(5.0, 3.0) + val normal2 = new NormalDistribution(10.0, 3.0) + val acceptableErr = 1e-6 + assert(densities(0) - (normal1.density(5.0) + normal2.density(5.0)) / 2 < acceptableErr) + assert(densities(0) - (normal1.density(6.0) + normal2.density(6.0)) / 2 < acceptableErr) + } +} diff --git a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala index 9347eaf9221a8..249b8eae19b17 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala @@ -29,11 +29,17 @@ import org.apache.spark.mllib.tree.configuration.FeatureType._ import org.apache.spark.mllib.tree.configuration.{QuantileStrategy, Strategy} import org.apache.spark.mllib.tree.impl.{BaggedPoint, DecisionTreeMetadata, TreePoint} import org.apache.spark.mllib.tree.impurity.{Entropy, Gini, Variance} -import org.apache.spark.mllib.tree.model.{InformationGainStats, DecisionTreeModel, Node} +import org.apache.spark.mllib.tree.model._ import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.util.Utils + class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext { + ///////////////////////////////////////////////////////////////////////////// + // Tests examining individual elements of training + ///////////////////////////////////////////////////////////////////////////// + test("Binary classification with continuous features: split and bin calculation") { val arr = DecisionTreeSuite.generateOrderedLabeledPointsWithLabel1() assert(arr.length === 1000) @@ -188,7 +194,7 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext { assert(splits.length === 2) assert(bins.length === 2) assert(splits(0).length === 3) - assert(bins(0).length === 6) + assert(bins(0).length === 0) // Expecting 2^2 - 1 = 3 bins/splits assert(splits(0)(0).feature === 0) @@ -226,41 +232,6 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext { assert(splits(1)(2).categories.contains(0.0)) assert(splits(1)(2).categories.contains(1.0)) - // Check bins. - - assert(bins(0)(0).category === Double.MinValue) - assert(bins(0)(0).lowSplit.categories.length === 0) - assert(bins(0)(0).highSplit.categories.length === 1) - assert(bins(0)(0).highSplit.categories.contains(0.0)) - assert(bins(1)(0).category === Double.MinValue) - assert(bins(1)(0).lowSplit.categories.length === 0) - assert(bins(1)(0).highSplit.categories.length === 1) - assert(bins(1)(0).highSplit.categories.contains(0.0)) - - assert(bins(0)(1).category === Double.MinValue) - assert(bins(0)(1).lowSplit.categories.length === 1) - assert(bins(0)(1).lowSplit.categories.contains(0.0)) - assert(bins(0)(1).highSplit.categories.length === 1) - assert(bins(0)(1).highSplit.categories.contains(1.0)) - assert(bins(1)(1).category === Double.MinValue) - assert(bins(1)(1).lowSplit.categories.length === 1) - assert(bins(1)(1).lowSplit.categories.contains(0.0)) - assert(bins(1)(1).highSplit.categories.length === 1) - assert(bins(1)(1).highSplit.categories.contains(1.0)) - - assert(bins(0)(2).category === Double.MinValue) - assert(bins(0)(2).lowSplit.categories.length === 1) - assert(bins(0)(2).lowSplit.categories.contains(1.0)) - assert(bins(0)(2).highSplit.categories.length === 2) - assert(bins(0)(2).highSplit.categories.contains(1.0)) - assert(bins(0)(2).highSplit.categories.contains(0.0)) - assert(bins(1)(2).category === Double.MinValue) - assert(bins(1)(2).lowSplit.categories.length === 1) - assert(bins(1)(2).lowSplit.categories.contains(1.0)) - assert(bins(1)(2).highSplit.categories.length === 2) - assert(bins(1)(2).highSplit.categories.contains(1.0)) - assert(bins(1)(2).highSplit.categories.contains(0.0)) - } test("Multiclass classification with ordered categorical features: split and bin calculations") { @@ -287,6 +258,165 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext { assert(bins(0).length === 0) } + test("Avoid aggregation on the last level") { + val arr = Array( + LabeledPoint(0.0, Vectors.dense(1.0, 0.0, 0.0)), + LabeledPoint(1.0, Vectors.dense(0.0, 1.0, 1.0)), + LabeledPoint(0.0, Vectors.dense(2.0, 0.0, 0.0)), + LabeledPoint(1.0, Vectors.dense(0.0, 2.0, 1.0))) + val input = sc.parallelize(arr) + + val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 1, + numClasses = 2, categoricalFeaturesInfo = Map(0 -> 3)) + val metadata = DecisionTreeMetadata.buildMetadata(input, strategy) + val (splits, bins) = DecisionTree.findSplitsBins(input, metadata) + + val treeInput = TreePoint.convertToTreeRDD(input, bins, metadata) + val baggedInput = BaggedPoint.convertToBaggedRDD(treeInput, 1.0, 1, false) + + val topNode = Node.emptyNode(nodeIndex = 1) + assert(topNode.predict.predict === Double.MinValue) + assert(topNode.impurity === -1.0) + assert(topNode.isLeaf === false) + + val nodesForGroup = Map((0, Array(topNode))) + val treeToNodeToIndexInfo = Map((0, Map( + (topNode.id, new RandomForest.NodeIndexInfo(0, None)) + ))) + val nodeQueue = new mutable.Queue[(Int, Node)]() + DecisionTree.findBestSplits(baggedInput, metadata, Array(topNode), + nodesForGroup, treeToNodeToIndexInfo, splits, bins, nodeQueue) + + // don't enqueue leaf nodes into node queue + assert(nodeQueue.isEmpty) + + // set impurity and predict for topNode + assert(topNode.predict.predict !== Double.MinValue) + assert(topNode.impurity !== -1.0) + + // set impurity and predict for child nodes + assert(topNode.leftNode.get.predict.predict === 0.0) + assert(topNode.rightNode.get.predict.predict === 1.0) + assert(topNode.leftNode.get.impurity === 0.0) + assert(topNode.rightNode.get.impurity === 0.0) + } + + test("Avoid aggregation if impurity is 0.0") { + val arr = Array( + LabeledPoint(0.0, Vectors.dense(1.0, 0.0, 0.0)), + LabeledPoint(1.0, Vectors.dense(0.0, 1.0, 1.0)), + LabeledPoint(0.0, Vectors.dense(2.0, 0.0, 0.0)), + LabeledPoint(1.0, Vectors.dense(0.0, 2.0, 1.0))) + val input = sc.parallelize(arr) + + val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 5, + numClasses = 2, categoricalFeaturesInfo = Map(0 -> 3)) + val metadata = DecisionTreeMetadata.buildMetadata(input, strategy) + val (splits, bins) = DecisionTree.findSplitsBins(input, metadata) + + val treeInput = TreePoint.convertToTreeRDD(input, bins, metadata) + val baggedInput = BaggedPoint.convertToBaggedRDD(treeInput, 1.0, 1, false) + + val topNode = Node.emptyNode(nodeIndex = 1) + assert(topNode.predict.predict === Double.MinValue) + assert(topNode.impurity === -1.0) + assert(topNode.isLeaf === false) + + val nodesForGroup = Map((0, Array(topNode))) + val treeToNodeToIndexInfo = Map((0, Map( + (topNode.id, new RandomForest.NodeIndexInfo(0, None)) + ))) + val nodeQueue = new mutable.Queue[(Int, Node)]() + DecisionTree.findBestSplits(baggedInput, metadata, Array(topNode), + nodesForGroup, treeToNodeToIndexInfo, splits, bins, nodeQueue) + + // don't enqueue a node into node queue if its impurity is 0.0 + assert(nodeQueue.isEmpty) + + // set impurity and predict for topNode + assert(topNode.predict.predict !== Double.MinValue) + assert(topNode.impurity !== -1.0) + + // set impurity and predict for child nodes + assert(topNode.leftNode.get.predict.predict === 0.0) + assert(topNode.rightNode.get.predict.predict === 1.0) + assert(topNode.leftNode.get.impurity === 0.0) + assert(topNode.rightNode.get.impurity === 0.0) + } + + test("Second level node building with vs. without groups") { + val arr = DecisionTreeSuite.generateOrderedLabeledPoints() + assert(arr.length === 1000) + val rdd = sc.parallelize(arr) + val strategy = new Strategy(Classification, Entropy, 3, 2, 100) + val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy) + val (splits, bins) = DecisionTree.findSplitsBins(rdd, metadata) + assert(splits.length === 2) + assert(splits(0).length === 99) + assert(bins.length === 2) + assert(bins(0).length === 100) + + // Train a 1-node model + val strategyOneNode = new Strategy(Classification, Entropy, maxDepth = 1, + numClasses = 2, maxBins = 100) + val modelOneNode = DecisionTree.train(rdd, strategyOneNode) + val rootNode1 = modelOneNode.topNode.deepCopy() + val rootNode2 = modelOneNode.topNode.deepCopy() + assert(rootNode1.leftNode.nonEmpty) + assert(rootNode1.rightNode.nonEmpty) + + val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata) + val baggedInput = BaggedPoint.convertToBaggedRDD(treeInput, 1.0, 1, false) + + // Single group second level tree construction. + val nodesForGroup = Map((0, Array(rootNode1.leftNode.get, rootNode1.rightNode.get))) + val treeToNodeToIndexInfo = Map((0, Map( + (rootNode1.leftNode.get.id, new RandomForest.NodeIndexInfo(0, None)), + (rootNode1.rightNode.get.id, new RandomForest.NodeIndexInfo(1, None))))) + val nodeQueue = new mutable.Queue[(Int, Node)]() + DecisionTree.findBestSplits(baggedInput, metadata, Array(rootNode1), + nodesForGroup, treeToNodeToIndexInfo, splits, bins, nodeQueue) + val children1 = new Array[Node](2) + children1(0) = rootNode1.leftNode.get + children1(1) = rootNode1.rightNode.get + + // Train one second-level node at a time. + val nodesForGroupA = Map((0, Array(rootNode2.leftNode.get))) + val treeToNodeToIndexInfoA = Map((0, Map( + (rootNode2.leftNode.get.id, new RandomForest.NodeIndexInfo(0, None))))) + nodeQueue.clear() + DecisionTree.findBestSplits(baggedInput, metadata, Array(rootNode2), + nodesForGroupA, treeToNodeToIndexInfoA, splits, bins, nodeQueue) + val nodesForGroupB = Map((0, Array(rootNode2.rightNode.get))) + val treeToNodeToIndexInfoB = Map((0, Map( + (rootNode2.rightNode.get.id, new RandomForest.NodeIndexInfo(0, None))))) + nodeQueue.clear() + DecisionTree.findBestSplits(baggedInput, metadata, Array(rootNode2), + nodesForGroupB, treeToNodeToIndexInfoB, splits, bins, nodeQueue) + val children2 = new Array[Node](2) + children2(0) = rootNode2.leftNode.get + children2(1) = rootNode2.rightNode.get + + // Verify whether the splits obtained using single group and multiple group level + // construction strategies are the same. + for (i <- 0 until 2) { + assert(children1(i).stats.nonEmpty && children1(i).stats.get.gain > 0) + assert(children2(i).stats.nonEmpty && children2(i).stats.get.gain > 0) + assert(children1(i).split === children2(i).split) + assert(children1(i).stats.nonEmpty && children2(i).stats.nonEmpty) + val stats1 = children1(i).stats.get + val stats2 = children2(i).stats.get + assert(stats1.gain === stats2.gain) + assert(stats1.impurity === stats2.impurity) + assert(stats1.leftImpurity === stats2.leftImpurity) + assert(stats1.rightImpurity === stats2.rightImpurity) + assert(children1(i).predict.predict === children2(i).predict.predict) + } + } + + ///////////////////////////////////////////////////////////////////////////// + // Tests calling train() + ///////////////////////////////////////////////////////////////////////////// test("Binary classification stump with ordered categorical features") { val arr = DecisionTreeSuite.generateCategoricalDataPoints() @@ -471,76 +601,6 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext { assert(rootNode.predict.predict === 1) } - test("Second level node building with vs. without groups") { - val arr = DecisionTreeSuite.generateOrderedLabeledPoints() - assert(arr.length === 1000) - val rdd = sc.parallelize(arr) - val strategy = new Strategy(Classification, Entropy, 3, 2, 100) - val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy) - val (splits, bins) = DecisionTree.findSplitsBins(rdd, metadata) - assert(splits.length === 2) - assert(splits(0).length === 99) - assert(bins.length === 2) - assert(bins(0).length === 100) - - // Train a 1-node model - val strategyOneNode = new Strategy(Classification, Entropy, maxDepth = 1, - numClasses = 2, maxBins = 100) - val modelOneNode = DecisionTree.train(rdd, strategyOneNode) - val rootNode1 = modelOneNode.topNode.deepCopy() - val rootNode2 = modelOneNode.topNode.deepCopy() - assert(rootNode1.leftNode.nonEmpty) - assert(rootNode1.rightNode.nonEmpty) - - val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata) - val baggedInput = BaggedPoint.convertToBaggedRDD(treeInput, 1.0, 1, false) - - // Single group second level tree construction. - val nodesForGroup = Map((0, Array(rootNode1.leftNode.get, rootNode1.rightNode.get))) - val treeToNodeToIndexInfo = Map((0, Map( - (rootNode1.leftNode.get.id, new RandomForest.NodeIndexInfo(0, None)), - (rootNode1.rightNode.get.id, new RandomForest.NodeIndexInfo(1, None))))) - val nodeQueue = new mutable.Queue[(Int, Node)]() - DecisionTree.findBestSplits(baggedInput, metadata, Array(rootNode1), - nodesForGroup, treeToNodeToIndexInfo, splits, bins, nodeQueue) - val children1 = new Array[Node](2) - children1(0) = rootNode1.leftNode.get - children1(1) = rootNode1.rightNode.get - - // Train one second-level node at a time. - val nodesForGroupA = Map((0, Array(rootNode2.leftNode.get))) - val treeToNodeToIndexInfoA = Map((0, Map( - (rootNode2.leftNode.get.id, new RandomForest.NodeIndexInfo(0, None))))) - nodeQueue.clear() - DecisionTree.findBestSplits(baggedInput, metadata, Array(rootNode2), - nodesForGroupA, treeToNodeToIndexInfoA, splits, bins, nodeQueue) - val nodesForGroupB = Map((0, Array(rootNode2.rightNode.get))) - val treeToNodeToIndexInfoB = Map((0, Map( - (rootNode2.rightNode.get.id, new RandomForest.NodeIndexInfo(0, None))))) - nodeQueue.clear() - DecisionTree.findBestSplits(baggedInput, metadata, Array(rootNode2), - nodesForGroupB, treeToNodeToIndexInfoB, splits, bins, nodeQueue) - val children2 = new Array[Node](2) - children2(0) = rootNode2.leftNode.get - children2(1) = rootNode2.rightNode.get - - // Verify whether the splits obtained using single group and multiple group level - // construction strategies are the same. - for (i <- 0 until 2) { - assert(children1(i).stats.nonEmpty && children1(i).stats.get.gain > 0) - assert(children2(i).stats.nonEmpty && children2(i).stats.get.gain > 0) - assert(children1(i).split === children2(i).split) - assert(children1(i).stats.nonEmpty && children2(i).stats.nonEmpty) - val stats1 = children1(i).stats.get - val stats2 = children2(i).stats.get - assert(stats1.gain === stats2.gain) - assert(stats1.impurity === stats2.impurity) - assert(stats1.leftImpurity === stats2.leftImpurity) - assert(stats1.rightImpurity === stats2.rightImpurity) - assert(children1(i).predict.predict === children2(i).predict.predict) - } - } - test("Multiclass classification stump with 3-ary (unordered) categorical features") { val arr = DecisionTreeSuite.generateCategoricalDataPointsForMulticlass() val rdd = sc.parallelize(arr) @@ -561,11 +621,11 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext { } test("Binary classification stump with 1 continuous feature, to check off-by-1 error") { - val arr = new Array[LabeledPoint](4) - arr(0) = new LabeledPoint(0.0, Vectors.dense(0.0)) - arr(1) = new LabeledPoint(1.0, Vectors.dense(1.0)) - arr(2) = new LabeledPoint(1.0, Vectors.dense(2.0)) - arr(3) = new LabeledPoint(1.0, Vectors.dense(3.0)) + val arr = Array( + LabeledPoint(0.0, Vectors.dense(0.0)), + LabeledPoint(1.0, Vectors.dense(1.0)), + LabeledPoint(1.0, Vectors.dense(2.0)), + LabeledPoint(1.0, Vectors.dense(3.0))) val rdd = sc.parallelize(arr) val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 4, numClasses = 2) @@ -577,11 +637,11 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext { } test("Binary classification stump with 2 continuous features") { - val arr = new Array[LabeledPoint](4) - arr(0) = new LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 0.0)))) - arr(1) = new LabeledPoint(1.0, Vectors.sparse(2, Seq((1, 1.0)))) - arr(2) = new LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 0.0)))) - arr(3) = new LabeledPoint(1.0, Vectors.sparse(2, Seq((1, 2.0)))) + val arr = Array( + LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 0.0)))), + LabeledPoint(1.0, Vectors.sparse(2, Seq((1, 1.0)))), + LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 0.0)))), + LabeledPoint(1.0, Vectors.sparse(2, Seq((1, 2.0))))) val rdd = sc.parallelize(arr) val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 4, @@ -701,11 +761,10 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext { } test("split must satisfy min instances per node requirements") { - val arr = new Array[LabeledPoint](3) - arr(0) = new LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 0.0)))) - arr(1) = new LabeledPoint(1.0, Vectors.sparse(2, Seq((1, 1.0)))) - arr(2) = new LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 1.0)))) - + val arr = Array( + LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 0.0)))), + LabeledPoint(1.0, Vectors.sparse(2, Seq((1, 1.0)))), + LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 1.0))))) val rdd = sc.parallelize(arr) val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 2, numClasses = 2, minInstancesPerNode = 2) @@ -728,11 +787,11 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext { test("do not choose split that does not satisfy min instance per node requirements") { // if a split does not satisfy min instances per node requirements, // this split is invalid, even though the information gain of split is large. - val arr = new Array[LabeledPoint](4) - arr(0) = new LabeledPoint(0.0, Vectors.dense(0.0, 1.0)) - arr(1) = new LabeledPoint(1.0, Vectors.dense(1.0, 1.0)) - arr(2) = new LabeledPoint(0.0, Vectors.dense(0.0, 0.0)) - arr(3) = new LabeledPoint(0.0, Vectors.dense(0.0, 0.0)) + val arr = Array( + LabeledPoint(0.0, Vectors.dense(0.0, 1.0)), + LabeledPoint(1.0, Vectors.dense(1.0, 1.0)), + LabeledPoint(0.0, Vectors.dense(0.0, 0.0)), + LabeledPoint(0.0, Vectors.dense(0.0, 0.0))) val rdd = sc.parallelize(arr) val strategy = new Strategy(algo = Classification, impurity = Gini, @@ -748,10 +807,10 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext { } test("split must satisfy min info gain requirements") { - val arr = new Array[LabeledPoint](3) - arr(0) = new LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 0.0)))) - arr(1) = new LabeledPoint(1.0, Vectors.sparse(2, Seq((1, 1.0)))) - arr(2) = new LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 1.0)))) + val arr = Array( + LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 0.0)))), + LabeledPoint(1.0, Vectors.sparse(2, Seq((1, 1.0)))), + LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 1.0))))) val input = sc.parallelize(arr) val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 2, @@ -772,94 +831,35 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext { assert(gain == InformationGainStats.invalidInformationGainStats) } - test("Avoid aggregation on the last level") { - val arr = new Array[LabeledPoint](4) - arr(0) = new LabeledPoint(0.0, Vectors.dense(1.0, 0.0, 0.0)) - arr(1) = new LabeledPoint(1.0, Vectors.dense(0.0, 1.0, 1.0)) - arr(2) = new LabeledPoint(0.0, Vectors.dense(2.0, 0.0, 0.0)) - arr(3) = new LabeledPoint(1.0, Vectors.dense(0.0, 2.0, 1.0)) - val input = sc.parallelize(arr) - - val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 1, - numClasses = 2, categoricalFeaturesInfo = Map(0 -> 3)) - val metadata = DecisionTreeMetadata.buildMetadata(input, strategy) - val (splits, bins) = DecisionTree.findSplitsBins(input, metadata) - - val treeInput = TreePoint.convertToTreeRDD(input, bins, metadata) - val baggedInput = BaggedPoint.convertToBaggedRDD(treeInput, 1.0, 1, false) + ///////////////////////////////////////////////////////////////////////////// + // Tests of model save/load + ///////////////////////////////////////////////////////////////////////////// - val topNode = Node.emptyNode(nodeIndex = 1) - assert(topNode.predict.predict === Double.MinValue) - assert(topNode.impurity === -1.0) - assert(topNode.isLeaf === false) - - val nodesForGroup = Map((0, Array(topNode))) - val treeToNodeToIndexInfo = Map((0, Map( - (topNode.id, new RandomForest.NodeIndexInfo(0, None)) - ))) - val nodeQueue = new mutable.Queue[(Int, Node)]() - DecisionTree.findBestSplits(baggedInput, metadata, Array(topNode), - nodesForGroup, treeToNodeToIndexInfo, splits, bins, nodeQueue) - - // don't enqueue leaf nodes into node queue - assert(nodeQueue.isEmpty) - - // set impurity and predict for topNode - assert(topNode.predict.predict !== Double.MinValue) - assert(topNode.impurity !== -1.0) - - // set impurity and predict for child nodes - assert(topNode.leftNode.get.predict.predict === 0.0) - assert(topNode.rightNode.get.predict.predict === 1.0) - assert(topNode.leftNode.get.impurity === 0.0) - assert(topNode.rightNode.get.impurity === 0.0) + test("Node.subtreeIterator") { + val model = DecisionTreeSuite.createModel(Classification) + val nodeIds = model.topNode.subtreeIterator.map(_.id).toArray.sorted + assert(nodeIds === DecisionTreeSuite.createdModelNodeIds) } - test("Avoid aggregation if impurity is 0.0") { - val arr = new Array[LabeledPoint](4) - arr(0) = new LabeledPoint(0.0, Vectors.dense(1.0, 0.0, 0.0)) - arr(1) = new LabeledPoint(1.0, Vectors.dense(0.0, 1.0, 1.0)) - arr(2) = new LabeledPoint(0.0, Vectors.dense(2.0, 0.0, 0.0)) - arr(3) = new LabeledPoint(1.0, Vectors.dense(0.0, 2.0, 1.0)) - val input = sc.parallelize(arr) - - val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 5, - numClasses = 2, categoricalFeaturesInfo = Map(0 -> 3)) - val metadata = DecisionTreeMetadata.buildMetadata(input, strategy) - val (splits, bins) = DecisionTree.findSplitsBins(input, metadata) - - val treeInput = TreePoint.convertToTreeRDD(input, bins, metadata) - val baggedInput = BaggedPoint.convertToBaggedRDD(treeInput, 1.0, 1, false) - - val topNode = Node.emptyNode(nodeIndex = 1) - assert(topNode.predict.predict === Double.MinValue) - assert(topNode.impurity === -1.0) - assert(topNode.isLeaf === false) - - val nodesForGroup = Map((0, Array(topNode))) - val treeToNodeToIndexInfo = Map((0, Map( - (topNode.id, new RandomForest.NodeIndexInfo(0, None)) - ))) - val nodeQueue = new mutable.Queue[(Int, Node)]() - DecisionTree.findBestSplits(baggedInput, metadata, Array(topNode), - nodesForGroup, treeToNodeToIndexInfo, splits, bins, nodeQueue) - - // don't enqueue a node into node queue if its impurity is 0.0 - assert(nodeQueue.isEmpty) - - // set impurity and predict for topNode - assert(topNode.predict.predict !== Double.MinValue) - assert(topNode.impurity !== -1.0) - - // set impurity and predict for child nodes - assert(topNode.leftNode.get.predict.predict === 0.0) - assert(topNode.rightNode.get.predict.predict === 1.0) - assert(topNode.leftNode.get.impurity === 0.0) - assert(topNode.rightNode.get.impurity === 0.0) + test("model save/load") { + val tempDir = Utils.createTempDir() + val path = tempDir.toURI.toString + + Array(Classification, Regression).foreach { algo => + val model = DecisionTreeSuite.createModel(algo) + // Save model, load it back, and compare. + try { + model.save(sc, path) + val sameModel = DecisionTreeModel.load(sc, path) + DecisionTreeSuite.checkEqual(model, sameModel) + } finally { + Utils.deleteRecursively(tempDir) + } + } } } -object DecisionTreeSuite { +object DecisionTreeSuite extends FunSuite { def validateClassifier( model: DecisionTreeModel, @@ -979,4 +979,96 @@ object DecisionTreeSuite { arr } + /** Create a leaf node with the given node ID */ + private def createLeafNode(id: Int): Node = { + Node(nodeIndex = id, new Predict(0.0, 1.0), impurity = 0.5, isLeaf = true) + } + + /** + * Create an internal node with the given node ID and feature type. + * Note: This does NOT set the child nodes. + */ + private def createInternalNode(id: Int, featureType: FeatureType): Node = { + val node = Node(nodeIndex = id, new Predict(0.0, 1.0), impurity = 0.5, isLeaf = false) + featureType match { + case Continuous => + node.split = Some(new Split(feature = 0, threshold = 0.5, Continuous, + categories = List.empty[Double])) + case Categorical => + node.split = Some(new Split(feature = 1, threshold = 0.0, Categorical, + categories = List(0.0, 1.0))) + } + // TODO: The information gain stats should be consistent with the same info stored in children. + node.stats = Some(new InformationGainStats(gain = 0.1, impurity = 0.2, + leftImpurity = 0.3, rightImpurity = 0.4, new Predict(1.0, 0.4), new Predict(0.0, 0.6))) + node + } + + /** + * Create a tree model. This is deterministic and contains a variety of node and feature types. + * TODO: Update this to be a correct tree (with matching probabilities, impurities, etc.) + */ + private[mllib] def createModel(algo: Algo): DecisionTreeModel = { + val topNode = createInternalNode(id = 1, Continuous) + val (node2, node3) = (createLeafNode(id = 2), createInternalNode(id = 3, Categorical)) + val (node6, node7) = (createLeafNode(id = 6), createLeafNode(id = 7)) + topNode.leftNode = Some(node2) + topNode.rightNode = Some(node3) + node3.leftNode = Some(node6) + node3.rightNode = Some(node7) + new DecisionTreeModel(topNode, algo) + } + + /** Sorted Node IDs matching the model returned by [[createModel()]] */ + private val createdModelNodeIds = Array(1, 2, 3, 6, 7) + + /** + * Check if the two trees are exactly the same. + * Note: I hesitate to override Node.equals since it could cause problems if users + * make mistakes such as creating loops of Nodes. + * If the trees are not equal, this prints the two trees and throws an exception. + */ + private[mllib] def checkEqual(a: DecisionTreeModel, b: DecisionTreeModel): Unit = { + try { + assert(a.algo === b.algo) + checkEqual(a.topNode, b.topNode) + } catch { + case ex: Exception => + throw new AssertionError("checkEqual failed since the two trees were not identical.\n" + + "TREE A:\n" + a.toDebugString + "\n" + + "TREE B:\n" + b.toDebugString + "\n", ex) + } + } + + /** + * Return true iff the two nodes and their descendents are exactly the same. + * Note: I hesitate to override Node.equals since it could cause problems if users + * make mistakes such as creating loops of Nodes. + */ + private def checkEqual(a: Node, b: Node): Unit = { + assert(a.id === b.id) + assert(a.predict === b.predict) + assert(a.impurity === b.impurity) + assert(a.isLeaf === b.isLeaf) + assert(a.split === b.split) + (a.stats, b.stats) match { + // TODO: Check other fields besides the infomation gain. + case (Some(aStats), Some(bStats)) => assert(aStats.gain === bStats.gain) + case (None, None) => + case _ => throw new AssertionError( + s"Only one instance has stats defined. (a.stats: ${a.stats}, b.stats: ${b.stats})") + } + (a.leftNode, b.leftNode) match { + case (Some(aNode), Some(bNode)) => checkEqual(aNode, bNode) + case (None, None) => + case _ => throw new AssertionError("Only one instance has leftNode defined. " + + s"(a.leftNode: ${a.leftNode}, b.leftNode: ${b.leftNode})") + } + (a.rightNode, b.rightNode) match { + case (Some(aNode: Node), Some(bNode: Node)) => checkEqual(aNode, bNode) + case (None, None) => + case _ => throw new AssertionError("Only one instance has rightNode defined. " + + s"(a.rightNode: ${a.rightNode}, b.rightNode: ${b.rightNode})") + } + } } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/tree/GradientBoostedTreesSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/tree/GradientBoostedTreesSuite.scala index e8341a5d0d104..55b0bac7d49fe 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/tree/GradientBoostedTreesSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/tree/GradientBoostedTreesSuite.scala @@ -24,8 +24,10 @@ import org.apache.spark.mllib.tree.configuration.Algo._ import org.apache.spark.mllib.tree.configuration.{BoostingStrategy, Strategy} import org.apache.spark.mllib.tree.impurity.Variance import org.apache.spark.mllib.tree.loss.{AbsoluteError, SquaredError, LogLoss} - +import org.apache.spark.mllib.tree.model.GradientBoostedTreesModel import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.util.Utils + /** * Test suite for [[GradientBoostedTrees]]. @@ -35,32 +37,30 @@ class GradientBoostedTreesSuite extends FunSuite with MLlibTestSparkContext { test("Regression with continuous features: SquaredError") { GradientBoostedTreesSuite.testCombinations.foreach { case (numIterations, learningRate, subsamplingRate) => - GradientBoostedTreesSuite.randomSeeds.foreach { randomSeed => - val rdd = sc.parallelize(GradientBoostedTreesSuite.data, 2) - - val treeStrategy = new Strategy(algo = Regression, impurity = Variance, maxDepth = 2, - categoricalFeaturesInfo = Map.empty, subsamplingRate = subsamplingRate) - val boostingStrategy = - new BoostingStrategy(treeStrategy, SquaredError, numIterations, learningRate) - - val gbt = GradientBoostedTrees.train(rdd, boostingStrategy) - - assert(gbt.trees.size === numIterations) - try { - EnsembleTestHelper.validateRegressor(gbt, GradientBoostedTreesSuite.data, 0.06) - } catch { - case e: java.lang.AssertionError => - println(s"FAILED for numIterations=$numIterations, learningRate=$learningRate," + - s" subsamplingRate=$subsamplingRate") - throw e - } + val rdd = sc.parallelize(GradientBoostedTreesSuite.data, 2) + + val treeStrategy = new Strategy(algo = Regression, impurity = Variance, maxDepth = 2, + categoricalFeaturesInfo = Map.empty, subsamplingRate = subsamplingRate) + val boostingStrategy = + new BoostingStrategy(treeStrategy, SquaredError, numIterations, learningRate) - val remappedInput = rdd.map(x => new LabeledPoint((x.label * 2) - 1, x.features)) - val dt = DecisionTree.train(remappedInput, treeStrategy) + val gbt = GradientBoostedTrees.train(rdd, boostingStrategy) - // Make sure trees are the same. - assert(gbt.trees.head.toString == dt.toString) + assert(gbt.trees.size === numIterations) + try { + EnsembleTestHelper.validateRegressor(gbt, GradientBoostedTreesSuite.data, 0.06) + } catch { + case e: java.lang.AssertionError => + println(s"FAILED for numIterations=$numIterations, learningRate=$learningRate," + + s" subsamplingRate=$subsamplingRate") + throw e } + + val remappedInput = rdd.map(x => new LabeledPoint((x.label * 2) - 1, x.features)) + val dt = DecisionTree.train(remappedInput, treeStrategy) + + // Make sure trees are the same. + assert(gbt.trees.head.toString == dt.toString) } } @@ -133,14 +133,85 @@ class GradientBoostedTreesSuite extends FunSuite with MLlibTestSparkContext { BoostingStrategy.defaultParams(algo) } } + + test("model save/load") { + val tempDir = Utils.createTempDir() + val path = tempDir.toURI.toString + + val trees = Range(0, 3).map(_ => DecisionTreeSuite.createModel(Regression)).toArray + val treeWeights = Array(0.1, 0.3, 1.1) + + Array(Classification, Regression).foreach { algo => + val model = new GradientBoostedTreesModel(algo, trees, treeWeights) + + // Save model, load it back, and compare. + try { + model.save(sc, path) + val sameModel = GradientBoostedTreesModel.load(sc, path) + assert(model.algo == sameModel.algo) + model.trees.zip(sameModel.trees).foreach { case (treeA, treeB) => + DecisionTreeSuite.checkEqual(treeA, treeB) + } + assert(model.treeWeights === sameModel.treeWeights) + } finally { + Utils.deleteRecursively(tempDir) + } + } + } + + test("runWithValidation stops early and performs better on a validation dataset") { + // Set numIterations large enough so that it stops early. + val numIterations = 20 + val trainRdd = sc.parallelize(GradientBoostedTreesSuite.trainData, 2) + val validateRdd = sc.parallelize(GradientBoostedTreesSuite.validateData, 2) + + val algos = Array(Regression, Regression, Classification) + val losses = Array(SquaredError, AbsoluteError, LogLoss) + (algos zip losses) map { + case (algo, loss) => { + val treeStrategy = new Strategy(algo = algo, impurity = Variance, maxDepth = 2, + categoricalFeaturesInfo = Map.empty) + val boostingStrategy = + new BoostingStrategy(treeStrategy, loss, numIterations, validationTol = 0.0) + val gbtValidate = new GradientBoostedTrees(boostingStrategy) + .runWithValidation(trainRdd, validateRdd) + val numTrees = gbtValidate.numTrees + assert(numTrees !== numIterations) + + // Test that it performs better on the validation dataset. + val gbt = new GradientBoostedTrees(boostingStrategy).run(trainRdd) + val (errorWithoutValidation, errorWithValidation) = { + if (algo == Classification) { + val remappedRdd = validateRdd.map(x => new LabeledPoint(2 * x.label - 1, x.features)) + (loss.computeError(gbt, remappedRdd), loss.computeError(gbtValidate, remappedRdd)) + } else { + (loss.computeError(gbt, validateRdd), loss.computeError(gbtValidate, validateRdd)) + } + } + assert(errorWithValidation <= errorWithoutValidation) + + // Test that results from evaluateEachIteration comply with runWithValidation. + // Note that convergenceTol is set to 0.0 + val evaluationArray = gbt.evaluateEachIteration(validateRdd, loss) + assert(evaluationArray.length === numIterations) + assert(evaluationArray(numTrees) > evaluationArray(numTrees - 1)) + var i = 1 + while (i < numTrees) { + assert(evaluationArray(i) <= evaluationArray(i - 1)) + i += 1 + } + } + } + } + } -object GradientBoostedTreesSuite { +private object GradientBoostedTreesSuite { // Combinations for estimators, learning rates and subsamplingRate val testCombinations = Array((10, 1.0, 1.0), (10, 0.1, 1.0), (10, 0.5, 0.75), (10, 0.1, 0.75)) - val randomSeeds = Array(681283, 4398) - val data = EnsembleTestHelper.generateOrderedLabeledPoints(numFeatures = 10, 100) + val trainData = EnsembleTestHelper.generateOrderedLabeledPoints(numFeatures = 20, 120) + val validateData = EnsembleTestHelper.generateOrderedLabeledPoints(numFeatures = 20, 80) } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/tree/RandomForestSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/tree/RandomForestSuite.scala index 55e963977b54f..ee3bc98486862 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/tree/RandomForestSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/tree/RandomForestSuite.scala @@ -27,8 +27,10 @@ import org.apache.spark.mllib.tree.configuration.Algo._ import org.apache.spark.mllib.tree.configuration.Strategy import org.apache.spark.mllib.tree.impl.DecisionTreeMetadata import org.apache.spark.mllib.tree.impurity.{Gini, Variance} -import org.apache.spark.mllib.tree.model.Node +import org.apache.spark.mllib.tree.model.{Node, RandomForestModel} import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.util.Utils + /** * Test suite for [[RandomForest]]. @@ -212,6 +214,26 @@ class RandomForestSuite extends FunSuite with MLlibTestSparkContext { assert(rf1.toDebugString != rf2.toDebugString) } -} - + test("model save/load") { + val tempDir = Utils.createTempDir() + val path = tempDir.toURI.toString + + Array(Classification, Regression).foreach { algo => + val trees = Range(0, 3).map(_ => DecisionTreeSuite.createModel(algo)).toArray + val model = new RandomForestModel(algo, trees) + + // Save model, load it back, and compare. + try { + model.save(sc, path) + val sameModel = RandomForestModel.load(sc, path) + assert(model.algo == sameModel.algo) + model.trees.zip(sameModel.trees).foreach { case (treeA, treeB) => + DecisionTreeSuite.checkEqual(treeA, treeB) + } + } finally { + Utils.deleteRecursively(tempDir) + } + } + } +} diff --git a/mllib/src/test/scala/org/apache/spark/mllib/util/TestingUtils.scala b/mllib/src/test/scala/org/apache/spark/mllib/util/TestingUtils.scala index e957fa5d25f4c..352193a67860c 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/util/TestingUtils.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/util/TestingUtils.scala @@ -95,16 +95,16 @@ object TestingUtils { /** * Comparison using absolute tolerance. */ - def absTol(eps: Double): CompareDoubleRightSide = CompareDoubleRightSide(AbsoluteErrorComparison, - x, eps, ABS_TOL_MSG) + def absTol(eps: Double): CompareDoubleRightSide = + CompareDoubleRightSide(AbsoluteErrorComparison, x, eps, ABS_TOL_MSG) /** * Comparison using relative tolerance. */ - def relTol(eps: Double): CompareDoubleRightSide = CompareDoubleRightSide(RelativeErrorComparison, - x, eps, REL_TOL_MSG) + def relTol(eps: Double): CompareDoubleRightSide = + CompareDoubleRightSide(RelativeErrorComparison, x, eps, REL_TOL_MSG) - override def toString = x.toString + override def toString: String = x.toString } case class CompareVectorRightSide( @@ -166,7 +166,7 @@ object TestingUtils { x.toArray.zip(y.toArray).forall(x => x._1 ~= x._2 relTol eps) }, x, eps, REL_TOL_MSG) - override def toString = x.toString + override def toString: String = x.toString } case class CompareMatrixRightSide( @@ -229,7 +229,7 @@ object TestingUtils { x.toArray.zip(y.toArray).forall(x => x._1 ~= x._2 relTol eps) }, x, eps, REL_TOL_MSG) - override def toString = x.toString + override def toString: String = x.toString } } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/util/TestingUtilsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/util/TestingUtilsSuite.scala index b0ecb33c28483..59e6c778806f4 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/util/TestingUtilsSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/util/TestingUtilsSuite.scala @@ -88,16 +88,20 @@ class TestingUtilsSuite extends FunSuite { assert(!(17.8 ~= 17.59 absTol 0.2)) // Comparisons of numbers very close to zero, and both side of zeros - assert(Double.MinPositiveValue ~== 4 * Double.MinPositiveValue absTol 5 * Double.MinPositiveValue) - assert(Double.MinPositiveValue !~== 6 * Double.MinPositiveValue absTol 5 * Double.MinPositiveValue) - - assert(-Double.MinPositiveValue ~== 3 * Double.MinPositiveValue absTol 5 * Double.MinPositiveValue) - assert(Double.MinPositiveValue !~== -4 * Double.MinPositiveValue absTol 5 * Double.MinPositiveValue) + assert( + Double.MinPositiveValue ~== 4 * Double.MinPositiveValue absTol 5 * Double.MinPositiveValue) + assert( + Double.MinPositiveValue !~== 6 * Double.MinPositiveValue absTol 5 * Double.MinPositiveValue) + + assert( + -Double.MinPositiveValue ~== 3 * Double.MinPositiveValue absTol 5 * Double.MinPositiveValue) + assert( + Double.MinPositiveValue !~== -4 * Double.MinPositiveValue absTol 5 * Double.MinPositiveValue) } test("Comparing vectors using relative error.") { - //Comparisons of two dense vectors + // Comparisons of two dense vectors assert(Vectors.dense(Array(3.1, 3.5)) ~== Vectors.dense(Array(3.130, 3.534)) relTol 0.01) assert(Vectors.dense(Array(3.1, 3.5)) !~== Vectors.dense(Array(3.135, 3.534)) relTol 0.01) assert(Vectors.dense(Array(3.1, 3.5)) ~= Vectors.dense(Array(3.130, 3.534)) relTol 0.01) @@ -130,7 +134,7 @@ class TestingUtilsSuite extends FunSuite { test("Comparing vectors using absolute error.") { - //Comparisons of two dense vectors + // Comparisons of two dense vectors assert(Vectors.dense(Array(3.1, 3.5, 0.0)) ~== Vectors.dense(Array(3.1 + 1E-8, 3.5 + 2E-7, 1E-8)) absTol 1E-6) diff --git a/network/common/pom.xml b/network/common/pom.xml index 8f7c924d6b3a3..22c738bde6d42 100644 --- a/network/common/pom.xml +++ b/network/common/pom.xml @@ -21,8 +21,8 @@ 4.0.0 org.apache.spark - spark-parent - 1.3.0-SNAPSHOT + spark-parent_2.10 + 1.4.0-SNAPSHOT ../../pom.xml @@ -80,6 +80,11 @@ mockito-all test + + org.slf4j + slf4j-log4j12 + test + diff --git a/network/common/src/main/java/org/apache/spark/network/TransportContext.java b/network/common/src/main/java/org/apache/spark/network/TransportContext.java index 5bc6e5a2418a9..f0a89c9d9116c 100644 --- a/network/common/src/main/java/org/apache/spark/network/TransportContext.java +++ b/network/common/src/main/java/org/apache/spark/network/TransportContext.java @@ -35,7 +35,6 @@ import org.apache.spark.network.server.TransportChannelHandler; import org.apache.spark.network.server.TransportRequestHandler; import org.apache.spark.network.server.TransportServer; -import org.apache.spark.network.server.StreamManager; import org.apache.spark.network.util.NettyUtils; import org.apache.spark.network.util.TransportConf; diff --git a/network/common/src/main/java/org/apache/spark/network/protocol/ChunkFetchFailure.java b/network/common/src/main/java/org/apache/spark/network/protocol/ChunkFetchFailure.java index 986957c1509fd..f76bb49e874fc 100644 --- a/network/common/src/main/java/org/apache/spark/network/protocol/ChunkFetchFailure.java +++ b/network/common/src/main/java/org/apache/spark/network/protocol/ChunkFetchFailure.java @@ -17,7 +17,6 @@ package org.apache.spark.network.protocol; -import com.google.common.base.Charsets; import com.google.common.base.Objects; import io.netty.buffer.ByteBuf; diff --git a/network/common/src/main/java/org/apache/spark/network/protocol/Encoders.java b/network/common/src/main/java/org/apache/spark/network/protocol/Encoders.java index 873c694250942..9162d0b977f83 100644 --- a/network/common/src/main/java/org/apache/spark/network/protocol/Encoders.java +++ b/network/common/src/main/java/org/apache/spark/network/protocol/Encoders.java @@ -20,7 +20,6 @@ import com.google.common.base.Charsets; import io.netty.buffer.ByteBuf; -import io.netty.buffer.Unpooled; /** Provides a canonical set of Encoders for simple types. */ public class Encoders { diff --git a/network/common/src/main/java/org/apache/spark/network/protocol/MessageEncoder.java b/network/common/src/main/java/org/apache/spark/network/protocol/MessageEncoder.java index 91d1e8a538a77..0f999f5dfe8d8 100644 --- a/network/common/src/main/java/org/apache/spark/network/protocol/MessageEncoder.java +++ b/network/common/src/main/java/org/apache/spark/network/protocol/MessageEncoder.java @@ -72,9 +72,11 @@ public void encode(ChannelHandlerContext ctx, Message in, List out) { in.encode(header); assert header.writableBytes() == 0; - out.add(header); if (body != null && bodyLength > 0) { - out.add(body); + out.add(new MessageWithHeader(header, body, bodyLength)); + } else { + out.add(header); } } + } diff --git a/network/common/src/main/java/org/apache/spark/network/protocol/MessageWithHeader.java b/network/common/src/main/java/org/apache/spark/network/protocol/MessageWithHeader.java new file mode 100644 index 0000000000000..d686a951467cf --- /dev/null +++ b/network/common/src/main/java/org/apache/spark/network/protocol/MessageWithHeader.java @@ -0,0 +1,109 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.network.protocol; + +import java.io.IOException; +import java.nio.channels.WritableByteChannel; + +import com.google.common.base.Preconditions; +import io.netty.buffer.ByteBuf; +import io.netty.channel.FileRegion; +import io.netty.util.AbstractReferenceCounted; +import io.netty.util.ReferenceCountUtil; + +/** + * A wrapper message that holds two separate pieces (a header and a body). + * + * The header must be a ByteBuf, while the body can be a ByteBuf or a FileRegion. + */ +class MessageWithHeader extends AbstractReferenceCounted implements FileRegion { + + private final ByteBuf header; + private final int headerLength; + private final Object body; + private final long bodyLength; + private long totalBytesTransferred; + + MessageWithHeader(ByteBuf header, Object body, long bodyLength) { + Preconditions.checkArgument(body instanceof ByteBuf || body instanceof FileRegion, + "Body must be a ByteBuf or a FileRegion."); + this.header = header; + this.headerLength = header.readableBytes(); + this.body = body; + this.bodyLength = bodyLength; + } + + @Override + public long count() { + return headerLength + bodyLength; + } + + @Override + public long position() { + return 0; + } + + @Override + public long transfered() { + return totalBytesTransferred; + } + + /** + * This code is more complicated than you would think because we might require multiple + * transferTo invocations in order to transfer a single MessageWithHeader to avoid busy waiting. + * + * The contract is that the caller will ensure position is properly set to the total number + * of bytes transferred so far (i.e. value returned by transfered()). + */ + @Override + public long transferTo(final WritableByteChannel target, final long position) throws IOException { + Preconditions.checkArgument(position == totalBytesTransferred, "Invalid position."); + // Bytes written for header in this call. + long writtenHeader = 0; + if (header.readableBytes() > 0) { + writtenHeader = copyByteBuf(header, target); + totalBytesTransferred += writtenHeader; + if (header.readableBytes() > 0) { + return writtenHeader; + } + } + + // Bytes written for body in this call. + long writtenBody = 0; + if (body instanceof FileRegion) { + writtenBody = ((FileRegion) body).transferTo(target, totalBytesTransferred - headerLength); + } else if (body instanceof ByteBuf) { + writtenBody = copyByteBuf((ByteBuf) body, target); + } + totalBytesTransferred += writtenBody; + + return writtenHeader + writtenBody; + } + + @Override + protected void deallocate() { + header.release(); + ReferenceCountUtil.release(body); + } + + private int copyByteBuf(ByteBuf buf, WritableByteChannel target) throws IOException { + int written = target.write(buf.nioBuffer()); + buf.skipBytes(written); + return written; + } +} diff --git a/network/common/src/main/java/org/apache/spark/network/protocol/RpcFailure.java b/network/common/src/main/java/org/apache/spark/network/protocol/RpcFailure.java index ebd764eb5eb5f..6b991375fc486 100644 --- a/network/common/src/main/java/org/apache/spark/network/protocol/RpcFailure.java +++ b/network/common/src/main/java/org/apache/spark/network/protocol/RpcFailure.java @@ -17,7 +17,6 @@ package org.apache.spark.network.protocol; -import com.google.common.base.Charsets; import com.google.common.base.Objects; import io.netty.buffer.ByteBuf; diff --git a/network/shuffle/src/main/java/org/apache/spark/network/sasl/SaslClientBootstrap.java b/network/common/src/main/java/org/apache/spark/network/sasl/SaslClientBootstrap.java similarity index 100% rename from network/shuffle/src/main/java/org/apache/spark/network/sasl/SaslClientBootstrap.java rename to network/common/src/main/java/org/apache/spark/network/sasl/SaslClientBootstrap.java diff --git a/network/shuffle/src/main/java/org/apache/spark/network/sasl/SaslMessage.java b/network/common/src/main/java/org/apache/spark/network/sasl/SaslMessage.java similarity index 100% rename from network/shuffle/src/main/java/org/apache/spark/network/sasl/SaslMessage.java rename to network/common/src/main/java/org/apache/spark/network/sasl/SaslMessage.java diff --git a/network/shuffle/src/main/java/org/apache/spark/network/sasl/SaslRpcHandler.java b/network/common/src/main/java/org/apache/spark/network/sasl/SaslRpcHandler.java similarity index 96% rename from network/shuffle/src/main/java/org/apache/spark/network/sasl/SaslRpcHandler.java rename to network/common/src/main/java/org/apache/spark/network/sasl/SaslRpcHandler.java index 3777a18e33f78..026cbd260d16c 100644 --- a/network/shuffle/src/main/java/org/apache/spark/network/sasl/SaslRpcHandler.java +++ b/network/common/src/main/java/org/apache/spark/network/sasl/SaslRpcHandler.java @@ -19,16 +19,13 @@ import java.util.concurrent.ConcurrentMap; -import com.google.common.base.Charsets; import com.google.common.collect.Maps; -import io.netty.buffer.ByteBuf; import io.netty.buffer.Unpooled; import org.slf4j.Logger; import org.slf4j.LoggerFactory; import org.apache.spark.network.client.RpcResponseCallback; import org.apache.spark.network.client.TransportClient; -import org.apache.spark.network.protocol.Encodable; import org.apache.spark.network.server.RpcHandler; import org.apache.spark.network.server.StreamManager; diff --git a/network/shuffle/src/main/java/org/apache/spark/network/sasl/SecretKeyHolder.java b/network/common/src/main/java/org/apache/spark/network/sasl/SecretKeyHolder.java similarity index 100% rename from network/shuffle/src/main/java/org/apache/spark/network/sasl/SecretKeyHolder.java rename to network/common/src/main/java/org/apache/spark/network/sasl/SecretKeyHolder.java diff --git a/network/shuffle/src/main/java/org/apache/spark/network/sasl/SparkSaslClient.java b/network/common/src/main/java/org/apache/spark/network/sasl/SparkSaslClient.java similarity index 100% rename from network/shuffle/src/main/java/org/apache/spark/network/sasl/SparkSaslClient.java rename to network/common/src/main/java/org/apache/spark/network/sasl/SparkSaslClient.java diff --git a/network/shuffle/src/main/java/org/apache/spark/network/sasl/SparkSaslServer.java b/network/common/src/main/java/org/apache/spark/network/sasl/SparkSaslServer.java similarity index 100% rename from network/shuffle/src/main/java/org/apache/spark/network/sasl/SparkSaslServer.java rename to network/common/src/main/java/org/apache/spark/network/sasl/SparkSaslServer.java diff --git a/network/common/src/main/java/org/apache/spark/network/server/TransportServer.java b/network/common/src/main/java/org/apache/spark/network/server/TransportServer.java index ef209991804b4..b7ce8541e565e 100644 --- a/network/common/src/main/java/org/apache/spark/network/server/TransportServer.java +++ b/network/common/src/main/java/org/apache/spark/network/server/TransportServer.java @@ -28,7 +28,6 @@ import io.netty.channel.ChannelOption; import io.netty.channel.EventLoopGroup; import io.netty.channel.socket.SocketChannel; -import io.netty.util.internal.PlatformDependent; import org.slf4j.Logger; import org.slf4j.LoggerFactory; diff --git a/network/common/src/main/java/org/apache/spark/network/util/JavaUtils.java b/network/common/src/main/java/org/apache/spark/network/util/JavaUtils.java index bf8a1fc42fc6d..b6fbace509a0e 100644 --- a/network/common/src/main/java/org/apache/spark/network/util/JavaUtils.java +++ b/network/common/src/main/java/org/apache/spark/network/util/JavaUtils.java @@ -17,19 +17,17 @@ package org.apache.spark.network.util; -import java.nio.ByteBuffer; - -import java.io.ByteArrayInputStream; -import java.io.ByteArrayOutputStream; import java.io.Closeable; import java.io.File; import java.io.IOException; -import java.io.ObjectInputStream; -import java.io.ObjectOutputStream; +import java.nio.ByteBuffer; +import java.util.concurrent.TimeUnit; +import java.util.regex.Matcher; +import java.util.regex.Pattern; -import com.google.common.base.Preconditions; -import com.google.common.io.Closeables; import com.google.common.base.Charsets; +import com.google.common.base.Preconditions; +import com.google.common.collect.ImmutableMap; import io.netty.buffer.Unpooled; import org.slf4j.Logger; import org.slf4j.LoggerFactory; @@ -127,4 +125,66 @@ private static boolean isSymlink(File file) throws IOException { } return !fileInCanonicalDir.getCanonicalFile().equals(fileInCanonicalDir.getAbsoluteFile()); } + + private static ImmutableMap timeSuffixes = + ImmutableMap.builder() + .put("us", TimeUnit.MICROSECONDS) + .put("ms", TimeUnit.MILLISECONDS) + .put("s", TimeUnit.SECONDS) + .put("m", TimeUnit.MINUTES) + .put("min", TimeUnit.MINUTES) + .put("h", TimeUnit.HOURS) + .put("d", TimeUnit.DAYS) + .build(); + + /** + * Convert a passed time string (e.g. 50s, 100ms, or 250us) to a time count for + * internal use. If no suffix is provided a direct conversion is attempted. + */ + private static long parseTimeString(String str, TimeUnit unit) { + String lower = str.toLowerCase().trim(); + + try { + String suffix; + long val; + Matcher m = Pattern.compile("(-?[0-9]+)([a-z]+)?").matcher(lower); + if (m.matches()) { + val = Long.parseLong(m.group(1)); + suffix = m.group(2); + } else { + throw new NumberFormatException("Failed to parse time string: " + str); + } + + // Check for invalid suffixes + if (suffix != null && !timeSuffixes.containsKey(suffix)) { + throw new NumberFormatException("Invalid suffix: \"" + suffix + "\""); + } + + // If suffix is valid use that, otherwise none was provided and use the default passed + return unit.convert(val, suffix != null ? timeSuffixes.get(suffix) : unit); + } catch (NumberFormatException e) { + String timeError = "Time must be specified as seconds (s), " + + "milliseconds (ms), microseconds (us), minutes (m or min) hour (h), or day (d). " + + "E.g. 50s, 100ms, or 250us."; + + throw new NumberFormatException(timeError + "\n" + e.getMessage()); + } + } + + /** + * Convert a time parameter such as (50s, 100ms, or 250us) to milliseconds for internal use. If + * no suffix is provided, the passed number is assumed to be in ms. + */ + public static long timeStringAsMs(String str) { + return parseTimeString(str, TimeUnit.MILLISECONDS); + } + + /** + * Convert a time parameter such as (50s, 100ms, or 250us) to seconds for internal use. If + * no suffix is provided, the passed number is assumed to be in seconds. + */ + public static long timeStringAsSec(String str) { + return parseTimeString(str, TimeUnit.SECONDS); + } + } diff --git a/network/common/src/main/java/org/apache/spark/network/util/NettyUtils.java b/network/common/src/main/java/org/apache/spark/network/util/NettyUtils.java index 2a4b88b64cdc9..dabd6261d2aa0 100644 --- a/network/common/src/main/java/org/apache/spark/network/util/NettyUtils.java +++ b/network/common/src/main/java/org/apache/spark/network/util/NettyUtils.java @@ -25,7 +25,6 @@ import io.netty.channel.Channel; import io.netty.channel.EventLoopGroup; import io.netty.channel.ServerChannel; -import io.netty.channel.epoll.Epoll; import io.netty.channel.epoll.EpollEventLoopGroup; import io.netty.channel.epoll.EpollServerSocketChannel; import io.netty.channel.epoll.EpollSocketChannel; diff --git a/network/common/src/main/java/org/apache/spark/network/util/TransportConf.java b/network/common/src/main/java/org/apache/spark/network/util/TransportConf.java index 2eaf3b71d9a49..0aef7f1987315 100644 --- a/network/common/src/main/java/org/apache/spark/network/util/TransportConf.java +++ b/network/common/src/main/java/org/apache/spark/network/util/TransportConf.java @@ -37,8 +37,11 @@ public boolean preferDirectBufs() { /** Connect timeout in milliseconds. Default 120 secs. */ public int connectionTimeoutMs() { - int defaultTimeout = conf.getInt("spark.network.timeout", 120); - return conf.getInt("spark.shuffle.io.connectionTimeout", defaultTimeout) * 1000; + long defaultNetworkTimeoutS = JavaUtils.timeStringAsSec( + conf.get("spark.network.timeout", "120s")); + long defaultTimeoutMs = JavaUtils.timeStringAsSec( + conf.get("spark.shuffle.io.connectionTimeout", defaultNetworkTimeoutS + "s")) * 1000; + return (int) defaultTimeoutMs; } /** Number of concurrent connections between two nodes for fetching data. */ @@ -68,7 +71,9 @@ public int numConnectionsPerPeer() { public int sendBuf() { return conf.getInt("spark.shuffle.io.sendBuffer", -1); } /** Timeout for a single round trip of SASL token exchange, in milliseconds. */ - public int saslRTTimeoutMs() { return conf.getInt("spark.shuffle.sasl.timeout", 30) * 1000; } + public int saslRTTimeoutMs() { + return (int) JavaUtils.timeStringAsSec(conf.get("spark.shuffle.sasl.timeout", "30s")) * 1000; + } /** * Max number of times we will try IO exceptions (such as connection timeouts) per request. @@ -80,7 +85,9 @@ public int numConnectionsPerPeer() { * Time (in milliseconds) that we will wait in order to perform a retry after an IOException. * Only relevant if maxIORetries > 0. */ - public int ioRetryWaitTimeMs() { return conf.getInt("spark.shuffle.io.retryWait", 5) * 1000; } + public int ioRetryWaitTimeMs() { + return (int) JavaUtils.timeStringAsSec(conf.get("spark.shuffle.io.retryWait", "5s")) * 1000; + } /** * Minimum size of a block that we should start using memory map rather than reading in through diff --git a/project/spark-style/src/main/scala/org/apache/spark/scalastyle/NonASCIICharacterChecker.scala b/network/common/src/test/java/org/apache/spark/network/ByteArrayWritableChannel.java similarity index 55% rename from project/spark-style/src/main/scala/org/apache/spark/scalastyle/NonASCIICharacterChecker.scala rename to network/common/src/test/java/org/apache/spark/network/ByteArrayWritableChannel.java index 3d43c35299555..b525ed69fc9fb 100644 --- a/project/spark-style/src/main/scala/org/apache/spark/scalastyle/NonASCIICharacterChecker.scala +++ b/network/common/src/test/java/org/apache/spark/network/ByteArrayWritableChannel.java @@ -15,25 +15,41 @@ * limitations under the License. */ +package org.apache.spark.network; -package org.apache.spark.scalastyle +import java.nio.ByteBuffer; +import java.nio.channels.WritableByteChannel; -import java.util.regex.Pattern +public class ByteArrayWritableChannel implements WritableByteChannel { -import org.scalastyle.{PositionError, ScalariformChecker, ScalastyleError} + private final byte[] data; + private int offset; -import scalariform.lexer.Token -import scalariform.parser.CompilationUnit + public ByteArrayWritableChannel(int size) { + this.data = new byte[size]; + this.offset = 0; + } + + public byte[] getData() { + return data; + } -class NonASCIICharacterChecker extends ScalariformChecker { - val errorKey: String = "non.ascii.character.disallowed" + @Override + public int write(ByteBuffer src) { + int available = src.remaining(); + src.get(data, offset, available); + offset += available; + return available; + } + + @Override + public void close() { - override def verify(ast: CompilationUnit): List[ScalastyleError] = { - ast.tokens.filter(hasNonAsciiChars).map(x => PositionError(x.offset)).toList } - private def hasNonAsciiChars(x: Token) = - x.rawText.trim.nonEmpty && !Pattern.compile( """\p{ASCII}+""", Pattern.DOTALL) - .matcher(x.text.trim).matches() + @Override + public boolean isOpen() { + return true; + } } diff --git a/network/common/src/test/java/org/apache/spark/network/ProtocolSuite.java b/network/common/src/test/java/org/apache/spark/network/ProtocolSuite.java index 43dc0cf8c7194..860dd6d9b3915 100644 --- a/network/common/src/test/java/org/apache/spark/network/ProtocolSuite.java +++ b/network/common/src/test/java/org/apache/spark/network/ProtocolSuite.java @@ -17,26 +17,34 @@ package org.apache.spark.network; +import java.util.List; + +import com.google.common.primitives.Ints; +import io.netty.buffer.Unpooled; +import io.netty.channel.ChannelHandlerContext; +import io.netty.channel.FileRegion; import io.netty.channel.embedded.EmbeddedChannel; +import io.netty.handler.codec.MessageToMessageEncoder; import org.junit.Test; import static org.junit.Assert.assertEquals; -import org.apache.spark.network.protocol.Message; -import org.apache.spark.network.protocol.StreamChunkId; -import org.apache.spark.network.protocol.ChunkFetchRequest; import org.apache.spark.network.protocol.ChunkFetchFailure; +import org.apache.spark.network.protocol.ChunkFetchRequest; import org.apache.spark.network.protocol.ChunkFetchSuccess; -import org.apache.spark.network.protocol.RpcRequest; -import org.apache.spark.network.protocol.RpcFailure; -import org.apache.spark.network.protocol.RpcResponse; +import org.apache.spark.network.protocol.Message; import org.apache.spark.network.protocol.MessageDecoder; import org.apache.spark.network.protocol.MessageEncoder; +import org.apache.spark.network.protocol.RpcFailure; +import org.apache.spark.network.protocol.RpcRequest; +import org.apache.spark.network.protocol.RpcResponse; +import org.apache.spark.network.protocol.StreamChunkId; import org.apache.spark.network.util.NettyUtils; public class ProtocolSuite { private void testServerToClient(Message msg) { - EmbeddedChannel serverChannel = new EmbeddedChannel(new MessageEncoder()); + EmbeddedChannel serverChannel = new EmbeddedChannel(new FileRegionEncoder(), + new MessageEncoder()); serverChannel.writeOutbound(msg); EmbeddedChannel clientChannel = new EmbeddedChannel( @@ -51,7 +59,8 @@ private void testServerToClient(Message msg) { } private void testClientToServer(Message msg) { - EmbeddedChannel clientChannel = new EmbeddedChannel(new MessageEncoder()); + EmbeddedChannel clientChannel = new EmbeddedChannel(new FileRegionEncoder(), + new MessageEncoder()); clientChannel.writeOutbound(msg); EmbeddedChannel serverChannel = new EmbeddedChannel( @@ -83,4 +92,25 @@ public void responses() { testServerToClient(new RpcFailure(0, "this is an error")); testServerToClient(new RpcFailure(0, "")); } + + /** + * Handler to transform a FileRegion into a byte buffer. EmbeddedChannel doesn't actually transfer + * bytes, but messages, so this is needed so that the frame decoder on the receiving side can + * understand what MessageWithHeader actually contains. + */ + private static class FileRegionEncoder extends MessageToMessageEncoder { + + @Override + public void encode(ChannelHandlerContext ctx, FileRegion in, List out) + throws Exception { + + ByteArrayWritableChannel channel = new ByteArrayWritableChannel(Ints.checkedCast(in.count())); + while (in.transfered() < in.count()) { + in.transferTo(channel, in.transfered()); + } + out.add(Unpooled.wrappedBuffer(channel.getData())); + } + + } + } diff --git a/network/common/src/test/java/org/apache/spark/network/protocol/MessageWithHeaderSuite.java b/network/common/src/test/java/org/apache/spark/network/protocol/MessageWithHeaderSuite.java new file mode 100644 index 0000000000000..ff985096d72d5 --- /dev/null +++ b/network/common/src/test/java/org/apache/spark/network/protocol/MessageWithHeaderSuite.java @@ -0,0 +1,129 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.network.protocol; + +import java.io.IOException; +import java.nio.ByteBuffer; +import java.nio.channels.WritableByteChannel; + +import io.netty.buffer.ByteBuf; +import io.netty.buffer.Unpooled; +import io.netty.channel.FileRegion; +import io.netty.util.AbstractReferenceCounted; +import org.junit.Test; + +import static org.junit.Assert.*; + +import org.apache.spark.network.ByteArrayWritableChannel; + +public class MessageWithHeaderSuite { + + @Test + public void testSingleWrite() throws Exception { + testFileRegionBody(8, 8); + } + + @Test + public void testShortWrite() throws Exception { + testFileRegionBody(8, 1); + } + + @Test + public void testByteBufBody() throws Exception { + ByteBuf header = Unpooled.copyLong(42); + ByteBuf body = Unpooled.copyLong(84); + MessageWithHeader msg = new MessageWithHeader(header, body, body.readableBytes()); + + ByteBuf result = doWrite(msg, 1); + assertEquals(msg.count(), result.readableBytes()); + assertEquals(42, result.readLong()); + assertEquals(84, result.readLong()); + } + + private void testFileRegionBody(int totalWrites, int writesPerCall) throws Exception { + ByteBuf header = Unpooled.copyLong(42); + int headerLength = header.readableBytes(); + TestFileRegion region = new TestFileRegion(totalWrites, writesPerCall); + MessageWithHeader msg = new MessageWithHeader(header, region, region.count()); + + ByteBuf result = doWrite(msg, totalWrites / writesPerCall); + assertEquals(headerLength + region.count(), result.readableBytes()); + assertEquals(42, result.readLong()); + for (long i = 0; i < 8; i++) { + assertEquals(i, result.readLong()); + } + } + + private ByteBuf doWrite(MessageWithHeader msg, int minExpectedWrites) throws Exception { + int writes = 0; + ByteArrayWritableChannel channel = new ByteArrayWritableChannel((int) msg.count()); + while (msg.transfered() < msg.count()) { + msg.transferTo(channel, msg.transfered()); + writes++; + } + assertTrue("Not enough writes!", minExpectedWrites <= writes); + return Unpooled.wrappedBuffer(channel.getData()); + } + + private static class TestFileRegion extends AbstractReferenceCounted implements FileRegion { + + private final int writeCount; + private final int writesPerCall; + private int written; + + TestFileRegion(int totalWrites, int writesPerCall) { + this.writeCount = totalWrites; + this.writesPerCall = writesPerCall; + } + + @Override + public long count() { + return 8 * writeCount; + } + + @Override + public long position() { + return 0; + } + + @Override + public long transfered() { + return 8 * written; + } + + @Override + public long transferTo(WritableByteChannel target, long position) throws IOException { + for (int i = 0; i < writesPerCall; i++) { + ByteBuf buf = Unpooled.copyLong((position / 8) + i); + ByteBuffer nio = buf.nioBuffer(); + while (nio.remaining() > 0) { + target.write(nio); + } + buf.release(); + written++; + } + return 8 * writesPerCall; + } + + @Override + protected void deallocate() { + } + + } + +} diff --git a/network/shuffle/src/test/java/org/apache/spark/network/sasl/SparkSaslSuite.java b/network/common/src/test/java/org/apache/spark/network/sasl/SparkSaslSuite.java similarity index 95% rename from network/shuffle/src/test/java/org/apache/spark/network/sasl/SparkSaslSuite.java rename to network/common/src/test/java/org/apache/spark/network/sasl/SparkSaslSuite.java index 67a07f38eb5a0..23b4e06f064e1 100644 --- a/network/shuffle/src/test/java/org/apache/spark/network/sasl/SparkSaslSuite.java +++ b/network/common/src/test/java/org/apache/spark/network/sasl/SparkSaslSuite.java @@ -17,12 +17,12 @@ package org.apache.spark.network.sasl; -import java.util.Map; +import static org.junit.Assert.assertFalse; +import static org.junit.Assert.assertTrue; +import static org.junit.Assert.fail; -import com.google.common.collect.ImmutableMap; import org.junit.Test; -import static org.junit.Assert.*; /** * Jointly tests SparkSaslClient and SparkSaslServer, as both are black boxes. diff --git a/network/common/src/test/resources/log4j.properties b/network/common/src/test/resources/log4j.properties new file mode 100644 index 0000000000000..e8da774f7ca9e --- /dev/null +++ b/network/common/src/test/resources/log4j.properties @@ -0,0 +1,27 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# Set everything to be logged to the file target/unit-tests.log +log4j.rootCategory=DEBUG, file +log4j.appender.file=org.apache.log4j.FileAppender +log4j.appender.file.append=true +log4j.appender.file.file=target/unit-tests.log +log4j.appender.file.layout=org.apache.log4j.PatternLayout +log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n + +# Silence verbose logs from 3rd-party libraries. +log4j.logger.io.netty=INFO diff --git a/network/shuffle/pom.xml b/network/shuffle/pom.xml index c2d0300ecd904..7dc7c65825e34 100644 --- a/network/shuffle/pom.xml +++ b/network/shuffle/pom.xml @@ -21,8 +21,8 @@ 4.0.0 org.apache.spark - spark-parent - 1.3.0-SNAPSHOT + spark-parent_2.10 + 1.4.0-SNAPSHOT ../../pom.xml diff --git a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/OneForOneBlockFetcher.java b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/OneForOneBlockFetcher.java index 8ed2e0b39ad23..e653f5cb147ee 100644 --- a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/OneForOneBlockFetcher.java +++ b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/OneForOneBlockFetcher.java @@ -29,7 +29,6 @@ import org.apache.spark.network.shuffle.protocol.BlockTransferMessage; import org.apache.spark.network.shuffle.protocol.OpenBlocks; import org.apache.spark.network.shuffle.protocol.StreamHandle; -import org.apache.spark.network.util.JavaUtils; /** * Simple wrapper on top of a TransportClient which interprets each chunk as a whole block, and diff --git a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/OpenBlocks.java b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/OpenBlocks.java index 62fce9b0d16cd..60485bace643c 100644 --- a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/OpenBlocks.java +++ b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/OpenBlocks.java @@ -23,7 +23,6 @@ import io.netty.buffer.ByteBuf; import org.apache.spark.network.protocol.Encoders; -import org.apache.spark.network.shuffle.protocol.BlockTransferMessage.Type; /** Request to read a set of blocks. Returns {@link StreamHandle}. */ public class OpenBlocks extends BlockTransferMessage { diff --git a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/RegisterExecutor.java b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/RegisterExecutor.java index 7eb4385044077..38acae3b31d64 100644 --- a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/RegisterExecutor.java +++ b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/RegisterExecutor.java @@ -21,7 +21,6 @@ import io.netty.buffer.ByteBuf; import org.apache.spark.network.protocol.Encoders; -import org.apache.spark.network.shuffle.protocol.BlockTransferMessage.Type; /** * Initial registration message between an executor and its local shuffle server. diff --git a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/StreamHandle.java b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/StreamHandle.java index bc9daa6158ba3..9a9220211a50c 100644 --- a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/StreamHandle.java +++ b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/StreamHandle.java @@ -20,8 +20,6 @@ import com.google.common.base.Objects; import io.netty.buffer.ByteBuf; -import org.apache.spark.network.shuffle.protocol.BlockTransferMessage.Type; - /** * Identifier for a fixed number of chunks to read from a stream created by an "open blocks" * message. This is used by {@link org.apache.spark.network.shuffle.OneForOneBlockFetcher}. diff --git a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/UploadBlock.java b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/UploadBlock.java index 0b23e112bd512..2ff9aaa650f92 100644 --- a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/UploadBlock.java +++ b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/UploadBlock.java @@ -23,7 +23,6 @@ import io.netty.buffer.ByteBuf; import org.apache.spark.network.protocol.Encoders; -import org.apache.spark.network.shuffle.protocol.BlockTransferMessage.Type; /** Request to upload a block with a certain StorageLevel. Returns nothing (empty byte array). */ diff --git a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/OneForOneBlockFetcherSuite.java b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/OneForOneBlockFetcherSuite.java index 842741e3d354f..b35a6d685dd02 100644 --- a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/OneForOneBlockFetcherSuite.java +++ b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/OneForOneBlockFetcherSuite.java @@ -28,11 +28,16 @@ import org.mockito.invocation.InvocationOnMock; import org.mockito.stubbing.Answer; -import static org.junit.Assert.*; import static org.junit.Assert.assertEquals; +import static org.junit.Assert.fail; import static org.mockito.Matchers.any; +import static org.mockito.Matchers.anyInt; +import static org.mockito.Matchers.anyLong; import static org.mockito.Matchers.eq; -import static org.mockito.Mockito.*; +import static org.mockito.Mockito.doAnswer; +import static org.mockito.Mockito.mock; +import static org.mockito.Mockito.times; +import static org.mockito.Mockito.verify; import org.apache.spark.network.buffer.ManagedBuffer; import org.apache.spark.network.buffer.NettyManagedBuffer; diff --git a/network/yarn/pom.xml b/network/yarn/pom.xml index acec8f18f2b5c..1e2e9c80af6cc 100644 --- a/network/yarn/pom.xml +++ b/network/yarn/pom.xml @@ -21,8 +21,8 @@ 4.0.0 org.apache.spark - spark-parent - 1.3.0-SNAPSHOT + spark-parent_2.10 + 1.4.0-SNAPSHOT ../../pom.xml @@ -33,6 +33,8 @@ http://spark.apache.org/ network-yarn + + provided @@ -47,7 +49,6 @@ org.apache.hadoop hadoop-client - provided diff --git a/pom.xml b/pom.xml index f6f176d2004b7..bcc2f57f1af5d 100644 --- a/pom.xml +++ b/pom.xml @@ -25,8 +25,8 @@ 14 org.apache.spark - spark-parent - 1.3.0-SNAPSHOT + spark-parent_2.10 + 1.4.0-SNAPSHOT pom Spark Project Parent POM http://spark.apache.org/ @@ -105,6 +105,7 @@ external/zeromq examples repl + launcher @@ -119,7 +120,7 @@ shaded-protobuf 1.7.10 1.2.17 - 1.0.4 + 2.2.0 2.4.1 ${hadoop.version} 0.98.7-hadoop1 @@ -140,13 +141,13 @@ 2.4.0 2.0.8 3.1.0 - 1.7.6 + 1.7.7 0.7.1 1.8.3 1.1.0 4.2.6 - 3.1.1 + 3.4.1 ${project.build.directory}/spark-test-classpath.txt 2.10.4 2.10 @@ -155,7 +156,10 @@ 3.6.3 1.8.8 2.4.4 - 1.1.1.6 + 1.1.1.7 + 1.1.2 + + ${java.home} @@ -395,7 +399,7 @@ provided - + org.apache.commons commons-lang3 @@ -404,7 +408,7 @@ commons-codec commons-codec - 1.5 + 1.10 org.apache.commons @@ -422,6 +426,13 @@ 2.42.2 test + + + xml-apis + xml-apis + 1.4.01 + test + org.slf4j slf4j-api @@ -583,10 +594,18 @@ jackson-databind ${fasterxml.jackson.version} + com.fasterxml.jackson.module jackson-module-scala_2.10 ${fasterxml.jackson.version} + + + com.google.guava + guava + + org.scala-lang @@ -619,19 +638,6 @@ 2.2.1 test - - org.easymock - easymockclassextension - 3.1 - test - - - - asm - asm - 3.3.1 - test - org.mockito mockito-all @@ -1096,6 +1102,12 @@ scala-maven-plugin 3.2.0 + + eclipse-add-source + + add-source + + scala-compile-first process-resources @@ -1178,13 +1190,20 @@ ${project.build.directory}/surefire-reports -Xmx3g -XX:MaxPermSize=${MaxPermGen} -XX:ReservedCodeCacheSize=512m + + + ${test_classpath} + ${test.java.home} + true - ${session.executionRootDirectory} + ${spark.test.home} 1 false false - ${test_classpath} true false @@ -1208,6 +1227,7 @@ launched by the tests have access to the correct test-time classpath. --> ${test_classpath} + ${test.java.home} true @@ -1249,6 +1269,7 @@ create-source-jar jar-no-fork + test-jar-no-fork @@ -1426,7 +1447,7 @@ org.scalastyle scalastyle-maven-plugin - 0.4.0 + 0.7.0 false true @@ -1435,12 +1456,12 @@ ${basedir}/src/main/scala ${basedir}/src/test/scala scalastyle-config.xml - scalastyle-output.xml - UTF-8 + ${basedir}/target/scalastyle-output.xml + ${project.build.sourceEncoding} + ${project.reporting.outputEncoding} - package check @@ -1456,6 +1477,25 @@ org.scalatest scalatest-maven-plugin + + + org.apache.maven.plugins + maven-jar-plugin + + + prepare-test-jar + prepare-package + + test-jar + + + + log4j.properties + + + + + @@ -1572,7 +1612,7 @@ 2.3.0 2.5.0 - 0.9.2 + 0.9.3 0.98.7-hadoop2 3.1.1 hadoop2 @@ -1585,7 +1625,7 @@ 2.4.0 2.5.0 - 0.9.2 + 0.9.3 0.98.7-hadoop2 3.1.1 hadoop2 @@ -1606,7 +1646,7 @@ 1.0.3-mapr-3.0.3 2.4.1-mapr-1408 - 0.94.17-mapr-1405 + 0.98.4-mapr-1408 3.4.5-mapr-1406 @@ -1616,7 +1656,7 @@ 2.4.1-mapr-1408 2.4.1-mapr-1408 - 0.94.17-mapr-1405-4.0.0-FCS + 0.98.4-mapr-1408 3.4.5-mapr-1406 @@ -1679,6 +1719,16 @@ + + test-java-home + + env.JAVA_HOME + + + ${env.JAVA_HOME} + + + scala-2.11 @@ -1712,5 +1762,8 @@ parquet-provided + + sparkr + diff --git a/project/MimaBuild.scala b/project/MimaBuild.scala index f0cbf4e57b8c5..dde92949fa175 100644 --- a/project/MimaBuild.scala +++ b/project/MimaBuild.scala @@ -91,7 +91,7 @@ object MimaBuild { def mimaSettings(sparkHome: File, projectRef: ProjectRef) = { val organization = "org.apache.spark" - val previousSparkVersion = "1.2.0" + val previousSparkVersion = "1.3.0" val fullId = "spark-" + projectRef.project + "_2.10" mimaDefaultSettings ++ Seq(previousArtifact := Some(organization % fullId % previousSparkVersion), diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala index 4065a562a1a18..7ef363a2f07ad 100644 --- a/project/MimaExcludes.scala +++ b/project/MimaExcludes.scala @@ -16,6 +16,7 @@ */ import com.typesafe.tools.mima.core._ +import com.typesafe.tools.mima.core.ProblemFilters._ /** * Additional excludes for checking of Spark's binary compatibility. @@ -33,6 +34,46 @@ import com.typesafe.tools.mima.core._ object MimaExcludes { def excludes(version: String) = version match { + case v if v.startsWith("1.4") => + Seq( + MimaBuild.excludeSparkPackage("deploy"), + MimaBuild.excludeSparkPackage("ml"), + // SPARK-5922 Adding a generalized diff(other: RDD[(VertexId, VD)]) to VertexRDD + ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.graphx.VertexRDD.diff"), + // These are needed if checking against the sbt build, since they are part of + // the maven-generated artifacts in 1.3. + excludePackage("org.spark-project.jetty"), + MimaBuild.excludeSparkPackage("unused"), + ProblemFilters.exclude[MissingClassProblem]("com.google.common.base.Optional"), + ProblemFilters.exclude[IncompatibleResultTypeProblem]( + "org.apache.spark.rdd.JdbcRDD.compute"), + ProblemFilters.exclude[IncompatibleResultTypeProblem]( + "org.apache.spark.broadcast.HttpBroadcastFactory.newBroadcast"), + ProblemFilters.exclude[IncompatibleResultTypeProblem]( + "org.apache.spark.broadcast.TorrentBroadcastFactory.newBroadcast"), + ProblemFilters.exclude[MissingClassProblem]( + "org.apache.spark.scheduler.OutputCommitCoordinator$OutputCommitCoordinatorActor") + ) ++ Seq( + // SPARK-4655 - Making Stage an Abstract class broke binary compatility even though + // the stage class is defined as private[spark] + ProblemFilters.exclude[AbstractClassProblem]("org.apache.spark.scheduler.Stage") + ) ++ Seq( + // SPARK-6510 Add a Graph#minus method acting as Set#difference + ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.graphx.VertexRDD.minus") + ) ++ Seq( + // SPARK-6492 Fix deadlock in SparkContext.stop() + ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.SparkContext.org$" + + "apache$spark$SparkContext$$SPARK_CONTEXT_CONSTRUCTOR_LOCK") + )++ Seq( + // SPARK-6693 add tostring with max lines and width for matrix + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.mllib.linalg.Matrix.toString") + )++ Seq( + // SPARK-6703 Add getOrCreate method to SparkContext + ProblemFilters.exclude[IncompatibleResultTypeProblem] + ("org.apache.spark.SparkContext.org$apache$spark$SparkContext$$activeContext") + ) + case v if v.startsWith("1.3") => Seq( MimaBuild.excludeSparkPackage("deploy"), @@ -148,6 +189,42 @@ object MimaExcludes { ProblemFilters.exclude[MissingTypesProblem]("org.apache.spark.mllib.linalg.VectorUDT"), ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.mllib.linalg.VectorUDT.serialize"), ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.mllib.linalg.VectorUDT.sqlType") + ) ++ Seq( + // SPARK-5814 + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$wrapDoubleArray"), + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$fillFullMatrix"), + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$iterations"), + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$makeOutLinkBlock"), + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$computeYtY"), + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$makeLinkRDDs"), + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$alpha"), + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$randomFactor"), + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$makeInLinkBlock"), + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$dspr"), + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$lambda"), + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$implicitPrefs"), + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$rank") + ) ++ Seq( + // SPARK-4682 + ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.RealClock"), + ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.Clock"), + ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.TestClock") + ) ++ Seq( + // SPARK-5922 Adding a generalized diff(other: RDD[(VertexId, VD)]) to VertexRDD + ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.graphx.VertexRDD.diff") ) case v if v.startsWith("1.2") => diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala index 95f8dfa3d270f..09b4976d10c26 100644 --- a/project/SparkBuild.scala +++ b/project/SparkBuild.scala @@ -15,7 +15,7 @@ * limitations under the License. */ -import java.io.File +import java.io._ import scala.util.Properties import scala.collection.JavaConversions._ @@ -34,11 +34,11 @@ object BuildCommons { val allProjects@Seq(bagel, catalyst, core, graphx, hive, hiveThriftServer, mllib, repl, sql, networkCommon, networkShuffle, streaming, streamingFlumeSink, streamingFlume, streamingKafka, - streamingMqtt, streamingTwitter, streamingZeromq) = + streamingMqtt, streamingTwitter, streamingZeromq, launcher) = Seq("bagel", "catalyst", "core", "graphx", "hive", "hive-thriftserver", "mllib", "repl", "sql", "network-common", "network-shuffle", "streaming", "streaming-flume-sink", "streaming-flume", "streaming-kafka", "streaming-mqtt", "streaming-twitter", - "streaming-zeromq").map(ProjectRef(buildLocation, _)) + "streaming-zeromq", "launcher").map(ProjectRef(buildLocation, _)) val optionallyEnabledProjects@Seq(yarn, yarnStable, java8Tests, sparkGangliaLgpl, sparkKinesisAsl) = Seq("yarn", "yarn-stable", "java8-tests", "ganglia-lgpl", @@ -119,7 +119,9 @@ object SparkBuild extends PomBuild { lazy val publishLocalBoth = TaskKey[Unit]("publish-local", "publish local for m2 and ivy") lazy val sharedSettings = graphSettings ++ genjavadocSettings ++ Seq ( - javaHome := Properties.envOrNone("JAVA_HOME").map(file), + javaHome := sys.env.get("JAVA_HOME") + .orElse(sys.props.get("java.home").map { p => new File(p).getParentFile().getAbsolutePath() }) + .map(file), incOptions := incOptions.value.withNameHashing(true), retrieveManaged := true, retrievePattern := "[type]s/[artifact](-[revision])(-[classifier]).[ext]", @@ -155,14 +157,18 @@ object SparkBuild extends PomBuild { (allProjects ++ optionallyEnabledProjects).foreach(enable(TestSettings.settings)) // TODO: Add Sql to mima checks + // TODO: remove launcher from this list after 1.3. allProjects.filterNot(x => Seq(spark, sql, hive, hiveThriftServer, catalyst, repl, - networkCommon, networkShuffle, networkYarn).contains(x)).foreach { + networkCommon, networkShuffle, networkYarn, launcher).contains(x)).foreach { x => enable(MimaBuild.mimaSettings(sparkHome, x))(x) } /* Enable Assembly for all assembly projects */ assemblyProjects.foreach(enable(Assembly.settings)) + /* Package pyspark artifacts in the main assembly. */ + enable(PySparkAssembly.settings)(assembly) + /* Enable unidoc only for the root spark project */ enable(Unidoc.settings)(spark) @@ -268,11 +274,10 @@ object SQL { |import org.apache.spark.sql.catalyst.plans.logical._ |import org.apache.spark.sql.catalyst.rules._ |import org.apache.spark.sql.catalyst.util._ - |import org.apache.spark.sql.Dsl._ |import org.apache.spark.sql.execution + |import org.apache.spark.sql.functions._ |import org.apache.spark.sql.test.TestSQLContext._ - |import org.apache.spark.sql.types._ - |import org.apache.spark.sql.parquet.ParquetTestData""".stripMargin, + |import org.apache.spark.sql.types._""".stripMargin, cleanupCommands in console := "sparkContext.stop()" ) } @@ -299,12 +304,11 @@ object Hive { |import org.apache.spark.sql.catalyst.plans.logical._ |import org.apache.spark.sql.catalyst.rules._ |import org.apache.spark.sql.catalyst.util._ - |import org.apache.spark.sql.Dsl._ |import org.apache.spark.sql.execution + |import org.apache.spark.sql.functions._ |import org.apache.spark.sql.hive._ |import org.apache.spark.sql.hive.test.TestHive._ - |import org.apache.spark.sql.types._ - |import org.apache.spark.sql.parquet.ParquetTestData""".stripMargin, + |import org.apache.spark.sql.types._""".stripMargin, cleanupCommands in console := "sparkContext.stop()", // Some of our log4j jars make it impossible to submit jobs from this JVM to Hive Map/Reduce // in order to generate golden files. This is only required for developers who are adding new @@ -315,6 +319,7 @@ object Hive { } object Assembly { + import sbtassembly.AssemblyUtils._ import sbtassembly.Plugin._ import AssemblyKeys._ @@ -346,6 +351,60 @@ object Assembly { ) } +object PySparkAssembly { + import sbtassembly.Plugin._ + import AssemblyKeys._ + + lazy val settings = Seq( + unmanagedJars in Compile += { BuildCommons.sparkHome / "python/lib/py4j-0.8.2.1-src.zip" }, + // Use a resource generator to copy all .py files from python/pyspark into a managed directory + // to be included in the assembly. We can't just add "python/" to the assembly's resource dir + // list since that will copy unneeded / unwanted files. + resourceGenerators in Compile <+= resourceManaged in Compile map { outDir: File => + val dst = new File(outDir, "pyspark") + if (!dst.isDirectory()) { + require(dst.mkdirs()) + } + + val src = new File(BuildCommons.sparkHome, "python/pyspark") + copy(src, dst) + } + ) + + private def copy(src: File, dst: File): Seq[File] = { + src.listFiles().flatMap { f => + val child = new File(dst, f.getName()) + if (f.isDirectory()) { + child.mkdir() + copy(f, child) + } else if (f.getName().endsWith(".py")) { + var in: Option[FileInputStream] = None + var out: Option[FileOutputStream] = None + try { + in = Some(new FileInputStream(f)) + out = Some(new FileOutputStream(child)) + + val bytes = new Array[Byte](1024) + var read = 0 + while (read >= 0) { + read = in.get.read(bytes) + if (read > 0) { + out.get.write(bytes, 0, read) + } + } + + Some(child) + } finally { + in.foreach(_.close()) + out.foreach(_.close()) + } + } else { + None + } + } + } +} + object Unidoc { import BuildCommons._ @@ -357,25 +416,38 @@ object Unidoc { names.map(s => "org.apache.spark." + s).mkString(":") } + private def ignoreUndocumentedPackages(packages: Seq[Seq[File]]): Seq[Seq[File]] = { + packages + .map(_.filterNot(_.getName.contains("$"))) + .map(_.filterNot(_.getCanonicalPath.contains("akka"))) + .map(_.filterNot(_.getCanonicalPath.contains("org/apache/spark/deploy"))) + .map(_.filterNot(_.getCanonicalPath.contains("org/apache/spark/network"))) + .map(_.filterNot(_.getCanonicalPath.contains("org/apache/spark/shuffle"))) + .map(_.filterNot(_.getCanonicalPath.contains("org/apache/spark/executor"))) + .map(_.filterNot(_.getCanonicalPath.contains("python"))) + .map(_.filterNot(_.getCanonicalPath.contains("org/apache/spark/util/collection"))) + .map(_.filterNot(_.getCanonicalPath.contains("org/apache/spark/sql/catalyst"))) + .map(_.filterNot(_.getCanonicalPath.contains("org/apache/spark/sql/execution"))) + .map(_.filterNot(_.getCanonicalPath.contains("org/apache/spark/sql/hive/test"))) + } + lazy val settings = scalaJavaUnidocSettings ++ Seq ( publish := {}, unidocProjectFilter in(ScalaUnidoc, unidoc) := - inAnyProject -- inProjects(OldDeps.project, repl, examples, tools, catalyst, streamingFlumeSink, yarn), + inAnyProject -- inProjects(OldDeps.project, repl, examples, tools, streamingFlumeSink, yarn), unidocProjectFilter in(JavaUnidoc, unidoc) := - inAnyProject -- inProjects(OldDeps.project, repl, bagel, examples, tools, catalyst, streamingFlumeSink, yarn), + inAnyProject -- inProjects(OldDeps.project, repl, bagel, examples, tools, streamingFlumeSink, yarn), + + // Skip actual catalyst, but include the subproject. + // Catalyst is not public API and contains quasiquotes which break scaladoc. + unidocAllSources in (ScalaUnidoc, unidoc) := { + ignoreUndocumentedPackages((unidocAllSources in (ScalaUnidoc, unidoc)).value) + }, // Skip class names containing $ and some internal packages in Javadocs unidocAllSources in (JavaUnidoc, unidoc) := { - (unidocAllSources in (JavaUnidoc, unidoc)).value - .map(_.filterNot(_.getName.contains("$"))) - .map(_.filterNot(_.getCanonicalPath.contains("akka"))) - .map(_.filterNot(_.getCanonicalPath.contains("deploy"))) - .map(_.filterNot(_.getCanonicalPath.contains("network"))) - .map(_.filterNot(_.getCanonicalPath.contains("shuffle"))) - .map(_.filterNot(_.getCanonicalPath.contains("executor"))) - .map(_.filterNot(_.getCanonicalPath.contains("python"))) - .map(_.filterNot(_.getCanonicalPath.contains("collection"))) + ignoreUndocumentedPackages((unidocAllSources in (JavaUnidoc, unidoc)).value) }, // Javadoc options: create a window title, and group key packages on index page @@ -394,7 +466,8 @@ object Unidoc { "mllib.tree.impurity", "mllib.tree.model", "mllib.util", "mllib.evaluation", "mllib.feature", "mllib.random", "mllib.stat.correlation", "mllib.stat.test", "mllib.tree.impl", "mllib.tree.loss", - "ml", "ml.classification", "ml.evaluation", "ml.feature", "ml.param", "ml.tuning" + "ml", "ml.attribute", "ml.classification", "ml.evaluation", "ml.feature", "ml.param", + "ml.tuning" ), "-group", "Spark SQL", packageList("sql.api.java", "sql.api.java.types", "sql.hive.api.java"), "-noqualifier", "java.lang" @@ -411,6 +484,12 @@ object TestSettings { lazy val settings = Seq ( // Fork new JVMs for tests and set Java options for those fork := true, + // Setting SPARK_DIST_CLASSPATH is a simple way to make sure any child processes + // launched by the tests have access to the correct test-time classpath. + envVars in Test ++= Map( + "SPARK_DIST_CLASSPATH" -> + (fullClasspath in Test).value.files.map(_.getAbsolutePath).mkString(":").stripSuffix(":"), + "JAVA_HOME" -> sys.env.get("JAVA_HOME").getOrElse(sys.props("java.home"))), javaOptions in Test += "-Dspark.test.home=" + sparkHome, javaOptions in Test += "-Dspark.testing=1", javaOptions in Test += "-Dspark.port.maxRetries=100", @@ -423,10 +502,6 @@ object TestSettings { javaOptions in Test += "-ea", javaOptions in Test ++= "-Xmx3g -XX:PermSize=128M -XX:MaxNewSize=256m -XX:MaxPermSize=1g" .split(" ").toSeq, - // This places test scope jars on the classpath of executors during tests. - javaOptions in Test += - "-Dspark.executor.extraClassPath=" + (fullClasspath in Test).value.files. - map(_.getAbsolutePath).mkString(":").stripSuffix(":"), javaOptions += "-Xmx3g", // Show full stack trace and duration in test cases. testOptions in Test += Tests.Argument("-oDF"), diff --git a/project/plugins.sbt b/project/plugins.sbt index ee45b6a51905e..7096b0d3ee7de 100644 --- a/project/plugins.sbt +++ b/project/plugins.sbt @@ -19,7 +19,7 @@ addSbtPlugin("com.github.mpeltonen" % "sbt-idea" % "1.6.0") addSbtPlugin("net.virtual-void" % "sbt-dependency-graph" % "0.7.4") -addSbtPlugin("org.scalastyle" %% "scalastyle-sbt-plugin" % "0.6.0") +addSbtPlugin("org.scalastyle" %% "scalastyle-sbt-plugin" % "0.7.0") addSbtPlugin("com.typesafe" % "sbt-mima-plugin" % "0.1.6") diff --git a/project/project/SparkPluginBuild.scala b/project/project/SparkPluginBuild.scala index 8863f272da415..471d00bd8223f 100644 --- a/project/project/SparkPluginBuild.scala +++ b/project/project/SparkPluginBuild.scala @@ -24,20 +24,6 @@ import sbt.Keys._ * becomes available for scalastyle sbt plugin. */ object SparkPluginDef extends Build { - lazy val root = Project("plugins", file(".")) dependsOn(sparkStyle, sbtPomReader) - lazy val sparkStyle = Project("spark-style", file("spark-style"), settings = styleSettings) + lazy val root = Project("plugins", file(".")) dependsOn(sbtPomReader) lazy val sbtPomReader = uri("https://github.com/ScrapCodes/sbt-pom-reader.git#ignore_artifact_id") - - // There is actually no need to publish this artifact. - def styleSettings = Defaults.defaultSettings ++ Seq ( - name := "spark-style", - organization := "org.apache.spark", - scalaVersion := "2.10.4", - scalacOptions := Seq("-unchecked", "-deprecation"), - libraryDependencies ++= Dependencies.scalaStyle - ) - - object Dependencies { - val scalaStyle = Seq("org.scalastyle" %% "scalastyle" % "0.4.0") - } } diff --git a/python/docs/conf.py b/python/docs/conf.py index b00dce95d65b4..163987dd8e5fa 100644 --- a/python/docs/conf.py +++ b/python/docs/conf.py @@ -48,16 +48,16 @@ # General information about the project. project = u'PySpark' -copyright = u'2014, Author' +copyright = u'' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. -version = '1.3-SNAPSHOT' +version = 'master' # The full version, including alpha/beta/rc tags. -release = '1.3-SNAPSHOT' +release = os.environ.get('RELEASE_VERSION', version) # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. @@ -97,6 +97,10 @@ # If true, keep warnings as "system message" paragraphs in the built documents. #keep_warnings = False +# -- Options for autodoc -------------------------------------------------- + +# Look at the first line of the docstring for function and method signatures. +autodoc_docstring_signature = True # -- Options for HTML output ---------------------------------------------- diff --git a/python/docs/index.rst b/python/docs/index.rst index d150de9d5c502..f7eede9c3c82a 100644 --- a/python/docs/index.rst +++ b/python/docs/index.rst @@ -29,6 +29,14 @@ Core classes: A Resilient Distributed Dataset (RDD), the basic abstraction in Spark. + :class:`pyspark.sql.SQLContext` + + Main entry point for DataFrame and SQL functionality. + + :class:`pyspark.sql.DataFrame` + + A distributed collection of data grouped into named columns. + Indices and tables ================== diff --git a/python/docs/pyspark.ml.rst b/python/docs/pyspark.ml.rst index f10d1339a9a8f..4da6d4a74a299 100644 --- a/python/docs/pyspark.ml.rst +++ b/python/docs/pyspark.ml.rst @@ -1,11 +1,8 @@ pyspark.ml package ===================== -Submodules ----------- - -pyspark.ml module ------------------ +Module Context +-------------- .. automodule:: pyspark.ml :members: diff --git a/python/docs/pyspark.mllib.rst b/python/docs/pyspark.mllib.rst index 4548b8739ed91..26ece4c2c389a 100644 --- a/python/docs/pyspark.mllib.rst +++ b/python/docs/pyspark.mllib.rst @@ -1,16 +1,13 @@ pyspark.mllib package ===================== -Submodules ----------- - pyspark.mllib.classification module ----------------------------------- .. automodule:: pyspark.mllib.classification :members: :undoc-members: - :show-inheritance: + :inherited-members: pyspark.mllib.clustering module ------------------------------- @@ -18,7 +15,13 @@ pyspark.mllib.clustering module .. automodule:: pyspark.mllib.clustering :members: :undoc-members: - :show-inheritance: + +pyspark.mllib.evaluation module +------------------------------- + +.. automodule:: pyspark.mllib.evaluation + :members: + :undoc-members: pyspark.mllib.feature module ------------------------------- @@ -28,6 +31,13 @@ pyspark.mllib.feature module :undoc-members: :show-inheritance: +pyspark.mllib.fpm module +------------------------ + +.. automodule:: pyspark.mllib.fpm + :members: + :undoc-members: + pyspark.mllib.linalg module --------------------------- @@ -42,7 +52,6 @@ pyspark.mllib.random module .. automodule:: pyspark.mllib.random :members: :undoc-members: - :show-inheritance: pyspark.mllib.recommendation module ----------------------------------- @@ -50,7 +59,6 @@ pyspark.mllib.recommendation module .. automodule:: pyspark.mllib.recommendation :members: :undoc-members: - :show-inheritance: pyspark.mllib.regression module ------------------------------- @@ -58,7 +66,7 @@ pyspark.mllib.regression module .. automodule:: pyspark.mllib.regression :members: :undoc-members: - :show-inheritance: + :inherited-members: pyspark.mllib.stat module ------------------------- @@ -66,7 +74,6 @@ pyspark.mllib.stat module .. automodule:: pyspark.mllib.stat :members: :undoc-members: - :show-inheritance: pyspark.mllib.tree module ------------------------- @@ -74,7 +81,7 @@ pyspark.mllib.tree module .. automodule:: pyspark.mllib.tree :members: :undoc-members: - :show-inheritance: + :inherited-members: pyspark.mllib.util module ------------------------- @@ -82,4 +89,3 @@ pyspark.mllib.util module .. automodule:: pyspark.mllib.util :members: :undoc-members: - :show-inheritance: diff --git a/python/docs/pyspark.sql.rst b/python/docs/pyspark.sql.rst index 65b3650ae10ab..6259379ed05b7 100644 --- a/python/docs/pyspark.sql.rst +++ b/python/docs/pyspark.sql.rst @@ -1,10 +1,23 @@ pyspark.sql module ================== -Module contents ---------------- +Module Context +-------------- .. automodule:: pyspark.sql :members: :undoc-members: - :show-inheritance: + + +pyspark.sql.types module +------------------------ +.. automodule:: pyspark.sql.types + :members: + :undoc-members: + + +pyspark.sql.functions module +---------------------------- +.. automodule:: pyspark.sql.functions + :members: + :undoc-members: diff --git a/python/docs/pyspark.streaming.rst b/python/docs/pyspark.streaming.rst index f08185627d0bc..50822c93faba1 100644 --- a/python/docs/pyspark.streaming.rst +++ b/python/docs/pyspark.streaming.rst @@ -8,3 +8,10 @@ Module contents :members: :undoc-members: :show-inheritance: + +pyspark.streaming.kafka module +------------------------------ +.. automodule:: pyspark.streaming.kafka + :members: + :undoc-members: + :show-inheritance: diff --git a/python/pyspark/__init__.py b/python/pyspark/__init__.py index d3efcdf221d82..5f70ac6ed8fe6 100644 --- a/python/pyspark/__init__.py +++ b/python/pyspark/__init__.py @@ -22,17 +22,17 @@ - :class:`SparkContext`: Main entry point for Spark functionality. - - L{RDD} + - :class:`RDD`: A Resilient Distributed Dataset (RDD), the basic abstraction in Spark. - - L{Broadcast} + - :class:`Broadcast`: A broadcast variable that gets reused across tasks. - - L{Accumulator} + - :class:`Accumulator`: An "add-only" shared variable that tasks can only add values to. - - L{SparkConf} + - :class:`SparkConf`: For configuring Spark. - - L{SparkFiles} + - :class:`SparkFiles`: Access files shipped with jobs. - - L{StorageLevel} + - :class:`StorageLevel`: Finer-grained cache persistence levels. """ @@ -45,6 +45,7 @@ from pyspark.accumulators import Accumulator, AccumulatorParam from pyspark.broadcast import Broadcast from pyspark.serializers import MarshalSerializer, PickleSerializer +from pyspark.status import * from pyspark.profiler import Profiler, BasicProfiler # for back compatibility @@ -53,5 +54,5 @@ __all__ = [ "SparkConf", "SparkContext", "SparkFiles", "RDD", "StorageLevel", "Broadcast", "Accumulator", "AccumulatorParam", "MarshalSerializer", "PickleSerializer", - "Profiler", "BasicProfiler", + "StatusTracker", "SparkJobInfo", "SparkStageInfo", "Profiler", "BasicProfiler", ] diff --git a/python/pyspark/accumulators.py b/python/pyspark/accumulators.py index ccbca67656c8d..7271809e43880 100644 --- a/python/pyspark/accumulators.py +++ b/python/pyspark/accumulators.py @@ -54,7 +54,7 @@ ... def zero(self, value): ... return [0.0] * len(value) ... def addInPlace(self, val1, val2): -... for i in xrange(len(val1)): +... for i in range(len(val1)): ... val1[i] += val2[i] ... return val1 >>> va = sc.accumulator([1.0, 2.0, 3.0], VectorAccumulatorParam()) @@ -86,9 +86,13 @@ Exception:... """ +import sys import select import struct -import SocketServer +if sys.version < '3': + import SocketServer +else: + import socketserver as SocketServer import threading from pyspark.cloudpickle import CloudPickler from pyspark.serializers import read_int, PickleSerializer @@ -247,6 +251,7 @@ class AccumulatorServer(SocketServer.TCPServer): def shutdown(self): self.server_shutdown = True SocketServer.TCPServer.shutdown(self) + self.server_close() def _start_update_server(): diff --git a/python/pyspark/broadcast.py b/python/pyspark/broadcast.py index 6b8a8b256a891..3de4615428bb6 100644 --- a/python/pyspark/broadcast.py +++ b/python/pyspark/broadcast.py @@ -16,10 +16,15 @@ # import os -import cPickle +import sys import gc from tempfile import NamedTemporaryFile +if sys.version < '3': + import cPickle as pickle +else: + import pickle + unicode = str __all__ = ['Broadcast'] @@ -70,33 +75,19 @@ def __init__(self, sc=None, value=None, pickle_registry=None, path=None): self._path = path def dump(self, value, f): - if isinstance(value, basestring): - if isinstance(value, unicode): - f.write('U') - value = value.encode('utf8') - else: - f.write('S') - f.write(value) - else: - f.write('P') - cPickle.dump(value, f, 2) + pickle.dump(value, f, 2) f.close() return f.name def load(self, path): with open(path, 'rb', 1 << 20) as f: - flag = f.read(1) - data = f.read() - if flag == 'P': - # cPickle.loads() may create lots of objects, disable GC - # temporary for better performance - gc.disable() - try: - return cPickle.loads(data) - finally: - gc.enable() - else: - return data.decode('utf8') if flag == 'U' else data + # pickle.load() may create lots of objects, disable GC + # temporary for better performance + gc.disable() + try: + return pickle.load(f) + finally: + gc.enable() @property def value(self): diff --git a/python/pyspark/cloudpickle.py b/python/pyspark/cloudpickle.py index bb0783555aa77..9ef93071d2e77 100644 --- a/python/pyspark/cloudpickle.py +++ b/python/pyspark/cloudpickle.py @@ -40,164 +40,126 @@ NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """ - +from __future__ import print_function import operator import os +import io import pickle import struct import sys import types from functools import partial import itertools -from copy_reg import _extension_registry, _inverted_registry, _extension_cache -import new import dis import traceback -import platform - -PyImp = platform.python_implementation() - -import logging -cloudLog = logging.getLogger("Cloud.Transport") +if sys.version < '3': + from pickle import Pickler + try: + from cStringIO import StringIO + except ImportError: + from StringIO import StringIO + PY3 = False +else: + types.ClassType = type + from pickle import _Pickler as Pickler + from io import BytesIO as StringIO + PY3 = True #relevant opcodes -STORE_GLOBAL = chr(dis.opname.index('STORE_GLOBAL')) -DELETE_GLOBAL = chr(dis.opname.index('DELETE_GLOBAL')) -LOAD_GLOBAL = chr(dis.opname.index('LOAD_GLOBAL')) +STORE_GLOBAL = dis.opname.index('STORE_GLOBAL') +DELETE_GLOBAL = dis.opname.index('DELETE_GLOBAL') +LOAD_GLOBAL = dis.opname.index('LOAD_GLOBAL') GLOBAL_OPS = [STORE_GLOBAL, DELETE_GLOBAL, LOAD_GLOBAL] +HAVE_ARGUMENT = dis.HAVE_ARGUMENT +EXTENDED_ARG = dis.EXTENDED_ARG -HAVE_ARGUMENT = chr(dis.HAVE_ARGUMENT) -EXTENDED_ARG = chr(dis.EXTENDED_ARG) - -if PyImp == "PyPy": - # register builtin type in `new` - new.method = types.MethodType - -try: - from cStringIO import StringIO -except ImportError: - from StringIO import StringIO -# These helper functions were copied from PiCloud's util module. def islambda(func): - return getattr(func,'func_name') == '' + return getattr(func,'__name__') == '' -def xrange_params(xrangeobj): - """Returns a 3 element tuple describing the xrange start, step, and len - respectively - Note: Only guarentees that elements of xrange are the same. parameters may - be different. - e.g. xrange(1,1) is interpretted as xrange(0,0); both behave the same - though w/ iteration - """ - - xrange_len = len(xrangeobj) - if not xrange_len: #empty - return (0,1,0) - start = xrangeobj[0] - if xrange_len == 1: #one element - return start, 1, 1 - return (start, xrangeobj[1] - xrangeobj[0], xrange_len) - -#debug variables intended for developer use: -printSerialization = False -printMemoization = False +_BUILTIN_TYPE_NAMES = {} +for k, v in types.__dict__.items(): + if type(v) is type: + _BUILTIN_TYPE_NAMES[v] = k -useForcedImports = True #Should I use forced imports for tracking? +def _builtin_type(name): + return getattr(types, name) -class CloudPickler(pickle.Pickler): +class CloudPickler(Pickler): - dispatch = pickle.Pickler.dispatch.copy() - savedForceImports = False - savedDjangoEnv = False #hack tro transport django environment + dispatch = Pickler.dispatch.copy() - def __init__(self, file, protocol=None, min_size_to_save= 0): - pickle.Pickler.__init__(self,file,protocol) - self.modules = set() #set of modules needed to depickle - self.globals_ref = {} # map ids to dictionary. used to ensure that functions can share global env + def __init__(self, file, protocol=None): + Pickler.__init__(self, file, protocol) + # set of modules to unpickle + self.modules = set() + # map ids to dictionary. used to ensure that functions can share global env + self.globals_ref = {} def dump(self, obj): - # note: not thread safe - # minimal side-effects, so not fixing - recurse_limit = 3000 - base_recurse = sys.getrecursionlimit() - if base_recurse < recurse_limit: - sys.setrecursionlimit(recurse_limit) self.inject_addons() try: - return pickle.Pickler.dump(self, obj) - except RuntimeError, e: + return Pickler.dump(self, obj) + except RuntimeError as e: if 'recursion' in e.args[0]: - msg = """Could not pickle object as excessively deep recursion required. - Try _fast_serialization=2 or contact PiCloud support""" + msg = """Could not pickle object as excessively deep recursion required.""" raise pickle.PicklingError(msg) - finally: - new_recurse = sys.getrecursionlimit() - if new_recurse == recurse_limit: - sys.setrecursionlimit(base_recurse) + + def save_memoryview(self, obj): + """Fallback to save_string""" + Pickler.save_string(self, str(obj)) def save_buffer(self, obj): """Fallback to save_string""" - pickle.Pickler.save_string(self,str(obj)) - dispatch[buffer] = save_buffer + Pickler.save_string(self,str(obj)) + if PY3: + dispatch[memoryview] = save_memoryview + else: + dispatch[buffer] = save_buffer - #block broken objects - def save_unsupported(self, obj, pack=None): + def save_unsupported(self, obj): raise pickle.PicklingError("Cannot pickle objects of type %s" % type(obj)) dispatch[types.GeneratorType] = save_unsupported - #python2.6+ supports slice pickling. some py2.5 extensions might as well. We just test it - try: - slice(0,1).__reduce__() - except TypeError: #can't pickle - - dispatch[slice] = save_unsupported - - #itertools objects do not pickle! + # itertools objects do not pickle! for v in itertools.__dict__.values(): if type(v) is type: dispatch[v] = save_unsupported - - def save_dict(self, obj): - """hack fix - If the dict is a global, deal with it in a special way - """ - #print 'saving', obj - if obj is __builtins__: - self.save_reduce(_get_module_builtins, (), obj=obj) - else: - pickle.Pickler.save_dict(self, obj) - dispatch[pickle.DictionaryType] = save_dict - - - def save_module(self, obj, pack=struct.pack): + def save_module(self, obj): """ Save a module as an import """ - #print 'try save import', obj.__name__ self.modules.add(obj) - self.save_reduce(subimport,(obj.__name__,), obj=obj) - dispatch[types.ModuleType] = save_module #new type + self.save_reduce(subimport, (obj.__name__,), obj=obj) + dispatch[types.ModuleType] = save_module - def save_codeobject(self, obj, pack=struct.pack): + def save_codeobject(self, obj): """ Save a code object """ - #print 'try to save codeobj: ', obj - args = ( - obj.co_argcount, obj.co_nlocals, obj.co_stacksize, obj.co_flags, obj.co_code, - obj.co_consts, obj.co_names, obj.co_varnames, obj.co_filename, obj.co_name, - obj.co_firstlineno, obj.co_lnotab, obj.co_freevars, obj.co_cellvars - ) + if PY3: + args = ( + obj.co_argcount, obj.co_kwonlyargcount, obj.co_nlocals, obj.co_stacksize, + obj.co_flags, obj.co_code, obj.co_consts, obj.co_names, obj.co_varnames, + obj.co_filename, obj.co_name, obj.co_firstlineno, obj.co_lnotab, obj.co_freevars, + obj.co_cellvars + ) + else: + args = ( + obj.co_argcount, obj.co_nlocals, obj.co_stacksize, obj.co_flags, obj.co_code, + obj.co_consts, obj.co_names, obj.co_varnames, obj.co_filename, obj.co_name, + obj.co_firstlineno, obj.co_lnotab, obj.co_freevars, obj.co_cellvars + ) self.save_reduce(types.CodeType, args, obj=obj) - dispatch[types.CodeType] = save_codeobject #new type + dispatch[types.CodeType] = save_codeobject - def save_function(self, obj, name=None, pack=struct.pack): + def save_function(self, obj, name=None): """ Registered with the dispatch to handle all function types. Determines what kind of function obj is (e.g. lambda, defined at @@ -205,12 +167,14 @@ def save_function(self, obj, name=None, pack=struct.pack): """ write = self.write - name = obj.__name__ + if name is None: + name = obj.__name__ modname = pickle.whichmodule(obj, name) - #print 'which gives %s %s %s' % (modname, obj, name) + # print('which gives %s %s %s' % (modname, obj, name)) try: themodule = sys.modules[modname] - except KeyError: # eval'd items such as namedtuple give invalid items for their function __module__ + except KeyError: + # eval'd items such as namedtuple give invalid items for their function __module__ modname = '__main__' if modname == '__main__': @@ -221,37 +185,18 @@ def save_function(self, obj, name=None, pack=struct.pack): if getattr(themodule, name, None) is obj: return self.save_global(obj, name) - if not self.savedDjangoEnv: - #hack for django - if we detect the settings module, we transport it - django_settings = os.environ.get('DJANGO_SETTINGS_MODULE', '') - if django_settings: - django_mod = sys.modules.get(django_settings) - if django_mod: - cloudLog.debug('Transporting django settings %s during save of %s', django_mod, name) - self.savedDjangoEnv = True - self.modules.add(django_mod) - write(pickle.MARK) - self.save_reduce(django_settings_load, (django_mod.__name__,), obj=django_mod) - write(pickle.POP_MARK) - - # if func is lambda, def'ed at prompt, is in main, or is nested, then # we'll pickle the actual function object rather than simply saving a # reference (as is done in default pickler), via save_function_tuple. - if islambda(obj) or obj.func_code.co_filename == '' or themodule is None: - #Force server to import modules that have been imported in main - modList = None - if themodule is None and not self.savedForceImports: - mainmod = sys.modules['__main__'] - if useForcedImports and hasattr(mainmod,'___pyc_forcedImports__'): - modList = list(mainmod.___pyc_forcedImports__) - self.savedForceImports = True - self.save_function_tuple(obj, modList) + if islambda(obj) or obj.__code__.co_filename == '' or themodule is None: + #print("save global", islambda(obj), obj.__code__.co_filename, modname, themodule) + self.save_function_tuple(obj) return - else: # func is nested + else: + # func is nested klass = getattr(themodule, name, None) if klass is None or klass is not obj: - self.save_function_tuple(obj, [themodule]) + self.save_function_tuple(obj) return if obj.__dict__: @@ -266,7 +211,7 @@ def save_function(self, obj, name=None, pack=struct.pack): self.memoize(obj) dispatch[types.FunctionType] = save_function - def save_function_tuple(self, func, forced_imports): + def save_function_tuple(self, func): """ Pickles an actual func object. A func comprises: code, globals, defaults, closure, and dict. We @@ -281,19 +226,6 @@ def save_function_tuple(self, func, forced_imports): save = self.save write = self.write - # save the modules (if any) - if forced_imports: - write(pickle.MARK) - save(_modules_to_main) - #print 'forced imports are', forced_imports - - forced_names = map(lambda m: m.__name__, forced_imports) - save((forced_names,)) - - #save((forced_imports,)) - write(pickle.REDUCE) - write(pickle.POP_MARK) - code, f_globals, defaults, closure, dct, base_globals = self.extract_func_data(func) save(_fill_function) # skeleton function updater @@ -318,6 +250,8 @@ def extract_code_globals(co): Find all globals names read or written to by codeblock co """ code = co.co_code + if not PY3: + code = [ord(c) for c in code] names = co.co_names out_names = set() @@ -327,18 +261,18 @@ def extract_code_globals(co): while i < n: op = code[i] - i = i+1 + i += 1 if op >= HAVE_ARGUMENT: - oparg = ord(code[i]) + ord(code[i+1])*256 + extended_arg + oparg = code[i] + code[i+1] * 256 + extended_arg extended_arg = 0 - i = i+2 + i += 2 if op == EXTENDED_ARG: - extended_arg = oparg*65536L + extended_arg = oparg*65536 if op in GLOBAL_OPS: out_names.add(names[oparg]) - #print 'extracted', out_names, ' from ', names - if co.co_consts: # see if nested function have any global refs + # see if nested function have any global refs + if co.co_consts: for const in co.co_consts: if type(const) is types.CodeType: out_names |= CloudPickler.extract_code_globals(const) @@ -350,46 +284,28 @@ def extract_func_data(self, func): Turn the function into a tuple of data necessary to recreate it: code, globals, defaults, closure, dict """ - code = func.func_code + code = func.__code__ # extract all global ref's - func_global_refs = CloudPickler.extract_code_globals(code) + func_global_refs = self.extract_code_globals(code) # process all variables referenced by global environment f_globals = {} for var in func_global_refs: - #Some names, such as class functions are not global - we don't need them - if func.func_globals.has_key(var): - f_globals[var] = func.func_globals[var] + if var in func.__globals__: + f_globals[var] = func.__globals__[var] # defaults requires no processing - defaults = func.func_defaults - - def get_contents(cell): - try: - return cell.cell_contents - except ValueError, e: #cell is empty error on not yet assigned - raise pickle.PicklingError('Function to be pickled has free variables that are referenced before assignment in enclosing scope') - + defaults = func.__defaults__ # process closure - if func.func_closure: - closure = map(get_contents, func.func_closure) - else: - closure = [] + closure = [c.cell_contents for c in func.__closure__] if func.__closure__ else [] # save the dict - dct = func.func_dict - - if printSerialization: - outvars = ['code: ' + str(code) ] - outvars.append('globals: ' + str(f_globals)) - outvars.append('defaults: ' + str(defaults)) - outvars.append('closure: ' + str(closure)) - print 'function ', func, 'is extracted to: ', ', '.join(outvars) + dct = func.__dict__ - base_globals = self.globals_ref.get(id(func.func_globals), {}) - self.globals_ref[id(func.func_globals)] = base_globals + base_globals = self.globals_ref.get(id(func.__globals__), {}) + self.globals_ref[id(func.__globals__)] = base_globals return (code, f_globals, defaults, closure, dct, base_globals) @@ -400,8 +316,9 @@ def save_builtin_function(self, obj): dispatch[types.BuiltinFunctionType] = save_builtin_function def save_global(self, obj, name=None, pack=struct.pack): - write = self.write - memo = self.memo + if obj.__module__ == "__builtin__" or obj.__module__ == "builtins": + if obj in _BUILTIN_TYPE_NAMES: + return self.save_reduce(_builtin_type, (_BUILTIN_TYPE_NAMES[obj],), obj=obj) if name is None: name = obj.__name__ @@ -410,98 +327,57 @@ def save_global(self, obj, name=None, pack=struct.pack): if modname is None: modname = pickle.whichmodule(obj, name) - try: - __import__(modname) - themodule = sys.modules[modname] - except (ImportError, KeyError, AttributeError): #should never occur - raise pickle.PicklingError( - "Can't pickle %r: Module %s cannot be found" % - (obj, modname)) - if modname == '__main__': themodule = None - - if themodule: + else: + __import__(modname) + themodule = sys.modules[modname] self.modules.add(themodule) - sendRef = True - typ = type(obj) - #print 'saving', obj, typ - try: - try: #Deal with case when getattribute fails with exceptions - klass = getattr(themodule, name) - except (AttributeError): - if modname == '__builtin__': #new.* are misrepeported - modname = 'new' - __import__(modname) - themodule = sys.modules[modname] - try: - klass = getattr(themodule, name) - except AttributeError, a: - # print themodule, name, obj, type(obj) - raise pickle.PicklingError("Can't pickle builtin %s" % obj) - else: - raise + if hasattr(themodule, name) and getattr(themodule, name) is obj: + return Pickler.save_global(self, obj, name) - except (ImportError, KeyError, AttributeError): - if typ == types.TypeType or typ == types.ClassType: - sendRef = False - else: #we can't deal with this - raise - else: - if klass is not obj and (typ == types.TypeType or typ == types.ClassType): - sendRef = False - if not sendRef: - #note: Third party types might crash this - add better checks! - d = dict(obj.__dict__) #copy dict proxy to a dict - if not isinstance(d.get('__dict__', None), property): # don't extract dict that are properties - d.pop('__dict__',None) - d.pop('__weakref__',None) + typ = type(obj) + if typ is not obj and isinstance(obj, (type, types.ClassType)): + d = dict(obj.__dict__) # copy dict proxy to a dict + if not isinstance(d.get('__dict__', None), property): + # don't extract dict that are properties + d.pop('__dict__', None) + d.pop('__weakref__', None) # hack as __new__ is stored differently in the __dict__ new_override = d.get('__new__', None) if new_override: d['__new__'] = obj.__new__ - self.save_reduce(type(obj),(obj.__name__,obj.__bases__, - d),obj=obj) - #print 'internal reduce dask %s %s' % (obj, d) - return - - if self.proto >= 2: - code = _extension_registry.get((modname, name)) - if code: - assert code > 0 - if code <= 0xff: - write(pickle.EXT1 + chr(code)) - elif code <= 0xffff: - write("%c%c%c" % (pickle.EXT2, code&0xff, code>>8)) - else: - write(pickle.EXT4 + pack("> sys.stderr, 'Cloud not import django settings %s:' % (name) - print_exec(sys.stderr) - if modified_env: - del os.environ['DJANGO_SETTINGS_MODULE'] - else: - #add project directory to sys,path: - if hasattr(module,'__file__'): - dirname = os.path.split(module.__file__)[0] + '/' - sys.path.append(dirname) # restores function attributes def _restore_attr(obj, attr): @@ -851,13 +636,16 @@ def _restore_attr(obj, attr): setattr(obj, key, val) return obj + def _get_module_builtins(): return pickle.__builtins__ + def print_exec(stream): ei = sys.exc_info() traceback.print_exception(ei[0], ei[1], ei[2], None, stream) + def _modules_to_main(modList): """Force every module in modList to be placed into main""" if not modList: @@ -868,22 +656,16 @@ def _modules_to_main(modList): if type(modname) is str: try: mod = __import__(modname) - except Exception, i: #catch all... - sys.stderr.write('warning: could not import %s\n. Your function may unexpectedly error due to this import failing; \ -A version mismatch is likely. Specific error was:\n' % modname) + except Exception as e: + sys.stderr.write('warning: could not import %s\n. ' + 'Your function may unexpectedly error due to this import failing;' + 'A version mismatch is likely. Specific error was:\n' % modname) print_exec(sys.stderr) else: - setattr(main,mod.__name__, mod) - else: - #REVERSE COMPATIBILITY FOR CLOUD CLIENT 1.5 (WITH EPD) - #In old version actual module was sent - setattr(main,modname.__name__, modname) + setattr(main, mod.__name__, mod) -#object generators: -def _build_xrange(start, step, len): - """Built xrange explicitly""" - return xrange(start, start + step*len, step) +#object generators: def _genpartial(func, args, kwds): if not args: args = () @@ -891,22 +673,26 @@ def _genpartial(func, args, kwds): kwds = {} return partial(func, *args, **kwds) + def _fill_function(func, globals, defaults, dict): """ Fills in the rest of function data into the skeleton function object that were created via _make_skel_func(). """ - func.func_globals.update(globals) - func.func_defaults = defaults - func.func_dict = dict + func.__globals__.update(globals) + func.__defaults__ = defaults + func.__dict__ = dict return func + def _make_cell(value): - return (lambda: value).func_closure[0] + return (lambda: value).__closure__[0] + def _reconstruct_closure(values): return tuple([_make_cell(v) for v in values]) + def _make_skel_func(code, closures, base_globals = None): """ Creates a skeleton function object that contains just the provided code and the correct number of cells in func_closure. All other @@ -928,40 +714,3 @@ def _make_skel_func(code, closures, base_globals = None): def _getobject(modname, attribute): mod = __import__(modname, fromlist=[attribute]) return mod.__dict__[attribute] - -def _generateImage(size, mode, str_rep): - """Generate image from string representation""" - import Image - i = Image.new(mode, size) - i.fromstring(str_rep) - return i - -def _lazyloadImage(fp): - import Image - fp.seek(0) #works in almost any case - return Image.open(fp) - -"""Timeseries""" -def _genTimeSeries(reduce_args, state): - import scikits.timeseries.tseries as ts - from numpy import ndarray - from numpy.ma import MaskedArray - - - time_series = ts._tsreconstruct(*reduce_args) - - #from setstate modified - (ver, shp, typ, isf, raw, msk, flv, dsh, dtm, dtyp, frq, infodict) = state - #print 'regenerating %s' % dtyp - - MaskedArray.__setstate__(time_series, (ver, shp, typ, isf, raw, msk, flv)) - _dates = time_series._dates - #_dates.__setstate__((ver, dsh, typ, isf, dtm, frq)) #use remote typ - ndarray.__setstate__(_dates,(dsh,dtyp, isf, dtm)) - _dates.freq = frq - _dates._cachedinfo.update(dict(full=None, hasdups=None, steps=None, - toobj=None, toord=None, tostr=None)) - # Update the _optinfo dictionary - time_series._optinfo.update(infodict) - return time_series - diff --git a/python/pyspark/conf.py b/python/pyspark/conf.py index dc7cd0bce56f3..924da3eecf214 100644 --- a/python/pyspark/conf.py +++ b/python/pyspark/conf.py @@ -44,7 +44,7 @@ >>> conf.get("spark.executorEnv.VAR1") u'value1' ->>> print conf.toDebugString() +>>> print(conf.toDebugString()) spark.executorEnv.VAR1=value1 spark.executorEnv.VAR3=value3 spark.executorEnv.VAR4=value4 @@ -56,6 +56,13 @@ __all__ = ['SparkConf'] +import sys +import re + +if sys.version > '3': + unicode = str + __doc__ = re.sub(r"(\W|^)[uU](['])", r'\1\2', __doc__) + class SparkConf(object): diff --git a/python/pyspark/context.py b/python/pyspark/context.py index bf1f61c8504ed..1dc2fec0ae5c8 100644 --- a/python/pyspark/context.py +++ b/python/pyspark/context.py @@ -15,12 +15,16 @@ # limitations under the License. # +from __future__ import print_function + import os import shutil import sys from threading import Lock from tempfile import NamedTemporaryFile +from py4j.java_collections import ListConverter + from pyspark import accumulators from pyspark.accumulators import Accumulator from pyspark.broadcast import Broadcast @@ -30,11 +34,13 @@ from pyspark.serializers import PickleSerializer, BatchedSerializer, UTF8Deserializer, \ PairDeserializer, AutoBatchedSerializer, NoOpSerializer from pyspark.storagelevel import StorageLevel -from pyspark.rdd import RDD +from pyspark.rdd import RDD, _load_from_socket, ignore_unicode_prefix from pyspark.traceback_utils import CallSite, first_spark_call +from pyspark.status import StatusTracker from pyspark.profiler import ProfilerCollector, BasicProfiler -from py4j.java_collections import ListConverter +if sys.version > '3': + xrange = range __all__ = ['SparkContext'] @@ -58,12 +64,13 @@ class SparkContext(object): _gateway = None _jvm = None - _writeToFile = None _next_accum_id = 0 _active_spark_context = None _lock = Lock() _python_includes = None # zip and egg files that need to be added to PYTHONPATH + PACKAGE_EXTENSIONS = ('.zip', '.egg', '.jar') + def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None, environment=None, batchSize=0, serializer=PickleSerializer(), conf=None, gateway=None, jsc=None, profiler_cls=BasicProfiler): @@ -131,7 +138,7 @@ def _do_init(self, master, appName, sparkHome, pyFiles, environment, batchSize, if sparkHome: self._conf.setSparkHome(sparkHome) if environment: - for key, value in environment.iteritems(): + for key, value in environment.items(): self._conf.setExecutorEnv(key, value) for key, value in DEFAULT_CONFIGS.items(): self._conf.setIfMissing(key, value) @@ -151,6 +158,10 @@ def _do_init(self, master, appName, sparkHome, pyFiles, environment, batchSize, if k.startswith("spark.executorEnv."): varName = k[len("spark.executorEnv."):] self.environment[varName] = v + if sys.version >= '3.3' and 'PYTHONHASHSEED' not in os.environ: + # disable randomness of hash of string in worker, if this is not + # launched by spark-submit + self.environment["PYTHONHASHSEED"] = "0" # Create the Java SparkContext through Py4J self._jsc = jsc or self._initialize_context(self._conf._jconf) @@ -185,7 +196,7 @@ def _do_init(self, master, appName, sparkHome, pyFiles, environment, batchSize, for path in self._conf.get("spark.submit.pyFiles", "").split(","): if path != "": (dirname, filename) = os.path.split(path) - if filename.lower().endswith("zip") or filename.lower().endswith("egg"): + if filename[-4:].lower() in self.PACKAGE_EXTENSIONS: self._python_includes.append(filename) sys.path.insert(1, os.path.join(SparkFiles.getRootDirectory(), filename)) @@ -218,7 +229,6 @@ def _ensure_initialized(cls, instance=None, gateway=None): if not SparkContext._gateway: SparkContext._gateway = gateway or launch_gateway() SparkContext._jvm = SparkContext._gateway.jvm - SparkContext._writeToFile = SparkContext._jvm.PythonRDD.writeToFile if instance: if (SparkContext._active_spark_context and @@ -322,7 +332,7 @@ def parallelize(self, c, numSlices=None): start0 = c[0] def getStart(split): - return start0 + (split * size / numSlices) * step + return start0 + int((split * size / numSlices)) * step def f(split, iterator): return xrange(getStart(split), getStart(split + 1), step) @@ -356,6 +366,7 @@ def pickleFile(self, name, minPartitions=None): minPartitions = minPartitions or self.defaultMinPartitions return RDD(self._jsc.objectFile(name, minPartitions), self) + @ignore_unicode_prefix def textFile(self, name, minPartitions=None, use_unicode=True): """ Read a text file from HDFS, a local file system (available on all @@ -368,7 +379,7 @@ def textFile(self, name, minPartitions=None, use_unicode=True): >>> path = os.path.join(tempdir, "sample-text.txt") >>> with open(path, "w") as testFile: - ... testFile.write("Hello world!") + ... _ = testFile.write("Hello world!") >>> textFile = sc.textFile(path) >>> textFile.collect() [u'Hello world!'] @@ -377,6 +388,7 @@ def textFile(self, name, minPartitions=None, use_unicode=True): return RDD(self._jsc.textFile(name, minPartitions), self, UTF8Deserializer(use_unicode)) + @ignore_unicode_prefix def wholeTextFiles(self, path, minPartitions=None, use_unicode=True): """ Read a directory of text files from HDFS, a local file system @@ -410,9 +422,9 @@ def wholeTextFiles(self, path, minPartitions=None, use_unicode=True): >>> dirPath = os.path.join(tempdir, "files") >>> os.mkdir(dirPath) >>> with open(os.path.join(dirPath, "1.txt"), "w") as file1: - ... file1.write("1") + ... _ = file1.write("1") >>> with open(os.path.join(dirPath, "2.txt"), "w") as file2: - ... file2.write("2") + ... _ = file2.write("2") >>> textFiles = sc.wholeTextFiles(dirPath) >>> sorted(textFiles.collect()) [(u'.../1.txt', u'1'), (u'.../2.txt', u'2')] @@ -455,7 +467,7 @@ def _dictToJavaMap(self, d): jm = self._jvm.java.util.HashMap() if not d: d = {} - for k, v in d.iteritems(): + for k, v in d.items(): jm[k] = v return jm @@ -607,6 +619,7 @@ def _checkpointFile(self, name, input_deserializer): jrdd = self._jsc.checkpointFile(name) return RDD(jrdd, self, input_deserializer) + @ignore_unicode_prefix def union(self, rdds): """ Build the union of a list of RDDs. @@ -617,7 +630,7 @@ def union(self, rdds): >>> path = os.path.join(tempdir, "union-text.txt") >>> with open(path, "w") as testFile: - ... testFile.write("Hello") + ... _ = testFile.write("Hello") >>> textFile = sc.textFile(path) >>> textFile.collect() [u'Hello'] @@ -676,7 +689,7 @@ def addFile(self, path): >>> from pyspark import SparkFiles >>> path = os.path.join(tempdir, "test.txt") >>> with open(path, "w") as testFile: - ... testFile.write("100") + ... _ = testFile.write("100") >>> sc.addFile(path) >>> def func(iterator): ... with open(SparkFiles.get("test.txt")) as testFile: @@ -704,11 +717,13 @@ def addPyFile(self, path): """ self.addFile(path) (dirname, filename) = os.path.split(path) # dirname may be directory or HDFS/S3 prefix - - if filename.endswith('.zip') or filename.endswith('.ZIP') or filename.endswith('.egg'): + if filename[-4:].lower() in self.PACKAGE_EXTENSIONS: self._python_includes.append(filename) # for tests in local mode sys.path.insert(1, os.path.join(SparkFiles.getRootDirectory(), filename)) + if sys.version > '3': + import importlib + importlib.invalidate_caches() def setCheckpointDir(self, dirName): """ @@ -743,7 +758,7 @@ def setJobGroup(self, groupId, description, interruptOnCancel=False): The application can use L{SparkContext.cancelJobGroup} to cancel all running jobs in this group. - >>> import thread, threading + >>> import threading >>> from time import sleep >>> result = "Not Set" >>> lock = threading.Lock() @@ -762,10 +777,10 @@ def setJobGroup(self, groupId, description, interruptOnCancel=False): ... sleep(5) ... sc.cancelJobGroup("job_to_cancel") >>> supress = lock.acquire() - >>> supress = thread.start_new_thread(start_job, (10,)) - >>> supress = thread.start_new_thread(stop_job, tuple()) + >>> supress = threading.Thread(target=start_job, args=(10,)).start() + >>> supress = threading.Thread(target=stop_job).start() >>> supress = lock.acquire() - >>> print result + >>> print(result) Cancelled If interruptOnCancel is set to true for the job group, then job cancellation will result @@ -808,6 +823,12 @@ def cancelAllJobs(self): """ self._jsc.sc().cancelAllJobs() + def statusTracker(self): + """ + Return :class:`StatusTracker` object + """ + return StatusTracker(self._jsc.statusTracker()) + def runJob(self, rdd, partitionFunc, partitions=None, allowLocal=False): """ Executes the given partitionFunc on the specified set of partitions, @@ -831,8 +852,9 @@ def runJob(self, rdd, partitionFunc, partitions=None, allowLocal=False): # by runJob() in order to avoid having to pass a Python lambda into # SparkContext#runJob. mappedRDD = rdd.mapPartitions(partitionFunc) - it = self._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, javaPartitions, allowLocal) - return list(mappedRDD._collect_iterator_through_file(it)) + port = self._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, javaPartitions, + allowLocal) + return list(_load_from_socket(port, mappedRDD._jrdd_deserializer)) def show_profiles(self): """ Print the profile stats to stdout """ diff --git a/python/pyspark/daemon.py b/python/pyspark/daemon.py index f09587f211708..7f06d4288c872 100644 --- a/python/pyspark/daemon.py +++ b/python/pyspark/daemon.py @@ -24,9 +24,10 @@ import traceback import time import gc -from errno import EINTR, ECHILD, EAGAIN +from errno import EINTR, EAGAIN from socket import AF_INET, SOCK_STREAM, SOMAXCONN from signal import SIGHUP, SIGTERM, SIGCHLD, SIG_DFL, SIG_IGN, SIGINT + from pyspark.worker import main as worker_main from pyspark.serializers import read_int, write_int @@ -53,29 +54,21 @@ def worker(sock): # Read the socket using fdopen instead of socket.makefile() because the latter # seems to be very slow; note that we need to dup() the file descriptor because # otherwise writes also cause a seek that makes us miss data on the read side. - infile = os.fdopen(os.dup(sock.fileno()), "a+", 65536) - outfile = os.fdopen(os.dup(sock.fileno()), "a+", 65536) + infile = os.fdopen(os.dup(sock.fileno()), "rb", 65536) + outfile = os.fdopen(os.dup(sock.fileno()), "wb", 65536) exit_code = 0 try: worker_main(infile, outfile) except SystemExit as exc: exit_code = compute_real_exit_code(exc.code) finally: - outfile.flush() + try: + outfile.flush() + except Exception: + pass return exit_code -# Cleanup zombie children -def cleanup_dead_children(): - try: - while True: - pid, _ = os.waitpid(0, os.WNOHANG) - if not pid: - break - except: - pass - - def manager(): # Create a new process group to corral our children os.setpgid(0, 0) @@ -85,8 +78,12 @@ def manager(): listen_sock.bind(('127.0.0.1', 0)) listen_sock.listen(max(1024, SOMAXCONN)) listen_host, listen_port = listen_sock.getsockname() - write_int(listen_port, sys.stdout) - sys.stdout.flush() + + # re-open stdin/stdout in 'wb' mode + stdin_bin = os.fdopen(sys.stdin.fileno(), 'rb', 4) + stdout_bin = os.fdopen(sys.stdout.fileno(), 'wb', 4) + write_int(listen_port, stdout_bin) + stdout_bin.flush() def shutdown(code): signal.signal(SIGTERM, SIG_DFL) @@ -98,6 +95,7 @@ def handle_sigterm(*args): shutdown(1) signal.signal(SIGTERM, handle_sigterm) # Gracefully exit on SIGTERM signal.signal(SIGHUP, SIG_IGN) # Don't die on SIGHUP + signal.signal(SIGCHLD, SIG_IGN) reuse = os.environ.get("SPARK_REUSE_WORKER") @@ -112,12 +110,9 @@ def handle_sigterm(*args): else: raise - # cleanup in signal handler will cause deadlock - cleanup_dead_children() - if 0 in ready_fds: try: - worker_pid = read_int(sys.stdin) + worker_pid = read_int(stdin_bin) except EOFError: # Spark told us to exit by closing stdin shutdown(0) @@ -142,7 +137,7 @@ def handle_sigterm(*args): time.sleep(1) pid = os.fork() # error here will shutdown daemon else: - outfile = sock.makefile('w') + outfile = sock.makefile(mode='wb') write_int(e.errno, outfile) # Signal that the fork failed outfile.flush() outfile.close() @@ -154,7 +149,7 @@ def handle_sigterm(*args): listen_sock.close() try: # Acknowledge that the fork was successful - outfile = sock.makefile("w") + outfile = sock.makefile(mode="wb") write_int(os.getpid(), outfile) outfile.flush() outfile.close() diff --git a/python/pyspark/heapq3.py b/python/pyspark/heapq3.py index bc441f138f7fc..4ef2afe03544f 100644 --- a/python/pyspark/heapq3.py +++ b/python/pyspark/heapq3.py @@ -627,51 +627,49 @@ def merge(iterables, key=None, reverse=False): if key is None: for order, it in enumerate(map(iter, iterables)): try: - next = it.next - h_append([next(), order * direction, next]) + h_append([next(it), order * direction, it]) except StopIteration: pass _heapify(h) while len(h) > 1: try: while True: - value, order, next = s = h[0] + value, order, it = s = h[0] yield value - s[0] = next() # raises StopIteration when exhausted + s[0] = next(it) # raises StopIteration when exhausted _heapreplace(h, s) # restore heap condition except StopIteration: _heappop(h) # remove empty iterator if h: # fast case when only a single iterator remains - value, order, next = h[0] + value, order, it = h[0] yield value - for value in next.__self__: + for value in it: yield value return for order, it in enumerate(map(iter, iterables)): try: - next = it.next - value = next() - h_append([key(value), order * direction, value, next]) + value = next(it) + h_append([key(value), order * direction, value, it]) except StopIteration: pass _heapify(h) while len(h) > 1: try: while True: - key_value, order, value, next = s = h[0] + key_value, order, value, it = s = h[0] yield value - value = next() + value = next(it) s[0] = key(value) s[2] = value _heapreplace(h, s) except StopIteration: _heappop(h) if h: - key_value, order, value, next = h[0] + key_value, order, value, it = h[0] yield value - for value in next.__self__: + for value in it: yield value diff --git a/python/pyspark/java_gateway.py b/python/pyspark/java_gateway.py index a0a028446d5fd..45bc38f7e61f8 100644 --- a/python/pyspark/java_gateway.py +++ b/python/pyspark/java_gateway.py @@ -17,60 +17,63 @@ import atexit import os -import sys +import select import signal import shlex +import socket import platform from subprocess import Popen, PIPE -from threading import Thread from py4j.java_gateway import java_import, JavaGateway, GatewayClient +from pyspark.serializers import read_int -def launch_gateway(): - SPARK_HOME = os.environ["SPARK_HOME"] - gateway_port = -1 +def launch_gateway(): if "PYSPARK_GATEWAY_PORT" in os.environ: gateway_port = int(os.environ["PYSPARK_GATEWAY_PORT"]) else: + SPARK_HOME = os.environ["SPARK_HOME"] # Launch the Py4j gateway using Spark's run command so that we pick up the # proper classpath and settings from spark-env.sh on_windows = platform.system() == "Windows" script = "./bin/spark-submit.cmd" if on_windows else "./bin/spark-submit" - submit_args = os.environ.get("PYSPARK_SUBMIT_ARGS") - submit_args = submit_args if submit_args is not None else "" - submit_args = shlex.split(submit_args) - command = [os.path.join(SPARK_HOME, script)] + submit_args + ["pyspark-shell"] + submit_args = os.environ.get("PYSPARK_SUBMIT_ARGS", "pyspark-shell") + command = [os.path.join(SPARK_HOME, script)] + shlex.split(submit_args) + + # Start a socket that will be used by PythonGatewayServer to communicate its port to us + callback_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + callback_socket.bind(('127.0.0.1', 0)) + callback_socket.listen(1) + callback_host, callback_port = callback_socket.getsockname() + env = dict(os.environ) + env['_PYSPARK_DRIVER_CALLBACK_HOST'] = callback_host + env['_PYSPARK_DRIVER_CALLBACK_PORT'] = str(callback_port) + + # Launch the Java gateway. + # We open a pipe to stdin so that the Java gateway can die when the pipe is broken if not on_windows: # Don't send ctrl-c / SIGINT to the Java gateway: def preexec_func(): signal.signal(signal.SIGINT, signal.SIG_IGN) - env = dict(os.environ) - env["IS_SUBPROCESS"] = "1" # tell JVM to exit after python exits - proc = Popen(command, stdout=PIPE, stdin=PIPE, preexec_fn=preexec_func, env=env) + proc = Popen(command, stdin=PIPE, preexec_fn=preexec_func, env=env) else: # preexec_fn not supported on Windows - proc = Popen(command, stdout=PIPE, stdin=PIPE) + proc = Popen(command, stdin=PIPE, env=env) - try: - # Determine which ephemeral port the server started on: - gateway_port = proc.stdout.readline() - gateway_port = int(gateway_port) - except ValueError: - # Grab the remaining lines of stdout - (stdout, _) = proc.communicate() - exit_code = proc.poll() - error_msg = "Launching GatewayServer failed" - error_msg += " with exit code %d!\n" % exit_code if exit_code else "!\n" - error_msg += "Warning: Expected GatewayServer to output a port, but found " - if gateway_port == "" and stdout == "": - error_msg += "no output.\n" - else: - error_msg += "the following:\n\n" - error_msg += "--------------------------------------------------------------\n" - error_msg += gateway_port + stdout - error_msg += "--------------------------------------------------------------\n" - raise Exception(error_msg) + gateway_port = None + # We use select() here in order to avoid blocking indefinitely if the subprocess dies + # before connecting + while gateway_port is None and proc.poll() is None: + timeout = 1 # (seconds) + readable, _, _ = select.select([callback_socket], [], [], timeout) + if callback_socket in readable: + gateway_connection = callback_socket.accept()[0] + # Determine which ephemeral port the server started on: + gateway_port = read_int(gateway_connection.makefile(mode="rb")) + gateway_connection.close() + callback_socket.close() + if gateway_port is None: + raise Exception("Java gateway process exited before sending the driver its port number") # In Windows, ensure the Java child processes do not linger after Python has exited. # In UNIX-based systems, the child process can kill itself on broken pipe (i.e. when @@ -88,21 +91,6 @@ def killChild(): Popen(["cmd", "/c", "taskkill", "/f", "/t", "/pid", str(proc.pid)]) atexit.register(killChild) - # Create a thread to echo output from the GatewayServer, which is required - # for Java log output to show up: - class EchoOutputThread(Thread): - - def __init__(self, stream): - Thread.__init__(self) - self.daemon = True - self.stream = stream - - def run(self): - while True: - line = self.stream.readline() - sys.stderr.write(line) - EchoOutputThread(proc.stdout).start() - # Connect to the gateway gateway = JavaGateway(GatewayClient(port=gateway_port), auto_convert=False) diff --git a/python/pyspark/join.py b/python/pyspark/join.py index b4a844713745a..94df3990164d6 100644 --- a/python/pyspark/join.py +++ b/python/pyspark/join.py @@ -32,11 +32,12 @@ """ from pyspark.resultiterable import ResultIterable +from functools import reduce def _do_python_join(rdd, other, numPartitions, dispatch): - vs = rdd.map(lambda (k, v): (k, (1, v))) - ws = other.map(lambda (k, v): (k, (2, v))) + vs = rdd.mapValues(lambda v: (1, v)) + ws = other.mapValues(lambda v: (2, v)) return vs.union(ws).groupByKey(numPartitions).flatMapValues(lambda x: dispatch(x.__iter__())) @@ -48,7 +49,7 @@ def dispatch(seq): vbuf.append(v) elif n == 2: wbuf.append(v) - return [(v, w) for v in vbuf for w in wbuf] + return ((v, w) for v in vbuf for w in wbuf) return _do_python_join(rdd, other, numPartitions, dispatch) @@ -62,7 +63,7 @@ def dispatch(seq): wbuf.append(v) if not vbuf: vbuf.append(None) - return [(v, w) for v in vbuf for w in wbuf] + return ((v, w) for v in vbuf for w in wbuf) return _do_python_join(rdd, other, numPartitions, dispatch) @@ -76,7 +77,7 @@ def dispatch(seq): wbuf.append(v) if not wbuf: wbuf.append(None) - return [(v, w) for v in vbuf for w in wbuf] + return ((v, w) for v in vbuf for w in wbuf) return _do_python_join(rdd, other, numPartitions, dispatch) @@ -98,14 +99,15 @@ def dispatch(seq): def python_cogroup(rdds, numPartitions): def make_mapper(i): - return lambda (k, v): (k, (i, v)) - vrdds = [rdd.map(make_mapper(i)) for i, rdd in enumerate(rdds)] + return lambda v: (i, v) + vrdds = [rdd.mapValues(make_mapper(i)) for i, rdd in enumerate(rdds)] union_vrdds = reduce(lambda acc, other: acc.union(other), vrdds) rdd_len = len(vrdds) def dispatch(seq): - bufs = [[] for i in range(rdd_len)] - for (n, v) in seq: + bufs = [[] for _ in range(rdd_len)] + for n, v in seq: bufs[n].append(v) - return tuple(map(ResultIterable, bufs)) + return tuple(ResultIterable(vs) for vs in bufs) + return union_vrdds.groupByKey(numPartitions).mapValues(dispatch) diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py index 6bd2aa8e47837..45754bc9d4b10 100644 --- a/python/pyspark/ml/classification.py +++ b/python/pyspark/ml/classification.py @@ -15,10 +15,11 @@ # limitations under the License. # -from pyspark.ml.util import inherit_doc +from pyspark.ml.util import keyword_only from pyspark.ml.wrapper import JavaEstimator, JavaModel from pyspark.ml.param.shared import HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,\ HasRegParam +from pyspark.mllib.common import inherit_doc __all__ = ['LogisticRegression', 'LogisticRegressionModel'] @@ -32,22 +33,47 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti >>> from pyspark.sql import Row >>> from pyspark.mllib.linalg import Vectors - >>> dataset = sqlCtx.inferSchema(sc.parallelize([ \ - Row(label=1.0, features=Vectors.dense(1.0)), \ - Row(label=0.0, features=Vectors.sparse(1, [], []))])) - >>> lr = LogisticRegression() \ - .setMaxIter(5) \ - .setRegParam(0.01) - >>> model = lr.fit(dataset) - >>> test0 = sqlCtx.inferSchema(sc.parallelize([Row(features=Vectors.dense(-1.0))])) - >>> print model.transform(test0).head().prediction + >>> df = sc.parallelize([ + ... Row(label=1.0, features=Vectors.dense(1.0)), + ... Row(label=0.0, features=Vectors.sparse(1, [], []))]).toDF() + >>> lr = LogisticRegression(maxIter=5, regParam=0.01) + >>> model = lr.fit(df) + >>> test0 = sc.parallelize([Row(features=Vectors.dense(-1.0))]).toDF() + >>> model.transform(test0).head().prediction 0.0 - >>> test1 = sqlCtx.inferSchema(sc.parallelize([Row(features=Vectors.sparse(1, [0], [1.0]))])) - >>> print model.transform(test1).head().prediction + >>> test1 = sc.parallelize([Row(features=Vectors.sparse(1, [0], [1.0]))]).toDF() + >>> model.transform(test1).head().prediction 1.0 + >>> lr.setParams("vector") + Traceback (most recent call last): + ... + TypeError: Method setParams forces keyword arguments. """ _java_class = "org.apache.spark.ml.classification.LogisticRegression" + @keyword_only + def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", + maxIter=100, regParam=0.1): + """ + __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ + maxIter=100, regParam=0.1) + """ + super(LogisticRegression, self).__init__() + self._setDefault(maxIter=100, regParam=0.1) + kwargs = self.__init__._input_kwargs + self.setParams(**kwargs) + + @keyword_only + def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", + maxIter=100, regParam=0.1): + """ + setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ + maxIter=100, regParam=0.1) + Sets params for logistic regression. + """ + kwargs = self.setParams._input_kwargs + return self._set(**kwargs) + def _create_model(self, java_model): return LogisticRegressionModel(java_model) @@ -66,9 +92,9 @@ class LogisticRegressionModel(JavaModel): # The small batch size here ensures that we see multiple batches, # even in these small test examples: sc = SparkContext("local[2]", "ml.feature tests") - sqlCtx = SQLContext(sc) + sqlContext = SQLContext(sc) globs['sc'] = sc - globs['sqlCtx'] = sqlCtx + globs['sqlContext'] = sqlContext (failure_count, test_count) = doctest.testmod( globs=globs, optionflags=doctest.ELLIPSIS) sc.stop() diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py index e088acd0ca82d..4e4614b859ac6 100644 --- a/python/pyspark/ml/feature.py +++ b/python/pyspark/ml/feature.py @@ -15,32 +15,62 @@ # limitations under the License. # +from pyspark.rdd import ignore_unicode_prefix from pyspark.ml.param.shared import HasInputCol, HasOutputCol, HasNumFeatures -from pyspark.ml.util import inherit_doc +from pyspark.ml.util import keyword_only from pyspark.ml.wrapper import JavaTransformer +from pyspark.mllib.common import inherit_doc __all__ = ['Tokenizer', 'HashingTF'] @inherit_doc +@ignore_unicode_prefix class Tokenizer(JavaTransformer, HasInputCol, HasOutputCol): """ A tokenizer that converts the input string to lowercase and then splits it by white spaces. >>> from pyspark.sql import Row - >>> dataset = sqlCtx.inferSchema(sc.parallelize([Row(text="a b c")])) - >>> tokenizer = Tokenizer() \ - .setInputCol("text") \ - .setOutputCol("words") - >>> print tokenizer.transform(dataset).head() + >>> df = sc.parallelize([Row(text="a b c")]).toDF() + >>> tokenizer = Tokenizer(inputCol="text", outputCol="words") + >>> tokenizer.transform(df).head() Row(text=u'a b c', words=[u'a', u'b', u'c']) - >>> print tokenizer.transform(dataset, {tokenizer.outputCol: "tokens"}).head() + >>> # Change a parameter. + >>> tokenizer.setParams(outputCol="tokens").transform(df).head() Row(text=u'a b c', tokens=[u'a', u'b', u'c']) + >>> # Temporarily modify a parameter. + >>> tokenizer.transform(df, {tokenizer.outputCol: "words"}).head() + Row(text=u'a b c', words=[u'a', u'b', u'c']) + >>> tokenizer.transform(df).head() + Row(text=u'a b c', tokens=[u'a', u'b', u'c']) + >>> # Must use keyword arguments to specify params. + >>> tokenizer.setParams("text") + Traceback (most recent call last): + ... + TypeError: Method setParams forces keyword arguments. """ _java_class = "org.apache.spark.ml.feature.Tokenizer" + @keyword_only + def __init__(self, inputCol=None, outputCol=None): + """ + __init__(self, inputCol=None, outputCol=None) + """ + super(Tokenizer, self).__init__() + kwargs = self.__init__._input_kwargs + self.setParams(**kwargs) + + @keyword_only + def setParams(self, inputCol=None, outputCol=None): + """ + setParams(self, inputCol="input", outputCol="output") + Sets params for this Tokenizer. + """ + kwargs = self.setParams._input_kwargs + return self._set(**kwargs) + @inherit_doc class HashingTF(JavaTransformer, HasInputCol, HasOutputCol, HasNumFeatures): @@ -49,20 +79,38 @@ class HashingTF(JavaTransformer, HasInputCol, HasOutputCol, HasNumFeatures): hashing trick. >>> from pyspark.sql import Row - >>> dataset = sqlCtx.inferSchema(sc.parallelize([Row(words=["a", "b", "c"])])) - >>> hashingTF = HashingTF() \ - .setNumFeatures(10) \ - .setInputCol("words") \ - .setOutputCol("features") - >>> print hashingTF.transform(dataset).head().features - (10,[7,8,9],[1.0,1.0,1.0]) + >>> df = sc.parallelize([Row(words=["a", "b", "c"])]).toDF() + >>> hashingTF = HashingTF(numFeatures=10, inputCol="words", outputCol="features") + >>> hashingTF.transform(df).head().features + SparseVector(10, {7: 1.0, 8: 1.0, 9: 1.0}) + >>> hashingTF.setParams(outputCol="freqs").transform(df).head().freqs + SparseVector(10, {7: 1.0, 8: 1.0, 9: 1.0}) >>> params = {hashingTF.numFeatures: 5, hashingTF.outputCol: "vector"} - >>> print hashingTF.transform(dataset, params).head().vector - (5,[2,3,4],[1.0,1.0,1.0]) + >>> hashingTF.transform(df, params).head().vector + SparseVector(5, {2: 1.0, 3: 1.0, 4: 1.0}) """ _java_class = "org.apache.spark.ml.feature.HashingTF" + @keyword_only + def __init__(self, numFeatures=1 << 18, inputCol=None, outputCol=None): + """ + __init__(self, numFeatures=1 << 18, inputCol=None, outputCol=None) + """ + super(HashingTF, self).__init__() + self._setDefault(numFeatures=1 << 18) + kwargs = self.__init__._input_kwargs + self.setParams(**kwargs) + + @keyword_only + def setParams(self, numFeatures=1 << 18, inputCol=None, outputCol=None): + """ + setParams(self, numFeatures=1 << 18, inputCol=None, outputCol=None) + Sets params for this HashingTF. + """ + kwargs = self.setParams._input_kwargs + return self._set(**kwargs) + if __name__ == "__main__": import doctest @@ -72,9 +120,9 @@ class HashingTF(JavaTransformer, HasInputCol, HasOutputCol, HasNumFeatures): # The small batch size here ensures that we see multiple batches, # even in these small test examples: sc = SparkContext("local[2]", "ml.feature tests") - sqlCtx = SQLContext(sc) + sqlContext = SQLContext(sc) globs['sc'] = sc - globs['sqlCtx'] = sqlCtx + globs['sqlContext'] = sqlContext (failure_count, test_count) = doctest.testmod( globs=globs, optionflags=doctest.ELLIPSIS) sc.stop() diff --git a/python/pyspark/ml/param/__init__.py b/python/pyspark/ml/param/__init__.py index 5566792cead48..9fccb65675185 100644 --- a/python/pyspark/ml/param/__init__.py +++ b/python/pyspark/ml/param/__init__.py @@ -25,23 +25,21 @@ class Param(object): """ - A param with self-contained documentation and optionally default value. + A param with self-contained documentation. """ - def __init__(self, parent, name, doc, defaultValue=None): - if not isinstance(parent, Identifiable): - raise ValueError("Parent must be identifiable but got type %s." % type(parent).__name__) + def __init__(self, parent, name, doc): + if not isinstance(parent, Params): + raise ValueError("Parent must be a Params but got type %s." % type(parent).__name__) self.parent = parent self.name = str(name) self.doc = str(doc) - self.defaultValue = defaultValue def __str__(self): - return str(self.parent) + "-" + self.name + return str(self.parent) + "__" + self.name def __repr__(self): - return "Param(parent=%r, name=%r, doc=%r, defaultValue=%r)" % \ - (self.parent, self.name, self.doc, self.defaultValue) + return "Param(parent=%r, name=%r, doc=%r)" % (self.parent, self.name, self.doc) class Params(Identifiable): @@ -52,26 +50,128 @@ class Params(Identifiable): __metaclass__ = ABCMeta - def __init__(self): - super(Params, self).__init__() - #: embedded param map - self.paramMap = {} + #: internal param map for user-supplied values param map + paramMap = {} + + #: internal param map for default values + defaultParamMap = {} @property def params(self): """ - Returns all params. The default implementation uses - :py:func:`dir` to get all attributes of type + Returns all params ordered by name. The default implementation + uses :py:func:`dir` to get all attributes of type :py:class:`Param`. """ - return filter(lambda attr: isinstance(attr, Param), - [getattr(self, x) for x in dir(self) if x != "params"]) + return list(filter(lambda attr: isinstance(attr, Param), + [getattr(self, x) for x in dir(self) if x != "params"])) + + def _explain(self, param): + """ + Explains a single param and returns its name, doc, and optional + default value and user-supplied value in a string. + """ + param = self._resolveParam(param) + values = [] + if self.isDefined(param): + if param in self.defaultParamMap: + values.append("default: %s" % self.defaultParamMap[param]) + if param in self.paramMap: + values.append("current: %s" % self.paramMap[param]) + else: + values.append("undefined") + valueStr = "(" + ", ".join(values) + ")" + return "%s: %s %s" % (param.name, param.doc, valueStr) + + def explainParams(self): + """ + Returns the documentation of all params with their optionally + default values and user-supplied values. + """ + return "\n".join([self._explain(param) for param in self.params]) + + def getParam(self, paramName): + """ + Gets a param by its name. + """ + param = getattr(self, paramName) + if isinstance(param, Param): + return param + else: + raise ValueError("Cannot find param with name %s." % paramName) + + def isSet(self, param): + """ + Checks whether a param is explicitly set by user. + """ + param = self._resolveParam(param) + return param in self.paramMap + + def hasDefault(self, param): + """ + Checks whether a param has a default value. + """ + param = self._resolveParam(param) + return param in self.defaultParamMap + + def isDefined(self, param): + """ + Checks whether a param is explicitly set by user or has a default value. + """ + return self.isSet(param) or self.hasDefault(param) - def _merge_params(self, params): - paramMap = self.paramMap.copy() - paramMap.update(params) + def getOrDefault(self, param): + """ + Gets the value of a param in the user-supplied param map or its + default value. Raises an error if either is set. + """ + if isinstance(param, Param): + if param in self.paramMap: + return self.paramMap[param] + else: + return self.defaultParamMap[param] + elif isinstance(param, str): + return self.getOrDefault(self.getParam(param)) + else: + raise KeyError("Cannot recognize %r as a param." % param) + + def extractParamMap(self, extraParamMap={}): + """ + Extracts the embedded default param values and user-supplied + values, and then merges them with extra values from input into + a flat param map, where the latter value is used if there exist + conflicts, i.e., with ordering: default param values < + user-supplied values < extraParamMap. + :param extraParamMap: extra param values + :return: merged param map + """ + paramMap = self.defaultParamMap.copy() + paramMap.update(self.paramMap) + paramMap.update(extraParamMap) return paramMap + def _shouldOwn(self, param): + """ + Validates that the input param belongs to this Params instance. + """ + if param.parent is not self: + raise ValueError("Param %r does not belong to %r." % (param, self)) + + def _resolveParam(self, param): + """ + Resolves a param and validates the ownership. + :param param: param name or the param instance, which must + belong to this Params instance + :return: resolved param instance + """ + if isinstance(param, Param): + self._shouldOwn(param) + return param + elif isinstance(param, str): + return self.getParam(param) + else: + raise ValueError("Cannot resolve %r as a param." % param) + @staticmethod def _dummy(): """ @@ -80,3 +180,19 @@ def _dummy(): dummy = Params() dummy.uid = "undefined" return dummy + + def _set(self, **kwargs): + """ + Sets user-supplied params. + """ + for param, value in kwargs.items(): + self.paramMap[getattr(self, param)] = value + return self + + def _setDefault(self, **kwargs): + """ + Sets default params. + """ + for param, value in kwargs.items(): + self.defaultParamMap[getattr(self, param)] = value + return self diff --git a/python/pyspark/ml/param/_gen_shared_params.py b/python/pyspark/ml/param/_shared_params_code_gen.py similarity index 67% rename from python/pyspark/ml/param/_gen_shared_params.py rename to python/pyspark/ml/param/_shared_params_code_gen.py index 5eb81106f116c..6a3192465d66d 100644 --- a/python/pyspark/ml/param/_gen_shared_params.py +++ b/python/pyspark/ml/param/_shared_params_code_gen.py @@ -15,6 +15,8 @@ # limitations under the License. # +from __future__ import print_function + header = """# # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with @@ -32,29 +34,34 @@ # limitations under the License. #""" +# Code generator for shared params (shared.py). Run under this folder with: +# python _shared_params_code_gen.py > shared.py + -def _gen_param_code(name, doc, defaultValue): +def _gen_param_code(name, doc, defaultValueStr): """ Generates Python code for a shared param class. :param name: param name :param doc: param doc - :param defaultValue: string representation of the param + :param defaultValueStr: string representation of the default value :return: code string """ # TODO: How to correctly inherit instance attributes? template = '''class Has$Name(Params): """ - Params with $name. + Mixin for param $name: $doc. """ # a placeholder to make it appear in the generated doc - $name = Param(Params._dummy(), "$name", "$doc", $defaultValue) + $name = Param(Params._dummy(), "$name", "$doc") def __init__(self): super(Has$Name, self).__init__() #: param for $doc - self.$name = Param(self, "$name", "$doc", $defaultValue) + self.$name = Param(self, "$name", "$doc") + if $defaultValueStr is not None: + self._setDefault($name=$defaultValueStr) def set$Name(self, value): """ @@ -67,32 +74,29 @@ def get$Name(self): """ Gets the value of $name or its default value. """ - if self.$name in self.paramMap: - return self.paramMap[self.$name] - else: - return self.$name.defaultValue''' + return self.getOrDefault(self.$name)''' - upperCamelName = name[0].upper() + name[1:] + Name = name[0].upper() + name[1:] return template \ .replace("$name", name) \ - .replace("$Name", upperCamelName) \ + .replace("$Name", Name) \ .replace("$doc", doc) \ - .replace("$defaultValue", defaultValue) + .replace("$defaultValueStr", str(defaultValueStr)) if __name__ == "__main__": - print header - print "\n# DO NOT MODIFY. The code is generated by _gen_shared_params.py.\n" - print "from pyspark.ml.param import Param, Params\n\n" + print(header) + print("\n# DO NOT MODIFY THIS FILE! It was generated by _shared_params_code_gen.py.\n") + print("from pyspark.ml.param import Param, Params\n\n") shared = [ - ("maxIter", "max number of iterations", "100"), - ("regParam", "regularization constant", "0.1"), + ("maxIter", "max number of iterations", None), + ("regParam", "regularization constant", None), ("featuresCol", "features column name", "'features'"), ("labelCol", "label column name", "'label'"), ("predictionCol", "prediction column name", "'prediction'"), - ("inputCol", "input column name", "'input'"), - ("outputCol", "output column name", "'output'"), - ("numFeatures", "number of features", "1 << 18")] + ("inputCol", "input column name", None), + ("outputCol", "output column name", None), + ("numFeatures", "number of features", None)] code = [] - for name, doc, defaultValue in shared: - code.append(_gen_param_code(name, doc, defaultValue)) - print "\n\n\n".join(code) + for name, doc, defaultValueStr in shared: + code.append(_gen_param_code(name, doc, defaultValueStr)) + print("\n\n\n".join(code)) diff --git a/python/pyspark/ml/param/shared.py b/python/pyspark/ml/param/shared.py index 586822f2de423..13b6749998ad0 100644 --- a/python/pyspark/ml/param/shared.py +++ b/python/pyspark/ml/param/shared.py @@ -15,23 +15,25 @@ # limitations under the License. # -# DO NOT MODIFY. The code is generated by _gen_shared_params.py. +# DO NOT MODIFY THIS FILE! It was generated by _shared_params_code_gen.py. from pyspark.ml.param import Param, Params class HasMaxIter(Params): """ - Params with maxIter. + Mixin for param maxIter: max number of iterations. """ # a placeholder to make it appear in the generated doc - maxIter = Param(Params._dummy(), "maxIter", "max number of iterations", 100) + maxIter = Param(Params._dummy(), "maxIter", "max number of iterations") def __init__(self): super(HasMaxIter, self).__init__() #: param for max number of iterations - self.maxIter = Param(self, "maxIter", "max number of iterations", 100) + self.maxIter = Param(self, "maxIter", "max number of iterations") + if None is not None: + self._setDefault(maxIter=None) def setMaxIter(self, value): """ @@ -44,24 +46,23 @@ def getMaxIter(self): """ Gets the value of maxIter or its default value. """ - if self.maxIter in self.paramMap: - return self.paramMap[self.maxIter] - else: - return self.maxIter.defaultValue + return self.getOrDefault(self.maxIter) class HasRegParam(Params): """ - Params with regParam. + Mixin for param regParam: regularization constant. """ # a placeholder to make it appear in the generated doc - regParam = Param(Params._dummy(), "regParam", "regularization constant", 0.1) + regParam = Param(Params._dummy(), "regParam", "regularization constant") def __init__(self): super(HasRegParam, self).__init__() #: param for regularization constant - self.regParam = Param(self, "regParam", "regularization constant", 0.1) + self.regParam = Param(self, "regParam", "regularization constant") + if None is not None: + self._setDefault(regParam=None) def setRegParam(self, value): """ @@ -74,24 +75,23 @@ def getRegParam(self): """ Gets the value of regParam or its default value. """ - if self.regParam in self.paramMap: - return self.paramMap[self.regParam] - else: - return self.regParam.defaultValue + return self.getOrDefault(self.regParam) class HasFeaturesCol(Params): """ - Params with featuresCol. + Mixin for param featuresCol: features column name. """ # a placeholder to make it appear in the generated doc - featuresCol = Param(Params._dummy(), "featuresCol", "features column name", 'features') + featuresCol = Param(Params._dummy(), "featuresCol", "features column name") def __init__(self): super(HasFeaturesCol, self).__init__() #: param for features column name - self.featuresCol = Param(self, "featuresCol", "features column name", 'features') + self.featuresCol = Param(self, "featuresCol", "features column name") + if 'features' is not None: + self._setDefault(featuresCol='features') def setFeaturesCol(self, value): """ @@ -104,24 +104,23 @@ def getFeaturesCol(self): """ Gets the value of featuresCol or its default value. """ - if self.featuresCol in self.paramMap: - return self.paramMap[self.featuresCol] - else: - return self.featuresCol.defaultValue + return self.getOrDefault(self.featuresCol) class HasLabelCol(Params): """ - Params with labelCol. + Mixin for param labelCol: label column name. """ # a placeholder to make it appear in the generated doc - labelCol = Param(Params._dummy(), "labelCol", "label column name", 'label') + labelCol = Param(Params._dummy(), "labelCol", "label column name") def __init__(self): super(HasLabelCol, self).__init__() #: param for label column name - self.labelCol = Param(self, "labelCol", "label column name", 'label') + self.labelCol = Param(self, "labelCol", "label column name") + if 'label' is not None: + self._setDefault(labelCol='label') def setLabelCol(self, value): """ @@ -134,24 +133,23 @@ def getLabelCol(self): """ Gets the value of labelCol or its default value. """ - if self.labelCol in self.paramMap: - return self.paramMap[self.labelCol] - else: - return self.labelCol.defaultValue + return self.getOrDefault(self.labelCol) class HasPredictionCol(Params): """ - Params with predictionCol. + Mixin for param predictionCol: prediction column name. """ # a placeholder to make it appear in the generated doc - predictionCol = Param(Params._dummy(), "predictionCol", "prediction column name", 'prediction') + predictionCol = Param(Params._dummy(), "predictionCol", "prediction column name") def __init__(self): super(HasPredictionCol, self).__init__() #: param for prediction column name - self.predictionCol = Param(self, "predictionCol", "prediction column name", 'prediction') + self.predictionCol = Param(self, "predictionCol", "prediction column name") + if 'prediction' is not None: + self._setDefault(predictionCol='prediction') def setPredictionCol(self, value): """ @@ -164,24 +162,23 @@ def getPredictionCol(self): """ Gets the value of predictionCol or its default value. """ - if self.predictionCol in self.paramMap: - return self.paramMap[self.predictionCol] - else: - return self.predictionCol.defaultValue + return self.getOrDefault(self.predictionCol) class HasInputCol(Params): """ - Params with inputCol. + Mixin for param inputCol: input column name. """ # a placeholder to make it appear in the generated doc - inputCol = Param(Params._dummy(), "inputCol", "input column name", 'input') + inputCol = Param(Params._dummy(), "inputCol", "input column name") def __init__(self): super(HasInputCol, self).__init__() #: param for input column name - self.inputCol = Param(self, "inputCol", "input column name", 'input') + self.inputCol = Param(self, "inputCol", "input column name") + if None is not None: + self._setDefault(inputCol=None) def setInputCol(self, value): """ @@ -194,24 +191,23 @@ def getInputCol(self): """ Gets the value of inputCol or its default value. """ - if self.inputCol in self.paramMap: - return self.paramMap[self.inputCol] - else: - return self.inputCol.defaultValue + return self.getOrDefault(self.inputCol) class HasOutputCol(Params): """ - Params with outputCol. + Mixin for param outputCol: output column name. """ # a placeholder to make it appear in the generated doc - outputCol = Param(Params._dummy(), "outputCol", "output column name", 'output') + outputCol = Param(Params._dummy(), "outputCol", "output column name") def __init__(self): super(HasOutputCol, self).__init__() #: param for output column name - self.outputCol = Param(self, "outputCol", "output column name", 'output') + self.outputCol = Param(self, "outputCol", "output column name") + if None is not None: + self._setDefault(outputCol=None) def setOutputCol(self, value): """ @@ -224,24 +220,23 @@ def getOutputCol(self): """ Gets the value of outputCol or its default value. """ - if self.outputCol in self.paramMap: - return self.paramMap[self.outputCol] - else: - return self.outputCol.defaultValue + return self.getOrDefault(self.outputCol) class HasNumFeatures(Params): """ - Params with numFeatures. + Mixin for param numFeatures: number of features. """ # a placeholder to make it appear in the generated doc - numFeatures = Param(Params._dummy(), "numFeatures", "number of features", 1 << 18) + numFeatures = Param(Params._dummy(), "numFeatures", "number of features") def __init__(self): super(HasNumFeatures, self).__init__() #: param for number of features - self.numFeatures = Param(self, "numFeatures", "number of features", 1 << 18) + self.numFeatures = Param(self, "numFeatures", "number of features") + if None is not None: + self._setDefault(numFeatures=None) def setNumFeatures(self, value): """ @@ -254,7 +249,4 @@ def getNumFeatures(self): """ Gets the value of numFeatures or its default value. """ - if self.numFeatures in self.paramMap: - return self.paramMap[self.numFeatures] - else: - return self.numFeatures.defaultValue + return self.getOrDefault(self.numFeatures) diff --git a/python/pyspark/ml/pipeline.py b/python/pyspark/ml/pipeline.py index 2d239f8c802a0..d94ecfff09f66 100644 --- a/python/pyspark/ml/pipeline.py +++ b/python/pyspark/ml/pipeline.py @@ -18,7 +18,8 @@ from abc import ABCMeta, abstractmethod from pyspark.ml.param import Param, Params -from pyspark.ml.util import inherit_doc +from pyspark.ml.util import keyword_only +from pyspark.mllib.common import inherit_doc __all__ = ['Estimator', 'Transformer', 'Pipeline', 'PipelineModel'] @@ -38,7 +39,7 @@ def fit(self, dataset, params={}): Fits a model to the input dataset with optional parameters. :param dataset: input dataset, which is an instance of - :py:class:`pyspark.sql.SchemaRDD` + :py:class:`pyspark.sql.DataFrame` :param params: an optional param map that overwrites embedded params :returns: fitted model @@ -61,7 +62,7 @@ def transform(self, dataset, params={}): Transforms the input dataset with optional parameters. :param dataset: input dataset, which is an instance of - :py:class:`pyspark.sql.SchemaRDD` + :py:class:`pyspark.sql.DataFrame` :param params: an optional param map that overwrites embedded params :returns: transformed dataset @@ -89,10 +90,16 @@ class Pipeline(Estimator): identity transformer. """ - def __init__(self): + @keyword_only + def __init__(self, stages=[]): + """ + __init__(self, stages=[]) + """ super(Pipeline, self).__init__() #: Param for pipeline stages. self.stages = Param(self, "stages", "pipeline stages") + kwargs = self.__init__._input_kwargs + self.setParams(**kwargs) def setStages(self, value): """ @@ -110,8 +117,17 @@ def getStages(self): if self.stages in self.paramMap: return self.paramMap[self.stages] + @keyword_only + def setParams(self, stages=[]): + """ + setParams(self, stages=[]) + Sets params for Pipeline. + """ + kwargs = self.setParams._input_kwargs + return self._set(**kwargs) + def fit(self, dataset, params={}): - paramMap = self._merge_params(params) + paramMap = self.extractParamMap(params) stages = paramMap[self.stages] for stage in stages: if not (isinstance(stage, Estimator) or isinstance(stage, Transformer)): @@ -148,7 +164,7 @@ def __init__(self, transformers): self.transformers = transformers def transform(self, dataset, params={}): - paramMap = self._merge_params(params) + paramMap = self.extractParamMap(params) for t in self.transformers: dataset = t.transform(dataset, paramMap) return dataset diff --git a/python/pyspark/ml/tests.py b/python/pyspark/ml/tests.py index b627c2b4e930b..3a42bcf723894 100644 --- a/python/pyspark/ml/tests.py +++ b/python/pyspark/ml/tests.py @@ -33,6 +33,7 @@ from pyspark.tests import ReusedPySparkTestCase as PySparkTestCase from pyspark.sql import DataFrame from pyspark.ml.param import Param +from pyspark.ml.param.shared import HasMaxIter, HasInputCol from pyspark.ml.pipeline import Transformer, Estimator, Pipeline @@ -46,7 +47,7 @@ class MockTransformer(Transformer): def __init__(self): super(MockTransformer, self).__init__() - self.fake = Param(self, "fake", "fake", None) + self.fake = Param(self, "fake", "fake") self.dataset_index = None self.fake_param_value = None @@ -62,7 +63,7 @@ class MockEstimator(Estimator): def __init__(self): super(MockEstimator, self).__init__() - self.fake = Param(self, "fake", "fake", None) + self.fake = Param(self, "fake", "fake") self.dataset_index = None self.fake_param_value = None self.model = None @@ -111,5 +112,52 @@ def test_pipeline(self): self.assertEqual(6, dataset.index) +class TestParams(HasMaxIter, HasInputCol): + """ + A subclass of Params mixed with HasMaxIter and HasInputCol. + """ + + def __init__(self): + super(TestParams, self).__init__() + self._setDefault(maxIter=10) + + +class ParamTests(PySparkTestCase): + + def test_param(self): + testParams = TestParams() + maxIter = testParams.maxIter + self.assertEqual(maxIter.name, "maxIter") + self.assertEqual(maxIter.doc, "max number of iterations") + self.assertTrue(maxIter.parent is testParams) + + def test_params(self): + testParams = TestParams() + maxIter = testParams.maxIter + inputCol = testParams.inputCol + + params = testParams.params + self.assertEqual(params, [inputCol, maxIter]) + + self.assertTrue(testParams.hasDefault(maxIter)) + self.assertFalse(testParams.isSet(maxIter)) + self.assertTrue(testParams.isDefined(maxIter)) + self.assertEqual(testParams.getMaxIter(), 10) + testParams.setMaxIter(100) + self.assertTrue(testParams.isSet(maxIter)) + self.assertEquals(testParams.getMaxIter(), 100) + + self.assertFalse(testParams.hasDefault(inputCol)) + self.assertFalse(testParams.isSet(inputCol)) + self.assertFalse(testParams.isDefined(inputCol)) + with self.assertRaises(KeyError): + testParams.getInputCol() + + self.assertEquals( + testParams.explainParams(), + "\n".join(["inputCol: input column name (undefined)", + "maxIter: max number of iterations (default: 10, current: 100)"])) + + if __name__ == "__main__": unittest.main() diff --git a/python/pyspark/ml/util.py b/python/pyspark/ml/util.py index b1caa84b6306a..d3cb100a9efa5 100644 --- a/python/pyspark/ml/util.py +++ b/python/pyspark/ml/util.py @@ -15,21 +15,22 @@ # limitations under the License. # +from functools import wraps import uuid -def inherit_doc(cls): - for name, func in vars(cls).items(): - # only inherit docstring for public functions - if name.startswith("_"): - continue - if not func.__doc__: - for parent in cls.__bases__: - parent_func = getattr(parent, name, None) - if parent_func and getattr(parent_func, "__doc__", None): - func.__doc__ = parent_func.__doc__ - break - return cls +def keyword_only(func): + """ + A decorator that forces keyword arguments in the wrapped method + and saves actual input keyword arguments in `_input_kwargs`. + """ + @wraps(func) + def wrapper(*args, **kwargs): + if len(args) > 1: + raise TypeError("Method %s forces keyword arguments." % func.__name__) + wrapper._input_kwargs = kwargs + return func(*args, **kwargs) + return wrapper class Identifiable(object): @@ -39,8 +40,8 @@ class Identifiable(object): def __init__(self): #: A unique id for the object. The default implementation - #: concatenates the class name, "-", and 8 random hex chars. - self.uid = type(self).__name__ + "-" + uuid.uuid4().hex[:8] + #: concatenates the class name, "_", and 8 random hex chars. + self.uid = type(self).__name__ + "_" + uuid.uuid4().hex[:8] def __repr__(self): return self.uid diff --git a/python/pyspark/ml/wrapper.py b/python/pyspark/ml/wrapper.py index 9e12ddc3d9b8f..394f23c5e9b12 100644 --- a/python/pyspark/ml/wrapper.py +++ b/python/pyspark/ml/wrapper.py @@ -21,7 +21,7 @@ from pyspark.sql import DataFrame from pyspark.ml.param import Params from pyspark.ml.pipeline import Estimator, Transformer -from pyspark.ml.util import inherit_doc +from pyspark.mllib.common import inherit_doc def _jvm(): @@ -64,7 +64,7 @@ def _transfer_params_to_java(self, params, java_obj): :param params: additional params (overwriting embedded values) :param java_obj: Java object to receive the params """ - paramMap = self._merge_params(params) + paramMap = self.extractParamMap(params) for param in self.params: if param in paramMap: java_obj.set(param.name, paramMap[param]) @@ -102,7 +102,7 @@ def _fit_java(self, dataset, params={}): """ Fits a Java model to the input dataset. :param dataset: input dataset, which is an instance of - :py:class:`pyspark.sql.SchemaRDD` + :py:class:`pyspark.sql.DataFrame` :param params: additional params (overwriting embedded values) :return: fitted Java model """ diff --git a/python/pyspark/mllib/__init__.py b/python/pyspark/mllib/__init__.py index c3217620e3c4e..07507b2ad0d05 100644 --- a/python/pyspark/mllib/__init__.py +++ b/python/pyspark/mllib/__init__.py @@ -18,18 +18,21 @@ """ Python bindings for MLlib. """ +from __future__ import absolute_import -# MLlib currently needs and NumPy 1.4+, so complain if lower +# MLlib currently needs NumPy 1.4+, so complain if lower import numpy if numpy.version.version < '1.4': raise Exception("MLlib requires NumPy 1.4+") -__all__ = ['classification', 'clustering', 'feature', 'linalg', 'random', +__all__ = ['classification', 'clustering', 'feature', 'fpm', 'linalg', 'random', 'recommendation', 'regression', 'stat', 'tree', 'util'] import sys -import rand as random -random.__name__ = 'random' -random.RandomRDDs.__module__ = __name__ + '.random' -sys.modules[__name__ + '.random'] = random +from . import rand as random +modname = __name__ + '.random' +random.__name__ = modname +random.RandomRDDs.__module__ = modname +sys.modules[modname] = random +del modname, sys diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.py index 00e2e76711e84..eda0b60f8b1e7 100644 --- a/python/pyspark/mllib/classification.py +++ b/python/pyspark/mllib/classification.py @@ -21,22 +21,23 @@ from numpy import array from pyspark import RDD -from pyspark.mllib.common import callMLlibFunc -from pyspark.mllib.linalg import SparseVector, _convert_to_vector +from pyspark.mllib.common import callMLlibFunc, _py2java, _java2py +from pyspark.mllib.linalg import DenseVector, SparseVector, _convert_to_vector from pyspark.mllib.regression import LabeledPoint, LinearModel, _regression_train_wrapper +from pyspark.mllib.util import Saveable, Loader, inherit_doc __all__ = ['LogisticRegressionModel', 'LogisticRegressionWithSGD', 'LogisticRegressionWithLBFGS', 'SVMModel', 'SVMWithSGD', 'NaiveBayesModel', 'NaiveBayes'] -class LinearBinaryClassificationModel(LinearModel): +class LinearClassificationModel(LinearModel): """ - Represents a linear binary classification model that predicts to whether an - example is positive (1.0) or negative (0.0). + A private abstract class representing a multiclass classification model. + The categories are represented by int values: 0, 1, 2, etc. """ def __init__(self, weights, intercept): - super(LinearBinaryClassificationModel, self).__init__(weights, intercept) + super(LinearClassificationModel, self).__init__(weights, intercept) self._threshold = None def setThreshold(self, value): @@ -46,14 +47,26 @@ def setThreshold(self, value): Sets the threshold that separates positive predictions from negative predictions. An example with prediction score greater than or equal to this threshold is identified as an positive, and negative otherwise. + It is used for binary classification only. """ self._threshold = value + @property + def threshold(self): + """ + .. note:: Experimental + + Returns the threshold (if any) used for converting raw prediction scores + into 0/1 predictions. It is used for binary classification only. + """ + return self._threshold + def clearThreshold(self): """ .. note:: Experimental Clears the threshold so that `predict` will output raw prediction scores. + It is used for binary classification only. """ self._threshold = None @@ -65,7 +78,7 @@ def predict(self, test): raise NotImplementedError -class LogisticRegressionModel(LinearBinaryClassificationModel): +class LogisticRegressionModel(LinearClassificationModel): """A linear binary classification model derived from logistic regression. @@ -99,10 +112,51 @@ class LogisticRegressionModel(LinearBinaryClassificationModel): 1 >>> lrm.predict(SparseVector(2, {0: 1.0})) 0 + >>> import os, tempfile + >>> path = tempfile.mkdtemp() + >>> lrm.save(sc, path) + >>> sameModel = LogisticRegressionModel.load(sc, path) + >>> sameModel.predict(array([0.0, 1.0])) + 1 + >>> sameModel.predict(SparseVector(2, {0: 1.0})) + 0 + >>> try: + ... os.removedirs(path) + ... except: + ... pass + >>> multi_class_data = [ + ... LabeledPoint(0.0, [0.0, 1.0, 0.0]), + ... LabeledPoint(1.0, [1.0, 0.0, 0.0]), + ... LabeledPoint(2.0, [0.0, 0.0, 1.0]) + ... ] + >>> mcm = LogisticRegressionWithLBFGS.train(data=sc.parallelize(multi_class_data), numClasses=3) + >>> mcm.predict([0.0, 0.5, 0.0]) + 0 + >>> mcm.predict([0.8, 0.0, 0.0]) + 1 + >>> mcm.predict([0.0, 0.0, 0.3]) + 2 """ - def __init__(self, weights, intercept): + def __init__(self, weights, intercept, numFeatures, numClasses): super(LogisticRegressionModel, self).__init__(weights, intercept) + self._numFeatures = int(numFeatures) + self._numClasses = int(numClasses) self._threshold = 0.5 + if self._numClasses == 2: + self._dataWithBiasSize = None + self._weightsMatrix = None + else: + self._dataWithBiasSize = self._coeff.size / (self._numClasses - 1) + self._weightsMatrix = self._coeff.toArray().reshape(self._numClasses - 1, + self._dataWithBiasSize) + + @property + def numFeatures(self): + return self._numFeatures + + @property + def numClasses(self): + return self._numClasses def predict(self, x): """ @@ -113,23 +167,60 @@ def predict(self, x): return x.map(lambda v: self.predict(v)) x = _convert_to_vector(x) - margin = self.weights.dot(x) + self._intercept - if margin > 0: - prob = 1 / (1 + exp(-margin)) + if self.numClasses == 2: + margin = self.weights.dot(x) + self._intercept + if margin > 0: + prob = 1 / (1 + exp(-margin)) + else: + exp_margin = exp(margin) + prob = exp_margin / (1 + exp_margin) + if self._threshold is None: + return prob + else: + return 1 if prob > self._threshold else 0 else: - exp_margin = exp(margin) - prob = exp_margin / (1 + exp_margin) - if self._threshold is None: - return prob - else: - return 1 if prob > self._threshold else 0 + best_class = 0 + max_margin = 0.0 + if x.size + 1 == self._dataWithBiasSize: + for i in range(0, self._numClasses - 1): + margin = x.dot(self._weightsMatrix[i][0:x.size]) + \ + self._weightsMatrix[i][x.size] + if margin > max_margin: + max_margin = margin + best_class = i + 1 + else: + for i in range(0, self._numClasses - 1): + margin = x.dot(self._weightsMatrix[i]) + if margin > max_margin: + max_margin = margin + best_class = i + 1 + return best_class + + def save(self, sc, path): + java_model = sc._jvm.org.apache.spark.mllib.classification.LogisticRegressionModel( + _py2java(sc, self._coeff), self.intercept, self.numFeatures, self.numClasses) + java_model.save(sc._jsc.sc(), path) + + @classmethod + def load(cls, sc, path): + java_model = sc._jvm.org.apache.spark.mllib.classification.LogisticRegressionModel.load( + sc._jsc.sc(), path) + weights = _java2py(sc, java_model.weights()) + intercept = java_model.intercept() + numFeatures = java_model.numFeatures() + numClasses = java_model.numClasses() + threshold = java_model.getThreshold().get() + model = LogisticRegressionModel(weights, intercept, numFeatures, numClasses) + model.setThreshold(threshold) + return model class LogisticRegressionWithSGD(object): @classmethod def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0, - initialWeights=None, regParam=0.01, regType="l2", intercept=False): + initialWeights=None, regParam=0.01, regType="l2", intercept=False, + validateData=True): """ Train a logistic regression model on the given data. @@ -155,11 +246,14 @@ def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0, or not of the augmented representation for training data (i.e. whether bias features are activated or not). + :param validateData: Boolean parameter which indicates if the + algorithm should validate data before training. + (default: True) """ def train(rdd, i): return callMLlibFunc("trainLogisticRegressionModelWithSGD", rdd, int(iterations), float(step), float(miniBatchFraction), i, float(regParam), regType, - bool(intercept)) + bool(intercept), bool(validateData)) return _regression_train_wrapper(train, LogisticRegressionModel, data, initialWeights) @@ -168,7 +262,7 @@ class LogisticRegressionWithLBFGS(object): @classmethod def train(cls, data, iterations=100, initialWeights=None, regParam=0.01, regType="l2", - intercept=False, corrections=10, tolerance=1e-4): + intercept=False, corrections=10, tolerance=1e-4, validateData=True, numClasses=2): """ Train a logistic regression model on the given data. @@ -194,6 +288,11 @@ def train(cls, data, iterations=100, initialWeights=None, regParam=0.01, regType update (default: 10). :param tolerance: The convergence tolerance of iterations for L-BFGS (default: 1e-4). + :param validateData: Boolean parameter which indicates if the + algorithm should validate data before training. + (default: True) + :param numClasses: The number of classes (i.e., outcomes) a label can take + in Multinomial Logistic Regression (default: 2). >>> data = [ ... LabeledPoint(0.0, [0.0, 1.0]), @@ -207,13 +306,21 @@ def train(cls, data, iterations=100, initialWeights=None, regParam=0.01, regType """ def train(rdd, i): return callMLlibFunc("trainLogisticRegressionModelWithLBFGS", rdd, int(iterations), i, - float(regParam), str(regType), bool(intercept), int(corrections), - float(tolerance)) - + float(regParam), regType, bool(intercept), int(corrections), + float(tolerance), bool(validateData), int(numClasses)) + + if initialWeights is None: + if numClasses == 2: + initialWeights = [0.0] * len(data.first().features) + else: + if intercept: + initialWeights = [0.0] * (len(data.first().features) + 1) * (numClasses - 1) + else: + initialWeights = [0.0] * len(data.first().features) * (numClasses - 1) return _regression_train_wrapper(train, LogisticRegressionModel, data, initialWeights) -class SVMModel(LinearBinaryClassificationModel): +class SVMModel(LinearClassificationModel): """A support vector machine. @@ -243,6 +350,18 @@ class SVMModel(LinearBinaryClassificationModel): 1 >>> svm.predict(SparseVector(2, {0: -1.0})) 0 + >>> import os, tempfile + >>> path = tempfile.mkdtemp() + >>> svm.save(sc, path) + >>> sameModel = SVMModel.load(sc, path) + >>> sameModel.predict(SparseVector(2, {1: 1.0})) + 1 + >>> sameModel.predict(SparseVector(2, {0: -1.0})) + 0 + >>> try: + ... os.removedirs(path) + ... except: + ... pass """ def __init__(self, weights, intercept): super(SVMModel, self).__init__(weights, intercept) @@ -263,12 +382,29 @@ def predict(self, x): else: return 1 if margin > self._threshold else 0 + def save(self, sc, path): + java_model = sc._jvm.org.apache.spark.mllib.classification.SVMModel( + _py2java(sc, self._coeff), self.intercept) + java_model.save(sc._jsc.sc(), path) + + @classmethod + def load(cls, sc, path): + java_model = sc._jvm.org.apache.spark.mllib.classification.SVMModel.load( + sc._jsc.sc(), path) + weights = _java2py(sc, java_model.weights()) + intercept = java_model.intercept() + threshold = java_model.getThreshold().get() + model = SVMModel(weights, intercept) + model.setThreshold(threshold) + return model + class SVMWithSGD(object): @classmethod def train(cls, data, iterations=100, step=1.0, regParam=0.01, - miniBatchFraction=1.0, initialWeights=None, regType="l2", intercept=False): + miniBatchFraction=1.0, initialWeights=None, regType="l2", + intercept=False, validateData=True): """ Train a support vector machine on the given data. @@ -294,16 +430,20 @@ def train(cls, data, iterations=100, step=1.0, regParam=0.01, or not of the augmented representation for training data (i.e. whether bias features are activated or not). + :param validateData: Boolean parameter which indicates if the + algorithm should validate data before training. + (default: True) """ def train(rdd, i): return callMLlibFunc("trainSVMModelWithSGD", rdd, int(iterations), float(step), float(regParam), float(miniBatchFraction), i, regType, - bool(intercept)) + bool(intercept), bool(validateData)) return _regression_train_wrapper(train, SVMModel, data, initialWeights) -class NaiveBayesModel(object): +@inherit_doc +class NaiveBayesModel(Saveable, Loader): """ Model for Naive Bayes classifiers. @@ -334,6 +474,16 @@ class NaiveBayesModel(object): 0.0 >>> model.predict(SparseVector(2, {0: 1.0})) 1.0 + >>> import os, tempfile + >>> path = tempfile.mkdtemp() + >>> model.save(sc, path) + >>> sameModel = NaiveBayesModel.load(sc, path) + >>> sameModel.predict(SparseVector(2, {0: 1.0})) == model.predict(SparseVector(2, {0: 1.0})) + True + >>> try: + ... os.removedirs(path) + ... except OSError: + ... pass """ def __init__(self, labels, pi, theta): @@ -348,6 +498,24 @@ def predict(self, x): x = _convert_to_vector(x) return self.labels[numpy.argmax(self.pi + x.dot(self.theta.transpose()))] + def save(self, sc, path): + java_labels = _py2java(sc, self.labels.tolist()) + java_pi = _py2java(sc, self.pi.tolist()) + java_theta = _py2java(sc, self.theta.tolist()) + java_model = sc._jvm.org.apache.spark.mllib.classification.NaiveBayesModel( + java_labels, java_pi, java_theta) + java_model.save(sc._jsc.sc(), path) + + @classmethod + def load(cls, sc, path): + java_model = sc._jvm.org.apache.spark.mllib.classification.NaiveBayesModel.load( + sc._jsc.sc(), path) + # Can not unpickle array.array from Pyrolite in Python3 with "bytes" + py_labels = _java2py(sc, java_model.labels(), "latin1") + py_pi = _java2py(sc, java_model.pi(), "latin1") + py_theta = _java2py(sc, java_model.theta(), "latin1") + return NaiveBayesModel(py_labels, py_pi, numpy.array(py_theta)) + class NaiveBayes(object): diff --git a/python/pyspark/mllib/clustering.py b/python/pyspark/mllib/clustering.py index f6b97abb1723c..abbb7cf60eece 100644 --- a/python/pyspark/mllib/clustering.py +++ b/python/pyspark/mllib/clustering.py @@ -15,18 +15,26 @@ # limitations under the License. # +import sys +import array as pyarray + +if sys.version > '3': + xrange = range + from numpy import array from pyspark import RDD from pyspark import SparkContext -from pyspark.mllib.common import callMLlibFunc, callJavaFunc -from pyspark.mllib.linalg import DenseVector, SparseVector, _convert_to_vector +from pyspark.mllib.common import callMLlibFunc, callJavaFunc, _py2java, _java2py +from pyspark.mllib.linalg import SparseVector, _convert_to_vector from pyspark.mllib.stat.distribution import MultivariateGaussian +from pyspark.mllib.util import Saveable, Loader, inherit_doc __all__ = ['KMeansModel', 'KMeans', 'GaussianMixtureModel', 'GaussianMixture'] -class KMeansModel(object): +@inherit_doc +class KMeansModel(Saveable, Loader): """A clustering model derived from the k-means method. @@ -53,8 +61,18 @@ class KMeansModel(object): True >>> model.predict(sparse_data[2]) == model.predict(sparse_data[3]) True - >>> type(model.clusterCenters) - + >>> isinstance(model.clusterCenters, list) + True + >>> import os, tempfile + >>> path = tempfile.mkdtemp() + >>> model.save(sc, path) + >>> sameModel = KMeansModel.load(sc, path) + >>> sameModel.predict(sparse_data[0]) == model.predict(sparse_data[0]) + True + >>> try: + ... os.removedirs(path) + ... except OSError: + ... pass """ def __init__(self, centers): @@ -77,6 +95,16 @@ def predict(self, x): best_distance = distance return best + def save(self, sc, path): + java_centers = _py2java(sc, [_convert_to_vector(c) for c in self.centers]) + java_model = sc._jvm.org.apache.spark.mllib.clustering.KMeansModel(java_centers) + java_model.save(sc._jsc.sc(), path) + + @classmethod + def load(cls, sc, path): + java_model = sc._jvm.org.apache.spark.mllib.clustering.KMeansModel.load(sc._jsc.sc(), path) + return KMeansModel(_java2py(sc, java_model.clusterCenters())) + class KMeans(object): @@ -111,7 +139,7 @@ class GaussianMixtureModel(object): ... 5.7048, 4.6567, 5.5026, ... 4.5605, 5.2043, 6.2734]).reshape(5, 3)) >>> model = GaussianMixture.train(clusterdata_2, 2, convergenceTol=0.0001, - ... maxIterations=150, seed=10) + ... maxIterations=150, seed=10) >>> labels = model.predict(clusterdata_2).collect() >>> labels[0]==labels[1]==labels[2] True @@ -146,13 +174,13 @@ def predictSoft(self, x): if isinstance(x, RDD): means, sigmas = zip(*[(g.mu, g.sigma) for g in self.gaussians]) membership_matrix = callMLlibFunc("predictSoftGMM", x.map(_convert_to_vector), - self.weights, means, sigmas) - return membership_matrix + _convert_to_vector(self.weights), means, sigmas) + return membership_matrix.map(lambda x: pyarray.array('d', x)) class GaussianMixture(object): """ - Estimate model parameters with the expectation-maximization algorithm. + Learning algorithm for Gaussian Mixtures using the expectation-maximization algorithm. :param data: RDD of data points :param k: Number of components diff --git a/python/pyspark/mllib/common.py b/python/pyspark/mllib/common.py index 3c5ee66cd8b64..ba6058978880a 100644 --- a/python/pyspark/mllib/common.py +++ b/python/pyspark/mllib/common.py @@ -15,6 +15,11 @@ # limitations under the License. # +import sys +if sys.version >= '3': + long = int + unicode = str + import py4j.protocol from py4j.protocol import Py4JJavaError from py4j.java_gateway import JavaObject @@ -36,7 +41,7 @@ def _new_smart_decode(obj): if isinstance(obj, float): - s = unicode(obj) + s = str(obj) return _float_str_mapping.get(s, s) return _old_smart_decode(obj) @@ -70,19 +75,19 @@ def _py2java(sc, obj): obj = _to_java_object_rdd(obj) elif isinstance(obj, SparkContext): obj = obj._jsc - elif isinstance(obj, list) and (obj or isinstance(obj[0], JavaObject)): - obj = ListConverter().convert(obj, sc._gateway._gateway_client) + elif isinstance(obj, list): + obj = ListConverter().convert([_py2java(sc, x) for x in obj], sc._gateway._gateway_client) elif isinstance(obj, JavaObject): pass - elif isinstance(obj, (int, long, float, bool, basestring)): + elif isinstance(obj, (int, long, float, bool, bytes, unicode)): pass else: - bytes = bytearray(PickleSerializer().dumps(obj)) - obj = sc._jvm.SerDe.loads(bytes) + data = bytearray(PickleSerializer().dumps(obj)) + obj = sc._jvm.SerDe.loads(data) return obj -def _java2py(sc, r): +def _java2py(sc, r, encoding="bytes"): if isinstance(r, JavaObject): clsName = r.getClass().getSimpleName() # convert RDD into JavaRDD @@ -102,8 +107,8 @@ def _java2py(sc, r): except Py4JJavaError: pass # not pickable - if isinstance(r, bytearray): - r = PickleSerializer().loads(str(r)) + if isinstance(r, (bytearray, bytes)): + r = PickleSerializer().loads(bytes(r), encoding=encoding) return r @@ -134,3 +139,20 @@ def __del__(self): def call(self, name, *a): """Call method of java_model""" return callJavaFunc(self._sc, getattr(self._java_model, name), *a) + + +def inherit_doc(cls): + """ + A decorator that makes a class inherit documentation from its parents. + """ + for name, func in vars(cls).items(): + # only inherit docstring for public functions + if name.startswith("_"): + continue + if not func.__doc__: + for parent in cls.__bases__: + parent_func = getattr(parent, name, None) + if parent_func and getattr(parent_func, "__doc__", None): + func.__doc__ = parent_func.__doc__ + break + return cls diff --git a/python/pyspark/mllib/evaluation.py b/python/pyspark/mllib/evaluation.py new file mode 100644 index 0000000000000..16cb49cc0cfff --- /dev/null +++ b/python/pyspark/mllib/evaluation.py @@ -0,0 +1,83 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from pyspark.mllib.common import JavaModelWrapper +from pyspark.sql import SQLContext +from pyspark.sql.types import StructField, StructType, DoubleType + + +class BinaryClassificationMetrics(JavaModelWrapper): + """ + Evaluator for binary classification. + + >>> scoreAndLabels = sc.parallelize([ + ... (0.1, 0.0), (0.1, 1.0), (0.4, 0.0), (0.6, 0.0), (0.6, 1.0), (0.6, 1.0), (0.8, 1.0)], 2) + >>> metrics = BinaryClassificationMetrics(scoreAndLabels) + >>> metrics.areaUnderROC() + 0.70... + >>> metrics.areaUnderPR() + 0.83... + >>> metrics.unpersist() + """ + + def __init__(self, scoreAndLabels): + """ + :param scoreAndLabels: an RDD of (score, label) pairs + """ + sc = scoreAndLabels.ctx + sql_ctx = SQLContext(sc) + df = sql_ctx.createDataFrame(scoreAndLabels, schema=StructType([ + StructField("score", DoubleType(), nullable=False), + StructField("label", DoubleType(), nullable=False)])) + java_class = sc._jvm.org.apache.spark.mllib.evaluation.BinaryClassificationMetrics + java_model = java_class(df._jdf) + super(BinaryClassificationMetrics, self).__init__(java_model) + + def areaUnderROC(self): + """ + Computes the area under the receiver operating characteristic + (ROC) curve. + """ + return self.call("areaUnderROC") + + def areaUnderPR(self): + """ + Computes the area under the precision-recall curve. + """ + return self.call("areaUnderPR") + + def unpersist(self): + """ + Unpersists intermediate RDDs used in the computation. + """ + self.call("unpersist") + + +def _test(): + import doctest + from pyspark import SparkContext + import pyspark.mllib.evaluation + globs = pyspark.mllib.evaluation.__dict__.copy() + globs['sc'] = SparkContext('local[4]', 'PythonTest') + (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS) + globs['sc'].stop() + if failure_count: + exit(-1) + + +if __name__ == "__main__": + _test() diff --git a/python/pyspark/mllib/feature.py b/python/pyspark/mllib/feature.py index 10df6288065b8..1140539a24e95 100644 --- a/python/pyspark/mllib/feature.py +++ b/python/pyspark/mllib/feature.py @@ -23,12 +23,17 @@ import sys import warnings import random +import binascii +if sys.version >= '3': + basestring = str + unicode = str from py4j.protocol import Py4JJavaError -from pyspark import RDD, SparkContext +from pyspark import SparkContext +from pyspark.rdd import RDD, ignore_unicode_prefix from pyspark.mllib.common import callMLlibFunc, JavaModelWrapper -from pyspark.mllib.linalg import Vectors, Vector, _convert_to_vector +from pyspark.mllib.linalg import Vectors, _convert_to_vector __all__ = ['Normalizer', 'StandardScalerModel', 'StandardScaler', 'HashingTF', 'IDFModel', 'IDF', 'Word2Vec', 'Word2VecModel'] @@ -58,7 +63,8 @@ class Normalizer(VectorTransformer): For any 1 <= `p` < float('inf'), normalizes samples using sum(abs(vector) :sup:`p`) :sup:`(1/p)` as norm. - For `p` = float('inf'), max(abs(vector)) will be used as norm for normalization. + For `p` = float('inf'), max(abs(vector)) will be used as norm for + normalization. >>> v = Vectors.dense(range(3)) >>> nor = Normalizer(1) @@ -120,12 +126,33 @@ def transform(self, vector): """ Applies standardization transformation on a vector. + Note: In Python, transform cannot currently be used within + an RDD transformation or action. + Call transform directly on the RDD instead. + :param vector: Vector or RDD of Vector to be standardized. - :return: Standardized vector. If the variance of a column is zero, - it will return default `0.0` for the column with zero variance. + :return: Standardized vector. If the variance of a column is + zero, it will return default `0.0` for the column with + zero variance. """ return JavaVectorTransformer.transform(self, vector) + def setWithMean(self, withMean): + """ + Setter of the boolean which decides + whether it uses mean or not + """ + self.call("setWithMean", withMean) + return self + + def setWithStd(self, withStd): + """ + Setter of the boolean which decides + whether it uses std or not + """ + self.call("setWithStd", withStd) + return self + class StandardScaler(object): """ @@ -148,9 +175,10 @@ def __init__(self, withMean=False, withStd=True): """ :param withMean: False by default. Centers the data with mean before scaling. It will build a dense output, so this - does not work on sparse input and will raise an exception. - :param withStd: True by default. Scales the data to unit standard - deviation. + does not work on sparse input and will raise an + exception. + :param withStd: True by default. Scales the data to unit + standard deviation. """ if not (withMean or withStd): warnings.warn("Both withMean and withStd are false. The model does nothing.") @@ -159,10 +187,11 @@ def __init__(self, withMean=False, withStd=True): def fit(self, dataset): """ - Computes the mean and variance and stores as a model to be used for later scaling. + Computes the mean and variance and stores as a model to be used + for later scaling. - :param data: The data used to compute the mean and variance to build - the transformation model. + :param data: The data used to compute the mean and variance + to build the transformation model. :return: a StandardScalarModel """ dataset = dataset.map(_convert_to_vector) @@ -174,14 +203,15 @@ class HashingTF(object): """ .. note:: Experimental - Maps a sequence of terms to their term frequencies using the hashing trick. + Maps a sequence of terms to their term frequencies using the hashing + trick. Note: the terms must be hashable (can not be dict/set/list...). >>> htf = HashingTF(100) >>> doc = "a a b b c d".split(" ") >>> htf.transform(doc) - SparseVector(100, {1: 1.0, 14: 1.0, 31: 2.0, 44: 2.0}) + SparseVector(100, {...}) """ def __init__(self, numFeatures=1 << 20): """ @@ -195,8 +225,9 @@ def indexOf(self, term): def transform(self, document): """ - Transforms the input document (list of terms) to term frequency vectors, - or transform the RDD of document to RDD of term frequency vectors. + Transforms the input document (list of terms) to term frequency + vectors, or transform the RDD of document to RDD of term + frequency vectors. """ if isinstance(document, RDD): return document.map(self.transform) @@ -220,7 +251,12 @@ def transform(self, x): the terms which occur in fewer than `minDocFreq` documents will have an entry of 0. - :param x: an RDD of term frequency vectors or a term frequency vector + Note: In Python, transform cannot currently be used within + an RDD transformation or action. + Call transform directly on the RDD instead. + + :param x: an RDD of term frequency vectors or a term frequency + vector :return: an RDD of TF-IDF vectors or a TF-IDF vector """ if isinstance(x, RDD): @@ -229,6 +265,12 @@ def transform(self, x): x = _convert_to_vector(x) return JavaVectorTransformer.transform(self, x) + def idf(self): + """ + Returns the current IDF vector. + """ + return self.call('idf') + class IDF(object): """ @@ -241,9 +283,9 @@ class IDF(object): of documents that contain term `t`. This implementation supports filtering out terms which do not appear - in a minimum number of documents (controlled by the variable `minDocFreq`). - For terms that are not in at least `minDocFreq` documents, the IDF is - found as 0, resulting in TF-IDFs of 0. + in a minimum number of documents (controlled by the variable + `minDocFreq`). For terms that are not in at least `minDocFreq` + documents, the IDF is found as 0, resulting in TF-IDFs of 0. >>> n = 4 >>> freqs = [Vectors.sparse(n, (1, 3), (1.0, 2.0)), @@ -316,7 +358,14 @@ def findSynonyms(self, word, num): words, similarity = self.call("findSynonyms", word, num) return zip(words, similarity) + def getVectors(self): + """ + Returns a map of words to their vector representations. + """ + return self.call("getVectors") + +@ignore_unicode_prefix class Word2Vec(object): """ Word2Vec creates vector representation of words in a text corpus. @@ -325,20 +374,21 @@ class Word2Vec(object): The vector representation can be used as features in natural language processing and machine learning algorithms. - We used skip-gram model in our implementation and hierarchical softmax - method to train the model. The variable names in the implementation - matches the original C implementation. + We used skip-gram model in our implementation and hierarchical + softmax method to train the model. The variable names in the + implementation matches the original C implementation. - For original C implementation, see https://code.google.com/p/word2vec/ + For original C implementation, + see https://code.google.com/p/word2vec/ For research papers, see Efficient Estimation of Word Representations in Vector Space - and - Distributed Representations of Words and Phrases and their Compositionality. + and Distributed Representations of Words and Phrases and their + Compositionality. >>> sentence = "a b " * 100 + "a c " * 10 >>> localDoc = [sentence, sentence] >>> doc = sc.parallelize(localDoc).map(lambda line: line.split(" ")) - >>> model = Word2Vec().setVectorSize(10).setSeed(42L).fit(doc) + >>> model = Word2Vec().setVectorSize(10).setSeed(42).fit(doc) >>> syms = model.findSynonyms("a", 2) >>> [s[0] for s in syms] @@ -356,7 +406,8 @@ def __init__(self): self.learningRate = 0.025 self.numPartitions = 1 self.numIterations = 1 - self.seed = random.randint(0, sys.maxint) + self.seed = random.randint(0, sys.maxsize) + self.minCount = 5 def setVectorSize(self, vectorSize): """ @@ -374,15 +425,16 @@ def setLearningRate(self, learningRate): def setNumPartitions(self, numPartitions): """ - Sets number of partitions (default: 1). Use a small number for accuracy. + Sets number of partitions (default: 1). Use a small number for + accuracy. """ self.numPartitions = numPartitions return self def setNumIterations(self, numIterations): """ - Sets number of iterations (default: 1), which should be smaller than or equal to number of - partitions. + Sets number of iterations (default: 1), which should be smaller + than or equal to number of partitions. """ self.numIterations = numIterations return self @@ -394,6 +446,14 @@ def setSeed(self, seed): self.seed = seed return self + def setMinCount(self, minCount): + """ + Sets minCount, the minimum number of times a token must appear + to be included in the word2vec model's vocabulary (default: 5). + """ + self.minCount = minCount + return self + def fit(self, data): """ Computes the vector representation of each word in vocabulary. @@ -405,7 +465,8 @@ def fit(self, data): raise TypeError("data should be an RDD of list of string") jmodel = callMLlibFunc("trainWord2Vec", data, int(self.vectorSize), float(self.learningRate), int(self.numPartitions), - int(self.numIterations), long(self.seed)) + int(self.numIterations), int(self.seed), + int(self.minCount)) return Word2VecModel(jmodel) diff --git a/python/pyspark/mllib/fpm.py b/python/pyspark/mllib/fpm.py new file mode 100644 index 0000000000000..628ccc01cf3cc --- /dev/null +++ b/python/pyspark/mllib/fpm.py @@ -0,0 +1,83 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from pyspark import SparkContext +from pyspark.rdd import ignore_unicode_prefix +from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc, inherit_doc + +__all__ = ['FPGrowth', 'FPGrowthModel'] + + +@inherit_doc +@ignore_unicode_prefix +class FPGrowthModel(JavaModelWrapper): + + """ + .. note:: Experimental + + A FP-Growth model for mining frequent itemsets + using the Parallel FP-Growth algorithm. + + >>> data = [["a", "b", "c"], ["a", "b", "d", "e"], ["a", "c", "e"], ["a", "c", "f"]] + >>> rdd = sc.parallelize(data, 2) + >>> model = FPGrowth.train(rdd, 0.6, 2) + >>> sorted(model.freqItemsets().collect()) + [([u'a'], 4), ([u'c'], 3), ([u'c', u'a'], 3)] + """ + + def freqItemsets(self): + """ + Get the frequent itemsets of this model + """ + return self.call("getFreqItemsets") + + +class FPGrowth(object): + """ + .. note:: Experimental + + A Parallel FP-growth algorithm to mine frequent itemsets. + """ + + @classmethod + def train(cls, data, minSupport=0.3, numPartitions=-1): + """ + Computes an FP-Growth model that contains frequent itemsets. + :param data: The input data set, each element + contains a transaction. + :param minSupport: The minimal support level + (default: `0.3`). + :param numPartitions: The number of partitions used by parallel + FP-growth (default: same as input data). + """ + model = callMLlibFunc("trainFPGrowthModel", data, float(minSupport), int(numPartitions)) + return FPGrowthModel(model) + + +def _test(): + import doctest + import pyspark.mllib.fpm + globs = pyspark.mllib.fpm.__dict__.copy() + globs['sc'] = SparkContext('local[4]', 'PythonTest') + (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS) + globs['sc'].stop() + if failure_count: + exit(-1) + + +if __name__ == "__main__": + _test() diff --git a/python/pyspark/mllib/linalg.py b/python/pyspark/mllib/linalg.py index 7f21190ed8c25..38b3aa3ad460e 100644 --- a/python/pyspark/mllib/linalg.py +++ b/python/pyspark/mllib/linalg.py @@ -25,11 +25,17 @@ import sys import array -import copy_reg + +if sys.version >= '3': + basestring = str + xrange = range + import copyreg as copy_reg +else: + import copy_reg import numpy as np -from pyspark.sql import UserDefinedType, StructField, StructType, ArrayType, DoubleType, \ +from pyspark.sql.types import UserDefinedType, StructField, StructType, ArrayType, DoubleType, \ IntegerType, ByteType @@ -57,7 +63,7 @@ def fast_pickle_array(ar): def _convert_to_vector(l): if isinstance(l, Vector): return l - elif type(l) in (array.array, np.array, np.ndarray, list, tuple): + elif type(l) in (array.array, np.array, np.ndarray, list, tuple, xrange): return DenseVector(l) elif _have_scipy and scipy.sparse.issparse(l): assert l.shape[1] == 1, "Expected column vector" @@ -88,7 +94,7 @@ def _vector_size(v): """ if isinstance(v, Vector): return len(v) - elif type(v) in (array.array, list, tuple): + elif type(v) in (array.array, list, tuple, xrange): return len(v) elif type(v) == np.ndarray: if v.ndim == 1 or (v.ndim == 2 and v.shape[1] == 1): @@ -152,6 +158,9 @@ def deserialize(self, datum): else: raise ValueError("do not recognize type %r" % tpe) + def simpleString(self): + return "vector" + class Vector(object): @@ -170,10 +179,27 @@ def toArray(self): class DenseVector(Vector): """ - A dense vector represented by a value array. + A dense vector represented by a value array. We use numpy array for + storage and arithmetics will be delegated to the underlying numpy + array. + + >>> v = Vectors.dense([1.0, 2.0]) + >>> u = Vectors.dense([3.0, 4.0]) + >>> v + u + DenseVector([4.0, 6.0]) + >>> 2 - v + DenseVector([1.0, 0.0]) + >>> v / 2 + DenseVector([0.5, 1.0]) + >>> v * u + DenseVector([3.0, 8.0]) + >>> u / v + DenseVector([3.0, 2.0]) + >>> u % 2 + DenseVector([1.0, 0.0]) """ def __init__(self, ar): - if isinstance(ar, basestring): + if isinstance(ar, bytes): ar = np.frombuffer(ar, dtype=np.float64) elif not isinstance(ar, np.ndarray): ar = np.array(ar, dtype=np.float64) @@ -289,6 +315,27 @@ def __ne__(self, other): def __getattr__(self, item): return getattr(self.array, item) + def _delegate(op): + def func(self, other): + if isinstance(other, DenseVector): + other = other.array + return DenseVector(getattr(self.array, op)(other)) + return func + + __neg__ = _delegate("__neg__") + __add__ = _delegate("__add__") + __sub__ = _delegate("__sub__") + __mul__ = _delegate("__mul__") + __div__ = _delegate("__div__") + __truediv__ = _delegate("__truediv__") + __mod__ = _delegate("__mod__") + __radd__ = _delegate("__radd__") + __rsub__ = _delegate("__rsub__") + __rmul__ = _delegate("__rmul__") + __rdiv__ = _delegate("__rdiv__") + __rtruediv__ = _delegate("__rtruediv__") + __rmod__ = _delegate("__rmod__") + class SparseVector(Vector): """ @@ -305,12 +352,12 @@ def __init__(self, size, *args): :param args: Non-zero entries, as a dictionary, list of tupes, or two sorted lists containing indices and values. - >>> print SparseVector(4, {1: 1.0, 3: 5.5}) - (4,[1,3],[1.0,5.5]) - >>> print SparseVector(4, [(1, 1.0), (3, 5.5)]) - (4,[1,3],[1.0,5.5]) - >>> print SparseVector(4, [1, 3], [1.0, 5.5]) - (4,[1,3],[1.0,5.5]) + >>> SparseVector(4, {1: 1.0, 3: 5.5}) + SparseVector(4, {1: 1.0, 3: 5.5}) + >>> SparseVector(4, [(1, 1.0), (3, 5.5)]) + SparseVector(4, {1: 1.0, 3: 5.5}) + >>> SparseVector(4, [1, 3], [1.0, 5.5]) + SparseVector(4, {1: 1.0, 3: 5.5}) """ self.size = int(size) assert 1 <= len(args) <= 2, "must pass either 2 or 3 arguments" @@ -322,8 +369,8 @@ def __init__(self, size, *args): self.indices = np.array([p[0] for p in pairs], dtype=np.int32) self.values = np.array([p[1] for p in pairs], dtype=np.float64) else: - if isinstance(args[0], basestring): - assert isinstance(args[1], str), "values should be string too" + if isinstance(args[0], bytes): + assert isinstance(args[1], bytes), "values should be string too" if args[0]: self.indices = np.frombuffer(args[0], np.int32) self.values = np.frombuffer(args[1], np.float64) @@ -552,12 +599,12 @@ def sparse(size, *args): :param args: Non-zero entries, as a dictionary, list of tupes, or two sorted lists containing indices and values. - >>> print Vectors.sparse(4, {1: 1.0, 3: 5.5}) - (4,[1,3],[1.0,5.5]) - >>> print Vectors.sparse(4, [(1, 1.0), (3, 5.5)]) - (4,[1,3],[1.0,5.5]) - >>> print Vectors.sparse(4, [1, 3], [1.0, 5.5]) - (4,[1,3],[1.0,5.5]) + >>> Vectors.sparse(4, {1: 1.0, 3: 5.5}) + SparseVector(4, {1: 1.0, 3: 5.5}) + >>> Vectors.sparse(4, [(1, 1.0), (3, 5.5)]) + SparseVector(4, {1: 1.0, 3: 5.5}) + >>> Vectors.sparse(4, [1, 3], [1.0, 5.5]) + SparseVector(4, {1: 1.0, 3: 5.5}) """ return SparseVector(size, *args) @@ -601,6 +648,15 @@ def toArray(self): """ raise NotImplementedError + @staticmethod + def _convert_to_array(array_like, dtype): + """ + Convert Matrix attributes which are array-like or buffer to array. + """ + if isinstance(array_like, bytes): + return np.frombuffer(array_like, dtype=dtype) + return np.asarray(array_like, dtype=dtype) + class DenseMatrix(Matrix): """ @@ -608,13 +664,8 @@ class DenseMatrix(Matrix): """ def __init__(self, numRows, numCols, values): Matrix.__init__(self, numRows, numCols) - if isinstance(values, basestring): - values = np.frombuffer(values, dtype=np.float64) - elif not isinstance(values, np.ndarray): - values = np.array(values, dtype=np.float64) + values = self._convert_to_array(values, np.float64) assert len(values) == numRows * numCols - if values.dtype != np.float64: - values.astype(np.float64) self.values = values def __reduce__(self): @@ -631,6 +682,27 @@ def toArray(self): """ return self.values.reshape((self.numRows, self.numCols), order='F') + def toSparse(self): + """Convert to SparseMatrix""" + indices = np.nonzero(self.values)[0] + colCounts = np.bincount(indices // self.numRows) + colPtrs = np.cumsum(np.hstack( + (0, colCounts, np.zeros(self.numCols - colCounts.size)))) + values = self.values[indices] + rowIndices = indices % self.numRows + + return SparseMatrix(self.numRows, self.numCols, colPtrs, rowIndices, values) + + def __getitem__(self, indices): + i, j = indices + if i < 0 or i >= self.numRows: + raise ValueError("Row index %d is out of range [0, %d)" + % (i, self.numRows)) + if j >= self.numCols or j < 0: + raise ValueError("Column index %d is out of range [0, %d)" + % (j, self.numCols)) + return self.values[i + j * self.numRows] + def __eq__(self, other): return (isinstance(other, DenseMatrix) and self.numRows == other.numRows and @@ -638,6 +710,82 @@ def __eq__(self, other): all(self.values == other.values)) +class SparseMatrix(Matrix): + """Sparse Matrix stored in CSC format.""" + def __init__(self, numRows, numCols, colPtrs, rowIndices, values, + isTransposed=False): + Matrix.__init__(self, numRows, numCols) + self.isTransposed = isTransposed + self.colPtrs = self._convert_to_array(colPtrs, np.int32) + self.rowIndices = self._convert_to_array(rowIndices, np.int32) + self.values = self._convert_to_array(values, np.float64) + + if self.isTransposed: + if self.colPtrs.size != numRows + 1: + raise ValueError("Expected colPtrs of size %d, got %d." + % (numRows + 1, self.colPtrs.size)) + else: + if self.colPtrs.size != numCols + 1: + raise ValueError("Expected colPtrs of size %d, got %d." + % (numCols + 1, self.colPtrs.size)) + if self.rowIndices.size != self.values.size: + raise ValueError("Expected rowIndices of length %d, got %d." + % (self.rowIndices.size, self.values.size)) + + def __reduce__(self): + return SparseMatrix, ( + self.numRows, self.numCols, self.colPtrs.tostring(), + self.rowIndices.tostring(), self.values.tostring(), + self.isTransposed) + + def __getitem__(self, indices): + i, j = indices + if i < 0 or i >= self.numRows: + raise ValueError("Row index %d is out of range [0, %d)" + % (i, self.numRows)) + if j < 0 or j >= self.numCols: + raise ValueError("Column index %d is out of range [0, %d)" + % (j, self.numCols)) + + # If a CSR matrix is given, then the row index should be searched + # for in ColPtrs, and the column index should be searched for in the + # corresponding slice obtained from rowIndices. + if self.isTransposed: + j, i = i, j + + colStart = self.colPtrs[j] + colEnd = self.colPtrs[j + 1] + nz = self.rowIndices[colStart: colEnd] + ind = np.searchsorted(nz, i) + colStart + if ind < colEnd and self.rowIndices[ind] == i: + return self.values[ind] + else: + return 0.0 + + def toArray(self): + """ + Return an numpy.ndarray + """ + A = np.zeros((self.numRows, self.numCols), dtype=np.float64, order='F') + for k in xrange(self.colPtrs.size - 1): + startptr = self.colPtrs[k] + endptr = self.colPtrs[k + 1] + if self.isTransposed: + A[k, self.rowIndices[startptr:endptr]] = self.values[startptr:endptr] + else: + A[self.rowIndices[startptr:endptr], k] = self.values[startptr:endptr] + return A + + def toDense(self): + densevals = np.reshape( + self.toArray(), (self.numRows * self.numCols), order='F') + return DenseMatrix(self.numRows, self.numCols, densevals) + + # TODO: More efficient implementation: + def __eq__(self, other): + return np.all(self.toArray == other.toArray) + + class Matrices(object): @staticmethod def dense(numRows, numCols, values): @@ -646,6 +794,13 @@ def dense(numRows, numCols, values): """ return DenseMatrix(numRows, numCols, values) + @staticmethod + def sparse(numRows, numCols, colPtrs, rowIndices, values): + """ + Create a SparseMatrix + """ + return SparseMatrix(numRows, numCols, colPtrs, rowIndices, values) + def _test(): import doctest diff --git a/python/pyspark/mllib/rand.py b/python/pyspark/mllib/rand.py index 20ee9d78bf5b0..06fbc0eb6aef0 100644 --- a/python/pyspark/mllib/rand.py +++ b/python/pyspark/mllib/rand.py @@ -88,10 +88,10 @@ def normalRDD(sc, size, numPartitions=None, seed=None): :param seed: Random seed (default: a random long integer). :return: RDD of float comprised of i.i.d. samples ~ N(0.0, 1.0). - >>> x = RandomRDDs.normalRDD(sc, 1000, seed=1L) + >>> x = RandomRDDs.normalRDD(sc, 1000, seed=1) >>> stats = x.stats() >>> stats.count() - 1000L + 1000 >>> abs(stats.mean() - 0.0) < 0.1 True >>> abs(stats.stdev() - 1.0) < 0.1 @@ -118,10 +118,10 @@ def logNormalRDD(sc, mean, std, size, numPartitions=None, seed=None): >>> std = 1.0 >>> expMean = exp(mean + 0.5 * std * std) >>> expStd = sqrt((exp(std * std) - 1.0) * exp(2.0 * mean + std * std)) - >>> x = RandomRDDs.logNormalRDD(sc, mean, std, 1000, seed=2L) + >>> x = RandomRDDs.logNormalRDD(sc, mean, std, 1000, seed=2) >>> stats = x.stats() >>> stats.count() - 1000L + 1000 >>> abs(stats.mean() - expMean) < 0.5 True >>> from math import sqrt @@ -145,10 +145,10 @@ def poissonRDD(sc, mean, size, numPartitions=None, seed=None): :return: RDD of float comprised of i.i.d. samples ~ Pois(mean). >>> mean = 100.0 - >>> x = RandomRDDs.poissonRDD(sc, mean, 1000, seed=2L) + >>> x = RandomRDDs.poissonRDD(sc, mean, 1000, seed=2) >>> stats = x.stats() >>> stats.count() - 1000L + 1000 >>> abs(stats.mean() - mean) < 0.5 True >>> from math import sqrt @@ -171,10 +171,10 @@ def exponentialRDD(sc, mean, size, numPartitions=None, seed=None): :return: RDD of float comprised of i.i.d. samples ~ Exp(mean). >>> mean = 2.0 - >>> x = RandomRDDs.exponentialRDD(sc, mean, 1000, seed=2L) + >>> x = RandomRDDs.exponentialRDD(sc, mean, 1000, seed=2) >>> stats = x.stats() >>> stats.count() - 1000L + 1000 >>> abs(stats.mean() - mean) < 0.5 True >>> from math import sqrt @@ -202,10 +202,10 @@ def gammaRDD(sc, shape, scale, size, numPartitions=None, seed=None): >>> scale = 2.0 >>> expMean = shape * scale >>> expStd = sqrt(shape * scale * scale) - >>> x = RandomRDDs.gammaRDD(sc, shape, scale, 1000, seed=2L) + >>> x = RandomRDDs.gammaRDD(sc, shape, scale, 1000, seed=2) >>> stats = x.stats() >>> stats.count() - 1000L + 1000 >>> abs(stats.mean() - expMean) < 0.5 True >>> abs(stats.stdev() - expStd) < 0.5 @@ -254,7 +254,7 @@ def normalVectorRDD(sc, numRows, numCols, numPartitions=None, seed=None): :return: RDD of Vector with vectors containing i.i.d. samples ~ `N(0.0, 1.0)`. >>> import numpy as np - >>> mat = np.matrix(RandomRDDs.normalVectorRDD(sc, 100, 100, seed=1L).collect()) + >>> mat = np.matrix(RandomRDDs.normalVectorRDD(sc, 100, 100, seed=1).collect()) >>> mat.shape (100, 100) >>> abs(mat.mean() - 0.0) < 0.1 @@ -286,8 +286,8 @@ def logNormalVectorRDD(sc, mean, std, numRows, numCols, numPartitions=None, seed >>> std = 1.0 >>> expMean = exp(mean + 0.5 * std * std) >>> expStd = sqrt((exp(std * std) - 1.0) * exp(2.0 * mean + std * std)) - >>> mat = np.matrix(RandomRDDs.logNormalVectorRDD(sc, mean, std, \ - 100, 100, seed=1L).collect()) + >>> m = RandomRDDs.logNormalVectorRDD(sc, mean, std, 100, 100, seed=1).collect() + >>> mat = np.matrix(m) >>> mat.shape (100, 100) >>> abs(mat.mean() - expMean) < 0.1 @@ -315,7 +315,7 @@ def poissonVectorRDD(sc, mean, numRows, numCols, numPartitions=None, seed=None): >>> import numpy as np >>> mean = 100.0 - >>> rdd = RandomRDDs.poissonVectorRDD(sc, mean, 100, 100, seed=1L) + >>> rdd = RandomRDDs.poissonVectorRDD(sc, mean, 100, 100, seed=1) >>> mat = np.mat(rdd.collect()) >>> mat.shape (100, 100) @@ -345,7 +345,7 @@ def exponentialVectorRDD(sc, mean, numRows, numCols, numPartitions=None, seed=No >>> import numpy as np >>> mean = 0.5 - >>> rdd = RandomRDDs.exponentialVectorRDD(sc, mean, 100, 100, seed=1L) + >>> rdd = RandomRDDs.exponentialVectorRDD(sc, mean, 100, 100, seed=1) >>> mat = np.mat(rdd.collect()) >>> mat.shape (100, 100) @@ -380,8 +380,7 @@ def gammaVectorRDD(sc, shape, scale, numRows, numCols, numPartitions=None, seed= >>> scale = 2.0 >>> expMean = shape * scale >>> expStd = sqrt(shape * scale * scale) - >>> mat = np.matrix(RandomRDDs.gammaVectorRDD(sc, shape, scale, \ - 100, 100, seed=1L).collect()) + >>> mat = np.matrix(RandomRDDs.gammaVectorRDD(sc, shape, scale, 100, 100, seed=1).collect()) >>> mat.shape (100, 100) >>> abs(mat.mean() - expMean) < 0.1 diff --git a/python/pyspark/mllib/recommendation.py b/python/pyspark/mllib/recommendation.py index 0d99e6dedfad9..80e0a356bb78a 100644 --- a/python/pyspark/mllib/recommendation.py +++ b/python/pyspark/mllib/recommendation.py @@ -15,11 +15,13 @@ # limitations under the License. # +import array from collections import namedtuple from pyspark import SparkContext from pyspark.rdd import RDD -from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc +from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc, inherit_doc +from pyspark.mllib.util import JavaLoader, JavaSaveable __all__ = ['MatrixFactorizationModel', 'ALS', 'Rating'] @@ -39,7 +41,8 @@ def __reduce__(self): return Rating, (int(self.user), int(self.product), float(self.rating)) -class MatrixFactorizationModel(JavaModelWrapper): +@inherit_doc +class MatrixFactorizationModel(JavaModelWrapper, JavaSaveable, JavaLoader): """A matrix factorisation model trained by regularized alternating least-squares. @@ -50,7 +53,7 @@ class MatrixFactorizationModel(JavaModelWrapper): >>> ratings = sc.parallelize([r1, r2, r3]) >>> model = ALS.trainImplicit(ratings, 1, seed=10) >>> model.predict(2, 2) - 0.43... + 0.4... >>> testset = sc.parallelize([(1, 2), (1, 1)]) >>> model = ALS.train(ratings, 2, seed=0) @@ -80,7 +83,20 @@ class MatrixFactorizationModel(JavaModelWrapper): >>> model = ALS.trainImplicit(ratings, 1, nonnegative=True, seed=10) >>> model.predict(2,2) - 0.43... + 0.4... + + >>> import os, tempfile + >>> path = tempfile.mkdtemp() + >>> model.save(sc, path) + >>> sameModel = MatrixFactorizationModel.load(sc, path) + >>> sameModel.predict(2,2) + 0.4... + >>> sameModel.predictAll(testset).collect() + [Rating(... + >>> try: + ... os.removedirs(path) + ... except OSError: + ... pass """ def predict(self, user, product): return self._java_model.predict(int(user), int(product)) @@ -89,14 +105,20 @@ def predictAll(self, user_product): assert isinstance(user_product, RDD), "user_product should be RDD of (user, product)" first = user_product.first() assert len(first) == 2, "user_product should be RDD of (user, product)" - user_product = user_product.map(lambda (u, p): (int(u), int(p))) + user_product = user_product.map(lambda u_p: (int(u_p[0]), int(u_p[1]))) return self.call("predict", user_product) def userFeatures(self): - return self.call("getUserFeatures") + return self.call("getUserFeatures").mapValues(lambda v: array.array('d', v)) def productFeatures(self): - return self.call("getProductFeatures") + return self.call("getProductFeatures").mapValues(lambda v: array.array('d', v)) + + @classmethod + def load(cls, sc, path): + model = cls._load_java(sc, path) + wrapper = sc._jvm.MatrixFactorizationModelWrapper(model) + return MatrixFactorizationModel(wrapper) class ALS(object): diff --git a/python/pyspark/mllib/regression.py b/python/pyspark/mllib/regression.py index 210060140fd91..cd7310a64f4ae 100644 --- a/python/pyspark/mllib/regression.py +++ b/python/pyspark/mllib/regression.py @@ -18,11 +18,14 @@ import numpy as np from numpy import array -from pyspark.mllib.common import callMLlibFunc +from pyspark.mllib.common import callMLlibFunc, _py2java, _java2py, inherit_doc from pyspark.mllib.linalg import SparseVector, _convert_to_vector +from pyspark.mllib.util import Saveable, Loader -__all__ = ['LabeledPoint', 'LinearModel', 'LinearRegressionModel', 'RidgeRegressionModel', - 'LinearRegressionWithSGD', 'LassoWithSGD', 'RidgeRegressionWithSGD'] +__all__ = ['LabeledPoint', 'LinearModel', + 'LinearRegressionModel', 'LinearRegressionWithSGD', + 'RidgeRegressionModel', 'RidgeRegressionWithSGD', + 'LassoModel', 'LassoWithSGD'] class LabeledPoint(object): @@ -31,8 +34,11 @@ class LabeledPoint(object): The features and labels of a data point. :param label: Label for this data point. - :param features: Vector of features for this point (NumPy array, list, - pyspark.mllib.linalg.SparseVector, or scipy.sparse column matrix) + :param features: Vector of features for this point (NumPy array, + list, pyspark.mllib.linalg.SparseVector, or scipy.sparse + column matrix) + + Note: 'label' and 'features' are accessible as class attributes. """ def __init__(self, label, features): @@ -69,6 +75,7 @@ def __repr__(self): return "(weights=%s, intercept=%r)" % (self._coeff, self._intercept) +@inherit_doc class LinearRegressionModelBase(LinearModel): """A linear regression model. @@ -89,6 +96,7 @@ def predict(self, x): return self.weights.dot(x) + self.intercept +@inherit_doc class LinearRegressionModel(LinearRegressionModelBase): """A linear regression model derived from a least-squares fit. @@ -107,6 +115,20 @@ class LinearRegressionModel(LinearRegressionModelBase): True >>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5 True + >>> import os, tempfile + >>> path = tempfile.mkdtemp() + >>> lrm.save(sc, path) + >>> sameModel = LinearRegressionModel.load(sc, path) + >>> abs(sameModel.predict(np.array([0.0])) - 0) < 0.5 + True + >>> abs(sameModel.predict(np.array([1.0])) - 1) < 0.5 + True + >>> abs(sameModel.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5 + True + >>> try: + ... os.removedirs(path) + ... except: + ... pass >>> data = [ ... LabeledPoint(0.0, SparseVector(1, {0: 0.0})), ... LabeledPoint(1.0, SparseVector(1, {0: 1.0})), @@ -118,26 +140,54 @@ class LinearRegressionModel(LinearRegressionModelBase): True >>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5 True + >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), iterations=100, step=1.0, + ... miniBatchFraction=1.0, initialWeights=array([1.0]), regParam=0.1, regType="l2", + ... intercept=True, validateData=True) + >>> abs(lrm.predict(array([0.0])) - 0) < 0.5 + True + >>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5 + True """ + def save(self, sc, path): + java_model = sc._jvm.org.apache.spark.mllib.regression.LinearRegressionModel( + _py2java(sc, self._coeff), self.intercept) + java_model.save(sc._jsc.sc(), path) + + @classmethod + def load(cls, sc, path): + java_model = sc._jvm.org.apache.spark.mllib.regression.LinearRegressionModel.load( + sc._jsc.sc(), path) + weights = _java2py(sc, java_model.weights()) + intercept = java_model.intercept() + model = LinearRegressionModel(weights, intercept) + return model # train_func should take two parameters, namely data and initial_weights, and # return the result of a call to the appropriate JVM stub. # _regression_train_wrapper is responsible for setup and error checking. def _regression_train_wrapper(train_func, modelClass, data, initial_weights): + from pyspark.mllib.classification import LogisticRegressionModel first = data.first() if not isinstance(first, LabeledPoint): raise ValueError("data should be an RDD of LabeledPoint, but got %s" % first) - initial_weights = initial_weights or [0.0] * len(data.first().features) - weights, intercept = train_func(data, _convert_to_vector(initial_weights)) - return modelClass(weights, intercept) + if initial_weights is None: + initial_weights = [0.0] * len(data.first().features) + if (modelClass == LogisticRegressionModel): + weights, intercept, numFeatures, numClasses = train_func( + data, _convert_to_vector(initial_weights)) + return modelClass(weights, intercept, numFeatures, numClasses) + else: + weights, intercept = train_func(data, _convert_to_vector(initial_weights)) + return modelClass(weights, intercept) class LinearRegressionWithSGD(object): @classmethod def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0, - initialWeights=None, regParam=0.0, regType=None, intercept=False): + initialWeights=None, regParam=0.0, regType=None, intercept=False, + validateData=True): """ Train a linear regression model on the given data. @@ -159,19 +209,23 @@ def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0, (default: None) - @param intercept: Boolean parameter which indicates the use + :param intercept: Boolean parameter which indicates the use or not of the augmented representation for training data (i.e. whether bias features - are activated or not). + are activated or not). (default: False) + :param validateData: Boolean parameter which indicates if the + algorithm should validate data before training. + (default: True) """ def train(rdd, i): return callMLlibFunc("trainLinearRegressionModelWithSGD", rdd, int(iterations), float(step), float(miniBatchFraction), i, float(regParam), - regType, bool(intercept)) + regType, bool(intercept), bool(validateData)) return _regression_train_wrapper(train, LinearRegressionModel, data, initialWeights) +@inherit_doc class LassoModel(LinearRegressionModelBase): """A linear regression model derived from a least-squares fit with an @@ -191,6 +245,20 @@ class LassoModel(LinearRegressionModelBase): True >>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5 True + >>> import os, tempfile + >>> path = tempfile.mkdtemp() + >>> lrm.save(sc, path) + >>> sameModel = LassoModel.load(sc, path) + >>> abs(sameModel.predict(np.array([0.0])) - 0) < 0.5 + True + >>> abs(sameModel.predict(np.array([1.0])) - 1) < 0.5 + True + >>> abs(sameModel.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5 + True + >>> try: + ... os.removedirs(path) + ... except: + ... pass >>> data = [ ... LabeledPoint(0.0, SparseVector(1, {0: 0.0})), ... LabeledPoint(1.0, SparseVector(1, {0: 1.0})), @@ -202,22 +270,45 @@ class LassoModel(LinearRegressionModelBase): True >>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5 True + >>> lrm = LassoWithSGD.train(sc.parallelize(data), iterations=100, step=1.0, + ... regParam=0.01, miniBatchFraction=1.0, initialWeights=array([1.0]), intercept=True, + ... validateData=True) + >>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5 + True + >>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5 + True """ + def save(self, sc, path): + java_model = sc._jvm.org.apache.spark.mllib.regression.LassoModel( + _py2java(sc, self._coeff), self.intercept) + java_model.save(sc._jsc.sc(), path) + + @classmethod + def load(cls, sc, path): + java_model = sc._jvm.org.apache.spark.mllib.regression.LassoModel.load( + sc._jsc.sc(), path) + weights = _java2py(sc, java_model.weights()) + intercept = java_model.intercept() + model = LassoModel(weights, intercept) + return model class LassoWithSGD(object): @classmethod def train(cls, data, iterations=100, step=1.0, regParam=0.01, - miniBatchFraction=1.0, initialWeights=None): + miniBatchFraction=1.0, initialWeights=None, intercept=False, + validateData=True): """Train a Lasso regression model on the given data.""" def train(rdd, i): return callMLlibFunc("trainLassoModelWithSGD", rdd, int(iterations), float(step), - float(regParam), float(miniBatchFraction), i) + float(regParam), float(miniBatchFraction), i, bool(intercept), + bool(validateData)) return _regression_train_wrapper(train, LassoModel, data, initialWeights) +@inherit_doc class RidgeRegressionModel(LinearRegressionModelBase): """A linear regression model derived from a least-squares fit with an @@ -237,6 +328,20 @@ class RidgeRegressionModel(LinearRegressionModelBase): True >>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5 True + >>> import os, tempfile + >>> path = tempfile.mkdtemp() + >>> lrm.save(sc, path) + >>> sameModel = RidgeRegressionModel.load(sc, path) + >>> abs(sameModel.predict(np.array([0.0])) - 0) < 0.5 + True + >>> abs(sameModel.predict(np.array([1.0])) - 1) < 0.5 + True + >>> abs(sameModel.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5 + True + >>> try: + ... os.removedirs(path) + ... except: + ... pass >>> data = [ ... LabeledPoint(0.0, SparseVector(1, {0: 0.0})), ... LabeledPoint(1.0, SparseVector(1, {0: 1.0})), @@ -248,18 +353,40 @@ class RidgeRegressionModel(LinearRegressionModelBase): True >>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5 True + >>> lrm = RidgeRegressionWithSGD.train(sc.parallelize(data), iterations=100, step=1.0, + ... regParam=0.01, miniBatchFraction=1.0, initialWeights=array([1.0]), intercept=True, + ... validateData=True) + >>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5 + True + >>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5 + True """ + def save(self, sc, path): + java_model = sc._jvm.org.apache.spark.mllib.regression.RidgeRegressionModel( + _py2java(sc, self._coeff), self.intercept) + java_model.save(sc._jsc.sc(), path) + + @classmethod + def load(cls, sc, path): + java_model = sc._jvm.org.apache.spark.mllib.regression.RidgeRegressionModel.load( + sc._jsc.sc(), path) + weights = _java2py(sc, java_model.weights()) + intercept = java_model.intercept() + model = RidgeRegressionModel(weights, intercept) + return model class RidgeRegressionWithSGD(object): @classmethod def train(cls, data, iterations=100, step=1.0, regParam=0.01, - miniBatchFraction=1.0, initialWeights=None): + miniBatchFraction=1.0, initialWeights=None, intercept=False, + validateData=True): """Train a ridge regression model on the given data.""" def train(rdd, i): return callMLlibFunc("trainRidgeModelWithSGD", rdd, int(iterations), float(step), - float(regParam), float(miniBatchFraction), i) + float(regParam), float(miniBatchFraction), i, bool(intercept), + bool(validateData)) return _regression_train_wrapper(train, RidgeRegressionModel, data, initialWeights) diff --git a/python/pyspark/mllib/stat/__init__.py b/python/pyspark/mllib/stat/__init__.py index b686d955a0080..e3e128513e0d7 100644 --- a/python/pyspark/mllib/stat/__init__.py +++ b/python/pyspark/mllib/stat/__init__.py @@ -21,5 +21,7 @@ from pyspark.mllib.stat._statistics import * from pyspark.mllib.stat.distribution import MultivariateGaussian +from pyspark.mllib.stat.test import ChiSqTestResult -__all__ = ["Statistics", "MultivariateStatisticalSummary", "MultivariateGaussian"] +__all__ = ["Statistics", "MultivariateStatisticalSummary", "ChiSqTestResult", + "MultivariateGaussian"] diff --git a/python/pyspark/mllib/stat/_statistics.py b/python/pyspark/mllib/stat/_statistics.py index 218ac148ca992..b475be4b4d953 100644 --- a/python/pyspark/mllib/stat/_statistics.py +++ b/python/pyspark/mllib/stat/_statistics.py @@ -15,7 +15,7 @@ # limitations under the License. # -from pyspark import RDD +from pyspark.rdd import RDD, ignore_unicode_prefix from pyspark.mllib.common import callMLlibFunc, JavaModelWrapper from pyspark.mllib.linalg import Matrix, _convert_to_vector from pyspark.mllib.regression import LabeledPoint @@ -38,7 +38,7 @@ def variance(self): return self.call("variance").toArray() def count(self): - return self.call("count") + return int(self.call("count")) def numNonzeros(self): return self.call("numNonzeros").toArray() @@ -49,6 +49,12 @@ def max(self): def min(self): return self.call("min").toArray() + def normL1(self): + return self.call("normL1").toArray() + + def normL2(self): + return self.call("normL2").toArray() + class Statistics(object): @@ -72,7 +78,7 @@ def colStats(rdd): >>> cStats.variance() array([ 4., 13., 0., 25.]) >>> cStats.count() - 3L + 3 >>> cStats.numNonzeros() array([ 3., 2., 0., 3.]) >>> cStats.max() @@ -118,20 +124,20 @@ def corr(x, y=None, method=None): >>> rdd = sc.parallelize([Vectors.dense([1, 0, 0, -2]), Vectors.dense([4, 5, 0, 3]), ... Vectors.dense([6, 7, 0, 8]), Vectors.dense([9, 0, 0, 1])]) >>> pearsonCorr = Statistics.corr(rdd) - >>> print str(pearsonCorr).replace('nan', 'NaN') + >>> print(str(pearsonCorr).replace('nan', 'NaN')) [[ 1. 0.05564149 NaN 0.40047142] [ 0.05564149 1. NaN 0.91359586] [ NaN NaN 1. NaN] [ 0.40047142 0.91359586 NaN 1. ]] >>> spearmanCorr = Statistics.corr(rdd, method="spearman") - >>> print str(spearmanCorr).replace('nan', 'NaN') + >>> print(str(spearmanCorr).replace('nan', 'NaN')) [[ 1. 0.10540926 NaN 0.4 ] [ 0.10540926 1. NaN 0.9486833 ] [ NaN NaN 1. NaN] [ 0.4 0.9486833 NaN 1. ]] >>> try: ... Statistics.corr(rdd, "spearman") - ... print "Method name as second argument without 'method=' shouldn't be allowed." + ... print("Method name as second argument without 'method=' shouldn't be allowed.") ... except TypeError: ... pass """ @@ -147,6 +153,7 @@ def corr(x, y=None, method=None): return callMLlibFunc("corr", x.map(float), y.map(float), method) @staticmethod + @ignore_unicode_prefix def chiSqTest(observed, expected=None): """ .. note:: Experimental @@ -182,11 +189,11 @@ def chiSqTest(observed, expected=None): >>> from pyspark.mllib.linalg import Vectors, Matrices >>> observed = Vectors.dense([4, 6, 5]) >>> pearson = Statistics.chiSqTest(observed) - >>> print pearson.statistic + >>> print(pearson.statistic) 0.4 >>> pearson.degreesOfFreedom 2 - >>> print round(pearson.pValue, 4) + >>> print(round(pearson.pValue, 4)) 0.8187 >>> pearson.method u'pearson' @@ -196,12 +203,12 @@ def chiSqTest(observed, expected=None): >>> observed = Vectors.dense([21, 38, 43, 80]) >>> expected = Vectors.dense([3, 5, 7, 20]) >>> pearson = Statistics.chiSqTest(observed, expected) - >>> print round(pearson.pValue, 4) + >>> print(round(pearson.pValue, 4)) 0.0027 >>> data = [40.0, 24.0, 29.0, 56.0, 32.0, 42.0, 31.0, 10.0, 0.0, 30.0, 15.0, 12.0] >>> chi = Statistics.chiSqTest(Matrices.dense(3, 4, data)) - >>> print round(chi.statistic, 4) + >>> print(round(chi.statistic, 4)) 21.9958 >>> data = [LabeledPoint(0.0, Vectors.dense([0.5, 10.0])), @@ -212,9 +219,9 @@ def chiSqTest(observed, expected=None): ... LabeledPoint(1.0, Vectors.dense([3.5, 40.0])),] >>> rdd = sc.parallelize(data, 4) >>> chi = Statistics.chiSqTest(rdd) - >>> print chi[0].statistic + >>> print(chi[0].statistic) 0.75 - >>> print chi[1].statistic + >>> print(chi[1].statistic) 1.5 """ if isinstance(observed, RDD): diff --git a/python/pyspark/mllib/stat/distribution.py b/python/pyspark/mllib/stat/distribution.py index 07792e1532046..46f7a1d2f277a 100644 --- a/python/pyspark/mllib/stat/distribution.py +++ b/python/pyspark/mllib/stat/distribution.py @@ -22,7 +22,8 @@ class MultivariateGaussian(namedtuple('MultivariateGaussian', ['mu', 'sigma'])): - """ Represents a (mu, sigma) tuple + """Represents a (mu, sigma) tuple + >>> m = MultivariateGaussian(Vectors.dense([11,12]),DenseMatrix(2, 2, (1.0, 3.0, 5.0, 2.0))) >>> (m.mu, m.sigma.toArray()) (DenseVector([11.0, 12.0]), array([[ 1., 5.],[ 3., 2.]])) diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py index 49e5c9d58e5db..c6ed5acd1770e 100644 --- a/python/pyspark/mllib/tests.py +++ b/python/pyspark/mllib/tests.py @@ -19,10 +19,12 @@ Fuller unit tests for Python MLlib. """ +import os import sys +import tempfile import array as pyarray -from numpy import array, array_equal +from numpy import array, array_equal, zeros from py4j.protocol import Py4JJavaError if sys.version_info[:2] <= (2, 6): @@ -34,11 +36,15 @@ else: import unittest +from pyspark.mllib.common import _to_java_object_rdd from pyspark.mllib.linalg import Vector, SparseVector, DenseVector, VectorUDT, _convert_to_vector,\ - DenseMatrix, Vectors, Matrices + DenseMatrix, SparseMatrix, Vectors, Matrices from pyspark.mllib.regression import LabeledPoint from pyspark.mllib.random import RandomRDDs from pyspark.mllib.stat import Statistics +from pyspark.mllib.feature import Word2Vec +from pyspark.mllib.feature import IDF +from pyspark.mllib.feature import StandardScaler from pyspark.serializers import PickleSerializer from pyspark.sql import SQLContext from pyspark.tests import ReusedPySparkTestCase as PySparkTestCase @@ -66,11 +72,11 @@ class VectorTests(PySparkTestCase): def _test_serialize(self, v): self.assertEqual(v, ser.loads(ser.dumps(v))) jvec = self.sc._jvm.SerDe.loads(bytearray(ser.dumps(v))) - nv = ser.loads(str(self.sc._jvm.SerDe.dumps(jvec))) + nv = ser.loads(bytes(self.sc._jvm.SerDe.dumps(jvec))) self.assertEqual(v, nv) vs = [v] * 100 jvecs = self.sc._jvm.SerDe.loads(bytearray(ser.dumps(vs))) - nvs = ser.loads(str(self.sc._jvm.SerDe.dumps(jvecs))) + nvs = ser.loads(bytes(self.sc._jvm.SerDe.dumps(jvecs))) self.assertEqual(vs, nvs) def test_serialize(self): @@ -132,6 +138,61 @@ def test_sparse_vector_indexing(self): for ind in [4, -5, 7.8]: self.assertRaises(ValueError, sv.__getitem__, ind) + def test_matrix_indexing(self): + mat = DenseMatrix(3, 2, [0, 1, 4, 6, 8, 10]) + expected = [[0, 6], [1, 8], [4, 10]] + for i in range(3): + for j in range(2): + self.assertEquals(mat[i, j], expected[i][j]) + + def test_sparse_matrix(self): + # Test sparse matrix creation. + sm1 = SparseMatrix( + 3, 4, [0, 2, 2, 4, 4], [1, 2, 1, 2], [1.0, 2.0, 4.0, 5.0]) + self.assertEquals(sm1.numRows, 3) + self.assertEquals(sm1.numCols, 4) + self.assertEquals(sm1.colPtrs.tolist(), [0, 2, 2, 4, 4]) + self.assertEquals(sm1.rowIndices.tolist(), [1, 2, 1, 2]) + self.assertEquals(sm1.values.tolist(), [1.0, 2.0, 4.0, 5.0]) + + # Test indexing + expected = [ + [0, 0, 0, 0], + [1, 0, 4, 0], + [2, 0, 5, 0]] + + for i in range(3): + for j in range(4): + self.assertEquals(expected[i][j], sm1[i, j]) + self.assertTrue(array_equal(sm1.toArray(), expected)) + + # Test conversion to dense and sparse. + smnew = sm1.toDense().toSparse() + self.assertEquals(sm1.numRows, smnew.numRows) + self.assertEquals(sm1.numCols, smnew.numCols) + self.assertTrue(array_equal(sm1.colPtrs, smnew.colPtrs)) + self.assertTrue(array_equal(sm1.rowIndices, smnew.rowIndices)) + self.assertTrue(array_equal(sm1.values, smnew.values)) + + sm1t = SparseMatrix( + 3, 4, [0, 2, 3, 5], [0, 1, 2, 0, 2], [3.0, 2.0, 4.0, 9.0, 8.0], + isTransposed=True) + self.assertEquals(sm1t.numRows, 3) + self.assertEquals(sm1t.numCols, 4) + self.assertEquals(sm1t.colPtrs.tolist(), [0, 2, 3, 5]) + self.assertEquals(sm1t.rowIndices.tolist(), [0, 1, 2, 0, 2]) + self.assertEquals(sm1t.values.tolist(), [3.0, 2.0, 4.0, 9.0, 8.0]) + + expected = [ + [3, 2, 0, 0], + [0, 0, 4, 0], + [9, 0, 8, 0]] + + for i in range(3): + for j in range(4): + self.assertEquals(expected[i][j], sm1t[i, j]) + self.assertTrue(array_equal(sm1t.toArray(), expected)) + class ListTests(PySparkTestCase): @@ -195,7 +256,8 @@ def test_gmm_deterministic(self): def test_classification(self): from pyspark.mllib.classification import LogisticRegressionWithSGD, SVMWithSGD, NaiveBayes - from pyspark.mllib.tree import DecisionTree, RandomForest, GradientBoostedTrees + from pyspark.mllib.tree import DecisionTree, DecisionTreeModel, RandomForest,\ + RandomForestModel, GradientBoostedTrees, GradientBoostedTreesModel data = [ LabeledPoint(0.0, [1, 0, 0]), LabeledPoint(1.0, [0, 1, 1]), @@ -205,6 +267,8 @@ def test_classification(self): rdd = self.sc.parallelize(data) features = [p.features.tolist() for p in data] + temp_dir = tempfile.mkdtemp() + lr_model = LogisticRegressionWithSGD.train(rdd) self.assertTrue(lr_model.predict(features[0]) <= 0) self.assertTrue(lr_model.predict(features[1]) > 0) @@ -231,6 +295,11 @@ def test_classification(self): self.assertTrue(dt_model.predict(features[2]) <= 0) self.assertTrue(dt_model.predict(features[3]) > 0) + dt_model_dir = os.path.join(temp_dir, "dt") + dt_model.save(self.sc, dt_model_dir) + same_dt_model = DecisionTreeModel.load(self.sc, dt_model_dir) + self.assertEqual(same_dt_model.toDebugString(), dt_model.toDebugString()) + rf_model = RandomForest.trainClassifier( rdd, numClasses=2, categoricalFeaturesInfo=categoricalFeaturesInfo, numTrees=100) self.assertTrue(rf_model.predict(features[0]) <= 0) @@ -238,6 +307,11 @@ def test_classification(self): self.assertTrue(rf_model.predict(features[2]) <= 0) self.assertTrue(rf_model.predict(features[3]) > 0) + rf_model_dir = os.path.join(temp_dir, "rf") + rf_model.save(self.sc, rf_model_dir) + same_rf_model = RandomForestModel.load(self.sc, rf_model_dir) + self.assertEqual(same_rf_model.toDebugString(), rf_model.toDebugString()) + gbt_model = GradientBoostedTrees.trainClassifier( rdd, categoricalFeaturesInfo=categoricalFeaturesInfo) self.assertTrue(gbt_model.predict(features[0]) <= 0) @@ -245,6 +319,16 @@ def test_classification(self): self.assertTrue(gbt_model.predict(features[2]) <= 0) self.assertTrue(gbt_model.predict(features[3]) > 0) + gbt_model_dir = os.path.join(temp_dir, "gbt") + gbt_model.save(self.sc, gbt_model_dir) + same_gbt_model = GradientBoostedTreesModel.load(self.sc, gbt_model_dir) + self.assertEqual(same_gbt_model.toDebugString(), gbt_model.toDebugString()) + + try: + os.removedirs(temp_dir) + except OSError: + pass + def test_regression(self): from pyspark.mllib.regression import LinearRegressionWithSGD, LassoWithSGD, \ RidgeRegressionWithSGD @@ -298,6 +382,13 @@ def test_regression(self): self.assertTrue(gbt_model.predict(features[2]) <= 0) self.assertTrue(gbt_model.predict(features[3]) > 0) + try: + LinearRegressionWithSGD.train(rdd, initialWeights=array([1.0, 1.0])) + LassoWithSGD.train(rdd, initialWeights=array([1.0, 1.0])) + RidgeRegressionWithSGD.train(rdd, initialWeights=array([1.0, 1.0])) + except ValueError: + self.fail() + class StatTests(PySparkTestCase): # SPARK-4023 @@ -315,6 +406,19 @@ def test_col_with_different_rdds(self): summary = Statistics.colStats(data) self.assertEqual(10, summary.count()) + def test_col_norms(self): + data = RandomRDDs.normalVectorRDD(self.sc, 1000, 10, 10) + summary = Statistics.colStats(data) + self.assertEqual(10, len(summary.normL1())) + self.assertEqual(10, len(summary.normL2())) + + data2 = self.sc.parallelize(range(10)).map(lambda x: Vectors.dense(x)) + summary2 = Statistics.colStats(data2) + self.assertEqual(array([45.0]), summary2.normL1()) + import math + expectedNormL2 = math.sqrt(sum(map(lambda x: x*x, range(10)))) + self.assertTrue(math.fabs(summary2.normL2()[0] - expectedNormL2) < 1e-14) + class VectorUDTTests(PySparkTestCase): @@ -334,11 +438,11 @@ def test_serialization(self): def test_infer_schema(self): sqlCtx = SQLContext(self.sc) rdd = self.sc.parallelize([LabeledPoint(1.0, self.dv1), LabeledPoint(0.0, self.sv1)]) - srdd = sqlCtx.inferSchema(rdd) - schema = srdd.schema() + df = rdd.toDF() + schema = df.schema field = [f for f in schema.fields if f.name == "features"][0] self.assertEqual(field.dataType, self.udt) - vectors = srdd.map(lambda p: p.features).collect() + vectors = df.map(lambda p: p.features).collect() self.assertEqual(len(vectors), 2) for v in vectors: if isinstance(v, SparseVector): @@ -588,9 +692,86 @@ def test_right_number_of_results(self): self.assertEqual(len(chi), num_cols) self.assertIsNotNone(chi[1000]) + +class SerDeTest(PySparkTestCase): + def test_to_java_object_rdd(self): # SPARK-6660 + data = RandomRDDs.uniformRDD(self.sc, 10, 5, seed=0) + self.assertEqual(_to_java_object_rdd(data).count(), 10) + + +class FeatureTest(PySparkTestCase): + def test_idf_model(self): + data = [ + Vectors.dense([1, 2, 6, 0, 2, 3, 1, 1, 0, 0, 3]), + Vectors.dense([1, 3, 0, 1, 3, 0, 0, 2, 0, 0, 1]), + Vectors.dense([1, 4, 1, 0, 0, 4, 9, 0, 1, 2, 0]), + Vectors.dense([2, 1, 0, 3, 0, 0, 5, 0, 2, 3, 9]) + ] + model = IDF().fit(self.sc.parallelize(data, 2)) + idf = model.idf() + self.assertEqual(len(idf), 11) + + +class Word2VecTests(PySparkTestCase): + def test_word2vec_setters(self): + data = [ + ["I", "have", "a", "pen"], + ["I", "like", "soccer", "very", "much"], + ["I", "live", "in", "Tokyo"] + ] + model = Word2Vec() \ + .setVectorSize(2) \ + .setLearningRate(0.01) \ + .setNumPartitions(2) \ + .setNumIterations(10) \ + .setSeed(1024) \ + .setMinCount(3) + self.assertEquals(model.vectorSize, 2) + self.assertTrue(model.learningRate < 0.02) + self.assertEquals(model.numPartitions, 2) + self.assertEquals(model.numIterations, 10) + self.assertEquals(model.seed, 1024) + self.assertEquals(model.minCount, 3) + + def test_word2vec_get_vectors(self): + data = [ + ["a", "b", "c", "d", "e", "f", "g"], + ["a", "b", "c", "d", "e", "f"], + ["a", "b", "c", "d", "e"], + ["a", "b", "c", "d"], + ["a", "b", "c"], + ["a", "b"], + ["a"] + ] + model = Word2Vec().fit(self.sc.parallelize(data)) + self.assertEquals(len(model.getVectors()), 3) + + +class StandardScalerTests(PySparkTestCase): + def test_model_setters(self): + data = [ + [1.0, 2.0, 3.0], + [2.0, 3.0, 4.0], + [3.0, 4.0, 5.0] + ] + model = StandardScaler().fit(self.sc.parallelize(data)) + self.assertIsNotNone(model.setWithMean(True)) + self.assertIsNotNone(model.setWithStd(True)) + self.assertEqual(model.transform([1.0, 2.0, 3.0]), DenseVector([-1.0, -1.0, -1.0])) + + def test_model_transform(self): + data = [ + [1.0, 2.0, 3.0], + [2.0, 3.0, 4.0], + [3.0, 4.0, 5.0] + ] + model = StandardScaler().fit(self.sc.parallelize(data)) + self.assertEqual(model.transform([1.0, 2.0, 3.0]), DenseVector([1.0, 2.0, 3.0])) + + if __name__ == "__main__": if not _have_scipy: - print "NOTE: Skipping SciPy tests as it does not seem to be installed" + print("NOTE: Skipping SciPy tests as it does not seem to be installed") unittest.main() if not _have_scipy: - print "NOTE: SciPy tests were skipped as it does not seem to be installed" + print("NOTE: SciPy tests were skipped as it does not seem to be installed") diff --git a/python/pyspark/mllib/tree.py b/python/pyspark/mllib/tree.py index aae48f213246b..0fe6e4fabe43a 100644 --- a/python/pyspark/mllib/tree.py +++ b/python/pyspark/mllib/tree.py @@ -20,19 +20,24 @@ import random from pyspark import SparkContext, RDD -from pyspark.mllib.common import callMLlibFunc, JavaModelWrapper +from pyspark.mllib.common import callMLlibFunc, inherit_doc, JavaModelWrapper from pyspark.mllib.linalg import _convert_to_vector from pyspark.mllib.regression import LabeledPoint +from pyspark.mllib.util import JavaLoader, JavaSaveable __all__ = ['DecisionTreeModel', 'DecisionTree', 'RandomForestModel', - 'RandomForest', 'GradientBoostedTrees'] + 'RandomForest', 'GradientBoostedTreesModel', 'GradientBoostedTrees'] -class TreeEnsembleModel(JavaModelWrapper): +class TreeEnsembleModel(JavaModelWrapper, JavaSaveable): def predict(self, x): """ Predict values for a single data point or an RDD of points using the model trained. + + Note: In Python, predict cannot currently be used within an RDD + transformation or action. + Call predict directly on the RDD instead. """ if isinstance(x, RDD): return self.call("predict", x.map(_convert_to_vector)) @@ -48,7 +53,8 @@ def numTrees(self): def totalNumNodes(self): """ - Get total number of nodes, summed over all trees in the ensemble. + Get total number of nodes, summed over all trees in the + ensemble. """ return self.call("totalNumNodes") @@ -61,7 +67,7 @@ def toDebugString(self): return self._java_model.toDebugString() -class DecisionTreeModel(JavaModelWrapper): +class DecisionTreeModel(JavaModelWrapper, JavaSaveable, JavaLoader): """ .. note:: Experimental @@ -71,6 +77,10 @@ def predict(self, x): """ Predict the label of one or more examples. + Note: In Python, predict cannot currently be used within an RDD + transformation or action. + Call predict directly on the RDD instead. + :param x: Data point (feature vector), or an RDD of data points (feature vectors). """ @@ -94,12 +104,17 @@ def toDebugString(self): """ full model. """ return self._java_model.toDebugString() + @classmethod + def _java_loader_class(cls): + return "org.apache.spark.mllib.tree.model.DecisionTreeModel" + class DecisionTree(object): """ .. note:: Experimental - Learning algorithm for a decision tree model for classification or regression. + Learning algorithm for a decision tree model for classification or + regression. """ @classmethod @@ -148,14 +163,16 @@ def trainClassifier(cls, data, numClasses, categoricalFeaturesInfo, ... LabeledPoint(1.0, [3.0]) ... ] >>> model = DecisionTree.trainClassifier(sc.parallelize(data), 2, {}) - >>> print model, # it already has newline + >>> print(model) DecisionTreeModel classifier of depth 1 with 3 nodes - >>> print model.toDebugString(), # it already has newline + + >>> print(model.toDebugString()) DecisionTreeModel classifier of depth 1 with 3 nodes If (feature 0 <= 0.0) Predict: 0.0 Else (feature 0 > 0.0) Predict: 1.0 + >>> model.predict(array([1.0])) 1.0 >>> model.predict(array([0.0])) @@ -176,17 +193,17 @@ def trainRegressor(cls, data, categoricalFeaturesInfo, :param data: Training data: RDD of LabeledPoint. Labels are real numbers. - :param categoricalFeaturesInfo: Map from categorical feature index - to number of categories. - Any feature not in this map - is treated as continuous. + :param categoricalFeaturesInfo: Map from categorical feature + index to number of categories. + Any feature not in this map is treated as continuous. :param impurity: Supported values: "variance" :param maxDepth: Max depth of tree. - E.g., depth 0 means 1 leaf node. - Depth 1 means 1 internal node + 2 leaf nodes. - :param maxBins: Number of bins used for finding splits at each node. - :param minInstancesPerNode: Min number of instances required at child - nodes to create the parent split + E.g., depth 0 means 1 leaf node. + Depth 1 means 1 internal node + 2 leaf nodes. + :param maxBins: Number of bins used for finding splits at each + node. + :param minInstancesPerNode: Min number of instances required at + child nodes to create the parent split :param minInfoGain: Min info gain required to create a split :return: DecisionTreeModel @@ -216,19 +233,25 @@ def trainRegressor(cls, data, categoricalFeaturesInfo, impurity, maxDepth, maxBins, minInstancesPerNode, minInfoGain) -class RandomForestModel(TreeEnsembleModel): +@inherit_doc +class RandomForestModel(TreeEnsembleModel, JavaLoader): """ .. note:: Experimental Represents a random forest model. """ + @classmethod + def _java_loader_class(cls): + return "org.apache.spark.mllib.tree.model.RandomForestModel" + class RandomForest(object): """ .. note:: Experimental - Learning algorithm for a random forest model for classification or regression. + Learning algorithm for a random forest model for classification or + regression. """ supportedFeatureSubsetStrategies = ("auto", "all", "sqrt", "log2", "onethird") @@ -255,26 +278,30 @@ def trainClassifier(cls, data, numClasses, categoricalFeaturesInfo, numTrees, Method to train a decision tree model for binary or multiclass classification. - :param data: Training dataset: RDD of LabeledPoint. Labels should take - values {0, 1, ..., numClasses-1}. + :param data: Training dataset: RDD of LabeledPoint. Labels + should take values {0, 1, ..., numClasses-1}. :param numClasses: number of classes for classification. - :param categoricalFeaturesInfo: Map storing arity of categorical features. - E.g., an entry (n -> k) indicates that feature n is categorical - with k categories indexed from 0: {0, 1, ..., k-1}. + :param categoricalFeaturesInfo: Map storing arity of categorical + features. E.g., an entry (n -> k) indicates that + feature n is categorical with k categories indexed + from 0: {0, 1, ..., k-1}. :param numTrees: Number of trees in the random forest. - :param featureSubsetStrategy: Number of features to consider for splits at - each node. - Supported: "auto" (default), "all", "sqrt", "log2", "onethird". - If "auto" is set, this parameter is set based on numTrees: - if numTrees == 1, set to "all"; - if numTrees > 1 (forest) set to "sqrt". + :param featureSubsetStrategy: Number of features to consider for + splits at each node. + Supported: "auto" (default), "all", "sqrt", "log2", "onethird". + If "auto" is set, this parameter is set based on numTrees: + if numTrees == 1, set to "all"; + if numTrees > 1 (forest) set to "sqrt". :param impurity: Criterion used for information gain calculation. Supported values: "gini" (recommended) or "entropy". - :param maxDepth: Maximum depth of the tree. E.g., depth 0 means 1 leaf node; - depth 1 means 1 internal node + 2 leaf nodes. (default: 4) - :param maxBins: maximum number of bins used for splitting features - (default: 100) - :param seed: Random seed for bootstrapping and choosing feature subsets. + :param maxDepth: Maximum depth of the tree. + E.g., depth 0 means 1 leaf node; depth 1 means + 1 internal node + 2 leaf nodes. (default: 4) + :param maxBins: maximum number of bins used for splitting + features + (default: 100) + :param seed: Random seed for bootstrapping and choosing feature + subsets. :return: RandomForestModel that can be used for prediction Example usage: @@ -293,9 +320,10 @@ def trainClassifier(cls, data, numClasses, categoricalFeaturesInfo, numTrees, 3 >>> model.totalNumNodes() 7 - >>> print model, + >>> print(model) TreeEnsembleModel classifier with 3 trees - >>> print model.toDebugString(), + + >>> print(model.toDebugString()) TreeEnsembleModel classifier with 3 trees Tree 0: @@ -310,6 +338,7 @@ def trainClassifier(cls, data, numClasses, categoricalFeaturesInfo, numTrees, Predict: 0.0 Else (feature 0 > 1.0) Predict: 1.0 + >>> model.predict([2.0]) 1.0 >>> model.predict([0.0]) @@ -336,19 +365,21 @@ def trainRegressor(cls, data, categoricalFeaturesInfo, numTrees, featureSubsetSt {0, 1, ..., k-1}. :param numTrees: Number of trees in the random forest. :param featureSubsetStrategy: Number of features to consider for - splits at each node. - Supported: "auto" (default), "all", "sqrt", "log2", "onethird". - If "auto" is set, this parameter is set based on numTrees: - if numTrees == 1, set to "all"; - if numTrees > 1 (forest) set to "onethird" for regression. - :param impurity: Criterion used for information gain calculation. - Supported values: "variance". - :param maxDepth: Maximum depth of the tree. E.g., depth 0 means 1 - leaf node; depth 1 means 1 internal node + 2 leaf nodes. - (default: 4) - :param maxBins: maximum number of bins used for splitting features - (default: 100) - :param seed: Random seed for bootstrapping and choosing feature subsets. + splits at each node. + Supported: "auto" (default), "all", "sqrt", "log2", "onethird". + If "auto" is set, this parameter is set based on numTrees: + if numTrees == 1, set to "all"; + if numTrees > 1 (forest) set to "onethird" for regression. + :param impurity: Criterion used for information gain + calculation. + Supported values: "variance". + :param maxDepth: Maximum depth of the tree. E.g., depth 0 means + 1 leaf node; depth 1 means 1 internal node + 2 leaf + nodes. (default: 4) + :param maxBins: maximum number of bins used for splitting + features (default: 100) + :param seed: Random seed for bootstrapping and choosing feature + subsets. :return: RandomForestModel that can be used for prediction Example usage: @@ -381,19 +412,25 @@ def trainRegressor(cls, data, categoricalFeaturesInfo, numTrees, featureSubsetSt featureSubsetStrategy, impurity, maxDepth, maxBins, seed) -class GradientBoostedTreesModel(TreeEnsembleModel): +@inherit_doc +class GradientBoostedTreesModel(TreeEnsembleModel, JavaLoader): """ .. note:: Experimental Represents a gradient-boosted tree model. """ + @classmethod + def _java_loader_class(cls): + return "org.apache.spark.mllib.tree.model.GradientBoostedTreesModel" + class GradientBoostedTrees(object): """ .. note:: Experimental - Learning algorithm for a gradient boosted trees model for classification or regression. + Learning algorithm for a gradient boosted trees model for + classification or regression. """ @classmethod @@ -409,24 +446,29 @@ def _train(cls, data, algo, categoricalFeaturesInfo, def trainClassifier(cls, data, categoricalFeaturesInfo, loss="logLoss", numIterations=100, learningRate=0.1, maxDepth=3): """ - Method to train a gradient-boosted trees model for classification. + Method to train a gradient-boosted trees model for + classification. - :param data: Training dataset: RDD of LabeledPoint. Labels should take values {0, 1}. + :param data: Training dataset: RDD of LabeledPoint. + Labels should take values {0, 1}. :param categoricalFeaturesInfo: Map storing arity of categorical features. E.g., an entry (n -> k) indicates that feature n is categorical with k categories indexed from 0: {0, 1, ..., k-1}. - :param loss: Loss function used for minimization during gradient boosting. - Supported: {"logLoss" (default), "leastSquaresError", "leastAbsoluteError"}. + :param loss: Loss function used for minimization during gradient + boosting. Supported: {"logLoss" (default), + "leastSquaresError", "leastAbsoluteError"}. :param numIterations: Number of iterations of boosting. (default: 100) - :param learningRate: Learning rate for shrinking the contribution of each estimator. - The learning rate should be between in the interval (0, 1] - (default: 0.1) - :param maxDepth: Maximum depth of the tree. E.g., depth 0 means 1 - leaf node; depth 1 means 1 internal node + 2 leaf nodes. - (default: 3) - :return: GradientBoostedTreesModel that can be used for prediction + :param learningRate: Learning rate for shrinking the + contribution of each estimator. The learning rate + should be between in the interval (0, 1]. + (default: 0.1) + :param maxDepth: Maximum depth of the tree. E.g., depth 0 means + 1 leaf node; depth 1 means 1 internal node + 2 leaf + nodes. (default: 3) + :return: GradientBoostedTreesModel that can be used for + prediction Example usage: @@ -445,8 +487,9 @@ def trainClassifier(cls, data, categoricalFeaturesInfo, 100 >>> model.totalNumNodes() 300 - >>> print model, # it already has newline + >>> print(model) # it already has newline TreeEnsembleModel classifier with 100 trees + >>> model.predict([2.0]) 1.0 >>> model.predict([0.0]) @@ -470,17 +513,20 @@ def trainRegressor(cls, data, categoricalFeaturesInfo, features. E.g., an entry (n -> k) indicates that feature n is categorical with k categories indexed from 0: {0, 1, ..., k-1}. - :param loss: Loss function used for minimization during gradient boosting. - Supported: {"logLoss" (default), "leastSquaresError", "leastAbsoluteError"}. + :param loss: Loss function used for minimization during gradient + boosting. Supported: {"logLoss" (default), + "leastSquaresError", "leastAbsoluteError"}. :param numIterations: Number of iterations of boosting. (default: 100) - :param learningRate: Learning rate for shrinking the contribution of each estimator. - The learning rate should be between in the interval (0, 1] - (default: 0.1) - :param maxDepth: Maximum depth of the tree. E.g., depth 0 means 1 - leaf node; depth 1 means 1 internal node + 2 leaf nodes. - (default: 3) - :return: GradientBoostedTreesModel that can be used for prediction + :param learningRate: Learning rate for shrinking the + contribution of each estimator. The learning rate + should be between in the interval (0, 1]. + (default: 0.1) + :param maxDepth: Maximum depth of the tree. E.g., depth 0 means + 1 leaf node; depth 1 means 1 internal node + 2 leaf + nodes. (default: 3) + :return: GradientBoostedTreesModel that can be used for + prediction Example usage: diff --git a/python/pyspark/mllib/util.py b/python/pyspark/mllib/util.py index 4ed978b45409c..16a90db146ef0 100644 --- a/python/pyspark/mllib/util.py +++ b/python/pyspark/mllib/util.py @@ -15,12 +15,15 @@ # limitations under the License. # +import sys import numpy as np import warnings -from pyspark.mllib.common import callMLlibFunc +if sys.version > '3': + xrange = range + +from pyspark.mllib.common import callMLlibFunc, inherit_doc from pyspark.mllib.linalg import Vectors, SparseVector, _convert_to_vector -from pyspark.mllib.regression import LabeledPoint class MLUtils(object): @@ -50,6 +53,7 @@ def _parse_libsvm_line(line, multiclass=None): @staticmethod def _convert_labeled_point_to_libsvm(p): """Converts a LabeledPoint to a string in LIBSVM format.""" + from pyspark.mllib.regression import LabeledPoint assert isinstance(p, LabeledPoint) items = [str(p.label)] v = _convert_to_vector(p.features) @@ -92,24 +96,20 @@ def loadLibSVMFile(sc, path, numFeatures=-1, minPartitions=None, multiclass=None >>> from tempfile import NamedTemporaryFile >>> from pyspark.mllib.util import MLUtils + >>> from pyspark.mllib.regression import LabeledPoint >>> tempFile = NamedTemporaryFile(delete=True) - >>> tempFile.write("+1 1:1.0 3:2.0 5:3.0\\n-1\\n-1 2:4.0 4:5.0 6:6.0") + >>> _ = tempFile.write(b"+1 1:1.0 3:2.0 5:3.0\\n-1\\n-1 2:4.0 4:5.0 6:6.0") >>> tempFile.flush() >>> examples = MLUtils.loadLibSVMFile(sc, tempFile.name).collect() >>> tempFile.close() - >>> type(examples[0]) == LabeledPoint - True - >>> print examples[0] - (1.0,(6,[0,2,4],[1.0,2.0,3.0])) - >>> type(examples[1]) == LabeledPoint - True - >>> print examples[1] - (-1.0,(6,[],[])) - >>> type(examples[2]) == LabeledPoint - True - >>> print examples[2] - (-1.0,(6,[1,3,5],[4.0,5.0,6.0])) + >>> examples[0] + LabeledPoint(1.0, (6,[0,2,4],[1.0,2.0,3.0])) + >>> examples[1] + LabeledPoint(-1.0, (6,[],[])) + >>> examples[2] + LabeledPoint(-1.0, (6,[1,3,5],[4.0,5.0,6.0])) """ + from pyspark.mllib.regression import LabeledPoint if multiclass is not None: warnings.warn("deprecated", DeprecationWarning) @@ -130,6 +130,7 @@ def saveAsLibSVMFile(data, dir): >>> from tempfile import NamedTemporaryFile >>> from fileinput import input + >>> from pyspark.mllib.regression import LabeledPoint >>> from glob import glob >>> from pyspark.mllib.util import MLUtils >>> examples = [LabeledPoint(1.1, Vectors.sparse(3, [(0, 1.23), (2, 4.56)])), \ @@ -156,6 +157,7 @@ def loadLabeledPoints(sc, path, minPartitions=None): >>> from tempfile import NamedTemporaryFile >>> from pyspark.mllib.util import MLUtils + >>> from pyspark.mllib.regression import LabeledPoint >>> examples = [LabeledPoint(1.1, Vectors.sparse(3, [(0, -1.23), (2, 4.56e-7)])), \ LabeledPoint(0.0, Vectors.dense([1.01, 2.02, 3.03]))] >>> tempFile = NamedTemporaryFile(delete=True) @@ -168,6 +170,93 @@ def loadLabeledPoints(sc, path, minPartitions=None): return callMLlibFunc("loadLabeledPoints", sc, path, minPartitions) +class Saveable(object): + """ + Mixin for models and transformers which may be saved as files. + """ + + def save(self, sc, path): + """ + Save this model to the given path. + + This saves: + * human-readable (JSON) model metadata to path/metadata/ + * Parquet formatted data to path/data/ + + The model may be loaded using py:meth:`Loader.load`. + + :param sc: Spark context used to save model data. + :param path: Path specifying the directory in which to save + this model. If the directory already exists, + this method throws an exception. + """ + raise NotImplementedError + + +@inherit_doc +class JavaSaveable(Saveable): + """ + Mixin for models that provide save() through their Scala + implementation. + """ + + def save(self, sc, path): + self._java_model.save(sc._jsc.sc(), path) + + +class Loader(object): + """ + Mixin for classes which can load saved models from files. + """ + + @classmethod + def load(cls, sc, path): + """ + Load a model from the given path. The model should have been + saved using py:meth:`Saveable.save`. + + :param sc: Spark context used for loading model files. + :param path: Path specifying the directory to which the model + was saved. + :return: model instance + """ + raise NotImplemented + + +@inherit_doc +class JavaLoader(Loader): + """ + Mixin for classes which can load saved models using its Scala + implementation. + """ + + @classmethod + def _java_loader_class(cls): + """ + Returns the full class name of the Java loader. The default + implementation replaces "pyspark" by "org.apache.spark" in + the Python full class name. + """ + java_package = cls.__module__.replace("pyspark", "org.apache.spark") + return ".".join([java_package, cls.__name__]) + + @classmethod + def _load_java(cls, sc, path): + """ + Load a Java model from the given path. + """ + java_class = cls._java_loader_class() + java_obj = sc._jvm + for name in java_class.split("."): + java_obj = getattr(java_obj, name) + return java_obj.load(sc._jsc.sc(), path) + + @classmethod + def load(cls, sc, path): + java_model = cls._load_java(sc, path) + return cls(java_model) + + def _test(): import doctest from pyspark.context import SparkContext diff --git a/python/pyspark/profiler.py b/python/pyspark/profiler.py index 4408996db0790..d18daaabfcb3c 100644 --- a/python/pyspark/profiler.py +++ b/python/pyspark/profiler.py @@ -84,11 +84,11 @@ class Profiler(object): >>> from pyspark import BasicProfiler >>> class MyCustomProfiler(BasicProfiler): ... def show(self, id): - ... print "My custom profiles for RDD:%s" % id + ... print("My custom profiles for RDD:%s" % id) ... >>> conf = SparkConf().set("spark.python.profile", "true") >>> sc = SparkContext('local', 'test', conf=conf, profiler_cls=MyCustomProfiler) - >>> sc.parallelize(list(range(1000))).map(lambda x: 2 * x).take(10) + >>> sc.parallelize(range(1000)).map(lambda x: 2 * x).take(10) [0, 2, 4, 6, 8, 10, 12, 14, 16, 18] >>> sc.show_profiles() My custom profiles for RDD:1 @@ -111,9 +111,9 @@ def show(self, id): """ Print the profile stats to stdout, id is the RDD id """ stats = self.stats() if stats: - print "=" * 60 - print "Profile of RDD" % id - print "=" * 60 + print("=" * 60) + print("Profile of RDD" % id) + print("=" * 60) stats.sort_stats("time", "cumulative").print_stats() def dump(self, id, path): diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py index bd4f16e058045..d9cdbb666f92a 100644 --- a/python/pyspark/rdd.py +++ b/python/pyspark/rdd.py @@ -16,21 +16,29 @@ # import copy -from collections import defaultdict -from itertools import chain, ifilter, imap -import operator -import os import sys +import os +import re +import operator import shlex -from subprocess import Popen, PIPE -from tempfile import NamedTemporaryFile -from threading import Thread import warnings import heapq import bisect import random +import socket +from subprocess import Popen, PIPE +from tempfile import NamedTemporaryFile +from threading import Thread +from collections import defaultdict +from itertools import chain +from functools import reduce from math import sqrt, log, isinf, isnan, pow, ceil +if sys.version > '3': + basestring = unicode = str +else: + from itertools import imap as map, ifilter as filter + from pyspark.serializers import NoOpSerializer, CartesianDeserializer, \ BatchedSerializer, CloudPickleSerializer, PairDeserializer, \ PickleSerializer, pack_long, AutoBatchedSerializer @@ -41,7 +49,7 @@ from pyspark.storagelevel import StorageLevel from pyspark.resultiterable import ResultIterable from pyspark.shuffle import Aggregator, InMemoryMerger, ExternalMerger, \ - get_used_memory, ExternalSorter + get_used_memory, ExternalSorter, ExternalGroupBy from pyspark.traceback_utils import SCCallSiteSync from py4j.java_collections import ListConverter, MapConverter @@ -50,20 +58,21 @@ __all__ = ["RDD"] -# TODO: for Python 3.3+, PYTHONHASHSEED should be reset to disable randomized -# hash for string def portable_hash(x): """ - This function returns consistant hash code for builtin types, especially + This function returns consistent hash code for builtin types, especially for None and tuple with None. - The algrithm is similar to that one used by CPython 2.7 + The algorithm is similar to that one used by CPython 2.7 >>> portable_hash(None) 0 >>> portable_hash((None, 1)) & 0xffffffff 219750521 """ + if sys.version >= '3.3' and 'PYTHONHASHSEED' not in os.environ: + raise Exception("Randomness of hash of string should be disabled via PYTHONHASHSEED") + if x is None: return 0 if isinstance(x, tuple): @@ -71,7 +80,7 @@ def portable_hash(x): for i in x: h ^= portable_hash(i) h *= 1000003 - h &= sys.maxint + h &= sys.maxsize h ^= len(x) if h == -1: h = -2 @@ -111,6 +120,44 @@ def _parse_memory(s): return int(float(s[:-1]) * units[s[-1].lower()]) +def _load_from_socket(port, serializer): + sock = socket.socket() + sock.settimeout(3) + try: + sock.connect(("localhost", port)) + rf = sock.makefile("rb", 65536) + for item in serializer.load_stream(rf): + yield item + finally: + sock.close() + + +def ignore_unicode_prefix(f): + """ + Ignore the 'u' prefix of string in doc tests, to make it works + in both python 2 and 3 + """ + if sys.version >= '3': + # the representation of unicode string in Python 3 does not have prefix 'u', + # so remove the prefix 'u' for doc tests + literal_re = re.compile(r"(\W|^)[uU](['])", re.UNICODE) + f.__doc__ = literal_re.sub(r'\1\2', f.__doc__) + return f + + +class Partitioner(object): + def __init__(self, numPartitions, partitionFunc): + self.numPartitions = numPartitions + self.partitionFunc = partitionFunc + + def __eq__(self, other): + return (isinstance(other, Partitioner) and self.numPartitions == other.numPartitions + and self.partitionFunc == other.partitionFunc) + + def __call__(self, k): + return self.partitionFunc(k) % self.numPartitions + + class RDD(object): """ @@ -126,7 +173,7 @@ def __init__(self, jrdd, ctx, jrdd_deserializer=AutoBatchedSerializer(PickleSeri self.ctx = ctx self._jrdd_deserializer = jrdd_deserializer self._id = jrdd.id() - self._partitionFunc = None + self.partitioner = None def _pickled(self): return self._reserialize(AutoBatchedSerializer(PickleSerializer())) @@ -226,7 +273,7 @@ def map(self, f, preservesPartitioning=False): [('a', 1), ('b', 1), ('c', 1)] """ def func(_, iterator): - return imap(f, iterator) + return map(f, iterator) return self.mapPartitionsWithIndex(func, preservesPartitioning) def flatMap(self, f, preservesPartitioning=False): @@ -241,7 +288,7 @@ def flatMap(self, f, preservesPartitioning=False): [(2, 2), (2, 2), (3, 3), (3, 3), (4, 4), (4, 4)] """ def func(s, iterator): - return chain.from_iterable(imap(f, iterator)) + return chain.from_iterable(map(f, iterator)) return self.mapPartitionsWithIndex(func, preservesPartitioning) def mapPartitions(self, f, preservesPartitioning=False): @@ -304,7 +351,7 @@ def filter(self, f): [2, 4] """ def func(iterator): - return ifilter(f, iterator) + return filter(f, iterator) return self.mapPartitions(func, True) def distinct(self, numPartitions=None): @@ -316,15 +363,21 @@ def distinct(self, numPartitions=None): """ return self.map(lambda x: (x, None)) \ .reduceByKey(lambda x, _: x, numPartitions) \ - .map(lambda (x, _): x) + .map(lambda x: x[0]) def sample(self, withReplacement, fraction, seed=None): """ Return a sampled subset of this RDD. + :param withReplacement: can elements be sampled multiple times (replaced when sampled out) + :param fraction: expected size of the sample as a fraction of this RDD's size + without replacement: probability that each element is chosen; fraction must be [0, 1] + with replacement: expected number of times each element is chosen; fraction must be >= 0 + :param seed: seed for the random number generator + >>> rdd = sc.parallelize(range(100), 4) - >>> rdd.sample(False, 0.1, 81).count() - 10 + >>> 6 <= rdd.sample(False, 0.1, 81).count() <= 14 + True """ assert fraction >= 0.0, "Negative fraction value: %s" % fraction return self.mapPartitionsWithIndex(RDDSampler(withReplacement, fraction, seed).func, True) @@ -337,12 +390,14 @@ def randomSplit(self, weights, seed=None): :param seed: random seed :return: split RDDs in a list - >>> rdd = sc.parallelize(range(5), 1) + >>> rdd = sc.parallelize(range(500), 1) >>> rdd1, rdd2 = rdd.randomSplit([2, 3], 17) - >>> rdd1.collect() - [1, 3] - >>> rdd2.collect() - [0, 2, 4] + >>> len(rdd1.collect() + rdd2.collect()) + 500 + >>> 150 < rdd1.count() < 250 + True + >>> 250 < rdd2.count() < 350 + True """ s = float(sum(weights)) cweights = [0.0] @@ -385,7 +440,7 @@ def takeSample(self, withReplacement, num, seed=None): rand.shuffle(samples) return samples - maxSampleSize = sys.maxint - int(numStDev * sqrt(sys.maxint)) + maxSampleSize = sys.maxsize - int(numStDev * sqrt(sys.maxsize)) if num > maxSampleSize: raise ValueError( "Sample size cannot be greater than %d." % maxSampleSize) @@ -399,7 +454,7 @@ def takeSample(self, withReplacement, num, seed=None): # See: scala/spark/RDD.scala while len(samples) < num: # TODO: add log warning for when more than one iteration was run - seed = rand.randint(0, sys.maxint) + seed = rand.randint(0, sys.maxsize) samples = self.sample(withReplacement, fraction, seed).collect() rand.shuffle(samples) @@ -450,14 +505,17 @@ def union(self, other): if self._jrdd_deserializer == other._jrdd_deserializer: rdd = RDD(self._jrdd.union(other._jrdd), self.ctx, self._jrdd_deserializer) - return rdd else: # These RDDs contain data in different serialized formats, so we # must normalize them to the default serializer. self_copy = self._reserialize() other_copy = other._reserialize() - return RDD(self_copy._jrdd.union(other_copy._jrdd), self.ctx, - self.ctx.serializer) + rdd = RDD(self_copy._jrdd.union(other_copy._jrdd), self.ctx, + self.ctx.serializer) + if (self.partitioner == other.partitioner and + self.getNumPartitions() == rdd.getNumPartitions()): + rdd.partitioner = self.partitioner + return rdd def intersection(self, other): """ @@ -473,7 +531,7 @@ def intersection(self, other): """ return self.map(lambda v: (v, None)) \ .cogroup(other.map(lambda v: (v, None))) \ - .filter(lambda (k, vs): all(vs)) \ + .filter(lambda k_vs: all(k_vs[1])) \ .keys() def _reserialize(self, serializer=None): @@ -515,7 +573,7 @@ def repartitionAndSortWithinPartitions(self, numPartitions=None, partitionFunc=p def sortPartition(iterator): sort = ExternalSorter(memory * 0.9, serializer).sorted if spill else sorted - return iter(sort(iterator, key=lambda (k, v): keyfunc(k), reverse=(not ascending))) + return iter(sort(iterator, key=lambda k_v: keyfunc(k_v[0]), reverse=(not ascending))) return self.partitionBy(numPartitions, partitionFunc).mapPartitions(sortPartition, True) @@ -539,13 +597,13 @@ def sortByKey(self, ascending=True, numPartitions=None, keyfunc=lambda x: x): if numPartitions is None: numPartitions = self._defaultReducePartitions() - spill = (self.ctx._conf.get("spark.shuffle.spill", 'True').lower() == 'true') - memory = _parse_memory(self.ctx._conf.get("spark.python.worker.memory", "512m")) + spill = self._can_spill() + memory = self._memory_limit() serializer = self._jrdd_deserializer def sortPartition(iterator): sort = ExternalSorter(memory * 0.9, serializer).sorted if spill else sorted - return iter(sort(iterator, key=lambda (k, v): keyfunc(k), reverse=(not ascending))) + return iter(sort(iterator, key=lambda kv: keyfunc(kv[0]), reverse=(not ascending))) if numPartitions == 1: if self.getNumPartitions() > 1: @@ -560,12 +618,12 @@ def sortPartition(iterator): return self # empty RDD maxSampleSize = numPartitions * 20.0 # constant from Spark's RangePartitioner fraction = min(maxSampleSize / max(rddSize, 1), 1.0) - samples = self.sample(False, fraction, 1).map(lambda (k, v): k).collect() - samples = sorted(samples, reverse=(not ascending), key=keyfunc) + samples = self.sample(False, fraction, 1).map(lambda kv: kv[0]).collect() + samples = sorted(samples, key=keyfunc) # we have numPartitions many parts but one of the them has # an implicit boundary - bounds = [samples[len(samples) * (i + 1) / numPartitions] + bounds = [samples[int(len(samples) * (i + 1) / numPartitions)] for i in range(0, numPartitions - 1)] def rangePartitioner(k): @@ -628,12 +686,13 @@ def groupBy(self, f, numPartitions=None): """ return self.map(lambda x: (f(x), x)).groupByKey(numPartitions) + @ignore_unicode_prefix def pipe(self, command, env={}): """ Return an RDD created by piping elements to a forked external process. >>> sc.parallelize(['1', '2', '', '3']).pipe('cat').collect() - ['1', '2', '', '3'] + [u'1', u'2', u'', u'3'] """ def func(iterator): pipe = Popen( @@ -641,17 +700,18 @@ def func(iterator): def pipe_objs(out): for obj in iterator: - out.write(str(obj).rstrip('\n') + '\n') + s = str(obj).rstrip('\n') + '\n' + out.write(s.encode('utf-8')) out.close() Thread(target=pipe_objs, args=[pipe.stdin]).start() - return (x.rstrip('\n') for x in iter(pipe.stdout.readline, '')) + return (x.rstrip(b'\n').decode('utf-8') for x in iter(pipe.stdout.readline, b'')) return self.mapPartitions(func) def foreach(self, f): """ Applies a function to all elements of this RDD. - >>> def f(x): print x + >>> def f(x): print(x) >>> sc.parallelize([1, 2, 3, 4, 5]).foreach(f) """ def processPartition(iterator): @@ -666,7 +726,7 @@ def foreachPartition(self, f): >>> def f(iterator): ... for x in iterator: - ... print x + ... print(x) >>> sc.parallelize([1, 2, 3, 4, 5]).foreachPartition(f) """ def func(it): @@ -682,21 +742,8 @@ def collect(self): Return a list that contains all of the elements in this RDD. """ with SCCallSiteSync(self.context) as css: - bytesInJava = self._jrdd.collect().iterator() - return list(self._collect_iterator_through_file(bytesInJava)) - - def _collect_iterator_through_file(self, iterator): - # Transferring lots of data through Py4J can be slow because - # socket.readline() is inefficient. Instead, we'll dump the data to a - # file and read it back. - tempFile = NamedTemporaryFile(delete=False, dir=self.ctx._temp_dir) - tempFile.close() - self.ctx._writeToFile(iterator, tempFile.name) - # Read the data into Python and deserialize it: - with open(tempFile.name, 'rb') as tempFile: - for item in self._jrdd_deserializer.load_stream(tempFile): - yield item - os.unlink(tempFile.name) + port = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd()) + return list(_load_from_socket(port, self._jrdd_deserializer)) def reduce(self, f): """ @@ -853,7 +900,7 @@ def aggregatePartition(iterator): # aggregation. while numPartitions > scale + numPartitions / scale: numPartitions /= scale - curNumPartitions = numPartitions + curNumPartitions = int(numPartitions) def mapPartition(i, iterator): for obj in iterator: @@ -963,7 +1010,7 @@ def histogram(self, buckets): (('a', 'b', 'c'), [2, 2]) """ - if isinstance(buckets, (int, long)): + if isinstance(buckets, int): if buckets < 1: raise ValueError("number of buckets must be >= 1") @@ -999,6 +1046,7 @@ def minmax(a, b): raise ValueError("Can not generate buckets with infinite value") # keep them as integer if possible + inc = int(inc) if inc * buckets != maxv - minv: inc = (maxv - minv) * 1.0 / buckets @@ -1116,7 +1164,7 @@ def countPartition(iterator): yield counts def mergeMaps(m1, m2): - for k, v in m2.iteritems(): + for k, v in m2.items(): m1[k] += v return m1 return self.mapPartitions(countPartition).reduce(mergeMaps) @@ -1176,7 +1224,7 @@ def take(self, num): [91, 92, 93] """ items = [] - totalParts = self._jrdd.partitions().size() + totalParts = self.getNumPartitions() partsScanned = 0 while len(items) < num and partsScanned < totalParts: @@ -1239,7 +1287,7 @@ def isEmpty(self): >>> sc.parallelize([1]).isEmpty() False """ - return self._jrdd.partitions().size() == 0 or len(self.take(1)) == 0 + return self.getNumPartitions() == 0 or len(self.take(1)) == 0 def saveAsNewAPIHadoopDataset(self, conf, keyConverter=None, valueConverter=None): """ @@ -1357,8 +1405,8 @@ def saveAsPickleFile(self, path, batchSize=10): >>> tmpFile = NamedTemporaryFile(delete=True) >>> tmpFile.close() >>> sc.parallelize([1, 2, 'spark', 'rdd']).saveAsPickleFile(tmpFile.name, 3) - >>> sorted(sc.pickleFile(tmpFile.name, 5).collect()) - [1, 2, 'rdd', 'spark'] + >>> sorted(sc.pickleFile(tmpFile.name, 5).map(str).collect()) + ['1', '2', 'rdd', 'spark'] """ if batchSize == 0: ser = AutoBatchedSerializer(PickleSerializer()) @@ -1366,6 +1414,7 @@ def saveAsPickleFile(self, path, batchSize=10): ser = BatchedSerializer(PickleSerializer(), batchSize) self._reserialize(ser)._jrdd.saveAsObjectFile(path) + @ignore_unicode_prefix def saveAsTextFile(self, path, compressionCodecClass=None): """ Save this RDD as a text file, using string representations of elements. @@ -1397,12 +1446,13 @@ def saveAsTextFile(self, path, compressionCodecClass=None): >>> codec = "org.apache.hadoop.io.compress.GzipCodec" >>> sc.parallelize(['foo', 'bar']).saveAsTextFile(tempFile3.name, codec) >>> from fileinput import input, hook_compressed - >>> ''.join(sorted(input(glob(tempFile3.name + "/part*.gz"), openhook=hook_compressed))) - 'bar\\nfoo\\n' + >>> result = sorted(input(glob(tempFile3.name + "/part*.gz"), openhook=hook_compressed)) + >>> b''.join(result).decode('utf-8') + u'bar\\nfoo\\n' """ def func(split, iterator): for x in iterator: - if not isinstance(x, basestring): + if not isinstance(x, (unicode, bytes)): x = unicode(x) if isinstance(x, unicode): x = x.encode("utf-8") @@ -1437,7 +1487,7 @@ def keys(self): >>> m.collect() [1, 3] """ - return self.map(lambda (k, v): k) + return self.map(lambda x: x[0]) def values(self): """ @@ -1447,7 +1497,7 @@ def values(self): >>> m.collect() [2, 4] """ - return self.map(lambda (k, v): v) + return self.map(lambda x: x[1]) def reduceByKey(self, func, numPartitions=None): """ @@ -1486,7 +1536,7 @@ def reducePartition(iterator): yield m def mergeMaps(m1, m2): - for k, v in m2.iteritems(): + for k, v in m2.items(): m1[k] = func(m1[k], v) if k in m1 else v return m1 return self.mapPartitions(reducePartition).reduce(mergeMaps) @@ -1583,11 +1633,14 @@ def partitionBy(self, numPartitions, partitionFunc=portable_hash): >>> pairs = sc.parallelize([1, 2, 3, 4, 2, 4, 1]).map(lambda x: (x, x)) >>> sets = pairs.partitionBy(2).glom().collect() - >>> set(sets[0]).intersection(set(sets[1])) - set([]) + >>> len(set(sets[0]).intersection(set(sets[1]))) + 0 """ if numPartitions is None: numPartitions = self._defaultReducePartitions() + partitioner = Partitioner(numPartitions, partitionFunc) + if self.partitioner == partitioner: + return self # Transferring O(n) objects to Java is too expensive. # Instead, we'll form the hash buckets in Python, @@ -1613,37 +1666,35 @@ def add_shuffle_key(split, iterator): if (c % 1000 == 0 and get_used_memory() > limit or c > batch): n, size = len(buckets), 0 - for split in buckets.keys(): + for split in list(buckets.keys()): yield pack_long(split) d = outputSerializer.dumps(buckets[split]) del buckets[split] yield d size += len(d) - avg = (size / n) >> 20 + avg = int(size / n) >> 20 # let 1M < avg < 10M if avg < 1: batch *= 1.5 elif avg > 10: - batch = max(batch / 1.5, 1) + batch = max(int(batch / 1.5), 1) c = 0 - for split, items in buckets.iteritems(): + for split, items in buckets.items(): yield pack_long(split) yield outputSerializer.dumps(items) - keyed = self.mapPartitionsWithIndex(add_shuffle_key) + keyed = self.mapPartitionsWithIndex(add_shuffle_key, preservesPartitioning=True) keyed._bypass_serializer = True with SCCallSiteSync(self.context) as css: pairRDD = self.ctx._jvm.PairwiseRDD( keyed._jrdd.rdd()).asJavaPairRDD() - partitioner = self.ctx._jvm.PythonPartitioner(numPartitions, - id(partitionFunc)) - jrdd = pairRDD.partitionBy(partitioner).values() + jpartitioner = self.ctx._jvm.PythonPartitioner(numPartitions, + id(partitionFunc)) + jrdd = self.ctx._jvm.PythonRDD.valueOfPair(pairRDD.partitionBy(jpartitioner)) rdd = RDD(jrdd, self.ctx, BatchedSerializer(outputSerializer)) - # This is required so that id(partitionFunc) remains unique, - # even if partitionFunc is a lambda: - rdd._partitionFunc = partitionFunc + rdd.partitioner = partitioner return rdd # TODO: add control over map-side aggregation @@ -1677,28 +1728,26 @@ def combineByKey(self, createCombiner, mergeValue, mergeCombiners, numPartitions = self._defaultReducePartitions() serializer = self.ctx.serializer - spill = (self.ctx._conf.get("spark.shuffle.spill", 'True').lower() - == 'true') - memory = _parse_memory(self.ctx._conf.get( - "spark.python.worker.memory", "512m")) + spill = self._can_spill() + memory = self._memory_limit() agg = Aggregator(createCombiner, mergeValue, mergeCombiners) def combineLocally(iterator): merger = ExternalMerger(agg, memory * 0.9, serializer) \ if spill else InMemoryMerger(agg) merger.mergeValues(iterator) - return merger.iteritems() + return merger.items() - locally_combined = self.mapPartitions(combineLocally) + locally_combined = self.mapPartitions(combineLocally, preservesPartitioning=True) shuffled = locally_combined.partitionBy(numPartitions) def _mergeCombiners(iterator): merger = ExternalMerger(agg, memory, serializer) \ if spill else InMemoryMerger(agg) merger.mergeCombiners(iterator) - return merger.iteritems() + return merger.items() - return shuffled.mapPartitions(_mergeCombiners, True) + return shuffled.mapPartitions(_mergeCombiners, preservesPartitioning=True) def aggregateByKey(self, zeroValue, seqFunc, combFunc, numPartitions=None): """ @@ -1725,7 +1774,7 @@ def foldByKey(self, zeroValue, func, numPartitions=None): >>> rdd = sc.parallelize([("a", 1), ("b", 1), ("a", 1)]) >>> from operator import add - >>> rdd.foldByKey(0, add).collect() + >>> sorted(rdd.foldByKey(0, add).collect()) [('a', 2), ('b', 1)] """ def createZero(): @@ -1733,21 +1782,28 @@ def createZero(): return self.combineByKey(lambda v: func(createZero(), v), func, func, numPartitions) + def _can_spill(self): + return self.ctx._conf.get("spark.shuffle.spill", "True").lower() == "true" + + def _memory_limit(self): + return _parse_memory(self.ctx._conf.get("spark.python.worker.memory", "512m")) + # TODO: support variant with custom partitioner def groupByKey(self, numPartitions=None): """ Group the values for each key in the RDD into a single sequence. - Hash-partitions the resulting RDD with into numPartitions partitions. + Hash-partitions the resulting RDD with numPartitions partitions. Note: If you are grouping in order to perform an aggregation (such as a sum or average) over each key, using reduceByKey or aggregateByKey will provide much better performance. - >>> x = sc.parallelize([("a", 1), ("b", 1), ("a", 1)]) - >>> map((lambda (x,y): (x, list(y))), sorted(x.groupByKey().collect())) + >>> rdd = sc.parallelize([("a", 1), ("b", 1), ("a", 1)]) + >>> sorted(rdd.groupByKey().mapValues(len).collect()) + [('a', 2), ('b', 1)] + >>> sorted(rdd.groupByKey().mapValues(list).collect()) [('a', [1, 1]), ('b', [1])] """ - def createCombiner(x): return [x] @@ -1759,8 +1815,27 @@ def mergeCombiners(a, b): a.extend(b) return a - return self.combineByKey(createCombiner, mergeValue, mergeCombiners, - numPartitions).mapValues(lambda x: ResultIterable(x)) + spill = self._can_spill() + memory = self._memory_limit() + serializer = self._jrdd_deserializer + agg = Aggregator(createCombiner, mergeValue, mergeCombiners) + + def combine(iterator): + merger = ExternalMerger(agg, memory * 0.9, serializer) \ + if spill else InMemoryMerger(agg) + merger.mergeValues(iterator) + return merger.items() + + locally_combined = self.mapPartitions(combine, preservesPartitioning=True) + shuffled = locally_combined.partitionBy(numPartitions) + + def groupByKey(it): + merger = ExternalGroupBy(agg, memory, serializer)\ + if spill else InMemoryMerger(agg) + merger.mergeCombiners(it) + return merger.items() + + return shuffled.mapPartitions(groupByKey, True).mapValues(ResultIterable) def flatMapValues(self, f): """ @@ -1773,7 +1848,7 @@ def flatMapValues(self, f): >>> x.flatMapValues(f).collect() [('a', 'x'), ('a', 'y'), ('a', 'z'), ('b', 'p'), ('b', 'r')] """ - flat_map_fn = lambda (k, v): ((k, x) for x in f(v)) + flat_map_fn = lambda kv: ((kv[0], x) for x in f(kv[1])) return self.flatMap(flat_map_fn, preservesPartitioning=True) def mapValues(self, f): @@ -1787,7 +1862,7 @@ def mapValues(self, f): >>> x.mapValues(f).collect() [('a', 3), ('b', 1)] """ - map_values_fn = lambda (k, v): (k, f(v)) + map_values_fn = lambda kv: (kv[0], f(kv[1])) return self.map(map_values_fn, preservesPartitioning=True) def groupWith(self, other, *others): @@ -1798,8 +1873,7 @@ def groupWith(self, other, *others): >>> x = sc.parallelize([("a", 1), ("b", 4)]) >>> y = sc.parallelize([("a", 2)]) >>> z = sc.parallelize([("b", 42)]) - >>> map((lambda (x,y): (x, (list(y[0]), list(y[1]), list(y[2]), list(y[3])))), \ - sorted(list(w.groupWith(x, y, z).collect()))) + >>> [(x, tuple(map(list, y))) for x, y in sorted(list(w.groupWith(x, y, z).collect()))] [('a', ([5], [1], [2], [])), ('b', ([6], [4], [], [42]))] """ @@ -1814,7 +1888,7 @@ def cogroup(self, other, numPartitions=None): >>> x = sc.parallelize([("a", 1), ("b", 4)]) >>> y = sc.parallelize([("a", 2)]) - >>> map((lambda (x,y): (x, (list(y[0]), list(y[1])))), sorted(list(x.cogroup(y).collect()))) + >>> [(x, tuple(map(list, y))) for x, y in sorted(list(x.cogroup(y).collect()))] [('a', ([1], [2])), ('b', ([4], []))] """ return python_cogroup((self, other), numPartitions) @@ -1850,8 +1924,9 @@ def subtractByKey(self, other, numPartitions=None): >>> sorted(x.subtractByKey(y).collect()) [('b', 4), ('b', 5)] """ - def filter_func((key, vals)): - return vals[0] and not vals[1] + def filter_func(pair): + key, (val1, val2) = pair + return val1 and not val2 return self.cogroup(other, numPartitions).filter(filter_func).flatMapValues(lambda x: x[0]) def subtract(self, other, numPartitions=None): @@ -1873,8 +1948,8 @@ def keyBy(self, f): >>> x = sc.parallelize(range(0,3)).keyBy(lambda x: x*x) >>> y = sc.parallelize(zip(range(0,5), range(0,5))) - >>> map((lambda (x,y): (x, (list(y[0]), (list(y[1]))))), sorted(x.cogroup(y).collect())) - [(0, ([0], [0])), (1, ([1], [1])), (2, ([], [2])), (3, ([], [3])), (4, ([2], [4]))] + >>> [(x, list(map(list, y))) for x, y in sorted(x.cogroup(y).collect())] + [(0, [[0], [0]]), (1, [[1], [1]]), (2, [[], [2]]), (3, [[], [3]]), (4, [[2], [4]])] """ return self.map(lambda x: (f(x), x)) @@ -1933,7 +2008,7 @@ def batch_as(rdd, batchSize): my_batch = get_batch_size(self._jrdd_deserializer) other_batch = get_batch_size(other._jrdd_deserializer) - if my_batch != other_batch: + if my_batch != other_batch or not my_batch: # use the smallest batchSize for both of them batchSize = min(my_batch, other_batch) if batchSize <= 0: @@ -2003,17 +2078,18 @@ def name(self): """ Return the name of this RDD. """ - name_ = self._jrdd.name() - if name_: - return name_.encode('utf-8') + n = self._jrdd.name() + if n: + return n + @ignore_unicode_prefix def setName(self, name): """ Assign a name to this RDD. - >>> rdd1 = sc.parallelize([1,2]) + >>> rdd1 = sc.parallelize([1, 2]) >>> rdd1.setName('RDD1').name() - 'RDD1' + u'RDD1' """ self._jrdd.setName(name) return self @@ -2075,10 +2151,10 @@ def lookup(self, key): >>> sorted.lookup(1024) [] """ - values = self.filter(lambda (k, v): k == key).values() + values = self.filter(lambda kv: kv[0] == key).values() - if self._partitionFunc is not None: - return self.ctx.runJob(values, lambda x: x, [self._partitionFunc(key)], False) + if self.partitioner is not None: + return self.ctx.runJob(values, lambda x: x, [self.partitioner(key)], False) return values.collect() @@ -2094,6 +2170,7 @@ def _to_java_object_rdd(self): def countApprox(self, timeout, confidence=0.95): """ .. note:: Experimental + Approximate version of count() that returns a potentially incomplete result within a timeout, even if not all tasks have finished. @@ -2107,11 +2184,12 @@ def countApprox(self, timeout, confidence=0.95): def sumApprox(self, timeout, confidence=0.95): """ .. note:: Experimental + Approximate operation to return the sum within a timeout or meet the confidence. >>> rdd = sc.parallelize(range(1000), 10) - >>> r = sum(xrange(1000)) + >>> r = sum(range(1000)) >>> (rdd.sumApprox(1000) - r) / r < 0.05 True """ @@ -2123,11 +2201,12 @@ def sumApprox(self, timeout, confidence=0.95): def meanApprox(self, timeout, confidence=0.95): """ .. note:: Experimental + Approximate operation to return the mean within a timeout or meet the confidence. >>> rdd = sc.parallelize(range(1000), 10) - >>> r = sum(xrange(1000)) / 1000.0 + >>> r = sum(range(1000)) / 1000.0 >>> (rdd.meanApprox(1000) - r) / r < 0.05 True """ @@ -2139,6 +2218,7 @@ def meanApprox(self, timeout, confidence=0.95): def countApproxDistinct(self, relativeSD=0.05): """ .. note:: Experimental + Return approximate number of distinct elements in the RDD. The algorithm used is based on streamlib's implementation of @@ -2151,10 +2231,10 @@ def countApproxDistinct(self, relativeSD=0.05): It must be greater than 0.000017. >>> n = sc.parallelize(range(1000)).map(str).countApproxDistinct() - >>> 950 < n < 1050 + >>> 900 < n < 1100 True >>> n = sc.parallelize([i % 20 for i in range(1000)]).countApproxDistinct() - >>> 18 < n < 22 + >>> 16 < n < 24 True """ if relativeSD < 0.000017: @@ -2173,8 +2253,7 @@ def toLocalIterator(self): >>> [x for x in rdd.toLocalIterator()] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] """ - partitions = xrange(self.getNumPartitions()) - for partition in partitions: + for partition in range(self.getNumPartitions()): rows = self.context.runJob(self, lambda x: x, [partition]) for row in rows: yield row @@ -2183,13 +2262,11 @@ def toLocalIterator(self): def _prepare_for_python_RDD(sc, command, obj=None): # the serialized command will be compressed by broadcast ser = CloudPickleSerializer() - pickled_command = ser.dumps(command) + pickled_command = ser.dumps((command, sys.version_info[:2])) if len(pickled_command) > (1 << 20): # 1M + # The broadcast will have same life cycle as created PythonRDD broadcast = sc.broadcast(pickled_command) pickled_command = ser.dumps(broadcast) - # tracking the life cycle by obj - if obj is not None: - obj._broadcast = broadcast broadcast_vars = ListConverter().convert( [x._jbroadcast for x in sc._pickled_broadcast_vars], sc._gateway._gateway_client) @@ -2243,13 +2320,10 @@ def pipeline_func(split, iterator): self._id = None self._jrdd_deserializer = self.ctx.serializer self._bypass_serializer = False - self._partitionFunc = prev._partitionFunc if self.preservesPartitioning else None - self._broadcast = None + self.partitioner = prev.partitioner if self.preservesPartitioning else None - def __del__(self): - if self._broadcast: - self._broadcast.unpersist() - self._broadcast = None + def getNumPartitions(self): + return self._prev_jrdd.partitions().size() @property def _jrdd(self): diff --git a/python/pyspark/rddsampler.py b/python/pyspark/rddsampler.py index 459e1427803cb..fe8f87324804b 100644 --- a/python/pyspark/rddsampler.py +++ b/python/pyspark/rddsampler.py @@ -23,7 +23,7 @@ class RDDSamplerBase(object): def __init__(self, withReplacement, seed=None): - self._seed = seed if seed is not None else random.randint(0, sys.maxint) + self._seed = seed if seed is not None else random.randint(0, sys.maxsize) self._withReplacement = withReplacement self._random = None @@ -31,7 +31,7 @@ def initRandomGenerator(self, split): self._random = random.Random(self._seed ^ split) # mixing because the initial seeds are close to each other - for _ in xrange(10): + for _ in range(10): self._random.randint(0, 1) def getUniformSample(self): diff --git a/python/pyspark/resultiterable.py b/python/pyspark/resultiterable.py index ef04c82866e6c..1ab5ce14c3531 100644 --- a/python/pyspark/resultiterable.py +++ b/python/pyspark/resultiterable.py @@ -15,15 +15,16 @@ # limitations under the License. # -__all__ = ["ResultIterable"] - import collections +__all__ = ["ResultIterable"] + class ResultIterable(collections.Iterable): """ - A special result iterable. This is used because the standard iterator can not be pickled + A special result iterable. This is used because the standard + iterator can not be pickled """ def __init__(self, data): diff --git a/python/pyspark/serializers.py b/python/pyspark/serializers.py index 0ffb41d02f6f6..d8cdcda3a3783 100644 --- a/python/pyspark/serializers.py +++ b/python/pyspark/serializers.py @@ -49,16 +49,24 @@ >>> sc.stop() """ -import cPickle -from itertools import chain, izip, product +import sys +from itertools import chain, product import marshal import struct -import sys import types import collections import zlib import itertools +if sys.version < '3': + import cPickle as pickle + protocol = 2 + from itertools import izip as zip +else: + import pickle + protocol = 3 + xrange = range + from pyspark import cloudpickle @@ -97,7 +105,7 @@ def _load_stream_without_unbatching(self, stream): # subclasses should override __eq__ as appropriate. def __eq__(self, other): - return isinstance(other, self.__class__) + return isinstance(other, self.__class__) and self.__dict__ == other.__dict__ def __ne__(self, other): return not self.__eq__(other) @@ -212,14 +220,33 @@ def load_stream(self, stream): def _load_stream_without_unbatching(self, stream): return self.serializer.load_stream(stream) - def __eq__(self, other): - return (isinstance(other, BatchedSerializer) and - other.serializer == self.serializer and other.batchSize == self.batchSize) - def __repr__(self): return "BatchedSerializer(%s, %d)" % (str(self.serializer), self.batchSize) +class FlattenedValuesSerializer(BatchedSerializer): + + """ + Serializes a stream of list of pairs, split the list of values + which contain more than a certain number of objects to make them + have similar sizes. + """ + def __init__(self, serializer, batchSize=10): + BatchedSerializer.__init__(self, serializer, batchSize) + + def _batched(self, iterator): + n = self.batchSize + for key, values in iterator: + for i in range(0, len(values), n): + yield key, values[i:i + n] + + def load_stream(self, stream): + return self.serializer.load_stream(stream) + + def __repr__(self): + return "FlattenedValuesSerializer(%s, %d)" % (self.serializer, self.batchSize) + + class AutoBatchedSerializer(BatchedSerializer): """ Choose the size of batch automatically based on the size of object @@ -247,12 +274,8 @@ def dump_stream(self, iterator, stream): elif size > best * 10 and batch > 1: batch /= 2 - def __eq__(self, other): - return (isinstance(other, AutoBatchedSerializer) and - other.serializer == self.serializer and other.bestSize == self.bestSize) - - def __str__(self): - return "AutoBatchedSerializer(%s)" % str(self.serializer) + def __repr__(self): + return "AutoBatchedSerializer(%s)" % self.serializer class CartesianDeserializer(FramedSerializer): @@ -262,6 +285,7 @@ class CartesianDeserializer(FramedSerializer): """ def __init__(self, key_ser, val_ser): + FramedSerializer.__init__(self) self.key_ser = key_ser self.val_ser = val_ser @@ -270,7 +294,7 @@ def prepare_keys_values(self, stream): val_stream = self.val_ser._load_stream_without_unbatching(stream) key_is_batched = isinstance(self.key_ser, BatchedSerializer) val_is_batched = isinstance(self.val_ser, BatchedSerializer) - for (keys, vals) in izip(key_stream, val_stream): + for (keys, vals) in zip(key_stream, val_stream): keys = keys if key_is_batched else [keys] vals = vals if val_is_batched else [vals] yield (keys, vals) @@ -280,10 +304,6 @@ def load_stream(self, stream): for pair in product(keys, vals): yield pair - def __eq__(self, other): - return (isinstance(other, CartesianDeserializer) and - self.key_ser == other.key_ser and self.val_ser == other.val_ser) - def __repr__(self): return "CartesianDeserializer(%s, %s)" % \ (str(self.key_ser), str(self.val_ser)) @@ -295,22 +315,14 @@ class PairDeserializer(CartesianDeserializer): Deserializes the JavaRDD zip() of two PythonRDDs. """ - def __init__(self, key_ser, val_ser): - self.key_ser = key_ser - self.val_ser = val_ser - def load_stream(self, stream): for (keys, vals) in self.prepare_keys_values(stream): if len(keys) != len(vals): raise ValueError("Can not deserialize RDD with different number of items" " in pair: (%d, %d)" % (len(keys), len(vals))) - for pair in izip(keys, vals): + for pair in zip(keys, vals): yield pair - def __eq__(self, other): - return (isinstance(other, PairDeserializer) and - self.key_ser == other.key_ser and self.val_ser == other.val_ser) - def __repr__(self): return "PairDeserializer(%s, %s)" % (str(self.key_ser), str(self.val_ser)) @@ -359,8 +371,8 @@ def _hijack_namedtuple(): global _old_namedtuple # or it will put in closure def _copy_func(f): - return types.FunctionType(f.func_code, f.func_globals, f.func_name, - f.func_defaults, f.func_closure) + return types.FunctionType(f.__code__, f.__globals__, f.__name__, + f.__defaults__, f.__closure__) _old_namedtuple = _copy_func(collections.namedtuple) @@ -369,15 +381,15 @@ def namedtuple(*args, **kwargs): return _hack_namedtuple(cls) # replace namedtuple with new one - collections.namedtuple.func_globals["_old_namedtuple"] = _old_namedtuple - collections.namedtuple.func_globals["_hack_namedtuple"] = _hack_namedtuple - collections.namedtuple.func_code = namedtuple.func_code + collections.namedtuple.__globals__["_old_namedtuple"] = _old_namedtuple + collections.namedtuple.__globals__["_hack_namedtuple"] = _hack_namedtuple + collections.namedtuple.__code__ = namedtuple.__code__ collections.namedtuple.__hijack = 1 # hack the cls already generated by namedtuple # those created in other module can be pickled as normal, # so only hack those in __main__ module - for n, o in sys.modules["__main__"].__dict__.iteritems(): + for n, o in sys.modules["__main__"].__dict__.items(): if (type(o) is type and o.__base__ is tuple and hasattr(o, "_fields") and "__reduce__" not in o.__dict__): @@ -390,7 +402,7 @@ def namedtuple(*args, **kwargs): class PickleSerializer(FramedSerializer): """ - Serializes objects using Python's cPickle serializer: + Serializes objects using Python's pickle serializer: http://docs.python.org/2/library/pickle.html @@ -399,10 +411,14 @@ class PickleSerializer(FramedSerializer): """ def dumps(self, obj): - return cPickle.dumps(obj, 2) + return pickle.dumps(obj, protocol) - def loads(self, obj): - return cPickle.loads(obj) + if sys.version >= '3': + def loads(self, obj, encoding="bytes"): + return pickle.loads(obj, encoding=encoding) + else: + def loads(self, obj, encoding=None): + return pickle.loads(obj) class CloudPickleSerializer(PickleSerializer): @@ -431,7 +447,7 @@ def loads(self, obj): class AutoSerializer(FramedSerializer): """ - Choose marshal or cPickle as serialization protocol automatically + Choose marshal or pickle as serialization protocol automatically """ def __init__(self): @@ -440,19 +456,19 @@ def __init__(self): def dumps(self, obj): if self._type is not None: - return 'P' + cPickle.dumps(obj, -1) + return b'P' + pickle.dumps(obj, -1) try: - return 'M' + marshal.dumps(obj) + return b'M' + marshal.dumps(obj) except Exception: - self._type = 'P' - return 'P' + cPickle.dumps(obj, -1) + self._type = b'P' + return b'P' + pickle.dumps(obj, -1) def loads(self, obj): _type = obj[0] - if _type == 'M': + if _type == b'M': return marshal.loads(obj[1:]) - elif _type == 'P': - return cPickle.loads(obj[1:]) + elif _type == b'P': + return pickle.loads(obj[1:]) else: raise ValueError("invalid sevialization type: %s" % _type) @@ -472,8 +488,8 @@ def dumps(self, obj): def loads(self, obj): return self.serializer.loads(zlib.decompress(obj)) - def __eq__(self, other): - return isinstance(other, CompressedSerializer) and self.serializer == other.serializer + def __repr__(self): + return "CompressedSerializer(%s)" % self.serializer class UTF8Deserializer(Serializer): @@ -482,7 +498,7 @@ class UTF8Deserializer(Serializer): Deserializes streams written by String.getBytes. """ - def __init__(self, use_unicode=False): + def __init__(self, use_unicode=True): self.use_unicode = use_unicode def loads(self, stream): @@ -503,13 +519,13 @@ def load_stream(self, stream): except EOFError: return - def __eq__(self, other): - return isinstance(other, UTF8Deserializer) and self.use_unicode == other.use_unicode + def __repr__(self): + return "UTF8Deserializer(%s)" % self.use_unicode def read_long(stream): length = stream.read(8) - if length == "": + if not length: raise EOFError return struct.unpack("!q", length)[0] @@ -524,7 +540,7 @@ def pack_long(value): def read_int(stream): length = stream.read(4) - if length == "": + if not length: raise EOFError return struct.unpack("!i", length)[0] diff --git a/python/pyspark/shell.py b/python/pyspark/shell.py index 89cf76920e353..144cdf0b0cdd5 100644 --- a/python/pyspark/shell.py +++ b/python/pyspark/shell.py @@ -21,23 +21,21 @@ This file is designed to be launched as a PYTHONSTARTUP script. """ -import sys -if sys.version_info[0] != 2: - print("Error: Default Python used is Python%s" % sys.version_info.major) - print("\tSet env variable PYSPARK_PYTHON to Python2 binary and re-run it.") - sys.exit(1) - - import atexit import os import platform + +import py4j + import pyspark from pyspark.context import SparkContext +from pyspark.sql import SQLContext, HiveContext from pyspark.storagelevel import StorageLevel -# this is the equivalent of ADD_JARS -add_files = (os.environ.get("ADD_FILES").split(',') - if os.environ.get("ADD_FILES") is not None else None) +# this is the deprecated equivalent of ADD_JARS +add_files = None +if os.environ.get("ADD_FILES") is not None: + add_files = os.environ.get("ADD_FILES").split(',') if os.environ.get("SPARK_EXECUTOR_URI"): SparkContext.setSystemProperty("spark.executor.uri", os.environ["SPARK_EXECUTOR_URI"]) @@ -45,6 +43,18 @@ sc = SparkContext(appName="PySparkShell", pyFiles=add_files) atexit.register(lambda: sc.stop()) +try: + # Try to access HiveConf, it will raise exception if Hive is not added + sc._jvm.org.apache.hadoop.hive.conf.HiveConf() + sqlContext = HiveContext(sc) +except py4j.protocol.Py4JError: + sqlContext = SQLContext(sc) +except TypeError: + sqlContext = SQLContext(sc) + +# for compatibility +sqlCtx = sqlContext + print("""Welcome to ____ __ / __/__ ___ _____/ /__ @@ -56,9 +66,10 @@ platform.python_version(), platform.python_build()[0], platform.python_build()[1])) -print("SparkContext available as sc.") +print("SparkContext available as sc, %s available as sqlContext." % sqlContext.__class__.__name__) if add_files is not None: + print("Warning: ADD_FILES environment variable is deprecated, use --py-files argument instead") print("Adding files: [%s]" % ", ".join(add_files)) # The ./bin/pyspark script stores the old PYTHONSTARTUP value in OLD_PYTHONSTARTUP, diff --git a/python/pyspark/shuffle.py b/python/pyspark/shuffle.py index 10a7ccd502000..b54baa57ec28a 100644 --- a/python/pyspark/shuffle.py +++ b/python/pyspark/shuffle.py @@ -16,28 +16,35 @@ # import os -import sys import platform import shutil import warnings import gc import itertools +import operator import random import pyspark.heapq3 as heapq -from pyspark.serializers import AutoBatchedSerializer, PickleSerializer +from pyspark.serializers import BatchedSerializer, PickleSerializer, FlattenedValuesSerializer, \ + CompressedSerializer, AutoBatchedSerializer + try: import psutil + process = None + def get_used_memory(): """ Return the used memory in MB """ - process = psutil.Process(os.getpid()) + global process + if process is None or process._pid != os.getpid(): + process = psutil.Process(os.getpid()) if hasattr(process, "memory_info"): info = process.memory_info() else: info = process.get_memory_info() return info.rss >> 20 + except ImportError: def get_used_memory(): @@ -46,6 +53,7 @@ def get_used_memory(): for line in open('/proc/self/status'): if line.startswith('VmRSS:'): return int(line.split()[1]) >> 10 + else: warnings.warn("Please install psutil to have better " "support with spilling") @@ -54,6 +62,7 @@ def get_used_memory(): rss = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss return rss >> 20 # TODO: support windows + return 0 @@ -69,8 +78,8 @@ def _get_local_dirs(sub): # global stats -MemoryBytesSpilled = 0L -DiskBytesSpilled = 0L +MemoryBytesSpilled = 0 +DiskBytesSpilled = 0 class Aggregator(object): @@ -117,7 +126,7 @@ def mergeCombiners(self, iterator): """ Merge the combined items by mergeCombiner """ raise NotImplementedError - def iteritems(self): + def items(self): """ Return the merged items ad iterator """ raise NotImplementedError @@ -147,9 +156,15 @@ def mergeCombiners(self, iterator): for k, v in iterator: d[k] = comb(d[k], v) if k in d else v - def iteritems(self): + def items(self): """ Return the merged items ad iterator """ - return self.data.iteritems() + return iter(self.data.items()) + + +def _compressed_serializer(self, serializer=None): + # always use PickleSerializer to simplify implementation + ser = PickleSerializer() + return AutoBatchedSerializer(CompressedSerializer(ser)) class ExternalMerger(Merger): @@ -173,7 +188,7 @@ class ExternalMerger(Merger): dict. Repeat this again until combine all the items. - Before return any items, it will load each partition and - combine them seperately. Yield them before loading next + combine them separately. Yield them before loading next partition. - During loading a partition, if the memory goes over limit, @@ -182,7 +197,7 @@ class ExternalMerger(Merger): `data` and `pdata` are used to hold the merged items in memory. At first, all the data are merged into `data`. Once the used - memory goes over limit, the items in `data` are dumped indo + memory goes over limit, the items in `data` are dumped into disks, `data` will be cleared, all rest of items will be merged into `pdata` and then dumped into disks. Before returning, all the items in `pdata` will be dumped into disks. @@ -193,16 +208,16 @@ class ExternalMerger(Merger): >>> agg = SimpleAggregator(lambda x, y: x + y) >>> merger = ExternalMerger(agg, 10) >>> N = 10000 - >>> merger.mergeValues(zip(xrange(N), xrange(N)) * 10) + >>> merger.mergeValues(zip(range(N), range(N))) >>> assert merger.spills > 0 - >>> sum(v for k,v in merger.iteritems()) - 499950000 + >>> sum(v for k,v in merger.items()) + 49995000 >>> merger = ExternalMerger(agg, 10) - >>> merger.mergeCombiners(zip(xrange(N), xrange(N)) * 10) + >>> merger.mergeCombiners(zip(range(N), range(N))) >>> assert merger.spills > 0 - >>> sum(v for k,v in merger.iteritems()) - 499950000 + >>> sum(v for k,v in merger.items()) + 49995000 """ # the max total partitions created recursively @@ -212,8 +227,7 @@ def __init__(self, aggregator, memory_limit=512, serializer=None, localdirs=None, scale=1, partitions=59, batch=1000): Merger.__init__(self, aggregator) self.memory_limit = memory_limit - # default serializer is only used for tests - self.serializer = serializer or AutoBatchedSerializer(PickleSerializer()) + self.serializer = _compressed_serializer(serializer) self.localdirs = localdirs or _get_local_dirs(str(id(self))) # number of partitions when spill data into disks self.partitions = partitions @@ -221,7 +235,7 @@ def __init__(self, aggregator, memory_limit=512, serializer=None, self.batch = batch # scale is used to scale down the hash of key for recursive hash map self.scale = scale - # unpartitioned merged data + # un-partitioned merged data self.data = {} # partitioned merged data, list of dicts self.pdata = [] @@ -244,72 +258,63 @@ def _next_limit(self): def mergeValues(self, iterator): """ Combine the items by creator and combiner """ - iterator = iter(iterator) # speedup attribute lookup creator, comb = self.agg.createCombiner, self.agg.mergeValue - d, c, batch = self.data, 0, self.batch + c, data, pdata, hfun, batch = 0, self.data, self.pdata, self._partition, self.batch + limit = self.memory_limit for k, v in iterator: + d = pdata[hfun(k)] if pdata else data d[k] = comb(d[k], v) if k in d else creator(v) c += 1 - if c % batch == 0 and get_used_memory() > self.memory_limit: - self._spill() - self._partitioned_mergeValues(iterator, self._next_limit()) - break + if c >= batch: + if get_used_memory() >= limit: + self._spill() + limit = self._next_limit() + batch /= 2 + c = 0 + else: + batch *= 1.5 + + if get_used_memory() >= limit: + self._spill() def _partition(self, key): """ Return the partition for key """ return hash((key, self._seed)) % self.partitions - def _partitioned_mergeValues(self, iterator, limit=0): - """ Partition the items by key, then combine them """ - # speedup attribute lookup - creator, comb = self.agg.createCombiner, self.agg.mergeValue - c, pdata, hfun, batch = 0, self.pdata, self._partition, self.batch - - for k, v in iterator: - d = pdata[hfun(k)] - d[k] = comb(d[k], v) if k in d else creator(v) - if not limit: - continue - - c += 1 - if c % batch == 0 and get_used_memory() > limit: - self._spill() - limit = self._next_limit() + def _object_size(self, obj): + """ How much of memory for this obj, assume that all the objects + consume similar bytes of memory + """ + return 1 - def mergeCombiners(self, iterator, check=True): + def mergeCombiners(self, iterator, limit=None): """ Merge (K,V) pair by mergeCombiner """ - iterator = iter(iterator) + if limit is None: + limit = self.memory_limit # speedup attribute lookup - d, comb, batch = self.data, self.agg.mergeCombiners, self.batch - c = 0 - for k, v in iterator: - d[k] = comb(d[k], v) if k in d else v - if not check: - continue - - c += 1 - if c % batch == 0 and get_used_memory() > self.memory_limit: - self._spill() - self._partitioned_mergeCombiners(iterator, self._next_limit()) - break - - def _partitioned_mergeCombiners(self, iterator, limit=0): - """ Partition the items by key, then merge them """ - comb, pdata = self.agg.mergeCombiners, self.pdata - c, hfun = 0, self._partition + comb, hfun, objsize = self.agg.mergeCombiners, self._partition, self._object_size + c, data, pdata, batch = 0, self.data, self.pdata, self.batch for k, v in iterator: - d = pdata[hfun(k)] + d = pdata[hfun(k)] if pdata else data d[k] = comb(d[k], v) if k in d else v if not limit: continue - c += 1 - if c % self.batch == 0 and get_used_memory() > limit: - self._spill() - limit = self._next_limit() + c += objsize(v) + if c > batch: + if get_used_memory() > limit: + self._spill() + limit = self._next_limit() + batch /= 2 + c = 0 + else: + batch *= 1.5 + + if limit and get_used_memory() >= limit: + self._spill() def _spill(self): """ @@ -330,12 +335,12 @@ def _spill(self): # above limit at the first time. # open all the files for writing - streams = [open(os.path.join(path, str(i)), 'w') + streams = [open(os.path.join(path, str(i)), 'wb') for i in range(self.partitions)] - for k, v in self.data.iteritems(): + for k, v in self.data.items(): h = self._partition(k) - # put one item in batch, make it compatitable with load_stream + # put one item in batch, make it compatible with load_stream # it will increase the memory if dump them in batch self.serializer.dump_stream([(k, v)], streams[h]) @@ -344,14 +349,14 @@ def _spill(self): s.close() self.data.clear() - self.pdata = [{} for i in range(self.partitions)] + self.pdata.extend([{} for i in range(self.partitions)]) else: for i in range(self.partitions): p = os.path.join(path, str(i)) - with open(p, "w") as f: + with open(p, "wb") as f: # dump items in batch - self.serializer.dump_stream(self.pdata[i].iteritems(), f) + self.serializer.dump_stream(iter(self.pdata[i].items()), f) self.pdata[i].clear() DiskBytesSpilled += os.path.getsize(p) @@ -359,10 +364,10 @@ def _spill(self): gc.collect() # release the memory as much as possible MemoryBytesSpilled += (used_memory - get_used_memory()) << 20 - def iteritems(self): + def items(self): """ Return all merged items as iterator """ if not self.pdata and not self.spills: - return self.data.iteritems() + return iter(self.data.items()) return self._external_items() def _external_items(self): @@ -370,29 +375,12 @@ def _external_items(self): assert not self.data if any(self.pdata): self._spill() - hard_limit = self._next_limit() + # disable partitioning and spilling when merge combiners from disk + self.pdata = [] try: for i in range(self.partitions): - self.data = {} - for j in range(self.spills): - path = self._get_spill_dir(j) - p = os.path.join(path, str(i)) - # do not check memory during merging - self.mergeCombiners(self.serializer.load_stream(open(p)), - False) - - # limit the total partitions - if (self.scale * self.partitions < self.MAX_TOTAL_PARTITIONS - and j < self.spills - 1 - and get_used_memory() > hard_limit): - self.data.clear() # will read from disk again - gc.collect() # release the memory as much as possible - for v in self._recursive_merged_items(i): - yield v - return - - for v in self.data.iteritems(): + for v in self._merged_items(i): yield v self.data.clear() @@ -400,53 +388,58 @@ def _external_items(self): for j in range(self.spills): path = self._get_spill_dir(j) os.remove(os.path.join(path, str(i))) - finally: self._cleanup() - def _cleanup(self): - """ Clean up all the files in disks """ - for d in self.localdirs: - shutil.rmtree(d, True) - - def _recursive_merged_items(self, start): + def _merged_items(self, index): + self.data = {} + limit = self._next_limit() + for j in range(self.spills): + path = self._get_spill_dir(j) + p = os.path.join(path, str(index)) + # do not check memory during merging + with open(p, "rb") as f: + self.mergeCombiners(self.serializer.load_stream(f), 0) + + # limit the total partitions + if (self.scale * self.partitions < self.MAX_TOTAL_PARTITIONS + and j < self.spills - 1 + and get_used_memory() > limit): + self.data.clear() # will read from disk again + gc.collect() # release the memory as much as possible + return self._recursive_merged_items(index) + + return self.data.items() + + def _recursive_merged_items(self, index): """ merge the partitioned items and return the as iterator If one partition can not be fit in memory, then them will be partitioned and merged recursively. """ - # make sure all the data are dumps into disks. - assert not self.data - if any(self.pdata): - self._spill() - assert self.spills > 0 - - for i in range(start, self.partitions): - subdirs = [os.path.join(d, "parts", str(i)) - for d in self.localdirs] - m = ExternalMerger(self.agg, self.memory_limit, self.serializer, - subdirs, self.scale * self.partitions, self.partitions) - m.pdata = [{} for _ in range(self.partitions)] - limit = self._next_limit() - - for j in range(self.spills): - path = self._get_spill_dir(j) - p = os.path.join(path, str(i)) - m._partitioned_mergeCombiners( - self.serializer.load_stream(open(p))) - - if get_used_memory() > limit: - m._spill() - limit = self._next_limit() + subdirs = [os.path.join(d, "parts", str(index)) for d in self.localdirs] + m = ExternalMerger(self.agg, self.memory_limit, self.serializer, subdirs, + self.scale * self.partitions, self.partitions, self.batch) + m.pdata = [{} for _ in range(self.partitions)] + limit = self._next_limit() + + for j in range(self.spills): + path = self._get_spill_dir(j) + p = os.path.join(path, str(index)) + with open(p, 'rb') as f: + m.mergeCombiners(self.serializer.load_stream(f), 0) + + if get_used_memory() > limit: + m._spill() + limit = self._next_limit() - for v in m._external_items(): - yield v + return m._external_items() - # remove the merged partition - for j in range(self.spills): - path = self._get_spill_dir(j) - os.remove(os.path.join(path, str(i))) + def _cleanup(self): + """ Clean up all the files in disks """ + for d in self.localdirs: + shutil.rmtree(d, True) class ExternalSorter(object): @@ -457,9 +450,10 @@ class ExternalSorter(object): The spilling will only happen when the used memory goes above the limit. + >>> sorter = ExternalSorter(1) # 1M >>> import random - >>> l = range(1024) + >>> l = list(range(1024)) >>> random.shuffle(l) >>> sorted(l) == list(sorter.sorted(l)) True @@ -469,7 +463,7 @@ class ExternalSorter(object): def __init__(self, memory_limit, serializer=None): self.memory_limit = memory_limit self.local_dirs = _get_local_dirs("sort") - self.serializer = serializer or AutoBatchedSerializer(PickleSerializer()) + self.serializer = _compressed_serializer(serializer) def _get_path(self, n): """ Choose one directory for spill by number n """ @@ -507,14 +501,22 @@ def sorted(self, iterator, key=None, reverse=False): # sort them inplace will save memory current_chunk.sort(key=key, reverse=reverse) path = self._get_path(len(chunks)) - with open(path, 'w') as f: + with open(path, 'wb') as f: self.serializer.dump_stream(current_chunk, f) - chunks.append(self.serializer.load_stream(open(path))) + + def load(f): + for v in self.serializer.load_stream(f): + yield v + # close the file explicit once we consume all the items + # to avoid ResourceWarning in Python3 + f.close() + chunks.append(load(open(path, 'rb'))) current_chunk = [] gc.collect() limit = self._next_limit() MemoryBytesSpilled += (used_memory - get_used_memory()) << 20 DiskBytesSpilled += os.path.getsize(path) + os.unlink(path) # data will be deleted after close elif not chunks: batch = min(batch * 2, 10000) @@ -529,6 +531,313 @@ def sorted(self, iterator, key=None, reverse=False): return heapq.merge(chunks, key=key, reverse=reverse) +class ExternalList(object): + """ + ExternalList can have many items which cannot be hold in memory in + the same time. + + >>> l = ExternalList(list(range(100))) + >>> len(l) + 100 + >>> l.append(10) + >>> len(l) + 101 + >>> for i in range(20240): + ... l.append(i) + >>> len(l) + 20341 + >>> import pickle + >>> l2 = pickle.loads(pickle.dumps(l)) + >>> len(l2) + 20341 + >>> list(l2)[100] + 10 + """ + LIMIT = 10240 + + def __init__(self, values): + self.values = values + self.count = len(values) + self._file = None + self._ser = None + + def __getstate__(self): + if self._file is not None: + self._file.flush() + with os.fdopen(os.dup(self._file.fileno()), "rb") as f: + f.seek(0) + serialized = f.read() + else: + serialized = b'' + return self.values, self.count, serialized + + def __setstate__(self, item): + self.values, self.count, serialized = item + if serialized: + self._open_file() + self._file.write(serialized) + else: + self._file = None + self._ser = None + + def __iter__(self): + if self._file is not None: + self._file.flush() + # read all items from disks first + with os.fdopen(os.dup(self._file.fileno()), 'rb') as f: + f.seek(0) + for v in self._ser.load_stream(f): + yield v + + for v in self.values: + yield v + + def __len__(self): + return self.count + + def append(self, value): + self.values.append(value) + self.count += 1 + # dump them into disk if the key is huge + if len(self.values) >= self.LIMIT: + self._spill() + + def _open_file(self): + dirs = _get_local_dirs("objects") + d = dirs[id(self) % len(dirs)] + if not os.path.exists(d): + os.makedirs(d) + p = os.path.join(d, str(id(self))) + self._file = open(p, "wb+", 65536) + self._ser = BatchedSerializer(CompressedSerializer(PickleSerializer()), 1024) + os.unlink(p) + + def __del__(self): + if self._file: + self._file.close() + self._file = None + + def _spill(self): + """ dump the values into disk """ + global MemoryBytesSpilled, DiskBytesSpilled + if self._file is None: + self._open_file() + + used_memory = get_used_memory() + pos = self._file.tell() + self._ser.dump_stream(self.values, self._file) + self.values = [] + gc.collect() + DiskBytesSpilled += self._file.tell() - pos + MemoryBytesSpilled += (used_memory - get_used_memory()) << 20 + + +class ExternalListOfList(ExternalList): + """ + An external list for list. + + >>> l = ExternalListOfList([[i, i] for i in range(100)]) + >>> len(l) + 200 + >>> l.append(range(10)) + >>> len(l) + 210 + >>> len(list(l)) + 210 + """ + + def __init__(self, values): + ExternalList.__init__(self, values) + self.count = sum(len(i) for i in values) + + def append(self, value): + ExternalList.append(self, value) + # already counted 1 in ExternalList.append + self.count += len(value) - 1 + + def __iter__(self): + for values in ExternalList.__iter__(self): + for v in values: + yield v + + +class GroupByKey(object): + """ + Group a sorted iterator as [(k1, it1), (k2, it2), ...] + + >>> k = [i // 3 for i in range(6)] + >>> v = [[i] for i in range(6)] + >>> g = GroupByKey(zip(k, v)) + >>> [(k, list(it)) for k, it in g] + [(0, [0, 1, 2]), (1, [3, 4, 5])] + """ + + def __init__(self, iterator): + self.iterator = iterator + + def __iter__(self): + key, values = None, None + for k, v in self.iterator: + if values is not None and k == key: + values.append(v) + else: + if values is not None: + yield (key, values) + key = k + values = ExternalListOfList([v]) + if values is not None: + yield (key, values) + + +class ExternalGroupBy(ExternalMerger): + + """ + Group by the items by key. If any partition of them can not been + hold in memory, it will do sort based group by. + + This class works as follows: + + - It repeatedly group the items by key and save them in one dict in + memory. + + - When the used memory goes above memory limit, it will split + the combined data into partitions by hash code, dump them + into disk, one file per partition. If the number of keys + in one partitions is smaller than 1000, it will sort them + by key before dumping into disk. + + - Then it goes through the rest of the iterator, group items + by key into different dict by hash. Until the used memory goes over + memory limit, it dump all the dicts into disks, one file per + dict. Repeat this again until combine all the items. It + also will try to sort the items by key in each partition + before dumping into disks. + + - It will yield the grouped items partitions by partitions. + If the data in one partitions can be hold in memory, then it + will load and combine them in memory and yield. + + - If the dataset in one partition cannot be hold in memory, + it will sort them first. If all the files are already sorted, + it merge them by heap.merge(), so it will do external sort + for all the files. + + - After sorting, `GroupByKey` class will put all the continuous + items with the same key as a group, yield the values as + an iterator. + """ + SORT_KEY_LIMIT = 1000 + + def flattened_serializer(self): + assert isinstance(self.serializer, BatchedSerializer) + ser = self.serializer + return FlattenedValuesSerializer(ser, 20) + + def _object_size(self, obj): + return len(obj) + + def _spill(self): + """ + dump already partitioned data into disks. + """ + global MemoryBytesSpilled, DiskBytesSpilled + path = self._get_spill_dir(self.spills) + if not os.path.exists(path): + os.makedirs(path) + + used_memory = get_used_memory() + if not self.pdata: + # The data has not been partitioned, it will iterator the + # data once, write them into different files, has no + # additional memory. It only called when the memory goes + # above limit at the first time. + + # open all the files for writing + streams = [open(os.path.join(path, str(i)), 'wb') + for i in range(self.partitions)] + + # If the number of keys is small, then the overhead of sort is small + # sort them before dumping into disks + self._sorted = len(self.data) < self.SORT_KEY_LIMIT + if self._sorted: + self.serializer = self.flattened_serializer() + for k in sorted(self.data.keys()): + h = self._partition(k) + self.serializer.dump_stream([(k, self.data[k])], streams[h]) + else: + for k, v in self.data.items(): + h = self._partition(k) + self.serializer.dump_stream([(k, v)], streams[h]) + + for s in streams: + DiskBytesSpilled += s.tell() + s.close() + + self.data.clear() + # self.pdata is cached in `mergeValues` and `mergeCombiners` + self.pdata.extend([{} for i in range(self.partitions)]) + + else: + for i in range(self.partitions): + p = os.path.join(path, str(i)) + with open(p, "wb") as f: + # dump items in batch + if self._sorted: + # sort by key only (stable) + sorted_items = sorted(self.pdata[i].items(), key=operator.itemgetter(0)) + self.serializer.dump_stream(sorted_items, f) + else: + self.serializer.dump_stream(self.pdata[i].items(), f) + self.pdata[i].clear() + DiskBytesSpilled += os.path.getsize(p) + + self.spills += 1 + gc.collect() # release the memory as much as possible + MemoryBytesSpilled += (used_memory - get_used_memory()) << 20 + + def _merged_items(self, index): + size = sum(os.path.getsize(os.path.join(self._get_spill_dir(j), str(index))) + for j in range(self.spills)) + # if the memory can not hold all the partition, + # then use sort based merge. Because of compression, + # the data on disks will be much smaller than needed memory + if size >= self.memory_limit << 17: # * 1M / 8 + return self._merge_sorted_items(index) + + self.data = {} + for j in range(self.spills): + path = self._get_spill_dir(j) + p = os.path.join(path, str(index)) + # do not check memory during merging + with open(p, "rb") as f: + self.mergeCombiners(self.serializer.load_stream(f), 0) + return self.data.items() + + def _merge_sorted_items(self, index): + """ load a partition from disk, then sort and group by key """ + def load_partition(j): + path = self._get_spill_dir(j) + p = os.path.join(path, str(index)) + with open(p, 'rb', 65536) as f: + for v in self.serializer.load_stream(f): + yield v + + disk_items = [load_partition(j) for j in range(self.spills)] + + if self._sorted: + # all the partitions are already sorted + sorted_items = heapq.merge(disk_items, key=operator.itemgetter(0)) + + else: + # Flatten the combined values, so it will not consume huge + # memory during merging sort. + ser = self.flattened_serializer() + sorter = ExternalSorter(self.memory_limit, ser) + sorted_items = sorter.sorted(itertools.chain(*disk_items), + key=operator.itemgetter(0)) + return ((k, vs) for k, vs in GroupByKey(sorted_items)) + + if __name__ == "__main__": import doctest doctest.testmod() diff --git a/python/pyspark/sql.py b/python/pyspark/sql.py deleted file mode 100644 index e55f285a778c4..0000000000000 --- a/python/pyspark/sql.py +++ /dev/null @@ -1,2711 +0,0 @@ -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -""" -public classes of Spark SQL: - - - L{SQLContext} - Main entry point for SQL functionality. - - L{DataFrame} - A Resilient Distributed Dataset (RDD) with Schema information for the data contained. In - addition to normal RDD operations, DataFrames also support SQL. - - L{GroupedData} - - L{Column} - Column is a DataFrame with a single column. - - L{Row} - A Row of data returned by a Spark SQL query. - - L{HiveContext} - Main entry point for accessing data stored in Apache Hive.. -""" - -import sys -import itertools -import decimal -import datetime -import keyword -import warnings -import json -import re -import random -import os -from tempfile import NamedTemporaryFile -from array import array -from operator import itemgetter -from itertools import imap - -from py4j.protocol import Py4JError -from py4j.java_collections import ListConverter, MapConverter - -from pyspark.context import SparkContext -from pyspark.rdd import RDD, _prepare_for_python_RDD -from pyspark.serializers import BatchedSerializer, AutoBatchedSerializer, PickleSerializer, \ - CloudPickleSerializer, UTF8Deserializer -from pyspark.storagelevel import StorageLevel -from pyspark.traceback_utils import SCCallSiteSync - - -__all__ = [ - "StringType", "BinaryType", "BooleanType", "DateType", "TimestampType", "DecimalType", - "DoubleType", "FloatType", "ByteType", "IntegerType", "LongType", - "ShortType", "ArrayType", "MapType", "StructField", "StructType", - "SQLContext", "HiveContext", "DataFrame", "GroupedData", "Column", "Row", "Dsl", - "SchemaRDD"] - - -class DataType(object): - - """Spark SQL DataType""" - - def __repr__(self): - return self.__class__.__name__ - - def __hash__(self): - return hash(str(self)) - - def __eq__(self, other): - return (isinstance(other, self.__class__) and - self.__dict__ == other.__dict__) - - def __ne__(self, other): - return not self.__eq__(other) - - @classmethod - def typeName(cls): - return cls.__name__[:-4].lower() - - def jsonValue(self): - return self.typeName() - - def json(self): - return json.dumps(self.jsonValue(), - separators=(',', ':'), - sort_keys=True) - - -class PrimitiveTypeSingleton(type): - - """Metaclass for PrimitiveType""" - - _instances = {} - - def __call__(cls): - if cls not in cls._instances: - cls._instances[cls] = super(PrimitiveTypeSingleton, cls).__call__() - return cls._instances[cls] - - -class PrimitiveType(DataType): - - """Spark SQL PrimitiveType""" - - __metaclass__ = PrimitiveTypeSingleton - - def __eq__(self, other): - # because they should be the same object - return self is other - - -class NullType(PrimitiveType): - - """Spark SQL NullType - - The data type representing None, used for the types which has not - been inferred. - """ - - -class StringType(PrimitiveType): - - """Spark SQL StringType - - The data type representing string values. - """ - - -class BinaryType(PrimitiveType): - - """Spark SQL BinaryType - - The data type representing bytearray values. - """ - - -class BooleanType(PrimitiveType): - - """Spark SQL BooleanType - - The data type representing bool values. - """ - - -class DateType(PrimitiveType): - - """Spark SQL DateType - - The data type representing datetime.date values. - """ - - -class TimestampType(PrimitiveType): - - """Spark SQL TimestampType - - The data type representing datetime.datetime values. - """ - - -class DecimalType(DataType): - - """Spark SQL DecimalType - - The data type representing decimal.Decimal values. - """ - - def __init__(self, precision=None, scale=None): - self.precision = precision - self.scale = scale - self.hasPrecisionInfo = precision is not None - - def jsonValue(self): - if self.hasPrecisionInfo: - return "decimal(%d,%d)" % (self.precision, self.scale) - else: - return "decimal" - - def __repr__(self): - if self.hasPrecisionInfo: - return "DecimalType(%d,%d)" % (self.precision, self.scale) - else: - return "DecimalType()" - - -class DoubleType(PrimitiveType): - - """Spark SQL DoubleType - - The data type representing float values. - """ - - -class FloatType(PrimitiveType): - - """Spark SQL FloatType - - The data type representing single precision floating-point values. - """ - - -class ByteType(PrimitiveType): - - """Spark SQL ByteType - - The data type representing int values with 1 singed byte. - """ - - -class IntegerType(PrimitiveType): - - """Spark SQL IntegerType - - The data type representing int values. - """ - - -class LongType(PrimitiveType): - - """Spark SQL LongType - - The data type representing long values. If the any value is - beyond the range of [-9223372036854775808, 9223372036854775807], - please use DecimalType. - """ - - -class ShortType(PrimitiveType): - - """Spark SQL ShortType - - The data type representing int values with 2 signed bytes. - """ - - -class ArrayType(DataType): - - """Spark SQL ArrayType - - The data type representing list values. An ArrayType object - comprises two fields, elementType (a DataType) and containsNull (a bool). - The field of elementType is used to specify the type of array elements. - The field of containsNull is used to specify if the array has None values. - - """ - - def __init__(self, elementType, containsNull=True): - """Creates an ArrayType - - :param elementType: the data type of elements. - :param containsNull: indicates whether the list contains None values. - - >>> ArrayType(StringType) == ArrayType(StringType, True) - True - >>> ArrayType(StringType, False) == ArrayType(StringType) - False - """ - self.elementType = elementType - self.containsNull = containsNull - - def __repr__(self): - return "ArrayType(%s,%s)" % (self.elementType, - str(self.containsNull).lower()) - - def jsonValue(self): - return {"type": self.typeName(), - "elementType": self.elementType.jsonValue(), - "containsNull": self.containsNull} - - @classmethod - def fromJson(cls, json): - return ArrayType(_parse_datatype_json_value(json["elementType"]), - json["containsNull"]) - - -class MapType(DataType): - - """Spark SQL MapType - - The data type representing dict values. A MapType object comprises - three fields, keyType (a DataType), valueType (a DataType) and - valueContainsNull (a bool). - - The field of keyType is used to specify the type of keys in the map. - The field of valueType is used to specify the type of values in the map. - The field of valueContainsNull is used to specify if values of this - map has None values. - - For values of a MapType column, keys are not allowed to have None values. - - """ - - def __init__(self, keyType, valueType, valueContainsNull=True): - """Creates a MapType - :param keyType: the data type of keys. - :param valueType: the data type of values. - :param valueContainsNull: indicates whether values contains - null values. - - >>> (MapType(StringType, IntegerType) - ... == MapType(StringType, IntegerType, True)) - True - >>> (MapType(StringType, IntegerType, False) - ... == MapType(StringType, FloatType)) - False - """ - self.keyType = keyType - self.valueType = valueType - self.valueContainsNull = valueContainsNull - - def __repr__(self): - return "MapType(%s,%s,%s)" % (self.keyType, self.valueType, - str(self.valueContainsNull).lower()) - - def jsonValue(self): - return {"type": self.typeName(), - "keyType": self.keyType.jsonValue(), - "valueType": self.valueType.jsonValue(), - "valueContainsNull": self.valueContainsNull} - - @classmethod - def fromJson(cls, json): - return MapType(_parse_datatype_json_value(json["keyType"]), - _parse_datatype_json_value(json["valueType"]), - json["valueContainsNull"]) - - -class StructField(DataType): - - """Spark SQL StructField - - Represents a field in a StructType. - A StructField object comprises three fields, name (a string), - dataType (a DataType) and nullable (a bool). The field of name - is the name of a StructField. The field of dataType specifies - the data type of a StructField. - - The field of nullable specifies if values of a StructField can - contain None values. - - """ - - def __init__(self, name, dataType, nullable=True, metadata=None): - """Creates a StructField - :param name: the name of this field. - :param dataType: the data type of this field. - :param nullable: indicates whether values of this field - can be null. - :param metadata: metadata of this field, which is a map from string - to simple type that can be serialized to JSON - automatically - - >>> (StructField("f1", StringType, True) - ... == StructField("f1", StringType, True)) - True - >>> (StructField("f1", StringType, True) - ... == StructField("f2", StringType, True)) - False - """ - self.name = name - self.dataType = dataType - self.nullable = nullable - self.metadata = metadata or {} - - def __repr__(self): - return "StructField(%s,%s,%s)" % (self.name, self.dataType, - str(self.nullable).lower()) - - def jsonValue(self): - return {"name": self.name, - "type": self.dataType.jsonValue(), - "nullable": self.nullable, - "metadata": self.metadata} - - @classmethod - def fromJson(cls, json): - return StructField(json["name"], - _parse_datatype_json_value(json["type"]), - json["nullable"], - json["metadata"]) - - -class StructType(DataType): - - """Spark SQL StructType - - The data type representing rows. - A StructType object comprises a list of L{StructField}. - - """ - - def __init__(self, fields): - """Creates a StructType - - >>> struct1 = StructType([StructField("f1", StringType, True)]) - >>> struct2 = StructType([StructField("f1", StringType, True)]) - >>> struct1 == struct2 - True - >>> struct1 = StructType([StructField("f1", StringType, True)]) - >>> struct2 = StructType([StructField("f1", StringType, True), - ... [StructField("f2", IntegerType, False)]]) - >>> struct1 == struct2 - False - """ - self.fields = fields - - def __repr__(self): - return ("StructType(List(%s))" % - ",".join(str(field) for field in self.fields)) - - def jsonValue(self): - return {"type": self.typeName(), - "fields": [f.jsonValue() for f in self.fields]} - - @classmethod - def fromJson(cls, json): - return StructType([StructField.fromJson(f) for f in json["fields"]]) - - -class UserDefinedType(DataType): - """ - .. note:: WARN: Spark Internal Use Only - SQL User-Defined Type (UDT). - """ - - @classmethod - def typeName(cls): - return cls.__name__.lower() - - @classmethod - def sqlType(cls): - """ - Underlying SQL storage type for this UDT. - """ - raise NotImplementedError("UDT must implement sqlType().") - - @classmethod - def module(cls): - """ - The Python module of the UDT. - """ - raise NotImplementedError("UDT must implement module().") - - @classmethod - def scalaUDT(cls): - """ - The class name of the paired Scala UDT. - """ - raise NotImplementedError("UDT must have a paired Scala UDT.") - - def serialize(self, obj): - """ - Converts the a user-type object into a SQL datum. - """ - raise NotImplementedError("UDT must implement serialize().") - - def deserialize(self, datum): - """ - Converts a SQL datum into a user-type object. - """ - raise NotImplementedError("UDT must implement deserialize().") - - def json(self): - return json.dumps(self.jsonValue(), separators=(',', ':'), sort_keys=True) - - def jsonValue(self): - schema = { - "type": "udt", - "class": self.scalaUDT(), - "pyClass": "%s.%s" % (self.module(), type(self).__name__), - "sqlType": self.sqlType().jsonValue() - } - return schema - - @classmethod - def fromJson(cls, json): - pyUDT = json["pyClass"] - split = pyUDT.rfind(".") - pyModule = pyUDT[:split] - pyClass = pyUDT[split+1:] - m = __import__(pyModule, globals(), locals(), [pyClass], -1) - UDT = getattr(m, pyClass) - return UDT() - - def __eq__(self, other): - return type(self) == type(other) - - -_all_primitive_types = dict((v.typeName(), v) - for v in globals().itervalues() - if type(v) is PrimitiveTypeSingleton and - v.__base__ == PrimitiveType) - - -_all_complex_types = dict((v.typeName(), v) - for v in [ArrayType, MapType, StructType]) - - -def _parse_datatype_json_string(json_string): - """Parses the given data type JSON string. - >>> def check_datatype(datatype): - ... scala_datatype = sqlCtx._ssql_ctx.parseDataType(datatype.json()) - ... python_datatype = _parse_datatype_json_string(scala_datatype.json()) - ... return datatype == python_datatype - >>> all(check_datatype(cls()) for cls in _all_primitive_types.values()) - True - >>> # Simple ArrayType. - >>> simple_arraytype = ArrayType(StringType(), True) - >>> check_datatype(simple_arraytype) - True - >>> # Simple MapType. - >>> simple_maptype = MapType(StringType(), LongType()) - >>> check_datatype(simple_maptype) - True - >>> # Simple StructType. - >>> simple_structtype = StructType([ - ... StructField("a", DecimalType(), False), - ... StructField("b", BooleanType(), True), - ... StructField("c", LongType(), True), - ... StructField("d", BinaryType(), False)]) - >>> check_datatype(simple_structtype) - True - >>> # Complex StructType. - >>> complex_structtype = StructType([ - ... StructField("simpleArray", simple_arraytype, True), - ... StructField("simpleMap", simple_maptype, True), - ... StructField("simpleStruct", simple_structtype, True), - ... StructField("boolean", BooleanType(), False), - ... StructField("withMeta", DoubleType(), False, {"name": "age"})]) - >>> check_datatype(complex_structtype) - True - >>> # Complex ArrayType. - >>> complex_arraytype = ArrayType(complex_structtype, True) - >>> check_datatype(complex_arraytype) - True - >>> # Complex MapType. - >>> complex_maptype = MapType(complex_structtype, - ... complex_arraytype, False) - >>> check_datatype(complex_maptype) - True - >>> check_datatype(ExamplePointUDT()) - True - >>> structtype_with_udt = StructType([StructField("label", DoubleType(), False), - ... StructField("point", ExamplePointUDT(), False)]) - >>> check_datatype(structtype_with_udt) - True - """ - return _parse_datatype_json_value(json.loads(json_string)) - - -_FIXED_DECIMAL = re.compile("decimal\\((\\d+),(\\d+)\\)") - - -def _parse_datatype_json_value(json_value): - if type(json_value) is unicode: - if json_value in _all_primitive_types.keys(): - return _all_primitive_types[json_value]() - elif json_value == u'decimal': - return DecimalType() - elif _FIXED_DECIMAL.match(json_value): - m = _FIXED_DECIMAL.match(json_value) - return DecimalType(int(m.group(1)), int(m.group(2))) - else: - raise ValueError("Could not parse datatype: %s" % json_value) - else: - tpe = json_value["type"] - if tpe in _all_complex_types: - return _all_complex_types[tpe].fromJson(json_value) - elif tpe == 'udt': - return UserDefinedType.fromJson(json_value) - else: - raise ValueError("not supported type: %s" % tpe) - - -# Mapping Python types to Spark SQL DataType -_type_mappings = { - type(None): NullType, - bool: BooleanType, - int: IntegerType, - long: LongType, - float: DoubleType, - str: StringType, - unicode: StringType, - bytearray: BinaryType, - decimal.Decimal: DecimalType, - datetime.date: DateType, - datetime.datetime: TimestampType, - datetime.time: TimestampType, -} - - -def _infer_type(obj): - """Infer the DataType from obj - - >>> p = ExamplePoint(1.0, 2.0) - >>> _infer_type(p) - ExamplePointUDT - """ - if obj is None: - raise ValueError("Can not infer type for None") - - if hasattr(obj, '__UDT__'): - return obj.__UDT__ - - dataType = _type_mappings.get(type(obj)) - if dataType is not None: - return dataType() - - if isinstance(obj, dict): - for key, value in obj.iteritems(): - if key is not None and value is not None: - return MapType(_infer_type(key), _infer_type(value), True) - else: - return MapType(NullType(), NullType(), True) - elif isinstance(obj, (list, array)): - for v in obj: - if v is not None: - return ArrayType(_infer_type(obj[0]), True) - else: - return ArrayType(NullType(), True) - else: - try: - return _infer_schema(obj) - except ValueError: - raise ValueError("not supported type: %s" % type(obj)) - - -def _infer_schema(row): - """Infer the schema from dict/namedtuple/object""" - if isinstance(row, dict): - items = sorted(row.items()) - - elif isinstance(row, tuple): - if hasattr(row, "_fields"): # namedtuple - items = zip(row._fields, tuple(row)) - elif hasattr(row, "__FIELDS__"): # Row - items = zip(row.__FIELDS__, tuple(row)) - elif all(isinstance(x, tuple) and len(x) == 2 for x in row): - items = row - else: - raise ValueError("Can't infer schema from tuple") - - elif hasattr(row, "__dict__"): # object - items = sorted(row.__dict__.items()) - - else: - raise ValueError("Can not infer schema for type: %s" % type(row)) - - fields = [StructField(k, _infer_type(v), True) for k, v in items] - return StructType(fields) - - -def _need_python_to_sql_conversion(dataType): - """ - Checks whether we need python to sql conversion for the given type. - For now, only UDTs need this conversion. - - >>> _need_python_to_sql_conversion(DoubleType()) - False - >>> schema0 = StructType([StructField("indices", ArrayType(IntegerType(), False), False), - ... StructField("values", ArrayType(DoubleType(), False), False)]) - >>> _need_python_to_sql_conversion(schema0) - False - >>> _need_python_to_sql_conversion(ExamplePointUDT()) - True - >>> schema1 = ArrayType(ExamplePointUDT(), False) - >>> _need_python_to_sql_conversion(schema1) - True - >>> schema2 = StructType([StructField("label", DoubleType(), False), - ... StructField("point", ExamplePointUDT(), False)]) - >>> _need_python_to_sql_conversion(schema2) - True - """ - if isinstance(dataType, StructType): - return any([_need_python_to_sql_conversion(f.dataType) for f in dataType.fields]) - elif isinstance(dataType, ArrayType): - return _need_python_to_sql_conversion(dataType.elementType) - elif isinstance(dataType, MapType): - return _need_python_to_sql_conversion(dataType.keyType) or \ - _need_python_to_sql_conversion(dataType.valueType) - elif isinstance(dataType, UserDefinedType): - return True - else: - return False - - -def _python_to_sql_converter(dataType): - """ - Returns a converter that converts a Python object into a SQL datum for the given type. - - >>> conv = _python_to_sql_converter(DoubleType()) - >>> conv(1.0) - 1.0 - >>> conv = _python_to_sql_converter(ArrayType(DoubleType(), False)) - >>> conv([1.0, 2.0]) - [1.0, 2.0] - >>> conv = _python_to_sql_converter(ExamplePointUDT()) - >>> conv(ExamplePoint(1.0, 2.0)) - [1.0, 2.0] - >>> schema = StructType([StructField("label", DoubleType(), False), - ... StructField("point", ExamplePointUDT(), False)]) - >>> conv = _python_to_sql_converter(schema) - >>> conv((1.0, ExamplePoint(1.0, 2.0))) - (1.0, [1.0, 2.0]) - """ - if not _need_python_to_sql_conversion(dataType): - return lambda x: x - - if isinstance(dataType, StructType): - names, types = zip(*[(f.name, f.dataType) for f in dataType.fields]) - converters = map(_python_to_sql_converter, types) - - def converter(obj): - if isinstance(obj, dict): - return tuple(c(obj.get(n)) for n, c in zip(names, converters)) - elif isinstance(obj, tuple): - if hasattr(obj, "_fields") or hasattr(obj, "__FIELDS__"): - return tuple(c(v) for c, v in zip(converters, obj)) - elif all(isinstance(x, tuple) and len(x) == 2 for x in obj): # k-v pairs - d = dict(obj) - return tuple(c(d.get(n)) for n, c in zip(names, converters)) - else: - return tuple(c(v) for c, v in zip(converters, obj)) - else: - raise ValueError("Unexpected tuple %r with type %r" % (obj, dataType)) - return converter - elif isinstance(dataType, ArrayType): - element_converter = _python_to_sql_converter(dataType.elementType) - return lambda a: [element_converter(v) for v in a] - elif isinstance(dataType, MapType): - key_converter = _python_to_sql_converter(dataType.keyType) - value_converter = _python_to_sql_converter(dataType.valueType) - return lambda m: dict([(key_converter(k), value_converter(v)) for k, v in m.items()]) - elif isinstance(dataType, UserDefinedType): - return lambda obj: dataType.serialize(obj) - else: - raise ValueError("Unexpected type %r" % dataType) - - -def _has_nulltype(dt): - """ Return whether there is NullType in `dt` or not """ - if isinstance(dt, StructType): - return any(_has_nulltype(f.dataType) for f in dt.fields) - elif isinstance(dt, ArrayType): - return _has_nulltype((dt.elementType)) - elif isinstance(dt, MapType): - return _has_nulltype(dt.keyType) or _has_nulltype(dt.valueType) - else: - return isinstance(dt, NullType) - - -def _merge_type(a, b): - if isinstance(a, NullType): - return b - elif isinstance(b, NullType): - return a - elif type(a) is not type(b): - # TODO: type cast (such as int -> long) - raise TypeError("Can not merge type %s and %s" % (a, b)) - - # same type - if isinstance(a, StructType): - nfs = dict((f.name, f.dataType) for f in b.fields) - fields = [StructField(f.name, _merge_type(f.dataType, nfs.get(f.name, NullType()))) - for f in a.fields] - names = set([f.name for f in fields]) - for n in nfs: - if n not in names: - fields.append(StructField(n, nfs[n])) - return StructType(fields) - - elif isinstance(a, ArrayType): - return ArrayType(_merge_type(a.elementType, b.elementType), True) - - elif isinstance(a, MapType): - return MapType(_merge_type(a.keyType, b.keyType), - _merge_type(a.valueType, b.valueType), - True) - else: - return a - - -def _create_converter(dataType): - """Create an converter to drop the names of fields in obj """ - if isinstance(dataType, ArrayType): - conv = _create_converter(dataType.elementType) - return lambda row: map(conv, row) - - elif isinstance(dataType, MapType): - kconv = _create_converter(dataType.keyType) - vconv = _create_converter(dataType.valueType) - return lambda row: dict((kconv(k), vconv(v)) for k, v in row.iteritems()) - - elif isinstance(dataType, NullType): - return lambda x: None - - elif not isinstance(dataType, StructType): - return lambda x: x - - # dataType must be StructType - names = [f.name for f in dataType.fields] - converters = [_create_converter(f.dataType) for f in dataType.fields] - - def convert_struct(obj): - if obj is None: - return - - if isinstance(obj, tuple): - if hasattr(obj, "_fields"): - d = dict(zip(obj._fields, obj)) - elif hasattr(obj, "__FIELDS__"): - d = dict(zip(obj.__FIELDS__, obj)) - elif all(isinstance(x, tuple) and len(x) == 2 for x in obj): - d = dict(obj) - else: - raise ValueError("unexpected tuple: %s" % str(obj)) - - elif isinstance(obj, dict): - d = obj - elif hasattr(obj, "__dict__"): # object - d = obj.__dict__ - else: - raise ValueError("Unexpected obj: %s" % obj) - - return tuple([conv(d.get(name)) for name, conv in zip(names, converters)]) - - return convert_struct - - -_BRACKETS = {'(': ')', '[': ']', '{': '}'} - - -def _split_schema_abstract(s): - """ - split the schema abstract into fields - - >>> _split_schema_abstract("a b c") - ['a', 'b', 'c'] - >>> _split_schema_abstract("a(a b)") - ['a(a b)'] - >>> _split_schema_abstract("a b[] c{a b}") - ['a', 'b[]', 'c{a b}'] - >>> _split_schema_abstract(" ") - [] - """ - - r = [] - w = '' - brackets = [] - for c in s: - if c == ' ' and not brackets: - if w: - r.append(w) - w = '' - else: - w += c - if c in _BRACKETS: - brackets.append(c) - elif c in _BRACKETS.values(): - if not brackets or c != _BRACKETS[brackets.pop()]: - raise ValueError("unexpected " + c) - - if brackets: - raise ValueError("brackets not closed: %s" % brackets) - if w: - r.append(w) - return r - - -def _parse_field_abstract(s): - """ - Parse a field in schema abstract - - >>> _parse_field_abstract("a") - StructField(a,None,true) - >>> _parse_field_abstract("b(c d)") - StructField(b,StructType(...c,None,true),StructField(d... - >>> _parse_field_abstract("a[]") - StructField(a,ArrayType(None,true),true) - >>> _parse_field_abstract("a{[]}") - StructField(a,MapType(None,ArrayType(None,true),true),true) - """ - if set(_BRACKETS.keys()) & set(s): - idx = min((s.index(c) for c in _BRACKETS if c in s)) - name = s[:idx] - return StructField(name, _parse_schema_abstract(s[idx:]), True) - else: - return StructField(s, None, True) - - -def _parse_schema_abstract(s): - """ - parse abstract into schema - - >>> _parse_schema_abstract("a b c") - StructType...a...b...c... - >>> _parse_schema_abstract("a[b c] b{}") - StructType...a,ArrayType...b...c...b,MapType... - >>> _parse_schema_abstract("c{} d{a b}") - StructType...c,MapType...d,MapType...a...b... - >>> _parse_schema_abstract("a b(t)").fields[1] - StructField(b,StructType(List(StructField(t,None,true))),true) - """ - s = s.strip() - if not s: - return - - elif s.startswith('('): - return _parse_schema_abstract(s[1:-1]) - - elif s.startswith('['): - return ArrayType(_parse_schema_abstract(s[1:-1]), True) - - elif s.startswith('{'): - return MapType(None, _parse_schema_abstract(s[1:-1])) - - parts = _split_schema_abstract(s) - fields = [_parse_field_abstract(p) for p in parts] - return StructType(fields) - - -def _infer_schema_type(obj, dataType): - """ - Fill the dataType with types inferred from obj - - >>> schema = _parse_schema_abstract("a b c d") - >>> row = (1, 1.0, "str", datetime.date(2014, 10, 10)) - >>> _infer_schema_type(row, schema) - StructType...IntegerType...DoubleType...StringType...DateType... - >>> row = [[1], {"key": (1, 2.0)}] - >>> schema = _parse_schema_abstract("a[] b{c d}") - >>> _infer_schema_type(row, schema) - StructType...a,ArrayType...b,MapType(StringType,...c,IntegerType... - """ - if dataType is None: - return _infer_type(obj) - - if not obj: - return NullType() - - if isinstance(dataType, ArrayType): - eType = _infer_schema_type(obj[0], dataType.elementType) - return ArrayType(eType, True) - - elif isinstance(dataType, MapType): - k, v = obj.iteritems().next() - return MapType(_infer_schema_type(k, dataType.keyType), - _infer_schema_type(v, dataType.valueType)) - - elif isinstance(dataType, StructType): - fs = dataType.fields - assert len(fs) == len(obj), \ - "Obj(%s) have different length with fields(%s)" % (obj, fs) - fields = [StructField(f.name, _infer_schema_type(o, f.dataType), True) - for o, f in zip(obj, fs)] - return StructType(fields) - - else: - raise ValueError("Unexpected dataType: %s" % dataType) - - -_acceptable_types = { - BooleanType: (bool,), - ByteType: (int, long), - ShortType: (int, long), - IntegerType: (int, long), - LongType: (int, long), - FloatType: (float,), - DoubleType: (float,), - DecimalType: (decimal.Decimal,), - StringType: (str, unicode), - BinaryType: (bytearray,), - DateType: (datetime.date,), - TimestampType: (datetime.datetime,), - ArrayType: (list, tuple, array), - MapType: (dict,), - StructType: (tuple, list), -} - - -def _verify_type(obj, dataType): - """ - Verify the type of obj against dataType, raise an exception if - they do not match. - - >>> _verify_type(None, StructType([])) - >>> _verify_type("", StringType()) - >>> _verify_type(0, IntegerType()) - >>> _verify_type(range(3), ArrayType(ShortType())) - >>> _verify_type(set(), ArrayType(StringType())) # doctest: +IGNORE_EXCEPTION_DETAIL - Traceback (most recent call last): - ... - TypeError:... - >>> _verify_type({}, MapType(StringType(), IntegerType())) - >>> _verify_type((), StructType([])) - >>> _verify_type([], StructType([])) - >>> _verify_type([1], StructType([])) # doctest: +IGNORE_EXCEPTION_DETAIL - Traceback (most recent call last): - ... - ValueError:... - >>> _verify_type(ExamplePoint(1.0, 2.0), ExamplePointUDT()) - >>> _verify_type([1.0, 2.0], ExamplePointUDT()) # doctest: +IGNORE_EXCEPTION_DETAIL - Traceback (most recent call last): - ... - ValueError:... - """ - # all objects are nullable - if obj is None: - return - - if isinstance(dataType, UserDefinedType): - if not (hasattr(obj, '__UDT__') and obj.__UDT__ == dataType): - raise ValueError("%r is not an instance of type %r" % (obj, dataType)) - _verify_type(dataType.serialize(obj), dataType.sqlType()) - return - - _type = type(dataType) - assert _type in _acceptable_types, "unkown datatype: %s" % dataType - - # subclass of them can not be deserialized in JVM - if type(obj) not in _acceptable_types[_type]: - raise TypeError("%s can not accept object in type %s" - % (dataType, type(obj))) - - if isinstance(dataType, ArrayType): - for i in obj: - _verify_type(i, dataType.elementType) - - elif isinstance(dataType, MapType): - for k, v in obj.iteritems(): - _verify_type(k, dataType.keyType) - _verify_type(v, dataType.valueType) - - elif isinstance(dataType, StructType): - if len(obj) != len(dataType.fields): - raise ValueError("Length of object (%d) does not match with" - "length of fields (%d)" % (len(obj), len(dataType.fields))) - for v, f in zip(obj, dataType.fields): - _verify_type(v, f.dataType) - - -_cached_cls = {} - - -def _restore_object(dataType, obj): - """ Restore object during unpickling. """ - # use id(dataType) as key to speed up lookup in dict - # Because of batched pickling, dataType will be the - # same object in most cases. - k = id(dataType) - cls = _cached_cls.get(k) - if cls is None: - # use dataType as key to avoid create multiple class - cls = _cached_cls.get(dataType) - if cls is None: - cls = _create_cls(dataType) - _cached_cls[dataType] = cls - _cached_cls[k] = cls - return cls(obj) - - -def _create_object(cls, v): - """ Create an customized object with class `cls`. """ - # datetime.date would be deserialized as datetime.datetime - # from java type, so we need to set it back. - if cls is datetime.date and isinstance(v, datetime.datetime): - return v.date() - return cls(v) if v is not None else v - - -def _create_getter(dt, i): - """ Create a getter for item `i` with schema """ - cls = _create_cls(dt) - - def getter(self): - return _create_object(cls, self[i]) - - return getter - - -def _has_struct_or_date(dt): - """Return whether `dt` is or has StructType/DateType in it""" - if isinstance(dt, StructType): - return True - elif isinstance(dt, ArrayType): - return _has_struct_or_date(dt.elementType) - elif isinstance(dt, MapType): - return _has_struct_or_date(dt.keyType) or _has_struct_or_date(dt.valueType) - elif isinstance(dt, DateType): - return True - elif isinstance(dt, UserDefinedType): - return True - return False - - -def _create_properties(fields): - """Create properties according to fields""" - ps = {} - for i, f in enumerate(fields): - name = f.name - if (name.startswith("__") and name.endswith("__") - or keyword.iskeyword(name)): - warnings.warn("field name %s can not be accessed in Python," - "use position to access it instead" % name) - if _has_struct_or_date(f.dataType): - # delay creating object until accessing it - getter = _create_getter(f.dataType, i) - else: - getter = itemgetter(i) - ps[name] = property(getter) - return ps - - -def _create_cls(dataType): - """ - Create an class by dataType - - The created class is similar to namedtuple, but can have nested schema. - - >>> schema = _parse_schema_abstract("a b c") - >>> row = (1, 1.0, "str") - >>> schema = _infer_schema_type(row, schema) - >>> obj = _create_cls(schema)(row) - >>> import pickle - >>> pickle.loads(pickle.dumps(obj)) - Row(a=1, b=1.0, c='str') - - >>> row = [[1], {"key": (1, 2.0)}] - >>> schema = _parse_schema_abstract("a[] b{c d}") - >>> schema = _infer_schema_type(row, schema) - >>> obj = _create_cls(schema)(row) - >>> pickle.loads(pickle.dumps(obj)) - Row(a=[1], b={'key': Row(c=1, d=2.0)}) - >>> pickle.loads(pickle.dumps(obj.a)) - [1] - >>> pickle.loads(pickle.dumps(obj.b)) - {'key': Row(c=1, d=2.0)} - """ - - if isinstance(dataType, ArrayType): - cls = _create_cls(dataType.elementType) - - def List(l): - if l is None: - return - return [_create_object(cls, v) for v in l] - - return List - - elif isinstance(dataType, MapType): - kcls = _create_cls(dataType.keyType) - vcls = _create_cls(dataType.valueType) - - def Dict(d): - if d is None: - return - return dict((_create_object(kcls, k), _create_object(vcls, v)) for k, v in d.items()) - - return Dict - - elif isinstance(dataType, DateType): - return datetime.date - - elif isinstance(dataType, UserDefinedType): - return lambda datum: dataType.deserialize(datum) - - elif not isinstance(dataType, StructType): - # no wrapper for primitive types - return lambda x: x - - class Row(tuple): - - """ Row in DataFrame """ - __DATATYPE__ = dataType - __FIELDS__ = tuple(f.name for f in dataType.fields) - __slots__ = () - - # create property for fast access - locals().update(_create_properties(dataType.fields)) - - def asDict(self): - """ Return as a dict """ - return dict((n, getattr(self, n)) for n in self.__FIELDS__) - - def __repr__(self): - # call collect __repr__ for nested objects - return ("Row(%s)" % ", ".join("%s=%r" % (n, getattr(self, n)) - for n in self.__FIELDS__)) - - def __reduce__(self): - return (_restore_object, (self.__DATATYPE__, tuple(self))) - - return Row - - -class SQLContext(object): - - """Main entry point for Spark SQL functionality. - - A SQLContext can be used create L{DataFrame}, register L{DataFrame} as - tables, execute SQL over tables, cache tables, and read parquet files. - """ - - def __init__(self, sparkContext, sqlContext=None): - """Create a new SQLContext. - - :param sparkContext: The SparkContext to wrap. - :param sqlContext: An optional JVM Scala SQLContext. If set, we do not instatiate a new - SQLContext in the JVM, instead we make all calls to this object. - - >>> df = sqlCtx.inferSchema(rdd) - >>> sqlCtx.inferSchema(df) # doctest: +IGNORE_EXCEPTION_DETAIL - Traceback (most recent call last): - ... - TypeError:... - - >>> bad_rdd = sc.parallelize([1,2,3]) - >>> sqlCtx.inferSchema(bad_rdd) # doctest: +IGNORE_EXCEPTION_DETAIL - Traceback (most recent call last): - ... - ValueError:... - - >>> from datetime import datetime - >>> allTypes = sc.parallelize([Row(i=1, s="string", d=1.0, l=1L, - ... b=True, list=[1, 2, 3], dict={"s": 0}, row=Row(a=1), - ... time=datetime(2014, 8, 1, 14, 1, 5))]) - >>> df = sqlCtx.inferSchema(allTypes) - >>> df.registerTempTable("allTypes") - >>> sqlCtx.sql('select i+1, d+1, not b, list[1], dict["s"], time, row.a ' - ... 'from allTypes where b and i > 0').collect() - [Row(c0=2, c1=2.0, c2=False, c3=2, c4=0...8, 1, 14, 1, 5), a=1)] - >>> df.map(lambda x: (x.i, x.s, x.d, x.l, x.b, x.time, - ... x.row.a, x.list)).collect() - [(1, u'string', 1.0, 1, True, ...(2014, 8, 1, 14, 1, 5), 1, [1, 2, 3])] - """ - self._sc = sparkContext - self._jsc = self._sc._jsc - self._jvm = self._sc._jvm - self._scala_SQLContext = sqlContext - - @property - def _ssql_ctx(self): - """Accessor for the JVM Spark SQL context. - - Subclasses can override this property to provide their own - JVM Contexts. - """ - if self._scala_SQLContext is None: - self._scala_SQLContext = self._jvm.SQLContext(self._jsc.sc()) - return self._scala_SQLContext - - def registerFunction(self, name, f, returnType=StringType()): - """Registers a lambda function as a UDF so it can be used in SQL statements. - - In addition to a name and the function itself, the return type can be optionally specified. - When the return type is not given it default to a string and conversion will automatically - be done. For any other return type, the produced object must match the specified type. - - >>> sqlCtx.registerFunction("stringLengthString", lambda x: len(x)) - >>> sqlCtx.sql("SELECT stringLengthString('test')").collect() - [Row(c0=u'4')] - >>> sqlCtx.registerFunction("stringLengthInt", lambda x: len(x), IntegerType()) - >>> sqlCtx.sql("SELECT stringLengthInt('test')").collect() - [Row(c0=4)] - """ - func = lambda _, it: imap(lambda x: f(*x), it) - ser = AutoBatchedSerializer(PickleSerializer()) - command = (func, None, ser, ser) - pickled_cmd, bvars, env, includes = _prepare_for_python_RDD(self._sc, command, self) - self._ssql_ctx.udf().registerPython(name, - bytearray(pickled_cmd), - env, - includes, - self._sc.pythonExec, - bvars, - self._sc._javaAccumulator, - returnType.json()) - - def inferSchema(self, rdd, samplingRatio=None): - """Infer and apply a schema to an RDD of L{Row}. - - When samplingRatio is specified, the schema is inferred by looking - at the types of each row in the sampled dataset. Otherwise, the - first 100 rows of the RDD are inspected. Nested collections are - supported, which can include array, dict, list, Row, tuple, - namedtuple, or object. - - Each row could be L{pyspark.sql.Row} object or namedtuple or objects. - Using top level dicts is deprecated, as dict is used to represent Maps. - - If a single column has multiple distinct inferred types, it may cause - runtime exceptions. - - >>> rdd = sc.parallelize( - ... [Row(field1=1, field2="row1"), - ... Row(field1=2, field2="row2"), - ... Row(field1=3, field2="row3")]) - >>> df = sqlCtx.inferSchema(rdd) - >>> df.collect()[0] - Row(field1=1, field2=u'row1') - - >>> NestedRow = Row("f1", "f2") - >>> nestedRdd1 = sc.parallelize([ - ... NestedRow(array('i', [1, 2]), {"row1": 1.0}), - ... NestedRow(array('i', [2, 3]), {"row2": 2.0})]) - >>> df = sqlCtx.inferSchema(nestedRdd1) - >>> df.collect() - [Row(f1=[1, 2], f2={u'row1': 1.0}), ..., f2={u'row2': 2.0})] - - >>> nestedRdd2 = sc.parallelize([ - ... NestedRow([[1, 2], [2, 3]], [1, 2]), - ... NestedRow([[2, 3], [3, 4]], [2, 3])]) - >>> df = sqlCtx.inferSchema(nestedRdd2) - >>> df.collect() - [Row(f1=[[1, 2], [2, 3]], f2=[1, 2]), ..., f2=[2, 3])] - - >>> from collections import namedtuple - >>> CustomRow = namedtuple('CustomRow', 'field1 field2') - >>> rdd = sc.parallelize( - ... [CustomRow(field1=1, field2="row1"), - ... CustomRow(field1=2, field2="row2"), - ... CustomRow(field1=3, field2="row3")]) - >>> df = sqlCtx.inferSchema(rdd) - >>> df.collect()[0] - Row(field1=1, field2=u'row1') - """ - - if isinstance(rdd, DataFrame): - raise TypeError("Cannot apply schema to DataFrame") - - first = rdd.first() - if not first: - raise ValueError("The first row in RDD is empty, " - "can not infer schema") - if type(first) is dict: - warnings.warn("Using RDD of dict to inferSchema is deprecated," - "please use pyspark.sql.Row instead") - - if samplingRatio is None: - schema = _infer_schema(first) - if _has_nulltype(schema): - for row in rdd.take(100)[1:]: - schema = _merge_type(schema, _infer_schema(row)) - if not _has_nulltype(schema): - break - else: - warnings.warn("Some of types cannot be determined by the " - "first 100 rows, please try again with sampling") - else: - if samplingRatio > 0.99: - rdd = rdd.sample(False, float(samplingRatio)) - schema = rdd.map(_infer_schema).reduce(_merge_type) - - converter = _create_converter(schema) - rdd = rdd.map(converter) - return self.applySchema(rdd, schema) - - def applySchema(self, rdd, schema): - """ - Applies the given schema to the given RDD of L{tuple} or L{list}. - - These tuples or lists can contain complex nested structures like - lists, maps or nested rows. - - The schema should be a StructType. - - It is important that the schema matches the types of the objects - in each row or exceptions could be thrown at runtime. - - >>> rdd2 = sc.parallelize([(1, "row1"), (2, "row2"), (3, "row3")]) - >>> schema = StructType([StructField("field1", IntegerType(), False), - ... StructField("field2", StringType(), False)]) - >>> df = sqlCtx.applySchema(rdd2, schema) - >>> sqlCtx.registerRDDAsTable(df, "table1") - >>> df2 = sqlCtx.sql("SELECT * from table1") - >>> df2.collect() - [Row(field1=1, field2=u'row1'),..., Row(field1=3, field2=u'row3')] - - >>> from datetime import date, datetime - >>> rdd = sc.parallelize([(127, -128L, -32768, 32767, 2147483647L, 1.0, - ... date(2010, 1, 1), - ... datetime(2010, 1, 1, 1, 1, 1), - ... {"a": 1}, (2,), [1, 2, 3], None)]) - >>> schema = StructType([ - ... StructField("byte1", ByteType(), False), - ... StructField("byte2", ByteType(), False), - ... StructField("short1", ShortType(), False), - ... StructField("short2", ShortType(), False), - ... StructField("int", IntegerType(), False), - ... StructField("float", FloatType(), False), - ... StructField("date", DateType(), False), - ... StructField("time", TimestampType(), False), - ... StructField("map", - ... MapType(StringType(), IntegerType(), False), False), - ... StructField("struct", - ... StructType([StructField("b", ShortType(), False)]), False), - ... StructField("list", ArrayType(ByteType(), False), False), - ... StructField("null", DoubleType(), True)]) - >>> df = sqlCtx.applySchema(rdd, schema) - >>> results = df.map( - ... lambda x: (x.byte1, x.byte2, x.short1, x.short2, x.int, x.float, x.date, - ... x.time, x.map["a"], x.struct.b, x.list, x.null)) - >>> results.collect()[0] # doctest: +NORMALIZE_WHITESPACE - (127, -128, -32768, 32767, 2147483647, 1.0, datetime.date(2010, 1, 1), - datetime.datetime(2010, 1, 1, 1, 1, 1), 1, 2, [1, 2, 3], None) - - >>> df.registerTempTable("table2") - >>> sqlCtx.sql( - ... "SELECT byte1 - 1 AS byte1, byte2 + 1 AS byte2, " + - ... "short1 + 1 AS short1, short2 - 1 AS short2, int - 1 AS int, " + - ... "float + 1.5 as float FROM table2").collect() - [Row(byte1=126, byte2=-127, short1=-32767, short2=32766, int=2147483646, float=2.5)] - - >>> rdd = sc.parallelize([(127, -32768, 1.0, - ... datetime(2010, 1, 1, 1, 1, 1), - ... {"a": 1}, (2,), [1, 2, 3])]) - >>> abstract = "byte short float time map{} struct(b) list[]" - >>> schema = _parse_schema_abstract(abstract) - >>> typedSchema = _infer_schema_type(rdd.first(), schema) - >>> df = sqlCtx.applySchema(rdd, typedSchema) - >>> df.collect() - [Row(byte=127, short=-32768, float=1.0, time=..., list=[1, 2, 3])] - """ - - if isinstance(rdd, DataFrame): - raise TypeError("Cannot apply schema to DataFrame") - - if not isinstance(schema, StructType): - raise TypeError("schema should be StructType") - - # take the first few rows to verify schema - rows = rdd.take(10) - # Row() cannot been deserialized by Pyrolite - if rows and isinstance(rows[0], tuple) and rows[0].__class__.__name__ == 'Row': - rdd = rdd.map(tuple) - rows = rdd.take(10) - - for row in rows: - _verify_type(row, schema) - - # convert python objects to sql data - converter = _python_to_sql_converter(schema) - rdd = rdd.map(converter) - - jrdd = self._jvm.SerDeUtil.toJavaArray(rdd._to_java_object_rdd()) - df = self._ssql_ctx.applySchemaToPythonRDD(jrdd.rdd(), schema.json()) - return DataFrame(df, self) - - def registerRDDAsTable(self, rdd, tableName): - """Registers the given RDD as a temporary table in the catalog. - - Temporary tables exist only during the lifetime of this instance of - SQLContext. - - >>> df = sqlCtx.inferSchema(rdd) - >>> sqlCtx.registerRDDAsTable(df, "table1") - """ - if (rdd.__class__ is DataFrame): - df = rdd._jdf - self._ssql_ctx.registerRDDAsTable(df, tableName) - else: - raise ValueError("Can only register DataFrame as table") - - def parquetFile(self, *paths): - """Loads a Parquet file, returning the result as a L{DataFrame}. - - >>> import tempfile, shutil - >>> parquetFile = tempfile.mkdtemp() - >>> shutil.rmtree(parquetFile) - >>> df = sqlCtx.inferSchema(rdd) - >>> df.saveAsParquetFile(parquetFile) - >>> df2 = sqlCtx.parquetFile(parquetFile) - >>> sorted(df.collect()) == sorted(df2.collect()) - True - """ - gateway = self._sc._gateway - jpath = paths[0] - jpaths = gateway.new_array(gateway.jvm.java.lang.String, len(paths) - 1) - for i in range(1, len(paths)): - jpaths[i] = paths[i] - jdf = self._ssql_ctx.parquetFile(jpath, jpaths) - return DataFrame(jdf, self) - - def jsonFile(self, path, schema=None, samplingRatio=1.0): - """ - Loads a text file storing one JSON object per line as a - L{DataFrame}. - - If the schema is provided, applies the given schema to this - JSON dataset. - - Otherwise, it samples the dataset with ratio `samplingRatio` to - determine the schema. - - >>> import tempfile, shutil - >>> jsonFile = tempfile.mkdtemp() - >>> shutil.rmtree(jsonFile) - >>> ofn = open(jsonFile, 'w') - >>> for json in jsonStrings: - ... print>>ofn, json - >>> ofn.close() - >>> df1 = sqlCtx.jsonFile(jsonFile) - >>> sqlCtx.registerRDDAsTable(df1, "table1") - >>> df2 = sqlCtx.sql( - ... "SELECT field1 AS f1, field2 as f2, field3 as f3, " - ... "field6 as f4 from table1") - >>> for r in df2.collect(): - ... print r - Row(f1=1, f2=u'row1', f3=Row(field4=11, field5=None), f4=None) - Row(f1=2, f2=None, f3=Row(field4=22,..., f4=[Row(field7=u'row2')]) - Row(f1=None, f2=u'row3', f3=Row(field4=33, field5=[]), f4=None) - - >>> df3 = sqlCtx.jsonFile(jsonFile, df1.schema()) - >>> sqlCtx.registerRDDAsTable(df3, "table2") - >>> df4 = sqlCtx.sql( - ... "SELECT field1 AS f1, field2 as f2, field3 as f3, " - ... "field6 as f4 from table2") - >>> for r in df4.collect(): - ... print r - Row(f1=1, f2=u'row1', f3=Row(field4=11, field5=None), f4=None) - Row(f1=2, f2=None, f3=Row(field4=22,..., f4=[Row(field7=u'row2')]) - Row(f1=None, f2=u'row3', f3=Row(field4=33, field5=[]), f4=None) - - >>> schema = StructType([ - ... StructField("field2", StringType(), True), - ... StructField("field3", - ... StructType([ - ... StructField("field5", - ... ArrayType(IntegerType(), False), True)]), False)]) - >>> df5 = sqlCtx.jsonFile(jsonFile, schema) - >>> sqlCtx.registerRDDAsTable(df5, "table3") - >>> df6 = sqlCtx.sql( - ... "SELECT field2 AS f1, field3.field5 as f2, " - ... "field3.field5[0] as f3 from table3") - >>> df6.collect() - [Row(f1=u'row1', f2=None, f3=None)...Row(f1=u'row3', f2=[], f3=None)] - """ - if schema is None: - df = self._ssql_ctx.jsonFile(path, samplingRatio) - else: - scala_datatype = self._ssql_ctx.parseDataType(schema.json()) - df = self._ssql_ctx.jsonFile(path, scala_datatype) - return DataFrame(df, self) - - def jsonRDD(self, rdd, schema=None, samplingRatio=1.0): - """Loads an RDD storing one JSON object per string as a L{DataFrame}. - - If the schema is provided, applies the given schema to this - JSON dataset. - - Otherwise, it samples the dataset with ratio `samplingRatio` to - determine the schema. - - >>> df1 = sqlCtx.jsonRDD(json) - >>> sqlCtx.registerRDDAsTable(df1, "table1") - >>> df2 = sqlCtx.sql( - ... "SELECT field1 AS f1, field2 as f2, field3 as f3, " - ... "field6 as f4 from table1") - >>> for r in df2.collect(): - ... print r - Row(f1=1, f2=u'row1', f3=Row(field4=11, field5=None), f4=None) - Row(f1=2, f2=None, f3=Row(field4=22..., f4=[Row(field7=u'row2')]) - Row(f1=None, f2=u'row3', f3=Row(field4=33, field5=[]), f4=None) - - >>> df3 = sqlCtx.jsonRDD(json, df1.schema()) - >>> sqlCtx.registerRDDAsTable(df3, "table2") - >>> df4 = sqlCtx.sql( - ... "SELECT field1 AS f1, field2 as f2, field3 as f3, " - ... "field6 as f4 from table2") - >>> for r in df4.collect(): - ... print r - Row(f1=1, f2=u'row1', f3=Row(field4=11, field5=None), f4=None) - Row(f1=2, f2=None, f3=Row(field4=22..., f4=[Row(field7=u'row2')]) - Row(f1=None, f2=u'row3', f3=Row(field4=33, field5=[]), f4=None) - - >>> schema = StructType([ - ... StructField("field2", StringType(), True), - ... StructField("field3", - ... StructType([ - ... StructField("field5", - ... ArrayType(IntegerType(), False), True)]), False)]) - >>> df5 = sqlCtx.jsonRDD(json, schema) - >>> sqlCtx.registerRDDAsTable(df5, "table3") - >>> df6 = sqlCtx.sql( - ... "SELECT field2 AS f1, field3.field5 as f2, " - ... "field3.field5[0] as f3 from table3") - >>> df6.collect() - [Row(f1=u'row1', f2=None,...Row(f1=u'row3', f2=[], f3=None)] - - >>> sqlCtx.jsonRDD(sc.parallelize(['{}', - ... '{"key0": {"key1": "value1"}}'])).collect() - [Row(key0=None), Row(key0=Row(key1=u'value1'))] - >>> sqlCtx.jsonRDD(sc.parallelize(['{"key0": null}', - ... '{"key0": {"key1": "value1"}}'])).collect() - [Row(key0=None), Row(key0=Row(key1=u'value1'))] - """ - - def func(iterator): - for x in iterator: - if not isinstance(x, basestring): - x = unicode(x) - if isinstance(x, unicode): - x = x.encode("utf-8") - yield x - keyed = rdd.mapPartitions(func) - keyed._bypass_serializer = True - jrdd = keyed._jrdd.map(self._jvm.BytesToString()) - if schema is None: - df = self._ssql_ctx.jsonRDD(jrdd.rdd(), samplingRatio) - else: - scala_datatype = self._ssql_ctx.parseDataType(schema.json()) - df = self._ssql_ctx.jsonRDD(jrdd.rdd(), scala_datatype) - return DataFrame(df, self) - - def sql(self, sqlQuery): - """Return a L{DataFrame} representing the result of the given query. - - >>> df = sqlCtx.inferSchema(rdd) - >>> sqlCtx.registerRDDAsTable(df, "table1") - >>> df2 = sqlCtx.sql("SELECT field1 AS f1, field2 as f2 from table1") - >>> df2.collect() - [Row(f1=1, f2=u'row1'), Row(f1=2, f2=u'row2'), Row(f1=3, f2=u'row3')] - """ - return DataFrame(self._ssql_ctx.sql(sqlQuery), self) - - def table(self, tableName): - """Returns the specified table as a L{DataFrame}. - - >>> df = sqlCtx.inferSchema(rdd) - >>> sqlCtx.registerRDDAsTable(df, "table1") - >>> df2 = sqlCtx.table("table1") - >>> sorted(df.collect()) == sorted(df2.collect()) - True - """ - return DataFrame(self._ssql_ctx.table(tableName), self) - - def cacheTable(self, tableName): - """Caches the specified table in-memory.""" - self._ssql_ctx.cacheTable(tableName) - - def uncacheTable(self, tableName): - """Removes the specified table from the in-memory cache.""" - self._ssql_ctx.uncacheTable(tableName) - - -class HiveContext(SQLContext): - - """A variant of Spark SQL that integrates with data stored in Hive. - - Configuration for Hive is read from hive-site.xml on the classpath. - It supports running both SQL and HiveQL commands. - """ - - def __init__(self, sparkContext, hiveContext=None): - """Create a new HiveContext. - - :param sparkContext: The SparkContext to wrap. - :param hiveContext: An optional JVM Scala HiveContext. If set, we do not instatiate a new - HiveContext in the JVM, instead we make all calls to this object. - """ - SQLContext.__init__(self, sparkContext) - - if hiveContext: - self._scala_HiveContext = hiveContext - - @property - def _ssql_ctx(self): - try: - if not hasattr(self, '_scala_HiveContext'): - self._scala_HiveContext = self._get_hive_ctx() - return self._scala_HiveContext - except Py4JError as e: - raise Exception("You must build Spark with Hive. " - "Export 'SPARK_HIVE=true' and run " - "build/sbt assembly", e) - - def _get_hive_ctx(self): - return self._jvm.HiveContext(self._jsc.sc()) - - -def _create_row(fields, values): - row = Row(*values) - row.__FIELDS__ = fields - return row - - -class Row(tuple): - - """ - A row in L{DataFrame}. The fields in it can be accessed like attributes. - - Row can be used to create a row object by using named arguments, - the fields will be sorted by names. - - >>> row = Row(name="Alice", age=11) - >>> row - Row(age=11, name='Alice') - >>> row.name, row.age - ('Alice', 11) - - Row also can be used to create another Row like class, then it - could be used to create Row objects, such as - - >>> Person = Row("name", "age") - >>> Person - - >>> Person("Alice", 11) - Row(name='Alice', age=11) - """ - - def __new__(self, *args, **kwargs): - if args and kwargs: - raise ValueError("Can not use both args " - "and kwargs to create Row") - if args: - # create row class or objects - return tuple.__new__(self, args) - - elif kwargs: - # create row objects - names = sorted(kwargs.keys()) - values = tuple(kwargs[n] for n in names) - row = tuple.__new__(self, values) - row.__FIELDS__ = names - return row - - else: - raise ValueError("No args or kwargs") - - def asDict(self): - """ - Return as an dict - """ - if not hasattr(self, "__FIELDS__"): - raise TypeError("Cannot convert a Row class into dict") - return dict(zip(self.__FIELDS__, self)) - - # let obect acs like class - def __call__(self, *args): - """create new Row object""" - return _create_row(self, args) - - def __getattr__(self, item): - if item.startswith("__"): - raise AttributeError(item) - try: - # it will be slow when it has many fields, - # but this will not be used in normal cases - idx = self.__FIELDS__.index(item) - return self[idx] - except IndexError: - raise AttributeError(item) - - def __reduce__(self): - if hasattr(self, "__FIELDS__"): - return (_create_row, (self.__FIELDS__, tuple(self))) - else: - return tuple.__reduce__(self) - - def __repr__(self): - if hasattr(self, "__FIELDS__"): - return "Row(%s)" % ", ".join("%s=%r" % (k, v) - for k, v in zip(self.__FIELDS__, self)) - else: - return "" % ", ".join(self) - - -class DataFrame(object): - - """A collection of rows that have the same columns. - - A :class:`DataFrame` is equivalent to a relational table in Spark SQL, - and can be created using various functions in :class:`SQLContext`:: - - people = sqlContext.parquetFile("...") - - Once created, it can be manipulated using the various domain-specific-language - (DSL) functions defined in: :class:`DataFrame`, :class:`Column`. - - To select a column from the data frame, use the apply method:: - - ageCol = people.age - - Note that the :class:`Column` type can also be manipulated - through its various functions:: - - # The following creates a new column that increases everybody's age by 10. - people.age + 10 - - - A more concrete example:: - - # To create DataFrame using SQLContext - people = sqlContext.parquetFile("...") - department = sqlContext.parquetFile("...") - - people.filter(people.age > 30).join(department, people.deptId == department.id)) \ - .groupBy(department.name, "gender").agg({"salary": "avg", "age": "max"}) - """ - - def __init__(self, jdf, sql_ctx): - self._jdf = jdf - self.sql_ctx = sql_ctx - self._sc = sql_ctx and sql_ctx._sc - self.is_cached = False - - @property - def rdd(self): - """ - Return the content of the :class:`DataFrame` as an :class:`RDD` - of :class:`Row` s. - """ - if not hasattr(self, '_lazy_rdd'): - jrdd = self._jdf.javaToPython() - rdd = RDD(jrdd, self.sql_ctx._sc, BatchedSerializer(PickleSerializer())) - schema = self.schema() - - def applySchema(it): - cls = _create_cls(schema) - return itertools.imap(cls, it) - - self._lazy_rdd = rdd.mapPartitions(applySchema) - - return self._lazy_rdd - - def toJSON(self, use_unicode=False): - """Convert a DataFrame into a MappedRDD of JSON documents; one document per row. - - >>> df1 = sqlCtx.jsonRDD(json) - >>> sqlCtx.registerRDDAsTable(df1, "table1") - >>> df2 = sqlCtx.sql( "SELECT * from table1") - >>> df2.toJSON().take(1)[0] == '{"field1":1,"field2":"row1","field3":{"field4":11}}' - True - >>> df3 = sqlCtx.sql( "SELECT field3.field4 from table1") - >>> df3.toJSON().collect() == ['{"field4":11}', '{"field4":22}', '{"field4":33}'] - True - """ - rdd = self._jdf.toJSON() - return RDD(rdd.toJavaRDD(), self._sc, UTF8Deserializer(use_unicode)) - - def saveAsParquetFile(self, path): - """Save the contents as a Parquet file, preserving the schema. - - Files that are written out using this method can be read back in as - a DataFrame using the L{SQLContext.parquetFile} method. - - >>> import tempfile, shutil - >>> parquetFile = tempfile.mkdtemp() - >>> shutil.rmtree(parquetFile) - >>> df.saveAsParquetFile(parquetFile) - >>> df2 = sqlCtx.parquetFile(parquetFile) - >>> sorted(df2.collect()) == sorted(df.collect()) - True - """ - self._jdf.saveAsParquetFile(path) - - def registerTempTable(self, name): - """Registers this RDD as a temporary table using the given name. - - The lifetime of this temporary table is tied to the L{SQLContext} - that was used to create this DataFrame. - - >>> df.registerTempTable("people") - >>> df2 = sqlCtx.sql("select * from people") - >>> sorted(df.collect()) == sorted(df2.collect()) - True - """ - self._jdf.registerTempTable(name) - - def registerAsTable(self, name): - """DEPRECATED: use registerTempTable() instead""" - warnings.warn("Use registerTempTable instead of registerAsTable.", DeprecationWarning) - self.registerTempTable(name) - - def insertInto(self, tableName, overwrite=False): - """Inserts the contents of this DataFrame into the specified table. - - Optionally overwriting any existing data. - """ - self._jdf.insertInto(tableName, overwrite) - - def saveAsTable(self, tableName): - """Creates a new table with the contents of this DataFrame.""" - self._jdf.saveAsTable(tableName) - - def schema(self): - """Returns the schema of this DataFrame (represented by - a L{StructType}). - - >>> df.schema() - StructType(List(StructField(age,IntegerType,true),StructField(name,StringType,true))) - """ - return _parse_datatype_json_string(self._jdf.schema().json()) - - def printSchema(self): - """Prints out the schema in the tree format. - - >>> df.printSchema() - root - |-- age: integer (nullable = true) - |-- name: string (nullable = true) - - """ - print (self._jdf.schema().treeString()) - - def count(self): - """Return the number of elements in this RDD. - - Unlike the base RDD implementation of count, this implementation - leverages the query optimizer to compute the count on the DataFrame, - which supports features such as filter pushdown. - - >>> df.count() - 2L - """ - return self._jdf.count() - - def collect(self): - """Return a list that contains all of the rows. - - Each object in the list is a Row, the fields can be accessed as - attributes. - - >>> df.collect() - [Row(age=2, name=u'Alice'), Row(age=5, name=u'Bob')] - """ - with SCCallSiteSync(self._sc) as css: - bytesInJava = self._jdf.javaToPython().collect().iterator() - tempFile = NamedTemporaryFile(delete=False, dir=self._sc._temp_dir) - tempFile.close() - self._sc._writeToFile(bytesInJava, tempFile.name) - # Read the data into Python and deserialize it: - with open(tempFile.name, 'rb') as tempFile: - rs = list(BatchedSerializer(PickleSerializer()).load_stream(tempFile)) - os.unlink(tempFile.name) - cls = _create_cls(self.schema()) - return [cls(r) for r in rs] - - def limit(self, num): - """Limit the result count to the number specified. - - >>> df.limit(1).collect() - [Row(age=2, name=u'Alice')] - >>> df.limit(0).collect() - [] - """ - jdf = self._jdf.limit(num) - return DataFrame(jdf, self.sql_ctx) - - def take(self, num): - """Take the first num rows of the RDD. - - Each object in the list is a Row, the fields can be accessed as - attributes. - - >>> df.take(2) - [Row(age=2, name=u'Alice'), Row(age=5, name=u'Bob')] - """ - return self.limit(num).collect() - - def map(self, f): - """ Return a new RDD by applying a function to each Row, it's a - shorthand for df.rdd.map() - - >>> df.map(lambda p: p.name).collect() - [u'Alice', u'Bob'] - """ - return self.rdd.map(f) - - def mapPartitions(self, f, preservesPartitioning=False): - """ - Return a new RDD by applying a function to each partition. - - >>> rdd = sc.parallelize([1, 2, 3, 4], 4) - >>> def f(iterator): yield 1 - >>> rdd.mapPartitions(f).sum() - 4 - """ - return self.rdd.mapPartitions(f, preservesPartitioning) - - def cache(self): - """ Persist with the default storage level (C{MEMORY_ONLY_SER}). - """ - self.is_cached = True - self._jdf.cache() - return self - - def persist(self, storageLevel=StorageLevel.MEMORY_ONLY_SER): - """ Set the storage level to persist its values across operations - after the first time it is computed. This can only be used to assign - a new storage level if the RDD does not have a storage level set yet. - If no storage level is specified defaults to (C{MEMORY_ONLY_SER}). - """ - self.is_cached = True - javaStorageLevel = self._sc._getJavaStorageLevel(storageLevel) - self._jdf.persist(javaStorageLevel) - return self - - def unpersist(self, blocking=True): - """ Mark it as non-persistent, and remove all blocks for it from - memory and disk. - """ - self.is_cached = False - self._jdf.unpersist(blocking) - return self - - # def coalesce(self, numPartitions, shuffle=False): - # rdd = self._jdf.coalesce(numPartitions, shuffle, None) - # return DataFrame(rdd, self.sql_ctx) - - def repartition(self, numPartitions): - """ Return a new :class:`DataFrame` that has exactly `numPartitions` - partitions. - """ - rdd = self._jdf.repartition(numPartitions, None) - return DataFrame(rdd, self.sql_ctx) - - def sample(self, withReplacement, fraction, seed=None): - """ - Return a sampled subset of this DataFrame. - - >>> df = sqlCtx.inferSchema(rdd) - >>> df.sample(False, 0.5, 97).count() - 2L - """ - assert fraction >= 0.0, "Negative fraction value: %s" % fraction - seed = seed if seed is not None else random.randint(0, sys.maxint) - rdd = self._jdf.sample(withReplacement, fraction, long(seed)) - return DataFrame(rdd, self.sql_ctx) - - # def takeSample(self, withReplacement, num, seed=None): - # """Return a fixed-size sampled subset of this DataFrame. - # - # >>> df = sqlCtx.inferSchema(rdd) - # >>> df.takeSample(False, 2, 97) - # [Row(field1=3, field2=u'row3'), Row(field1=1, field2=u'row1')] - # """ - # seed = seed if seed is not None else random.randint(0, sys.maxint) - # with SCCallSiteSync(self.context) as css: - # bytesInJava = self._jdf \ - # .takeSampleToPython(withReplacement, num, long(seed)) \ - # .iterator() - # cls = _create_cls(self.schema()) - # return map(cls, self._collect_iterator_through_file(bytesInJava)) - - @property - def dtypes(self): - """Return all column names and their data types as a list. - - >>> df.dtypes - [('age', 'integer'), ('name', 'string')] - """ - return [(str(f.name), f.dataType.jsonValue()) for f in self.schema().fields] - - @property - def columns(self): - """ Return all column names as a list. - - >>> df.columns - [u'age', u'name'] - """ - return [f.name for f in self.schema().fields] - - def join(self, other, joinExprs=None, joinType=None): - """ - Join with another DataFrame, using the given join expression. - The following performs a full outer join between `df1` and `df2`:: - - :param other: Right side of the join - :param joinExprs: Join expression - :param joinType: One of `inner`, `outer`, `left_outer`, `right_outer`, `semijoin`. - - >>> df.join(df2, df.name == df2.name, 'outer').select(df.name, df2.height).collect() - [Row(name=None, height=80), Row(name=u'Bob', height=85), Row(name=u'Alice', height=None)] - """ - - if joinExprs is None: - jdf = self._jdf.join(other._jdf) - else: - assert isinstance(joinExprs, Column), "joinExprs should be Column" - if joinType is None: - jdf = self._jdf.join(other._jdf, joinExprs._jc) - else: - assert isinstance(joinType, basestring), "joinType should be basestring" - jdf = self._jdf.join(other._jdf, joinExprs._jc, joinType) - return DataFrame(jdf, self.sql_ctx) - - def sort(self, *cols): - """ Return a new :class:`DataFrame` sorted by the specified column. - - :param cols: The columns or expressions used for sorting - - >>> df.sort(df.age.desc()).collect() - [Row(age=5, name=u'Bob'), Row(age=2, name=u'Alice')] - >>> df.sortBy(df.age.desc()).collect() - [Row(age=5, name=u'Bob'), Row(age=2, name=u'Alice')] - """ - if not cols: - raise ValueError("should sort by at least one column") - jcols = ListConverter().convert([_to_java_column(c) for c in cols], - self._sc._gateway._gateway_client) - jdf = self._jdf.sort(self._sc._jvm.PythonUtils.toSeq(jcols)) - return DataFrame(jdf, self.sql_ctx) - - sortBy = sort - - def head(self, n=None): - """ Return the first `n` rows or the first row if n is None. - - >>> df.head() - Row(age=2, name=u'Alice') - >>> df.head(1) - [Row(age=2, name=u'Alice')] - """ - if n is None: - rs = self.head(1) - return rs[0] if rs else None - return self.take(n) - - def first(self): - """ Return the first row. - - >>> df.first() - Row(age=2, name=u'Alice') - """ - return self.head() - - def __getitem__(self, item): - """ Return the column by given name - - >>> df['age'].collect() - [Row(age=2), Row(age=5)] - >>> df[ ["name", "age"]].collect() - [Row(name=u'Alice', age=2), Row(name=u'Bob', age=5)] - >>> df[ df.age > 3 ].collect() - [Row(age=5, name=u'Bob')] - """ - if isinstance(item, basestring): - jc = self._jdf.apply(item) - return Column(jc, self.sql_ctx) - elif isinstance(item, Column): - return self.filter(item) - elif isinstance(item, list): - return self.select(*item) - else: - raise IndexError("unexpected index: %s" % item) - - def __getattr__(self, name): - """ Return the column by given name - - >>> df.age.collect() - [Row(age=2), Row(age=5)] - """ - if name.startswith("__"): - raise AttributeError(name) - jc = self._jdf.apply(name) - return Column(jc, self.sql_ctx) - - def select(self, *cols): - """ Selecting a set of expressions. - - >>> df.select().collect() - [Row(age=2, name=u'Alice'), Row(age=5, name=u'Bob')] - >>> df.select('*').collect() - [Row(age=2, name=u'Alice'), Row(age=5, name=u'Bob')] - >>> df.select('name', 'age').collect() - [Row(name=u'Alice', age=2), Row(name=u'Bob', age=5)] - >>> df.select(df.name, (df.age + 10).alias('age')).collect() - [Row(name=u'Alice', age=12), Row(name=u'Bob', age=15)] - """ - if not cols: - cols = ["*"] - jcols = ListConverter().convert([_to_java_column(c) for c in cols], - self._sc._gateway._gateway_client) - jdf = self._jdf.select(self.sql_ctx._sc._jvm.PythonUtils.toSeq(jcols)) - return DataFrame(jdf, self.sql_ctx) - - def selectExpr(self, *expr): - """ - Selects a set of SQL expressions. This is a variant of - `select` that accepts SQL expressions. - - >>> df.selectExpr("age * 2", "abs(age)").collect() - [Row(('age * 2)=4, Abs('age)=2), Row(('age * 2)=10, Abs('age)=5)] - """ - jexpr = ListConverter().convert(expr, self._sc._gateway._gateway_client) - jdf = self._jdf.selectExpr(self._sc._jvm.PythonUtils.toSeq(jexpr)) - return DataFrame(jdf, self.sql_ctx) - - def filter(self, condition): - """ Filtering rows using the given condition, which could be - Column expression or string of SQL expression. - - where() is an alias for filter(). - - >>> df.filter(df.age > 3).collect() - [Row(age=5, name=u'Bob')] - >>> df.where(df.age == 2).collect() - [Row(age=2, name=u'Alice')] - - >>> df.filter("age > 3").collect() - [Row(age=5, name=u'Bob')] - >>> df.where("age = 2").collect() - [Row(age=2, name=u'Alice')] - """ - if isinstance(condition, basestring): - jdf = self._jdf.filter(condition) - elif isinstance(condition, Column): - jdf = self._jdf.filter(condition._jc) - else: - raise TypeError("condition should be string or Column") - return DataFrame(jdf, self.sql_ctx) - - where = filter - - def groupBy(self, *cols): - """ Group the :class:`DataFrame` using the specified columns, - so we can run aggregation on them. See :class:`GroupedData` - for all the available aggregate functions. - - >>> df.groupBy().avg().collect() - [Row(AVG(age#0)=3.5)] - >>> df.groupBy('name').agg({'age': 'mean'}).collect() - [Row(name=u'Bob', AVG(age#0)=5.0), Row(name=u'Alice', AVG(age#0)=2.0)] - >>> df.groupBy(df.name).avg().collect() - [Row(name=u'Bob', AVG(age#0)=5.0), Row(name=u'Alice', AVG(age#0)=2.0)] - """ - jcols = ListConverter().convert([_to_java_column(c) for c in cols], - self._sc._gateway._gateway_client) - jdf = self._jdf.groupBy(self.sql_ctx._sc._jvm.PythonUtils.toSeq(jcols)) - return GroupedData(jdf, self.sql_ctx) - - def agg(self, *exprs): - """ Aggregate on the entire :class:`DataFrame` without groups - (shorthand for df.groupBy.agg()). - - >>> df.agg({"age": "max"}).collect() - [Row(MAX(age#0)=5)] - >>> from pyspark.sql import Dsl - >>> df.agg(Dsl.min(df.age)).collect() - [Row(MIN(age#0)=2)] - """ - return self.groupBy().agg(*exprs) - - def unionAll(self, other): - """ Return a new DataFrame containing union of rows in this - frame and another frame. - - This is equivalent to `UNION ALL` in SQL. - """ - return DataFrame(self._jdf.unionAll(other._jdf), self.sql_ctx) - - def intersect(self, other): - """ Return a new :class:`DataFrame` containing rows only in - both this frame and another frame. - - This is equivalent to `INTERSECT` in SQL. - """ - return DataFrame(self._jdf.intersect(other._jdf), self.sql_ctx) - - def subtract(self, other): - """ Return a new :class:`DataFrame` containing rows in this frame - but not in another frame. - - This is equivalent to `EXCEPT` in SQL. - """ - return DataFrame(getattr(self._jdf, "except")(other._jdf), self.sql_ctx) - - def addColumn(self, colName, col): - """ Return a new :class:`DataFrame` by adding a column. - - >>> df.addColumn('age2', df.age + 2).collect() - [Row(age=2, name=u'Alice', age2=4), Row(age=5, name=u'Bob', age2=7)] - """ - return self.select('*', col.alias(colName)) - - -# Having SchemaRDD for backward compatibility (for docs) -class SchemaRDD(DataFrame): - """ - SchemaRDD is deprecated, please use DataFrame - """ - - -def dfapi(f): - def _api(self): - name = f.__name__ - jdf = getattr(self._jdf, name)() - return DataFrame(jdf, self.sql_ctx) - _api.__name__ = f.__name__ - _api.__doc__ = f.__doc__ - return _api - - -class GroupedData(object): - - """ - A set of methods for aggregations on a :class:`DataFrame`, - created by DataFrame.groupBy(). - """ - - def __init__(self, jdf, sql_ctx): - self._jdf = jdf - self.sql_ctx = sql_ctx - - def agg(self, *exprs): - """ Compute aggregates by specifying a map from column name - to aggregate methods. - - The available aggregate methods are `avg`, `max`, `min`, - `sum`, `count`. - - :param exprs: list or aggregate columns or a map from column - name to aggregate methods. - - >>> gdf = df.groupBy(df.name) - >>> gdf.agg({"age": "max"}).collect() - [Row(name=u'Bob', MAX(age#0)=5), Row(name=u'Alice', MAX(age#0)=2)] - >>> from pyspark.sql import Dsl - >>> gdf.agg(Dsl.min(df.age)).collect() - [Row(MIN(age#0)=5), Row(MIN(age#0)=2)] - """ - assert exprs, "exprs should not be empty" - if len(exprs) == 1 and isinstance(exprs[0], dict): - jmap = MapConverter().convert(exprs[0], - self.sql_ctx._sc._gateway._gateway_client) - jdf = self._jdf.agg(jmap) - else: - # Columns - assert all(isinstance(c, Column) for c in exprs), "all exprs should be Column" - jcols = ListConverter().convert([c._jc for c in exprs[1:]], - self.sql_ctx._sc._gateway._gateway_client) - jdf = self._jdf.agg(exprs[0]._jc, self.sql_ctx._sc._jvm.PythonUtils.toSeq(jcols)) - return DataFrame(jdf, self.sql_ctx) - - @dfapi - def count(self): - """ Count the number of rows for each group. - - >>> df.groupBy(df.age).count().collect() - [Row(age=2, count=1), Row(age=5, count=1)] - """ - - @dfapi - def mean(self): - """Compute the average value for each numeric columns - for each group. This is an alias for `avg`.""" - - @dfapi - def avg(self): - """Compute the average value for each numeric columns - for each group.""" - - @dfapi - def max(self): - """Compute the max value for each numeric columns for - each group. """ - - @dfapi - def min(self): - """Compute the min value for each numeric column for - each group.""" - - @dfapi - def sum(self): - """Compute the sum for each numeric columns for each - group.""" - - -def _create_column_from_literal(literal): - sc = SparkContext._active_spark_context - return sc._jvm.Dsl.lit(literal) - - -def _create_column_from_name(name): - sc = SparkContext._active_spark_context - return sc._jvm.Dsl.col(name) - - -def _to_java_column(col): - if isinstance(col, Column): - jcol = col._jc - else: - jcol = _create_column_from_name(col) - return jcol - - -def _unary_op(name, doc="unary operator"): - """ Create a method for given unary operator """ - def _(self): - jc = getattr(self._jc, name)() - return Column(jc, self.sql_ctx) - _.__doc__ = doc - return _ - - -def _dsl_op(name, doc=''): - def _(self): - jc = getattr(self._sc._jvm.Dsl, name)(self._jc) - return Column(jc, self.sql_ctx) - _.__doc__ = doc - return _ - - -def _bin_op(name, doc="binary operator"): - """ Create a method for given binary operator - """ - def _(self, other): - jc = other._jc if isinstance(other, Column) else other - njc = getattr(self._jc, name)(jc) - return Column(njc, self.sql_ctx) - _.__doc__ = doc - return _ - - -def _reverse_op(name, doc="binary operator"): - """ Create a method for binary operator (this object is on right side) - """ - def _(self, other): - jother = _create_column_from_literal(other) - jc = getattr(jother, name)(self._jc) - return Column(jc, self.sql_ctx) - _.__doc__ = doc - return _ - - -class Column(DataFrame): - - """ - A column in a DataFrame. - - `Column` instances can be created by:: - - # 1. Select a column out of a DataFrame - df.colName - df["colName"] - - # 2. Create from an expression - df.colName + 1 - 1 / df.colName - """ - - def __init__(self, jc, sql_ctx=None): - self._jc = jc - super(Column, self).__init__(jc, sql_ctx) - - # arithmetic operators - __neg__ = _dsl_op("negate") - __add__ = _bin_op("plus") - __sub__ = _bin_op("minus") - __mul__ = _bin_op("multiply") - __div__ = _bin_op("divide") - __mod__ = _bin_op("mod") - __radd__ = _bin_op("plus") - __rsub__ = _reverse_op("minus") - __rmul__ = _bin_op("multiply") - __rdiv__ = _reverse_op("divide") - __rmod__ = _reverse_op("mod") - - # logistic operators - __eq__ = _bin_op("equalTo") - __ne__ = _bin_op("notEqual") - __lt__ = _bin_op("lt") - __le__ = _bin_op("leq") - __ge__ = _bin_op("geq") - __gt__ = _bin_op("gt") - - # `and`, `or`, `not` cannot be overloaded in Python, - # so use bitwise operators as boolean operators - __and__ = _bin_op('and') - __or__ = _bin_op('or') - __invert__ = _dsl_op('not') - __rand__ = _bin_op("and") - __ror__ = _bin_op("or") - - # container operators - __contains__ = _bin_op("contains") - __getitem__ = _bin_op("getItem") - getField = _bin_op("getField", "An expression that gets a field by name in a StructField.") - - # string methods - rlike = _bin_op("rlike") - like = _bin_op("like") - startswith = _bin_op("startsWith") - endswith = _bin_op("endsWith") - - def substr(self, startPos, length): - """ - Return a Column which is a substring of the column - - :param startPos: start position (int or Column) - :param length: length of the substring (int or Column) - - >>> df.name.substr(1, 3).collect() - [Row(col=u'Ali'), Row(col=u'Bob')] - """ - if type(startPos) != type(length): - raise TypeError("Can not mix the type") - if isinstance(startPos, (int, long)): - jc = self._jc.substr(startPos, length) - elif isinstance(startPos, Column): - jc = self._jc.substr(startPos._jc, length._jc) - else: - raise TypeError("Unexpected type: %s" % type(startPos)) - return Column(jc, self.sql_ctx) - - __getslice__ = substr - - # order - asc = _unary_op("asc") - desc = _unary_op("desc") - - isNull = _unary_op("isNull", "True if the current expression is null.") - isNotNull = _unary_op("isNotNull", "True if the current expression is not null.") - - def alias(self, alias): - """Return a alias for this column - - >>> df.age.alias("age2").collect() - [Row(age2=2), Row(age2=5)] - """ - return Column(getattr(self._jc, "as")(alias), self.sql_ctx) - - def cast(self, dataType): - """ Convert the column into type `dataType` - - >>> df.select(df.age.cast("string").alias('ages')).collect() - [Row(ages=u'2'), Row(ages=u'5')] - >>> df.select(df.age.cast(StringType()).alias('ages')).collect() - [Row(ages=u'2'), Row(ages=u'5')] - """ - if self.sql_ctx is None: - sc = SparkContext._active_spark_context - ssql_ctx = sc._jvm.SQLContext(sc._jsc.sc()) - else: - ssql_ctx = self.sql_ctx._ssql_ctx - if isinstance(dataType, basestring): - jc = self._jc.cast(dataType) - elif isinstance(dataType, DataType): - jdt = ssql_ctx.parseDataType(dataType.json()) - jc = self._jc.cast(jdt) - return Column(jc, self.sql_ctx) - - -def _aggregate_func(name, doc=""): - """ Create a function for aggregator by name""" - def _(col): - sc = SparkContext._active_spark_context - jc = getattr(sc._jvm.Dsl, name)(_to_java_column(col)) - return Column(jc) - _.__name__ = name - _.__doc__ = doc - return staticmethod(_) - - -class UserDefinedFunction(object): - def __init__(self, func, returnType): - self.func = func - self.returnType = returnType - self._broadcast = None - self._judf = self._create_judf() - - def _create_judf(self): - f = self.func # put it in closure `func` - func = lambda _, it: imap(lambda x: f(*x), it) - ser = AutoBatchedSerializer(PickleSerializer()) - command = (func, None, ser, ser) - sc = SparkContext._active_spark_context - pickled_command, broadcast_vars, env, includes = _prepare_for_python_RDD(sc, command, self) - ssql_ctx = sc._jvm.SQLContext(sc._jsc.sc()) - jdt = ssql_ctx.parseDataType(self.returnType.json()) - judf = sc._jvm.UserDefinedPythonFunction(f.__name__, bytearray(pickled_command), env, - includes, sc.pythonExec, broadcast_vars, - sc._javaAccumulator, jdt) - return judf - - def __del__(self): - if self._broadcast is not None: - self._broadcast.unpersist() - self._broadcast = None - - def __call__(self, *cols): - sc = SparkContext._active_spark_context - jcols = ListConverter().convert([_to_java_column(c) for c in cols], - sc._gateway._gateway_client) - jc = self._judf.apply(sc._jvm.PythonUtils.toSeq(jcols)) - return Column(jc) - - -class Dsl(object): - """ - A collections of builtin aggregators - """ - DSLS = { - 'lit': 'Creates a :class:`Column` of literal value.', - 'col': 'Returns a :class:`Column` based on the given column name.', - 'column': 'Returns a :class:`Column` based on the given column name.', - 'upper': 'Converts a string expression to upper case.', - 'lower': 'Converts a string expression to upper case.', - 'sqrt': 'Computes the square root of the specified float value.', - 'abs': 'Computes the absolutle value.', - - 'max': 'Aggregate function: returns the maximum value of the expression in a group.', - 'min': 'Aggregate function: returns the minimum value of the expression in a group.', - 'first': 'Aggregate function: returns the first value in a group.', - 'last': 'Aggregate function: returns the last value in a group.', - 'count': 'Aggregate function: returns the number of items in a group.', - 'sum': 'Aggregate function: returns the sum of all values in the expression.', - 'avg': 'Aggregate function: returns the average of the values in a group.', - 'mean': 'Aggregate function: returns the average of the values in a group.', - 'sumDistinct': 'Aggregate function: returns the sum of distinct values in the expression.', - } - - for _name, _doc in DSLS.items(): - locals()[_name] = _aggregate_func(_name, _doc) - del _name, _doc - - @staticmethod - def countDistinct(col, *cols): - """ Return a new Column for distinct count of (col, *cols) - - >>> from pyspark.sql import Dsl - >>> df.agg(Dsl.countDistinct(df.age, df.name).alias('c')).collect() - [Row(c=2)] - - >>> df.agg(Dsl.countDistinct("age", "name").alias('c')).collect() - [Row(c=2)] - """ - sc = SparkContext._active_spark_context - jcols = ListConverter().convert([_to_java_column(c) for c in cols], - sc._gateway._gateway_client) - jc = sc._jvm.Dsl.countDistinct(_to_java_column(col), - sc._jvm.PythonUtils.toSeq(jcols)) - return Column(jc) - - @staticmethod - def approxCountDistinct(col, rsd=None): - """ Return a new Column for approxiate distinct count of (col, *cols) - - >>> from pyspark.sql import Dsl - >>> df.agg(Dsl.approxCountDistinct(df.age).alias('c')).collect() - [Row(c=2)] - """ - sc = SparkContext._active_spark_context - if rsd is None: - jc = sc._jvm.Dsl.approxCountDistinct(_to_java_column(col)) - else: - jc = sc._jvm.Dsl.approxCountDistinct(_to_java_column(col), rsd) - return Column(jc) - - @staticmethod - def udf(f, returnType=StringType()): - """Create a user defined function (UDF) - - >>> slen = Dsl.udf(lambda s: len(s), IntegerType()) - >>> df.select(slen(df.name).alias('slen')).collect() - [Row(slen=5), Row(slen=3)] - """ - return UserDefinedFunction(f, returnType) - - -def _test(): - import doctest - from pyspark.context import SparkContext - # let doctest run in pyspark.sql, so DataTypes can be picklable - import pyspark.sql - from pyspark.sql import Row, SQLContext - from pyspark.sql_tests import ExamplePoint, ExamplePointUDT - globs = pyspark.sql.__dict__.copy() - sc = SparkContext('local[4]', 'PythonTest') - globs['sc'] = sc - globs['sqlCtx'] = sqlCtx = SQLContext(sc) - globs['rdd'] = sc.parallelize( - [Row(field1=1, field2="row1"), - Row(field1=2, field2="row2"), - Row(field1=3, field2="row3")] - ) - rdd2 = sc.parallelize([Row(name='Alice', age=2), Row(name='Bob', age=5)]) - rdd3 = sc.parallelize([Row(name='Tom', height=80), Row(name='Bob', height=85)]) - globs['df'] = sqlCtx.inferSchema(rdd2) - globs['df2'] = sqlCtx.inferSchema(rdd3) - globs['ExamplePoint'] = ExamplePoint - globs['ExamplePointUDT'] = ExamplePointUDT - jsonStrings = [ - '{"field1": 1, "field2": "row1", "field3":{"field4":11}}', - '{"field1" : 2, "field3":{"field4":22, "field5": [10, 11]},' - '"field6":[{"field7": "row2"}]}', - '{"field1" : null, "field2": "row3", ' - '"field3":{"field4":33, "field5": []}}' - ] - globs['jsonStrings'] = jsonStrings - globs['json'] = sc.parallelize(jsonStrings) - (failure_count, test_count) = doctest.testmod( - pyspark.sql, globs=globs, optionflags=doctest.ELLIPSIS) - globs['sc'].stop() - if failure_count: - exit(-1) - - -if __name__ == "__main__": - _test() diff --git a/python/pyspark/sql/__init__.py b/python/pyspark/sql/__init__.py new file mode 100644 index 0000000000000..6d54b9e49ed10 --- /dev/null +++ b/python/pyspark/sql/__init__.py @@ -0,0 +1,60 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +""" +Important classes of Spark SQL and DataFrames: + + - L{SQLContext} + Main entry point for :class:`DataFrame` and SQL functionality. + - L{DataFrame} + A distributed collection of data grouped into named columns. + - L{Column} + A column expression in a :class:`DataFrame`. + - L{Row} + A row of data in a :class:`DataFrame`. + - L{HiveContext} + Main entry point for accessing data stored in Apache Hive. + - L{GroupedData} + Aggregation methods, returned by :func:`DataFrame.groupBy`. + - L{DataFrameNaFunctions} + Methods for handling missing data (null values). + - L{functions} + List of built-in functions available for :class:`DataFrame`. + - L{types} + List of data types available. +""" +from __future__ import absolute_import + +# fix the module name conflict for Python 3+ +import sys +from . import _types as types +modname = __name__ + '.types' +types.__name__ = modname +# update the __module__ for all objects, make them picklable +for v in types.__dict__.values(): + if hasattr(v, "__module__") and v.__module__.endswith('._types'): + v.__module__ = modname +sys.modules[modname] = types +del modname, sys + +from pyspark.sql.types import Row +from pyspark.sql.context import SQLContext, HiveContext +from pyspark.sql.dataframe import DataFrame, GroupedData, Column, SchemaRDD, DataFrameNaFunctions + +__all__ = [ + 'SQLContext', 'HiveContext', 'DataFrame', 'GroupedData', 'Column', 'Row', 'DataFrameNaFunctions' +] diff --git a/python/pyspark/sql/_types.py b/python/pyspark/sql/_types.py new file mode 100644 index 0000000000000..492c0cbdcf693 --- /dev/null +++ b/python/pyspark/sql/_types.py @@ -0,0 +1,1261 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import sys +import decimal +import datetime +import keyword +import warnings +import json +import re +import weakref +from array import array +from operator import itemgetter + +if sys.version >= "3": + long = int + unicode = str + +__all__ = [ + "DataType", "NullType", "StringType", "BinaryType", "BooleanType", "DateType", + "TimestampType", "DecimalType", "DoubleType", "FloatType", "ByteType", "IntegerType", + "LongType", "ShortType", "ArrayType", "MapType", "StructField", "StructType"] + + +class DataType(object): + """Base class for data types.""" + + def __repr__(self): + return self.__class__.__name__ + + def __hash__(self): + return hash(str(self)) + + def __eq__(self, other): + return isinstance(other, self.__class__) and self.__dict__ == other.__dict__ + + def __ne__(self, other): + return not self.__eq__(other) + + @classmethod + def typeName(cls): + return cls.__name__[:-4].lower() + + def simpleString(self): + return self.typeName() + + def jsonValue(self): + return self.typeName() + + def json(self): + return json.dumps(self.jsonValue(), + separators=(',', ':'), + sort_keys=True) + + +# This singleton pattern does not work with pickle, you will get +# another object after pickle and unpickle +class PrimitiveTypeSingleton(type): + """Metaclass for PrimitiveType""" + + _instances = {} + + def __call__(cls): + if cls not in cls._instances: + cls._instances[cls] = super(PrimitiveTypeSingleton, cls).__call__() + return cls._instances[cls] + + +class PrimitiveType(DataType): + """Spark SQL PrimitiveType""" + + __metaclass__ = PrimitiveTypeSingleton + + +class NullType(PrimitiveType): + """Null type. + + The data type representing None, used for the types that cannot be inferred. + """ + + +class StringType(PrimitiveType): + """String data type. + """ + + +class BinaryType(PrimitiveType): + """Binary (byte array) data type. + """ + + +class BooleanType(PrimitiveType): + """Boolean data type. + """ + + +class DateType(PrimitiveType): + """Date (datetime.date) data type. + """ + + +class TimestampType(PrimitiveType): + """Timestamp (datetime.datetime) data type. + """ + + +class DecimalType(DataType): + """Decimal (decimal.Decimal) data type. + """ + + def __init__(self, precision=None, scale=None): + self.precision = precision + self.scale = scale + self.hasPrecisionInfo = precision is not None + + def simpleString(self): + if self.hasPrecisionInfo: + return "decimal(%d,%d)" % (self.precision, self.scale) + else: + return "decimal(10,0)" + + def jsonValue(self): + if self.hasPrecisionInfo: + return "decimal(%d,%d)" % (self.precision, self.scale) + else: + return "decimal" + + def __repr__(self): + if self.hasPrecisionInfo: + return "DecimalType(%d,%d)" % (self.precision, self.scale) + else: + return "DecimalType()" + + +class DoubleType(PrimitiveType): + """Double data type, representing double precision floats. + """ + + +class FloatType(PrimitiveType): + """Float data type, representing single precision floats. + """ + + +class ByteType(PrimitiveType): + """Byte data type, i.e. a signed integer in a single byte. + """ + def simpleString(self): + return 'tinyint' + + +class IntegerType(PrimitiveType): + """Int data type, i.e. a signed 32-bit integer. + """ + def simpleString(self): + return 'int' + + +class LongType(PrimitiveType): + """Long data type, i.e. a signed 64-bit integer. + + If the values are beyond the range of [-9223372036854775808, 9223372036854775807], + please use :class:`DecimalType`. + """ + def simpleString(self): + return 'bigint' + + +class ShortType(PrimitiveType): + """Short data type, i.e. a signed 16-bit integer. + """ + def simpleString(self): + return 'smallint' + + +class ArrayType(DataType): + """Array data type. + + :param elementType: :class:`DataType` of each element in the array. + :param containsNull: boolean, whether the array can contain null (None) values. + """ + + def __init__(self, elementType, containsNull=True): + """ + >>> ArrayType(StringType()) == ArrayType(StringType(), True) + True + >>> ArrayType(StringType(), False) == ArrayType(StringType()) + False + """ + assert isinstance(elementType, DataType), "elementType should be DataType" + self.elementType = elementType + self.containsNull = containsNull + + def simpleString(self): + return 'array<%s>' % self.elementType.simpleString() + + def __repr__(self): + return "ArrayType(%s,%s)" % (self.elementType, + str(self.containsNull).lower()) + + def jsonValue(self): + return {"type": self.typeName(), + "elementType": self.elementType.jsonValue(), + "containsNull": self.containsNull} + + @classmethod + def fromJson(cls, json): + return ArrayType(_parse_datatype_json_value(json["elementType"]), + json["containsNull"]) + + +class MapType(DataType): + """Map data type. + + :param keyType: :class:`DataType` of the keys in the map. + :param valueType: :class:`DataType` of the values in the map. + :param valueContainsNull: indicates whether values can contain null (None) values. + + Keys in a map data type are not allowed to be null (None). + """ + + def __init__(self, keyType, valueType, valueContainsNull=True): + """ + >>> (MapType(StringType(), IntegerType()) + ... == MapType(StringType(), IntegerType(), True)) + True + >>> (MapType(StringType(), IntegerType(), False) + ... == MapType(StringType(), FloatType())) + False + """ + assert isinstance(keyType, DataType), "keyType should be DataType" + assert isinstance(valueType, DataType), "valueType should be DataType" + self.keyType = keyType + self.valueType = valueType + self.valueContainsNull = valueContainsNull + + def simpleString(self): + return 'map<%s,%s>' % (self.keyType.simpleString(), self.valueType.simpleString()) + + def __repr__(self): + return "MapType(%s,%s,%s)" % (self.keyType, self.valueType, + str(self.valueContainsNull).lower()) + + def jsonValue(self): + return {"type": self.typeName(), + "keyType": self.keyType.jsonValue(), + "valueType": self.valueType.jsonValue(), + "valueContainsNull": self.valueContainsNull} + + @classmethod + def fromJson(cls, json): + return MapType(_parse_datatype_json_value(json["keyType"]), + _parse_datatype_json_value(json["valueType"]), + json["valueContainsNull"]) + + +class StructField(DataType): + """A field in :class:`StructType`. + + :param name: string, name of the field. + :param dataType: :class:`DataType` of the field. + :param nullable: boolean, whether the field can be null (None) or not. + :param metadata: a dict from string to simple type that can be serialized to JSON automatically + """ + + def __init__(self, name, dataType, nullable=True, metadata=None): + """ + >>> (StructField("f1", StringType(), True) + ... == StructField("f1", StringType(), True)) + True + >>> (StructField("f1", StringType(), True) + ... == StructField("f2", StringType(), True)) + False + """ + assert isinstance(dataType, DataType), "dataType should be DataType" + self.name = name + self.dataType = dataType + self.nullable = nullable + self.metadata = metadata or {} + + def simpleString(self): + return '%s:%s' % (self.name, self.dataType.simpleString()) + + def __repr__(self): + return "StructField(%s,%s,%s)" % (self.name, self.dataType, + str(self.nullable).lower()) + + def jsonValue(self): + return {"name": self.name, + "type": self.dataType.jsonValue(), + "nullable": self.nullable, + "metadata": self.metadata} + + @classmethod + def fromJson(cls, json): + return StructField(json["name"], + _parse_datatype_json_value(json["type"]), + json["nullable"], + json["metadata"]) + + +class StructType(DataType): + """Struct type, consisting of a list of :class:`StructField`. + + This is the data type representing a :class:`Row`. + """ + + def __init__(self, fields): + """ + >>> struct1 = StructType([StructField("f1", StringType(), True)]) + >>> struct2 = StructType([StructField("f1", StringType(), True)]) + >>> struct1 == struct2 + True + >>> struct1 = StructType([StructField("f1", StringType(), True)]) + >>> struct2 = StructType([StructField("f1", StringType(), True), + ... StructField("f2", IntegerType(), False)]) + >>> struct1 == struct2 + False + """ + assert all(isinstance(f, DataType) for f in fields), "fields should be a list of DataType" + self.fields = fields + + def simpleString(self): + return 'struct<%s>' % (','.join(f.simpleString() for f in self.fields)) + + def __repr__(self): + return ("StructType(List(%s))" % + ",".join(str(field) for field in self.fields)) + + def jsonValue(self): + return {"type": self.typeName(), + "fields": [f.jsonValue() for f in self.fields]} + + @classmethod + def fromJson(cls, json): + return StructType([StructField.fromJson(f) for f in json["fields"]]) + + +class UserDefinedType(DataType): + """User-defined type (UDT). + + .. note:: WARN: Spark Internal Use Only + """ + + @classmethod + def typeName(cls): + return cls.__name__.lower() + + @classmethod + def sqlType(cls): + """ + Underlying SQL storage type for this UDT. + """ + raise NotImplementedError("UDT must implement sqlType().") + + @classmethod + def module(cls): + """ + The Python module of the UDT. + """ + raise NotImplementedError("UDT must implement module().") + + @classmethod + def scalaUDT(cls): + """ + The class name of the paired Scala UDT. + """ + raise NotImplementedError("UDT must have a paired Scala UDT.") + + def serialize(self, obj): + """ + Converts the a user-type object into a SQL datum. + """ + raise NotImplementedError("UDT must implement serialize().") + + def deserialize(self, datum): + """ + Converts a SQL datum into a user-type object. + """ + raise NotImplementedError("UDT must implement deserialize().") + + def simpleString(self): + return 'udt' + + def json(self): + return json.dumps(self.jsonValue(), separators=(',', ':'), sort_keys=True) + + def jsonValue(self): + schema = { + "type": "udt", + "class": self.scalaUDT(), + "pyClass": "%s.%s" % (self.module(), type(self).__name__), + "sqlType": self.sqlType().jsonValue() + } + return schema + + @classmethod + def fromJson(cls, json): + pyUDT = json["pyClass"] + split = pyUDT.rfind(".") + pyModule = pyUDT[:split] + pyClass = pyUDT[split+1:] + m = __import__(pyModule, globals(), locals(), [pyClass]) + UDT = getattr(m, pyClass) + return UDT() + + def __eq__(self, other): + return type(self) == type(other) + + +_all_primitive_types = dict((v.typeName(), v) + for v in list(globals().values()) + if (type(v) is type or type(v) is PrimitiveTypeSingleton) + and v.__base__ == PrimitiveType) + +_all_complex_types = dict((v.typeName(), v) + for v in [ArrayType, MapType, StructType]) + + +def _parse_datatype_json_string(json_string): + """Parses the given data type JSON string. + >>> import pickle + >>> def check_datatype(datatype): + ... pickled = pickle.loads(pickle.dumps(datatype)) + ... assert datatype == pickled + ... scala_datatype = sqlContext._ssql_ctx.parseDataType(datatype.json()) + ... python_datatype = _parse_datatype_json_string(scala_datatype.json()) + ... assert datatype == python_datatype + >>> for cls in _all_primitive_types.values(): + ... check_datatype(cls()) + + >>> # Simple ArrayType. + >>> simple_arraytype = ArrayType(StringType(), True) + >>> check_datatype(simple_arraytype) + + >>> # Simple MapType. + >>> simple_maptype = MapType(StringType(), LongType()) + >>> check_datatype(simple_maptype) + + >>> # Simple StructType. + >>> simple_structtype = StructType([ + ... StructField("a", DecimalType(), False), + ... StructField("b", BooleanType(), True), + ... StructField("c", LongType(), True), + ... StructField("d", BinaryType(), False)]) + >>> check_datatype(simple_structtype) + + >>> # Complex StructType. + >>> complex_structtype = StructType([ + ... StructField("simpleArray", simple_arraytype, True), + ... StructField("simpleMap", simple_maptype, True), + ... StructField("simpleStruct", simple_structtype, True), + ... StructField("boolean", BooleanType(), False), + ... StructField("withMeta", DoubleType(), False, {"name": "age"})]) + >>> check_datatype(complex_structtype) + + >>> # Complex ArrayType. + >>> complex_arraytype = ArrayType(complex_structtype, True) + >>> check_datatype(complex_arraytype) + + >>> # Complex MapType. + >>> complex_maptype = MapType(complex_structtype, + ... complex_arraytype, False) + >>> check_datatype(complex_maptype) + + >>> check_datatype(ExamplePointUDT()) + >>> structtype_with_udt = StructType([StructField("label", DoubleType(), False), + ... StructField("point", ExamplePointUDT(), False)]) + >>> check_datatype(structtype_with_udt) + """ + return _parse_datatype_json_value(json.loads(json_string)) + + +_FIXED_DECIMAL = re.compile("decimal\\((\\d+),(\\d+)\\)") + + +def _parse_datatype_json_value(json_value): + if not isinstance(json_value, dict): + if json_value in _all_primitive_types.keys(): + return _all_primitive_types[json_value]() + elif json_value == 'decimal': + return DecimalType() + elif _FIXED_DECIMAL.match(json_value): + m = _FIXED_DECIMAL.match(json_value) + return DecimalType(int(m.group(1)), int(m.group(2))) + else: + raise ValueError("Could not parse datatype: %s" % json_value) + else: + tpe = json_value["type"] + if tpe in _all_complex_types: + return _all_complex_types[tpe].fromJson(json_value) + elif tpe == 'udt': + return UserDefinedType.fromJson(json_value) + else: + raise ValueError("not supported type: %s" % tpe) + + +# Mapping Python types to Spark SQL DataType +_type_mappings = { + type(None): NullType, + bool: BooleanType, + int: LongType, + float: DoubleType, + str: StringType, + bytearray: BinaryType, + decimal.Decimal: DecimalType, + datetime.date: DateType, + datetime.datetime: TimestampType, + datetime.time: TimestampType, +} + +if sys.version < "3": + _type_mappings.update({ + unicode: StringType, + long: LongType, + }) + + +def _infer_type(obj): + """Infer the DataType from obj + + >>> p = ExamplePoint(1.0, 2.0) + >>> _infer_type(p) + ExamplePointUDT + """ + if obj is None: + return NullType() + + if hasattr(obj, '__UDT__'): + return obj.__UDT__ + + dataType = _type_mappings.get(type(obj)) + if dataType is not None: + return dataType() + + if isinstance(obj, dict): + for key, value in obj.items(): + if key is not None and value is not None: + return MapType(_infer_type(key), _infer_type(value), True) + else: + return MapType(NullType(), NullType(), True) + elif isinstance(obj, (list, array)): + for v in obj: + if v is not None: + return ArrayType(_infer_type(obj[0]), True) + else: + return ArrayType(NullType(), True) + else: + try: + return _infer_schema(obj) + except ValueError: + raise ValueError("not supported type: %s" % type(obj)) + + +def _infer_schema(row): + """Infer the schema from dict/namedtuple/object""" + if isinstance(row, dict): + items = sorted(row.items()) + + elif isinstance(row, (tuple, list)): + if hasattr(row, "__fields__"): # Row + items = zip(row.__fields__, tuple(row)) + elif hasattr(row, "_fields"): # namedtuple + items = zip(row._fields, tuple(row)) + else: + names = ['_%d' % i for i in range(1, len(row) + 1)] + items = zip(names, row) + + elif hasattr(row, "__dict__"): # object + items = sorted(row.__dict__.items()) + + else: + raise ValueError("Can not infer schema for type: %s" % type(row)) + + fields = [StructField(k, _infer_type(v), True) for k, v in items] + return StructType(fields) + + +def _need_python_to_sql_conversion(dataType): + """ + Checks whether we need python to sql conversion for the given type. + For now, only UDTs need this conversion. + + >>> _need_python_to_sql_conversion(DoubleType()) + False + >>> schema0 = StructType([StructField("indices", ArrayType(IntegerType(), False), False), + ... StructField("values", ArrayType(DoubleType(), False), False)]) + >>> _need_python_to_sql_conversion(schema0) + False + >>> _need_python_to_sql_conversion(ExamplePointUDT()) + True + >>> schema1 = ArrayType(ExamplePointUDT(), False) + >>> _need_python_to_sql_conversion(schema1) + True + >>> schema2 = StructType([StructField("label", DoubleType(), False), + ... StructField("point", ExamplePointUDT(), False)]) + >>> _need_python_to_sql_conversion(schema2) + True + """ + if isinstance(dataType, StructType): + return any([_need_python_to_sql_conversion(f.dataType) for f in dataType.fields]) + elif isinstance(dataType, ArrayType): + return _need_python_to_sql_conversion(dataType.elementType) + elif isinstance(dataType, MapType): + return _need_python_to_sql_conversion(dataType.keyType) or \ + _need_python_to_sql_conversion(dataType.valueType) + elif isinstance(dataType, UserDefinedType): + return True + else: + return False + + +def _python_to_sql_converter(dataType): + """ + Returns a converter that converts a Python object into a SQL datum for the given type. + + >>> conv = _python_to_sql_converter(DoubleType()) + >>> conv(1.0) + 1.0 + >>> conv = _python_to_sql_converter(ArrayType(DoubleType(), False)) + >>> conv([1.0, 2.0]) + [1.0, 2.0] + >>> conv = _python_to_sql_converter(ExamplePointUDT()) + >>> conv(ExamplePoint(1.0, 2.0)) + [1.0, 2.0] + >>> schema = StructType([StructField("label", DoubleType(), False), + ... StructField("point", ExamplePointUDT(), False)]) + >>> conv = _python_to_sql_converter(schema) + >>> conv((1.0, ExamplePoint(1.0, 2.0))) + (1.0, [1.0, 2.0]) + """ + if not _need_python_to_sql_conversion(dataType): + return lambda x: x + + if isinstance(dataType, StructType): + names, types = zip(*[(f.name, f.dataType) for f in dataType.fields]) + converters = map(_python_to_sql_converter, types) + + def converter(obj): + if isinstance(obj, dict): + return tuple(c(obj.get(n)) for n, c in zip(names, converters)) + elif isinstance(obj, tuple): + if hasattr(obj, "__fields__") or hasattr(obj, "_fields"): + return tuple(c(v) for c, v in zip(converters, obj)) + elif all(isinstance(x, tuple) and len(x) == 2 for x in obj): # k-v pairs + d = dict(obj) + return tuple(c(d.get(n)) for n, c in zip(names, converters)) + else: + return tuple(c(v) for c, v in zip(converters, obj)) + else: + raise ValueError("Unexpected tuple %r with type %r" % (obj, dataType)) + return converter + elif isinstance(dataType, ArrayType): + element_converter = _python_to_sql_converter(dataType.elementType) + return lambda a: [element_converter(v) for v in a] + elif isinstance(dataType, MapType): + key_converter = _python_to_sql_converter(dataType.keyType) + value_converter = _python_to_sql_converter(dataType.valueType) + return lambda m: dict([(key_converter(k), value_converter(v)) for k, v in m.items()]) + elif isinstance(dataType, UserDefinedType): + return lambda obj: dataType.serialize(obj) + else: + raise ValueError("Unexpected type %r" % dataType) + + +def _has_nulltype(dt): + """ Return whether there is NullType in `dt` or not """ + if isinstance(dt, StructType): + return any(_has_nulltype(f.dataType) for f in dt.fields) + elif isinstance(dt, ArrayType): + return _has_nulltype((dt.elementType)) + elif isinstance(dt, MapType): + return _has_nulltype(dt.keyType) or _has_nulltype(dt.valueType) + else: + return isinstance(dt, NullType) + + +def _merge_type(a, b): + if isinstance(a, NullType): + return b + elif isinstance(b, NullType): + return a + elif type(a) is not type(b): + # TODO: type cast (such as int -> long) + raise TypeError("Can not merge type %s and %s" % (a, b)) + + # same type + if isinstance(a, StructType): + nfs = dict((f.name, f.dataType) for f in b.fields) + fields = [StructField(f.name, _merge_type(f.dataType, nfs.get(f.name, NullType()))) + for f in a.fields] + names = set([f.name for f in fields]) + for n in nfs: + if n not in names: + fields.append(StructField(n, nfs[n])) + return StructType(fields) + + elif isinstance(a, ArrayType): + return ArrayType(_merge_type(a.elementType, b.elementType), True) + + elif isinstance(a, MapType): + return MapType(_merge_type(a.keyType, b.keyType), + _merge_type(a.valueType, b.valueType), + True) + else: + return a + + +def _need_converter(dataType): + if isinstance(dataType, StructType): + return True + elif isinstance(dataType, ArrayType): + return _need_converter(dataType.elementType) + elif isinstance(dataType, MapType): + return _need_converter(dataType.keyType) or _need_converter(dataType.valueType) + elif isinstance(dataType, NullType): + return True + else: + return False + + +def _create_converter(dataType): + """Create an converter to drop the names of fields in obj """ + if not _need_converter(dataType): + return lambda x: x + + if isinstance(dataType, ArrayType): + conv = _create_converter(dataType.elementType) + return lambda row: [conv(v) for v in row] + + elif isinstance(dataType, MapType): + kconv = _create_converter(dataType.keyType) + vconv = _create_converter(dataType.valueType) + return lambda row: dict((kconv(k), vconv(v)) for k, v in row.items()) + + elif isinstance(dataType, NullType): + return lambda x: None + + elif not isinstance(dataType, StructType): + return lambda x: x + + # dataType must be StructType + names = [f.name for f in dataType.fields] + converters = [_create_converter(f.dataType) for f in dataType.fields] + convert_fields = any(_need_converter(f.dataType) for f in dataType.fields) + + def convert_struct(obj): + if obj is None: + return + + if isinstance(obj, (tuple, list)): + if convert_fields: + return tuple(conv(v) for v, conv in zip(obj, converters)) + else: + return tuple(obj) + + if isinstance(obj, dict): + d = obj + elif hasattr(obj, "__dict__"): # object + d = obj.__dict__ + else: + raise ValueError("Unexpected obj: %s" % obj) + + if convert_fields: + return tuple([conv(d.get(name)) for name, conv in zip(names, converters)]) + else: + return tuple([d.get(name) for name in names]) + + return convert_struct + + +_BRACKETS = {'(': ')', '[': ']', '{': '}'} + + +def _split_schema_abstract(s): + """ + split the schema abstract into fields + + >>> _split_schema_abstract("a b c") + ['a', 'b', 'c'] + >>> _split_schema_abstract("a(a b)") + ['a(a b)'] + >>> _split_schema_abstract("a b[] c{a b}") + ['a', 'b[]', 'c{a b}'] + >>> _split_schema_abstract(" ") + [] + """ + + r = [] + w = '' + brackets = [] + for c in s: + if c == ' ' and not brackets: + if w: + r.append(w) + w = '' + else: + w += c + if c in _BRACKETS: + brackets.append(c) + elif c in _BRACKETS.values(): + if not brackets or c != _BRACKETS[brackets.pop()]: + raise ValueError("unexpected " + c) + + if brackets: + raise ValueError("brackets not closed: %s" % brackets) + if w: + r.append(w) + return r + + +def _parse_field_abstract(s): + """ + Parse a field in schema abstract + + >>> _parse_field_abstract("a") + StructField(a,NullType,true) + >>> _parse_field_abstract("b(c d)") + StructField(b,StructType(...c,NullType,true),StructField(d... + >>> _parse_field_abstract("a[]") + StructField(a,ArrayType(NullType,true),true) + >>> _parse_field_abstract("a{[]}") + StructField(a,MapType(NullType,ArrayType(NullType,true),true),true) + """ + if set(_BRACKETS.keys()) & set(s): + idx = min((s.index(c) for c in _BRACKETS if c in s)) + name = s[:idx] + return StructField(name, _parse_schema_abstract(s[idx:]), True) + else: + return StructField(s, NullType(), True) + + +def _parse_schema_abstract(s): + """ + parse abstract into schema + + >>> _parse_schema_abstract("a b c") + StructType...a...b...c... + >>> _parse_schema_abstract("a[b c] b{}") + StructType...a,ArrayType...b...c...b,MapType... + >>> _parse_schema_abstract("c{} d{a b}") + StructType...c,MapType...d,MapType...a...b... + >>> _parse_schema_abstract("a b(t)").fields[1] + StructField(b,StructType(List(StructField(t,NullType,true))),true) + """ + s = s.strip() + if not s: + return NullType() + + elif s.startswith('('): + return _parse_schema_abstract(s[1:-1]) + + elif s.startswith('['): + return ArrayType(_parse_schema_abstract(s[1:-1]), True) + + elif s.startswith('{'): + return MapType(NullType(), _parse_schema_abstract(s[1:-1])) + + parts = _split_schema_abstract(s) + fields = [_parse_field_abstract(p) for p in parts] + return StructType(fields) + + +def _infer_schema_type(obj, dataType): + """ + Fill the dataType with types inferred from obj + + >>> schema = _parse_schema_abstract("a b c d") + >>> row = (1, 1.0, "str", datetime.date(2014, 10, 10)) + >>> _infer_schema_type(row, schema) + StructType...LongType...DoubleType...StringType...DateType... + >>> row = [[1], {"key": (1, 2.0)}] + >>> schema = _parse_schema_abstract("a[] b{c d}") + >>> _infer_schema_type(row, schema) + StructType...a,ArrayType...b,MapType(StringType,...c,LongType... + """ + if isinstance(dataType, NullType): + return _infer_type(obj) + + if not obj: + return NullType() + + if isinstance(dataType, ArrayType): + eType = _infer_schema_type(obj[0], dataType.elementType) + return ArrayType(eType, True) + + elif isinstance(dataType, MapType): + k, v = next(iter(obj.items())) + return MapType(_infer_schema_type(k, dataType.keyType), + _infer_schema_type(v, dataType.valueType)) + + elif isinstance(dataType, StructType): + fs = dataType.fields + assert len(fs) == len(obj), \ + "Obj(%s) have different length with fields(%s)" % (obj, fs) + fields = [StructField(f.name, _infer_schema_type(o, f.dataType), True) + for o, f in zip(obj, fs)] + return StructType(fields) + + else: + raise ValueError("Unexpected dataType: %s" % dataType) + + +_acceptable_types = { + BooleanType: (bool,), + ByteType: (int, long), + ShortType: (int, long), + IntegerType: (int, long), + LongType: (int, long), + FloatType: (float,), + DoubleType: (float,), + DecimalType: (decimal.Decimal,), + StringType: (str, unicode), + BinaryType: (bytearray,), + DateType: (datetime.date,), + TimestampType: (datetime.datetime,), + ArrayType: (list, tuple, array), + MapType: (dict,), + StructType: (tuple, list), +} + + +def _verify_type(obj, dataType): + """ + Verify the type of obj against dataType, raise an exception if + they do not match. + + >>> _verify_type(None, StructType([])) + >>> _verify_type("", StringType()) + >>> _verify_type(0, LongType()) + >>> _verify_type(list(range(3)), ArrayType(ShortType())) + >>> _verify_type(set(), ArrayType(StringType())) # doctest: +IGNORE_EXCEPTION_DETAIL + Traceback (most recent call last): + ... + TypeError:... + >>> _verify_type({}, MapType(StringType(), IntegerType())) + >>> _verify_type((), StructType([])) + >>> _verify_type([], StructType([])) + >>> _verify_type([1], StructType([])) # doctest: +IGNORE_EXCEPTION_DETAIL + Traceback (most recent call last): + ... + ValueError:... + >>> _verify_type(ExamplePoint(1.0, 2.0), ExamplePointUDT()) + >>> _verify_type([1.0, 2.0], ExamplePointUDT()) # doctest: +IGNORE_EXCEPTION_DETAIL + Traceback (most recent call last): + ... + ValueError:... + """ + # all objects are nullable + if obj is None: + return + + if isinstance(dataType, UserDefinedType): + if not (hasattr(obj, '__UDT__') and obj.__UDT__ == dataType): + raise ValueError("%r is not an instance of type %r" % (obj, dataType)) + _verify_type(dataType.serialize(obj), dataType.sqlType()) + return + + _type = type(dataType) + assert _type in _acceptable_types, "unknown datatype: %s" % dataType + + # subclass of them can not be deserialized in JVM + if type(obj) not in _acceptable_types[_type]: + raise TypeError("%s can not accept object in type %s" + % (dataType, type(obj))) + + if isinstance(dataType, ArrayType): + for i in obj: + _verify_type(i, dataType.elementType) + + elif isinstance(dataType, MapType): + for k, v in obj.items(): + _verify_type(k, dataType.keyType) + _verify_type(v, dataType.valueType) + + elif isinstance(dataType, StructType): + if len(obj) != len(dataType.fields): + raise ValueError("Length of object (%d) does not match with " + "length of fields (%d)" % (len(obj), len(dataType.fields))) + for v, f in zip(obj, dataType.fields): + _verify_type(v, f.dataType) + +_cached_cls = weakref.WeakValueDictionary() + + +def _restore_object(dataType, obj): + """ Restore object during unpickling. """ + # use id(dataType) as key to speed up lookup in dict + # Because of batched pickling, dataType will be the + # same object in most cases. + k = id(dataType) + cls = _cached_cls.get(k) + if cls is None or cls.__datatype is not dataType: + # use dataType as key to avoid create multiple class + cls = _cached_cls.get(dataType) + if cls is None: + cls = _create_cls(dataType) + _cached_cls[dataType] = cls + cls.__datatype = dataType + _cached_cls[k] = cls + return cls(obj) + + +def _create_object(cls, v): + """ Create an customized object with class `cls`. """ + # datetime.date would be deserialized as datetime.datetime + # from java type, so we need to set it back. + if cls is datetime.date and isinstance(v, datetime.datetime): + return v.date() + return cls(v) if v is not None else v + + +def _create_getter(dt, i): + """ Create a getter for item `i` with schema """ + cls = _create_cls(dt) + + def getter(self): + return _create_object(cls, self[i]) + + return getter + + +def _has_struct_or_date(dt): + """Return whether `dt` is or has StructType/DateType in it""" + if isinstance(dt, StructType): + return True + elif isinstance(dt, ArrayType): + return _has_struct_or_date(dt.elementType) + elif isinstance(dt, MapType): + return _has_struct_or_date(dt.keyType) or _has_struct_or_date(dt.valueType) + elif isinstance(dt, DateType): + return True + elif isinstance(dt, UserDefinedType): + return True + return False + + +def _create_properties(fields): + """Create properties according to fields""" + ps = {} + for i, f in enumerate(fields): + name = f.name + if (name.startswith("__") and name.endswith("__") + or keyword.iskeyword(name)): + warnings.warn("field name %s can not be accessed in Python," + "use position to access it instead" % name) + if _has_struct_or_date(f.dataType): + # delay creating object until accessing it + getter = _create_getter(f.dataType, i) + else: + getter = itemgetter(i) + ps[name] = property(getter) + return ps + + +def _create_cls(dataType): + """ + Create an class by dataType + + The created class is similar to namedtuple, but can have nested schema. + + >>> schema = _parse_schema_abstract("a b c") + >>> row = (1, 1.0, "str") + >>> schema = _infer_schema_type(row, schema) + >>> obj = _create_cls(schema)(row) + >>> import pickle + >>> pickle.loads(pickle.dumps(obj)) + Row(a=1, b=1.0, c='str') + + >>> row = [[1], {"key": (1, 2.0)}] + >>> schema = _parse_schema_abstract("a[] b{c d}") + >>> schema = _infer_schema_type(row, schema) + >>> obj = _create_cls(schema)(row) + >>> pickle.loads(pickle.dumps(obj)) + Row(a=[1], b={'key': Row(c=1, d=2.0)}) + >>> pickle.loads(pickle.dumps(obj.a)) + [1] + >>> pickle.loads(pickle.dumps(obj.b)) + {'key': Row(c=1, d=2.0)} + """ + + if isinstance(dataType, ArrayType): + cls = _create_cls(dataType.elementType) + + def List(l): + if l is None: + return + return [_create_object(cls, v) for v in l] + + return List + + elif isinstance(dataType, MapType): + kcls = _create_cls(dataType.keyType) + vcls = _create_cls(dataType.valueType) + + def Dict(d): + if d is None: + return + return dict((_create_object(kcls, k), _create_object(vcls, v)) for k, v in d.items()) + + return Dict + + elif isinstance(dataType, DateType): + return datetime.date + + elif isinstance(dataType, UserDefinedType): + return lambda datum: dataType.deserialize(datum) + + elif not isinstance(dataType, StructType): + # no wrapper for primitive types + return lambda x: x + + class Row(tuple): + + """ Row in DataFrame """ + __datatype = dataType + __fields__ = tuple(f.name for f in dataType.fields) + __slots__ = () + + # create property for fast access + locals().update(_create_properties(dataType.fields)) + + def asDict(self): + """ Return as a dict """ + return dict((n, getattr(self, n)) for n in self.__fields__) + + def __repr__(self): + # call collect __repr__ for nested objects + return ("Row(%s)" % ", ".join("%s=%r" % (n, getattr(self, n)) + for n in self.__fields__)) + + def __reduce__(self): + return (_restore_object, (self.__datatype, tuple(self))) + + return Row + + +def _create_row(fields, values): + row = Row(*values) + row.__fields__ = fields + return row + + +class Row(tuple): + + """ + A row in L{DataFrame}. The fields in it can be accessed like attributes. + + Row can be used to create a row object by using named arguments, + the fields will be sorted by names. + + >>> row = Row(name="Alice", age=11) + >>> row + Row(age=11, name='Alice') + >>> row.name, row.age + ('Alice', 11) + + Row also can be used to create another Row like class, then it + could be used to create Row objects, such as + + >>> Person = Row("name", "age") + >>> Person + + >>> Person("Alice", 11) + Row(name='Alice', age=11) + """ + + def __new__(self, *args, **kwargs): + if args and kwargs: + raise ValueError("Can not use both args " + "and kwargs to create Row") + if args: + # create row class or objects + return tuple.__new__(self, args) + + elif kwargs: + # create row objects + names = sorted(kwargs.keys()) + row = tuple.__new__(self, [kwargs[n] for n in names]) + row.__fields__ = names + return row + + else: + raise ValueError("No args or kwargs") + + def asDict(self): + """ + Return as an dict + """ + if not hasattr(self, "__fields__"): + raise TypeError("Cannot convert a Row class into dict") + return dict(zip(self.__fields__, self)) + + # let object acts like class + def __call__(self, *args): + """create new Row object""" + return _create_row(self, args) + + def __getattr__(self, item): + if item.startswith("__"): + raise AttributeError(item) + try: + # it will be slow when it has many fields, + # but this will not be used in normal cases + idx = self.__fields__.index(item) + return self[idx] + except IndexError: + raise AttributeError(item) + except ValueError: + raise AttributeError(item) + + def __reduce__(self): + if hasattr(self, "__fields__"): + return (_create_row, (self.__fields__, tuple(self))) + else: + return tuple.__reduce__(self) + + def __repr__(self): + if hasattr(self, "__fields__"): + return "Row(%s)" % ", ".join("%s=%r" % (k, v) + for k, v in zip(self.__fields__, tuple(self))) + else: + return "" % ", ".join(self) + + +def _test(): + import doctest + from pyspark.context import SparkContext + # let doctest run in pyspark.sql.types, so DataTypes can be picklable + import pyspark.sql.types + from pyspark.sql import Row, SQLContext + from pyspark.sql.tests import ExamplePoint, ExamplePointUDT + globs = pyspark.sql.types.__dict__.copy() + sc = SparkContext('local[4]', 'PythonTest') + globs['sc'] = sc + globs['sqlContext'] = SQLContext(sc) + globs['ExamplePoint'] = ExamplePoint + globs['ExamplePointUDT'] = ExamplePointUDT + (failure_count, test_count) = doctest.testmod( + pyspark.sql.types, globs=globs, optionflags=doctest.ELLIPSIS) + globs['sc'].stop() + if failure_count: + exit(-1) + + +if __name__ == "__main__": + _test() diff --git a/python/pyspark/sql/context.py b/python/pyspark/sql/context.py new file mode 100644 index 0000000000000..c90afc326ca0e --- /dev/null +++ b/python/pyspark/sql/context.py @@ -0,0 +1,640 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import sys +import warnings +import json + +if sys.version >= '3': + basestring = unicode = str +else: + from itertools import imap as map + +from py4j.protocol import Py4JError +from py4j.java_collections import MapConverter + +from pyspark.rdd import RDD, _prepare_for_python_RDD, ignore_unicode_prefix +from pyspark.serializers import AutoBatchedSerializer, PickleSerializer +from pyspark.sql.types import Row, StringType, StructType, _verify_type, \ + _infer_schema, _has_nulltype, _merge_type, _create_converter, _python_to_sql_converter +from pyspark.sql.dataframe import DataFrame + +try: + import pandas + has_pandas = True +except ImportError: + has_pandas = False + +__all__ = ["SQLContext", "HiveContext", "UDFRegistration"] + + +def _monkey_patch_RDD(sqlContext): + def toDF(self, schema=None, sampleRatio=None): + """ + Converts current :class:`RDD` into a :class:`DataFrame` + + This is a shorthand for ``sqlContext.createDataFrame(rdd, schema, sampleRatio)`` + + :param schema: a StructType or list of names of columns + :param samplingRatio: the sample ratio of rows used for inferring + :return: a DataFrame + + >>> rdd.toDF().collect() + [Row(name=u'Alice', age=1)] + """ + return sqlContext.createDataFrame(self, schema, sampleRatio) + + RDD.toDF = toDF + + +class SQLContext(object): + """Main entry point for Spark SQL functionality. + + A SQLContext can be used create :class:`DataFrame`, register :class:`DataFrame` as + tables, execute SQL over tables, cache tables, and read parquet files. + + :param sparkContext: The :class:`SparkContext` backing this SQLContext. + :param sqlContext: An optional JVM Scala SQLContext. If set, we do not instantiate a new + SQLContext in the JVM, instead we make all calls to this object. + """ + + @ignore_unicode_prefix + def __init__(self, sparkContext, sqlContext=None): + """Creates a new SQLContext. + + >>> from datetime import datetime + >>> sqlContext = SQLContext(sc) + >>> allTypes = sc.parallelize([Row(i=1, s="string", d=1.0, l=1, + ... b=True, list=[1, 2, 3], dict={"s": 0}, row=Row(a=1), + ... time=datetime(2014, 8, 1, 14, 1, 5))]) + >>> df = allTypes.toDF() + >>> df.registerTempTable("allTypes") + >>> sqlContext.sql('select i+1, d+1, not b, list[1], dict["s"], time, row.a ' + ... 'from allTypes where b and i > 0').collect() + [Row(c0=2, c1=2.0, c2=False, c3=2, c4=0, time=datetime.datetime(2014, 8, 1, 14, 1, 5), a=1)] + >>> df.map(lambda x: (x.i, x.s, x.d, x.l, x.b, x.time, x.row.a, x.list)).collect() + [(1, u'string', 1.0, 1, True, datetime.datetime(2014, 8, 1, 14, 1, 5), 1, [1, 2, 3])] + """ + self._sc = sparkContext + self._jsc = self._sc._jsc + self._jvm = self._sc._jvm + self._scala_SQLContext = sqlContext + _monkey_patch_RDD(self) + + @property + def _ssql_ctx(self): + """Accessor for the JVM Spark SQL context. + + Subclasses can override this property to provide their own + JVM Contexts. + """ + if self._scala_SQLContext is None: + self._scala_SQLContext = self._jvm.SQLContext(self._jsc.sc()) + return self._scala_SQLContext + + def setConf(self, key, value): + """Sets the given Spark SQL configuration property. + """ + self._ssql_ctx.setConf(key, value) + + def getConf(self, key, defaultValue): + """Returns the value of Spark SQL configuration property for the given key. + + If the key is not set, returns defaultValue. + """ + return self._ssql_ctx.getConf(key, defaultValue) + + @property + def udf(self): + """Returns a :class:`UDFRegistration` for UDF registration.""" + return UDFRegistration(self) + + @ignore_unicode_prefix + def registerFunction(self, name, f, returnType=StringType()): + """Registers a lambda function as a UDF so it can be used in SQL statements. + + In addition to a name and the function itself, the return type can be optionally specified. + When the return type is not given it default to a string and conversion will automatically + be done. For any other return type, the produced object must match the specified type. + + :param name: name of the UDF + :param samplingRatio: lambda function + :param returnType: a :class:`DataType` object + + >>> sqlContext.registerFunction("stringLengthString", lambda x: len(x)) + >>> sqlContext.sql("SELECT stringLengthString('test')").collect() + [Row(c0=u'4')] + + >>> from pyspark.sql.types import IntegerType + >>> sqlContext.registerFunction("stringLengthInt", lambda x: len(x), IntegerType()) + >>> sqlContext.sql("SELECT stringLengthInt('test')").collect() + [Row(c0=4)] + + >>> from pyspark.sql.types import IntegerType + >>> sqlContext.udf.register("stringLengthInt", lambda x: len(x), IntegerType()) + >>> sqlContext.sql("SELECT stringLengthInt('test')").collect() + [Row(c0=4)] + """ + func = lambda _, it: map(lambda x: f(*x), it) + ser = AutoBatchedSerializer(PickleSerializer()) + command = (func, None, ser, ser) + pickled_cmd, bvars, env, includes = _prepare_for_python_RDD(self._sc, command, self) + self._ssql_ctx.udf().registerPython(name, + bytearray(pickled_cmd), + env, + includes, + self._sc.pythonExec, + bvars, + self._sc._javaAccumulator, + returnType.json()) + + def _inferSchema(self, rdd, samplingRatio=None): + first = rdd.first() + if not first: + raise ValueError("The first row in RDD is empty, " + "can not infer schema") + if type(first) is dict: + warnings.warn("Using RDD of dict to inferSchema is deprecated," + "please use pyspark.sql.Row instead") + + if samplingRatio is None: + schema = _infer_schema(first) + if _has_nulltype(schema): + for row in rdd.take(100)[1:]: + schema = _merge_type(schema, _infer_schema(row)) + if not _has_nulltype(schema): + break + else: + raise ValueError("Some of types cannot be determined by the " + "first 100 rows, please try again with sampling") + else: + if samplingRatio < 0.99: + rdd = rdd.sample(False, float(samplingRatio)) + schema = rdd.map(_infer_schema).reduce(_merge_type) + return schema + + @ignore_unicode_prefix + def inferSchema(self, rdd, samplingRatio=None): + """::note: Deprecated in 1.3, use :func:`createDataFrame` instead. + """ + warnings.warn("inferSchema is deprecated, please use createDataFrame instead") + + if isinstance(rdd, DataFrame): + raise TypeError("Cannot apply schema to DataFrame") + + return self.createDataFrame(rdd, None, samplingRatio) + + @ignore_unicode_prefix + def applySchema(self, rdd, schema): + """::note: Deprecated in 1.3, use :func:`createDataFrame` instead. + """ + warnings.warn("applySchema is deprecated, please use createDataFrame instead") + + if isinstance(rdd, DataFrame): + raise TypeError("Cannot apply schema to DataFrame") + + if not isinstance(schema, StructType): + raise TypeError("schema should be StructType, but got %s" % schema) + + return self.createDataFrame(rdd, schema) + + @ignore_unicode_prefix + def createDataFrame(self, data, schema=None, samplingRatio=None): + """ + Creates a :class:`DataFrame` from an :class:`RDD` of :class:`tuple`/:class:`list`, + list or :class:`pandas.DataFrame`. + + When ``schema`` is a list of column names, the type of each column + will be inferred from ``data``. + + When ``schema`` is ``None``, it will try to infer the schema (column names and types) + from ``data``, which should be an RDD of :class:`Row`, + or :class:`namedtuple`, or :class:`dict`. + + If schema inference is needed, ``samplingRatio`` is used to determined the ratio of + rows used for schema inference. The first row will be used if ``samplingRatio`` is ``None``. + + :param data: an RDD of :class:`Row`/:class:`tuple`/:class:`list`/:class:`dict`, + :class:`list`, or :class:`pandas.DataFrame`. + :param schema: a :class:`StructType` or list of column names. default None. + :param samplingRatio: the sample ratio of rows used for inferring + + >>> l = [('Alice', 1)] + >>> sqlContext.createDataFrame(l).collect() + [Row(_1=u'Alice', _2=1)] + >>> sqlContext.createDataFrame(l, ['name', 'age']).collect() + [Row(name=u'Alice', age=1)] + + >>> d = [{'name': 'Alice', 'age': 1}] + >>> sqlContext.createDataFrame(d).collect() + [Row(age=1, name=u'Alice')] + + >>> rdd = sc.parallelize(l) + >>> sqlContext.createDataFrame(rdd).collect() + [Row(_1=u'Alice', _2=1)] + >>> df = sqlContext.createDataFrame(rdd, ['name', 'age']) + >>> df.collect() + [Row(name=u'Alice', age=1)] + + >>> from pyspark.sql import Row + >>> Person = Row('name', 'age') + >>> person = rdd.map(lambda r: Person(*r)) + >>> df2 = sqlContext.createDataFrame(person) + >>> df2.collect() + [Row(name=u'Alice', age=1)] + + >>> from pyspark.sql.types import * + >>> schema = StructType([ + ... StructField("name", StringType(), True), + ... StructField("age", IntegerType(), True)]) + >>> df3 = sqlContext.createDataFrame(rdd, schema) + >>> df3.collect() + [Row(name=u'Alice', age=1)] + + >>> sqlContext.createDataFrame(df.toPandas()).collect() # doctest: +SKIP + [Row(name=u'Alice', age=1)] + """ + if isinstance(data, DataFrame): + raise TypeError("data is already a DataFrame") + + if has_pandas and isinstance(data, pandas.DataFrame): + if schema is None: + schema = list(data.columns) + data = [r.tolist() for r in data.to_records(index=False)] + + if not isinstance(data, RDD): + try: + # data could be list, tuple, generator ... + rdd = self._sc.parallelize(data) + except Exception: + raise ValueError("cannot create an RDD from type: %s" % type(data)) + else: + rdd = data + + if schema is None: + schema = self._inferSchema(rdd, samplingRatio) + converter = _create_converter(schema) + rdd = rdd.map(converter) + + if isinstance(schema, (list, tuple)): + first = rdd.first() + if not isinstance(first, (list, tuple)): + raise ValueError("each row in `rdd` should be list or tuple, " + "but got %r" % type(first)) + row_cls = Row(*schema) + schema = self._inferSchema(rdd.map(lambda r: row_cls(*r)), samplingRatio) + + # take the first few rows to verify schema + rows = rdd.take(10) + # Row() cannot been deserialized by Pyrolite + if rows and isinstance(rows[0], tuple) and rows[0].__class__.__name__ == 'Row': + rdd = rdd.map(tuple) + rows = rdd.take(10) + + for row in rows: + _verify_type(row, schema) + + # convert python objects to sql data + converter = _python_to_sql_converter(schema) + rdd = rdd.map(converter) + + jrdd = self._jvm.SerDeUtil.toJavaArray(rdd._to_java_object_rdd()) + df = self._ssql_ctx.applySchemaToPythonRDD(jrdd.rdd(), schema.json()) + return DataFrame(df, self) + + def registerDataFrameAsTable(self, df, tableName): + """Registers the given :class:`DataFrame` as a temporary table in the catalog. + + Temporary tables exist only during the lifetime of this instance of :class:`SQLContext`. + + >>> sqlContext.registerDataFrameAsTable(df, "table1") + """ + if (df.__class__ is DataFrame): + self._ssql_ctx.registerDataFrameAsTable(df._jdf, tableName) + else: + raise ValueError("Can only register DataFrame as table") + + def parquetFile(self, *paths): + """Loads a Parquet file, returning the result as a :class:`DataFrame`. + + >>> import tempfile, shutil + >>> parquetFile = tempfile.mkdtemp() + >>> shutil.rmtree(parquetFile) + >>> df.saveAsParquetFile(parquetFile) + >>> df2 = sqlContext.parquetFile(parquetFile) + >>> sorted(df.collect()) == sorted(df2.collect()) + True + """ + gateway = self._sc._gateway + jpaths = gateway.new_array(gateway.jvm.java.lang.String, len(paths)) + for i in range(0, len(paths)): + jpaths[i] = paths[i] + jdf = self._ssql_ctx.parquetFile(jpaths) + return DataFrame(jdf, self) + + def jsonFile(self, path, schema=None, samplingRatio=1.0): + """Loads a text file storing one JSON object per line as a :class:`DataFrame`. + + If the schema is provided, applies the given schema to this JSON dataset. + Otherwise, it samples the dataset with ratio ``samplingRatio`` to determine the schema. + + >>> import tempfile, shutil + >>> jsonFile = tempfile.mkdtemp() + >>> shutil.rmtree(jsonFile) + >>> with open(jsonFile, 'w') as f: + ... f.writelines(jsonStrings) + >>> df1 = sqlContext.jsonFile(jsonFile) + >>> df1.printSchema() + root + |-- field1: long (nullable = true) + |-- field2: string (nullable = true) + |-- field3: struct (nullable = true) + | |-- field4: long (nullable = true) + + >>> from pyspark.sql.types import * + >>> schema = StructType([ + ... StructField("field2", StringType()), + ... StructField("field3", + ... StructType([StructField("field5", ArrayType(IntegerType()))]))]) + >>> df2 = sqlContext.jsonFile(jsonFile, schema) + >>> df2.printSchema() + root + |-- field2: string (nullable = true) + |-- field3: struct (nullable = true) + | |-- field5: array (nullable = true) + | | |-- element: integer (containsNull = true) + """ + if schema is None: + df = self._ssql_ctx.jsonFile(path, samplingRatio) + else: + scala_datatype = self._ssql_ctx.parseDataType(schema.json()) + df = self._ssql_ctx.jsonFile(path, scala_datatype) + return DataFrame(df, self) + + @ignore_unicode_prefix + def jsonRDD(self, rdd, schema=None, samplingRatio=1.0): + """Loads an RDD storing one JSON object per string as a :class:`DataFrame`. + + If the schema is provided, applies the given schema to this JSON dataset. + Otherwise, it samples the dataset with ratio ``samplingRatio`` to determine the schema. + + >>> df1 = sqlContext.jsonRDD(json) + >>> df1.first() + Row(field1=1, field2=u'row1', field3=Row(field4=11, field5=None), field6=None) + + >>> df2 = sqlContext.jsonRDD(json, df1.schema) + >>> df2.first() + Row(field1=1, field2=u'row1', field3=Row(field4=11, field5=None), field6=None) + + >>> from pyspark.sql.types import * + >>> schema = StructType([ + ... StructField("field2", StringType()), + ... StructField("field3", + ... StructType([StructField("field5", ArrayType(IntegerType()))])) + ... ]) + >>> df3 = sqlContext.jsonRDD(json, schema) + >>> df3.first() + Row(field2=u'row1', field3=Row(field5=None)) + """ + + def func(iterator): + for x in iterator: + if not isinstance(x, basestring): + x = unicode(x) + if isinstance(x, unicode): + x = x.encode("utf-8") + yield x + keyed = rdd.mapPartitions(func) + keyed._bypass_serializer = True + jrdd = keyed._jrdd.map(self._jvm.BytesToString()) + if schema is None: + df = self._ssql_ctx.jsonRDD(jrdd.rdd(), samplingRatio) + else: + scala_datatype = self._ssql_ctx.parseDataType(schema.json()) + df = self._ssql_ctx.jsonRDD(jrdd.rdd(), scala_datatype) + return DataFrame(df, self) + + def load(self, path=None, source=None, schema=None, **options): + """Returns the dataset in a data source as a :class:`DataFrame`. + + The data source is specified by the ``source`` and a set of ``options``. + If ``source`` is not specified, the default data source configured by + ``spark.sql.sources.default`` will be used. + + Optionally, a schema can be provided as the schema of the returned DataFrame. + """ + if path is not None: + options["path"] = path + if source is None: + source = self.getConf("spark.sql.sources.default", + "org.apache.spark.sql.parquet") + joptions = MapConverter().convert(options, + self._sc._gateway._gateway_client) + if schema is None: + df = self._ssql_ctx.load(source, joptions) + else: + if not isinstance(schema, StructType): + raise TypeError("schema should be StructType") + scala_datatype = self._ssql_ctx.parseDataType(schema.json()) + df = self._ssql_ctx.load(source, scala_datatype, joptions) + return DataFrame(df, self) + + def createExternalTable(self, tableName, path=None, source=None, + schema=None, **options): + """Creates an external table based on the dataset in a data source. + + It returns the DataFrame associated with the external table. + + The data source is specified by the ``source`` and a set of ``options``. + If ``source`` is not specified, the default data source configured by + ``spark.sql.sources.default`` will be used. + + Optionally, a schema can be provided as the schema of the returned :class:`DataFrame` and + created external table. + """ + if path is not None: + options["path"] = path + if source is None: + source = self.getConf("spark.sql.sources.default", + "org.apache.spark.sql.parquet") + joptions = MapConverter().convert(options, + self._sc._gateway._gateway_client) + if schema is None: + df = self._ssql_ctx.createExternalTable(tableName, source, joptions) + else: + if not isinstance(schema, StructType): + raise TypeError("schema should be StructType") + scala_datatype = self._ssql_ctx.parseDataType(schema.json()) + df = self._ssql_ctx.createExternalTable(tableName, source, scala_datatype, + joptions) + return DataFrame(df, self) + + @ignore_unicode_prefix + def sql(self, sqlQuery): + """Returns a :class:`DataFrame` representing the result of the given query. + + >>> sqlContext.registerDataFrameAsTable(df, "table1") + >>> df2 = sqlContext.sql("SELECT field1 AS f1, field2 as f2 from table1") + >>> df2.collect() + [Row(f1=1, f2=u'row1'), Row(f1=2, f2=u'row2'), Row(f1=3, f2=u'row3')] + """ + return DataFrame(self._ssql_ctx.sql(sqlQuery), self) + + def table(self, tableName): + """Returns the specified table as a :class:`DataFrame`. + + >>> sqlContext.registerDataFrameAsTable(df, "table1") + >>> df2 = sqlContext.table("table1") + >>> sorted(df.collect()) == sorted(df2.collect()) + True + """ + return DataFrame(self._ssql_ctx.table(tableName), self) + + @ignore_unicode_prefix + def tables(self, dbName=None): + """Returns a :class:`DataFrame` containing names of tables in the given database. + + If ``dbName`` is not specified, the current database will be used. + + The returned DataFrame has two columns: ``tableName`` and ``isTemporary`` + (a column with :class:`BooleanType` indicating if a table is a temporary one or not). + + >>> sqlContext.registerDataFrameAsTable(df, "table1") + >>> df2 = sqlContext.tables() + >>> df2.filter("tableName = 'table1'").first() + Row(tableName=u'table1', isTemporary=True) + """ + if dbName is None: + return DataFrame(self._ssql_ctx.tables(), self) + else: + return DataFrame(self._ssql_ctx.tables(dbName), self) + + def tableNames(self, dbName=None): + """Returns a list of names of tables in the database ``dbName``. + + If ``dbName`` is not specified, the current database will be used. + + >>> sqlContext.registerDataFrameAsTable(df, "table1") + >>> "table1" in sqlContext.tableNames() + True + >>> "table1" in sqlContext.tableNames("db") + True + """ + if dbName is None: + return [name for name in self._ssql_ctx.tableNames()] + else: + return [name for name in self._ssql_ctx.tableNames(dbName)] + + def cacheTable(self, tableName): + """Caches the specified table in-memory.""" + self._ssql_ctx.cacheTable(tableName) + + def uncacheTable(self, tableName): + """Removes the specified table from the in-memory cache.""" + self._ssql_ctx.uncacheTable(tableName) + + def clearCache(self): + """Removes all cached tables from the in-memory cache. """ + self._ssql_ctx.clearCache() + + +class HiveContext(SQLContext): + """A variant of Spark SQL that integrates with data stored in Hive. + + Configuration for Hive is read from ``hive-site.xml`` on the classpath. + It supports running both SQL and HiveQL commands. + + :param sparkContext: The SparkContext to wrap. + :param hiveContext: An optional JVM Scala HiveContext. If set, we do not instantiate a new + :class:`HiveContext` in the JVM, instead we make all calls to this object. + """ + + def __init__(self, sparkContext, hiveContext=None): + SQLContext.__init__(self, sparkContext) + if hiveContext: + self._scala_HiveContext = hiveContext + + @property + def _ssql_ctx(self): + try: + if not hasattr(self, '_scala_HiveContext'): + self._scala_HiveContext = self._get_hive_ctx() + return self._scala_HiveContext + except Py4JError as e: + raise Exception("You must build Spark with Hive. " + "Export 'SPARK_HIVE=true' and run " + "build/sbt assembly", e) + + def _get_hive_ctx(self): + return self._jvm.HiveContext(self._jsc.sc()) + + def refreshTable(self, tableName): + """Invalidate and refresh all the cached the metadata of the given + table. For performance reasons, Spark SQL or the external data source + library it uses might cache certain metadata about a table, such as the + location of blocks. When those change outside of Spark SQL, users should + call this function to invalidate the cache. + """ + self._ssql_ctx.refreshTable(tableName) + + +class UDFRegistration(object): + """Wrapper for user-defined function registration.""" + + def __init__(self, sqlContext): + self.sqlContext = sqlContext + + def register(self, name, f, returnType=StringType()): + return self.sqlContext.registerFunction(name, f, returnType) + + register.__doc__ = SQLContext.registerFunction.__doc__ + + +def _test(): + import doctest + from pyspark.context import SparkContext + from pyspark.sql import Row, SQLContext + import pyspark.sql.context + globs = pyspark.sql.context.__dict__.copy() + sc = SparkContext('local[4]', 'PythonTest') + globs['sc'] = sc + globs['sqlContext'] = SQLContext(sc) + globs['rdd'] = rdd = sc.parallelize( + [Row(field1=1, field2="row1"), + Row(field1=2, field2="row2"), + Row(field1=3, field2="row3")] + ) + globs['df'] = rdd.toDF() + jsonStrings = [ + '{"field1": 1, "field2": "row1", "field3":{"field4":11}}', + '{"field1" : 2, "field3":{"field4":22, "field5": [10, 11]},' + '"field6":[{"field7": "row2"}]}', + '{"field1" : null, "field2": "row3", ' + '"field3":{"field4":33, "field5": []}}' + ] + globs['jsonStrings'] = jsonStrings + globs['json'] = sc.parallelize(jsonStrings) + (failure_count, test_count) = doctest.testmod( + pyspark.sql.context, globs=globs, + optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE) + globs['sc'].stop() + if failure_count: + exit(-1) + + +if __name__ == "__main__": + _test() diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py new file mode 100644 index 0000000000000..d70c5b0a6930c --- /dev/null +++ b/python/pyspark/sql/dataframe.py @@ -0,0 +1,1310 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import sys +import warnings +import random + +if sys.version >= '3': + basestring = unicode = str + long = int +else: + from itertools import imap as map + +from py4j.java_collections import ListConverter, MapConverter + +from pyspark.context import SparkContext +from pyspark.rdd import RDD, _load_from_socket, ignore_unicode_prefix +from pyspark.serializers import BatchedSerializer, PickleSerializer, UTF8Deserializer +from pyspark.storagelevel import StorageLevel +from pyspark.traceback_utils import SCCallSiteSync +from pyspark.sql.types import * +from pyspark.sql.types import _create_cls, _parse_datatype_json_string + + +__all__ = ["DataFrame", "GroupedData", "Column", "SchemaRDD", "DataFrameNaFunctions"] + + +class DataFrame(object): + """A distributed collection of data grouped into named columns. + + A :class:`DataFrame` is equivalent to a relational table in Spark SQL, + and can be created using various functions in :class:`SQLContext`:: + + people = sqlContext.parquetFile("...") + + Once created, it can be manipulated using the various domain-specific-language + (DSL) functions defined in: :class:`DataFrame`, :class:`Column`. + + To select a column from the data frame, use the apply method:: + + ageCol = people.age + + A more concrete example:: + + # To create DataFrame using SQLContext + people = sqlContext.parquetFile("...") + department = sqlContext.parquetFile("...") + + people.filter(people.age > 30).join(department, people.deptId == department.id)) \ + .groupBy(department.name, "gender").agg({"salary": "avg", "age": "max"}) + """ + + def __init__(self, jdf, sql_ctx): + self._jdf = jdf + self.sql_ctx = sql_ctx + self._sc = sql_ctx and sql_ctx._sc + self.is_cached = False + self._schema = None # initialized lazily + self._lazy_rdd = None + + @property + def rdd(self): + """Returns the content as an :class:`pyspark.RDD` of :class:`Row`. + """ + if self._lazy_rdd is None: + jrdd = self._jdf.javaToPython() + rdd = RDD(jrdd, self.sql_ctx._sc, BatchedSerializer(PickleSerializer())) + schema = self.schema + + def applySchema(it): + cls = _create_cls(schema) + return map(cls, it) + + self._lazy_rdd = rdd.mapPartitions(applySchema) + + return self._lazy_rdd + + @property + def na(self): + """Returns a :class:`DataFrameNaFunctions` for handling missing values. + """ + return DataFrameNaFunctions(self) + + @ignore_unicode_prefix + def toJSON(self, use_unicode=True): + """Converts a :class:`DataFrame` into a :class:`RDD` of string. + + Each row is turned into a JSON document as one element in the returned RDD. + + >>> df.toJSON().first() + u'{"age":2,"name":"Alice"}' + """ + rdd = self._jdf.toJSON() + return RDD(rdd.toJavaRDD(), self._sc, UTF8Deserializer(use_unicode)) + + def saveAsParquetFile(self, path): + """Saves the contents as a Parquet file, preserving the schema. + + Files that are written out using this method can be read back in as + a :class:`DataFrame` using :func:`SQLContext.parquetFile`. + + >>> import tempfile, shutil + >>> parquetFile = tempfile.mkdtemp() + >>> shutil.rmtree(parquetFile) + >>> df.saveAsParquetFile(parquetFile) + >>> df2 = sqlContext.parquetFile(parquetFile) + >>> sorted(df2.collect()) == sorted(df.collect()) + True + """ + self._jdf.saveAsParquetFile(path) + + def registerTempTable(self, name): + """Registers this RDD as a temporary table using the given name. + + The lifetime of this temporary table is tied to the :class:`SQLContext` + that was used to create this :class:`DataFrame`. + + >>> df.registerTempTable("people") + >>> df2 = sqlContext.sql("select * from people") + >>> sorted(df.collect()) == sorted(df2.collect()) + True + """ + self._jdf.registerTempTable(name) + + def registerAsTable(self, name): + """DEPRECATED: use :func:`registerTempTable` instead""" + warnings.warn("Use registerTempTable instead of registerAsTable.", DeprecationWarning) + self.registerTempTable(name) + + def insertInto(self, tableName, overwrite=False): + """Inserts the contents of this :class:`DataFrame` into the specified table. + + Optionally overwriting any existing data. + """ + self._jdf.insertInto(tableName, overwrite) + + def _java_save_mode(self, mode): + """Returns the Java save mode based on the Python save mode represented by a string. + """ + jSaveMode = self._sc._jvm.org.apache.spark.sql.SaveMode + jmode = jSaveMode.ErrorIfExists + mode = mode.lower() + if mode == "append": + jmode = jSaveMode.Append + elif mode == "overwrite": + jmode = jSaveMode.Overwrite + elif mode == "ignore": + jmode = jSaveMode.Ignore + elif mode == "error": + pass + else: + raise ValueError( + "Only 'append', 'overwrite', 'ignore', and 'error' are acceptable save mode.") + return jmode + + def saveAsTable(self, tableName, source=None, mode="error", **options): + """Saves the contents of this :class:`DataFrame` to a data source as a table. + + The data source is specified by the ``source`` and a set of ``options``. + If ``source`` is not specified, the default data source configured by + ``spark.sql.sources.default`` will be used. + + Additionally, mode is used to specify the behavior of the saveAsTable operation when + table already exists in the data source. There are four modes: + + * `append`: Append contents of this :class:`DataFrame` to existing data. + * `overwrite`: Overwrite existing data. + * `error`: Throw an exception if data already exists. + * `ignore`: Silently ignore this operation if data already exists. + """ + if source is None: + source = self.sql_ctx.getConf("spark.sql.sources.default", + "org.apache.spark.sql.parquet") + jmode = self._java_save_mode(mode) + joptions = MapConverter().convert(options, + self.sql_ctx._sc._gateway._gateway_client) + self._jdf.saveAsTable(tableName, source, jmode, joptions) + + def save(self, path=None, source=None, mode="error", **options): + """Saves the contents of the :class:`DataFrame` to a data source. + + The data source is specified by the ``source`` and a set of ``options``. + If ``source`` is not specified, the default data source configured by + ``spark.sql.sources.default`` will be used. + + Additionally, mode is used to specify the behavior of the save operation when + data already exists in the data source. There are four modes: + + * `append`: Append contents of this :class:`DataFrame` to existing data. + * `overwrite`: Overwrite existing data. + * `error`: Throw an exception if data already exists. + * `ignore`: Silently ignore this operation if data already exists. + """ + if path is not None: + options["path"] = path + if source is None: + source = self.sql_ctx.getConf("spark.sql.sources.default", + "org.apache.spark.sql.parquet") + jmode = self._java_save_mode(mode) + joptions = MapConverter().convert(options, + self._sc._gateway._gateway_client) + self._jdf.save(source, jmode, joptions) + + @property + def schema(self): + """Returns the schema of this :class:`DataFrame` as a :class:`types.StructType`. + + >>> df.schema + StructType(List(StructField(age,IntegerType,true),StructField(name,StringType,true))) + """ + if self._schema is None: + self._schema = _parse_datatype_json_string(self._jdf.schema().json()) + return self._schema + + def printSchema(self): + """Prints out the schema in the tree format. + + >>> df.printSchema() + root + |-- age: integer (nullable = true) + |-- name: string (nullable = true) + + """ + print(self._jdf.schema().treeString()) + + def explain(self, extended=False): + """Prints the (logical and physical) plans to the console for debugging purpose. + + :param extended: boolean, default ``False``. If ``False``, prints only the physical plan. + + >>> df.explain() + PhysicalRDD [age#0,name#1], MapPartitionsRDD[...] at mapPartitions at SQLContext.scala:... + + >>> df.explain(True) + == Parsed Logical Plan == + ... + == Analyzed Logical Plan == + ... + == Optimized Logical Plan == + ... + == Physical Plan == + ... + == RDD == + """ + if extended: + print(self._jdf.queryExecution().toString()) + else: + print(self._jdf.queryExecution().executedPlan().toString()) + + def isLocal(self): + """Returns ``True`` if the :func:`collect` and :func:`take` methods can be run locally + (without any Spark executors). + """ + return self._jdf.isLocal() + + def show(self, n=20): + """Prints the first ``n`` rows to the console. + + >>> df + DataFrame[age: int, name: string] + >>> df.show() + age name + 2 Alice + 5 Bob + """ + print(self._jdf.showString(n)) + + def __repr__(self): + return "DataFrame[%s]" % (", ".join("%s: %s" % c for c in self.dtypes)) + + def count(self): + """Returns the number of rows in this :class:`DataFrame`. + + >>> df.count() + 2 + """ + return int(self._jdf.count()) + + @ignore_unicode_prefix + def collect(self): + """Returns all the records as a list of :class:`Row`. + + >>> df.collect() + [Row(age=2, name=u'Alice'), Row(age=5, name=u'Bob')] + """ + with SCCallSiteSync(self._sc) as css: + port = self._sc._jvm.PythonRDD.collectAndServe(self._jdf.javaToPython().rdd()) + rs = list(_load_from_socket(port, BatchedSerializer(PickleSerializer()))) + cls = _create_cls(self.schema) + return [cls(r) for r in rs] + + @ignore_unicode_prefix + def limit(self, num): + """Limits the result count to the number specified. + + >>> df.limit(1).collect() + [Row(age=2, name=u'Alice')] + >>> df.limit(0).collect() + [] + """ + jdf = self._jdf.limit(num) + return DataFrame(jdf, self.sql_ctx) + + @ignore_unicode_prefix + def take(self, num): + """Returns the first ``num`` rows as a :class:`list` of :class:`Row`. + + >>> df.take(2) + [Row(age=2, name=u'Alice'), Row(age=5, name=u'Bob')] + """ + return self.limit(num).collect() + + @ignore_unicode_prefix + def map(self, f): + """ Returns a new :class:`RDD` by applying a the ``f`` function to each :class:`Row`. + + This is a shorthand for ``df.rdd.map()``. + + >>> df.map(lambda p: p.name).collect() + [u'Alice', u'Bob'] + """ + return self.rdd.map(f) + + @ignore_unicode_prefix + def flatMap(self, f): + """ Returns a new :class:`RDD` by first applying the ``f`` function to each :class:`Row`, + and then flattening the results. + + This is a shorthand for ``df.rdd.flatMap()``. + + >>> df.flatMap(lambda p: p.name).collect() + [u'A', u'l', u'i', u'c', u'e', u'B', u'o', u'b'] + """ + return self.rdd.flatMap(f) + + def mapPartitions(self, f, preservesPartitioning=False): + """Returns a new :class:`RDD` by applying the ``f`` function to each partition. + + This is a shorthand for ``df.rdd.mapPartitions()``. + + >>> rdd = sc.parallelize([1, 2, 3, 4], 4) + >>> def f(iterator): yield 1 + >>> rdd.mapPartitions(f).sum() + 4 + """ + return self.rdd.mapPartitions(f, preservesPartitioning) + + def foreach(self, f): + """Applies the ``f`` function to all :class:`Row` of this :class:`DataFrame`. + + This is a shorthand for ``df.rdd.foreach()``. + + >>> def f(person): + ... print(person.name) + >>> df.foreach(f) + """ + return self.rdd.foreach(f) + + def foreachPartition(self, f): + """Applies the ``f`` function to each partition of this :class:`DataFrame`. + + This a shorthand for ``df.rdd.foreachPartition()``. + + >>> def f(people): + ... for person in people: + ... print(person.name) + >>> df.foreachPartition(f) + """ + return self.rdd.foreachPartition(f) + + def cache(self): + """ Persists with the default storage level (C{MEMORY_ONLY_SER}). + """ + self.is_cached = True + self._jdf.cache() + return self + + def persist(self, storageLevel=StorageLevel.MEMORY_ONLY_SER): + """Sets the storage level to persist its values across operations + after the first time it is computed. This can only be used to assign + a new storage level if the RDD does not have a storage level set yet. + If no storage level is specified defaults to (C{MEMORY_ONLY_SER}). + """ + self.is_cached = True + javaStorageLevel = self._sc._getJavaStorageLevel(storageLevel) + self._jdf.persist(javaStorageLevel) + return self + + def unpersist(self, blocking=True): + """Marks the :class:`DataFrame` as non-persistent, and remove all blocks for it from + memory and disk. + """ + self.is_cached = False + self._jdf.unpersist(blocking) + return self + + # def coalesce(self, numPartitions, shuffle=False): + # rdd = self._jdf.coalesce(numPartitions, shuffle, None) + # return DataFrame(rdd, self.sql_ctx) + + def repartition(self, numPartitions): + """Returns a new :class:`DataFrame` that has exactly ``numPartitions`` partitions. + + >>> df.repartition(10).rdd.getNumPartitions() + 10 + """ + return DataFrame(self._jdf.repartition(numPartitions), self.sql_ctx) + + def distinct(self): + """Returns a new :class:`DataFrame` containing the distinct rows in this :class:`DataFrame`. + + >>> df.distinct().count() + 2 + """ + return DataFrame(self._jdf.distinct(), self.sql_ctx) + + def sample(self, withReplacement, fraction, seed=None): + """Returns a sampled subset of this :class:`DataFrame`. + + >>> df.sample(False, 0.5, 97).count() + 1 + """ + assert fraction >= 0.0, "Negative fraction value: %s" % fraction + seed = seed if seed is not None else random.randint(0, sys.maxsize) + rdd = self._jdf.sample(withReplacement, fraction, long(seed)) + return DataFrame(rdd, self.sql_ctx) + + @property + def dtypes(self): + """Returns all column names and their data types as a list. + + >>> df.dtypes + [('age', 'int'), ('name', 'string')] + """ + return [(str(f.name), f.dataType.simpleString()) for f in self.schema.fields] + + @property + @ignore_unicode_prefix + def columns(self): + """Returns all column names as a list. + + >>> df.columns + [u'age', u'name'] + """ + return [f.name for f in self.schema.fields] + + @ignore_unicode_prefix + def join(self, other, joinExprs=None, joinType=None): + """Joins with another :class:`DataFrame`, using the given join expression. + + The following performs a full outer join between ``df1`` and ``df2``. + + :param other: Right side of the join + :param joinExprs: Join expression + :param joinType: str, default 'inner'. + One of `inner`, `outer`, `left_outer`, `right_outer`, `semijoin`. + + >>> df.join(df2, df.name == df2.name, 'outer').select(df.name, df2.height).collect() + [Row(name=None, height=80), Row(name=u'Alice', height=None), Row(name=u'Bob', height=85)] + """ + + if joinExprs is None: + jdf = self._jdf.join(other._jdf) + else: + assert isinstance(joinExprs, Column), "joinExprs should be Column" + if joinType is None: + jdf = self._jdf.join(other._jdf, joinExprs._jc) + else: + assert isinstance(joinType, basestring), "joinType should be basestring" + jdf = self._jdf.join(other._jdf, joinExprs._jc, joinType) + return DataFrame(jdf, self.sql_ctx) + + @ignore_unicode_prefix + def sort(self, *cols, **kwargs): + """Returns a new :class:`DataFrame` sorted by the specified column(s). + + :param cols: list of :class:`Column` or column names to sort by. + :param ascending: boolean or list of boolean (default True). + Sort ascending vs. descending. Specify list for multiple sort orders. + If a list is specified, length of the list must equal length of the `cols`. + + >>> df.sort(df.age.desc()).collect() + [Row(age=5, name=u'Bob'), Row(age=2, name=u'Alice')] + >>> df.sort("age", ascending=False).collect() + [Row(age=5, name=u'Bob'), Row(age=2, name=u'Alice')] + >>> df.orderBy(df.age.desc()).collect() + [Row(age=5, name=u'Bob'), Row(age=2, name=u'Alice')] + >>> from pyspark.sql.functions import * + >>> df.sort(asc("age")).collect() + [Row(age=2, name=u'Alice'), Row(age=5, name=u'Bob')] + >>> df.orderBy(desc("age"), "name").collect() + [Row(age=5, name=u'Bob'), Row(age=2, name=u'Alice')] + >>> df.orderBy(["age", "name"], ascending=[0, 1]).collect() + [Row(age=5, name=u'Bob'), Row(age=2, name=u'Alice')] + """ + if not cols: + raise ValueError("should sort by at least one column") + if len(cols) == 1 and isinstance(cols[0], list): + cols = cols[0] + jcols = [_to_java_column(c) for c in cols] + ascending = kwargs.get('ascending', True) + if isinstance(ascending, (bool, int)): + if not ascending: + jcols = [jc.desc() for jc in jcols] + elif isinstance(ascending, list): + jcols = [jc if asc else jc.desc() + for asc, jc in zip(ascending, jcols)] + else: + raise TypeError("ascending can only be boolean or list, but got %s" % type(ascending)) + + jdf = self._jdf.sort(self._jseq(jcols)) + return DataFrame(jdf, self.sql_ctx) + + orderBy = sort + + def _jseq(self, cols, converter=None): + """Return a JVM Seq of Columns from a list of Column or names""" + return _to_seq(self.sql_ctx._sc, cols, converter) + + def _jcols(self, *cols): + """Return a JVM Seq of Columns from a list of Column or column names + + If `cols` has only one list in it, cols[0] will be used as the list. + """ + if len(cols) == 1 and isinstance(cols[0], list): + cols = cols[0] + return self._jseq(cols, _to_java_column) + + def describe(self, *cols): + """Computes statistics for numeric columns. + + This include count, mean, stddev, min, and max. If no columns are + given, this function computes statistics for all numerical columns. + + >>> df.describe().show() + summary age + count 2 + mean 3.5 + stddev 1.5 + min 2 + max 5 + """ + jdf = self._jdf.describe(self._jseq(cols)) + return DataFrame(jdf, self.sql_ctx) + + @ignore_unicode_prefix + def head(self, n=None): + """ + Returns the first ``n`` rows as a list of :class:`Row`, + or the first :class:`Row` if ``n`` is ``None.`` + + >>> df.head() + Row(age=2, name=u'Alice') + >>> df.head(1) + [Row(age=2, name=u'Alice')] + """ + if n is None: + rs = self.head(1) + return rs[0] if rs else None + return self.take(n) + + @ignore_unicode_prefix + def first(self): + """Returns the first row as a :class:`Row`. + + >>> df.first() + Row(age=2, name=u'Alice') + """ + return self.head() + + @ignore_unicode_prefix + def __getitem__(self, item): + """Returns the column as a :class:`Column`. + + >>> df.select(df['age']).collect() + [Row(age=2), Row(age=5)] + >>> df[ ["name", "age"]].collect() + [Row(name=u'Alice', age=2), Row(name=u'Bob', age=5)] + >>> df[ df.age > 3 ].collect() + [Row(age=5, name=u'Bob')] + >>> df[df[0] > 3].collect() + [Row(age=5, name=u'Bob')] + """ + if isinstance(item, basestring): + if item not in self.columns: + raise IndexError("no such column: %s" % item) + jc = self._jdf.apply(item) + return Column(jc) + elif isinstance(item, Column): + return self.filter(item) + elif isinstance(item, (list, tuple)): + return self.select(*item) + elif isinstance(item, int): + jc = self._jdf.apply(self.columns[item]) + return Column(jc) + else: + raise TypeError("unexpected type: %s" % type(item)) + + def __getattr__(self, name): + """Returns the :class:`Column` denoted by ``name``. + + >>> df.select(df.age).collect() + [Row(age=2), Row(age=5)] + """ + if name not in self.columns: + raise AttributeError("No such column: %s" % name) + jc = self._jdf.apply(name) + return Column(jc) + + @ignore_unicode_prefix + def select(self, *cols): + """Projects a set of expressions and returns a new :class:`DataFrame`. + + :param cols: list of column names (string) or expressions (:class:`Column`). + If one of the column names is '*', that column is expanded to include all columns + in the current DataFrame. + + >>> df.select('*').collect() + [Row(age=2, name=u'Alice'), Row(age=5, name=u'Bob')] + >>> df.select('name', 'age').collect() + [Row(name=u'Alice', age=2), Row(name=u'Bob', age=5)] + >>> df.select(df.name, (df.age + 10).alias('age')).collect() + [Row(name=u'Alice', age=12), Row(name=u'Bob', age=15)] + """ + jdf = self._jdf.select(self._jcols(*cols)) + return DataFrame(jdf, self.sql_ctx) + + def selectExpr(self, *expr): + """Projects a set of SQL expressions and returns a new :class:`DataFrame`. + + This is a variant of :func:`select` that accepts SQL expressions. + + >>> df.selectExpr("age * 2", "abs(age)").collect() + [Row((age * 2)=4, Abs(age)=2), Row((age * 2)=10, Abs(age)=5)] + """ + if len(expr) == 1 and isinstance(expr[0], list): + expr = expr[0] + jdf = self._jdf.selectExpr(self._jseq(expr)) + return DataFrame(jdf, self.sql_ctx) + + @ignore_unicode_prefix + def filter(self, condition): + """Filters rows using the given condition. + + :func:`where` is an alias for :func:`filter`. + + :param condition: a :class:`Column` of :class:`types.BooleanType` + or a string of SQL expression. + + >>> df.filter(df.age > 3).collect() + [Row(age=5, name=u'Bob')] + >>> df.where(df.age == 2).collect() + [Row(age=2, name=u'Alice')] + + >>> df.filter("age > 3").collect() + [Row(age=5, name=u'Bob')] + >>> df.where("age = 2").collect() + [Row(age=2, name=u'Alice')] + """ + if isinstance(condition, basestring): + jdf = self._jdf.filter(condition) + elif isinstance(condition, Column): + jdf = self._jdf.filter(condition._jc) + else: + raise TypeError("condition should be string or Column") + return DataFrame(jdf, self.sql_ctx) + + where = filter + + @ignore_unicode_prefix + def groupBy(self, *cols): + """Groups the :class:`DataFrame` using the specified columns, + so we can run aggregation on them. See :class:`GroupedData` + for all the available aggregate functions. + + :func:`groupby` is an alias for :func:`groupBy`. + + :param cols: list of columns to group by. + Each element should be a column name (string) or an expression (:class:`Column`). + + >>> df.groupBy().avg().collect() + [Row(AVG(age)=3.5)] + >>> df.groupBy('name').agg({'age': 'mean'}).collect() + [Row(name=u'Alice', AVG(age)=2.0), Row(name=u'Bob', AVG(age)=5.0)] + >>> df.groupBy(df.name).avg().collect() + [Row(name=u'Alice', AVG(age)=2.0), Row(name=u'Bob', AVG(age)=5.0)] + >>> df.groupBy(['name', df.age]).count().collect() + [Row(name=u'Bob', age=5, count=1), Row(name=u'Alice', age=2, count=1)] + """ + jdf = self._jdf.groupBy(self._jcols(*cols)) + return GroupedData(jdf, self.sql_ctx) + + groupby = groupBy + + def agg(self, *exprs): + """ Aggregate on the entire :class:`DataFrame` without groups + (shorthand for ``df.groupBy.agg()``). + + >>> df.agg({"age": "max"}).collect() + [Row(MAX(age)=5)] + >>> from pyspark.sql import functions as F + >>> df.agg(F.min(df.age)).collect() + [Row(MIN(age)=2)] + """ + return self.groupBy().agg(*exprs) + + def unionAll(self, other): + """ Return a new :class:`DataFrame` containing union of rows in this + frame and another frame. + + This is equivalent to `UNION ALL` in SQL. + """ + return DataFrame(self._jdf.unionAll(other._jdf), self.sql_ctx) + + def intersect(self, other): + """ Return a new :class:`DataFrame` containing rows only in + both this frame and another frame. + + This is equivalent to `INTERSECT` in SQL. + """ + return DataFrame(self._jdf.intersect(other._jdf), self.sql_ctx) + + def subtract(self, other): + """ Return a new :class:`DataFrame` containing rows in this frame + but not in another frame. + + This is equivalent to `EXCEPT` in SQL. + """ + return DataFrame(getattr(self._jdf, "except")(other._jdf), self.sql_ctx) + + def dropna(self, how='any', thresh=None, subset=None): + """Returns a new :class:`DataFrame` omitting rows with null values. + + This is an alias for ``na.drop()``. + + :param how: 'any' or 'all'. + If 'any', drop a row if it contains any nulls. + If 'all', drop a row only if all its values are null. + :param thresh: int, default None + If specified, drop rows that have less than `thresh` non-null values. + This overwrites the `how` parameter. + :param subset: optional list of column names to consider. + + >>> df4.dropna().show() + age height name + 10 80 Alice + + >>> df4.na.drop().show() + age height name + 10 80 Alice + """ + if how is not None and how not in ['any', 'all']: + raise ValueError("how ('" + how + "') should be 'any' or 'all'") + + if subset is None: + subset = self.columns + elif isinstance(subset, basestring): + subset = [subset] + elif not isinstance(subset, (list, tuple)): + raise ValueError("subset should be a list or tuple of column names") + + if thresh is None: + thresh = len(subset) if how == 'any' else 1 + + return DataFrame(self._jdf.na().drop(thresh, self._jseq(subset)), self.sql_ctx) + + def fillna(self, value, subset=None): + """Replace null values, alias for ``na.fill()``. + + :param value: int, long, float, string, or dict. + Value to replace null values with. + If the value is a dict, then `subset` is ignored and `value` must be a mapping + from column name (string) to replacement value. The replacement value must be + an int, long, float, or string. + :param subset: optional list of column names to consider. + Columns specified in subset that do not have matching data type are ignored. + For example, if `value` is a string, and subset contains a non-string column, + then the non-string column is simply ignored. + + >>> df4.fillna(50).show() + age height name + 10 80 Alice + 5 50 Bob + 50 50 Tom + 50 50 null + + >>> df4.fillna({'age': 50, 'name': 'unknown'}).show() + age height name + 10 80 Alice + 5 null Bob + 50 null Tom + 50 null unknown + + >>> df4.na.fill({'age': 50, 'name': 'unknown'}).show() + age height name + 10 80 Alice + 5 null Bob + 50 null Tom + 50 null unknown + """ + if not isinstance(value, (float, int, long, basestring, dict)): + raise ValueError("value should be a float, int, long, string, or dict") + + if isinstance(value, (int, long)): + value = float(value) + + if isinstance(value, dict): + value = MapConverter().convert(value, self.sql_ctx._sc._gateway._gateway_client) + return DataFrame(self._jdf.na().fill(value), self.sql_ctx) + elif subset is None: + return DataFrame(self._jdf.na().fill(value), self.sql_ctx) + else: + if isinstance(subset, basestring): + subset = [subset] + elif not isinstance(subset, (list, tuple)): + raise ValueError("subset should be a list or tuple of column names") + + return DataFrame(self._jdf.na().fill(value, self._jseq(subset)), self.sql_ctx) + + @ignore_unicode_prefix + def withColumn(self, colName, col): + """Returns a new :class:`DataFrame` by adding a column. + + :param colName: string, name of the new column. + :param col: a :class:`Column` expression for the new column. + + >>> df.withColumn('age2', df.age + 2).collect() + [Row(age=2, name=u'Alice', age2=4), Row(age=5, name=u'Bob', age2=7)] + """ + return self.select('*', col.alias(colName)) + + @ignore_unicode_prefix + def withColumnRenamed(self, existing, new): + """REturns a new :class:`DataFrame` by renaming an existing column. + + :param existing: string, name of the existing column to rename. + :param col: string, new name of the column. + + >>> df.withColumnRenamed('age', 'age2').collect() + [Row(age2=2, name=u'Alice'), Row(age2=5, name=u'Bob')] + """ + cols = [Column(_to_java_column(c)).alias(new) + if c == existing else c + for c in self.columns] + return self.select(*cols) + + def toPandas(self): + """Returns the contents of this :class:`DataFrame` as Pandas ``pandas.DataFrame``. + + This is only available if Pandas is installed and available. + + >>> df.toPandas() # doctest: +SKIP + age name + 0 2 Alice + 1 5 Bob + """ + import pandas as pd + return pd.DataFrame.from_records(self.collect(), columns=self.columns) + + +# Having SchemaRDD for backward compatibility (for docs) +class SchemaRDD(DataFrame): + """SchemaRDD is deprecated, please use :class:`DataFrame`. + """ + + +def dfapi(f): + def _api(self): + name = f.__name__ + jdf = getattr(self._jdf, name)() + return DataFrame(jdf, self.sql_ctx) + _api.__name__ = f.__name__ + _api.__doc__ = f.__doc__ + return _api + + +def df_varargs_api(f): + def _api(self, *args): + name = f.__name__ + jdf = getattr(self._jdf, name)(_to_seq(self.sql_ctx._sc, args)) + return DataFrame(jdf, self.sql_ctx) + _api.__name__ = f.__name__ + _api.__doc__ = f.__doc__ + return _api + + +class GroupedData(object): + """ + A set of methods for aggregations on a :class:`DataFrame`, + created by :func:`DataFrame.groupBy`. + """ + + def __init__(self, jdf, sql_ctx): + self._jdf = jdf + self.sql_ctx = sql_ctx + + @ignore_unicode_prefix + def agg(self, *exprs): + """Compute aggregates and returns the result as a :class:`DataFrame`. + + The available aggregate functions are `avg`, `max`, `min`, `sum`, `count`. + + If ``exprs`` is a single :class:`dict` mapping from string to string, then the key + is the column to perform aggregation on, and the value is the aggregate function. + + Alternatively, ``exprs`` can also be a list of aggregate :class:`Column` expressions. + + :param exprs: a dict mapping from column name (string) to aggregate functions (string), + or a list of :class:`Column`. + + >>> gdf = df.groupBy(df.name) + >>> gdf.agg({"*": "count"}).collect() + [Row(name=u'Alice', COUNT(1)=1), Row(name=u'Bob', COUNT(1)=1)] + + >>> from pyspark.sql import functions as F + >>> gdf.agg(F.min(df.age)).collect() + [Row(MIN(age)=2), Row(MIN(age)=5)] + """ + assert exprs, "exprs should not be empty" + if len(exprs) == 1 and isinstance(exprs[0], dict): + jmap = MapConverter().convert(exprs[0], + self.sql_ctx._sc._gateway._gateway_client) + jdf = self._jdf.agg(jmap) + else: + # Columns + assert all(isinstance(c, Column) for c in exprs), "all exprs should be Column" + jdf = self._jdf.agg(exprs[0]._jc, + _to_seq(self.sql_ctx._sc, [c._jc for c in exprs[1:]])) + return DataFrame(jdf, self.sql_ctx) + + @dfapi + def count(self): + """Counts the number of records for each group. + + >>> df.groupBy(df.age).count().collect() + [Row(age=2, count=1), Row(age=5, count=1)] + """ + + @df_varargs_api + def mean(self, *cols): + """Computes average values for each numeric columns for each group. + + :func:`mean` is an alias for :func:`avg`. + + :param cols: list of column names (string). Non-numeric columns are ignored. + + >>> df.groupBy().mean('age').collect() + [Row(AVG(age)=3.5)] + >>> df3.groupBy().mean('age', 'height').collect() + [Row(AVG(age)=3.5, AVG(height)=82.5)] + """ + + @df_varargs_api + def avg(self, *cols): + """Computes average values for each numeric columns for each group. + + :func:`mean` is an alias for :func:`avg`. + + :param cols: list of column names (string). Non-numeric columns are ignored. + + >>> df.groupBy().avg('age').collect() + [Row(AVG(age)=3.5)] + >>> df3.groupBy().avg('age', 'height').collect() + [Row(AVG(age)=3.5, AVG(height)=82.5)] + """ + + @df_varargs_api + def max(self, *cols): + """Computes the max value for each numeric columns for each group. + + >>> df.groupBy().max('age').collect() + [Row(MAX(age)=5)] + >>> df3.groupBy().max('age', 'height').collect() + [Row(MAX(age)=5, MAX(height)=85)] + """ + + @df_varargs_api + def min(self, *cols): + """Computes the min value for each numeric column for each group. + + :param cols: list of column names (string). Non-numeric columns are ignored. + + >>> df.groupBy().min('age').collect() + [Row(MIN(age)=2)] + >>> df3.groupBy().min('age', 'height').collect() + [Row(MIN(age)=2, MIN(height)=80)] + """ + + @df_varargs_api + def sum(self, *cols): + """Compute the sum for each numeric columns for each group. + + :param cols: list of column names (string). Non-numeric columns are ignored. + + >>> df.groupBy().sum('age').collect() + [Row(SUM(age)=7)] + >>> df3.groupBy().sum('age', 'height').collect() + [Row(SUM(age)=7, SUM(height)=165)] + """ + + +def _create_column_from_literal(literal): + sc = SparkContext._active_spark_context + return sc._jvm.functions.lit(literal) + + +def _create_column_from_name(name): + sc = SparkContext._active_spark_context + return sc._jvm.functions.col(name) + + +def _to_java_column(col): + if isinstance(col, Column): + jcol = col._jc + else: + jcol = _create_column_from_name(col) + return jcol + + +def _to_seq(sc, cols, converter=None): + """ + Convert a list of Column (or names) into a JVM Seq of Column. + + An optional `converter` could be used to convert items in `cols` + into JVM Column objects. + """ + if converter: + cols = [converter(c) for c in cols] + jcols = ListConverter().convert(cols, sc._gateway._gateway_client) + return sc._jvm.PythonUtils.toSeq(jcols) + + +def _unary_op(name, doc="unary operator"): + """ Create a method for given unary operator """ + def _(self): + jc = getattr(self._jc, name)() + return Column(jc) + _.__doc__ = doc + return _ + + +def _func_op(name, doc=''): + def _(self): + sc = SparkContext._active_spark_context + jc = getattr(sc._jvm.functions, name)(self._jc) + return Column(jc) + _.__doc__ = doc + return _ + + +def _bin_op(name, doc="binary operator"): + """ Create a method for given binary operator + """ + def _(self, other): + jc = other._jc if isinstance(other, Column) else other + njc = getattr(self._jc, name)(jc) + return Column(njc) + _.__doc__ = doc + return _ + + +def _reverse_op(name, doc="binary operator"): + """ Create a method for binary operator (this object is on right side) + """ + def _(self, other): + jother = _create_column_from_literal(other) + jc = getattr(jother, name)(self._jc) + return Column(jc) + _.__doc__ = doc + return _ + + +class Column(object): + + """ + A column in a DataFrame. + + :class:`Column` instances can be created by:: + + # 1. Select a column out of a DataFrame + + df.colName + df["colName"] + + # 2. Create from an expression + df.colName + 1 + 1 / df.colName + """ + + def __init__(self, jc): + self._jc = jc + + # arithmetic operators + __neg__ = _func_op("negate") + __add__ = _bin_op("plus") + __sub__ = _bin_op("minus") + __mul__ = _bin_op("multiply") + __div__ = _bin_op("divide") + __truediv__ = _bin_op("divide") + __mod__ = _bin_op("mod") + __radd__ = _bin_op("plus") + __rsub__ = _reverse_op("minus") + __rmul__ = _bin_op("multiply") + __rdiv__ = _reverse_op("divide") + __rtruediv__ = _reverse_op("divide") + __rmod__ = _reverse_op("mod") + + # logistic operators + __eq__ = _bin_op("equalTo") + __ne__ = _bin_op("notEqual") + __lt__ = _bin_op("lt") + __le__ = _bin_op("leq") + __ge__ = _bin_op("geq") + __gt__ = _bin_op("gt") + + # `and`, `or`, `not` cannot be overloaded in Python, + # so use bitwise operators as boolean operators + __and__ = _bin_op('and') + __or__ = _bin_op('or') + __invert__ = _func_op('not') + __rand__ = _bin_op("and") + __ror__ = _bin_op("or") + + # container operators + __contains__ = _bin_op("contains") + __getitem__ = _bin_op("getItem") + + def getItem(self, key): + """An expression that gets an item at position `ordinal` out of a list, + or gets an item by key out of a dict. + + >>> df = sc.parallelize([([1, 2], {"key": "value"})]).toDF(["l", "d"]) + >>> df.select(df.l.getItem(0), df.d.getItem("key")).show() + l[0] d[key] + 1 value + >>> df.select(df.l[0], df.d["key"]).show() + l[0] d[key] + 1 value + """ + return self[key] + + def getField(self, name): + """An expression that gets a field by name in a StructField. + + >>> from pyspark.sql import Row + >>> df = sc.parallelize([Row(r=Row(a=1, b="b"))]).toDF() + >>> df.select(df.r.getField("b")).show() + r.b + b + >>> df.select(df.r.a).show() + r.a + 1 + """ + return Column(self._jc.getField(name)) + + def __getattr__(self, item): + if item.startswith("__"): + raise AttributeError(item) + return self.getField(item) + + # string methods + rlike = _bin_op("rlike") + like = _bin_op("like") + startswith = _bin_op("startsWith") + endswith = _bin_op("endsWith") + + @ignore_unicode_prefix + def substr(self, startPos, length): + """ + Return a :class:`Column` which is a substring of the column + + :param startPos: start position (int or Column) + :param length: length of the substring (int or Column) + + >>> df.select(df.name.substr(1, 3).alias("col")).collect() + [Row(col=u'Ali'), Row(col=u'Bob')] + """ + if type(startPos) != type(length): + raise TypeError("Can not mix the type") + if isinstance(startPos, (int, long)): + jc = self._jc.substr(startPos, length) + elif isinstance(startPos, Column): + jc = self._jc.substr(startPos._jc, length._jc) + else: + raise TypeError("Unexpected type: %s" % type(startPos)) + return Column(jc) + + __getslice__ = substr + + @ignore_unicode_prefix + def inSet(self, *cols): + """ A boolean expression that is evaluated to true if the value of this + expression is contained by the evaluated values of the arguments. + + >>> df[df.name.inSet("Bob", "Mike")].collect() + [Row(age=5, name=u'Bob')] + >>> df[df.age.inSet([1, 2, 3])].collect() + [Row(age=2, name=u'Alice')] + """ + if len(cols) == 1 and isinstance(cols[0], (list, set)): + cols = cols[0] + cols = [c._jc if isinstance(c, Column) else _create_column_from_literal(c) for c in cols] + sc = SparkContext._active_spark_context + jc = getattr(self._jc, "in")(_to_seq(sc, cols)) + return Column(jc) + + # order + asc = _unary_op("asc", "Returns a sort expression based on the" + " ascending order of the given column name.") + desc = _unary_op("desc", "Returns a sort expression based on the" + " descending order of the given column name.") + + isNull = _unary_op("isNull", "True if the current expression is null.") + isNotNull = _unary_op("isNotNull", "True if the current expression is not null.") + + def alias(self, alias): + """Return a alias for this column + + >>> df.select(df.age.alias("age2")).collect() + [Row(age2=2), Row(age2=5)] + """ + return Column(getattr(self._jc, "as")(alias)) + + @ignore_unicode_prefix + def cast(self, dataType): + """ Convert the column into type `dataType` + + >>> df.select(df.age.cast("string").alias('ages')).collect() + [Row(ages=u'2'), Row(ages=u'5')] + >>> df.select(df.age.cast(StringType()).alias('ages')).collect() + [Row(ages=u'2'), Row(ages=u'5')] + """ + if isinstance(dataType, basestring): + jc = self._jc.cast(dataType) + elif isinstance(dataType, DataType): + sc = SparkContext._active_spark_context + ssql_ctx = sc._jvm.SQLContext(sc._jsc.sc()) + jdt = ssql_ctx.parseDataType(dataType.json()) + jc = self._jc.cast(jdt) + else: + raise TypeError("unexpected type: %s" % type(dataType)) + return Column(jc) + + def __repr__(self): + return 'Column<%s>' % self._jc.toString().encode('utf8') + + +class DataFrameNaFunctions(object): + """Functionality for working with missing data in :class:`DataFrame`. + """ + + def __init__(self, df): + self.df = df + + def drop(self, how='any', thresh=None, subset=None): + return self.df.dropna(how=how, thresh=thresh, subset=subset) + + drop.__doc__ = DataFrame.dropna.__doc__ + + def fill(self, value, subset=None): + return self.df.fillna(value=value, subset=subset) + + fill.__doc__ = DataFrame.fillna.__doc__ + + +def _test(): + import doctest + from pyspark.context import SparkContext + from pyspark.sql import Row, SQLContext + import pyspark.sql.dataframe + globs = pyspark.sql.dataframe.__dict__.copy() + sc = SparkContext('local[4]', 'PythonTest') + globs['sc'] = sc + globs['sqlContext'] = SQLContext(sc) + globs['df'] = sc.parallelize([(2, 'Alice'), (5, 'Bob')])\ + .toDF(StructType([StructField('age', IntegerType()), + StructField('name', StringType())])) + globs['df2'] = sc.parallelize([Row(name='Tom', height=80), Row(name='Bob', height=85)]).toDF() + globs['df3'] = sc.parallelize([Row(name='Alice', age=2, height=80), + Row(name='Bob', age=5, height=85)]).toDF() + + globs['df4'] = sc.parallelize([Row(name='Alice', age=10, height=80), + Row(name='Bob', age=5, height=None), + Row(name='Tom', age=None, height=None), + Row(name=None, age=None, height=None)]).toDF() + + (failure_count, test_count) = doctest.testmod( + pyspark.sql.dataframe, globs=globs, + optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE | doctest.REPORT_NDIFF) + globs['sc'].stop() + if failure_count: + exit(-1) + + +if __name__ == "__main__": + _test() diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py new file mode 100644 index 0000000000000..bb47923f24b82 --- /dev/null +++ b/python/pyspark/sql/functions.py @@ -0,0 +1,172 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +""" +A collections of builtin functions +""" +import sys + +if sys.version < "3": + from itertools import imap as map + +from pyspark import SparkContext +from pyspark.rdd import _prepare_for_python_RDD +from pyspark.serializers import PickleSerializer, AutoBatchedSerializer +from pyspark.sql.types import StringType +from pyspark.sql.dataframe import Column, _to_java_column, _to_seq + + +__all__ = ['countDistinct', 'approxCountDistinct', 'udf'] + + +def _create_function(name, doc=""): + """ Create a function for aggregator by name""" + def _(col): + sc = SparkContext._active_spark_context + jc = getattr(sc._jvm.functions, name)(col._jc if isinstance(col, Column) else col) + return Column(jc) + _.__name__ = name + _.__doc__ = doc + return _ + + +_functions = { + 'lit': 'Creates a :class:`Column` of literal value.', + 'col': 'Returns a :class:`Column` based on the given column name.', + 'column': 'Returns a :class:`Column` based on the given column name.', + 'asc': 'Returns a sort expression based on the ascending order of the given column name.', + 'desc': 'Returns a sort expression based on the descending order of the given column name.', + + 'upper': 'Converts a string expression to upper case.', + 'lower': 'Converts a string expression to upper case.', + 'sqrt': 'Computes the square root of the specified float value.', + 'abs': 'Computes the absolutle value.', + + 'max': 'Aggregate function: returns the maximum value of the expression in a group.', + 'min': 'Aggregate function: returns the minimum value of the expression in a group.', + 'first': 'Aggregate function: returns the first value in a group.', + 'last': 'Aggregate function: returns the last value in a group.', + 'count': 'Aggregate function: returns the number of items in a group.', + 'sum': 'Aggregate function: returns the sum of all values in the expression.', + 'avg': 'Aggregate function: returns the average of the values in a group.', + 'mean': 'Aggregate function: returns the average of the values in a group.', + 'sumDistinct': 'Aggregate function: returns the sum of distinct values in the expression.', +} + + +for _name, _doc in _functions.items(): + globals()[_name] = _create_function(_name, _doc) +del _name, _doc +__all__ += _functions.keys() +__all__.sort() + + +def countDistinct(col, *cols): + """Returns a new :class:`Column` for distinct count of ``col`` or ``cols``. + + >>> df.agg(countDistinct(df.age, df.name).alias('c')).collect() + [Row(c=2)] + + >>> df.agg(countDistinct("age", "name").alias('c')).collect() + [Row(c=2)] + """ + sc = SparkContext._active_spark_context + jc = sc._jvm.functions.countDistinct(_to_java_column(col), _to_seq(sc, cols, _to_java_column)) + return Column(jc) + + +def approxCountDistinct(col, rsd=None): + """Returns a new :class:`Column` for approximate distinct count of ``col``. + + >>> df.agg(approxCountDistinct(df.age).alias('c')).collect() + [Row(c=2)] + """ + sc = SparkContext._active_spark_context + if rsd is None: + jc = sc._jvm.functions.approxCountDistinct(_to_java_column(col)) + else: + jc = sc._jvm.functions.approxCountDistinct(_to_java_column(col), rsd) + return Column(jc) + + +class UserDefinedFunction(object): + """ + User defined function in Python + """ + def __init__(self, func, returnType): + self.func = func + self.returnType = returnType + self._broadcast = None + self._judf = self._create_judf() + + def _create_judf(self): + f = self.func # put it in closure `func` + func = lambda _, it: map(lambda x: f(*x), it) + ser = AutoBatchedSerializer(PickleSerializer()) + command = (func, None, ser, ser) + sc = SparkContext._active_spark_context + pickled_command, broadcast_vars, env, includes = _prepare_for_python_RDD(sc, command, self) + ssql_ctx = sc._jvm.SQLContext(sc._jsc.sc()) + jdt = ssql_ctx.parseDataType(self.returnType.json()) + fname = f.__name__ if hasattr(f, '__name__') else f.__class__.__name__ + judf = sc._jvm.UserDefinedPythonFunction(fname, bytearray(pickled_command), env, + includes, sc.pythonExec, broadcast_vars, + sc._javaAccumulator, jdt) + return judf + + def __del__(self): + if self._broadcast is not None: + self._broadcast.unpersist() + self._broadcast = None + + def __call__(self, *cols): + sc = SparkContext._active_spark_context + jc = self._judf.apply(_to_seq(sc, cols, _to_java_column)) + return Column(jc) + + +def udf(f, returnType=StringType()): + """Creates a :class:`Column` expression representing a user defined function (UDF). + + >>> from pyspark.sql.types import IntegerType + >>> slen = udf(lambda s: len(s), IntegerType()) + >>> df.select(slen(df.name).alias('slen')).collect() + [Row(slen=5), Row(slen=3)] + """ + return UserDefinedFunction(f, returnType) + + +def _test(): + import doctest + from pyspark.context import SparkContext + from pyspark.sql import Row, SQLContext + import pyspark.sql.functions + globs = pyspark.sql.functions.__dict__.copy() + sc = SparkContext('local[4]', 'PythonTest') + globs['sc'] = sc + globs['sqlContext'] = SQLContext(sc) + globs['df'] = sc.parallelize([Row(name='Alice', age=2), Row(name='Bob', age=5)]).toDF() + (failure_count, test_count) = doctest.testmod( + pyspark.sql.functions, globs=globs, + optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE) + globs['sc'].stop() + if failure_count: + exit(-1) + + +if __name__ == "__main__": + _test() diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py new file mode 100644 index 0000000000000..aa3aa1d164d9f --- /dev/null +++ b/python/pyspark/sql/tests.py @@ -0,0 +1,644 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +""" +Unit tests for pyspark.sql; additional tests are implemented as doctests in +individual modules. +""" +import os +import sys +import pydoc +import shutil +import tempfile +import pickle +import functools + +import py4j + +if sys.version_info[:2] <= (2, 6): + try: + import unittest2 as unittest + except ImportError: + sys.stderr.write('Please install unittest2 to test with Python 2.6 or earlier') + sys.exit(1) +else: + import unittest + +from pyspark.sql import SQLContext, HiveContext, Column, Row +from pyspark.sql.types import * +from pyspark.sql.types import UserDefinedType, _infer_type +from pyspark.tests import ReusedPySparkTestCase +from pyspark.sql.functions import UserDefinedFunction + + +class ExamplePointUDT(UserDefinedType): + """ + User-defined type (UDT) for ExamplePoint. + """ + + @classmethod + def sqlType(self): + return ArrayType(DoubleType(), False) + + @classmethod + def module(cls): + return 'pyspark.tests' + + @classmethod + def scalaUDT(cls): + return 'org.apache.spark.sql.test.ExamplePointUDT' + + def serialize(self, obj): + return [obj.x, obj.y] + + def deserialize(self, datum): + return ExamplePoint(datum[0], datum[1]) + + +class ExamplePoint: + """ + An example class to demonstrate UDT in Scala, Java, and Python. + """ + + __UDT__ = ExamplePointUDT() + + def __init__(self, x, y): + self.x = x + self.y = y + + def __repr__(self): + return "ExamplePoint(%s,%s)" % (self.x, self.y) + + def __str__(self): + return "(%s,%s)" % (self.x, self.y) + + def __eq__(self, other): + return isinstance(other, ExamplePoint) and \ + other.x == self.x and other.y == self.y + + +class DataTypeTests(unittest.TestCase): + # regression test for SPARK-6055 + def test_data_type_eq(self): + lt = LongType() + lt2 = pickle.loads(pickle.dumps(LongType())) + self.assertEquals(lt, lt2) + + +class SQLTests(ReusedPySparkTestCase): + + @classmethod + def setUpClass(cls): + ReusedPySparkTestCase.setUpClass() + cls.tempdir = tempfile.NamedTemporaryFile(delete=False) + os.unlink(cls.tempdir.name) + cls.sqlCtx = SQLContext(cls.sc) + cls.testData = [Row(key=i, value=str(i)) for i in range(100)] + rdd = cls.sc.parallelize(cls.testData) + cls.df = rdd.toDF() + + @classmethod + def tearDownClass(cls): + ReusedPySparkTestCase.tearDownClass() + shutil.rmtree(cls.tempdir.name, ignore_errors=True) + + def test_udf_with_callable(self): + d = [Row(number=i, squared=i**2) for i in range(10)] + rdd = self.sc.parallelize(d) + data = self.sqlCtx.createDataFrame(rdd) + + class PlusFour: + def __call__(self, col): + if col is not None: + return col + 4 + + call = PlusFour() + pudf = UserDefinedFunction(call, LongType()) + res = data.select(pudf(data['number']).alias('plus_four')) + self.assertEqual(res.agg({'plus_four': 'sum'}).collect()[0][0], 85) + + def test_udf_with_partial_function(self): + d = [Row(number=i, squared=i**2) for i in range(10)] + rdd = self.sc.parallelize(d) + data = self.sqlCtx.createDataFrame(rdd) + + def some_func(col, param): + if col is not None: + return col + param + + pfunc = functools.partial(some_func, param=4) + pudf = UserDefinedFunction(pfunc, LongType()) + res = data.select(pudf(data['number']).alias('plus_four')) + self.assertEqual(res.agg({'plus_four': 'sum'}).collect()[0][0], 85) + + def test_udf(self): + self.sqlCtx.registerFunction("twoArgs", lambda x, y: len(x) + y, IntegerType()) + [row] = self.sqlCtx.sql("SELECT twoArgs('test', 1)").collect() + self.assertEqual(row[0], 5) + + def test_udf2(self): + self.sqlCtx.registerFunction("strlen", lambda string: len(string), IntegerType()) + self.sqlCtx.createDataFrame(self.sc.parallelize([Row(a="test")])).registerTempTable("test") + [res] = self.sqlCtx.sql("SELECT strlen(a) FROM test WHERE strlen(a) > 1").collect() + self.assertEqual(4, res[0]) + + def test_udf_with_array_type(self): + d = [Row(l=list(range(3)), d={"key": list(range(5))})] + rdd = self.sc.parallelize(d) + self.sqlCtx.createDataFrame(rdd).registerTempTable("test") + self.sqlCtx.registerFunction("copylist", lambda l: list(l), ArrayType(IntegerType())) + self.sqlCtx.registerFunction("maplen", lambda d: len(d), IntegerType()) + [(l1, l2)] = self.sqlCtx.sql("select copylist(l), maplen(d) from test").collect() + self.assertEqual(list(range(3)), l1) + self.assertEqual(1, l2) + + def test_broadcast_in_udf(self): + bar = {"a": "aa", "b": "bb", "c": "abc"} + foo = self.sc.broadcast(bar) + self.sqlCtx.registerFunction("MYUDF", lambda x: foo.value[x] if x else '') + [res] = self.sqlCtx.sql("SELECT MYUDF('c')").collect() + self.assertEqual("abc", res[0]) + [res] = self.sqlCtx.sql("SELECT MYUDF('')").collect() + self.assertEqual("", res[0]) + + def test_basic_functions(self): + rdd = self.sc.parallelize(['{"foo":"bar"}', '{"foo":"baz"}']) + df = self.sqlCtx.jsonRDD(rdd) + df.count() + df.collect() + df.schema + + # cache and checkpoint + self.assertFalse(df.is_cached) + df.persist() + df.unpersist() + df.cache() + self.assertTrue(df.is_cached) + self.assertEqual(2, df.count()) + + df.registerTempTable("temp") + df = self.sqlCtx.sql("select foo from temp") + df.count() + df.collect() + + def test_apply_schema_to_row(self): + df = self.sqlCtx.jsonRDD(self.sc.parallelize(["""{"a":2}"""])) + df2 = self.sqlCtx.createDataFrame(df.map(lambda x: x), df.schema) + self.assertEqual(df.collect(), df2.collect()) + + rdd = self.sc.parallelize(range(10)).map(lambda x: Row(a=x)) + df3 = self.sqlCtx.createDataFrame(rdd, df.schema) + self.assertEqual(10, df3.count()) + + def test_serialize_nested_array_and_map(self): + d = [Row(l=[Row(a=1, b='s')], d={"key": Row(c=1.0, d="2")})] + rdd = self.sc.parallelize(d) + df = self.sqlCtx.createDataFrame(rdd) + row = df.head() + self.assertEqual(1, len(row.l)) + self.assertEqual(1, row.l[0].a) + self.assertEqual("2", row.d["key"].d) + + l = df.map(lambda x: x.l).first() + self.assertEqual(1, len(l)) + self.assertEqual('s', l[0].b) + + d = df.map(lambda x: x.d).first() + self.assertEqual(1, len(d)) + self.assertEqual(1.0, d["key"].c) + + row = df.map(lambda x: x.d["key"]).first() + self.assertEqual(1.0, row.c) + self.assertEqual("2", row.d) + + def test_infer_schema(self): + d = [Row(l=[], d={}, s=None), + Row(l=[Row(a=1, b='s')], d={"key": Row(c=1.0, d="2")}, s="")] + rdd = self.sc.parallelize(d) + df = self.sqlCtx.createDataFrame(rdd) + self.assertEqual([], df.map(lambda r: r.l).first()) + self.assertEqual([None, ""], df.map(lambda r: r.s).collect()) + df.registerTempTable("test") + result = self.sqlCtx.sql("SELECT l[0].a from test where d['key'].d = '2'") + self.assertEqual(1, result.head()[0]) + + df2 = self.sqlCtx.createDataFrame(rdd, samplingRatio=1.0) + self.assertEqual(df.schema, df2.schema) + self.assertEqual({}, df2.map(lambda r: r.d).first()) + self.assertEqual([None, ""], df2.map(lambda r: r.s).collect()) + df2.registerTempTable("test2") + result = self.sqlCtx.sql("SELECT l[0].a from test2 where d['key'].d = '2'") + self.assertEqual(1, result.head()[0]) + + def test_infer_nested_schema(self): + NestedRow = Row("f1", "f2") + nestedRdd1 = self.sc.parallelize([NestedRow([1, 2], {"row1": 1.0}), + NestedRow([2, 3], {"row2": 2.0})]) + df = self.sqlCtx.inferSchema(nestedRdd1) + self.assertEqual(Row(f1=[1, 2], f2={u'row1': 1.0}), df.collect()[0]) + + nestedRdd2 = self.sc.parallelize([NestedRow([[1, 2], [2, 3]], [1, 2]), + NestedRow([[2, 3], [3, 4]], [2, 3])]) + df = self.sqlCtx.inferSchema(nestedRdd2) + self.assertEqual(Row(f1=[[1, 2], [2, 3]], f2=[1, 2]), df.collect()[0]) + + from collections import namedtuple + CustomRow = namedtuple('CustomRow', 'field1 field2') + rdd = self.sc.parallelize([CustomRow(field1=1, field2="row1"), + CustomRow(field1=2, field2="row2"), + CustomRow(field1=3, field2="row3")]) + df = self.sqlCtx.inferSchema(rdd) + self.assertEquals(Row(field1=1, field2=u'row1'), df.first()) + + def test_apply_schema(self): + from datetime import date, datetime + rdd = self.sc.parallelize([(127, -128, -32768, 32767, 2147483647, 1.0, + date(2010, 1, 1), datetime(2010, 1, 1, 1, 1, 1), + {"a": 1}, (2,), [1, 2, 3], None)]) + schema = StructType([ + StructField("byte1", ByteType(), False), + StructField("byte2", ByteType(), False), + StructField("short1", ShortType(), False), + StructField("short2", ShortType(), False), + StructField("int1", IntegerType(), False), + StructField("float1", FloatType(), False), + StructField("date1", DateType(), False), + StructField("time1", TimestampType(), False), + StructField("map1", MapType(StringType(), IntegerType(), False), False), + StructField("struct1", StructType([StructField("b", ShortType(), False)]), False), + StructField("list1", ArrayType(ByteType(), False), False), + StructField("null1", DoubleType(), True)]) + df = self.sqlCtx.createDataFrame(rdd, schema) + results = df.map(lambda x: (x.byte1, x.byte2, x.short1, x.short2, x.int1, x.float1, x.date1, + x.time1, x.map1["a"], x.struct1.b, x.list1, x.null1)) + r = (127, -128, -32768, 32767, 2147483647, 1.0, date(2010, 1, 1), + datetime(2010, 1, 1, 1, 1, 1), 1, 2, [1, 2, 3], None) + self.assertEqual(r, results.first()) + + df.registerTempTable("table2") + r = self.sqlCtx.sql("SELECT byte1 - 1 AS byte1, byte2 + 1 AS byte2, " + + "short1 + 1 AS short1, short2 - 1 AS short2, int1 - 1 AS int1, " + + "float1 + 1.5 as float1 FROM table2").first() + + self.assertEqual((126, -127, -32767, 32766, 2147483646, 2.5), tuple(r)) + + from pyspark.sql.types import _parse_schema_abstract, _infer_schema_type + rdd = self.sc.parallelize([(127, -32768, 1.0, datetime(2010, 1, 1, 1, 1, 1), + {"a": 1}, (2,), [1, 2, 3])]) + abstract = "byte1 short1 float1 time1 map1{} struct1(b) list1[]" + schema = _parse_schema_abstract(abstract) + typedSchema = _infer_schema_type(rdd.first(), schema) + df = self.sqlCtx.applySchema(rdd, typedSchema) + r = (127, -32768, 1.0, datetime(2010, 1, 1, 1, 1, 1), {"a": 1}, Row(b=2), [1, 2, 3]) + self.assertEqual(r, tuple(df.first())) + + def test_struct_in_map(self): + d = [Row(m={Row(i=1): Row(s="")})] + df = self.sc.parallelize(d).toDF() + k, v = list(df.head().m.items())[0] + self.assertEqual(1, k.i) + self.assertEqual("", v.s) + + def test_convert_row_to_dict(self): + row = Row(l=[Row(a=1, b='s')], d={"key": Row(c=1.0, d="2")}) + self.assertEqual(1, row.asDict()['l'][0].a) + df = self.sc.parallelize([row]).toDF() + df.registerTempTable("test") + row = self.sqlCtx.sql("select l, d from test").head() + self.assertEqual(1, row.asDict()["l"][0].a) + self.assertEqual(1.0, row.asDict()['d']['key'].c) + + def test_infer_schema_with_udt(self): + from pyspark.sql.tests import ExamplePoint, ExamplePointUDT + row = Row(label=1.0, point=ExamplePoint(1.0, 2.0)) + df = self.sc.parallelize([row]).toDF() + schema = df.schema + field = [f for f in schema.fields if f.name == "point"][0] + self.assertEqual(type(field.dataType), ExamplePointUDT) + df.registerTempTable("labeled_point") + point = self.sqlCtx.sql("SELECT point FROM labeled_point").head().point + self.assertEqual(point, ExamplePoint(1.0, 2.0)) + + def test_apply_schema_with_udt(self): + from pyspark.sql.tests import ExamplePoint, ExamplePointUDT + row = (1.0, ExamplePoint(1.0, 2.0)) + rdd = self.sc.parallelize([row]) + schema = StructType([StructField("label", DoubleType(), False), + StructField("point", ExamplePointUDT(), False)]) + df = rdd.toDF(schema) + point = df.head().point + self.assertEquals(point, ExamplePoint(1.0, 2.0)) + + def test_parquet_with_udt(self): + from pyspark.sql.tests import ExamplePoint + row = Row(label=1.0, point=ExamplePoint(1.0, 2.0)) + df0 = self.sc.parallelize([row]).toDF() + output_dir = os.path.join(self.tempdir.name, "labeled_point") + df0.saveAsParquetFile(output_dir) + df1 = self.sqlCtx.parquetFile(output_dir) + point = df1.head().point + self.assertEquals(point, ExamplePoint(1.0, 2.0)) + + def test_column_operators(self): + ci = self.df.key + cs = self.df.value + c = ci == cs + self.assertTrue(isinstance((- ci - 1 - 2) % 3 * 2.5 / 3.5, Column)) + rcc = (1 + ci), (1 - ci), (1 * ci), (1 / ci), (1 % ci) + self.assertTrue(all(isinstance(c, Column) for c in rcc)) + cb = [ci == 5, ci != 0, ci > 3, ci < 4, ci >= 0, ci <= 7, ci and cs, ci or cs] + self.assertTrue(all(isinstance(c, Column) for c in cb)) + cbool = (ci & ci), (ci | ci), (~ci) + self.assertTrue(all(isinstance(c, Column) for c in cbool)) + css = cs.like('a'), cs.rlike('a'), cs.asc(), cs.desc(), cs.startswith('a'), cs.endswith('a') + self.assertTrue(all(isinstance(c, Column) for c in css)) + self.assertTrue(isinstance(ci.cast(LongType()), Column)) + + def test_column_select(self): + df = self.df + self.assertEqual(self.testData, df.select("*").collect()) + self.assertEqual(self.testData, df.select(df.key, df.value).collect()) + self.assertEqual([Row(value='1')], df.where(df.key == 1).select(df.value).collect()) + + def test_aggregator(self): + df = self.df + g = df.groupBy() + self.assertEqual([99, 100], sorted(g.agg({'key': 'max', 'value': 'count'}).collect()[0])) + self.assertEqual([Row(**{"AVG(key#0)": 49.5})], g.mean().collect()) + + from pyspark.sql import functions + self.assertEqual((0, u'99'), + tuple(g.agg(functions.first(df.key), functions.last(df.value)).first())) + self.assertTrue(95 < g.agg(functions.approxCountDistinct(df.key)).first()[0]) + self.assertEqual(100, g.agg(functions.countDistinct(df.value)).first()[0]) + + def test_save_and_load(self): + df = self.df + tmpPath = tempfile.mkdtemp() + shutil.rmtree(tmpPath) + df.save(tmpPath, "org.apache.spark.sql.json", "error") + actual = self.sqlCtx.load(tmpPath, "org.apache.spark.sql.json") + self.assertTrue(sorted(df.collect()) == sorted(actual.collect())) + + schema = StructType([StructField("value", StringType(), True)]) + actual = self.sqlCtx.load(tmpPath, "org.apache.spark.sql.json", schema) + self.assertTrue(sorted(df.select("value").collect()) == sorted(actual.collect())) + + df.save(tmpPath, "org.apache.spark.sql.json", "overwrite") + actual = self.sqlCtx.load(tmpPath, "org.apache.spark.sql.json") + self.assertTrue(sorted(df.collect()) == sorted(actual.collect())) + + df.save(source="org.apache.spark.sql.json", mode="overwrite", path=tmpPath, + noUse="this options will not be used in save.") + actual = self.sqlCtx.load(source="org.apache.spark.sql.json", path=tmpPath, + noUse="this options will not be used in load.") + self.assertTrue(sorted(df.collect()) == sorted(actual.collect())) + + defaultDataSourceName = self.sqlCtx.getConf("spark.sql.sources.default", + "org.apache.spark.sql.parquet") + self.sqlCtx.sql("SET spark.sql.sources.default=org.apache.spark.sql.json") + actual = self.sqlCtx.load(path=tmpPath) + self.assertTrue(sorted(df.collect()) == sorted(actual.collect())) + self.sqlCtx.sql("SET spark.sql.sources.default=" + defaultDataSourceName) + + shutil.rmtree(tmpPath) + + def test_help_command(self): + # Regression test for SPARK-5464 + rdd = self.sc.parallelize(['{"foo":"bar"}', '{"foo":"baz"}']) + df = self.sqlCtx.jsonRDD(rdd) + # render_doc() reproduces the help() exception without printing output + pydoc.render_doc(df) + pydoc.render_doc(df.foo) + pydoc.render_doc(df.take(1)) + + def test_access_column(self): + df = self.df + self.assertTrue(isinstance(df.key, Column)) + self.assertTrue(isinstance(df['key'], Column)) + self.assertTrue(isinstance(df[0], Column)) + self.assertRaises(IndexError, lambda: df[2]) + self.assertRaises(IndexError, lambda: df["bad_key"]) + self.assertRaises(TypeError, lambda: df[{}]) + + def test_access_nested_types(self): + df = self.sc.parallelize([Row(l=[1], r=Row(a=1, b="b"), d={"k": "v"})]).toDF() + self.assertEqual(1, df.select(df.l[0]).first()[0]) + self.assertEqual(1, df.select(df.l.getItem(0)).first()[0]) + self.assertEqual(1, df.select(df.r.a).first()[0]) + self.assertEqual("b", df.select(df.r.getField("b")).first()[0]) + self.assertEqual("v", df.select(df.d["k"]).first()[0]) + self.assertEqual("v", df.select(df.d.getItem("k")).first()[0]) + + def test_infer_long_type(self): + longrow = [Row(f1='a', f2=100000000000000)] + df = self.sc.parallelize(longrow).toDF() + self.assertEqual(df.schema.fields[1].dataType, LongType()) + + # this saving as Parquet caused issues as well. + output_dir = os.path.join(self.tempdir.name, "infer_long_type") + df.saveAsParquetFile(output_dir) + df1 = self.sqlCtx.parquetFile(output_dir) + self.assertEquals('a', df1.first().f1) + self.assertEquals(100000000000000, df1.first().f2) + + self.assertEqual(_infer_type(1), LongType()) + self.assertEqual(_infer_type(2**10), LongType()) + self.assertEqual(_infer_type(2**20), LongType()) + self.assertEqual(_infer_type(2**31 - 1), LongType()) + self.assertEqual(_infer_type(2**31), LongType()) + self.assertEqual(_infer_type(2**61), LongType()) + self.assertEqual(_infer_type(2**71), LongType()) + + def test_dropna(self): + schema = StructType([ + StructField("name", StringType(), True), + StructField("age", IntegerType(), True), + StructField("height", DoubleType(), True)]) + + # shouldn't drop a non-null row + self.assertEqual(self.sqlCtx.createDataFrame( + [(u'Alice', 50, 80.1)], schema).dropna().count(), + 1) + + # dropping rows with a single null value + self.assertEqual(self.sqlCtx.createDataFrame( + [(u'Alice', None, 80.1)], schema).dropna().count(), + 0) + self.assertEqual(self.sqlCtx.createDataFrame( + [(u'Alice', None, 80.1)], schema).dropna(how='any').count(), + 0) + + # if how = 'all', only drop rows if all values are null + self.assertEqual(self.sqlCtx.createDataFrame( + [(u'Alice', None, 80.1)], schema).dropna(how='all').count(), + 1) + self.assertEqual(self.sqlCtx.createDataFrame( + [(None, None, None)], schema).dropna(how='all').count(), + 0) + + # how and subset + self.assertEqual(self.sqlCtx.createDataFrame( + [(u'Alice', 50, None)], schema).dropna(how='any', subset=['name', 'age']).count(), + 1) + self.assertEqual(self.sqlCtx.createDataFrame( + [(u'Alice', None, None)], schema).dropna(how='any', subset=['name', 'age']).count(), + 0) + + # threshold + self.assertEqual(self.sqlCtx.createDataFrame( + [(u'Alice', None, 80.1)], schema).dropna(thresh=2).count(), + 1) + self.assertEqual(self.sqlCtx.createDataFrame( + [(u'Alice', None, None)], schema).dropna(thresh=2).count(), + 0) + + # threshold and subset + self.assertEqual(self.sqlCtx.createDataFrame( + [(u'Alice', 50, None)], schema).dropna(thresh=2, subset=['name', 'age']).count(), + 1) + self.assertEqual(self.sqlCtx.createDataFrame( + [(u'Alice', None, 180.9)], schema).dropna(thresh=2, subset=['name', 'age']).count(), + 0) + + # thresh should take precedence over how + self.assertEqual(self.sqlCtx.createDataFrame( + [(u'Alice', 50, None)], schema).dropna( + how='any', thresh=2, subset=['name', 'age']).count(), + 1) + + def test_fillna(self): + schema = StructType([ + StructField("name", StringType(), True), + StructField("age", IntegerType(), True), + StructField("height", DoubleType(), True)]) + + # fillna shouldn't change non-null values + row = self.sqlCtx.createDataFrame([(u'Alice', 10, 80.1)], schema).fillna(50).first() + self.assertEqual(row.age, 10) + + # fillna with int + row = self.sqlCtx.createDataFrame([(u'Alice', None, None)], schema).fillna(50).first() + self.assertEqual(row.age, 50) + self.assertEqual(row.height, 50.0) + + # fillna with double + row = self.sqlCtx.createDataFrame([(u'Alice', None, None)], schema).fillna(50.1).first() + self.assertEqual(row.age, 50) + self.assertEqual(row.height, 50.1) + + # fillna with string + row = self.sqlCtx.createDataFrame([(None, None, None)], schema).fillna("hello").first() + self.assertEqual(row.name, u"hello") + self.assertEqual(row.age, None) + + # fillna with subset specified for numeric cols + row = self.sqlCtx.createDataFrame( + [(None, None, None)], schema).fillna(50, subset=['name', 'age']).first() + self.assertEqual(row.name, None) + self.assertEqual(row.age, 50) + self.assertEqual(row.height, None) + + # fillna with subset specified for numeric cols + row = self.sqlCtx.createDataFrame( + [(None, None, None)], schema).fillna("haha", subset=['name', 'age']).first() + self.assertEqual(row.name, "haha") + self.assertEqual(row.age, None) + self.assertEqual(row.height, None) + + +class HiveContextSQLTests(ReusedPySparkTestCase): + + @classmethod + def setUpClass(cls): + ReusedPySparkTestCase.setUpClass() + cls.tempdir = tempfile.NamedTemporaryFile(delete=False) + try: + cls.sc._jvm.org.apache.hadoop.hive.conf.HiveConf() + except py4j.protocol.Py4JError: + cls.sqlCtx = None + return + except TypeError: + cls.sqlCtx = None + return + os.unlink(cls.tempdir.name) + _scala_HiveContext =\ + cls.sc._jvm.org.apache.spark.sql.hive.test.TestHiveContext(cls.sc._jsc.sc()) + cls.sqlCtx = HiveContext(cls.sc, _scala_HiveContext) + cls.testData = [Row(key=i, value=str(i)) for i in range(100)] + cls.df = cls.sc.parallelize(cls.testData).toDF() + + @classmethod + def tearDownClass(cls): + ReusedPySparkTestCase.tearDownClass() + shutil.rmtree(cls.tempdir.name, ignore_errors=True) + + def test_save_and_load_table(self): + if self.sqlCtx is None: + return # no hive available, skipped + + df = self.df + tmpPath = tempfile.mkdtemp() + shutil.rmtree(tmpPath) + df.saveAsTable("savedJsonTable", "org.apache.spark.sql.json", "append", path=tmpPath) + actual = self.sqlCtx.createExternalTable("externalJsonTable", tmpPath, + "org.apache.spark.sql.json") + self.assertTrue( + sorted(df.collect()) == + sorted(self.sqlCtx.sql("SELECT * FROM savedJsonTable").collect())) + self.assertTrue( + sorted(df.collect()) == + sorted(self.sqlCtx.sql("SELECT * FROM externalJsonTable").collect())) + self.assertTrue(sorted(df.collect()) == sorted(actual.collect())) + self.sqlCtx.sql("DROP TABLE externalJsonTable") + + df.saveAsTable("savedJsonTable", "org.apache.spark.sql.json", "overwrite", path=tmpPath) + schema = StructType([StructField("value", StringType(), True)]) + actual = self.sqlCtx.createExternalTable("externalJsonTable", + source="org.apache.spark.sql.json", + schema=schema, path=tmpPath, + noUse="this options will not be used") + self.assertTrue( + sorted(df.collect()) == + sorted(self.sqlCtx.sql("SELECT * FROM savedJsonTable").collect())) + self.assertTrue( + sorted(df.select("value").collect()) == + sorted(self.sqlCtx.sql("SELECT * FROM externalJsonTable").collect())) + self.assertTrue(sorted(df.select("value").collect()) == sorted(actual.collect())) + self.sqlCtx.sql("DROP TABLE savedJsonTable") + self.sqlCtx.sql("DROP TABLE externalJsonTable") + + defaultDataSourceName = self.sqlCtx.getConf("spark.sql.sources.default", + "org.apache.spark.sql.parquet") + self.sqlCtx.sql("SET spark.sql.sources.default=org.apache.spark.sql.json") + df.saveAsTable("savedJsonTable", path=tmpPath, mode="overwrite") + actual = self.sqlCtx.createExternalTable("externalJsonTable", path=tmpPath) + self.assertTrue( + sorted(df.collect()) == + sorted(self.sqlCtx.sql("SELECT * FROM savedJsonTable").collect())) + self.assertTrue( + sorted(df.collect()) == + sorted(self.sqlCtx.sql("SELECT * FROM externalJsonTable").collect())) + self.assertTrue(sorted(df.collect()) == sorted(actual.collect())) + self.sqlCtx.sql("DROP TABLE savedJsonTable") + self.sqlCtx.sql("DROP TABLE externalJsonTable") + self.sqlCtx.sql("SET spark.sql.sources.default=" + defaultDataSourceName) + + shutil.rmtree(tmpPath) + +if __name__ == "__main__": + unittest.main() diff --git a/python/pyspark/sql_tests.py b/python/pyspark/sql_tests.py deleted file mode 100644 index d314f46e8d2d5..0000000000000 --- a/python/pyspark/sql_tests.py +++ /dev/null @@ -1,299 +0,0 @@ -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -""" -Unit tests for pyspark.sql; additional tests are implemented as doctests in -individual modules. -""" -import os -import sys -import pydoc -import shutil -import tempfile - -if sys.version_info[:2] <= (2, 6): - try: - import unittest2 as unittest - except ImportError: - sys.stderr.write('Please install unittest2 to test with Python 2.6 or earlier') - sys.exit(1) -else: - import unittest - -from pyspark.sql import SQLContext, IntegerType, Row, ArrayType, StructType, StructField, \ - UserDefinedType, DoubleType -from pyspark.tests import ReusedPySparkTestCase - - -class ExamplePointUDT(UserDefinedType): - """ - User-defined type (UDT) for ExamplePoint. - """ - - @classmethod - def sqlType(self): - return ArrayType(DoubleType(), False) - - @classmethod - def module(cls): - return 'pyspark.tests' - - @classmethod - def scalaUDT(cls): - return 'org.apache.spark.sql.test.ExamplePointUDT' - - def serialize(self, obj): - return [obj.x, obj.y] - - def deserialize(self, datum): - return ExamplePoint(datum[0], datum[1]) - - -class ExamplePoint: - """ - An example class to demonstrate UDT in Scala, Java, and Python. - """ - - __UDT__ = ExamplePointUDT() - - def __init__(self, x, y): - self.x = x - self.y = y - - def __repr__(self): - return "ExamplePoint(%s,%s)" % (self.x, self.y) - - def __str__(self): - return "(%s,%s)" % (self.x, self.y) - - def __eq__(self, other): - return isinstance(other, ExamplePoint) and \ - other.x == self.x and other.y == self.y - - -class SQLTests(ReusedPySparkTestCase): - - @classmethod - def setUpClass(cls): - ReusedPySparkTestCase.setUpClass() - cls.tempdir = tempfile.NamedTemporaryFile(delete=False) - os.unlink(cls.tempdir.name) - cls.sqlCtx = SQLContext(cls.sc) - cls.testData = [Row(key=i, value=str(i)) for i in range(100)] - rdd = cls.sc.parallelize(cls.testData) - cls.df = cls.sqlCtx.inferSchema(rdd) - - @classmethod - def tearDownClass(cls): - ReusedPySparkTestCase.tearDownClass() - shutil.rmtree(cls.tempdir.name, ignore_errors=True) - - def test_udf(self): - self.sqlCtx.registerFunction("twoArgs", lambda x, y: len(x) + y, IntegerType()) - [row] = self.sqlCtx.sql("SELECT twoArgs('test', 1)").collect() - self.assertEqual(row[0], 5) - - def test_udf2(self): - self.sqlCtx.registerFunction("strlen", lambda string: len(string), IntegerType()) - self.sqlCtx.inferSchema(self.sc.parallelize([Row(a="test")])).registerTempTable("test") - [res] = self.sqlCtx.sql("SELECT strlen(a) FROM test WHERE strlen(a) > 1").collect() - self.assertEqual(4, res[0]) - - def test_udf_with_array_type(self): - d = [Row(l=range(3), d={"key": range(5)})] - rdd = self.sc.parallelize(d) - self.sqlCtx.inferSchema(rdd).registerTempTable("test") - self.sqlCtx.registerFunction("copylist", lambda l: list(l), ArrayType(IntegerType())) - self.sqlCtx.registerFunction("maplen", lambda d: len(d), IntegerType()) - [(l1, l2)] = self.sqlCtx.sql("select copylist(l), maplen(d) from test").collect() - self.assertEqual(range(3), l1) - self.assertEqual(1, l2) - - def test_broadcast_in_udf(self): - bar = {"a": "aa", "b": "bb", "c": "abc"} - foo = self.sc.broadcast(bar) - self.sqlCtx.registerFunction("MYUDF", lambda x: foo.value[x] if x else '') - [res] = self.sqlCtx.sql("SELECT MYUDF('c')").collect() - self.assertEqual("abc", res[0]) - [res] = self.sqlCtx.sql("SELECT MYUDF('')").collect() - self.assertEqual("", res[0]) - - def test_basic_functions(self): - rdd = self.sc.parallelize(['{"foo":"bar"}', '{"foo":"baz"}']) - df = self.sqlCtx.jsonRDD(rdd) - df.count() - df.collect() - df.schema() - - # cache and checkpoint - self.assertFalse(df.is_cached) - df.persist() - df.unpersist() - df.cache() - self.assertTrue(df.is_cached) - self.assertEqual(2, df.count()) - - df.registerTempTable("temp") - df = self.sqlCtx.sql("select foo from temp") - df.count() - df.collect() - - def test_apply_schema_to_row(self): - df = self.sqlCtx.jsonRDD(self.sc.parallelize(["""{"a":2}"""])) - df2 = self.sqlCtx.applySchema(df.map(lambda x: x), df.schema()) - self.assertEqual(df.collect(), df2.collect()) - - rdd = self.sc.parallelize(range(10)).map(lambda x: Row(a=x)) - df3 = self.sqlCtx.applySchema(rdd, df.schema()) - self.assertEqual(10, df3.count()) - - def test_serialize_nested_array_and_map(self): - d = [Row(l=[Row(a=1, b='s')], d={"key": Row(c=1.0, d="2")})] - rdd = self.sc.parallelize(d) - df = self.sqlCtx.inferSchema(rdd) - row = df.head() - self.assertEqual(1, len(row.l)) - self.assertEqual(1, row.l[0].a) - self.assertEqual("2", row.d["key"].d) - - l = df.map(lambda x: x.l).first() - self.assertEqual(1, len(l)) - self.assertEqual('s', l[0].b) - - d = df.map(lambda x: x.d).first() - self.assertEqual(1, len(d)) - self.assertEqual(1.0, d["key"].c) - - row = df.map(lambda x: x.d["key"]).first() - self.assertEqual(1.0, row.c) - self.assertEqual("2", row.d) - - def test_infer_schema(self): - d = [Row(l=[], d={}), - Row(l=[Row(a=1, b='s')], d={"key": Row(c=1.0, d="2")}, s="")] - rdd = self.sc.parallelize(d) - df = self.sqlCtx.inferSchema(rdd) - self.assertEqual([], df.map(lambda r: r.l).first()) - self.assertEqual([None, ""], df.map(lambda r: r.s).collect()) - df.registerTempTable("test") - result = self.sqlCtx.sql("SELECT l[0].a from test where d['key'].d = '2'") - self.assertEqual(1, result.head()[0]) - - df2 = self.sqlCtx.inferSchema(rdd, 1.0) - self.assertEqual(df.schema(), df2.schema()) - self.assertEqual({}, df2.map(lambda r: r.d).first()) - self.assertEqual([None, ""], df2.map(lambda r: r.s).collect()) - df2.registerTempTable("test2") - result = self.sqlCtx.sql("SELECT l[0].a from test2 where d['key'].d = '2'") - self.assertEqual(1, result.head()[0]) - - def test_struct_in_map(self): - d = [Row(m={Row(i=1): Row(s="")})] - rdd = self.sc.parallelize(d) - df = self.sqlCtx.inferSchema(rdd) - k, v = df.head().m.items()[0] - self.assertEqual(1, k.i) - self.assertEqual("", v.s) - - def test_convert_row_to_dict(self): - row = Row(l=[Row(a=1, b='s')], d={"key": Row(c=1.0, d="2")}) - self.assertEqual(1, row.asDict()['l'][0].a) - rdd = self.sc.parallelize([row]) - df = self.sqlCtx.inferSchema(rdd) - df.registerTempTable("test") - row = self.sqlCtx.sql("select l, d from test").head() - self.assertEqual(1, row.asDict()["l"][0].a) - self.assertEqual(1.0, row.asDict()['d']['key'].c) - - def test_infer_schema_with_udt(self): - from pyspark.sql_tests import ExamplePoint, ExamplePointUDT - row = Row(label=1.0, point=ExamplePoint(1.0, 2.0)) - rdd = self.sc.parallelize([row]) - df = self.sqlCtx.inferSchema(rdd) - schema = df.schema() - field = [f for f in schema.fields if f.name == "point"][0] - self.assertEqual(type(field.dataType), ExamplePointUDT) - df.registerTempTable("labeled_point") - point = self.sqlCtx.sql("SELECT point FROM labeled_point").head().point - self.assertEqual(point, ExamplePoint(1.0, 2.0)) - - def test_apply_schema_with_udt(self): - from pyspark.sql_tests import ExamplePoint, ExamplePointUDT - row = (1.0, ExamplePoint(1.0, 2.0)) - rdd = self.sc.parallelize([row]) - schema = StructType([StructField("label", DoubleType(), False), - StructField("point", ExamplePointUDT(), False)]) - df = self.sqlCtx.applySchema(rdd, schema) - point = df.head().point - self.assertEquals(point, ExamplePoint(1.0, 2.0)) - - def test_parquet_with_udt(self): - from pyspark.sql_tests import ExamplePoint - row = Row(label=1.0, point=ExamplePoint(1.0, 2.0)) - rdd = self.sc.parallelize([row]) - df0 = self.sqlCtx.inferSchema(rdd) - output_dir = os.path.join(self.tempdir.name, "labeled_point") - df0.saveAsParquetFile(output_dir) - df1 = self.sqlCtx.parquetFile(output_dir) - point = df1.head().point - self.assertEquals(point, ExamplePoint(1.0, 2.0)) - - def test_column_operators(self): - from pyspark.sql import Column, LongType - ci = self.df.key - cs = self.df.value - c = ci == cs - self.assertTrue(isinstance((- ci - 1 - 2) % 3 * 2.5 / 3.5, Column)) - rcc = (1 + ci), (1 - ci), (1 * ci), (1 / ci), (1 % ci) - self.assertTrue(all(isinstance(c, Column) for c in rcc)) - cb = [ci == 5, ci != 0, ci > 3, ci < 4, ci >= 0, ci <= 7, ci and cs, ci or cs] - self.assertTrue(all(isinstance(c, Column) for c in cb)) - cbool = (ci & ci), (ci | ci), (~ci) - self.assertTrue(all(isinstance(c, Column) for c in cbool)) - css = cs.like('a'), cs.rlike('a'), cs.asc(), cs.desc(), cs.startswith('a'), cs.endswith('a') - self.assertTrue(all(isinstance(c, Column) for c in css)) - self.assertTrue(isinstance(ci.cast(LongType()), Column)) - - def test_column_select(self): - df = self.df - self.assertEqual(self.testData, df.select("*").collect()) - self.assertEqual(self.testData, df.select(df.key, df.value).collect()) - self.assertEqual([Row(value='1')], df.where(df.key == 1).select(df.value).collect()) - - def test_aggregator(self): - df = self.df - g = df.groupBy() - self.assertEqual([99, 100], sorted(g.agg({'key': 'max', 'value': 'count'}).collect()[0])) - self.assertEqual([Row(**{"AVG(key#0)": 49.5})], g.mean().collect()) - - from pyspark.sql import Dsl - self.assertEqual((0, u'99'), tuple(g.agg(Dsl.first(df.key), Dsl.last(df.value)).first())) - self.assertTrue(95 < g.agg(Dsl.approxCountDistinct(df.key)).first()[0]) - self.assertEqual(100, g.agg(Dsl.countDistinct(df.value)).first()[0]) - - def test_help_command(self): - # Regression test for SPARK-5464 - rdd = self.sc.parallelize(['{"foo":"bar"}', '{"foo":"baz"}']) - df = self.sqlCtx.jsonRDD(rdd) - # render_doc() reproduces the help() exception without printing output - pydoc.render_doc(df) - pydoc.render_doc(df.foo) - pydoc.render_doc(df.take(1)) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/pyspark/statcounter.py b/python/pyspark/statcounter.py index 1e597d64e03fe..944fa414b0c0e 100644 --- a/python/pyspark/statcounter.py +++ b/python/pyspark/statcounter.py @@ -31,7 +31,7 @@ class StatCounter(object): def __init__(self, values=[]): - self.n = 0L # Running count of our values + self.n = 0 # Running count of our values self.mu = 0.0 # Running mean of our values self.m2 = 0.0 # Running variance numerator (sum of (x - mean)^2) self.maxValue = float("-inf") @@ -87,7 +87,7 @@ def copy(self): return copy.deepcopy(self) def count(self): - return self.n + return int(self.n) def mean(self): return self.mu diff --git a/python/pyspark/status.py b/python/pyspark/status.py new file mode 100644 index 0000000000000..a6fa7dd3144d4 --- /dev/null +++ b/python/pyspark/status.py @@ -0,0 +1,96 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from collections import namedtuple + +__all__ = ["SparkJobInfo", "SparkStageInfo", "StatusTracker"] + + +class SparkJobInfo(namedtuple("SparkJobInfo", "jobId stageIds status")): + """ + Exposes information about Spark Jobs. + """ + + +class SparkStageInfo(namedtuple("SparkStageInfo", + "stageId currentAttemptId name numTasks numActiveTasks " + "numCompletedTasks numFailedTasks")): + """ + Exposes information about Spark Stages. + """ + + +class StatusTracker(object): + """ + Low-level status reporting APIs for monitoring job and stage progress. + + These APIs intentionally provide very weak consistency semantics; + consumers of these APIs should be prepared to handle empty / missing + information. For example, a job's stage ids may be known but the status + API may not have any information about the details of those stages, so + `getStageInfo` could potentially return `None` for a valid stage id. + + To limit memory usage, these APIs only provide information on recent + jobs / stages. These APIs will provide information for the last + `spark.ui.retainedStages` stages and `spark.ui.retainedJobs` jobs. + """ + def __init__(self, jtracker): + self._jtracker = jtracker + + def getJobIdsForGroup(self, jobGroup=None): + """ + Return a list of all known jobs in a particular job group. If + `jobGroup` is None, then returns all known jobs that are not + associated with a job group. + + The returned list may contain running, failed, and completed jobs, + and may vary across invocations of this method. This method does + not guarantee the order of the elements in its result. + """ + return list(self._jtracker.getJobIdsForGroup(jobGroup)) + + def getActiveStageIds(self): + """ + Returns an array containing the ids of all active stages. + """ + return sorted(list(self._jtracker.getActiveStageIds())) + + def getActiveJobsIds(self): + """ + Returns an array containing the ids of all active jobs. + """ + return sorted((list(self._jtracker.getActiveJobIds()))) + + def getJobInfo(self, jobId): + """ + Returns a :class:`SparkJobInfo` object, or None if the job info + could not be found or was garbage collected. + """ + job = self._jtracker.getJobInfo(jobId) + if job is not None: + return SparkJobInfo(jobId, job.stageIds(), str(job.status())) + + def getStageInfo(self, stageId): + """ + Returns a :class:`SparkStageInfo` object, or None if the stage + info could not be found or was garbage collected. + """ + stage = self._jtracker.getStageInfo(stageId) + if stage is not None: + # TODO: fetch them in batch for better performance + attrs = [getattr(stage, f)() for f in SparkStageInfo._fields[1:]] + return SparkStageInfo(stageId, *attrs) diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py index b06ab650370bd..4590c58839266 100644 --- a/python/pyspark/streaming/context.py +++ b/python/pyspark/streaming/context.py @@ -14,6 +14,9 @@ # See the License for the specific language governing permissions and # limitations under the License. # + +from __future__ import print_function + import os import sys @@ -157,7 +160,7 @@ def getOrCreate(cls, checkpointPath, setupFunc): try: jssc = gw.jvm.JavaStreamingContext(checkpointPath) except Exception: - print >>sys.stderr, "failed to load StreamingContext from checkpoint" + print("failed to load StreamingContext from checkpoint", file=sys.stderr) raise jsc = jssc.sparkContext() @@ -189,7 +192,7 @@ def awaitTermination(self, timeout=None): if timeout is None: self._jssc.awaitTermination() else: - self._jssc.awaitTermination(int(timeout * 1000)) + self._jssc.awaitTerminationOrTimeout(int(timeout * 1000)) def awaitTerminationOrTimeout(self, timeout): """ diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py index 2fe39392ff081..ff097985fae3e 100644 --- a/python/pyspark/streaming/dstream.py +++ b/python/pyspark/streaming/dstream.py @@ -15,11 +15,15 @@ # limitations under the License. # -from itertools import chain, ifilter, imap +import sys import operator import time +from itertools import chain from datetime import datetime +if sys.version < "3": + from itertools import imap as map, ifilter as filter + from py4j.protocol import Py4JJavaError from pyspark import RDD @@ -76,7 +80,7 @@ def filter(self, f): Return a new DStream containing only the elements that satisfy predicate. """ def func(iterator): - return ifilter(f, iterator) + return filter(f, iterator) return self.mapPartitions(func, True) def flatMap(self, f, preservesPartitioning=False): @@ -85,7 +89,7 @@ def flatMap(self, f, preservesPartitioning=False): this DStream, and then flattening the results """ def func(s, iterator): - return chain.from_iterable(imap(f, iterator)) + return chain.from_iterable(map(f, iterator)) return self.mapPartitionsWithIndex(func, preservesPartitioning) def map(self, f, preservesPartitioning=False): @@ -93,7 +97,7 @@ def map(self, f, preservesPartitioning=False): Return a new DStream by applying a function to each element of DStream. """ def func(iterator): - return imap(f, iterator) + return map(f, iterator) return self.mapPartitions(func, preservesPartitioning) def mapPartitions(self, f, preservesPartitioning=False): @@ -150,7 +154,7 @@ def foreachRDD(self, func): """ Apply a function to each RDD in this DStream. """ - if func.func_code.co_argcount == 1: + if func.__code__.co_argcount == 1: old_func = func func = lambda t, rdd: old_func(rdd) jfunc = TransformFunction(self._sc, func, self._jrdd_deserializer) @@ -165,14 +169,14 @@ def pprint(self, num=10): """ def takeAndPrint(time, rdd): taken = rdd.take(num + 1) - print "-------------------------------------------" - print "Time: %s" % time - print "-------------------------------------------" + print("-------------------------------------------") + print("Time: %s" % time) + print("-------------------------------------------") for record in taken[:num]: - print record + print(record) if len(taken) > num: - print "..." - print + print("...") + print() self.foreachRDD(takeAndPrint) @@ -181,7 +185,7 @@ def mapValues(self, f): Return a new DStream by applying a map function to the value of each key-value pairs in this DStream without changing the key. """ - map_values_fn = lambda (k, v): (k, f(v)) + map_values_fn = lambda kv: (kv[0], f(kv[1])) return self.map(map_values_fn, preservesPartitioning=True) def flatMapValues(self, f): @@ -189,7 +193,7 @@ def flatMapValues(self, f): Return a new DStream by applying a flatmap function to the value of each key-value pairs in this DStream without changing the key. """ - flat_map_fn = lambda (k, v): ((k, x) for x in f(v)) + flat_map_fn = lambda kv: ((kv[0], x) for x in f(kv[1])) return self.flatMap(flat_map_fn, preservesPartitioning=True) def glom(self): @@ -286,10 +290,10 @@ def transform(self, func): `func` can have one argument of `rdd`, or have two arguments of (`time`, `rdd`) """ - if func.func_code.co_argcount == 1: + if func.__code__.co_argcount == 1: oldfunc = func func = lambda t, rdd: oldfunc(rdd) - assert func.func_code.co_argcount == 2, "func should take one or two arguments" + assert func.__code__.co_argcount == 2, "func should take one or two arguments" return TransformedDStream(self, func) def transformWith(self, func, other, keepSerializer=False): @@ -300,10 +304,10 @@ def transformWith(self, func, other, keepSerializer=False): `func` can have two arguments of (`rdd_a`, `rdd_b`) or have three arguments of (`time`, `rdd_a`, `rdd_b`) """ - if func.func_code.co_argcount == 2: + if func.__code__.co_argcount == 2: oldfunc = func func = lambda t, a, b: oldfunc(a, b) - assert func.func_code.co_argcount == 3, "func should take two or three arguments" + assert func.__code__.co_argcount == 3, "func should take two or three arguments" jfunc = TransformFunction(self._sc, func, self._jrdd_deserializer, other._jrdd_deserializer) dstream = self._sc._jvm.PythonTransformed2DStream(self._jdstream.dstream(), other._jdstream.dstream(), jfunc) @@ -460,7 +464,7 @@ def reduceByWindow(self, reduceFunc, invReduceFunc, windowDuration, slideDuratio keyed = self.map(lambda x: (1, x)) reduced = keyed.reduceByKeyAndWindow(reduceFunc, invReduceFunc, windowDuration, slideDuration, 1) - return reduced.map(lambda (k, v): v) + return reduced.map(lambda kv: kv[1]) def countByWindow(self, windowDuration, slideDuration): """ @@ -489,7 +493,7 @@ def countByValueAndWindow(self, windowDuration, slideDuration, numPartitions=Non keyed = self.map(lambda x: (x, 1)) counted = keyed.reduceByKeyAndWindow(operator.add, operator.sub, windowDuration, slideDuration, numPartitions) - return counted.filter(lambda (k, v): v > 0).count() + return counted.filter(lambda kv: kv[1] > 0).count() def groupByKeyAndWindow(self, windowDuration, slideDuration, numPartitions=None): """ @@ -548,7 +552,8 @@ def reduceFunc(t, a, b): def invReduceFunc(t, a, b): b = b.reduceByKey(func, numPartitions) joined = a.leftOuterJoin(b, numPartitions) - return joined.mapValues(lambda (v1, v2): invFunc(v1, v2) if v2 is not None else v1) + return joined.mapValues(lambda kv: invFunc(kv[0], kv[1]) + if kv[1] is not None else kv[0]) jreduceFunc = TransformFunction(self._sc, reduceFunc, reduced._jrdd_deserializer) if invReduceFunc: @@ -578,10 +583,10 @@ def reduceFunc(t, a, b): if a is None: g = b.groupByKey(numPartitions).mapValues(lambda vs: (list(vs), None)) else: - g = a.cogroup(b, numPartitions) - g = g.mapValues(lambda (va, vb): (list(vb), list(va)[0] if len(va) else None)) - state = g.mapValues(lambda (vs, s): updateFunc(vs, s)) - return state.filter(lambda (k, v): v is not None) + g = a.cogroup(b.partitionBy(numPartitions), numPartitions) + g = g.mapValues(lambda ab: (list(ab[1]), list(ab[0])[0] if len(ab[0]) else None)) + state = g.mapValues(lambda vs_s: updateFunc(vs_s[0], vs_s[1])) + return state.filter(lambda k_v: k_v[1] is not None) jreduceFunc = TransformFunction(self._sc, reduceFunc, self._sc.serializer, self._jrdd_deserializer) diff --git a/python/pyspark/streaming/kafka.py b/python/pyspark/streaming/kafka.py index 19ad71f99d4d5..7a7b6e1d9a527 100644 --- a/python/pyspark/streaming/kafka.py +++ b/python/pyspark/streaming/kafka.py @@ -16,7 +16,7 @@ # from py4j.java_collections import MapConverter -from py4j.java_gateway import java_import, Py4JError +from py4j.java_gateway import java_import, Py4JError, Py4JJavaError from pyspark.storagelevel import StorageLevel from pyspark.serializers import PairDeserializer, NoOpSerializer @@ -50,8 +50,6 @@ def createStream(ssc, zkQuorum, groupId, topics, kafkaParams={}, :param valueDecoder: A function used to decode value (default is utf8_decoder) :return: A DStream object """ - java_import(ssc._jvm, "org.apache.spark.streaming.kafka.KafkaUtils") - kafkaParams.update({ "zookeeper.connect": zkQuorum, "group.id": groupId, @@ -63,21 +61,35 @@ def createStream(ssc, zkQuorum, groupId, topics, kafkaParams={}, jparam = MapConverter().convert(kafkaParams, ssc.sparkContext._gateway._gateway_client) jlevel = ssc._sc._getJavaStorageLevel(storageLevel) - def getClassByName(name): - return ssc._jvm.org.apache.spark.util.Utils.classForName(name) - try: - array = getClassByName("[B") - decoder = getClassByName("kafka.serializer.DefaultDecoder") - jstream = ssc._jvm.KafkaUtils.createStream(ssc._jssc, array, array, decoder, decoder, - jparam, jtopics, jlevel) - except Py4JError, e: + # Use KafkaUtilsPythonHelper to access Scala's KafkaUtils (see SPARK-6027) + helperClass = ssc._jvm.java.lang.Thread.currentThread().getContextClassLoader()\ + .loadClass("org.apache.spark.streaming.kafka.KafkaUtilsPythonHelper") + helper = helperClass.newInstance() + jstream = helper.createStream(ssc._jssc, jparam, jtopics, jlevel) + except Py4JJavaError as e: # TODO: use --jar once it also work on driver - if not e.message or 'call a package' in e.message: - print "No kafka package, please put the assembly jar into classpath:" - print " $ bin/spark-submit --driver-class-path external/kafka-assembly/target/" + \ - "scala-*/spark-streaming-kafka-assembly-*.jar" + if 'ClassNotFoundException' in str(e.java_exception): + print(""" +________________________________________________________________________________________________ + + Spark Streaming's Kafka libraries not found in class path. Try one of the following. + + 1. Include the Kafka library and its dependencies with in the + spark-submit command as + + $ bin/spark-submit --packages org.apache.spark:spark-streaming-kafka:%s ... + + 2. Download the JAR of the artifact from Maven Central http://search.maven.org/, + Group Id = org.apache.spark, Artifact Id = spark-streaming-kafka-assembly, Version = %s. + Then, include the jar in the spark-submit command as + + $ bin/spark-submit --jars ... + +________________________________________________________________________________________________ + +""" % (ssc.sparkContext.version, ssc.sparkContext.version)) raise e ser = PairDeserializer(NoOpSerializer(), NoOpSerializer()) stream = DStream(jstream, ssc, ser) - return stream.map(lambda (k, v): (keyDecoder(k), valueDecoder(v))) + return stream.map(lambda k_v: (keyDecoder(k_v[0]), valueDecoder(k_v[1]))) diff --git a/python/pyspark/streaming/tests.py b/python/pyspark/streaming/tests.py index 608f8e26473a6..06d22154373bc 100644 --- a/python/pyspark/streaming/tests.py +++ b/python/pyspark/streaming/tests.py @@ -22,14 +22,18 @@ import unittest import tempfile import struct +from functools import reduce + +from py4j.java_collections import MapConverter from pyspark.context import SparkConf, SparkContext, RDD from pyspark.streaming.context import StreamingContext +from pyspark.streaming.kafka import KafkaUtils class PySparkStreamingTestCase(unittest.TestCase): - timeout = 10 # seconds + timeout = 20 # seconds duration = 1 def setUp(self): @@ -48,7 +52,7 @@ def wait_for(self, result, n): while len(result) < n and time.time() - start_time < self.timeout: time.sleep(0.01) if len(result) < n: - print "timeout after", self.timeout + print("timeout after", self.timeout) def _take(self, dstream, n): """ @@ -128,7 +132,7 @@ def test_map(self): def func(dstream): return dstream.map(str) - expected = map(lambda x: map(str, x), input) + expected = [list(map(str, x)) for x in input] self._test_func(input, func, expected) def test_flatMap(self): @@ -137,8 +141,8 @@ def test_flatMap(self): def func(dstream): return dstream.flatMap(lambda x: (x, x * 2)) - expected = map(lambda x: list(chain.from_iterable((map(lambda y: [y, y * 2], x)))), - input) + expected = [list(chain.from_iterable((map(lambda y: [y, y * 2], x)))) + for x in input] self._test_func(input, func, expected) def test_filter(self): @@ -147,7 +151,7 @@ def test_filter(self): def func(dstream): return dstream.filter(lambda x: x % 2 == 0) - expected = map(lambda x: filter(lambda y: y % 2 == 0, x), input) + expected = [[y for y in x if y % 2 == 0] for x in input] self._test_func(input, func, expected) def test_count(self): @@ -156,7 +160,7 @@ def test_count(self): def func(dstream): return dstream.count() - expected = map(lambda x: [len(x)], input) + expected = [[len(x)] for x in input] self._test_func(input, func, expected) def test_reduce(self): @@ -165,7 +169,7 @@ def test_reduce(self): def func(dstream): return dstream.reduce(operator.add) - expected = map(lambda x: [reduce(operator.add, x)], input) + expected = [[reduce(operator.add, x)] for x in input] self._test_func(input, func, expected) def test_reduceByKey(self): @@ -182,27 +186,27 @@ def func(dstream): def test_mapValues(self): """Basic operation test for DStream.mapValues.""" input = [[("a", 2), ("b", 2), ("c", 1), ("d", 1)], - [("", 4), (1, 1), (2, 2), (3, 3)], + [(0, 4), (1, 1), (2, 2), (3, 3)], [(1, 1), (2, 1), (3, 1), (4, 1)]] def func(dstream): return dstream.mapValues(lambda x: x + 10) expected = [[("a", 12), ("b", 12), ("c", 11), ("d", 11)], - [("", 14), (1, 11), (2, 12), (3, 13)], + [(0, 14), (1, 11), (2, 12), (3, 13)], [(1, 11), (2, 11), (3, 11), (4, 11)]] self._test_func(input, func, expected, sort=True) def test_flatMapValues(self): """Basic operation test for DStream.flatMapValues.""" input = [[("a", 2), ("b", 2), ("c", 1), ("d", 1)], - [("", 4), (1, 1), (2, 1), (3, 1)], + [(0, 4), (1, 1), (2, 1), (3, 1)], [(1, 1), (2, 1), (3, 1), (4, 1)]] def func(dstream): return dstream.flatMapValues(lambda x: (x, x + 10)) expected = [[("a", 2), ("a", 12), ("b", 2), ("b", 12), ("c", 1), ("c", 11), ("d", 1), ("d", 11)], - [("", 4), ("", 14), (1, 1), (1, 11), (2, 1), (2, 11), (3, 1), (3, 11)], + [(0, 4), (0, 14), (1, 1), (1, 11), (2, 1), (2, 11), (3, 1), (3, 11)], [(1, 1), (1, 11), (2, 1), (2, 11), (3, 1), (3, 11), (4, 1), (4, 11)]] self._test_func(input, func, expected) @@ -230,7 +234,7 @@ def f(iterator): def test_countByValue(self): """Basic operation test for DStream.countByValue.""" - input = [range(1, 5) * 2, range(5, 7) + range(5, 9), ["a", "a", "b", ""]] + input = [list(range(1, 5)) * 2, list(range(5, 7)) + list(range(5, 9)), ["a", "a", "b", ""]] def func(dstream): return dstream.countByValue() @@ -282,7 +286,7 @@ def test_union(self): def func(d1, d2): return d1.union(d2) - expected = [range(6), range(6), range(6)] + expected = [list(range(6)), list(range(6)), list(range(6))] self._test_func(input1, func, expected, input2=input2) def test_cogroup(self): @@ -421,7 +425,7 @@ class StreamingContextTests(PySparkStreamingTestCase): duration = 0.1 def _add_input_stream(self): - inputs = map(lambda x: range(1, x), range(101)) + inputs = [range(1, x) for x in range(101)] stream = self.ssc.queueStream(inputs) self._collect(stream, 1, block=False) @@ -438,7 +442,7 @@ def test_stop_multiple_times(self): self.ssc.stop() def test_queue_stream(self): - input = [range(i + 1) for i in range(3)] + input = [list(range(i + 1)) for i in range(3)] dstream = self.ssc.queueStream(input) result = self._collect(dstream, 3) self.assertEqual(input, result) @@ -454,13 +458,13 @@ def test_text_file_stream(self): with open(os.path.join(d, name), "w") as f: f.writelines(["%d\n" % i for i in range(10)]) self.wait_for(result, 2) - self.assertEqual([range(10), range(10)], result) + self.assertEqual([list(range(10)), list(range(10))], result) def test_binary_records_stream(self): d = tempfile.mkdtemp() self.ssc = StreamingContext(self.sc, self.duration) dstream = self.ssc.binaryRecordsStream(d, 10).map( - lambda v: struct.unpack("10b", str(v))) + lambda v: struct.unpack("10b", bytes(v))) result = self._collect(dstream, 2, block=False) self.ssc.start() for name in ('a', 'b'): @@ -468,10 +472,10 @@ def test_binary_records_stream(self): with open(os.path.join(d, name), "wb") as f: f.write(bytearray(range(10))) self.wait_for(result, 2) - self.assertEqual([range(10), range(10)], map(lambda v: list(v[0]), result)) + self.assertEqual([list(range(10)), list(range(10))], [list(v[0]) for v in result]) def test_union(self): - input = [range(i + 1) for i in range(3)] + input = [list(range(i + 1)) for i in range(3)] dstream = self.ssc.queueStream(input) dstream2 = self.ssc.queueStream(input) dstream3 = self.ssc.union(dstream, dstream2) @@ -556,5 +560,43 @@ def check_output(n): check_output(3) +class KafkaStreamTests(PySparkStreamingTestCase): + + def setUp(self): + super(KafkaStreamTests, self).setUp() + + kafkaTestUtilsClz = self.ssc._jvm.java.lang.Thread.currentThread().getContextClassLoader()\ + .loadClass("org.apache.spark.streaming.kafka.KafkaTestUtils") + self._kafkaTestUtils = kafkaTestUtilsClz.newInstance() + self._kafkaTestUtils.setup() + + def tearDown(self): + if self._kafkaTestUtils is not None: + self._kafkaTestUtils.teardown() + self._kafkaTestUtils = None + + super(KafkaStreamTests, self).tearDown() + + def test_kafka_stream(self): + """Test the Python Kafka stream API.""" + topic = "topic1" + sendData = {"a": 3, "b": 5, "c": 10} + jSendData = MapConverter().convert(sendData, + self.ssc.sparkContext._gateway._gateway_client) + + self._kafkaTestUtils.createTopic(topic) + self._kafkaTestUtils.sendMessages(topic, jSendData) + + stream = KafkaUtils.createStream(self.ssc, self._kafkaTestUtils.zkAddress(), + "test-streaming-consumer", {topic: 1}, + {"auto.offset.reset": "smallest"}) + + result = {} + for i in chain.from_iterable(self._collect(stream.map(lambda x: x[1]), + sum(sendData.values()))): + result[i] = result.get(i, 0) + 1 + + self.assertEqual(sendData, result) + if __name__ == "__main__": unittest.main() diff --git a/python/pyspark/streaming/util.py b/python/pyspark/streaming/util.py index 86ee5aa04f252..34291f30a5652 100644 --- a/python/pyspark/streaming/util.py +++ b/python/pyspark/streaming/util.py @@ -91,9 +91,9 @@ def dumps(self, id): except Exception: traceback.print_exc() - def loads(self, bytes): + def loads(self, data): try: - f, deserializers = self.serializer.loads(str(bytes)) + f, deserializers = self.serializer.loads(bytes(data)) return TransformFunction(self.ctx, f, *deserializers) except Exception: traceback.print_exc() @@ -116,7 +116,7 @@ def rddToFileName(prefix, suffix, timestamp): """ if isinstance(timestamp, datetime): seconds = time.mktime(timestamp.timetuple()) - timestamp = long(seconds * 1000) + timestamp.microsecond / 1000 + timestamp = int(seconds * 1000) + timestamp.microsecond // 1000 if suffix is None: return prefix + "-" + str(timestamp) else: diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py index b5e28c498040b..75f39d9e75f38 100644 --- a/python/pyspark/tests.py +++ b/python/pyspark/tests.py @@ -19,8 +19,8 @@ Unit tests for PySpark; additional tests are implemented as doctests in individual modules. """ + from array import array -from fileinput import input from glob import glob import os import re @@ -31,9 +31,12 @@ import time import zipfile import random +import itertools import threading import hashlib +from py4j.protocol import Py4JJavaError + if sys.version_info[:2] <= (2, 6): try: import unittest2 as unittest @@ -42,6 +45,9 @@ sys.exit(1) else: import unittest + if sys.version_info[0] >= 3: + xrange = range + basestring = str from pyspark.conf import SparkConf @@ -49,7 +55,9 @@ from pyspark.rdd import RDD from pyspark.files import SparkFiles from pyspark.serializers import read_int, BatchedSerializer, MarshalSerializer, PickleSerializer, \ - CloudPickleSerializer, CompressedSerializer, UTF8Deserializer, NoOpSerializer + CloudPickleSerializer, CompressedSerializer, UTF8Deserializer, NoOpSerializer, \ + PairDeserializer, CartesianDeserializer, AutoBatchedSerializer, AutoSerializer, \ + FlattenedValuesSerializer from pyspark.shuffle import Aggregator, InMemoryMerger, ExternalMerger, ExternalSorter from pyspark import shuffle from pyspark.profiler import BasicProfiler @@ -76,9 +84,9 @@ class MergerTests(unittest.TestCase): def setUp(self): - self.N = 1 << 14 + self.N = 1 << 12 self.l = [i for i in xrange(self.N)] - self.data = zip(self.l, self.l) + self.data = list(zip(self.l, self.l)) self.agg = Aggregator(lambda x: [x], lambda x, y: x.append(y) or x, lambda x, y: x.extend(y) or x) @@ -86,84 +94,110 @@ def setUp(self): def test_in_memory(self): m = InMemoryMerger(self.agg) m.mergeValues(self.data) - self.assertEqual(sum(sum(v) for k, v in m.iteritems()), + self.assertEqual(sum(sum(v) for k, v in m.items()), sum(xrange(self.N))) m = InMemoryMerger(self.agg) - m.mergeCombiners(map(lambda (x, y): (x, [y]), self.data)) - self.assertEqual(sum(sum(v) for k, v in m.iteritems()), + m.mergeCombiners(map(lambda x_y: (x_y[0], [x_y[1]]), self.data)) + self.assertEqual(sum(sum(v) for k, v in m.items()), sum(xrange(self.N))) def test_small_dataset(self): m = ExternalMerger(self.agg, 1000) m.mergeValues(self.data) self.assertEqual(m.spills, 0) - self.assertEqual(sum(sum(v) for k, v in m.iteritems()), + self.assertEqual(sum(sum(v) for k, v in m.items()), sum(xrange(self.N))) m = ExternalMerger(self.agg, 1000) - m.mergeCombiners(map(lambda (x, y): (x, [y]), self.data)) + m.mergeCombiners(map(lambda x_y1: (x_y1[0], [x_y1[1]]), self.data)) self.assertEqual(m.spills, 0) - self.assertEqual(sum(sum(v) for k, v in m.iteritems()), + self.assertEqual(sum(sum(v) for k, v in m.items()), sum(xrange(self.N))) def test_medium_dataset(self): - m = ExternalMerger(self.agg, 10) + m = ExternalMerger(self.agg, 20) m.mergeValues(self.data) self.assertTrue(m.spills >= 1) - self.assertEqual(sum(sum(v) for k, v in m.iteritems()), + self.assertEqual(sum(sum(v) for k, v in m.items()), sum(xrange(self.N))) m = ExternalMerger(self.agg, 10) - m.mergeCombiners(map(lambda (x, y): (x, [y]), self.data * 3)) + m.mergeCombiners(map(lambda x_y2: (x_y2[0], [x_y2[1]]), self.data * 3)) self.assertTrue(m.spills >= 1) - self.assertEqual(sum(sum(v) for k, v in m.iteritems()), + self.assertEqual(sum(sum(v) for k, v in m.items()), sum(xrange(self.N)) * 3) def test_huge_dataset(self): - m = ExternalMerger(self.agg, 10, partitions=3) - m.mergeCombiners(map(lambda (k, v): (k, [str(v)]), self.data * 10)) + m = ExternalMerger(self.agg, 5, partitions=3) + m.mergeCombiners(map(lambda k_v: (k_v[0], [str(k_v[1])]), self.data * 10)) self.assertTrue(m.spills >= 1) - self.assertEqual(sum(len(v) for k, v in m._recursive_merged_items(0)), + self.assertEqual(sum(len(v) for k, v in m.items()), self.N * 10) m._cleanup() + def test_group_by_key(self): + + def gen_data(N, step): + for i in range(1, N + 1, step): + for j in range(i): + yield (i, [j]) + + def gen_gs(N, step=1): + return shuffle.GroupByKey(gen_data(N, step)) + + self.assertEqual(1, len(list(gen_gs(1)))) + self.assertEqual(2, len(list(gen_gs(2)))) + self.assertEqual(100, len(list(gen_gs(100)))) + self.assertEqual(list(range(1, 101)), [k for k, _ in gen_gs(100)]) + self.assertTrue(all(list(range(k)) == list(vs) for k, vs in gen_gs(100))) + + for k, vs in gen_gs(50002, 10000): + self.assertEqual(k, len(vs)) + self.assertEqual(list(range(k)), list(vs)) + + ser = PickleSerializer() + l = ser.loads(ser.dumps(list(gen_gs(50002, 30000)))) + for k, vs in l: + self.assertEqual(k, len(vs)) + self.assertEqual(list(range(k)), list(vs)) + class SorterTests(unittest.TestCase): def test_in_memory_sort(self): - l = range(1024) + l = list(range(1024)) random.shuffle(l) sorter = ExternalSorter(1024) - self.assertEquals(sorted(l), list(sorter.sorted(l))) - self.assertEquals(sorted(l, reverse=True), list(sorter.sorted(l, reverse=True))) - self.assertEquals(sorted(l, key=lambda x: -x), list(sorter.sorted(l, key=lambda x: -x))) - self.assertEquals(sorted(l, key=lambda x: -x, reverse=True), - list(sorter.sorted(l, key=lambda x: -x, reverse=True))) + self.assertEqual(sorted(l), list(sorter.sorted(l))) + self.assertEqual(sorted(l, reverse=True), list(sorter.sorted(l, reverse=True))) + self.assertEqual(sorted(l, key=lambda x: -x), list(sorter.sorted(l, key=lambda x: -x))) + self.assertEqual(sorted(l, key=lambda x: -x, reverse=True), + list(sorter.sorted(l, key=lambda x: -x, reverse=True))) def test_external_sort(self): - l = range(1024) + l = list(range(1024)) random.shuffle(l) sorter = ExternalSorter(1) - self.assertEquals(sorted(l), list(sorter.sorted(l))) + self.assertEqual(sorted(l), list(sorter.sorted(l))) self.assertGreater(shuffle.DiskBytesSpilled, 0) last = shuffle.DiskBytesSpilled - self.assertEquals(sorted(l, reverse=True), list(sorter.sorted(l, reverse=True))) + self.assertEqual(sorted(l, reverse=True), list(sorter.sorted(l, reverse=True))) self.assertGreater(shuffle.DiskBytesSpilled, last) last = shuffle.DiskBytesSpilled - self.assertEquals(sorted(l, key=lambda x: -x), list(sorter.sorted(l, key=lambda x: -x))) + self.assertEqual(sorted(l, key=lambda x: -x), list(sorter.sorted(l, key=lambda x: -x))) self.assertGreater(shuffle.DiskBytesSpilled, last) last = shuffle.DiskBytesSpilled - self.assertEquals(sorted(l, key=lambda x: -x, reverse=True), - list(sorter.sorted(l, key=lambda x: -x, reverse=True))) + self.assertEqual(sorted(l, key=lambda x: -x, reverse=True), + list(sorter.sorted(l, key=lambda x: -x, reverse=True))) self.assertGreater(shuffle.DiskBytesSpilled, last) def test_external_sort_in_rdd(self): conf = SparkConf().set("spark.python.worker.memory", "1m") sc = SparkContext(conf=conf) - l = range(10240) + l = list(range(10240)) random.shuffle(l) - rdd = sc.parallelize(l, 10) - self.assertEquals(sorted(l), rdd.sortBy(lambda x: x).collect()) + rdd = sc.parallelize(l, 2) + self.assertEqual(sorted(l), rdd.sortBy(lambda x: x).collect()) sc.stop() @@ -171,11 +205,11 @@ class SerializationTestCase(unittest.TestCase): def test_namedtuple(self): from collections import namedtuple - from cPickle import dumps, loads + from pickle import dumps, loads P = namedtuple("P", "x y") p1 = P(1, 3) p2 = loads(dumps(p1, 2)) - self.assertEquals(p1, p2) + self.assertEqual(p1, p2) def test_itemgetter(self): from operator import itemgetter @@ -217,7 +251,7 @@ def test_pickling_file_handles(self): ser = CloudPickleSerializer() out1 = sys.stderr out2 = ser.loads(ser.dumps(out1)) - self.assertEquals(out1, out2) + self.assertEqual(out1, out2) def test_func_globals(self): @@ -234,19 +268,36 @@ def __reduce__(self): def foo(): sys.exit(0) - self.assertTrue("exit" in foo.func_code.co_names) + self.assertTrue("exit" in foo.__code__.co_names) ser.dumps(foo) def test_compressed_serializer(self): ser = CompressedSerializer(PickleSerializer()) - from StringIO import StringIO + try: + from StringIO import StringIO + except ImportError: + from io import BytesIO as StringIO io = StringIO() ser.dump_stream(["abc", u"123", range(5)], io) io.seek(0) self.assertEqual(["abc", u"123", range(5)], list(ser.load_stream(io))) ser.dump_stream(range(1000), io) io.seek(0) - self.assertEqual(["abc", u"123", range(5)] + range(1000), list(ser.load_stream(io))) + self.assertEqual(["abc", u"123", range(5)] + list(range(1000)), list(ser.load_stream(io))) + io.close() + + def test_hash_serializer(self): + hash(NoOpSerializer()) + hash(UTF8Deserializer()) + hash(PickleSerializer()) + hash(MarshalSerializer()) + hash(AutoSerializer()) + hash(BatchedSerializer(PickleSerializer())) + hash(AutoBatchedSerializer(MarshalSerializer())) + hash(PairDeserializer(NoOpSerializer(), UTF8Deserializer())) + hash(CartesianDeserializer(NoOpSerializer(), UTF8Deserializer())) + hash(CompressedSerializer(PickleSerializer())) + hash(FlattenedValuesSerializer(PickleSerializer())) class PySparkTestCase(unittest.TestCase): @@ -311,7 +362,7 @@ def test_checkpoint_and_restore(self): self.assertTrue(flatMappedRDD.getCheckpointFile() is not None) recovered = self.sc._checkpointFile(flatMappedRDD.getCheckpointFile(), flatMappedRDD._jrdd_deserializer) - self.assertEquals([1, 2, 3, 4], recovered.collect()) + self.assertEqual([1, 2, 3, 4], recovered.collect()) class AddFileTests(PySparkTestCase): @@ -327,8 +378,7 @@ def test_add_py_file(self): def func(x): from userlibrary import UserClass return UserClass().hello() - self.assertRaises(Exception, - self.sc.parallelize(range(2)).map(func).first) + self.assertRaises(Exception, self.sc.parallelize(range(2)).map(func).first) log4j.LogManager.getRootLogger().setLevel(old_level) # Add the file, so the job should now succeed: @@ -343,7 +393,7 @@ def test_add_file_locally(self): download_path = SparkFiles.get("hello.txt") self.assertNotEqual(path, download_path) with open(download_path) as test_file: - self.assertEquals("Hello World!\n", test_file.readline()) + self.assertEqual("Hello World!\n", test_file.readline()) def test_add_py_file_locally(self): # To ensure that we're actually testing addPyFile's effects, check that @@ -352,7 +402,7 @@ def func(): from userlibrary import UserClass self.assertRaises(ImportError, func) path = os.path.join(SPARK_HOME, "python/test_support/userlibrary.py") - self.sc.addFile(path) + self.sc.addPyFile(path) from userlibrary import UserClass self.assertEqual("Hello World!", UserClass().hello()) @@ -362,7 +412,7 @@ def test_add_egg_file_locally(self): def func(): from userlib import UserClass self.assertRaises(ImportError, func) - path = os.path.join(SPARK_HOME, "python/test_support/userlib-0.1-py2.7.egg") + path = os.path.join(SPARK_HOME, "python/test_support/userlib-0.1.zip") self.sc.addPyFile(path) from userlib import UserClass self.assertEqual("Hello World from inside a package!", UserClass().hello()) @@ -398,8 +448,9 @@ def test_save_as_textfile_with_unicode(self): tempFile = tempfile.NamedTemporaryFile(delete=True) tempFile.close() data.saveAsTextFile(tempFile.name) - raw_contents = ''.join(input(glob(tempFile.name + "/part-0000*"))) - self.assertEqual(x, unicode(raw_contents.strip(), "utf-8")) + raw_contents = b''.join(open(p, 'rb').read() + for p in glob(tempFile.name + "/part-0000*")) + self.assertEqual(x, raw_contents.strip().decode("utf-8")) def test_save_as_textfile_with_utf8(self): x = u"\u00A1Hola, mundo!" @@ -407,19 +458,20 @@ def test_save_as_textfile_with_utf8(self): tempFile = tempfile.NamedTemporaryFile(delete=True) tempFile.close() data.saveAsTextFile(tempFile.name) - raw_contents = ''.join(input(glob(tempFile.name + "/part-0000*"))) - self.assertEqual(x, unicode(raw_contents.strip(), "utf-8")) + raw_contents = b''.join(open(p, 'rb').read() + for p in glob(tempFile.name + "/part-0000*")) + self.assertEqual(x, raw_contents.strip().decode('utf8')) def test_transforming_cartesian_result(self): # Regression test for SPARK-1034 rdd1 = self.sc.parallelize([1, 2]) rdd2 = self.sc.parallelize([3, 4]) cart = rdd1.cartesian(rdd2) - result = cart.map(lambda (x, y): x + y).collect() + result = cart.map(lambda x_y3: x_y3[0] + x_y3[1]).collect() def test_transforming_pickle_file(self): # Regression test for SPARK-2601 - data = self.sc.parallelize(["Hello", "World!"]) + data = self.sc.parallelize([u"Hello", u"World!"]) tempFile = tempfile.NamedTemporaryFile(delete=True) tempFile.close() data.saveAsPickleFile(tempFile.name) @@ -432,13 +484,13 @@ def test_cartesian_on_textfile(self): a = self.sc.textFile(path) result = a.cartesian(a).collect() (x, y) = result[0] - self.assertEqual("Hello World!", x.strip()) - self.assertEqual("Hello World!", y.strip()) + self.assertEqual(u"Hello World!", x.strip()) + self.assertEqual(u"Hello World!", y.strip()) def test_deleting_input_files(self): # Regression test for SPARK-1025 tempFile = tempfile.NamedTemporaryFile(delete=False) - tempFile.write("Hello World!") + tempFile.write(b"Hello World!") tempFile.close() data = self.sc.textFile(tempFile.name) filtered_data = data.filter(lambda x: True) @@ -481,21 +533,21 @@ def test_namedtuple_in_rdd(self): jon = Person(1, "Jon", "Doe") jane = Person(2, "Jane", "Doe") theDoes = self.sc.parallelize([jon, jane]) - self.assertEquals([jon, jane], theDoes.collect()) + self.assertEqual([jon, jane], theDoes.collect()) def test_large_broadcast(self): N = 100000 data = [[float(i) for i in range(300)] for i in range(N)] bdata = self.sc.broadcast(data) # 270MB m = self.sc.parallelize(range(1), 1).map(lambda x: len(bdata.value)).sum() - self.assertEquals(N, m) + self.assertEqual(N, m) def test_multiple_broadcasts(self): N = 1 << 21 b1 = self.sc.broadcast(set(range(N))) # multiple blocks in JVM - r = range(1 << 15) + r = list(range(1 << 15)) random.shuffle(r) - s = str(r) + s = str(r).encode() checksum = hashlib.md5(s).hexdigest() b2 = self.sc.broadcast(s) r = list(set(self.sc.parallelize(range(10), 10).map( @@ -506,7 +558,7 @@ def test_multiple_broadcasts(self): self.assertEqual(checksum, csum) random.shuffle(r) - s = str(r) + s = str(r).encode() checksum = hashlib.md5(s).hexdigest() b2 = self.sc.broadcast(s) r = list(set(self.sc.parallelize(range(10), 10).map( @@ -520,11 +572,9 @@ def test_large_closure(self): N = 1000000 data = [float(i) for i in xrange(N)] rdd = self.sc.parallelize(range(1), 1).map(lambda x: len(data)) - self.assertEquals(N, rdd.first()) - self.assertTrue(rdd._broadcast is not None) - rdd = self.sc.parallelize(range(1), 1).map(lambda x: 1) - self.assertEqual(1, rdd.first()) - self.assertTrue(rdd._broadcast is None) + self.assertEqual(N, rdd.first()) + # regression test for SPARK-6886 + self.assertEqual(1, rdd.map(lambda x: (x, 1)).groupByKey().count()) def test_zip_with_different_serializers(self): a = self.sc.parallelize(range(5)) @@ -543,6 +593,12 @@ def test_zip_with_different_serializers(self): # regression test for bug in _reserializer() self.assertEqual(cnt, t.zip(rdd).count()) + def test_zip_with_different_object_sizes(self): + # regress test for SPARK-5973 + a = self.sc.parallelize(range(10000)).map(lambda i: '*' * i) + b = self.sc.parallelize(range(10000, 20000)).map(lambda i: '*' * i) + self.assertEqual(10000, a.zip(b).count()) + def test_zip_with_different_number_of_items(self): a = self.sc.parallelize(range(5), 2) # different number of partitions @@ -557,15 +613,15 @@ def test_zip_with_different_number_of_items(self): # same total number of items, but different distributions a = self.sc.parallelize([2, 3], 2).flatMap(range) b = self.sc.parallelize([3, 2], 2).flatMap(range) - self.assertEquals(a.count(), b.count()) + self.assertEqual(a.count(), b.count()) self.assertRaises(Exception, lambda: a.zip(b).count()) def test_count_approx_distinct(self): rdd = self.sc.parallelize(range(1000)) - self.assertTrue(950 < rdd.countApproxDistinct(0.04) < 1050) - self.assertTrue(950 < rdd.map(float).countApproxDistinct(0.04) < 1050) - self.assertTrue(950 < rdd.map(str).countApproxDistinct(0.04) < 1050) - self.assertTrue(950 < rdd.map(lambda x: (x, -x)).countApproxDistinct(0.04) < 1050) + self.assertTrue(950 < rdd.countApproxDistinct(0.03) < 1050) + self.assertTrue(950 < rdd.map(float).countApproxDistinct(0.03) < 1050) + self.assertTrue(950 < rdd.map(str).countApproxDistinct(0.03) < 1050) + self.assertTrue(950 < rdd.map(lambda x: (x, -x)).countApproxDistinct(0.03) < 1050) rdd = self.sc.parallelize([i % 20 for i in range(1000)], 7) self.assertTrue(18 < rdd.countApproxDistinct() < 22) @@ -579,59 +635,59 @@ def test_count_approx_distinct(self): def test_histogram(self): # empty rdd = self.sc.parallelize([]) - self.assertEquals([0], rdd.histogram([0, 10])[1]) - self.assertEquals([0, 0], rdd.histogram([0, 4, 10])[1]) + self.assertEqual([0], rdd.histogram([0, 10])[1]) + self.assertEqual([0, 0], rdd.histogram([0, 4, 10])[1]) self.assertRaises(ValueError, lambda: rdd.histogram(1)) # out of range rdd = self.sc.parallelize([10.01, -0.01]) - self.assertEquals([0], rdd.histogram([0, 10])[1]) - self.assertEquals([0, 0], rdd.histogram((0, 4, 10))[1]) + self.assertEqual([0], rdd.histogram([0, 10])[1]) + self.assertEqual([0, 0], rdd.histogram((0, 4, 10))[1]) # in range with one bucket rdd = self.sc.parallelize(range(1, 5)) - self.assertEquals([4], rdd.histogram([0, 10])[1]) - self.assertEquals([3, 1], rdd.histogram([0, 4, 10])[1]) + self.assertEqual([4], rdd.histogram([0, 10])[1]) + self.assertEqual([3, 1], rdd.histogram([0, 4, 10])[1]) # in range with one bucket exact match - self.assertEquals([4], rdd.histogram([1, 4])[1]) + self.assertEqual([4], rdd.histogram([1, 4])[1]) # out of range with two buckets rdd = self.sc.parallelize([10.01, -0.01]) - self.assertEquals([0, 0], rdd.histogram([0, 5, 10])[1]) + self.assertEqual([0, 0], rdd.histogram([0, 5, 10])[1]) # out of range with two uneven buckets rdd = self.sc.parallelize([10.01, -0.01]) - self.assertEquals([0, 0], rdd.histogram([0, 4, 10])[1]) + self.assertEqual([0, 0], rdd.histogram([0, 4, 10])[1]) # in range with two buckets rdd = self.sc.parallelize([1, 2, 3, 5, 6]) - self.assertEquals([3, 2], rdd.histogram([0, 5, 10])[1]) + self.assertEqual([3, 2], rdd.histogram([0, 5, 10])[1]) # in range with two bucket and None rdd = self.sc.parallelize([1, 2, 3, 5, 6, None, float('nan')]) - self.assertEquals([3, 2], rdd.histogram([0, 5, 10])[1]) + self.assertEqual([3, 2], rdd.histogram([0, 5, 10])[1]) # in range with two uneven buckets rdd = self.sc.parallelize([1, 2, 3, 5, 6]) - self.assertEquals([3, 2], rdd.histogram([0, 5, 11])[1]) + self.assertEqual([3, 2], rdd.histogram([0, 5, 11])[1]) # mixed range with two uneven buckets rdd = self.sc.parallelize([-0.01, 0.0, 1, 2, 3, 5, 6, 11.0, 11.01]) - self.assertEquals([4, 3], rdd.histogram([0, 5, 11])[1]) + self.assertEqual([4, 3], rdd.histogram([0, 5, 11])[1]) # mixed range with four uneven buckets rdd = self.sc.parallelize([-0.01, 0.0, 1, 2, 3, 5, 6, 11.01, 12.0, 199.0, 200.0, 200.1]) - self.assertEquals([4, 2, 1, 3], rdd.histogram([0.0, 5.0, 11.0, 12.0, 200.0])[1]) + self.assertEqual([4, 2, 1, 3], rdd.histogram([0.0, 5.0, 11.0, 12.0, 200.0])[1]) # mixed range with uneven buckets and NaN rdd = self.sc.parallelize([-0.01, 0.0, 1, 2, 3, 5, 6, 11.01, 12.0, 199.0, 200.0, 200.1, None, float('nan')]) - self.assertEquals([4, 2, 1, 3], rdd.histogram([0.0, 5.0, 11.0, 12.0, 200.0])[1]) + self.assertEqual([4, 2, 1, 3], rdd.histogram([0.0, 5.0, 11.0, 12.0, 200.0])[1]) # out of range with infinite buckets rdd = self.sc.parallelize([10.01, -0.01, float('nan'), float("inf")]) - self.assertEquals([1, 2], rdd.histogram([float('-inf'), 0, float('inf')])[1]) + self.assertEqual([1, 2], rdd.histogram([float('-inf'), 0, float('inf')])[1]) # invalid buckets self.assertRaises(ValueError, lambda: rdd.histogram([])) @@ -641,25 +697,25 @@ def test_histogram(self): # without buckets rdd = self.sc.parallelize(range(1, 5)) - self.assertEquals(([1, 4], [4]), rdd.histogram(1)) + self.assertEqual(([1, 4], [4]), rdd.histogram(1)) # without buckets single element rdd = self.sc.parallelize([1]) - self.assertEquals(([1, 1], [1]), rdd.histogram(1)) + self.assertEqual(([1, 1], [1]), rdd.histogram(1)) # without bucket no range rdd = self.sc.parallelize([1] * 4) - self.assertEquals(([1, 1], [4]), rdd.histogram(1)) + self.assertEqual(([1, 1], [4]), rdd.histogram(1)) # without buckets basic two rdd = self.sc.parallelize(range(1, 5)) - self.assertEquals(([1, 2.5, 4], [2, 2]), rdd.histogram(2)) + self.assertEqual(([1, 2.5, 4], [2, 2]), rdd.histogram(2)) # without buckets with more requested than elements rdd = self.sc.parallelize([1, 2]) buckets = [1 + 0.2 * i for i in range(6)] hist = [1, 0, 0, 0, 1] - self.assertEquals((buckets, hist), rdd.histogram(5)) + self.assertEqual((buckets, hist), rdd.histogram(5)) # invalid RDDs rdd = self.sc.parallelize([1, float('inf')]) @@ -669,15 +725,8 @@ def test_histogram(self): # string rdd = self.sc.parallelize(["ab", "ac", "b", "bd", "ef"], 2) - self.assertEquals([2, 2], rdd.histogram(["a", "b", "c"])[1]) - self.assertEquals((["ab", "ef"], [5]), rdd.histogram(1)) - self.assertRaises(TypeError, lambda: rdd.histogram(2)) - - # mixed RDD - rdd = self.sc.parallelize([1, 4, "ab", "ac", "b"], 2) - self.assertEquals([1, 1], rdd.histogram([0, 4, 10])[1]) - self.assertEquals([2, 1], rdd.histogram(["a", "b", "c"])[1]) - self.assertEquals(([1, "b"], [5]), rdd.histogram(1)) + self.assertEqual([2, 2], rdd.histogram(["a", "b", "c"])[1]) + self.assertEqual((["ab", "ef"], [5]), rdd.histogram(1)) self.assertRaises(TypeError, lambda: rdd.histogram(2)) def test_repartitionAndSortWithinPartitions(self): @@ -685,16 +734,31 @@ def test_repartitionAndSortWithinPartitions(self): repartitioned = rdd.repartitionAndSortWithinPartitions(2, lambda key: key % 2) partitions = repartitioned.glom().collect() - self.assertEquals(partitions[0], [(0, 5), (0, 8), (2, 6)]) - self.assertEquals(partitions[1], [(1, 3), (3, 8), (3, 8)]) + self.assertEqual(partitions[0], [(0, 5), (0, 8), (2, 6)]) + self.assertEqual(partitions[1], [(1, 3), (3, 8), (3, 8)]) def test_distinct(self): rdd = self.sc.parallelize((1, 2, 3)*10, 10) - self.assertEquals(rdd.getNumPartitions(), 10) - self.assertEquals(rdd.distinct().count(), 3) + self.assertEqual(rdd.getNumPartitions(), 10) + self.assertEqual(rdd.distinct().count(), 3) result = rdd.distinct(5) - self.assertEquals(result.getNumPartitions(), 5) - self.assertEquals(result.count(), 3) + self.assertEqual(result.getNumPartitions(), 5) + self.assertEqual(result.count(), 3) + + def test_external_group_by_key(self): + self.sc._conf.set("spark.python.worker.memory", "1m") + N = 200001 + kv = self.sc.parallelize(range(N)).map(lambda x: (x % 3, x)) + gkv = kv.groupByKey().cache() + self.assertEqual(3, gkv.count()) + filtered = gkv.filter(lambda kv: kv[0] == 1) + self.assertEqual(1, filtered.count()) + self.assertEqual([(1, N // 3)], filtered.mapValues(len).collect()) + self.assertEqual([(N // 3, N // 3)], + filtered.values().map(lambda x: (len(x), len(list(x)))).collect()) + result = filtered.collect()[0][1] + self.assertEqual(N // 3, len(result)) + self.assertTrue(isinstance(result.data, shuffle.ExternalListOfList)) def test_sort_on_empty_rdd(self): self.assertEqual([], self.sc.parallelize(zip([], [])).sortByKey().collect()) @@ -719,7 +783,7 @@ def test_null_in_rdd(self): rdd = RDD(jrdd, self.sc, UTF8Deserializer()) self.assertEqual([u"a", None, u"b"], rdd.collect()) rdd = RDD(jrdd, self.sc, NoOpSerializer()) - self.assertEqual(["a", None, "b"], rdd.collect()) + self.assertEqual([b"a", None, b"b"], rdd.collect()) def test_multiple_python_java_RDD_conversions(self): # Regression test for SPARK-5361 @@ -727,7 +791,6 @@ def test_multiple_python_java_RDD_conversions(self): (u'1', {u'director': u'David Lean'}), (u'2', {u'director': u'Andrew Dominik'}) ] - from pyspark.rdd import RDD data_rdd = self.sc.parallelize(data) data_java_rdd = data_rdd._to_java_object_rdd() data_python_rdd = self.sc._jvm.SerDe.javaToPython(data_java_rdd) @@ -740,6 +803,59 @@ def test_multiple_python_java_RDD_conversions(self): converted_rdd = RDD(data_python_rdd, self.sc) self.assertEqual(2, converted_rdd.count()) + def test_narrow_dependency_in_join(self): + rdd = self.sc.parallelize(range(10)).map(lambda x: (x, x)) + parted = rdd.partitionBy(2) + self.assertEqual(2, parted.union(parted).getNumPartitions()) + self.assertEqual(rdd.getNumPartitions() + 2, parted.union(rdd).getNumPartitions()) + self.assertEqual(rdd.getNumPartitions() + 2, rdd.union(parted).getNumPartitions()) + + tracker = self.sc.statusTracker() + + self.sc.setJobGroup("test1", "test", True) + d = sorted(parted.join(parted).collect()) + self.assertEqual(10, len(d)) + self.assertEqual((0, (0, 0)), d[0]) + jobId = tracker.getJobIdsForGroup("test1")[0] + self.assertEqual(2, len(tracker.getJobInfo(jobId).stageIds)) + + self.sc.setJobGroup("test2", "test", True) + d = sorted(parted.join(rdd).collect()) + self.assertEqual(10, len(d)) + self.assertEqual((0, (0, 0)), d[0]) + jobId = tracker.getJobIdsForGroup("test2")[0] + self.assertEqual(3, len(tracker.getJobInfo(jobId).stageIds)) + + self.sc.setJobGroup("test3", "test", True) + d = sorted(parted.cogroup(parted).collect()) + self.assertEqual(10, len(d)) + self.assertEqual([[0], [0]], list(map(list, d[0][1]))) + jobId = tracker.getJobIdsForGroup("test3")[0] + self.assertEqual(2, len(tracker.getJobInfo(jobId).stageIds)) + + self.sc.setJobGroup("test4", "test", True) + d = sorted(parted.cogroup(rdd).collect()) + self.assertEqual(10, len(d)) + self.assertEqual([[0], [0]], list(map(list, d[0][1]))) + jobId = tracker.getJobIdsForGroup("test4")[0] + self.assertEqual(3, len(tracker.getJobInfo(jobId).stageIds)) + + # Regression test for SPARK-6294 + def test_take_on_jrdd(self): + rdd = self.sc.parallelize(range(1 << 20)).map(lambda x: str(x)) + rdd._jrdd.first() + + def test_sortByKey_uses_all_partitions_not_only_first_and_last(self): + # Regression test for SPARK-5969 + seq = [(i * 59 % 101, i) for i in range(101)] # unsorted sequence + rdd = self.sc.parallelize(seq) + for ascending in [True, False]: + sort = rdd.sortByKey(ascending=ascending, numPartitions=5) + self.assertEqual(sort.collect(), sorted(seq, reverse=not ascending)) + sizes = sort.glom().map(len).collect() + for size in sizes: + self.assertGreater(size, 0) + class ProfilerTests(PySparkTestCase): @@ -806,6 +922,7 @@ def tearDownClass(cls): ReusedPySparkTestCase.tearDownClass() shutil.rmtree(cls.tempdir.name) + @unittest.skipIf(sys.version >= "3", "serialize array of byte") def test_sequencefiles(self): basepath = self.tempdir.name ints = sorted(self.sc.sequenceFile(basepath + "/sftestdata/sfint/", @@ -854,15 +971,16 @@ def test_sequencefiles(self): en = [(1, None), (1, None), (2, None), (2, None), (2, None), (3, None)] self.assertEqual(nulls, en) - maps = sorted(self.sc.sequenceFile(basepath + "/sftestdata/sfmap/", - "org.apache.hadoop.io.IntWritable", - "org.apache.hadoop.io.MapWritable").collect()) + maps = self.sc.sequenceFile(basepath + "/sftestdata/sfmap/", + "org.apache.hadoop.io.IntWritable", + "org.apache.hadoop.io.MapWritable").collect() em = [(1, {}), (1, {3.0: u'bb'}), (2, {1.0: u'aa'}), (2, {1.0: u'cc'}), (3, {2.0: u'dd'})] - self.assertEqual(maps, em) + for v in maps: + self.assertTrue(v in em) # arrays get pickled to tuples by default tuples = sorted(self.sc.sequenceFile( @@ -989,8 +1107,8 @@ def test_converters(self): def test_binary_files(self): path = os.path.join(self.tempdir.name, "binaryfiles") os.mkdir(path) - data = "short binary data" - with open(os.path.join(path, "part-0000"), 'w') as f: + data = b"short binary data" + with open(os.path.join(path, "part-0000"), 'wb') as f: f.write(data) [(p, d)] = self.sc.binaryFiles(path).collect() self.assertTrue(p.endswith("part-0000")) @@ -1003,7 +1121,7 @@ def test_binary_records(self): for i in range(100): f.write('%04d' % i) result = self.sc.binaryRecords(path, 4).map(int).collect() - self.assertEqual(range(100), result) + self.assertEqual(list(range(100)), result) class OutputFormatTests(ReusedPySparkTestCase): @@ -1015,6 +1133,7 @@ def setUp(self): def tearDown(self): shutil.rmtree(self.tempdir.name, ignore_errors=True) + @unittest.skipIf(sys.version >= "3", "serialize array of byte") def test_sequencefiles(self): basepath = self.tempdir.name ei = [(1, u'aa'), (1, u'aa'), (2, u'aa'), (2, u'bb'), (2, u'bb'), (3, u'cc')] @@ -1055,8 +1174,9 @@ def test_sequencefiles(self): (2, {1.0: u'cc'}), (3, {2.0: u'dd'})] self.sc.parallelize(em).saveAsSequenceFile(basepath + "/sfmap/") - maps = sorted(self.sc.sequenceFile(basepath + "/sfmap/").collect()) - self.assertEqual(maps, em) + maps = self.sc.sequenceFile(basepath + "/sfmap/").collect() + for v in maps: + self.assertTrue(v, em) def test_oldhadoop(self): basepath = self.tempdir.name @@ -1068,12 +1188,13 @@ def test_oldhadoop(self): "org.apache.hadoop.mapred.SequenceFileOutputFormat", "org.apache.hadoop.io.IntWritable", "org.apache.hadoop.io.MapWritable") - result = sorted(self.sc.hadoopFile( + result = self.sc.hadoopFile( basepath + "/oldhadoop/", "org.apache.hadoop.mapred.SequenceFileInputFormat", "org.apache.hadoop.io.IntWritable", - "org.apache.hadoop.io.MapWritable").collect()) - self.assertEqual(result, dict_data) + "org.apache.hadoop.io.MapWritable").collect() + for v in result: + self.assertTrue(v, dict_data) conf = { "mapred.output.format.class": "org.apache.hadoop.mapred.SequenceFileOutputFormat", @@ -1083,12 +1204,13 @@ def test_oldhadoop(self): } self.sc.parallelize(dict_data).saveAsHadoopDataset(conf) input_conf = {"mapred.input.dir": basepath + "/olddataset/"} - old_dataset = sorted(self.sc.hadoopRDD( + result = self.sc.hadoopRDD( "org.apache.hadoop.mapred.SequenceFileInputFormat", "org.apache.hadoop.io.IntWritable", "org.apache.hadoop.io.MapWritable", - conf=input_conf).collect()) - self.assertEqual(old_dataset, dict_data) + conf=input_conf).collect() + for v in result: + self.assertTrue(v, dict_data) def test_newhadoop(self): basepath = self.tempdir.name @@ -1123,6 +1245,7 @@ def test_newhadoop(self): conf=input_conf).collect()) self.assertEqual(new_dataset, data) + @unittest.skipIf(sys.version >= "3", "serialize of array") def test_newhadoop_with_array(self): basepath = self.tempdir.name # use custom ArrayWritable types and converters to handle arrays @@ -1203,7 +1326,7 @@ def test_reserialization(self): basepath = self.tempdir.name x = range(1, 5) y = range(1001, 1005) - data = zip(x, y) + data = list(zip(x, y)) rdd = self.sc.parallelize(x).zip(self.sc.parallelize(y)) rdd.saveAsSequenceFile(basepath + "/reserialize/sequence") result1 = sorted(self.sc.sequenceFile(basepath + "/reserialize/sequence").collect()) @@ -1254,7 +1377,7 @@ def connect(self, port): sock = socket(AF_INET, SOCK_STREAM) sock.connect(('127.0.0.1', port)) # send a split index of -1 to shutdown the worker - sock.send("\xFF\xFF\xFF\xFF") + sock.send(b"\xFF\xFF\xFF\xFF") sock.close() return True @@ -1295,7 +1418,6 @@ def test_termination_sigterm(self): class WorkerTests(PySparkTestCase): - def test_cancel_task(self): temp = tempfile.NamedTemporaryFile(delete=True) temp.close() @@ -1310,7 +1432,7 @@ def sleep(x): # start job in background thread def run(): - self.sc.parallelize(range(1)).foreach(sleep) + self.sc.parallelize(range(1), 1).foreach(sleep) import threading t = threading.Thread(target=run) t.daemon = True @@ -1319,7 +1441,8 @@ def run(): daemon_pid, worker_pid = 0, 0 while True: if os.path.exists(path): - data = open(path).read().split(' ') + with open(path) as f: + data = f.read().split(' ') daemon_pid, worker_pid = map(int, data) break time.sleep(0.1) @@ -1355,7 +1478,7 @@ def raise_exception(_): def test_after_jvm_exception(self): tempFile = tempfile.NamedTemporaryFile(delete=False) - tempFile.write("Hello World!") + tempFile.write(b"Hello World!") tempFile.close() data = self.sc.textFile(tempFile.name, 1) filtered_data = data.filter(lambda x: True) @@ -1394,6 +1517,20 @@ def count(): self.assertTrue(not t.isAlive()) self.assertEqual(100000, rdd.count()) + def test_with_different_versions_of_python(self): + rdd = self.sc.parallelize(range(10)) + rdd.count() + version = sys.version_info + sys.version_info = (2, 0, 0) + log4j = self.sc._jvm.org.apache.log4j + old_level = log4j.LogManager.getRootLogger().getLevel() + log4j.LogManager.getRootLogger().setLevel(log4j.Level.FATAL) + try: + self.assertRaises(Py4JJavaError, lambda: rdd.count()) + finally: + sys.version_info = version + log4j.LogManager.getRootLogger().setLevel(old_level) + class SparkSubmitTests(unittest.TestCase): @@ -1404,43 +1541,71 @@ def setUp(self): def tearDown(self): shutil.rmtree(self.programDir) - def createTempFile(self, name, content): + def createTempFile(self, name, content, dir=None): """ Create a temp file with the given name and content and return its path. Strips leading spaces from content up to the first '|' in each line. """ pattern = re.compile(r'^ *\|', re.MULTILINE) content = re.sub(pattern, '', content.strip()) - path = os.path.join(self.programDir, name) + if dir is None: + path = os.path.join(self.programDir, name) + else: + os.makedirs(os.path.join(self.programDir, dir)) + path = os.path.join(self.programDir, dir, name) with open(path, "w") as f: f.write(content) return path - def createFileInZip(self, name, content): + def createFileInZip(self, name, content, ext=".zip", dir=None, zip_name=None): """ Create a zip archive containing a file with the given content and return its path. Strips leading spaces from content up to the first '|' in each line. """ pattern = re.compile(r'^ *\|', re.MULTILINE) content = re.sub(pattern, '', content.strip()) - path = os.path.join(self.programDir, name + ".zip") + if dir is None: + path = os.path.join(self.programDir, name + ext) + else: + path = os.path.join(self.programDir, dir, zip_name + ext) zip = zipfile.ZipFile(path, 'w') zip.writestr(name, content) zip.close() return path + def create_spark_package(self, artifact_name): + group_id, artifact_id, version = artifact_name.split(":") + self.createTempFile("%s-%s.pom" % (artifact_id, version), (""" + | + | + | 4.0.0 + | %s + | %s + | %s + | + """ % (group_id, artifact_id, version)).lstrip(), + os.path.join(group_id, artifact_id, version)) + self.createFileInZip("%s.py" % artifact_id, """ + |def myfunc(x): + | return x + 1 + """, ".jar", os.path.join(group_id, artifact_id, version), + "%s-%s" % (artifact_id, version)) + def test_single_script(self): """Submit and test a single script file""" script = self.createTempFile("test.py", """ |from pyspark import SparkContext | |sc = SparkContext() - |print sc.parallelize([1, 2, 3]).map(lambda x: x * 2).collect() + |print(sc.parallelize([1, 2, 3]).map(lambda x: x * 2).collect()) """) proc = subprocess.Popen([self.sparkSubmit, script], stdout=subprocess.PIPE) out, err = proc.communicate() self.assertEqual(0, proc.returncode) - self.assertIn("[2, 4, 6]", out) + self.assertIn("[2, 4, 6]", out.decode('utf-8')) def test_script_with_local_functions(self): """Submit and test a single script file calling a global function""" @@ -1451,12 +1616,12 @@ def test_script_with_local_functions(self): | return x * 3 | |sc = SparkContext() - |print sc.parallelize([1, 2, 3]).map(foo).collect() + |print(sc.parallelize([1, 2, 3]).map(foo).collect()) """) proc = subprocess.Popen([self.sparkSubmit, script], stdout=subprocess.PIPE) out, err = proc.communicate() self.assertEqual(0, proc.returncode) - self.assertIn("[3, 6, 9]", out) + self.assertIn("[3, 6, 9]", out.decode('utf-8')) def test_module_dependency(self): """Submit and test a script with a dependency on another module""" @@ -1465,7 +1630,7 @@ def test_module_dependency(self): |from mylib import myfunc | |sc = SparkContext() - |print sc.parallelize([1, 2, 3]).map(myfunc).collect() + |print(sc.parallelize([1, 2, 3]).map(myfunc).collect()) """) zip = self.createFileInZip("mylib.py", """ |def myfunc(x): @@ -1475,7 +1640,7 @@ def test_module_dependency(self): stdout=subprocess.PIPE) out, err = proc.communicate() self.assertEqual(0, proc.returncode) - self.assertIn("[2, 3, 4]", out) + self.assertIn("[2, 3, 4]", out.decode('utf-8')) def test_module_dependency_on_cluster(self): """Submit and test a script with a dependency on another module on a cluster""" @@ -1484,7 +1649,7 @@ def test_module_dependency_on_cluster(self): |from mylib import myfunc | |sc = SparkContext() - |print sc.parallelize([1, 2, 3]).map(myfunc).collect() + |print(sc.parallelize([1, 2, 3]).map(myfunc).collect()) """) zip = self.createFileInZip("mylib.py", """ |def myfunc(x): @@ -1495,7 +1660,40 @@ def test_module_dependency_on_cluster(self): stdout=subprocess.PIPE) out, err = proc.communicate() self.assertEqual(0, proc.returncode) - self.assertIn("[2, 3, 4]", out) + self.assertIn("[2, 3, 4]", out.decode('utf-8')) + + def test_package_dependency(self): + """Submit and test a script with a dependency on a Spark Package""" + script = self.createTempFile("test.py", """ + |from pyspark import SparkContext + |from mylib import myfunc + | + |sc = SparkContext() + |print(sc.parallelize([1, 2, 3]).map(myfunc).collect()) + """) + self.create_spark_package("a:mylib:0.1") + proc = subprocess.Popen([self.sparkSubmit, "--packages", "a:mylib:0.1", "--repositories", + "file:" + self.programDir, script], stdout=subprocess.PIPE) + out, err = proc.communicate() + self.assertEqual(0, proc.returncode) + self.assertIn("[2, 3, 4]", out.decode('utf-8')) + + def test_package_dependency_on_cluster(self): + """Submit and test a script with a dependency on a Spark Package on a cluster""" + script = self.createTempFile("test.py", """ + |from pyspark import SparkContext + |from mylib import myfunc + | + |sc = SparkContext() + |print(sc.parallelize([1, 2, 3]).map(myfunc).collect()) + """) + self.create_spark_package("a:mylib:0.1") + proc = subprocess.Popen([self.sparkSubmit, "--packages", "a:mylib:0.1", "--repositories", + "file:" + self.programDir, "--master", + "local-cluster[1,1,512]", script], stdout=subprocess.PIPE) + out, err = proc.communicate() + self.assertEqual(0, proc.returncode) + self.assertIn("[2, 3, 4]", out.decode('utf-8')) def test_single_script_on_cluster(self): """Submit and test a single script on a cluster""" @@ -1506,7 +1704,7 @@ def test_single_script_on_cluster(self): | return x * 2 | |sc = SparkContext() - |print sc.parallelize([1, 2, 3]).map(foo).collect() + |print(sc.parallelize([1, 2, 3]).map(foo).collect()) """) # this will fail if you have different spark.executor.memory # in conf/spark-defaults.conf @@ -1515,7 +1713,7 @@ def test_single_script_on_cluster(self): stdout=subprocess.PIPE) out, err = proc.communicate() self.assertEqual(0, proc.returncode) - self.assertIn("[2, 4, 6]", out) + self.assertIn("[2, 4, 6]", out.decode('utf-8')) class ContextTests(unittest.TestCase): @@ -1550,6 +1748,37 @@ def test_with_stop(self): sc.stop() self.assertEqual(SparkContext._active_spark_context, None) + def test_progress_api(self): + with SparkContext() as sc: + sc.setJobGroup('test_progress_api', '', True) + + rdd = sc.parallelize(range(10)).map(lambda x: time.sleep(100)) + t = threading.Thread(target=rdd.collect) + t.daemon = True + t.start() + # wait for scheduler to start + time.sleep(1) + + tracker = sc.statusTracker() + jobIds = tracker.getJobIdsForGroup('test_progress_api') + self.assertEqual(1, len(jobIds)) + job = tracker.getJobInfo(jobIds[0]) + self.assertEqual(1, len(job.stageIds)) + stage = tracker.getStageInfo(job.stageIds[0]) + self.assertEqual(rdd.getNumPartitions(), stage.numTasks) + + sc.cancelAllJobs() + t.join() + # wait for event listener to update the status + time.sleep(1) + + job = tracker.getJobInfo(jobIds[0]) + self.assertEqual('FAILED', job.status) + self.assertEqual([], tracker.getActiveJobsIds()) + self.assertEqual([], tracker.getActiveStageIds()) + + sc.stop() + @unittest.skipIf(not _have_scipy, "SciPy not installed") class SciPyTests(PySparkTestCase): @@ -1559,7 +1788,7 @@ class SciPyTests(PySparkTestCase): def test_serialize(self): from scipy.special import gammaln x = range(1, 5) - expected = map(gammaln, x) + expected = list(map(gammaln, x)) observed = self.sc.parallelize(x).map(gammaln).collect() self.assertEqual(expected, observed) @@ -1580,11 +1809,11 @@ def test_statcounter_array(self): if __name__ == "__main__": if not _have_scipy: - print "NOTE: Skipping SciPy tests as it does not seem to be installed" + print("NOTE: Skipping SciPy tests as it does not seem to be installed") if not _have_numpy: - print "NOTE: Skipping NumPy tests as it does not seem to be installed" + print("NOTE: Skipping NumPy tests as it does not seem to be installed") unittest.main() if not _have_scipy: - print "NOTE: SciPy tests were skipped as it does not seem to be installed" + print("NOTE: SciPy tests were skipped as it does not seem to be installed") if not _have_numpy: - print "NOTE: NumPy tests were skipped as it does not seem to be installed" + print("NOTE: NumPy tests were skipped as it does not seem to be installed") diff --git a/python/pyspark/worker.py b/python/pyspark/worker.py index 8a93c320ec5d3..fbdaf3a5814cd 100644 --- a/python/pyspark/worker.py +++ b/python/pyspark/worker.py @@ -18,6 +18,7 @@ """ Worker that receives input from Piped RDD. """ +from __future__ import print_function import os import sys import time @@ -37,9 +38,9 @@ def report_times(outfile, boot, init, finish): write_int(SpecialLengths.TIMING_DATA, outfile) - write_long(1000 * boot, outfile) - write_long(1000 * init, outfile) - write_long(1000 * finish, outfile) + write_long(int(1000 * boot), outfile) + write_long(int(1000 * init), outfile) + write_long(int(1000 * finish), outfile) def add_path(path): @@ -72,6 +73,9 @@ def main(infile, outfile): for _ in range(num_python_includes): filename = utf8_deserializer.loads(infile) add_path(os.path.join(spark_files_dir, filename)) + if sys.version > '3': + import importlib + importlib.invalidate_caches() # fetch names and values of broadcast variables num_broadcast_variables = read_int(infile) @@ -88,7 +92,11 @@ def main(infile, outfile): command = pickleSer._read_with_length(infile) if isinstance(command, Broadcast): command = pickleSer.loads(command.value) - (func, profiler, deserializer, serializer) = command + (func, profiler, deserializer, serializer), version = command + if version != sys.version_info[:2]: + raise Exception(("Python in worker has different version %s than that in " + + "driver %s, PySpark cannot run with different minor versions") % + (sys.version_info[:2], version)) init_time = time.time() def process(): @@ -102,14 +110,14 @@ def process(): except Exception: try: write_int(SpecialLengths.PYTHON_EXCEPTION_THROWN, outfile) - write_with_length(traceback.format_exc(), outfile) + write_with_length(traceback.format_exc().encode("utf-8"), outfile) except IOError: # JVM close the socket pass except Exception: # Write the error to stderr if it happened while serializing - print >> sys.stderr, "PySpark worker failed with exception:" - print >> sys.stderr, traceback.format_exc() + print("PySpark worker failed with exception:", file=sys.stderr) + print(traceback.format_exc(), file=sys.stderr) exit(-1) finish_time = time.time() report_times(outfile, boot_time, init_time, finish_time) diff --git a/python/run-tests b/python/run-tests index 649a2c44d187b..ed3e819ef30c1 100755 --- a/python/run-tests +++ b/python/run-tests @@ -21,6 +21,8 @@ # Figure out where the Spark framework is installed FWDIR="$(cd "`dirname "$0"`"; cd ../; pwd)" +. "$FWDIR"/bin/load-spark-env.sh + # CD into the python directory to find things on the right path cd "$FWDIR/python" @@ -35,7 +37,7 @@ rm -rf metastore warehouse function run_test() { echo "Running test: $1" | tee -a $LOG_FILE - SPARK_TESTING=1 time "$FWDIR"/bin/pyspark $1 2>&1 | tee -a $LOG_FILE + SPARK_TESTING=1 time "$FWDIR"/bin/pyspark $1 > $LOG_FILE 2>&1 FAILED=$((PIPESTATUS[0]||$FAILED)) @@ -57,22 +59,27 @@ function run_core_tests() { PYSPARK_DOC_TEST=1 run_test "pyspark/broadcast.py" PYSPARK_DOC_TEST=1 run_test "pyspark/accumulators.py" run_test "pyspark/serializers.py" - run_test "pyspark/profiler.py" + run_test "pyspark/profiler.py" run_test "pyspark/shuffle.py" run_test "pyspark/tests.py" } function run_sql_tests() { echo "Run sql tests ..." - run_test "pyspark/sql.py" - run_test "pyspark/sql_tests.py" + run_test "pyspark/sql/_types.py" + run_test "pyspark/sql/context.py" + run_test "pyspark/sql/dataframe.py" + run_test "pyspark/sql/functions.py" + run_test "pyspark/sql/tests.py" } function run_mllib_tests() { echo "Run mllib tests ..." run_test "pyspark/mllib/classification.py" run_test "pyspark/mllib/clustering.py" + run_test "pyspark/mllib/evaluation.py" run_test "pyspark/mllib/feature.py" + run_test "pyspark/mllib/fpm.py" run_test "pyspark/mllib/linalg.py" run_test "pyspark/mllib/rand.py" run_test "pyspark/mllib/recommendation.py" @@ -92,6 +99,21 @@ function run_ml_tests() { function run_streaming_tests() { echo "Run streaming tests ..." + + KAFKA_ASSEMBLY_DIR="$FWDIR"/external/kafka-assembly + JAR_PATH="${KAFKA_ASSEMBLY_DIR}/target/scala-${SPARK_SCALA_VERSION}" + for f in "${JAR_PATH}"/spark-streaming-kafka-assembly-*.jar; do + if [[ ! -e "$f" ]]; then + echo "Failed to find Spark Streaming Kafka assembly jar in $KAFKA_ASSEMBLY_DIR" 1>&2 + echo "You need to build Spark with " \ + "'build/sbt assembly/assembly streaming-kafka-assembly/assembly' or" \ + "'build/mvn package' before running this program" 1>&2 + exit 1 + fi + KAFKA_ASSEMBLY_JAR="$f" + done + + export PYSPARK_SUBMIT_ARGS="--jars ${KAFKA_ASSEMBLY_JAR} pyspark-shell" run_test "pyspark/streaming/util.py" run_test "pyspark/streaming/tests.py" } @@ -114,6 +136,19 @@ run_mllib_tests run_ml_tests run_streaming_tests +# Try to test with Python 3 +if [ $(which python3.4) ]; then + export PYSPARK_PYTHON="python3.4" + echo "Testing with Python3.4 version:" + $PYSPARK_PYTHON --version + + run_core_tests + run_sql_tests + run_mllib_tests + run_ml_tests + run_streaming_tests +fi + # Try to test with PyPy if [ $(which pypy) ]; then export PYSPARK_PYTHON="pypy" diff --git a/python/test_support/userlib-0.1-py2.7.egg b/python/test_support/userlib-0.1-py2.7.egg deleted file mode 100644 index 1674c9cb2227e..0000000000000 Binary files a/python/test_support/userlib-0.1-py2.7.egg and /dev/null differ diff --git a/python/test_support/userlib-0.1.zip b/python/test_support/userlib-0.1.zip new file mode 100644 index 0000000000000..496e1349aa967 Binary files /dev/null and b/python/test_support/userlib-0.1.zip differ diff --git a/repl/pom.xml b/repl/pom.xml index 3d4adf8fd5b03..03053b4c3b287 100644 --- a/repl/pom.xml +++ b/repl/pom.xml @@ -20,8 +20,8 @@ 4.0.0 org.apache.spark - spark-parent - 1.3.0-SNAPSHOT + spark-parent_2.10 + 1.4.0-SNAPSHOT ../pom.xml @@ -33,8 +33,6 @@ repl - /usr/share/spark - root scala-2.10/src/main/scala scala-2.10/src/test/scala @@ -86,6 +84,11 @@ scalacheck_${scala.binary.version} test + + org.mockito + mockito-all + test + diff --git a/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkILoop.scala b/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkILoop.scala index b4db3df795177..8dc0e0c965923 100644 --- a/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkILoop.scala +++ b/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkILoop.scala @@ -1064,15 +1064,16 @@ class SparkILoop( private def main(settings: Settings): Unit = process(settings) } -object SparkILoop { +object SparkILoop extends Logging { implicit def loopToInterpreter(repl: SparkILoop): SparkIMain = repl.intp private def echo(msg: String) = Console println msg def getAddedJars: Array[String] = { val envJars = sys.env.get("ADD_JARS") - val propJars = sys.props.get("spark.jars").flatMap { p => - if (p == "") None else Some(p) + if (envJars.isDefined) { + logWarning("ADD_JARS environment variable is deprecated, use --jar spark submit argument instead") } + val propJars = sys.props.get("spark.jars").flatMap { p => if (p == "") None else Some(p) } val jars = propJars.orElse(envJars).getOrElse("") Utils.resolveURIs(jars).split(",").filter(_.nonEmpty) } diff --git a/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkILoopInit.scala b/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkILoopInit.scala index 0cf2de6d399b0..05faef8786d2c 100644 --- a/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkILoopInit.scala +++ b/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkILoopInit.scala @@ -137,7 +137,7 @@ private[repl] trait SparkILoopInit { command("import org.apache.spark.SparkContext._") command("import sqlContext.implicits._") command("import sqlContext.sql") - command("import org.apache.spark.sql.Dsl._") + command("import org.apache.spark.sql.functions._") } } diff --git a/repl/scala-2.10/src/test/scala/org/apache/spark/repl/ReplSuite.scala b/repl/scala-2.10/src/test/scala/org/apache/spark/repl/ReplSuite.scala index 201f2672d5474..934daaeaafca1 100644 --- a/repl/scala-2.10/src/test/scala/org/apache/spark/repl/ReplSuite.scala +++ b/repl/scala-2.10/src/test/scala/org/apache/spark/repl/ReplSuite.scala @@ -121,9 +121,9 @@ class ReplSuite extends FunSuite { val output = runInterpreter("local", """ |var v = 7 - |sc.parallelize(1 to 10).map(x => v).collect.reduceLeft(_+_) + |sc.parallelize(1 to 10).map(x => v).collect().reduceLeft(_+_) |v = 10 - |sc.parallelize(1 to 10).map(x => v).collect.reduceLeft(_+_) + |sc.parallelize(1 to 10).map(x => v).collect().reduceLeft(_+_) """.stripMargin) assertDoesNotContain("error:", output) assertDoesNotContain("Exception", output) @@ -137,7 +137,7 @@ class ReplSuite extends FunSuite { |class C { |def foo = 5 |} - |sc.parallelize(1 to 10).map(x => (new C).foo).collect.reduceLeft(_+_) + |sc.parallelize(1 to 10).map(x => (new C).foo).collect().reduceLeft(_+_) """.stripMargin) assertDoesNotContain("error:", output) assertDoesNotContain("Exception", output) @@ -148,7 +148,7 @@ class ReplSuite extends FunSuite { val output = runInterpreter("local", """ |def double(x: Int) = x + x - |sc.parallelize(1 to 10).map(x => double(x)).collect.reduceLeft(_+_) + |sc.parallelize(1 to 10).map(x => double(x)).collect().reduceLeft(_+_) """.stripMargin) assertDoesNotContain("error:", output) assertDoesNotContain("Exception", output) @@ -160,9 +160,9 @@ class ReplSuite extends FunSuite { """ |var v = 7 |def getV() = v - |sc.parallelize(1 to 10).map(x => getV()).collect.reduceLeft(_+_) + |sc.parallelize(1 to 10).map(x => getV()).collect().reduceLeft(_+_) |v = 10 - |sc.parallelize(1 to 10).map(x => getV()).collect.reduceLeft(_+_) + |sc.parallelize(1 to 10).map(x => getV()).collect().reduceLeft(_+_) """.stripMargin) assertDoesNotContain("error:", output) assertDoesNotContain("Exception", output) @@ -178,9 +178,9 @@ class ReplSuite extends FunSuite { """ |var array = new Array[Int](5) |val broadcastArray = sc.broadcast(array) - |sc.parallelize(0 to 4).map(x => broadcastArray.value(x)).collect + |sc.parallelize(0 to 4).map(x => broadcastArray.value(x)).collect() |array(0) = 5 - |sc.parallelize(0 to 4).map(x => broadcastArray.value(x)).collect + |sc.parallelize(0 to 4).map(x => broadcastArray.value(x)).collect() """.stripMargin) assertDoesNotContain("error:", output) assertDoesNotContain("Exception", output) @@ -216,14 +216,14 @@ class ReplSuite extends FunSuite { """ |var v = 7 |def getV() = v - |sc.parallelize(1 to 10).map(x => getV()).collect.reduceLeft(_+_) + |sc.parallelize(1 to 10).map(x => getV()).collect().reduceLeft(_+_) |v = 10 - |sc.parallelize(1 to 10).map(x => getV()).collect.reduceLeft(_+_) + |sc.parallelize(1 to 10).map(x => getV()).collect().reduceLeft(_+_) |var array = new Array[Int](5) |val broadcastArray = sc.broadcast(array) - |sc.parallelize(0 to 4).map(x => broadcastArray.value(x)).collect + |sc.parallelize(0 to 4).map(x => broadcastArray.value(x)).collect() |array(0) = 5 - |sc.parallelize(0 to 4).map(x => broadcastArray.value(x)).collect + |sc.parallelize(0 to 4).map(x => broadcastArray.value(x)).collect() """.stripMargin) assertDoesNotContain("error:", output) assertDoesNotContain("Exception", output) @@ -262,7 +262,7 @@ class ReplSuite extends FunSuite { |val sqlContext = new org.apache.spark.sql.SQLContext(sc) |import sqlContext.implicits._ |case class TestCaseClass(value: Int) - |sc.parallelize(1 to 10).map(x => TestCaseClass(x)).toDataFrame.collect() + |sc.parallelize(1 to 10).map(x => TestCaseClass(x)).toDF().collect() """.stripMargin) assertDoesNotContain("error:", output) assertDoesNotContain("Exception", output) @@ -275,26 +275,26 @@ class ReplSuite extends FunSuite { |val t = new TestClass |import t.testMethod |case class TestCaseClass(value: Int) - |sc.parallelize(1 to 10).map(x => TestCaseClass(x)).collect + |sc.parallelize(1 to 10).map(x => TestCaseClass(x)).collect() """.stripMargin) assertDoesNotContain("error:", output) assertDoesNotContain("Exception", output) } - if (System.getenv("MESOS_NATIVE_LIBRARY") != null) { + if (System.getenv("MESOS_NATIVE_JAVA_LIBRARY") != null) { test("running on Mesos") { val output = runInterpreter("localquiet", """ |var v = 7 |def getV() = v - |sc.parallelize(1 to 10).map(x => getV()).collect.reduceLeft(_+_) + |sc.parallelize(1 to 10).map(x => getV()).collect().reduceLeft(_+_) |v = 10 - |sc.parallelize(1 to 10).map(x => getV()).collect.reduceLeft(_+_) + |sc.parallelize(1 to 10).map(x => getV()).collect().reduceLeft(_+_) |var array = new Array[Int](5) |val broadcastArray = sc.broadcast(array) - |sc.parallelize(0 to 4).map(x => broadcastArray.value(x)).collect + |sc.parallelize(0 to 4).map(x => broadcastArray.value(x)).collect() |array(0) = 5 - |sc.parallelize(0 to 4).map(x => broadcastArray.value(x)).collect + |sc.parallelize(0 to 4).map(x => broadcastArray.value(x)).collect() """.stripMargin) assertDoesNotContain("error:", output) assertDoesNotContain("Exception", output) @@ -309,10 +309,22 @@ class ReplSuite extends FunSuite { val output = runInterpreter("local[2]", """ |case class Foo(i: Int) - |val ret = sc.parallelize((1 to 100).map(Foo), 10).collect + |val ret = sc.parallelize((1 to 100).map(Foo), 10).collect() """.stripMargin) assertDoesNotContain("error:", output) assertDoesNotContain("Exception", output) assertContains("ret: Array[Foo] = Array(Foo(1),", output) } + + test("collecting objects of class defined in repl - shuffling") { + val output = runInterpreter("local-cluster[1,1,512]", + """ + |case class Foo(i: Int) + |val list = List((1, Foo(1)), (1, Foo(2))) + |val ret = sc.parallelize(list).groupByKey().collect() + """.stripMargin) + assertDoesNotContain("error:", output) + assertDoesNotContain("Exception", output) + assertContains("ret: Array[(Int, Iterable[Foo])] = Array((1,", output) + } } diff --git a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/Main.scala b/repl/scala-2.11/src/main/scala/org/apache/spark/repl/Main.scala index dc25692749aad..2210fbaafeadb 100644 --- a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/Main.scala +++ b/repl/scala-2.11/src/main/scala/org/apache/spark/repl/Main.scala @@ -51,6 +51,9 @@ object Main extends Logging { def getAddedJars: Array[String] = { val envJars = sys.env.get("ADD_JARS") + if (envJars.isDefined) { + logWarning("ADD_JARS environment variable is deprecated, use --jar spark submit argument instead") + } val propJars = sys.props.get("spark.jars").flatMap { p => if (p == "") None else Some(p) } val jars = propJars.orElse(envJars).getOrElse("") Utils.resolveURIs(jars).split(",").filter(_.nonEmpty) diff --git a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkILoop.scala b/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkILoop.scala index 1bd2a6991404b..7a5e94da5cbf3 100644 --- a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkILoop.scala +++ b/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkILoop.scala @@ -77,7 +77,7 @@ class SparkILoop(in0: Option[BufferedReader], protected val out: JPrintWriter) command("import org.apache.spark.SparkContext._") command("import sqlContext.implicits._") command("import sqlContext.sql") - command("import org.apache.spark.sql.Dsl._") + command("import org.apache.spark.sql.functions._") } } diff --git a/repl/scala-2.11/src/test/scala/org/apache/spark/repl/ReplSuite.scala b/repl/scala-2.11/src/test/scala/org/apache/spark/repl/ReplSuite.scala index f966f25c5a14c..14f5e9ed4f25e 100644 --- a/repl/scala-2.11/src/test/scala/org/apache/spark/repl/ReplSuite.scala +++ b/repl/scala-2.11/src/test/scala/org/apache/spark/repl/ReplSuite.scala @@ -21,11 +21,9 @@ import java.io._ import java.net.URLClassLoader import scala.collection.mutable.ArrayBuffer -import scala.concurrent.Await import scala.concurrent.duration._ import scala.tools.nsc.interpreter.SparkILoop -import com.google.common.io.Files import org.scalatest.FunSuite import org.apache.commons.lang3.StringEscapeUtils import org.apache.spark.SparkContext @@ -128,9 +126,9 @@ class ReplSuite extends FunSuite { val output = runInterpreter("local", """ |var v = 7 - |sc.parallelize(1 to 10).map(x => v).collect.reduceLeft(_+_) + |sc.parallelize(1 to 10).map(x => v).collect().reduceLeft(_+_) |v = 10 - |sc.parallelize(1 to 10).map(x => v).collect.reduceLeft(_+_) + |sc.parallelize(1 to 10).map(x => v).collect().reduceLeft(_+_) """.stripMargin) assertDoesNotContain("error:", output) assertDoesNotContain("Exception", output) @@ -144,7 +142,7 @@ class ReplSuite extends FunSuite { |class C { |def foo = 5 |} - |sc.parallelize(1 to 10).map(x => (new C).foo).collect.reduceLeft(_+_) + |sc.parallelize(1 to 10).map(x => (new C).foo).collect().reduceLeft(_+_) """.stripMargin) assertDoesNotContain("error:", output) assertDoesNotContain("Exception", output) @@ -155,7 +153,7 @@ class ReplSuite extends FunSuite { val output = runInterpreter("local", """ |def double(x: Int) = x + x - |sc.parallelize(1 to 10).map(x => double(x)).collect.reduceLeft(_+_) + |sc.parallelize(1 to 10).map(x => double(x)).collect().reduceLeft(_+_) """.stripMargin) assertDoesNotContain("error:", output) assertDoesNotContain("Exception", output) @@ -167,9 +165,9 @@ class ReplSuite extends FunSuite { """ |var v = 7 |def getV() = v - |sc.parallelize(1 to 10).map(x => getV()).collect.reduceLeft(_+_) + |sc.parallelize(1 to 10).map(x => getV()).collect().reduceLeft(_+_) |v = 10 - |sc.parallelize(1 to 10).map(x => getV()).collect.reduceLeft(_+_) + |sc.parallelize(1 to 10).map(x => getV()).collect().reduceLeft(_+_) """.stripMargin) assertDoesNotContain("error:", output) assertDoesNotContain("Exception", output) @@ -185,9 +183,9 @@ class ReplSuite extends FunSuite { """ |var array = new Array[Int](5) |val broadcastArray = sc.broadcast(array) - |sc.parallelize(0 to 4).map(x => broadcastArray.value(x)).collect + |sc.parallelize(0 to 4).map(x => broadcastArray.value(x)).collect() |array(0) = 5 - |sc.parallelize(0 to 4).map(x => broadcastArray.value(x)).collect + |sc.parallelize(0 to 4).map(x => broadcastArray.value(x)).collect() """.stripMargin) assertDoesNotContain("error:", output) assertDoesNotContain("Exception", output) @@ -196,8 +194,7 @@ class ReplSuite extends FunSuite { } test("interacting with files") { - val tempDir = Files.createTempDir() - tempDir.deleteOnExit() + val tempDir = Utils.createTempDir() val out = new FileWriter(tempDir + "/input") out.write("Hello world!\n") out.write("What's up?\n") @@ -224,14 +221,14 @@ class ReplSuite extends FunSuite { """ |var v = 7 |def getV() = v - |sc.parallelize(1 to 10).map(x => getV()).collect.reduceLeft(_+_) + |sc.parallelize(1 to 10).map(x => getV()).collect().reduceLeft(_+_) |v = 10 - |sc.parallelize(1 to 10).map(x => getV()).collect.reduceLeft(_+_) + |sc.parallelize(1 to 10).map(x => getV()).collect().reduceLeft(_+_) |var array = new Array[Int](5) |val broadcastArray = sc.broadcast(array) - |sc.parallelize(0 to 4).map(x => broadcastArray.value(x)).collect + |sc.parallelize(0 to 4).map(x => broadcastArray.value(x)).collect() |array(0) = 5 - |sc.parallelize(0 to 4).map(x => broadcastArray.value(x)).collect + |sc.parallelize(0 to 4).map(x => broadcastArray.value(x)).collect() """.stripMargin) assertDoesNotContain("error:", output) assertDoesNotContain("Exception", output) @@ -263,14 +260,14 @@ class ReplSuite extends FunSuite { assertDoesNotContain("Exception", output) } - test("SPARK-2576 importing SQLContext.createSchemaRDD.") { + test("SPARK-2576 importing SQLContext.createDataFrame.") { // We need to use local-cluster to test this case. val output = runInterpreter("local-cluster[1,1,512]", """ |val sqlContext = new org.apache.spark.sql.SQLContext(sc) - |import sqlContext.createSchemaRDD + |import sqlContext.implicits._ |case class TestCaseClass(value: Int) - |sc.parallelize(1 to 10).map(x => TestCaseClass(x)).toSchemaRDD.collect + |sc.parallelize(1 to 10).map(x => TestCaseClass(x)).toDF().collect() """.stripMargin) assertDoesNotContain("error:", output) assertDoesNotContain("Exception", output) @@ -283,26 +280,26 @@ class ReplSuite extends FunSuite { |val t = new TestClass |import t.testMethod |case class TestCaseClass(value: Int) - |sc.parallelize(1 to 10).map(x => TestCaseClass(x)).collect + |sc.parallelize(1 to 10).map(x => TestCaseClass(x)).collect() """.stripMargin) assertDoesNotContain("error:", output) assertDoesNotContain("Exception", output) } - if (System.getenv("MESOS_NATIVE_LIBRARY") != null) { + if (System.getenv("MESOS_NATIVE_JAVA_LIBRARY") != null) { test("running on Mesos") { val output = runInterpreter("localquiet", """ |var v = 7 |def getV() = v - |sc.parallelize(1 to 10).map(x => getV()).collect.reduceLeft(_+_) + |sc.parallelize(1 to 10).map(x => getV()).collect().reduceLeft(_+_) |v = 10 - |sc.parallelize(1 to 10).map(x => getV()).collect.reduceLeft(_+_) + |sc.parallelize(1 to 10).map(x => getV()).collect().reduceLeft(_+_) |var array = new Array[Int](5) |val broadcastArray = sc.broadcast(array) - |sc.parallelize(0 to 4).map(x => broadcastArray.value(x)).collect + |sc.parallelize(0 to 4).map(x => broadcastArray.value(x)).collect() |array(0) = 5 - |sc.parallelize(0 to 4).map(x => broadcastArray.value(x)).collect + |sc.parallelize(0 to 4).map(x => broadcastArray.value(x)).collect() """.stripMargin) assertDoesNotContain("error:", output) assertDoesNotContain("Exception", output) @@ -317,10 +314,22 @@ class ReplSuite extends FunSuite { val output = runInterpreter("local[2]", """ |case class Foo(i: Int) - |val ret = sc.parallelize((1 to 100).map(Foo), 10).collect + |val ret = sc.parallelize((1 to 100).map(Foo), 10).collect() """.stripMargin) assertDoesNotContain("error:", output) assertDoesNotContain("Exception", output) assertContains("ret: Array[Foo] = Array(Foo(1),", output) } + + test("collecting objects of class defined in repl - shuffling") { + val output = runInterpreter("local-cluster[1,1,512]", + """ + |case class Foo(i: Int) + |val list = List((1, Foo(1)), (1, Foo(2))) + |val ret = sc.parallelize(list).groupByKey().collect() + """.stripMargin) + assertDoesNotContain("error:", output) + assertDoesNotContain("Exception", output) + assertContains("ret: Array[(Int, Iterable[Foo])] = Array((1,", output) + } } diff --git a/repl/src/main/scala/org/apache/spark/repl/ExecutorClassLoader.scala b/repl/src/main/scala/org/apache/spark/repl/ExecutorClassLoader.scala index 9805609120005..004941d5f50ae 100644 --- a/repl/src/main/scala/org/apache/spark/repl/ExecutorClassLoader.scala +++ b/repl/src/main/scala/org/apache/spark/repl/ExecutorClassLoader.scala @@ -17,9 +17,10 @@ package org.apache.spark.repl -import java.io.{ByteArrayOutputStream, InputStream, FileNotFoundException} -import java.net.{URI, URL, URLEncoder} -import java.util.concurrent.{Executors, ExecutorService} +import java.io.{IOException, ByteArrayOutputStream, InputStream} +import java.net.{HttpURLConnection, URI, URL, URLEncoder} + +import scala.util.control.NonFatal import org.apache.hadoop.fs.{FileSystem, Path} @@ -43,6 +44,9 @@ class ExecutorClassLoader(conf: SparkConf, classUri: String, parent: ClassLoader val parentLoader = new ParentClassLoader(parent) + // Allows HTTP connect and read timeouts to be controlled for testing / debugging purposes + private[repl] var httpUrlConnectionTimeoutMillis: Int = -1 + // Hadoop FileSystem object for our URI, if it isn't using HTTP var fileSystem: FileSystem = { if (Set("http", "https", "ftp").contains(uri.getScheme)) { @@ -71,30 +75,66 @@ class ExecutorClassLoader(conf: SparkConf, classUri: String, parent: ClassLoader } } + private def getClassFileInputStreamFromHttpServer(pathInDirectory: String): InputStream = { + val url = if (SparkEnv.get.securityManager.isAuthenticationEnabled()) { + val uri = new URI(classUri + "/" + urlEncode(pathInDirectory)) + val newuri = Utils.constructURIForAuthentication(uri, SparkEnv.get.securityManager) + newuri.toURL + } else { + new URL(classUri + "/" + urlEncode(pathInDirectory)) + } + val connection: HttpURLConnection = Utils.setupSecureURLConnection(url.openConnection(), + SparkEnv.get.securityManager).asInstanceOf[HttpURLConnection] + // Set the connection timeouts (for testing purposes) + if (httpUrlConnectionTimeoutMillis != -1) { + connection.setConnectTimeout(httpUrlConnectionTimeoutMillis) + connection.setReadTimeout(httpUrlConnectionTimeoutMillis) + } + connection.connect() + try { + if (connection.getResponseCode != 200) { + // Close the error stream so that the connection is eligible for re-use + try { + connection.getErrorStream.close() + } catch { + case ioe: IOException => + logError("Exception while closing error stream", ioe) + } + throw new ClassNotFoundException(s"Class file not found at URL $url") + } else { + connection.getInputStream + } + } catch { + case NonFatal(e) if !e.isInstanceOf[ClassNotFoundException] => + connection.disconnect() + throw e + } + } + + private def getClassFileInputStreamFromFileSystem(pathInDirectory: String): InputStream = { + val path = new Path(directory, pathInDirectory) + if (fileSystem.exists(path)) { + fileSystem.open(path) + } else { + throw new ClassNotFoundException(s"Class file not found at path $path") + } + } + def findClassLocally(name: String): Option[Class[_]] = { + val pathInDirectory = name.replace('.', '/') + ".class" + var inputStream: InputStream = null try { - val pathInDirectory = name.replace('.', '/') + ".class" - val inputStream = { + inputStream = { if (fileSystem != null) { - fileSystem.open(new Path(directory, pathInDirectory)) + getClassFileInputStreamFromFileSystem(pathInDirectory) } else { - val url = if (SparkEnv.get.securityManager.isAuthenticationEnabled()) { - val uri = new URI(classUri + "/" + urlEncode(pathInDirectory)) - val newuri = Utils.constructURIForAuthentication(uri, SparkEnv.get.securityManager) - newuri.toURL - } else { - new URL(classUri + "/" + urlEncode(pathInDirectory)) - } - - Utils.setupSecureURLConnection(url.openConnection(), SparkEnv.get.securityManager) - .getInputStream + getClassFileInputStreamFromHttpServer(pathInDirectory) } } val bytes = readAndTransformClass(name, inputStream) - inputStream.close() Some(defineClass(name, bytes, 0, bytes.length)) } catch { - case e: FileNotFoundException => + case e: ClassNotFoundException => // We did not find the class logDebug(s"Did not load class $name from REPL class server at $uri", e) None @@ -102,6 +142,15 @@ class ExecutorClassLoader(conf: SparkConf, classUri: String, parent: ClassLoader // Something bad happened while checking if the class exists logError(s"Failed to check existence of class $name on REPL class server at $uri", e) None + } finally { + if (inputStream != null) { + try { + inputStream.close() + } catch { + case e: Exception => + logError("Exception while closing inputStream", e) + } + } } } diff --git a/repl/src/test/resources/log4j.properties b/repl/src/test/resources/log4j.properties index e7e4a4113174a..e2ee9c963a4da 100644 --- a/repl/src/test/resources/log4j.properties +++ b/repl/src/test/resources/log4j.properties @@ -24,4 +24,4 @@ log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n # Ignore messages below warning level from Jetty, because it's a bit verbose -log4j.logger.org.eclipse.jetty=WARN +log4j.logger.org.spark-project.jetty=WARN diff --git a/repl/src/test/scala/org/apache/spark/repl/ExecutorClassLoaderSuite.scala b/repl/src/test/scala/org/apache/spark/repl/ExecutorClassLoaderSuite.scala index 6a79e76a34db8..c709cde740748 100644 --- a/repl/src/test/scala/org/apache/spark/repl/ExecutorClassLoaderSuite.scala +++ b/repl/src/test/scala/org/apache/spark/repl/ExecutorClassLoaderSuite.scala @@ -20,13 +20,25 @@ package org.apache.spark.repl import java.io.File import java.net.{URL, URLClassLoader} +import scala.concurrent.duration._ +import scala.language.implicitConversions +import scala.language.postfixOps + import org.scalatest.BeforeAndAfterAll import org.scalatest.FunSuite +import org.scalatest.concurrent.Interruptor +import org.scalatest.concurrent.Timeouts._ +import org.scalatest.mock.MockitoSugar +import org.mockito.Mockito._ -import org.apache.spark.{SparkConf, TestUtils} +import org.apache.spark._ import org.apache.spark.util.Utils -class ExecutorClassLoaderSuite extends FunSuite with BeforeAndAfterAll { +class ExecutorClassLoaderSuite + extends FunSuite + with BeforeAndAfterAll + with MockitoSugar + with Logging { val childClassNames = List("ReplFakeClass1", "ReplFakeClass2") val parentClassNames = List("ReplFakeClass1", "ReplFakeClass2", "ReplFakeClass3") @@ -34,6 +46,7 @@ class ExecutorClassLoaderSuite extends FunSuite with BeforeAndAfterAll { var tempDir2: File = _ var url1: String = _ var urls2: Array[URL] = _ + var classServer: HttpServer = _ override def beforeAll() { super.beforeAll() @@ -47,8 +60,12 @@ class ExecutorClassLoaderSuite extends FunSuite with BeforeAndAfterAll { override def afterAll() { super.afterAll() + if (classServer != null) { + classServer.stop() + } Utils.deleteRecursively(tempDir1) Utils.deleteRecursively(tempDir2) + SparkEnv.set(null) } test("child first") { @@ -83,4 +100,53 @@ class ExecutorClassLoaderSuite extends FunSuite with BeforeAndAfterAll { } } + test("failing to fetch classes from HTTP server should not leak resources (SPARK-6209)") { + // This is a regression test for SPARK-6209, a bug where each failed attempt to load a class + // from the driver's class server would leak a HTTP connection, causing the class server's + // thread / connection pool to be exhausted. + val conf = new SparkConf() + val securityManager = new SecurityManager(conf) + classServer = new HttpServer(conf, tempDir1, securityManager) + classServer.start() + // ExecutorClassLoader uses SparkEnv's SecurityManager, so we need to mock this + val mockEnv = mock[SparkEnv] + when(mockEnv.securityManager).thenReturn(securityManager) + SparkEnv.set(mockEnv) + // Create an ExecutorClassLoader that's configured to load classes from the HTTP server + val parentLoader = new URLClassLoader(Array.empty, null) + val classLoader = new ExecutorClassLoader(conf, classServer.uri, parentLoader, false) + classLoader.httpUrlConnectionTimeoutMillis = 500 + // Check that this class loader can actually load classes that exist + val fakeClass = classLoader.loadClass("ReplFakeClass2").newInstance() + val fakeClassVersion = fakeClass.toString + assert(fakeClassVersion === "1") + // Try to perform a full GC now, since GC during the test might mask resource leaks + System.gc() + // When the original bug occurs, the test thread becomes blocked in a classloading call + // and does not respond to interrupts. Therefore, use a custom ScalaTest interruptor to + // shut down the HTTP server when the test times out + val interruptor: Interruptor = new Interruptor { + override def apply(thread: Thread): Unit = { + classServer.stop() + classServer = null + thread.interrupt() + } + } + def tryAndFailToLoadABunchOfClasses(): Unit = { + // The number of trials here should be much larger than Jetty's thread / connection limit + // in order to expose thread or connection leaks + for (i <- 1 to 1000) { + if (Thread.currentThread().isInterrupted) { + throw new InterruptedException() + } + // Incorporate the iteration number into the class name in order to avoid any response + // caching that might be added in the future + intercept[ClassNotFoundException] { + classLoader.loadClass(s"ReplFakeClassDoesNotExist$i").newInstance() + } + } + } + failAfter(10 seconds)(tryAndFailToLoadABunchOfClasses())(interruptor) + } + } diff --git a/sbin/spark-daemon.sh b/sbin/spark-daemon.sh index 89608bc41b71d..de762acc8fa0e 100755 --- a/sbin/spark-daemon.sh +++ b/sbin/spark-daemon.sh @@ -29,7 +29,7 @@ # SPARK_NICENESS The scheduling priority for daemons. Defaults to 0. ## -usage="Usage: spark-daemon.sh [--config ] (start|stop) " +usage="Usage: spark-daemon.sh [--config ] (start|stop|status) " # if no args specified, show usage if [ $# -le 1 ]; then @@ -121,60 +121,97 @@ if [ "$SPARK_NICENESS" = "" ]; then export SPARK_NICENESS=0 fi +run_command() { + mode="$1" + shift -case $option in + mkdir -p "$SPARK_PID_DIR" - (start|spark-submit) + if [ -f "$pid" ]; then + TARGET_ID="$(cat "$pid")" + if [[ $(ps -p "$TARGET_ID" -o comm=) =~ "java" ]]; then + echo "$command running as process $TARGET_ID. Stop it first." + exit 1 + fi + fi - mkdir -p "$SPARK_PID_DIR" + if [ "$SPARK_MASTER" != "" ]; then + echo rsync from "$SPARK_MASTER" + rsync -a -e ssh --delete --exclude=.svn --exclude='logs/*' --exclude='contrib/hod/logs/*' "$SPARK_MASTER/" "$SPARK_HOME" + fi - if [ -f $pid ]; then - if kill -0 `cat $pid` > /dev/null 2>&1; then - echo $command running as process `cat $pid`. Stop it first. - exit 1 - fi - fi + spark_rotate_log "$log" + echo "starting $command, logging to $log" + + case "$mode" in + (class) + nohup nice -n "$SPARK_NICENESS" "$SPARK_PREFIX"/bin/spark-class $command "$@" >> "$log" 2>&1 < /dev/null & + newpid="$!" + ;; + + (submit) + nohup nice -n "$SPARK_NICENESS" "$SPARK_PREFIX"/bin/spark-submit --class $command "$@" >> "$log" 2>&1 < /dev/null & + newpid="$!" + ;; + + (*) + echo "unknown mode: $mode" + exit 1 + ;; + esac + + echo "$newpid" > "$pid" + sleep 2 + # Check if the process has died; in that case we'll tail the log so the user can see + if [[ ! $(ps -p "$newpid" -o comm=) =~ "java" ]]; then + echo "failed to launch $command:" + tail -2 "$log" | sed 's/^/ /' + echo "full log in $log" + fi +} - if [ "$SPARK_MASTER" != "" ]; then - echo rsync from "$SPARK_MASTER" - rsync -a -e ssh --delete --exclude=.svn --exclude='logs/*' --exclude='contrib/hod/logs/*' $SPARK_MASTER/ "$SPARK_HOME" - fi +case $option in - spark_rotate_log "$log" - echo starting $command, logging to $log - if [ $option == spark-submit ]; then - source "$SPARK_HOME"/bin/utils.sh - gatherSparkSubmitOpts "$@" - nohup nice -n $SPARK_NICENESS "$SPARK_PREFIX"/bin/spark-submit --class $command \ - "${SUBMISSION_OPTS[@]}" spark-internal "${APPLICATION_OPTS[@]}" >> "$log" 2>&1 < /dev/null & - else - nohup nice -n $SPARK_NICENESS "$SPARK_PREFIX"/bin/spark-class $command "$@" >> "$log" 2>&1 < /dev/null & - fi - newpid=$! - echo $newpid > $pid - sleep 2 - # Check if the process has died; in that case we'll tail the log so the user can see - if ! kill -0 $newpid >/dev/null 2>&1; then - echo "failed to launch $command:" - tail -2 "$log" | sed 's/^/ /' - echo "full log in $log" - fi + (submit) + run_command submit "$@" + ;; + + (start) + run_command class "$@" ;; (stop) if [ -f $pid ]; then - if kill -0 `cat $pid` > /dev/null 2>&1; then - echo stopping $command - kill `cat $pid` + TARGET_ID="$(cat "$pid")" + if [[ $(ps -p "$TARGET_ID" -o comm=) =~ "java" ]]; then + echo "stopping $command" + kill "$TARGET_ID" && rm -f "$pid" else - echo no $command to stop + echo "no $command to stop" fi else - echo no $command to stop + echo "no $command to stop" fi ;; + (status) + + if [ -f $pid ]; then + TARGET_ID="$(cat "$pid")" + if [[ $(ps -p "$TARGET_ID" -o comm=) =~ "java" ]]; then + echo $command is running. + exit 0 + else + echo $pid file is present but $command not running + exit 1 + fi + else + echo $command not running. + exit 2 + fi + ;; + (*) echo $usage exit 1 diff --git a/sbin/start-slave.sh b/sbin/start-slave.sh index 2fc35309f4ca5..4c919ff76a8f5 100755 --- a/sbin/start-slave.sh +++ b/sbin/start-slave.sh @@ -17,10 +17,69 @@ # limitations under the License. # -# Usage: start-slave.sh -# where is like "spark://localhost:7077" +# Starts a slave on the machine this script is executed on. +# +# Environment Variables +# +# SPARK_WORKER_INSTANCES The number of worker instances to run on this +# slave. Default is 1. +# SPARK_WORKER_PORT The base port number for the first worker. If set, +# subsequent workers will increment this number. If +# unset, Spark will find a valid port number, but +# with no guarantee of a predictable pattern. +# SPARK_WORKER_WEBUI_PORT The base port for the web interface of the first +# worker. Subsequent workers will increment this +# number. Default is 8081. + +usage="Usage: start-slave.sh where is like spark://localhost:7077" + +if [ $# -lt 1 ]; then + echo $usage + echo Called as start-slave.sh $* + exit 1 +fi sbin="`dirname "$0"`" sbin="`cd "$sbin"; pwd`" -"$sbin"/spark-daemon.sh start org.apache.spark.deploy.worker.Worker "$@" +. "$sbin/spark-config.sh" + +. "$SPARK_PREFIX/bin/load-spark-env.sh" + +# First argument should be the master; we need to store it aside because we may +# need to insert arguments between it and the other arguments +MASTER=$1 +shift + +# Determine desired worker port +if [ "$SPARK_WORKER_WEBUI_PORT" = "" ]; then + SPARK_WORKER_WEBUI_PORT=8081 +fi + +# Start up the appropriate number of workers on this machine. +# quick local function to start a worker +function start_instance { + WORKER_NUM=$1 + shift + + if [ "$SPARK_WORKER_PORT" = "" ]; then + PORT_FLAG= + PORT_NUM= + else + PORT_FLAG="--port" + PORT_NUM=$(( $SPARK_WORKER_PORT + $WORKER_NUM - 1 )) + fi + WEBUI_PORT=$(( $SPARK_WORKER_WEBUI_PORT + $WORKER_NUM - 1 )) + + "$sbin"/spark-daemon.sh start org.apache.spark.deploy.worker.Worker $WORKER_NUM \ + --webui-port "$WEBUI_PORT" $PORT_FLAG $PORT_NUM $MASTER "$@" +} + +if [ "$SPARK_WORKER_INSTANCES" = "" ]; then + start_instance 1 "$@" +else + for ((i=0; i<$SPARK_WORKER_INSTANCES; i++)); do + start_instance $(( 1 + $i )) "$@" + done +fi + diff --git a/sbin/start-slaves.sh b/sbin/start-slaves.sh index ba1a84abc1fef..24d6268815ed3 100755 --- a/sbin/start-slaves.sh +++ b/sbin/start-slaves.sh @@ -17,6 +17,8 @@ # limitations under the License. # +# Starts a slave instance on each machine specified in the conf/slaves file. + sbin="`dirname "$0"`" sbin="`cd "$sbin"; pwd`" @@ -57,13 +59,4 @@ if [ "$START_TACHYON" == "true" ]; then fi # Launch the slaves -if [ "$SPARK_WORKER_INSTANCES" = "" ]; then - exec "$sbin/slaves.sh" cd "$SPARK_HOME" \; "$sbin/start-slave.sh" 1 "spark://$SPARK_MASTER_IP:$SPARK_MASTER_PORT" -else - if [ "$SPARK_WORKER_WEBUI_PORT" = "" ]; then - SPARK_WORKER_WEBUI_PORT=8081 - fi - for ((i=0; i<$SPARK_WORKER_INSTANCES; i++)); do - "$sbin/slaves.sh" cd "$SPARK_HOME" \; "$sbin/start-slave.sh" $(( $i + 1 )) "spark://$SPARK_MASTER_IP:$SPARK_MASTER_PORT" --webui-port $(( $SPARK_WORKER_WEBUI_PORT + $i )) - done -fi +"$sbin/slaves.sh" cd "$SPARK_HOME" \; "$sbin/start-slave.sh" "spark://$SPARK_MASTER_IP:$SPARK_MASTER_PORT" diff --git a/sbin/start-thriftserver.sh b/sbin/start-thriftserver.sh index 070cc7a87e6f2..5b0aeb177fff3 100755 --- a/sbin/start-thriftserver.sh +++ b/sbin/start-thriftserver.sh @@ -52,4 +52,4 @@ fi export SUBMIT_USAGE_FUNCTION=usage -exec "$FWDIR"/sbin/spark-daemon.sh spark-submit $CLASS 1 "$@" +exec "$FWDIR"/sbin/spark-daemon.sh submit $CLASS 1 "$@" diff --git a/sbin/stop-all.sh b/sbin/stop-all.sh index 971d5d49da664..1a9abe07db844 100755 --- a/sbin/stop-all.sh +++ b/sbin/stop-all.sh @@ -17,8 +17,8 @@ # limitations under the License. # -# Start all spark daemons. -# Run this on the master nde +# Stop all spark daemons. +# Run this on the master node. sbin="`dirname "$0"`" diff --git a/sbin/stop-master.sh b/sbin/stop-master.sh index b6bdaa4db373c..729702d92191e 100755 --- a/sbin/stop-master.sh +++ b/sbin/stop-master.sh @@ -17,7 +17,7 @@ # limitations under the License. # -# Starts the master on the machine this script is executed on. +# Stops the master on the machine this script is executed on. sbin=`dirname "$0"` sbin=`cd "$sbin"; pwd` diff --git a/sbin/stop-slave.sh b/sbin/stop-slave.sh new file mode 100755 index 0000000000000..3d1da5b254f2a --- /dev/null +++ b/sbin/stop-slave.sh @@ -0,0 +1,43 @@ +#!/usr/bin/env bash + +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# A shell script to stop all workers on a single slave +# +# Environment variables +# +# SPARK_WORKER_INSTANCES The number of worker instances that should be +# running on this slave. Default is 1. + +# Usage: stop-slave.sh +# Stops all slaves on this worker machine + +sbin="`dirname "$0"`" +sbin="`cd "$sbin"; pwd`" + +. "$sbin/spark-config.sh" + +. "$SPARK_PREFIX/bin/load-spark-env.sh" + +if [ "$SPARK_WORKER_INSTANCES" = "" ]; then + "$sbin"/spark-daemon.sh stop org.apache.spark.deploy.worker.Worker 1 +else + for ((i=0; i<$SPARK_WORKER_INSTANCES; i++)); do + "$sbin"/spark-daemon.sh stop org.apache.spark.deploy.worker.Worker $(( $i + 1 )) + done +fi diff --git a/sbin/stop-slaves.sh b/sbin/stop-slaves.sh index 7c2201100ef97..54c9bd46803a9 100755 --- a/sbin/stop-slaves.sh +++ b/sbin/stop-slaves.sh @@ -17,8 +17,8 @@ # limitations under the License. # -sbin=`dirname "$0"` -sbin=`cd "$sbin"; pwd` +sbin="`dirname "$0"`" +sbin="`cd "$sbin"; pwd`" . "$sbin/spark-config.sh" @@ -29,10 +29,4 @@ if [ -e "$sbin"/../tachyon/bin/tachyon ]; then "$sbin/slaves.sh" cd "$SPARK_HOME" \; "$sbin"/../tachyon/bin/tachyon killAll tachyon.worker.Worker fi -if [ "$SPARK_WORKER_INSTANCES" = "" ]; then - "$sbin"/spark-daemons.sh stop org.apache.spark.deploy.worker.Worker 1 -else - for ((i=0; i<$SPARK_WORKER_INSTANCES; i++)); do - "$sbin"/spark-daemons.sh stop org.apache.spark.deploy.worker.Worker $(( $i + 1 )) - done -fi +"$sbin/slaves.sh" cd "$SPARK_HOME" \; "$sbin"/stop-slave.sh diff --git a/scalastyle-config.xml b/scalastyle-config.xml index 0ff521706c71a..7168d5b2a8e26 100644 --- a/scalastyle-config.xml +++ b/scalastyle-config.xml @@ -137,9 +137,9 @@ - + - + diff --git a/sql/README.md b/sql/README.md index a79249965ee67..237620e3fa808 100644 --- a/sql/README.md +++ b/sql/README.md @@ -36,12 +36,11 @@ import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.logical._ import org.apache.spark.sql.catalyst.rules._ import org.apache.spark.sql.catalyst.util._ -import org.apache.spark.sql.Dsl._ import org.apache.spark.sql.execution +import org.apache.spark.sql.functions._ import org.apache.spark.sql.hive._ import org.apache.spark.sql.hive.test.TestHive._ import org.apache.spark.sql.types._ -import org.apache.spark.sql.parquet.ParquetTestData Type in expressions to have them evaluated. Type :help for more information. @@ -57,6 +56,6 @@ res2: Array[org.apache.spark.sql.Row] = Array([238,val_238], [86,val_86], [311,v You can also build further queries on top of these `DataFrames` using the query DSL. ``` -scala> query.where('key > 30).select(avg('key)).collect() +scala> query.where(query("key") > 30).select(avg(query("key"))).collect() res3: Array[org.apache.spark.sql.Row] = Array([274.79025423728814]) ``` diff --git a/sql/catalyst/pom.xml b/sql/catalyst/pom.xml index a1947fb022e54..3dea2ee76542f 100644 --- a/sql/catalyst/pom.xml +++ b/sql/catalyst/pom.xml @@ -21,8 +21,8 @@ 4.0.0 org.apache.spark - spark-parent - 1.3.0-SNAPSHOT + spark-parent_2.10 + 1.4.0-SNAPSHOT ../../pom.xml diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/AnalysisException.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/AnalysisException.scala new file mode 100644 index 0000000000000..f9992185a4563 --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/AnalysisException.scala @@ -0,0 +1,44 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql + +import org.apache.spark.annotation.DeveloperApi + +/** + * :: DeveloperApi :: + * Thrown when a query fails to analyze, usually because the query itself is invalid. + */ +@DeveloperApi +class AnalysisException protected[sql] ( + val message: String, + val line: Option[Int] = None, + val startPosition: Option[Int] = None) + extends Exception with Serializable { + + def withPosition(line: Option[Int], startPosition: Option[Int]): AnalysisException = { + val newException = new AnalysisException(message, line, startPosition) + newException.setStackTrace(getStackTrace) + newException + } + + override def getMessage: String = { + val lineAnnotation = line.map(l => s" line $l").getOrElse("") + val positionAnnotation = startPosition.map(p => s" pos $p").getOrElse("") + s"$message;$lineAnnotation$positionAnnotation" + } +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala index 3a70d25534968..ac8a782976465 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala @@ -20,7 +20,7 @@ package org.apache.spark.sql import scala.util.hashing.MurmurHash3 import org.apache.spark.sql.catalyst.expressions.GenericRow -import org.apache.spark.sql.types.DateUtils +import org.apache.spark.sql.types.StructType object Row { /** @@ -122,6 +122,11 @@ trait Row extends Serializable { /** Number of elements in the Row. */ def length: Int + /** + * Schema for the row. + */ + def schema: StructType = null + /** * Returns the value at position i. If the value is null, null is returned. The following * is a mapping between Spark SQL types and return types: @@ -252,6 +257,7 @@ trait Row extends Serializable { * * @throws ClassCastException when data type does not match. */ + // TODO(davies): This is not the right default implementation, we use Int as Date internally def getDate(i: Int): java.sql.Date = apply(i).asInstanceOf[java.sql.Date] /** diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala index 366be00473d1c..3823584287741 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala @@ -26,7 +26,7 @@ import scala.util.parsing.input.CharArrayReader.EofCh import org.apache.spark.sql.catalyst.plans.logical._ private[sql] object KeywordNormalizer { - def apply(str: String) = str.toLowerCase() + def apply(str: String): String = str.toLowerCase() } private[sql] abstract class AbstractSparkSQLParser @@ -42,7 +42,7 @@ private[sql] abstract class AbstractSparkSQLParser } protected case class Keyword(str: String) { - def normalize = KeywordNormalizer(str) + def normalize: String = KeywordNormalizer(str) def parser: Parser[String] = normalize } @@ -81,7 +81,7 @@ private[sql] abstract class AbstractSparkSQLParser class SqlLexical extends StdLexical { case class FloatLit(chars: String) extends Token { - override def toString = chars + override def toString: String = chars } /* This is a work around to support the lazy setting */ @@ -120,7 +120,7 @@ class SqlLexical extends StdLexical { | failure("illegal character") ) - override def identChar = letter | elem('_') + override def identChar: Parser[Elem] = letter | elem('_') override def whitespace: Parser[Any] = ( whitespaceChar diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala new file mode 100644 index 0000000000000..d4f9fdacda4fb --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala @@ -0,0 +1,332 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst + +import java.util.{Map => JavaMap} + +import scala.collection.mutable.HashMap + +import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.types._ + +/** + * Functions to convert Scala types to Catalyst types and vice versa. + */ +object CatalystTypeConverters { + // The Predef.Map is scala.collection.immutable.Map. + // Since the map values can be mutable, we explicitly import scala.collection.Map at here. + import scala.collection.Map + + /** + * Converts Scala objects to catalyst rows / types. This method is slow, and for batch + * conversion you should be using converter produced by createToCatalystConverter. + * Note: This is always called after schemaFor has been called. + * This ordering is important for UDT registration. + */ + def convertToCatalyst(a: Any, dataType: DataType): Any = (a, dataType) match { + // Check UDT first since UDTs can override other types + case (obj, udt: UserDefinedType[_]) => + udt.serialize(obj) + + case (o: Option[_], _) => + o.map(convertToCatalyst(_, dataType)).orNull + + case (s: Seq[_], arrayType: ArrayType) => + s.map(convertToCatalyst(_, arrayType.elementType)) + + case (s: Array[_], arrayType: ArrayType) => + s.toSeq.map(convertToCatalyst(_, arrayType.elementType)) + + case (m: Map[_, _], mapType: MapType) => + m.map { case (k, v) => + convertToCatalyst(k, mapType.keyType) -> convertToCatalyst(v, mapType.valueType) + } + + case (jmap: JavaMap[_, _], mapType: MapType) => + val iter = jmap.entrySet.iterator + var listOfEntries: List[(Any, Any)] = List() + while (iter.hasNext) { + val entry = iter.next() + listOfEntries :+= (convertToCatalyst(entry.getKey, mapType.keyType), + convertToCatalyst(entry.getValue, mapType.valueType)) + } + listOfEntries.toMap + + case (p: Product, structType: StructType) => + val ar = new Array[Any](structType.size) + val iter = p.productIterator + var idx = 0 + while (idx < structType.size) { + ar(idx) = convertToCatalyst(iter.next(), structType.fields(idx).dataType) + idx += 1 + } + new GenericRowWithSchema(ar, structType) + + case (d: String, _) => + UTF8String(d) + + case (d: BigDecimal, _) => + Decimal(d) + + case (d: java.math.BigDecimal, _) => + Decimal(d) + + case (d: java.sql.Date, _) => + DateUtils.fromJavaDate(d) + + case (r: Row, structType: StructType) => + val converters = structType.fields.map { + f => (item: Any) => convertToCatalyst(item, f.dataType) + } + convertRowWithConverters(r, structType, converters) + + case (other, _) => + other + } + + /** + * Creates a converter function that will convert Scala objects to the specified catalyst type. + * Typical use case would be converting a collection of rows that have the same schema. You will + * call this function once to get a converter, and apply it to every row. + */ + private[sql] def createToCatalystConverter(dataType: DataType): Any => Any = { + def extractOption(item: Any): Any = item match { + case opt: Option[_] => opt.orNull + case other => other + } + + dataType match { + // Check UDT first since UDTs can override other types + case udt: UserDefinedType[_] => + (item) => extractOption(item) match { + case null => null + case other => udt.serialize(other) + } + + case arrayType: ArrayType => + val elementConverter = createToCatalystConverter(arrayType.elementType) + (item: Any) => { + extractOption(item) match { + case a: Array[_] => a.toSeq.map(elementConverter) + case s: Seq[_] => s.map(elementConverter) + case null => null + } + } + + case mapType: MapType => + val keyConverter = createToCatalystConverter(mapType.keyType) + val valueConverter = createToCatalystConverter(mapType.valueType) + (item: Any) => { + extractOption(item) match { + case m: Map[_, _] => + m.map { case (k, v) => + keyConverter(k) -> valueConverter(v) + } + + case jmap: JavaMap[_, _] => + val iter = jmap.entrySet.iterator + val convertedMap: HashMap[Any, Any] = HashMap() + while (iter.hasNext) { + val entry = iter.next() + convertedMap(keyConverter(entry.getKey)) = valueConverter(entry.getValue) + } + convertedMap + + case null => null + } + } + + case structType: StructType => + val converters = structType.fields.map(f => createToCatalystConverter(f.dataType)) + (item: Any) => { + extractOption(item) match { + case r: Row => + convertRowWithConverters(r, structType, converters) + + case p: Product => + val ar = new Array[Any](structType.size) + val iter = p.productIterator + var idx = 0 + while (idx < structType.size) { + ar(idx) = converters(idx)(iter.next()) + idx += 1 + } + new GenericRowWithSchema(ar, structType) + + case null => + null + } + } + + case dateType: DateType => (item: Any) => extractOption(item) match { + case d: java.sql.Date => DateUtils.fromJavaDate(d) + case other => other + } + + case dataType: StringType => (item: Any) => extractOption(item) match { + case s: String => UTF8String(s) + case other => other + } + + case _ => + (item: Any) => extractOption(item) match { + case d: BigDecimal => Decimal(d) + case d: java.math.BigDecimal => Decimal(d) + case other => other + } + } + } + + /** + * Converts Scala objects to catalyst rows / types. + * + * Note: This should be called before do evaluation on Row + * (It does not support UDT) + * This is used to create an RDD or test results with correct types for Catalyst. + */ + def convertToCatalyst(a: Any): Any = a match { + case s: String => UTF8String(s) + case d: java.sql.Date => DateUtils.fromJavaDate(d) + case d: BigDecimal => Decimal(d) + case d: java.math.BigDecimal => Decimal(d) + case seq: Seq[Any] => seq.map(convertToCatalyst) + case r: Row => Row(r.toSeq.map(convertToCatalyst): _*) + case arr: Array[Any] => arr.toSeq.map(convertToCatalyst).toArray + case m: Map[Any, Any] => + m.map { case (k, v) => (convertToCatalyst(k), convertToCatalyst(v)) }.toMap + case other => other + } + + /** + * Converts Catalyst types used internally in rows to standard Scala types + * This method is slow, and for batch conversion you should be using converter + * produced by createToScalaConverter. + */ + def convertToScala(a: Any, dataType: DataType): Any = (a, dataType) match { + // Check UDT first since UDTs can override other types + case (d, udt: UserDefinedType[_]) => + udt.deserialize(d) + + case (s: Seq[_], arrayType: ArrayType) => + s.map(convertToScala(_, arrayType.elementType)) + + case (m: Map[_, _], mapType: MapType) => + m.map { case (k, v) => + convertToScala(k, mapType.keyType) -> convertToScala(v, mapType.valueType) + } + + case (r: Row, s: StructType) => + convertRowToScala(r, s) + + case (d: Decimal, _: DecimalType) => + d.toJavaBigDecimal + + case (i: Int, DateType) => + DateUtils.toJavaDate(i) + + case (s: UTF8String, StringType) => + s.toString() + + case (other, _) => + other + } + + /** + * Creates a converter function that will convert Catalyst types to Scala type. + * Typical use case would be converting a collection of rows that have the same schema. You will + * call this function once to get a converter, and apply it to every row. + */ + private[sql] def createToScalaConverter(dataType: DataType): Any => Any = dataType match { + // Check UDT first since UDTs can override other types + case udt: UserDefinedType[_] => + (item: Any) => if (item == null) null else udt.deserialize(item) + + case arrayType: ArrayType => + val elementConverter = createToScalaConverter(arrayType.elementType) + (item: Any) => if (item == null) null else item.asInstanceOf[Seq[_]].map(elementConverter) + + case mapType: MapType => + val keyConverter = createToScalaConverter(mapType.keyType) + val valueConverter = createToScalaConverter(mapType.valueType) + (item: Any) => if (item == null) { + null + } else { + item.asInstanceOf[Map[_, _]].map { case (k, v) => + keyConverter(k) -> valueConverter(v) + } + } + + case s: StructType => + val converters = s.fields.map(f => createToScalaConverter(f.dataType)) + (item: Any) => { + if (item == null) { + null + } else { + convertRowWithConverters(item.asInstanceOf[Row], s, converters) + } + } + + case _: DecimalType => + (item: Any) => item match { + case d: Decimal => d.toJavaBigDecimal + case other => other + } + + case DateType => + (item: Any) => item match { + case i: Int => DateUtils.toJavaDate(i) + case other => other + } + + case StringType => + (item: Any) => item match { + case s: UTF8String => s.toString() + case other => other + } + + case other => + (item: Any) => item + } + + def convertRowToScala(r: Row, schema: StructType): Row = { + val ar = new Array[Any](r.size) + var idx = 0 + while (idx < r.size) { + ar(idx) = convertToScala(r(idx), schema.fields(idx).dataType) + idx += 1 + } + new GenericRowWithSchema(ar, schema) + } + + /** + * Converts a row by applying the provided set of converter functions. It is used for both + * toScala and toCatalyst conversions. + */ + private[sql] def convertRowWithConverters( + row: Row, + schema: StructType, + converters: Array[Any => Any]): Row = { + val ar = new Array[Any](row.size) + var idx = 0 + while (idx < row.size) { + ar(idx) = converters(idx)(row(idx)) + idx += 1 + } + new GenericRowWithSchema(ar, schema) + } +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala index 5d9c331ca5178..d9521953cad73 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala @@ -20,7 +20,7 @@ package org.apache.spark.sql.catalyst import java.sql.Timestamp import org.apache.spark.util.Utils -import org.apache.spark.sql.catalyst.expressions.{GenericRow, Attribute, AttributeReference, Row} +import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.logical.LocalRelation import org.apache.spark.sql.types._ @@ -46,56 +46,6 @@ trait ScalaReflection { case class Schema(dataType: DataType, nullable: Boolean) - /** - * Converts Scala objects to catalyst rows / types. - * Note: This is always called after schemaFor has been called. - * This ordering is important for UDT registration. - */ - def convertToCatalyst(a: Any, dataType: DataType): Any = (a, dataType) match { - // Check UDT first since UDTs can override other types - case (obj, udt: UserDefinedType[_]) => udt.serialize(obj) - case (o: Option[_], _) => o.map(convertToCatalyst(_, dataType)).orNull - case (s: Seq[_], arrayType: ArrayType) => s.map(convertToCatalyst(_, arrayType.elementType)) - case (s: Array[_], arrayType: ArrayType) => if (arrayType.elementType.isPrimitive) { - s.toSeq - } else { - s.toSeq.map(convertToCatalyst(_, arrayType.elementType)) - } - case (m: Map[_, _], mapType: MapType) => m.map { case (k, v) => - convertToCatalyst(k, mapType.keyType) -> convertToCatalyst(v, mapType.valueType) - } - case (p: Product, structType: StructType) => - new GenericRow( - p.productIterator.toSeq.zip(structType.fields).map { case (elem, field) => - convertToCatalyst(elem, field.dataType) - }.toArray) - case (d: BigDecimal, _) => Decimal(d) - case (d: java.math.BigDecimal, _) => Decimal(d) - case (d: java.sql.Date, _) => DateUtils.fromJavaDate(d) - case (other, _) => other - } - - /** Converts Catalyst types used internally in rows to standard Scala types */ - def convertToScala(a: Any, dataType: DataType): Any = (a, dataType) match { - // Check UDT first since UDTs can override other types - case (d, udt: UserDefinedType[_]) => udt.deserialize(d) - case (s: Seq[_], arrayType: ArrayType) => s.map(convertToScala(_, arrayType.elementType)) - case (m: Map[_, _], mapType: MapType) => m.map { case (k, v) => - convertToScala(k, mapType.keyType) -> convertToScala(v, mapType.valueType) - } - case (r: Row, s: StructType) => convertRowToScala(r, s) - case (d: Decimal, _: DecimalType) => d.toJavaBigDecimal - case (i: Int, DateType) => DateUtils.toJavaDate(i) - case (other, _) => other - } - - def convertRowToScala(r: Row, schema: StructType): Row = { - // TODO: This is very slow!!! - new GenericRow( - r.toSeq.zip(schema.fields.map(_.dataType)) - .map(r_dt => convertToScala(r_dt._1, r_dt._2)).toArray) - } - /** Returns a Sequence of attributes for the given case class type. */ def attributesFor[T: TypeTag]: Seq[Attribute] = schemaFor[T] match { case Schema(s: StructType, _) => @@ -122,6 +72,21 @@ trait ScalaReflection { case t if t <:< typeOf[Option[_]] => val TypeRef(_, _, Seq(optType)) = t Schema(schemaFor(optType).dataType, nullable = true) + // Need to decide if we actually need a special type here. + case t if t <:< typeOf[Array[Byte]] => Schema(BinaryType, nullable = true) + case t if t <:< typeOf[Array[_]] => + val TypeRef(_, _, Seq(elementType)) = t + val Schema(dataType, nullable) = schemaFor(elementType) + Schema(ArrayType(dataType, containsNull = nullable), nullable = true) + case t if t <:< typeOf[Seq[_]] => + val TypeRef(_, _, Seq(elementType)) = t + val Schema(dataType, nullable) = schemaFor(elementType) + Schema(ArrayType(dataType, containsNull = nullable), nullable = true) + case t if t <:< typeOf[Map[_, _]] => + val TypeRef(_, _, Seq(keyType, valueType)) = t + val Schema(valueDataType, valueNullable) = schemaFor(valueType) + Schema(MapType(schemaFor(keyType).dataType, + valueDataType, valueContainsNull = valueNullable), nullable = true) case t if t <:< typeOf[Product] => val formalTypeArgs = t.typeSymbol.asClass.typeParams val TypeRef(_, _, actualTypeArgs) = t @@ -144,21 +109,6 @@ trait ScalaReflection { schemaFor(p.typeSignature.substituteTypes(formalTypeArgs, actualTypeArgs)) StructField(p.name.toString, dataType, nullable) }), nullable = true) - // Need to decide if we actually need a special type here. - case t if t <:< typeOf[Array[Byte]] => Schema(BinaryType, nullable = true) - case t if t <:< typeOf[Array[_]] => - val TypeRef(_, _, Seq(elementType)) = t - val Schema(dataType, nullable) = schemaFor(elementType) - Schema(ArrayType(dataType, containsNull = nullable), nullable = true) - case t if t <:< typeOf[Seq[_]] => - val TypeRef(_, _, Seq(elementType)) = t - val Schema(dataType, nullable) = schemaFor(elementType) - Schema(ArrayType(dataType, containsNull = nullable), nullable = true) - case t if t <:< typeOf[Map[_, _]] => - val TypeRef(_, _, Seq(keyType, valueType)) = t - val Schema(valueDataType, valueNullable) = schemaFor(valueType) - Schema(MapType(schemaFor(keyType).dataType, - valueDataType, valueContainsNull = valueNullable), nullable = true) case t if t <:< typeOf[String] => Schema(StringType, nullable = true) case t if t <:< typeOf[Timestamp] => Schema(TimestampType, nullable = true) case t if t <:< typeOf[java.sql.Date] => Schema(DateType, nullable = true) @@ -179,6 +129,8 @@ trait ScalaReflection { case t if t <:< definitions.ShortTpe => Schema(ShortType, nullable = false) case t if t <:< definitions.ByteTpe => Schema(ByteType, nullable = false) case t if t <:< definitions.BooleanTpe => Schema(BooleanType, nullable = false) + case other => + throw new UnsupportedOperationException(s"Schema for type $other is not supported") } } @@ -186,6 +138,7 @@ trait ScalaReflection { // The data type can be determined without ambiguity. case obj: BooleanType.JvmType => BooleanType case obj: BinaryType.JvmType => BinaryType + case obj: String => StringType case obj: StringType.JvmType => StringType case obj: ByteType.JvmType => ByteType case obj: ShortType.JvmType => ShortType diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala old mode 100755 new mode 100644 index 124f083669358..0af969cc5cc67 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala @@ -35,12 +35,12 @@ import org.apache.spark.sql.types._ * This is currently included mostly for illustrative purposes. Users wanting more complete support * for a SQL like language should checkout the HiveQL support in the sql/hive sub-project. */ -class SqlParser extends AbstractSparkSQLParser { +class SqlParser extends AbstractSparkSQLParser with DataTypeParser { def parseExpression(input: String): Expression = { // Initialize the Keywords. lexical.initialize(reservedWords) - phrase(expression)(new lexical.Scanner(input)) match { + phrase(projection)(new lexical.Scanner(input)) match { case Success(plan, _) => plan case failureOrError => sys.error(failureOrError.toString) } @@ -61,11 +61,8 @@ class SqlParser extends AbstractSparkSQLParser { protected val CAST = Keyword("CAST") protected val COALESCE = Keyword("COALESCE") protected val COUNT = Keyword("COUNT") - protected val DATE = Keyword("DATE") - protected val DECIMAL = Keyword("DECIMAL") protected val DESC = Keyword("DESC") protected val DISTINCT = Keyword("DISTINCT") - protected val DOUBLE = Keyword("DOUBLE") protected val ELSE = Keyword("ELSE") protected val END = Keyword("END") protected val EXCEPT = Keyword("EXCEPT") @@ -104,18 +101,17 @@ class SqlParser extends AbstractSparkSQLParser { protected val SELECT = Keyword("SELECT") protected val SEMI = Keyword("SEMI") protected val SQRT = Keyword("SQRT") - protected val STRING = Keyword("STRING") protected val SUBSTR = Keyword("SUBSTR") protected val SUBSTRING = Keyword("SUBSTRING") protected val SUM = Keyword("SUM") protected val TABLE = Keyword("TABLE") protected val THEN = Keyword("THEN") - protected val TIMESTAMP = Keyword("TIMESTAMP") protected val TRUE = Keyword("TRUE") protected val UNION = Keyword("UNION") protected val UPPER = Keyword("UPPER") protected val WHEN = Keyword("WHEN") protected val WHERE = Keyword("WHERE") + protected val WITH = Keyword("WITH") protected def assignAliases(exprs: Seq[Expression]): Seq[NamedExpression] = { exprs.zipWithIndex.map { @@ -125,13 +121,14 @@ class SqlParser extends AbstractSparkSQLParser { } protected lazy val start: Parser[LogicalPlan] = - ( (select | ("(" ~> select <~ ")")) * - ( UNION ~ ALL ^^^ { (q1: LogicalPlan, q2: LogicalPlan) => Union(q1, q2) } - | INTERSECT ^^^ { (q1: LogicalPlan, q2: LogicalPlan) => Intersect(q1, q2) } - | EXCEPT ^^^ { (q1: LogicalPlan, q2: LogicalPlan) => Except(q1, q2)} - | UNION ~ DISTINCT.? ^^^ { (q1: LogicalPlan, q2: LogicalPlan) => Distinct(Union(q1, q2)) } - ) - | insert + start1 | insert | cte + + protected lazy val start1: Parser[LogicalPlan] = + (select | ("(" ~> select <~ ")")) * + ( UNION ~ ALL ^^^ { (q1: LogicalPlan, q2: LogicalPlan) => Union(q1, q2) } + | INTERSECT ^^^ { (q1: LogicalPlan, q2: LogicalPlan) => Intersect(q1, q2) } + | EXCEPT ^^^ { (q1: LogicalPlan, q2: LogicalPlan) => Except(q1, q2)} + | UNION ~ DISTINCT.? ^^^ { (q1: LogicalPlan, q2: LogicalPlan) => Distinct(Union(q1, q2)) } ) protected lazy val select: Parser[LogicalPlan] = @@ -144,7 +141,7 @@ class SqlParser extends AbstractSparkSQLParser { sortType.? ~ (LIMIT ~> expression).? ^^ { case d ~ p ~ r ~ f ~ g ~ h ~ o ~ l => - val base = r.getOrElse(NoRelation) + val base = r.getOrElse(OneRowRelation) val withFilter = f.map(Filter(_, base)).getOrElse(base) val withProjection = g .map(Aggregate(_, assignAliases(p), withFilter)) @@ -158,7 +155,12 @@ class SqlParser extends AbstractSparkSQLParser { protected lazy val insert: Parser[LogicalPlan] = INSERT ~> (OVERWRITE ^^^ true | INTO ^^^ false) ~ (TABLE ~> relation) ~ select ^^ { - case o ~ r ~ s => InsertIntoTable(r, Map.empty[String, Option[String]], s, o) + case o ~ r ~ s => InsertIntoTable(r, Map.empty[String, Option[String]], s, o, false) + } + + protected lazy val cte: Parser[LogicalPlan] = + WITH ~> rep1sep(ident ~ ( AS ~ "(" ~> start1 <~ ")"), ",") ~ (start1 | insert) ^^ { + case r ~ s => With(s, r.map({case n ~ s => (n, Subquery(n, s))}).toMap) } protected lazy val projection: Parser[Expression] = @@ -314,18 +316,20 @@ class SqlParser extends AbstractSparkSQLParser { ) protected lazy val cast: Parser[Expression] = - CAST ~ "(" ~> expression ~ (AS ~> dataType) <~ ")" ^^ { case exp ~ t => Cast(exp, t) } + CAST ~ "(" ~> expression ~ (AS ~> dataType) <~ ")" ^^ { + case exp ~ t => Cast(exp, t) + } protected lazy val literal: Parser[Literal] = ( numericLiteral | booleanLiteral - | stringLit ^^ {case s => Literal(s, StringType) } - | NULL ^^^ Literal(null, NullType) + | stringLit ^^ {case s => Literal.create(s, StringType) } + | NULL ^^^ Literal.create(null, NullType) ) protected lazy val booleanLiteral: Parser[Literal] = - ( TRUE ^^^ Literal(true, BooleanType) - | FALSE ^^^ Literal(false, BooleanType) + ( TRUE ^^^ Literal.create(true, BooleanType) + | FALSE ^^^ Literal.create(false, BooleanType) ) protected lazy val numericLiteral: Parser[Literal] = @@ -377,27 +381,13 @@ class SqlParser extends AbstractSparkSQLParser { | "(" ~> expression <~ ")" | function | dotExpressionHeader - | ident ^^ UnresolvedAttribute + | ident ^^ {case i => UnresolvedAttribute.quoted(i)} | signedPrimary | "~" ~> expression ^^ BitwiseNot ) protected lazy val dotExpressionHeader: Parser[Expression] = (ident <~ ".") ~ ident ~ rep("." ~> ident) ^^ { - case i1 ~ i2 ~ rest => UnresolvedAttribute(i1 + "." + i2 + rest.mkString(".", ".", "")) - } - - protected lazy val dataType: Parser[DataType] = - ( STRING ^^^ StringType - | TIMESTAMP ^^^ TimestampType - | DOUBLE ^^^ DoubleType - | fixedDecimalType - | DECIMAL ^^^ DecimalType.Unlimited - | DATE ^^^ DateType - ) - - protected lazy val fixedDecimalType: Parser[DataType] = - (DECIMAL ~ "(" ~> numericLit) ~ ("," ~> numericLit <~ ")") ^^ { - case precision ~ scale => DecimalType(precision.toInt, scale.toInt) + case i1 ~ i2 ~ rest => UnresolvedAttribute(Seq(i1, i2) ++ rest) } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala index 0b59ed1739566..cb49e5ad5586f 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala @@ -18,16 +18,16 @@ package org.apache.spark.sql.catalyst.analysis import org.apache.spark.util.collection.OpenHashSet +import org.apache.spark.sql.AnalysisException import org.apache.spark.sql.catalyst.errors.TreeNodeException import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.logical._ import org.apache.spark.sql.catalyst.rules._ -import org.apache.spark.sql.types.StructType -import org.apache.spark.sql.types.IntegerType +import org.apache.spark.sql.types._ /** * A trivial [[Analyzer]] with an [[EmptyCatalog]] and [[EmptyFunctionRegistry]]. Used for testing - * when all relations are already filled in and the analyser needs only to resolve attribute + * when all relations are already filled in and the analyzer needs only to resolve attribute * references. */ object SimpleAnalyzer extends Analyzer(EmptyCatalog, EmptyFunctionRegistry, true) @@ -37,11 +37,12 @@ object SimpleAnalyzer extends Analyzer(EmptyCatalog, EmptyFunctionRegistry, true * [[UnresolvedRelation]]s into fully typed objects using information in a schema [[Catalog]] and * a [[FunctionRegistry]]. */ -class Analyzer(catalog: Catalog, - registry: FunctionRegistry, - caseSensitive: Boolean, - maxIterations: Int = 100) - extends RuleExecutor[LogicalPlan] with HiveTypeCoercion { +class Analyzer( + catalog: Catalog, + registry: FunctionRegistry, + caseSensitive: Boolean, + maxIterations: Int = 100) + extends RuleExecutor[LogicalPlan] with HiveTypeCoercion with CheckAnalysis { val resolver = if (caseSensitive) caseSensitiveResolution else caseInsensitiveResolution @@ -50,52 +51,23 @@ class Analyzer(catalog: Catalog, /** * Override to provide additional rules for the "Resolution" batch. */ - val extendedRules: Seq[Rule[LogicalPlan]] = Nil + val extendedResolutionRules: Seq[Rule[LogicalPlan]] = Nil lazy val batches: Seq[Batch] = Seq( - Batch("MultiInstanceRelations", Once, - NewRelationInstances), Batch("Resolution", fixedPoint, - ResolveReferences :: ResolveRelations :: + ResolveReferences :: ResolveGroupingAnalytics :: ResolveSortReferences :: - NewRelationInstances :: ImplicitGenerate :: ResolveFunctions :: GlobalAggregates :: UnresolvedHavingClauseAttributes :: TrimGroupingAliases :: typeCoercionRules ++ - extendedRules : _*), - Batch("Check Analysis", Once, - CheckResolution :: - CheckAggregation :: - Nil: _*), - Batch("AnalysisOperators", fixedPoint, - EliminateAnalysisOperators) + extendedResolutionRules : _*) ) - /** - * Makes sure all attributes and logical plans have been resolved. - */ - object CheckResolution extends Rule[LogicalPlan] { - def apply(plan: LogicalPlan): LogicalPlan = { - plan.transform { - case p if p.expressions.exists(!_.resolved) => - throw new TreeNodeException(p, - s"Unresolved attributes: ${p.expressions.filterNot(_.resolved).mkString(",")}") - case p if !p.resolved && p.childrenResolved => - throw new TreeNodeException(p, "Unresolved plan found") - } match { - // As a backstop, use the root node to check that the entire plan tree is resolved. - case p if !p.resolved => - throw new TreeNodeException(p, "Unresolved plan in tree") - case p => p - } - } - } - /** * Removes no-op Alias expressions from the plan. */ @@ -168,10 +140,10 @@ class Analyzer(catalog: Catalog, case x: Expression if nonSelectedGroupExprSet.contains(x) => // if the input attribute in the Invalid Grouping Expression set of for this group // replace it with constant null - Literal(null, expr.dataType) + Literal.create(null, expr.dataType) case x if x == g.gid => // replace the groupingId with concrete value (the bit mask) - Literal(bitmask, IntegerType) + Literal.create(bitmask, IntegerType) }) result += GroupExpression(substitution) @@ -194,46 +166,39 @@ class Analyzer(catalog: Catalog, } /** - * Checks for non-aggregated attributes with aggregation + * Replaces [[UnresolvedRelation]]s with concrete relations from the catalog. */ - object CheckAggregation extends Rule[LogicalPlan] { - def apply(plan: LogicalPlan): LogicalPlan = { - plan.transform { - case aggregatePlan @ Aggregate(groupingExprs, aggregateExprs, child) => - def isValidAggregateExpression(expr: Expression): Boolean = expr match { - case _: AggregateExpression => true - case e: Attribute => groupingExprs.contains(e) - case e if groupingExprs.contains(e) => true - case e if e.references.isEmpty => true - case e => e.children.forall(isValidAggregateExpression) - } - - aggregateExprs.find { e => - !isValidAggregateExpression(e.transform { - // Should trim aliases around `GetField`s. These aliases are introduced while - // resolving struct field accesses, because `GetField` is not a `NamedExpression`. - // (Should we just turn `GetField` into a `NamedExpression`?) - case Alias(g: GetField, _) => g - }) - }.foreach { e => - throw new TreeNodeException(plan, s"Expression not in GROUP BY: $e") - } - - aggregatePlan + object ResolveRelations extends Rule[LogicalPlan] { + def getTable(u: UnresolvedRelation, cteRelations: Map[String, LogicalPlan]): LogicalPlan = { + try { + // In hive, if there is same table name in database and CTE definition, + // hive will use the table in database, not the CTE one. + // Taking into account the reasonableness and the implementation complexity, + // here use the CTE definition first, check table name only and ignore database name + cteRelations.get(u.tableIdentifier.last) + .map(relation => u.alias.map(Subquery(_, relation)).getOrElse(relation)) + .getOrElse(catalog.lookupRelation(u.tableIdentifier, u.alias)) + } catch { + case _: NoSuchTableException => + u.failAnalysis(s"no such table ${u.tableName}") } } - } - /** - * Replaces [[UnresolvedRelation]]s with concrete relations from the catalog. - */ - object ResolveRelations extends Rule[LogicalPlan] { - def apply(plan: LogicalPlan): LogicalPlan = plan transform { - case i @ InsertIntoTable(UnresolvedRelation(tableIdentifier, alias), _, _, _) => - i.copy( - table = EliminateAnalysisOperators(catalog.lookupRelation(tableIdentifier, alias))) - case UnresolvedRelation(tableIdentifier, alias) => - catalog.lookupRelation(tableIdentifier, alias) + def apply(plan: LogicalPlan): LogicalPlan = { + val (realPlan, cteRelations) = plan match { + // TODO allow subquery to define CTE + // Add cte table to a temp relation map,drop `with` plan and keep its child + case With(child, relations) => (child, relations) + case other => (other, Map.empty[String, LogicalPlan]) + } + + realPlan transform { + case i@InsertIntoTable(u: UnresolvedRelation, _, _, _, _) => + i.copy( + table = EliminateSubQueries(getTable(u, cteRelations))) + case u: UnresolvedRelation => + getTable(u, cteRelations) + } } } @@ -263,6 +228,12 @@ class Analyzer(catalog: Catalog, case o => o :: Nil } Alias(c.copy(children = expandedArgs), name)() :: Nil + case Alias(c @ CreateStruct(args), name) if containsStar(args) => + val expandedArgs = args.flatMap { + case s: Star => s.expand(child.output, resolver) + case o => o :: Nil + } + Alias(c.copy(children = expandedArgs), name)() :: Nil case o => o :: Nil }, child) @@ -283,53 +254,85 @@ class Analyzer(catalog: Catalog, } ) + // Special handling for cases when self-join introduce duplicate expression ids. + case j @ Join(left, right, _, _) if left.outputSet.intersect(right.outputSet).nonEmpty => + val conflictingAttributes = left.outputSet.intersect(right.outputSet) + logDebug(s"Conflicting attributes ${conflictingAttributes.mkString(",")} in $j") + + val (oldRelation, newRelation) = right.collect { + // Handle base relations that might appear more than once. + case oldVersion: MultiInstanceRelation + if oldVersion.outputSet.intersect(conflictingAttributes).nonEmpty => + val newVersion = oldVersion.newInstance() + (oldVersion, newVersion) + + // Handle projects that create conflicting aliases. + case oldVersion @ Project(projectList, _) + if findAliases(projectList).intersect(conflictingAttributes).nonEmpty => + (oldVersion, oldVersion.copy(projectList = newAliases(projectList))) + + case oldVersion @ Aggregate(_, aggregateExpressions, _) + if findAliases(aggregateExpressions).intersect(conflictingAttributes).nonEmpty => + (oldVersion, oldVersion.copy(aggregateExpressions = newAliases(aggregateExpressions))) + }.headOption.getOrElse { // Only handle first case, others will be fixed on the next pass. + sys.error( + s""" + |Failure when resolving conflicting references in Join: + |$plan + | + |Conflicting attributes: ${conflictingAttributes.mkString(",")} + """.stripMargin) + } + + val attributeRewrites = AttributeMap(oldRelation.output.zip(newRelation.output)) + val newRight = right transformUp { + case r if r == oldRelation => newRelation + } transformUp { + case other => other transformExpressions { + case a: Attribute => attributeRewrites.get(a).getOrElse(a) + } + } + j.copy(right = newRight) + case q: LogicalPlan => logTrace(s"Attempting to resolve ${q.simpleString}") q transformExpressionsUp { - case u @ UnresolvedAttribute(name) if resolver(name, VirtualColumn.groupingIdName) && + case u @ UnresolvedAttribute(nameParts) if nameParts.length == 1 && + resolver(nameParts(0), VirtualColumn.groupingIdName) && q.isInstanceOf[GroupingAnalytics] => // Resolve the virtual column GROUPING__ID for the operator GroupingAnalytics q.asInstanceOf[GroupingAnalytics].gid - case u @ UnresolvedAttribute(name) => + case u @ UnresolvedAttribute(nameParts) => // Leave unchanged if resolution fails. Hopefully will be resolved next round. - val result = q.resolveChildren(name, resolver).getOrElse(u) + val result = + withPosition(u) { q.resolveChildren(nameParts, resolver).getOrElse(u) } logDebug(s"Resolving $u to $result") result case UnresolvedGetField(child, fieldName) if child.resolved => - resolveGetField(child, fieldName) + GetField(child, fieldName, resolver) } } + def newAliases(expressions: Seq[NamedExpression]): Seq[NamedExpression] = { + expressions.map { + case a: Alias => Alias(a.child, a.name)() + case other => other + } + } + + def findAliases(projectList: Seq[NamedExpression]): AttributeSet = { + AttributeSet(projectList.collect { case a: Alias => a.toAttribute }) + } + /** * Returns true if `exprs` contains a [[Star]]. */ protected def containsStar(exprs: Seq[Expression]): Boolean = exprs.exists(_.collect { case _: Star => true }.nonEmpty) - - /** - * Returns the resolved `GetField`, and report error if no desired field or over one - * desired fields are found. - */ - protected def resolveGetField(expr: Expression, fieldName: String): Expression = { - expr.dataType match { - case StructType(fields) => - val actualField = fields.filter(f => resolver(f.name, fieldName)) - if (actualField.length == 0) { - sys.error( - s"No such struct field $fieldName in ${fields.map(_.name).mkString(", ")}") - } else if (actualField.length == 1) { - val field = actualField(0) - GetField(expr, field, fields.indexOf(field)) - } else { - sys.error(s"Ambiguous reference to fields ${actualField.mkString(", ")}") - } - case otherType => sys.error(s"GetField is not valid on fields of type $otherType") - } - } } /** - * In many dialects of SQL is it valid to sort by attributes that are not present in the SELECT + * In many dialects of SQL it is valid to sort by attributes that are not present in the SELECT * clause. This rule detects such queries and adds the required attributes to the original * projection, so that they will be available during sorting. Another projection is added to * remove these attributes after sorting. @@ -338,18 +341,16 @@ class Analyzer(catalog: Catalog, def apply(plan: LogicalPlan): LogicalPlan = plan transformUp { case s @ Sort(ordering, global, p @ Project(projectList, child)) if !s.resolved && p.resolved => - val unresolved = ordering.flatMap(_.collect { case UnresolvedAttribute(name) => name }) - val resolved = unresolved.flatMap(child.resolve(_, resolver)) - val requiredAttributes = AttributeSet(resolved.collect { case a: Attribute => a }) + val (resolvedOrdering, missing) = resolveAndFindMissing(ordering, p, child) - val missingInProject = requiredAttributes -- p.output - if (missingInProject.nonEmpty) { + // If this rule was not a no-op, return the transformed plan, otherwise return the original. + if (missing.nonEmpty) { // Add missing attributes and then project them away after the sort. - Project(projectList.map(_.toAttribute), - Sort(ordering, global, - Project(projectList ++ missingInProject, child))) + Project(p.output, + Sort(resolvedOrdering, global, + Project(projectList ++ missing, child))) } else { - logDebug(s"Failed to find $missingInProject in ${p.output.mkString(", ")}") + logDebug(s"Failed to find $missing in ${p.output.mkString(", ")}") s // Nothing we can do here. Return original plan. } case s @ Sort(ordering, global, a @ Aggregate(grouping, aggs, child)) @@ -361,18 +362,54 @@ class Analyzer(catalog: Catalog, grouping.collect { case ne: NamedExpression => ne.toAttribute } ) - logDebug(s"Grouping expressions: $groupingRelation") - val resolved = unresolved.flatMap(groupingRelation.resolve(_, resolver)) - val missingInAggs = resolved.filterNot(a.outputSet.contains) - logDebug(s"Resolved: $resolved Missing in aggs: $missingInAggs") - if (missingInAggs.nonEmpty) { + val (resolvedOrdering, missing) = resolveAndFindMissing(ordering, a, groupingRelation) + + if (missing.nonEmpty) { // Add missing grouping exprs and then project them away after the sort. Project(a.output, - Sort(ordering, global, Aggregate(grouping, aggs ++ missingInAggs, child))) + Sort(resolvedOrdering, global, + Aggregate(grouping, aggs ++ missing, child))) } else { s // Nothing we can do here. Return original plan. } } + + /** + * Given a child and a grandchild that are present beneath a sort operator, returns + * a resolved sort ordering and a list of attributes that are missing from the child + * but are present in the grandchild. + */ + def resolveAndFindMissing( + ordering: Seq[SortOrder], + child: LogicalPlan, + grandchild: LogicalPlan): (Seq[SortOrder], Seq[Attribute]) = { + // Find any attributes that remain unresolved in the sort. + val unresolved: Seq[Seq[String]] = + ordering.flatMap(_.collect { case UnresolvedAttribute(nameParts) => nameParts }) + + // Create a map from name, to resolved attributes, when the desired name can be found + // prior to the projection. + val resolved: Map[Seq[String], NamedExpression] = + unresolved.flatMap(u => grandchild.resolve(u, resolver).map(a => u -> a)).toMap + + // Construct a set that contains all of the attributes that we need to evaluate the + // ordering. + val requiredAttributes = AttributeSet(resolved.values) + + // Figure out which ones are missing from the projection, so that we can add them and + // remove them after the sort. + val missingInProject = requiredAttributes -- child.output + + // Now that we have all the attributes we need, reconstruct a resolved ordering. + // It is important to do it here, instead of waiting for the standard resolved as adding + // attributes to the project below can actually introduce ambiquity that was not present + // before. + val resolvedOrdering = ordering.map(_ transform { + case u @ UnresolvedAttribute(name) => resolved.getOrElse(name, u) + }).asInstanceOf[Seq[SortOrder]] + + (resolvedOrdering, missingInProject.toSeq) + } } /** @@ -448,7 +485,7 @@ class Analyzer(catalog: Catalog, * only required to provide scoping information for attributes and can be removed once analysis is * complete. */ -object EliminateAnalysisOperators extends Rule[LogicalPlan] { +object EliminateSubQueries extends Rule[LogicalPlan] { def apply(plan: LogicalPlan): LogicalPlan = plan transform { case Subquery(_, child) => child } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala index df8d03b86c533..b2f8157a1a61f 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala @@ -21,6 +21,12 @@ import scala.collection.mutable import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Subquery} +/** + * Thrown by a catalog when a table cannot be found. The analyzer will rethrow the exception + * as an AnalysisException with the correct position information. + */ +class NoSuchTableException extends Exception + /** * An interface for looking up relations by name. Used by an [[Analyzer]]. */ @@ -34,6 +40,14 @@ trait Catalog { tableIdentifier: Seq[String], alias: Option[String] = None): LogicalPlan + /** + * Returns tuples of (tableName, isTemporary) for all tables in the given database. + * isTemporary is a Boolean value indicates if a table is a temporary or not. + */ + def getTables(databaseName: Option[String]): Seq[(String, Boolean)] + + def refreshTable(databaseName: String, tableName: String): Unit + def registerTable(tableIdentifier: Seq[String], plan: LogicalPlan): Unit def unregisterTable(tableIdentifier: Seq[String]): Unit @@ -72,12 +86,12 @@ class SimpleCatalog(val caseSensitive: Boolean) extends Catalog { tables += ((getDbTableName(tableIdent), plan)) } - override def unregisterTable(tableIdentifier: Seq[String]) = { + override def unregisterTable(tableIdentifier: Seq[String]): Unit = { val tableIdent = processTableIdentifier(tableIdentifier) tables -= getDbTableName(tableIdent) } - override def unregisterAllTables() = { + override def unregisterAllTables(): Unit = { tables.clear() } @@ -101,6 +115,16 @@ class SimpleCatalog(val caseSensitive: Boolean) extends Catalog { // properly qualified with this alias. alias.map(a => Subquery(a, tableWithQualifiers)).getOrElse(tableWithQualifiers) } + + override def getTables(databaseName: Option[String]): Seq[(String, Boolean)] = { + tables.map { + case (name, _) => (name, true) + }.toSeq + } + + override def refreshTable(databaseName: String, tableName: String): Unit = { + throw new UnsupportedOperationException + } } /** @@ -123,8 +147,8 @@ trait OverrideCatalog extends Catalog { } abstract override def lookupRelation( - tableIdentifier: Seq[String], - alias: Option[String] = None): LogicalPlan = { + tableIdentifier: Seq[String], + alias: Option[String] = None): LogicalPlan = { val tableIdent = processTableIdentifier(tableIdentifier) val overriddenTable = overrides.get(getDBTable(tableIdent)) val tableWithQualifers = overriddenTable.map(r => Subquery(tableIdent.last, r)) @@ -137,6 +161,27 @@ trait OverrideCatalog extends Catalog { withAlias.getOrElse(super.lookupRelation(tableIdentifier, alias)) } + abstract override def getTables(databaseName: Option[String]): Seq[(String, Boolean)] = { + val dbName = if (!caseSensitive) { + if (databaseName.isDefined) Some(databaseName.get.toLowerCase) else None + } else { + databaseName + } + + val temporaryTables = overrides.filter { + // If a temporary table does not have an associated database, we should return its name. + case ((None, _), _) => true + // If a temporary table does have an associated database, we should return it if the database + // matches the given database name. + case ((db: Some[String], _), _) if db == dbName => true + case _ => false + }.map { + case ((_, tableName), _) => (tableName, true) + }.toSeq + + temporaryTables ++ super.getTables(databaseName) + } + override def registerTable( tableIdentifier: Seq[String], plan: LogicalPlan): Unit = { @@ -156,29 +201,37 @@ trait OverrideCatalog extends Catalog { /** * A trivial catalog that returns an error when a relation is requested. Used for testing when all - * relations are already filled in and the analyser needs only to resolve attribute references. + * relations are already filled in and the analyzer needs only to resolve attribute references. */ object EmptyCatalog extends Catalog { - val caseSensitive: Boolean = true + override val caseSensitive: Boolean = true - def tableExists(tableIdentifier: Seq[String]): Boolean = { + override def tableExists(tableIdentifier: Seq[String]): Boolean = { throw new UnsupportedOperationException } - def lookupRelation( - tableIdentifier: Seq[String], - alias: Option[String] = None) = { + override def lookupRelation( + tableIdentifier: Seq[String], + alias: Option[String] = None): LogicalPlan = { + throw new UnsupportedOperationException + } + + override def getTables(databaseName: Option[String]): Seq[(String, Boolean)] = { throw new UnsupportedOperationException } - def registerTable(tableIdentifier: Seq[String], plan: LogicalPlan): Unit = { + override def registerTable(tableIdentifier: Seq[String], plan: LogicalPlan): Unit = { throw new UnsupportedOperationException } - def unregisterTable(tableIdentifier: Seq[String]): Unit = { + override def unregisterTable(tableIdentifier: Seq[String]): Unit = { throw new UnsupportedOperationException } override def unregisterAllTables(): Unit = {} + + override def refreshTable(databaseName: String, tableName: String): Unit = { + throw new UnsupportedOperationException + } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala new file mode 100644 index 0000000000000..1155dac28fc78 --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala @@ -0,0 +1,119 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.analysis + +import org.apache.spark.sql.AnalysisException +import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.catalyst.plans.logical._ +import org.apache.spark.sql.types._ + +/** + * Throws user facing errors when passed invalid queries that fail to analyze. + */ +trait CheckAnalysis { + self: Analyzer => + + /** + * Override to provide additional checks for correct analysis. + * These rules will be evaluated after our built-in check rules. + */ + val extendedCheckRules: Seq[LogicalPlan => Unit] = Nil + + protected def failAnalysis(msg: String): Nothing = { + throw new AnalysisException(msg) + } + + def checkAnalysis(plan: LogicalPlan): Unit = { + // We transform up and order the rules so as to catch the first possible failure instead + // of the result of cascading resolution failures. + plan.foreachUp { + case operator: LogicalPlan => + operator transformExpressionsUp { + case a: Attribute if !a.resolved => + if (operator.childrenResolved) { + val nameParts = a match { + case UnresolvedAttribute(nameParts) => nameParts + case _ => Seq(a.name) + } + // Throw errors for specific problems with get field. + operator.resolveChildren(nameParts, resolver, throwErrors = true) + } + + val from = operator.inputSet.map(_.name).mkString(", ") + a.failAnalysis(s"cannot resolve '${a.prettyString}' given input columns $from") + + case c: Cast if !c.resolved => + failAnalysis( + s"invalid cast from ${c.child.dataType.simpleString} to ${c.dataType.simpleString}") + + case b: BinaryExpression if !b.resolved => + failAnalysis( + s"invalid expression ${b.prettyString} " + + s"between ${b.left.simpleString} and ${b.right.simpleString}") + } + + operator match { + case f: Filter if f.condition.dataType != BooleanType => + failAnalysis( + s"filter expression '${f.condition.prettyString}' " + + s"of type ${f.condition.dataType.simpleString} is not a boolean.") + + case Aggregate(groupingExprs, aggregateExprs, child) => + def checkValidAggregateExpression(expr: Expression): Unit = expr match { + case _: AggregateExpression => // OK + case e: Attribute if !groupingExprs.contains(e) => + failAnalysis( + s"expression '${e.prettyString}' is neither present in the group by, " + + s"nor is it an aggregate function. " + + "Add to group by or wrap in first() if you don't care which value you get.") + case e if groupingExprs.contains(e) => // OK + case e if e.references.isEmpty => // OK + case e => e.children.foreach(checkValidAggregateExpression) + } + + val cleaned = aggregateExprs.map(_.transform { + // Should trim aliases around `GetField`s. These aliases are introduced while + // resolving struct field accesses, because `GetField` is not a `NamedExpression`. + // (Should we just turn `GetField` into a `NamedExpression`?) + case Alias(g, _) => g + }) + + cleaned.foreach(checkValidAggregateExpression) + + case _ => // Fallbacks to the following checks + } + + operator match { + case o if o.children.nonEmpty && o.missingInput.nonEmpty => + val missingAttributes = o.missingInput.mkString(",") + val input = o.inputSet.mkString(",") + + failAnalysis( + s"resolved attribute(s) $missingAttributes missing from $input " + + s"in operator ${operator.simpleString}") + + case o if !o.resolved => + failAnalysis( + s"unresolved operator ${operator.simpleString}") + + case _ => // Analysis successful! + } + } + extendedCheckRules.foreach(_(plan)) + } +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala index 9f334f6d42ad1..16ca5bcd57a72 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala @@ -35,7 +35,7 @@ trait OverrideFunctionRegistry extends FunctionRegistry { val functionBuilders = StringKeyHashMap[FunctionBuilder](caseSensitive) - def registerFunction(name: String, builder: FunctionBuilder) = { + override def registerFunction(name: String, builder: FunctionBuilder): Unit = { functionBuilders.put(name, builder) } @@ -47,7 +47,7 @@ trait OverrideFunctionRegistry extends FunctionRegistry { class SimpleFunctionRegistry(val caseSensitive: Boolean) extends FunctionRegistry { val functionBuilders = StringKeyHashMap[FunctionBuilder](caseSensitive) - def registerFunction(name: String, builder: FunctionBuilder) = { + override def registerFunction(name: String, builder: FunctionBuilder): Unit = { functionBuilders.put(name, builder) } @@ -57,17 +57,19 @@ class SimpleFunctionRegistry(val caseSensitive: Boolean) extends FunctionRegistr } /** - * A trivial catalog that returns an error when a function is requested. Used for testing when all - * functions are already filled in and the analyser needs only to resolve attribute references. + * A trivial catalog that returns an error when a function is requested. Used for testing when all + * functions are already filled in and the analyzer needs only to resolve attribute references. */ object EmptyFunctionRegistry extends FunctionRegistry { - def registerFunction(name: String, builder: FunctionBuilder) = ??? + override def registerFunction(name: String, builder: FunctionBuilder): Unit = { + throw new UnsupportedOperationException + } - def lookupFunction(name: String, children: Seq[Expression]): Expression = { + override def lookupFunction(name: String, children: Seq[Expression]): Expression = { throw new UnsupportedOperationException } - def caseSensitive: Boolean = ??? + override def caseSensitive: Boolean = throw new UnsupportedOperationException } /** @@ -76,7 +78,7 @@ object EmptyFunctionRegistry extends FunctionRegistry { * TODO move this into util folder? */ object StringKeyHashMap { - def apply[T](caseSensitive: Boolean) = caseSensitive match { + def apply[T](caseSensitive: Boolean): StringKeyHashMap[T] = caseSensitive match { case false => new StringKeyHashMap[T](_.toLowerCase) case true => new StringKeyHashMap[T](identity) } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala index 34ef7d28cc7f2..35c7f00d4e42a 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala @@ -78,6 +78,7 @@ trait HiveTypeCoercion { FunctionArgumentConversion :: CaseWhenCoercion :: Division :: + PropagateTypes :: Nil /** @@ -114,7 +115,7 @@ trait HiveTypeCoercion { * the appropriate numeric equivalent. */ object ConvertNaNs extends Rule[LogicalPlan] { - val stringNaN = Literal("NaN", StringType) + val stringNaN = Literal("NaN") def apply(plan: LogicalPlan): LogicalPlan = plan transform { case q: LogicalPlan => q transformExpressions { @@ -284,6 +285,7 @@ trait HiveTypeCoercion { * Calculates and propagates precision for fixed-precision decimals. Hive has a number of * rules for this based on the SQL standard and MS SQL: * https://cwiki.apache.org/confluence/download/attachments/27362075/Hive_Decimal_Precision_Scale_Support.pdf + * https://msdn.microsoft.com/en-us/library/ms190476.aspx * * In particular, if we have expressions e1 and e2 with precision/scale p1/s2 and p2/s2 * respectively, then the following operations have the following precision / scale: @@ -295,6 +297,7 @@ trait HiveTypeCoercion { * e1 * e2 p1 + p2 + 1 s1 + s2 * e1 / e2 p1 - s1 + s2 + max(6, s1 + p2 + 1) max(6, s1 + p2 + 1) * e1 % e2 min(p1-s1, p2-s2) + max(s1, s2) max(s1, s2) + * e1 union e2 max(s1, s2) + max(p1-s1, p2-s2) max(s1, s2) * sum(e1) p1 + 10 s1 * avg(e1) p1 + 4 s1 + 4 * @@ -310,7 +313,12 @@ trait HiveTypeCoercion { * - SHORT gets turned into DECIMAL(5, 0) * - INT gets turned into DECIMAL(10, 0) * - LONG gets turned into DECIMAL(20, 0) - * - FLOAT and DOUBLE cause fixed-length decimals to turn into DOUBLE (this is the same as Hive, + * - FLOAT and DOUBLE + * 1. Union operation: + * FLOAT gets turned into DECIMAL(7, 7), DOUBLE gets turned into DECIMAL(15, 15) (this is the + * same as Hive) + * 2. Other operation: + * FLOAT and DOUBLE cause fixed-length decimals to turn into DOUBLE (this is the same as Hive, * but note that unlimited decimals are considered bigger than doubles in WidenTypes) */ // scalastyle:on @@ -327,76 +335,127 @@ trait HiveTypeCoercion { def isFloat(t: DataType): Boolean = t == FloatType || t == DoubleType - def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions { - // Skip nodes whose children have not been resolved yet - case e if !e.childrenResolved => e + // Conversion rules for float and double into fixed-precision decimals + val floatTypeToFixed: Map[DataType, DecimalType] = Map( + FloatType -> DecimalType(7, 7), + DoubleType -> DecimalType(15, 15) + ) - case Add(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) => - Cast( - Add(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)), - DecimalType(max(s1, s2) + max(p1 - s1, p2 - s2) + 1, max(s1, s2)) - ) - - case Subtract(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) => - Cast( - Subtract(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)), - DecimalType(max(s1, s2) + max(p1 - s1, p2 - s2) + 1, max(s1, s2)) - ) - - case Multiply(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) => - Cast( - Multiply(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)), - DecimalType(p1 + p2 + 1, s1 + s2) - ) - - case Divide(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) => - Cast( - Divide(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)), - DecimalType(p1 - s1 + s2 + max(6, s1 + p2 + 1), max(6, s1 + p2 + 1)) - ) - - case Remainder(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) => - Cast( - Remainder(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)), - DecimalType(min(p1 - s1, p2 - s2) + max(s1, s2), max(s1, s2)) - ) - - case LessThan(e1 @ DecimalType.Expression(p1, s1), - e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 => - LessThan(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)) - - case LessThanOrEqual(e1 @ DecimalType.Expression(p1, s1), - e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 => - LessThanOrEqual(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)) - - case GreaterThan(e1 @ DecimalType.Expression(p1, s1), - e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 => - GreaterThan(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)) - - case GreaterThanOrEqual(e1 @ DecimalType.Expression(p1, s1), - e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 => - GreaterThanOrEqual(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)) - - // Promote integers inside a binary expression with fixed-precision decimals to decimals, - // and fixed-precision decimals in an expression with floats / doubles to doubles - case b: BinaryExpression if b.left.dataType != b.right.dataType => - (b.left.dataType, b.right.dataType) match { - case (t, DecimalType.Fixed(p, s)) if intTypeToFixed.contains(t) => - b.makeCopy(Array(Cast(b.left, intTypeToFixed(t)), b.right)) - case (DecimalType.Fixed(p, s), t) if intTypeToFixed.contains(t) => - b.makeCopy(Array(b.left, Cast(b.right, intTypeToFixed(t)))) - case (t, DecimalType.Fixed(p, s)) if isFloat(t) => - b.makeCopy(Array(b.left, Cast(b.right, DoubleType))) - case (DecimalType.Fixed(p, s), t) if isFloat(t) => - b.makeCopy(Array(Cast(b.left, DoubleType), b.right)) - case _ => - b + def apply(plan: LogicalPlan): LogicalPlan = plan transform { + // fix decimal precision for union + case u @ Union(left, right) if u.childrenResolved && !u.resolved => + val castedInput = left.output.zip(right.output).map { + case (l, r) if l.dataType != r.dataType => + (l.dataType, r.dataType) match { + case (DecimalType.Fixed(p1, s1), DecimalType.Fixed(p2, s2)) => + // Union decimals with precision/scale p1/s2 and p2/s2 will be promoted to + // DecimalType(max(s1, s2) + max(p1-s1, p2-s2), max(s1, s2)) + val fixedType = DecimalType(max(s1, s2) + max(p1 - s1, p2 - s2), max(s1, s2)) + (Alias(Cast(l, fixedType), l.name)(), Alias(Cast(r, fixedType), r.name)()) + case (t, DecimalType.Fixed(p, s)) if intTypeToFixed.contains(t) => + (Alias(Cast(l, intTypeToFixed(t)), l.name)(), r) + case (DecimalType.Fixed(p, s), t) if intTypeToFixed.contains(t) => + (l, Alias(Cast(r, intTypeToFixed(t)), r.name)()) + case (t, DecimalType.Fixed(p, s)) if floatTypeToFixed.contains(t) => + (Alias(Cast(l, floatTypeToFixed(t)), l.name)(), r) + case (DecimalType.Fixed(p, s), t) if floatTypeToFixed.contains(t) => + (l, Alias(Cast(r, floatTypeToFixed(t)), r.name)()) + case _ => (l, r) + } + case other => other } - // TODO: MaxOf, MinOf, etc might want other rules + val (castedLeft, castedRight) = castedInput.unzip - // SUM and AVERAGE are handled by the implementations of those expressions + val newLeft = + if (castedLeft.map(_.dataType) != left.output.map(_.dataType)) { + Project(castedLeft, left) + } else { + left + } + + val newRight = + if (castedRight.map(_.dataType) != right.output.map(_.dataType)) { + Project(castedRight, right) + } else { + right + } + + Union(newLeft, newRight) + + // fix decimal precision for expressions + case q => q.transformExpressions { + // Skip nodes whose children have not been resolved yet + case e if !e.childrenResolved => e + + case Add(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) => + Cast( + Add(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)), + DecimalType(max(s1, s2) + max(p1 - s1, p2 - s2) + 1, max(s1, s2)) + ) + + case Subtract(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) => + Cast( + Subtract(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)), + DecimalType(max(s1, s2) + max(p1 - s1, p2 - s2) + 1, max(s1, s2)) + ) + + case Multiply(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) => + Cast( + Multiply(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)), + DecimalType(p1 + p2 + 1, s1 + s2) + ) + + case Divide(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) => + Cast( + Divide(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)), + DecimalType(p1 - s1 + s2 + max(6, s1 + p2 + 1), max(6, s1 + p2 + 1)) + ) + + case Remainder(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) => + Cast( + Remainder(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)), + DecimalType(min(p1 - s1, p2 - s2) + max(s1, s2), max(s1, s2)) + ) + + case LessThan(e1 @ DecimalType.Expression(p1, s1), + e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 => + LessThan(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)) + + case LessThanOrEqual(e1 @ DecimalType.Expression(p1, s1), + e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 => + LessThanOrEqual(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)) + + case GreaterThan(e1 @ DecimalType.Expression(p1, s1), + e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 => + GreaterThan(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)) + + case GreaterThanOrEqual(e1 @ DecimalType.Expression(p1, s1), + e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 => + GreaterThanOrEqual(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)) + + // Promote integers inside a binary expression with fixed-precision decimals to decimals, + // and fixed-precision decimals in an expression with floats / doubles to doubles + case b: BinaryExpression if b.left.dataType != b.right.dataType => + (b.left.dataType, b.right.dataType) match { + case (t, DecimalType.Fixed(p, s)) if intTypeToFixed.contains(t) => + b.makeCopy(Array(Cast(b.left, intTypeToFixed(t)), b.right)) + case (DecimalType.Fixed(p, s), t) if intTypeToFixed.contains(t) => + b.makeCopy(Array(b.left, Cast(b.right, intTypeToFixed(t)))) + case (t, DecimalType.Fixed(p, s)) if isFloat(t) => + b.makeCopy(Array(b.left, Cast(b.right, DoubleType))) + case (DecimalType.Fixed(p, s), t) if isFloat(t) => + b.makeCopy(Array(Cast(b.left, DoubleType), b.right)) + case _ => + b + } + + // TODO: MaxOf, MinOf, etc might want other rules + + // SUM and AVERAGE are handled by the implementations of those expressions + } } + } /** @@ -504,6 +563,10 @@ trait HiveTypeCoercion { case Sum(e @ TimestampType()) => Sum(Cast(e, DoubleType)) case Average(e @ TimestampType()) => Average(Cast(e, DoubleType)) + // Compatible with Hive + case Substring(e, start, len) if e.dataType != StringType => + Substring(Cast(e, StringType), start, len) + // Coalesce should return the first non-null value, which could be any column // from the list. So we need to make sure the return type is deterministic and // compatible with every child column. diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/MultiInstanceRelation.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/MultiInstanceRelation.scala index 4c5fb3f45bf49..35b74024a4cab 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/MultiInstanceRelation.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/MultiInstanceRelation.scala @@ -26,28 +26,9 @@ import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan * produced by distinct operators in a query tree as this breaks the guarantee that expression * ids, which are used to differentiate attributes, are unique. * - * Before analysis, all operators that include this trait will be asked to produce a new version + * During analysis, operators that include this trait may be asked to produce a new version * of itself with globally unique expression ids. */ trait MultiInstanceRelation { - def newInstance(): this.type -} - -/** - * If any MultiInstanceRelation appears more than once in the query plan then the plan is updated so - * that each instance has unique expression ids for the attributes produced. - */ -object NewRelationInstances extends Rule[LogicalPlan] { - def apply(plan: LogicalPlan): LogicalPlan = { - val localRelations = plan collect { case l: MultiInstanceRelation => l} - val multiAppearance = localRelations - .groupBy(identity[MultiInstanceRelation]) - .filter { case (_, ls) => ls.size > 1 } - .map(_._1) - .toSet - - plan transform { - case l: MultiInstanceRelation if multiAppearance.contains(l) => l.newInstance() - } - } + def newInstance(): LogicalPlan } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/package.scala index 3f672a3e0fd91..7731336d247db 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/package.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/package.scala @@ -17,6 +17,9 @@ package org.apache.spark.sql.catalyst +import org.apache.spark.sql.AnalysisException +import org.apache.spark.sql.catalyst.trees.TreeNode + /** * Provides a logical query plan [[Analyzer]] and supporting classes for performing analysis. * Analysis consists of translating [[UnresolvedAttribute]]s and [[UnresolvedRelation]]s @@ -25,11 +28,26 @@ package org.apache.spark.sql.catalyst package object analysis { /** - * Responsible for resolving which identifiers refer to the same entity. For example, by using - * case insensitive equality. + * Resolver should return true if the first string refers to the same entity as the second string. + * For example, by using case insensitive equality. */ type Resolver = (String, String) => Boolean val caseInsensitiveResolution = (a: String, b: String) => a.equalsIgnoreCase(b) val caseSensitiveResolution = (a: String, b: String) => a == b + + implicit class AnalysisErrorAt(t: TreeNode[_]) { + /** Fails the analysis at the point where a specific tree node was parsed. */ + def failAnalysis(msg: String): Nothing = { + throw new AnalysisException(msg, t.origin.line, t.origin.startPosition) + } + } + + /** Catches any AnalysisExceptions thrown by `f` and attaches `t`'s position if any. */ + def withPosition[A](t: TreeNode[_])(f: => A): A = { + try f catch { + case a: AnalysisException => + throw a.withPosition(t.origin.line, t.origin.startPosition) + } + } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala index f959a50564011..3f567e3e8b2a6 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala @@ -22,6 +22,7 @@ import org.apache.spark.sql.catalyst.errors.TreeNodeException import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.logical.LeafNode import org.apache.spark.sql.catalyst.trees.TreeNode +import org.apache.spark.sql.types.DataType /** * Thrown when an invalid attempt is made to access a property of a tree that has yet to be fully @@ -36,24 +37,34 @@ class UnresolvedException[TreeType <: TreeNode[_]](tree: TreeType, function: Str case class UnresolvedRelation( tableIdentifier: Seq[String], alias: Option[String] = None) extends LeafNode { - override def output = Nil + + /** Returns a `.` separated name for this relation. */ + def tableName: String = tableIdentifier.mkString(".") + + override def output: Seq[Attribute] = Nil + override lazy val resolved = false } /** * Holds the name of an attribute that has yet to be resolved. */ -case class UnresolvedAttribute(name: String) extends Attribute with trees.LeafNode[Expression] { - override def exprId = throw new UnresolvedException(this, "exprId") - override def dataType = throw new UnresolvedException(this, "dataType") - override def nullable = throw new UnresolvedException(this, "nullable") - override def qualifiers = throw new UnresolvedException(this, "qualifiers") +case class UnresolvedAttribute(nameParts: Seq[String]) + extends Attribute with trees.LeafNode[Expression] { + + def name: String = + nameParts.map(n => if (n.contains(".")) s"`$n`" else n).mkString(".") + + override def exprId: ExprId = throw new UnresolvedException(this, "exprId") + override def dataType: DataType = throw new UnresolvedException(this, "dataType") + override def nullable: Boolean = throw new UnresolvedException(this, "nullable") + override def qualifiers: Seq[String] = throw new UnresolvedException(this, "qualifiers") override lazy val resolved = false - override def newInstance() = this - override def withNullability(newNullability: Boolean) = this - override def withQualifiers(newQualifiers: Seq[String]) = this - override def withName(newName: String) = UnresolvedAttribute(name) + override def newInstance(): UnresolvedAttribute = this + override def withNullability(newNullability: Boolean): UnresolvedAttribute = this + override def withQualifiers(newQualifiers: Seq[String]): UnresolvedAttribute = this + override def withName(newName: String): UnresolvedAttribute = UnresolvedAttribute.quoted(newName) // Unresolved attributes are transient at compile time and don't get evaluated during execution. override def eval(input: Row = null): EvaluatedType = @@ -62,17 +73,22 @@ case class UnresolvedAttribute(name: String) extends Attribute with trees.LeafNo override def toString: String = s"'$name" } +object UnresolvedAttribute { + def apply(name: String): UnresolvedAttribute = new UnresolvedAttribute(name.split("\\.")) + def quoted(name: String): UnresolvedAttribute = new UnresolvedAttribute(Seq(name)) +} + case class UnresolvedFunction(name: String, children: Seq[Expression]) extends Expression { - override def dataType = throw new UnresolvedException(this, "dataType") - override def foldable = throw new UnresolvedException(this, "foldable") - override def nullable = throw new UnresolvedException(this, "nullable") + override def dataType: DataType = throw new UnresolvedException(this, "dataType") + override def foldable: Boolean = throw new UnresolvedException(this, "foldable") + override def nullable: Boolean = throw new UnresolvedException(this, "nullable") override lazy val resolved = false // Unresolved functions are transient at compile time and don't get evaluated during execution. override def eval(input: Row = null): EvaluatedType = throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}") - override def toString = s"'$name(${children.mkString(",")})" + override def toString: String = s"'$name(${children.mkString(",")})" } /** @@ -82,17 +98,17 @@ case class UnresolvedFunction(name: String, children: Seq[Expression]) extends E trait Star extends Attribute with trees.LeafNode[Expression] { self: Product => - override def name = throw new UnresolvedException(this, "name") - override def exprId = throw new UnresolvedException(this, "exprId") - override def dataType = throw new UnresolvedException(this, "dataType") - override def nullable = throw new UnresolvedException(this, "nullable") - override def qualifiers = throw new UnresolvedException(this, "qualifiers") + override def name: String = throw new UnresolvedException(this, "name") + override def exprId: ExprId = throw new UnresolvedException(this, "exprId") + override def dataType: DataType = throw new UnresolvedException(this, "dataType") + override def nullable: Boolean = throw new UnresolvedException(this, "nullable") + override def qualifiers: Seq[String] = throw new UnresolvedException(this, "qualifiers") override lazy val resolved = false - override def newInstance() = this - override def withNullability(newNullability: Boolean) = this - override def withQualifiers(newQualifiers: Seq[String]) = this - override def withName(newName: String) = this + override def newInstance(): Star = this + override def withNullability(newNullability: Boolean): Star = this + override def withQualifiers(newQualifiers: Seq[String]): Star = this + override def withName(newName: String): Star = this // Star gets expanded at runtime so we never evaluate a Star. override def eval(input: Row = null): EvaluatedType = @@ -125,7 +141,7 @@ case class UnresolvedStar(table: Option[String]) extends Star { } } - override def toString = table.map(_ + ".").getOrElse("") + "*" + override def toString: String = table.map(_ + ".").getOrElse("") + "*" } /** @@ -140,25 +156,25 @@ case class UnresolvedStar(table: Option[String]) extends Star { case class MultiAlias(child: Expression, names: Seq[String]) extends Attribute with trees.UnaryNode[Expression] { - override def name = throw new UnresolvedException(this, "name") + override def name: String = throw new UnresolvedException(this, "name") - override def exprId = throw new UnresolvedException(this, "exprId") + override def exprId: ExprId = throw new UnresolvedException(this, "exprId") - override def dataType = throw new UnresolvedException(this, "dataType") + override def dataType: DataType = throw new UnresolvedException(this, "dataType") - override def nullable = throw new UnresolvedException(this, "nullable") + override def nullable: Boolean = throw new UnresolvedException(this, "nullable") - override def qualifiers = throw new UnresolvedException(this, "qualifiers") + override def qualifiers: Seq[String] = throw new UnresolvedException(this, "qualifiers") override lazy val resolved = false - override def newInstance = this + override def newInstance(): MultiAlias = this - override def withNullability(newNullability: Boolean) = this + override def withNullability(newNullability: Boolean): MultiAlias = this - override def withQualifiers(newQualifiers: Seq[String]) = this + override def withQualifiers(newQualifiers: Seq[String]): MultiAlias = this - override def withName(newName: String) = this + override def withName(newName: String): MultiAlias = this override def eval(input: Row = null): EvaluatedType = throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}") @@ -175,17 +191,17 @@ case class MultiAlias(child: Expression, names: Seq[String]) */ case class ResolvedStar(expressions: Seq[NamedExpression]) extends Star { override def expand(input: Seq[Attribute], resolver: Resolver): Seq[NamedExpression] = expressions - override def toString = expressions.mkString("ResolvedStar(", ", ", ")") + override def toString: String = expressions.mkString("ResolvedStar(", ", ", ")") } case class UnresolvedGetField(child: Expression, fieldName: String) extends UnaryExpression { - override def dataType = throw new UnresolvedException(this, "dataType") - override def foldable = throw new UnresolvedException(this, "foldable") - override def nullable = throw new UnresolvedException(this, "nullable") + override def dataType: DataType = throw new UnresolvedException(this, "dataType") + override def foldable: Boolean = throw new UnresolvedException(this, "foldable") + override def nullable: Boolean = throw new UnresolvedException(this, "nullable") override lazy val resolved = false override def eval(input: Row = null): EvaluatedType = throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}") - override def toString = s"$child.$fieldName" + override def toString: String = s"$child.$fieldName" } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala old mode 100755 new mode 100644 index 51a09ac0e1249..21c15ad14fd19 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala @@ -22,7 +22,7 @@ import java.sql.{Date, Timestamp} import scala.language.implicitConversions import scala.reflect.runtime.universe.{TypeTag, typeTag} -import org.apache.spark.sql.catalyst.analysis.{UnresolvedGetField, UnresolvedAttribute} +import org.apache.spark.sql.catalyst.analysis.{EliminateSubQueries, UnresolvedGetField, UnresolvedAttribute} import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.logical._ import org.apache.spark.sql.catalyst.plans.{Inner, JoinType} @@ -61,60 +61,60 @@ package object dsl { trait ImplicitOperators { def expr: Expression - def unary_- = UnaryMinus(expr) - def unary_! = Not(expr) - def unary_~ = BitwiseNot(expr) - - def + (other: Expression) = Add(expr, other) - def - (other: Expression) = Subtract(expr, other) - def * (other: Expression) = Multiply(expr, other) - def / (other: Expression) = Divide(expr, other) - def % (other: Expression) = Remainder(expr, other) - def & (other: Expression) = BitwiseAnd(expr, other) - def | (other: Expression) = BitwiseOr(expr, other) - def ^ (other: Expression) = BitwiseXor(expr, other) - - def && (other: Expression) = And(expr, other) - def || (other: Expression) = Or(expr, other) - - def < (other: Expression) = LessThan(expr, other) - def <= (other: Expression) = LessThanOrEqual(expr, other) - def > (other: Expression) = GreaterThan(expr, other) - def >= (other: Expression) = GreaterThanOrEqual(expr, other) - def === (other: Expression) = EqualTo(expr, other) - def <=> (other: Expression) = EqualNullSafe(expr, other) - def !== (other: Expression) = Not(EqualTo(expr, other)) - - def in(list: Expression*) = In(expr, list) - - def like(other: Expression) = Like(expr, other) - def rlike(other: Expression) = RLike(expr, other) - def contains(other: Expression) = Contains(expr, other) - def startsWith(other: Expression) = StartsWith(expr, other) - def endsWith(other: Expression) = EndsWith(expr, other) - def substr(pos: Expression, len: Expression = Literal(Int.MaxValue)) = + def unary_- : Expression= UnaryMinus(expr) + def unary_! : Predicate = Not(expr) + def unary_~ : Expression = BitwiseNot(expr) + + def + (other: Expression): Expression = Add(expr, other) + def - (other: Expression): Expression = Subtract(expr, other) + def * (other: Expression): Expression = Multiply(expr, other) + def / (other: Expression): Expression = Divide(expr, other) + def % (other: Expression): Expression = Remainder(expr, other) + def & (other: Expression): Expression = BitwiseAnd(expr, other) + def | (other: Expression): Expression = BitwiseOr(expr, other) + def ^ (other: Expression): Expression = BitwiseXor(expr, other) + + def && (other: Expression): Predicate = And(expr, other) + def || (other: Expression): Predicate = Or(expr, other) + + def < (other: Expression): Predicate = LessThan(expr, other) + def <= (other: Expression): Predicate = LessThanOrEqual(expr, other) + def > (other: Expression): Predicate = GreaterThan(expr, other) + def >= (other: Expression): Predicate = GreaterThanOrEqual(expr, other) + def === (other: Expression): Predicate = EqualTo(expr, other) + def <=> (other: Expression): Predicate = EqualNullSafe(expr, other) + def !== (other: Expression): Predicate = Not(EqualTo(expr, other)) + + def in(list: Expression*): Expression = In(expr, list) + + def like(other: Expression): Expression = Like(expr, other) + def rlike(other: Expression): Expression = RLike(expr, other) + def contains(other: Expression): Expression = Contains(expr, other) + def startsWith(other: Expression): Expression = StartsWith(expr, other) + def endsWith(other: Expression): Expression = EndsWith(expr, other) + def substr(pos: Expression, len: Expression = Literal(Int.MaxValue)): Expression = Substring(expr, pos, len) - def substring(pos: Expression, len: Expression = Literal(Int.MaxValue)) = + def substring(pos: Expression, len: Expression = Literal(Int.MaxValue)): Expression = Substring(expr, pos, len) - def isNull = IsNull(expr) - def isNotNull = IsNotNull(expr) + def isNull: Predicate = IsNull(expr) + def isNotNull: Predicate = IsNotNull(expr) - def getItem(ordinal: Expression) = GetItem(expr, ordinal) - def getField(fieldName: String) = UnresolvedGetField(expr, fieldName) + def getItem(ordinal: Expression): Expression = GetItem(expr, ordinal) + def getField(fieldName: String): UnresolvedGetField = UnresolvedGetField(expr, fieldName) - def cast(to: DataType) = Cast(expr, to) + def cast(to: DataType): Expression = Cast(expr, to) - def asc = SortOrder(expr, Ascending) - def desc = SortOrder(expr, Descending) + def asc: SortOrder = SortOrder(expr, Ascending) + def desc: SortOrder = SortOrder(expr, Descending) - def as(alias: String) = Alias(expr, alias)() - def as(alias: Symbol) = Alias(expr, alias.name)() + def as(alias: String): NamedExpression = Alias(expr, alias)() + def as(alias: Symbol): NamedExpression = Alias(expr, alias.name)() } trait ExpressionConversions { implicit class DslExpression(e: Expression) extends ImplicitOperators { - def expr = e + def expr: Expression = e } implicit def booleanToLiteral(b: Boolean): Literal = Literal(b) @@ -144,94 +144,100 @@ package object dsl { } } - def sum(e: Expression) = Sum(e) - def sumDistinct(e: Expression) = SumDistinct(e) - def count(e: Expression) = Count(e) - def countDistinct(e: Expression*) = CountDistinct(e) - def approxCountDistinct(e: Expression, rsd: Double = 0.05) = ApproxCountDistinct(e, rsd) - def avg(e: Expression) = Average(e) - def first(e: Expression) = First(e) - def last(e: Expression) = Last(e) - def min(e: Expression) = Min(e) - def max(e: Expression) = Max(e) - def upper(e: Expression) = Upper(e) - def lower(e: Expression) = Lower(e) - def sqrt(e: Expression) = Sqrt(e) - def abs(e: Expression) = Abs(e) - - implicit class DslSymbol(sym: Symbol) extends ImplicitAttribute { def s = sym.name } + def sum(e: Expression): Expression = Sum(e) + def sumDistinct(e: Expression): Expression = SumDistinct(e) + def count(e: Expression): Expression = Count(e) + def countDistinct(e: Expression*): Expression = CountDistinct(e) + def approxCountDistinct(e: Expression, rsd: Double = 0.05): Expression = + ApproxCountDistinct(e, rsd) + def avg(e: Expression): Expression = Average(e) + def first(e: Expression): Expression = First(e) + def last(e: Expression): Expression = Last(e) + def min(e: Expression): Expression = Min(e) + def max(e: Expression): Expression = Max(e) + def upper(e: Expression): Expression = Upper(e) + def lower(e: Expression): Expression = Lower(e) + def sqrt(e: Expression): Expression = Sqrt(e) + def abs(e: Expression): Expression = Abs(e) + + implicit class DslSymbol(sym: Symbol) extends ImplicitAttribute { def s: String = sym.name } // TODO more implicit class for literal? implicit class DslString(val s: String) extends ImplicitOperators { override def expr: Expression = Literal(s) - def attr = analysis.UnresolvedAttribute(s) + def attr: UnresolvedAttribute = analysis.UnresolvedAttribute(s) } abstract class ImplicitAttribute extends ImplicitOperators { def s: String - def expr = attr - def attr = analysis.UnresolvedAttribute(s) + def expr: UnresolvedAttribute = attr + def attr: UnresolvedAttribute = analysis.UnresolvedAttribute(s) /** Creates a new AttributeReference of type boolean */ - def boolean = AttributeReference(s, BooleanType, nullable = true)() + def boolean: AttributeReference = AttributeReference(s, BooleanType, nullable = true)() /** Creates a new AttributeReference of type byte */ - def byte = AttributeReference(s, ByteType, nullable = true)() + def byte: AttributeReference = AttributeReference(s, ByteType, nullable = true)() /** Creates a new AttributeReference of type short */ - def short = AttributeReference(s, ShortType, nullable = true)() + def short: AttributeReference = AttributeReference(s, ShortType, nullable = true)() /** Creates a new AttributeReference of type int */ - def int = AttributeReference(s, IntegerType, nullable = true)() + def int: AttributeReference = AttributeReference(s, IntegerType, nullable = true)() /** Creates a new AttributeReference of type long */ - def long = AttributeReference(s, LongType, nullable = true)() + def long: AttributeReference = AttributeReference(s, LongType, nullable = true)() /** Creates a new AttributeReference of type float */ - def float = AttributeReference(s, FloatType, nullable = true)() + def float: AttributeReference = AttributeReference(s, FloatType, nullable = true)() /** Creates a new AttributeReference of type double */ - def double = AttributeReference(s, DoubleType, nullable = true)() + def double: AttributeReference = AttributeReference(s, DoubleType, nullable = true)() /** Creates a new AttributeReference of type string */ - def string = AttributeReference(s, StringType, nullable = true)() + def string: AttributeReference = AttributeReference(s, StringType, nullable = true)() /** Creates a new AttributeReference of type date */ - def date = AttributeReference(s, DateType, nullable = true)() + def date: AttributeReference = AttributeReference(s, DateType, nullable = true)() /** Creates a new AttributeReference of type decimal */ - def decimal = AttributeReference(s, DecimalType.Unlimited, nullable = true)() + def decimal: AttributeReference = + AttributeReference(s, DecimalType.Unlimited, nullable = true)() /** Creates a new AttributeReference of type decimal */ - def decimal(precision: Int, scale: Int) = + def decimal(precision: Int, scale: Int): AttributeReference = AttributeReference(s, DecimalType(precision, scale), nullable = true)() /** Creates a new AttributeReference of type timestamp */ - def timestamp = AttributeReference(s, TimestampType, nullable = true)() + def timestamp: AttributeReference = AttributeReference(s, TimestampType, nullable = true)() /** Creates a new AttributeReference of type binary */ - def binary = AttributeReference(s, BinaryType, nullable = true)() + def binary: AttributeReference = AttributeReference(s, BinaryType, nullable = true)() /** Creates a new AttributeReference of type array */ - def array(dataType: DataType) = AttributeReference(s, ArrayType(dataType), nullable = true)() + def array(dataType: DataType): AttributeReference = + AttributeReference(s, ArrayType(dataType), nullable = true)() /** Creates a new AttributeReference of type map */ def map(keyType: DataType, valueType: DataType): AttributeReference = map(MapType(keyType, valueType)) - def map(mapType: MapType) = AttributeReference(s, mapType, nullable = true)() + + def map(mapType: MapType): AttributeReference = + AttributeReference(s, mapType, nullable = true)() /** Creates a new AttributeReference of type struct */ def struct(fields: StructField*): AttributeReference = struct(StructType(fields)) - def struct(structType: StructType) = AttributeReference(s, structType, nullable = true)() + def struct(structType: StructType): AttributeReference = + AttributeReference(s, structType, nullable = true)() } implicit class DslAttribute(a: AttributeReference) { - def notNull = a.withNullability(false) - def nullable = a.withNullability(true) + def notNull: AttributeReference = a.withNullability(false) + def nullable: AttributeReference = a.withNullability(true) // Protobuf terminology - def required = a.withNullability(false) + def required: AttributeReference = a.withNullability(false) - def at(ordinal: Int) = BoundReference(ordinal, a.dataType, a.nullable) + def at(ordinal: Int): BoundReference = BoundReference(ordinal, a.dataType, a.nullable) } } @@ -241,23 +247,23 @@ package object dsl { abstract class LogicalPlanFunctions { def logicalPlan: LogicalPlan - def select(exprs: NamedExpression*) = Project(exprs, logicalPlan) + def select(exprs: NamedExpression*): LogicalPlan = Project(exprs, logicalPlan) - def where(condition: Expression) = Filter(condition, logicalPlan) + def where(condition: Expression): LogicalPlan = Filter(condition, logicalPlan) - def limit(limitExpr: Expression) = Limit(limitExpr, logicalPlan) + def limit(limitExpr: Expression): LogicalPlan = Limit(limitExpr, logicalPlan) def join( otherPlan: LogicalPlan, joinType: JoinType = Inner, - condition: Option[Expression] = None) = + condition: Option[Expression] = None): LogicalPlan = Join(logicalPlan, otherPlan, joinType, condition) - def orderBy(sortExprs: SortOrder*) = Sort(sortExprs, true, logicalPlan) + def orderBy(sortExprs: SortOrder*): LogicalPlan = Sort(sortExprs, true, logicalPlan) - def sortBy(sortExprs: SortOrder*) = Sort(sortExprs, false, logicalPlan) + def sortBy(sortExprs: SortOrder*): LogicalPlan = Sort(sortExprs, false, logicalPlan) - def groupBy(groupingExprs: Expression*)(aggregateExprs: Expression*) = { + def groupBy(groupingExprs: Expression*)(aggregateExprs: Expression*): LogicalPlan = { val aliasedExprs = aggregateExprs.map { case ne: NamedExpression => ne case e => Alias(e, e.toString)() @@ -265,41 +271,43 @@ package object dsl { Aggregate(groupingExprs, aliasedExprs, logicalPlan) } - def subquery(alias: Symbol) = Subquery(alias.name, logicalPlan) + def subquery(alias: Symbol): LogicalPlan = Subquery(alias.name, logicalPlan) - def unionAll(otherPlan: LogicalPlan) = Union(logicalPlan, otherPlan) + def unionAll(otherPlan: LogicalPlan): LogicalPlan = Union(logicalPlan, otherPlan) - def sfilter[T1](arg1: Symbol)(udf: (T1) => Boolean) = + def sfilter[T1](arg1: Symbol)(udf: (T1) => Boolean): LogicalPlan = Filter(ScalaUdf(udf, BooleanType, Seq(UnresolvedAttribute(arg1.name))), logicalPlan) def sample( fraction: Double, withReplacement: Boolean = true, - seed: Int = (math.random * 1000).toInt) = + seed: Int = (math.random * 1000).toInt): LogicalPlan = Sample(fraction, withReplacement, seed, logicalPlan) def generate( generator: Generator, join: Boolean = false, outer: Boolean = false, - alias: Option[String] = None) = + alias: Option[String] = None): LogicalPlan = Generate(generator, join, outer, None, logicalPlan) - def insertInto(tableName: String, overwrite: Boolean = false) = + def insertInto(tableName: String, overwrite: Boolean = false): LogicalPlan = InsertIntoTable( - analysis.UnresolvedRelation(Seq(tableName)), Map.empty, logicalPlan, overwrite) + analysis.UnresolvedRelation(Seq(tableName)), Map.empty, logicalPlan, overwrite, false) - def analyze = analysis.SimpleAnalyzer(logicalPlan) + def analyze: LogicalPlan = EliminateSubQueries(analysis.SimpleAnalyzer(logicalPlan)) } object plans { // scalastyle:ignore implicit class DslLogicalPlan(val logicalPlan: LogicalPlan) extends LogicalPlanFunctions { - def writeToFile(path: String) = WriteToFile(path, logicalPlan) + def writeToFile(path: String): LogicalPlan = WriteToFile(path, logicalPlan) } } case class ScalaUdfBuilder[T: TypeTag](f: AnyRef) { - def call(args: Expression*) = ScalaUdf(f, ScalaReflection.schemaFor(typeTag[T]).dataType, args) + def call(args: Expression*): ScalaUdf = { + ScalaUdf(f, ScalaReflection.schemaFor(typeTag[T]).dataType, args) + } } // scalastyle:off diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/AttributeMap.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/AttributeMap.scala index 82e760b6c6916..96a11e352ec50 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/AttributeMap.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/AttributeMap.scala @@ -23,7 +23,9 @@ package org.apache.spark.sql.catalyst.expressions * of the name, or the expected nullability). */ object AttributeMap { - def apply[A](kvs: Seq[(Attribute, A)]) = new AttributeMap(kvs.map(kv => (kv._1.exprId, kv)).toMap) + def apply[A](kvs: Seq[(Attribute, A)]): AttributeMap[A] = { + new AttributeMap(kvs.map(kv => (kv._1.exprId, kv)).toMap) + } } class AttributeMap[A](baseMap: Map[ExprId, (Attribute, A)]) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/AttributeSet.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/AttributeSet.scala index 171845ad14e3e..5345696570b41 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/AttributeSet.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/AttributeSet.scala @@ -17,26 +17,29 @@ package org.apache.spark.sql.catalyst.expressions -import org.apache.spark.sql.catalyst.analysis.Star protected class AttributeEquals(val a: Attribute) { - override def hashCode() = a.exprId.hashCode() - override def equals(other: Any) = (a, other.asInstanceOf[AttributeEquals].a) match { + override def hashCode(): Int = a match { + case ar: AttributeReference => ar.exprId.hashCode() + case a => a.hashCode() + } + + override def equals(other: Any): Boolean = (a, other.asInstanceOf[AttributeEquals].a) match { case (a1: AttributeReference, a2: AttributeReference) => a1.exprId == a2.exprId case (a1, a2) => a1 == a2 } } object AttributeSet { - def apply(a: Attribute) = - new AttributeSet(Set(new AttributeEquals(a))) + def apply(a: Attribute): AttributeSet = new AttributeSet(Set(new AttributeEquals(a))) /** Constructs a new [[AttributeSet]] given a sequence of [[Expression Expressions]]. */ - def apply(baseSet: Seq[Expression]) = + def apply(baseSet: Iterable[Expression]): AttributeSet = { new AttributeSet( baseSet .flatMap(_.references) .map(new AttributeEquals(_)).toSet) + } } /** @@ -54,8 +57,9 @@ class AttributeSet private (val baseSet: Set[AttributeEquals]) extends Traversable[Attribute] with Serializable { /** Returns true if the members of this AttributeSet and other are the same. */ - override def equals(other: Any) = other match { - case otherSet: AttributeSet => baseSet.map(_.a).forall(otherSet.contains) + override def equals(other: Any): Boolean = other match { + case otherSet: AttributeSet => + otherSet.size == baseSet.size && baseSet.map(_.a).forall(otherSet.contains) case _ => false } @@ -78,32 +82,34 @@ class AttributeSet private (val baseSet: Set[AttributeEquals]) * Returns true if the [[Attribute Attributes]] in this set are a subset of the Attributes in * `other`. */ - def subsetOf(other: AttributeSet) = baseSet.subsetOf(other.baseSet) + def subsetOf(other: AttributeSet): Boolean = baseSet.subsetOf(other.baseSet) /** * Returns a new [[AttributeSet]] that does not contain any of the [[Attribute Attributes]] found * in `other`. */ - def --(other: Traversable[NamedExpression]) = + def --(other: Traversable[NamedExpression]): AttributeSet = new AttributeSet(baseSet -- other.map(a => new AttributeEquals(a.toAttribute))) /** * Returns a new [[AttributeSet]] that contains all of the [[Attribute Attributes]] found * in `other`. */ - def ++(other: AttributeSet) = new AttributeSet(baseSet ++ other.baseSet) + def ++(other: AttributeSet): AttributeSet = new AttributeSet(baseSet ++ other.baseSet) /** * Returns a new [[AttributeSet]] contain only the [[Attribute Attributes]] where `f` evaluates to * true. */ - override def filter(f: Attribute => Boolean) = new AttributeSet(baseSet.filter(ae => f(ae.a))) + override def filter(f: Attribute => Boolean): AttributeSet = + new AttributeSet(baseSet.filter(ae => f(ae.a))) /** * Returns a new [[AttributeSet]] that only contains [[Attribute Attributes]] that are found in * `this` and `other`. */ - def intersect(other: AttributeSet) = new AttributeSet(baseSet.intersect(other.baseSet)) + def intersect(other: AttributeSet): AttributeSet = + new AttributeSet(baseSet.intersect(other.baseSet)) override def foreach[U](f: (Attribute) => U): Unit = baseSet.map(_.a).foreach(f) @@ -111,7 +117,7 @@ class AttributeSet private (val baseSet: Set[AttributeEquals]) // sorts of things in its closure. override def toSeq: Seq[Attribute] = baseSet.map(_.a).toArray.toSeq - override def toString = "{" + baseSet.map(_.a).mkString(", ") + "}" + override def toString: String = "{" + baseSet.map(_.a).mkString(", ") + "}" override def isEmpty: Boolean = baseSet.isEmpty } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala index 76a9f08dea85f..2225621dbaabd 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala @@ -32,7 +32,7 @@ case class BoundReference(ordinal: Int, dataType: DataType, nullable: Boolean) type EvaluatedType = Any - override def toString = s"input[$ordinal]" + override def toString: String = s"input[$ordinal]" override def eval(input: Row): Any = input(ordinal) } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala index b1bc858478ee1..adf941ab2a45f 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala @@ -21,7 +21,6 @@ import java.sql.{Date, Timestamp} import java.text.{DateFormat, SimpleDateFormat} import org.apache.spark.Logging -import org.apache.spark.sql.catalyst.errors.TreeNodeException import org.apache.spark.sql.types._ /** Cast the child expression to the target data type. */ @@ -29,9 +28,9 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w override lazy val resolved = childrenResolved && resolve(child.dataType, dataType) - override def foldable = child.foldable + override def foldable: Boolean = child.foldable - override def nullable = forceNullable(child.dataType, dataType) || child.nullable + override def nullable: Boolean = forceNullable(child.dataType, dataType) || child.nullable private[this] def forceNullable(from: DataType, to: DataType) = (from, to) match { case (StringType, _: NumericType) => true @@ -103,7 +102,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w } } - override def toString = s"CAST($child, $dataType)" + override def toString: String = s"CAST($child, $dataType)" type EvaluatedType = Any @@ -112,21 +111,21 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w // UDFToString private[this] def castToString(from: DataType): Any => Any = from match { - case BinaryType => buildCast[Array[Byte]](_, new String(_, "UTF-8")) - case DateType => buildCast[Int](_, d => DateUtils.toString(d)) - case TimestampType => buildCast[Timestamp](_, timestampToString) - case _ => buildCast[Any](_, _.toString) + case BinaryType => buildCast[Array[Byte]](_, UTF8String(_)) + case DateType => buildCast[Int](_, d => UTF8String(DateUtils.toString(d))) + case TimestampType => buildCast[Timestamp](_, t => UTF8String(timestampToString(t))) + case _ => buildCast[Any](_, o => UTF8String(o.toString)) } // BinaryConverter private[this] def castToBinary(from: DataType): Any => Any = from match { - case StringType => buildCast[String](_, _.getBytes("UTF-8")) + case StringType => buildCast[UTF8String](_, _.getBytes) } // UDFToBoolean private[this] def castToBoolean(from: DataType): Any => Any = from match { case StringType => - buildCast[String](_, _.length() != 0) + buildCast[UTF8String](_, _.length() != 0) case TimestampType => buildCast[Timestamp](_, t => t.getTime() != 0 || t.getNanos() != 0) case DateType => @@ -151,8 +150,9 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w // TimestampConverter private[this] def castToTimestamp(from: DataType): Any => Any = from match { case StringType => - buildCast[String](_, s => { + buildCast[UTF8String](_, utfs => { // Throw away extra if more than 9 decimal places + val s = utfs.toString val periodIdx = s.indexOf(".") var n = s if (periodIdx != -1 && n.length() - periodIdx > 9) { @@ -227,8 +227,8 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w // DateConverter private[this] def castToDate(from: DataType): Any => Any = from match { case StringType => - buildCast[String](_, s => - try DateUtils.fromJavaDate(Date.valueOf(s)) + buildCast[UTF8String](_, s => + try DateUtils.fromJavaDate(Date.valueOf(s.toString)) catch { case _: java.lang.IllegalArgumentException => null } ) case TimestampType => @@ -245,7 +245,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w // LongConverter private[this] def castToLong(from: DataType): Any => Any = from match { case StringType => - buildCast[String](_, s => try s.toLong catch { + buildCast[UTF8String](_, s => try s.toString.toLong catch { case _: NumberFormatException => null }) case BooleanType => @@ -261,7 +261,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w // IntConverter private[this] def castToInt(from: DataType): Any => Any = from match { case StringType => - buildCast[String](_, s => try s.toInt catch { + buildCast[UTF8String](_, s => try s.toString.toInt catch { case _: NumberFormatException => null }) case BooleanType => @@ -277,7 +277,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w // ShortConverter private[this] def castToShort(from: DataType): Any => Any = from match { case StringType => - buildCast[String](_, s => try s.toShort catch { + buildCast[UTF8String](_, s => try s.toString.toShort catch { case _: NumberFormatException => null }) case BooleanType => @@ -293,7 +293,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w // ByteConverter private[this] def castToByte(from: DataType): Any => Any = from match { case StringType => - buildCast[String](_, s => try s.toByte catch { + buildCast[UTF8String](_, s => try s.toString.toByte catch { case _: NumberFormatException => null }) case BooleanType => @@ -323,7 +323,9 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w private[this] def castToDecimal(from: DataType, target: DecimalType): Any => Any = from match { case StringType => - buildCast[String](_, s => try changePrecision(Decimal(s.toDouble), target) catch { + buildCast[UTF8String](_, s => try { + changePrecision(Decimal(s.toString.toDouble), target) + } catch { case _: NumberFormatException => null }) case BooleanType => @@ -348,7 +350,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w // DoubleConverter private[this] def castToDouble(from: DataType): Any => Any = from match { case StringType => - buildCast[String](_, s => try s.toDouble catch { + buildCast[UTF8String](_, s => try s.toString.toDouble catch { case _: NumberFormatException => null }) case BooleanType => @@ -364,7 +366,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w // FloatConverter private[this] def castToFloat(from: DataType): Any => Any = from match { case StringType => - buildCast[String](_, s => try s.toFloat catch { + buildCast[UTF8String](_, s => try s.toString.toFloat catch { case _: NumberFormatException => null }) case BooleanType => @@ -394,10 +396,17 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w val casts = from.fields.zip(to.fields).map { case (fromField, toField) => cast(fromField.dataType, toField.dataType) } - // TODO: This is very slow! - buildCast[Row](_, row => Row(row.toSeq.zip(casts).map { - case (v, cast) => if (v == null) null else cast(v) - }: _*)) + // TODO: Could be faster? + val newRow = new GenericMutableRow(from.fields.size) + buildCast[Row](_, row => { + var i = 0 + while (i < row.length) { + val v = row(i) + newRow.update(i, if (v == null) null else casts(i)(v)) + i += 1 + } + newRow.copy() + }) } private[this] def cast(from: DataType, to: DataType): Any => Any = to match { @@ -430,14 +439,14 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w object Cast { // `SimpleDateFormat` is not thread-safe. private[sql] val threadLocalTimestampFormat = new ThreadLocal[DateFormat] { - override def initialValue() = { + override def initialValue(): SimpleDateFormat = { new SimpleDateFormat("yyyy-MM-dd HH:mm:ss") } } // `SimpleDateFormat` is not thread-safe. private[sql] val threadLocalDateFormat = new ThreadLocal[DateFormat] { - override def initialValue() = { + override def initialValue(): SimpleDateFormat = { new SimpleDateFormat("yyyy-MM-dd") } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala index cf14992ef835c..4e3bbc06a5b4c 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala @@ -17,6 +17,7 @@ package org.apache.spark.sql.catalyst.expressions +import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute import org.apache.spark.sql.catalyst.errors.TreeNodeException import org.apache.spark.sql.catalyst.trees import org.apache.spark.sql.catalyst.trees.TreeNode @@ -64,206 +65,17 @@ abstract class Expression extends TreeNode[Expression] { * Returns true if all the children of this expression have been resolved to a specific schema * and false if any still contains any unresolved placeholders. */ - def childrenResolved = !children.exists(!_.resolved) + def childrenResolved: Boolean = !children.exists(!_.resolved) /** - * A set of helper functions that return the correct descendant of `scala.math.Numeric[T]` type - * and do any casting necessary of child evaluation. + * Returns a string representation of this expression that does not have developer centric + * debugging information like the expression id. */ - @inline - def n1(e: Expression, i: Row, f: ((Numeric[Any], Any) => Any)): Any = { - val evalE = e.eval(i) - if (evalE == null) { - null - } else { - e.dataType match { - case n: NumericType => - val castedFunction = f.asInstanceOf[(Numeric[n.JvmType], n.JvmType) => n.JvmType] - castedFunction(n.numeric, evalE.asInstanceOf[n.JvmType]) - case other => sys.error(s"Type $other does not support numeric operations") - } - } - } - - /** - * Evaluation helper function for 2 Numeric children expressions. Those expressions are supposed - * to be in the same data type, and also the return type. - * Either one of the expressions result is null, the evaluation result should be null. - */ - @inline - protected final def n2( - i: Row, - e1: Expression, - e2: Expression, - f: ((Numeric[Any], Any, Any) => Any)): Any = { - - if (e1.dataType != e2.dataType) { - throw new TreeNodeException(this, s"Types do not match ${e1.dataType} != ${e2.dataType}") - } - - val evalE1 = e1.eval(i) - if(evalE1 == null) { - null - } else { - val evalE2 = e2.eval(i) - if (evalE2 == null) { - null - } else { - e1.dataType match { - case n: NumericType => - f.asInstanceOf[(Numeric[n.JvmType], n.JvmType, n.JvmType) => n.JvmType]( - n.numeric, evalE1.asInstanceOf[n.JvmType], evalE2.asInstanceOf[n.JvmType]) - case other => sys.error(s"Type $other does not support numeric operations") - } - } - } - } - - /** - * Evaluation helper function for 2 Fractional children expressions. Those expressions are - * supposed to be in the same data type, and also the return type. - * Either one of the expressions result is null, the evaluation result should be null. - */ - @inline - protected final def f2( - i: Row, - e1: Expression, - e2: Expression, - f: ((Fractional[Any], Any, Any) => Any)): Any = { - if (e1.dataType != e2.dataType) { - throw new TreeNodeException(this, s"Types do not match ${e1.dataType} != ${e2.dataType}") - } - - val evalE1 = e1.eval(i: Row) - if(evalE1 == null) { - null - } else { - val evalE2 = e2.eval(i: Row) - if (evalE2 == null) { - null - } else { - e1.dataType match { - case ft: FractionalType => - f.asInstanceOf[(Fractional[ft.JvmType], ft.JvmType, ft.JvmType) => ft.JvmType]( - ft.fractional, evalE1.asInstanceOf[ft.JvmType], evalE2.asInstanceOf[ft.JvmType]) - case other => sys.error(s"Type $other does not support fractional operations") - } - } - } - } - - /** - * Evaluation helper function for 1 Fractional children expression. - * if the expression result is null, the evaluation result should be null. - */ - @inline - protected final def f1(i: Row, e1: Expression, f: ((Fractional[Any], Any) => Any)): Any = { - val evalE1 = e1.eval(i: Row) - if(evalE1 == null) { - null - } else { - e1.dataType match { - case ft: FractionalType => - f.asInstanceOf[(Fractional[ft.JvmType], ft.JvmType) => ft.JvmType]( - ft.fractional, evalE1.asInstanceOf[ft.JvmType]) - case other => sys.error(s"Type $other does not support fractional operations") - } - } - } - - /** - * Evaluation helper function for 2 Integral children expressions. Those expressions are - * supposed to be in the same data type, and also the return type. - * Either one of the expressions result is null, the evaluation result should be null. - */ - @inline - protected final def i2( - i: Row, - e1: Expression, - e2: Expression, - f: ((Integral[Any], Any, Any) => Any)): Any = { - if (e1.dataType != e2.dataType) { - throw new TreeNodeException(this, s"Types do not match ${e1.dataType} != ${e2.dataType}") - } - - val evalE1 = e1.eval(i) - if(evalE1 == null) { - null - } else { - val evalE2 = e2.eval(i) - if (evalE2 == null) { - null - } else { - e1.dataType match { - case i: IntegralType => - f.asInstanceOf[(Integral[i.JvmType], i.JvmType, i.JvmType) => i.JvmType]( - i.integral, evalE1.asInstanceOf[i.JvmType], evalE2.asInstanceOf[i.JvmType]) - case i: FractionalType => - f.asInstanceOf[(Integral[i.JvmType], i.JvmType, i.JvmType) => i.JvmType]( - i.asIntegral, evalE1.asInstanceOf[i.JvmType], evalE2.asInstanceOf[i.JvmType]) - case other => sys.error(s"Type $other does not support numeric operations") - } - } - } - } - - /** - * Evaluation helper function for 1 Integral children expression. - * if the expression result is null, the evaluation result should be null. - */ - @inline - protected final def i1(i: Row, e1: Expression, f: ((Integral[Any], Any) => Any)): Any = { - val evalE1 = e1.eval(i) - if(evalE1 == null) { - null - } else { - e1.dataType match { - case i: IntegralType => - f.asInstanceOf[(Integral[i.JvmType], i.JvmType) => i.JvmType]( - i.integral, evalE1.asInstanceOf[i.JvmType]) - case i: FractionalType => - f.asInstanceOf[(Integral[i.JvmType], i.JvmType) => i.JvmType]( - i.asIntegral, evalE1.asInstanceOf[i.JvmType]) - case other => sys.error(s"Type $other does not support numeric operations") - } - } - } - - /** - * Evaluation helper function for 2 Comparable children expressions. Those expressions are - * supposed to be in the same data type, and the return type should be Integer: - * Negative value: 1st argument less than 2nd argument - * Zero: 1st argument equals 2nd argument - * Positive value: 1st argument greater than 2nd argument - * - * Either one of the expressions result is null, the evaluation result should be null. - */ - @inline - protected final def c2( - i: Row, - e1: Expression, - e2: Expression, - f: ((Ordering[Any], Any, Any) => Any)): Any = { - if (e1.dataType != e2.dataType) { - throw new TreeNodeException(this, s"Types do not match ${e1.dataType} != ${e2.dataType}") - } - - val evalE1 = e1.eval(i) - if(evalE1 == null) { - null - } else { - val evalE2 = e2.eval(i) - if (evalE2 == null) { - null - } else { - e1.dataType match { - case i: NativeType => - f.asInstanceOf[(Ordering[i.JvmType], i.JvmType, i.JvmType) => Boolean]( - i.ordering, evalE1.asInstanceOf[i.JvmType], evalE2.asInstanceOf[i.JvmType]) - case other => sys.error(s"Type $other does not support ordered operations") - } - } - } + def prettyString: String = { + transform { + case a: AttributeReference => PrettyAttribute(a.name) + case u: UnresolvedAttribute => PrettyAttribute(u.name) + }.toString } } @@ -272,9 +84,9 @@ abstract class BinaryExpression extends Expression with trees.BinaryNode[Express def symbol: String - override def foldable = left.foldable && right.foldable + override def foldable: Boolean = left.foldable && right.foldable - override def toString = s"($left $symbol $right)" + override def toString: String = s"($left $symbol $right)" } abstract class LeafExpression extends Expression with trees.LeafNode[Expression] { @@ -292,8 +104,8 @@ abstract class UnaryExpression extends Expression with trees.UnaryNode[Expressio case class GroupExpression(children: Seq[Expression]) extends Expression { self: Product => type EvaluatedType = Seq[Any] - override def eval(input: Row): EvaluatedType = ??? - override def nullable = false - override def foldable = false - override def dataType = ??? + override def eval(input: Row): EvaluatedType = throw new UnsupportedOperationException + override def nullable: Boolean = false + override def foldable: Boolean = false + override def dataType: DataType = throw new UnsupportedOperationException } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala index db5d897ee569f..c2866cd955409 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala @@ -40,7 +40,7 @@ class InterpretedProjection(expressions: Seq[Expression]) extends Projection { new GenericRow(outputArray) } - override def toString = s"Row => [${exprArray.mkString(",")}]" + override def toString: String = s"Row => [${exprArray.mkString(",")}]" } /** @@ -107,12 +107,12 @@ class JoinedRow extends Row { override def toSeq: Seq[Any] = row1.toSeq ++ row2.toSeq - override def length = row1.length + row2.length + override def length: Int = row1.length + row2.length - override def apply(i: Int) = + override def apply(i: Int): Any = if (i < row1.length) row1(i) else row2(i - row1.length) - override def isNullAt(i: Int) = + override def isNullAt(i: Int): Boolean = if (i < row1.length) row1.isNullAt(i) else row2.isNullAt(i - row1.length) override def getInt(i: Int): Int = @@ -142,7 +142,7 @@ class JoinedRow extends Row { override def getAs[T](i: Int): T = if (i < row1.length) row1.getAs[T](i) else row2.getAs[T](i - row1.length) - override def copy() = { + override def copy(): Row = { val totalSize = row1.length + row2.length val copiedValues = new Array[Any](totalSize) var i = 0 @@ -153,7 +153,7 @@ class JoinedRow extends Row { new GenericRow(copiedValues) } - override def toString() = { + override def toString: String = { // Make sure toString never throws NullPointerException. if ((row1 eq null) && (row2 eq null)) { "[ empty row ]" @@ -207,12 +207,12 @@ class JoinedRow2 extends Row { override def toSeq: Seq[Any] = row1.toSeq ++ row2.toSeq - override def length = row1.length + row2.length + override def length: Int = row1.length + row2.length - override def apply(i: Int) = + override def apply(i: Int): Any = if (i < row1.length) row1(i) else row2(i - row1.length) - override def isNullAt(i: Int) = + override def isNullAt(i: Int): Boolean = if (i < row1.length) row1.isNullAt(i) else row2.isNullAt(i - row1.length) override def getInt(i: Int): Int = @@ -242,7 +242,7 @@ class JoinedRow2 extends Row { override def getAs[T](i: Int): T = if (i < row1.length) row1.getAs[T](i) else row2.getAs[T](i - row1.length) - override def copy() = { + override def copy(): Row = { val totalSize = row1.length + row2.length val copiedValues = new Array[Any](totalSize) var i = 0 @@ -253,7 +253,7 @@ class JoinedRow2 extends Row { new GenericRow(copiedValues) } - override def toString() = { + override def toString: String = { // Make sure toString never throws NullPointerException. if ((row1 eq null) && (row2 eq null)) { "[ empty row ]" @@ -301,12 +301,12 @@ class JoinedRow3 extends Row { override def toSeq: Seq[Any] = row1.toSeq ++ row2.toSeq - override def length = row1.length + row2.length + override def length: Int = row1.length + row2.length - override def apply(i: Int) = + override def apply(i: Int): Any = if (i < row1.length) row1(i) else row2(i - row1.length) - override def isNullAt(i: Int) = + override def isNullAt(i: Int): Boolean = if (i < row1.length) row1.isNullAt(i) else row2.isNullAt(i - row1.length) override def getInt(i: Int): Int = @@ -336,7 +336,7 @@ class JoinedRow3 extends Row { override def getAs[T](i: Int): T = if (i < row1.length) row1.getAs[T](i) else row2.getAs[T](i - row1.length) - override def copy() = { + override def copy(): Row = { val totalSize = row1.length + row2.length val copiedValues = new Array[Any](totalSize) var i = 0 @@ -347,7 +347,7 @@ class JoinedRow3 extends Row { new GenericRow(copiedValues) } - override def toString() = { + override def toString: String = { // Make sure toString never throws NullPointerException. if ((row1 eq null) && (row2 eq null)) { "[ empty row ]" @@ -395,12 +395,12 @@ class JoinedRow4 extends Row { override def toSeq: Seq[Any] = row1.toSeq ++ row2.toSeq - override def length = row1.length + row2.length + override def length: Int = row1.length + row2.length - override def apply(i: Int) = + override def apply(i: Int): Any = if (i < row1.length) row1(i) else row2(i - row1.length) - override def isNullAt(i: Int) = + override def isNullAt(i: Int): Boolean = if (i < row1.length) row1.isNullAt(i) else row2.isNullAt(i - row1.length) override def getInt(i: Int): Int = @@ -430,7 +430,7 @@ class JoinedRow4 extends Row { override def getAs[T](i: Int): T = if (i < row1.length) row1.getAs[T](i) else row2.getAs[T](i - row1.length) - override def copy() = { + override def copy(): Row = { val totalSize = row1.length + row2.length val copiedValues = new Array[Any](totalSize) var i = 0 @@ -441,7 +441,7 @@ class JoinedRow4 extends Row { new GenericRow(copiedValues) } - override def toString() = { + override def toString: String = { // Make sure toString never throws NullPointerException. if ((row1 eq null) && (row2 eq null)) { "[ empty row ]" @@ -489,12 +489,12 @@ class JoinedRow5 extends Row { override def toSeq: Seq[Any] = row1.toSeq ++ row2.toSeq - override def length = row1.length + row2.length + override def length: Int = row1.length + row2.length - override def apply(i: Int) = + override def apply(i: Int): Any = if (i < row1.length) row1(i) else row2(i - row1.length) - override def isNullAt(i: Int) = + override def isNullAt(i: Int): Boolean = if (i < row1.length) row1.isNullAt(i) else row2.isNullAt(i - row1.length) override def getInt(i: Int): Int = @@ -524,7 +524,7 @@ class JoinedRow5 extends Row { override def getAs[T](i: Int): T = if (i < row1.length) row1.getAs[T](i) else row2.getAs[T](i - row1.length) - override def copy() = { + override def copy(): Row = { val totalSize = row1.length + row2.length val copiedValues = new Array[Any](totalSize) var i = 0 @@ -535,7 +535,7 @@ class JoinedRow5 extends Row { new GenericRow(copiedValues) } - override def toString() = { + override def toString: String = { // Make sure toString never throws NullPointerException. if ((row1 eq null) && (row2 eq null)) { "[ empty row ]" diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Rand.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Rand.scala index b2c6d3029031d..f5fea3f015dc4 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Rand.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Rand.scala @@ -18,16 +18,19 @@ package org.apache.spark.sql.catalyst.expressions import java.util.Random -import org.apache.spark.sql.types.DoubleType + +import org.apache.spark.sql.types.{DataType, DoubleType} case object Rand extends LeafExpression { - override def dataType = DoubleType - override def nullable = false + override def dataType: DataType = DoubleType + override def nullable: Boolean = false private[this] lazy val rand = new Random - override def eval(input: Row = null) = rand.nextDouble().asInstanceOf[EvaluatedType] + override def eval(input: Row = null): EvaluatedType = { + rand.nextDouble().asInstanceOf[EvaluatedType] + } - override def toString = "RAND()" + override def toString: String = "RAND()" } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala index 8a36c6810790d..9a77ca624ebe2 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala @@ -17,7 +17,7 @@ package org.apache.spark.sql.catalyst.expressions -import org.apache.spark.sql.catalyst.ScalaReflection +import org.apache.spark.sql.catalyst.CatalystTypeConverters import org.apache.spark.sql.types.DataType /** @@ -29,9 +29,9 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi type EvaluatedType = Any - def nullable = true + override def nullable: Boolean = true - override def toString = s"scalaUDF(${children.mkString(",")})" + override def toString: String = s"scalaUDF(${children.mkString(",")})" // scalastyle:off @@ -39,363 +39,924 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi (1 to 22).map { x => val anys = (1 to x).map(x => "Any").reduce(_ + ", " + _) - val evals = (0 to x - 1).map(x => s" ScalaReflection.convertToScala(children($x).eval(input), children($x).dataType)").reduce(_ + ",\n " + _) - - s""" - case $x => - function.asInstanceOf[($anys) => Any]( - $evals) - """ + val childs = (0 to x - 1).map(x => s"val child$x = children($x)").reduce(_ + "\n " + _) + lazy val converters = (0 to x - 1).map(x => s"lazy val converter$x = CatalystTypeConverters.createToScalaConverter(child$x.dataType)").reduce(_ + "\n " + _) + val evals = (0 to x - 1).map(x => s"converter$x(child$x.eval(input))").reduce(_ + ",\n " + _) + + s"""case $x => + val func = function.asInstanceOf[($anys) => Any] + $childs + $converters + (input: Row) => { + func( + $evals) + } + """ }.foreach(println) */ - - override def eval(input: Row): Any = { - val result = children.size match { - case 0 => function.asInstanceOf[() => Any]() - case 1 => - function.asInstanceOf[(Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType)) - - - case 2 => - function.asInstanceOf[(Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType)) - - - case 3 => - function.asInstanceOf[(Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType)) - - - case 4 => - function.asInstanceOf[(Any, Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType), - ScalaReflection.convertToScala(children(3).eval(input), children(3).dataType)) - - - case 5 => - function.asInstanceOf[(Any, Any, Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType), - ScalaReflection.convertToScala(children(3).eval(input), children(3).dataType), - ScalaReflection.convertToScala(children(4).eval(input), children(4).dataType)) - - - case 6 => - function.asInstanceOf[(Any, Any, Any, Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType), - ScalaReflection.convertToScala(children(3).eval(input), children(3).dataType), - ScalaReflection.convertToScala(children(4).eval(input), children(4).dataType), - ScalaReflection.convertToScala(children(5).eval(input), children(5).dataType)) - - - case 7 => - function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType), - ScalaReflection.convertToScala(children(3).eval(input), children(3).dataType), - ScalaReflection.convertToScala(children(4).eval(input), children(4).dataType), - ScalaReflection.convertToScala(children(5).eval(input), children(5).dataType), - ScalaReflection.convertToScala(children(6).eval(input), children(6).dataType)) - - - case 8 => - function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType), - ScalaReflection.convertToScala(children(3).eval(input), children(3).dataType), - ScalaReflection.convertToScala(children(4).eval(input), children(4).dataType), - ScalaReflection.convertToScala(children(5).eval(input), children(5).dataType), - ScalaReflection.convertToScala(children(6).eval(input), children(6).dataType), - ScalaReflection.convertToScala(children(7).eval(input), children(7).dataType)) - - - case 9 => - function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType), - ScalaReflection.convertToScala(children(3).eval(input), children(3).dataType), - ScalaReflection.convertToScala(children(4).eval(input), children(4).dataType), - ScalaReflection.convertToScala(children(5).eval(input), children(5).dataType), - ScalaReflection.convertToScala(children(6).eval(input), children(6).dataType), - ScalaReflection.convertToScala(children(7).eval(input), children(7).dataType), - ScalaReflection.convertToScala(children(8).eval(input), children(8).dataType)) - - - case 10 => - function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType), - ScalaReflection.convertToScala(children(3).eval(input), children(3).dataType), - ScalaReflection.convertToScala(children(4).eval(input), children(4).dataType), - ScalaReflection.convertToScala(children(5).eval(input), children(5).dataType), - ScalaReflection.convertToScala(children(6).eval(input), children(6).dataType), - ScalaReflection.convertToScala(children(7).eval(input), children(7).dataType), - ScalaReflection.convertToScala(children(8).eval(input), children(8).dataType), - ScalaReflection.convertToScala(children(9).eval(input), children(9).dataType)) - - - case 11 => - function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType), - ScalaReflection.convertToScala(children(3).eval(input), children(3).dataType), - ScalaReflection.convertToScala(children(4).eval(input), children(4).dataType), - ScalaReflection.convertToScala(children(5).eval(input), children(5).dataType), - ScalaReflection.convertToScala(children(6).eval(input), children(6).dataType), - ScalaReflection.convertToScala(children(7).eval(input), children(7).dataType), - ScalaReflection.convertToScala(children(8).eval(input), children(8).dataType), - ScalaReflection.convertToScala(children(9).eval(input), children(9).dataType), - ScalaReflection.convertToScala(children(10).eval(input), children(10).dataType)) - - - case 12 => - function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType), - ScalaReflection.convertToScala(children(3).eval(input), children(3).dataType), - ScalaReflection.convertToScala(children(4).eval(input), children(4).dataType), - ScalaReflection.convertToScala(children(5).eval(input), children(5).dataType), - ScalaReflection.convertToScala(children(6).eval(input), children(6).dataType), - ScalaReflection.convertToScala(children(7).eval(input), children(7).dataType), - ScalaReflection.convertToScala(children(8).eval(input), children(8).dataType), - ScalaReflection.convertToScala(children(9).eval(input), children(9).dataType), - ScalaReflection.convertToScala(children(10).eval(input), children(10).dataType), - ScalaReflection.convertToScala(children(11).eval(input), children(11).dataType)) - - - case 13 => - function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType), - ScalaReflection.convertToScala(children(3).eval(input), children(3).dataType), - ScalaReflection.convertToScala(children(4).eval(input), children(4).dataType), - ScalaReflection.convertToScala(children(5).eval(input), children(5).dataType), - ScalaReflection.convertToScala(children(6).eval(input), children(6).dataType), - ScalaReflection.convertToScala(children(7).eval(input), children(7).dataType), - ScalaReflection.convertToScala(children(8).eval(input), children(8).dataType), - ScalaReflection.convertToScala(children(9).eval(input), children(9).dataType), - ScalaReflection.convertToScala(children(10).eval(input), children(10).dataType), - ScalaReflection.convertToScala(children(11).eval(input), children(11).dataType), - ScalaReflection.convertToScala(children(12).eval(input), children(12).dataType)) - - - case 14 => - function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType), - ScalaReflection.convertToScala(children(3).eval(input), children(3).dataType), - ScalaReflection.convertToScala(children(4).eval(input), children(4).dataType), - ScalaReflection.convertToScala(children(5).eval(input), children(5).dataType), - ScalaReflection.convertToScala(children(6).eval(input), children(6).dataType), - ScalaReflection.convertToScala(children(7).eval(input), children(7).dataType), - ScalaReflection.convertToScala(children(8).eval(input), children(8).dataType), - ScalaReflection.convertToScala(children(9).eval(input), children(9).dataType), - ScalaReflection.convertToScala(children(10).eval(input), children(10).dataType), - ScalaReflection.convertToScala(children(11).eval(input), children(11).dataType), - ScalaReflection.convertToScala(children(12).eval(input), children(12).dataType), - ScalaReflection.convertToScala(children(13).eval(input), children(13).dataType)) - - - case 15 => - function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType), - ScalaReflection.convertToScala(children(3).eval(input), children(3).dataType), - ScalaReflection.convertToScala(children(4).eval(input), children(4).dataType), - ScalaReflection.convertToScala(children(5).eval(input), children(5).dataType), - ScalaReflection.convertToScala(children(6).eval(input), children(6).dataType), - ScalaReflection.convertToScala(children(7).eval(input), children(7).dataType), - ScalaReflection.convertToScala(children(8).eval(input), children(8).dataType), - ScalaReflection.convertToScala(children(9).eval(input), children(9).dataType), - ScalaReflection.convertToScala(children(10).eval(input), children(10).dataType), - ScalaReflection.convertToScala(children(11).eval(input), children(11).dataType), - ScalaReflection.convertToScala(children(12).eval(input), children(12).dataType), - ScalaReflection.convertToScala(children(13).eval(input), children(13).dataType), - ScalaReflection.convertToScala(children(14).eval(input), children(14).dataType)) - - - case 16 => - function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType), - ScalaReflection.convertToScala(children(3).eval(input), children(3).dataType), - ScalaReflection.convertToScala(children(4).eval(input), children(4).dataType), - ScalaReflection.convertToScala(children(5).eval(input), children(5).dataType), - ScalaReflection.convertToScala(children(6).eval(input), children(6).dataType), - ScalaReflection.convertToScala(children(7).eval(input), children(7).dataType), - ScalaReflection.convertToScala(children(8).eval(input), children(8).dataType), - ScalaReflection.convertToScala(children(9).eval(input), children(9).dataType), - ScalaReflection.convertToScala(children(10).eval(input), children(10).dataType), - ScalaReflection.convertToScala(children(11).eval(input), children(11).dataType), - ScalaReflection.convertToScala(children(12).eval(input), children(12).dataType), - ScalaReflection.convertToScala(children(13).eval(input), children(13).dataType), - ScalaReflection.convertToScala(children(14).eval(input), children(14).dataType), - ScalaReflection.convertToScala(children(15).eval(input), children(15).dataType)) - - - case 17 => - function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType), - ScalaReflection.convertToScala(children(3).eval(input), children(3).dataType), - ScalaReflection.convertToScala(children(4).eval(input), children(4).dataType), - ScalaReflection.convertToScala(children(5).eval(input), children(5).dataType), - ScalaReflection.convertToScala(children(6).eval(input), children(6).dataType), - ScalaReflection.convertToScala(children(7).eval(input), children(7).dataType), - ScalaReflection.convertToScala(children(8).eval(input), children(8).dataType), - ScalaReflection.convertToScala(children(9).eval(input), children(9).dataType), - ScalaReflection.convertToScala(children(10).eval(input), children(10).dataType), - ScalaReflection.convertToScala(children(11).eval(input), children(11).dataType), - ScalaReflection.convertToScala(children(12).eval(input), children(12).dataType), - ScalaReflection.convertToScala(children(13).eval(input), children(13).dataType), - ScalaReflection.convertToScala(children(14).eval(input), children(14).dataType), - ScalaReflection.convertToScala(children(15).eval(input), children(15).dataType), - ScalaReflection.convertToScala(children(16).eval(input), children(16).dataType)) - - - case 18 => - function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType), - ScalaReflection.convertToScala(children(3).eval(input), children(3).dataType), - ScalaReflection.convertToScala(children(4).eval(input), children(4).dataType), - ScalaReflection.convertToScala(children(5).eval(input), children(5).dataType), - ScalaReflection.convertToScala(children(6).eval(input), children(6).dataType), - ScalaReflection.convertToScala(children(7).eval(input), children(7).dataType), - ScalaReflection.convertToScala(children(8).eval(input), children(8).dataType), - ScalaReflection.convertToScala(children(9).eval(input), children(9).dataType), - ScalaReflection.convertToScala(children(10).eval(input), children(10).dataType), - ScalaReflection.convertToScala(children(11).eval(input), children(11).dataType), - ScalaReflection.convertToScala(children(12).eval(input), children(12).dataType), - ScalaReflection.convertToScala(children(13).eval(input), children(13).dataType), - ScalaReflection.convertToScala(children(14).eval(input), children(14).dataType), - ScalaReflection.convertToScala(children(15).eval(input), children(15).dataType), - ScalaReflection.convertToScala(children(16).eval(input), children(16).dataType), - ScalaReflection.convertToScala(children(17).eval(input), children(17).dataType)) - - - case 19 => - function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType), - ScalaReflection.convertToScala(children(3).eval(input), children(3).dataType), - ScalaReflection.convertToScala(children(4).eval(input), children(4).dataType), - ScalaReflection.convertToScala(children(5).eval(input), children(5).dataType), - ScalaReflection.convertToScala(children(6).eval(input), children(6).dataType), - ScalaReflection.convertToScala(children(7).eval(input), children(7).dataType), - ScalaReflection.convertToScala(children(8).eval(input), children(8).dataType), - ScalaReflection.convertToScala(children(9).eval(input), children(9).dataType), - ScalaReflection.convertToScala(children(10).eval(input), children(10).dataType), - ScalaReflection.convertToScala(children(11).eval(input), children(11).dataType), - ScalaReflection.convertToScala(children(12).eval(input), children(12).dataType), - ScalaReflection.convertToScala(children(13).eval(input), children(13).dataType), - ScalaReflection.convertToScala(children(14).eval(input), children(14).dataType), - ScalaReflection.convertToScala(children(15).eval(input), children(15).dataType), - ScalaReflection.convertToScala(children(16).eval(input), children(16).dataType), - ScalaReflection.convertToScala(children(17).eval(input), children(17).dataType), - ScalaReflection.convertToScala(children(18).eval(input), children(18).dataType)) - - - case 20 => - function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType), - ScalaReflection.convertToScala(children(3).eval(input), children(3).dataType), - ScalaReflection.convertToScala(children(4).eval(input), children(4).dataType), - ScalaReflection.convertToScala(children(5).eval(input), children(5).dataType), - ScalaReflection.convertToScala(children(6).eval(input), children(6).dataType), - ScalaReflection.convertToScala(children(7).eval(input), children(7).dataType), - ScalaReflection.convertToScala(children(8).eval(input), children(8).dataType), - ScalaReflection.convertToScala(children(9).eval(input), children(9).dataType), - ScalaReflection.convertToScala(children(10).eval(input), children(10).dataType), - ScalaReflection.convertToScala(children(11).eval(input), children(11).dataType), - ScalaReflection.convertToScala(children(12).eval(input), children(12).dataType), - ScalaReflection.convertToScala(children(13).eval(input), children(13).dataType), - ScalaReflection.convertToScala(children(14).eval(input), children(14).dataType), - ScalaReflection.convertToScala(children(15).eval(input), children(15).dataType), - ScalaReflection.convertToScala(children(16).eval(input), children(16).dataType), - ScalaReflection.convertToScala(children(17).eval(input), children(17).dataType), - ScalaReflection.convertToScala(children(18).eval(input), children(18).dataType), - ScalaReflection.convertToScala(children(19).eval(input), children(19).dataType)) - - - case 21 => - function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType), - ScalaReflection.convertToScala(children(3).eval(input), children(3).dataType), - ScalaReflection.convertToScala(children(4).eval(input), children(4).dataType), - ScalaReflection.convertToScala(children(5).eval(input), children(5).dataType), - ScalaReflection.convertToScala(children(6).eval(input), children(6).dataType), - ScalaReflection.convertToScala(children(7).eval(input), children(7).dataType), - ScalaReflection.convertToScala(children(8).eval(input), children(8).dataType), - ScalaReflection.convertToScala(children(9).eval(input), children(9).dataType), - ScalaReflection.convertToScala(children(10).eval(input), children(10).dataType), - ScalaReflection.convertToScala(children(11).eval(input), children(11).dataType), - ScalaReflection.convertToScala(children(12).eval(input), children(12).dataType), - ScalaReflection.convertToScala(children(13).eval(input), children(13).dataType), - ScalaReflection.convertToScala(children(14).eval(input), children(14).dataType), - ScalaReflection.convertToScala(children(15).eval(input), children(15).dataType), - ScalaReflection.convertToScala(children(16).eval(input), children(16).dataType), - ScalaReflection.convertToScala(children(17).eval(input), children(17).dataType), - ScalaReflection.convertToScala(children(18).eval(input), children(18).dataType), - ScalaReflection.convertToScala(children(19).eval(input), children(19).dataType), - ScalaReflection.convertToScala(children(20).eval(input), children(20).dataType)) - - - case 22 => - function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType), - ScalaReflection.convertToScala(children(3).eval(input), children(3).dataType), - ScalaReflection.convertToScala(children(4).eval(input), children(4).dataType), - ScalaReflection.convertToScala(children(5).eval(input), children(5).dataType), - ScalaReflection.convertToScala(children(6).eval(input), children(6).dataType), - ScalaReflection.convertToScala(children(7).eval(input), children(7).dataType), - ScalaReflection.convertToScala(children(8).eval(input), children(8).dataType), - ScalaReflection.convertToScala(children(9).eval(input), children(9).dataType), - ScalaReflection.convertToScala(children(10).eval(input), children(10).dataType), - ScalaReflection.convertToScala(children(11).eval(input), children(11).dataType), - ScalaReflection.convertToScala(children(12).eval(input), children(12).dataType), - ScalaReflection.convertToScala(children(13).eval(input), children(13).dataType), - ScalaReflection.convertToScala(children(14).eval(input), children(14).dataType), - ScalaReflection.convertToScala(children(15).eval(input), children(15).dataType), - ScalaReflection.convertToScala(children(16).eval(input), children(16).dataType), - ScalaReflection.convertToScala(children(17).eval(input), children(17).dataType), - ScalaReflection.convertToScala(children(18).eval(input), children(18).dataType), - ScalaReflection.convertToScala(children(19).eval(input), children(19).dataType), - ScalaReflection.convertToScala(children(20).eval(input), children(20).dataType), - ScalaReflection.convertToScala(children(21).eval(input), children(21).dataType)) - - } - // scalastyle:on - - ScalaReflection.convertToCatalyst(result, dataType) + + val f = children.size match { + case 0 => + val func = function.asInstanceOf[() => Any] + (input: Row) => { + func() + } + + case 1 => + val func = function.asInstanceOf[(Any) => Any] + val child0 = children(0) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + (input: Row) => { + func( + converter0(child0.eval(input))) + } + + case 2 => + val func = function.asInstanceOf[(Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + (input: Row) => { + func( + converter0(child0.eval(input)), + converter1(child1.eval(input))) + } + + case 3 => + val func = function.asInstanceOf[(Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) + (input: Row) => { + func( + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input))) + } + + case 4 => + val func = function.asInstanceOf[(Any, Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + val child3 = children(3) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) + lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) + (input: Row) => { + func( + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input)), + converter3(child3.eval(input))) + } + + case 5 => + val func = function.asInstanceOf[(Any, Any, Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + val child3 = children(3) + val child4 = children(4) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) + lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) + lazy val converter4 = CatalystTypeConverters.createToScalaConverter(child4.dataType) + (input: Row) => { + func( + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input)), + converter3(child3.eval(input)), + converter4(child4.eval(input))) + } + + case 6 => + val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + val child3 = children(3) + val child4 = children(4) + val child5 = children(5) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) + lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) + lazy val converter4 = CatalystTypeConverters.createToScalaConverter(child4.dataType) + lazy val converter5 = CatalystTypeConverters.createToScalaConverter(child5.dataType) + (input: Row) => { + func( + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input)), + converter3(child3.eval(input)), + converter4(child4.eval(input)), + converter5(child5.eval(input))) + } + + case 7 => + val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + val child3 = children(3) + val child4 = children(4) + val child5 = children(5) + val child6 = children(6) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) + lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) + lazy val converter4 = CatalystTypeConverters.createToScalaConverter(child4.dataType) + lazy val converter5 = CatalystTypeConverters.createToScalaConverter(child5.dataType) + lazy val converter6 = CatalystTypeConverters.createToScalaConverter(child6.dataType) + (input: Row) => { + func( + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input)), + converter3(child3.eval(input)), + converter4(child4.eval(input)), + converter5(child5.eval(input)), + converter6(child6.eval(input))) + } + + case 8 => + val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + val child3 = children(3) + val child4 = children(4) + val child5 = children(5) + val child6 = children(6) + val child7 = children(7) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) + lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) + lazy val converter4 = CatalystTypeConverters.createToScalaConverter(child4.dataType) + lazy val converter5 = CatalystTypeConverters.createToScalaConverter(child5.dataType) + lazy val converter6 = CatalystTypeConverters.createToScalaConverter(child6.dataType) + lazy val converter7 = CatalystTypeConverters.createToScalaConverter(child7.dataType) + (input: Row) => { + func( + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input)), + converter3(child3.eval(input)), + converter4(child4.eval(input)), + converter5(child5.eval(input)), + converter6(child6.eval(input)), + converter7(child7.eval(input))) + } + + case 9 => + val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + val child3 = children(3) + val child4 = children(4) + val child5 = children(5) + val child6 = children(6) + val child7 = children(7) + val child8 = children(8) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) + lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) + lazy val converter4 = CatalystTypeConverters.createToScalaConverter(child4.dataType) + lazy val converter5 = CatalystTypeConverters.createToScalaConverter(child5.dataType) + lazy val converter6 = CatalystTypeConverters.createToScalaConverter(child6.dataType) + lazy val converter7 = CatalystTypeConverters.createToScalaConverter(child7.dataType) + lazy val converter8 = CatalystTypeConverters.createToScalaConverter(child8.dataType) + (input: Row) => { + func( + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input)), + converter3(child3.eval(input)), + converter4(child4.eval(input)), + converter5(child5.eval(input)), + converter6(child6.eval(input)), + converter7(child7.eval(input)), + converter8(child8.eval(input))) + } + + case 10 => + val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + val child3 = children(3) + val child4 = children(4) + val child5 = children(5) + val child6 = children(6) + val child7 = children(7) + val child8 = children(8) + val child9 = children(9) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) + lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) + lazy val converter4 = CatalystTypeConverters.createToScalaConverter(child4.dataType) + lazy val converter5 = CatalystTypeConverters.createToScalaConverter(child5.dataType) + lazy val converter6 = CatalystTypeConverters.createToScalaConverter(child6.dataType) + lazy val converter7 = CatalystTypeConverters.createToScalaConverter(child7.dataType) + lazy val converter8 = CatalystTypeConverters.createToScalaConverter(child8.dataType) + lazy val converter9 = CatalystTypeConverters.createToScalaConverter(child9.dataType) + (input: Row) => { + func( + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input)), + converter3(child3.eval(input)), + converter4(child4.eval(input)), + converter5(child5.eval(input)), + converter6(child6.eval(input)), + converter7(child7.eval(input)), + converter8(child8.eval(input)), + converter9(child9.eval(input))) + } + + case 11 => + val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + val child3 = children(3) + val child4 = children(4) + val child5 = children(5) + val child6 = children(6) + val child7 = children(7) + val child8 = children(8) + val child9 = children(9) + val child10 = children(10) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) + lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) + lazy val converter4 = CatalystTypeConverters.createToScalaConverter(child4.dataType) + lazy val converter5 = CatalystTypeConverters.createToScalaConverter(child5.dataType) + lazy val converter6 = CatalystTypeConverters.createToScalaConverter(child6.dataType) + lazy val converter7 = CatalystTypeConverters.createToScalaConverter(child7.dataType) + lazy val converter8 = CatalystTypeConverters.createToScalaConverter(child8.dataType) + lazy val converter9 = CatalystTypeConverters.createToScalaConverter(child9.dataType) + lazy val converter10 = CatalystTypeConverters.createToScalaConverter(child10.dataType) + (input: Row) => { + func( + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input)), + converter3(child3.eval(input)), + converter4(child4.eval(input)), + converter5(child5.eval(input)), + converter6(child6.eval(input)), + converter7(child7.eval(input)), + converter8(child8.eval(input)), + converter9(child9.eval(input)), + converter10(child10.eval(input))) + } + + case 12 => + val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + val child3 = children(3) + val child4 = children(4) + val child5 = children(5) + val child6 = children(6) + val child7 = children(7) + val child8 = children(8) + val child9 = children(9) + val child10 = children(10) + val child11 = children(11) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) + lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) + lazy val converter4 = CatalystTypeConverters.createToScalaConverter(child4.dataType) + lazy val converter5 = CatalystTypeConverters.createToScalaConverter(child5.dataType) + lazy val converter6 = CatalystTypeConverters.createToScalaConverter(child6.dataType) + lazy val converter7 = CatalystTypeConverters.createToScalaConverter(child7.dataType) + lazy val converter8 = CatalystTypeConverters.createToScalaConverter(child8.dataType) + lazy val converter9 = CatalystTypeConverters.createToScalaConverter(child9.dataType) + lazy val converter10 = CatalystTypeConverters.createToScalaConverter(child10.dataType) + lazy val converter11 = CatalystTypeConverters.createToScalaConverter(child11.dataType) + (input: Row) => { + func( + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input)), + converter3(child3.eval(input)), + converter4(child4.eval(input)), + converter5(child5.eval(input)), + converter6(child6.eval(input)), + converter7(child7.eval(input)), + converter8(child8.eval(input)), + converter9(child9.eval(input)), + converter10(child10.eval(input)), + converter11(child11.eval(input))) + } + + case 13 => + val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + val child3 = children(3) + val child4 = children(4) + val child5 = children(5) + val child6 = children(6) + val child7 = children(7) + val child8 = children(8) + val child9 = children(9) + val child10 = children(10) + val child11 = children(11) + val child12 = children(12) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) + lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) + lazy val converter4 = CatalystTypeConverters.createToScalaConverter(child4.dataType) + lazy val converter5 = CatalystTypeConverters.createToScalaConverter(child5.dataType) + lazy val converter6 = CatalystTypeConverters.createToScalaConverter(child6.dataType) + lazy val converter7 = CatalystTypeConverters.createToScalaConverter(child7.dataType) + lazy val converter8 = CatalystTypeConverters.createToScalaConverter(child8.dataType) + lazy val converter9 = CatalystTypeConverters.createToScalaConverter(child9.dataType) + lazy val converter10 = CatalystTypeConverters.createToScalaConverter(child10.dataType) + lazy val converter11 = CatalystTypeConverters.createToScalaConverter(child11.dataType) + lazy val converter12 = CatalystTypeConverters.createToScalaConverter(child12.dataType) + (input: Row) => { + func( + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input)), + converter3(child3.eval(input)), + converter4(child4.eval(input)), + converter5(child5.eval(input)), + converter6(child6.eval(input)), + converter7(child7.eval(input)), + converter8(child8.eval(input)), + converter9(child9.eval(input)), + converter10(child10.eval(input)), + converter11(child11.eval(input)), + converter12(child12.eval(input))) + } + + case 14 => + val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + val child3 = children(3) + val child4 = children(4) + val child5 = children(5) + val child6 = children(6) + val child7 = children(7) + val child8 = children(8) + val child9 = children(9) + val child10 = children(10) + val child11 = children(11) + val child12 = children(12) + val child13 = children(13) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) + lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) + lazy val converter4 = CatalystTypeConverters.createToScalaConverter(child4.dataType) + lazy val converter5 = CatalystTypeConverters.createToScalaConverter(child5.dataType) + lazy val converter6 = CatalystTypeConverters.createToScalaConverter(child6.dataType) + lazy val converter7 = CatalystTypeConverters.createToScalaConverter(child7.dataType) + lazy val converter8 = CatalystTypeConverters.createToScalaConverter(child8.dataType) + lazy val converter9 = CatalystTypeConverters.createToScalaConverter(child9.dataType) + lazy val converter10 = CatalystTypeConverters.createToScalaConverter(child10.dataType) + lazy val converter11 = CatalystTypeConverters.createToScalaConverter(child11.dataType) + lazy val converter12 = CatalystTypeConverters.createToScalaConverter(child12.dataType) + lazy val converter13 = CatalystTypeConverters.createToScalaConverter(child13.dataType) + (input: Row) => { + func( + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input)), + converter3(child3.eval(input)), + converter4(child4.eval(input)), + converter5(child5.eval(input)), + converter6(child6.eval(input)), + converter7(child7.eval(input)), + converter8(child8.eval(input)), + converter9(child9.eval(input)), + converter10(child10.eval(input)), + converter11(child11.eval(input)), + converter12(child12.eval(input)), + converter13(child13.eval(input))) + } + + case 15 => + val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + val child3 = children(3) + val child4 = children(4) + val child5 = children(5) + val child6 = children(6) + val child7 = children(7) + val child8 = children(8) + val child9 = children(9) + val child10 = children(10) + val child11 = children(11) + val child12 = children(12) + val child13 = children(13) + val child14 = children(14) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) + lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) + lazy val converter4 = CatalystTypeConverters.createToScalaConverter(child4.dataType) + lazy val converter5 = CatalystTypeConverters.createToScalaConverter(child5.dataType) + lazy val converter6 = CatalystTypeConverters.createToScalaConverter(child6.dataType) + lazy val converter7 = CatalystTypeConverters.createToScalaConverter(child7.dataType) + lazy val converter8 = CatalystTypeConverters.createToScalaConverter(child8.dataType) + lazy val converter9 = CatalystTypeConverters.createToScalaConverter(child9.dataType) + lazy val converter10 = CatalystTypeConverters.createToScalaConverter(child10.dataType) + lazy val converter11 = CatalystTypeConverters.createToScalaConverter(child11.dataType) + lazy val converter12 = CatalystTypeConverters.createToScalaConverter(child12.dataType) + lazy val converter13 = CatalystTypeConverters.createToScalaConverter(child13.dataType) + lazy val converter14 = CatalystTypeConverters.createToScalaConverter(child14.dataType) + (input: Row) => { + func( + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input)), + converter3(child3.eval(input)), + converter4(child4.eval(input)), + converter5(child5.eval(input)), + converter6(child6.eval(input)), + converter7(child7.eval(input)), + converter8(child8.eval(input)), + converter9(child9.eval(input)), + converter10(child10.eval(input)), + converter11(child11.eval(input)), + converter12(child12.eval(input)), + converter13(child13.eval(input)), + converter14(child14.eval(input))) + } + + case 16 => + val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + val child3 = children(3) + val child4 = children(4) + val child5 = children(5) + val child6 = children(6) + val child7 = children(7) + val child8 = children(8) + val child9 = children(9) + val child10 = children(10) + val child11 = children(11) + val child12 = children(12) + val child13 = children(13) + val child14 = children(14) + val child15 = children(15) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) + lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) + lazy val converter4 = CatalystTypeConverters.createToScalaConverter(child4.dataType) + lazy val converter5 = CatalystTypeConverters.createToScalaConverter(child5.dataType) + lazy val converter6 = CatalystTypeConverters.createToScalaConverter(child6.dataType) + lazy val converter7 = CatalystTypeConverters.createToScalaConverter(child7.dataType) + lazy val converter8 = CatalystTypeConverters.createToScalaConverter(child8.dataType) + lazy val converter9 = CatalystTypeConverters.createToScalaConverter(child9.dataType) + lazy val converter10 = CatalystTypeConverters.createToScalaConverter(child10.dataType) + lazy val converter11 = CatalystTypeConverters.createToScalaConverter(child11.dataType) + lazy val converter12 = CatalystTypeConverters.createToScalaConverter(child12.dataType) + lazy val converter13 = CatalystTypeConverters.createToScalaConverter(child13.dataType) + lazy val converter14 = CatalystTypeConverters.createToScalaConverter(child14.dataType) + lazy val converter15 = CatalystTypeConverters.createToScalaConverter(child15.dataType) + (input: Row) => { + func( + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input)), + converter3(child3.eval(input)), + converter4(child4.eval(input)), + converter5(child5.eval(input)), + converter6(child6.eval(input)), + converter7(child7.eval(input)), + converter8(child8.eval(input)), + converter9(child9.eval(input)), + converter10(child10.eval(input)), + converter11(child11.eval(input)), + converter12(child12.eval(input)), + converter13(child13.eval(input)), + converter14(child14.eval(input)), + converter15(child15.eval(input))) + } + + case 17 => + val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + val child3 = children(3) + val child4 = children(4) + val child5 = children(5) + val child6 = children(6) + val child7 = children(7) + val child8 = children(8) + val child9 = children(9) + val child10 = children(10) + val child11 = children(11) + val child12 = children(12) + val child13 = children(13) + val child14 = children(14) + val child15 = children(15) + val child16 = children(16) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) + lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) + lazy val converter4 = CatalystTypeConverters.createToScalaConverter(child4.dataType) + lazy val converter5 = CatalystTypeConverters.createToScalaConverter(child5.dataType) + lazy val converter6 = CatalystTypeConverters.createToScalaConverter(child6.dataType) + lazy val converter7 = CatalystTypeConverters.createToScalaConverter(child7.dataType) + lazy val converter8 = CatalystTypeConverters.createToScalaConverter(child8.dataType) + lazy val converter9 = CatalystTypeConverters.createToScalaConverter(child9.dataType) + lazy val converter10 = CatalystTypeConverters.createToScalaConverter(child10.dataType) + lazy val converter11 = CatalystTypeConverters.createToScalaConverter(child11.dataType) + lazy val converter12 = CatalystTypeConverters.createToScalaConverter(child12.dataType) + lazy val converter13 = CatalystTypeConverters.createToScalaConverter(child13.dataType) + lazy val converter14 = CatalystTypeConverters.createToScalaConverter(child14.dataType) + lazy val converter15 = CatalystTypeConverters.createToScalaConverter(child15.dataType) + lazy val converter16 = CatalystTypeConverters.createToScalaConverter(child16.dataType) + (input: Row) => { + func( + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input)), + converter3(child3.eval(input)), + converter4(child4.eval(input)), + converter5(child5.eval(input)), + converter6(child6.eval(input)), + converter7(child7.eval(input)), + converter8(child8.eval(input)), + converter9(child9.eval(input)), + converter10(child10.eval(input)), + converter11(child11.eval(input)), + converter12(child12.eval(input)), + converter13(child13.eval(input)), + converter14(child14.eval(input)), + converter15(child15.eval(input)), + converter16(child16.eval(input))) + } + + case 18 => + val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + val child3 = children(3) + val child4 = children(4) + val child5 = children(5) + val child6 = children(6) + val child7 = children(7) + val child8 = children(8) + val child9 = children(9) + val child10 = children(10) + val child11 = children(11) + val child12 = children(12) + val child13 = children(13) + val child14 = children(14) + val child15 = children(15) + val child16 = children(16) + val child17 = children(17) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) + lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) + lazy val converter4 = CatalystTypeConverters.createToScalaConverter(child4.dataType) + lazy val converter5 = CatalystTypeConverters.createToScalaConverter(child5.dataType) + lazy val converter6 = CatalystTypeConverters.createToScalaConverter(child6.dataType) + lazy val converter7 = CatalystTypeConverters.createToScalaConverter(child7.dataType) + lazy val converter8 = CatalystTypeConverters.createToScalaConverter(child8.dataType) + lazy val converter9 = CatalystTypeConverters.createToScalaConverter(child9.dataType) + lazy val converter10 = CatalystTypeConverters.createToScalaConverter(child10.dataType) + lazy val converter11 = CatalystTypeConverters.createToScalaConverter(child11.dataType) + lazy val converter12 = CatalystTypeConverters.createToScalaConverter(child12.dataType) + lazy val converter13 = CatalystTypeConverters.createToScalaConverter(child13.dataType) + lazy val converter14 = CatalystTypeConverters.createToScalaConverter(child14.dataType) + lazy val converter15 = CatalystTypeConverters.createToScalaConverter(child15.dataType) + lazy val converter16 = CatalystTypeConverters.createToScalaConverter(child16.dataType) + lazy val converter17 = CatalystTypeConverters.createToScalaConverter(child17.dataType) + (input: Row) => { + func( + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input)), + converter3(child3.eval(input)), + converter4(child4.eval(input)), + converter5(child5.eval(input)), + converter6(child6.eval(input)), + converter7(child7.eval(input)), + converter8(child8.eval(input)), + converter9(child9.eval(input)), + converter10(child10.eval(input)), + converter11(child11.eval(input)), + converter12(child12.eval(input)), + converter13(child13.eval(input)), + converter14(child14.eval(input)), + converter15(child15.eval(input)), + converter16(child16.eval(input)), + converter17(child17.eval(input))) + } + + case 19 => + val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + val child3 = children(3) + val child4 = children(4) + val child5 = children(5) + val child6 = children(6) + val child7 = children(7) + val child8 = children(8) + val child9 = children(9) + val child10 = children(10) + val child11 = children(11) + val child12 = children(12) + val child13 = children(13) + val child14 = children(14) + val child15 = children(15) + val child16 = children(16) + val child17 = children(17) + val child18 = children(18) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) + lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) + lazy val converter4 = CatalystTypeConverters.createToScalaConverter(child4.dataType) + lazy val converter5 = CatalystTypeConverters.createToScalaConverter(child5.dataType) + lazy val converter6 = CatalystTypeConverters.createToScalaConverter(child6.dataType) + lazy val converter7 = CatalystTypeConverters.createToScalaConverter(child7.dataType) + lazy val converter8 = CatalystTypeConverters.createToScalaConverter(child8.dataType) + lazy val converter9 = CatalystTypeConverters.createToScalaConverter(child9.dataType) + lazy val converter10 = CatalystTypeConverters.createToScalaConverter(child10.dataType) + lazy val converter11 = CatalystTypeConverters.createToScalaConverter(child11.dataType) + lazy val converter12 = CatalystTypeConverters.createToScalaConverter(child12.dataType) + lazy val converter13 = CatalystTypeConverters.createToScalaConverter(child13.dataType) + lazy val converter14 = CatalystTypeConverters.createToScalaConverter(child14.dataType) + lazy val converter15 = CatalystTypeConverters.createToScalaConverter(child15.dataType) + lazy val converter16 = CatalystTypeConverters.createToScalaConverter(child16.dataType) + lazy val converter17 = CatalystTypeConverters.createToScalaConverter(child17.dataType) + lazy val converter18 = CatalystTypeConverters.createToScalaConverter(child18.dataType) + (input: Row) => { + func( + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input)), + converter3(child3.eval(input)), + converter4(child4.eval(input)), + converter5(child5.eval(input)), + converter6(child6.eval(input)), + converter7(child7.eval(input)), + converter8(child8.eval(input)), + converter9(child9.eval(input)), + converter10(child10.eval(input)), + converter11(child11.eval(input)), + converter12(child12.eval(input)), + converter13(child13.eval(input)), + converter14(child14.eval(input)), + converter15(child15.eval(input)), + converter16(child16.eval(input)), + converter17(child17.eval(input)), + converter18(child18.eval(input))) + } + + case 20 => + val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + val child3 = children(3) + val child4 = children(4) + val child5 = children(5) + val child6 = children(6) + val child7 = children(7) + val child8 = children(8) + val child9 = children(9) + val child10 = children(10) + val child11 = children(11) + val child12 = children(12) + val child13 = children(13) + val child14 = children(14) + val child15 = children(15) + val child16 = children(16) + val child17 = children(17) + val child18 = children(18) + val child19 = children(19) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) + lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) + lazy val converter4 = CatalystTypeConverters.createToScalaConverter(child4.dataType) + lazy val converter5 = CatalystTypeConverters.createToScalaConverter(child5.dataType) + lazy val converter6 = CatalystTypeConverters.createToScalaConverter(child6.dataType) + lazy val converter7 = CatalystTypeConverters.createToScalaConverter(child7.dataType) + lazy val converter8 = CatalystTypeConverters.createToScalaConverter(child8.dataType) + lazy val converter9 = CatalystTypeConverters.createToScalaConverter(child9.dataType) + lazy val converter10 = CatalystTypeConverters.createToScalaConverter(child10.dataType) + lazy val converter11 = CatalystTypeConverters.createToScalaConverter(child11.dataType) + lazy val converter12 = CatalystTypeConverters.createToScalaConverter(child12.dataType) + lazy val converter13 = CatalystTypeConverters.createToScalaConverter(child13.dataType) + lazy val converter14 = CatalystTypeConverters.createToScalaConverter(child14.dataType) + lazy val converter15 = CatalystTypeConverters.createToScalaConverter(child15.dataType) + lazy val converter16 = CatalystTypeConverters.createToScalaConverter(child16.dataType) + lazy val converter17 = CatalystTypeConverters.createToScalaConverter(child17.dataType) + lazy val converter18 = CatalystTypeConverters.createToScalaConverter(child18.dataType) + lazy val converter19 = CatalystTypeConverters.createToScalaConverter(child19.dataType) + (input: Row) => { + func( + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input)), + converter3(child3.eval(input)), + converter4(child4.eval(input)), + converter5(child5.eval(input)), + converter6(child6.eval(input)), + converter7(child7.eval(input)), + converter8(child8.eval(input)), + converter9(child9.eval(input)), + converter10(child10.eval(input)), + converter11(child11.eval(input)), + converter12(child12.eval(input)), + converter13(child13.eval(input)), + converter14(child14.eval(input)), + converter15(child15.eval(input)), + converter16(child16.eval(input)), + converter17(child17.eval(input)), + converter18(child18.eval(input)), + converter19(child19.eval(input))) + } + + case 21 => + val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + val child3 = children(3) + val child4 = children(4) + val child5 = children(5) + val child6 = children(6) + val child7 = children(7) + val child8 = children(8) + val child9 = children(9) + val child10 = children(10) + val child11 = children(11) + val child12 = children(12) + val child13 = children(13) + val child14 = children(14) + val child15 = children(15) + val child16 = children(16) + val child17 = children(17) + val child18 = children(18) + val child19 = children(19) + val child20 = children(20) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) + lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) + lazy val converter4 = CatalystTypeConverters.createToScalaConverter(child4.dataType) + lazy val converter5 = CatalystTypeConverters.createToScalaConverter(child5.dataType) + lazy val converter6 = CatalystTypeConverters.createToScalaConverter(child6.dataType) + lazy val converter7 = CatalystTypeConverters.createToScalaConverter(child7.dataType) + lazy val converter8 = CatalystTypeConverters.createToScalaConverter(child8.dataType) + lazy val converter9 = CatalystTypeConverters.createToScalaConverter(child9.dataType) + lazy val converter10 = CatalystTypeConverters.createToScalaConverter(child10.dataType) + lazy val converter11 = CatalystTypeConverters.createToScalaConverter(child11.dataType) + lazy val converter12 = CatalystTypeConverters.createToScalaConverter(child12.dataType) + lazy val converter13 = CatalystTypeConverters.createToScalaConverter(child13.dataType) + lazy val converter14 = CatalystTypeConverters.createToScalaConverter(child14.dataType) + lazy val converter15 = CatalystTypeConverters.createToScalaConverter(child15.dataType) + lazy val converter16 = CatalystTypeConverters.createToScalaConverter(child16.dataType) + lazy val converter17 = CatalystTypeConverters.createToScalaConverter(child17.dataType) + lazy val converter18 = CatalystTypeConverters.createToScalaConverter(child18.dataType) + lazy val converter19 = CatalystTypeConverters.createToScalaConverter(child19.dataType) + lazy val converter20 = CatalystTypeConverters.createToScalaConverter(child20.dataType) + (input: Row) => { + func( + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input)), + converter3(child3.eval(input)), + converter4(child4.eval(input)), + converter5(child5.eval(input)), + converter6(child6.eval(input)), + converter7(child7.eval(input)), + converter8(child8.eval(input)), + converter9(child9.eval(input)), + converter10(child10.eval(input)), + converter11(child11.eval(input)), + converter12(child12.eval(input)), + converter13(child13.eval(input)), + converter14(child14.eval(input)), + converter15(child15.eval(input)), + converter16(child16.eval(input)), + converter17(child17.eval(input)), + converter18(child18.eval(input)), + converter19(child19.eval(input)), + converter20(child20.eval(input))) + } + + case 22 => + val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + val child3 = children(3) + val child4 = children(4) + val child5 = children(5) + val child6 = children(6) + val child7 = children(7) + val child8 = children(8) + val child9 = children(9) + val child10 = children(10) + val child11 = children(11) + val child12 = children(12) + val child13 = children(13) + val child14 = children(14) + val child15 = children(15) + val child16 = children(16) + val child17 = children(17) + val child18 = children(18) + val child19 = children(19) + val child20 = children(20) + val child21 = children(21) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) + lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) + lazy val converter4 = CatalystTypeConverters.createToScalaConverter(child4.dataType) + lazy val converter5 = CatalystTypeConverters.createToScalaConverter(child5.dataType) + lazy val converter6 = CatalystTypeConverters.createToScalaConverter(child6.dataType) + lazy val converter7 = CatalystTypeConverters.createToScalaConverter(child7.dataType) + lazy val converter8 = CatalystTypeConverters.createToScalaConverter(child8.dataType) + lazy val converter9 = CatalystTypeConverters.createToScalaConverter(child9.dataType) + lazy val converter10 = CatalystTypeConverters.createToScalaConverter(child10.dataType) + lazy val converter11 = CatalystTypeConverters.createToScalaConverter(child11.dataType) + lazy val converter12 = CatalystTypeConverters.createToScalaConverter(child12.dataType) + lazy val converter13 = CatalystTypeConverters.createToScalaConverter(child13.dataType) + lazy val converter14 = CatalystTypeConverters.createToScalaConverter(child14.dataType) + lazy val converter15 = CatalystTypeConverters.createToScalaConverter(child15.dataType) + lazy val converter16 = CatalystTypeConverters.createToScalaConverter(child16.dataType) + lazy val converter17 = CatalystTypeConverters.createToScalaConverter(child17.dataType) + lazy val converter18 = CatalystTypeConverters.createToScalaConverter(child18.dataType) + lazy val converter19 = CatalystTypeConverters.createToScalaConverter(child19.dataType) + lazy val converter20 = CatalystTypeConverters.createToScalaConverter(child20.dataType) + lazy val converter21 = CatalystTypeConverters.createToScalaConverter(child21.dataType) + (input: Row) => { + func( + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input)), + converter3(child3.eval(input)), + converter4(child4.eval(input)), + converter5(child5.eval(input)), + converter6(child6.eval(input)), + converter7(child7.eval(input)), + converter8(child8.eval(input)), + converter9(child9.eval(input)), + converter10(child10.eval(input)), + converter11(child11.eval(input)), + converter12(child12.eval(input)), + converter13(child13.eval(input)), + converter14(child14.eval(input)), + converter15(child15.eval(input)), + converter16(child16.eval(input)), + converter17(child17.eval(input)), + converter18(child18.eval(input)), + converter19(child19.eval(input)), + converter20(child20.eval(input)), + converter21(child21.eval(input))) + } } + + // scalastyle:on + + override def eval(input: Row): Any = CatalystTypeConverters.convertToCatalyst(f(input), dataType) + } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala index d00b2ac09745c..83074eb1e6310 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala @@ -19,6 +19,7 @@ package org.apache.spark.sql.catalyst.expressions import org.apache.spark.sql.catalyst.errors.TreeNodeException import org.apache.spark.sql.catalyst.trees +import org.apache.spark.sql.types.DataType abstract sealed class SortDirection case object Ascending extends SortDirection @@ -31,12 +32,12 @@ case object Descending extends SortDirection case class SortOrder(child: Expression, direction: SortDirection) extends Expression with trees.UnaryNode[Expression] { - override def dataType = child.dataType - override def nullable = child.nullable + override def dataType: DataType = child.dataType + override def nullable: Boolean = child.nullable // SortOrder itself is never evaluated. override def eval(input: Row = null): EvaluatedType = throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}") - override def toString = s"$child ${if (direction == Ascending) "ASC" else "DESC"}" + override def toString: String = s"$child ${if (direction == Ascending) "ASC" else "DESC"}" } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala index 7434165f654f8..3475ed05f4454 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala @@ -62,126 +62,126 @@ abstract class MutableValue extends Serializable { var isNull: Boolean = true def boxed: Any def update(v: Any) - def copy(): this.type + def copy(): MutableValue } final class MutableInt extends MutableValue { var value: Int = 0 - def boxed = if (isNull) null else value - def update(v: Any) = value = { + override def boxed: Any = if (isNull) null else value + override def update(v: Any): Unit = { isNull = false - v.asInstanceOf[Int] + value = v.asInstanceOf[Int] } - def copy() = { + override def copy(): MutableInt = { val newCopy = new MutableInt newCopy.isNull = isNull newCopy.value = value - newCopy.asInstanceOf[this.type] + newCopy.asInstanceOf[MutableInt] } } final class MutableFloat extends MutableValue { var value: Float = 0 - def boxed = if (isNull) null else value - def update(v: Any) = value = { + override def boxed: Any = if (isNull) null else value + override def update(v: Any): Unit = { isNull = false - v.asInstanceOf[Float] + value = v.asInstanceOf[Float] } - def copy() = { + override def copy(): MutableFloat = { val newCopy = new MutableFloat newCopy.isNull = isNull newCopy.value = value - newCopy.asInstanceOf[this.type] + newCopy.asInstanceOf[MutableFloat] } } final class MutableBoolean extends MutableValue { var value: Boolean = false - def boxed = if (isNull) null else value - def update(v: Any) = value = { + override def boxed: Any = if (isNull) null else value + override def update(v: Any): Unit = { isNull = false - v.asInstanceOf[Boolean] + value = v.asInstanceOf[Boolean] } - def copy() = { + override def copy(): MutableBoolean = { val newCopy = new MutableBoolean newCopy.isNull = isNull newCopy.value = value - newCopy.asInstanceOf[this.type] + newCopy.asInstanceOf[MutableBoolean] } } final class MutableDouble extends MutableValue { var value: Double = 0 - def boxed = if (isNull) null else value - def update(v: Any) = value = { + override def boxed: Any = if (isNull) null else value + override def update(v: Any): Unit = { isNull = false - v.asInstanceOf[Double] + value = v.asInstanceOf[Double] } - def copy() = { + override def copy(): MutableDouble = { val newCopy = new MutableDouble newCopy.isNull = isNull newCopy.value = value - newCopy.asInstanceOf[this.type] + newCopy.asInstanceOf[MutableDouble] } } final class MutableShort extends MutableValue { var value: Short = 0 - def boxed = if (isNull) null else value - def update(v: Any) = value = { + override def boxed: Any = if (isNull) null else value + override def update(v: Any): Unit = value = { isNull = false v.asInstanceOf[Short] } - def copy() = { + override def copy(): MutableShort = { val newCopy = new MutableShort newCopy.isNull = isNull newCopy.value = value - newCopy.asInstanceOf[this.type] + newCopy.asInstanceOf[MutableShort] } } final class MutableLong extends MutableValue { var value: Long = 0 - def boxed = if (isNull) null else value - def update(v: Any) = value = { + override def boxed: Any = if (isNull) null else value + override def update(v: Any): Unit = value = { isNull = false v.asInstanceOf[Long] } - def copy() = { + override def copy(): MutableLong = { val newCopy = new MutableLong newCopy.isNull = isNull newCopy.value = value - newCopy.asInstanceOf[this.type] + newCopy.asInstanceOf[MutableLong] } } final class MutableByte extends MutableValue { var value: Byte = 0 - def boxed = if (isNull) null else value - def update(v: Any) = value = { + override def boxed: Any = if (isNull) null else value + override def update(v: Any): Unit = value = { isNull = false v.asInstanceOf[Byte] } - def copy() = { + override def copy(): MutableByte = { val newCopy = new MutableByte newCopy.isNull = isNull newCopy.value = value - newCopy.asInstanceOf[this.type] + newCopy.asInstanceOf[MutableByte] } } final class MutableAny extends MutableValue { var value: Any = _ - def boxed = if (isNull) null else value - def update(v: Any) = value = { + override def boxed: Any = if (isNull) null else value + override def update(v: Any): Unit = { isNull = false - v.asInstanceOf[Any] + value = v.asInstanceOf[Any] } - def copy() = { + override def copy(): MutableAny = { val newCopy = new MutableAny newCopy.isNull = isNull newCopy.value = value - newCopy.asInstanceOf[this.type] + newCopy.asInstanceOf[MutableAny] } } @@ -220,22 +220,27 @@ final class SpecificMutableRow(val values: Array[MutableValue]) extends MutableR override def isNullAt(i: Int): Boolean = values(i).isNull override def copy(): Row = { - val newValues = new Array[MutableValue](values.length) + val newValues = new Array[Any](values.length) var i = 0 while (i < values.length) { - newValues(i) = values(i).copy() + newValues(i) = values(i).boxed i += 1 } - new SpecificMutableRow(newValues) + + new GenericRow(newValues) } - override def update(ordinal: Int, value: Any): Unit = { - if (value == null) setNullAt(ordinal) else values(ordinal).update(value) + override def update(ordinal: Int, value: Any) { + if (value == null) { + setNullAt(ordinal) + } else { + values(ordinal).update(value) + } } - override def setString(ordinal: Int, value: String) = update(ordinal, value) + override def setString(ordinal: Int, value: String): Unit = update(ordinal, UTF8String(value)) - override def getString(ordinal: Int) = apply(ordinal).asInstanceOf[String] + override def getString(ordinal: Int): String = apply(ordinal).toString override def setInt(ordinal: Int, value: Int): Unit = { val currentValue = values(ordinal).asInstanceOf[MutableInt] diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala old mode 100755 new mode 100644 index 735b7488fdcbd..f3830c6d3bcf2 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala @@ -79,27 +79,29 @@ abstract class AggregateFunction /** Base should return the generic aggregate expression that this function is computing */ val base: AggregateExpression - override def nullable = base.nullable - override def dataType = base.dataType + override def nullable: Boolean = base.nullable + override def dataType: DataType = base.dataType def update(input: Row): Unit // Do we really need this? - override def newInstance() = makeCopy(productIterator.map { case a: AnyRef => a }.toArray) + override def newInstance(): AggregateFunction = { + makeCopy(productIterator.map { case a: AnyRef => a }.toArray) + } } case class Min(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] { - override def nullable = true - override def dataType = child.dataType - override def toString = s"MIN($child)" + override def nullable: Boolean = true + override def dataType: DataType = child.dataType + override def toString: String = s"MIN($child)" override def asPartial: SplitEvaluation = { val partialMin = Alias(Min(child), "PartialMin")() SplitEvaluation(Min(partialMin.toAttribute), partialMin :: Nil) } - override def newInstance() = new MinFunction(child, this) + override def newInstance(): MinFunction = new MinFunction(child, this) } case class MinFunction(expr: Expression, base: AggregateExpression) extends AggregateFunction { @@ -121,16 +123,16 @@ case class MinFunction(expr: Expression, base: AggregateExpression) extends Aggr case class Max(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] { - override def nullable = true - override def dataType = child.dataType - override def toString = s"MAX($child)" + override def nullable: Boolean = true + override def dataType: DataType = child.dataType + override def toString: String = s"MAX($child)" override def asPartial: SplitEvaluation = { val partialMax = Alias(Max(child), "PartialMax")() SplitEvaluation(Max(partialMax.toAttribute), partialMax :: Nil) } - override def newInstance() = new MaxFunction(child, this) + override def newInstance(): MaxFunction = new MaxFunction(child, this) } case class MaxFunction(expr: Expression, base: AggregateExpression) extends AggregateFunction { @@ -152,29 +154,29 @@ case class MaxFunction(expr: Expression, base: AggregateExpression) extends Aggr case class Count(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] { - override def nullable = false - override def dataType = LongType - override def toString = s"COUNT($child)" + override def nullable: Boolean = false + override def dataType: LongType.type = LongType + override def toString: String = s"COUNT($child)" override def asPartial: SplitEvaluation = { val partialCount = Alias(Count(child), "PartialCount")() SplitEvaluation(Coalesce(Seq(Sum(partialCount.toAttribute), Literal(0L))), partialCount :: Nil) } - override def newInstance() = new CountFunction(child, this) + override def newInstance(): CountFunction = new CountFunction(child, this) } case class CountDistinct(expressions: Seq[Expression]) extends PartialAggregate { def this() = this(null) - override def children = expressions + override def children: Seq[Expression] = expressions - override def nullable = false - override def dataType = LongType - override def toString = s"COUNT(DISTINCT ${expressions.mkString(",")})" - override def newInstance() = new CountDistinctFunction(expressions, this) + override def nullable: Boolean = false + override def dataType: DataType = LongType + override def toString: String = s"COUNT(DISTINCT ${expressions.mkString(",")})" + override def newInstance(): CountDistinctFunction = new CountDistinctFunction(expressions, this) - override def asPartial = { + override def asPartial: SplitEvaluation = { val partialSet = Alias(CollectHashSet(expressions), "partialSets")() SplitEvaluation( CombineSetsAndCount(partialSet.toAttribute), @@ -185,11 +187,12 @@ case class CountDistinct(expressions: Seq[Expression]) extends PartialAggregate case class CollectHashSet(expressions: Seq[Expression]) extends AggregateExpression { def this() = this(null) - override def children = expressions - override def nullable = false - override def dataType = ArrayType(expressions.head.dataType) - override def toString = s"AddToHashSet(${expressions.mkString(",")})" - override def newInstance() = new CollectHashSetFunction(expressions, this) + override def children: Seq[Expression] = expressions + override def nullable: Boolean = false + override def dataType: OpenHashSetUDT = new OpenHashSetUDT(expressions.head.dataType) + override def toString: String = s"AddToHashSet(${expressions.mkString(",")})" + override def newInstance(): CollectHashSetFunction = + new CollectHashSetFunction(expressions, this) } case class CollectHashSetFunction( @@ -219,11 +222,13 @@ case class CollectHashSetFunction( case class CombineSetsAndCount(inputSet: Expression) extends AggregateExpression { def this() = this(null) - override def children = inputSet :: Nil - override def nullable = false - override def dataType = LongType - override def toString = s"CombineAndCount($inputSet)" - override def newInstance() = new CombineSetsAndCountFunction(inputSet, this) + override def children: Seq[Expression] = inputSet :: Nil + override def nullable: Boolean = false + override def dataType: DataType = LongType + override def toString: String = s"CombineAndCount($inputSet)" + override def newInstance(): CombineSetsAndCountFunction = { + new CombineSetsAndCountFunction(inputSet, this) + } } case class CombineSetsAndCountFunction( @@ -246,30 +251,51 @@ case class CombineSetsAndCountFunction( override def eval(input: Row): Any = seen.size.toLong } +/** The data type of ApproxCountDistinctPartition since its output is a HyperLogLog object. */ +private[sql] case object HyperLogLogUDT extends UserDefinedType[HyperLogLog] { + + override def sqlType: DataType = BinaryType + + /** Since we are using HyperLogLog internally, usually it will not be called. */ + override def serialize(obj: Any): Array[Byte] = + obj.asInstanceOf[HyperLogLog].getBytes + + + /** Since we are using HyperLogLog internally, usually it will not be called. */ + override def deserialize(datum: Any): HyperLogLog = + HyperLogLog.Builder.build(datum.asInstanceOf[Array[Byte]]) + + override def userClass: Class[HyperLogLog] = classOf[HyperLogLog] +} + case class ApproxCountDistinctPartition(child: Expression, relativeSD: Double) extends AggregateExpression with trees.UnaryNode[Expression] { - override def nullable = false - override def dataType = child.dataType - override def toString = s"APPROXIMATE COUNT(DISTINCT $child)" - override def newInstance() = new ApproxCountDistinctPartitionFunction(child, this, relativeSD) + override def nullable: Boolean = false + override def dataType: DataType = HyperLogLogUDT + override def toString: String = s"APPROXIMATE COUNT(DISTINCT $child)" + override def newInstance(): ApproxCountDistinctPartitionFunction = { + new ApproxCountDistinctPartitionFunction(child, this, relativeSD) + } } case class ApproxCountDistinctMerge(child: Expression, relativeSD: Double) extends AggregateExpression with trees.UnaryNode[Expression] { - override def nullable = false - override def dataType = LongType - override def toString = s"APPROXIMATE COUNT(DISTINCT $child)" - override def newInstance() = new ApproxCountDistinctMergeFunction(child, this, relativeSD) + override def nullable: Boolean = false + override def dataType: LongType.type = LongType + override def toString: String = s"APPROXIMATE COUNT(DISTINCT $child)" + override def newInstance(): ApproxCountDistinctMergeFunction = { + new ApproxCountDistinctMergeFunction(child, this, relativeSD) + } } case class ApproxCountDistinct(child: Expression, relativeSD: Double = 0.05) extends PartialAggregate with trees.UnaryNode[Expression] { - override def nullable = false - override def dataType = LongType - override def toString = s"APPROXIMATE COUNT(DISTINCT $child)" + override def nullable: Boolean = false + override def dataType: LongType.type = LongType + override def toString: String = s"APPROXIMATE COUNT(DISTINCT $child)" override def asPartial: SplitEvaluation = { val partialCount = @@ -280,14 +306,14 @@ case class ApproxCountDistinct(child: Expression, relativeSD: Double = 0.05) partialCount :: Nil) } - override def newInstance() = new CountDistinctFunction(child :: Nil, this) + override def newInstance(): CountDistinctFunction = new CountDistinctFunction(child :: Nil, this) } case class Average(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] { - override def nullable = true + override def nullable: Boolean = true - override def dataType = child.dataType match { + override def dataType: DataType = child.dataType match { case DecimalType.Fixed(precision, scale) => DecimalType(precision + 4, scale + 4) // Add 4 digits after decimal point, like Hive case DecimalType.Unlimited => @@ -296,11 +322,11 @@ case class Average(child: Expression) extends PartialAggregate with trees.UnaryN DoubleType } - override def toString = s"AVG($child)" + override def toString: String = s"AVG($child)" override def asPartial: SplitEvaluation = { child.dataType match { - case DecimalType.Fixed(_, _) => + case DecimalType.Fixed(_, _) | DecimalType.Unlimited => // Turn the child to unlimited decimals for calculation, before going back to fixed val partialSum = Alias(Sum(Cast(child, DecimalType.Unlimited)), "PartialSum")() val partialCount = Alias(Count(child), "PartialCount")() @@ -323,14 +349,14 @@ case class Average(child: Expression) extends PartialAggregate with trees.UnaryN } } - override def newInstance() = new AverageFunction(child, this) + override def newInstance(): AverageFunction = new AverageFunction(child, this) } case class Sum(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] { - override def nullable = true + override def nullable: Boolean = true - override def dataType = child.dataType match { + override def dataType: DataType = child.dataType match { case DecimalType.Fixed(precision, scale) => DecimalType(precision + 10, scale) // Add 10 digits left of decimal point, like Hive case DecimalType.Unlimited => @@ -339,33 +365,57 @@ case class Sum(child: Expression) extends PartialAggregate with trees.UnaryNode[ child.dataType } - override def toString = s"SUM($child)" + override def toString: String = s"SUM($child)" override def asPartial: SplitEvaluation = { child.dataType match { case DecimalType.Fixed(_, _) => val partialSum = Alias(Sum(Cast(child, DecimalType.Unlimited)), "PartialSum")() SplitEvaluation( - Cast(Sum(partialSum.toAttribute), dataType), + Cast(CombineSum(partialSum.toAttribute), dataType), partialSum :: Nil) case _ => val partialSum = Alias(Sum(child), "PartialSum")() SplitEvaluation( - Sum(partialSum.toAttribute), + CombineSum(partialSum.toAttribute), partialSum :: Nil) } } - override def newInstance() = new SumFunction(child, this) + override def newInstance(): SumFunction = new SumFunction(child, this) +} + +/** + * Sum should satisfy 3 cases: + * 1) sum of all null values = zero + * 2) sum for table column with no data = null + * 3) sum of column with null and not null values = sum of not null values + * Require separate CombineSum Expression and function as it has to distinguish "No data" case + * versus "data equals null" case, while aggregating results and at each partial expression.i.e., + * Combining PartitionLevel InputData + * <-- null + * Zero <-- Zero <-- null + * + * <-- null <-- no data + * null <-- null <-- no data + */ +case class CombineSum(child: Expression) extends AggregateExpression { + def this() = this(null) + + override def children: Seq[Expression] = child :: Nil + override def nullable: Boolean = true + override def dataType: DataType = child.dataType + override def toString: String = s"CombineSum($child)" + override def newInstance(): CombineSumFunction = new CombineSumFunction(child, this) } case class SumDistinct(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] { def this() = this(null) - override def nullable = true - override def dataType = child.dataType match { + override def nullable: Boolean = true + override def dataType: DataType = child.dataType match { case DecimalType.Fixed(precision, scale) => DecimalType(precision + 10, scale) // Add 10 digits left of decimal point, like Hive case DecimalType.Unlimited => @@ -373,10 +423,10 @@ case class SumDistinct(child: Expression) case _ => child.dataType } - override def toString = s"SUM(DISTINCT ${child})" - override def newInstance() = new SumDistinctFunction(child, this) + override def toString: String = s"SUM(DISTINCT $child)" + override def newInstance(): SumDistinctFunction = new SumDistinctFunction(child, this) - override def asPartial = { + override def asPartial: SplitEvaluation = { val partialSet = Alias(CollectHashSet(child :: Nil), "partialSets")() SplitEvaluation( CombineSetsAndSum(partialSet.toAttribute, this), @@ -387,11 +437,13 @@ case class SumDistinct(child: Expression) case class CombineSetsAndSum(inputSet: Expression, base: Expression) extends AggregateExpression { def this() = this(null, null) - override def children = inputSet :: Nil - override def nullable = true - override def dataType = base.dataType - override def toString = s"CombineAndSum($inputSet)" - override def newInstance() = new CombineSetsAndSumFunction(inputSet, this) + override def children: Seq[Expression] = inputSet :: Nil + override def nullable: Boolean = true + override def dataType: DataType = base.dataType + override def toString: String = s"CombineAndSum($inputSet)" + override def newInstance(): CombineSetsAndSumFunction = { + new CombineSetsAndSumFunction(inputSet, this) + } } case class CombineSetsAndSumFunction( @@ -425,9 +477,9 @@ case class CombineSetsAndSumFunction( } case class First(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] { - override def nullable = true - override def dataType = child.dataType - override def toString = s"FIRST($child)" + override def nullable: Boolean = true + override def dataType: DataType = child.dataType + override def toString: String = s"FIRST($child)" override def asPartial: SplitEvaluation = { val partialFirst = Alias(First(child), "PartialFirst")() @@ -435,14 +487,14 @@ case class First(child: Expression) extends PartialAggregate with trees.UnaryNod First(partialFirst.toAttribute), partialFirst :: Nil) } - override def newInstance() = new FirstFunction(child, this) + override def newInstance(): FirstFunction = new FirstFunction(child, this) } case class Last(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] { - override def references = child.references - override def nullable = true - override def dataType = child.dataType - override def toString = s"LAST($child)" + override def references: AttributeSet = child.references + override def nullable: Boolean = true + override def dataType: DataType = child.dataType + override def toString: String = s"LAST($child)" override def asPartial: SplitEvaluation = { val partialLast = Alias(Last(child), "PartialLast")() @@ -450,7 +502,7 @@ case class Last(child: Expression) extends PartialAggregate with trees.UnaryNode Last(partialLast.toAttribute), partialLast :: Nil) } - override def newInstance() = new LastFunction(child, this) + override def newInstance(): LastFunction = new LastFunction(child, this) } case class AverageFunction(expr: Expression, base: AggregateExpression) @@ -471,7 +523,8 @@ case class AverageFunction(expr: Expression, base: AggregateExpression) private var count: Long = _ private val sum = MutableLiteral(zero.eval(null), calcType) - private def addFunction(value: Any) = Add(sum, Cast(Literal(value, expr.dataType), calcType)) + private def addFunction(value: Any) = Add(sum, + Cast(Literal.create(value, expr.dataType), calcType)) override def eval(input: Row): Any = { if (count == 0L) { @@ -565,7 +618,8 @@ case class SumFunction(expr: Expression, base: AggregateExpression) extends Aggr private val sum = MutableLiteral(null, calcType) - private val addFunction = Coalesce(Seq(Add(Coalesce(Seq(sum, zero)), Cast(expr, calcType)), sum)) + private val addFunction = + Coalesce(Seq(Add(Coalesce(Seq(sum, zero)), Cast(expr, calcType)), sum, zero)) override def update(input: Row): Unit = { sum.update(addFunction, input) @@ -580,6 +634,43 @@ case class SumFunction(expr: Expression, base: AggregateExpression) extends Aggr } } +case class CombineSumFunction(expr: Expression, base: AggregateExpression) + extends AggregateFunction { + + def this() = this(null, null) // Required for serialization. + + private val calcType = + expr.dataType match { + case DecimalType.Fixed(_, _) => + DecimalType.Unlimited + case _ => + expr.dataType + } + + private val zero = Cast(Literal(0), calcType) + + private val sum = MutableLiteral(null, calcType) + + private val addFunction = + Coalesce(Seq(Add(Coalesce(Seq(sum, zero)), Cast(expr, calcType)), sum, zero)) + + override def update(input: Row): Unit = { + val result = expr.eval(input) + // partial sum result can be null only when no input rows present + if(result != null) { + sum.update(addFunction, input) + } + } + + override def eval(input: Row): Any = { + expr.dataType match { + case DecimalType.Fixed(_, _) => + Cast(sum, dataType).eval(null) + case _ => sum.eval(null) + } + } +} + case class SumDistinctFunction(expr: Expression, base: AggregateExpression) extends AggregateFunction { @@ -651,6 +742,7 @@ case class LastFunction(expr: Expression, base: AggregateExpression) extends Agg result = input } - override def eval(input: Row): Any = if (result != null) expr.eval(result.asInstanceOf[Row]) - else null + override def eval(input: Row): Any = { + if (result != null) expr.eval(result.asInstanceOf[Row]) else null + } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala index 574907f566c0f..566b34f7c3a6a 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala @@ -18,41 +18,53 @@ package org.apache.spark.sql.catalyst.expressions import org.apache.spark.sql.catalyst.analysis.UnresolvedException +import org.apache.spark.sql.catalyst.errors.TreeNodeException import org.apache.spark.sql.types._ case class UnaryMinus(child: Expression) extends UnaryExpression { type EvaluatedType = Any - def dataType = child.dataType - override def foldable = child.foldable - def nullable = child.nullable - override def toString = s"-$child" + override def dataType: DataType = child.dataType + override def foldable: Boolean = child.foldable + override def nullable: Boolean = child.nullable + override def toString: String = s"-$child" + + lazy val numeric = dataType match { + case n: NumericType => n.numeric.asInstanceOf[Numeric[Any]] + case other => sys.error(s"Type $other does not support numeric operations") + } override def eval(input: Row): Any = { - n1(child, input, _.negate(_)) + val evalE = child.eval(input) + if (evalE == null) { + null + } else { + numeric.negate(evalE) + } } } case class Sqrt(child: Expression) extends UnaryExpression { type EvaluatedType = Any - def dataType = DoubleType - override def foldable = child.foldable - def nullable = true - override def toString = s"SQRT($child)" + override def dataType: DataType = DoubleType + override def foldable: Boolean = child.foldable + override def nullable: Boolean = true + override def toString: String = s"SQRT($child)" + + lazy val numeric = child.dataType match { + case n: NumericType => n.numeric.asInstanceOf[Numeric[Any]] + case other => sys.error(s"Type $other does not support non-negative numeric operations") + } override def eval(input: Row): Any = { val evalE = child.eval(input) if (evalE == null) { null } else { - child.dataType match { - case n: NumericType => - val value = n.numeric.toDouble(evalE.asInstanceOf[n.JvmType]) - if (value < 0) null - else math.sqrt(value) - case other => sys.error(s"Type $other does not support non-negative numeric operations") - } + val value = numeric.toDouble(evalE) + if (value < 0) null + else math.sqrt(value) } } } @@ -62,14 +74,14 @@ abstract class BinaryArithmetic extends BinaryExpression { type EvaluatedType = Any - def nullable = left.nullable || right.nullable + def nullable: Boolean = left.nullable || right.nullable override lazy val resolved = left.resolved && right.resolved && left.dataType == right.dataType && !DecimalType.isFixed(left.dataType) - def dataType = { + def dataType: DataType = { if (!resolved) { throw new UnresolvedException(this, s"datatype. Can not resolve due to differing types ${left.dataType}, ${right.dataType}") @@ -96,51 +108,122 @@ abstract class BinaryArithmetic extends BinaryExpression { } case class Add(left: Expression, right: Expression) extends BinaryArithmetic { - def symbol = "+" + override def symbol: String = "+" + + lazy val numeric = dataType match { + case n: NumericType => n.numeric.asInstanceOf[Numeric[Any]] + case other => sys.error(s"Type $other does not support numeric operations") + } - override def eval(input: Row): Any = n2(input, left, right, _.plus(_, _)) + override def eval(input: Row): Any = { + val evalE1 = left.eval(input) + if(evalE1 == null) { + null + } else { + val evalE2 = right.eval(input) + if (evalE2 == null) { + null + } else { + numeric.plus(evalE1, evalE2) + } + } + } } case class Subtract(left: Expression, right: Expression) extends BinaryArithmetic { - def symbol = "-" + override def symbol: String = "-" - override def eval(input: Row): Any = n2(input, left, right, _.minus(_, _)) + lazy val numeric = dataType match { + case n: NumericType => n.numeric.asInstanceOf[Numeric[Any]] + case other => sys.error(s"Type $other does not support numeric operations") + } + + override def eval(input: Row): Any = { + val evalE1 = left.eval(input) + if(evalE1 == null) { + null + } else { + val evalE2 = right.eval(input) + if (evalE2 == null) { + null + } else { + numeric.minus(evalE1, evalE2) + } + } + } } case class Multiply(left: Expression, right: Expression) extends BinaryArithmetic { - def symbol = "*" + override def symbol: String = "*" + + lazy val numeric = dataType match { + case n: NumericType => n.numeric.asInstanceOf[Numeric[Any]] + case other => sys.error(s"Type $other does not support numeric operations") + } - override def eval(input: Row): Any = n2(input, left, right, _.times(_, _)) + override def eval(input: Row): Any = { + val evalE1 = left.eval(input) + if(evalE1 == null) { + null + } else { + val evalE2 = right.eval(input) + if (evalE2 == null) { + null + } else { + numeric.times(evalE1, evalE2) + } + } + } } case class Divide(left: Expression, right: Expression) extends BinaryArithmetic { - def symbol = "/" + override def symbol: String = "/" - override def nullable = true + override def nullable: Boolean = true + lazy val div: (Any, Any) => Any = dataType match { + case ft: FractionalType => ft.fractional.asInstanceOf[Fractional[Any]].div + case it: IntegralType => it.integral.asInstanceOf[Integral[Any]].quot + case other => sys.error(s"Type $other does not support numeric operations") + } + override def eval(input: Row): Any = { val evalE2 = right.eval(input) - dataType match { - case _ if evalE2 == null => null - case _ if evalE2 == 0 => null - case ft: FractionalType => f1(input, left, _.div(_, evalE2.asInstanceOf[ft.JvmType])) - case it: IntegralType => i1(input, left, _.quot(_, evalE2.asInstanceOf[it.JvmType])) + if (evalE2 == null || evalE2 == 0) { + null + } else { + val evalE1 = left.eval(input) + if (evalE1 == null) { + null + } else { + div(evalE1, evalE2) + } } } - } case class Remainder(left: Expression, right: Expression) extends BinaryArithmetic { - def symbol = "%" + override def symbol: String = "%" - override def nullable = true + override def nullable: Boolean = true + + lazy val integral = dataType match { + case i: IntegralType => i.integral.asInstanceOf[Integral[Any]] + case i: FractionalType => i.asIntegral.asInstanceOf[Integral[Any]] + case other => sys.error(s"Type $other does not support numeric operations") + } override def eval(input: Row): Any = { val evalE2 = right.eval(input) - dataType match { - case _ if evalE2 == null => null - case _ if evalE2 == 0 => null - case nt: NumericType => i1(input, left, _.rem(_, evalE2.asInstanceOf[nt.JvmType])) + if (evalE2 == null || evalE2 == 0) { + null + } else { + val evalE1 = left.eval(input) + if (evalE1 == null) { + null + } else { + integral.rem(evalE1, evalE2) + } } } } @@ -149,45 +232,63 @@ case class Remainder(left: Expression, right: Expression) extends BinaryArithmet * A function that calculates bitwise and(&) of two numbers. */ case class BitwiseAnd(left: Expression, right: Expression) extends BinaryArithmetic { - def symbol = "&" - - override def evalInternal(evalE1: EvaluatedType, evalE2: EvaluatedType): Any = dataType match { - case ByteType => (evalE1.asInstanceOf[Byte] & evalE2.asInstanceOf[Byte]).toByte - case ShortType => (evalE1.asInstanceOf[Short] & evalE2.asInstanceOf[Short]).toShort - case IntegerType => evalE1.asInstanceOf[Int] & evalE2.asInstanceOf[Int] - case LongType => evalE1.asInstanceOf[Long] & evalE2.asInstanceOf[Long] + override def symbol: String = "&" + + lazy val and: (Any, Any) => Any = dataType match { + case ByteType => + ((evalE1: Byte, evalE2: Byte) => (evalE1 & evalE2).toByte).asInstanceOf[(Any, Any) => Any] + case ShortType => + ((evalE1: Short, evalE2: Short) => (evalE1 & evalE2).toShort).asInstanceOf[(Any, Any) => Any] + case IntegerType => + ((evalE1: Int, evalE2: Int) => evalE1 & evalE2).asInstanceOf[(Any, Any) => Any] + case LongType => + ((evalE1: Long, evalE2: Long) => evalE1 & evalE2).asInstanceOf[(Any, Any) => Any] case other => sys.error(s"Unsupported bitwise & operation on $other") } + + override def evalInternal(evalE1: EvaluatedType, evalE2: EvaluatedType): Any = and(evalE1, evalE2) } /** * A function that calculates bitwise or(|) of two numbers. */ case class BitwiseOr(left: Expression, right: Expression) extends BinaryArithmetic { - def symbol = "|" - - override def evalInternal(evalE1: EvaluatedType, evalE2: EvaluatedType): Any = dataType match { - case ByteType => (evalE1.asInstanceOf[Byte] | evalE2.asInstanceOf[Byte]).toByte - case ShortType => (evalE1.asInstanceOf[Short] | evalE2.asInstanceOf[Short]).toShort - case IntegerType => evalE1.asInstanceOf[Int] | evalE2.asInstanceOf[Int] - case LongType => evalE1.asInstanceOf[Long] | evalE2.asInstanceOf[Long] + override def symbol: String = "|" + + lazy val or: (Any, Any) => Any = dataType match { + case ByteType => + ((evalE1: Byte, evalE2: Byte) => (evalE1 | evalE2).toByte).asInstanceOf[(Any, Any) => Any] + case ShortType => + ((evalE1: Short, evalE2: Short) => (evalE1 | evalE2).toShort).asInstanceOf[(Any, Any) => Any] + case IntegerType => + ((evalE1: Int, evalE2: Int) => evalE1 | evalE2).asInstanceOf[(Any, Any) => Any] + case LongType => + ((evalE1: Long, evalE2: Long) => evalE1 | evalE2).asInstanceOf[(Any, Any) => Any] case other => sys.error(s"Unsupported bitwise | operation on $other") } + + override def evalInternal(evalE1: EvaluatedType, evalE2: EvaluatedType): Any = or(evalE1, evalE2) } /** * A function that calculates bitwise xor(^) of two numbers. */ case class BitwiseXor(left: Expression, right: Expression) extends BinaryArithmetic { - def symbol = "^" - - override def evalInternal(evalE1: EvaluatedType, evalE2: EvaluatedType): Any = dataType match { - case ByteType => (evalE1.asInstanceOf[Byte] ^ evalE2.asInstanceOf[Byte]).toByte - case ShortType => (evalE1.asInstanceOf[Short] ^ evalE2.asInstanceOf[Short]).toShort - case IntegerType => evalE1.asInstanceOf[Int] ^ evalE2.asInstanceOf[Int] - case LongType => evalE1.asInstanceOf[Long] ^ evalE2.asInstanceOf[Long] + override def symbol: String = "^" + + lazy val xor: (Any, Any) => Any = dataType match { + case ByteType => + ((evalE1: Byte, evalE2: Byte) => (evalE1 ^ evalE2).toByte).asInstanceOf[(Any, Any) => Any] + case ShortType => + ((evalE1: Short, evalE2: Short) => (evalE1 ^ evalE2).toShort).asInstanceOf[(Any, Any) => Any] + case IntegerType => + ((evalE1: Int, evalE2: Int) => evalE1 ^ evalE2).asInstanceOf[(Any, Any) => Any] + case LongType => + ((evalE1: Long, evalE2: Long) => evalE1 ^ evalE2).asInstanceOf[(Any, Any) => Any] case other => sys.error(s"Unsupported bitwise ^ operation on $other") } + + override def evalInternal(evalE1: EvaluatedType, evalE2: EvaluatedType): Any = xor(evalE1, evalE2) } /** @@ -196,23 +297,29 @@ case class BitwiseXor(left: Expression, right: Expression) extends BinaryArithme case class BitwiseNot(child: Expression) extends UnaryExpression { type EvaluatedType = Any - def dataType = child.dataType - override def foldable = child.foldable - def nullable = child.nullable - override def toString = s"~$child" + override def dataType: DataType = child.dataType + override def foldable: Boolean = child.foldable + override def nullable: Boolean = child.nullable + override def toString: String = s"~$child" + + lazy val not: (Any) => Any = dataType match { + case ByteType => + ((evalE: Byte) => (~evalE).toByte).asInstanceOf[(Any) => Any] + case ShortType => + ((evalE: Short) => (~evalE).toShort).asInstanceOf[(Any) => Any] + case IntegerType => + ((evalE: Int) => ~evalE).asInstanceOf[(Any) => Any] + case LongType => + ((evalE: Long) => ~evalE).asInstanceOf[(Any) => Any] + case other => sys.error(s"Unsupported bitwise ~ operation on $other") + } override def eval(input: Row): Any = { val evalE = child.eval(input) if (evalE == null) { null } else { - dataType match { - case ByteType => (~evalE.asInstanceOf[Byte]).toByte - case ShortType => (~evalE.asInstanceOf[Short]).toShort - case IntegerType => ~evalE.asInstanceOf[Int] - case LongType => ~evalE.asInstanceOf[Long] - case other => sys.error(s"Unsupported bitwise ~ operation on $other") - } + not(evalE) } } } @@ -220,32 +327,91 @@ case class BitwiseNot(child: Expression) extends UnaryExpression { case class MaxOf(left: Expression, right: Expression) extends Expression { type EvaluatedType = Any - override def foldable = left.foldable && right.foldable + override def foldable: Boolean = left.foldable && right.foldable + + override def nullable: Boolean = left.nullable && right.nullable + + override def children: Seq[Expression] = left :: right :: Nil + + override lazy val resolved = + left.resolved && right.resolved && + left.dataType == right.dataType + + override def dataType: DataType = { + if (!resolved) { + throw new UnresolvedException(this, + s"datatype. Can not resolve due to differing types ${left.dataType}, ${right.dataType}") + } + left.dataType + } + + lazy val ordering = left.dataType match { + case i: NativeType => i.ordering.asInstanceOf[Ordering[Any]] + case other => sys.error(s"Type $other does not support ordered operations") + } + + override def eval(input: Row): Any = { + val evalE1 = left.eval(input) + val evalE2 = right.eval(input) + if (evalE1 == null) { + evalE2 + } else if (evalE2 == null) { + evalE1 + } else { + if (ordering.compare(evalE1, evalE2) < 0) { + evalE2 + } else { + evalE1 + } + } + } + + override def toString: String = s"MaxOf($left, $right)" +} + +case class MinOf(left: Expression, right: Expression) extends Expression { + type EvaluatedType = Any + + override def foldable: Boolean = left.foldable && right.foldable - override def nullable = left.nullable && right.nullable + override def nullable: Boolean = left.nullable && right.nullable - override def children = left :: right :: Nil + override def children: Seq[Expression] = left :: right :: Nil - override def dataType = left.dataType + override lazy val resolved = + left.resolved && right.resolved && + left.dataType == right.dataType + + override def dataType: DataType = { + if (!resolved) { + throw new UnresolvedException(this, + s"datatype. Can not resolve due to differing types ${left.dataType}, ${right.dataType}") + } + left.dataType + } + + lazy val ordering = left.dataType match { + case i: NativeType => i.ordering.asInstanceOf[Ordering[Any]] + case other => sys.error(s"Type $other does not support ordered operations") + } override def eval(input: Row): Any = { - val leftEval = left.eval(input) - val rightEval = right.eval(input) - if (leftEval == null) { - rightEval - } else if (rightEval == null) { - leftEval + val evalE1 = left.eval(input) + val evalE2 = right.eval(input) + if (evalE1 == null) { + evalE2 + } else if (evalE2 == null) { + evalE1 } else { - val numeric = left.dataType.asInstanceOf[NumericType].numeric.asInstanceOf[Numeric[Any]] - if (numeric.compare(leftEval, rightEval) < 0) { - rightEval + if (ordering.compare(evalE1, evalE2) < 0) { + evalE1 } else { - leftEval + evalE2 } } } - override def toString = s"MaxOf($left, $right)" + override def toString: String = s"MinOf($left, $right)" } /** @@ -254,10 +420,22 @@ case class MaxOf(left: Expression, right: Expression) extends Expression { case class Abs(child: Expression) extends UnaryExpression { type EvaluatedType = Any - def dataType = child.dataType - override def foldable = child.foldable - def nullable = child.nullable - override def toString = s"Abs($child)" + override def dataType: DataType = child.dataType + override def foldable: Boolean = child.foldable + override def nullable: Boolean = child.nullable + override def toString: String = s"Abs($child)" + + lazy val numeric = dataType match { + case n: NumericType => n.numeric.asInstanceOf[Numeric[Any]] + case other => sys.error(s"Type $other does not support numeric operations") + } - override def eval(input: Row): Any = n1(child, input, _.abs(_)) + override def eval(input: Row): Any = { + val evalE = child.eval(input) + if (evalE == null) { + null + } else { + numeric.abs(evalE) + } + } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala index 1f80d84b744a1..be2c101d63a63 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala @@ -91,7 +91,7 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin val startTime = System.nanoTime() val result = create(in) val endTime = System.nanoTime() - def timeMs = (endTime - startTime).toDouble / 1000000 + def timeMs: Double = (endTime - startTime).toDouble / 1000000 logInfo(s"Code generated expression $in in $timeMs ms") result } @@ -121,7 +121,7 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin * @param nullTerm A term that holds a boolean value representing whether the expression evaluated * to null. * @param primitiveTerm A term for a possible primitive value of the result of the evaluation. Not - * valid if `nullTerm` is set to `false`. + * valid if `nullTerm` is set to `true`. * @param objectTerm A possibly boxed version of the result of evaluating this expression. */ protected case class EvaluatedExpression( @@ -216,10 +216,11 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin val $primitiveTerm: ${termForType(dataType)} = $value """.children - case expressions.Literal(value: String, dataType) => + case expressions.Literal(value: UTF8String, dataType) => q""" val $nullTerm = ${value == null} - val $primitiveTerm: ${termForType(dataType)} = $value + val $primitiveTerm: ${termForType(dataType)} = + org.apache.spark.sql.types.UTF8String(${value.getBytes}) """.children case expressions.Literal(value: Int, dataType) => @@ -243,11 +244,14 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin if($nullTerm) ${defaultPrimitive(StringType)} else - new String(${eval.primitiveTerm}.asInstanceOf[Array[Byte]]) + org.apache.spark.sql.types.UTF8String(${eval.primitiveTerm}.asInstanceOf[Array[Byte]]) """.children case Cast(child @ DateType(), StringType) => - child.castOrNull(c => q"org.apache.spark.sql.types.DateUtils.toString($c)", StringType) + child.castOrNull(c => + q"""org.apache.spark.sql.types.UTF8String( + org.apache.spark.sql.types.DateUtils.toString($c))""", + StringType) case Cast(child @ NumericType(), IntegerType) => child.castOrNull(c => q"$c.toInt", IntegerType) @@ -259,7 +263,7 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin child.castOrNull(c => q"$c.toDouble", DoubleType) case Cast(child @ NumericType(), FloatType) => - child.castOrNull(c => q"$c.toFloat", IntegerType) + child.castOrNull(c => q"$c.toFloat", FloatType) // Special handling required for timestamps in hive test cases since the toString function // does not match the expected output. @@ -272,9 +276,18 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin if($nullTerm) ${defaultPrimitive(StringType)} else - ${eval.primitiveTerm}.toString + org.apache.spark.sql.types.UTF8String(${eval.primitiveTerm}.toString) """.children + case EqualTo(e1: BinaryType, e2: BinaryType) => + (e1, e2).evaluateAs (BooleanType) { + case (eval1, eval2) => + q""" + java.util.Arrays.equals($eval1.asInstanceOf[Array[Byte]], + $eval2.asInstanceOf[Array[Byte]]) + """ + } + case EqualTo(e1, e2) => (e1, e2).evaluateAs (BooleanType) { case (eval1, eval2) => q"$eval1 == $eval2" } @@ -464,7 +477,7 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin val itemEval = expressionEvaluator(item) val setEval = expressionEvaluator(set) - val ArrayType(elementType, _) = set.dataType + val elementType = set.dataType.asInstanceOf[OpenHashSetUDT].elementType itemEval.code ++ setEval.code ++ q""" @@ -482,7 +495,7 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin val leftEval = expressionEvaluator(left) val rightEval = expressionEvaluator(right) - val ArrayType(elementType, _) = left.dataType + val elementType = left.dataType.asInstanceOf[OpenHashSetUDT].elementType leftEval.code ++ rightEval.code ++ q""" @@ -524,6 +537,30 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin } """.children + case MinOf(e1, e2) => + val eval1 = expressionEvaluator(e1) + val eval2 = expressionEvaluator(e2) + + eval1.code ++ eval2.code ++ + q""" + var $nullTerm = false + var $primitiveTerm: ${termForType(e1.dataType)} = ${defaultPrimitive(e1.dataType)} + + if (${eval1.nullTerm}) { + $nullTerm = ${eval2.nullTerm} + $primitiveTerm = ${eval2.primitiveTerm} + } else if (${eval2.nullTerm}) { + $nullTerm = ${eval1.nullTerm} + $primitiveTerm = ${eval1.primitiveTerm} + } else { + if (${eval1.primitiveTerm} < ${eval2.primitiveTerm}) { + $primitiveTerm = ${eval1.primitiveTerm} + } else { + $primitiveTerm = ${eval2.primitiveTerm} + } + } + """.children + case UnscaledValue(child) => val childEval = expressionEvaluator(child) @@ -573,7 +610,8 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin val localLogger = log val localLoggerTree = reify { localLogger } q""" - $localLoggerTree.debug(${e.toString} + ": " + (if($nullTerm) "null" else $primitiveTerm)) + $localLoggerTree.debug( + ${e.toString} + ": " + (if ($nullTerm) "null" else $primitiveTerm.toString)) """ :: Nil } else { Nil @@ -584,6 +622,7 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin protected def getColumn(inputRow: TermName, dataType: DataType, ordinal: Int) = { dataType match { + case StringType => q"$inputRow($ordinal).asInstanceOf[org.apache.spark.sql.types.UTF8String]" case dt @ NativeType() => q"$inputRow.${accessorForType(dt)}($ordinal)" case _ => q"$inputRow.apply($ordinal).asInstanceOf[${termForType(dataType)}]" } @@ -595,6 +634,7 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin ordinal: Int, value: TermName) = { dataType match { + case StringType => q"$destinationRow.update($ordinal, $value)" case dt @ NativeType() => q"$destinationRow.${mutatorForType(dt)}($ordinal, $value)" case _ => q"$destinationRow.update($ordinal, $value)" } @@ -618,15 +658,15 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin case DoubleType => "Double" case FloatType => "Float" case BooleanType => "Boolean" - case StringType => "String" + case StringType => "org.apache.spark.sql.types.UTF8String" } protected def defaultPrimitive(dt: DataType) = dt match { case BooleanType => ru.Literal(Constant(false)) case FloatType => ru.Literal(Constant(-1.0.toFloat)) - case StringType => ru.Literal(Constant("")) + case StringType => q"""org.apache.spark.sql.types.UTF8String("")""" case ShortType => ru.Literal(Constant(-1.toShort)) - case LongType => ru.Literal(Constant(1L)) + case LongType => ru.Literal(Constant(-1L)) case ByteType => ru.Literal(Constant(-1.toByte)) case DoubleType => ru.Literal(Constant(-1.toDouble)) case DecimalType() => q"org.apache.spark.sql.types.Decimal(-1)" diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala index 0db29eb404bd1..fc2a2b60703e4 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala @@ -19,7 +19,7 @@ package org.apache.spark.sql.catalyst.expressions.codegen import org.apache.spark.Logging import org.apache.spark.sql.catalyst.expressions._ -import org.apache.spark.sql.types.{StringType, NumericType} +import org.apache.spark.sql.types.{BinaryType, StringType, NumericType} /** * Generates bytecode for an [[Ordering]] of [[Row Rows]] for a given set of @@ -43,6 +43,18 @@ object GenerateOrdering extends CodeGenerator[Seq[SortOrder], Ordering[Row]] wit val evalB = expressionEvaluator(order.child) val compare = order.child.dataType match { + case BinaryType => + q""" + val x = ${if (order.direction == Ascending) evalA.primitiveTerm else evalB.primitiveTerm} + val y = ${if (order.direction != Ascending) evalB.primitiveTerm else evalA.primitiveTerm} + var i = 0 + while (i < x.length && i < y.length) { + val res = x(i).compareTo(y(i)) + if (res != 0) return res + i = i+1 + } + return x.length - y.length + """ case _: NumericType => q""" val comp = ${evalA.primitiveTerm} - ${evalB.primitiveTerm} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala index 69397a73a8880..6f572ff959fb4 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala @@ -111,36 +111,54 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] { val specificAccessorFunctions = NativeType.all.map { dataType => val ifStatements = expressions.zipWithIndex.flatMap { - case (e, i) if e.dataType == dataType => + // getString() is not used by expressions + case (e, i) if e.dataType == dataType && dataType != StringType => val elementName = newTermName(s"c$i") // TODO: The string of ifs gets pretty inefficient as the row grows in size. // TODO: Optional null checks? q"if(i == $i) return $elementName" :: Nil case _ => Nil } - - q""" - override def ${accessorForType(dataType)}(i: Int):${termForType(dataType)} = { - ..$ifStatements; - $accessorFailure - }""" + dataType match { + // Row() need this interface to compile + case StringType => + q""" + override def getString(i: Int): String = { + $accessorFailure + }""" + case other => + q""" + override def ${accessorForType(dataType)}(i: Int): ${termForType(dataType)} = { + ..$ifStatements; + $accessorFailure + }""" + } } val specificMutatorFunctions = NativeType.all.map { dataType => val ifStatements = expressions.zipWithIndex.flatMap { - case (e, i) if e.dataType == dataType => + // setString() is not used by expressions + case (e, i) if e.dataType == dataType && dataType != StringType => val elementName = newTermName(s"c$i") // TODO: The string of ifs gets pretty inefficient as the row grows in size. // TODO: Optional null checks? q"if(i == $i) { nullBits($i) = false; $elementName = value; return }" :: Nil case _ => Nil } - - q""" - override def ${mutatorForType(dataType)}(i: Int, value: ${termForType(dataType)}): Unit = { - ..$ifStatements; - $accessorFailure - }""" + dataType match { + case StringType => + // MutableRow() need this interface to compile + q""" + override def setString(i: Int, value: String) { + $accessorFailure + }""" + case other => + q""" + override def ${mutatorForType(dataType)}(i: Int, value: ${termForType(dataType)}) { + ..$ifStatements; + $accessorFailure + }""" + } } val hashValues = expressions.zipWithIndex.map { case (e,i) => diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/package.scala index 80c7dfd376c96..528e38a50a740 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/package.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/package.scala @@ -19,7 +19,7 @@ package org.apache.spark.sql.catalyst.expressions import org.apache.spark.annotation.DeveloperApi import org.apache.spark.sql.catalyst.rules -import org.apache.spark.sql.catalyst.util +import org.apache.spark.util.Utils /** * A collection of generators that build custom bytecode at runtime for performing the evaluation @@ -52,7 +52,7 @@ package object codegen { @DeveloperApi object DumpByteCode { import scala.sys.process._ - val dumpDirectory = util.getTempFilePath("sparkSqlByteCode") + val dumpDirectory = Utils.createTempDir() dumpDirectory.mkdir() def apply(obj: Any): Unit = { diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala index 66e2e5c4bafce..fc1f69655963d 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala @@ -19,6 +19,8 @@ package org.apache.spark.sql.catalyst.expressions import scala.collection.Map +import org.apache.spark.sql.AnalysisException +import org.apache.spark.sql.catalyst.analysis.Resolver import org.apache.spark.sql.types._ /** @@ -27,12 +29,12 @@ import org.apache.spark.sql.types._ case class GetItem(child: Expression, ordinal: Expression) extends Expression { type EvaluatedType = Any - val children = child :: ordinal :: Nil + val children: Seq[Expression] = child :: ordinal :: Nil /** `Null` is returned for invalid ordinals. */ - override def nullable = true - override def foldable = child.foldable && ordinal.foldable + override def nullable: Boolean = true + override def foldable: Boolean = child.foldable && ordinal.foldable - def dataType = child.dataType match { + override def dataType: DataType = child.dataType match { case ArrayType(dt, _) => dt case MapType(_, vt, _) => vt } @@ -40,7 +42,7 @@ case class GetItem(child: Expression, ordinal: Expression) extends Expression { childrenResolved && (child.dataType.isInstanceOf[ArrayType] || child.dataType.isInstanceOf[MapType]) - override def toString = s"$child[$ordinal]" + override def toString: String = s"$child[$ordinal]" override def eval(input: Row): Any = { val value = child.eval(input) @@ -70,22 +72,83 @@ case class GetItem(child: Expression, ordinal: Expression) extends Expression { } } + +trait GetField extends UnaryExpression { + self: Product => + + type EvaluatedType = Any + override def foldable: Boolean = child.foldable + override def toString: String = s"$child.${field.name}" + + def field: StructField +} + +object GetField { + /** + * Returns the resolved `GetField`, and report error if no desired field or over one + * desired fields are found. + */ + def apply( + expr: Expression, + fieldName: String, + resolver: Resolver): GetField = { + def findField(fields: Array[StructField]): Int = { + val checkField = (f: StructField) => resolver(f.name, fieldName) + val ordinal = fields.indexWhere(checkField) + if (ordinal == -1) { + throw new AnalysisException( + s"No such struct field $fieldName in ${fields.map(_.name).mkString(", ")}") + } else if (fields.indexWhere(checkField, ordinal + 1) != -1) { + throw new AnalysisException( + s"Ambiguous reference to fields ${fields.filter(checkField).mkString(", ")}") + } else { + ordinal + } + } + expr.dataType match { + case StructType(fields) => + val ordinal = findField(fields) + StructGetField(expr, fields(ordinal), ordinal) + case ArrayType(StructType(fields), containsNull) => + val ordinal = findField(fields) + ArrayGetField(expr, fields(ordinal), ordinal, containsNull) + case otherType => + throw new AnalysisException(s"GetField is not valid on fields of type $otherType") + } + } +} + /** * Returns the value of fields in the Struct `child`. */ -case class GetField(child: Expression, field: StructField, ordinal: Int) extends UnaryExpression { - type EvaluatedType = Any +case class StructGetField(child: Expression, field: StructField, ordinal: Int) extends GetField { - def dataType = field.dataType - override def nullable = child.nullable || field.nullable - override def foldable = child.foldable + override def dataType: DataType = field.dataType + override def nullable: Boolean = child.nullable || field.nullable override def eval(input: Row): Any = { val baseValue = child.eval(input).asInstanceOf[Row] if (baseValue == null) null else baseValue(ordinal) } +} + +/** + * Returns the array of value of fields in the Array of Struct `child`. + */ +case class ArrayGetField(child: Expression, field: StructField, ordinal: Int, containsNull: Boolean) + extends GetField { - override def toString = s"$child.${field.name}" + override def dataType: DataType = ArrayType(field.dataType, containsNull) + override def nullable: Boolean = child.nullable + + override def eval(input: Row): Any = { + val baseValue = child.eval(input).asInstanceOf[Seq[Row]] + if (baseValue == null) null else { + baseValue.map { row => + if (row == null) null else row(ordinal) + } + } + } } /** @@ -94,7 +157,7 @@ case class GetField(child: Expression, field: StructField, ordinal: Int) extends case class CreateArray(children: Seq[Expression]) extends Expression { override type EvaluatedType = Any - override def foldable = !children.exists(!_.foldable) + override def foldable: Boolean = children.forall(_.foldable) lazy val childTypes = children.map(_.dataType).distinct @@ -114,5 +177,32 @@ case class CreateArray(children: Seq[Expression]) extends Expression { children.map(_.eval(input)) } - override def toString = s"Array(${children.mkString(",")})" + override def toString: String = s"Array(${children.mkString(",")})" +} + +/** + * Returns a Row containing the evaluation of all children expressions. + * TODO: [[CreateStruct]] does not support codegen. + */ +case class CreateStruct(children: Seq[NamedExpression]) extends Expression { + override type EvaluatedType = Row + + override def foldable: Boolean = children.forall(_.foldable) + + override lazy val resolved: Boolean = childrenResolved + + override lazy val dataType: StructType = { + assert(resolved, + s"CreateStruct contains unresolvable children: ${children.filterNot(_.resolved)}.") + val fields = children.map { child => + StructField(child.name, child.dataType, child.nullable, child.metadata) + } + StructType(fields) + } + + override def nullable: Boolean = false + + override def eval(input: Row): EvaluatedType = { + Row(children.map(_.eval(input)): _*) + } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala index 83d8c1d42bca4..adb94df7d1c7b 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala @@ -24,9 +24,9 @@ case class UnscaledValue(child: Expression) extends UnaryExpression { override type EvaluatedType = Any override def dataType: DataType = LongType - override def foldable = child.foldable - def nullable = child.nullable - override def toString = s"UnscaledValue($child)" + override def foldable: Boolean = child.foldable + override def nullable: Boolean = child.nullable + override def toString: String = s"UnscaledValue($child)" override def eval(input: Row): Any = { val childResult = child.eval(input) @@ -43,9 +43,9 @@ case class MakeDecimal(child: Expression, precision: Int, scale: Int) extends Un override type EvaluatedType = Decimal override def dataType: DataType = DecimalType(precision, scale) - override def foldable = child.foldable - def nullable = child.nullable - override def toString = s"MakeDecimal($child,$precision,$scale)" + override def foldable: Boolean = child.foldable + override def nullable: Boolean = child.nullable + override def toString: String = s"MakeDecimal($child,$precision,$scale)" override def eval(input: Row): Decimal = { val childResult = child.eval(input) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala index 43b6482c0171c..67caadb839ff9 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala @@ -19,7 +19,7 @@ package org.apache.spark.sql.catalyst.expressions import scala.collection.Map -import org.apache.spark.sql.catalyst.trees +import org.apache.spark.sql.catalyst.{CatalystTypeConverters, trees} import org.apache.spark.sql.types._ /** @@ -45,7 +45,7 @@ abstract class Generator extends Expression { override lazy val dataType = ArrayType(StructType(output.map(a => StructField(a.name, a.dataType, a.nullable, a.metadata)))) - override def nullable = false + override def nullable: Boolean = false /** * Should be overridden by specific generators. Called only once for each instance to ensure @@ -73,6 +73,28 @@ abstract class Generator extends Expression { } } +/** + * A generator that produces its output using the provided lambda function. + */ +case class UserDefinedGenerator( + schema: Seq[Attribute], + function: Row => TraversableOnce[Row], + children: Seq[Expression]) + extends Generator{ + + override protected def makeOutput(): Seq[Attribute] = schema + + override def eval(input: Row): TraversableOnce[Row] = { + // TODO(davies): improve this + // Convert the objects into Scala Type before calling function, we need schema to support UDT + val inputSchema = StructType(children.map(e => StructField(e.simpleString, e.dataType, true))) + val inputRow = new InterpretedProjection(children) + function(CatalystTypeConverters.convertToScala(inputRow(input), inputSchema).asInstanceOf[Row]) + } + + override def toString: String = s"UserDefinedGenerator(${children.mkString(",")})" +} + /** * Given an input array produces a sequence of rows for each value in the array. */ @@ -111,5 +133,5 @@ case class Explode(attributeNames: Seq[String], child: Expression) } } - override def toString() = s"explode($child)" + override def toString: String = s"explode($child)" } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala index 97bb96f48e2c7..18cba4cc46707 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala @@ -19,6 +19,7 @@ package org.apache.spark.sql.catalyst.expressions import java.sql.{Date, Timestamp} +import org.apache.spark.sql.catalyst.CatalystTypeConverters import org.apache.spark.sql.types._ object Literal { @@ -29,7 +30,7 @@ object Literal { case f: Float => Literal(f, FloatType) case b: Byte => Literal(b, ByteType) case s: Short => Literal(s, ShortType) - case s: String => Literal(s, StringType) + case s: String => Literal(UTF8String(s), StringType) case b: Boolean => Literal(b, BooleanType) case d: BigDecimal => Literal(Decimal(d), DecimalType.Unlimited) case d: java.math.BigDecimal => Literal(Decimal(d), DecimalType.Unlimited) @@ -38,6 +39,12 @@ object Literal { case d: Date => Literal(DateUtils.fromJavaDate(d), DateType) case a: Array[Byte] => Literal(a, BinaryType) case null => Literal(null, NullType) + case _ => + throw new RuntimeException("Unsupported literal type " + v.getClass + " " + v) + } + + def create(v: Any, dataType: DataType): Literal = { + Literal(CatalystTypeConverters.convertToCatalyst(v), dataType) } } @@ -60,16 +67,18 @@ object IntegerLiteral { } } -case class Literal(value: Any, dataType: DataType) extends LeafExpression { - - override def foldable = true - def nullable = value == null +/** + * In order to do type checking, use Literal.create() instead of constructor + */ +case class Literal protected (value: Any, dataType: DataType) extends LeafExpression { + override def foldable: Boolean = true + override def nullable: Boolean = value == null - override def toString = if (value != null) value.toString else "null" + override def toString: String = if (value != null) value.toString else "null" type EvaluatedType = Any - override def eval(input: Row):Any = value + override def eval(input: Row): Any = value } // TODO: Specialize @@ -77,9 +86,9 @@ case class MutableLiteral(var value: Any, dataType: DataType, nullable: Boolean extends LeafExpression { type EvaluatedType = Any - def update(expression: Expression, input: Row) = { + def update(expression: Expression, input: Row): Unit = { value = expression.eval(input) } - override def eval(input: Row) = value + override def eval(input: Row): Any = value } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala index e6ab1fd8d7939..bcbcbeb31c7b5 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala @@ -20,11 +20,12 @@ package org.apache.spark.sql.catalyst.expressions import org.apache.spark.sql.catalyst.trees import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute import org.apache.spark.sql.catalyst.errors.TreeNodeException +import org.apache.spark.sql.catalyst.trees.LeafNode import org.apache.spark.sql.types._ object NamedExpression { private val curId = new java.util.concurrent.atomic.AtomicLong() - def newExprId = ExprId(curId.getAndIncrement()) + def newExprId: ExprId = ExprId(curId.getAndIncrement()) def unapply(expr: NamedExpression): Option[(String, DataType)] = Some(expr.name, expr.dataType) } @@ -40,6 +41,24 @@ abstract class NamedExpression extends Expression { def name: String def exprId: ExprId + + /** + * Returns a dot separated fully qualified name for this attribute. Given that there can be + * multiple qualifiers, it is possible that there are other possible way to refer to this + * attribute. + */ + def qualifiedName: String = (qualifiers.headOption.toSeq :+ name).mkString(".") + + /** + * All possible qualifiers for the expression. + * + * For now, since we do not allow using original table name to qualify a column name once the + * table is aliased, this can only be: + * + * 1. Empty Seq: when an attribute doesn't have a qualifier, + * e.g. top level attributes aliased in the SELECT clause, or column from a LocalRelation. + * 2. Single element: either the table name or the alias name of the table. + */ def qualifiers: Seq[String] def toAttribute: Attribute @@ -61,13 +80,13 @@ abstract class NamedExpression extends Expression { abstract class Attribute extends NamedExpression { self: Product => - override def references = AttributeSet(this) + override def references: AttributeSet = AttributeSet(this) def withNullability(newNullability: Boolean): Attribute def withQualifiers(newQualifiers: Seq[String]): Attribute def withName(newName: String): Attribute - def toAttribute = this + def toAttribute: Attribute = this def newInstance(): Attribute } @@ -84,25 +103,30 @@ abstract class Attribute extends NamedExpression { * @param name the name to be associated with the result of computing [[child]]. * @param exprId A globally unique id used to check if an [[AttributeReference]] refers to this * alias. Auto-assigned if left blank. + * @param explicitMetadata Explicit metadata associated with this alias that overwrites child's. */ -case class Alias(child: Expression, name: String) - (val exprId: ExprId = NamedExpression.newExprId, val qualifiers: Seq[String] = Nil) +case class Alias(child: Expression, name: String)( + val exprId: ExprId = NamedExpression.newExprId, + val qualifiers: Seq[String] = Nil, + val explicitMetadata: Option[Metadata] = None) extends NamedExpression with trees.UnaryNode[Expression] { override type EvaluatedType = Any - override def eval(input: Row) = child.eval(input) + override def eval(input: Row): Any = child.eval(input) - override def dataType = child.dataType - override def nullable = child.nullable + override def dataType: DataType = child.dataType + override def nullable: Boolean = child.nullable override def metadata: Metadata = { - child match { - case named: NamedExpression => named.metadata - case _ => Metadata.empty + explicitMetadata.getOrElse { + child match { + case named: NamedExpression => named.metadata + case _ => Metadata.empty + } } } - override def toAttribute = { + override def toAttribute: Attribute = { if (resolved) { AttributeReference(name, child.dataType, child.nullable, metadata)(exprId, qualifiers) } else { @@ -112,7 +136,16 @@ case class Alias(child: Expression, name: String) override def toString: String = s"$child AS $name#${exprId.id}$typeSuffix" - override protected final def otherCopyArgs = exprId :: qualifiers :: Nil + override protected final def otherCopyArgs: Seq[AnyRef] = { + exprId :: qualifiers :: explicitMetadata :: Nil + } + + override def equals(other: Any): Boolean = other match { + case a: Alias => + name == a.name && exprId == a.exprId && child == a.child && qualifiers == a.qualifiers && + explicitMetadata == a.explicitMetadata + case _ => false + } } /** @@ -136,7 +169,7 @@ case class AttributeReference( val exprId: ExprId = NamedExpression.newExprId, val qualifiers: Seq[String] = Nil) extends Attribute with trees.LeafNode[Expression] { - override def equals(other: Any) = other match { + override def equals(other: Any): Boolean = other match { case ar: AttributeReference => name == ar.name && exprId == ar.exprId && dataType == ar.dataType case _ => false } @@ -150,7 +183,7 @@ case class AttributeReference( h } - override def newInstance() = + override def newInstance(): AttributeReference = AttributeReference(name, dataType, nullable, metadata)(qualifiers = qualifiers) /** @@ -175,7 +208,7 @@ case class AttributeReference( /** * Returns a copy of this [[AttributeReference]] with new qualifiers. */ - override def withQualifiers(newQualifiers: Seq[String]) = { + override def withQualifiers(newQualifiers: Seq[String]): AttributeReference = { if (newQualifiers.toSet == qualifiers.toSet) { this } else { @@ -190,7 +223,29 @@ case class AttributeReference( override def toString: String = s"$name#${exprId.id}$typeSuffix" } +/** + * A place holder used when printing expressions without debugging information such as the + * expression id or the unresolved indicator. + */ +case class PrettyAttribute(name: String) extends Attribute with trees.LeafNode[Expression] { + type EvaluatedType = Any + + override def toString: String = name + + override def withNullability(newNullability: Boolean): Attribute = + throw new UnsupportedOperationException + override def newInstance(): Attribute = throw new UnsupportedOperationException + override def withQualifiers(newQualifiers: Seq[String]): Attribute = + throw new UnsupportedOperationException + override def withName(newName: String): Attribute = throw new UnsupportedOperationException + override def qualifiers: Seq[String] = throw new UnsupportedOperationException + override def exprId: ExprId = throw new UnsupportedOperationException + override def eval(input: Row): EvaluatedType = throw new UnsupportedOperationException + override def nullable: Boolean = throw new UnsupportedOperationException + override def dataType: DataType = NullType +} + object VirtualColumn { - val groupingIdName = "grouping__id" - def newGroupingId = AttributeReference(groupingIdName, IntegerType, false)() + val groupingIdName: String = "grouping__id" + def newGroupingId: AttributeReference = AttributeReference(groupingIdName, IntegerType, false)() } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala index 08b982bc671e7..f9161cf34f0c9 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala @@ -19,22 +19,23 @@ package org.apache.spark.sql.catalyst.expressions import org.apache.spark.sql.catalyst.trees import org.apache.spark.sql.catalyst.analysis.UnresolvedException +import org.apache.spark.sql.types.DataType case class Coalesce(children: Seq[Expression]) extends Expression { type EvaluatedType = Any /** Coalesce is nullable if all of its children are nullable, or if it has no children. */ - def nullable = !children.exists(!_.nullable) + override def nullable: Boolean = !children.exists(!_.nullable) // Coalesce is foldable if all children are foldable. - override def foldable = !children.exists(!_.foldable) + override def foldable: Boolean = !children.exists(!_.foldable) // Only resolved if all the children are of the same type. override lazy val resolved = childrenResolved && (children.map(_.dataType).distinct.size == 1) - override def toString = s"Coalesce(${children.mkString(",")})" + override def toString: String = s"Coalesce(${children.mkString(",")})" - def dataType = if (resolved) { + override def dataType: DataType = if (resolved) { children.head.dataType } else { val childTypes = children.map(c => s"$c: ${c.dataType}").mkString(", ") @@ -54,22 +55,45 @@ case class Coalesce(children: Seq[Expression]) extends Expression { } case class IsNull(child: Expression) extends Predicate with trees.UnaryNode[Expression] { - override def foldable = child.foldable - def nullable = false + override def foldable: Boolean = child.foldable + override def nullable: Boolean = false override def eval(input: Row): Any = { child.eval(input) == null } - override def toString = s"IS NULL $child" + override def toString: String = s"IS NULL $child" } case class IsNotNull(child: Expression) extends Predicate with trees.UnaryNode[Expression] { - override def foldable = child.foldable - def nullable = false - override def toString = s"IS NOT NULL $child" + override def foldable: Boolean = child.foldable + override def nullable: Boolean = false + override def toString: String = s"IS NOT NULL $child" override def eval(input: Row): Any = { child.eval(input) != null } } + +/** + * A predicate that is evaluated to be true if there are at least `n` non-null values. + */ +case class AtLeastNNonNulls(n: Int, children: Seq[Expression]) extends Predicate { + override def nullable: Boolean = false + override def foldable: Boolean = false + override def toString: String = s"AtLeastNNulls(n, ${children.mkString(",")})" + + private[this] val childrenArray = children.toArray + + override def eval(input: Row): Boolean = { + var numNonNulls = 0 + var i = 0 + while (i < childrenArray.length && numNonNulls < n) { + if (childrenArray(i).eval(input) != null) { + numNonNulls += 1 + } + i += 1 + } + numNonNulls >= n + } +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala index 365b1685a8e71..fcd6352079b4d 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala @@ -18,8 +18,9 @@ package org.apache.spark.sql.catalyst.expressions import org.apache.spark.sql.catalyst.analysis.UnresolvedException +import org.apache.spark.sql.catalyst.errors.TreeNodeException import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan -import org.apache.spark.sql.types.{BinaryType, BooleanType} +import org.apache.spark.sql.types.{DataType, BinaryType, BooleanType, NativeType} object InterpretedPredicate { def apply(expression: Expression, inputSchema: Seq[Attribute]): (Row => Boolean) = @@ -33,7 +34,7 @@ object InterpretedPredicate { trait Predicate extends Expression { self: Product => - def dataType = BooleanType + override def dataType: DataType = BooleanType type EvaluatedType = Any } @@ -71,13 +72,13 @@ trait PredicateHelper { abstract class BinaryPredicate extends BinaryExpression with Predicate { self: Product => - def nullable = left.nullable || right.nullable + override def nullable: Boolean = left.nullable || right.nullable } case class Not(child: Expression) extends UnaryExpression with Predicate { - override def foldable = child.foldable - def nullable = child.nullable - override def toString = s"NOT $child" + override def foldable: Boolean = child.foldable + override def nullable: Boolean = child.nullable + override def toString: String = s"NOT $child" override def eval(input: Row): Any = { child.eval(input) match { @@ -91,10 +92,10 @@ case class Not(child: Expression) extends UnaryExpression with Predicate { * Evaluates to `true` if `list` contains `value`. */ case class In(value: Expression, list: Seq[Expression]) extends Predicate { - def children = value +: list + override def children: Seq[Expression] = value +: list - def nullable = true // TODO: Figure out correct nullability semantics of IN. - override def toString = s"$value IN ${list.mkString("(", ",", ")")}" + override def nullable: Boolean = true // TODO: Figure out correct nullability semantics of IN. + override def toString: String = s"$value IN ${list.mkString("(", ",", ")")}" override def eval(input: Row): Any = { val evaluatedValue = value.eval(input) @@ -109,10 +110,10 @@ case class In(value: Expression, list: Seq[Expression]) extends Predicate { case class InSet(value: Expression, hset: Set[Any]) extends Predicate { - def children = value :: Nil + override def children: Seq[Expression] = value :: Nil - def nullable = true // TODO: Figure out correct nullability semantics of IN. - override def toString = s"$value INSET ${hset.mkString("(", ",", ")")}" + override def nullable: Boolean = true // TODO: Figure out correct nullability semantics of IN. + override def toString: String = s"$value INSET ${hset.mkString("(", ",", ")")}" override def eval(input: Row): Any = { hset.contains(value.eval(input)) @@ -120,7 +121,7 @@ case class InSet(value: Expression, hset: Set[Any]) } case class And(left: Expression, right: Expression) extends BinaryPredicate { - def symbol = "&&" + override def symbol: String = "&&" override def eval(input: Row): Any = { val l = left.eval(input) @@ -142,7 +143,7 @@ case class And(left: Expression, right: Expression) extends BinaryPredicate { } case class Or(left: Expression, right: Expression) extends BinaryPredicate { - def symbol = "||" + override def symbol: String = "||" override def eval(input: Row): Any = { val l = left.eval(input) @@ -168,7 +169,8 @@ abstract class BinaryComparison extends BinaryPredicate { } case class EqualTo(left: Expression, right: Expression) extends BinaryComparison { - def symbol = "=" + override def symbol: String = "=" + override def eval(input: Row): Any = { val l = left.eval(input) if (l == null) { @@ -177,15 +179,16 @@ case class EqualTo(left: Expression, right: Expression) extends BinaryComparison val r = right.eval(input) if (r == null) null else if (left.dataType != BinaryType) l == r - else BinaryType.ordering.compare( - l.asInstanceOf[Array[Byte]], r.asInstanceOf[Array[Byte]]) == 0 + else java.util.Arrays.equals(l.asInstanceOf[Array[Byte]], r.asInstanceOf[Array[Byte]]) } } } case class EqualNullSafe(left: Expression, right: Expression) extends BinaryComparison { - def symbol = "<=>" - override def nullable = false + override def symbol: String = "<=>" + + override def nullable: Boolean = false + override def eval(input: Row): Any = { val l = left.eval(input) val r = right.eval(input) @@ -200,33 +203,129 @@ case class EqualNullSafe(left: Expression, right: Expression) extends BinaryComp } case class LessThan(left: Expression, right: Expression) extends BinaryComparison { - def symbol = "<" - override def eval(input: Row): Any = c2(input, left, right, _.lt(_, _)) + override def symbol: String = "<" + + lazy val ordering: Ordering[Any] = { + if (left.dataType != right.dataType) { + throw new TreeNodeException(this, + s"Types do not match ${left.dataType} != ${right.dataType}") + } + left.dataType match { + case i: NativeType => i.ordering.asInstanceOf[Ordering[Any]] + case other => sys.error(s"Type $other does not support ordered operations") + } + } + + override def eval(input: Row): Any = { + val evalE1 = left.eval(input) + if (evalE1 == null) { + null + } else { + val evalE2 = right.eval(input) + if (evalE2 == null) { + null + } else { + ordering.lt(evalE1, evalE2) + } + } + } } case class LessThanOrEqual(left: Expression, right: Expression) extends BinaryComparison { - def symbol = "<=" - override def eval(input: Row): Any = c2(input, left, right, _.lteq(_, _)) + override def symbol: String = "<=" + + lazy val ordering: Ordering[Any] = { + if (left.dataType != right.dataType) { + throw new TreeNodeException(this, + s"Types do not match ${left.dataType} != ${right.dataType}") + } + left.dataType match { + case i: NativeType => i.ordering.asInstanceOf[Ordering[Any]] + case other => sys.error(s"Type $other does not support ordered operations") + } + } + + override def eval(input: Row): Any = { + val evalE1 = left.eval(input) + if (evalE1 == null) { + null + } else { + val evalE2 = right.eval(input) + if (evalE2 == null) { + null + } else { + ordering.lteq(evalE1, evalE2) + } + } + } } case class GreaterThan(left: Expression, right: Expression) extends BinaryComparison { - def symbol = ">" - override def eval(input: Row): Any = c2(input, left, right, _.gt(_, _)) + override def symbol: String = ">" + + lazy val ordering: Ordering[Any] = { + if (left.dataType != right.dataType) { + throw new TreeNodeException(this, + s"Types do not match ${left.dataType} != ${right.dataType}") + } + left.dataType match { + case i: NativeType => i.ordering.asInstanceOf[Ordering[Any]] + case other => sys.error(s"Type $other does not support ordered operations") + } + } + + override def eval(input: Row): Any = { + val evalE1 = left.eval(input) + if(evalE1 == null) { + null + } else { + val evalE2 = right.eval(input) + if (evalE2 == null) { + null + } else { + ordering.gt(evalE1, evalE2) + } + } + } } case class GreaterThanOrEqual(left: Expression, right: Expression) extends BinaryComparison { - def symbol = ">=" - override def eval(input: Row): Any = c2(input, left, right, _.gteq(_, _)) + override def symbol: String = ">=" + + lazy val ordering: Ordering[Any] = { + if (left.dataType != right.dataType) { + throw new TreeNodeException(this, + s"Types do not match ${left.dataType} != ${right.dataType}") + } + left.dataType match { + case i: NativeType => i.ordering.asInstanceOf[Ordering[Any]] + case other => sys.error(s"Type $other does not support ordered operations") + } + } + + override def eval(input: Row): Any = { + val evalE1 = left.eval(input) + if (evalE1 == null) { + null + } else { + val evalE2 = right.eval(input) + if (evalE2 == null) { + null + } else { + ordering.gteq(evalE1, evalE2) + } + } + } } case class If(predicate: Expression, trueValue: Expression, falseValue: Expression) - extends Expression { + extends Expression { - def children = predicate :: trueValue :: falseValue :: Nil - override def nullable = trueValue.nullable || falseValue.nullable + override def children: Seq[Expression] = predicate :: trueValue :: falseValue :: Nil + override def nullable: Boolean = trueValue.nullable || falseValue.nullable override lazy val resolved = childrenResolved && trueValue.dataType == falseValue.dataType - def dataType = { + override def dataType: DataType = { if (!resolved) { throw new UnresolvedException( this, @@ -245,7 +344,7 @@ case class If(predicate: Expression, trueValue: Expression, falseValue: Expressi } } - override def toString = s"if ($predicate) $trueValue else $falseValue" + override def toString: String = s"if ($predicate) $trueValue else $falseValue" } // scalastyle:off @@ -265,9 +364,10 @@ case class If(predicate: Expression, trueValue: Expression, falseValue: Expressi // scalastyle:on case class CaseWhen(branches: Seq[Expression]) extends Expression { type EvaluatedType = Any - def children = branches - def dataType = { + override def children: Seq[Expression] = branches + + override def dataType: DataType = { if (!resolved) { throw new UnresolvedException(this, "cannot resolve due to differing types in some branches") } @@ -282,12 +382,12 @@ case class CaseWhen(branches: Seq[Expression]) extends Expression { @transient private[this] lazy val elseValue = if (branches.length % 2 == 0) None else Option(branches.last) - override def nullable = { + override def nullable: Boolean = { // If no value is nullable and no elseValue is provided, the whole statement defaults to null. values.exists(_.nullable) || (elseValue.map(_.nullable).getOrElse(true)) } - override lazy val resolved = { + override lazy val resolved: Boolean = { if (!childrenResolved) { false } else { @@ -318,7 +418,7 @@ case class CaseWhen(branches: Seq[Expression]) extends Expression { res } - override def toString = { + override def toString: String = { "CASE" + branches.sliding(2, 2).map { case Seq(cond, value) => s" WHEN $cond THEN $value" case Seq(elseValue) => s" ELSE $elseValue" diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala index 73ec7a6d114f5..b6ec7d3417ef8 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala @@ -17,8 +17,7 @@ package org.apache.spark.sql.catalyst.expressions -import org.apache.spark.sql.types.NativeType - +import org.apache.spark.sql.types.{UTF8String, DataType, StructType, NativeType} /** * An extended interface to [[Row]] that allows the values for each column to be updated. Setting @@ -37,6 +36,7 @@ trait MutableRow extends Row { def setByte(ordinal: Int, value: Byte) def setFloat(ordinal: Int, value: Float) def setString(ordinal: Int, value: String) + // TODO(davies): add setDate() and setDecimal() } /** @@ -44,8 +44,8 @@ trait MutableRow extends Row { */ object EmptyRow extends Row { override def apply(i: Int): Any = throw new UnsupportedOperationException - override def toSeq = Seq.empty - override def length = 0 + override def toSeq: Seq[Any] = Seq.empty + override def length: Int = 0 override def isNullAt(i: Int): Boolean = throw new UnsupportedOperationException override def getInt(i: Int): Int = throw new UnsupportedOperationException override def getLong(i: Int): Long = throw new UnsupportedOperationException @@ -56,7 +56,7 @@ object EmptyRow extends Row { override def getByte(i: Int): Byte = throw new UnsupportedOperationException override def getString(i: Int): String = throw new UnsupportedOperationException override def getAs[T](i: Int): T = throw new UnsupportedOperationException - def copy() = this + override def copy(): Row = this } /** @@ -66,17 +66,17 @@ object EmptyRow extends Row { */ class GenericRow(protected[sql] val values: Array[Any]) extends Row { /** No-arg constructor for serialization. */ - def this() = this(null) + protected def this() = this(null) def this(size: Int) = this(new Array[Any](size)) - override def toSeq = values.toSeq + override def toSeq: Seq[Any] = values.toSeq - override def length = values.length + override def length: Int = values.length - override def apply(i: Int) = values(i) + override def apply(i: Int): Any = values(i) - override def isNullAt(i: Int) = values(i) == null + override def isNullAt(i: Int): Boolean = values(i) == null override def getInt(i: Int): Int = { if (values(i) == null) sys.error("Failed to check null bit for primitive int value.") @@ -114,9 +114,15 @@ class GenericRow(protected[sql] val values: Array[Any]) extends Row { } override def getString(i: Int): String = { - values(i).asInstanceOf[String] + values(i) match { + case null => null + case s: String => s + case utf8: UTF8String => utf8.toString + } } + // TODO(davies): add getDate and getDecimal + // Custom hashCode function that matches the efficient code generated version. override def hashCode: Int = { var result: Int = 37 @@ -146,12 +152,40 @@ class GenericRow(protected[sql] val values: Array[Any]) extends Row { result } - def copy() = this + override def equals(o: Any): Boolean = o match { + case other: Row => + if (values.length != other.length) { + return false + } + + var i = 0 + while (i < values.length) { + if (isNullAt(i) != other.isNullAt(i)) { + return false + } + if (apply(i) != other.apply(i)) { + return false + } + i += 1 + } + true + + case _ => false + } + + override def copy(): Row = this +} + +class GenericRowWithSchema(values: Array[Any], override val schema: StructType) + extends GenericRow(values) { + + /** No-arg constructor for serialization. */ + protected def this() = this(null, null) } class GenericMutableRow(v: Array[Any]) extends GenericRow(v) with MutableRow { /** No-arg constructor for serialization. */ - def this() = this(null) + protected def this() = this(null) def this(size: Int) = this(new Array[Any](size)) @@ -161,15 +195,14 @@ class GenericMutableRow(v: Array[Any]) extends GenericRow(v) with MutableRow { override def setFloat(ordinal: Int, value: Float): Unit = { values(ordinal) = value } override def setInt(ordinal: Int, value: Int): Unit = { values(ordinal) = value } override def setLong(ordinal: Int, value: Long): Unit = { values(ordinal) = value } - override def setString(ordinal: Int, value: String): Unit = { values(ordinal) = value } - + override def setString(ordinal: Int, value: String) { values(ordinal) = UTF8String(value)} override def setNullAt(i: Int): Unit = { values(i) = null } override def setShort(ordinal: Int, value: Short): Unit = { values(ordinal) = value } override def update(ordinal: Int, value: Any): Unit = { values(ordinal) = value } - override def copy() = new GenericRow(values.clone()) + override def copy(): Row = new GenericRow(values.clone()) } @@ -196,6 +229,7 @@ class RowOrdering(ordering: Seq[SortOrder]) extends Ordering[Row] { n.ordering.asInstanceOf[Ordering[Any]].compare(left, right) case n: NativeType if order.direction == Descending => n.ordering.asInstanceOf[Ordering[Any]].reverse.compare(left, right) + case other => sys.error(s"Type $other does not support ordered operations") } if (comparison != 0) return comparison } @@ -204,3 +238,10 @@ class RowOrdering(ordering: Seq[SortOrder]) extends Ordering[Row] { return 0 } } + +object RowOrdering { + def forSchema(dataTypes: Seq[DataType]): RowOrdering = + new RowOrdering(dataTypes.zipWithIndex.map { + case(dt, index) => new SortOrder(BoundReference(index, dt, nullable = true), Ascending) + }) +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala index 3a5bdca1f07c3..4c44182278207 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala @@ -20,23 +20,48 @@ package org.apache.spark.sql.catalyst.expressions import org.apache.spark.sql.types._ import org.apache.spark.util.collection.OpenHashSet +/** The data type for expressions returning an OpenHashSet as the result. */ +private[sql] class OpenHashSetUDT( + val elementType: DataType) extends UserDefinedType[OpenHashSet[Any]] { + + override def sqlType: DataType = ArrayType(elementType) + + /** Since we are using OpenHashSet internally, usually it will not be called. */ + override def serialize(obj: Any): Seq[Any] = { + obj.asInstanceOf[OpenHashSet[Any]].iterator.toSeq + } + + /** Since we are using OpenHashSet internally, usually it will not be called. */ + override def deserialize(datum: Any): OpenHashSet[Any] = { + val iterator = datum.asInstanceOf[Seq[Any]].iterator + val set = new OpenHashSet[Any] + while(iterator.hasNext) { + set.add(iterator.next()) + } + + set + } + + override def userClass: Class[OpenHashSet[Any]] = classOf[OpenHashSet[Any]] + + private[spark] override def asNullable: OpenHashSetUDT = this +} + /** * Creates a new set of the specified type */ case class NewSet(elementType: DataType) extends LeafExpression { type EvaluatedType = Any - def nullable = false + override def nullable: Boolean = false - // We are currently only using these Expressions internally for aggregation. However, if we ever - // expose these to users we'll want to create a proper type instead of hijacking ArrayType. - def dataType = ArrayType(elementType) + override def dataType: OpenHashSetUDT = new OpenHashSetUDT(elementType) - def eval(input: Row): Any = { + override def eval(input: Row): Any = { new OpenHashSet[Any]() } - override def toString = s"new Set($dataType)" + override def toString: String = s"new Set($dataType)" } /** @@ -46,12 +71,13 @@ case class NewSet(elementType: DataType) extends LeafExpression { case class AddItemToSet(item: Expression, set: Expression) extends Expression { type EvaluatedType = Any - def children = item :: set :: Nil + override def children: Seq[Expression] = item :: set :: Nil + + override def nullable: Boolean = set.nullable - def nullable = set.nullable + override def dataType: OpenHashSetUDT = set.dataType.asInstanceOf[OpenHashSetUDT] - def dataType = set.dataType - def eval(input: Row): Any = { + override def eval(input: Row): Any = { val itemEval = item.eval(input) val setEval = set.eval(input).asInstanceOf[OpenHashSet[Any]] @@ -67,7 +93,7 @@ case class AddItemToSet(item: Expression, set: Expression) extends Expression { } } - override def toString = s"$set += $item" + override def toString: String = s"$set += $item" } /** @@ -77,13 +103,13 @@ case class AddItemToSet(item: Expression, set: Expression) extends Expression { case class CombineSets(left: Expression, right: Expression) extends BinaryExpression { type EvaluatedType = Any - def nullable = left.nullable || right.nullable + override def nullable: Boolean = left.nullable || right.nullable - def dataType = left.dataType + override def dataType: OpenHashSetUDT = left.dataType.asInstanceOf[OpenHashSetUDT] - def symbol = "++=" + override def symbol: String = "++=" - def eval(input: Row): Any = { + override def eval(input: Row): Any = { val leftEval = left.eval(input).asInstanceOf[OpenHashSet[Any]] if(leftEval != null) { val rightEval = right.eval(input).asInstanceOf[OpenHashSet[Any]] @@ -109,16 +135,16 @@ case class CombineSets(left: Expression, right: Expression) extends BinaryExpres case class CountSet(child: Expression) extends UnaryExpression { type EvaluatedType = Any - def nullable = child.nullable + override def nullable: Boolean = child.nullable - def dataType = LongType + override def dataType: DataType = LongType - def eval(input: Row): Any = { + override def eval(input: Row): Any = { val childEval = child.eval(input).asInstanceOf[OpenHashSet[Any]] if (childEval != null) { childEval.size.toLong } } - override def toString = s"$child.count()" + override def toString: String = s"$child.count()" } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala index f85ee0a9bb6d8..d597bf7ce756a 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala @@ -19,11 +19,8 @@ package org.apache.spark.sql.catalyst.expressions import java.util.regex.Pattern -import scala.collection.IndexedSeqOptimized - - import org.apache.spark.sql.catalyst.analysis.UnresolvedException -import org.apache.spark.sql.types.{BinaryType, BooleanType, DataType, StringType} +import org.apache.spark.sql.types._ trait StringRegexExpression { self: BinaryExpression => @@ -33,8 +30,8 @@ trait StringRegexExpression { def escape(v: String): String def matches(regex: Pattern, str: String): Boolean - def nullable: Boolean = left.nullable || right.nullable - def dataType: DataType = BooleanType + override def nullable: Boolean = left.nullable || right.nullable + override def dataType: DataType = BooleanType // try cache the pattern for Literal private lazy val cache: Pattern = right match { @@ -60,49 +57,28 @@ trait StringRegexExpression { if(r == null) { null } else { - val regex = pattern(r.asInstanceOf[String]) + val regex = pattern(r.asInstanceOf[UTF8String].toString) if(regex == null) { null } else { - matches(regex, l.asInstanceOf[String]) + matches(regex, l.asInstanceOf[UTF8String].toString) } } } } } -trait CaseConversionExpression { - self: UnaryExpression => - - type EvaluatedType = Any - - def convert(v: String): String - - override def foldable: Boolean = child.foldable - def nullable: Boolean = child.nullable - def dataType: DataType = StringType - - override def eval(input: Row): Any = { - val evaluated = child.eval(input) - if (evaluated == null) { - null - } else { - convert(evaluated.toString) - } - } -} - /** * Simple RegEx pattern matching function */ case class Like(left: Expression, right: Expression) extends BinaryExpression with StringRegexExpression { - def symbol = "LIKE" + override def symbol: String = "LIKE" // replace the _ with .{1} exactly match 1 time of any character // replace the % with .*, match 0 or more times with any character - override def escape(v: String) = + override def escape(v: String): String = if (!v.isEmpty) { "(?s)" + (' ' +: v.init).zip(v).flatMap { case (prev, '\\') => "" @@ -129,19 +105,40 @@ case class Like(left: Expression, right: Expression) case class RLike(left: Expression, right: Expression) extends BinaryExpression with StringRegexExpression { - def symbol = "RLIKE" + override def symbol: String = "RLIKE" override def escape(v: String): String = v override def matches(regex: Pattern, str: String): Boolean = regex.matcher(str).find(0) } +trait CaseConversionExpression { + self: UnaryExpression => + + type EvaluatedType = Any + + def convert(v: UTF8String): UTF8String + + override def foldable: Boolean = child.foldable + def nullable: Boolean = child.nullable + def dataType: DataType = StringType + + override def eval(input: Row): Any = { + val evaluated = child.eval(input) + if (evaluated == null) { + null + } else { + convert(evaluated.asInstanceOf[UTF8String]) + } + } +} + /** * A function that converts the characters of a string to uppercase. */ case class Upper(child: Expression) extends UnaryExpression with CaseConversionExpression { - override def convert(v: String): String = v.toUpperCase() + override def convert(v: UTF8String): UTF8String = v.toUpperCase - override def toString() = s"Upper($child)" + override def toString: String = s"Upper($child)" } /** @@ -149,59 +146,59 @@ case class Upper(child: Expression) extends UnaryExpression with CaseConversionE */ case class Lower(child: Expression) extends UnaryExpression with CaseConversionExpression { - override def convert(v: String): String = v.toLowerCase() + override def convert(v: UTF8String): UTF8String = v.toLowerCase - override def toString() = s"Lower($child)" + override def toString: String = s"Lower($child)" } /** A base trait for functions that compare two strings, returning a boolean. */ trait StringComparison { - self: BinaryExpression => + self: BinaryPredicate => - type EvaluatedType = Any + override type EvaluatedType = Any - def nullable: Boolean = left.nullable || right.nullable - override def dataType: DataType = BooleanType + override def nullable: Boolean = left.nullable || right.nullable - def compare(l: String, r: String): Boolean + def compare(l: UTF8String, r: UTF8String): Boolean override def eval(input: Row): Any = { - val leftEval = left.eval(input).asInstanceOf[String] + val leftEval = left.eval(input) if(leftEval == null) { null } else { - val rightEval = right.eval(input).asInstanceOf[String] - if (rightEval == null) null else compare(leftEval, rightEval) + val rightEval = right.eval(input) + if (rightEval == null) null + else compare(leftEval.asInstanceOf[UTF8String], rightEval.asInstanceOf[UTF8String]) } } - def symbol: String = nodeName + override def symbol: String = nodeName - override def toString() = s"$nodeName($left, $right)" + override def toString: String = s"$nodeName($left, $right)" } /** * A function that returns true if the string `left` contains the string `right`. */ case class Contains(left: Expression, right: Expression) - extends BinaryExpression with StringComparison { - override def compare(l: String, r: String) = l.contains(r) + extends BinaryPredicate with StringComparison { + override def compare(l: UTF8String, r: UTF8String): Boolean = l.contains(r) } /** * A function that returns true if the string `left` starts with the string `right`. */ case class StartsWith(left: Expression, right: Expression) - extends BinaryExpression with StringComparison { - def compare(l: String, r: String) = l.startsWith(r) + extends BinaryPredicate with StringComparison { + override def compare(l: UTF8String, r: UTF8String): Boolean = l.startsWith(r) } /** * A function that returns true if the string `left` ends with the string `right`. */ case class EndsWith(left: Expression, right: Expression) - extends BinaryExpression with StringComparison { - def compare(l: String, r: String) = l.endsWith(r) + extends BinaryPredicate with StringComparison { + override def compare(l: UTF8String, r: UTF8String): Boolean = l.endsWith(r) } /** @@ -212,22 +209,20 @@ case class Substring(str: Expression, pos: Expression, len: Expression) extends type EvaluatedType = Any - override def foldable = str.foldable && pos.foldable && len.foldable + override def foldable: Boolean = str.foldable && pos.foldable && len.foldable - def nullable: Boolean = str.nullable || pos.nullable || len.nullable - def dataType: DataType = { + override def nullable: Boolean = str.nullable || pos.nullable || len.nullable + override def dataType: DataType = { if (!resolved) { throw new UnresolvedException(this, s"Cannot resolve since $children are not resolved") } if (str.dataType == BinaryType) str.dataType else StringType } - override def children = str :: pos :: len :: Nil + override def children: Seq[Expression] = str :: pos :: len :: Nil @inline - def slice[T, C <: Any](str: C, startPos: Int, sliceLen: Int) - (implicit ev: (C=>IndexedSeqOptimized[T,_])): Any = { - val len = str.length + def slicePos(startPos: Int, sliceLen: Int, length: () => Int): (Int, Int) = { // Hive and SQL use one-based indexing for SUBSTR arguments but also accept zero and // negative indices for start positions. If a start index i is greater than 0, it // refers to element i-1 in the sequence. If a start index i is less than 0, it refers @@ -236,7 +231,7 @@ case class Substring(str: Expression, pos: Expression, len: Expression) extends val start = startPos match { case pos if pos > 0 => pos - 1 - case neg if neg < 0 => len + neg + case neg if neg < 0 => length() + neg case _ => 0 } @@ -245,12 +240,11 @@ case class Substring(str: Expression, pos: Expression, len: Expression) extends case x => start + x } - str.slice(start, end) + (start, end) } override def eval(input: Row): Any = { val string = str.eval(input) - val po = pos.eval(input) val ln = len.eval(input) @@ -258,16 +252,20 @@ case class Substring(str: Expression, pos: Expression, len: Expression) extends null } else { val start = po.asInstanceOf[Int] - val length = ln.asInstanceOf[Int] - + val length = ln.asInstanceOf[Int] string match { - case ba: Array[Byte] => slice(ba, start, length) - case other => slice(other.toString, start, length) + case ba: Array[Byte] => + val (st, end) = slicePos(start, length, () => ba.length) + ba.slice(st, end) + case s: UTF8String => + val (st, end) = slicePos(start, length, () => s.length) + s.slice(st, end) } } } - override def toString = len match { + override def toString: String = len match { + // TODO: This is broken because max is not an integer value. case max if max == Integer.MAX_VALUE => s"SUBSTR($str, $pos)" case _ => s"SUBSTR($str, $pos, $len)" } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala index 8c8f2896eb99b..7c80634d2c852 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala @@ -18,6 +18,7 @@ package org.apache.spark.sql.catalyst.optimizer import scala.collection.immutable.HashSet +import org.apache.spark.sql.catalyst.analysis.EliminateSubQueries import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.Inner import org.apache.spark.sql.catalyst.plans.FullOuter @@ -32,6 +33,9 @@ abstract class Optimizer extends RuleExecutor[LogicalPlan] object DefaultOptimizer extends Optimizer { val batches = + // SubQueries are only needed for analysis and can be removed before execution. + Batch("Remove SubQueries", FixedPoint(100), + EliminateSubQueries) :: Batch("Combine Limits", FixedPoint(100), CombineLimits) :: Batch("ConstantFolding", FixedPoint(100), @@ -50,7 +54,10 @@ object DefaultOptimizer extends Optimizer { CombineFilters, PushPredicateThroughProject, PushPredicateThroughJoin, - ColumnPruning) :: Nil + PushPredicateThroughGenerate, + ColumnPruning) :: + Batch("LocalRelation", FixedPoint(100), + ConvertToLocalRelation) :: Nil } /** @@ -116,6 +123,15 @@ object ColumnPruning extends Rule[LogicalPlan] { case a @ Aggregate(_, _, child) if (child.outputSet -- a.references).nonEmpty => a.copy(child = Project(a.references.toSeq, child)) + case p @ Project(projectList, a @ Aggregate(groupingExpressions, aggregateExpressions, child)) + if (a.outputSet -- p.references).nonEmpty => + Project( + projectList, + Aggregate( + groupingExpressions, + aggregateExpressions.filter(e => p.references.contains(e)), + child)) + // Eliminate unneeded attributes from either side of a Join. case Project(projectList, Join(left, right, joinType, condition)) => // Collect the list of all references required either above or to evaluate the condition. @@ -125,7 +141,7 @@ object ColumnPruning extends Rule[LogicalPlan] { condition.map(_.references).getOrElse(AttributeSet(Seq.empty)) /** Applies a projection only when the child is producing unnecessary attributes */ - def pruneJoinChild(c: LogicalPlan) = prunedChild(c, allReferences) + def pruneJoinChild(c: LogicalPlan): LogicalPlan = prunedChild(c, allReferences) Project(projectList, Join(pruneJoinChild(left), pruneJoinChild(right), joinType, condition)) @@ -182,14 +198,19 @@ object LikeSimplification extends Rule[LogicalPlan] { val equalTo = "([^_%]*)".r def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions { - case Like(l, Literal(startsWith(pattern), StringType)) if !pattern.endsWith("\\") => - StartsWith(l, Literal(pattern)) - case Like(l, Literal(endsWith(pattern), StringType)) => - EndsWith(l, Literal(pattern)) - case Like(l, Literal(contains(pattern), StringType)) if !pattern.endsWith("\\") => - Contains(l, Literal(pattern)) - case Like(l, Literal(equalTo(pattern), StringType)) => - EqualTo(l, Literal(pattern)) + case Like(l, Literal(utf, StringType)) => + utf.toString match { + case startsWith(pattern) if !pattern.endsWith("\\") => + StartsWith(l, Literal(pattern)) + case endsWith(pattern) => + EndsWith(l, Literal(pattern)) + case contains(pattern) if !pattern.endsWith("\\") => + Contains(l, Literal(pattern)) + case equalTo(pattern) => + EqualTo(l, Literal(pattern)) + case _ => + Like(l, Literal.create(utf, StringType)) + } } } @@ -202,11 +223,12 @@ object NullPropagation extends Rule[LogicalPlan] { def apply(plan: LogicalPlan): LogicalPlan = plan transform { case q: LogicalPlan => q transformExpressionsUp { case e @ Count(Literal(null, _)) => Cast(Literal(0L), e.dataType) - case e @ IsNull(c) if !c.nullable => Literal(false, BooleanType) - case e @ IsNotNull(c) if !c.nullable => Literal(true, BooleanType) - case e @ GetItem(Literal(null, _), _) => Literal(null, e.dataType) - case e @ GetItem(_, Literal(null, _)) => Literal(null, e.dataType) - case e @ GetField(Literal(null, _), _, _) => Literal(null, e.dataType) + case e @ IsNull(c) if !c.nullable => Literal.create(false, BooleanType) + case e @ IsNotNull(c) if !c.nullable => Literal.create(true, BooleanType) + case e @ GetItem(Literal(null, _), _) => Literal.create(null, e.dataType) + case e @ GetItem(_, Literal(null, _)) => Literal.create(null, e.dataType) + case e @ StructGetField(Literal(null, _), _, _) => Literal.create(null, e.dataType) + case e @ ArrayGetField(Literal(null, _), _, _, _) => Literal.create(null, e.dataType) case e @ EqualNullSafe(Literal(null, _), r) => IsNull(r) case e @ EqualNullSafe(l, Literal(null, _)) => IsNull(l) case e @ Count(expr) if !expr.nullable => Count(Literal(1)) @@ -218,36 +240,36 @@ object NullPropagation extends Rule[LogicalPlan] { case _ => true } if (newChildren.length == 0) { - Literal(null, e.dataType) + Literal.create(null, e.dataType) } else if (newChildren.length == 1) { newChildren(0) } else { Coalesce(newChildren) } - case e @ Substring(Literal(null, _), _, _) => Literal(null, e.dataType) - case e @ Substring(_, Literal(null, _), _) => Literal(null, e.dataType) - case e @ Substring(_, _, Literal(null, _)) => Literal(null, e.dataType) + case e @ Substring(Literal(null, _), _, _) => Literal.create(null, e.dataType) + case e @ Substring(_, Literal(null, _), _) => Literal.create(null, e.dataType) + case e @ Substring(_, _, Literal(null, _)) => Literal.create(null, e.dataType) // Put exceptional cases above if any case e: BinaryArithmetic => e.children match { - case Literal(null, _) :: right :: Nil => Literal(null, e.dataType) - case left :: Literal(null, _) :: Nil => Literal(null, e.dataType) + case Literal(null, _) :: right :: Nil => Literal.create(null, e.dataType) + case left :: Literal(null, _) :: Nil => Literal.create(null, e.dataType) case _ => e } case e: BinaryComparison => e.children match { - case Literal(null, _) :: right :: Nil => Literal(null, e.dataType) - case left :: Literal(null, _) :: Nil => Literal(null, e.dataType) + case Literal(null, _) :: right :: Nil => Literal.create(null, e.dataType) + case left :: Literal(null, _) :: Nil => Literal.create(null, e.dataType) case _ => e } case e: StringRegexExpression => e.children match { - case Literal(null, _) :: right :: Nil => Literal(null, e.dataType) - case left :: Literal(null, _) :: Nil => Literal(null, e.dataType) + case Literal(null, _) :: right :: Nil => Literal.create(null, e.dataType) + case left :: Literal(null, _) :: Nil => Literal.create(null, e.dataType) case _ => e } case e: StringComparison => e.children match { - case Literal(null, _) :: right :: Nil => Literal(null, e.dataType) - case left :: Literal(null, _) :: Nil => Literal(null, e.dataType) + case Literal(null, _) :: right :: Nil => Literal.create(null, e.dataType) + case left :: Literal(null, _) :: Nil => Literal.create(null, e.dataType) case _ => e } } @@ -267,13 +289,13 @@ object ConstantFolding extends Rule[LogicalPlan] { case l: Literal => l // Fold expressions that are foldable. - case e if e.foldable => Literal(e.eval(null), e.dataType) + case e if e.foldable => Literal.create(e.eval(null), e.dataType) // Fold "literal in (item1, item2, ..., literal, ...)" into true directly. case In(Literal(v, _), list) if list.exists { case Literal(candidate, _) if candidate == v => true case _ => false - } => Literal(true, BooleanType) + } => Literal.create(true, BooleanType) } } } @@ -453,6 +475,30 @@ object PushPredicateThroughProject extends Rule[LogicalPlan] { } } +/** + * Push [[Filter]] operators through [[Generate]] operators. Parts of the predicate that reference + * attributes generated in [[Generate]] will remain above, and the rest should be pushed beneath. + */ +object PushPredicateThroughGenerate extends Rule[LogicalPlan] with PredicateHelper { + + def apply(plan: LogicalPlan): LogicalPlan = plan transform { + case filter @ Filter(condition, + generate @ Generate(generator, join, outer, alias, grandChild)) => + // Predicates that reference attributes produced by the `Generate` operator cannot + // be pushed below the operator. + val (pushDown, stayUp) = splitConjunctivePredicates(condition).partition { + conjunct => conjunct.references subsetOf grandChild.outputSet + } + if (pushDown.nonEmpty) { + val pushDownPredicate = pushDown.reduce(And) + val withPushdown = generate.copy(child = Filter(pushDownPredicate, grandChild)) + stayUp.reduceOption(And).map(Filter(_, withPushdown)).getOrElse(withPushdown) + } else { + filter + } + } +} + /** * Pushes down [[Filter]] operators where the `condition` can be * evaluated using only the attributes of the left or right side of a join. Other @@ -606,7 +652,21 @@ object DecimalAggregates extends Rule[LogicalPlan] { case Average(e @ DecimalType.Expression(prec, scale)) if prec + 4 <= MAX_DOUBLE_DIGITS => Cast( - Divide(Average(UnscaledValue(e)), Literal(math.pow(10.0, scale), DoubleType)), + Divide(Average(UnscaledValue(e)), Literal.create(math.pow(10.0, scale), DoubleType)), DecimalType(prec + 4, scale + 4)) } } + +/** + * Converts local operations (i.e. ones that don't require data exchange) on LocalRelation to + * another LocalRelation. + * + * This is relatively simple as it currently handles only a single case: Project. + */ +object ConvertToLocalRelation extends Rule[LogicalPlan] { + def apply(plan: LogicalPlan): LogicalPlan = plan transform { + case Project(projectList, LocalRelation(output, data)) => + val projection = new InterpretedProjection(projectList, output) + LocalRelation(projectList.map(_.toAttribute), data.map(projection)) + } +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala index b4c445b3badf1..9c8c643f7d17a 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala @@ -91,16 +91,18 @@ object PhysicalOperation extends PredicateHelper { (None, Nil, other, Map.empty) } - def collectAliases(fields: Seq[Expression]) = fields.collect { + def collectAliases(fields: Seq[Expression]): Map[Attribute, Expression] = fields.collect { case a @ Alias(child, _) => a.toAttribute.asInstanceOf[Attribute] -> child }.toMap - def substitute(aliases: Map[Attribute, Expression])(expr: Expression) = expr.transform { - case a @ Alias(ref: AttributeReference, name) => - aliases.get(ref).map(Alias(_, name)(a.exprId, a.qualifiers)).getOrElse(a) + def substitute(aliases: Map[Attribute, Expression])(expr: Expression): Expression = { + expr.transform { + case a @ Alias(ref: AttributeReference, name) => + aliases.get(ref).map(Alias(_, name)(a.exprId, a.qualifiers)).getOrElse(a) - case a: AttributeReference => - aliases.get(a).map(Alias(_, a.name)(a.exprId, a.qualifiers)).getOrElse(a) + case a: AttributeReference => + aliases.get(a).map(Alias(_, a.name)(a.exprId, a.qualifiers)).getOrElse(a) + } } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala index 619f42859cbb8..7967189cacb24 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala @@ -17,7 +17,7 @@ package org.apache.spark.sql.catalyst.plans -import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeSet, Expression} +import org.apache.spark.sql.catalyst.expressions.{VirtualColumn, Attribute, AttributeSet, Expression} import org.apache.spark.sql.catalyst.trees.TreeNode import org.apache.spark.sql.types.{ArrayType, DataType, StructField, StructType} @@ -47,8 +47,12 @@ abstract class QueryPlan[PlanType <: TreeNode[PlanType]] extends TreeNode[PlanTy * Attributes that are referenced by expressions but not provided by this nodes children. * Subclasses should override this method if they produce attributes internally as it is used by * assertions designed to prevent the construction of invalid plans. + * + * Note that virtual columns should be excluded. Currently, we only support the grouping ID + * virtual column. */ - def missingInput: AttributeSet = references -- inputSet + def missingInput: AttributeSet = + (references -- inputSet).filter(_.name != VirtualColumn.groupingIdName) /** * Runs [[transform]] with `rule` on all expressions present in this query operator. @@ -67,7 +71,7 @@ abstract class QueryPlan[PlanType <: TreeNode[PlanType]] extends TreeNode[PlanTy def transformExpressionsDown(rule: PartialFunction[Expression, Expression]): this.type = { var changed = false - @inline def transformExpressionDown(e: Expression) = { + @inline def transformExpressionDown(e: Expression): Expression = { val newE = e.transformDown(rule) if (newE.fastEquals(e)) { e @@ -81,6 +85,7 @@ abstract class QueryPlan[PlanType <: TreeNode[PlanType]] extends TreeNode[PlanTy case e: Expression => transformExpressionDown(e) case Some(e: Expression) => Some(transformExpressionDown(e)) case m: Map[_,_] => m + case d: DataType => d // Avoid unpacking Structs case seq: Traversable[_] => seq.map { case e: Expression => transformExpressionDown(e) case other => other @@ -99,7 +104,7 @@ abstract class QueryPlan[PlanType <: TreeNode[PlanType]] extends TreeNode[PlanTy def transformExpressionsUp(rule: PartialFunction[Expression, Expression]): this.type = { var changed = false - @inline def transformExpressionUp(e: Expression) = { + @inline def transformExpressionUp(e: Expression): Expression = { val newE = e.transformUp(rule) if (newE.fastEquals(e)) { e @@ -113,6 +118,7 @@ abstract class QueryPlan[PlanType <: TreeNode[PlanType]] extends TreeNode[PlanTy case e: Expression => transformExpressionUp(e) case Some(e: Expression) => Some(transformExpressionUp(e)) case m: Map[_,_] => m + case d: DataType => d // Avoid unpacking Structs case seq: Traversable[_] => seq.map { case e: Expression => transformExpressionUp(e) case other => other @@ -144,7 +150,7 @@ abstract class QueryPlan[PlanType <: TreeNode[PlanType]] extends TreeNode[PlanTy }.toSeq } - def schema: StructType = StructType.fromAttributes(output) + lazy val schema: StructType = StructType.fromAttributes(output) /** Returns the output schema in the tree format. */ def schemaString: String = schema.treeString @@ -152,7 +158,12 @@ abstract class QueryPlan[PlanType <: TreeNode[PlanType]] extends TreeNode[PlanTy /** Prints out the schema in the tree format */ def printSchema(): Unit = println(schemaString) + /** + * A prefix string used when printing the plan. + * + * We use "!" to indicate an invalid plan, and "'" to indicate an unresolved plan. + */ protected def statePrefix = if (missingInput.nonEmpty && children.nonEmpty) "!" else "" - override def simpleString = statePrefix + super.simpleString + override def simpleString: String = statePrefix + super.simpleString } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LocalRelation.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LocalRelation.scala index 92bd057c6f4b6..e3e070f0ff307 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LocalRelation.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LocalRelation.scala @@ -18,9 +18,9 @@ package org.apache.spark.sql.catalyst.plans.logical import org.apache.spark.sql.Row -import org.apache.spark.sql.catalyst.analysis +import org.apache.spark.sql.catalyst.{CatalystTypeConverters, analysis} import org.apache.spark.sql.catalyst.expressions.Attribute -import org.apache.spark.sql.types.{DataTypeConversions, StructType, StructField} +import org.apache.spark.sql.types.{StructType, StructField} object LocalRelation { def apply(output: Attribute*): LocalRelation = new LocalRelation(output) @@ -31,7 +31,8 @@ object LocalRelation { def fromProduct(output: Seq[Attribute], data: Seq[Product]): LocalRelation = { val schema = StructType.fromAttributes(output) - LocalRelation(output, data.map(row => DataTypeConversions.productToRow(row, schema))) + val converter = CatalystTypeConverters.createToCatalystConverter(schema) + LocalRelation(output, data.map(converter(_).asInstanceOf[Row])) } } @@ -54,4 +55,7 @@ case class LocalRelation(output: Seq[Attribute], data: Seq[Row] = Nil) otherOutput.map(_.dataType) == output.map(_.dataType) && otherData == data case _ => false } + + override lazy val statistics = + Statistics(sizeInBytes = output.map(_.dataType.defaultSize).sum * data.length) } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala index 8d30528328946..ae4620a4e5abf 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala @@ -18,38 +18,29 @@ package org.apache.spark.sql.catalyst.plans.logical import org.apache.spark.Logging -import org.apache.spark.sql.catalyst.analysis.{UnresolvedGetField, Resolver} -import org.apache.spark.sql.catalyst.errors.TreeNodeException +import org.apache.spark.sql.AnalysisException +import org.apache.spark.sql.catalyst.analysis.{UnresolvedAttribute, EliminateSubQueries, Resolver} import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.QueryPlan import org.apache.spark.sql.catalyst.trees.TreeNode -import org.apache.spark.sql.types.StructType import org.apache.spark.sql.catalyst.trees -/** - * Estimates of various statistics. The default estimation logic simply lazily multiplies the - * corresponding statistic produced by the children. To override this behavior, override - * `statistics` and assign it an overriden version of `Statistics`. - * - * '''NOTE''': concrete and/or overriden versions of statistics fields should pay attention to the - * performance of the implementations. The reason is that estimations might get triggered in - * performance-critical processes, such as query plan planning. - * - * @param sizeInBytes Physical size in bytes. For leaf operators this defaults to 1, otherwise it - * defaults to the product of children's `sizeInBytes`. - */ -private[sql] case class Statistics(sizeInBytes: BigInt) abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging { self: Product => + /** + * Computes [[Statistics]] for this plan. The default implementation assumes the output + * cardinality is the product of of all child plan's cardinality, i.e. applies in the case + * of cartesian joins. + * + * [[LeafNode]]s must override this. + */ def statistics: Statistics = { if (children.size == 0) { throw new UnsupportedOperationException(s"LeafNode $nodeName must implement statistics.") } - - Statistics( - sizeInBytes = children.map(_.statistics).map(_.sizeInBytes).product) + Statistics(sizeInBytes = children.map(_.statistics.sizeInBytes).product) } /** @@ -82,12 +73,16 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging { * can do better should override this function. */ def sameResult(plan: LogicalPlan): Boolean = { - plan.getClass == this.getClass && - plan.children.size == children.size && { - logDebug(s"[${cleanArgs.mkString(", ")}] == [${plan.cleanArgs.mkString(", ")}]") - cleanArgs == plan.cleanArgs + val cleanLeft = EliminateSubQueries(this) + val cleanRight = EliminateSubQueries(plan) + + cleanLeft.getClass == cleanRight.getClass && + cleanLeft.children.size == cleanRight.children.size && { + logDebug( + s"[${cleanRight.cleanArgs.mkString(", ")}] == [${cleanLeft.cleanArgs.mkString(", ")}]") + cleanRight.cleanArgs == cleanLeft.cleanArgs } && - (plan.children, children).zipped.forall(_ sameResult _) + (cleanLeft.children, cleanRight.children).zipped.forall(_ sameResult _) } /** Args that have cleaned such that differences in expression id should not affect equality */ @@ -114,53 +109,112 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging { * nodes of this LogicalPlan. The attribute is expressed as * as string in the following form: `[scope].AttributeName.[nested].[fields]...`. */ - def resolveChildren(name: String, resolver: Resolver): Option[NamedExpression] = - resolve(name, children.flatMap(_.output), resolver) + def resolveChildren( + nameParts: Seq[String], + resolver: Resolver, + throwErrors: Boolean = false): Option[NamedExpression] = + resolve(nameParts, children.flatMap(_.output), resolver, throwErrors) /** * Optionally resolves the given string to a [[NamedExpression]] based on the output of this * LogicalPlan. The attribute is expressed as string in the following form: * `[scope].AttributeName.[nested].[fields]...`. */ - def resolve(name: String, resolver: Resolver): Option[NamedExpression] = - resolve(name, output, resolver) + def resolve( + nameParts: Seq[String], + resolver: Resolver, + throwErrors: Boolean = false): Option[NamedExpression] = + resolve(nameParts, output, resolver, throwErrors) + + /** + * Resolve the given `name` string against the given attribute, returning either 0 or 1 match. + * + * This assumes `name` has multiple parts, where the 1st part is a qualifier + * (i.e. table name, alias, or subquery alias). + * See the comment above `candidates` variable in resolve() for semantics the returned data. + */ + private def resolveAsTableColumn( + nameParts: Seq[String], + resolver: Resolver, + attribute: Attribute): Option[(Attribute, List[String])] = { + assert(nameParts.length > 1) + if (attribute.qualifiers.exists(resolver(_, nameParts.head))) { + // At least one qualifier matches. See if remaining parts match. + val remainingParts = nameParts.tail + resolveAsColumn(remainingParts, resolver, attribute) + } else { + None + } + } + + /** + * Resolve the given `name` string against the given attribute, returning either 0 or 1 match. + * + * Different from resolveAsTableColumn, this assumes `name` does NOT start with a qualifier. + * See the comment above `candidates` variable in resolve() for semantics the returned data. + */ + private def resolveAsColumn( + nameParts: Seq[String], + resolver: Resolver, + attribute: Attribute): Option[(Attribute, List[String])] = { + if (resolver(attribute.name, nameParts.head)) { + Option((attribute.withName(nameParts.head), nameParts.tail.toList)) + } else { + None + } + } /** Performs attribute resolution given a name and a sequence of possible attributes. */ protected def resolve( - name: String, + nameParts: Seq[String], input: Seq[Attribute], - resolver: Resolver): Option[NamedExpression] = { - - val parts = name.split("\\.") - - // Collect all attributes that are output by this nodes children where either the first part - // matches the name or where the first part matches the scope and the second part matches the - // name. Return these matches along with any remaining parts, which represent dotted access to - // struct fields. - val options = input.flatMap { option => - // If the first part of the desired name matches a qualifier for this possible match, drop it. - val remainingParts = - if (option.qualifiers.find(resolver(_, parts.head)).nonEmpty && parts.size > 1) { - parts.drop(1) - } else { - parts + resolver: Resolver, + throwErrors: Boolean): Option[NamedExpression] = { + + // A sequence of possible candidate matches. + // Each candidate is a tuple. The first element is a resolved attribute, followed by a list + // of parts that are to be resolved. + // For example, consider an example where "a" is the table name, "b" is the column name, + // and "c" is the struct field name, i.e. "a.b.c". In this case, Attribute will be "a.b", + // and the second element will be List("c"). + var candidates: Seq[(Attribute, List[String])] = { + // If the name has 2 or more parts, try to resolve it as `table.column` first. + if (nameParts.length > 1) { + input.flatMap { option => + resolveAsTableColumn(nameParts, resolver, option) } - - if (resolver(option.name, remainingParts.head)) { - // Preserve the case of the user's attribute reference. - (option.withName(remainingParts.head), remainingParts.tail.toList) :: Nil } else { - Nil + Seq.empty + } + } + + // If none of attributes match `table.column` pattern, we try to resolve it as a column. + if (candidates.isEmpty) { + candidates = input.flatMap { candidate => + resolveAsColumn(nameParts, resolver, candidate) } } - options.distinct match { + def name = UnresolvedAttribute(nameParts).name + + candidates.distinct match { // One match, no nested fields, use it. case Seq((a, Nil)) => Some(a) // One match, but we also need to extract the requested nested field. case Seq((a, nestedFields)) => - Some(Alias(nestedFields.foldLeft(a: Expression)(UnresolvedGetField), nestedFields.last)()) + try { + // The foldLeft adds GetFields for every remaining parts of the identifier, + // and aliases it with the last part of the identifier. + // For example, consider "a.b.c", where "a" is resolved to an existing attribute. + // Then this will add GetField("c", GetField("b", a)), and alias + // the final expression as "c". + val fieldExprs = nestedFields.foldLeft(a: Expression)(GetField(_, _, resolver)) + val aliasName = nestedFields.last + Some(Alias(fieldExprs, aliasName)()) + } catch { + case a: AnalysisException if !throwErrors => None + } // No matches. case Seq() => @@ -169,8 +223,9 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging { // More than one match. case ambiguousReferences => - throw new TreeNodeException( - this, s"Ambiguous references to $name: ${ambiguousReferences.mkString(",")}") + val referenceNames = ambiguousReferences.map(_._1).mkString(", ") + throw new AnalysisException( + s"Reference '$name' is ambiguous, could be: $referenceNames.") } } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/ScriptTransformation.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/ScriptTransformation.scala index cfe2c7a39a17c..ccf5291219add 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/ScriptTransformation.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/ScriptTransformation.scala @@ -17,7 +17,7 @@ package org.apache.spark.sql.catalyst.plans.logical -import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression} +import org.apache.spark.sql.catalyst.expressions.{AttributeSet, Attribute, Expression} /** * Transforms the input by forking and running the specified script. @@ -32,7 +32,9 @@ case class ScriptTransformation( script: String, output: Seq[Attribute], child: LogicalPlan, - ioschema: ScriptInputOutputSchema) extends UnaryNode + ioschema: ScriptInputOutputSchema) extends UnaryNode { + override def references: AttributeSet = AttributeSet(input.flatMap(_.references)) +} /** * A placeholder for implementation specific input and output properties when passing data diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/Statistics.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/Statistics.scala new file mode 100644 index 0000000000000..9ac4c3a2a56c8 --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/Statistics.scala @@ -0,0 +1,35 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.plans.logical + +/** + * Estimates of various statistics. The default estimation logic simply lazily multiplies the + * corresponding statistic produced by the children. To override this behavior, override + * `statistics` and assign it an overridden version of `Statistics`. + * + * '''NOTE''': concrete and/or overridden versions of statistics fields should pay attention to the + * performance of the implementations. The reason is that estimations might get triggered in + * performance-critical processes, such as query plan planning. + * + * Note that we are using a BigInt here since it is easy to overflow a 64-bit integer in + * cardinality estimation (e.g. cartesian joins). + * + * @param sizeInBytes Physical size in bytes. For leaf operators this defaults to 1, otherwise it + * defaults to the product of children's `sizeInBytes`. + */ +private[sql] case class Statistics(sizeInBytes: BigInt) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala index 9628e93274a11..17522976dc2c9 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala @@ -22,7 +22,17 @@ import org.apache.spark.sql.catalyst.plans._ import org.apache.spark.sql.types._ case class Project(projectList: Seq[NamedExpression], child: LogicalPlan) extends UnaryNode { - def output = projectList.map(_.toAttribute) + override def output: Seq[Attribute] = projectList.map(_.toAttribute) + + override lazy val resolved: Boolean = { + val containsAggregatesOrGenerators = projectList.exists ( _.collect { + case agg: AggregateExpression => agg + case generator: Generator => generator + }.nonEmpty + ) + + !expressions.exists(!_.resolved) && childrenResolved && !containsAggregatesOrGenerators + } } /** @@ -56,21 +66,26 @@ case class Generate( } } - override def output = + override def output: Seq[Attribute] = if (join) child.output ++ generatorOutput else generatorOutput } case class Filter(condition: Expression, child: LogicalPlan) extends UnaryNode { - override def output = child.output + override def output: Seq[Attribute] = child.output } case class Union(left: LogicalPlan, right: LogicalPlan) extends BinaryNode { // TODO: These aren't really the same attributes as nullability etc might change. - override def output = left.output + override def output: Seq[Attribute] = left.output - override lazy val resolved = + override lazy val resolved: Boolean = childrenResolved && - !left.output.zip(right.output).exists { case (l,r) => l.dataType != r.dataType } + left.output.zip(right.output).forall { case (l,r) => l.dataType == r.dataType } + + override def statistics: Statistics = { + val sizeInBytes = left.statistics.sizeInBytes + right.statistics.sizeInBytes + Statistics(sizeInBytes = sizeInBytes) + } } case class Join( @@ -79,7 +94,7 @@ case class Join( joinType: JoinType, condition: Option[Expression]) extends BinaryNode { - override def output = { + override def output: Seq[Attribute] = { joinType match { case LeftSemi => left.output @@ -93,24 +108,34 @@ case class Join( left.output ++ right.output } } + + private def selfJoinResolved: Boolean = left.outputSet.intersect(right.outputSet).isEmpty + + // Joins are only resolved if they don't introduce ambiguious expression ids. + override lazy val resolved: Boolean = { + childrenResolved && !expressions.exists(!_.resolved) && selfJoinResolved + } } case class Except(left: LogicalPlan, right: LogicalPlan) extends BinaryNode { - def output = left.output + override def output: Seq[Attribute] = left.output } case class InsertIntoTable( table: LogicalPlan, partition: Map[String, Option[String]], child: LogicalPlan, - overwrite: Boolean) + overwrite: Boolean, + ifNotExists: Boolean) extends LogicalPlan { - override def children = child :: Nil - override def output = child.output + override def children: Seq[LogicalPlan] = child :: Nil + override def output: Seq[Attribute] = child.output - override lazy val resolved = childrenResolved && child.output.zip(table.output).forall { - case (childAttr, tableAttr) => childAttr.dataType == tableAttr.dataType + assert(overwrite || !ifNotExists) + override lazy val resolved: Boolean = childrenResolved && child.output.zip(table.output).forall { + case (childAttr, tableAttr) => + DataType.equalsIgnoreCompatibleNullability(childAttr.dataType, tableAttr.dataType) } } @@ -120,14 +145,26 @@ case class CreateTableAsSelect[T]( child: LogicalPlan, allowExisting: Boolean, desc: Option[T] = None) extends UnaryNode { - override def output = Seq.empty[Attribute] - override lazy val resolved = databaseName != None && childrenResolved + override def output: Seq[Attribute] = Seq.empty[Attribute] + override lazy val resolved: Boolean = databaseName != None && childrenResolved +} + +/** + * A container for holding named common table expressions (CTEs) and a query plan. + * This operator will be removed during analysis and the relations will be substituted into child. + * @param child The final query of this CTE. + * @param cteRelations Queries that this CTE defined, + * key is the alias of the CTE definition, + * value is the CTE definition. + */ +case class With(child: LogicalPlan, cteRelations: Map[String, Subquery]) extends UnaryNode { + override def output: Seq[Attribute] = child.output } case class WriteToFile( path: String, child: LogicalPlan) extends UnaryNode { - override def output = child.output + override def output: Seq[Attribute] = child.output } /** @@ -140,7 +177,7 @@ case class Sort( order: Seq[SortOrder], global: Boolean, child: LogicalPlan) extends UnaryNode { - override def output = child.output + override def output: Seq[Attribute] = child.output } case class Aggregate( @@ -149,7 +186,7 @@ case class Aggregate( child: LogicalPlan) extends UnaryNode { - override def output = aggregateExpressions.map(_.toAttribute) + override def output: Seq[Attribute] = aggregateExpressions.map(_.toAttribute) } /** @@ -163,7 +200,12 @@ case class Aggregate( case class Expand( projections: Seq[GroupExpression], output: Seq[Attribute], - child: LogicalPlan) extends UnaryNode + child: LogicalPlan) extends UnaryNode { + override def statistics: Statistics = { + val sizeInBytes = child.statistics.sizeInBytes * projections.length + Statistics(sizeInBytes = sizeInBytes) + } +} trait GroupingAnalytics extends UnaryNode { self: Product => @@ -171,7 +213,7 @@ trait GroupingAnalytics extends UnaryNode { def groupByExprs: Seq[Expression] def aggregations: Seq[NamedExpression] - override def output = aggregations.map(_.toAttribute) + override def output: Seq[Attribute] = aggregations.map(_.toAttribute) } /** @@ -236,7 +278,7 @@ case class Rollup( gid: AttributeReference = VirtualColumn.newGroupingId) extends GroupingAnalytics case class Limit(limitExpr: Expression, child: LogicalPlan) extends UnaryNode { - override def output = child.output + override def output: Seq[Attribute] = child.output override lazy val statistics: Statistics = { val limit = limitExpr.eval(null).asInstanceOf[Int] @@ -246,23 +288,35 @@ case class Limit(limitExpr: Expression, child: LogicalPlan) extends UnaryNode { } case class Subquery(alias: String, child: LogicalPlan) extends UnaryNode { - override def output = child.output.map(_.withQualifiers(alias :: Nil)) + override def output: Seq[Attribute] = child.output.map(_.withQualifiers(alias :: Nil)) } case class Sample(fraction: Double, withReplacement: Boolean, seed: Long, child: LogicalPlan) extends UnaryNode { - override def output = child.output + override def output: Seq[Attribute] = child.output } case class Distinct(child: LogicalPlan) extends UnaryNode { - override def output = child.output + override def output: Seq[Attribute] = child.output } -case object NoRelation extends LeafNode { - override def output = Nil +/** + * A relation with one row. This is used in "SELECT ..." without a from clause. + */ +case object OneRowRelation extends LeafNode { + override def output: Seq[Attribute] = Nil + + /** + * Computes [[Statistics]] for this plan. The default implementation assumes the output + * cardinality is the product of of all child plan's cardinality, i.e. applies in the case + * of cartesian joins. + * + * [[LeafNode]]s must override this. + */ + override def statistics: Statistics = Statistics(sizeInBytes = 1) } case class Intersect(left: LogicalPlan, right: LogicalPlan) extends BinaryNode { - override def output = left.output + override def output: Seq[Attribute] = left.output } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/partitioning.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/partitioning.scala index 72b0c5c8e7a26..e737418d9c3bc 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/partitioning.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/partitioning.scala @@ -17,7 +17,7 @@ package org.apache.spark.sql.catalyst.plans.logical -import org.apache.spark.sql.catalyst.expressions.{Expression, SortOrder} +import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression, SortOrder} /** * Performs a physical redistribution of the data. Used when the consumer of the query @@ -26,14 +26,11 @@ import org.apache.spark.sql.catalyst.expressions.{Expression, SortOrder} abstract class RedistributeData extends UnaryNode { self: Product => - def output = child.output + override def output: Seq[Attribute] = child.output } case class SortPartitions(sortExpressions: Seq[SortOrder], child: LogicalPlan) - extends RedistributeData { -} + extends RedistributeData case class Repartition(partitionExpressions: Seq[Expression], child: LogicalPlan) - extends RedistributeData { -} - + extends RedistributeData diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala index 3c3d7a3119064..fb4217a44807b 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala @@ -19,7 +19,7 @@ package org.apache.spark.sql.catalyst.plans.physical import org.apache.spark.sql.catalyst.errors.TreeNodeException import org.apache.spark.sql.catalyst.expressions.{Expression, Row, SortOrder} -import org.apache.spark.sql.types.IntegerType +import org.apache.spark.sql.types.{DataType, IntegerType} /** * Specifies how tuples that share common expressions will be distributed when a query is executed @@ -72,7 +72,7 @@ case class OrderedDistribution(ordering: Seq[SortOrder]) extends Distribution { "a single partition.") // TODO: This is not really valid... - def clustering = ordering.map(_.child).toSet + def clustering: Set[Expression] = ordering.map(_.child).toSet } sealed trait Partitioning { @@ -94,6 +94,9 @@ sealed trait Partitioning { * only compatible if the `numPartitions` of them is the same. */ def compatibleWith(other: Partitioning): Boolean + + /** Returns the expressions that are used to key the partitioning. */ + def keyExpressions: Seq[Expression] } case class UnknownPartitioning(numPartitions: Int) extends Partitioning { @@ -106,6 +109,8 @@ case class UnknownPartitioning(numPartitions: Int) extends Partitioning { case UnknownPartitioning(_) => true case _ => false } + + override def keyExpressions: Seq[Expression] = Nil } case object SinglePartition extends Partitioning { @@ -113,10 +118,12 @@ case object SinglePartition extends Partitioning { override def satisfies(required: Distribution): Boolean = true - override def compatibleWith(other: Partitioning) = other match { + override def compatibleWith(other: Partitioning): Boolean = other match { case SinglePartition => true case _ => false } + + override def keyExpressions: Seq[Expression] = Nil } case object BroadcastPartitioning extends Partitioning { @@ -124,10 +131,12 @@ case object BroadcastPartitioning extends Partitioning { override def satisfies(required: Distribution): Boolean = true - override def compatibleWith(other: Partitioning) = other match { + override def compatibleWith(other: Partitioning): Boolean = other match { case SinglePartition => true case _ => false } + + override def keyExpressions: Seq[Expression] = Nil } /** @@ -139,9 +148,9 @@ case class HashPartitioning(expressions: Seq[Expression], numPartitions: Int) extends Expression with Partitioning { - override def children = expressions - override def nullable = false - override def dataType = IntegerType + override def children: Seq[Expression] = expressions + override def nullable: Boolean = false + override def dataType: DataType = IntegerType private[this] lazy val clusteringSet = expressions.toSet @@ -152,12 +161,14 @@ case class HashPartitioning(expressions: Seq[Expression], numPartitions: Int) case _ => false } - override def compatibleWith(other: Partitioning) = other match { + override def compatibleWith(other: Partitioning): Boolean = other match { case BroadcastPartitioning => true case h: HashPartitioning if h == this => true case _ => false } + override def keyExpressions: Seq[Expression] = expressions + override def eval(input: Row = null): EvaluatedType = throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}") } @@ -178,9 +189,9 @@ case class RangePartitioning(ordering: Seq[SortOrder], numPartitions: Int) extends Expression with Partitioning { - override def children = ordering - override def nullable = false - override def dataType = IntegerType + override def children: Seq[SortOrder] = ordering + override def nullable: Boolean = false + override def dataType: DataType = IntegerType private[this] lazy val clusteringSet = ordering.map(_.child).toSet @@ -194,12 +205,14 @@ case class RangePartitioning(ordering: Seq[SortOrder], numPartitions: Int) case _ => false } - override def compatibleWith(other: Partitioning) = other match { + override def compatibleWith(other: Partitioning): Boolean = other match { case BroadcastPartitioning => true case r: RangePartitioning if r == this => true case _ => false } + override def keyExpressions: Seq[Expression] = ordering.map(_.child) + override def eval(input: Row): EvaluatedType = throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}") } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala index 2013ae4f7bd13..97502ed3afe72 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala @@ -18,19 +18,53 @@ package org.apache.spark.sql.catalyst.trees import org.apache.spark.sql.catalyst.errors._ +import org.apache.spark.sql.types.DataType /** Used by [[TreeNode.getNodeNumbered]] when traversing the tree for a given number */ private class MutableInt(var i: Int) +case class Origin( + line: Option[Int] = None, + startPosition: Option[Int] = None) + +/** + * Provides a location for TreeNodes to ask about the context of their origin. For example, which + * line of code is currently being parsed. + */ +object CurrentOrigin { + private val value = new ThreadLocal[Origin]() { + override def initialValue: Origin = Origin() + } + + def get: Origin = value.get() + def set(o: Origin): Unit = value.set(o) + + def reset(): Unit = value.set(Origin()) + + def setPosition(line: Int, start: Int): Unit = { + value.set( + value.get.copy(line = Some(line), startPosition = Some(start))) + } + + def withOrigin[A](o: Origin)(f: => A): A = { + set(o) + val ret = try f finally { reset() } + reset() + ret + } +} + abstract class TreeNode[BaseType <: TreeNode[BaseType]] { self: BaseType with Product => + val origin: Origin = CurrentOrigin.get + /** Returns a Seq of the children of this node */ def children: Seq[BaseType] /** * Faster version of equality which short-circuits when two treeNodes are the same instance. - * We don't just override Object.Equals, as doing so prevents the scala compiler from from + * We don't just override Object.equals, as doing so prevents the scala compiler from * generating case class `equals` methods */ def fastEquals(other: TreeNode[_]): Boolean = { @@ -46,6 +80,15 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] { children.foreach(_.foreach(f)) } + /** + * Runs the given function recursively on [[children]] then on this node. + * @param f the function to be applied to each node in the tree. + */ + def foreachUp(f: BaseType => Unit): Unit = { + children.foreach(_.foreachUp(f)) + f(this) + } + /** * Returns a Seq containing the result of applying the given function to each * node in this tree in a preorder traversal. @@ -141,7 +184,10 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] { * @param rule the function used to transform this nodes children */ def transformDown(rule: PartialFunction[BaseType, BaseType]): BaseType = { - val afterRule = rule.applyOrElse(this, identity[BaseType]) + val afterRule = CurrentOrigin.withOrigin(origin) { + rule.applyOrElse(this, identity[BaseType]) + } + // Check if unchanged and then possibly return old copy to avoid gc churn. if (this fastEquals afterRule) { transformChildrenDown(rule) @@ -175,6 +221,7 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] { Some(arg) } case m: Map[_,_] => m + case d: DataType => d // Avoid unpacking Structs case args: Traversable[_] => args.map { case arg: TreeNode[_] if children contains arg => val newChild = arg.asInstanceOf[BaseType].transformDown(rule) @@ -201,9 +248,13 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] { def transformUp(rule: PartialFunction[BaseType, BaseType]): BaseType = { val afterRuleOnChildren = transformChildrenUp(rule); if (this fastEquals afterRuleOnChildren) { - rule.applyOrElse(this, identity[BaseType]) + CurrentOrigin.withOrigin(origin) { + rule.applyOrElse(this, identity[BaseType]) + } } else { - rule.applyOrElse(afterRuleOnChildren, identity[BaseType]) + CurrentOrigin.withOrigin(origin) { + rule.applyOrElse(afterRuleOnChildren, identity[BaseType]) + } } } @@ -227,6 +278,7 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] { Some(arg) } case m: Map[_,_] => m + case d: DataType => d // Avoid unpacking Structs case args: Traversable[_] => args.map { case arg: TreeNode[_] if children contains arg => val newChild = arg.asInstanceOf[BaseType].transformUp(rule) @@ -258,29 +310,42 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] { * @param newArgs the new product arguments. */ def makeCopy(newArgs: Array[AnyRef]): this.type = attachTree(this, "makeCopy") { + val defaultCtor = + getClass.getConstructors + .find(_.getParameterTypes.size != 0) + .headOption + .getOrElse(sys.error(s"No valid constructor for $nodeName")) + try { - // Skip no-arg constructors that are just there for kryo. - val defaultCtor = getClass.getConstructors.find(_.getParameterTypes.size != 0).head - if (otherCopyArgs.isEmpty) { - defaultCtor.newInstance(newArgs: _*).asInstanceOf[this.type] - } else { - defaultCtor.newInstance((newArgs ++ otherCopyArgs).toArray: _*).asInstanceOf[this.type] + CurrentOrigin.withOrigin(origin) { + // Skip no-arg constructors that are just there for kryo. + if (otherCopyArgs.isEmpty) { + defaultCtor.newInstance(newArgs: _*).asInstanceOf[this.type] + } else { + defaultCtor.newInstance((newArgs ++ otherCopyArgs).toArray: _*).asInstanceOf[this.type] + } } } catch { case e: java.lang.IllegalArgumentException => throw new TreeNodeException( - this, s"Failed to copy node. Is otherCopyArgs specified correctly for $nodeName? " - + s"Exception message: ${e.getMessage}.") + this, + s""" + |Failed to copy node. + |Is otherCopyArgs specified correctly for $nodeName. + |Exception message: ${e.getMessage} + |ctor: $defaultCtor? + |args: ${newArgs.mkString(", ")} + """.stripMargin) } } /** Returns the name of this type of TreeNode. Defaults to the class name. */ - def nodeName = getClass.getSimpleName + def nodeName: String = getClass.getSimpleName /** * The arguments that should be included in the arg string. Defaults to the `productIterator`. */ - protected def stringArgs = productIterator + protected def stringArgs: Iterator[Any] = productIterator /** Returns a string representing the arguments to this node, minus any children */ def argString: String = productIterator.flatMap { @@ -292,18 +357,18 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] { }.mkString(", ") /** String representation of this node without any children */ - def simpleString = s"$nodeName $argString" + def simpleString: String = s"$nodeName $argString".trim override def toString: String = treeString /** Returns a string representation of the nodes in this tree */ - def treeString = generateTreeString(0, new StringBuilder).toString + def treeString: String = generateTreeString(0, new StringBuilder).toString /** * Returns a string representation of the nodes in this tree, where each operator is numbered. * The numbers can be used with [[trees.TreeNode.apply apply]] to easily access specific subtrees. */ - def numberedTreeString = + def numberedTreeString: String = treeString.split("\n").zipWithIndex.map { case (line, i) => f"$i%02d $line" }.mkString("\n") /** @@ -355,14 +420,14 @@ trait BinaryNode[BaseType <: TreeNode[BaseType]] { def left: BaseType def right: BaseType - def children = Seq(left, right) + def children: Seq[BaseType] = Seq(left, right) } /** * A [[TreeNode]] with no children. */ trait LeafNode[BaseType <: TreeNode[BaseType]] { - def children = Nil + def children: Seq[BaseType] = Nil } /** @@ -370,6 +435,5 @@ trait LeafNode[BaseType <: TreeNode[BaseType]] { */ trait UnaryNode[BaseType <: TreeNode[BaseType]] { def child: BaseType - def children = child :: Nil + def children: Seq[BaseType] = child :: Nil } - diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/package.scala index 79a8e06d4b4d4..ea6aa1850db4c 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/package.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/package.scala @@ -41,11 +41,11 @@ package object trees extends Logging { * A [[TreeNode]] companion for reference equality for Hash based Collection. */ class TreeNodeRef(val obj: TreeNode[_]) { - override def equals(o: Any) = o match { + override def equals(o: Any): Boolean = o match { case that: TreeNodeRef => that.obj.eq(obj) case _ => false } - override def hashCode = if (obj == null) 0 else obj.hashCode + override def hashCode: Int = if (obj == null) 0 else obj.hashCode } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/package.scala index d8da45ae70c4b..c86214a2aa944 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/package.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/package.scala @@ -19,22 +19,11 @@ package org.apache.spark.sql.catalyst import java.io.{PrintWriter, ByteArrayOutputStream, FileInputStream, File} -import org.apache.spark.util.{Utils => SparkUtils} +import org.apache.spark.util.Utils package object util { - /** - * Returns a path to a temporary file that probably does not exist. - * Note, there is always the race condition that someone created this - * file since the last time we checked. Thus, this shouldn't be used - * for anything security conscious. - */ - def getTempFilePath(prefix: String, suffix: String = ""): File = { - val tempFile = File.createTempFile(prefix, suffix) - tempFile.delete() - tempFile - } - def fileToString(file: File, encoding: String = "UTF-8") = { + def fileToString(file: File, encoding: String = "UTF-8"): String = { val inStream = new FileInputStream(file) val outStream = new ByteArrayOutputStream try { @@ -56,7 +45,7 @@ package object util { def resourceToString( resource:String, encoding: String = "UTF-8", - classLoader: ClassLoader = SparkUtils.getSparkClassLoader) = { + classLoader: ClassLoader = Utils.getSparkClassLoader): String = { val inStream = classLoader.getResourceAsStream(resource) val outStream = new ByteArrayOutputStream try { @@ -104,7 +93,7 @@ package object util { new String(out.toByteArray) } - def stringOrNull(a: AnyRef) = if (a == null) null else a.toString + def stringOrNull(a: AnyRef): String = if (a == null) null else a.toString def benchmark[A](f: => A): A = { val startTime = System.nanoTime() diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataTypeConversions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataTypeConversions.scala deleted file mode 100644 index c243be07a91b6..0000000000000 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataTypeConversions.scala +++ /dev/null @@ -1,77 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.sql.types - -import java.text.SimpleDateFormat - -import org.apache.spark.sql.Row -import org.apache.spark.sql.catalyst.ScalaReflection -import org.apache.spark.sql.catalyst.expressions.GenericMutableRow - - -protected[sql] object DataTypeConversions { - - def productToRow(product: Product, schema: StructType): Row = { - val mutableRow = new GenericMutableRow(product.productArity) - val schemaFields = schema.fields.toArray - - var i = 0 - while (i < mutableRow.length) { - mutableRow(i) = - ScalaReflection.convertToCatalyst(product.productElement(i), schemaFields(i).dataType) - i += 1 - } - - mutableRow - } - - def stringToTime(s: String): java.util.Date = { - if (!s.contains('T')) { - // JDBC escape string - if (s.contains(' ')) { - java.sql.Timestamp.valueOf(s) - } else { - java.sql.Date.valueOf(s) - } - } else if (s.endsWith("Z")) { - // this is zero timezone of ISO8601 - stringToTime(s.substring(0, s.length - 1) + "GMT-00:00") - } else if (s.indexOf("GMT") == -1) { - // timezone with ISO8601 - val inset = "+00.00".length - val s0 = s.substring(0, s.length - inset) - val s1 = s.substring(s.length - inset, s.length) - if (s0.substring(s0.lastIndexOf(':')).contains('.')) { - stringToTime(s0 + "GMT" + s1) - } else { - stringToTime(s0 + ".0GMT" + s1) - } - } else { - // ISO8601 with GMT insert - val ISO8601GMT: SimpleDateFormat = new SimpleDateFormat( "yyyy-MM-dd'T'HH:mm:ss.SSSz" ) - ISO8601GMT.parse(s) - } - } - - /** Converts Java objects to catalyst rows / types */ - def convertJavaToCatalyst(a: Any, dataType: DataType): Any = (a, dataType) match { - case (obj, udt: UserDefinedType[_]) => ScalaReflection.convertToCatalyst(obj, udt) // Scala type - case (d: java.math.BigDecimal, _) => Decimal(d) - case (other, _) => other - } -} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataTypeParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataTypeParser.scala new file mode 100644 index 0000000000000..5163f05879e42 --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataTypeParser.scala @@ -0,0 +1,115 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.types + +import scala.language.implicitConversions +import scala.util.matching.Regex +import scala.util.parsing.combinator.syntactical.StandardTokenParsers + +import org.apache.spark.sql.catalyst.SqlLexical + +/** + * This is a data type parser that can be used to parse string representations of data types + * provided in SQL queries. This parser is mixed in with DDLParser and SqlParser. + */ +private[sql] trait DataTypeParser extends StandardTokenParsers { + + // This is used to create a parser from a regex. We are using regexes for data type strings + // since these strings can be also used as column names or field names. + import lexical.Identifier + implicit def regexToParser(regex: Regex): Parser[String] = acceptMatch( + s"identifier matching regex ${regex}", + { case Identifier(str) if regex.unapplySeq(str).isDefined => str } + ) + + protected lazy val primitiveType: Parser[DataType] = + "(?i)string".r ^^^ StringType | + "(?i)float".r ^^^ FloatType | + "(?i)(?:int|integer)".r ^^^ IntegerType | + "(?i)tinyint".r ^^^ ByteType | + "(?i)smallint".r ^^^ ShortType | + "(?i)double".r ^^^ DoubleType | + "(?i)bigint".r ^^^ LongType | + "(?i)binary".r ^^^ BinaryType | + "(?i)boolean".r ^^^ BooleanType | + fixedDecimalType | + "(?i)decimal".r ^^^ DecimalType.Unlimited | + "(?i)date".r ^^^ DateType | + "(?i)timestamp".r ^^^ TimestampType | + varchar + + protected lazy val fixedDecimalType: Parser[DataType] = + ("(?i)decimal".r ~> "(" ~> numericLit) ~ ("," ~> numericLit <~ ")") ^^ { + case precision ~ scale => + DecimalType(precision.toInt, scale.toInt) + } + + protected lazy val varchar: Parser[DataType] = + "(?i)varchar".r ~> "(" ~> (numericLit <~ ")") ^^^ StringType + + protected lazy val arrayType: Parser[DataType] = + "(?i)array".r ~> "<" ~> dataType <~ ">" ^^ { + case tpe => ArrayType(tpe) + } + + protected lazy val mapType: Parser[DataType] = + "(?i)map".r ~> "<" ~> dataType ~ "," ~ dataType <~ ">" ^^ { + case t1 ~ _ ~ t2 => MapType(t1, t2) + } + + protected lazy val structField: Parser[StructField] = + ident ~ ":" ~ dataType ^^ { + case name ~ _ ~ tpe => StructField(name, tpe, nullable = true) + } + + protected lazy val structType: Parser[DataType] = + ("(?i)struct".r ~> "<" ~> repsep(structField, ",") <~ ">" ^^ { + case fields => new StructType(fields.toArray) + }) | + ("(?i)struct".r ~ "<>" ^^^ StructType(Nil)) + + protected lazy val dataType: Parser[DataType] = + arrayType | + mapType | + structType | + primitiveType + + def toDataType(dataTypeString: String): DataType = synchronized { + phrase(dataType)(new lexical.Scanner(dataTypeString)) match { + case Success(result, _) => result + case failure: NoSuccess => throw new DataTypeException(failMessage(dataTypeString)) + } + } + + private def failMessage(dataTypeString: String): String = { + s"Unsupported dataType: $dataTypeString. If you have a struct and a field name of it has " + + "any special characters, please use backticks (`) to quote that field name, e.g. `x+y`. " + + "Please note that backtick itself is not supported in a field name." + } +} + +private[sql] object DataTypeParser { + lazy val dataTypeParser = new DataTypeParser { + override val lexical = new SqlLexical + } + + def apply(dataTypeString: String): DataType = dataTypeParser.toDataType(dataTypeString) +} + +/** The exception thrown from the [[DataTypeParser]]. */ +private[sql] class DataTypeException(message: String) extends Exception(message) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DateUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DateUtils.scala index 8a1a3b81b3d2c..d36a49159b87f 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DateUtils.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DateUtils.scala @@ -18,6 +18,7 @@ package org.apache.spark.sql.types import java.sql.Date +import java.text.SimpleDateFormat import java.util.{Calendar, TimeZone} import org.apache.spark.sql.catalyst.expressions.Cast @@ -39,6 +40,7 @@ object DateUtils { millisToDays(d.getTime) } + // we should use the exact day as Int, for example, (year, month, day) -> day def millisToDays(millisLocal: Long): Int = { ((millisLocal + LOCAL_TIMEZONE.get().getOffset(millisLocal)) / MILLIS_PER_DAY).toInt } @@ -57,4 +59,32 @@ object DateUtils { } def toString(days: Int): String = Cast.threadLocalDateFormat.get.format(toJavaDate(days)) + + def stringToTime(s: String): java.util.Date = { + if (!s.contains('T')) { + // JDBC escape string + if (s.contains(' ')) { + java.sql.Timestamp.valueOf(s) + } else { + java.sql.Date.valueOf(s) + } + } else if (s.endsWith("Z")) { + // this is zero timezone of ISO8601 + stringToTime(s.substring(0, s.length - 1) + "GMT-00:00") + } else if (s.indexOf("GMT") == -1) { + // timezone with ISO8601 + val inset = "+00.00".length + val s0 = s.substring(0, s.length - inset) + val s1 = s.substring(s.length - inset, s.length) + if (s0.substring(s0.lastIndexOf(':')).contains('.')) { + stringToTime(s0 + "GMT" + s1) + } else { + stringToTime(s0 + ".0GMT" + s1) + } + } else { + // ISO8601 with GMT insert + val ISO8601GMT: SimpleDateFormat = new SimpleDateFormat( "yyyy-MM-dd'T'HH:mm:ss.SSSz" ) + ISO8601GMT.parse(s) + } + } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala index 21cc6cea4bf54..994c5202c15dc 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala @@ -246,7 +246,7 @@ final class Decimal extends Ordered[Decimal] with Serializable { } } - override def equals(other: Any) = other match { + override def equals(other: Any): Boolean = other match { case d: Decimal => compare(d) == 0 case _ => diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Metadata.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Metadata.scala old mode 100755 new mode 100644 index e50e9761431f5..6ee24ee0c1913 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Metadata.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Metadata.scala @@ -41,6 +41,9 @@ import org.apache.spark.annotation.DeveloperApi sealed class Metadata private[types] (private[types] val map: Map[String, Any]) extends Serializable { + /** No-arg constructor for kryo. */ + protected def this() = this(null) + /** Tests whether this Metadata contains a binding for a key. */ def contains(key: String): Boolean = map.contains(key) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/UTF8String.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/UTF8String.scala new file mode 100644 index 0000000000000..fc02ba6c9c43e --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/UTF8String.scala @@ -0,0 +1,214 @@ +/* +* Licensed to the Apache Software Foundation (ASF) under one or more +* contributor license agreements. See the NOTICE file distributed with +* this work for additional information regarding copyright ownership. +* The ASF licenses this file to You under the Apache License, Version 2.0 +* (the "License"); you may not use this file except in compliance with +* the License. You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +*/ + +package org.apache.spark.sql.types + +import java.util.Arrays + +/** + * A UTF-8 String, as internal representation of StringType in SparkSQL + * + * A String encoded in UTF-8 as an Array[Byte], which can be used for comparison, + * search, see http://en.wikipedia.org/wiki/UTF-8 for details. + * + * Note: This is not designed for general use cases, should not be used outside SQL. + */ + +final class UTF8String extends Ordered[UTF8String] with Serializable { + + private[this] var bytes: Array[Byte] = _ + + /** + * Update the UTF8String with String. + */ + def set(str: String): UTF8String = { + bytes = str.getBytes("utf-8") + this + } + + /** + * Update the UTF8String with Array[Byte], which should be encoded in UTF-8 + */ + def set(bytes: Array[Byte]): UTF8String = { + this.bytes = bytes + this + } + + /** + * Return the number of bytes for a code point with the first byte as `b` + * @param b The first byte of a code point + */ + @inline + private[this] def numOfBytes(b: Byte): Int = { + val offset = (b & 0xFF) - 192 + if (offset >= 0) UTF8String.bytesOfCodePointInUTF8(offset) else 1 + } + + /** + * Return the number of code points in it. + * + * This is only used by Substring() when `start` is negative. + */ + def length(): Int = { + var len = 0 + var i: Int = 0 + while (i < bytes.length) { + i += numOfBytes(bytes(i)) + len += 1 + } + len + } + + def getBytes: Array[Byte] = { + bytes + } + + /** + * Return a substring of this, + * @param start the position of first code point + * @param until the position after last code point + */ + def slice(start: Int, until: Int): UTF8String = { + if (until <= start || start >= bytes.length || bytes == null) { + new UTF8String + } + + var c = 0 + var i: Int = 0 + while (c < start && i < bytes.length) { + i += numOfBytes(bytes(i)) + c += 1 + } + var j = i + while (c < until && j < bytes.length) { + j += numOfBytes(bytes(j)) + c += 1 + } + UTF8String(Arrays.copyOfRange(bytes, i, j)) + } + + def contains(sub: UTF8String): Boolean = { + val b = sub.getBytes + if (b.length == 0) { + return true + } + var i: Int = 0 + while (i <= bytes.length - b.length) { + // In worst case, it's O(N*K), but should works fine with SQL + if (bytes(i) == b(0) && Arrays.equals(Arrays.copyOfRange(bytes, i, i + b.length), b)) { + return true + } + i += 1 + } + false + } + + def startsWith(prefix: UTF8String): Boolean = { + val b = prefix.getBytes + if (b.length > bytes.length) { + return false + } + Arrays.equals(Arrays.copyOfRange(bytes, 0, b.length), b) + } + + def endsWith(suffix: UTF8String): Boolean = { + val b = suffix.getBytes + if (b.length > bytes.length) { + return false + } + Arrays.equals(Arrays.copyOfRange(bytes, bytes.length - b.length, bytes.length), b) + } + + def toUpperCase(): UTF8String = { + // upper case depends on locale, fallback to String. + UTF8String(toString().toUpperCase) + } + + def toLowerCase(): UTF8String = { + // lower case depends on locale, fallback to String. + UTF8String(toString().toLowerCase) + } + + override def toString(): String = { + new String(bytes, "utf-8") + } + + override def clone(): UTF8String = new UTF8String().set(this.bytes) + + override def compare(other: UTF8String): Int = { + var i: Int = 0 + val b = other.getBytes + while (i < bytes.length && i < b.length) { + val res = bytes(i).compareTo(b(i)) + if (res != 0) return res + i += 1 + } + bytes.length - b.length + } + + override def compareTo(other: UTF8String): Int = { + compare(other) + } + + override def equals(other: Any): Boolean = other match { + case s: UTF8String => + Arrays.equals(bytes, s.getBytes) + case s: String => + // This is only used for Catalyst unit tests + // fail fast + bytes.length >= s.length && length() == s.length && toString() == s + case _ => + false + } + + override def hashCode(): Int = { + Arrays.hashCode(bytes) + } +} + +object UTF8String { + // number of tailing bytes in a UTF8 sequence for a code point + // see http://en.wikipedia.org/wiki/UTF-8, 192-256 of Byte 1 + private[types] val bytesOfCodePointInUTF8: Array[Int] = Array(2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, + 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, + 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, + 4, 4, 4, 4, 4, 4, 4, 4, + 5, 5, 5, 5, + 6, 6, 6, 6) + + /** + * Create a UTF-8 String from String + */ + def apply(s: String): UTF8String = { + if (s != null) { + new UTF8String().set(s) + } else{ + null + } + } + + /** + * Create a UTF-8 String from Array[Byte], which should be encoded in UTF-8 + */ + def apply(bytes: Array[Byte]): UTF8String = { + if (bytes != null) { + new UTF8String().set(bytes) + } else { + null + } + } +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala index 91efe320546a7..c6fb22c26bd3c 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala @@ -20,6 +20,7 @@ package org.apache.spark.sql.types import java.sql.Timestamp import scala.collection.mutable.ArrayBuffer +import scala.math._ import scala.math.Numeric.{FloatAsIfIntegral, DoubleAsIfIntegral} import scala.reflect.ClassTag import scala.reflect.runtime.universe.{TypeTag, runtimeMirror, typeTag} @@ -181,7 +182,7 @@ object DataType { /** * Compares two types, ignoring nullability of ArrayType, MapType, StructType. */ - private[sql] def equalsIgnoreNullability(left: DataType, right: DataType): Boolean = { + private[types] def equalsIgnoreNullability(left: DataType, right: DataType): Boolean = { (left, right) match { case (ArrayType(leftElementType, _), ArrayType(rightElementType, _)) => equalsIgnoreNullability(leftElementType, rightElementType) @@ -198,12 +199,48 @@ object DataType { case (left, right) => left == right } } + + /** + * Compares two types, ignoring compatible nullability of ArrayType, MapType, StructType. + * + * Compatible nullability is defined as follows: + * - If `from` and `to` are ArrayTypes, `from` has a compatible nullability with `to` + * if and only if `to.containsNull` is true, or both of `from.containsNull` and + * `to.containsNull` are false. + * - If `from` and `to` are MapTypes, `from` has a compatible nullability with `to` + * if and only if `to.valueContainsNull` is true, or both of `from.valueContainsNull` and + * `to.valueContainsNull` are false. + * - If `from` and `to` are StructTypes, `from` has a compatible nullability with `to` + * if and only if for all every pair of fields, `to.nullable` is true, or both + * of `fromField.nullable` and `toField.nullable` are false. + */ + private[sql] def equalsIgnoreCompatibleNullability(from: DataType, to: DataType): Boolean = { + (from, to) match { + case (ArrayType(fromElement, fn), ArrayType(toElement, tn)) => + (tn || !fn) && equalsIgnoreCompatibleNullability(fromElement, toElement) + + case (MapType(fromKey, fromValue, fn), MapType(toKey, toValue, tn)) => + (tn || !fn) && + equalsIgnoreCompatibleNullability(fromKey, toKey) && + equalsIgnoreCompatibleNullability(fromValue, toValue) + + case (StructType(fromFields), StructType(toFields)) => + fromFields.size == toFields.size && + fromFields.zip(toFields).forall { + case (fromField, toField) => + fromField.name == toField.name && + (toField.nullable || !fromField.nullable) && + equalsIgnoreCompatibleNullability(fromField.dataType, toField.dataType) + } + + case (fromDataType, toDataType) => fromDataType == toDataType + } + } } /** * :: DeveloperApi :: - * * The base type of all Spark SQL data types. * * @group dataType @@ -230,20 +267,38 @@ abstract class DataType { def prettyJson: String = pretty(render(jsonValue)) def simpleString: String = typeName + + /** Check if `this` and `other` are the same data type when ignoring nullability + * (`StructField.nullable`, `ArrayType.containsNull`, and `MapType.valueContainsNull`). + */ + private[spark] def sameType(other: DataType): Boolean = + DataType.equalsIgnoreNullability(this, other) + + /** Returns the same data type but set all nullability fields are true + * (`StructField.nullable`, `ArrayType.containsNull`, and `MapType.valueContainsNull`). + */ + private[spark] def asNullable: DataType } /** * :: DeveloperApi :: - * * The data type representing `NULL` values. Please use the singleton [[DataTypes.NullType]]. * * @group dataType */ @DeveloperApi -case object NullType extends DataType { +class NullType private() extends DataType { + // The companion object and this class is separated so the companion object also subclasses + // this type. Otherwise, the companion object would be of type "NullType$" in byte code. + // Defined with a private constructor so the companion object is the only possible instantiation. override def defaultSize: Int = 1 + + private[spark] override def asNullable: NullType = this } +case object NullType extends NullType + + protected[sql] object NativeType { val all = Seq( IntegerType, BooleanType, LongType, DoubleType, FloatType, ShortType, ByteType, StringType) @@ -253,7 +308,7 @@ protected[sql] object NativeType { protected[sql] trait PrimitiveType extends DataType { - override def isPrimitive = true + override def isPrimitive: Boolean = true } @@ -286,14 +341,16 @@ protected[sql] abstract class NativeType extends DataType { /** * :: DeveloperApi :: - * * The data type representing `String` values. Please use the singleton [[DataTypes.StringType]]. * * @group dataType */ @DeveloperApi -case object StringType extends NativeType with PrimitiveType { - private[sql] type JvmType = String +class StringType private() extends NativeType with PrimitiveType { + // The companion object and this class is separated so the companion object also subclasses + // this type. Otherwise, the companion object would be of type "StringType$" in byte code. + // Defined with a private constructor so the companion object is the only possible instantiation. + private[sql] type JvmType = UTF8String @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] } private[sql] val ordering = implicitly[Ordering[JvmType]] @@ -301,19 +358,25 @@ case object StringType extends NativeType with PrimitiveType { * The default size of a value of the StringType is 4096 bytes. */ override def defaultSize: Int = 4096 + + private[spark] override def asNullable: StringType = this } +case object StringType extends StringType + /** * :: DeveloperApi :: - * * The data type representing `Array[Byte]` values. * Please use the singleton [[DataTypes.BinaryType]]. * * @group dataType */ @DeveloperApi -case object BinaryType extends NativeType with PrimitiveType { +class BinaryType private() extends NativeType with PrimitiveType { + // The companion object and this class is separated so the companion object also subclasses + // this type. Otherwise, the companion object would be of type "BinaryType$" in byte code. + // Defined with a private constructor so the companion object is the only possible instantiation. private[sql] type JvmType = Array[Byte] @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] } private[sql] val ordering = new Ordering[JvmType] { @@ -330,18 +393,24 @@ case object BinaryType extends NativeType with PrimitiveType { * The default size of a value of the BinaryType is 4096 bytes. */ override def defaultSize: Int = 4096 + + private[spark] override def asNullable: BinaryType = this } +case object BinaryType extends BinaryType + /** * :: DeveloperApi :: - * * The data type representing `Boolean` values. Please use the singleton [[DataTypes.BooleanType]]. * *@group dataType */ @DeveloperApi -case object BooleanType extends NativeType with PrimitiveType { +class BooleanType private() extends NativeType with PrimitiveType { + // The companion object and this class is separated so the companion object also subclasses + // this type. Otherwise, the companion object would be of type "BooleanType$" in byte code. + // Defined with a private constructor so the companion object is the only possible instantiation. private[sql] type JvmType = Boolean @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] } private[sql] val ordering = implicitly[Ordering[JvmType]] @@ -350,44 +419,56 @@ case object BooleanType extends NativeType with PrimitiveType { * The default size of a value of the BooleanType is 1 byte. */ override def defaultSize: Int = 1 + + private[spark] override def asNullable: BooleanType = this } +case object BooleanType extends BooleanType + /** * :: DeveloperApi :: - * * The data type representing `java.sql.Timestamp` values. * Please use the singleton [[DataTypes.TimestampType]]. * * @group dataType */ @DeveloperApi -case object TimestampType extends NativeType { +class TimestampType private() extends NativeType { + // The companion object and this class is separated so the companion object also subclasses + // this type. Otherwise, the companion object would be of type "TimestampType$" in byte code. + // Defined with a private constructor so the companion object is the only possible instantiation. private[sql] type JvmType = Timestamp @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] } private[sql] val ordering = new Ordering[JvmType] { - def compare(x: Timestamp, y: Timestamp) = x.compareTo(y) + def compare(x: Timestamp, y: Timestamp): Int = x.compareTo(y) } /** * The default size of a value of the TimestampType is 12 bytes. */ override def defaultSize: Int = 12 + + private[spark] override def asNullable: TimestampType = this } +case object TimestampType extends TimestampType + /** * :: DeveloperApi :: - * * The data type representing `java.sql.Date` values. * Please use the singleton [[DataTypes.DateType]]. * * @group dataType */ @DeveloperApi -case object DateType extends NativeType { +class DateType private() extends NativeType { + // The companion object and this class is separated so the companion object also subclasses + // this type. Otherwise, the companion object would be of type "DateType$" in byte code. + // Defined with a private constructor so the companion object is the only possible instantiation. private[sql] type JvmType = Int @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] } @@ -398,9 +479,19 @@ case object DateType extends NativeType { * The default size of a value of the DateType is 4 bytes. */ override def defaultSize: Int = 4 + + private[spark] override def asNullable: DateType = this } +case object DateType extends DateType + +/** + * :: DeveloperApi :: + * Numeric data types. + * + * @group dataType + */ abstract class NumericType extends NativeType with PrimitiveType { // Unfortunately we can't get this implicitly as that breaks Spark Serialization. In order for // implicitly[Numeric[JvmType]] to be valid, we have to change JvmType from a type variable to a @@ -432,13 +523,15 @@ protected[sql] sealed abstract class IntegralType extends NumericType { /** * :: DeveloperApi :: - * * The data type representing `Long` values. Please use the singleton [[DataTypes.LongType]]. * * @group dataType */ @DeveloperApi -case object LongType extends IntegralType { +class LongType private() extends IntegralType { + // The companion object and this class is separated so the companion object also subclasses + // this type. Otherwise, the companion object would be of type "LongType$" in byte code. + // Defined with a private constructor so the companion object is the only possible instantiation. private[sql] type JvmType = Long @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] } private[sql] val numeric = implicitly[Numeric[Long]] @@ -450,19 +543,25 @@ case object LongType extends IntegralType { */ override def defaultSize: Int = 8 - override def simpleString = "bigint" + override def simpleString: String = "bigint" + + private[spark] override def asNullable: LongType = this } +case object LongType extends LongType + /** * :: DeveloperApi :: - * * The data type representing `Int` values. Please use the singleton [[DataTypes.IntegerType]]. * * @group dataType */ @DeveloperApi -case object IntegerType extends IntegralType { +class IntegerType private() extends IntegralType { + // The companion object and this class is separated so the companion object also subclasses + // this type. Otherwise, the companion object would be of type "IntegerType$" in byte code. + // Defined with a private constructor so the companion object is the only possible instantiation. private[sql] type JvmType = Int @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] } private[sql] val numeric = implicitly[Numeric[Int]] @@ -474,19 +573,25 @@ case object IntegerType extends IntegralType { */ override def defaultSize: Int = 4 - override def simpleString = "int" + override def simpleString: String = "int" + + private[spark] override def asNullable: IntegerType = this } +case object IntegerType extends IntegerType + /** * :: DeveloperApi :: - * * The data type representing `Short` values. Please use the singleton [[DataTypes.ShortType]]. * * @group dataType */ @DeveloperApi -case object ShortType extends IntegralType { +class ShortType private() extends IntegralType { + // The companion object and this class is separated so the companion object also subclasses + // this type. Otherwise, the companion object would be of type "ShortType$" in byte code. + // Defined with a private constructor so the companion object is the only possible instantiation. private[sql] type JvmType = Short @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] } private[sql] val numeric = implicitly[Numeric[Short]] @@ -498,19 +603,25 @@ case object ShortType extends IntegralType { */ override def defaultSize: Int = 2 - override def simpleString = "smallint" + override def simpleString: String = "smallint" + + private[spark] override def asNullable: ShortType = this } +case object ShortType extends ShortType + /** * :: DeveloperApi :: - * * The data type representing `Byte` values. Please use the singleton [[DataTypes.ByteType]]. * * @group dataType */ @DeveloperApi -case object ByteType extends IntegralType { +class ByteType private() extends IntegralType { + // The companion object and this class is separated so the companion object also subclasses + // this type. Otherwise, the companion object would be of type "ByteType$" in byte code. + // Defined with a private constructor so the companion object is the only possible instantiation. private[sql] type JvmType = Byte @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] } private[sql] val numeric = implicitly[Numeric[Byte]] @@ -522,9 +633,13 @@ case object ByteType extends IntegralType { */ override def defaultSize: Int = 1 - override def simpleString = "tinyint" + override def simpleString: String = "tinyint" + + private[spark] override def asNullable: ByteType = this } +case object ByteType extends ByteType + /** Matcher for any expressions that evaluate to [[FractionalType]]s */ protected[sql] object FractionalType { @@ -547,7 +662,6 @@ case class PrecisionInfo(precision: Int, scale: Int) /** * :: DeveloperApi :: - * * The data type representing `java.math.BigDecimal` values. * A Decimal that might have fixed precision and scale, or unlimited values for these. * @@ -557,6 +671,10 @@ case class PrecisionInfo(precision: Int, scale: Int) */ @DeveloperApi case class DecimalType(precisionInfo: Option[PrecisionInfo]) extends FractionalType { + + /** No-arg constructor for kryo. */ + protected def this() = this(null) + private[sql] type JvmType = Decimal @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] } private[sql] val numeric = Decimal.DecimalIsFractional @@ -583,10 +701,12 @@ case class DecimalType(precisionInfo: Option[PrecisionInfo]) extends FractionalT */ override def defaultSize: Int = 4096 - override def simpleString = precisionInfo match { + override def simpleString: String = precisionInfo match { case Some(PrecisionInfo(precision, scale)) => s"decimal($precision,$scale)" case None => "decimal(10,0)" } + + private[spark] override def asNullable: DecimalType = this } @@ -624,13 +744,15 @@ object DecimalType { /** * :: DeveloperApi :: - * * The data type representing `Double` values. Please use the singleton [[DataTypes.DoubleType]]. * * @group dataType */ @DeveloperApi -case object DoubleType extends FractionalType { +class DoubleType private() extends FractionalType { + // The companion object and this class is separated so the companion object also subclasses + // this type. Otherwise, the companion object would be of type "DoubleType$" in byte code. + // Defined with a private constructor so the companion object is the only possible instantiation. private[sql] type JvmType = Double @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] } private[sql] val numeric = implicitly[Numeric[Double]] @@ -642,18 +764,24 @@ case object DoubleType extends FractionalType { * The default size of a value of the DoubleType is 8 bytes. */ override def defaultSize: Int = 8 + + private[spark] override def asNullable: DoubleType = this } +case object DoubleType extends DoubleType + /** * :: DeveloperApi :: - * * The data type representing `Float` values. Please use the singleton [[DataTypes.FloatType]]. * * @group dataType */ @DeveloperApi -case object FloatType extends FractionalType { +class FloatType private() extends FractionalType { + // The companion object and this class is separated so the companion object also subclasses + // this type. Otherwise, the companion object would be of type "FloatType$" in byte code. + // Defined with a private constructor so the companion object is the only possible instantiation. private[sql] type JvmType = Float @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] } private[sql] val numeric = implicitly[Numeric[Float]] @@ -665,8 +793,12 @@ case object FloatType extends FractionalType { * The default size of a value of the FloatType is 4 bytes. */ override def defaultSize: Int = 4 + + private[spark] override def asNullable: FloatType = this } +case object FloatType extends FloatType + object ArrayType { /** Construct a [[ArrayType]] object with the given element type. The `containsNull` is true. */ @@ -676,7 +808,6 @@ object ArrayType { /** * :: DeveloperApi :: - * * The data type for collections of multiple values. * Internally these are represented as columns that contain a ``scala.collection.Seq``. * @@ -693,6 +824,10 @@ object ArrayType { */ @DeveloperApi case class ArrayType(elementType: DataType, containsNull: Boolean) extends DataType { + + /** No-arg constructor for kryo. */ + protected def this() = this(null, false) + private[sql] def buildFormattedString(prefix: String, builder: StringBuilder): Unit = { builder.append( s"$prefix-- element: ${elementType.typeName} (containsNull = $containsNull)\n") @@ -710,13 +845,15 @@ case class ArrayType(elementType: DataType, containsNull: Boolean) extends DataT */ override def defaultSize: Int = 100 * elementType.defaultSize - override def simpleString = s"array<${elementType.simpleString}>" + override def simpleString: String = s"array<${elementType.simpleString}>" + + private[spark] override def asNullable: ArrayType = + ArrayType(elementType.asNullable, containsNull = true) } /** * A field inside a StructType. - * * @param name The name of this field. * @param dataType The data type of this field. * @param nullable Indicates if values of this field can be `null` values. @@ -729,6 +866,9 @@ case class StructField( nullable: Boolean = true, metadata: Metadata = Metadata.empty) { + /** No-arg constructor for kryo. */ + protected def this() = this(null, null) + private[sql] def buildFormattedString(prefix: String, builder: StringBuilder): Unit = { builder.append(s"$prefix-- $name: ${dataType.typeName} (nullable = $nullable)\n") DataType.buildFormattedString(dataType, s"$prefix |", builder) @@ -795,7 +935,9 @@ object StructType { case (DecimalType.Fixed(leftPrecision, leftScale), DecimalType.Fixed(rightPrecision, rightScale)) => - DecimalType(leftPrecision.max(rightPrecision), leftScale.max(rightScale)) + DecimalType( + max(leftScale, rightScale) + max(leftPrecision - leftScale, rightPrecision - rightScale), + max(leftScale, rightScale)) case (leftUdt: UserDefinedType[_], rightUdt: UserDefinedType[_]) if leftUdt.userClass == rightUdt.userClass => leftUdt @@ -811,7 +953,6 @@ object StructType { /** * :: DeveloperApi :: - * * A [[StructType]] object can be constructed by * {{{ * StructType(fields: Seq[StructField]) @@ -876,6 +1017,9 @@ object StructType { @DeveloperApi case class StructType(fields: Array[StructField]) extends DataType with Seq[StructField] { + /** No-arg constructor for kryo. */ + protected def this() = this(null) + /** Returns all field names in an array. */ def fieldNames: Array[String] = fields.map(_.name) @@ -938,7 +1082,7 @@ case class StructType(fields: Array[StructField]) extends DataType with Seq[Stru */ override def defaultSize: Int = fields.map(_.dataType.defaultSize).sum - override def simpleString = { + override def simpleString: String = { val fieldTypes = fields.map(field => s"${field.name}:${field.dataType.simpleString}") s"struct<${fieldTypes.mkString(",")}>" } @@ -956,6 +1100,15 @@ case class StructType(fields: Array[StructField]) extends DataType with Seq[Stru */ private[sql] def merge(that: StructType): StructType = StructType.merge(this, that).asInstanceOf[StructType] + + private[spark] override def asNullable: StructType = { + val newFields = fields.map { + case StructField(name, dataType, nullable, metadata) => + StructField(name, dataType.asNullable, nullable = true, metadata) + } + + StructType(newFields) + } } @@ -971,7 +1124,6 @@ object MapType { /** * :: DeveloperApi :: - * * The data type for Maps. Keys in a map are not allowed to have `null` values. * * Please use [[DataTypes.createMapType()]] to create a specific instance. @@ -986,6 +1138,10 @@ case class MapType( keyType: DataType, valueType: DataType, valueContainsNull: Boolean) extends DataType { + + /** No-arg constructor for kryo. */ + def this() = this(null, null, false) + private[sql] def buildFormattedString(prefix: String, builder: StringBuilder): Unit = { builder.append(s"$prefix-- key: ${keyType.typeName}\n") builder.append(s"$prefix-- value: ${valueType.typeName} " + @@ -1007,7 +1163,10 @@ case class MapType( */ override def defaultSize: Int = 100 * (keyType.defaultSize + valueType.defaultSize) - override def simpleString = s"map<${keyType.simpleString},${valueType.simpleString}>" + override def simpleString: String = s"map<${keyType.simpleString},${valueType.simpleString}>" + + private[spark] override def asNullable: MapType = + MapType(keyType.asNullable, valueType.asNullable, valueContainsNull = true) } @@ -1037,8 +1196,8 @@ abstract class UserDefinedType[UserType] extends DataType with Serializable { /** * Convert the user type to a SQL datum * - * TODO: Can we make this take obj: UserType? The issue is in ScalaReflection.convertToCatalyst, - * where we need to convert Any to UserType. + * TODO: Can we make this take obj: UserType? The issue is in + * CatalystTypeConverters.convertToCatalyst, where we need to convert Any to UserType. */ def serialize(obj: Any): Any @@ -1061,4 +1220,10 @@ abstract class UserDefinedType[UserType] extends DataType with Serializable { * The default size of a value of the UserDefinedType is 4096 bytes. */ override def defaultSize: Int = 4096 + + /** + * For UDT, asNullable will not change the nullability of its internal sqlType and just returns + * itself. + */ + private[spark] override def asNullable: UserDefinedType[UserType] = this } diff --git a/sql/catalyst/src/test/resources/log4j.properties b/sql/catalyst/src/test/resources/log4j.properties index 287c8e3563503..eb3b1999eb996 100644 --- a/sql/catalyst/src/test/resources/log4j.properties +++ b/sql/catalyst/src/test/resources/log4j.properties @@ -24,5 +24,5 @@ log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n # Ignore messages below warning level from Jetty, because it's a bit verbose -log4j.logger.org.eclipse.jetty=WARN -org.eclipse.jetty.LEVEL=WARN +log4j.logger.org.spark-project.jetty=WARN +org.spark-project.jetty.LEVEL=WARN diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/DistributionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/DistributionSuite.scala index 46b2250aab231..ea82cd2622de9 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/DistributionSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/DistributionSuite.scala @@ -30,7 +30,7 @@ class DistributionSuite extends FunSuite { inputPartitioning: Partitioning, requiredDistribution: Distribution, satisfied: Boolean) { - if (inputPartitioning.satisfies(requiredDistribution) != satisfied) + if (inputPartitioning.satisfies(requiredDistribution) != satisfied) { fail( s""" |== Input Partitioning == @@ -40,6 +40,7 @@ class DistributionSuite extends FunSuite { |== Does input partitioning satisfy required distribution? == |Expected $satisfied got ${inputPartitioning.satisfies(requiredDistribution)} """.stripMargin) + } } test("HashPartitioning is the output partitioning") { diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala index d0f547d187ecb..bbc0b661a0c0c 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala @@ -61,6 +61,7 @@ case class OptionalData( case class ComplexData( arrayField: Seq[Int], arrayField1: Array[Int], + arrayField2: List[Int], arrayFieldContainsNull: Seq[java.lang.Integer], mapField: Map[Int, Long], mapFieldValueContainsNull: Map[Int, java.lang.Long], @@ -137,6 +138,10 @@ class ScalaReflectionSuite extends FunSuite { "arrayField1", ArrayType(IntegerType, containsNull = false), nullable = true), + StructField( + "arrayField2", + ArrayType(IntegerType, containsNull = false), + nullable = true), StructField( "arrayFieldContainsNull", ArrayType(IntegerType, containsNull = true), @@ -255,7 +260,7 @@ class ScalaReflectionSuite extends FunSuite { val data = PrimitiveData(1, 1, 1, 1, 1, 1, true) val convertedData = Row(1, 1.toLong, 1.toDouble, 1.toFloat, 1.toShort, 1.toByte, true) val dataType = schemaFor[PrimitiveData].dataType - assert(convertToCatalyst(data, dataType) === convertedData) + assert(CatalystTypeConverters.convertToCatalyst(data, dataType) === convertedData) } test("convert Option[Product] to catalyst") { @@ -265,7 +270,7 @@ class ScalaReflectionSuite extends FunSuite { val dataType = schemaFor[OptionalData].dataType val convertedData = Row(2, 2.toLong, 2.toDouble, 2.toFloat, 2.toShort, 2.toByte, true, Row(1, 1, 1, 1, 1, 1, true)) - assert(convertToCatalyst(data, dataType) === convertedData) + assert(CatalystTypeConverters.convertToCatalyst(data, dataType) === convertedData) } test("infer schema from case class with multiple constructors") { diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala index 60060bf02913b..e10ddfdf5127c 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala @@ -19,8 +19,8 @@ package org.apache.spark.sql.catalyst.analysis import org.scalatest.{BeforeAndAfter, FunSuite} -import org.apache.spark.sql.catalyst.expressions.{Alias, AttributeReference} -import org.apache.spark.sql.catalyst.errors.TreeNodeException +import org.apache.spark.sql.AnalysisException +import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.logical._ import org.apache.spark.sql.types._ @@ -30,10 +30,22 @@ import org.apache.spark.sql.catalyst.dsl.plans._ class AnalysisSuite extends FunSuite with BeforeAndAfter { val caseSensitiveCatalog = new SimpleCatalog(true) val caseInsensitiveCatalog = new SimpleCatalog(false) - val caseSensitiveAnalyze = - new Analyzer(caseSensitiveCatalog, EmptyFunctionRegistry, caseSensitive = true) - val caseInsensitiveAnalyze = - new Analyzer(caseInsensitiveCatalog, EmptyFunctionRegistry, caseSensitive = false) + + val caseSensitiveAnalyzer = + new Analyzer(caseSensitiveCatalog, EmptyFunctionRegistry, caseSensitive = true) { + override val extendedResolutionRules = EliminateSubQueries :: Nil + } + val caseInsensitiveAnalyzer = + new Analyzer(caseInsensitiveCatalog, EmptyFunctionRegistry, caseSensitive = false) { + override val extendedResolutionRules = EliminateSubQueries :: Nil + } + + + def caseSensitiveAnalyze(plan: LogicalPlan): Unit = + caseSensitiveAnalyzer.checkAnalysis(caseSensitiveAnalyzer(plan)) + + def caseInsensitiveAnalyze(plan: LogicalPlan): Unit = + caseInsensitiveAnalyzer.checkAnalysis(caseInsensitiveAnalyzer(plan)) val testRelation = LocalRelation(AttributeReference("a", IntegerType, nullable = true)()) val testRelation2 = LocalRelation( @@ -43,6 +55,21 @@ class AnalysisSuite extends FunSuite with BeforeAndAfter { AttributeReference("d", DecimalType.Unlimited)(), AttributeReference("e", ShortType)()) + val nestedRelation = LocalRelation( + AttributeReference("top", StructType( + StructField("duplicateField", StringType) :: + StructField("duplicateField", StringType) :: + StructField("differentCase", StringType) :: + StructField("differentcase", StringType) :: Nil + ))()) + + val nestedRelation2 = LocalRelation( + AttributeReference("top", StructType( + StructField("aField", StringType) :: + StructField("bField", StringType) :: + StructField("cField", StringType) :: Nil + ))()) + before { caseSensitiveCatalog.registerTable(Seq("TaBlE"), testRelation) caseInsensitiveCatalog.registerTable(Seq("TaBlE"), testRelation) @@ -55,35 +82,46 @@ class AnalysisSuite extends FunSuite with BeforeAndAfter { a.select(UnresolvedStar(None)).select('a).unionAll(b.select(UnresolvedStar(None))) } - assert(caseInsensitiveAnalyze(plan).resolved) + assert(caseInsensitiveAnalyzer(plan).resolved) + } + + test("check project's resolved") { + assert(Project(testRelation.output, testRelation).resolved) + + assert(!Project(Seq(UnresolvedAttribute("a")), testRelation).resolved) + + val explode = Explode(Nil, AttributeReference("a", IntegerType, nullable = true)()) + assert(!Project(Seq(Alias(explode, "explode")()), testRelation).resolved) + + assert(!Project(Seq(Alias(Count(Literal(1)), "count")()), testRelation).resolved) } test("analyze project") { assert( - caseSensitiveAnalyze(Project(Seq(UnresolvedAttribute("a")), testRelation)) === + caseSensitiveAnalyzer(Project(Seq(UnresolvedAttribute("a")), testRelation)) === Project(testRelation.output, testRelation)) assert( - caseSensitiveAnalyze( + caseSensitiveAnalyzer( Project(Seq(UnresolvedAttribute("TbL.a")), UnresolvedRelation(Seq("TaBlE"), Some("TbL")))) === Project(testRelation.output, testRelation)) - val e = intercept[TreeNodeException[_]] { + val e = intercept[AnalysisException] { caseSensitiveAnalyze( Project(Seq(UnresolvedAttribute("tBl.a")), UnresolvedRelation(Seq("TaBlE"), Some("TbL")))) } - assert(e.getMessage().toLowerCase.contains("unresolved")) + assert(e.getMessage().toLowerCase.contains("cannot resolve")) assert( - caseInsensitiveAnalyze( + caseInsensitiveAnalyzer( Project(Seq(UnresolvedAttribute("TbL.a")), UnresolvedRelation(Seq("TaBlE"), Some("TbL")))) === Project(testRelation.output, testRelation)) assert( - caseInsensitiveAnalyze( + caseInsensitiveAnalyzer( Project(Seq(UnresolvedAttribute("tBl.a")), UnresolvedRelation(Seq("TaBlE"), Some("TbL")))) === Project(testRelation.output, testRelation)) @@ -96,36 +134,83 @@ class AnalysisSuite extends FunSuite with BeforeAndAfter { assert(e.getMessage == "Table Not Found: tAbLe") assert( - caseSensitiveAnalyze(UnresolvedRelation(Seq("TaBlE"), None)) === - testRelation) + caseSensitiveAnalyzer(UnresolvedRelation(Seq("TaBlE"), None)) === testRelation) assert( - caseInsensitiveAnalyze(UnresolvedRelation(Seq("tAbLe"), None)) === - testRelation) + caseInsensitiveAnalyzer(UnresolvedRelation(Seq("tAbLe"), None)) === testRelation) assert( - caseInsensitiveAnalyze(UnresolvedRelation(Seq("TaBlE"), None)) === - testRelation) + caseInsensitiveAnalyzer(UnresolvedRelation(Seq("TaBlE"), None)) === testRelation) } - test("throw errors for unresolved attributes during analysis") { - val e = intercept[TreeNodeException[_]] { - caseSensitiveAnalyze(Project(Seq(UnresolvedAttribute("abcd")), testRelation)) + def errorTest( + name: String, + plan: LogicalPlan, + errorMessages: Seq[String], + caseSensitive: Boolean = true): Unit = { + test(name) { + val error = intercept[AnalysisException] { + if(caseSensitive) { + caseSensitiveAnalyze(plan) + } else { + caseInsensitiveAnalyze(plan) + } + } + + errorMessages.foreach(m => assert(error.getMessage contains m)) } - assert(e.getMessage().toLowerCase.contains("unresolved attribute")) } - test("throw errors for unresolved plans during analysis") { - case class UnresolvedTestPlan() extends LeafNode { - override lazy val resolved = false - override def output = Nil - } - val e = intercept[TreeNodeException[_]] { - caseSensitiveAnalyze(UnresolvedTestPlan()) - } - assert(e.getMessage().toLowerCase.contains("unresolved plan")) + errorTest( + "unresolved attributes", + testRelation.select('abcd), + "cannot resolve" :: "abcd" :: Nil) + + errorTest( + "bad casts", + testRelation.select(Literal(1).cast(BinaryType).as('badCast)), + "invalid cast" :: Literal(1).dataType.simpleString :: BinaryType.simpleString :: Nil) + + errorTest( + "non-boolean filters", + testRelation.where(Literal(1)), + "filter" :: "'1'" :: "not a boolean" :: Literal(1).dataType.simpleString :: Nil) + + errorTest( + "missing group by", + testRelation2.groupBy('a)('b), + "'b'" :: "group by" :: Nil + ) + + errorTest( + "ambiguous field", + nestedRelation.select($"top.duplicateField"), + "Ambiguous reference to fields" :: "duplicateField" :: Nil, + caseSensitive = false) + + errorTest( + "ambiguous field due to case insensitivity", + nestedRelation.select($"top.differentCase"), + "Ambiguous reference to fields" :: "differentCase" :: "differentcase" :: Nil, + caseSensitive = false) + + errorTest( + "missing field", + nestedRelation2.select($"top.c"), + "No such struct field" :: "aField" :: "bField" :: "cField" :: Nil, + caseSensitive = false) + + case class UnresolvedTestPlan() extends LeafNode { + override lazy val resolved = false + override def output: Seq[Attribute] = Nil } + errorTest( + "catch all unresolved plan", + UnresolvedTestPlan(), + "unresolved" :: Nil) + + test("divide should be casted into fractional types") { val testRelation2 = LocalRelation( AttributeReference("a", StringType)(), @@ -134,22 +219,37 @@ class AnalysisSuite extends FunSuite with BeforeAndAfter { AttributeReference("d", DecimalType.Unlimited)(), AttributeReference("e", ShortType)()) - val expr0 = 'a / 2 - val expr1 = 'a / 'b - val expr2 = 'a / 'c - val expr3 = 'a / 'd - val expr4 = 'e / 'e - val plan = caseInsensitiveAnalyze(Project( - Alias(expr0, s"Analyzer($expr0)")() :: - Alias(expr1, s"Analyzer($expr1)")() :: - Alias(expr2, s"Analyzer($expr2)")() :: - Alias(expr3, s"Analyzer($expr3)")() :: - Alias(expr4, s"Analyzer($expr4)")() :: Nil, testRelation2)) + val plan = caseInsensitiveAnalyzer( + testRelation2.select( + 'a / Literal(2) as 'div1, + 'a / 'b as 'div2, + 'a / 'c as 'div3, + 'a / 'd as 'div4, + 'e / 'e as 'div5)) val pl = plan.asInstanceOf[Project].projectList + assert(pl(0).dataType == DoubleType) assert(pl(1).dataType == DoubleType) assert(pl(2).dataType == DoubleType) assert(pl(3).dataType == DecimalType.Unlimited) assert(pl(4).dataType == DoubleType) } + + test("SPARK-6452 regression test") { + // CheckAnalysis should throw AnalysisException when Aggregate contains missing attribute(s) + val plan = + Aggregate( + Nil, + Alias(Sum(AttributeReference("a", StringType)(exprId = ExprId(1))), "b")() :: Nil, + LocalRelation( + AttributeReference("a", StringType)(exprId = ExprId(2)))) + + assert(plan.resolved) + + val message = intercept[AnalysisException] { + caseSensitiveAnalyze(plan) + }.getMessage + + assert(message.contains("resolved attribute(s) a#1 missing from a#2")) + } } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecisionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecisionSuite.scala index bc2ec754d5865..67bec999dfbd1 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecisionSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecisionSuite.scala @@ -18,7 +18,7 @@ package org.apache.spark.sql.catalyst.analysis import org.apache.spark.sql.catalyst.expressions._ -import org.apache.spark.sql.catalyst.plans.logical.{Project, LocalRelation} +import org.apache.spark.sql.catalyst.plans.logical.{Union, Project, LocalRelation} import org.apache.spark.sql.types._ import org.scalatest.{BeforeAndAfter, FunSuite} @@ -31,7 +31,8 @@ class DecimalPrecisionSuite extends FunSuite with BeforeAndAfter { AttributeReference("d1", DecimalType(2, 1))(), AttributeReference("d2", DecimalType(5, 2))(), AttributeReference("u", DecimalType.Unlimited)(), - AttributeReference("f", FloatType)() + AttributeReference("f", FloatType)(), + AttributeReference("b", DoubleType)() ) val i: Expression = UnresolvedAttribute("i") @@ -39,6 +40,7 @@ class DecimalPrecisionSuite extends FunSuite with BeforeAndAfter { val d2: Expression = UnresolvedAttribute("d2") val u: Expression = UnresolvedAttribute("u") val f: Expression = UnresolvedAttribute("f") + val b: Expression = UnresolvedAttribute("b") before { catalog.registerTable(Seq("table"), relation) @@ -58,6 +60,17 @@ class DecimalPrecisionSuite extends FunSuite with BeforeAndAfter { assert(comparison.right.dataType === expectedType) } + private def checkUnion(left: Expression, right: Expression, expectedType: DataType): Unit = { + val plan = + Union(Project(Seq(Alias(left, "l")()), relation), + Project(Seq(Alias(right, "r")()), relation)) + val (l, r) = analyzer(plan).collect { + case Union(left, right) => (left.output.head, right.output.head) + }.head + assert(l.dataType === expectedType) + assert(r.dataType === expectedType) + } + test("basic operations") { checkType(Add(d1, d2), DecimalType(6, 2)) checkType(Subtract(d1, d2), DecimalType(6, 2)) @@ -82,6 +95,19 @@ class DecimalPrecisionSuite extends FunSuite with BeforeAndAfter { checkComparison(GreaterThan(d2, d2), DecimalType(5, 2)) } + test("decimal precision for union") { + checkUnion(d1, i, DecimalType(11, 1)) + checkUnion(i, d2, DecimalType(12, 2)) + checkUnion(d1, d2, DecimalType(5, 2)) + checkUnion(d2, d1, DecimalType(5, 2)) + checkUnion(d1, f, DecimalType(8, 7)) + checkUnion(f, d2, DecimalType(10, 7)) + checkUnion(d1, b, DecimalType(16, 15)) + checkUnion(b, d2, DecimalType(18, 15)) + checkUnion(d1, u, DecimalType.Unlimited) + checkUnion(u, d2, DecimalType.Unlimited) + } + test("bringing in primitive types") { checkType(Add(d1, i), DecimalType(12, 1)) checkType(Add(d1, f), DoubleType) diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala index 85798d0871fda..fcd745f43cfbf 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala @@ -17,13 +17,13 @@ package org.apache.spark.sql.catalyst.analysis -import org.scalatest.FunSuite +import org.apache.spark.sql.catalyst.plans.PlanTest import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, Project} import org.apache.spark.sql.types._ -class HiveTypeCoercionSuite extends FunSuite { +class HiveTypeCoercionSuite extends PlanTest { test("tightest common bound for types") { def widenTest(t1: DataType, t2: DataType, tightestCommon: Option[DataType]) { @@ -96,7 +96,9 @@ class HiveTypeCoercionSuite extends FunSuite { widenTest(StringType, TimestampType, None) // ComplexType - widenTest(NullType, MapType(IntegerType, StringType, false), Some(MapType(IntegerType, StringType, false))) + widenTest(NullType, + MapType(IntegerType, StringType, false), + Some(MapType(IntegerType, StringType, false))) widenTest(NullType, StructType(Seq()), Some(StructType(Seq()))) widenTest(StringType, MapType(IntegerType, StringType, true), None) widenTest(ArrayType(IntegerType), StructType(Seq()), None) @@ -106,30 +108,34 @@ class HiveTypeCoercionSuite extends FunSuite { val booleanCasts = new HiveTypeCoercion { }.BooleanCasts def ruleTest(initial: Expression, transformed: Expression) { val testRelation = LocalRelation(AttributeReference("a", IntegerType)()) - assert(booleanCasts(Project(Seq(Alias(initial, "a")()), testRelation)) == + comparePlans( + booleanCasts(Project(Seq(Alias(initial, "a")()), testRelation)), Project(Seq(Alias(transformed, "a")()), testRelation)) } // Remove superflous boolean -> boolean casts. ruleTest(Cast(Literal(true), BooleanType), Literal(true)) // Stringify boolean when casting to string. - ruleTest(Cast(Literal(false), StringType), If(Literal(false), Literal("true"), Literal("false"))) + ruleTest( + Cast(Literal(false), StringType), + If(Literal(false), Literal("true"), Literal("false"))) } test("coalesce casts") { val fac = new HiveTypeCoercion { }.FunctionArgumentConversion def ruleTest(initial: Expression, transformed: Expression) { val testRelation = LocalRelation(AttributeReference("a", IntegerType)()) - assert(fac(Project(Seq(Alias(initial, "a")()), testRelation)) == + comparePlans( + fac(Project(Seq(Alias(initial, "a")()), testRelation)), Project(Seq(Alias(transformed, "a")()), testRelation)) } ruleTest( Coalesce(Literal(1.0) :: Literal(1) - :: Literal(1.0, FloatType) + :: Literal.create(1.0, FloatType) :: Nil), Coalesce(Cast(Literal(1.0), DoubleType) :: Cast(Literal(1), DoubleType) - :: Cast(Literal(1.0, FloatType), DoubleType) + :: Cast(Literal.create(1.0, FloatType), DoubleType) :: Nil)) ruleTest( Coalesce(Literal(1L) diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/AttributeSetSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/AttributeSetSuite.scala new file mode 100644 index 0000000000000..f2f3a84d19380 --- /dev/null +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/AttributeSetSuite.scala @@ -0,0 +1,82 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.expressions + +import org.scalatest.FunSuite + +import org.apache.spark.sql.types.IntegerType + +class AttributeSetSuite extends FunSuite { + + val aUpper = AttributeReference("A", IntegerType)(exprId = ExprId(1)) + val aLower = AttributeReference("a", IntegerType)(exprId = ExprId(1)) + val fakeA = AttributeReference("a", IntegerType)(exprId = ExprId(3)) + val aSet = AttributeSet(aLower :: Nil) + + val bUpper = AttributeReference("B", IntegerType)(exprId = ExprId(2)) + val bLower = AttributeReference("b", IntegerType)(exprId = ExprId(2)) + val bSet = AttributeSet(bUpper :: Nil) + + val aAndBSet = AttributeSet(aUpper :: bUpper :: Nil) + + test("sanity check") { + assert(aUpper != aLower) + assert(bUpper != bLower) + } + + test("checks by id not name") { + assert(aSet.contains(aUpper) === true) + assert(aSet.contains(aLower) === true) + assert(aSet.contains(fakeA) === false) + + assert(aSet.contains(bUpper) === false) + assert(aSet.contains(bLower) === false) + } + + test("++ preserves AttributeSet") { + assert((aSet ++ bSet).contains(aUpper) === true) + assert((aSet ++ bSet).contains(aLower) === true) + } + + test("extracts all references references") { + val addSet = AttributeSet(Add(aUpper, Alias(bUpper, "test")()):: Nil) + assert(addSet.contains(aUpper)) + assert(addSet.contains(aLower)) + assert(addSet.contains(bUpper)) + assert(addSet.contains(bLower)) + } + + test("dedups attributes") { + assert(AttributeSet(aUpper :: aLower :: Nil).size === 1) + } + + test("subset") { + assert(aSet.subsetOf(aAndBSet) === true) + assert(aAndBSet.subsetOf(aSet) === false) + } + + test("equality") { + assert(aSet != aAndBSet) + assert(aAndBSet != aSet) + assert(aSet != bSet) + assert(bSet != aSet) + + assert(aSet == aSet) + assert(aSet == AttributeSet(aUpper :: Nil)) + } +} diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala index 7cf6c80194f6c..76298f03c94ae 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala @@ -25,12 +25,44 @@ import org.scalactic.TripleEqualsSupport.Spread import org.scalatest.FunSuite import org.scalatest.Matchers._ -import org.apache.spark.sql.catalyst.dsl.expressions._ +import org.apache.spark.sql.catalyst.CatalystTypeConverters import org.apache.spark.sql.catalyst.analysis.UnresolvedGetField +import org.apache.spark.sql.catalyst.dsl.expressions._ import org.apache.spark.sql.types._ -class ExpressionEvaluationSuite extends FunSuite { +class ExpressionEvaluationBaseSuite extends FunSuite { + + def evaluate(expression: Expression, inputRow: Row = EmptyRow): Any = { + expression.eval(inputRow) + } + + def checkEvaluation(expression: Expression, expected: Any, inputRow: Row = EmptyRow): Unit = { + val actual = try evaluate(expression, inputRow) catch { + case e: Exception => fail(s"Exception evaluating $expression", e) + } + if(actual != expected) { + val input = if(inputRow == EmptyRow) "" else s", input: $inputRow" + fail(s"Incorrect Evaluation: $expression, actual: $actual, expected: $expected$input") + } + } + + def checkDoubleEvaluation( + expression: Expression, + expected: Spread[Double], + inputRow: Row = EmptyRow): Unit = { + val actual = try evaluate(expression, inputRow) catch { + case e: Exception => fail(s"Exception evaluating $expression", e) + } + actual.asInstanceOf[Double] shouldBe expected + } +} + +class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { + + def create_row(values: Any*): Row = { + new GenericRow(values.map(CatalystTypeConverters.convertToCatalyst).toArray) + } test("literals") { checkEvaluation(Literal(1), 1) @@ -55,10 +87,13 @@ class ExpressionEvaluationSuite extends FunSuite { assert(BitwiseNot(1.toByte).eval(EmptyRow).isInstanceOf[Byte]) } + // scalastyle:off /** * Checks for three-valued-logic. Based on: * http://en.wikipedia.org/wiki/Null_(SQL)#Comparisons_with_NULL_and_the_three-valued_logic_.283VL.29 - * I.e. in flat cpo "False -> Unknown -> True", OR is lowest upper bound, AND is greatest lower bound. + * I.e. in flat cpo "False -> Unknown -> True", + * OR is lowest upper bound, + * AND is greatest lower bound. * p q p OR q p AND q p = q * True True True True True * True False True False False @@ -75,7 +110,7 @@ class ExpressionEvaluationSuite extends FunSuite { * False True * Unknown Unknown */ - + // scalastyle:on val notTrueTable = (true, false) :: (false, true) :: @@ -84,7 +119,7 @@ class ExpressionEvaluationSuite extends FunSuite { test("3VL Not") { notTrueTable.foreach { case (v, answer) => - checkEvaluation(!Literal(v, BooleanType), answer) + checkEvaluation(!Literal.create(v, BooleanType), answer) } } @@ -128,38 +163,19 @@ class ExpressionEvaluationSuite extends FunSuite { test(s"3VL $name") { truthTable.foreach { case (l,r,answer) => - val expr = op(Literal(l, BooleanType), Literal(r, BooleanType)) + val expr = op(Literal.create(l, BooleanType), Literal.create(r, BooleanType)) checkEvaluation(expr, answer) } } } - def evaluate(expression: Expression, inputRow: Row = EmptyRow): Any = { - expression.eval(inputRow) - } - - def checkEvaluation(expression: Expression, expected: Any, inputRow: Row = EmptyRow): Unit = { - val actual = try evaluate(expression, inputRow) catch { - case e: Exception => fail(s"Exception evaluating $expression", e) - } - if(actual != expected) { - val input = if(inputRow == EmptyRow) "" else s", input: $inputRow" - fail(s"Incorrect Evaluation: $expression, actual: $actual, expected: $expected$input") - } - } - - def checkDoubleEvaluation(expression: Expression, expected: Spread[Double], inputRow: Row = EmptyRow): Unit = { - val actual = try evaluate(expression, inputRow) catch { - case e: Exception => fail(s"Exception evaluating $expression", e) - } - actual.asInstanceOf[Double] shouldBe expected - } - test("IN") { checkEvaluation(In(Literal(1), Seq(Literal(1), Literal(2))), true) checkEvaluation(In(Literal(2), Seq(Literal(1), Literal(2))), true) checkEvaluation(In(Literal(3), Seq(Literal(1), Literal(2))), false) - checkEvaluation(In(Literal(1), Seq(Literal(1), Literal(2))) && In(Literal(2), Seq(Literal(1), Literal(2))), true) + checkEvaluation( + In(Literal(1), Seq(Literal(1), Literal(2))) && In(Literal(2), Seq(Literal(1), Literal(2))), + true) } test("Divide") { @@ -169,12 +185,13 @@ class ExpressionEvaluationSuite extends FunSuite { checkEvaluation(Divide(Literal(1), Literal(0)), null) checkEvaluation(Divide(Literal(1.0), Literal(0.0)), null) checkEvaluation(Divide(Literal(0.0), Literal(0.0)), null) - checkEvaluation(Divide(Literal(0), Literal(null, IntegerType)), null) - checkEvaluation(Divide(Literal(1), Literal(null, IntegerType)), null) - checkEvaluation(Divide(Literal(null, IntegerType), Literal(0)), null) - checkEvaluation(Divide(Literal(null, DoubleType), Literal(0.0)), null) - checkEvaluation(Divide(Literal(null, IntegerType), Literal(1)), null) - checkEvaluation(Divide(Literal(null, IntegerType), Literal(null, IntegerType)), null) + checkEvaluation(Divide(Literal(0), Literal.create(null, IntegerType)), null) + checkEvaluation(Divide(Literal(1), Literal.create(null, IntegerType)), null) + checkEvaluation(Divide(Literal.create(null, IntegerType), Literal(0)), null) + checkEvaluation(Divide(Literal.create(null, DoubleType), Literal(0.0)), null) + checkEvaluation(Divide(Literal.create(null, IntegerType), Literal(1)), null) + checkEvaluation(Divide(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), + null) } test("Remainder") { @@ -184,12 +201,13 @@ class ExpressionEvaluationSuite extends FunSuite { checkEvaluation(Remainder(Literal(1), Literal(0)), null) checkEvaluation(Remainder(Literal(1.0), Literal(0.0)), null) checkEvaluation(Remainder(Literal(0.0), Literal(0.0)), null) - checkEvaluation(Remainder(Literal(0), Literal(null, IntegerType)), null) - checkEvaluation(Remainder(Literal(1), Literal(null, IntegerType)), null) - checkEvaluation(Remainder(Literal(null, IntegerType), Literal(0)), null) - checkEvaluation(Remainder(Literal(null, DoubleType), Literal(0.0)), null) - checkEvaluation(Remainder(Literal(null, IntegerType), Literal(1)), null) - checkEvaluation(Remainder(Literal(null, IntegerType), Literal(null, IntegerType)), null) + checkEvaluation(Remainder(Literal(0), Literal.create(null, IntegerType)), null) + checkEvaluation(Remainder(Literal(1), Literal.create(null, IntegerType)), null) + checkEvaluation(Remainder(Literal.create(null, IntegerType), Literal(0)), null) + checkEvaluation(Remainder(Literal.create(null, DoubleType), Literal(0.0)), null) + checkEvaluation(Remainder(Literal.create(null, IntegerType), Literal(1)), null) + checkEvaluation(Remainder(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), + null) } test("INSET") { @@ -216,14 +234,24 @@ class ExpressionEvaluationSuite extends FunSuite { checkEvaluation(MaxOf(1L, 2L), 2L) checkEvaluation(MaxOf(2L, 1L), 2L) - checkEvaluation(MaxOf(Literal(null, IntegerType), 2), 2) - checkEvaluation(MaxOf(2, Literal(null, IntegerType)), 2) + checkEvaluation(MaxOf(Literal.create(null, IntegerType), 2), 2) + checkEvaluation(MaxOf(2, Literal.create(null, IntegerType)), 2) + } + + test("MinOf") { + checkEvaluation(MinOf(1, 2), 1) + checkEvaluation(MinOf(2, 1), 1) + checkEvaluation(MinOf(1L, 2L), 1L) + checkEvaluation(MinOf(2L, 1L), 1L) + + checkEvaluation(MinOf(Literal.create(null, IntegerType), 1), 1) + checkEvaluation(MinOf(1, Literal.create(null, IntegerType)), 1) } test("LIKE literal Regular Expression") { - checkEvaluation(Literal(null, StringType).like("a"), null) - checkEvaluation(Literal("a", StringType).like(Literal(null, StringType)), null) - checkEvaluation(Literal(null, StringType).like(Literal(null, StringType)), null) + checkEvaluation(Literal.create(null, StringType).like("a"), null) + checkEvaluation(Literal.create("a", StringType).like(Literal.create(null, StringType)), null) + checkEvaluation(Literal.create(null, StringType).like(Literal.create(null, StringType)), null) checkEvaluation("abdef" like "abdef", true) checkEvaluation("a_%b" like "a\\__b", true) checkEvaluation("addb" like "a_%b", true) @@ -242,29 +270,29 @@ class ExpressionEvaluationSuite extends FunSuite { test("LIKE Non-literal Regular Expression") { val regEx = 'a.string.at(0) - checkEvaluation("abcd" like regEx, null, new GenericRow(Array[Any](null))) - checkEvaluation("abdef" like regEx, true, new GenericRow(Array[Any]("abdef"))) - checkEvaluation("a_%b" like regEx, true, new GenericRow(Array[Any]("a\\__b"))) - checkEvaluation("addb" like regEx, true, new GenericRow(Array[Any]("a_%b"))) - checkEvaluation("addb" like regEx, false, new GenericRow(Array[Any]("a\\__b"))) - checkEvaluation("addb" like regEx, false, new GenericRow(Array[Any]("a%\\%b"))) - checkEvaluation("a_%b" like regEx, true, new GenericRow(Array[Any]("a%\\%b"))) - checkEvaluation("addb" like regEx, true, new GenericRow(Array[Any]("a%"))) - checkEvaluation("addb" like regEx, false, new GenericRow(Array[Any]("**"))) - checkEvaluation("abc" like regEx, true, new GenericRow(Array[Any]("a%"))) - checkEvaluation("abc" like regEx, false, new GenericRow(Array[Any]("b%"))) - checkEvaluation("abc" like regEx, false, new GenericRow(Array[Any]("bc%"))) - checkEvaluation("a\nb" like regEx, true, new GenericRow(Array[Any]("a_b"))) - checkEvaluation("ab" like regEx, true, new GenericRow(Array[Any]("a%b"))) - checkEvaluation("a\nb" like regEx, true, new GenericRow(Array[Any]("a%b"))) - - checkEvaluation(Literal(null, StringType) like regEx, null, new GenericRow(Array[Any]("bc%"))) + checkEvaluation("abcd" like regEx, null, create_row(null)) + checkEvaluation("abdef" like regEx, true, create_row("abdef")) + checkEvaluation("a_%b" like regEx, true, create_row("a\\__b")) + checkEvaluation("addb" like regEx, true, create_row("a_%b")) + checkEvaluation("addb" like regEx, false, create_row("a\\__b")) + checkEvaluation("addb" like regEx, false, create_row("a%\\%b")) + checkEvaluation("a_%b" like regEx, true, create_row("a%\\%b")) + checkEvaluation("addb" like regEx, true, create_row("a%")) + checkEvaluation("addb" like regEx, false, create_row("**")) + checkEvaluation("abc" like regEx, true, create_row("a%")) + checkEvaluation("abc" like regEx, false, create_row("b%")) + checkEvaluation("abc" like regEx, false, create_row("bc%")) + checkEvaluation("a\nb" like regEx, true, create_row("a_b")) + checkEvaluation("ab" like regEx, true, create_row("a%b")) + checkEvaluation("a\nb" like regEx, true, create_row("a%b")) + + checkEvaluation(Literal.create(null, StringType) like regEx, null, create_row("bc%")) } test("RLIKE literal Regular Expression") { - checkEvaluation(Literal(null, StringType) rlike "abdef", null) - checkEvaluation("abdef" rlike Literal(null, StringType), null) - checkEvaluation(Literal(null, StringType) rlike Literal(null, StringType), null) + checkEvaluation(Literal.create(null, StringType) rlike "abdef", null) + checkEvaluation("abdef" rlike Literal.create(null, StringType), null) + checkEvaluation(Literal.create(null, StringType) rlike Literal.create(null, StringType), null) checkEvaluation("abdef" rlike "abdef", true) checkEvaluation("abbbbc" rlike "a.*c", true) @@ -289,14 +317,14 @@ class ExpressionEvaluationSuite extends FunSuite { test("RLIKE Non-literal Regular Expression") { val regEx = 'a.string.at(0) - checkEvaluation("abdef" rlike regEx, true, new GenericRow(Array[Any]("abdef"))) - checkEvaluation("abbbbc" rlike regEx, true, new GenericRow(Array[Any]("a.*c"))) - checkEvaluation("fofo" rlike regEx, true, new GenericRow(Array[Any]("^fo"))) - checkEvaluation("fo\no" rlike regEx, true, new GenericRow(Array[Any]("^fo\no$"))) - checkEvaluation("Bn" rlike regEx, true, new GenericRow(Array[Any]("^Ba*n"))) + checkEvaluation("abdef" rlike regEx, true, create_row("abdef")) + checkEvaluation("abbbbc" rlike regEx, true, create_row("a.*c")) + checkEvaluation("fofo" rlike regEx, true, create_row("^fo")) + checkEvaluation("fo\no" rlike regEx, true, create_row("^fo\no$")) + checkEvaluation("Bn" rlike regEx, true, create_row("^Ba*n")) intercept[java.util.regex.PatternSyntaxException] { - evaluate("abbbbc" rlike regEx, new GenericRow(Array[Any]("**"))) + evaluate("abbbbc" rlike regEx, create_row("**")) } } @@ -375,7 +403,7 @@ class ExpressionEvaluationSuite extends FunSuite { assert(("abcdef" cast DoubleType).nullable === true) assert(("abcdef" cast FloatType).nullable === true) - checkEvaluation(Cast(Literal(null, IntegerType), ShortType), null) + checkEvaluation(Cast(Literal.create(null, IntegerType), ShortType), null) } test("date") { @@ -501,8 +529,10 @@ class ExpressionEvaluationSuite extends FunSuite { } test("array casting") { - val array = Literal(Seq("123", "abc", "", null), ArrayType(StringType, containsNull = true)) - val array_notNull = Literal(Seq("123", "abc", ""), ArrayType(StringType, containsNull = false)) + val array = Literal.create(Seq("123", "abc", "", null), + ArrayType(StringType, containsNull = true)) + val array_notNull = Literal.create(Seq("123", "abc", ""), + ArrayType(StringType, containsNull = false)) { val cast = Cast(array, ArrayType(IntegerType, containsNull = true)) @@ -550,10 +580,10 @@ class ExpressionEvaluationSuite extends FunSuite { } test("map casting") { - val map = Literal( + val map = Literal.create( Map("a" -> "123", "b" -> "abc", "c" -> "", "d" -> null), MapType(StringType, StringType, valueContainsNull = true)) - val map_notNull = Literal( + val map_notNull = Literal.create( Map("a" -> "123", "b" -> "abc", "c" -> ""), MapType(StringType, StringType, valueContainsNull = false)) @@ -611,14 +641,14 @@ class ExpressionEvaluationSuite extends FunSuite { } test("struct casting") { - val struct = Literal( + val struct = Literal.create( Row("123", "abc", "", null), StructType(Seq( StructField("a", StringType, nullable = true), StructField("b", StringType, nullable = true), StructField("c", StringType, nullable = true), StructField("d", StringType, nullable = true)))) - val struct_notNull = Literal( + val struct_notNull = Literal.create( Row("123", "abc", ""), StructType(Seq( StructField("a", StringType, nullable = false), @@ -706,7 +736,7 @@ class ExpressionEvaluationSuite extends FunSuite { } test("complex casting") { - val complex = Literal( + val complex = Literal.create( Row( Seq("123", "abc", ""), Map("a" -> "123", "b" -> "abc", "c" -> ""), @@ -737,7 +767,7 @@ class ExpressionEvaluationSuite extends FunSuite { } test("null checking") { - val row = new GenericRow(Array[Any]("^Ba*n", null, true, null)) + val row = create_row("^Ba*n", null, true, null) val c1 = 'a.string.at(0) val c2 = 'a.string.at(1) val c3 = 'a.boolean.at(2) @@ -749,34 +779,35 @@ class ExpressionEvaluationSuite extends FunSuite { checkEvaluation(c2.isNull, true, row) checkEvaluation(c2.isNotNull, false, row) - checkEvaluation(Literal(1, ShortType).isNull, false) - checkEvaluation(Literal(1, ShortType).isNotNull, true) + checkEvaluation(Literal.create(1, ShortType).isNull, false) + checkEvaluation(Literal.create(1, ShortType).isNotNull, true) - checkEvaluation(Literal(null, ShortType).isNull, true) - checkEvaluation(Literal(null, ShortType).isNotNull, false) + checkEvaluation(Literal.create(null, ShortType).isNull, true) + checkEvaluation(Literal.create(null, ShortType).isNotNull, false) checkEvaluation(Coalesce(c1 :: c2 :: Nil), "^Ba*n", row) - checkEvaluation(Coalesce(Literal(null, StringType) :: Nil), null, row) - checkEvaluation(Coalesce(Literal(null, StringType) :: c1 :: c2 :: Nil), "^Ba*n", row) + checkEvaluation(Coalesce(Literal.create(null, StringType) :: Nil), null, row) + checkEvaluation(Coalesce(Literal.create(null, StringType) :: c1 :: c2 :: Nil), "^Ba*n", row) - checkEvaluation(If(c3, Literal("a", StringType), Literal("b", StringType)), "a", row) + checkEvaluation( + If(c3, Literal.create("a", StringType), Literal.create("b", StringType)), "a", row) checkEvaluation(If(c3, c1, c2), "^Ba*n", row) checkEvaluation(If(c4, c2, c1), "^Ba*n", row) - checkEvaluation(If(Literal(null, BooleanType), c2, c1), "^Ba*n", row) - checkEvaluation(If(Literal(true, BooleanType), c1, c2), "^Ba*n", row) - checkEvaluation(If(Literal(false, BooleanType), c2, c1), "^Ba*n", row) - checkEvaluation(If(Literal(false, BooleanType), - Literal("a", StringType), Literal("b", StringType)), "b", row) + checkEvaluation(If(Literal.create(null, BooleanType), c2, c1), "^Ba*n", row) + checkEvaluation(If(Literal.create(true, BooleanType), c1, c2), "^Ba*n", row) + checkEvaluation(If(Literal.create(false, BooleanType), c2, c1), "^Ba*n", row) + checkEvaluation(If(Literal.create(false, BooleanType), + Literal.create("a", StringType), Literal.create("b", StringType)), "b", row) checkEvaluation(c1 in (c1, c2), true, row) checkEvaluation( - Literal("^Ba*n", StringType) in (Literal("^Ba*n", StringType)), true, row) + Literal.create("^Ba*n", StringType) in (Literal.create("^Ba*n", StringType)), true, row) checkEvaluation( - Literal("^Ba*n", StringType) in (Literal("^Ba*n", StringType), c2), true, row) + Literal.create("^Ba*n", StringType) in (Literal.create("^Ba*n", StringType), c2), true, row) } test("case when") { - val row = new GenericRow(Array[Any](null, false, true, "a", "b", "c")) + val row = create_row(null, false, true, "a", "b", "c") val c1 = 'a.boolean.at(0) val c2 = 'a.boolean.at(1) val c3 = 'a.boolean.at(2) @@ -787,9 +818,9 @@ class ExpressionEvaluationSuite extends FunSuite { checkEvaluation(CaseWhen(Seq(c1, c4, c6)), "c", row) checkEvaluation(CaseWhen(Seq(c2, c4, c6)), "c", row) checkEvaluation(CaseWhen(Seq(c3, c4, c6)), "a", row) - checkEvaluation(CaseWhen(Seq(Literal(null, BooleanType), c4, c6)), "c", row) - checkEvaluation(CaseWhen(Seq(Literal(false, BooleanType), c4, c6)), "c", row) - checkEvaluation(CaseWhen(Seq(Literal(true, BooleanType), c4, c6)), "a", row) + checkEvaluation(CaseWhen(Seq(Literal.create(null, BooleanType), c4, c6)), "c", row) + checkEvaluation(CaseWhen(Seq(Literal.create(false, BooleanType), c4, c6)), "c", row) + checkEvaluation(CaseWhen(Seq(Literal.create(true, BooleanType), c4, c6)), "a", row) checkEvaluation(CaseWhen(Seq(c3, c4, c2, c5, c6)), "a", row) checkEvaluation(CaseWhen(Seq(c2, c4, c3, c5, c6)), "b", row) @@ -819,13 +850,13 @@ class ExpressionEvaluationSuite extends FunSuite { } test("complex type") { - val row = new GenericRow(Array[Any]( - "^Ba*n", // 0 - null.asInstanceOf[String], // 1 - new GenericRow(Array[Any]("aa", "bb")), // 2 - Map("aa"->"bb"), // 3 - Seq("aa", "bb") // 4 - )) + val row = create_row( + "^Ba*n", // 0 + null.asInstanceOf[UTF8String], // 1 + create_row("aa", "bb"), // 2 + Map("aa"->"bb"), // 3 + Seq("aa", "bb") // 4 + ) val typeS = StructType( StructField("a", StringType, true) :: StructField("b", StringType, true) :: Nil @@ -835,30 +866,34 @@ class ExpressionEvaluationSuite extends FunSuite { checkEvaluation(GetItem(BoundReference(3, typeMap, true), Literal("aa")), "bb", row) - checkEvaluation(GetItem(Literal(null, typeMap), Literal("aa")), null, row) - checkEvaluation(GetItem(Literal(null, typeMap), Literal(null, StringType)), null, row) + checkEvaluation(GetItem(Literal.create(null, typeMap), Literal("aa")), null, row) + checkEvaluation( + GetItem(Literal.create(null, typeMap), Literal.create(null, StringType)), null, row) checkEvaluation(GetItem(BoundReference(3, typeMap, true), - Literal(null, StringType)), null, row) + Literal.create(null, StringType)), null, row) checkEvaluation(GetItem(BoundReference(4, typeArray, true), Literal(1)), "bb", row) - checkEvaluation(GetItem(Literal(null, typeArray), Literal(1)), null, row) - checkEvaluation(GetItem(Literal(null, typeArray), Literal(null, IntegerType)), null, row) + checkEvaluation(GetItem(Literal.create(null, typeArray), Literal(1)), null, row) + checkEvaluation( + GetItem(Literal.create(null, typeArray), Literal.create(null, IntegerType)), null, row) checkEvaluation(GetItem(BoundReference(4, typeArray, true), - Literal(null, IntegerType)), null, row) + Literal.create(null, IntegerType)), null, row) - def quickBuildGetField(expr: Expression, fieldName: String) = { + def quickBuildGetField(expr: Expression, fieldName: String): StructGetField = { expr.dataType match { case StructType(fields) => val field = fields.find(_.name == fieldName).get - GetField(expr, field, fields.indexOf(field)) + StructGetField(expr, field, fields.indexOf(field)) } } - def quickResolve(u: UnresolvedGetField) = quickBuildGetField(u.child, u.fieldName) + def quickResolve(u: UnresolvedGetField): StructGetField = { + quickBuildGetField(u.child, u.fieldName) + } checkEvaluation(quickBuildGetField(BoundReference(2, typeS, nullable = true), "a"), "aa", row) - checkEvaluation(quickBuildGetField(Literal(null, typeS), "a"), null, row) + checkEvaluation(quickBuildGetField(Literal.create(null, typeS), "a"), null, row) val typeS_notNullable = StructType( StructField("a", StringType, nullable = false) @@ -866,10 +901,11 @@ class ExpressionEvaluationSuite extends FunSuite { ) assert(quickBuildGetField(BoundReference(2,typeS, nullable = true), "a").nullable === true) - assert(quickBuildGetField(BoundReference(2, typeS_notNullable, nullable = false), "a").nullable === false) + assert(quickBuildGetField(BoundReference(2, typeS_notNullable, nullable = false), "a").nullable + === false) - assert(quickBuildGetField(Literal(null, typeS), "a").nullable === true) - assert(quickBuildGetField(Literal(null, typeS_notNullable), "a").nullable === true) + assert(quickBuildGetField(Literal.create(null, typeS), "a").nullable === true) + assert(quickBuildGetField(Literal.create(null, typeS_notNullable), "a").nullable === true) checkEvaluation('c.map(typeMap).at(3).getItem("aa"), "bb", row) checkEvaluation('c.array(typeArray.elementType).at(4).getItem(1), "bb", row) @@ -877,20 +913,21 @@ class ExpressionEvaluationSuite extends FunSuite { } test("arithmetic") { - val row = new GenericRow(Array[Any](1, 2, 3, null)) + val row = create_row(1, 2, 3, null) val c1 = 'a.int.at(0) val c2 = 'a.int.at(1) val c3 = 'a.int.at(2) val c4 = 'a.int.at(3) checkEvaluation(UnaryMinus(c1), -1, row) - checkEvaluation(UnaryMinus(Literal(100, IntegerType)), -100) + checkEvaluation(UnaryMinus(Literal.create(100, IntegerType)), -100) checkEvaluation(Add(c1, c4), null, row) checkEvaluation(Add(c1, c2), 3, row) - checkEvaluation(Add(c1, Literal(null, IntegerType)), null, row) - checkEvaluation(Add(Literal(null, IntegerType), c2), null, row) - checkEvaluation(Add(Literal(null, IntegerType), Literal(null, IntegerType)), null, row) + checkEvaluation(Add(c1, Literal.create(null, IntegerType)), null, row) + checkEvaluation(Add(Literal.create(null, IntegerType), c2), null, row) + checkEvaluation( + Add(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null, row) checkEvaluation(-c1, -1, row) checkEvaluation(c1 + c2, 3, row) @@ -901,19 +938,20 @@ class ExpressionEvaluationSuite extends FunSuite { } test("fractional arithmetic") { - val row = new GenericRow(Array[Any](1.1, 2.0, 3.1, null)) + val row = create_row(1.1, 2.0, 3.1, null) val c1 = 'a.double.at(0) val c2 = 'a.double.at(1) val c3 = 'a.double.at(2) val c4 = 'a.double.at(3) checkEvaluation(UnaryMinus(c1), -1.1, row) - checkEvaluation(UnaryMinus(Literal(100.0, DoubleType)), -100.0) + checkEvaluation(UnaryMinus(Literal.create(100.0, DoubleType)), -100.0) checkEvaluation(Add(c1, c4), null, row) checkEvaluation(Add(c1, c2), 3.1, row) - checkEvaluation(Add(c1, Literal(null, DoubleType)), null, row) - checkEvaluation(Add(Literal(null, DoubleType), c2), null, row) - checkEvaluation(Add(Literal(null, DoubleType), Literal(null, DoubleType)), null, row) + checkEvaluation(Add(c1, Literal.create(null, DoubleType)), null, row) + checkEvaluation(Add(Literal.create(null, DoubleType), c2), null, row) + checkEvaluation( + Add(Literal.create(null, DoubleType), Literal.create(null, DoubleType)), null, row) checkEvaluation(-c1, -1.1, row) checkEvaluation(c1 + c2, 3.1, row) @@ -924,7 +962,7 @@ class ExpressionEvaluationSuite extends FunSuite { } test("BinaryComparison") { - val row = new GenericRow(Array[Any](1, 2, 3, null, 3, null)) + val row = create_row(1, 2, 3, null, 3, null) val c1 = 'a.int.at(0) val c2 = 'a.int.at(1) val c3 = 'a.int.at(2) @@ -934,9 +972,10 @@ class ExpressionEvaluationSuite extends FunSuite { checkEvaluation(LessThan(c1, c4), null, row) checkEvaluation(LessThan(c1, c2), true, row) - checkEvaluation(LessThan(c1, Literal(null, IntegerType)), null, row) - checkEvaluation(LessThan(Literal(null, IntegerType), c2), null, row) - checkEvaluation(LessThan(Literal(null, IntegerType), Literal(null, IntegerType)), null, row) + checkEvaluation(LessThan(c1, Literal.create(null, IntegerType)), null, row) + checkEvaluation(LessThan(Literal.create(null, IntegerType), c2), null, row) + checkEvaluation( + LessThan(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null, row) checkEvaluation(c1 < c2, true, row) checkEvaluation(c1 <= c2, true, row) @@ -948,85 +987,115 @@ class ExpressionEvaluationSuite extends FunSuite { checkEvaluation(c1 <=> c4, false, row) checkEvaluation(c4 <=> c6, true, row) checkEvaluation(c3 <=> c5, true, row) - checkEvaluation(Literal(true) <=> Literal(null, BooleanType), false, row) - checkEvaluation(Literal(null, BooleanType) <=> Literal(true), false, row) + checkEvaluation(Literal(true) <=> Literal.create(null, BooleanType), false, row) + checkEvaluation(Literal.create(null, BooleanType) <=> Literal(true), false, row) } test("StringComparison") { - val row = new GenericRow(Array[Any]("abc", null)) + val row = create_row("abc", null) val c1 = 'a.string.at(0) val c2 = 'a.string.at(1) checkEvaluation(c1 contains "b", true, row) checkEvaluation(c1 contains "x", false, row) checkEvaluation(c2 contains "b", null, row) - checkEvaluation(c1 contains Literal(null, StringType), null, row) + checkEvaluation(c1 contains Literal.create(null, StringType), null, row) checkEvaluation(c1 startsWith "a", true, row) checkEvaluation(c1 startsWith "b", false, row) checkEvaluation(c2 startsWith "a", null, row) - checkEvaluation(c1 startsWith Literal(null, StringType), null, row) + checkEvaluation(c1 startsWith Literal.create(null, StringType), null, row) checkEvaluation(c1 endsWith "c", true, row) checkEvaluation(c1 endsWith "b", false, row) checkEvaluation(c2 endsWith "b", null, row) - checkEvaluation(c1 endsWith Literal(null, StringType), null, row) + checkEvaluation(c1 endsWith Literal.create(null, StringType), null, row) } test("Substring") { - val row = new GenericRow(Array[Any]("example", "example".toArray.map(_.toByte))) + val row = create_row("example", "example".toArray.map(_.toByte)) val s = 'a.string.at(0) // substring from zero position with less-than-full length - checkEvaluation(Substring(s, Literal(0, IntegerType), Literal(2, IntegerType)), "ex", row) - checkEvaluation(Substring(s, Literal(1, IntegerType), Literal(2, IntegerType)), "ex", row) + checkEvaluation( + Substring(s, Literal.create(0, IntegerType), Literal.create(2, IntegerType)), "ex", row) + checkEvaluation( + Substring(s, Literal.create(1, IntegerType), Literal.create(2, IntegerType)), "ex", row) // substring from zero position with full length - checkEvaluation(Substring(s, Literal(0, IntegerType), Literal(7, IntegerType)), "example", row) - checkEvaluation(Substring(s, Literal(1, IntegerType), Literal(7, IntegerType)), "example", row) + checkEvaluation( + Substring(s, Literal.create(0, IntegerType), Literal.create(7, IntegerType)), "example", row) + checkEvaluation( + Substring(s, Literal.create(1, IntegerType), Literal.create(7, IntegerType)), "example", row) // substring from zero position with greater-than-full length - checkEvaluation(Substring(s, Literal(0, IntegerType), Literal(100, IntegerType)), "example", row) - checkEvaluation(Substring(s, Literal(1, IntegerType), Literal(100, IntegerType)), "example", row) + checkEvaluation(Substring(s, Literal.create(0, IntegerType), Literal.create(100, IntegerType)), + "example", row) + checkEvaluation(Substring(s, Literal.create(1, IntegerType), Literal.create(100, IntegerType)), + "example", row) // substring from nonzero position with less-than-full length - checkEvaluation(Substring(s, Literal(2, IntegerType), Literal(2, IntegerType)), "xa", row) + checkEvaluation(Substring(s, Literal.create(2, IntegerType), Literal.create(2, IntegerType)), + "xa", row) // substring from nonzero position with full length - checkEvaluation(Substring(s, Literal(2, IntegerType), Literal(6, IntegerType)), "xample", row) + checkEvaluation(Substring(s, Literal.create(2, IntegerType), Literal.create(6, IntegerType)), + "xample", row) // substring from nonzero position with greater-than-full length - checkEvaluation(Substring(s, Literal(2, IntegerType), Literal(100, IntegerType)), "xample", row) + checkEvaluation(Substring(s, Literal.create(2, IntegerType), Literal.create(100, IntegerType)), + "xample", row) // zero-length substring (within string bounds) - checkEvaluation(Substring(s, Literal(0, IntegerType), Literal(0, IntegerType)), "", row) + checkEvaluation(Substring(s, Literal.create(0, IntegerType), Literal.create(0, IntegerType)), + "", row) // zero-length substring (beyond string bounds) - checkEvaluation(Substring(s, Literal(100, IntegerType), Literal(4, IntegerType)), "", row) + checkEvaluation(Substring(s, Literal.create(100, IntegerType), Literal.create(4, IntegerType)), + "", row) // substring(null, _, _) -> null - checkEvaluation(Substring(s, Literal(100, IntegerType), Literal(4, IntegerType)), null, new GenericRow(Array[Any](null))) + checkEvaluation(Substring(s, Literal.create(100, IntegerType), Literal.create(4, IntegerType)), + null, create_row(null)) // substring(_, null, _) -> null - checkEvaluation(Substring(s, Literal(null, IntegerType), Literal(4, IntegerType)), null, row) + checkEvaluation(Substring(s, Literal.create(null, IntegerType), Literal.create(4, IntegerType)), + null, row) // substring(_, _, null) -> null - checkEvaluation(Substring(s, Literal(100, IntegerType), Literal(null, IntegerType)), null, row) + checkEvaluation( + Substring(s, Literal.create(100, IntegerType), Literal.create(null, IntegerType)), + null, + row) // 2-arg substring from zero position - checkEvaluation(Substring(s, Literal(0, IntegerType), Literal(Integer.MAX_VALUE, IntegerType)), "example", row) - checkEvaluation(Substring(s, Literal(1, IntegerType), Literal(Integer.MAX_VALUE, IntegerType)), "example", row) + checkEvaluation( + Substring(s, Literal.create(0, IntegerType), Literal.create(Integer.MAX_VALUE, IntegerType)), + "example", + row) + checkEvaluation( + Substring(s, Literal.create(1, IntegerType), Literal.create(Integer.MAX_VALUE, IntegerType)), + "example", + row) // 2-arg substring from nonzero position - checkEvaluation(Substring(s, Literal(2, IntegerType), Literal(Integer.MAX_VALUE, IntegerType)), "xample", row) + checkEvaluation( + Substring(s, Literal.create(2, IntegerType), Literal.create(Integer.MAX_VALUE, IntegerType)), + "xample", + row) val s_notNull = 'a.string.notNull.at(0) - assert(Substring(s, Literal(0, IntegerType), Literal(2, IntegerType)).nullable === true) - assert(Substring(s_notNull, Literal(0, IntegerType), Literal(2, IntegerType)).nullable === false) - assert(Substring(s_notNull, Literal(null, IntegerType), Literal(2, IntegerType)).nullable === true) - assert(Substring(s_notNull, Literal(0, IntegerType), Literal(null, IntegerType)).nullable === true) + assert(Substring(s, Literal.create(0, IntegerType), Literal.create(2, IntegerType)).nullable + === true) + assert( + Substring(s_notNull, Literal.create(0, IntegerType), Literal.create(2, IntegerType)).nullable + === false) + assert(Substring(s_notNull, + Literal.create(null, IntegerType), Literal.create(2, IntegerType)).nullable === true) + assert(Substring(s_notNull, + Literal.create(0, IntegerType), Literal.create(null, IntegerType)).nullable === true) checkEvaluation(s.substr(0, 2), "ex", row) checkEvaluation(s.substr(0), "example", row) @@ -1037,20 +1106,20 @@ class ExpressionEvaluationSuite extends FunSuite { test("SQRT") { val inputSequence = (1 to (1<<24) by 511).map(_ * (1L<<24)) val expectedResults = inputSequence.map(l => math.sqrt(l.toDouble)) - val rowSequence = inputSequence.map(l => new GenericRow(Array[Any](l.toDouble))) + val rowSequence = inputSequence.map(l => create_row(l.toDouble)) val d = 'a.double.at(0) for ((row, expected) <- rowSequence zip expectedResults) { checkEvaluation(Sqrt(d), expected, row) } - checkEvaluation(Sqrt(Literal(null, DoubleType)), null, new GenericRow(Array[Any](null))) + checkEvaluation(Sqrt(Literal.create(null, DoubleType)), null, create_row(null)) checkEvaluation(Sqrt(-1), null, EmptyRow) checkEvaluation(Sqrt(-1.5), null, EmptyRow) } test("Bitwise operations") { - val row = new GenericRow(Array[Any](1, 2, 3, null)) + val row = create_row(1, 2, 3, null) val c1 = 'a.int.at(0) val c2 = 'a.int.at(1) val c3 = 'a.int.at(2) @@ -1058,22 +1127,25 @@ class ExpressionEvaluationSuite extends FunSuite { checkEvaluation(BitwiseAnd(c1, c4), null, row) checkEvaluation(BitwiseAnd(c1, c2), 0, row) - checkEvaluation(BitwiseAnd(c1, Literal(null, IntegerType)), null, row) - checkEvaluation(BitwiseAnd(Literal(null, IntegerType), Literal(null, IntegerType)), null, row) + checkEvaluation(BitwiseAnd(c1, Literal.create(null, IntegerType)), null, row) + checkEvaluation( + BitwiseAnd(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null, row) checkEvaluation(BitwiseOr(c1, c4), null, row) checkEvaluation(BitwiseOr(c1, c2), 3, row) - checkEvaluation(BitwiseOr(c1, Literal(null, IntegerType)), null, row) - checkEvaluation(BitwiseOr(Literal(null, IntegerType), Literal(null, IntegerType)), null, row) + checkEvaluation(BitwiseOr(c1, Literal.create(null, IntegerType)), null, row) + checkEvaluation( + BitwiseOr(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null, row) checkEvaluation(BitwiseXor(c1, c4), null, row) checkEvaluation(BitwiseXor(c1, c2), 3, row) - checkEvaluation(BitwiseXor(c1, Literal(null, IntegerType)), null, row) - checkEvaluation(BitwiseXor(Literal(null, IntegerType), Literal(null, IntegerType)), null, row) + checkEvaluation(BitwiseXor(c1, Literal.create(null, IntegerType)), null, row) + checkEvaluation( + BitwiseXor(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null, row) checkEvaluation(BitwiseNot(c4), null, row) checkEvaluation(BitwiseNot(c1), -2, row) - checkEvaluation(BitwiseNot(Literal(null, IntegerType)), null, row) + checkEvaluation(BitwiseNot(Literal.create(null, IntegerType)), null, row) checkEvaluation(c1 & c2, 0, row) checkEvaluation(c1 | c2, 3, row) @@ -1081,3 +1153,14 @@ class ExpressionEvaluationSuite extends FunSuite { checkEvaluation(~c1, -2, row) } } + +// TODO: Make the tests work with codegen. +class ExpressionEvaluationWithoutCodeGenSuite extends ExpressionEvaluationBaseSuite { + + test("CreateStruct") { + val row = Row(1, 2, 3) + val c1 = 'a.int.at(0).as("a") + val c3 = 'c.int.at(2).as("c") + checkEvaluation(CreateStruct(Seq(c1, c3)), Row(1, 3), row) + } +} diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/GeneratedMutableEvaluationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/GeneratedMutableEvaluationSuite.scala index 275ea2627ebcd..bcc0c404d2cfb 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/GeneratedMutableEvaluationSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/GeneratedMutableEvaluationSuite.scala @@ -17,7 +17,7 @@ package org.apache.spark.sql.catalyst.expressions -import org.apache.spark.sql.catalyst.dsl.expressions._ +import org.apache.spark.sql.catalyst.CatalystTypeConverters import org.apache.spark.sql.catalyst.expressions.codegen._ /** @@ -43,7 +43,7 @@ class GeneratedMutableEvaluationSuite extends ExpressionEvaluationSuite { } val actual = plan(inputRow) - val expectedRow = new GenericRow(Array[Any](expected)) + val expectedRow = new GenericRow(Array[Any](CatalystTypeConverters.convertToCatalyst(expected))) if (actual.hashCode() != expectedRow.hashCode()) { fail( s""" diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/BooleanSimplificationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/BooleanSimplificationSuite.scala index 264a0eff37d34..72f06e26e05f1 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/BooleanSimplificationSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/BooleanSimplificationSuite.scala @@ -17,7 +17,7 @@ package org.apache.spark.sql.catalyst.optimizer -import org.apache.spark.sql.catalyst.analysis.EliminateAnalysisOperators +import org.apache.spark.sql.catalyst.analysis.EliminateSubQueries import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.logical._ import org.apache.spark.sql.catalyst.plans.PlanTest @@ -30,7 +30,7 @@ class BooleanSimplificationSuite extends PlanTest with PredicateHelper { object Optimize extends RuleExecutor[LogicalPlan] { val batches = Batch("AnalysisNodes", Once, - EliminateAnalysisOperators) :: + EliminateSubQueries) :: Batch("Constant Folding", FixedPoint(50), NullPropagation, ConstantFolding, diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala index e22c62505860a..4396bd0dda9a9 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala @@ -17,7 +17,7 @@ package org.apache.spark.sql.catalyst.optimizer -import org.apache.spark.sql.catalyst.analysis.{UnresolvedGetField, EliminateAnalysisOperators} +import org.apache.spark.sql.catalyst.analysis.{UnresolvedGetField, EliminateSubQueries} import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, LogicalPlan} import org.apache.spark.sql.catalyst.plans.PlanTest @@ -33,7 +33,7 @@ class ConstantFoldingSuite extends PlanTest { object Optimize extends RuleExecutor[LogicalPlan] { val batches = Batch("AnalysisNodes", Once, - EliminateAnalysisOperators) :: + EliminateSubQueries) :: Batch("ConstantFolding", Once, ConstantFolding, BooleanSimplification) :: Nil @@ -176,40 +176,39 @@ class ConstantFoldingSuite extends PlanTest { } test("Constant folding test: expressions have null literals") { - val originalQuery = - testRelation - .select( - IsNull(Literal(null)) as 'c1, - IsNotNull(Literal(null)) as 'c2, + val originalQuery = testRelation.select( + IsNull(Literal(null)) as 'c1, + IsNotNull(Literal(null)) as 'c2, - GetItem(Literal(null, ArrayType(IntegerType)), 1) as 'c3, - GetItem(Literal(Seq(1), ArrayType(IntegerType)), Literal(null, IntegerType)) as 'c4, - UnresolvedGetField( - Literal(null, StructType(Seq(StructField("a", IntegerType, true)))), - "a") as 'c5, + GetItem(Literal.create(null, ArrayType(IntegerType)), 1) as 'c3, + GetItem( + Literal.create(Seq(1), ArrayType(IntegerType)), Literal.create(null, IntegerType)) as 'c4, + UnresolvedGetField( + Literal.create(null, StructType(Seq(StructField("a", IntegerType, true)))), + "a") as 'c5, - UnaryMinus(Literal(null, IntegerType)) as 'c6, - Cast(Literal(null), IntegerType) as 'c7, - Not(Literal(null, BooleanType)) as 'c8, + UnaryMinus(Literal.create(null, IntegerType)) as 'c6, + Cast(Literal(null), IntegerType) as 'c7, + Not(Literal.create(null, BooleanType)) as 'c8, - Add(Literal(null, IntegerType), 1) as 'c9, - Add(1, Literal(null, IntegerType)) as 'c10, + Add(Literal.create(null, IntegerType), 1) as 'c9, + Add(1, Literal.create(null, IntegerType)) as 'c10, - EqualTo(Literal(null, IntegerType), 1) as 'c11, - EqualTo(1, Literal(null, IntegerType)) as 'c12, + EqualTo(Literal.create(null, IntegerType), 1) as 'c11, + EqualTo(1, Literal.create(null, IntegerType)) as 'c12, - Like(Literal(null, StringType), "abc") as 'c13, - Like("abc", Literal(null, StringType)) as 'c14, + Like(Literal.create(null, StringType), "abc") as 'c13, + Like("abc", Literal.create(null, StringType)) as 'c14, - Upper(Literal(null, StringType)) as 'c15, + Upper(Literal.create(null, StringType)) as 'c15, - Substring(Literal(null, StringType), 0, 1) as 'c16, - Substring("abc", Literal(null, IntegerType), 1) as 'c17, - Substring("abc", 0, Literal(null, IntegerType)) as 'c18, + Substring(Literal.create(null, StringType), 0, 1) as 'c16, + Substring("abc", Literal.create(null, IntegerType), 1) as 'c17, + Substring("abc", 0, Literal.create(null, IntegerType)) as 'c18, - Contains(Literal(null, StringType), "abc") as 'c19, - Contains("abc", Literal(null, StringType)) as 'c20 - ) + Contains(Literal.create(null, StringType), "abc") as 'c19, + Contains("abc", Literal.create(null, StringType)) as 'c20 + ) val optimized = Optimize(originalQuery.analyze) @@ -219,31 +218,31 @@ class ConstantFoldingSuite extends PlanTest { Literal(true) as 'c1, Literal(false) as 'c2, - Literal(null, IntegerType) as 'c3, - Literal(null, IntegerType) as 'c4, - Literal(null, IntegerType) as 'c5, + Literal.create(null, IntegerType) as 'c3, + Literal.create(null, IntegerType) as 'c4, + Literal.create(null, IntegerType) as 'c5, - Literal(null, IntegerType) as 'c6, - Literal(null, IntegerType) as 'c7, - Literal(null, BooleanType) as 'c8, + Literal.create(null, IntegerType) as 'c6, + Literal.create(null, IntegerType) as 'c7, + Literal.create(null, BooleanType) as 'c8, - Literal(null, IntegerType) as 'c9, - Literal(null, IntegerType) as 'c10, + Literal.create(null, IntegerType) as 'c9, + Literal.create(null, IntegerType) as 'c10, - Literal(null, BooleanType) as 'c11, - Literal(null, BooleanType) as 'c12, + Literal.create(null, BooleanType) as 'c11, + Literal.create(null, BooleanType) as 'c12, - Literal(null, BooleanType) as 'c13, - Literal(null, BooleanType) as 'c14, + Literal.create(null, BooleanType) as 'c13, + Literal.create(null, BooleanType) as 'c14, - Literal(null, StringType) as 'c15, + Literal.create(null, StringType) as 'c15, - Literal(null, StringType) as 'c16, - Literal(null, StringType) as 'c17, - Literal(null, StringType) as 'c18, + Literal.create(null, StringType) as 'c16, + Literal.create(null, StringType) as 'c17, + Literal.create(null, StringType) as 'c18, - Literal(null, BooleanType) as 'c19, - Literal(null, BooleanType) as 'c20 + Literal.create(null, BooleanType) as 'c19, + Literal.create(null, BooleanType) as 'c20 ).analyze comparePlans(optimized, correctAnswer) diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConvertToLocalRelationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConvertToLocalRelationSuite.scala new file mode 100644 index 0000000000000..cf42d43823399 --- /dev/null +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConvertToLocalRelationSuite.scala @@ -0,0 +1,57 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.optimizer + +import org.apache.spark.sql.Row +import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute +import org.apache.spark.sql.catalyst.dsl.plans._ +import org.apache.spark.sql.catalyst.dsl.expressions._ +import org.apache.spark.sql.catalyst.plans.PlanTest +import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, LogicalPlan} +import org.apache.spark.sql.catalyst.rules.RuleExecutor + + +class ConvertToLocalRelationSuite extends PlanTest { + + object Optimize extends RuleExecutor[LogicalPlan] { + val batches = + Batch("LocalRelation", FixedPoint(100), + ConvertToLocalRelation) :: Nil + } + + test("Project on LocalRelation should be turned into a single LocalRelation") { + val testRelation = LocalRelation( + LocalRelation('a.int, 'b.int).output, + Row(1, 2) :: + Row(4, 5) :: Nil) + + val correctAnswer = LocalRelation( + LocalRelation('a1.int, 'b1.int).output, + Row(1, 3) :: + Row(4, 6) :: Nil) + + val projectOnLocal = testRelation.select( + UnresolvedAttribute("a").as("a1"), + (UnresolvedAttribute("b") + 1).as("b1")) + + val optimized = Optimize(projectOnLocal.analyze) + + comparePlans(optimized, correctAnswer) + } + +} diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ExpressionOptimizationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ExpressionOptimizationSuite.scala index ae99a3f9ba287..2f3704be59a9d 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ExpressionOptimizationSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ExpressionOptimizationSuite.scala @@ -29,7 +29,7 @@ class ExpressionOptimizationSuite extends ExpressionEvaluationSuite { expression: Expression, expected: Any, inputRow: Row = EmptyRow): Unit = { - val plan = Project(Alias(expression, s"Optimized($expression)")() :: Nil, NoRelation) + val plan = Project(Alias(expression, s"Optimized($expression)")() :: Nil, OneRowRelation) val optimizedPlan = DefaultOptimizer(plan) super.checkEvaluation(optimizedPlan.expressions.head, expected, inputRow) } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala index ebb123c1f909e..1448098c770aa 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala @@ -18,23 +18,27 @@ package org.apache.spark.sql.catalyst.optimizer import org.apache.spark.sql.catalyst.analysis -import org.apache.spark.sql.catalyst.analysis.EliminateAnalysisOperators +import org.apache.spark.sql.catalyst.analysis.EliminateSubQueries +import org.apache.spark.sql.catalyst.expressions.{Count, Explode} import org.apache.spark.sql.catalyst.plans.logical._ import org.apache.spark.sql.catalyst.plans.{PlanTest, LeftOuter, RightOuter} import org.apache.spark.sql.catalyst.rules._ import org.apache.spark.sql.catalyst.dsl.plans._ import org.apache.spark.sql.catalyst.dsl.expressions._ +import org.apache.spark.sql.types.IntegerType class FilterPushdownSuite extends PlanTest { object Optimize extends RuleExecutor[LogicalPlan] { val batches = Batch("Subqueries", Once, - EliminateAnalysisOperators) :: + EliminateSubQueries) :: Batch("Filter Pushdown", Once, CombineFilters, PushPredicateThroughProject, - PushPredicateThroughJoin) :: Nil + PushPredicateThroughJoin, + PushPredicateThroughGenerate, + ColumnPruning) :: Nil } val testRelation = LocalRelation('a.int, 'b.int, 'c.int) @@ -55,6 +59,38 @@ class FilterPushdownSuite extends PlanTest { comparePlans(optimized, correctAnswer) } + test("column pruning for group") { + val originalQuery = + testRelation + .groupBy('a)('a, Count('b)) + .select('a) + + val optimized = Optimize(originalQuery.analyze) + val correctAnswer = + testRelation + .select('a) + .groupBy('a)('a) + .select('a).analyze + + comparePlans(optimized, correctAnswer) + } + + test("column pruning for group with alias") { + val originalQuery = + testRelation + .groupBy('a)('a as 'c, Count('b)) + .select('c) + + val optimized = Optimize(originalQuery.analyze) + val correctAnswer = + testRelation + .select('a) + .groupBy('a)('a as 'c) + .select('c).analyze + + comparePlans(optimized, correctAnswer) + } + // After this line is unimplemented. test("simple push down") { val originalQuery = @@ -348,7 +384,7 @@ class FilterPushdownSuite extends PlanTest { } val optimized = Optimize(originalQuery.analyze) - comparePlans(analysis.EliminateAnalysisOperators(originalQuery.analyze), optimized) + comparePlans(analysis.EliminateSubQueries(originalQuery.analyze), optimized) } test("joins: conjunctive predicates") { @@ -367,7 +403,7 @@ class FilterPushdownSuite extends PlanTest { left.join(right, condition = Some("x.b".attr === "y.b".attr)) .analyze - comparePlans(optimized, analysis.EliminateAnalysisOperators(correctAnswer)) + comparePlans(optimized, analysis.EliminateSubQueries(correctAnswer)) } test("joins: conjunctive predicates #2") { @@ -386,7 +422,7 @@ class FilterPushdownSuite extends PlanTest { left.join(right, condition = Some("x.b".attr === "y.b".attr)) .analyze - comparePlans(optimized, analysis.EliminateAnalysisOperators(correctAnswer)) + comparePlans(optimized, analysis.EliminateSubQueries(correctAnswer)) } test("joins: conjunctive predicates #3") { @@ -396,7 +432,8 @@ class FilterPushdownSuite extends PlanTest { val originalQuery = { z.join(x.join(y)) - .where(("x.b".attr === "y.b".attr) && ("x.a".attr === 1) && ("z.a".attr >= 3) && ("z.a".attr === "x.b".attr)) + .where(("x.b".attr === "y.b".attr) && ("x.a".attr === 1) && + ("z.a".attr >= 3) && ("z.a".attr === "x.b".attr)) } val optimized = Optimize(originalQuery.analyze) @@ -409,6 +446,64 @@ class FilterPushdownSuite extends PlanTest { condition = Some("z.a".attr === "x.b".attr)) .analyze - comparePlans(optimized, analysis.EliminateAnalysisOperators(correctAnswer)) + comparePlans(optimized, analysis.EliminateSubQueries(correctAnswer)) + } + + val testRelationWithArrayType = LocalRelation('a.int, 'b.int, 'c_arr.array(IntegerType)) + + test("generate: predicate referenced no generated column") { + val originalQuery = { + testRelationWithArrayType + .generate(Explode(Seq("c"), 'c_arr), true, false, Some("arr")) + .where(('b >= 5) && ('a > 6)) + } + val optimized = Optimize(originalQuery.analyze) + val correctAnswer = { + testRelationWithArrayType + .where(('b >= 5) && ('a > 6)) + .generate(Explode(Seq("c"), 'c_arr), true, false, Some("arr")).analyze + } + + comparePlans(optimized, correctAnswer) + } + + test("generate: part of conjuncts referenced generated column") { + val generator = Explode(Seq("c"), 'c_arr) + val originalQuery = { + testRelationWithArrayType + .generate(generator, true, false, Some("arr")) + .where(('b >= 5) && ('c > 6)) + } + val optimized = Optimize(originalQuery.analyze) + val referenceResult = { + testRelationWithArrayType + .where('b >= 5) + .generate(generator, true, false, Some("arr")) + .where('c > 6).analyze + } + + // Since newly generated columns get different ids every time being analyzed + // e.g. comparePlans(originalQuery.analyze, originalQuery.analyze) fails. + // So we check operators manually here. + // Filter("c" > 6) + assertResult(classOf[Filter])(optimized.getClass) + assertResult(1)(optimized.asInstanceOf[Filter].condition.references.size) + assertResult("c"){ + optimized.asInstanceOf[Filter].condition.references.toSeq(0).name + } + + // the rest part + comparePlans(optimized.children(0), referenceResult.children(0)) + } + + test("generate: all conjuncts referenced generated column") { + val originalQuery = { + testRelationWithArrayType + .generate(Explode(Seq("c"), 'c_arr), true, false, Some("arr")) + .where(('c > 6) || ('b > 5)).analyze + } + val optimized = Optimize(originalQuery) + + comparePlans(optimized, originalQuery) } } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/OptimizeInSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/OptimizeInSuite.scala index da912ab382179..966bc9ada1e6e 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/OptimizeInSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/OptimizeInSuite.scala @@ -18,7 +18,7 @@ package org.apache.spark.sql.catalyst.optimizer import scala.collection.immutable.HashSet -import org.apache.spark.sql.catalyst.analysis.{EliminateAnalysisOperators, UnresolvedAttribute} +import org.apache.spark.sql.catalyst.analysis.{EliminateSubQueries, UnresolvedAttribute} import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, LogicalPlan} import org.apache.spark.sql.catalyst.plans.PlanTest @@ -34,7 +34,7 @@ class OptimizeInSuite extends PlanTest { object Optimize extends RuleExecutor[LogicalPlan] { val batches = Batch("AnalysisNodes", Once, - EliminateAnalysisOperators) :: + EliminateSubQueries) :: Batch("ConstantFolding", Once, ConstantFolding, BooleanSimplification, @@ -52,7 +52,7 @@ class OptimizeInSuite extends PlanTest { val optimized = Optimize(originalQuery.analyze) val correctAnswer = testRelation - .where(InSet(UnresolvedAttribute("a"), HashSet[Any]()+1+2)) + .where(InSet(UnresolvedAttribute("a"), HashSet[Any]() + 1 + 2)) .analyze comparePlans(optimized, correctAnswer) diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/UnionPushdownSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/UnionPushdownSuite.scala index dfef87bd9133d..a54751dfa9a12 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/UnionPushdownSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/UnionPushdownSuite.scala @@ -18,7 +18,7 @@ package org.apache.spark.sql.catalyst.optimizer import org.apache.spark.sql.catalyst.analysis -import org.apache.spark.sql.catalyst.analysis.EliminateAnalysisOperators +import org.apache.spark.sql.catalyst.analysis.EliminateSubQueries import org.apache.spark.sql.catalyst.plans.logical._ import org.apache.spark.sql.catalyst.plans.{PlanTest, LeftOuter, RightOuter} import org.apache.spark.sql.catalyst.rules._ @@ -29,7 +29,7 @@ class UnionPushdownSuite extends PlanTest { object Optimize extends RuleExecutor[LogicalPlan] { val batches = Batch("Subqueries", Once, - EliminateAnalysisOperators) :: + EliminateSubQueries) :: Batch("Union Pushdown", Once, UnionPushdown) :: Nil } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala index c4a1f899d8a13..e7cafcc96de87 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala @@ -19,8 +19,8 @@ package org.apache.spark.sql.catalyst.plans import org.scalatest.FunSuite -import org.apache.spark.sql.catalyst.expressions.{ExprId, AttributeReference} -import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan +import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.catalyst.plans.logical.{OneRowRelation, Filter, LogicalPlan} import org.apache.spark.sql.catalyst.util._ /** @@ -33,11 +33,11 @@ class PlanTest extends FunSuite { * we must normalize them to check if two different queries are identical. */ protected def normalizeExprIds(plan: LogicalPlan) = { - val list = plan.flatMap(_.expressions.flatMap(_.references).map(_.exprId.id)) - val minId = if (list.isEmpty) 0 else list.min plan transformAllExpressions { case a: AttributeReference => - AttributeReference(a.name, a.dataType, a.nullable)(exprId = ExprId(a.exprId.id - minId)) + AttributeReference(a.name, a.dataType, a.nullable)(exprId = ExprId(0)) + case a: Alias => + Alias(a.child, a.name)(exprId = ExprId(0)) } } @@ -45,11 +45,17 @@ class PlanTest extends FunSuite { protected def comparePlans(plan1: LogicalPlan, plan2: LogicalPlan) { val normalized1 = normalizeExprIds(plan1) val normalized2 = normalizeExprIds(plan2) - if (normalized1 != normalized2) + if (normalized1 != normalized2) { fail( s""" |== FAIL: Plans do not match === |${sideBySide(normalized1.treeString, normalized2.treeString).mkString("\n")} - """.stripMargin) + """.stripMargin) + } + } + + /** Fails the test if the two expressions do not match */ + protected def compareExpressions(e1: Expression, e2: Expression): Unit = { + comparePlans(Filter(e1, OneRowRelation), Filter(e2, OneRowRelation)) } } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/SameResultSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/SameResultSuite.scala index 11e6831b24768..1273921f6394c 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/SameResultSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/SameResultSuite.scala @@ -32,7 +32,7 @@ class SameResultSuite extends FunSuite { val testRelation = LocalRelation('a.int, 'b.int, 'c.int) val testRelation2 = LocalRelation('a.int, 'b.int, 'c.int) - def assertSameResult(a: LogicalPlan, b: LogicalPlan, result: Boolean = true) = { + def assertSameResult(a: LogicalPlan, b: LogicalPlan, result: Boolean = true): Unit = { val aAnalyzed = a.analyze val bAnalyzed = b.analyze diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala index cdb843f959704..6b393327cc97a 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala @@ -25,12 +25,12 @@ import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.types.{StringType, NullType} case class Dummy(optKey: Option[Expression]) extends Expression { - def children = optKey.toSeq - def nullable = true - def dataType = NullType + def children: Seq[Expression] = optKey.toSeq + def nullable: Boolean = true + def dataType: NullType = NullType override lazy val resolved = true type EvaluatedType = Any - def eval(input: Row) = null.asInstanceOf[Any] + def eval(input: Row): Any = null.asInstanceOf[Any] } class TreeNodeSuite extends FunSuite { @@ -90,7 +90,7 @@ class TreeNodeSuite extends FunSuite { } test("transform works on nodes with Option children") { - val dummy1 = Dummy(Some(Literal("1", StringType))) + val dummy1 = Dummy(Some(Literal.create("1", StringType))) val dummy2 = Dummy(None) val toZero: PartialFunction[Expression, Expression] = { case Literal(_, _) => Literal(0) } @@ -104,4 +104,30 @@ class TreeNodeSuite extends FunSuite { assert(actual === Dummy(None)) } + test("preserves origin") { + CurrentOrigin.setPosition(1,1) + val add = Add(Literal(1), Literal(1)) + CurrentOrigin.reset() + + val transformed = add transform { + case Literal(1, _) => Literal(2) + } + + assert(transformed.origin.line.isDefined) + assert(transformed.origin.startPosition.isDefined) + } + + test("foreach up") { + val actual = new ArrayBuffer[String]() + val expected = Seq("1", "2", "3", "4", "-", "*", "+") + val expression = Add(Literal(1), Multiply(Literal(2), Subtract(Literal(3), Literal(4)))) + expression foreachUp { + case b: BinaryExpression => actual.append(b.symbol); + case l: Literal => actual.append(l.toString); + } + + assert(expected === actual) + } + + } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/MetadataSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/MetadataSuite.scala old mode 100755 new mode 100644 diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeParserSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeParserSuite.scala new file mode 100644 index 0000000000000..169125264a803 --- /dev/null +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeParserSuite.scala @@ -0,0 +1,118 @@ +/* +* Licensed to the Apache Software Foundation (ASF) under one or more +* contributor license agreements. See the NOTICE file distributed with +* this work for additional information regarding copyright ownership. +* The ASF licenses this file to You under the Apache License, Version 2.0 +* (the "License"); you may not use this file except in compliance with +* the License. You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +*/ + +package org.apache.spark.sql.types + +import org.scalatest.FunSuite + +class DataTypeParserSuite extends FunSuite { + + def checkDataType(dataTypeString: String, expectedDataType: DataType): Unit = { + test(s"parse ${dataTypeString.replace("\n", "")}") { + assert(DataTypeParser(dataTypeString) === expectedDataType) + } + } + + def unsupported(dataTypeString: String): Unit = { + test(s"$dataTypeString is not supported") { + intercept[DataTypeException](DataTypeParser(dataTypeString)) + } + } + + checkDataType("int", IntegerType) + checkDataType("integer", IntegerType) + checkDataType("BooLean", BooleanType) + checkDataType("tinYint", ByteType) + checkDataType("smallINT", ShortType) + checkDataType("INT", IntegerType) + checkDataType("INTEGER", IntegerType) + checkDataType("bigint", LongType) + checkDataType("float", FloatType) + checkDataType("dOUBle", DoubleType) + checkDataType("decimal(10, 5)", DecimalType(10, 5)) + checkDataType("decimal", DecimalType.Unlimited) + checkDataType("DATE", DateType) + checkDataType("timestamp", TimestampType) + checkDataType("string", StringType) + checkDataType("varchAr(20)", StringType) + checkDataType("BINARY", BinaryType) + + checkDataType("array", ArrayType(DoubleType, true)) + checkDataType("Array>", ArrayType(MapType(IntegerType, ByteType, true), true)) + checkDataType( + "array>", + ArrayType(StructType(StructField("tinYint", ByteType, true) :: Nil), true) + ) + checkDataType("MAP", MapType(IntegerType, StringType, true)) + checkDataType("MAp>", MapType(IntegerType, ArrayType(DoubleType), true)) + checkDataType( + "MAP>", + MapType(IntegerType, StructType(StructField("varchar", StringType, true) :: Nil), true) + ) + + checkDataType( + "struct", + StructType( + StructField("intType", IntegerType, true) :: + StructField("ts", TimestampType, true) :: Nil) + ) + // It is fine to use the data type string as the column name. + checkDataType( + "Struct", + StructType( + StructField("int", IntegerType, true) :: + StructField("timestamp", TimestampType, true) :: Nil) + ) + checkDataType( + """ + |struct< + | struct:struct, + | MAP:Map, + | arrAy:Array> + """.stripMargin, + StructType( + StructField("struct", + StructType( + StructField("deciMal", DecimalType.Unlimited, true) :: + StructField("anotherDecimal", DecimalType(5, 2), true) :: Nil), true) :: + StructField("MAP", MapType(TimestampType, StringType), true) :: + StructField("arrAy", ArrayType(DoubleType, true), true) :: Nil) + ) + // A column name can be a reserved word in our DDL parser and SqlParser. + checkDataType( + "Struct", + StructType( + StructField("TABLE", StringType, true) :: + StructField("CASE", BooleanType, true) :: Nil) + ) + // Use backticks to quote column names having special characters. + checkDataType( + "struct<`x+y`:int, `!@#$%^&*()`:string, `1_2.345<>:\"`:varchar(20)>", + StructType( + StructField("x+y", IntegerType, true) :: + StructField("!@#$%^&*()", StringType, true) :: + StructField("1_2.345<>:\"", StringType, true) :: Nil) + ) + // Empty struct. + checkDataType("strUCt<>", StructType(Nil)) + + unsupported("it is not a data type") + unsupported("struct") + unsupported("struct") + unsupported("struct<`x``y` int>") +} diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeSuite.scala index c97e0bec3e3a2..a1341ea13d810 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeSuite.scala @@ -115,4 +115,87 @@ class DataTypeSuite extends FunSuite { checkDefaultSize(MapType(IntegerType, StringType, true), 410000) checkDefaultSize(MapType(IntegerType, ArrayType(DoubleType), false), 80400) checkDefaultSize(structType, 812) + + def checkEqualsIgnoreCompatibleNullability( + from: DataType, + to: DataType, + expected: Boolean): Unit = { + val testName = + s"equalsIgnoreCompatibleNullability: (from: ${from}, to: ${to})" + test(testName) { + assert(DataType.equalsIgnoreCompatibleNullability(from, to) === expected) + } + } + + checkEqualsIgnoreCompatibleNullability( + from = ArrayType(DoubleType, containsNull = true), + to = ArrayType(DoubleType, containsNull = true), + expected = true) + checkEqualsIgnoreCompatibleNullability( + from = ArrayType(DoubleType, containsNull = false), + to = ArrayType(DoubleType, containsNull = false), + expected = true) + checkEqualsIgnoreCompatibleNullability( + from = ArrayType(DoubleType, containsNull = false), + to = ArrayType(DoubleType, containsNull = true), + expected = true) + checkEqualsIgnoreCompatibleNullability( + from = ArrayType(DoubleType, containsNull = true), + to = ArrayType(DoubleType, containsNull = false), + expected = false) + checkEqualsIgnoreCompatibleNullability( + from = ArrayType(DoubleType, containsNull = false), + to = ArrayType(StringType, containsNull = false), + expected = false) + + checkEqualsIgnoreCompatibleNullability( + from = MapType(StringType, DoubleType, valueContainsNull = true), + to = MapType(StringType, DoubleType, valueContainsNull = true), + expected = true) + checkEqualsIgnoreCompatibleNullability( + from = MapType(StringType, DoubleType, valueContainsNull = false), + to = MapType(StringType, DoubleType, valueContainsNull = false), + expected = true) + checkEqualsIgnoreCompatibleNullability( + from = MapType(StringType, DoubleType, valueContainsNull = false), + to = MapType(StringType, DoubleType, valueContainsNull = true), + expected = true) + checkEqualsIgnoreCompatibleNullability( + from = MapType(StringType, DoubleType, valueContainsNull = true), + to = MapType(StringType, DoubleType, valueContainsNull = false), + expected = false) + checkEqualsIgnoreCompatibleNullability( + from = MapType(StringType, ArrayType(IntegerType, true), valueContainsNull = true), + to = MapType(StringType, ArrayType(IntegerType, false), valueContainsNull = true), + expected = false) + checkEqualsIgnoreCompatibleNullability( + from = MapType(StringType, ArrayType(IntegerType, false), valueContainsNull = true), + to = MapType(StringType, ArrayType(IntegerType, true), valueContainsNull = true), + expected = true) + + + checkEqualsIgnoreCompatibleNullability( + from = StructType(StructField("a", StringType, nullable = true) :: Nil), + to = StructType(StructField("a", StringType, nullable = true) :: Nil), + expected = true) + checkEqualsIgnoreCompatibleNullability( + from = StructType(StructField("a", StringType, nullable = false) :: Nil), + to = StructType(StructField("a", StringType, nullable = false) :: Nil), + expected = true) + checkEqualsIgnoreCompatibleNullability( + from = StructType(StructField("a", StringType, nullable = false) :: Nil), + to = StructType(StructField("a", StringType, nullable = true) :: Nil), + expected = true) + checkEqualsIgnoreCompatibleNullability( + from = StructType(StructField("a", StringType, nullable = true) :: Nil), + to = StructType(StructField("a", StringType, nullable = false) :: Nil), + expected = false) + checkEqualsIgnoreCompatibleNullability( + from = StructType( + StructField("a", StringType, nullable = false) :: + StructField("b", StringType, nullable = true) :: Nil), + to = StructType( + StructField("a", StringType, nullable = false) :: + StructField("b", StringType, nullable = false) :: Nil), + expected = false) } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/UTF8StringSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/UTF8StringSuite.scala new file mode 100644 index 0000000000000..a22aa6f244c48 --- /dev/null +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/UTF8StringSuite.scala @@ -0,0 +1,70 @@ +/* +* Licensed to the Apache Software Foundation (ASF) under one or more +* contributor license agreements. See the NOTICE file distributed with +* this work for additional information regarding copyright ownership. +* The ASF licenses this file to You under the Apache License, Version 2.0 +* (the "License"); you may not use this file except in compliance with +* the License. You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +*/ + +package org.apache.spark.sql.types + +import org.scalatest.FunSuite + +// scalastyle:off +class UTF8StringSuite extends FunSuite { + test("basic") { + def check(str: String, len: Int) { + + assert(UTF8String(str).length == len) + assert(UTF8String(str.getBytes("utf8")).length() == len) + + assert(UTF8String(str) == str) + assert(UTF8String(str.getBytes("utf8")) == str) + assert(UTF8String(str).toString == str) + assert(UTF8String(str.getBytes("utf8")).toString == str) + assert(UTF8String(str.getBytes("utf8")) == UTF8String(str)) + + assert(UTF8String(str).hashCode() == UTF8String(str.getBytes("utf8")).hashCode()) + } + + check("hello", 5) + check("世 界", 3) + } + + test("contains") { + assert(UTF8String("hello").contains(UTF8String("ello"))) + assert(!UTF8String("hello").contains(UTF8String("vello"))) + assert(UTF8String("大千世界").contains(UTF8String("千世"))) + assert(!UTF8String("大千世界").contains(UTF8String("世千"))) + } + + test("prefix") { + assert(UTF8String("hello").startsWith(UTF8String("hell"))) + assert(!UTF8String("hello").startsWith(UTF8String("ell"))) + assert(UTF8String("大千世界").startsWith(UTF8String("大千"))) + assert(!UTF8String("大千世界").startsWith(UTF8String("千"))) + } + + test("suffix") { + assert(UTF8String("hello").endsWith(UTF8String("ello"))) + assert(!UTF8String("hello").endsWith(UTF8String("ellov"))) + assert(UTF8String("大千世界").endsWith(UTF8String("世界"))) + assert(!UTF8String("大千世界").endsWith(UTF8String("世"))) + } + + test("slice") { + assert(UTF8String("hello").slice(1, 3) == UTF8String("el")) + assert(UTF8String("大千世界").slice(0, 1) == UTF8String("大")) + assert(UTF8String("大千世界").slice(1, 3) == UTF8String("千世")) + assert(UTF8String("大千世界").slice(3, 5) == UTF8String("界")) + } +} diff --git a/sql/core/pom.xml b/sql/core/pom.xml index 03a5c9e7c24a0..e3a6b1fe72435 100644 --- a/sql/core/pom.xml +++ b/sql/core/pom.xml @@ -21,8 +21,8 @@ 4.0.0 org.apache.spark - spark-parent - 1.3.0-SNAPSHOT + spark-parent_2.10 + 1.4.0-SNAPSHOT ../../pom.xml @@ -99,15 +99,17 @@ 9.3-1102-jdbc41 test - - com.spotify - docker-client - 2.7.5 - test - target/scala-${scala.binary.version}/classes target/scala-${scala.binary.version}/test-classes + + + ../../python + + pyspark/sql/*.py + + + diff --git a/sql/core/src/main/java/org/apache/spark/sql/SaveMode.java b/sql/core/src/main/java/org/apache/spark/sql/SaveMode.java new file mode 100644 index 0000000000000..a40be526d0d11 --- /dev/null +++ b/sql/core/src/main/java/org/apache/spark/sql/SaveMode.java @@ -0,0 +1,45 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package org.apache.spark.sql; + +/** + * SaveMode is used to specify the expected behavior of saving a DataFrame to a data source. + */ +public enum SaveMode { + /** + * Append mode means that when saving a DataFrame to a data source, if data/table already exists, + * contents of the DataFrame are expected to be appended to existing data. + */ + Append, + /** + * Overwrite mode means that when saving a DataFrame to a data source, + * if data/table already exists, existing data is expected to be overwritten by the contents of + * the DataFrame. + */ + Overwrite, + /** + * ErrorIfExists mode means that when saving a DataFrame to a data source, if data already exists, + * an exception is expected to be thrown. + */ + ErrorIfExists, + /** + * Ignore mode means that when saving a DataFrame to a data source, if data already exists, + * the save operation is expected to not save the contents of the DataFrame and to not + * change the existing data. + */ + Ignore +} diff --git a/sql/core/src/main/java/org/apache/spark/sql/jdbc/JDBCUtils.java b/sql/core/src/main/java/org/apache/spark/sql/jdbc/JDBCUtils.java deleted file mode 100644 index aa441b2096f18..0000000000000 --- a/sql/core/src/main/java/org/apache/spark/sql/jdbc/JDBCUtils.java +++ /dev/null @@ -1,59 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.sql.jdbc; - -import org.apache.spark.Partition; -import org.apache.spark.sql.SQLContext; -import org.apache.spark.sql.DataFrame; - -public class JDBCUtils { - /** - * Construct a DataFrame representing the JDBC table at the database - * specified by url with table name table. - */ - public static DataFrame jdbcRDD(SQLContext sql, String url, String table) { - Partition[] parts = new Partition[1]; - parts[0] = new JDBCPartition(null, 0); - return sql.baseRelationToDataFrame( - new JDBCRelation(url, table, parts, sql)); - } - - /** - * Construct a DataFrame representing the JDBC table at the database - * specified by url with table name table partitioned by parts. - * Here, parts is an array of expressions suitable for insertion into a WHERE - * clause; each one defines one partition. - */ - public static DataFrame jdbcRDD(SQLContext sql, String url, String table, String[] parts) { - Partition[] partitions = new Partition[parts.length]; - for (int i = 0; i < parts.length; i++) - partitions[i] = new JDBCPartition(parts[i], i); - return sql.baseRelationToDataFrame( - new JDBCRelation(url, table, partitions, sql)); - } - - private static JavaJDBCTrampoline trampoline = new JavaJDBCTrampoline(); - - public static void createJDBCTable(DataFrame rdd, String url, String table, boolean allowExisting) { - trampoline.createJDBCTable(rdd, url, table, allowExisting); - } - - public static void insertIntoJDBC(DataFrame rdd, String url, String table, boolean overwrite) { - trampoline.insertIntoJDBC(rdd, url, table, overwrite); - } -} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/CacheManager.scala b/sql/core/src/main/scala/org/apache/spark/sql/CacheManager.scala index f1949aa5dd74b..18584c2dcf797 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/CacheManager.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/CacheManager.scala @@ -71,11 +71,17 @@ private[sql] class CacheManager(sqlContext: SQLContext) extends Logging { } } + /** Clears all cached tables. */ private[sql] def clearCache(): Unit = writeLock { cachedData.foreach(_.cachedRepresentation.cachedColumnBuffers.unpersist()) cachedData.clear() } + /** Checks if the cache is empty. */ + private[sql] def isEmpty: Boolean = readLock { + cachedData.isEmpty + } + /** * Caches the data produced by the logical representation of the given schema rdd. Unlike * `RDD.cache()`, the default storage level is set to be `MEMORY_AND_DISK` because recomputing @@ -106,7 +112,7 @@ private[sql] class CacheManager(sqlContext: SQLContext) extends Logging { val planToCache = query.queryExecution.analyzed val dataIndex = cachedData.indexWhere(cd => planToCache.sameResult(cd.plan)) require(dataIndex >= 0, s"Table $query is not cached.") - cachedData(dataIndex).cachedRepresentation.cachedColumnBuffers.unpersist(blocking) + cachedData(dataIndex).cachedRepresentation.uncache(blocking) cachedData.remove(dataIndex) } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala index 878b2b0556de7..edb229c059e6b 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala @@ -17,91 +17,55 @@ package org.apache.spark.sql -import scala.annotation.tailrec import scala.language.implicitConversions -import org.apache.spark.sql.Dsl.lit +import org.apache.spark.annotation.Experimental +import org.apache.spark.Logging +import org.apache.spark.sql.functions.lit import org.apache.spark.sql.catalyst.expressions._ -import org.apache.spark.sql.catalyst.plans.logical.{Subquery, Project, LogicalPlan} -import org.apache.spark.sql.catalyst.analysis.UnresolvedGetField +import org.apache.spark.sql.catalyst.analysis.{UnresolvedAttribute, UnresolvedStar, UnresolvedGetField} import org.apache.spark.sql.types._ private[sql] object Column { - def apply(colName: String): Column = new IncomputableColumn(colName) + def apply(colName: String): Column = new Column(colName) - def apply(expr: Expression): Column = new IncomputableColumn(expr) - - def apply(sqlContext: SQLContext, plan: LogicalPlan, expr: Expression): Column = { - new ComputableColumn(sqlContext, plan, expr) - } + def apply(expr: Expression): Column = new Column(expr) def unapply(col: Column): Option[Expression] = Some(col.expr) } /** + * :: Experimental :: * A column in a [[DataFrame]]. * - * `Column` instances can be created by: - * {{{ - * // 1. Select a column out of a DataFrame - * df("colName") - * - * // 2. Create a literal expression - * Literal(1) - * - * // 3. Create new columns from - * }}} - * + * @groupname java_expr_ops Java-specific expression operators. + * @groupname expr_ops Expression operators. + * @groupname df_ops DataFrame functions. + * @groupname Ungrouped Support functions for DataFrames. */ -// TODO: Improve documentation. -trait Column extends DataFrame { +@Experimental +class Column(protected[sql] val expr: Expression) extends Logging { - protected[sql] def expr: Expression + def this(name: String) = this(name match { + case "*" => UnresolvedStar(None) + case _ if name.endsWith(".*") => UnresolvedStar(Some(name.substring(0, name.length - 2))) + case _ => UnresolvedAttribute(name) + }) - /** - * Returns true iff the [[Column]] is computable. - */ - def isComputable: Boolean - - private def computableCol(baseCol: ComputableColumn, expr: Expression) = { - val plan = Project(Seq(expr match { - case named: NamedExpression => named - case unnamed: Expression => Alias(unnamed, "col")() - }), baseCol.plan) - Column(baseCol.sqlContext, plan, expr) - } + /** Creates a column based on the given expression. */ + implicit private def exprToColumn(newExpr: Expression): Column = new Column(newExpr) - private def constructColumn(otherValue: Any)(newExpr: Column => Expression): Column = { - // Removes all the top level projection and subquery so we can get to the underlying plan. - @tailrec def stripProject(p: LogicalPlan): LogicalPlan = p match { - case Project(_, child) => stripProject(child) - case Subquery(_, child) => stripProject(child) - case _ => p - } + override def toString: String = expr.prettyString - (this, lit(otherValue)) match { - case (left: ComputableColumn, right: ComputableColumn) => - if (stripProject(left.plan).sameResult(stripProject(right.plan))) { - computableCol(right, newExpr(right)) - } else { - Column(newExpr(right)) - } - case (left: ComputableColumn, right) => computableCol(left, newExpr(right)) - case (_, right: ComputableColumn) => computableCol(right, newExpr(right)) - case (_, right) => Column(newExpr(right)) - } + override def equals(that: Any): Boolean = that match { + case that: Column => that.expr.equals(this.expr) + case _ => false } - /** Creates a column based on the given expression. */ - private def exprToColumn(newExpr: Expression, computable: Boolean = true): Column = { - this match { - case c: ComputableColumn if computable => computableCol(c, newExpr) - case _ => Column(newExpr) - } - } + override def hashCode: Int = this.expr.hashCode /** * Unary minus, i.e. negate the expression. @@ -110,11 +74,13 @@ trait Column extends DataFrame { * df.select( -df("amount") ) * * // Java: - * import static org.apache.spark.sql.Dsl.*; + * import static org.apache.spark.sql.functions.*; * df.select( negate(col("amount") ); * }}} + * + * @group expr_ops */ - def unary_- : Column = exprToColumn(UnaryMinus(expr)) + def unary_- : Column = UnaryMinus(expr) /** * Inversion of boolean expression, i.e. NOT. @@ -123,11 +89,13 @@ trait Column extends DataFrame { * df.filter( !df("isActive") ) * * // Java: - * import static org.apache.spark.sql.Dsl.*; + * import static org.apache.spark.sql.functions.*; * df.filter( not(df.col("isActive")) ); * }} + * + * @group expr_ops */ - def unary_! : Column = exprToColumn(Not(expr)) + def unary_! : Column = Not(expr) /** * Equality test. @@ -136,12 +104,20 @@ trait Column extends DataFrame { * df.filter( df("colA") === df("colB") ) * * // Java - * import static org.apache.spark.sql.Dsl.*; + * import static org.apache.spark.sql.functions.*; * df.filter( col("colA").equalTo(col("colB")) ); * }}} - */ - def === (other: Any): Column = constructColumn(other) { o => - EqualTo(expr, o.expr) + * + * @group expr_ops + */ + def === (other: Any): Column = { + val right = lit(other).expr + if (this.expr == right) { + logWarning( + s"Constructing trivially true equals predicate, '${this.expr} = $right'. " + + "Perhaps you need to use aliases.") + } + EqualTo(expr, right) } /** @@ -151,9 +127,11 @@ trait Column extends DataFrame { * df.filter( df("colA") === df("colB") ) * * // Java - * import static org.apache.spark.sql.Dsl.*; + * import static org.apache.spark.sql.functions.*; * df.filter( col("colA").equalTo(col("colB")) ); * }}} + * + * @group expr_ops */ def equalTo(other: Any): Column = this === other @@ -165,13 +143,13 @@ trait Column extends DataFrame { * df.select( !(df("colA") === df("colB")) ) * * // Java: - * import static org.apache.spark.sql.Dsl.*; + * import static org.apache.spark.sql.functions.*; * df.filter( col("colA").notEqual(col("colB")) ); * }}} + * + * @group expr_ops */ - def !== (other: Any): Column = constructColumn(other) { o => - Not(EqualTo(expr, o.expr)) - } + def !== (other: Any): Column = Not(EqualTo(expr, lit(other).expr)) /** * Inequality test. @@ -181,13 +159,13 @@ trait Column extends DataFrame { * df.select( !(df("colA") === df("colB")) ) * * // Java: - * import static org.apache.spark.sql.Dsl.*; + * import static org.apache.spark.sql.functions.*; * df.filter( col("colA").notEqual(col("colB")) ); * }}} + * + * @group java_expr_ops */ - def notEqual(other: Any): Column = constructColumn(other) { o => - Not(EqualTo(expr, o.expr)) - } + def notEqual(other: Any): Column = Not(EqualTo(expr, lit(other).expr)) /** * Greater than. @@ -196,13 +174,13 @@ trait Column extends DataFrame { * people.select( people("age") > 21 ) * * // Java: - * import static org.apache.spark.sql.Dsl.*; + * import static org.apache.spark.sql.functions.*; * people.select( people("age").gt(21) ); * }}} + * + * @group expr_ops */ - def > (other: Any): Column = constructColumn(other) { o => - GreaterThan(expr, o.expr) - } + def > (other: Any): Column = GreaterThan(expr, lit(other).expr) /** * Greater than. @@ -211,9 +189,11 @@ trait Column extends DataFrame { * people.select( people("age") > lit(21) ) * * // Java: - * import static org.apache.spark.sql.Dsl.*; + * import static org.apache.spark.sql.functions.*; * people.select( people("age").gt(21) ); * }}} + * + * @group java_expr_ops */ def gt(other: Any): Column = this > other @@ -226,10 +206,10 @@ trait Column extends DataFrame { * // Java: * people.select( people("age").lt(21) ); * }}} + * + * @group expr_ops */ - def < (other: Any): Column = constructColumn(other) { o => - LessThan(expr, o.expr) - } + def < (other: Any): Column = LessThan(expr, lit(other).expr) /** * Less than. @@ -240,6 +220,8 @@ trait Column extends DataFrame { * // Java: * people.select( people("age").lt(21) ); * }}} + * + * @group java_expr_ops */ def lt(other: Any): Column = this < other @@ -252,10 +234,10 @@ trait Column extends DataFrame { * // Java: * people.select( people("age").leq(21) ); * }}} + * + * @group expr_ops */ - def <= (other: Any): Column = constructColumn(other) { o => - LessThanOrEqual(expr, o.expr) - } + def <= (other: Any): Column = LessThanOrEqual(expr, lit(other).expr) /** * Less than or equal to. @@ -266,6 +248,8 @@ trait Column extends DataFrame { * // Java: * people.select( people("age").leq(21) ); * }}} + * + * @group java_expr_ops */ def leq(other: Any): Column = this <= other @@ -278,10 +262,10 @@ trait Column extends DataFrame { * // Java: * people.select( people("age").geq(21) ) * }}} + * + * @group expr_ops */ - def >= (other: Any): Column = constructColumn(other) { o => - GreaterThanOrEqual(expr, o.expr) - } + def >= (other: Any): Column = GreaterThanOrEqual(expr, lit(other).expr) /** * Greater than or equal to an expression. @@ -292,30 +276,38 @@ trait Column extends DataFrame { * // Java: * people.select( people("age").geq(21) ) * }}} + * + * @group java_expr_ops */ def geq(other: Any): Column = this >= other /** * Equality test that is safe for null values. + * + * @group expr_ops */ - def <=> (other: Any): Column = constructColumn(other) { o => - EqualNullSafe(expr, o.expr) - } + def <=> (other: Any): Column = EqualNullSafe(expr, lit(other).expr) /** * Equality test that is safe for null values. + * + * @group java_expr_ops */ def eqNullSafe(other: Any): Column = this <=> other /** * True if the current expression is null. + * + * @group expr_ops */ - def isNull: Column = exprToColumn(IsNull(expr)) + def isNull: Column = IsNull(expr) /** * True if the current expression is NOT null. + * + * @group expr_ops */ - def isNotNull: Column = exprToColumn(IsNotNull(expr)) + def isNotNull: Column = IsNotNull(expr) /** * Boolean OR. @@ -326,10 +318,10 @@ trait Column extends DataFrame { * // Java: * people.filter( people("inSchool").or(people("isEmployed")) ); * }}} + * + * @group expr_ops */ - def || (other: Any): Column = constructColumn(other) { o => - Or(expr, o.expr) - } + def || (other: Any): Column = Or(expr, lit(other).expr) /** * Boolean OR. @@ -340,6 +332,8 @@ trait Column extends DataFrame { * // Java: * people.filter( people("inSchool").or(people("isEmployed")) ); * }}} + * + * @group java_expr_ops */ def or(other: Column): Column = this || other @@ -352,10 +346,10 @@ trait Column extends DataFrame { * // Java: * people.select( people("inSchool").and(people("isEmployed")) ); * }}} + * + * @group expr_ops */ - def && (other: Any): Column = constructColumn(other) { o => - And(expr, o.expr) - } + def && (other: Any): Column = And(expr, lit(other).expr) /** * Boolean AND. @@ -366,6 +360,8 @@ trait Column extends DataFrame { * // Java: * people.select( people("inSchool").and(people("isEmployed")) ); * }}} + * + * @group java_expr_ops */ def and(other: Column): Column = this && other @@ -378,10 +374,10 @@ trait Column extends DataFrame { * // Java: * people.select( people("height").plus(people("weight")) ); * }}} + * + * @group expr_ops */ - def + (other: Any): Column = constructColumn(other) { o => - Add(expr, o.expr) - } + def + (other: Any): Column = Add(expr, lit(other).expr) /** * Sum of this expression and another expression. @@ -392,6 +388,8 @@ trait Column extends DataFrame { * // Java: * people.select( people("height").plus(people("weight")) ); * }}} + * + * @group java_expr_ops */ def plus(other: Any): Column = this + other @@ -404,10 +402,10 @@ trait Column extends DataFrame { * // Java: * people.select( people("height").minus(people("weight")) ); * }}} + * + * @group expr_ops */ - def - (other: Any): Column = constructColumn(other) { o => - Subtract(expr, o.expr) - } + def - (other: Any): Column = Subtract(expr, lit(other).expr) /** * Subtraction. Subtract the other expression from this expression. @@ -418,6 +416,8 @@ trait Column extends DataFrame { * // Java: * people.select( people("height").minus(people("weight")) ); * }}} + * + * @group java_expr_ops */ def minus(other: Any): Column = this - other @@ -430,10 +430,10 @@ trait Column extends DataFrame { * // Java: * people.select( people("height").multiply(people("weight")) ); * }}} + * + * @group expr_ops */ - def * (other: Any): Column = constructColumn(other) { o => - Multiply(expr, o.expr) - } + def * (other: Any): Column = Multiply(expr, lit(other).expr) /** * Multiplication of this expression and another expression. @@ -444,6 +444,8 @@ trait Column extends DataFrame { * // Java: * people.select( people("height").multiply(people("weight")) ); * }}} + * + * @group java_expr_ops */ def multiply(other: Any): Column = this * other @@ -456,10 +458,10 @@ trait Column extends DataFrame { * // Java: * people.select( people("height").divide(people("weight")) ); * }}} + * + * @group expr_ops */ - def / (other: Any): Column = constructColumn(other) { o => - Divide(expr, o.expr) - } + def / (other: Any): Column = Divide(expr, lit(other).expr) /** * Division this expression by another expression. @@ -470,74 +472,114 @@ trait Column extends DataFrame { * // Java: * people.select( people("height").divide(people("weight")) ); * }}} + * + * @group java_expr_ops */ def divide(other: Any): Column = this / other /** * Modulo (a.k.a. remainder) expression. + * + * @group expr_ops */ - def % (other: Any): Column = constructColumn(other) { o => - Remainder(expr, o.expr) - } + def % (other: Any): Column = Remainder(expr, lit(other).expr) /** * Modulo (a.k.a. remainder) expression. + * + * @group java_expr_ops */ def mod(other: Any): Column = this % other /** * A boolean expression that is evaluated to true if the value of this expression is contained * by the evaluated values of the arguments. + * + * @group expr_ops */ @scala.annotation.varargs - def in(list: Column*): Column = { - new IncomputableColumn(In(expr, list.map(_.expr))) - } + def in(list: Column*): Column = In(expr, list.map(_.expr)) - def like(literal: String): Column = exprToColumn(Like(expr, lit(literal).expr)) + /** + * SQL like expression. + * + * @group expr_ops + */ + def like(literal: String): Column = Like(expr, lit(literal).expr) - def rlike(literal: String): Column = exprToColumn(RLike(expr, lit(literal).expr)) + /** + * SQL RLIKE expression (LIKE with Regex). + * + * @group expr_ops + */ + def rlike(literal: String): Column = RLike(expr, lit(literal).expr) /** - * An expression that gets an item at position `ordinal` out of an array. + * An expression that gets an item at position `ordinal` out of an array, + * or gets a value by key `key` in a [[MapType]]. + * + * @group expr_ops */ - def getItem(ordinal: Int): Column = exprToColumn(GetItem(expr, Literal(ordinal))) + def getItem(key: Any): Column = GetItem(expr, Literal(key)) /** - * An expression that gets a field by name in a [[StructField]]. + * An expression that gets a field by name in a [[StructType]]. + * + * @group expr_ops */ - def getField(fieldName: String): Column = exprToColumn(UnresolvedGetField(expr, fieldName)) + def getField(fieldName: String): Column = UnresolvedGetField(expr, fieldName) /** * An expression that returns a substring. * @param startPos expression for the starting position. * @param len expression for the length of the substring. + * + * @group expr_ops */ - def substr(startPos: Column, len: Column): Column = - exprToColumn(Substring(expr, startPos.expr, len.expr), computable = false) + def substr(startPos: Column, len: Column): Column = Substring(expr, startPos.expr, len.expr) /** * An expression that returns a substring. * @param startPos starting position. * @param len length of the substring. + * + * @group expr_ops */ - def substr(startPos: Int, len: Int): Column = - exprToColumn(Substring(expr, lit(startPos).expr, lit(len).expr)) + def substr(startPos: Int, len: Int): Column = Substring(expr, lit(startPos).expr, lit(len).expr) - def contains(other: Any): Column = constructColumn(other) { o => - Contains(expr, o.expr) - } + /** + * Contains the other element. + * + * @group expr_ops + */ + def contains(other: Any): Column = Contains(expr, lit(other).expr) - def startsWith(other: Column): Column = constructColumn(other) { o => - StartsWith(expr, o.expr) - } + /** + * String starts with. + * + * @group expr_ops + */ + def startsWith(other: Column): Column = StartsWith(expr, lit(other).expr) + /** + * String starts with another string literal. + * + * @group expr_ops + */ def startsWith(literal: String): Column = this.startsWith(lit(literal)) - def endsWith(other: Column): Column = constructColumn(other) { o => - EndsWith(expr, o.expr) - } + /** + * String ends with. + * + * @group expr_ops + */ + def endsWith(other: Column): Column = EndsWith(expr, lit(other).expr) + /** + * String ends with another string literal. + * + * @group expr_ops + */ def endsWith(literal: String): Column = this.endsWith(lit(literal)) /** @@ -546,8 +588,34 @@ trait Column extends DataFrame { * // Renames colA to colB in select output. * df.select($"colA".as("colB")) * }}} + * + * @group expr_ops + */ + def as(alias: String): Column = Alias(expr, alias)() + + /** + * Gives the column an alias. + * {{{ + * // Renames colA to colB in select output. + * df.select($"colA".as('colB)) + * }}} + * + * @group expr_ops + */ + def as(alias: Symbol): Column = Alias(expr, alias.name)() + + /** + * Gives the column an alias with metadata. + * {{{ + * val metadata: Metadata = ... + * df.select($"colA".as("colB", metadata)) + * }}} + * + * @group expr_ops */ - override def as(alias: String): Column = exprToColumn(Alias(expr, alias)()) + def as(alias: String, metadata: Metadata): Column = { + Alias(expr, alias)(explicitMetadata = Some(metadata)) + } /** * Casts the column to a different data type. @@ -559,8 +627,14 @@ trait Column extends DataFrame { * // equivalent to * df.select(df("colA").cast("int")) * }}} + * + * @group expr_ops */ - def cast(to: DataType): Column = exprToColumn(Cast(expr, to)) + def cast(to: DataType): Column = expr match { + // Lift alias out of cast so we can support col.as("name").cast(IntegerType) + case Alias(childExpr, name) => Alias(Cast(childExpr, to), name)() + case _ => Cast(expr, to) + } /** * Casts the column to a different data type, using the canonical string representation @@ -570,31 +644,60 @@ trait Column extends DataFrame { * // Casts colA to integer. * df.select(df("colA").cast("int")) * }}} + * + * @group expr_ops + */ + def cast(to: String): Column = cast(DataTypeParser(to)) + + /** + * Returns an ordering used in sorting. + * {{{ + * // Scala: sort a DataFrame by age column in descending order. + * df.sort(df("age").desc) + * + * // Java + * df.sort(df.col("age").desc()); + * }}} + * + * @group expr_ops + */ + def desc: Column = SortOrder(expr, Descending) + + /** + * Returns an ordering used in sorting. + * {{{ + * // Scala: sort a DataFrame by age column in ascending order. + * df.sort(df("age").asc) + * + * // Java + * df.sort(df.col("age").asc()); + * }}} + * + * @group expr_ops */ - def cast(to: String): Column = exprToColumn( - Cast(expr, to.toLowerCase match { - case "string" => StringType - case "boolean" => BooleanType - case "byte" => ByteType - case "short" => ShortType - case "int" => IntegerType - case "long" => LongType - case "float" => FloatType - case "double" => DoubleType - case "decimal" => DecimalType.Unlimited - case "date" => DateType - case "timestamp" => TimestampType - case _ => throw new RuntimeException(s"""Unsupported cast type: "$to"""") - }) - ) - - def desc: Column = exprToColumn(SortOrder(expr, Descending), computable = false) - - def asc: Column = exprToColumn(SortOrder(expr, Ascending), computable = false) + def asc: Column = SortOrder(expr, Ascending) + + /** + * Prints the expression to the console for debugging purpose. + * + * @group df_ops + */ + def explain(extended: Boolean): Unit = { + if (extended) { + println(expr) + } else { + println(expr.prettyString) + } + } } -class ColumnName(name: String) extends IncomputableColumn(name) { +/** + * :: Experimental :: + * A convenient class used for constructing schema. + */ +@Experimental +class ColumnName(name: String) extends Column(name) { /** Creates a new AttributeReference of type boolean */ def boolean: StructField = StructField(name, BooleanType) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala index 8ad6526f872e5..17c21f6e3a0e9 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala @@ -17,20 +17,38 @@ package org.apache.spark.sql +import java.io.CharArrayWriter +import java.sql.DriverManager + +import scala.collection.JavaConversions._ +import scala.language.implicitConversions import scala.reflect.ClassTag +import scala.reflect.runtime.universe.TypeTag +import scala.util.control.NonFatal + +import com.fasterxml.jackson.core.JsonFactory import org.apache.spark.annotation.{DeveloperApi, Experimental} import org.apache.spark.api.java.JavaRDD +import org.apache.spark.api.python.SerDeUtil import org.apache.spark.rdd.RDD import org.apache.spark.storage.StorageLevel +import org.apache.spark.sql.catalyst.{CatalystTypeConverters, ScalaReflection, SqlParser} +import org.apache.spark.sql.catalyst.analysis.{UnresolvedRelation, ResolvedStar} +import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.catalyst.plans.{JoinType, Inner} import org.apache.spark.sql.catalyst.plans.logical._ -import org.apache.spark.sql.types.StructType +import org.apache.spark.sql.execution.{EvaluatePython, ExplainCommand, LogicalRDD} +import org.apache.spark.sql.jdbc.JDBCWriteDetails +import org.apache.spark.sql.json.JsonRDD +import org.apache.spark.sql.types._ +import org.apache.spark.sql.sources.{ResolvedDataSource, CreateTableUsingAsSelect} import org.apache.spark.util.Utils private[sql] object DataFrame { def apply(sqlContext: SQLContext, logicalPlan: LogicalPlan): DataFrame = { - new DataFrameImpl(sqlContext, logicalPlan) + new DataFrame(sqlContext, logicalPlan) } } @@ -39,82 +57,278 @@ private[sql] object DataFrame { * :: Experimental :: * A distributed collection of data organized into named columns. * - * A [[DataFrame]] is equivalent to a relational table in Spark SQL, and can be created using - * various functions in [[SQLContext]]. + * A [[DataFrame]] is equivalent to a relational table in Spark SQL. There are multiple ways + * to create a [[DataFrame]]: * {{{ + * // Create a DataFrame from Parquet files * val people = sqlContext.parquetFile("...") + * + * // Create a DataFrame from data sources + * val df = sqlContext.load("...", "json") * }}} * * Once created, it can be manipulated using the various domain-specific-language (DSL) functions - * defined in: [[DataFrame]] (this class), [[Column]], [[Dsl]] for the DSL. + * defined in: [[DataFrame]] (this class), [[Column]], and [[functions]]. * - * To select a column from the data frame, use the apply method: + * To select a column from the data frame, use `apply` method in Scala and `col` in Java. * {{{ * val ageCol = people("age") // in Scala - * Column ageCol = people.apply("age") // in Java + * Column ageCol = people.col("age") // in Java * }}} * * Note that the [[Column]] type can also be manipulated through its various functions. * {{{ * // The following creates a new column that increases everybody's age by 10. * people("age") + 10 // in Scala + * people.col("age").plus(10); // in Java * }}} * - * A more concrete example: + * A more concrete example in Scala: * {{{ * // To create DataFrame using SQLContext * val people = sqlContext.parquetFile("...") * val department = sqlContext.parquetFile("...") * - * people.filter("age" > 30) + * people.filter("age > 30") * .join(department, people("deptId") === department("id")) * .groupBy(department("name"), "gender") * .agg(avg(people("salary")), max(people("age"))) * }}} + * + * and in Java: + * {{{ + * // To create DataFrame using SQLContext + * DataFrame people = sqlContext.parquetFile("..."); + * DataFrame department = sqlContext.parquetFile("..."); + * + * people.filter("age".gt(30)) + * .join(department, people.col("deptId").equalTo(department("id"))) + * .groupBy(department.col("name"), "gender") + * .agg(avg(people.col("salary")), max(people.col("age"))); + * }}} + * + * @groupname basic Basic DataFrame functions + * @groupname dfops Language Integrated Queries + * @groupname rdd RDD Operations + * @groupname output Output Operations + * @groupname action Actions */ // TODO: Improve documentation. @Experimental -trait DataFrame extends RDDApi[Row] { +class DataFrame private[sql]( + @transient val sqlContext: SQLContext, + @DeveloperApi @transient val queryExecution: SQLContext#QueryExecution) + extends RDDApi[Row] with Serializable { - val sqlContext: SQLContext + /** + * A constructor that automatically analyzes the logical plan. + * + * This reports error eagerly as the [[DataFrame]] is constructed, unless + * [[SQLConf.dataFrameEagerAnalysis]] is turned off. + */ + def this(sqlContext: SQLContext, logicalPlan: LogicalPlan) = { + this(sqlContext, { + val qe = sqlContext.executePlan(logicalPlan) + if (sqlContext.conf.dataFrameEagerAnalysis) { + qe.assertAnalyzed() // This should force analysis and throw errors if there are any + } + qe + }) + } - @DeveloperApi - def queryExecution: SQLContext#QueryExecution + @transient protected[sql] val logicalPlan: LogicalPlan = queryExecution.logical match { + // For various commands (like DDL) and queries with side effects, we force query optimization to + // happen right away to let these side effects take place eagerly. + case _: Command | + _: InsertIntoTable | + _: CreateTableAsSelect[_] | + _: CreateTableUsingAsSelect | + _: WriteToFile => + LogicalRDD(queryExecution.analyzed.output, queryExecution.toRdd)(sqlContext) + case _ => + queryExecution.analyzed + } - protected[sql] def logicalPlan: LogicalPlan + /** + * An implicit conversion function internal to this class for us to avoid doing + * "new DataFrame(...)" everywhere. + */ + @inline private implicit def logicalPlanToDataFrame(logicalPlan: LogicalPlan): DataFrame = { + new DataFrame(sqlContext, logicalPlan) + } + + protected[sql] def resolve(colName: String): NamedExpression = { + queryExecution.analyzed.resolve(colName.split("\\."), sqlContext.analyzer.resolver).getOrElse { + throw new AnalysisException( + s"""Cannot resolve column name "$colName" among (${schema.fieldNames.mkString(", ")})""") + } + } + + protected[sql] def numericColumns: Seq[Expression] = { + schema.fields.filter(_.dataType.isInstanceOf[NumericType]).map { n => + queryExecution.analyzed.resolve(n.name.split("\\."), sqlContext.analyzer.resolver).get + } + } + + /** + * Internal API for Python + * @param numRows Number of rows to show + */ + private[sql] def showString(numRows: Int): String = { + val data = take(numRows) + val numCols = schema.fieldNames.length + + // For cells that are beyond 20 characters, replace it with the first 17 and "..." + val rows: Seq[Seq[String]] = schema.fieldNames.toSeq +: data.map { row => + row.toSeq.map { cell => + val str = if (cell == null) "null" else cell.toString + if (str.length > 20) str.substring(0, 17) + "..." else str + }: Seq[String] + } + + // Compute the width of each column + val colWidths = Array.fill(numCols)(0) + for (row <- rows) { + for ((cell, i) <- row.zipWithIndex) { + colWidths(i) = math.max(colWidths(i), cell.length) + } + } + + // Pad the cells + rows.map { row => + row.zipWithIndex.map { case (cell, i) => + String.format(s"%-${colWidths(i)}s", cell) + }.mkString(" ") + }.mkString("\n") + } + + override def toString: String = { + try { + schema.map(f => s"${f.name}: ${f.dataType.simpleString}").mkString("[", ", ", "]") + } catch { + case NonFatal(e) => + s"Invalid tree; ${e.getMessage}:\n$queryExecution" + } + } /** Left here for backward compatibility. */ - @deprecated("1.3.0", "use toDataFrame") + @deprecated("1.3.0", "use toDF") def toSchemaRDD: DataFrame = this /** - * Returns the object itself. Used to force an implicit conversion from RDD to DataFrame in Scala. + * Returns the object itself. + * @group basic */ - def toDataFrame: DataFrame = this + // This is declared with parentheses to prevent the Scala compiler from treating + // `rdd.toDF("1")` as invoking this toDF and then apply on the returned DataFrame. + def toDF(): DataFrame = this /** * Returns a new [[DataFrame]] with columns renamed. This can be quite convenient in conversion * from a RDD of tuples into a [[DataFrame]] with meaningful names. For example: * {{{ * val rdd: RDD[(Int, String)] = ... - * rdd.toDataFrame // this implicit conversion creates a DataFrame with column name _1 and _2 - * rdd.toDataFrame("id", "name") // this creates a DataFrame with column name "id" and "name" + * rdd.toDF() // this implicit conversion creates a DataFrame with column name _1 and _2 + * rdd.toDF("id", "name") // this creates a DataFrame with column name "id" and "name" * }}} + * @group basic */ @scala.annotation.varargs - def toDataFrame(colName: String, colNames: String*): DataFrame + def toDF(colNames: String*): DataFrame = { + require(schema.size == colNames.size, + "The number of columns doesn't match.\n" + + s"Old column names (${schema.size}): " + schema.fields.map(_.name).mkString(", ") + "\n" + + s"New column names (${colNames.size}): " + colNames.mkString(", ")) + + val newCols = logicalPlan.output.zip(colNames).map { case (oldAttribute, newName) => + Column(oldAttribute).as(newName) + } + select(newCols :_*) + } - /** Returns the schema of this [[DataFrame]]. */ - def schema: StructType + /** + * Returns the schema of this [[DataFrame]]. + * @group basic + */ + def schema: StructType = queryExecution.analyzed.schema - /** Returns all column names and their data types as an array. */ - def dtypes: Array[(String, String)] + /** + * Returns all column names and their data types as an array. + * @group basic + */ + def dtypes: Array[(String, String)] = schema.fields.map { field => + (field.name, field.dataType.toString) + } - /** Returns all column names as an array. */ + /** + * Returns all column names as an array. + * @group basic + */ def columns: Array[String] = schema.fields.map(_.name) - /** Prints the schema to the console in a nice tree format. */ - def printSchema(): Unit + /** + * Prints the schema to the console in a nice tree format. + * @group basic + */ + def printSchema(): Unit = println(schema.treeString) + + /** + * Prints the plans (logical and physical) to the console for debugging purposes. + * @group basic + */ + def explain(extended: Boolean): Unit = { + ExplainCommand( + queryExecution.logical, + extended = extended).queryExecution.executedPlan.executeCollect().map { + r => println(r.getString(0)) + } + } + + /** + * Only prints the physical plan to the console for debugging purposes. + * @group basic + */ + def explain(): Unit = explain(extended = false) + + /** + * Returns true if the `collect` and `take` methods can be run locally + * (without any Spark executors). + * @group basic + */ + def isLocal: Boolean = logicalPlan.isInstanceOf[LocalRelation] + + /** + * Displays the [[DataFrame]] in a tabular form. For example: + * {{{ + * year month AVG('Adj Close) MAX('Adj Close) + * 1980 12 0.503218 0.595103 + * 1981 01 0.523289 0.570307 + * 1982 02 0.436504 0.475256 + * 1983 03 0.410516 0.442194 + * 1984 04 0.450090 0.483521 + * }}} + * @param numRows Number of rows to show + * + * @group action + */ + def show(numRows: Int): Unit = println(showString(numRows)) + + /** + * Displays the top 20 rows of [[DataFrame]] in a tabular form. + * @group action + */ + def show(): Unit = show(20) + + /** + * Returns a [[DataFrameNaFunctions]] for working with missing data. + * {{{ + * // Dropping rows containing any null values. + * df.na.drop() + * }}} + * + * @group dfops + */ + def na: DataFrameNaFunctions = new DataFrameNaFunctions(this) /** * Cartesian join with another [[DataFrame]]. @@ -122,8 +336,11 @@ trait DataFrame extends RDDApi[Row] { * Note that cartesian joins are very expensive without an extra filter that can be pushed down. * * @param right Right side of the join operation. + * @group dfops */ - def join(right: DataFrame): DataFrame + def join(right: DataFrame): DataFrame = { + Join(logicalPlan, right.logicalPlan, joinType = Inner, None) + } /** * Inner join with another [[DataFrame]], using the given join expression. @@ -133,28 +350,34 @@ trait DataFrame extends RDDApi[Row] { * df1.join(df2, $"df1Key" === $"df2Key") * df1.join(df2).where($"df1Key" === $"df2Key") * }}} + * @group dfops */ - def join(right: DataFrame, joinExprs: Column): DataFrame + def join(right: DataFrame, joinExprs: Column): DataFrame = { + Join(logicalPlan, right.logicalPlan, joinType = Inner, Some(joinExprs.expr)) + } /** - * Join with another [[DataFrame]], usin g the given join expression. The following performs + * Join with another [[DataFrame]], using the given join expression. The following performs * a full outer join between `df1` and `df2`. * * {{{ * // Scala: - * import org.apache.spark.sql.dsl._ - * df1.join(df2, "outer", $"df1Key" === $"df2Key") + * import org.apache.spark.sql.functions._ + * df1.join(df2, $"df1Key" === $"df2Key", "outer") * * // Java: - * import static org.apache.spark.sql.Dsl.*; - * df1.join(df2, "outer", col("df1Key") === col("df2Key")); + * import static org.apache.spark.sql.functions.*; + * df1.join(df2, col("df1Key").equalTo(col("df2Key")), "outer"); * }}} * * @param right Right side of the join. * @param joinExprs Join expression. * @param joinType One of: `inner`, `outer`, `left_outer`, `right_outer`, `semijoin`. + * @group dfops */ - def join(right: DataFrame, joinExprs: Column, joinType: String): DataFrame + def join(right: DataFrame, joinExprs: Column, joinType: String): DataFrame = { + Join(logicalPlan, right.logicalPlan, JoinType(joinType), Some(joinExprs.expr)) + } /** * Returns a new [[DataFrame]] sorted by the specified column, all in ascending order. @@ -164,66 +387,94 @@ trait DataFrame extends RDDApi[Row] { * df.sort($"sortcol") * df.sort($"sortcol".asc) * }}} + * @group dfops */ @scala.annotation.varargs - def sort(sortCol: String, sortCols: String*): DataFrame + def sort(sortCol: String, sortCols: String*): DataFrame = { + sort((sortCol +: sortCols).map(apply) :_*) + } /** * Returns a new [[DataFrame]] sorted by the given expressions. For example: * {{{ * df.sort($"col1", $"col2".desc) * }}} + * @group dfops */ @scala.annotation.varargs - def sort(sortExprs: Column*): DataFrame + def sort(sortExprs: Column*): DataFrame = { + val sortOrder: Seq[SortOrder] = sortExprs.map { col => + col.expr match { + case expr: SortOrder => + expr + case expr: Expression => + SortOrder(expr, Ascending) + } + } + Sort(sortOrder, global = true, logicalPlan) + } /** * Returns a new [[DataFrame]] sorted by the given expressions. * This is an alias of the `sort` function. + * @group dfops */ @scala.annotation.varargs - def orderBy(sortCol: String, sortCols: String*): DataFrame + def orderBy(sortCol: String, sortCols: String*): DataFrame = sort(sortCol, sortCols :_*) /** * Returns a new [[DataFrame]] sorted by the given expressions. * This is an alias of the `sort` function. + * @group dfops */ @scala.annotation.varargs - def orderBy(sortExprs: Column*): DataFrame + def orderBy(sortExprs: Column*): DataFrame = sort(sortExprs :_*) /** * Selects column based on the column name and return it as a [[Column]]. + * @group dfops */ def apply(colName: String): Column = col(colName) /** * Selects column based on the column name and return it as a [[Column]]. + * @group dfops */ - def col(colName: String): Column + def col(colName: String): Column = colName match { + case "*" => + Column(ResolvedStar(schema.fieldNames.map(resolve))) + case _ => + val expr = resolve(colName) + Column(expr) + } /** - * Selects a set of expressions, wrapped in a Product. - * {{{ - * // The following two are equivalent: - * df.apply(($"colA", $"colB" + 1)) - * df.select($"colA", $"colB" + 1) - * }}} + * Returns a new [[DataFrame]] with an alias set. + * @group dfops */ - def apply(projection: Product): DataFrame + def as(alias: String): DataFrame = Subquery(alias, logicalPlan) /** - * Returns a new [[DataFrame]] with an alias set. + * (Scala-specific) Returns a new [[DataFrame]] with an alias set. + * @group dfops */ - def as(name: String): DataFrame + def as(alias: Symbol): DataFrame = as(alias.name) /** * Selects a set of expressions. * {{{ * df.select($"colA", $"colB" + 1) * }}} + * @group dfops */ @scala.annotation.varargs - def select(cols: Column*): DataFrame + def select(cols: Column*): DataFrame = { + val namedExpressions = cols.map { + case Column(expr: NamedExpression) => expr + case Column(expr: Expression) => Alias(expr, expr.prettyString)() + } + Project(namedExpressions.toSeq, logicalPlan) + } /** * Selects a set of columns. This is a variant of `select` that can only select @@ -234,9 +485,10 @@ trait DataFrame extends RDDApi[Row] { * df.select("colA", "colB") * df.select($"colA", $"colB") * }}} + * @group dfops */ @scala.annotation.varargs - def select(col: String, cols: String*): DataFrame + def select(col: String, cols: String*): DataFrame = select((col +: cols).map(Column(_)) :_*) /** * Selects a set of SQL expressions. This is a variant of `select` that accepts @@ -245,9 +497,14 @@ trait DataFrame extends RDDApi[Row] { * {{{ * df.selectExpr("colA", "colB as newName", "abs(colC)") * }}} + * @group dfops */ @scala.annotation.varargs - def selectExpr(exprs: String*): DataFrame + def selectExpr(exprs: String*): DataFrame = { + select(exprs.map { expr => + Column(new SqlParser().parseExpression(expr)) + }: _*) + } /** * Filters rows using the given condition. @@ -257,16 +514,20 @@ trait DataFrame extends RDDApi[Row] { * peopleDf.where($"age" > 15) * peopleDf($"age" > 15) * }}} + * @group dfops */ - def filter(condition: Column): DataFrame + def filter(condition: Column): DataFrame = Filter(condition.expr, logicalPlan) /** * Filters rows using the given SQL expression. * {{{ * peopleDf.filter("age > 15") * }}} + * @group dfops */ - def filter(conditionExpr: String): DataFrame + def filter(conditionExpr: String): DataFrame = { + filter(Column(new SqlParser().parseExpression(conditionExpr))) + } /** * Filters rows using the given condition. This is an alias for `filter`. @@ -276,19 +537,9 @@ trait DataFrame extends RDDApi[Row] { * peopleDf.where($"age" > 15) * peopleDf($"age" > 15) * }}} + * @group dfops */ - def where(condition: Column): DataFrame - - /** - * Filters rows using the given condition. This is a shorthand meant for Scala. - * {{{ - * // The following are equivalent: - * peopleDf.filter($"age" > 15) - * peopleDf.where($"age" > 15) - * peopleDf($"age" > 15) - * }}} - */ - def apply(condition: Column): DataFrame + def where(condition: Column): DataFrame = filter(condition) /** * Groups the [[DataFrame]] using the specified columns, so we can run aggregation on them. @@ -304,9 +555,10 @@ trait DataFrame extends RDDApi[Row] { * "age" -> "max" * )) * }}} + * @group dfops */ @scala.annotation.varargs - def groupBy(cols: Column*): GroupedData + def groupBy(cols: Column*): GroupedData = new GroupedData(this, cols.map(_.expr)) /** * Groups the [[DataFrame]] using the specified columns, so we can run aggregation on them. @@ -325,9 +577,13 @@ trait DataFrame extends RDDApi[Row] { * "age" -> "max" * )) * }}} + * @group dfops */ @scala.annotation.varargs - def groupBy(col1: String, cols: String*): GroupedData + def groupBy(col1: String, cols: String*): GroupedData = { + val colNames: Seq[String] = col1 +: cols + new GroupedData(this, colNames.map(colName => resolve(colName))) + } /** * (Scala-specific) Compute aggregates by specifying a map from column name to @@ -341,6 +597,7 @@ trait DataFrame extends RDDApi[Row] { * "expense" -> "sum" * ) * }}} + * @group dfops */ def agg(aggExpr: (String, String), aggExprs: (String, String)*): DataFrame = { groupBy().agg(aggExpr, aggExprs :_*) @@ -353,6 +610,7 @@ trait DataFrame extends RDDApi[Row] { * df.agg(Map("age" -> "max", "salary" -> "avg")) * df.groupBy().agg(Map("age" -> "max", "salary" -> "avg")) * }} + * @group dfops */ def agg(exprs: Map[String, String]): DataFrame = groupBy().agg(exprs) @@ -363,6 +621,7 @@ trait DataFrame extends RDDApi[Row] { * df.agg(Map("age" -> "max", "salary" -> "avg")) * df.groupBy().agg(Map("age" -> "max", "salary" -> "avg")) * }} + * @group dfops */ def agg(exprs: java.util.Map[String, String]): DataFrame = groupBy().agg(exprs) @@ -373,6 +632,7 @@ trait DataFrame extends RDDApi[Row] { * df.agg(max($"age"), avg($"salary")) * df.groupBy().agg(max($"age"), avg($"salary")) * }} + * @group dfops */ @scala.annotation.varargs def agg(expr: Column, exprs: Column*): DataFrame = groupBy().agg(expr, exprs :_*) @@ -380,26 +640,30 @@ trait DataFrame extends RDDApi[Row] { /** * Returns a new [[DataFrame]] by taking the first `n` rows. The difference between this function * and `head` is that `head` returns an array while `limit` returns a new [[DataFrame]]. + * @group dfops */ - def limit(n: Int): DataFrame + def limit(n: Int): DataFrame = Limit(Literal(n), logicalPlan) /** * Returns a new [[DataFrame]] containing union of rows in this frame and another frame. * This is equivalent to `UNION ALL` in SQL. + * @group dfops */ - def unionAll(other: DataFrame): DataFrame + def unionAll(other: DataFrame): DataFrame = Union(logicalPlan, other.logicalPlan) /** * Returns a new [[DataFrame]] containing rows only in both this frame and another frame. * This is equivalent to `INTERSECT` in SQL. + * @group dfops */ - def intersect(other: DataFrame): DataFrame + def intersect(other: DataFrame): DataFrame = Intersect(logicalPlan, other.logicalPlan) /** * Returns a new [[DataFrame]] containing rows in this frame but not in another frame. * This is equivalent to `EXCEPT` in SQL. + * @group dfops */ - def except(other: DataFrame): DataFrame + def except(other: DataFrame): DataFrame = Except(logicalPlan, other.logicalPlan) /** * Returns a new [[DataFrame]] by sampling a fraction of rows. @@ -407,215 +671,652 @@ trait DataFrame extends RDDApi[Row] { * @param withReplacement Sample with replacement or not. * @param fraction Fraction of rows to generate. * @param seed Seed for sampling. + * @group dfops */ - def sample(withReplacement: Boolean, fraction: Double, seed: Long): DataFrame + def sample(withReplacement: Boolean, fraction: Double, seed: Long): DataFrame = { + Sample(fraction, withReplacement, seed, logicalPlan) + } /** * Returns a new [[DataFrame]] by sampling a fraction of rows, using a random seed. * * @param withReplacement Sample with replacement or not. * @param fraction Fraction of rows to generate. + * @group dfops */ def sample(withReplacement: Boolean, fraction: Double): DataFrame = { sample(withReplacement, fraction, Utils.random.nextLong) } + /** + * (Scala-specific) Returns a new [[DataFrame]] where each row has been expanded to zero or more + * rows by the provided function. This is similar to a `LATERAL VIEW` in HiveQL. The columns of + * the input row are implicitly joined with each row that is output by the function. + * + * The following example uses this function to count the number of books which contain + * a given word: + * + * {{{ + * case class Book(title: String, words: String) + * val df: RDD[Book] + * + * case class Word(word: String) + * val allWords = df.explode('words) { + * case Row(words: String) => words.split(" ").map(Word(_)) + * } + * + * val bookCountPerWord = allWords.groupBy("word").agg(countDistinct("title")) + * }}} + * @group dfops + */ + def explode[A <: Product : TypeTag](input: Column*)(f: Row => TraversableOnce[A]): DataFrame = { + val schema = ScalaReflection.schemaFor[A].dataType.asInstanceOf[StructType] + val attributes = schema.toAttributes + val rowFunction = + f.andThen(_.map(CatalystTypeConverters.convertToCatalyst(_, schema).asInstanceOf[Row])) + val generator = UserDefinedGenerator(attributes, rowFunction, input.map(_.expr)) + + Generate(generator, join = true, outer = false, None, logicalPlan) + } + + /** + * (Scala-specific) Returns a new [[DataFrame]] where a single column has been expanded to zero + * or more rows by the provided function. This is similar to a `LATERAL VIEW` in HiveQL. All + * columns of the input row are implicitly joined with each value that is output by the function. + * + * {{{ + * df.explode("words", "word")(words: String => words.split(" ")) + * }}} + * @group dfops + */ + def explode[A, B : TypeTag](inputColumn: String, outputColumn: String)(f: A => TraversableOnce[B]) + : DataFrame = { + val dataType = ScalaReflection.schemaFor[B].dataType + val attributes = AttributeReference(outputColumn, dataType)() :: Nil + def rowFunction(row: Row): TraversableOnce[Row] = { + f(row(0).asInstanceOf[A]).map(o => Row(CatalystTypeConverters.convertToCatalyst(o, dataType))) + } + val generator = UserDefinedGenerator(attributes, rowFunction, apply(inputColumn).expr :: Nil) + + Generate(generator, join = true, outer = false, None, logicalPlan) + } + ///////////////////////////////////////////////////////////////////////////// /** * Returns a new [[DataFrame]] by adding a column. + * @group dfops */ - def addColumn(colName: String, col: Column): DataFrame + def withColumn(colName: String, col: Column): DataFrame = select(Column("*"), col.as(colName)) /** * Returns a new [[DataFrame]] with a column renamed. + * @group dfops */ - def renameColumn(existingName: String, newName: String): DataFrame + def withColumnRenamed(existingName: String, newName: String): DataFrame = { + val resolver = sqlContext.analyzer.resolver + val colNames = schema.map { field => + val name = field.name + if (resolver(name, existingName)) Column(name).as(newName) else Column(name) + } + select(colNames :_*) + } + + /** + * Computes statistics for numeric columns, including count, mean, stddev, min, and max. + * If no columns are given, this function computes statistics for all numerical columns. + * + * This function is meant for exploratory data analysis, as we make no guarantee about the + * backward compatibility of the schema of the resulting [[DataFrame]]. If you want to + * programmatically compute summary statistics, use the `agg` function instead. + * + * {{{ + * df.describe("age", "height").show() + * + * // output: + * // summary age height + * // count 10.0 10.0 + * // mean 53.3 178.05 + * // stddev 11.6 15.7 + * // min 18.0 163.0 + * // max 92.0 192.0 + * }}} + * + * @group action + */ + @scala.annotation.varargs + def describe(cols: String*): DataFrame = { + + // TODO: Add stddev as an expression, and remove it from here. + def stddevExpr(expr: Expression): Expression = + Sqrt(Subtract(Average(Multiply(expr, expr)), Multiply(Average(expr), Average(expr)))) + + // The list of summary statistics to compute, in the form of expressions. + val statistics = List[(String, Expression => Expression)]( + "count" -> Count, + "mean" -> Average, + "stddev" -> stddevExpr, + "min" -> Min, + "max" -> Max) + + val outputCols = (if (cols.isEmpty) numericColumns.map(_.prettyString) else cols).toList + + val ret: Seq[Row] = if (outputCols.nonEmpty) { + val aggExprs = statistics.flatMap { case (_, colToAgg) => + outputCols.map(c => Column(colToAgg(Column(c).expr)).as(c)) + } + + val row = agg(aggExprs.head, aggExprs.tail: _*).head().toSeq + + // Pivot the data so each summary is one row + row.grouped(outputCols.size).toSeq.zip(statistics).map { + case (aggregation, (statistic, _)) => Row(statistic :: aggregation.toList: _*) + } + } else { + // If there are no output columns, just output a single column that contains the stats. + statistics.map { case (name, _) => Row(name) } + } + + // The first column is string type, and the rest are double type. + val schema = StructType( + StructField("summary", StringType) :: outputCols.map(StructField(_, DoubleType))).toAttributes + LocalRelation(schema, ret) + } /** * Returns the first `n` rows. + * @group action */ - def head(n: Int): Array[Row] + def head(n: Int): Array[Row] = limit(n).collect() /** * Returns the first row. + * @group action */ - def head(): Row + def head(): Row = head(1).head /** * Returns the first row. Alias for head(). + * @group action */ - override def first(): Row + override def first(): Row = head() /** * Returns a new RDD by applying a function to all rows of this DataFrame. + * @group rdd */ - override def map[R: ClassTag](f: Row => R): RDD[R] + override def map[R: ClassTag](f: Row => R): RDD[R] = rdd.map(f) /** * Returns a new RDD by first applying a function to all rows of this [[DataFrame]], * and then flattening the results. + * @group rdd */ - override def flatMap[R: ClassTag](f: Row => TraversableOnce[R]): RDD[R] + override def flatMap[R: ClassTag](f: Row => TraversableOnce[R]): RDD[R] = rdd.flatMap(f) /** * Returns a new RDD by applying a function to each partition of this DataFrame. + * @group rdd */ - override def mapPartitions[R: ClassTag](f: Iterator[Row] => Iterator[R]): RDD[R] + override def mapPartitions[R: ClassTag](f: Iterator[Row] => Iterator[R]): RDD[R] = { + rdd.mapPartitions(f) + } + /** * Applies a function `f` to all rows. + * @group rdd */ - override def foreach(f: Row => Unit): Unit + override def foreach(f: Row => Unit): Unit = rdd.foreach(f) /** * Applies a function f to each partition of this [[DataFrame]]. + * @group rdd */ - override def foreachPartition(f: Iterator[Row] => Unit): Unit + override def foreachPartition(f: Iterator[Row] => Unit): Unit = rdd.foreachPartition(f) /** * Returns the first `n` rows in the [[DataFrame]]. + * @group action */ - override def take(n: Int): Array[Row] + override def take(n: Int): Array[Row] = head(n) /** * Returns an array that contains all of [[Row]]s in this [[DataFrame]]. + * @group action */ - override def collect(): Array[Row] + override def collect(): Array[Row] = queryExecution.executedPlan.executeCollect() /** * Returns a Java list that contains all of [[Row]]s in this [[DataFrame]]. + * @group action */ - override def collectAsList(): java.util.List[Row] + override def collectAsList(): java.util.List[Row] = java.util.Arrays.asList(rdd.collect() :_*) /** * Returns the number of rows in the [[DataFrame]]. + * @group action */ - override def count(): Long + override def count(): Long = groupBy().count().collect().head.getLong(0) /** * Returns a new [[DataFrame]] that has exactly `numPartitions` partitions. + * @group rdd */ - override def repartition(numPartitions: Int): DataFrame + override def repartition(numPartitions: Int): DataFrame = { + sqlContext.createDataFrame( + queryExecution.toRdd.map(_.copy()).repartition(numPartitions), + schema, needsConversion = false) + } - override def persist(): this.type + /** + * Returns a new [[DataFrame]] that has exactly `numPartitions` partitions. + * Similar to coalesce defined on an [[RDD]], this operation results in a narrow dependency, e.g. + * if you go from 1000 partitions to 100 partitions, there will not be a shuffle, instead each of + * the 100 new partitions will claim 10 of the current partitions. + * @group rdd + */ + override def coalesce(numPartitions: Int): DataFrame = { + sqlContext.createDataFrame( + queryExecution.toRdd.coalesce(numPartitions), + schema, + needsConversion = false) + } - override def persist(newLevel: StorageLevel): this.type + /** + * Returns a new [[DataFrame]] that contains only the unique rows from this [[DataFrame]]. + * @group dfops + */ + override def distinct: DataFrame = Distinct(logicalPlan) - override def unpersist(blocking: Boolean): this.type + /** + * @group basic + */ + override def persist(): this.type = { + sqlContext.cacheManager.cacheQuery(this) + this + } + + /** + * @group basic + */ + override def cache(): this.type = persist() + + /** + * @group basic + */ + override def persist(newLevel: StorageLevel): this.type = { + sqlContext.cacheManager.cacheQuery(this, None, newLevel) + this + } + + /** + * @group basic + */ + override def unpersist(blocking: Boolean): this.type = { + sqlContext.cacheManager.tryUncacheQuery(this, blocking) + this + } + + /** + * @group basic + */ + override def unpersist(): this.type = unpersist(blocking = false) ///////////////////////////////////////////////////////////////////////////// // I/O ///////////////////////////////////////////////////////////////////////////// /** - * Returns the content of the [[DataFrame]] as an [[RDD]] of [[Row]]s. + * Represents the content of the [[DataFrame]] as an [[RDD]] of [[Row]]s. Note that the RDD is + * memoized. Once called, it won't change even if you change any query planning related Spark SQL + * configurations (e.g. `spark.sql.shuffle.partitions`). + * @group rdd */ - def rdd: RDD[Row] + lazy val rdd: RDD[Row] = { + // use a local variable to make sure the map closure doesn't capture the whole DataFrame + val schema = this.schema + queryExecution.executedPlan.execute().mapPartitions { rows => + val converter = CatalystTypeConverters.createToScalaConverter(schema) + rows.map(converter(_).asInstanceOf[Row]) + } + } /** * Returns the content of the [[DataFrame]] as a [[JavaRDD]] of [[Row]]s. + * @group rdd */ def toJavaRDD: JavaRDD[Row] = rdd.toJavaRDD() /** * Returns the content of the [[DataFrame]] as a [[JavaRDD]] of [[Row]]s. + * @group rdd */ def javaRDD: JavaRDD[Row] = toJavaRDD /** - * Registers this RDD as a temporary table using the given name. The lifetime of this temporary - * table is tied to the [[SQLContext]] that was used to create this DataFrame. + * Registers this [[DataFrame]] as a temporary table using the given name. The lifetime of this + * temporary table is tied to the [[SQLContext]] that was used to create this DataFrame. * - * @group schema + * @group basic */ - def registerTempTable(tableName: String): Unit + def registerTempTable(tableName: String): Unit = { + sqlContext.registerDataFrameAsTable(this, tableName) + } /** * Saves the contents of this [[DataFrame]] as a parquet file, preserving the schema. * Files that are written out using this method can be read back in as a [[DataFrame]] * using the `parquetFile` function in [[SQLContext]]. + * @group output */ - def saveAsParquetFile(path: String): Unit + def saveAsParquetFile(path: String): Unit = { + if (sqlContext.conf.parquetUseDataSourceApi) { + save("org.apache.spark.sql.parquet", SaveMode.ErrorIfExists, Map("path" -> path)) + } else { + sqlContext.executePlan(WriteToFile(path, logicalPlan)).toRdd + } + } /** * :: Experimental :: - * Creates a table from the the contents of this DataFrame. This will fail if the table already - * exists. + * Creates a table from the the contents of this DataFrame. + * It will use the default data source configured by spark.sql.sources.default. + * This will fail if the table already exists. + * + * Note that this currently only works with DataFrames that are created from a HiveContext as + * there is no notion of a persisted catalog in a standard SQL context. Instead you can write + * an RDD out to a parquet file, and then register that file as a table. This "table" can then + * be the target of an `insertInto`. + * @group output + */ + @Experimental + def saveAsTable(tableName: String): Unit = { + saveAsTable(tableName, SaveMode.ErrorIfExists) + } + + /** + * :: Experimental :: + * Creates a table from the the contents of this DataFrame, using the default data source + * configured by spark.sql.sources.default and [[SaveMode.ErrorIfExists]] as the save mode. + * + * Note that this currently only works with DataFrames that are created from a HiveContext as + * there is no notion of a persisted catalog in a standard SQL context. Instead you can write + * an RDD out to a parquet file, and then register that file as a table. This "table" can then + * be the target of an `insertInto`. + * @group output + */ + @Experimental + def saveAsTable(tableName: String, mode: SaveMode): Unit = { + if (sqlContext.catalog.tableExists(Seq(tableName)) && mode == SaveMode.Append) { + // If table already exists and the save mode is Append, + // we will just call insertInto to append the contents of this DataFrame. + insertInto(tableName, overwrite = false) + } else { + val dataSourceName = sqlContext.conf.defaultDataSourceName + saveAsTable(tableName, dataSourceName, mode) + } + } + + /** + * :: Experimental :: + * Creates a table at the given path from the the contents of this DataFrame + * based on a given data source and a set of options, + * using [[SaveMode.ErrorIfExists]] as the save mode. + * + * Note that this currently only works with DataFrames that are created from a HiveContext as + * there is no notion of a persisted catalog in a standard SQL context. Instead you can write + * an RDD out to a parquet file, and then register that file as a table. This "table" can then + * be the target of an `insertInto`. + * @group output + */ + @Experimental + def saveAsTable(tableName: String, source: String): Unit = { + saveAsTable(tableName, source, SaveMode.ErrorIfExists) + } + + /** + * :: Experimental :: + * Creates a table at the given path from the the contents of this DataFrame + * based on a given data source, [[SaveMode]] specified by mode, and a set of options. * * Note that this currently only works with DataFrames that are created from a HiveContext as * there is no notion of a persisted catalog in a standard SQL context. Instead you can write * an RDD out to a parquet file, and then register that file as a table. This "table" can then * be the target of an `insertInto`. + * @group output */ @Experimental - def saveAsTable(tableName: String): Unit + def saveAsTable(tableName: String, source: String, mode: SaveMode): Unit = { + saveAsTable(tableName, source, mode, Map.empty[String, String]) + } /** * :: Experimental :: - * Creates a table from the the contents of this DataFrame based on a given data source and - * a set of options. This will fail if the table already exists. + * Creates a table at the given path from the the contents of this DataFrame + * based on a given data source, [[SaveMode]] specified by mode, and a set of options. * * Note that this currently only works with DataFrames that are created from a HiveContext as * there is no notion of a persisted catalog in a standard SQL context. Instead you can write * an RDD out to a parquet file, and then register that file as a table. This "table" can then * be the target of an `insertInto`. + * @group output */ @Experimental def saveAsTable( tableName: String, - dataSourceName: String, - option: (String, String), - options: (String, String)*): Unit + source: String, + mode: SaveMode, + options: java.util.Map[String, String]): Unit = { + saveAsTable(tableName, source, mode, options.toMap) + } /** * :: Experimental :: - * Creates a table from the the contents of this DataFrame based on a given data source and - * a set of options. This will fail if the table already exists. + * (Scala-specific) + * Creates a table from the the contents of this DataFrame based on a given data source, + * [[SaveMode]] specified by mode, and a set of options. * * Note that this currently only works with DataFrames that are created from a HiveContext as * there is no notion of a persisted catalog in a standard SQL context. Instead you can write * an RDD out to a parquet file, and then register that file as a table. This "table" can then * be the target of an `insertInto`. + * @group output */ @Experimental def saveAsTable( tableName: String, - dataSourceName: String, - options: java.util.Map[String, String]): Unit + source: String, + mode: SaveMode, + options: Map[String, String]): Unit = { + val cmd = + CreateTableUsingAsSelect( + tableName, + source, + temporary = false, + mode, + options, + logicalPlan) + + sqlContext.executePlan(cmd).toRdd + } + /** + * :: Experimental :: + * Saves the contents of this DataFrame to the given path, + * using the default data source configured by spark.sql.sources.default and + * [[SaveMode.ErrorIfExists]] as the save mode. + * @group output + */ @Experimental - def save(path: String): Unit + def save(path: String): Unit = { + save(path, SaveMode.ErrorIfExists) + } + /** + * :: Experimental :: + * Saves the contents of this DataFrame to the given path and [[SaveMode]] specified by mode, + * using the default data source configured by spark.sql.sources.default. + * @group output + */ + @Experimental + def save(path: String, mode: SaveMode): Unit = { + val dataSourceName = sqlContext.conf.defaultDataSourceName + save(path, dataSourceName, mode) + } + + /** + * :: Experimental :: + * Saves the contents of this DataFrame to the given path based on the given data source, + * using [[SaveMode.ErrorIfExists]] as the save mode. + * @group output + */ + @Experimental + def save(path: String, source: String): Unit = { + save(source, SaveMode.ErrorIfExists, Map("path" -> path)) + } + + /** + * :: Experimental :: + * Saves the contents of this DataFrame to the given path based on the given data source and + * [[SaveMode]] specified by mode. + * @group output + */ + @Experimental + def save(path: String, source: String, mode: SaveMode): Unit = { + save(source, mode, Map("path" -> path)) + } + + /** + * :: Experimental :: + * Saves the contents of this DataFrame based on the given data source, + * [[SaveMode]] specified by mode, and a set of options. + * @group output + */ @Experimental def save( - dataSourceName: String, - option: (String, String), - options: (String, String)*): Unit + source: String, + mode: SaveMode, + options: java.util.Map[String, String]): Unit = { + save(source, mode, options.toMap) + } + /** + * :: Experimental :: + * (Scala-specific) + * Saves the contents of this DataFrame based on the given data source, + * [[SaveMode]] specified by mode, and a set of options + * @group output + */ @Experimental def save( - dataSourceName: String, - options: java.util.Map[String, String]): Unit + source: String, + mode: SaveMode, + options: Map[String, String]): Unit = { + ResolvedDataSource(sqlContext, source, mode, options, this) + } /** * :: Experimental :: * Adds the rows from this RDD to the specified table, optionally overwriting the existing data. + * @group output */ @Experimental - def insertInto(tableName: String, overwrite: Boolean): Unit + def insertInto(tableName: String, overwrite: Boolean): Unit = { + sqlContext.executePlan(InsertIntoTable(UnresolvedRelation(Seq(tableName)), + Map.empty, logicalPlan, overwrite, ifNotExists = false)).toRdd + } /** * :: Experimental :: * Adds the rows from this RDD to the specified table. * Throws an exception if the table already exists. + * @group output */ @Experimental def insertInto(tableName: String): Unit = insertInto(tableName, overwrite = false) /** * Returns the content of the [[DataFrame]] as a RDD of JSON strings. + * @group rdd + */ + def toJSON: RDD[String] = { + val rowSchema = this.schema + this.mapPartitions { iter => + val writer = new CharArrayWriter() + // create the Generator without separator inserted between 2 records + val gen = new JsonFactory().createGenerator(writer).setRootValueSeparator(null) + + new Iterator[String] { + override def hasNext: Boolean = iter.hasNext + override def next(): String = { + JsonRDD.rowToJSON(rowSchema, gen)(iter.next()) + gen.flush() + + val json = writer.toString + if (hasNext) { + writer.reset() + } else { + gen.close() + } + + json + } + } + } + } + + //////////////////////////////////////////////////////////////////////////// + // JDBC Write Support + //////////////////////////////////////////////////////////////////////////// + + /** + * Save this [[DataFrame]] to a JDBC database at `url` under the table name `table`. + * This will run a `CREATE TABLE` and a bunch of `INSERT INTO` statements. + * If you pass `true` for `allowExisting`, it will drop any table with the + * given name; if you pass `false`, it will throw if the table already + * exists. + * @group output */ - def toJSON: RDD[String] + def createJDBCTable(url: String, table: String, allowExisting: Boolean): Unit = { + val conn = DriverManager.getConnection(url) + try { + if (allowExisting) { + val sql = s"DROP TABLE IF EXISTS $table" + conn.prepareStatement(sql).executeUpdate() + } + val schema = JDBCWriteDetails.schemaString(this, url) + val sql = s"CREATE TABLE $table ($schema)" + conn.prepareStatement(sql).executeUpdate() + } finally { + conn.close() + } + JDBCWriteDetails.saveTable(this, url, table) + } + + /** + * Save this [[DataFrame]] to a JDBC database at `url` under the table name `table`. + * Assumes the table already exists and has a compatible schema. If you + * pass `true` for `overwrite`, it will `TRUNCATE` the table before + * performing the `INSERT`s. + * + * The table must already exist on the database. It must have a schema + * that is compatible with the schema of this RDD; inserting the rows of + * the RDD in order via the simple statement + * `INSERT INTO table VALUES (?, ?, ..., ?)` should not fail. + * @group output + */ + def insertIntoJDBC(url: String, table: String, overwrite: Boolean): Unit = { + if (overwrite) { + val conn = DriverManager.getConnection(url) + try { + val sql = s"TRUNCATE TABLE $table" + conn.prepareStatement(sql).executeUpdate() + } finally { + conn.close() + } + } + JDBCWriteDetails.saveTable(this, url, table) + } //////////////////////////////////////////////////////////////////////////// // for Python API @@ -624,5 +1325,9 @@ trait DataFrame extends RDDApi[Row] { /** * Converts a JavaRDD to a PythonRDD. */ - protected[sql] def javaToPython: JavaRDD[Array[Byte]] + protected[sql] def javaToPython: JavaRDD[Array[Byte]] = { + val fieldTypes = schema.fields.map(_.dataType) + val jrdd = rdd.map(EvaluatePython.rowToArray(_, fieldTypes)).toJavaRDD() + SerDeUtil.javaToPython(jrdd) + } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/ComputableColumn.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameHolder.scala similarity index 65% rename from sql/core/src/main/scala/org/apache/spark/sql/ComputableColumn.scala rename to sql/core/src/main/scala/org/apache/spark/sql/DataFrameHolder.scala index ac479b26a7c6a..a3187fe3230fd 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/ComputableColumn.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameHolder.scala @@ -17,17 +17,14 @@ package org.apache.spark.sql -import scala.language.implicitConversions +/** + * A container for a [[DataFrame]], used for implicit conversions. + */ +private[sql] case class DataFrameHolder(df: DataFrame) { -import org.apache.spark.sql.catalyst.expressions.Expression -import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan + // This is declared with parentheses to prevent the Scala compiler from treating + // `rdd.toDF("1")` as invoking this toDF and then apply on the returned DataFrame. + def toDF(): DataFrame = df - -private[sql] class ComputableColumn protected[sql]( - sqlContext: SQLContext, - protected[sql] val plan: LogicalPlan, - protected[sql] val expr: Expression) - extends DataFrameImpl(sqlContext, plan) with Column { - - override def isComputable: Boolean = true + def toDF(colNames: String*): DataFrame = df.toDF(colNames :_*) } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameImpl.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameImpl.scala deleted file mode 100644 index 789bcf6184b3e..0000000000000 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameImpl.scala +++ /dev/null @@ -1,394 +0,0 @@ -/* -* Licensed to the Apache Software Foundation (ASF) under one or more -* contributor license agreements. See the NOTICE file distributed with -* this work for additional information regarding copyright ownership. -* The ASF licenses this file to You under the Apache License, Version 2.0 -* (the "License"); you may not use this file except in compliance with -* the License. You may obtain a copy of the License at -* -* http://www.apache.org/licenses/LICENSE-2.0 -* -* Unless required by applicable law or agreed to in writing, software -* distributed under the License is distributed on an "AS IS" BASIS, -* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -* See the License for the specific language governing permissions and -* limitations under the License. -*/ - -package org.apache.spark.sql - -import scala.language.implicitConversions -import scala.reflect.ClassTag -import scala.collection.JavaConversions._ - -import com.fasterxml.jackson.core.JsonFactory - -import org.apache.spark.api.java.JavaRDD -import org.apache.spark.api.python.SerDeUtil -import org.apache.spark.rdd.RDD -import org.apache.spark.storage.StorageLevel -import org.apache.spark.sql.catalyst.{SqlParser, ScalaReflection} -import org.apache.spark.sql.catalyst.analysis.{ResolvedStar, UnresolvedRelation} -import org.apache.spark.sql.catalyst.expressions._ -import org.apache.spark.sql.catalyst.plans.{JoinType, Inner} -import org.apache.spark.sql.catalyst.plans.logical._ -import org.apache.spark.sql.execution.{LogicalRDD, EvaluatePython} -import org.apache.spark.sql.json.JsonRDD -import org.apache.spark.sql.sources.{ResolvedDataSource, CreateTableUsingAsLogicalPlan} -import org.apache.spark.sql.types.{NumericType, StructType} - - -/** - * Internal implementation of [[DataFrame]]. Users of the API should use [[DataFrame]] directly. - */ -private[sql] class DataFrameImpl protected[sql]( - override val sqlContext: SQLContext, - val queryExecution: SQLContext#QueryExecution) - extends DataFrame { - - /** - * A constructor that automatically analyzes the logical plan. This reports error eagerly - * as the [[DataFrame]] is constructed. - */ - def this(sqlContext: SQLContext, logicalPlan: LogicalPlan) = { - this(sqlContext, { - val qe = sqlContext.executePlan(logicalPlan) - if (sqlContext.conf.dataFrameEagerAnalysis) { - qe.analyzed // This should force analysis and throw errors if there are any - } - qe - }) - } - - @transient protected[sql] override val logicalPlan: LogicalPlan = queryExecution.logical match { - // For various commands (like DDL) and queries with side effects, we force query optimization to - // happen right away to let these side effects take place eagerly. - case _: Command | _: InsertIntoTable | _: CreateTableAsSelect[_] |_: WriteToFile => - LogicalRDD(queryExecution.analyzed.output, queryExecution.toRdd)(sqlContext) - case _ => - queryExecution.logical - } - - /** - * An implicit conversion function internal to this class for us to avoid doing - * "new DataFrameImpl(...)" everywhere. - */ - @inline private implicit def logicalPlanToDataFrame(logicalPlan: LogicalPlan): DataFrame = { - new DataFrameImpl(sqlContext, logicalPlan) - } - - protected[sql] def resolve(colName: String): NamedExpression = { - queryExecution.analyzed.resolve(colName, sqlContext.analyzer.resolver).getOrElse { - throw new RuntimeException( - s"""Cannot resolve column name "$colName" among (${schema.fieldNames.mkString(", ")})""") - } - } - - protected[sql] def numericColumns: Seq[Expression] = { - schema.fields.filter(_.dataType.isInstanceOf[NumericType]).map { n => - queryExecution.analyzed.resolve(n.name, sqlContext.analyzer.resolver).get - } - } - - override def toDataFrame(colName: String, colNames: String*): DataFrame = { - val newNames = colName +: colNames - require(schema.size == newNames.size, - "The number of columns doesn't match.\n" + - "Old column names: " + schema.fields.map(_.name).mkString(", ") + "\n" + - "New column names: " + newNames.mkString(", ")) - - val newCols = schema.fieldNames.zip(newNames).map { case (oldName, newName) => - apply(oldName).as(newName) - } - select(newCols :_*) - } - - override def schema: StructType = queryExecution.analyzed.schema - - override def dtypes: Array[(String, String)] = schema.fields.map { field => - (field.name, field.dataType.toString) - } - - override def columns: Array[String] = schema.fields.map(_.name) - - override def printSchema(): Unit = println(schema.treeString) - - override def join(right: DataFrame): DataFrame = { - Join(logicalPlan, right.logicalPlan, joinType = Inner, None) - } - - override def join(right: DataFrame, joinExprs: Column): DataFrame = { - Join(logicalPlan, right.logicalPlan, Inner, Some(joinExprs.expr)) - } - - override def join(right: DataFrame, joinExprs: Column, joinType: String): DataFrame = { - Join(logicalPlan, right.logicalPlan, JoinType(joinType), Some(joinExprs.expr)) - } - - override def sort(sortCol: String, sortCols: String*): DataFrame = { - sort((sortCol +: sortCols).map(apply) :_*) - } - - override def sort(sortExprs: Column*): DataFrame = { - val sortOrder: Seq[SortOrder] = sortExprs.map { col => - col.expr match { - case expr: SortOrder => - expr - case expr: Expression => - SortOrder(expr, Ascending) - } - } - Sort(sortOrder, global = true, logicalPlan) - } - - override def orderBy(sortCol: String, sortCols: String*): DataFrame = { - sort(sortCol, sortCols :_*) - } - - override def orderBy(sortExprs: Column*): DataFrame = { - sort(sortExprs :_*) - } - - override def col(colName: String): Column = colName match { - case "*" => - Column(ResolvedStar(schema.fieldNames.map(resolve))) - case _ => - val expr = resolve(colName) - Column(sqlContext, Project(Seq(expr), logicalPlan), expr) - } - - override def apply(projection: Product): DataFrame = { - require(projection.productArity >= 1) - select(projection.productIterator.map { - case c: Column => c - case o: Any => Column(Literal(o)) - }.toSeq :_*) - } - - override def as(name: String): DataFrame = Subquery(name, logicalPlan) - - override def select(cols: Column*): DataFrame = { - val exprs = cols.zipWithIndex.map { - case (Column(expr: NamedExpression), _) => - expr - case (Column(expr: Expression), _) => - Alias(expr, expr.toString)() - } - Project(exprs.toSeq, logicalPlan) - } - - override def select(col: String, cols: String*): DataFrame = { - select((col +: cols).map(Column(_)) :_*) - } - - override def selectExpr(exprs: String*): DataFrame = { - select(exprs.map { expr => - Column(new SqlParser().parseExpression(expr)) - } :_*) - } - - override def filter(condition: Column): DataFrame = { - Filter(condition.expr, logicalPlan) - } - - override def filter(conditionExpr: String): DataFrame = { - filter(Column(new SqlParser().parseExpression(conditionExpr))) - } - - override def where(condition: Column): DataFrame = { - filter(condition) - } - - override def apply(condition: Column): DataFrame = { - filter(condition) - } - - override def groupBy(cols: Column*): GroupedData = { - new GroupedData(this, cols.map(_.expr)) - } - - override def groupBy(col1: String, cols: String*): GroupedData = { - val colNames: Seq[String] = col1 +: cols - new GroupedData(this, colNames.map(colName => resolve(colName))) - } - - override def limit(n: Int): DataFrame = { - Limit(Literal(n), logicalPlan) - } - - override def unionAll(other: DataFrame): DataFrame = { - Union(logicalPlan, other.logicalPlan) - } - - override def intersect(other: DataFrame): DataFrame = { - Intersect(logicalPlan, other.logicalPlan) - } - - override def except(other: DataFrame): DataFrame = { - Except(logicalPlan, other.logicalPlan) - } - - override def sample(withReplacement: Boolean, fraction: Double, seed: Long): DataFrame = { - Sample(fraction, withReplacement, seed, logicalPlan) - } - - ///////////////////////////////////////////////////////////////////////////// - - override def addColumn(colName: String, col: Column): DataFrame = { - select(Column("*"), col.as(colName)) - } - - override def renameColumn(existingName: String, newName: String): DataFrame = { - val colNames = schema.map { field => - val name = field.name - if (name == existingName) Column(name).as(newName) else Column(name) - } - select(colNames :_*) - } - - override def head(n: Int): Array[Row] = limit(n).collect() - - override def head(): Row = head(1).head - - override def first(): Row = head() - - override def map[R: ClassTag](f: Row => R): RDD[R] = rdd.map(f) - - override def flatMap[R: ClassTag](f: Row => TraversableOnce[R]): RDD[R] = rdd.flatMap(f) - - override def mapPartitions[R: ClassTag](f: Iterator[Row] => Iterator[R]): RDD[R] = { - rdd.mapPartitions(f) - } - - override def foreach(f: Row => Unit): Unit = rdd.foreach(f) - - override def foreachPartition(f: Iterator[Row] => Unit): Unit = rdd.foreachPartition(f) - - override def take(n: Int): Array[Row] = head(n) - - override def collect(): Array[Row] = queryExecution.executedPlan.executeCollect() - - override def collectAsList(): java.util.List[Row] = java.util.Arrays.asList(rdd.collect() :_*) - - override def count(): Long = groupBy().count().rdd.collect().head.getLong(0) - - override def repartition(numPartitions: Int): DataFrame = { - sqlContext.applySchema(rdd.repartition(numPartitions), schema) - } - - override def persist(): this.type = { - sqlContext.cacheManager.cacheQuery(this) - this - } - - override def persist(newLevel: StorageLevel): this.type = { - sqlContext.cacheManager.cacheQuery(this, None, newLevel) - this - } - - override def unpersist(blocking: Boolean): this.type = { - sqlContext.cacheManager.tryUncacheQuery(this, blocking) - this - } - - ///////////////////////////////////////////////////////////////////////////// - // I/O - ///////////////////////////////////////////////////////////////////////////// - - override def rdd: RDD[Row] = { - val schema = this.schema - queryExecution.executedPlan.execute().map(ScalaReflection.convertRowToScala(_, schema)) - } - - override def registerTempTable(tableName: String): Unit = { - sqlContext.registerRDDAsTable(this, tableName) - } - - override def saveAsParquetFile(path: String): Unit = { - if (sqlContext.conf.parquetUseDataSourceApi) { - save("org.apache.spark.sql.parquet", "path" -> path) - } else { - sqlContext.executePlan(WriteToFile(path, logicalPlan)).toRdd - } - } - - override def saveAsTable(tableName: String): Unit = { - val dataSourceName = sqlContext.conf.defaultDataSourceName - val cmd = - CreateTableUsingAsLogicalPlan( - tableName, - dataSourceName, - temporary = false, - Map.empty, - allowExisting = false, - logicalPlan) - - sqlContext.executePlan(cmd).toRdd - } - - override def saveAsTable( - tableName: String, - dataSourceName: String, - option: (String, String), - options: (String, String)*): Unit = { - val cmd = - CreateTableUsingAsLogicalPlan( - tableName, - dataSourceName, - temporary = false, - (option +: options).toMap, - allowExisting = false, - logicalPlan) - - sqlContext.executePlan(cmd).toRdd - } - - override def saveAsTable( - tableName: String, - dataSourceName: String, - options: java.util.Map[String, String]): Unit = { - val opts = options.toSeq - saveAsTable(tableName, dataSourceName, opts.head, opts.tail:_*) - } - - override def save(path: String): Unit = { - val dataSourceName = sqlContext.conf.defaultDataSourceName - save(dataSourceName, "path" -> path) - } - - override def save( - dataSourceName: String, - option: (String, String), - options: (String, String)*): Unit = { - ResolvedDataSource(sqlContext, dataSourceName, (option +: options).toMap, this) - } - - override def save( - dataSourceName: String, - options: java.util.Map[String, String]): Unit = { - val opts = options.toSeq - save(dataSourceName, opts.head, opts.tail:_*) - } - - override def insertInto(tableName: String, overwrite: Boolean): Unit = { - sqlContext.executePlan(InsertIntoTable(UnresolvedRelation(Seq(tableName)), - Map.empty, logicalPlan, overwrite)).toRdd - } - - override def toJSON: RDD[String] = { - val rowSchema = this.schema - this.mapPartitions { iter => - val jsonFactory = new JsonFactory() - iter.map(JsonRDD.rowToJSON(rowSchema, jsonFactory)) - } - } - - //////////////////////////////////////////////////////////////////////////// - // for Python API - //////////////////////////////////////////////////////////////////////////// - protected[sql] override def javaToPython: JavaRDD[Array[Byte]] = { - val fieldTypes = schema.fields.map(_.dataType) - val jrdd = rdd.map(EvaluatePython.rowToArray(_, fieldTypes)).toJavaRDD() - SerDeUtil.javaToPython(jrdd) - } -} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala new file mode 100644 index 0000000000000..481ed4924857e --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala @@ -0,0 +1,375 @@ +/* +* Licensed to the Apache Software Foundation (ASF) under one or more +* contributor license agreements. See the NOTICE file distributed with +* this work for additional information regarding copyright ownership. +* The ASF licenses this file to You under the Apache License, Version 2.0 +* (the "License"); you may not use this file except in compliance with +* the License. You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +*/ + +package org.apache.spark.sql + +import java.{lang => jl} + +import scala.collection.JavaConversions._ + +import org.apache.spark.annotation.Experimental +import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.types._ + + +/** + * :: Experimental :: + * Functionality for working with missing data in [[DataFrame]]s. + */ +@Experimental +final class DataFrameNaFunctions private[sql](df: DataFrame) { + + /** + * Returns a new [[DataFrame]] that drops rows containing any null values. + */ + def drop(): DataFrame = drop("any", df.columns) + + /** + * Returns a new [[DataFrame]] that drops rows containing null values. + * + * If `how` is "any", then drop rows containing any null values. + * If `how` is "all", then drop rows only if every column is null for that row. + */ + def drop(how: String): DataFrame = drop(how, df.columns) + + /** + * Returns a new [[DataFrame]] that drops rows containing any null values + * in the specified columns. + */ + def drop(cols: Array[String]): DataFrame = drop(cols.toSeq) + + /** + * (Scala-specific) Returns a new [[DataFrame ]] that drops rows containing any null values + * in the specified columns. + */ + def drop(cols: Seq[String]): DataFrame = drop(cols.size, cols) + + /** + * Returns a new [[DataFrame]] that drops rows containing null values + * in the specified columns. + * + * If `how` is "any", then drop rows containing any null values in the specified columns. + * If `how` is "all", then drop rows only if every specified column is null for that row. + */ + def drop(how: String, cols: Array[String]): DataFrame = drop(how, cols.toSeq) + + /** + * (Scala-specific) Returns a new [[DataFrame]] that drops rows containing null values + * in the specified columns. + * + * If `how` is "any", then drop rows containing any null values in the specified columns. + * If `how` is "all", then drop rows only if every specified column is null for that row. + */ + def drop(how: String, cols: Seq[String]): DataFrame = { + how.toLowerCase match { + case "any" => drop(cols.size, cols) + case "all" => drop(1, cols) + case _ => throw new IllegalArgumentException(s"how ($how) must be 'any' or 'all'") + } + } + + /** + * Returns a new [[DataFrame]] that drops rows containing less than `minNonNulls` non-null values. + */ + def drop(minNonNulls: Int): DataFrame = drop(minNonNulls, df.columns) + + /** + * Returns a new [[DataFrame]] that drops rows containing less than `minNonNulls` non-null + * values in the specified columns. + */ + def drop(minNonNulls: Int, cols: Array[String]): DataFrame = drop(minNonNulls, cols.toSeq) + + /** + * (Scala-specific) Returns a new [[DataFrame]] that drops rows containing less than + * `minNonNulls` non-null values in the specified columns. + */ + def drop(minNonNulls: Int, cols: Seq[String]): DataFrame = { + // Filtering condition -- only keep the row if it has at least `minNonNulls` non-null values. + val predicate = AtLeastNNonNulls(minNonNulls, cols.map(name => df.resolve(name))) + df.filter(Column(predicate)) + } + + /** + * Returns a new [[DataFrame]] that replaces null values in numeric columns with `value`. + */ + def fill(value: Double): DataFrame = fill(value, df.columns) + + /** + * Returns a new [[DataFrame ]] that replaces null values in string columns with `value`. + */ + def fill(value: String): DataFrame = fill(value, df.columns) + + /** + * Returns a new [[DataFrame]] that replaces null values in specified numeric columns. + * If a specified column is not a numeric column, it is ignored. + */ + def fill(value: Double, cols: Array[String]): DataFrame = fill(value, cols.toSeq) + + /** + * (Scala-specific) Returns a new [[DataFrame]] that replaces null values in specified + * numeric columns. If a specified column is not a numeric column, it is ignored. + */ + def fill(value: Double, cols: Seq[String]): DataFrame = { + val columnEquals = df.sqlContext.analyzer.resolver + val projections = df.schema.fields.map { f => + // Only fill if the column is part of the cols list. + if (f.dataType.isInstanceOf[NumericType] && cols.exists(col => columnEquals(f.name, col))) { + fillCol[Double](f, value) + } else { + df.col(f.name) + } + } + df.select(projections : _*) + } + + /** + * Returns a new [[DataFrame]] that replaces null values in specified string columns. + * If a specified column is not a string column, it is ignored. + */ + def fill(value: String, cols: Array[String]): DataFrame = fill(value, cols.toSeq) + + /** + * (Scala-specific) Returns a new [[DataFrame]] that replaces null values in + * specified string columns. If a specified column is not a string column, it is ignored. + */ + def fill(value: String, cols: Seq[String]): DataFrame = { + val columnEquals = df.sqlContext.analyzer.resolver + val projections = df.schema.fields.map { f => + // Only fill if the column is part of the cols list. + if (f.dataType.isInstanceOf[StringType] && cols.exists(col => columnEquals(f.name, col))) { + fillCol[String](f, value) + } else { + df.col(f.name) + } + } + df.select(projections : _*) + } + + /** + * Returns a new [[DataFrame]] that replaces null values. + * + * The key of the map is the column name, and the value of the map is the replacement value. + * The value must be of the following type: `Integer`, `Long`, `Float`, `Double`, `String`. + * + * For example, the following replaces null values in column "A" with string "unknown", and + * null values in column "B" with numeric value 1.0. + * {{{ + * import com.google.common.collect.ImmutableMap; + * df.na.fill(ImmutableMap.of("A", "unknown", "B", 1.0)); + * }}} + */ + def fill(valueMap: java.util.Map[String, Any]): DataFrame = fill0(valueMap.toSeq) + + /** + * (Scala-specific) Returns a new [[DataFrame]] that replaces null values. + * + * The key of the map is the column name, and the value of the map is the replacement value. + * The value must be of the following type: `Int`, `Long`, `Float`, `Double`, `String`. + * + * For example, the following replaces null values in column "A" with string "unknown", and + * null values in column "B" with numeric value 1.0. + * {{{ + * df.na.fill(Map( + * "A" -> "unknown", + * "B" -> 1.0 + * )) + * }}} + */ + def fill(valueMap: Map[String, Any]): DataFrame = fill0(valueMap.toSeq) + + /** + * Replaces values matching keys in `replacement` map with the corresponding values. + * Key and value of `replacement` map must have the same type, and can only be doubles or strings. + * If `col` is "*", then the replacement is applied on all string columns or numeric columns. + * + * {{{ + * import com.google.common.collect.ImmutableMap; + * + * // Replaces all occurrences of 1.0 with 2.0 in column "height". + * df.replace("height", ImmutableMap.of(1.0, 2.0)); + * + * // Replaces all occurrences of "UNKNOWN" with "unnamed" in column "name". + * df.replace("name", ImmutableMap.of("UNKNOWN", "unnamed")); + * + * // Replaces all occurrences of "UNKNOWN" with "unnamed" in all string columns. + * df.replace("*", ImmutableMap.of("UNKNOWN", "unnamed")); + * }}} + * + * @param col name of the column to apply the value replacement + * @param replacement value replacement map, as explained above + */ + def replace[T](col: String, replacement: java.util.Map[T, T]): DataFrame = { + replace[T](col, replacement.toMap : Map[T, T]) + } + + /** + * Replaces values matching keys in `replacement` map with the corresponding values. + * Key and value of `replacement` map must have the same type, and can only be doubles or strings. + * + * {{{ + * import com.google.common.collect.ImmutableMap; + * + * // Replaces all occurrences of 1.0 with 2.0 in column "height" and "weight". + * df.replace(new String[] {"height", "weight"}, ImmutableMap.of(1.0, 2.0)); + * + * // Replaces all occurrences of "UNKNOWN" with "unnamed" in column "firstname" and "lastname". + * df.replace(new String[] {"firstname", "lastname"}, ImmutableMap.of("UNKNOWN", "unnamed")); + * }}} + * + * @param cols list of columns to apply the value replacement + * @param replacement value replacement map, as explained above + */ + def replace[T](cols: Array[String], replacement: java.util.Map[T, T]): DataFrame = { + replace(cols.toSeq, replacement.toMap) + } + + /** + * (Scala-specific) Replaces values matching keys in `replacement` map. + * Key and value of `replacement` map must have the same type, and can only be doubles or strings. + * If `col` is "*", then the replacement is applied on all string columns or numeric columns. + * + * {{{ + * // Replaces all occurrences of 1.0 with 2.0 in column "height". + * df.replace("height", Map(1.0 -> 2.0)) + * + * // Replaces all occurrences of "UNKNOWN" with "unnamed" in column "name". + * df.replace("name", Map("UNKNOWN" -> "unnamed") + * + * // Replaces all occurrences of "UNKNOWN" with "unnamed" in all string columns. + * df.replace("*", Map("UNKNOWN" -> "unnamed") + * }}} + * + * @param col name of the column to apply the value replacement + * @param replacement value replacement map, as explained above + */ + def replace[T](col: String, replacement: Map[T, T]): DataFrame = { + if (col == "*") { + replace0(df.columns, replacement) + } else { + replace0(Seq(col), replacement) + } + } + + /** + * (Scala-specific) Replaces values matching keys in `replacement` map. + * Key and value of `replacement` map must have the same type, and can only be doubles or strings. + * + * {{{ + * // Replaces all occurrences of 1.0 with 2.0 in column "height" and "weight". + * df.replace("height" :: "weight" :: Nil, Map(1.0 -> 2.0)); + * + * // Replaces all occurrences of "UNKNOWN" with "unnamed" in column "firstname" and "lastname". + * df.replace("firstname" :: "lastname" :: Nil, Map("UNKNOWN" -> "unnamed"); + * }}} + * + * @param cols list of columns to apply the value replacement + * @param replacement value replacement map, as explained above + */ + def replace[T](cols: Seq[String], replacement: Map[T, T]): DataFrame = replace0(cols, replacement) + + private def replace0[T](cols: Seq[String], replacement: Map[T, T]): DataFrame = { + if (replacement.isEmpty || cols.isEmpty) { + return df + } + + // replacementMap is either Map[String, String] or Map[Double, Double] + val replacementMap: Map[_, _] = replacement.head._2 match { + case v: String => replacement + case _ => replacement.map { case (k, v) => (convertToDouble(k), convertToDouble(v)) } + } + + // targetColumnType is either DoubleType or StringType + val targetColumnType = replacement.head._1 match { + case _: jl.Double | _: jl.Float | _: jl.Integer | _: jl.Long => DoubleType + case _: String => StringType + } + + val columnEquals = df.sqlContext.analyzer.resolver + val projections = df.schema.fields.map { f => + val shouldReplace = cols.exists(colName => columnEquals(colName, f.name)) + if (f.dataType.isInstanceOf[NumericType] && targetColumnType == DoubleType && shouldReplace) { + replaceCol(f, replacementMap) + } else if (f.dataType == targetColumnType && shouldReplace) { + replaceCol(f, replacementMap) + } else { + df.col(f.name) + } + } + df.select(projections : _*) + } + + private def fill0(values: Seq[(String, Any)]): DataFrame = { + // Error handling + values.foreach { case (colName, replaceValue) => + // Check column name exists + df.resolve(colName) + + // Check data type + replaceValue match { + case _: jl.Double | _: jl.Float | _: jl.Integer | _: jl.Long | _: String => + // This is good + case _ => throw new IllegalArgumentException( + s"Unsupported value type ${replaceValue.getClass.getName} ($replaceValue).") + } + } + + val columnEquals = df.sqlContext.analyzer.resolver + val projections = df.schema.fields.map { f => + values.find { case (k, _) => columnEquals(k, f.name) }.map { case (_, v) => + v match { + case v: jl.Float => fillCol[Double](f, v.toDouble) + case v: jl.Double => fillCol[Double](f, v) + case v: jl.Long => fillCol[Double](f, v.toDouble) + case v: jl.Integer => fillCol[Double](f, v.toDouble) + case v: String => fillCol[String](f, v) + } + }.getOrElse(df.col(f.name)) + } + df.select(projections : _*) + } + + /** + * Returns a [[Column]] expression that replaces null value in `col` with `replacement`. + */ + private def fillCol[T](col: StructField, replacement: T): Column = { + coalesce(df.col(col.name), lit(replacement).cast(col.dataType)).as(col.name) + } + + /** + * Returns a [[Column]] expression that replaces value matching key in `replacementMap` with + * value in `replacementMap`, using [[CaseWhen]]. + * + * TODO: This can be optimized to use broadcast join when replacementMap is large. + */ + private def replaceCol(col: StructField, replacementMap: Map[_, _]): Column = { + val branches: Seq[Expression] = replacementMap.flatMap { case (source, target) => + df.col(col.name).equalTo(lit(source).cast(col.dataType)).expr :: + lit(target).cast(col.dataType).expr :: Nil + }.toSeq + new Column(CaseWhen(branches ++ Seq(df.col(col.name).expr))).as(col.name) + } + + private def convertToDouble(v: Any): Double = v match { + case v: Float => v.toDouble + case v: Double => v + case v: Long => v.toDouble + case v: Int => v.toDouble + case v => throw new IllegalArgumentException( + s"Unsupported value type ${v.getClass.getName} ($v).") + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/ExperimentalMethods.scala b/sql/core/src/main/scala/org/apache/spark/sql/ExperimentalMethods.scala index f0e6a8f332188..d5d7e35a6b35d 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/ExperimentalMethods.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/ExperimentalMethods.scala @@ -20,8 +20,13 @@ package org.apache.spark.sql import org.apache.spark.annotation.Experimental /** + * :: Experimental :: * Holder for experimental methods for the bravest. We make NO guarantee about the stability * regarding binary compatibility and source compatibility of methods here. + * + * {{{ + * sqlContext.experimental.extraStrategies += ... + * }}} */ @Experimental class ExperimentalMethods protected[sql](sqlContext: SQLContext) { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala b/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala index 3c20676355c9d..53ad67372e024 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala @@ -17,32 +17,53 @@ package org.apache.spark.sql -import scala.language.implicitConversions import scala.collection.JavaConversions._ +import scala.language.implicitConversions +import org.apache.spark.annotation.Experimental +import org.apache.spark.sql.catalyst.analysis.Star import org.apache.spark.sql.catalyst.expressions._ -import org.apache.spark.sql.catalyst.expressions.{Literal => LiteralExpr} import org.apache.spark.sql.catalyst.plans.logical.Aggregate +import org.apache.spark.sql.types.NumericType /** + * :: Experimental :: * A set of methods for aggregations on a [[DataFrame]], created by [[DataFrame.groupBy]]. */ -class GroupedData protected[sql](df: DataFrameImpl, groupingExprs: Seq[Expression]) { +@Experimental +class GroupedData protected[sql](df: DataFrame, groupingExprs: Seq[Expression]) { - private[this] implicit def toDataFrame(aggExprs: Seq[NamedExpression]): DataFrame = { + private[sql] implicit def toDF(aggExprs: Seq[NamedExpression]): DataFrame = { val namedGroupingExprs = groupingExprs.map { case expr: NamedExpression => expr - case expr: Expression => Alias(expr, expr.toString)() + case expr: Expression => Alias(expr, expr.prettyString)() } DataFrame( df.sqlContext, Aggregate(groupingExprs, namedGroupingExprs ++ aggExprs, df.logicalPlan)) } - private[this] def aggregateNumericColumns(f: Expression => Expression): Seq[NamedExpression] = { - df.numericColumns.map { c => + private[this] def aggregateNumericColumns(colNames: String*)(f: Expression => Expression) + : Seq[NamedExpression] = { + + val columnExprs = if (colNames.isEmpty) { + // No columns specified. Use all numeric columns. + df.numericColumns + } else { + // Make sure all specified columns are numeric. + colNames.map { colName => + val namedExpr = df.resolve(colName) + if (!namedExpr.dataType.isInstanceOf[NumericType]) { + throw new AnalysisException( + s""""$colName" is not a numeric column. """ + + "Aggregation function can only be applied on a numeric column.") + } + namedExpr + } + } + columnExprs.map { c => val a = f(c) - Alias(a, a.toString)() + Alias(a, a.prettyString)() } } @@ -52,7 +73,12 @@ class GroupedData protected[sql](df: DataFrameImpl, groupingExprs: Seq[Expressio case "max" => Max case "min" => Min case "sum" => Sum - case "count" | "size" => Count + case "count" | "size" => + // Turn count(*) into count(1) + (inputExpr: Expression) => inputExpr match { + case s: Star => Count(Literal(1)) + case _ => Count(inputExpr) + } } } @@ -89,7 +115,7 @@ class GroupedData protected[sql](df: DataFrameImpl, groupingExprs: Seq[Expressio def agg(exprs: Map[String, String]): DataFrame = { exprs.map { case (colName, expr) => val a = strToExpr(expr)(df(colName).expr) - Alias(a, a.toString)() + Alias(a, a.prettyString)() }.toSeq } @@ -101,10 +127,7 @@ class GroupedData protected[sql](df: DataFrameImpl, groupingExprs: Seq[Expressio * {{{ * // Selects the age of the oldest employee and the aggregate expense for each department * import com.google.common.collect.ImmutableMap; - * df.groupBy("department").agg(ImmutableMap.builder() - * .put("age", "max") - * .put("expense", "sum") - * .build()); + * df.groupBy("department").agg(ImmutableMap.of("age", "max", "expense", "sum")); * }}} */ def agg(exprs: java.util.Map[String, String]): DataFrame = { @@ -115,17 +138,17 @@ class GroupedData protected[sql](df: DataFrameImpl, groupingExprs: Seq[Expressio * Compute aggregates by specifying a series of aggregate columns. Unlike other methods in this * class, the resulting [[DataFrame]] won't automatically include the grouping columns. * - * The available aggregate methods are defined in [[org.apache.spark.sql.Dsl]]. + * The available aggregate methods are defined in [[org.apache.spark.sql.functions]]. * * {{{ * // Selects the age of the oldest employee and the aggregate expense for each department * * // Scala: - * import org.apache.spark.sql.dsl._ + * import org.apache.spark.sql.functions._ * df.groupBy("department").agg($"department", max($"age"), sum($"expense")) * * // Java: - * import static org.apache.spark.sql.Dsl.*; + * import static org.apache.spark.sql.functions.*; * df.groupBy("department").agg(col("department"), max(col("age")), sum(col("expense"))); * }}} */ @@ -133,7 +156,7 @@ class GroupedData protected[sql](df: DataFrameImpl, groupingExprs: Seq[Expressio def agg(expr: Column, exprs: Column*): DataFrame = { val aggExprs = (expr +: exprs).map(_.expr).map { case expr: NamedExpression => expr - case expr: Expression => Alias(expr, expr.toString)() + case expr: Expression => Alias(expr, expr.prettyString)() } DataFrame(df.sqlContext, Aggregate(groupingExprs, aggExprs, df.logicalPlan)) } @@ -142,35 +165,55 @@ class GroupedData protected[sql](df: DataFrameImpl, groupingExprs: Seq[Expressio * Count the number of rows for each group. * The resulting [[DataFrame]] will also contain the grouping columns. */ - def count(): DataFrame = Seq(Alias(Count(LiteralExpr(1)), "count")()) + def count(): DataFrame = Seq(Alias(Count(Literal(1)), "count")()) /** * Compute the average value for each numeric columns for each group. This is an alias for `avg`. * The resulting [[DataFrame]] will also contain the grouping columns. + * When specified columns are given, only compute the average values for them. */ - def mean(): DataFrame = aggregateNumericColumns(Average) - + @scala.annotation.varargs + def mean(colNames: String*): DataFrame = { + aggregateNumericColumns(colNames:_*)(Average) + } + /** * Compute the max value for each numeric columns for each group. * The resulting [[DataFrame]] will also contain the grouping columns. + * When specified columns are given, only compute the max values for them. */ - def max(): DataFrame = aggregateNumericColumns(Max) + @scala.annotation.varargs + def max(colNames: String*): DataFrame = { + aggregateNumericColumns(colNames:_*)(Max) + } /** * Compute the mean value for each numeric columns for each group. * The resulting [[DataFrame]] will also contain the grouping columns. + * When specified columns are given, only compute the mean values for them. */ - def avg(): DataFrame = aggregateNumericColumns(Average) + @scala.annotation.varargs + def avg(colNames: String*): DataFrame = { + aggregateNumericColumns(colNames:_*)(Average) + } /** * Compute the min value for each numeric column for each group. * The resulting [[DataFrame]] will also contain the grouping columns. + * When specified columns are given, only compute the min values for them. */ - def min(): DataFrame = aggregateNumericColumns(Min) + @scala.annotation.varargs + def min(colNames: String*): DataFrame = { + aggregateNumericColumns(colNames:_*)(Min) + } /** * Compute the sum for each numeric columns for each group. * The resulting [[DataFrame]] will also contain the grouping columns. + * When specified columns are given, only compute the sum for them. */ - def sum(): DataFrame = aggregateNumericColumns(Sum) + @scala.annotation.varargs + def sum(colNames: String*): DataFrame = { + aggregateNumericColumns(colNames:_*)(Sum) + } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/IncomputableColumn.scala b/sql/core/src/main/scala/org/apache/spark/sql/IncomputableColumn.scala deleted file mode 100644 index 6043fb4dee01d..0000000000000 --- a/sql/core/src/main/scala/org/apache/spark/sql/IncomputableColumn.scala +++ /dev/null @@ -1,180 +0,0 @@ -/* -* Licensed to the Apache Software Foundation (ASF) under one or more -* contributor license agreements. See the NOTICE file distributed with -* this work for additional information regarding copyright ownership. -* The ASF licenses this file to You under the Apache License, Version 2.0 -* (the "License"); you may not use this file except in compliance with -* the License. You may obtain a copy of the License at -* -* http://www.apache.org/licenses/LICENSE-2.0 -* -* Unless required by applicable law or agreed to in writing, software -* distributed under the License is distributed on an "AS IS" BASIS, -* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -* See the License for the specific language governing permissions and -* limitations under the License. -*/ - -package org.apache.spark.sql - -import scala.reflect.ClassTag - -import org.apache.spark.api.java.JavaRDD -import org.apache.spark.rdd.RDD -import org.apache.spark.sql.catalyst.analysis.{UnresolvedAttribute, UnresolvedStar} -import org.apache.spark.sql.catalyst.expressions._ -import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan -import org.apache.spark.storage.StorageLevel -import org.apache.spark.sql.types.StructType - - -private[sql] class IncomputableColumn(protected[sql] val expr: Expression) extends Column { - - def this(name: String) = this(name match { - case "*" => UnresolvedStar(None) - case _ if name.endsWith(".*") => UnresolvedStar(Some(name.substring(0, name.length - 2))) - case _ => UnresolvedAttribute(name) - }) - - private def err[T](): T = { - throw new UnsupportedOperationException("Cannot run this method on an UncomputableColumn") - } - - override def isComputable: Boolean = false - - override val sqlContext: SQLContext = null - - override def queryExecution = err() - - protected[sql] override def logicalPlan: LogicalPlan = err() - - override def toDataFrame(colName: String, colNames: String*): DataFrame = err() - - override def schema: StructType = err() - - override def dtypes: Array[(String, String)] = err() - - override def columns: Array[String] = err() - - override def printSchema(): Unit = err() - - override def join(right: DataFrame): DataFrame = err() - - override def join(right: DataFrame, joinExprs: Column): DataFrame = err() - - override def join(right: DataFrame, joinExprs: Column, joinType: String): DataFrame = err() - - override def sort(sortCol: String, sortCols: String*): DataFrame = err() - - override def sort(sortExprs: Column*): DataFrame = err() - - override def orderBy(sortCol: String, sortCols: String*): DataFrame = err() - - override def orderBy(sortExprs: Column*): DataFrame = err() - - override def col(colName: String): Column = err() - - override def apply(projection: Product): DataFrame = err() - - override def select(cols: Column*): DataFrame = err() - - override def select(col: String, cols: String*): DataFrame = err() - - override def selectExpr(exprs: String*): DataFrame = err() - - override def filter(condition: Column): DataFrame = err() - - override def filter(conditionExpr: String): DataFrame = err() - - override def where(condition: Column): DataFrame = err() - - override def apply(condition: Column): DataFrame = err() - - override def groupBy(cols: Column*): GroupedData = err() - - override def groupBy(col1: String, cols: String*): GroupedData = err() - - override def limit(n: Int): DataFrame = err() - - override def unionAll(other: DataFrame): DataFrame = err() - - override def intersect(other: DataFrame): DataFrame = err() - - override def except(other: DataFrame): DataFrame = err() - - override def sample(withReplacement: Boolean, fraction: Double, seed: Long): DataFrame = err() - - ///////////////////////////////////////////////////////////////////////////// - - override def addColumn(colName: String, col: Column): DataFrame = err() - - override def renameColumn(existingName: String, newName: String): DataFrame = err() - - override def head(n: Int): Array[Row] = err() - - override def head(): Row = err() - - override def first(): Row = err() - - override def map[R: ClassTag](f: Row => R): RDD[R] = err() - - override def flatMap[R: ClassTag](f: Row => TraversableOnce[R]): RDD[R] = err() - - override def mapPartitions[R: ClassTag](f: Iterator[Row] => Iterator[R]): RDD[R] = err() - - override def foreach(f: Row => Unit): Unit = err() - - override def foreachPartition(f: Iterator[Row] => Unit): Unit = err() - - override def take(n: Int): Array[Row] = err() - - override def collect(): Array[Row] = err() - - override def collectAsList(): java.util.List[Row] = err() - - override def count(): Long = err() - - override def repartition(numPartitions: Int): DataFrame = err() - - override def persist(): this.type = err() - - override def persist(newLevel: StorageLevel): this.type = err() - - override def unpersist(blocking: Boolean): this.type = err() - - override def rdd: RDD[Row] = err() - - override def registerTempTable(tableName: String): Unit = err() - - override def saveAsParquetFile(path: String): Unit = err() - - override def saveAsTable(tableName: String): Unit = err() - - override def saveAsTable( - tableName: String, - dataSourceName: String, - option: (String, String), - options: (String, String)*): Unit = err() - - override def saveAsTable( - tableName: String, - dataSourceName: String, - options: java.util.Map[String, String]): Unit = err() - - override def save(path: String): Unit = err() - - override def save( - dataSourceName: String, - option: (String, String), - options: (String, String)*): Unit = err() - - override def save( - dataSourceName: String, - options: java.util.Map[String, String]): Unit = err() - - override def insertInto(tableName: String, overwrite: Boolean): Unit = err() - - override def toJSON: RDD[String] = err() - - protected[sql] override def javaToPython: JavaRDD[Array[Byte]] = err() -} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/RDDApi.scala b/sql/core/src/main/scala/org/apache/spark/sql/RDDApi.scala index 38e6382f171d5..63dbab19947c0 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/RDDApi.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/RDDApi.scala @@ -29,13 +29,13 @@ import org.apache.spark.storage.StorageLevel */ private[sql] trait RDDApi[T] { - def cache(): this.type = persist() + def cache(): this.type def persist(): this.type def persist(newLevel: StorageLevel): this.type - def unpersist(): this.type = unpersist(blocking = false) + def unpersist(): this.type def unpersist(blocking: Boolean): this.type @@ -60,4 +60,8 @@ private[sql] trait RDDApi[T] { def first(): T def repartition(numPartitions: Int): DataFrame + + def coalesce(numPartitions: Int): DataFrame + + def distinct: DataFrame } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala index 180f5e765fb91..5c65f04ee8497 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala @@ -39,21 +39,30 @@ private[spark] object SQLConf { val PARQUET_FILTER_PUSHDOWN_ENABLED = "spark.sql.parquet.filterPushdown" val PARQUET_USE_DATA_SOURCE_API = "spark.sql.parquet.useDataSourceApi" + val HIVE_VERIFY_PARTITIONPATH = "spark.sql.hive.verifyPartitionPath" + val COLUMN_NAME_OF_CORRUPT_RECORD = "spark.sql.columnNameOfCorruptRecord" val BROADCAST_TIMEOUT = "spark.sql.broadcastTimeout" // Options that control which operators can be chosen by the query planner. These should be // considered hints and may be ignored by future versions of Spark SQL. val EXTERNAL_SORT = "spark.sql.planner.externalSort" + val SORTMERGE_JOIN = "spark.sql.planner.sortMergeJoin" // This is only used for the thriftserver val THRIFTSERVER_POOL = "spark.sql.thriftserver.scheduler.pool" // This is used to set the default data source - val DEFAULT_DATA_SOURCE_NAME = "spark.sql.default.datasource" + val DEFAULT_DATA_SOURCE_NAME = "spark.sql.sources.default" + // This is used to control the when we will split a schema's JSON string to multiple pieces + // in order to fit the JSON string in metastore's table property (by default, the value has + // a length restriction of 4000 characters). We will split the JSON string of a schema + // to its length exceeds the threshold. + val SCHEMA_STRING_LENGTH_THRESHOLD = "spark.sql.sources.schemaStringLengthThreshold" - // Whether to perform eager analysis on a DataFrame. - val DATAFRAME_EAGER_ANALYSIS = "spark.sql.dataframe.eagerAnalysis" + // Whether to perform eager analysis when constructing a dataframe. + // Set to false when debugging requires the ability to look at invalid query plans. + val DATAFRAME_EAGER_ANALYSIS = "spark.sql.eagerAnalysis" object Deprecated { val MAPRED_REDUCE_TASKS = "mapred.reduce.tasks" @@ -113,9 +122,20 @@ private[sql] class SQLConf extends Serializable { private[spark] def parquetUseDataSourceApi = getConf(PARQUET_USE_DATA_SOURCE_API, "true").toBoolean + /** When true uses verifyPartitionPath to prune the path which is not exists. */ + private[spark] def verifyPartitionPath = + getConf(HIVE_VERIFY_PARTITIONPATH, "true").toBoolean + /** When true the planner will use the external sort, which may spill to disk. */ private[spark] def externalSortEnabled: Boolean = getConf(EXTERNAL_SORT, "false").toBoolean + /** + * Sort merge join would sort the two side of join first, and then iterate both sides together + * only once to get all matches. Using sort merge join can save a lot of memory usage compared + * to HashJoin. + */ + private[spark] def sortMergeJoinEnabled: Boolean = getConf(SORTMERGE_JOIN, "false").toBoolean + /** * When set to true, Spark SQL will use the Scala compiler at runtime to generate custom bytecode * that evaluates expressions found in queries. In general this custom code runs much faster @@ -176,6 +196,11 @@ private[sql] class SQLConf extends Serializable { private[spark] def defaultDataSourceName: String = getConf(DEFAULT_DATA_SOURCE_NAME, "org.apache.spark.sql.parquet") + // Do not use a value larger than 4000 as the default value of this property. + // See the comments of SCHEMA_STRING_LENGTH_THRESHOLD above for more information. + private[spark] def schemaStringLengthThreshold: Int = + getConf(SCHEMA_STRING_LENGTH_THRESHOLD, "4000").toInt + private[spark] def dataFrameEagerAnalysis: Boolean = getConf(DATAFRAME_EAGER_ANALYSIS, "true").toBoolean diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala index bf3990671029e..f9f3eb2e03817 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala @@ -25,32 +25,36 @@ import scala.collection.immutable import scala.language.implicitConversions import scala.reflect.runtime.universe.TypeTag -import org.apache.spark.annotation.{AlphaComponent, DeveloperApi, Experimental} +import org.apache.spark.annotation.{DeveloperApi, Experimental} import org.apache.spark.api.java.{JavaRDD, JavaSparkContext} import org.apache.spark.rdd.RDD -import org.apache.spark.sql.catalyst.ScalaReflection import org.apache.spark.sql.catalyst.analysis._ import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.optimizer.{DefaultOptimizer, Optimizer} import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, LogicalPlan} import org.apache.spark.sql.catalyst.rules.RuleExecutor -import org.apache.spark.sql.execution._ +import org.apache.spark.sql.catalyst.{CatalystTypeConverters, ScalaReflection, expressions} +import org.apache.spark.sql.execution.{Filter, _} import org.apache.spark.sql.jdbc.{JDBCPartition, JDBCPartitioningInfo, JDBCRelation} import org.apache.spark.sql.json._ -import org.apache.spark.sql.sources.{BaseRelation, DDLParser, DataSourceStrategy, LogicalRelation, _} +import org.apache.spark.sql.sources._ import org.apache.spark.sql.types._ import org.apache.spark.util.Utils import org.apache.spark.{Partition, SparkContext} /** - * :: AlphaComponent :: - * The entry point for running relational queries using Spark. Allows the creation of [[DataFrame]] - * objects and the execution of SQL queries. + * The entry point for working with structured data (rows and columns) in Spark. Allows the + * creation of [[DataFrame]] objects as well as the execution of SQL queries. * - * @groupname userf Spark SQL Functions + * @groupname basic Basic Operations + * @groupname ddl_ops Persistent Catalog DDL + * @groupname cachemgmt Cached Table Management + * @groupname genericdata Generic Data Sources + * @groupname specificdata Specific Data Sources + * @groupname config Configuration + * @groupname dataframes Custom DataFrame Creation * @groupname Ungrouped Support functions for language integrated queries. */ -@AlphaComponent class SQLContext(@transient val sparkContext: SparkContext) extends org.apache.spark.Logging with Serializable { @@ -59,49 +63,74 @@ class SQLContext(@transient val sparkContext: SparkContext) def this(sparkContext: JavaSparkContext) = this(sparkContext.sc) - // Note that this is a lazy val so we can override the default value in subclasses. - protected[sql] lazy val conf: SQLConf = new SQLConf + /** + * @return Spark SQL configuration + */ + protected[sql] def conf = tlSession.get().conf - /** Set Spark SQL configuration properties. */ + /** + * Set Spark SQL configuration properties. + * + * @group config + */ def setConf(props: Properties): Unit = conf.setConf(props) - /** Set the given Spark SQL configuration property. */ + /** + * Set the given Spark SQL configuration property. + * + * @group config + */ def setConf(key: String, value: String): Unit = conf.setConf(key, value) - /** Return the value of Spark SQL configuration property for the given key. */ + /** + * Return the value of Spark SQL configuration property for the given key. + * + * @group config + */ def getConf(key: String): String = conf.getConf(key) /** * Return the value of Spark SQL configuration property for the given key. If the key is not set * yet, return `defaultValue`. + * + * @group config */ def getConf(key: String, defaultValue: String): String = conf.getConf(key, defaultValue) /** * Return all the configuration properties that have been set (i.e. not the default). * This creates a new copy of the config properties in the form of a Map. + * + * @group config */ def getAllConfs: immutable.Map[String, String] = conf.getAllConfs + // TODO how to handle the temp table per user session? @transient protected[sql] lazy val catalog: Catalog = new SimpleCatalog(true) + // TODO how to handle the temp function per user session? @transient protected[sql] lazy val functionRegistry: FunctionRegistry = new SimpleFunctionRegistry(true) @transient protected[sql] lazy val analyzer: Analyzer = new Analyzer(catalog, functionRegistry, caseSensitive = true) { - override val extendedRules = + override val extendedResolutionRules = + ExtractPythonUdfs :: sources.PreInsertCastAndRename :: Nil + + override val extendedCheckRules = Seq( + sources.PreWriteCheck(catalog) + ) } @transient protected[sql] lazy val optimizer: Optimizer = DefaultOptimizer @transient - protected[sql] val ddlParser = new DDLParser + protected[sql] val ddlParser = new DDLParser(sqlParser.apply(_)) @transient protected[sql] val sqlParser = { @@ -117,19 +146,43 @@ class SQLContext(@transient val sparkContext: SparkContext) protected[sql] def executePlan(plan: LogicalPlan) = new this.QueryExecution(plan) + @transient + protected[sql] val tlSession = new ThreadLocal[SQLSession]() { + override def initialValue: SQLSession = defaultSession + } + + @transient + protected[sql] val defaultSession = createSession() + sparkContext.getConf.getAll.foreach { case (key, value) if key.startsWith("spark.sql") => setConf(key, value) case _ => } + @transient protected[sql] val cacheManager = new CacheManager(this) /** + * :: Experimental :: * A collection of methods that are considered experimental, but can be used to hook into - * the query planner for advanced functionalities. + * the query planner for advanced functionality. + * + * @group basic */ + @Experimental + @transient val experimental: ExperimentalMethods = new ExperimentalMethods(this) + /** + * :: Experimental :: + * Returns a [[DataFrame]] with no rows or columns. + * + * @group basic + */ + @Experimental + @transient + lazy val emptyDataFrame: DataFrame = createDataFrame(sparkContext.emptyRDD[Row], StructType(Nil)) + /** * A collection of methods for registering user-defined functions (UDF). * @@ -155,48 +208,126 @@ class SQLContext(@transient val sparkContext: SparkContext) * (Integer arg1, String arg2) -> arg2 + arg1), * DataTypes.StringType); * }}} + * + * @group basic + * TODO move to SQLSession? */ + @transient val udf: UDFRegistration = new UDFRegistration(this) - /** Returns true if the table is currently cached in-memory. */ + /** + * Returns true if the table is currently cached in-memory. + * @group cachemgmt + */ def isCached(tableName: String): Boolean = cacheManager.isCached(tableName) - /** Caches the specified table in-memory. */ + /** + * Caches the specified table in-memory. + * @group cachemgmt + */ def cacheTable(tableName: String): Unit = cacheManager.cacheTable(tableName) - /** Removes the specified table from the in-memory cache. */ + /** + * Removes the specified table from the in-memory cache. + * @group cachemgmt + */ def uncacheTable(tableName: String): Unit = cacheManager.uncacheTable(tableName) + /** + * Removes all cached tables from the in-memory cache. + */ + def clearCache(): Unit = cacheManager.clearCache() + // scalastyle:off // Disable style checker so "implicits" object can start with lowercase i /** - * (Scala-specific) - * Implicit methods available in Scala for converting common Scala objects into [[DataFrame]]s. + * :: Experimental :: + * (Scala-specific) Implicit methods available in Scala for converting + * common Scala objects into [[DataFrame]]s. + * + * {{{ + * val sqlContext = new SQLContext(sc) + * import sqlContext.implicits._ + * }}} + * + * @group basic */ - object implicits { + @Experimental + object implicits extends Serializable { // scalastyle:on - /** - * Creates a DataFrame from an RDD of case classes. - * - * @group userf - */ - implicit def createDataFrame[A <: Product : TypeTag](rdd: RDD[A]): DataFrame = { - self.createDataFrame(rdd) + + /** Converts $"col name" into an [[Column]]. */ + implicit class StringToColumn(val sc: StringContext) { + def $(args: Any*): ColumnName = { + new ColumnName(sc.s(args :_*)) + } } - /** - * Creates a DataFrame from a local Seq of Product. - */ - implicit def createDataFrame[A <: Product : TypeTag](data: Seq[A]): DataFrame = { - self.createDataFrame(data) + /** An implicit conversion that turns a Scala `Symbol` into a [[Column]]. */ + implicit def symbolToColumn(s: Symbol): ColumnName = new ColumnName(s.name) + + /** Creates a DataFrame from an RDD of case classes or tuples. */ + implicit def rddToDataFrameHolder[A <: Product : TypeTag](rdd: RDD[A]): DataFrameHolder = { + DataFrameHolder(self.createDataFrame(rdd)) + } + + /** Creates a DataFrame from a local Seq of Product. */ + implicit def localSeqToDataFrameHolder[A <: Product : TypeTag](data: Seq[A]): DataFrameHolder = + { + DataFrameHolder(self.createDataFrame(data)) + } + + // Do NOT add more implicit conversions. They are likely to break source compatibility by + // making existing implicit conversions ambiguous. In particular, RDD[Double] is dangerous + // because of [[DoubleRDDFunctions]]. + + /** Creates a single column DataFrame from an RDD[Int]. */ + implicit def intRddToDataFrameHolder(data: RDD[Int]): DataFrameHolder = { + val dataType = IntegerType + val rows = data.mapPartitions { iter => + val row = new SpecificMutableRow(dataType :: Nil) + iter.map { v => + row.setInt(0, v) + row: Row + } + } + DataFrameHolder(self.createDataFrame(rows, StructType(StructField("_1", dataType) :: Nil))) + } + + /** Creates a single column DataFrame from an RDD[Long]. */ + implicit def longRddToDataFrameHolder(data: RDD[Long]): DataFrameHolder = { + val dataType = LongType + val rows = data.mapPartitions { iter => + val row = new SpecificMutableRow(dataType :: Nil) + iter.map { v => + row.setLong(0, v) + row: Row + } + } + DataFrameHolder(self.createDataFrame(rows, StructType(StructField("_1", dataType) :: Nil))) + } + + /** Creates a single column DataFrame from an RDD[String]. */ + implicit def stringRddToDataFrameHolder(data: RDD[String]): DataFrameHolder = { + val dataType = StringType + val rows = data.mapPartitions { iter => + val row = new SpecificMutableRow(dataType :: Nil) + iter.map { v => + row.setString(0, v) + row: Row + } + } + DataFrameHolder(self.createDataFrame(rows, StructType(StructField("_1", dataType) :: Nil))) } } /** + * :: Experimental :: * Creates a DataFrame from an RDD of case classes. * - * @group userf + * @group dataframes */ + @Experimental def createDataFrame[A <: Product : TypeTag](rdd: RDD[A]): DataFrame = { SparkPlan.currentContext.set(self) val schema = ScalaReflection.schemaFor[A].dataType.asInstanceOf[StructType] @@ -206,8 +337,12 @@ class SQLContext(@transient val sparkContext: SparkContext) } /** + * :: Experimental :: * Creates a DataFrame from a local Seq of Product. + * + * @group dataframes */ + @Experimental def createDataFrame[A <: Product : TypeTag](data: Seq[A]): DataFrame = { SparkPlan.currentContext.set(self) val schema = ScalaReflection.schemaFor[A].dataType.asInstanceOf[StructType] @@ -217,6 +352,8 @@ class SQLContext(@transient val sparkContext: SparkContext) /** * Convert a [[BaseRelation]] created for external data sources into a [[DataFrame]]. + * + * @group dataframes */ def baseRelationToDataFrame(baseRelation: BaseRelation): DataFrame = { DataFrame(this, LogicalRelation(baseRelation)) @@ -224,12 +361,13 @@ class SQLContext(@transient val sparkContext: SparkContext) /** * :: DeveloperApi :: - * Creates a [[DataFrame]] from an [[RDD]] containing [[Row]]s by applying a schema to this RDD. + * Creates a [[DataFrame]] from an [[RDD]] containing [[Row]]s using the given schema. * It is important to make sure that the structure of every [[Row]] of the provided RDD matches * the provided schema. Otherwise, there will be runtime exception. * Example: * {{{ * import org.apache.spark.sql._ + * import org.apache.spark.sql.types._ * val sqlContext = new org.apache.spark.sql.SQLContext(sc) * * val schema = @@ -240,7 +378,7 @@ class SQLContext(@transient val sparkContext: SparkContext) * val people = * sc.textFile("examples/src/main/resources/people.txt").map( * _.split(",")).map(p => Row(p(0), p(1).trim.toInt)) - * val dataFrame = sqlContext. applySchema(people, schema) + * val dataFrame = sqlContext.createDataFrame(people, schema) * dataFrame.printSchema * // root * // |-- name: string (nullable = false) @@ -250,19 +388,56 @@ class SQLContext(@transient val sparkContext: SparkContext) * sqlContext.sql("select name from people").collect.foreach(println) * }}} * - * @group userf + * @group dataframes */ @DeveloperApi - def applySchema(rowRDD: RDD[Row], schema: StructType): DataFrame = { + def createDataFrame(rowRDD: RDD[Row], schema: StructType): DataFrame = { + createDataFrame(rowRDD, schema, needsConversion = true) + } + + /** + * Creates a DataFrame from an RDD[Row]. User can specify whether the input rows should be + * converted to Catalyst rows. + */ + private[sql] + def createDataFrame(rowRDD: RDD[Row], schema: StructType, needsConversion: Boolean) = { // TODO: use MutableProjection when rowRDD is another DataFrame and the applied // schema differs from the existing schema on any field data type. - val logicalPlan = LogicalRDD(schema.toAttributes, rowRDD)(self) + val catalystRows = if (needsConversion) { + val converter = CatalystTypeConverters.createToCatalystConverter(schema) + rowRDD.map(converter(_).asInstanceOf[Row]) + } else { + rowRDD + } + val logicalPlan = LogicalRDD(schema.toAttributes, catalystRows)(self) DataFrame(this, logicalPlan) } + /** + * :: DeveloperApi :: + * Creates a [[DataFrame]] from an [[JavaRDD]] containing [[Row]]s using the given schema. + * It is important to make sure that the structure of every [[Row]] of the provided RDD matches + * the provided schema. Otherwise, there will be runtime exception. + * + * @group dataframes + */ @DeveloperApi - def applySchema(rowRDD: JavaRDD[Row], schema: StructType): DataFrame = { - applySchema(rowRDD.rdd, schema); + def createDataFrame(rowRDD: JavaRDD[Row], schema: StructType): DataFrame = { + createDataFrame(rowRDD.rdd, schema) + } + + /** + * Creates a [[DataFrame]] from an [[JavaRDD]] containing [[Row]]s by applying + * a seq of names of columns to this RDD, the data type for each column will + * be inferred by the first row. + * + * @param rowRDD an JavaRDD of Row + * @param columns names for each column + * @return DataFrame + * @group dataframes + */ + def createDataFrame(rowRDD: JavaRDD[Row], columns: java.util.List[String]): DataFrame = { + createDataFrame(rowRDD.rdd, columns.toSeq) } /** @@ -270,8 +445,9 @@ class SQLContext(@transient val sparkContext: SparkContext) * * WARNING: Since there is no guaranteed ordering for fields in a Java Bean, * SELECT * queries will return the columns in an undefined order. + * @group dataframes */ - def applySchema(rdd: RDD[_], beanClass: Class[_]): DataFrame = { + def createDataFrame(rdd: RDD[_], beanClass: Class[_]): DataFrame = { val attributeSeq = getSchema(beanClass) val className = beanClass.getName val rowRdd = rdd.mapPartitions { iter => @@ -284,7 +460,7 @@ class SQLContext(@transient val sparkContext: SparkContext) iter.map { row => new GenericRow( extractors.zip(attributeSeq).map { case (e, attr) => - DataTypeConversions.convertJavaToCatalyst(e.invoke(row), attr.dataType) + CatalystTypeConverters.convertToCatalyst(e.invoke(row), attr.dataType) }.toArray[Any] ) : Row } @@ -292,35 +468,101 @@ class SQLContext(@transient val sparkContext: SparkContext) DataFrame(this, LogicalRDD(attributeSeq, rowRdd)(this)) } + /** + * Applies a schema to an RDD of Java Beans. + * + * WARNING: Since there is no guaranteed ordering for fields in a Java Bean, + * SELECT * queries will return the columns in an undefined order. + * @group dataframes + */ + def createDataFrame(rdd: JavaRDD[_], beanClass: Class[_]): DataFrame = { + createDataFrame(rdd.rdd, beanClass) + } + + /** + * :: DeveloperApi :: + * Creates a [[DataFrame]] from an [[RDD]] containing [[Row]]s by applying a schema to this RDD. + * It is important to make sure that the structure of every [[Row]] of the provided RDD matches + * the provided schema. Otherwise, there will be runtime exception. + * Example: + * {{{ + * import org.apache.spark.sql._ + * import org.apache.spark.sql.types._ + * val sqlContext = new org.apache.spark.sql.SQLContext(sc) + * + * val schema = + * StructType( + * StructField("name", StringType, false) :: + * StructField("age", IntegerType, true) :: Nil) + * + * val people = + * sc.textFile("examples/src/main/resources/people.txt").map( + * _.split(",")).map(p => Row(p(0), p(1).trim.toInt)) + * val dataFrame = sqlContext. applySchema(people, schema) + * dataFrame.printSchema + * // root + * // |-- name: string (nullable = false) + * // |-- age: integer (nullable = true) + * + * dataFrame.registerTempTable("people") + * sqlContext.sql("select name from people").collect.foreach(println) + * }}} + */ + @deprecated("use createDataFrame", "1.3.0") + def applySchema(rowRDD: RDD[Row], schema: StructType): DataFrame = { + createDataFrame(rowRDD, schema) + } + + @deprecated("use createDataFrame", "1.3.0") + def applySchema(rowRDD: JavaRDD[Row], schema: StructType): DataFrame = { + createDataFrame(rowRDD, schema) + } + + /** + * Applies a schema to an RDD of Java Beans. + * + * WARNING: Since there is no guaranteed ordering for fields in a Java Bean, + * SELECT * queries will return the columns in an undefined order. + */ + @deprecated("use createDataFrame", "1.3.0") + def applySchema(rdd: RDD[_], beanClass: Class[_]): DataFrame = { + createDataFrame(rdd, beanClass) + } + /** * Applies a schema to an RDD of Java Beans. * * WARNING: Since there is no guaranteed ordering for fields in a Java Bean, * SELECT * queries will return the columns in an undefined order. */ + @deprecated("use createDataFrame", "1.3.0") def applySchema(rdd: JavaRDD[_], beanClass: Class[_]): DataFrame = { - applySchema(rdd.rdd, beanClass) + createDataFrame(rdd, beanClass) } /** - * Loads a Parquet file, returning the result as a [[DataFrame]]. + * Loads a Parquet file, returning the result as a [[DataFrame]]. This function returns an empty + * [[DataFrame]] if no paths are passed in. * - * @group userf + * @group specificdata */ @scala.annotation.varargs - def parquetFile(path: String, paths: String*): DataFrame = - if (conf.parquetUseDataSourceApi) { - baseRelationToDataFrame(parquet.ParquetRelation2(path +: paths, Map.empty)(this)) + def parquetFile(paths: String*): DataFrame = { + if (paths.isEmpty) { + emptyDataFrame + } else if (conf.parquetUseDataSourceApi) { + baseRelationToDataFrame(parquet.ParquetRelation2(paths, Map.empty)(this)) } else { DataFrame(this, parquet.ParquetRelation( paths.mkString(","), Some(sparkContext.hadoopConfiguration), this)) } + } /** * Loads a JSON file (one object per line), returning the result as a [[DataFrame]]. * It goes through the entire dataset once to determine the schema. * - * @group userf + * @group specificdata */ def jsonFile(path: String): DataFrame = jsonFile(path, 1.0) @@ -329,32 +571,37 @@ class SQLContext(@transient val sparkContext: SparkContext) * Loads a JSON file (one object per line) and applies the given schema, * returning the result as a [[DataFrame]]. * - * @group userf + * @group specificdata */ @Experimental - def jsonFile(path: String, schema: StructType): DataFrame = { - val json = sparkContext.textFile(path) - jsonRDD(json, schema) - } + def jsonFile(path: String, schema: StructType): DataFrame = + load("json", schema, Map("path" -> path)) /** * :: Experimental :: + * @group specificdata */ @Experimental - def jsonFile(path: String, samplingRatio: Double): DataFrame = { - val json = sparkContext.textFile(path) - jsonRDD(json, samplingRatio) - } + def jsonFile(path: String, samplingRatio: Double): DataFrame = + load("json", Map("path" -> path, "samplingRatio" -> samplingRatio.toString)) /** * Loads an RDD[String] storing JSON objects (one object per record), returning the result as a * [[DataFrame]]. * It goes through the entire dataset once to determine the schema. * - * @group userf + * @group specificdata */ def jsonRDD(json: RDD[String]): DataFrame = jsonRDD(json, 1.0) + + /** + * Loads an RDD[String] storing JSON objects (one object per record), returning the result as a + * [[DataFrame]]. + * It goes through the entire dataset once to determine the schema. + * + * @group specificdata + */ def jsonRDD(json: JavaRDD[String]): DataFrame = jsonRDD(json.rdd, 1.0) /** @@ -362,7 +609,7 @@ class SQLContext(@transient val sparkContext: SparkContext) * Loads an RDD[String] storing JSON objects (one object per record) and applies the given schema, * returning the result as a [[DataFrame]]. * - * @group userf + * @group specificdata */ @Experimental def jsonRDD(json: RDD[String], schema: StructType): DataFrame = { @@ -372,9 +619,16 @@ class SQLContext(@transient val sparkContext: SparkContext) JsonRDD.nullTypeToStringType( JsonRDD.inferSchema(json, 1.0, columnNameOfCorruptJsonRecord))) val rowRDD = JsonRDD.jsonStringToRow(json, appliedSchema, columnNameOfCorruptJsonRecord) - applySchema(rowRDD, appliedSchema) + createDataFrame(rowRDD, appliedSchema, needsConversion = false) } + /** + * :: Experimental :: + * Loads an JavaRDD storing JSON objects (one object per record) and applies the given + * schema, returning the result as a [[DataFrame]]. + * + * @group specificdata + */ @Experimental def jsonRDD(json: JavaRDD[String], schema: StructType): DataFrame = { jsonRDD(json.rdd, schema) @@ -382,6 +636,10 @@ class SQLContext(@transient val sparkContext: SparkContext) /** * :: Experimental :: + * Loads an RDD[String] storing JSON objects (one object per record) inferring the + * schema, returning the result as a [[DataFrame]]. + * + * @group specificdata */ @Experimental def jsonRDD(json: RDD[String], samplingRatio: Double): DataFrame = { @@ -390,91 +648,279 @@ class SQLContext(@transient val sparkContext: SparkContext) JsonRDD.nullTypeToStringType( JsonRDD.inferSchema(json, samplingRatio, columnNameOfCorruptJsonRecord)) val rowRDD = JsonRDD.jsonStringToRow(json, appliedSchema, columnNameOfCorruptJsonRecord) - applySchema(rowRDD, appliedSchema) + createDataFrame(rowRDD, appliedSchema, needsConversion = false) } + /** + * :: Experimental :: + * Loads a JavaRDD[String] storing JSON objects (one object per record) inferring the + * schema, returning the result as a [[DataFrame]]. + * + * @group specificdata + */ @Experimental def jsonRDD(json: JavaRDD[String], samplingRatio: Double): DataFrame = { jsonRDD(json.rdd, samplingRatio); } + /** + * :: Experimental :: + * Returns the dataset stored at path as a DataFrame, + * using the default data source configured by spark.sql.sources.default. + * + * @group genericdata + */ @Experimental def load(path: String): DataFrame = { val dataSourceName = conf.defaultDataSourceName - load(dataSourceName, ("path", path)) + load(path, dataSourceName) } + /** + * :: Experimental :: + * Returns the dataset stored at path as a DataFrame, using the given data source. + * + * @group genericdata + */ @Experimental - def load( - dataSourceName: String, - option: (String, String), - options: (String, String)*): DataFrame = { - val resolved = ResolvedDataSource(this, None, dataSourceName, (option +: options).toMap) + def load(path: String, source: String): DataFrame = { + load(source, Map("path" -> path)) + } + + /** + * :: Experimental :: + * (Java-specific) Returns the dataset specified by the given data source and + * a set of options as a DataFrame. + * + * @group genericdata + */ + @Experimental + def load(source: String, options: java.util.Map[String, String]): DataFrame = { + load(source, options.toMap) + } + + /** + * :: Experimental :: + * (Scala-specific) Returns the dataset specified by the given data source and + * a set of options as a DataFrame. + * + * @group genericdata + */ + @Experimental + def load(source: String, options: Map[String, String]): DataFrame = { + val resolved = ResolvedDataSource(this, None, source, options) DataFrame(this, LogicalRelation(resolved.relation)) } + /** + * :: Experimental :: + * (Java-specific) Returns the dataset specified by the given data source and + * a set of options as a DataFrame, using the given schema as the schema of the DataFrame. + * + * @group genericdata + */ @Experimental def load( - dataSourceName: String, + source: String, + schema: StructType, options: java.util.Map[String, String]): DataFrame = { - val opts = options.toSeq - load(dataSourceName, opts.head, opts.tail:_*) + load(source, schema, options.toMap) } /** * :: Experimental :: - * Construct an RDD representing the database table accessible via JDBC URL + * (Scala-specific) Returns the dataset specified by the given data source and + * a set of options as a DataFrame, using the given schema as the schema of the DataFrame. + * @group genericdata + */ + @Experimental + def load( + source: String, + schema: StructType, + options: Map[String, String]): DataFrame = { + val resolved = ResolvedDataSource(this, Some(schema), source, options) + DataFrame(this, LogicalRelation(resolved.relation)) + } + + /** + * :: Experimental :: + * Creates an external table from the given path and returns the corresponding DataFrame. + * It will use the default data source configured by spark.sql.sources.default. + * + * @group ddl_ops + */ + @Experimental + def createExternalTable(tableName: String, path: String): DataFrame = { + val dataSourceName = conf.defaultDataSourceName + createExternalTable(tableName, path, dataSourceName) + } + + /** + * :: Experimental :: + * Creates an external table from the given path based on a data source + * and returns the corresponding DataFrame. + * + * @group ddl_ops + */ + @Experimental + def createExternalTable( + tableName: String, + path: String, + source: String): DataFrame = { + createExternalTable(tableName, source, Map("path" -> path)) + } + + /** + * :: Experimental :: + * Creates an external table from the given path based on a data source and a set of options. + * Then, returns the corresponding DataFrame. + * + * @group ddl_ops + */ + @Experimental + def createExternalTable( + tableName: String, + source: String, + options: java.util.Map[String, String]): DataFrame = { + createExternalTable(tableName, source, options.toMap) + } + + /** + * :: Experimental :: + * (Scala-specific) + * Creates an external table from the given path based on a data source and a set of options. + * Then, returns the corresponding DataFrame. + * + * @group ddl_ops + */ + @Experimental + def createExternalTable( + tableName: String, + source: String, + options: Map[String, String]): DataFrame = { + val cmd = + CreateTableUsing( + tableName, + userSpecifiedSchema = None, + source, + temporary = false, + options, + allowExisting = false, + managedIfNoPath = false) + executePlan(cmd).toRdd + table(tableName) + } + + /** + * :: Experimental :: + * Create an external table from the given path based on a data source, a schema and + * a set of options. Then, returns the corresponding DataFrame. + * + * @group ddl_ops + */ + @Experimental + def createExternalTable( + tableName: String, + source: String, + schema: StructType, + options: java.util.Map[String, String]): DataFrame = { + createExternalTable(tableName, source, schema, options.toMap) + } + + /** + * :: Experimental :: + * (Scala-specific) + * Create an external table from the given path based on a data source, a schema and + * a set of options. Then, returns the corresponding DataFrame. + * + * @group ddl_ops + */ + @Experimental + def createExternalTable( + tableName: String, + source: String, + schema: StructType, + options: Map[String, String]): DataFrame = { + val cmd = + CreateTableUsing( + tableName, + userSpecifiedSchema = Some(schema), + source, + temporary = false, + options, + allowExisting = false, + managedIfNoPath = false) + executePlan(cmd).toRdd + table(tableName) + } + + /** + * :: Experimental :: + * Construct a [[DataFrame]] representing the database table accessible via JDBC URL * url named table. + * + * @group specificdata */ @Experimental - def jdbcRDD(url: String, table: String): DataFrame = { - jdbcRDD(url, table, null.asInstanceOf[JDBCPartitioningInfo]) + def jdbc(url: String, table: String): DataFrame = { + jdbc(url, table, JDBCRelation.columnPartition(null)) } /** * :: Experimental :: - * Construct an RDD representing the database table accessible via JDBC URL - * url named table. The PartitioningInfo parameter - * gives the name of a column of integral type, a number of partitions, and - * advisory minimum and maximum values for the column. The RDD is - * partitioned according to said column. + * Construct a [[DataFrame]] representing the database table accessible via JDBC URL + * url named table. Partitions of the table will be retrieved in parallel based on the parameters + * passed to this function. + * + * @param columnName the name of a column of integral type that will be used for partitioning. + * @param lowerBound the minimum value of `columnName` used to decide partition stride + * @param upperBound the maximum value of `columnName` used to decide partition stride + * @param numPartitions the number of partitions. the range `minValue`-`maxValue` will be split + * evenly into this many partitions + * + * @group specificdata */ @Experimental - def jdbcRDD(url: String, table: String, partitioning: JDBCPartitioningInfo): - DataFrame = { + def jdbc( + url: String, + table: String, + columnName: String, + lowerBound: Long, + upperBound: Long, + numPartitions: Int): DataFrame = { + val partitioning = JDBCPartitioningInfo(columnName, lowerBound, upperBound, numPartitions) val parts = JDBCRelation.columnPartition(partitioning) - jdbcRDD(url, table, parts) + jdbc(url, table, parts) } /** * :: Experimental :: - * Construct an RDD representing the database table accessible via JDBC URL + * Construct a [[DataFrame]] representing the database table accessible via JDBC URL * url named table. The theParts parameter gives a list expressions * suitable for inclusion in WHERE clauses; each one defines one partition - * of the RDD. + * of the [[DataFrame]]. + * + * @group specificdata */ @Experimental - def jdbcRDD(url: String, table: String, theParts: Array[String]): - DataFrame = { - val parts: Array[Partition] = theParts.zipWithIndex.map( - x => JDBCPartition(x._1, x._2).asInstanceOf[Partition]) - jdbcRDD(url, table, parts) + def jdbc(url: String, table: String, theParts: Array[String]): DataFrame = { + val parts: Array[Partition] = theParts.zipWithIndex.map { case (part, i) => + JDBCPartition(part, i) : Partition + } + jdbc(url, table, parts) } - private def jdbcRDD(url: String, table: String, parts: Array[Partition]): - DataFrame = { + private def jdbc(url: String, table: String, parts: Array[Partition]): DataFrame = { val relation = JDBCRelation(url, table, parts)(this) baseRelationToDataFrame(relation) } /** - * Registers the given RDD as a temporary table in the catalog. Temporary tables exist only - * during the lifetime of this instance of SQLContext. - * - * @group userf + * Registers the given [[DataFrame]] as a temporary table in the catalog. Temporary tables exist + * only during the lifetime of this instance of SQLContext. */ - def registerRDDAsTable(rdd: DataFrame, tableName: String): Unit = { - catalog.registerTable(Seq(tableName), rdd.logicalPlan) + private[sql] def registerDataFrameAsTable(df: DataFrame, tableName: String): Unit = { + catalog.registerTable(Seq(tableName), df.logicalPlan) } /** @@ -483,7 +929,7 @@ class SQLContext(@transient val sparkContext: SparkContext) * * @param tableName the name of the table to be unregistered. * - * @group userf + * @group basic */ def dropTempTable(tableName: String): Unit = { cacheManager.tryUncacheQuery(table(tableName)) @@ -494,7 +940,7 @@ class SQLContext(@transient val sparkContext: SparkContext) * Executes a SQL query using Spark, returning the result as a [[DataFrame]]. The dialect that is * used for SQL parsing can be configured with 'spark.sql.dialect'. * - * @group userf + * @group basic */ def sql(sqlText: String): DataFrame = { if (conf.dialect == "sql") { @@ -504,18 +950,66 @@ class SQLContext(@transient val sparkContext: SparkContext) } } - /** Returns the specified table as a [[DataFrame]]. */ + /** + * Returns the specified table as a [[DataFrame]]. + * + * @group ddl_ops + */ def table(tableName: String): DataFrame = DataFrame(this, catalog.lookupRelation(Seq(tableName))) + /** + * Returns a [[DataFrame]] containing names of existing tables in the current database. + * The returned DataFrame has two columns, tableName and isTemporary (a Boolean + * indicating if a table is a temporary one or not). + * + * @group ddl_ops + */ + def tables(): DataFrame = { + DataFrame(this, ShowTablesCommand(None)) + } + + /** + * Returns a [[DataFrame]] containing names of existing tables in the given database. + * The returned DataFrame has two columns, tableName and isTemporary (a Boolean + * indicating if a table is a temporary one or not). + * + * @group ddl_ops + */ + def tables(databaseName: String): DataFrame = { + DataFrame(this, ShowTablesCommand(Some(databaseName))) + } + + /** + * Returns the names of tables in the current database as an array. + * + * @group ddl_ops + */ + def tableNames(): Array[String] = { + catalog.getTables(None).map { + case (tableName, _) => tableName + }.toArray + } + + /** + * Returns the names of tables in the given database as an array. + * + * @group ddl_ops + */ + def tableNames(databaseName: String): Array[String] = { + catalog.getTables(Some(databaseName)).map { + case (tableName, _) => tableName + }.toArray + } + protected[sql] class SparkPlanner extends SparkStrategies { val sparkContext: SparkContext = self.sparkContext val sqlContext: SQLContext = self - def codegenEnabled = self.conf.codegenEnabled + def codegenEnabled: Boolean = self.conf.codegenEnabled - def numPartitions = self.conf.numShufflePartitions + def numPartitions: Int = self.conf.numShufflePartitions def strategies: Seq[Strategy] = experimental.extraStrategies ++ ( @@ -552,7 +1046,8 @@ class SQLContext(@transient val sparkContext: SparkContext) val projectSet = AttributeSet(projectList.flatMap(_.references)) val filterSet = AttributeSet(filterPredicates.flatMap(_.references)) - val filterCondition = prunePushedDownFilters(filterPredicates).reduceLeftOption(And) + val filterCondition = + prunePushedDownFilters(filterPredicates).reduceLeftOption(expressions.And) // Right now we still use a projection even if the only evaluation is applying an alias // to a column. Since this is a no-op, it could be avoided. However, using this @@ -586,7 +1081,32 @@ class SQLContext(@transient val sparkContext: SparkContext) @transient protected[sql] val prepareForExecution = new RuleExecutor[SparkPlan] { val batches = - Batch("Add exchange", Once, AddExchange(self)) :: Nil + Batch("Add exchange", Once, EnsureRequirements(self)) :: Nil + } + + protected[sql] def openSession(): SQLSession = { + detachSession() + val session = createSession() + tlSession.set(session) + + session + } + + protected[sql] def currentSession(): SQLSession = { + tlSession.get() + } + + protected[sql] def createSession(): SQLSession = { + new this.SQLSession() + } + + protected[sql] def detachSession(): Unit = { + tlSession.remove() + } + + protected[sql] class SQLSession { + // Note that this is a lazy val so we can override the default value in subclasses. + protected[sql] lazy val conf: SQLConf = new SQLConf } /** @@ -596,9 +1116,13 @@ class SQLContext(@transient val sparkContext: SparkContext) */ @DeveloperApi protected[sql] class QueryExecution(val logical: LogicalPlan) { + def assertAnalyzed(): Unit = analyzer.checkAnalysis(analyzed) - lazy val analyzed: LogicalPlan = ExtractPythonUdfs(analyzer(logical)) - lazy val withCachedData: LogicalPlan = cacheManager.useCachedData(analyzed) + lazy val analyzed: LogicalPlan = analyzer(logical) + lazy val withCachedData: LogicalPlan = { + assertAnalyzed() + cacheManager.useCachedData(analyzed) + } lazy val optimizedPlan: LogicalPlan = optimizer(withCachedData) // TODO: Don't just pick the first one... @@ -667,9 +1191,11 @@ class SQLContext(@transient val sparkContext: SparkContext) def needsConversion(dataType: DataType): Boolean = dataType match { case ByteType => true case ShortType => true + case LongType => true case FloatType => true case DateType => true case TimestampType => true + case StringType => true case ArrayType(_, _) => true case MapType(_, _, _) => true case StructType(_) => true @@ -696,38 +1222,56 @@ class SQLContext(@transient val sparkContext: SparkContext) * Returns a Catalyst Schema for the given java bean class. */ protected def getSchema(beanClass: Class[_]): Seq[AttributeReference] = { + val (dataType, _) = inferDataType(beanClass) + dataType.asInstanceOf[StructType].fields.map { f => + AttributeReference(f.name, f.dataType, f.nullable)() + } + } + + /** + * Infers the corresponding SQL data type of a Java class. + * @param clazz Java class + * @return (SQL data type, nullable) + */ + private def inferDataType(clazz: Class[_]): (DataType, Boolean) = { // TODO: All of this could probably be moved to Catalyst as it is mostly not Spark specific. - val beanInfo = Introspector.getBeanInfo(beanClass) - - // Note: The ordering of elements may differ from when the schema is inferred in Scala. - // This is because beanInfo.getPropertyDescriptors gives no guarantees about - // element ordering. - val fields = beanInfo.getPropertyDescriptors.filterNot(_.getName == "class") - fields.map { property => - val (dataType, nullable) = property.getPropertyType match { - case c: Class[_] if c.isAnnotationPresent(classOf[SQLUserDefinedType]) => - (c.getAnnotation(classOf[SQLUserDefinedType]).udt().newInstance(), true) - case c: Class[_] if c == classOf[java.lang.String] => (StringType, true) - case c: Class[_] if c == java.lang.Short.TYPE => (ShortType, false) - case c: Class[_] if c == java.lang.Integer.TYPE => (IntegerType, false) - case c: Class[_] if c == java.lang.Long.TYPE => (LongType, false) - case c: Class[_] if c == java.lang.Double.TYPE => (DoubleType, false) - case c: Class[_] if c == java.lang.Byte.TYPE => (ByteType, false) - case c: Class[_] if c == java.lang.Float.TYPE => (FloatType, false) - case c: Class[_] if c == java.lang.Boolean.TYPE => (BooleanType, false) - - case c: Class[_] if c == classOf[java.lang.Short] => (ShortType, true) - case c: Class[_] if c == classOf[java.lang.Integer] => (IntegerType, true) - case c: Class[_] if c == classOf[java.lang.Long] => (LongType, true) - case c: Class[_] if c == classOf[java.lang.Double] => (DoubleType, true) - case c: Class[_] if c == classOf[java.lang.Byte] => (ByteType, true) - case c: Class[_] if c == classOf[java.lang.Float] => (FloatType, true) - case c: Class[_] if c == classOf[java.lang.Boolean] => (BooleanType, true) - case c: Class[_] if c == classOf[java.math.BigDecimal] => (DecimalType(), true) - case c: Class[_] if c == classOf[java.sql.Date] => (DateType, true) - case c: Class[_] if c == classOf[java.sql.Timestamp] => (TimestampType, true) - } - AttributeReference(property.getName, dataType, nullable)() + clazz match { + case c: Class[_] if c.isAnnotationPresent(classOf[SQLUserDefinedType]) => + (c.getAnnotation(classOf[SQLUserDefinedType]).udt().newInstance(), true) + + case c: Class[_] if c == classOf[java.lang.String] => (StringType, true) + case c: Class[_] if c == java.lang.Short.TYPE => (ShortType, false) + case c: Class[_] if c == java.lang.Integer.TYPE => (IntegerType, false) + case c: Class[_] if c == java.lang.Long.TYPE => (LongType, false) + case c: Class[_] if c == java.lang.Double.TYPE => (DoubleType, false) + case c: Class[_] if c == java.lang.Byte.TYPE => (ByteType, false) + case c: Class[_] if c == java.lang.Float.TYPE => (FloatType, false) + case c: Class[_] if c == java.lang.Boolean.TYPE => (BooleanType, false) + + case c: Class[_] if c == classOf[java.lang.Short] => (ShortType, true) + case c: Class[_] if c == classOf[java.lang.Integer] => (IntegerType, true) + case c: Class[_] if c == classOf[java.lang.Long] => (LongType, true) + case c: Class[_] if c == classOf[java.lang.Double] => (DoubleType, true) + case c: Class[_] if c == classOf[java.lang.Byte] => (ByteType, true) + case c: Class[_] if c == classOf[java.lang.Float] => (FloatType, true) + case c: Class[_] if c == classOf[java.lang.Boolean] => (BooleanType, true) + + case c: Class[_] if c == classOf[java.math.BigDecimal] => (DecimalType(), true) + case c: Class[_] if c == classOf[java.sql.Date] => (DateType, true) + case c: Class[_] if c == classOf[java.sql.Timestamp] => (TimestampType, true) + + case c: Class[_] if c.isArray => + val (dataType, nullable) = inferDataType(c.getComponentType) + (ArrayType(dataType, nullable), true) + + case _ => + val beanInfo = Introspector.getBeanInfo(clazz) + val properties = beanInfo.getPropertyDescriptors.filterNot(_.getName == "class") + val fields = properties.map { property => + val (dataType, nullable) = inferDataType(property.getPropertyType) + new StructField(property.getName, dataType, nullable) + } + (new StructType(fields), true) } } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SparkSQLParser.scala b/sql/core/src/main/scala/org/apache/spark/sql/SparkSQLParser.scala index f1a4053b79113..5921eaf5e63f4 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SparkSQLParser.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SparkSQLParser.scala @@ -23,7 +23,7 @@ import scala.util.parsing.combinator.RegexParsers import org.apache.spark.sql.catalyst.AbstractSparkSQLParser import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference} import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan -import org.apache.spark.sql.execution.{UncacheTableCommand, CacheTableCommand, SetCommand} +import org.apache.spark.sql.execution._ import org.apache.spark.sql.types.StringType @@ -57,12 +57,16 @@ private[sql] class SparkSQLParser(fallback: String => LogicalPlan) extends Abstr protected val AS = Keyword("AS") protected val CACHE = Keyword("CACHE") + protected val CLEAR = Keyword("CLEAR") + protected val IN = Keyword("IN") protected val LAZY = Keyword("LAZY") protected val SET = Keyword("SET") + protected val SHOW = Keyword("SHOW") protected val TABLE = Keyword("TABLE") + protected val TABLES = Keyword("TABLES") protected val UNCACHE = Keyword("UNCACHE") - override protected lazy val start: Parser[LogicalPlan] = cache | uncache | set | others + override protected lazy val start: Parser[LogicalPlan] = cache | uncache | set | show | others private lazy val cache: Parser[LogicalPlan] = CACHE ~> LAZY.? ~ (TABLE ~> ident) ~ (AS ~> restInput).? ^^ { @@ -71,15 +75,22 @@ private[sql] class SparkSQLParser(fallback: String => LogicalPlan) extends Abstr } private lazy val uncache: Parser[LogicalPlan] = - UNCACHE ~ TABLE ~> ident ^^ { - case tableName => UncacheTableCommand(tableName) - } + ( UNCACHE ~ TABLE ~> ident ^^ { + case tableName => UncacheTableCommand(tableName) + } + | CLEAR ~ CACHE ^^^ ClearCacheCommand + ) private lazy val set: Parser[LogicalPlan] = SET ~> restInput ^^ { case input => SetCommandParser(input) } + private lazy val show: Parser[LogicalPlan] = + SHOW ~> TABLES ~ (IN ~> ident).? ^^ { + case _ ~ dbName => ShowTablesCommand(dbName) + } + private lazy val others: Parser[LogicalPlan] = wholeInput ^^ { case input => fallback(input) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala b/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala index d8b0a3b26dbab..b97aaf73529a3 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala @@ -32,9 +32,9 @@ import org.apache.spark.sql.types.DataType /** - * Functions for registering user-defined functions. + * Functions for registering user-defined functions. Use [[SQLContext.udf]] to access this. */ -class UDFRegistration(sqlContext: SQLContext) extends Logging { +class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging { private val functionRegistry = sqlContext.functionRegistry @@ -61,7 +61,7 @@ class UDFRegistration(sqlContext: SQLContext) extends Logging { val dataType = sqlContext.parseDataType(stringDataType) - def builder(e: Seq[Expression]) = + def builder(e: Seq[Expression]): PythonUDF = PythonUDF( name, command, diff --git a/sql/core/src/main/scala/org/apache/spark/sql/UserDefinedFunction.scala b/sql/core/src/main/scala/org/apache/spark/sql/UserDefinedFunction.scala index c60d4070942a9..295db539adfc4 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/UserDefinedFunction.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/UserDefinedFunction.scala @@ -27,7 +27,7 @@ import org.apache.spark.sql.execution.PythonUDF import org.apache.spark.sql.types.DataType /** - * A user-defined function. To create one, use the `udf` functions in [[Dsl]]. + * A user-defined function. To create one, use the `udf` functions in [[functions]]. * As an example: * {{{ * // Defined a UDF that returns true or false based on some numeric score. @@ -37,7 +37,7 @@ import org.apache.spark.sql.types.DataType * df.select( predict(df("score")) ) * }}} */ -case class UserDefinedFunction(f: AnyRef, dataType: DataType) { +case class UserDefinedFunction protected[sql] (f: AnyRef, dataType: DataType) { def apply(exprs: Column*): Column = { Column(ScalaUdf(f, dataType, exprs.map(_.expr))) @@ -45,7 +45,7 @@ case class UserDefinedFunction(f: AnyRef, dataType: DataType) { } /** - * A user-defined Python function. To create one, use the `pythonUDF` functions in [[Dsl]]. + * A user-defined Python function. To create one, use the `pythonUDF` functions in [[functions]]. * This is used by Python API. */ private[sql] case class UserDefinedPythonFunction( @@ -58,6 +58,7 @@ private[sql] case class UserDefinedPythonFunction( accumulator: Accumulator[JList[Array[Byte]]], dataType: DataType) { + /** Returns a [[Column]] that will evaluate to calling this UDF with the given input. */ def apply(exprs: Column*): Column = { val udf = PythonUDF(name, command, envVars, pythonIncludes, pythonExec, broadcastVars, accumulator, dataType, exprs.map(_.expr)) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/api/package.scala b/sql/core/src/main/scala/org/apache/spark/sql/api/package.scala new file mode 100644 index 0000000000000..cbbd005228d44 --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/api/package.scala @@ -0,0 +1,23 @@ +/* +* Licensed to the Apache Software Foundation (ASF) under one or more +* contributor license agreements. See the NOTICE file distributed with +* this work for additional information regarding copyright ownership. +* The ASF licenses this file to You under the Apache License, Version 2.0 +* (the "License"); you may not use this file except in compliance with +* the License. You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +*/ + +package org.apache.spark.sql + +/** + * Contains API classes that are specific to a single language (i.e. Java). + */ +package object api diff --git a/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala new file mode 100644 index 0000000000000..ae77f72998a22 --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala @@ -0,0 +1,153 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.api.r + +import java.io.{ByteArrayInputStream, ByteArrayOutputStream, DataInputStream, DataOutputStream} + +import org.apache.spark.api.java.{JavaRDD, JavaSparkContext} +import org.apache.spark.api.r.SerDe +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.catalyst.expressions.{Alias, Expression, NamedExpression} +import org.apache.spark.sql.types._ +import org.apache.spark.sql.{Column, DataFrame, GroupedData, Row, SQLContext, SaveMode} + +private[r] object SQLUtils { + def createSQLContext(jsc: JavaSparkContext): SQLContext = { + new SQLContext(jsc) + } + + def getJavaSparkContext(sqlCtx: SQLContext): JavaSparkContext = { + new JavaSparkContext(sqlCtx.sparkContext) + } + + def toSeq[T](arr: Array[T]): Seq[T] = { + arr.toSeq + } + + def createStructType(fields : Seq[StructField]): StructType = { + StructType(fields) + } + + def getSQLDataType(dataType: String): DataType = { + dataType match { + case "byte" => org.apache.spark.sql.types.ByteType + case "integer" => org.apache.spark.sql.types.IntegerType + case "double" => org.apache.spark.sql.types.DoubleType + case "numeric" => org.apache.spark.sql.types.DoubleType + case "character" => org.apache.spark.sql.types.StringType + case "string" => org.apache.spark.sql.types.StringType + case "binary" => org.apache.spark.sql.types.BinaryType + case "raw" => org.apache.spark.sql.types.BinaryType + case "logical" => org.apache.spark.sql.types.BooleanType + case "boolean" => org.apache.spark.sql.types.BooleanType + case "timestamp" => org.apache.spark.sql.types.TimestampType + case "date" => org.apache.spark.sql.types.DateType + case _ => throw new IllegalArgumentException(s"Invaid type $dataType") + } + } + + def createStructField(name: String, dataType: String, nullable: Boolean): StructField = { + val dtObj = getSQLDataType(dataType) + StructField(name, dtObj, nullable) + } + + def createDF(rdd: RDD[Array[Byte]], schema: StructType, sqlContext: SQLContext): DataFrame = { + val num = schema.fields.size + val rowRDD = rdd.map(bytesToRow) + sqlContext.createDataFrame(rowRDD, schema) + } + + // A helper to include grouping columns in Agg() + def aggWithGrouping(gd: GroupedData, exprs: Column*): DataFrame = { + val aggExprs = exprs.map { col => + col.expr match { + case expr: NamedExpression => expr + case expr: Expression => Alias(expr, expr.simpleString)() + } + } + gd.toDF(aggExprs) + } + + def dfToRowRDD(df: DataFrame): JavaRDD[Array[Byte]] = { + df.map(r => rowToRBytes(r)) + } + + private[this] def bytesToRow(bytes: Array[Byte]): Row = { + val bis = new ByteArrayInputStream(bytes) + val dis = new DataInputStream(bis) + val num = SerDe.readInt(dis) + Row.fromSeq((0 until num).map { i => + SerDe.readObject(dis) + }.toSeq) + } + + private[this] def rowToRBytes(row: Row): Array[Byte] = { + val bos = new ByteArrayOutputStream() + val dos = new DataOutputStream(bos) + + SerDe.writeInt(dos, row.length) + (0 until row.length).map { idx => + val obj: Object = row(idx).asInstanceOf[Object] + SerDe.writeObject(dos, obj) + } + bos.toByteArray() + } + + def dfToCols(df: DataFrame): Array[Array[Byte]] = { + // localDF is Array[Row] + val localDF = df.collect() + val numCols = df.columns.length + // dfCols is Array[Array[Any]] + val dfCols = convertRowsToColumns(localDF, numCols) + + dfCols.map { col => + colToRBytes(col) + } + } + + def convertRowsToColumns(localDF: Array[Row], numCols: Int): Array[Array[Any]] = { + (0 until numCols).map { colIdx => + localDF.map { row => + row(colIdx) + } + }.toArray + } + + def colToRBytes(col: Array[Any]): Array[Byte] = { + val numRows = col.length + val bos = new ByteArrayOutputStream() + val dos = new DataOutputStream(bos) + + SerDe.writeInt(dos, numRows) + + col.map { item => + val obj: Object = item.asInstanceOf[Object] + SerDe.writeObject(dos, obj) + } + bos.toByteArray() + } + + def saveMode(mode: String): SaveMode = { + mode match { + case "append" => SaveMode.Append + case "overwrite" => SaveMode.Overwrite + case "error" => SaveMode.ErrorIfExists + case "ignore" => SaveMode.Ignore + } + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnAccessor.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnAccessor.scala index 91c4c105b14e6..f615fb33a7c35 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnAccessor.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnAccessor.scala @@ -21,7 +21,7 @@ import java.nio.{ByteBuffer, ByteOrder} import org.apache.spark.sql.catalyst.expressions.MutableRow import org.apache.spark.sql.columnar.compression.CompressibleColumnAccessor -import org.apache.spark.sql.types.{BinaryType, DataType, NativeType} +import org.apache.spark.sql.types._ /** * An `Iterator` like trait used to extract values from columnar byte buffer. When a value is @@ -48,9 +48,9 @@ private[sql] abstract class BasicColumnAccessor[T <: DataType, JvmType]( protected def initialize() {} - def hasNext = buffer.hasRemaining + override def hasNext: Boolean = buffer.hasRemaining - def extractTo(row: MutableRow, ordinal: Int): Unit = { + override def extractTo(row: MutableRow, ordinal: Int): Unit = { extractSingle(row, ordinal) } @@ -89,6 +89,9 @@ private[sql] class DoubleColumnAccessor(buffer: ByteBuffer) private[sql] class FloatColumnAccessor(buffer: ByteBuffer) extends NativeColumnAccessor(buffer, FLOAT) +private[sql] class FixedDecimalColumnAccessor(buffer: ByteBuffer, precision: Int, scale: Int) + extends NativeColumnAccessor(buffer, FIXED_DECIMAL(precision, scale)) + private[sql] class StringColumnAccessor(buffer: ByteBuffer) extends NativeColumnAccessor(buffer, STRING) @@ -107,24 +110,28 @@ private[sql] class GenericColumnAccessor(buffer: ByteBuffer) with NullableColumnAccessor private[sql] object ColumnAccessor { - def apply(buffer: ByteBuffer): ColumnAccessor = { + def apply(dataType: DataType, buffer: ByteBuffer): ColumnAccessor = { val dup = buffer.duplicate().order(ByteOrder.nativeOrder) - // The first 4 bytes in the buffer indicate the column type. - val columnTypeId = dup.getInt() - - columnTypeId match { - case INT.typeId => new IntColumnAccessor(dup) - case LONG.typeId => new LongColumnAccessor(dup) - case FLOAT.typeId => new FloatColumnAccessor(dup) - case DOUBLE.typeId => new DoubleColumnAccessor(dup) - case BOOLEAN.typeId => new BooleanColumnAccessor(dup) - case BYTE.typeId => new ByteColumnAccessor(dup) - case SHORT.typeId => new ShortColumnAccessor(dup) - case STRING.typeId => new StringColumnAccessor(dup) - case DATE.typeId => new DateColumnAccessor(dup) - case TIMESTAMP.typeId => new TimestampColumnAccessor(dup) - case BINARY.typeId => new BinaryColumnAccessor(dup) - case GENERIC.typeId => new GenericColumnAccessor(dup) + + // The first 4 bytes in the buffer indicate the column type. This field is not used now, + // because we always know the data type of the column ahead of time. + dup.getInt() + + dataType match { + case IntegerType => new IntColumnAccessor(dup) + case LongType => new LongColumnAccessor(dup) + case FloatType => new FloatColumnAccessor(dup) + case DoubleType => new DoubleColumnAccessor(dup) + case BooleanType => new BooleanColumnAccessor(dup) + case ByteType => new ByteColumnAccessor(dup) + case ShortType => new ShortColumnAccessor(dup) + case StringType => new StringColumnAccessor(dup) + case BinaryType => new BinaryColumnAccessor(dup) + case DateType => new DateColumnAccessor(dup) + case TimestampType => new TimestampColumnAccessor(dup) + case DecimalType.Fixed(precision, scale) if precision < 19 => + new FixedDecimalColumnAccessor(dup, precision, scale) + case _ => new GenericColumnAccessor(dup) } } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala index 3a4977b836af7..00ed70430b84d 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala @@ -58,7 +58,7 @@ private[sql] class BasicColumnBuilder[T <: DataType, JvmType]( override def initialize( initialSize: Int, columnName: String = "", - useCompression: Boolean = false) = { + useCompression: Boolean = false): Unit = { val size = if (initialSize == 0) DEFAULT_INITIAL_BUFFER_SIZE else initialSize this.columnName = columnName @@ -73,7 +73,7 @@ private[sql] class BasicColumnBuilder[T <: DataType, JvmType]( columnType.append(row, ordinal, buffer) } - override def build() = { + override def build(): ByteBuffer = { buffer.flip().asInstanceOf[ByteBuffer] } } @@ -106,6 +106,13 @@ private[sql] class DoubleColumnBuilder extends NativeColumnBuilder(new DoubleCol private[sql] class FloatColumnBuilder extends NativeColumnBuilder(new FloatColumnStats, FLOAT) +private[sql] class FixedDecimalColumnBuilder( + precision: Int, + scale: Int) + extends NativeColumnBuilder( + new FixedDecimalColumnStats, + FIXED_DECIMAL(precision, scale)) + private[sql] class StringColumnBuilder extends NativeColumnBuilder(new StringColumnStats, STRING) private[sql] class DateColumnBuilder extends NativeColumnBuilder(new DateColumnStats, DATE) @@ -139,25 +146,26 @@ private[sql] object ColumnBuilder { } def apply( - typeId: Int, + dataType: DataType, initialSize: Int = 0, columnName: String = "", useCompression: Boolean = false): ColumnBuilder = { - - val builder = (typeId match { - case INT.typeId => new IntColumnBuilder - case LONG.typeId => new LongColumnBuilder - case FLOAT.typeId => new FloatColumnBuilder - case DOUBLE.typeId => new DoubleColumnBuilder - case BOOLEAN.typeId => new BooleanColumnBuilder - case BYTE.typeId => new ByteColumnBuilder - case SHORT.typeId => new ShortColumnBuilder - case STRING.typeId => new StringColumnBuilder - case BINARY.typeId => new BinaryColumnBuilder - case GENERIC.typeId => new GenericColumnBuilder - case DATE.typeId => new DateColumnBuilder - case TIMESTAMP.typeId => new TimestampColumnBuilder - }).asInstanceOf[ColumnBuilder] + val builder: ColumnBuilder = dataType match { + case IntegerType => new IntColumnBuilder + case LongType => new LongColumnBuilder + case FloatType => new FloatColumnBuilder + case DoubleType => new DoubleColumnBuilder + case BooleanType => new BooleanColumnBuilder + case ByteType => new ByteColumnBuilder + case ShortType => new ShortColumnBuilder + case StringType => new StringColumnBuilder + case BinaryType => new BinaryColumnBuilder + case DateType => new DateColumnBuilder + case TimestampType => new TimestampColumnBuilder + case DecimalType.Fixed(precision, scale) if precision < 19 => + new FixedDecimalColumnBuilder(precision, scale) + case _ => new GenericColumnBuilder + } builder.initialize(initialSize, columnName, useCompression) builder diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala index cad0667b46435..b0f983c180673 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala @@ -76,7 +76,7 @@ private[sql] sealed trait ColumnStats extends Serializable { private[sql] class NoopColumnStats extends ColumnStats { override def gatherStats(row: Row, ordinal: Int): Unit = super.gatherStats(row, ordinal) - def collectedStatistics = Row(null, null, nullCount, count, 0L) + override def collectedStatistics: Row = Row(null, null, nullCount, count, 0L) } private[sql] class BooleanColumnStats extends ColumnStats { @@ -93,7 +93,7 @@ private[sql] class BooleanColumnStats extends ColumnStats { } } - def collectedStatistics = Row(lower, upper, nullCount, count, sizeInBytes) + override def collectedStatistics: Row = Row(lower, upper, nullCount, count, sizeInBytes) } private[sql] class ByteColumnStats extends ColumnStats { @@ -110,7 +110,7 @@ private[sql] class ByteColumnStats extends ColumnStats { } } - def collectedStatistics = Row(lower, upper, nullCount, count, sizeInBytes) + override def collectedStatistics: Row = Row(lower, upper, nullCount, count, sizeInBytes) } private[sql] class ShortColumnStats extends ColumnStats { @@ -127,7 +127,7 @@ private[sql] class ShortColumnStats extends ColumnStats { } } - def collectedStatistics = Row(lower, upper, nullCount, count, sizeInBytes) + override def collectedStatistics: Row = Row(lower, upper, nullCount, count, sizeInBytes) } private[sql] class LongColumnStats extends ColumnStats { @@ -144,7 +144,7 @@ private[sql] class LongColumnStats extends ColumnStats { } } - def collectedStatistics = Row(lower, upper, nullCount, count, sizeInBytes) + override def collectedStatistics: Row = Row(lower, upper, nullCount, count, sizeInBytes) } private[sql] class DoubleColumnStats extends ColumnStats { @@ -161,7 +161,7 @@ private[sql] class DoubleColumnStats extends ColumnStats { } } - def collectedStatistics = Row(lower, upper, nullCount, count, sizeInBytes) + override def collectedStatistics: Row = Row(lower, upper, nullCount, count, sizeInBytes) } private[sql] class FloatColumnStats extends ColumnStats { @@ -178,7 +178,24 @@ private[sql] class FloatColumnStats extends ColumnStats { } } - def collectedStatistics = Row(lower, upper, nullCount, count, sizeInBytes) + override def collectedStatistics: Row = Row(lower, upper, nullCount, count, sizeInBytes) +} + +private[sql] class FixedDecimalColumnStats extends ColumnStats { + protected var upper: Decimal = null + protected var lower: Decimal = null + + override def gatherStats(row: Row, ordinal: Int): Unit = { + super.gatherStats(row, ordinal) + if (!row.isNullAt(ordinal)) { + val value = row(ordinal).asInstanceOf[Decimal] + if (upper == null || value.compareTo(upper) > 0) upper = value + if (lower == null || value.compareTo(lower) < 0) lower = value + sizeInBytes += FIXED_DECIMAL.defaultSize + } + } + + override def collectedStatistics: Row = Row(lower, upper, nullCount, count, sizeInBytes) } private[sql] class IntColumnStats extends ColumnStats { @@ -195,24 +212,24 @@ private[sql] class IntColumnStats extends ColumnStats { } } - def collectedStatistics = Row(lower, upper, nullCount, count, sizeInBytes) + override def collectedStatistics: Row = Row(lower, upper, nullCount, count, sizeInBytes) } private[sql] class StringColumnStats extends ColumnStats { - protected var upper: String = null - protected var lower: String = null + protected var upper: UTF8String = null + protected var lower: UTF8String = null override def gatherStats(row: Row, ordinal: Int): Unit = { super.gatherStats(row, ordinal) if (!row.isNullAt(ordinal)) { - val value = row.getString(ordinal) + val value = row(ordinal).asInstanceOf[UTF8String] if (upper == null || value.compareTo(upper) > 0) upper = value if (lower == null || value.compareTo(lower) < 0) lower = value sizeInBytes += STRING.actualSize(row, ordinal) } } - def collectedStatistics = Row(lower, upper, nullCount, count, sizeInBytes) + override def collectedStatistics: Row = Row(lower, upper, nullCount, count, sizeInBytes) } private[sql] class DateColumnStats extends IntColumnStats @@ -231,7 +248,7 @@ private[sql] class TimestampColumnStats extends ColumnStats { } } - def collectedStatistics = Row(lower, upper, nullCount, count, sizeInBytes) + override def collectedStatistics: Row = Row(lower, upper, nullCount, count, sizeInBytes) } private[sql] class BinaryColumnStats extends ColumnStats { @@ -242,7 +259,7 @@ private[sql] class BinaryColumnStats extends ColumnStats { } } - def collectedStatistics = Row(null, null, nullCount, count, sizeInBytes) + override def collectedStatistics: Row = Row(null, null, nullCount, count, sizeInBytes) } private[sql] class GenericColumnStats extends ColumnStats { @@ -253,5 +270,5 @@ private[sql] class GenericColumnStats extends ColumnStats { } } - def collectedStatistics = Row(null, null, nullCount, count, sizeInBytes) + override def collectedStatistics: Row = Row(null, null, nullCount, count, sizeInBytes) } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala index db5bc0de363c7..1b9e0df2dcb5e 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala @@ -18,7 +18,7 @@ package org.apache.spark.sql.columnar import java.nio.ByteBuffer -import java.sql.{Date, Timestamp} +import java.sql.Timestamp import scala.reflect.runtime.universe.TypeTag @@ -98,7 +98,7 @@ private[sql] sealed abstract class ColumnType[T <: DataType, JvmType]( */ def clone(v: JvmType): JvmType = v - override def toString = getClass.getSimpleName.stripSuffix("$") + override def toString: String = getClass.getSimpleName.stripSuffix("$") } private[sql] abstract class NativeColumnType[T <: NativeType]( @@ -114,7 +114,7 @@ private[sql] abstract class NativeColumnType[T <: NativeType]( } private[sql] object INT extends NativeColumnType(IntegerType, 0, 4) { - def append(v: Int, buffer: ByteBuffer): Unit = { + override def append(v: Int, buffer: ByteBuffer): Unit = { buffer.putInt(v) } @@ -122,7 +122,7 @@ private[sql] object INT extends NativeColumnType(IntegerType, 0, 4) { buffer.putInt(row.getInt(ordinal)) } - def extract(buffer: ByteBuffer) = { + override def extract(buffer: ByteBuffer): Int = { buffer.getInt() } @@ -134,7 +134,7 @@ private[sql] object INT extends NativeColumnType(IntegerType, 0, 4) { row.setInt(ordinal, value) } - override def getField(row: Row, ordinal: Int) = row.getInt(ordinal) + override def getField(row: Row, ordinal: Int): Int = row.getInt(ordinal) override def copyField(from: Row, fromOrdinal: Int, to: MutableRow, toOrdinal: Int): Unit = { to.setInt(toOrdinal, from.getInt(fromOrdinal)) @@ -150,7 +150,7 @@ private[sql] object LONG extends NativeColumnType(LongType, 1, 8) { buffer.putLong(row.getLong(ordinal)) } - override def extract(buffer: ByteBuffer) = { + override def extract(buffer: ByteBuffer): Long = { buffer.getLong() } @@ -162,7 +162,7 @@ private[sql] object LONG extends NativeColumnType(LongType, 1, 8) { row.setLong(ordinal, value) } - override def getField(row: Row, ordinal: Int) = row.getLong(ordinal) + override def getField(row: Row, ordinal: Int): Long = row.getLong(ordinal) override def copyField(from: Row, fromOrdinal: Int, to: MutableRow, toOrdinal: Int): Unit = { to.setLong(toOrdinal, from.getLong(fromOrdinal)) @@ -178,7 +178,7 @@ private[sql] object FLOAT extends NativeColumnType(FloatType, 2, 4) { buffer.putFloat(row.getFloat(ordinal)) } - override def extract(buffer: ByteBuffer) = { + override def extract(buffer: ByteBuffer): Float = { buffer.getFloat() } @@ -190,7 +190,7 @@ private[sql] object FLOAT extends NativeColumnType(FloatType, 2, 4) { row.setFloat(ordinal, value) } - override def getField(row: Row, ordinal: Int) = row.getFloat(ordinal) + override def getField(row: Row, ordinal: Int): Float = row.getFloat(ordinal) override def copyField(from: Row, fromOrdinal: Int, to: MutableRow, toOrdinal: Int): Unit = { to.setFloat(toOrdinal, from.getFloat(fromOrdinal)) @@ -206,7 +206,7 @@ private[sql] object DOUBLE extends NativeColumnType(DoubleType, 3, 8) { buffer.putDouble(row.getDouble(ordinal)) } - override def extract(buffer: ByteBuffer) = { + override def extract(buffer: ByteBuffer): Double = { buffer.getDouble() } @@ -218,7 +218,7 @@ private[sql] object DOUBLE extends NativeColumnType(DoubleType, 3, 8) { row.setDouble(ordinal, value) } - override def getField(row: Row, ordinal: Int) = row.getDouble(ordinal) + override def getField(row: Row, ordinal: Int): Double = row.getDouble(ordinal) override def copyField(from: Row, fromOrdinal: Int, to: MutableRow, toOrdinal: Int): Unit = { to.setDouble(toOrdinal, from.getDouble(fromOrdinal)) @@ -234,7 +234,7 @@ private[sql] object BOOLEAN extends NativeColumnType(BooleanType, 4, 1) { buffer.put(if (row.getBoolean(ordinal)) 1: Byte else 0: Byte) } - override def extract(buffer: ByteBuffer) = buffer.get() == 1 + override def extract(buffer: ByteBuffer): Boolean = buffer.get() == 1 override def extract(buffer: ByteBuffer, row: MutableRow, ordinal: Int): Unit = { row.setBoolean(ordinal, buffer.get() == 1) @@ -244,7 +244,7 @@ private[sql] object BOOLEAN extends NativeColumnType(BooleanType, 4, 1) { row.setBoolean(ordinal, value) } - override def getField(row: Row, ordinal: Int) = row.getBoolean(ordinal) + override def getField(row: Row, ordinal: Int): Boolean = row.getBoolean(ordinal) override def copyField(from: Row, fromOrdinal: Int, to: MutableRow, toOrdinal: Int): Unit = { to.setBoolean(toOrdinal, from.getBoolean(fromOrdinal)) @@ -260,7 +260,7 @@ private[sql] object BYTE extends NativeColumnType(ByteType, 5, 1) { buffer.put(row.getByte(ordinal)) } - override def extract(buffer: ByteBuffer) = { + override def extract(buffer: ByteBuffer): Byte = { buffer.get() } @@ -272,7 +272,7 @@ private[sql] object BYTE extends NativeColumnType(ByteType, 5, 1) { row.setByte(ordinal, value) } - override def getField(row: Row, ordinal: Int) = row.getByte(ordinal) + override def getField(row: Row, ordinal: Int): Byte = row.getByte(ordinal) override def copyField(from: Row, fromOrdinal: Int, to: MutableRow, toOrdinal: Int): Unit = { to.setByte(toOrdinal, from.getByte(fromOrdinal)) @@ -288,7 +288,7 @@ private[sql] object SHORT extends NativeColumnType(ShortType, 6, 2) { buffer.putShort(row.getShort(ordinal)) } - override def extract(buffer: ByteBuffer) = { + override def extract(buffer: ByteBuffer): Short = { buffer.getShort() } @@ -300,7 +300,7 @@ private[sql] object SHORT extends NativeColumnType(ShortType, 6, 2) { row.setShort(ordinal, value) } - override def getField(row: Row, ordinal: Int) = row.getShort(ordinal) + override def getField(row: Row, ordinal: Int): Short = row.getShort(ordinal) override def copyField(from: Row, fromOrdinal: Int, to: MutableRow, toOrdinal: Int): Unit = { to.setShort(toOrdinal, from.getShort(fromOrdinal)) @@ -312,31 +312,33 @@ private[sql] object STRING extends NativeColumnType(StringType, 7, 8) { row.getString(ordinal).getBytes("utf-8").length + 4 } - override def append(v: String, buffer: ByteBuffer): Unit = { - val stringBytes = v.getBytes("utf-8") + override def append(v: UTF8String, buffer: ByteBuffer): Unit = { + val stringBytes = v.getBytes buffer.putInt(stringBytes.length).put(stringBytes, 0, stringBytes.length) } - override def extract(buffer: ByteBuffer) = { + override def extract(buffer: ByteBuffer): UTF8String = { val length = buffer.getInt() val stringBytes = new Array[Byte](length) buffer.get(stringBytes, 0, length) - new String(stringBytes, "utf-8") + UTF8String(stringBytes) } - override def setField(row: MutableRow, ordinal: Int, value: String): Unit = { - row.setString(ordinal, value) + override def setField(row: MutableRow, ordinal: Int, value: UTF8String): Unit = { + row.update(ordinal, value) } - override def getField(row: Row, ordinal: Int) = row.getString(ordinal) + override def getField(row: Row, ordinal: Int): UTF8String = { + row(ordinal).asInstanceOf[UTF8String] + } override def copyField(from: Row, fromOrdinal: Int, to: MutableRow, toOrdinal: Int): Unit = { - to.setString(toOrdinal, from.getString(fromOrdinal)) + to.update(toOrdinal, from(fromOrdinal)) } } private[sql] object DATE extends NativeColumnType(DateType, 8, 4) { - override def extract(buffer: ByteBuffer) = { + override def extract(buffer: ByteBuffer): Int = { buffer.getInt } @@ -344,7 +346,7 @@ private[sql] object DATE extends NativeColumnType(DateType, 8, 4) { buffer.putInt(v) } - override def getField(row: Row, ordinal: Int) = { + override def getField(row: Row, ordinal: Int): Int = { row(ordinal).asInstanceOf[Int] } @@ -354,7 +356,7 @@ private[sql] object DATE extends NativeColumnType(DateType, 8, 4) { } private[sql] object TIMESTAMP extends NativeColumnType(TimestampType, 9, 12) { - override def extract(buffer: ByteBuffer) = { + override def extract(buffer: ByteBuffer): Timestamp = { val timestamp = new Timestamp(buffer.getLong()) timestamp.setNanos(buffer.getInt()) timestamp @@ -364,7 +366,7 @@ private[sql] object TIMESTAMP extends NativeColumnType(TimestampType, 9, 12) { buffer.putLong(v.getTime).putInt(v.getNanos) } - override def getField(row: Row, ordinal: Int) = { + override def getField(row: Row, ordinal: Int): Timestamp = { row(ordinal).asInstanceOf[Timestamp] } @@ -373,12 +375,39 @@ private[sql] object TIMESTAMP extends NativeColumnType(TimestampType, 9, 12) { } } +private[sql] case class FIXED_DECIMAL(precision: Int, scale: Int) + extends NativeColumnType( + DecimalType(Some(PrecisionInfo(precision, scale))), + 10, + FIXED_DECIMAL.defaultSize) { + + override def extract(buffer: ByteBuffer): Decimal = { + Decimal(buffer.getLong(), precision, scale) + } + + override def append(v: Decimal, buffer: ByteBuffer): Unit = { + buffer.putLong(v.toUnscaledLong) + } + + override def getField(row: Row, ordinal: Int): Decimal = { + row(ordinal).asInstanceOf[Decimal] + } + + override def setField(row: MutableRow, ordinal: Int, value: Decimal): Unit = { + row(ordinal) = value + } +} + +private[sql] object FIXED_DECIMAL { + val defaultSize = 8 +} + private[sql] sealed abstract class ByteArrayColumnType[T <: DataType]( typeId: Int, defaultSize: Int) extends ColumnType[T, Array[Byte]](typeId, defaultSize) { - override def actualSize(row: Row, ordinal: Int) = { + override def actualSize(row: Row, ordinal: Int): Int = { getField(row, ordinal).length + 4 } @@ -386,7 +415,7 @@ private[sql] sealed abstract class ByteArrayColumnType[T <: DataType]( buffer.putInt(v.length).put(v, 0, v.length) } - override def extract(buffer: ByteBuffer) = { + override def extract(buffer: ByteBuffer): Array[Byte] = { val length = buffer.getInt() val bytes = new Array[Byte](length) buffer.get(bytes, 0, length) @@ -394,40 +423,46 @@ private[sql] sealed abstract class ByteArrayColumnType[T <: DataType]( } } -private[sql] object BINARY extends ByteArrayColumnType[BinaryType.type](10, 16) { +private[sql] object BINARY extends ByteArrayColumnType[BinaryType.type](11, 16) { override def setField(row: MutableRow, ordinal: Int, value: Array[Byte]): Unit = { row(ordinal) = value } - override def getField(row: Row, ordinal: Int) = row(ordinal).asInstanceOf[Array[Byte]] + override def getField(row: Row, ordinal: Int): Array[Byte] = { + row(ordinal).asInstanceOf[Array[Byte]] + } } // Used to process generic objects (all types other than those listed above). Objects should be // serialized first before appending to the column `ByteBuffer`, and is also extracted as serialized // byte array. -private[sql] object GENERIC extends ByteArrayColumnType[DataType](11, 16) { +private[sql] object GENERIC extends ByteArrayColumnType[DataType](12, 16) { override def setField(row: MutableRow, ordinal: Int, value: Array[Byte]): Unit = { row(ordinal) = SparkSqlSerializer.deserialize[Any](value) } - override def getField(row: Row, ordinal: Int) = SparkSqlSerializer.serialize(row(ordinal)) + override def getField(row: Row, ordinal: Int): Array[Byte] = { + SparkSqlSerializer.serialize(row(ordinal)) + } } private[sql] object ColumnType { def apply(dataType: DataType): ColumnType[_, _] = { dataType match { - case IntegerType => INT - case LongType => LONG - case FloatType => FLOAT - case DoubleType => DOUBLE - case BooleanType => BOOLEAN - case ByteType => BYTE - case ShortType => SHORT - case StringType => STRING - case BinaryType => BINARY - case DateType => DATE + case IntegerType => INT + case LongType => LONG + case FloatType => FLOAT + case DoubleType => DOUBLE + case BooleanType => BOOLEAN + case ByteType => BYTE + case ShortType => SHORT + case StringType => STRING + case BinaryType => BINARY + case DateType => DATE case TimestampType => TIMESTAMP - case _ => GENERIC + case DecimalType.Fixed(precision, scale) if precision < 19 => + FIXED_DECIMAL(precision, scale) + case _ => GENERIC } } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala index 11d5943fb427f..d9b6fb43ab83d 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala @@ -19,10 +19,15 @@ package org.apache.spark.sql.columnar import java.nio.ByteBuffer +import org.apache.spark.{Accumulable, Accumulator, Accumulators} +import org.apache.spark.sql.catalyst.expressions + import scala.collection.mutable.ArrayBuffer +import scala.collection.mutable.HashMap import org.apache.spark.rdd.RDD import org.apache.spark.sql.Row +import org.apache.spark.SparkContext import org.apache.spark.sql.catalyst.analysis.MultiInstanceRelation import org.apache.spark.sql.catalyst.dsl.expressions._ import org.apache.spark.sql.catalyst.expressions._ @@ -50,11 +55,16 @@ private[sql] case class InMemoryRelation( child: SparkPlan, tableName: Option[String])( private var _cachedColumnBuffers: RDD[CachedBatch] = null, - private var _statistics: Statistics = null) + private var _statistics: Statistics = null, + private var _batchStats: Accumulable[ArrayBuffer[Row], Row] = null) extends LogicalPlan with MultiInstanceRelation { - private val batchStats = - child.sqlContext.sparkContext.accumulableCollection(ArrayBuffer.empty[Row]) + private val batchStats: Accumulable[ArrayBuffer[Row], Row] = + if (_batchStats == null) { + child.sqlContext.sparkContext.accumulableCollection(ArrayBuffer.empty[Row]) + } else { + _batchStats + } val partitionStatistics = new PartitionStatistics(output) @@ -77,20 +87,23 @@ private[sql] case class InMemoryRelation( _statistics } - override def statistics = if (_statistics == null) { - if (batchStats.value.isEmpty) { - // Underlying columnar RDD hasn't been materialized, no useful statistics information - // available, return the default statistics. - Statistics(sizeInBytes = child.sqlContext.conf.defaultSizeInBytes) + override def statistics: Statistics = { + if (_statistics == null) { + if (batchStats.value.isEmpty) { + // Underlying columnar RDD hasn't been materialized, no useful statistics information + // available, return the default statistics. + Statistics(sizeInBytes = child.sqlContext.conf.defaultSizeInBytes) + } else { + // Underlying columnar RDD has been materialized, required information has also been + // collected via the `batchStats` accumulator, compute the final statistics, + // and update `_statistics`. + _statistics = Statistics(sizeInBytes = computeSizeInBytes) + _statistics + } } else { - // Underlying columnar RDD has been materialized, required information has also been collected - // via the `batchStats` accumulator, compute the final statistics, and update `_statistics`. - _statistics = Statistics(sizeInBytes = computeSizeInBytes) + // Pre-computed statistics _statistics } - } else { - // Pre-computed statistics - _statistics } // If the cached column buffers were not passed in, we calculate them in the constructor. @@ -99,7 +112,7 @@ private[sql] case class InMemoryRelation( buildBuffers() } - def recache() = { + def recache(): Unit = { _cachedColumnBuffers.unpersist() _cachedColumnBuffers = null buildBuffers() @@ -109,16 +122,27 @@ private[sql] case class InMemoryRelation( val output = child.output val cached = child.execute().mapPartitions { rowIterator => new Iterator[CachedBatch] { - def next() = { + def next(): CachedBatch = { val columnBuilders = output.map { attribute => val columnType = ColumnType(attribute.dataType) val initialBufferSize = columnType.defaultSize * batchSize - ColumnBuilder(columnType.typeId, initialBufferSize, attribute.name, useCompression) + ColumnBuilder(attribute.dataType, initialBufferSize, attribute.name, useCompression) }.toArray var rowCount = 0 while (rowIterator.hasNext && rowCount < batchSize) { val row = rowIterator.next() + + // Added for SPARK-6082. This assertion can be useful for scenarios when something + // like Hive TRANSFORM is used. The external data generation script used in TRANSFORM + // may result malformed rows, causing ArrayIndexOutOfBoundsException, which is somewhat + // hard to decipher. + assert( + row.size == columnBuilders.size, + s"""Row column number mismatch, expected ${output.size} columns, but got ${row.size}. + |Row content: $row + """.stripMargin) + var i = 0 while (i < row.length) { columnBuilders(i).appendFrom(row, i) @@ -133,7 +157,7 @@ private[sql] case class InMemoryRelation( CachedBatch(columnBuilders.map(_.build().array()), stats) } - def hasNext = rowIterator.hasNext + def hasNext: Boolean = rowIterator.hasNext } }.persist(storageLevel) @@ -144,12 +168,12 @@ private[sql] case class InMemoryRelation( def withOutput(newOutput: Seq[Attribute]): InMemoryRelation = { InMemoryRelation( newOutput, useCompression, batchSize, storageLevel, child, tableName)( - _cachedColumnBuffers, statisticsToBePropagated) + _cachedColumnBuffers, statisticsToBePropagated, batchStats) } - override def children = Seq.empty + override def children: Seq[LogicalPlan] = Seq.empty - override def newInstance() = { + override def newInstance(): this.type = { new InMemoryRelation( output.map(_.newInstance()), useCompression, @@ -158,13 +182,20 @@ private[sql] case class InMemoryRelation( child, tableName)( _cachedColumnBuffers, - statisticsToBePropagated).asInstanceOf[this.type] + statisticsToBePropagated, + batchStats).asInstanceOf[this.type] } - def cachedColumnBuffers = _cachedColumnBuffers + def cachedColumnBuffers: RDD[CachedBatch] = _cachedColumnBuffers override protected def otherCopyArgs: Seq[AnyRef] = - Seq(_cachedColumnBuffers, statisticsToBePropagated) + Seq(_cachedColumnBuffers, statisticsToBePropagated, batchStats) + + private[sql] def uncache(blocking: Boolean): Unit = { + Accumulators.remove(batchStats.id) + cachedColumnBuffers.unpersist(blocking) + _cachedColumnBuffers = null + } } private[sql] case class InMemoryColumnarTableScan( @@ -209,7 +240,7 @@ private[sql] case class InMemoryColumnarTableScan( case IsNotNull(a: Attribute) => statsFor(a).count - statsFor(a).nullCount > 0 } - val partitionFilters = { + val partitionFilters: Seq[Expression] = { predicates.flatMap { p => val filter = buildFilter.lift(p) val boundFilter = @@ -227,15 +258,20 @@ private[sql] case class InMemoryColumnarTableScan( } } + lazy val enableAccumulators: Boolean = + sqlContext.getConf("spark.sql.inMemoryTableScanStatistics.enable", "false").toBoolean + // Accumulators used for testing purposes - val readPartitions = sparkContext.accumulator(0) - val readBatches = sparkContext.accumulator(0) + lazy val readPartitions: Accumulator[Int] = sparkContext.accumulator(0) + lazy val readBatches: Accumulator[Int] = sparkContext.accumulator(0) private val inMemoryPartitionPruningEnabled = sqlContext.conf.inMemoryPartitionPruning - override def execute() = { - readPartitions.setValue(0) - readBatches.setValue(0) + override def execute(): RDD[Row] = { + if (enableAccumulators) { + readPartitions.setValue(0) + readBatches.setValue(0) + } relation.cachedColumnBuffers.mapPartitions { cachedBatchIterator => val partitionFilter = newPredicate( @@ -260,17 +296,19 @@ private[sql] case class InMemoryColumnarTableScan( val nextRow = new SpecificMutableRow(requestedColumnDataTypes) - def cachedBatchesToRows(cacheBatches: Iterator[CachedBatch]) = { + def cachedBatchesToRows(cacheBatches: Iterator[CachedBatch]): Iterator[Row] = { val rows = cacheBatches.flatMap { cachedBatch => // Build column accessors - val columnAccessors = requestedColumnIndices.map { batch => - ColumnAccessor(ByteBuffer.wrap(cachedBatch.buffers(batch))) + val columnAccessors = requestedColumnIndices.map { batchColumnIndex => + ColumnAccessor( + relation.output(batchColumnIndex).dataType, + ByteBuffer.wrap(cachedBatch.buffers(batchColumnIndex))) } // Extract rows via column accessors new Iterator[Row] { private[this] val rowLen = nextRow.length - override def next() = { + override def next(): Row = { var i = 0 while (i < rowLen) { columnAccessors(i).extractTo(nextRow, i) @@ -279,11 +317,11 @@ private[sql] case class InMemoryColumnarTableScan( nextRow } - override def hasNext = columnAccessors(0).hasNext + override def hasNext: Boolean = columnAccessors(0).hasNext } } - if (rows.hasNext) { + if (rows.hasNext && enableAccumulators) { readPartitions += 1 } @@ -295,14 +333,16 @@ private[sql] case class InMemoryColumnarTableScan( if (inMemoryPartitionPruningEnabled) { cachedBatchIterator.filter { cachedBatch => if (!partitionFilter(cachedBatch.stats)) { - def statsString = relation.partitionStatistics.schema + def statsString: String = relation.partitionStatistics.schema .zip(cachedBatch.stats.toSeq) .map { case (a, s) => s"${a.name}: $s" } .mkString(", ") logInfo(s"Skipping partition based on stats $statsString") false } else { - readBatches += 1 + if (enableAccumulators) { + readBatches += 1 + } true } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/NullableColumnAccessor.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/NullableColumnAccessor.scala index 965782a40031b..4d35650d4b1eb 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/NullableColumnAccessor.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/NullableColumnAccessor.scala @@ -55,5 +55,5 @@ private[sql] trait NullableColumnAccessor extends ColumnAccessor { pos += 1 } - abstract override def hasNext = seenNulls < nullCount || super.hasNext + abstract override def hasNext: Boolean = seenNulls < nullCount || super.hasNext } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnAccessor.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnAccessor.scala index 7dff9deac8dc0..d0b602a834dfe 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnAccessor.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnAccessor.scala @@ -26,12 +26,12 @@ private[sql] trait CompressibleColumnAccessor[T <: NativeType] extends ColumnAcc private var decoder: Decoder[T] = _ - abstract override protected def initialize() = { + abstract override protected def initialize(): Unit = { super.initialize() decoder = CompressionScheme(underlyingBuffer.getInt()).decoder(buffer, columnType) } - abstract override def hasNext = super.hasNext || decoder.hasNext + abstract override def hasNext: Boolean = super.hasNext || decoder.hasNext override def extractSingle(row: MutableRow, ordinal: Int): Unit = { decoder.next(row, ordinal) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnBuilder.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnBuilder.scala index aead768ecdf0a..b9cfc5df550d1 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnBuilder.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnBuilder.scala @@ -81,7 +81,7 @@ private[sql] trait CompressibleColumnBuilder[T <: NativeType] } } - override def build() = { + override def build(): ByteBuffer = { val nonNullBuffer = buildNonNulls() val typeId = nonNullBuffer.getInt() val encoder: Encoder[T] = { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala index 68a5b1de7691b..8727d71c48bb7 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala @@ -33,22 +33,23 @@ import org.apache.spark.util.Utils private[sql] case object PassThrough extends CompressionScheme { override val typeId = 0 - override def supports(columnType: ColumnType[_, _]) = true + override def supports(columnType: ColumnType[_, _]): Boolean = true - override def encoder[T <: NativeType](columnType: NativeColumnType[T]) = { + override def encoder[T <: NativeType](columnType: NativeColumnType[T]): Encoder[T] = { new this.Encoder[T](columnType) } - override def decoder[T <: NativeType](buffer: ByteBuffer, columnType: NativeColumnType[T]) = { + override def decoder[T <: NativeType]( + buffer: ByteBuffer, columnType: NativeColumnType[T]): Decoder[T] = { new this.Decoder(buffer, columnType) } class Encoder[T <: NativeType](columnType: NativeColumnType[T]) extends compression.Encoder[T] { - override def uncompressedSize = 0 + override def uncompressedSize: Int = 0 - override def compressedSize = 0 + override def compressedSize: Int = 0 - override def compress(from: ByteBuffer, to: ByteBuffer) = { + override def compress(from: ByteBuffer, to: ByteBuffer): ByteBuffer = { // Writes compression type ID and copies raw contents to.putInt(PassThrough.typeId).put(from).rewind() to @@ -62,22 +63,23 @@ private[sql] case object PassThrough extends CompressionScheme { columnType.extract(buffer, row, ordinal) } - override def hasNext = buffer.hasRemaining + override def hasNext: Boolean = buffer.hasRemaining } } private[sql] case object RunLengthEncoding extends CompressionScheme { override val typeId = 1 - override def encoder[T <: NativeType](columnType: NativeColumnType[T]) = { + override def encoder[T <: NativeType](columnType: NativeColumnType[T]): Encoder[T] = { new this.Encoder[T](columnType) } - override def decoder[T <: NativeType](buffer: ByteBuffer, columnType: NativeColumnType[T]) = { + override def decoder[T <: NativeType]( + buffer: ByteBuffer, columnType: NativeColumnType[T]): Decoder[T] = { new this.Decoder(buffer, columnType) } - override def supports(columnType: ColumnType[_, _]) = columnType match { + override def supports(columnType: ColumnType[_, _]): Boolean = columnType match { case INT | LONG | SHORT | BYTE | STRING | BOOLEAN => true case _ => false } @@ -90,9 +92,9 @@ private[sql] case object RunLengthEncoding extends CompressionScheme { private val lastValue = new SpecificMutableRow(Seq(columnType.dataType)) private var lastRun = 0 - override def uncompressedSize = _uncompressedSize + override def uncompressedSize: Int = _uncompressedSize - override def compressedSize = _compressedSize + override def compressedSize: Int = _compressedSize override def gatherCompressibilityStats(row: Row, ordinal: Int): Unit = { val value = columnType.getField(row, ordinal) @@ -114,7 +116,7 @@ private[sql] case object RunLengthEncoding extends CompressionScheme { } } - override def compress(from: ByteBuffer, to: ByteBuffer) = { + override def compress(from: ByteBuffer, to: ByteBuffer): ByteBuffer = { to.putInt(RunLengthEncoding.typeId) if (from.hasRemaining) { @@ -169,7 +171,7 @@ private[sql] case object RunLengthEncoding extends CompressionScheme { columnType.setField(row, ordinal, currentValue) } - override def hasNext = valueCount < run || buffer.hasRemaining + override def hasNext: Boolean = valueCount < run || buffer.hasRemaining } } @@ -179,15 +181,16 @@ private[sql] case object DictionaryEncoding extends CompressionScheme { // 32K unique values allowed val MAX_DICT_SIZE = Short.MaxValue - override def decoder[T <: NativeType](buffer: ByteBuffer, columnType: NativeColumnType[T]) = { + override def decoder[T <: NativeType](buffer: ByteBuffer, columnType: NativeColumnType[T]) + : Decoder[T] = { new this.Decoder(buffer, columnType) } - override def encoder[T <: NativeType](columnType: NativeColumnType[T]) = { + override def encoder[T <: NativeType](columnType: NativeColumnType[T]): Encoder[T] = { new this.Encoder[T](columnType) } - override def supports(columnType: ColumnType[_, _]) = columnType match { + override def supports(columnType: ColumnType[_, _]): Boolean = columnType match { case INT | LONG | STRING => true case _ => false } @@ -237,7 +240,7 @@ private[sql] case object DictionaryEncoding extends CompressionScheme { } } - override def compress(from: ByteBuffer, to: ByteBuffer) = { + override def compress(from: ByteBuffer, to: ByteBuffer): ByteBuffer = { if (overflow) { throw new IllegalStateException( "Dictionary encoding should not be used because of dictionary overflow.") @@ -260,9 +263,9 @@ private[sql] case object DictionaryEncoding extends CompressionScheme { to } - override def uncompressedSize = _uncompressedSize + override def uncompressedSize: Int = _uncompressedSize - override def compressedSize = if (overflow) Int.MaxValue else dictionarySize + count * 2 + override def compressedSize: Int = if (overflow) Int.MaxValue else dictionarySize + count * 2 } class Decoder[T <: NativeType](buffer: ByteBuffer, columnType: NativeColumnType[T]) @@ -284,7 +287,7 @@ private[sql] case object DictionaryEncoding extends CompressionScheme { columnType.setField(row, ordinal, dictionary(buffer.getShort())) } - override def hasNext = buffer.hasRemaining + override def hasNext: Boolean = buffer.hasRemaining } } @@ -293,15 +296,16 @@ private[sql] case object BooleanBitSet extends CompressionScheme { val BITS_PER_LONG = 64 - override def decoder[T <: NativeType](buffer: ByteBuffer, columnType: NativeColumnType[T]) = { + override def decoder[T <: NativeType](buffer: ByteBuffer, columnType: NativeColumnType[T]) + : compression.Decoder[T] = { new this.Decoder(buffer).asInstanceOf[compression.Decoder[T]] } - override def encoder[T <: NativeType](columnType: NativeColumnType[T]) = { + override def encoder[T <: NativeType](columnType: NativeColumnType[T]): compression.Encoder[T] = { (new this.Encoder).asInstanceOf[compression.Encoder[T]] } - override def supports(columnType: ColumnType[_, _]) = columnType == BOOLEAN + override def supports(columnType: ColumnType[_, _]): Boolean = columnType == BOOLEAN class Encoder extends compression.Encoder[BooleanType.type] { private var _uncompressedSize = 0 @@ -310,7 +314,7 @@ private[sql] case object BooleanBitSet extends CompressionScheme { _uncompressedSize += BOOLEAN.defaultSize } - override def compress(from: ByteBuffer, to: ByteBuffer) = { + override def compress(from: ByteBuffer, to: ByteBuffer): ByteBuffer = { to.putInt(BooleanBitSet.typeId) // Total element count (1 byte per Boolean value) .putInt(from.remaining) @@ -347,9 +351,9 @@ private[sql] case object BooleanBitSet extends CompressionScheme { to } - override def uncompressedSize = _uncompressedSize + override def uncompressedSize: Int = _uncompressedSize - override def compressedSize = { + override def compressedSize: Int = { val extra = if (_uncompressedSize % BITS_PER_LONG == 0) 0 else 1 (_uncompressedSize / BITS_PER_LONG + extra) * 8 + 4 } @@ -380,22 +384,23 @@ private[sql] case object BooleanBitSet extends CompressionScheme { private[sql] case object IntDelta extends CompressionScheme { override def typeId: Int = 4 - override def decoder[T <: NativeType](buffer: ByteBuffer, columnType: NativeColumnType[T]) = { + override def decoder[T <: NativeType](buffer: ByteBuffer, columnType: NativeColumnType[T]) + : compression.Decoder[T] = { new Decoder(buffer, INT).asInstanceOf[compression.Decoder[T]] } - override def encoder[T <: NativeType](columnType: NativeColumnType[T]) = { + override def encoder[T <: NativeType](columnType: NativeColumnType[T]): compression.Encoder[T] = { (new Encoder).asInstanceOf[compression.Encoder[T]] } - override def supports(columnType: ColumnType[_, _]) = columnType == INT + override def supports(columnType: ColumnType[_, _]): Boolean = columnType == INT class Encoder extends compression.Encoder[IntegerType.type] { protected var _compressedSize: Int = 0 protected var _uncompressedSize: Int = 0 - override def compressedSize = _compressedSize - override def uncompressedSize = _uncompressedSize + override def compressedSize: Int = _compressedSize + override def uncompressedSize: Int = _uncompressedSize private var prevValue: Int = _ @@ -459,22 +464,23 @@ private[sql] case object IntDelta extends CompressionScheme { private[sql] case object LongDelta extends CompressionScheme { override def typeId: Int = 5 - override def decoder[T <: NativeType](buffer: ByteBuffer, columnType: NativeColumnType[T]) = { + override def decoder[T <: NativeType](buffer: ByteBuffer, columnType: NativeColumnType[T]) + : compression.Decoder[T] = { new Decoder(buffer, LONG).asInstanceOf[compression.Decoder[T]] } - override def encoder[T <: NativeType](columnType: NativeColumnType[T]) = { + override def encoder[T <: NativeType](columnType: NativeColumnType[T]): compression.Encoder[T] = { (new Encoder).asInstanceOf[compression.Encoder[T]] } - override def supports(columnType: ColumnType[_, _]) = columnType == LONG + override def supports(columnType: ColumnType[_, _]): Boolean = columnType == LONG class Encoder extends compression.Encoder[LongType.type] { protected var _compressedSize: Int = 0 protected var _uncompressedSize: Int = 0 - override def compressedSize = _compressedSize - override def uncompressedSize = _uncompressedSize + override def compressedSize: Int = _compressedSize + override def uncompressedSize: Int = _uncompressedSize private var prevValue: Long = _ diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala old mode 100755 new mode 100644 index ad44a01d0e164..18b1ba4c5c4b9 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala @@ -21,6 +21,7 @@ import java.util.HashMap import org.apache.spark.annotation.DeveloperApi import org.apache.spark.SparkContext +import org.apache.spark.rdd.RDD import org.apache.spark.sql.catalyst.errors._ import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.physical._ @@ -45,7 +46,7 @@ case class Aggregate( child: SparkPlan) extends UnaryNode { - override def requiredChildDistribution = + override def requiredChildDistribution: List[Distribution] = { if (partial) { UnspecifiedDistribution :: Nil } else { @@ -55,8 +56,9 @@ case class Aggregate( ClusteredDistribution(groupingExpressions) :: Nil } } + } - override def output = aggregateExpressions.map(_.toAttribute) + override def output: Seq[Attribute] = aggregateExpressions.map(_.toAttribute) /** * An aggregate that needs to be computed for each row in a group. @@ -119,7 +121,7 @@ case class Aggregate( } } - override def execute() = attachTree(this, "execute") { + override def execute(): RDD[Row] = attachTree(this, "execute") { if (groupingExpressions.isEmpty) { child.execute().mapPartitions { iter => val buffer = newAggregateBuffer() diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala index 7c0b72aab448e..69a620e1ec929 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala @@ -20,23 +20,42 @@ package org.apache.spark.sql.execution import org.apache.spark.annotation.DeveloperApi import org.apache.spark.shuffle.sort.SortShuffleManager import org.apache.spark.{SparkEnv, HashPartitioner, RangePartitioner, SparkConf} -import org.apache.spark.rdd.ShuffledRDD +import org.apache.spark.rdd.{RDD, ShuffledRDD} import org.apache.spark.sql.{SQLContext, Row} import org.apache.spark.sql.catalyst.errors.attachTree -import org.apache.spark.sql.catalyst.expressions.RowOrdering +import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.physical._ import org.apache.spark.sql.catalyst.rules.Rule import org.apache.spark.util.MutablePair +object Exchange { + /** + * Returns true when the ordering expressions are a subset of the key. + * if true, ShuffledRDD can use `setKeyOrdering(orderingKey)` to sort within [[Exchange]]. + */ + def canSortWithShuffle(partitioning: Partitioning, desiredOrdering: Seq[SortOrder]): Boolean = { + desiredOrdering.map(_.child).toSet.subsetOf(partitioning.keyExpressions.toSet) + } +} + /** * :: DeveloperApi :: + * Performs a shuffle that will result in the desired `newPartitioning`. Optionally sorts each + * resulting partition based on expressions from the partition key. It is invalid to construct an + * exchange operator with a `newOrdering` that cannot be calculated using the partitioning key. */ @DeveloperApi -case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends UnaryNode { +case class Exchange( + newPartitioning: Partitioning, + newOrdering: Seq[SortOrder], + child: SparkPlan) + extends UnaryNode { + + override def outputPartitioning: Partitioning = newPartitioning - override def outputPartitioning = newPartitioning + override def outputOrdering: Seq[SortOrder] = newOrdering - override def output = child.output + override def output: Seq[Attribute] = child.output /** We must copy rows when sort based shuffle is on */ protected def sortBasedShuffleOn = SparkEnv.get.shuffleManager.isInstanceOf[SortShuffleManager] @@ -44,7 +63,23 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una private val bypassMergeThreshold = child.sqlContext.sparkContext.conf.getInt("spark.shuffle.sort.bypassMergeThreshold", 200) - override def execute() = attachTree(this , "execute") { + private val keyOrdering = { + if (newOrdering.nonEmpty) { + val key = newPartitioning.keyExpressions + val boundOrdering = newOrdering.map { o => + val ordinal = key.indexOf(o.child) + if (ordinal == -1) sys.error(s"Invalid ordering on $o requested for $newPartitioning") + o.copy(child = BoundReference(ordinal, o.child.dataType, o.child.nullable)) + } + new RowOrdering(boundOrdering) + } else { + null // Ordering will not be used + } + } + + override def execute(): RDD[Row] = attachTree(this , "execute") { + lazy val sparkConf = child.sqlContext.sparkContext.getConf + newPartitioning match { case HashPartitioning(expressions, numPartitions) => // TODO: Eliminate redundant expressions in grouping key and value. @@ -55,7 +90,9 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una // we can avoid the defensive copies to improve performance. In the long run, we probably // want to include information in shuffle dependencies to indicate whether elements in the // source RDD should be copied. - val rdd = if (sortBasedShuffleOn && numPartitions > bypassMergeThreshold) { + val willMergeSort = sortBasedShuffleOn && numPartitions > bypassMergeThreshold + + val rdd = if (willMergeSort || newOrdering.nonEmpty) { child.execute().mapPartitions { iter => val hashExpressions = newMutableProjection(expressions, child.output)() iter.map(r => (hashExpressions(r).copy(), r.copy())) @@ -68,12 +105,17 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una } } val part = new HashPartitioner(numPartitions) - val shuffled = new ShuffledRDD[Row, Row, Row](rdd, part) - shuffled.setSerializer(new SparkSqlSerializer(new SparkConf(false))) + val shuffled = + if (newOrdering.nonEmpty) { + new ShuffledRDD[Row, Row, Row](rdd, part).setKeyOrdering(keyOrdering) + } else { + new ShuffledRDD[Row, Row, Row](rdd, part) + } + shuffled.setSerializer(new SparkSqlSerializer(sparkConf)) shuffled.map(_._2) case RangePartitioning(sortingExpressions, numPartitions) => - val rdd = if (sortBasedShuffleOn) { + val rdd = if (sortBasedShuffleOn || newOrdering.nonEmpty) { child.execute().mapPartitions { iter => iter.map(row => (row.copy(), null))} } else { child.execute().mapPartitions { iter => @@ -86,9 +128,13 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una implicit val ordering = new RowOrdering(sortingExpressions, child.output) val part = new RangePartitioner(numPartitions, rdd, ascending = true) - val shuffled = new ShuffledRDD[Row, Null, Null](rdd, part) - shuffled.setSerializer(new SparkSqlSerializer(new SparkConf(false))) - + val shuffled = + if (newOrdering.nonEmpty) { + new ShuffledRDD[Row, Null, Null](rdd, part).setKeyOrdering(keyOrdering) + } else { + new ShuffledRDD[Row, Null, Null](rdd, part) + } + shuffled.setSerializer(new SparkSqlSerializer(sparkConf)) shuffled.map(_._1) case SinglePartition => @@ -106,7 +152,7 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una } val partitioner = new HashPartitioner(1) val shuffled = new ShuffledRDD[Null, Row, Row](rdd, partitioner) - shuffled.setSerializer(new SparkSqlSerializer(new SparkConf(false))) + shuffled.setSerializer(new SparkSqlSerializer(sparkConf)) shuffled.map(_._2) case _ => sys.error(s"Exchange not implemented for $newPartitioning") @@ -119,27 +165,34 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una * Ensures that the [[org.apache.spark.sql.catalyst.plans.physical.Partitioning Partitioning]] * of input data meets the * [[org.apache.spark.sql.catalyst.plans.physical.Distribution Distribution]] requirements for - * each operator by inserting [[Exchange]] Operators where required. + * each operator by inserting [[Exchange]] Operators where required. Also ensure that the + * required input partition ordering requirements are met. */ -private[sql] case class AddExchange(sqlContext: SQLContext) extends Rule[SparkPlan] { +private[sql] case class EnsureRequirements(sqlContext: SQLContext) extends Rule[SparkPlan] { // TODO: Determine the number of partitions. - def numPartitions = sqlContext.conf.numShufflePartitions + def numPartitions: Int = sqlContext.conf.numShufflePartitions def apply(plan: SparkPlan): SparkPlan = plan.transformUp { case operator: SparkPlan => - // Check if every child's outputPartitioning satisfies the corresponding + // True iff every child's outputPartitioning satisfies the corresponding // required data distribution. - def meetsRequirements = - !operator.requiredChildDistribution.zip(operator.children).map { + def meetsRequirements: Boolean = + operator.requiredChildDistribution.zip(operator.children).forall { case (required, child) => val valid = child.outputPartitioning.satisfies(required) logDebug( s"${if (valid) "Valid" else "Invalid"} distribution," + s"required: $required current: ${child.outputPartitioning}") valid - }.exists(!_) + } - // Check if outputPartitionings of children are compatible with each other. + // True iff any of the children are incorrectly sorted. + def needsAnySort: Boolean = + operator.requiredChildOrdering.zip(operator.children).exists { + case (required, child) => required.nonEmpty && required != child.outputOrdering + } + + // True iff outputPartitionings of children are compatible with each other. // It is possible that every child satisfies its required data distribution // but two children have incompatible outputPartitionings. For example, // A dataset is range partitioned by "a.asc" (RangePartitioning) and another @@ -147,7 +200,7 @@ private[sql] case class AddExchange(sqlContext: SQLContext) extends Rule[SparkPl // datasets are both clustered by "a", but these two outputPartitionings are not // compatible. // TODO: ASSUMES TRANSITIVITY? - def compatible = + def compatible: Boolean = !operator.children .map(_.outputPartitioning) .sliding(2) @@ -156,28 +209,69 @@ private[sql] case class AddExchange(sqlContext: SQLContext) extends Rule[SparkPl case Seq(a,b) => a compatibleWith b }.exists(!_) - // Check if the partitioning we want to ensure is the same as the child's output - // partitioning. If so, we do not need to add the Exchange operator. - def addExchangeIfNecessary(partitioning: Partitioning, child: SparkPlan) = - if (child.outputPartitioning != partitioning) Exchange(partitioning, child) else child + // Adds Exchange or Sort operators as required + def addOperatorsIfNecessary( + partitioning: Partitioning, + rowOrdering: Seq[SortOrder], + child: SparkPlan): SparkPlan = { + val needSort = rowOrdering.nonEmpty && child.outputOrdering != rowOrdering + val needsShuffle = child.outputPartitioning != partitioning + val canSortWithShuffle = Exchange.canSortWithShuffle(partitioning, rowOrdering) + + if (needSort && needsShuffle && canSortWithShuffle) { + Exchange(partitioning, rowOrdering, child) + } else { + val withShuffle = if (needsShuffle) { + Exchange(partitioning, Nil, child) + } else { + child + } + + val withSort = if (needSort) { + if (sqlContext.conf.externalSortEnabled) { + ExternalSort(rowOrdering, global = false, withShuffle) + } else { + Sort(rowOrdering, global = false, withShuffle) + } + } else { + withShuffle + } + + withSort + } + } - if (meetsRequirements && compatible) { + if (meetsRequirements && compatible && !needsAnySort) { operator } else { // At least one child does not satisfies its required data distribution or // at least one child's outputPartitioning is not compatible with another child's // outputPartitioning. In this case, we need to add Exchange operators. - val repartitionedChildren = operator.requiredChildDistribution.zip(operator.children).map { - case (AllTuples, child) => - addExchangeIfNecessary(SinglePartition, child) - case (ClusteredDistribution(clustering), child) => - addExchangeIfNecessary(HashPartitioning(clustering, numPartitions), child) - case (OrderedDistribution(ordering), child) => - addExchangeIfNecessary(RangePartitioning(ordering, numPartitions), child) - case (UnspecifiedDistribution, child) => child - case (dist, _) => sys.error(s"Don't know how to ensure $dist") + val requirements = + (operator.requiredChildDistribution, operator.requiredChildOrdering, operator.children) + + val fixedChildren = requirements.zipped.map { + case (AllTuples, rowOrdering, child) => + addOperatorsIfNecessary(SinglePartition, rowOrdering, child) + case (ClusteredDistribution(clustering), rowOrdering, child) => + addOperatorsIfNecessary(HashPartitioning(clustering, numPartitions), rowOrdering, child) + case (OrderedDistribution(ordering), rowOrdering, child) => + addOperatorsIfNecessary(RangePartitioning(ordering, numPartitions), rowOrdering, child) + + case (UnspecifiedDistribution, Seq(), child) => + child + case (UnspecifiedDistribution, rowOrdering, child) => + if (sqlContext.conf.externalSortEnabled) { + ExternalSort(rowOrdering, global = false, child) + } else { + Sort(rowOrdering, global = false, child) + } + + case (dist, ordering, _) => + sys.error(s"Don't know how to ensure $dist with ordering $ordering") } - operator.withNewChildren(repartitionedChildren) + + operator.withNewChildren(fixedChildren) } } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala index 248dc1512b4d3..1fd387eec7e57 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala @@ -19,12 +19,12 @@ package org.apache.spark.sql.execution import org.apache.spark.annotation.DeveloperApi import org.apache.spark.rdd.RDD -import org.apache.spark.sql.{Row, SQLContext} -import org.apache.spark.sql.catalyst.ScalaReflection +import org.apache.spark.sql.catalyst.CatalystTypeConverters import org.apache.spark.sql.catalyst.analysis.MultiInstanceRelation -import org.apache.spark.sql.catalyst.expressions.{Attribute, GenericMutableRow} +import org.apache.spark.sql.catalyst.expressions.{Attribute, GenericMutableRow, SpecificMutableRow} import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Statistics} import org.apache.spark.sql.types.StructType +import org.apache.spark.sql.{Row, SQLContext} /** * :: DeveloperApi :: @@ -37,13 +37,42 @@ object RDDConversions { Iterator.empty } else { val bufferedIterator = iterator.buffered - val mutableRow = new GenericMutableRow(bufferedIterator.head.productArity) + val mutableRow = new SpecificMutableRow(schema.fields.map(_.dataType)) + val schemaFields = schema.fields.toArray + val converters = schemaFields.map { + f => CatalystTypeConverters.createToCatalystConverter(f.dataType) + } + bufferedIterator.map { r => + var i = 0 + while (i < mutableRow.length) { + mutableRow(i) = converters(i)(r.productElement(i)) + i += 1 + } + + mutableRow + } + } + } + } + + /** + * Convert the objects inside Row into the types Catalyst expected. + */ + def rowToRowRdd(data: RDD[Row], schema: StructType): RDD[Row] = { + data.mapPartitions { iterator => + if (iterator.isEmpty) { + Iterator.empty + } else { + val bufferedIterator = iterator.buffered + val mutableRow = new GenericMutableRow(bufferedIterator.head.toSeq.toArray) val schemaFields = schema.fields.toArray + val converters = schemaFields.map { + f => CatalystTypeConverters.createToCatalystConverter(f.dataType) + } bufferedIterator.map { r => var i = 0 while (i < mutableRow.length) { - mutableRow(i) = - ScalaReflection.convertToCatalyst(r.productElement(i), schemaFields(i).dataType) + mutableRow(i) = converters(i)(r(i)) i += 1 } @@ -58,17 +87,17 @@ object RDDConversions { case class LogicalRDD(output: Seq[Attribute], rdd: RDD[Row])(sqlContext: SQLContext) extends LogicalPlan with MultiInstanceRelation { - override def children = Nil + override def children: Seq[LogicalPlan] = Nil - override def newInstance() = + override def newInstance(): LogicalRDD.this.type = LogicalRDD(output.map(_.newInstance()), rdd)(sqlContext).asInstanceOf[this.type] - override def sameResult(plan: LogicalPlan) = plan match { + override def sameResult(plan: LogicalPlan): Boolean = plan match { case LogicalRDD(_, otherRDD) => rdd.id == otherRDD.id case _ => false } - @transient override lazy val statistics = Statistics( + @transient override lazy val statistics: Statistics = Statistics( // TODO: Instead of returning a default value here, find a way to return a meaningful size // estimate for RDDs. See PR 1238 for more discussions. sizeInBytes = BigInt(sqlContext.conf.defaultSizeInBytes) @@ -77,24 +106,24 @@ case class LogicalRDD(output: Seq[Attribute], rdd: RDD[Row])(sqlContext: SQLCont /** Physical plan node for scanning data from an RDD. */ case class PhysicalRDD(output: Seq[Attribute], rdd: RDD[Row]) extends LeafNode { - override def execute() = rdd + override def execute(): RDD[Row] = rdd } /** Logical plan node for scanning data from a local collection. */ case class LogicalLocalTable(output: Seq[Attribute], rows: Seq[Row])(sqlContext: SQLContext) extends LogicalPlan with MultiInstanceRelation { - override def children = Nil + override def children: Seq[LogicalPlan] = Nil - override def newInstance() = + override def newInstance(): this.type = LogicalLocalTable(output.map(_.newInstance()), rows)(sqlContext).asInstanceOf[this.type] - override def sameResult(plan: LogicalPlan) = plan match { + override def sameResult(plan: LogicalPlan): Boolean = plan match { case LogicalRDD(_, otherRDD) => rows == rows case _ => false } - @transient override lazy val statistics = Statistics( + @transient override lazy val statistics: Statistics = Statistics( // TODO: Improve the statistics estimation. // This is made small enough so it can be broadcasted. sizeInBytes = sqlContext.conf.autoBroadcastJoinThreshold - 1 diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Expand.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Expand.scala index 95172420608f9..575849481faad 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Expand.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Expand.scala @@ -18,6 +18,7 @@ package org.apache.spark.sql.execution import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.rdd.RDD import org.apache.spark.sql.SQLContext import org.apache.spark.sql.catalyst.errors._ import org.apache.spark.sql.catalyst.expressions._ @@ -42,7 +43,7 @@ case class Expand( // as UNKNOWN partitioning override def outputPartitioning: Partitioning = UnknownPartitioning(0) - override def execute() = attachTree(this, "execute") { + override def execute(): RDD[Row] = attachTree(this, "execute") { child.execute().mapPartitions { iter => // TODO Move out projection objects creation and transfer to // workers via closure. However we can't assume the Projection @@ -55,7 +56,7 @@ case class Expand( private[this] var idx = -1 // -1 means the initial state private[this] var input: Row = _ - override final def hasNext = (-1 < idx && idx < groups.length) || iter.hasNext + override final def hasNext: Boolean = (-1 < idx && idx < groups.length) || iter.hasNext override final def next(): Row = { if (idx <= 0) { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Generate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Generate.scala index 38877c28de3a8..12271048bb39c 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Generate.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Generate.scala @@ -18,6 +18,7 @@ package org.apache.spark.sql.execution import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.rdd.RDD import org.apache.spark.sql.catalyst.expressions._ /** @@ -54,7 +55,7 @@ case class Generate( val boundGenerator = BindReferences.bindReference(generator, child.output) - override def execute() = { + override def execute(): RDD[Row] = { if (join) { child.execute().mapPartitions { iter => val nullValues = Seq.fill(generator.output.size)(Literal(null)) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala index 4abe26fe4afc6..b1ef6556de1e9 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala @@ -18,6 +18,7 @@ package org.apache.spark.sql.execution import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.rdd.RDD import org.apache.spark.sql.catalyst.trees._ import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.physical._ @@ -49,7 +50,7 @@ case class GeneratedAggregate( child: SparkPlan) extends UnaryNode { - override def requiredChildDistribution = + override def requiredChildDistribution: Seq[Distribution] = if (partial) { UnspecifiedDistribution :: Nil } else { @@ -60,13 +61,15 @@ case class GeneratedAggregate( } } - override def output = aggregateExpressions.map(_.toAttribute) + override def output: Seq[Attribute] = aggregateExpressions.map(_.toAttribute) - override def execute() = { + override def execute(): RDD[Row] = { val aggregatesToCompute = aggregateExpressions.flatMap { a => a.collect { case agg: AggregateExpression => agg} } + // If you add any new function support, please add tests in org.apache.spark.sql.SQLQuerySuite + // (in test "aggregation with codegen"). val computeFunctions = aggregatesToCompute.map { case c @ Count(expr) => // If we're evaluating UnscaledValue(x), we can do Count on x directly, since its @@ -92,13 +95,16 @@ case class GeneratedAggregate( } val currentSum = AttributeReference("currentSum", calcType, nullable = true)() - val initialValue = Literal(null, calcType) + val initialValue = Literal.create(null, calcType) - // Coalasce avoids double calculation... + // Coalesce avoids double calculation... // but really, common sub expression elimination would be better.... val zero = Cast(Literal(0), calcType) val updateFunction = Coalesce( - Add(Coalesce(currentSum :: zero :: Nil), Cast(expr, calcType)) :: currentSum :: Nil) + Add( + Coalesce(currentSum :: zero :: Nil), + Cast(expr, calcType) + ) :: currentSum :: zero :: Nil) val result = expr.dataType match { case DecimalType.Fixed(_, _) => @@ -108,8 +114,8 @@ case class GeneratedAggregate( AggregateEvaluation(currentSum :: Nil, initialValue :: Nil, updateFunction :: Nil, result) - case a @ Average(expr) => - val calcType = + case cs @ CombineSum(expr) => + val calcType = expr.dataType expr.dataType match { case DecimalType.Fixed(_, _) => DecimalType.Unlimited @@ -117,45 +123,39 @@ case class GeneratedAggregate( expr.dataType } - val currentCount = AttributeReference("currentCount", LongType, nullable = false)() - val currentSum = AttributeReference("currentSum", calcType, nullable = false)() - val initialCount = Literal(0L) - val initialSum = Cast(Literal(0L), calcType) + val currentSum = AttributeReference("currentSum", calcType, nullable = true)() + val initialValue = Literal.create(null, calcType) + // Coalasce avoids double calculation... + // but really, common sub expression elimination would be better.... + val zero = Cast(Literal(0), calcType) // If we're evaluating UnscaledValue(x), we can do Count on x directly, since its // UnscaledValue will be null if and only if x is null; helps with Average on decimals - val toCount = expr match { + val actualExpr = expr match { case UnscaledValue(e) => e case _ => expr } - - val updateCount = If(IsNotNull(toCount), Add(currentCount, Literal(1L)), currentCount) - val updateSum = Coalesce(Add(Cast(expr, calcType), currentSum) :: currentSum :: Nil) - + // partial sum result can be null only when no input rows present + val updateFunction = If( + IsNotNull(actualExpr), + Coalesce( + Add( + Coalesce(currentSum :: zero :: Nil), + Cast(expr, calcType)) :: currentSum :: zero :: Nil), + currentSum) + val result = expr.dataType match { case DecimalType.Fixed(_, _) => - If(EqualTo(currentCount, Literal(0L)), - Literal(null, a.dataType), - Cast(Divide( - Cast(currentSum, DecimalType.Unlimited), - Cast(currentCount, DecimalType.Unlimited)), a.dataType)) - case _ => - If(EqualTo(currentCount, Literal(0L)), - Literal(null, a.dataType), - Divide(Cast(currentSum, a.dataType), Cast(currentCount, a.dataType))) + Cast(currentSum, cs.dataType) + case _ => currentSum } - AggregateEvaluation( - currentCount :: currentSum :: Nil, - initialCount :: initialSum :: Nil, - updateCount :: updateSum :: Nil, - result - ) - + AggregateEvaluation(currentSum :: Nil, initialValue :: Nil, updateFunction :: Nil, result) + case m @ Max(expr) => val currentMax = AttributeReference("currentMax", expr.dataType, nullable = true)() - val initialValue = Literal(null, expr.dataType) + val initialValue = Literal.create(null, expr.dataType) val updateMax = MaxOf(currentMax, expr) AggregateEvaluation( @@ -164,8 +164,20 @@ case class GeneratedAggregate( updateMax :: Nil, currentMax) + case m @ Min(expr) => + val currentMin = AttributeReference("currentMin", expr.dataType, nullable = true)() + val initialValue = Literal.create(null, expr.dataType) + val updateMin = MinOf(currentMin, expr) + + AggregateEvaluation( + currentMin :: Nil, + initialValue :: Nil, + updateMin :: Nil, + currentMin) + case CollectHashSet(Seq(expr)) => - val set = AttributeReference("hashSet", ArrayType(expr.dataType), nullable = false)() + val set = + AttributeReference("hashSet", new OpenHashSetUDT(expr.dataType), nullable = false)() val initialValue = NewSet(expr.dataType) val addToSet = AddItemToSet(expr, set) @@ -176,9 +188,10 @@ case class GeneratedAggregate( set) case CombineSetsAndCount(inputSet) => - val ArrayType(inputType, _) = inputSet.dataType - val set = AttributeReference("hashSet", inputSet.dataType, nullable = false)() - val initialValue = NewSet(inputType) + val elementType = inputSet.dataType.asInstanceOf[OpenHashSetUDT].elementType + val set = + AttributeReference("hashSet", new OpenHashSetUDT(elementType), nullable = false)() + val initialValue = NewSet(elementType) val collectSets = CombineSets(set, inputSet) AggregateEvaluation( @@ -186,6 +199,8 @@ case class GeneratedAggregate( initialValue :: Nil, collectSets :: Nil, CountSet(set)) + + case o => sys.error(s"$o can't be codegened.") } val computationSchema = computeFunctions.flatMap(_.schema) @@ -271,9 +286,9 @@ case class GeneratedAggregate( private[this] val resultIterator = buffers.entrySet.iterator() private[this] val resultProjection = resultProjectionBuilder() - def hasNext = resultIterator.hasNext + def hasNext: Boolean = resultIterator.hasNext - def next() = { + def next(): Row = { val currentGroup = resultIterator.next() resultProjection(joinedRow(currentGroup.getKey, currentGroup.getValue)) } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala index d6d8258f46a9a..8a8c3a404323a 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala @@ -17,7 +17,9 @@ package org.apache.spark.sql.execution +import org.apache.spark.rdd.RDD import org.apache.spark.sql.Row +import org.apache.spark.sql.catalyst.CatalystTypeConverters import org.apache.spark.sql.catalyst.expressions.Attribute @@ -28,9 +30,17 @@ case class LocalTableScan(output: Seq[Attribute], rows: Seq[Row]) extends LeafNo private lazy val rdd = sqlContext.sparkContext.parallelize(rows) - override def execute() = rdd + override def execute(): RDD[Row] = rdd - override def executeCollect() = rows.toArray - override def executeTake(limit: Int) = rows.take(limit).toArray + override def executeCollect(): Array[Row] = { + val converter = CatalystTypeConverters.createToScalaConverter(schema) + rows.map(converter(_).asInstanceOf[Row]).toArray + } + + + override def executeTake(limit: Int): Array[Row] = { + val converter = CatalystTypeConverters.createToScalaConverter(schema) + rows.map(converter(_).asInstanceOf[Row]).take(limit).toArray + } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala index 052766c20abc2..e159ffe66cb24 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala @@ -21,7 +21,7 @@ import org.apache.spark.annotation.DeveloperApi import org.apache.spark.Logging import org.apache.spark.rdd.RDD import org.apache.spark.sql.SQLContext -import org.apache.spark.sql.catalyst.{ScalaReflection, trees} +import org.apache.spark.sql.catalyst.{CatalystTypeConverters, trees} import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.expressions.codegen._ import org.apache.spark.sql.catalyst.plans.QueryPlan @@ -67,10 +67,17 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ // TODO: Move to `DistributedPlan` /** Specifies how data is partitioned across different nodes in the cluster. */ def outputPartitioning: Partitioning = UnknownPartitioning(0) // TODO: WRONG WIDTH! + /** Specifies any partition requirements on the input data for this operator. */ def requiredChildDistribution: Seq[Distribution] = Seq.fill(children.size)(UnspecifiedDistribution) + /** Specifies how data is ordered in each partition. */ + def outputOrdering: Seq[SortOrder] = Nil + + /** Specifies sort order for each partition requirements on the input data for this operator. */ + def requiredChildOrdering: Seq[Seq[SortOrder]] = Seq.fill(children.size)(Nil) + /** * Runs this query returning the result as an RDD. */ @@ -79,8 +86,12 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ /** * Runs this query returning the result as an array. */ + def executeCollect(): Array[Row] = { - execute().map(ScalaReflection.convertRowToScala(_, schema)).collect() + execute().mapPartitions { iter => + val converter = CatalystTypeConverters.createToScalaConverter(schema) + iter.map(converter(_).asInstanceOf[Row]) + }.collect() } /** @@ -124,7 +135,8 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ partsScanned += numPartsToTry } - buf.toArray.map(ScalaReflection.convertRowToScala(_, this.schema)) + val converter = CatalystTypeConverters.createToScalaConverter(schema) + buf.toArray.map(converter(_).asInstanceOf[Row]) } protected def newProjection( diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala index 30564e14fa896..eea15aff5dbcf 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala @@ -18,6 +18,7 @@ package org.apache.spark.sql.execution import java.nio.ByteBuffer +import java.util.{HashMap => JavaHashMap} import org.apache.spark.sql.types.Decimal @@ -26,20 +27,19 @@ import scala.reflect.ClassTag import com.clearspring.analytics.stream.cardinality.HyperLogLog import com.esotericsoftware.kryo.io.{Input, Output} import com.esotericsoftware.kryo.{Serializer, Kryo} -import com.twitter.chill.{AllScalaRegistrar, ResourcePool} +import com.twitter.chill.ResourcePool import org.apache.spark.{SparkEnv, SparkConf} import org.apache.spark.serializer.{SerializerInstance, KryoSerializer} import org.apache.spark.sql.catalyst.expressions.GenericRow import org.apache.spark.util.collection.OpenHashSet import org.apache.spark.util.MutablePair -import org.apache.spark.util.Utils import org.apache.spark.sql.catalyst.expressions.codegen.{IntegerHashSet, LongHashSet} private[sql] class SparkSqlSerializer(conf: SparkConf) extends KryoSerializer(conf) { override def newKryo(): Kryo = { - val kryo = new Kryo() + val kryo = super.newKryo() kryo.setRegistrationRequired(false) kryo.register(classOf[MutablePair[_, _]]) kryo.register(classOf[org.apache.spark.sql.catalyst.expressions.GenericRow]) @@ -55,10 +55,9 @@ private[sql] class SparkSqlSerializer(conf: SparkConf) extends KryoSerializer(co kryo.register(classOf[org.apache.spark.util.collection.OpenHashSet[_]], new OpenHashSetSerializer) kryo.register(classOf[Decimal]) + kryo.register(classOf[JavaHashMap[_, _]]) kryo.setReferences(false) - kryo.setClassLoader(Utils.getSparkClassLoader) - new AllScalaRegistrar().apply(kryo) kryo } } @@ -66,15 +65,12 @@ private[sql] class SparkSqlSerializer(conf: SparkConf) extends KryoSerializer(co private[execution] class KryoResourcePool(size: Int) extends ResourcePool[SerializerInstance](size) { - val ser: KryoSerializer = { + val ser: SparkSqlSerializer = { val sparkConf = Option(SparkEnv.get).map(_.conf).getOrElse(new SparkConf()) - // TODO (lian) Using KryoSerializer here is workaround, needs further investigation - // Using SparkSqlSerializer here makes BasicQuerySuite to fail because of Kryo serialization - // related error. - new KryoSerializer(sparkConf) + new SparkSqlSerializer(sparkConf) } - def newInstance() = ser.newInstance() + def newInstance(): SerializerInstance = ser.newInstance() } private[sql] object SparkSqlSerializer { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala index 81bcf5a6f32dd..e687d01f57520 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala @@ -90,6 +90,14 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] { left.statistics.sizeInBytes <= sqlContext.conf.autoBroadcastJoinThreshold => makeBroadcastHashJoin(leftKeys, rightKeys, left, right, condition, joins.BuildLeft) + // If the sort merge join option is set, we want to use sort merge join prior to hashjoin + // for now let's support inner join first, then add outer join + case ExtractEquiJoinKeys(Inner, leftKeys, rightKeys, condition, left, right) + if sqlContext.conf.sortMergeJoinEnabled => + val mergeJoin = + joins.SortMergeJoin(leftKeys, rightKeys, planLater(left), planLater(right)) + condition.map(Filter(_, mergeJoin)).getOrElse(mergeJoin) :: Nil + case ExtractEquiJoinKeys(Inner, leftKeys, rightKeys, condition, left, right) => val buildSide = if (right.statistics.sizeInBytes <= left.statistics.sizeInBytes) { @@ -154,15 +162,15 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] { case _ => Nil } - def canBeCodeGened(aggs: Seq[AggregateExpression]) = !aggs.exists { - case _: Sum | _: Count | _: Max | _: CombineSetsAndCount => false + def canBeCodeGened(aggs: Seq[AggregateExpression]): Boolean = !aggs.exists { + case _: CombineSum | _: Sum | _: Count | _: Max | _: Min | _: CombineSetsAndCount => false // The generated set implementation is pretty limited ATM. case CollectHashSet(exprs) if exprs.size == 1 && Seq(IntegerType, LongType).contains(exprs.head.dataType) => false case _ => true } - def allAggregates(exprs: Seq[Expression]) = + def allAggregates(exprs: Seq[Expression]): Seq[AggregateExpression] = exprs.flatMap(_.collect { case a: AggregateExpression => a }) } @@ -211,9 +219,15 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] { ParquetRelation.create(path, child, sparkContext.hadoopConfiguration, sqlContext) // Note: overwrite=false because otherwise the metadata we just created will be deleted InsertIntoParquetTable(relation, planLater(child), overwrite = false) :: Nil - case logical.InsertIntoTable(table: ParquetRelation, partition, child, overwrite) => + case logical.InsertIntoTable( + table: ParquetRelation, partition, child, overwrite, ifNotExists) => InsertIntoParquetTable(table, planLater(child), overwrite) :: Nil case PhysicalOperation(projectList, filters: Seq[Expression], relation: ParquetRelation) => + val partitionColNames = relation.partitioningAttributes.map(_.name).toSet + val filtersToPush = filters.filter { pred => + val referencedColNames = pred.references.map(_.name).toSet + referencedColNames.intersect(partitionColNames).isEmpty + } val prunePushedDownFilters = if (sqlContext.conf.parquetFilterPushDown) { (predicates: Seq[Expression]) => { @@ -225,6 +239,10 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] { // "A AND B" in the higher-level filter, not just "B". predicates.map(p => p -> ParquetFilters.createFilter(p)).collect { case (predicate, None) => predicate + // Filter needs to be applied above when it contains partitioning + // columns + case (predicate, _) if(!predicate.references.map(_.name).toSet + .intersect (partitionColNames).isEmpty) => predicate } } } else { @@ -237,7 +255,7 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] { ParquetTableScan( _, relation, - if (sqlContext.conf.parquetFilterPushDown) filters else Nil)) :: Nil + if (sqlContext.conf.parquetFilterPushDown) filtersToPush else Nil)) :: Nil case _ => Nil } @@ -257,7 +275,7 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] { // Can we automate these 'pass through' operations? object BasicOperators extends Strategy { - def numPartitions = self.numPartitions + def numPartitions: Int = self.numPartitions def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match { case r: RunnableCommand => ExecutedCommand(r) :: Nil @@ -296,10 +314,11 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] { execution.Intersect(planLater(left), planLater(right)) :: Nil case logical.Generate(generator, join, outer, _, child) => execution.Generate(generator, join = join, outer = outer, planLater(child)) :: Nil - case logical.NoRelation => + case logical.OneRowRelation => execution.PhysicalRDD(Nil, singleRowRdd) :: Nil case logical.Repartition(expressions, child) => - execution.Exchange(HashPartitioning(expressions, numPartitions), planLater(child)) :: Nil + execution.Exchange( + HashPartitioning(expressions, numPartitions), Nil, planLater(child)) :: Nil case e @ EvaluatePython(udf, child, _) => BatchPythonEvaluation(udf, e.output, planLater(child)) :: Nil case LogicalRDD(output, rdd) => PhysicalRDD(output, rdd) :: Nil @@ -309,7 +328,7 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] { object DDLStrategy extends Strategy { def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match { - case CreateTableUsing(tableName, userSpecifiedSchema, provider, true, opts, false) => + case CreateTableUsing(tableName, userSpecifiedSchema, provider, true, opts, false, _) => ExecutedCommand( CreateTempTableUsing( tableName, userSpecifiedSchema, provider, opts)) :: Nil @@ -318,29 +337,12 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] { case c: CreateTableUsing if c.temporary && c.allowExisting => sys.error("allowExisting should be set to false when creating a temporary table.") - case CreateTableUsingAsSelect(tableName, provider, true, opts, false, query) => - val logicalPlan = sqlContext.parseSql(query) + case CreateTableUsingAsSelect(tableName, provider, true, mode, opts, query) => val cmd = - CreateTempTableUsingAsSelect(tableName, provider, opts, logicalPlan) + CreateTempTableUsingAsSelect(tableName, provider, mode, opts, query) ExecutedCommand(cmd) :: Nil case c: CreateTableUsingAsSelect if !c.temporary => sys.error("Tables created with SQLContext must be TEMPORARY. Use a HiveContext instead.") - case c: CreateTableUsingAsSelect if c.temporary && c.allowExisting => - sys.error("allowExisting should be set to false when creating a temporary table.") - - case CreateTableUsingAsLogicalPlan(tableName, provider, true, opts, false, query) => - val cmd = - CreateTempTableUsingAsSelect(tableName, provider, opts, query) - ExecutedCommand(cmd) :: Nil - case c: CreateTableUsingAsLogicalPlan if !c.temporary => - sys.error("Tables created with SQLContext must be TEMPORARY. Use a HiveContext instead.") - case c: CreateTableUsingAsLogicalPlan if c.temporary && c.allowExisting => - sys.error("allowExisting should be set to false when creating a temporary table.") - - case LogicalDescribeCommand(table, isExtended) => - val resultPlan = self.sqlContext.executePlan(table).executedPlan - ExecutedCommand( - RunnableDescribeCommand(resultPlan, resultPlan.output, isExtended)) :: Nil case LogicalDescribeCommand(table, isExtended) => val resultPlan = self.sqlContext.executePlan(table).executedPlan diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala index 66aed5d5113d1..d286fe81bee5f 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala @@ -17,18 +17,15 @@ package org.apache.spark.sql.execution -import scala.collection.mutable.ArrayBuffer -import scala.reflect.runtime.universe.TypeTag - import org.apache.spark.{SparkEnv, HashPartitioner, SparkConf} import org.apache.spark.annotation.DeveloperApi import org.apache.spark.rdd.{RDD, ShuffledRDD} import org.apache.spark.shuffle.sort.SortShuffleManager -import org.apache.spark.sql.catalyst.ScalaReflection +import org.apache.spark.sql.catalyst.CatalystTypeConverters import org.apache.spark.sql.catalyst.errors._ import org.apache.spark.sql.catalyst.expressions._ -import org.apache.spark.sql.catalyst.plans.physical.{ClusteredDistribution, OrderedDistribution, SinglePartition, UnspecifiedDistribution} -import org.apache.spark.util.MutablePair +import org.apache.spark.sql.catalyst.plans.physical._ +import org.apache.spark.util.{CompletionIterator, MutablePair} import org.apache.spark.util.collection.ExternalSorter /** @@ -36,14 +33,16 @@ import org.apache.spark.util.collection.ExternalSorter */ @DeveloperApi case class Project(projectList: Seq[NamedExpression], child: SparkPlan) extends UnaryNode { - override def output = projectList.map(_.toAttribute) + override def output: Seq[Attribute] = projectList.map(_.toAttribute) @transient lazy val buildProjection = newMutableProjection(projectList, child.output) - def execute() = child.execute().mapPartitions { iter => + override def execute(): RDD[Row] = child.execute().mapPartitions { iter => val resuableProjection = buildProjection() iter.map(resuableProjection) } + + override def outputOrdering: Seq[SortOrder] = child.outputOrdering } /** @@ -51,13 +50,15 @@ case class Project(projectList: Seq[NamedExpression], child: SparkPlan) extends */ @DeveloperApi case class Filter(condition: Expression, child: SparkPlan) extends UnaryNode { - override def output = child.output + override def output: Seq[Attribute] = child.output - @transient lazy val conditionEvaluator = newPredicate(condition, child.output) + @transient lazy val conditionEvaluator: (Row) => Boolean = newPredicate(condition, child.output) - def execute() = child.execute().mapPartitions { iter => + override def execute(): RDD[Row] = child.execute().mapPartitions { iter => iter.filter(conditionEvaluator) } + + override def outputOrdering: Seq[SortOrder] = child.outputOrdering } /** @@ -67,10 +68,12 @@ case class Filter(condition: Expression, child: SparkPlan) extends UnaryNode { case class Sample(fraction: Double, withReplacement: Boolean, seed: Long, child: SparkPlan) extends UnaryNode { - override def output = child.output + override def output: Seq[Attribute] = child.output // TODO: How to pick seed? - override def execute() = child.execute().map(_.copy()).sample(withReplacement, fraction, seed) + override def execute(): RDD[Row] = { + child.execute().map(_.copy()).sample(withReplacement, fraction, seed) + } } /** @@ -79,8 +82,8 @@ case class Sample(fraction: Double, withReplacement: Boolean, seed: Long, child: @DeveloperApi case class Union(children: Seq[SparkPlan]) extends SparkPlan { // TODO: attributes output by union should be distinct for nullability purposes - override def output = children.head.output - override def execute() = sparkContext.union(children.map(_.execute())) + override def output: Seq[Attribute] = children.head.output + override def execute(): RDD[Row] = sparkContext.union(children.map(_.execute())) } /** @@ -100,12 +103,12 @@ case class Limit(limit: Int, child: SparkPlan) /** We must copy rows when sort based shuffle is on */ private def sortBasedShuffleOn = SparkEnv.get.shuffleManager.isInstanceOf[SortShuffleManager] - override def output = child.output - override def outputPartitioning = SinglePartition + override def output: Seq[Attribute] = child.output + override def outputPartitioning: Partitioning = SinglePartition override def executeCollect(): Array[Row] = child.executeTake(limit) - override def execute() = { + override def execute(): RDD[Row] = { val rdd: RDD[_ <: Product2[Boolean, Row]] = if (sortBasedShuffleOn) { child.execute().mapPartitions { iter => iter.take(limit).map(row => (false, row.copy())) @@ -118,7 +121,7 @@ case class Limit(limit: Int, child: SparkPlan) } val part = new HashPartitioner(1) val shuffled = new ShuffledRDD[Boolean, Row, Row](rdd, part) - shuffled.setSerializer(new SparkSqlSerializer(new SparkConf(false))) + shuffled.setSerializer(new SparkSqlSerializer(child.sqlContext.sparkContext.getConf)) shuffled.mapPartitions(_.take(limit).map(_._2)) } } @@ -132,18 +135,24 @@ case class Limit(limit: Int, child: SparkPlan) @DeveloperApi case class TakeOrdered(limit: Int, sortOrder: Seq[SortOrder], child: SparkPlan) extends UnaryNode { - override def output = child.output - override def outputPartitioning = SinglePartition + override def output: Seq[Attribute] = child.output + + override def outputPartitioning: Partitioning = SinglePartition + + private val ord: RowOrdering = new RowOrdering(sortOrder, child.output) - val ord = new RowOrdering(sortOrder, child.output) + private def collectData(): Array[Row] = child.execute().map(_.copy()).takeOrdered(limit)(ord) - // TODO: Is this copying for no reason? - override def executeCollect() = child.execute().map(_.copy()).takeOrdered(limit)(ord) - .map(ScalaReflection.convertRowToScala(_, this.schema)) + override def executeCollect(): Array[Row] = { + val converter = CatalystTypeConverters.createToScalaConverter(schema) + collectData().map(converter(_).asInstanceOf[Row]) + } // TODO: Terminal split should be implemented differently from non-terminal split. // TODO: Pick num splits based on |limit|. - override def execute() = sparkContext.makeRDD(executeCollect(), 1) + override def execute(): RDD[Row] = sparkContext.makeRDD(collectData(), 1) + + override def outputOrdering: Seq[SortOrder] = sortOrder } /** @@ -158,17 +167,19 @@ case class Sort( global: Boolean, child: SparkPlan) extends UnaryNode { - override def requiredChildDistribution = + override def requiredChildDistribution: Seq[Distribution] = if (global) OrderedDistribution(sortOrder) :: Nil else UnspecifiedDistribution :: Nil - override def execute() = attachTree(this, "sort") { + override def execute(): RDD[Row] = attachTree(this, "sort") { child.execute().mapPartitions( { iterator => val ordering = newOrdering(sortOrder, child.output) iterator.map(_.copy()).toArray.sorted(ordering).iterator }, preservesPartitioning = true) } - override def output = child.output + override def output: Seq[Attribute] = child.output + + override def outputOrdering: Seq[SortOrder] = sortOrder } /** @@ -183,19 +194,24 @@ case class ExternalSort( global: Boolean, child: SparkPlan) extends UnaryNode { - override def requiredChildDistribution = + + override def requiredChildDistribution: Seq[Distribution] = if (global) OrderedDistribution(sortOrder) :: Nil else UnspecifiedDistribution :: Nil - override def execute() = attachTree(this, "sort") { + override def execute(): RDD[Row] = attachTree(this, "sort") { child.execute().mapPartitions( { iterator => val ordering = newOrdering(sortOrder, child.output) val sorter = new ExternalSorter[Row, Null, Row](ordering = Some(ordering)) - sorter.insertAll(iterator.map(r => (r, null))) - sorter.iterator.map(_._1) + sorter.insertAll(iterator.map(r => (r.copy, null))) + val baseIterator = sorter.iterator.map(_._1) + // TODO(marmbrus): The complex type signature below thwarts inference for no reason. + CompletionIterator[Row, Iterator[Row]](baseIterator, sorter.stop()) }, preservesPartitioning = true) } - override def output = child.output + override def output: Seq[Attribute] = child.output + + override def outputOrdering: Seq[SortOrder] = sortOrder } /** @@ -207,12 +223,12 @@ case class ExternalSort( */ @DeveloperApi case class Distinct(partial: Boolean, child: SparkPlan) extends UnaryNode { - override def output = child.output + override def output: Seq[Attribute] = child.output - override def requiredChildDistribution = + override def requiredChildDistribution: Seq[Distribution] = if (partial) UnspecifiedDistribution :: Nil else ClusteredDistribution(child.output) :: Nil - override def execute() = { + override def execute(): RDD[Row] = { child.execute().mapPartitions { iter => val hashSet = new scala.collection.mutable.HashSet[Row]() @@ -237,9 +253,9 @@ case class Distinct(partial: Boolean, child: SparkPlan) extends UnaryNode { */ @DeveloperApi case class Except(left: SparkPlan, right: SparkPlan) extends BinaryNode { - override def output = left.output + override def output: Seq[Attribute] = left.output - override def execute() = { + override def execute(): RDD[Row] = { left.execute().map(_.copy()).subtract(right.execute().map(_.copy())) } } @@ -251,9 +267,9 @@ case class Except(left: SparkPlan, right: SparkPlan) extends BinaryNode { */ @DeveloperApi case class Intersect(left: SparkPlan, right: SparkPlan) extends BinaryNode { - override def output = children.head.output + override def output: Seq[Attribute] = children.head.output - override def execute() = { + override def execute(): RDD[Row] = { left.execute().map(_.copy()).intersection(right.execute().map(_.copy())) } } @@ -266,6 +282,7 @@ case class Intersect(left: SparkPlan, right: SparkPlan) extends BinaryNode { */ @DeveloperApi case class OutputFaker(output: Seq[Attribute], child: SparkPlan) extends SparkPlan { - def children = child :: Nil - def execute() = child.execute() + def children: Seq[SparkPlan] = child :: Nil + + def execute(): RDD[Row] = child.execute() } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala index 335757087deef..99f24910fd61f 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala @@ -20,12 +20,13 @@ package org.apache.spark.sql.execution import org.apache.spark.Logging import org.apache.spark.annotation.DeveloperApi import org.apache.spark.rdd.RDD -import org.apache.spark.sql.{DataFrame, SQLConf, SQLContext} +import org.apache.spark.sql.catalyst.CatalystTypeConverters import org.apache.spark.sql.catalyst.errors.TreeNodeException -import org.apache.spark.sql.catalyst.expressions.{Row, Attribute} +import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference, Row} import org.apache.spark.sql.catalyst.plans.logical import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan -import scala.collection.mutable.ArrayBuffer +import org.apache.spark.sql.types._ +import org.apache.spark.sql.{DataFrame, SQLConf, SQLContext} /** * A logical command that is executed for its side-effects. `RunnableCommand`s are @@ -53,15 +54,19 @@ case class ExecutedCommand(cmd: RunnableCommand) extends SparkPlan { */ protected[sql] lazy val sideEffectResult: Seq[Row] = cmd.run(sqlContext) - override def output = cmd.output + override def output: Seq[Attribute] = cmd.output - override def children = Nil + override def children: Seq[SparkPlan] = Nil override def executeCollect(): Array[Row] = sideEffectResult.toArray override def executeTake(limit: Int): Array[Row] = sideEffectResult.take(limit).toArray - override def execute(): RDD[Row] = sqlContext.sparkContext.parallelize(sideEffectResult, 1) + override def execute(): RDD[Row] = { + val converted = sideEffectResult.map(r => + CatalystTypeConverters.convertToCatalyst(r, schema).asInstanceOf[Row]) + sqlContext.sparkContext.parallelize(converted, 1) + } } /** @@ -70,9 +75,10 @@ case class ExecutedCommand(cmd: RunnableCommand) extends SparkPlan { @DeveloperApi case class SetCommand( kv: Option[(String, Option[String])], - override val output: Seq[Attribute]) extends RunnableCommand with Logging { + override val output: Seq[Attribute]) + extends RunnableCommand with Logging { - override def run(sqlContext: SQLContext) = kv match { + override def run(sqlContext: SQLContext): Seq[Row] = kv match { // Configures the deprecated "mapred.reduce.tasks" property. case Some((SQLConf.Deprecated.MAPRED_REDUCE_TASKS, Some(value))) => logWarning( @@ -116,10 +122,13 @@ case class SetCommand( @DeveloperApi case class ExplainCommand( logicalPlan: LogicalPlan, - override val output: Seq[Attribute], extended: Boolean = false) extends RunnableCommand { + override val output: Seq[Attribute] = + Seq(AttributeReference("plan", StringType, nullable = false)()), + extended: Boolean = false) + extends RunnableCommand { // Run through the optimizer to generate the physical plan. - override def run(sqlContext: SQLContext) = try { + override def run(sqlContext: SQLContext): Seq[Row] = try { // TODO in Hive, the "extended" ExplainCommand prints the AST as well, and detailed properties. val queryExecution = sqlContext.executePlan(logicalPlan) val outputString = if (extended) queryExecution.toString else queryExecution.simpleString @@ -137,11 +146,12 @@ case class ExplainCommand( case class CacheTableCommand( tableName: String, plan: Option[LogicalPlan], - isLazy: Boolean) extends RunnableCommand { + isLazy: Boolean) + extends RunnableCommand { - override def run(sqlContext: SQLContext) = { + override def run(sqlContext: SQLContext): Seq[Row] = { plan.foreach { logicalPlan => - sqlContext.registerRDDAsTable(DataFrame(sqlContext, logicalPlan), tableName) + sqlContext.registerDataFrameAsTable(DataFrame(sqlContext, logicalPlan), tableName) } sqlContext.cacheTable(tableName) @@ -163,7 +173,7 @@ case class CacheTableCommand( @DeveloperApi case class UncacheTableCommand(tableName: String) extends RunnableCommand { - override def run(sqlContext: SQLContext) = { + override def run(sqlContext: SQLContext): Seq[Row] = { sqlContext.table(tableName).unpersist(blocking = false) Seq.empty[Row] } @@ -171,6 +181,21 @@ case class UncacheTableCommand(tableName: String) extends RunnableCommand { override def output: Seq[Attribute] = Seq.empty } +/** + * :: DeveloperApi :: + * Clear all cached data from the in-memory cache. + */ +@DeveloperApi +case object ClearCacheCommand extends RunnableCommand { + + override def run(sqlContext: SQLContext): Seq[Row] = { + sqlContext.clearCache() + Seq.empty[Row] + } + + override def output: Seq[Attribute] = Seq.empty +} + /** * :: DeveloperApi :: */ @@ -178,9 +203,10 @@ case class UncacheTableCommand(tableName: String) extends RunnableCommand { case class DescribeCommand( child: SparkPlan, override val output: Seq[Attribute], - isExtended: Boolean) extends RunnableCommand { + isExtended: Boolean) + extends RunnableCommand { - override def run(sqlContext: SQLContext) = { + override def run(sqlContext: SQLContext): Seq[Row] = { child.schema.fields.map { field => val cmtKey = "comment" val comment = if (field.metadata.contains(cmtKey)) field.metadata.getString(cmtKey) else "" @@ -188,3 +214,35 @@ case class DescribeCommand( } } } + +/** + * A command for users to get tables in the given database. + * If a databaseName is not given, the current database will be used. + * The syntax of using this command in SQL is: + * {{{ + * SHOW TABLES [IN databaseName] + * }}} + * :: DeveloperApi :: + */ +@DeveloperApi +case class ShowTablesCommand(databaseName: Option[String]) extends RunnableCommand { + + // The result of SHOW TABLES has two columns, tableName and isTemporary. + override val output: Seq[Attribute] = { + val schema = StructType( + StructField("tableName", StringType, false) :: + StructField("isTemporary", BooleanType, false) :: Nil) + + schema.toAttributes + } + + override def run(sqlContext: SQLContext): Seq[Row] = { + // Since we need to return a Seq of rows, we will call getTables directly + // instead of calling tables in sqlContext. + val rows = sqlContext.catalog.getTables(databaseName).map { + case (tableName, isTemporary) => Row(tableName, isTemporary) + } + + rows + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala index 5cc67cdd13944..710787096e6cb 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala @@ -17,12 +17,14 @@ package org.apache.spark.sql.execution +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.catalyst.expressions.Attribute + import scala.collection.mutable.HashSet -import org.apache.spark.{AccumulatorParam, Accumulator, SparkContext} +import org.apache.spark.{AccumulatorParam, Accumulator} import org.apache.spark.annotation.DeveloperApi -import org.apache.spark.SparkContext._ -import org.apache.spark.sql.{DataFrame, Row} +import org.apache.spark.sql.{SQLConf, SQLContext, DataFrame, Row} import org.apache.spark.sql.catalyst.trees.TreeNodeRef import org.apache.spark.sql.types._ @@ -32,11 +34,22 @@ import org.apache.spark.sql.types._ * * Usage: * {{{ - * sql("SELECT key FROM src").debug + * import org.apache.spark.sql.execution.debug._ + * sql("SELECT key FROM src").debug() + * dataFrame.typeCheck() * }}} */ package object debug { + /** + * Augments [[SQLContext]] with debug methods. + */ + implicit class DebugSQLContext(sqlContext: SQLContext) { + def debug(): Unit = { + sqlContext.setConf(SQLConf.DATAFRAME_EAGER_ANALYSIS, "false") + } + } + /** * :: DeveloperApi :: * Augments [[DataFrame]]s with debug methods. @@ -77,7 +90,7 @@ package object debug { } private[sql] case class DebugNode(child: SparkPlan) extends UnaryNode { - def output = child.output + def output: Seq[Attribute] = child.output implicit object SetAccumulatorParam extends AccumulatorParam[HashSet[String]] { def zero(initialValue: HashSet[String]): HashSet[String] = { @@ -98,10 +111,10 @@ package object debug { */ case class ColumnMetrics( elementTypes: Accumulator[HashSet[String]] = sparkContext.accumulator(HashSet.empty)) - val tupleCount = sparkContext.accumulator[Int](0) + val tupleCount: Accumulator[Int] = sparkContext.accumulator[Int](0) - val numColumns = child.output.size - val columnStats = Array.fill(child.output.size)(new ColumnMetrics()) + val numColumns: Int = child.output.size + val columnStats: Array[ColumnMetrics] = Array.fill(child.output.size)(new ColumnMetrics()) def dumpStats(): Unit = { println(s"== ${child.simpleString} ==") @@ -112,11 +125,11 @@ package object debug { } } - def execute() = { + def execute(): RDD[Row] = { child.execute().mapPartitions { iter => new Iterator[Row] { - def hasNext = iter.hasNext - def next() = { + def hasNext: Boolean = iter.hasNext + def next(): Row = { val currentRow = iter.next() tupleCount += 1 var i = 0 @@ -135,11 +148,9 @@ package object debug { } /** - * :: DeveloperApi :: * Helper functions for checking that runtime types match a given schema. */ - @DeveloperApi - object TypeCheck { + private[sql] object TypeCheck { def typeCheck(data: Any, schema: DataType): Unit = (data, schema) match { case (null, _) => @@ -153,37 +164,36 @@ package object debug { case (_: Long, LongType) => case (_: Int, IntegerType) => - case (_: String, StringType) => + case (_: UTF8String, StringType) => case (_: Float, FloatType) => case (_: Byte, ByteType) => case (_: Short, ShortType) => case (_: Boolean, BooleanType) => case (_: Double, DoubleType) => + case (v, udt: UserDefinedType[_]) => typeCheck(v, udt.sqlType) case (d, t) => sys.error(s"Invalid data found: got $d (${d.getClass}) expected $t") } } /** - * :: DeveloperApi :: * Augments [[DataFrame]]s with debug methods. */ - @DeveloperApi private[sql] case class TypeCheck(child: SparkPlan) extends SparkPlan { import TypeCheck._ - override def nodeName = "" + override def nodeName: String = "" /* Only required when defining this class in a REPL. override def makeCopy(args: Array[Object]): this.type = TypeCheck(args(0).asInstanceOf[SparkPlan]).asInstanceOf[this.type] */ - def output = child.output + def output: Seq[Attribute] = child.output - def children = child :: Nil + def children: List[SparkPlan] = child :: Nil - def execute() = { + def execute(): RDD[Row] = { child.execute().map { row => try typeCheck(row, child.schema) catch { case e: Exception => diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala index 2dd22c020ef12..926f5e6c137ee 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala @@ -17,13 +17,15 @@ package org.apache.spark.sql.execution.joins +import org.apache.spark.rdd.RDD + import scala.concurrent._ import scala.concurrent.duration._ import scala.concurrent.ExecutionContext.Implicits.global import org.apache.spark.annotation.DeveloperApi import org.apache.spark.sql.catalyst.expressions.{Row, Expression} -import org.apache.spark.sql.catalyst.plans.physical.{Partitioning, UnspecifiedDistribution} +import org.apache.spark.sql.catalyst.plans.physical.{Distribution, Partitioning, UnspecifiedDistribution} import org.apache.spark.sql.execution.{BinaryNode, SparkPlan} /** @@ -42,7 +44,7 @@ case class BroadcastHashJoin( right: SparkPlan) extends BinaryNode with HashJoin { - val timeout = { + val timeout: Duration = { val timeoutValue = sqlContext.conf.broadcastTimeout if (timeoutValue < 0) { Duration.Inf @@ -53,7 +55,7 @@ case class BroadcastHashJoin( override def outputPartitioning: Partitioning = streamedPlan.outputPartitioning - override def requiredChildDistribution = + override def requiredChildDistribution: Seq[Distribution] = UnspecifiedDistribution :: UnspecifiedDistribution :: Nil @transient @@ -64,7 +66,7 @@ case class BroadcastHashJoin( sparkContext.broadcast(hashed) } - override def execute() = { + override def execute(): RDD[Row] = { val broadcastRelation = Await.result(broadcastFuture, timeout) streamedPlan.execute().mapPartitions { streamedIter => diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala index 2ab064fd0151e..3ef1e0d7fbdd4 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala @@ -18,8 +18,8 @@ package org.apache.spark.sql.execution.joins import org.apache.spark.annotation.DeveloperApi -import org.apache.spark.sql.catalyst.expressions.{Expression, Row} -import org.apache.spark.sql.catalyst.plans.physical.ClusteredDistribution +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression, Row} import org.apache.spark.sql.execution.{BinaryNode, SparkPlan} /** @@ -34,11 +34,11 @@ case class BroadcastLeftSemiJoinHash( left: SparkPlan, right: SparkPlan) extends BinaryNode with HashJoin { - override val buildSide = BuildRight + override val buildSide: BuildSide = BuildRight - override def output = left.output + override def output: Seq[Attribute] = left.output - override def execute() = { + override def execute(): RDD[Row] = { val buildIter= buildPlan.execute().map(_.copy()).collect().toIterator val hashSet = new java.util.HashSet[Row]() var currentRow: Row = null diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala index 36aad13778bd2..83b1a83765153 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala @@ -18,6 +18,7 @@ package org.apache.spark.sql.execution.joins import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.rdd.RDD import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.physical.Partitioning import org.apache.spark.sql.catalyst.plans.{FullOuter, JoinType, LeftOuter, RightOuter} @@ -44,7 +45,7 @@ case class BroadcastNestedLoopJoin( override def outputPartitioning: Partitioning = streamed.outputPartitioning - override def output = { + override def output: Seq[Attribute] = { joinType match { case LeftOuter => left.output ++ right.output.map(_.withNullability(true)) @@ -63,7 +64,7 @@ case class BroadcastNestedLoopJoin( .map(c => BindReferences.bindReference(c, left.output ++ right.output)) .getOrElse(Literal(true))) - override def execute() = { + override def execute(): RDD[Row] = { val broadcastedRelation = sparkContext.broadcast(broadcast.execute().map(_.copy()).collect().toIndexedSeq) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/CartesianProduct.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/CartesianProduct.scala index 76c14c02aab34..1cbc98354d673 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/CartesianProduct.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/CartesianProduct.scala @@ -18,7 +18,9 @@ package org.apache.spark.sql.execution.joins import org.apache.spark.annotation.DeveloperApi -import org.apache.spark.sql.catalyst.expressions.JoinedRow +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.Row +import org.apache.spark.sql.catalyst.expressions.{Attribute, JoinedRow} import org.apache.spark.sql.execution.{BinaryNode, SparkPlan} /** @@ -26,9 +28,9 @@ import org.apache.spark.sql.execution.{BinaryNode, SparkPlan} */ @DeveloperApi case class CartesianProduct(left: SparkPlan, right: SparkPlan) extends BinaryNode { - override def output = left.output ++ right.output + override def output: Seq[Attribute] = left.output ++ right.output - override def execute() = { + override def execute(): RDD[Row] = { val leftResults = left.execute().map(_.copy()) val rightResults = right.execute().map(_.copy()) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala index 4012d757d5f9a..851de1685509a 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala @@ -41,7 +41,7 @@ trait HashJoin { case BuildRight => (rightKeys, leftKeys) } - override def output = left.output ++ right.output + override def output: Seq[Attribute] = left.output ++ right.output @transient protected lazy val buildSideKeyGenerator: Projection = newProjection(buildKeys, buildPlan.output) @@ -65,7 +65,7 @@ trait HashJoin { (currentMatchPosition != -1 && currentMatchPosition < currentHashMatches.size) || (streamIter.hasNext && fetchNext()) - override final def next() = { + override final def next(): Row = { val ret = buildSide match { case BuildRight => joinRow(currentStreamedRow, currentHashMatches(currentMatchPosition)) case BuildLeft => joinRow(currentHashMatches(currentMatchPosition), currentStreamedRow) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala index 59ef904272545..a396c0f5d56ee 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala @@ -19,6 +19,8 @@ package org.apache.spark.sql.execution.joins import java.util.{HashMap => JavaHashMap} +import org.apache.spark.rdd.RDD + import scala.collection.JavaConversions._ import org.apache.spark.annotation.DeveloperApi @@ -49,10 +51,10 @@ case class HashOuterJoin( case x => throw new Exception(s"HashOuterJoin should not take $x as the JoinType") } - override def requiredChildDistribution = + override def requiredChildDistribution: Seq[ClusteredDistribution] = ClusteredDistribution(leftKeys) :: ClusteredDistribution(rightKeys) :: Nil - override def output = { + override def output: Seq[Attribute] = { joinType match { case LeftOuter => left.output ++ right.output.map(_.withNullability(true)) @@ -78,12 +80,12 @@ case class HashOuterJoin( private[this] def leftOuterIterator( key: Row, joinedRow: JoinedRow, rightIter: Iterable[Row]): Iterator[Row] = { - val ret: Iterable[Row] = ( + val ret: Iterable[Row] = { if (!key.anyNull) { val temp = rightIter.collect { - case r if (boundCondition(joinedRow.withRight(r))) => joinedRow.copy + case r if boundCondition(joinedRow.withRight(r)) => joinedRow.copy() } - if (temp.size == 0) { + if (temp.size == 0) { joinedRow.withRight(rightNullRow).copy :: Nil } else { temp @@ -91,19 +93,19 @@ case class HashOuterJoin( } else { joinedRow.withRight(rightNullRow).copy :: Nil } - ) + } ret.iterator } private[this] def rightOuterIterator( key: Row, leftIter: Iterable[Row], joinedRow: JoinedRow): Iterator[Row] = { - val ret: Iterable[Row] = ( + val ret: Iterable[Row] = { if (!key.anyNull) { val temp = leftIter.collect { - case l if (boundCondition(joinedRow.withLeft(l))) => joinedRow.copy + case l if boundCondition(joinedRow.withLeft(l)) => joinedRow.copy } - if (temp.size == 0) { + if (temp.size == 0) { joinedRow.withLeft(leftNullRow).copy :: Nil } else { temp @@ -111,7 +113,7 @@ case class HashOuterJoin( } else { joinedRow.withLeft(leftNullRow).copy :: Nil } - ) + } ret.iterator } @@ -130,12 +132,12 @@ case class HashOuterJoin( // 1. For those matched (satisfy the join condition) records with both sides filled, // append them directly - case (r, idx) if (boundCondition(joinedRow.withRight(r)))=> { + case (r, idx) if boundCondition(joinedRow.withRight(r)) => matched = true // if the row satisfy the join condition, add its index into the matched set rightMatchedSet.add(idx) - joinedRow.copy - } + joinedRow.copy() + } ++ DUMMY_LIST.filter(_ => !matched).map( _ => { // 2. For those unmatched records in left, append additional records with empty right. @@ -143,22 +145,21 @@ case class HashOuterJoin( // as we don't know whether we need to append it until finish iterating all // of the records in right side. // If we didn't get any proper row, then append a single row with empty right. - joinedRow.withRight(rightNullRow).copy + joinedRow.withRight(rightNullRow).copy() }) } ++ rightIter.zipWithIndex.collect { // 3. For those unmatched records in right, append additional records with empty left. // Re-visiting the records in right, and append additional row with empty left, if its not // in the matched set. - case (r, idx) if (!rightMatchedSet.contains(idx)) => { - joinedRow(leftNullRow, r).copy - } + case (r, idx) if !rightMatchedSet.contains(idx) => + joinedRow(leftNullRow, r).copy() } } else { leftIter.iterator.map[Row] { l => - joinedRow(l, rightNullRow).copy + joinedRow(l, rightNullRow).copy() } ++ rightIter.iterator.map[Row] { r => - joinedRow(leftNullRow, r).copy + joinedRow(leftNullRow, r).copy() } } } @@ -182,13 +183,13 @@ case class HashOuterJoin( hashTable } - override def execute() = { + override def execute(): RDD[Row] = { val joinedRow = new JoinedRow() left.execute().zipPartitions(right.execute()) { (leftIter, rightIter) => // TODO this probably can be replaced by external sort (sort merged join?) joinType match { - case LeftOuter => { + case LeftOuter => val rightHashTable = buildHashTable(rightIter, newProjection(rightKeys, right.output)) val keyGenerator = newProjection(leftKeys, left.output) leftIter.flatMap( currentRow => { @@ -196,8 +197,8 @@ case class HashOuterJoin( joinedRow.withLeft(currentRow) leftOuterIterator(rowKey, joinedRow, rightHashTable.getOrElse(rowKey, EMPTY_LIST)) }) - } - case RightOuter => { + + case RightOuter => val leftHashTable = buildHashTable(leftIter, newProjection(leftKeys, left.output)) val keyGenerator = newProjection(rightKeys, right.output) rightIter.flatMap ( currentRow => { @@ -205,8 +206,8 @@ case class HashOuterJoin( joinedRow.withRight(currentRow) rightOuterIterator(rowKey, leftHashTable.getOrElse(rowKey, EMPTY_LIST), joinedRow) }) - } - case FullOuter => { + + case FullOuter => val leftHashTable = buildHashTable(leftIter, newProjection(leftKeys, left.output)) val rightHashTable = buildHashTable(rightIter, newProjection(rightKeys, right.output)) (leftHashTable.keySet ++ rightHashTable.keySet).iterator.flatMap { key => @@ -214,7 +215,7 @@ case class HashOuterJoin( leftHashTable.getOrElse(key, EMPTY_LIST), rightHashTable.getOrElse(key, EMPTY_LIST), joinedRow) } - } + case x => throw new Exception(s"HashOuterJoin should not take $x as the JoinType") } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala index 38b8993b03f82..ab84c123e0c0b 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala @@ -17,9 +17,11 @@ package org.apache.spark.sql.execution.joins +import java.io.{ObjectInput, ObjectOutput, Externalizable} import java.util.{HashMap => JavaHashMap} import org.apache.spark.sql.catalyst.expressions.{Projection, Row} +import org.apache.spark.sql.execution.SparkSqlSerializer import org.apache.spark.util.collection.CompactBuffer @@ -29,16 +31,43 @@ import org.apache.spark.util.collection.CompactBuffer */ private[joins] sealed trait HashedRelation { def get(key: Row): CompactBuffer[Row] + + // This is a helper method to implement Externalizable, and is used by + // GeneralHashedRelation and UniqueKeyHashedRelation + protected def writeBytes(out: ObjectOutput, serialized: Array[Byte]): Unit = { + out.writeInt(serialized.length) // Write the length of serialized bytes first + out.write(serialized) + } + + // This is a helper method to implement Externalizable, and is used by + // GeneralHashedRelation and UniqueKeyHashedRelation + protected def readBytes(in: ObjectInput): Array[Byte] = { + val serializedSize = in.readInt() // Read the length of serialized bytes first + val bytes = new Array[Byte](serializedSize) + in.readFully(bytes) + bytes + } } /** * A general [[HashedRelation]] backed by a hash map that maps the key into a sequence of values. */ -private[joins] final class GeneralHashedRelation(hashTable: JavaHashMap[Row, CompactBuffer[Row]]) - extends HashedRelation with Serializable { +private[joins] final class GeneralHashedRelation( + private var hashTable: JavaHashMap[Row, CompactBuffer[Row]]) + extends HashedRelation with Externalizable { + + def this() = this(null) // Needed for serialization + + override def get(key: Row): CompactBuffer[Row] = hashTable.get(key) + + override def writeExternal(out: ObjectOutput): Unit = { + writeBytes(out, SparkSqlSerializer.serialize(hashTable)) + } - override def get(key: Row) = hashTable.get(key) + override def readExternal(in: ObjectInput): Unit = { + hashTable = SparkSqlSerializer.deserialize(readBytes(in)) + } } @@ -46,15 +75,25 @@ private[joins] final class GeneralHashedRelation(hashTable: JavaHashMap[Row, Com * A specialized [[HashedRelation]] that maps key into a single value. This implementation * assumes the key is unique. */ -private[joins] final class UniqueKeyHashedRelation(hashTable: JavaHashMap[Row, Row]) - extends HashedRelation with Serializable { +private[joins] final class UniqueKeyHashedRelation(private var hashTable: JavaHashMap[Row, Row]) + extends HashedRelation with Externalizable { - override def get(key: Row) = { + def this() = this(null) // Needed for serialization + + override def get(key: Row): CompactBuffer[Row] = { val v = hashTable.get(key) if (v eq null) null else CompactBuffer(v) } def getValue(key: Row): Row = hashTable.get(key) + + override def writeExternal(out: ObjectOutput): Unit = { + writeBytes(out, SparkSqlSerializer.serialize(hashTable)) + } + + override def readExternal(in: ObjectInput): Unit = { + hashTable = SparkSqlSerializer.deserialize(readBytes(in)) + } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinBNL.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinBNL.scala index 60003d1900d85..1fa7e7bd0406c 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinBNL.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinBNL.scala @@ -18,6 +18,7 @@ package org.apache.spark.sql.execution.joins import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.rdd.RDD import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.physical.Partitioning import org.apache.spark.sql.execution.{BinaryNode, SparkPlan} @@ -35,12 +36,13 @@ case class LeftSemiJoinBNL( override def outputPartitioning: Partitioning = streamed.outputPartitioning - override def output = left.output + override def output: Seq[Attribute] = left.output /** The Streamed Relation */ - override def left = streamed + override def left: SparkPlan = streamed + /** The Broadcast relation */ - override def right = broadcast + override def right: SparkPlan = broadcast @transient private lazy val boundCondition = InterpretedPredicate( @@ -48,7 +50,7 @@ case class LeftSemiJoinBNL( .map(c => BindReferences.bindReference(c, left.output ++ right.output)) .getOrElse(Literal(true))) - override def execute() = { + override def execute(): RDD[Row] = { val broadcastedRelation = sparkContext.broadcast(broadcast.execute().map(_.copy()).collect().toIndexedSeq) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinHash.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinHash.scala index ea7babf3be948..a04f2a63b5a55 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinHash.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinHash.scala @@ -18,7 +18,8 @@ package org.apache.spark.sql.execution.joins import org.apache.spark.annotation.DeveloperApi -import org.apache.spark.sql.catalyst.expressions.{Expression, Row} +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression, Row} import org.apache.spark.sql.catalyst.plans.physical.ClusteredDistribution import org.apache.spark.sql.execution.{BinaryNode, SparkPlan} @@ -34,14 +35,14 @@ case class LeftSemiJoinHash( left: SparkPlan, right: SparkPlan) extends BinaryNode with HashJoin { - override val buildSide = BuildRight + override val buildSide: BuildSide = BuildRight - override def requiredChildDistribution = + override def requiredChildDistribution: Seq[ClusteredDistribution] = ClusteredDistribution(leftKeys) :: ClusteredDistribution(rightKeys) :: Nil - override def output = left.output + override def output: Seq[Attribute] = left.output - override def execute() = { + override def execute(): RDD[Row] = { buildPlan.execute().zipPartitions(streamedPlan.execute()) { (buildIter, streamIter) => val hashSet = new java.util.HashSet[Row]() var currentRow: Row = null diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoin.scala index 418c1c23e5546..a6cd8337c1c3e 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoin.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoin.scala @@ -18,6 +18,8 @@ package org.apache.spark.sql.execution.joins import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.Row import org.apache.spark.sql.catalyst.expressions.Expression import org.apache.spark.sql.catalyst.plans.physical.{ClusteredDistribution, Partitioning} import org.apache.spark.sql.execution.{BinaryNode, SparkPlan} @@ -38,10 +40,10 @@ case class ShuffledHashJoin( override def outputPartitioning: Partitioning = left.outputPartitioning - override def requiredChildDistribution = + override def requiredChildDistribution: Seq[ClusteredDistribution] = ClusteredDistribution(leftKeys) :: ClusteredDistribution(rightKeys) :: Nil - override def execute() = { + override def execute(): RDD[Row] = { buildPlan.execute().zipPartitions(streamedPlan.execute()) { (buildIter, streamIter) => val hashed = HashedRelation(buildIter, buildSideKeyGenerator) hashJoin(streamIter, hashed) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala new file mode 100644 index 0000000000000..b5123668ba11e --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala @@ -0,0 +1,169 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.execution.joins + +import java.util.NoSuchElementException + +import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.Row +import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.catalyst.plans._ +import org.apache.spark.sql.catalyst.plans.physical._ +import org.apache.spark.sql.execution.{BinaryNode, SparkPlan} +import org.apache.spark.util.collection.CompactBuffer + +/** + * :: DeveloperApi :: + * Performs an sort merge join of two child relations. + */ +@DeveloperApi +case class SortMergeJoin( + leftKeys: Seq[Expression], + rightKeys: Seq[Expression], + left: SparkPlan, + right: SparkPlan) extends BinaryNode { + + override def output: Seq[Attribute] = left.output ++ right.output + + override def outputPartitioning: Partitioning = left.outputPartitioning + + override def requiredChildDistribution: Seq[Distribution] = + ClusteredDistribution(leftKeys) :: ClusteredDistribution(rightKeys) :: Nil + + // this is to manually construct an ordering that can be used to compare keys from both sides + private val keyOrdering: RowOrdering = RowOrdering.forSchema(leftKeys.map(_.dataType)) + + override def outputOrdering: Seq[SortOrder] = requiredOrders(leftKeys) + + override def requiredChildOrdering: Seq[Seq[SortOrder]] = + requiredOrders(leftKeys) :: requiredOrders(rightKeys) :: Nil + + @transient protected lazy val leftKeyGenerator = newProjection(leftKeys, left.output) + @transient protected lazy val rightKeyGenerator = newProjection(rightKeys, right.output) + + private def requiredOrders(keys: Seq[Expression]): Seq[SortOrder] = + keys.map(SortOrder(_, Ascending)) + + override def execute(): RDD[Row] = { + val leftResults = left.execute().map(_.copy()) + val rightResults = right.execute().map(_.copy()) + + leftResults.zipPartitions(rightResults) { (leftIter, rightIter) => + new Iterator[Row] { + // Mutable per row objects. + private[this] val joinRow = new JoinedRow5 + private[this] var leftElement: Row = _ + private[this] var rightElement: Row = _ + private[this] var leftKey: Row = _ + private[this] var rightKey: Row = _ + private[this] var rightMatches: CompactBuffer[Row] = _ + private[this] var rightPosition: Int = -1 + private[this] var stop: Boolean = false + private[this] var matchKey: Row = _ + + // initialize iterator + initialize() + + override final def hasNext: Boolean = nextMatchingPair() + + override final def next(): Row = { + if (hasNext) { + // we are using the buffered right rows and run down left iterator + val joinedRow = joinRow(leftElement, rightMatches(rightPosition)) + rightPosition += 1 + if (rightPosition >= rightMatches.size) { + rightPosition = 0 + fetchLeft() + if (leftElement == null || keyOrdering.compare(leftKey, matchKey) != 0) { + stop = false + rightMatches = null + } + } + joinedRow + } else { + // no more result + throw new NoSuchElementException + } + } + + private def fetchLeft() = { + if (leftIter.hasNext) { + leftElement = leftIter.next() + leftKey = leftKeyGenerator(leftElement) + } else { + leftElement = null + } + } + + private def fetchRight() = { + if (rightIter.hasNext) { + rightElement = rightIter.next() + rightKey = rightKeyGenerator(rightElement) + } else { + rightElement = null + } + } + + private def initialize() = { + fetchLeft() + fetchRight() + } + + /** + * Searches the right iterator for the next rows that have matches in left side, and store + * them in a buffer. + * + * @return true if the search is successful, and false if the right iterator runs out of + * tuples. + */ + private def nextMatchingPair(): Boolean = { + if (!stop && rightElement != null) { + // run both side to get the first match pair + while (!stop && leftElement != null && rightElement != null) { + val comparing = keyOrdering.compare(leftKey, rightKey) + // for inner join, we need to filter those null keys + stop = comparing == 0 && !leftKey.anyNull + if (comparing > 0 || rightKey.anyNull) { + fetchRight() + } else if (comparing < 0 || leftKey.anyNull) { + fetchLeft() + } + } + rightMatches = new CompactBuffer[Row]() + if (stop) { + stop = false + // iterate the right side to buffer all rows that matches + // as the records should be ordered, exit when we meet the first that not match + while (!stop && rightElement != null) { + rightMatches += rightElement + fetchRight() + stop = keyOrdering.compare(leftKey, rightKey) != 0 + } + if (rightMatches.size > 0) { + rightPosition = 0 + matchKey = leftKey + } + } + } + rightMatches != null && rightMatches.size > 0 + } + } + } + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala index 3a2f8d75dac5e..7a43bfd8bc8d9 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala @@ -19,6 +19,8 @@ package org.apache.spark.sql.execution import java.util.{List => JList, Map => JMap} +import org.apache.spark.rdd.RDD + import scala.collection.JavaConversions._ import scala.collection.JavaConverters._ @@ -48,11 +50,13 @@ private[spark] case class PythonUDF( dataType: DataType, children: Seq[Expression]) extends Expression with SparkLogging { - override def toString = s"PythonUDF#$name(${children.mkString(",")})" + override def toString: String = s"PythonUDF#$name(${children.mkString(",")})" def nullable: Boolean = true - override def eval(input: Row) = sys.error("PythonUDFs can not be directly evaluated.") + override def eval(input: Row): PythonUDF.this.EvaluatedType = { + sys.error("PythonUDFs can not be directly evaluated.") + } } /** @@ -63,7 +67,7 @@ private[spark] case class PythonUDF( * multiple child operators. */ private[spark] object ExtractPythonUdfs extends Rule[LogicalPlan] { - def apply(plan: LogicalPlan) = plan transform { + def apply(plan: LogicalPlan): LogicalPlan = plan transform { // Skip EvaluatePython nodes. case p: EvaluatePython => p @@ -107,7 +111,7 @@ private[spark] object ExtractPythonUdfs extends Rule[LogicalPlan] { } object EvaluatePython { - def apply(udf: PythonUDF, child: LogicalPlan) = + def apply(udf: PythonUDF, child: LogicalPlan): EvaluatePython = new EvaluatePython(udf, child, AttributeReference("pythonUDF", udf.dataType)()) /** @@ -136,6 +140,7 @@ object EvaluatePython { case (ud, udt: UserDefinedType[_]) => toJava(udt.serialize(ud), udt.sqlType) case (date: Int, DateType) => DateUtils.toJavaDate(date) + case (s: UTF8String, StringType) => s.toString // Pyrolite can handle Timestamp and Decimal case (other, _) => other @@ -186,8 +191,10 @@ object EvaluatePython { case (c: Int, ShortType) => c.toShort case (c: Long, ShortType) => c.toShort case (c: Long, IntegerType) => c.toInt + case (c: Int, LongType) => c.toLong case (c: Double, FloatType) => c.toFloat - case (c, StringType) if !c.isInstanceOf[String] => c.toString + case (c: String, StringType) => UTF8String(c) + case (c, StringType) if !c.isInstanceOf[String] => UTF8String(c.toString) case (c, _) => c } @@ -204,7 +211,10 @@ case class EvaluatePython( resultAttribute: AttributeReference) extends logical.UnaryNode { - def output = child.output :+ resultAttribute + def output: Seq[Attribute] = child.output :+ resultAttribute + + // References should not include the produced attribute. + override def references: AttributeSet = udf.references } /** @@ -215,9 +225,10 @@ case class EvaluatePython( @DeveloperApi case class BatchPythonEvaluation(udf: PythonUDF, output: Seq[Attribute], child: SparkPlan) extends SparkPlan { - def children = child :: Nil - def execute() = { + def children: Seq[SparkPlan] = child :: Nil + + def execute(): RDD[Row] = { // TODO: Clean up after ourselves? val childResults = child.execute().map(_.copy()).cache() diff --git a/sql/core/src/main/scala/org/apache/spark/sql/Dsl.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala similarity index 71% rename from sql/core/src/main/scala/org/apache/spark/sql/Dsl.scala rename to sql/core/src/main/scala/org/apache/spark/sql/functions.scala index 6bf21dd1bc79b..ff91e1d74bc2c 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/Dsl.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala @@ -20,35 +20,41 @@ package org.apache.spark.sql import scala.language.implicitConversions import scala.reflect.runtime.universe.{TypeTag, typeTag} +import org.apache.spark.annotation.Experimental import org.apache.spark.sql.catalyst.ScalaReflection +import org.apache.spark.sql.catalyst.analysis.{UnresolvedFunction, Star} import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.types._ /** - * Domain specific functions available for [[DataFrame]]. + * :: Experimental :: + * Functions available for [[DataFrame]]. + * + * @groupname udf_funcs UDF functions + * @groupname agg_funcs Aggregate functions + * @groupname sort_funcs Sorting functions + * @groupname normal_funcs Non-aggregate functions + * @groupname Ungrouped Support functions for DataFrames. */ -object Dsl { - - /** An implicit conversion that turns a Scala `Symbol` into a [[Column]]. */ - implicit def symbolToColumn(s: Symbol): ColumnName = new ColumnName(s.name) - - /** Converts $"col name" into an [[Column]]. */ - implicit class StringToColumn(val sc: StringContext) extends AnyVal { - def $(args: Any*): ColumnName = { - new ColumnName(sc.s(args :_*)) - } - } +@Experimental +// scalastyle:off +object functions { +// scalastyle:on private[this] implicit def toColumn(expr: Expression): Column = Column(expr) /** * Returns a [[Column]] based on the given column name. + * + * @group normal_funcs */ def col(colName: String): Column = Column(colName) /** * Returns a [[Column]] based on the given column name. Alias of [[col]]. + * + * @group normal_funcs */ def column(colName: String): Column = Column(colName) @@ -58,6 +64,8 @@ object Dsl { * The passed in object is returned directly if it is already a [[Column]]. * If the object is a Scala Symbol, it is converted into a [[Column]] also. * Otherwise, a new [[Column]] is created to represent the literal value. + * + * @group normal_funcs */ def lit(literal: Any): Column = { literal match { @@ -66,106 +74,219 @@ object Dsl { case _ => // continue } - val literalExpr = literal match { - case v: Boolean => Literal(v, BooleanType) - case v: Byte => Literal(v, ByteType) - case v: Short => Literal(v, ShortType) - case v: Int => Literal(v, IntegerType) - case v: Long => Literal(v, LongType) - case v: Float => Literal(v, FloatType) - case v: Double => Literal(v, DoubleType) - case v: String => Literal(v, StringType) - case v: BigDecimal => Literal(Decimal(v), DecimalType.Unlimited) - case v: java.math.BigDecimal => Literal(Decimal(v), DecimalType.Unlimited) - case v: Decimal => Literal(v, DecimalType.Unlimited) - case v: java.sql.Timestamp => Literal(v, TimestampType) - case v: java.sql.Date => Literal(v, DateType) - case v: Array[Byte] => Literal(v, BinaryType) - case null => Literal(null, NullType) - case _ => - throw new RuntimeException("Unsupported literal type " + literal.getClass + " " + literal) - } + val literalExpr = Literal(literal) Column(literalExpr) } ////////////////////////////////////////////////////////////////////////////////////////////// + // Sort functions + ////////////////////////////////////////////////////////////////////////////////////////////// + + /** + * Returns a sort expression based on ascending order of the column. + * {{ + * // Sort by dept in ascending order, and then age in descending order. + * df.sort(asc("dept"), desc("age")) + * }} + * + * @group sort_funcs + */ + def asc(columnName: String): Column = Column(columnName).asc + + /** + * Returns a sort expression based on the descending order of the column. + * {{ + * // Sort by dept in ascending order, and then age in descending order. + * df.sort(asc("dept"), desc("age")) + * }} + * + * @group sort_funcs + */ + def desc(columnName: String): Column = Column(columnName).desc + + ////////////////////////////////////////////////////////////////////////////////////////////// + // Aggregate functions ////////////////////////////////////////////////////////////////////////////////////////////// - /** Aggregate function: returns the sum of all values in the expression. */ + /** + * Aggregate function: returns the sum of all values in the expression. + * + * @group agg_funcs + */ def sum(e: Column): Column = Sum(e.expr) - /** Aggregate function: returns the sum of all values in the given column. */ + /** + * Aggregate function: returns the sum of all values in the given column. + * + * @group agg_funcs + */ def sum(columnName: String): Column = sum(Column(columnName)) - /** Aggregate function: returns the sum of distinct values in the expression. */ + /** + * Aggregate function: returns the sum of distinct values in the expression. + * + * @group agg_funcs + */ def sumDistinct(e: Column): Column = SumDistinct(e.expr) - /** Aggregate function: returns the sum of distinct values in the expression. */ + /** + * Aggregate function: returns the sum of distinct values in the expression. + * + * @group agg_funcs + */ def sumDistinct(columnName: String): Column = sumDistinct(Column(columnName)) - /** Aggregate function: returns the number of items in a group. */ - def count(e: Column): Column = Count(e.expr) + /** + * Aggregate function: returns the number of items in a group. + * + * @group agg_funcs + */ + def count(e: Column): Column = e.expr match { + // Turn count(*) into count(1) + case s: Star => Count(Literal(1)) + case _ => Count(e.expr) + } - /** Aggregate function: returns the number of items in a group. */ + /** + * Aggregate function: returns the number of items in a group. + * + * @group agg_funcs + */ def count(columnName: String): Column = count(Column(columnName)) - /** Aggregate function: returns the number of distinct items in a group. */ + /** + * Aggregate function: returns the number of distinct items in a group. + * + * @group agg_funcs + */ @scala.annotation.varargs def countDistinct(expr: Column, exprs: Column*): Column = CountDistinct((expr +: exprs).map(_.expr)) - /** Aggregate function: returns the number of distinct items in a group. */ + /** + * Aggregate function: returns the number of distinct items in a group. + * + * @group agg_funcs + */ @scala.annotation.varargs def countDistinct(columnName: String, columnNames: String*): Column = countDistinct(Column(columnName), columnNames.map(Column.apply) :_*) - /** Aggregate function: returns the approximate number of distinct items in a group. */ + /** + * Aggregate function: returns the approximate number of distinct items in a group. + * + * @group agg_funcs + */ def approxCountDistinct(e: Column): Column = ApproxCountDistinct(e.expr) - /** Aggregate function: returns the approximate number of distinct items in a group. */ + /** + * Aggregate function: returns the approximate number of distinct items in a group. + * + * @group agg_funcs + */ def approxCountDistinct(columnName: String): Column = approxCountDistinct(column(columnName)) - /** Aggregate function: returns the approximate number of distinct items in a group. */ + /** + * Aggregate function: returns the approximate number of distinct items in a group. + * + * @group agg_funcs + */ def approxCountDistinct(e: Column, rsd: Double): Column = ApproxCountDistinct(e.expr, rsd) - /** Aggregate function: returns the approximate number of distinct items in a group. */ + /** + * Aggregate function: returns the approximate number of distinct items in a group. + * + * @group agg_funcs + */ def approxCountDistinct(columnName: String, rsd: Double): Column = { approxCountDistinct(Column(columnName), rsd) } - /** Aggregate function: returns the average of the values in a group. */ + /** + * Aggregate function: returns the average of the values in a group. + * + * @group agg_funcs + */ def avg(e: Column): Column = Average(e.expr) - /** Aggregate function: returns the average of the values in a group. */ + /** + * Aggregate function: returns the average of the values in a group. + * + * @group agg_funcs + */ def avg(columnName: String): Column = avg(Column(columnName)) - /** Aggregate function: returns the first value in a group. */ + /** + * Aggregate function: returns the first value in a group. + * + * @group agg_funcs + */ def first(e: Column): Column = First(e.expr) - /** Aggregate function: returns the first value of a column in a group. */ + /** + * Aggregate function: returns the first value of a column in a group. + * + * @group agg_funcs + */ def first(columnName: String): Column = first(Column(columnName)) - /** Aggregate function: returns the last value in a group. */ + /** + * Aggregate function: returns the last value in a group. + * + * @group agg_funcs + */ def last(e: Column): Column = Last(e.expr) - /** Aggregate function: returns the last value of the column in a group. */ + /** + * Aggregate function: returns the last value of the column in a group. + * + * @group agg_funcs + */ def last(columnName: String): Column = last(Column(columnName)) - /** Aggregate function: returns the minimum value of the expression in a group. */ + /** + * Aggregate function: returns the minimum value of the expression in a group. + * + * @group agg_funcs + */ def min(e: Column): Column = Min(e.expr) - /** Aggregate function: returns the minimum value of the column in a group. */ + /** + * Aggregate function: returns the minimum value of the column in a group. + * + * @group agg_funcs + */ def min(columnName: String): Column = min(Column(columnName)) - /** Aggregate function: returns the maximum value of the expression in a group. */ + /** + * Aggregate function: returns the maximum value of the expression in a group. + * + * @group agg_funcs + */ def max(e: Column): Column = Max(e.expr) - /** Aggregate function: returns the maximum value of the column in a group. */ + /** + * Aggregate function: returns the maximum value of the column in a group. + * + * @group agg_funcs + */ def max(columnName: String): Column = max(Column(columnName)) ////////////////////////////////////////////////////////////////////////////////////////////// + // Non-aggregate functions ////////////////////////////////////////////////////////////////////////////////////////////// + /** + * Returns the first column that is not null. + * {{{ + * df.select(coalesce(df("a"), df("b"))) + * }}} + * + * @group normal_funcs + */ + @scala.annotation.varargs + def coalesce(e: Column*): Column = Coalesce(e.map(_.expr)) + /** * Unary minus, i.e. negate the expression. * {{{ @@ -176,6 +297,8 @@ object Dsl { * // Java: * df.select( negate(df.col("amount")) ); * }}} + * + * @group normal_funcs */ def negate(e: Column): Column = -e @@ -188,19 +311,37 @@ object Dsl { * // Java: * df.filter( not(df.col("isActive")) ); * }} + * + * @group normal_funcs */ def not(e: Column): Column = !e - /** Converts a string expression to upper case. */ + /** + * Converts a string expression to upper case. + * + * @group normal_funcs + */ def upper(e: Column): Column = Upper(e.expr) - /** Converts a string exprsesion to lower case. */ + /** + * Converts a string exprsesion to lower case. + * + * @group normal_funcs + */ def lower(e: Column): Column = Lower(e.expr) - /** Computes the square root of the specified float value. */ + /** + * Computes the square root of the specified float value. + * + * @group normal_funcs + */ def sqrt(e: Column): Column = Sqrt(e.expr) - /** Computes the absolutle value. */ + /** + * Computes the absolutle value. + * + * @group normal_funcs + */ def abs(e: Column): Column = Abs(e.expr) ////////////////////////////////////////////////////////////////////////////////////////////// @@ -216,6 +357,8 @@ object Dsl { /** * Defines a user-defined function of ${x} arguments as user-defined function (UDF). * The data types are automatically inferred based on the function's signature. + * + * @group udf_funcs */ def udf[$typeTags](f: Function$x[$types]): UserDefinedFunction = { UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType) @@ -230,6 +373,8 @@ object Dsl { /** * Call a Scala function of ${x} arguments as user-defined function (UDF). This requires * you to specify the return data type. + * + * @group udf_funcs */ def callUDF(f: Function$x[$fTypes], returnType: DataType${if (args.length > 0) ", " + args else ""}): Column = { ScalaUdf(f, returnType, Seq($argsInUdf)) @@ -240,6 +385,8 @@ object Dsl { /** * Defines a user-defined function of 0 arguments as user-defined function (UDF). * The data types are automatically inferred based on the function's signature. + * + * @group udf_funcs */ def udf[RT: TypeTag](f: Function0[RT]): UserDefinedFunction = { UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType) @@ -248,6 +395,8 @@ object Dsl { /** * Defines a user-defined function of 1 arguments as user-defined function (UDF). * The data types are automatically inferred based on the function's signature. + * + * @group udf_funcs */ def udf[RT: TypeTag, A1: TypeTag](f: Function1[A1, RT]): UserDefinedFunction = { UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType) @@ -256,6 +405,8 @@ object Dsl { /** * Defines a user-defined function of 2 arguments as user-defined function (UDF). * The data types are automatically inferred based on the function's signature. + * + * @group udf_funcs */ def udf[RT: TypeTag, A1: TypeTag, A2: TypeTag](f: Function2[A1, A2, RT]): UserDefinedFunction = { UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType) @@ -264,6 +415,8 @@ object Dsl { /** * Defines a user-defined function of 3 arguments as user-defined function (UDF). * The data types are automatically inferred based on the function's signature. + * + * @group udf_funcs */ def udf[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag](f: Function3[A1, A2, A3, RT]): UserDefinedFunction = { UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType) @@ -272,6 +425,8 @@ object Dsl { /** * Defines a user-defined function of 4 arguments as user-defined function (UDF). * The data types are automatically inferred based on the function's signature. + * + * @group udf_funcs */ def udf[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag](f: Function4[A1, A2, A3, A4, RT]): UserDefinedFunction = { UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType) @@ -280,6 +435,8 @@ object Dsl { /** * Defines a user-defined function of 5 arguments as user-defined function (UDF). * The data types are automatically inferred based on the function's signature. + * + * @group udf_funcs */ def udf[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag](f: Function5[A1, A2, A3, A4, A5, RT]): UserDefinedFunction = { UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType) @@ -288,6 +445,8 @@ object Dsl { /** * Defines a user-defined function of 6 arguments as user-defined function (UDF). * The data types are automatically inferred based on the function's signature. + * + * @group udf_funcs */ def udf[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag](f: Function6[A1, A2, A3, A4, A5, A6, RT]): UserDefinedFunction = { UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType) @@ -296,6 +455,8 @@ object Dsl { /** * Defines a user-defined function of 7 arguments as user-defined function (UDF). * The data types are automatically inferred based on the function's signature. + * + * @group udf_funcs */ def udf[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag](f: Function7[A1, A2, A3, A4, A5, A6, A7, RT]): UserDefinedFunction = { UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType) @@ -304,6 +465,8 @@ object Dsl { /** * Defines a user-defined function of 8 arguments as user-defined function (UDF). * The data types are automatically inferred based on the function's signature. + * + * @group udf_funcs */ def udf[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag](f: Function8[A1, A2, A3, A4, A5, A6, A7, A8, RT]): UserDefinedFunction = { UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType) @@ -312,6 +475,8 @@ object Dsl { /** * Defines a user-defined function of 9 arguments as user-defined function (UDF). * The data types are automatically inferred based on the function's signature. + * + * @group udf_funcs */ def udf[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag](f: Function9[A1, A2, A3, A4, A5, A6, A7, A8, A9, RT]): UserDefinedFunction = { UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType) @@ -320,6 +485,8 @@ object Dsl { /** * Defines a user-defined function of 10 arguments as user-defined function (UDF). * The data types are automatically inferred based on the function's signature. + * + * @group udf_funcs */ def udf[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag](f: Function10[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, RT]): UserDefinedFunction = { UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType) @@ -330,6 +497,8 @@ object Dsl { /** * Call a Scala function of 0 arguments as user-defined function (UDF). This requires * you to specify the return data type. + * + * @group udf_funcs */ def callUDF(f: Function0[_], returnType: DataType): Column = { ScalaUdf(f, returnType, Seq()) @@ -338,6 +507,8 @@ object Dsl { /** * Call a Scala function of 1 arguments as user-defined function (UDF). This requires * you to specify the return data type. + * + * @group udf_funcs */ def callUDF(f: Function1[_, _], returnType: DataType, arg1: Column): Column = { ScalaUdf(f, returnType, Seq(arg1.expr)) @@ -346,6 +517,8 @@ object Dsl { /** * Call a Scala function of 2 arguments as user-defined function (UDF). This requires * you to specify the return data type. + * + * @group udf_funcs */ def callUDF(f: Function2[_, _, _], returnType: DataType, arg1: Column, arg2: Column): Column = { ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr)) @@ -354,6 +527,8 @@ object Dsl { /** * Call a Scala function of 3 arguments as user-defined function (UDF). This requires * you to specify the return data type. + * + * @group udf_funcs */ def callUDF(f: Function3[_, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column): Column = { ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr)) @@ -362,6 +537,8 @@ object Dsl { /** * Call a Scala function of 4 arguments as user-defined function (UDF). This requires * you to specify the return data type. + * + * @group udf_funcs */ def callUDF(f: Function4[_, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column): Column = { ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr)) @@ -370,6 +547,8 @@ object Dsl { /** * Call a Scala function of 5 arguments as user-defined function (UDF). This requires * you to specify the return data type. + * + * @group udf_funcs */ def callUDF(f: Function5[_, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column): Column = { ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr)) @@ -378,6 +557,8 @@ object Dsl { /** * Call a Scala function of 6 arguments as user-defined function (UDF). This requires * you to specify the return data type. + * + * @group udf_funcs */ def callUDF(f: Function6[_, _, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column): Column = { ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr)) @@ -386,6 +567,8 @@ object Dsl { /** * Call a Scala function of 7 arguments as user-defined function (UDF). This requires * you to specify the return data type. + * + * @group udf_funcs */ def callUDF(f: Function7[_, _, _, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column): Column = { ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr)) @@ -394,6 +577,8 @@ object Dsl { /** * Call a Scala function of 8 arguments as user-defined function (UDF). This requires * you to specify the return data type. + * + * @group udf_funcs */ def callUDF(f: Function8[_, _, _, _, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column): Column = { ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr)) @@ -402,6 +587,8 @@ object Dsl { /** * Call a Scala function of 9 arguments as user-defined function (UDF). This requires * you to specify the return data type. + * + * @group udf_funcs */ def callUDF(f: Function9[_, _, _, _, _, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column, arg9: Column): Column = { ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr, arg9.expr)) @@ -410,10 +597,31 @@ object Dsl { /** * Call a Scala function of 10 arguments as user-defined function (UDF). This requires * you to specify the return data type. + * + * @group udf_funcs */ def callUDF(f: Function10[_, _, _, _, _, _, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column, arg9: Column, arg10: Column): Column = { ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr, arg9.expr, arg10.expr)) } // scalastyle:on + + /** + * Call an user-defined function. + * Example: + * {{{ + * import org.apache.spark.sql._ + * + * val df = Seq(("id1", 1), ("id2", 4), ("id3", 5)).toDF("id", "value") + * val sqlContext = df.sqlContext + * sqlContext.udf.register("simpleUdf", (v: Int) => v * v) + * df.select($"id", callUdf("simpleUdf", $"value")) + * }}} + * + * @group udf_funcs + */ + def callUdf(udfName: String, cols: Column*): Column = { + UnresolvedFunction(udfName, cols.map(_.expr)) + } + } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/DriverQuirks.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/DriverQuirks.scala index 1704be7fcbd30..0feabc4282f4a 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/DriverQuirks.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/DriverQuirks.scala @@ -49,9 +49,9 @@ private[sql] object DriverQuirks { * Fetch the DriverQuirks class corresponding to a given database url. */ def get(url: String): DriverQuirks = { - if (url.substring(0, 10).equals("jdbc:mysql")) { + if (url.startsWith("jdbc:mysql")) { new MySQLQuirks() - } else if (url.substring(0, 15).equals("jdbc:postgresql")) { + } else if (url.startsWith("jdbc:postgresql")) { new PostgresQuirks() } else { new NoQuirks() diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala index a2f94675fb5a3..b9022fcd9e3ad 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala @@ -17,13 +17,12 @@ package org.apache.spark.sql.jdbc -import java.sql.{Connection, DatabaseMetaData, DriverManager, ResultSet, ResultSetMetaData, SQLException} -import scala.collection.mutable.ArrayBuffer +import java.sql.{Connection, DriverManager, ResultSet, ResultSetMetaData, SQLException} +import java.util.Properties +import org.apache.commons.lang.StringEscapeUtils.escapeSql import org.apache.spark.{Logging, Partition, SparkContext, TaskContext} import org.apache.spark.rdd.RDD -import org.apache.spark.util.NextIterator -import org.apache.spark.sql.catalyst.analysis.HiveTypeCoercion import org.apache.spark.sql.catalyst.expressions.{Row, SpecificMutableRow} import org.apache.spark.sql.types._ import org.apache.spark.sql.sources._ @@ -92,15 +91,15 @@ private[sql] object JDBCRDD extends Logging { * @throws SQLException if the table specification is garbage. * @throws SQLException if the table contains an unsupported type. */ - def resolveTable(url: String, table: String): StructType = { + def resolveTable(url: String, table: String, properties: Properties): StructType = { val quirks = DriverQuirks.get(url) - val conn: Connection = DriverManager.getConnection(url) + val conn: Connection = DriverManager.getConnection(url, properties) try { val rs = conn.prepareStatement(s"SELECT * FROM $table WHERE 1=0").executeQuery() try { val rsmd = rs.getMetaData val ncols = rsmd.getColumnCount - var fields = new Array[StructField](ncols); + val fields = new Array[StructField](ncols) var i = 0 while (i < ncols) { val columnName = rsmd.getColumnName(i + 1) @@ -149,7 +148,7 @@ private[sql] object JDBCRDD extends Logging { * * @return A function that loads the driver and connects to the url. */ - def getConnector(driver: String, url: String): () => Connection = { + def getConnector(driver: String, url: String, properties: Properties): () => Connection = { () => { try { if (driver != null) Class.forName(driver) @@ -158,7 +157,7 @@ private[sql] object JDBCRDD extends Logging { logWarning(s"Couldn't find class $driver", e); } } - DriverManager.getConnection(url) + DriverManager.getConnection(url, properties) } } /** @@ -176,23 +175,28 @@ private[sql] object JDBCRDD extends Logging { * * @return An RDD representing "SELECT requiredColumns FROM fqTable". */ - def scanTable(sc: SparkContext, - schema: StructType, - driver: String, - url: String, - fqTable: String, - requiredColumns: Array[String], - filters: Array[Filter], - parts: Array[Partition]): RDD[Row] = { + def scanTable( + sc: SparkContext, + schema: StructType, + driver: String, + url: String, + properties: Properties, + fqTable: String, + requiredColumns: Array[String], + filters: Array[Filter], + parts: Array[Partition]): RDD[Row] = { + val prunedSchema = pruneSchema(schema, requiredColumns) - return new JDBCRDD(sc, - getConnector(driver, url), - prunedSchema, - fqTable, - requiredColumns, - filters, - parts) + return new + JDBCRDD( + sc, + getConnector(driver, url, properties), + prunedSchema, + fqTable, + requiredColumns, + filters, + parts) } } @@ -225,16 +229,24 @@ private[sql] class JDBCRDD( if (sb.length == 0) "1" else sb.substring(1) } + /** + * Converts value to SQL expression. + */ + private def compileValue(value: Any): Any = value match { + case stringValue: UTF8String => s"'${escapeSql(stringValue.toString)}'" + case _ => value + } + /** * Turns a single Filter into a String representing a SQL expression. * Returns null for an unhandled filter. */ private def compileFilter(f: Filter): String = f match { - case EqualTo(attr, value) => s"$attr = $value" - case LessThan(attr, value) => s"$attr < $value" - case GreaterThan(attr, value) => s"$attr > $value" - case LessThanOrEqual(attr, value) => s"$attr <= $value" - case GreaterThanOrEqual(attr, value) => s"$attr >= $value" + case EqualTo(attr, value) => s"$attr = ${compileValue(value)}" + case LessThan(attr, value) => s"$attr < ${compileValue(value)}" + case GreaterThan(attr, value) => s"$attr > ${compileValue(value)}" + case LessThanOrEqual(attr, value) => s"$attr <= ${compileValue(value)}" + case GreaterThanOrEqual(attr, value) => s"$attr >= ${compileValue(value)}" case _ => null } @@ -305,7 +317,8 @@ private[sql] class JDBCRDD( /** * Runs the SQL query against the JDBC driver. */ - override def compute(thePart: Partition, context: TaskContext) = new Iterator[Row] { + override def compute(thePart: Partition, context: TaskContext): Iterator[Row] = new Iterator[Row] + { var closed = false var finished = false var gotNext = false @@ -336,12 +349,14 @@ private[sql] class JDBCRDD( val pos = i + 1 conversions(i) match { case BooleanConversion => mutableRow.setBoolean(i, rs.getBoolean(pos)) + // TODO(davies): convert Date into Int case DateConversion => mutableRow.update(i, rs.getDate(pos)) case DecimalConversion => mutableRow.update(i, rs.getBigDecimal(pos)) case DoubleConversion => mutableRow.setDouble(i, rs.getDouble(pos)) case FloatConversion => mutableRow.setFloat(i, rs.getFloat(pos)) case IntegerConversion => mutableRow.setInt(i, rs.getInt(pos)) case LongConversion => mutableRow.setLong(i, rs.getLong(pos)) + // TODO(davies): use getBytes for better performance, if the encoding is UTF-8 case StringConversion => mutableRow.setString(i, rs.getString(pos)) case TimestampConversion => mutableRow.update(i, rs.getTimestamp(pos)) case BinaryConversion => mutableRow.update(i, rs.getBytes(pos)) @@ -350,7 +365,7 @@ private[sql] class JDBCRDD( var ans = 0L var j = 0 while (j < bytes.size) { - ans = 256*ans + (255 & bytes(j)) + ans = 256 * ans + (255 & bytes(j)) j = j + 1; } mutableRow.setLong(i, ans) @@ -369,21 +384,21 @@ private[sql] class JDBCRDD( def close() { if (closed) return try { - if (null != rs && ! rs.isClosed()) { + if (null != rs) { rs.close() } } catch { case e: Exception => logWarning("Exception closing resultset", e) } try { - if (null != stmt && ! stmt.isClosed()) { + if (null != stmt) { stmt.close() } } catch { case e: Exception => logWarning("Exception closing statement", e) } try { - if (null != conn && ! conn.isClosed()) { + if (null != conn) { conn.close() } logInfo("closed connection") @@ -412,6 +427,5 @@ private[sql] class JDBCRDD( gotNext = false nextValue } - } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala index e09125e406ba2..5f480083d5a49 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala @@ -17,12 +17,18 @@ package org.apache.spark.sql.jdbc -import scala.collection.mutable.ArrayBuffer import java.sql.DriverManager +import java.util.Properties + +import scala.collection.mutable.ArrayBuffer import org.apache.spark.Partition +import org.apache.spark.rdd.RDD import org.apache.spark.sql.SQLContext +import org.apache.spark.sql.catalyst.expressions.Row import org.apache.spark.sql.sources._ +import org.apache.spark.sql.types.StructType +import org.apache.spark.util.Utils /** * Data corresponding to one partition of a JDBCRDD. @@ -48,11 +54,6 @@ private[sql] object JDBCRelation { * exactly once. The parameters minValue and maxValue are advisory in that * incorrect values may cause the partitioning to be poor, but no data * will fail to be represented. - * - * @param column - Column name. Must refer to a column of integral type. - * @param numPartitions - Number of partitions - * @param minValue - Smallest value of column. Advisory. - * @param maxValue - Largest value of column. Advisory. */ def columnPartition(partitioning: JDBCPartitioningInfo): Array[Partition] = { if (partitioning == null) return Array[Partition](JDBCPartition(null, 0)) @@ -68,12 +69,17 @@ private[sql] object JDBCRelation { var currentValue: Long = partitioning.lowerBound var ans = new ArrayBuffer[Partition]() while (i < numPartitions) { - val lowerBound = (if (i != 0) s"$column >= $currentValue" else null) + val lowerBound = if (i != 0) s"$column >= $currentValue" else null currentValue += stride - val upperBound = (if (i != numPartitions - 1) s"$column < $currentValue" else null) - val whereClause = (if (upperBound == null) lowerBound - else if (lowerBound == null) upperBound - else s"$lowerBound AND $upperBound") + val upperBound = if (i != numPartitions - 1) s"$column < $currentValue" else null + val whereClause = + if (upperBound == null) { + lowerBound + } else if (lowerBound == null) { + upperBound + } else { + s"$lowerBound AND $upperBound" + } ans += JDBCPartition(whereClause, i) i = i + 1 } @@ -94,9 +100,9 @@ private[sql] class DefaultSource extends RelationProvider { val upperBound = parameters.getOrElse("upperBound", null) val numPartitions = parameters.getOrElse("numPartitions", null) - if (driver != null) Class.forName(driver) + if (driver != null) Utils.getContextOrSparkClassLoader.loadClass(driver) - if ( partitionColumn != null + if (partitionColumn != null && (lowerBound == null || upperBound == null || numPartitions == null)) { sys.error("Partitioning incompletely specified") } @@ -104,30 +110,42 @@ private[sql] class DefaultSource extends RelationProvider { val partitionInfo = if (partitionColumn == null) { null } else { - JDBCPartitioningInfo(partitionColumn, - lowerBound.toLong, upperBound.toLong, - numPartitions.toInt) + JDBCPartitioningInfo( + partitionColumn, + lowerBound.toLong, + upperBound.toLong, + numPartitions.toInt) } val parts = JDBCRelation.columnPartition(partitionInfo) - JDBCRelation(url, table, parts)(sqlContext) + val properties = new Properties() // Additional properties that we will pass to getConnection + parameters.foreach(kv => properties.setProperty(kv._1, kv._2)) + JDBCRelation(url, table, parts, properties)(sqlContext) } } -private[sql] case class JDBCRelation(url: String, - table: String, - parts: Array[Partition])( - @transient val sqlContext: SQLContext) - extends PrunedFilteredScan { +private[sql] case class JDBCRelation( + url: String, + table: String, + parts: Array[Partition], + properties: Properties = new Properties())(@transient val sqlContext: SQLContext) + extends BaseRelation + with PrunedFilteredScan { + + override val needConversion: Boolean = false - override val schema = JDBCRDD.resolveTable(url, table) + override val schema: StructType = JDBCRDD.resolveTable(url, table, properties) - override def buildScan(requiredColumns: Array[String], filters: Array[Filter]) = { + override def buildScan(requiredColumns: Array[String], filters: Array[Filter]): RDD[Row] = { val driver: String = DriverManager.getDriver(url).getClass.getCanonicalName - JDBCRDD.scanTable(sqlContext.sparkContext, - schema, - driver, url, - table, - requiredColumns, filters, - parts) + JDBCRDD.scanTable( + sqlContext.sparkContext, + schema, + driver, + url, + properties, + table, + requiredColumns, + filters, + parts) } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala index 34a83f0a5dad8..d4e0abc040bc6 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala @@ -18,19 +18,16 @@ package org.apache.spark.sql import java.sql.{Connection, DriverManager, PreparedStatement} -import org.apache.spark.{Logging, Partition} -import org.apache.spark.sql._ -import org.apache.spark.sql.sources.LogicalRelation -import org.apache.spark.sql.jdbc.{JDBCPartitioningInfo, JDBCRelation, JDBCPartition} +import org.apache.spark.Logging import org.apache.spark.sql.types._ package object jdbc { - object JDBCWriteDetails extends Logging { + private[sql] object JDBCWriteDetails extends Logging { /** * Returns a PreparedStatement that inserts a row into table via conn. */ - private def insertStatement(conn: Connection, table: String, rddSchema: StructType): + def insertStatement(conn: Connection, table: String, rddSchema: StructType): PreparedStatement = { val sql = new StringBuilder(s"INSERT INTO $table VALUES (") var fieldsLeft = rddSchema.fields.length @@ -56,7 +53,7 @@ package object jdbc { * non-Serializable. Instead, we explicitly close over all variables that * are used. */ - private[jdbc] def savePartition(url: String, table: String, iterator: Iterator[Row], + def savePartition(url: String, table: String, iterator: Iterator[Row], rddSchema: StructType, nullTypes: Array[Int]): Iterator[Byte] = { val conn = DriverManager.getConnection(url) var committed = false @@ -117,19 +114,14 @@ package object jdbc { } Array[Byte]().iterator } - } - /** - * Make it so that you can call createJDBCTable and insertIntoJDBC on a DataFrame. - */ - implicit class JDBCDataFrame(rdd: DataFrame) { /** * Compute the schema string for this RDD. */ - private def schemaString(url: String): String = { + def schemaString(df: DataFrame, url: String): String = { val sb = new StringBuilder() val quirks = DriverQuirks.get(url) - rdd.schema.fields foreach { field => { + df.schema.fields foreach { field => { val name = field.name var typ: String = quirks.getJDBCType(field.dataType)._1 if (typ == null) typ = field.dataType match { @@ -156,9 +148,9 @@ package object jdbc { /** * Saves the RDD to the database in a single transaction. */ - private def saveTable(url: String, table: String) { + def saveTable(df: DataFrame, url: String, table: String) { val quirks = DriverQuirks.get(url) - var nullTypes: Array[Int] = rdd.schema.fields.map(field => { + var nullTypes: Array[Int] = df.schema.fields.map(field => { var nullType: Option[Int] = quirks.getJDBCType(field.dataType)._2 if (nullType.isEmpty) { field.dataType match { @@ -175,61 +167,16 @@ package object jdbc { case DateType => java.sql.Types.DATE case DecimalType.Unlimited => java.sql.Types.DECIMAL case _ => throw new IllegalArgumentException( - s"Can't translate null value for field $field") + s"Can't translate null value for field $field") } } else nullType.get }).toArray - val rddSchema = rdd.schema - rdd.mapPartitions(iterator => JDBCWriteDetails.savePartition( - url, table, iterator, rddSchema, nullTypes)).collect() - } - - /** - * Save this RDD to a JDBC database at `url` under the table name `table`. - * This will run a `CREATE TABLE` and a bunch of `INSERT INTO` statements. - * If you pass `true` for `allowExisting`, it will drop any table with the - * given name; if you pass `false`, it will throw if the table already - * exists. - */ - def createJDBCTable(url: String, table: String, allowExisting: Boolean) { - val conn = DriverManager.getConnection(url) - try { - if (allowExisting) { - val sql = s"DROP TABLE IF EXISTS $table" - conn.prepareStatement(sql).executeUpdate() - } - val schema = schemaString(url) - val sql = s"CREATE TABLE $table ($schema)" - conn.prepareStatement(sql).executeUpdate() - } finally { - conn.close() + val rddSchema = df.schema + df.foreachPartition { iterator => + JDBCWriteDetails.savePartition(url, table, iterator, rddSchema, nullTypes) } - saveTable(url, table) } - /** - * Save this RDD to a JDBC database at `url` under the table name `table`. - * Assumes the table already exists and has a compatible schema. If you - * pass `true` for `overwrite`, it will `TRUNCATE` the table before - * performing the `INSERT`s. - * - * The table must already exist on the database. It must have a schema - * that is compatible with the schema of this RDD; inserting the rows of - * the RDD in order via the simple statement - * `INSERT INTO table VALUES (?, ?, ..., ?)` should not fail. - */ - def insertIntoJDBC(url: String, table: String, overwrite: Boolean) { - if (overwrite) { - val conn = DriverManager.getConnection(url) - try { - val sql = s"TRUNCATE TABLE $table" - conn.prepareStatement(sql).executeUpdate() - } finally { - conn.close() - } - } - saveTable(url, table) - } - } // implicit class JDBCDataFrame + } } // package object jdbc diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala index c4e14c6c92908..e3352d02787fd 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala @@ -20,20 +20,26 @@ package org.apache.spark.sql.json import java.io.IOException import org.apache.hadoop.fs.Path + import org.apache.spark.rdd.RDD -import org.apache.spark.sql.{DataFrame, Row, SQLContext} +import org.apache.spark.sql.catalyst.expressions.Row import org.apache.spark.sql.sources._ import org.apache.spark.sql.types.StructType +import org.apache.spark.sql.{DataFrame, SQLContext, SaveMode} private[sql] class DefaultSource extends RelationProvider with SchemaRelationProvider with CreatableRelationProvider { + private def checkPath(parameters: Map[String, String]): String = { + parameters.getOrElse("path", sys.error("'path' must be specified for json data.")) + } + /** Returns a new base relation with the parameters. */ override def createRelation( sqlContext: SQLContext, parameters: Map[String, String]): BaseRelation = { - val path = parameters.getOrElse("path", sys.error("Option 'path' not specified")) + val path = checkPath(parameters) val samplingRatio = parameters.get("samplingRatio").map(_.toDouble).getOrElse(1.0) JSONRelation(path, samplingRatio, None)(sqlContext) @@ -44,7 +50,7 @@ private[sql] class DefaultSource sqlContext: SQLContext, parameters: Map[String, String], schema: StructType): BaseRelation = { - val path = parameters.getOrElse("path", sys.error("Option 'path' not specified")) + val path = checkPath(parameters) val samplingRatio = parameters.get("samplingRatio").map(_.toDouble).getOrElse(1.0) JSONRelation(path, samplingRatio, Some(schema))(sqlContext) @@ -52,15 +58,44 @@ private[sql] class DefaultSource override def createRelation( sqlContext: SQLContext, + mode: SaveMode, parameters: Map[String, String], data: DataFrame): BaseRelation = { - val path = parameters.getOrElse("path", sys.error("Option 'path' not specified")) + val path = checkPath(parameters) val filesystemPath = new Path(path) val fs = filesystemPath.getFileSystem(sqlContext.sparkContext.hadoopConfiguration) - if (fs.exists(filesystemPath)) { - sys.error(s"path $path already exists.") + val doSave = if (fs.exists(filesystemPath)) { + mode match { + case SaveMode.Append => + sys.error(s"Append mode is not supported by ${this.getClass.getCanonicalName}") + case SaveMode.Overwrite => { + var success: Boolean = false + try { + success = fs.delete(filesystemPath, true) + } catch { + case e: IOException => + throw new IOException( + s"Unable to clear output directory ${filesystemPath.toString} prior" + + s" to writing to JSON table:\n${e.toString}") + } + if (!success) { + throw new IOException( + s"Unable to clear output directory ${filesystemPath.toString} prior" + + s" to writing to JSON table.") + } + true + } + case SaveMode.ErrorIfExists => + sys.error(s"path $path already exists.") + case SaveMode.Ignore => false + } + } else { + true + } + if (doSave) { + // Only save data when the save mode is not ignore. + data.toJSON.saveAsTextFile(path) } - data.toJSON.saveAsTextFile(path) createRelation(sqlContext, parameters, data.schema) } @@ -71,10 +106,15 @@ private[sql] case class JSONRelation( samplingRatio: Double, userSpecifiedSchema: Option[StructType])( @transient val sqlContext: SQLContext) - extends TableScan with InsertableRelation { + extends BaseRelation + with TableScan + with InsertableRelation { + // TODO: Support partitioned JSON relation. private def baseRDD = sqlContext.sparkContext.textFile(path) + override val needConversion: Boolean = false + override val schema = userSpecifiedSchema.getOrElse( JsonRDD.nullTypeToStringType( JsonRDD.inferSchema( @@ -82,21 +122,29 @@ private[sql] case class JSONRelation( samplingRatio, sqlContext.conf.columnNameOfCorruptRecord))) - override def buildScan() = + override def buildScan(): RDD[Row] = JsonRDD.jsonStringToRow(baseRDD, schema, sqlContext.conf.columnNameOfCorruptRecord) - override def insert(data: DataFrame, overwrite: Boolean) = { + override def insert(data: DataFrame, overwrite: Boolean): Unit = { val filesystemPath = new Path(path) val fs = filesystemPath.getFileSystem(sqlContext.sparkContext.hadoopConfiguration) if (overwrite) { - try { - fs.delete(filesystemPath, true) - } catch { - case e: IOException => + if (fs.exists(filesystemPath)) { + var success: Boolean = false + try { + success = fs.delete(filesystemPath, true) + } catch { + case e: IOException => + throw new IOException( + s"Unable to clear output directory ${filesystemPath.toString} prior" + + s" to writing to JSON table:\n${e.toString}") + } + if (!success) { throw new IOException( s"Unable to clear output directory ${filesystemPath.toString} prior" - + s" to INSERT OVERWRITE a JSON table:\n${e.toString}") + + s" to writing to JSON table.") + } } // Write the data. data.toJSON.saveAsTextFile(path) @@ -112,7 +160,7 @@ private[sql] case class JSONRelation( override def equals(other: Any): Boolean = other match { case that: JSONRelation => - (this.path == that.path) && (this.schema == that.schema) + (this.path == that.path) && this.schema.sameType(that.schema) case _ => false } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala index 33ce71b51b213..29de7401dda71 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala @@ -17,14 +17,12 @@ package org.apache.spark.sql.json -import java.io.StringWriter -import java.sql.{Date, Timestamp} +import java.sql.Timestamp import scala.collection.Map import scala.collection.convert.Wrappers.{JMapWrapper, JListWrapper} -import com.fasterxml.jackson.core.JsonProcessingException -import com.fasterxml.jackson.core.JsonFactory +import com.fasterxml.jackson.core.{JsonGenerator, JsonProcessingException} import com.fasterxml.jackson.databind.ObjectMapper import org.apache.spark.rdd.RDD @@ -50,7 +48,11 @@ private[sql] object JsonRDD extends Logging { require(samplingRatio > 0, s"samplingRatio ($samplingRatio) should be greater than 0") val schemaData = if (samplingRatio > 0.99) json else json.sample(false, samplingRatio, 1) val allKeys = - parseJson(schemaData, columnNameOfCorruptRecords).map(allKeysWithValueTypes).reduce(_ ++ _) + if (schemaData.isEmpty()) { + Set.empty[(String, DataType)] + } else { + parseJson(schemaData, columnNameOfCorruptRecords).map(allKeysWithValueTypes).reduce(_ ++ _) + } createSchema(allKeys) } @@ -179,7 +181,12 @@ private[sql] object JsonRDD extends Logging { } private def typeOfPrimitiveValue: PartialFunction[Any, DataType] = { - ScalaReflection.typeOfObject orElse { + // For Integer values, use LongType by default. + val useLongType: PartialFunction[Any, DataType] = { + case value: IntegerType.JvmType => LongType + } + + useLongType orElse ScalaReflection.typeOfObject orElse { // Since we do not have a data type backed by BigInteger, // when we see a Java BigInteger, we use DecimalType. case value: java.math.BigInteger => DecimalType.Unlimited @@ -196,13 +203,12 @@ private[sql] object JsonRDD extends Logging { * type conflicts. */ private def typeOfArray(l: Seq[Any]): ArrayType = { - val containsNull = l.exists(v => v == null) val elements = l.flatMap(v => Option(v)) if (elements.isEmpty) { // If this JSON array is empty, we use NullType as a placeholder. // If this array is not empty in other JSON objects, we can resolve // the type after we have passed through all JSON objects. - ArrayType(NullType, containsNull) + ArrayType(NullType, containsNull = true) } else { val elementType = elements.map { e => e match { @@ -214,7 +220,7 @@ private[sql] object JsonRDD extends Logging { } }.reduce((type1: DataType, type2: DataType) => compatibleType(type1, type2)) - ArrayType(elementType, containsNull) + ArrayType(elementType, containsNull = true) } } @@ -242,7 +248,7 @@ private[sql] object JsonRDD extends Logging { // The value associated with the key is an array. // Handle inner structs of an array. def buildKeyPathForInnerStructs(v: Any, t: DataType): Seq[(String, DataType)] = t match { - case ArrayType(e: StructType, containsNull) => { + case ArrayType(e: StructType, _) => { // The elements of this arrays are structs. v.asInstanceOf[Seq[Map[String, Any]]].flatMap(Option(_)).flatMap { element => allKeysWithValueTypes(element) @@ -250,7 +256,7 @@ private[sql] object JsonRDD extends Logging { case (k, t) => (s"$key.$k", t) } } - case ArrayType(t1, containsNull) => + case ArrayType(t1, _) => v.asInstanceOf[Seq[Any]].flatMap(Option(_)).flatMap { element => buildKeyPathForInnerStructs(element, t1) } @@ -303,6 +309,10 @@ private[sql] object JsonRDD extends Logging { val parsed = mapper.readValue(record, classOf[Object]) match { case map: java.util.Map[_, _] => scalafy(map).asInstanceOf[Map[String, Any]] :: Nil case list: java.util.List[_] => scalafy(list).asInstanceOf[Seq[Map[String, Any]]] + case _ => + sys.error( + s"Failed to parse record $record. Please make sure that each line of the file " + + "(or each string in the RDD) is a valid JSON object or an array of JSON objects.") } parsed @@ -381,7 +391,7 @@ private[sql] object JsonRDD extends Logging { value match { // only support string as date case value: java.lang.String => - DateUtils.millisToDays(DataTypeConversions.stringToTime(value).getTime) + DateUtils.millisToDays(DateUtils.stringToTime(value).getTime) case value: java.sql.Date => DateUtils.fromJavaDate(value) } } @@ -390,7 +400,7 @@ private[sql] object JsonRDD extends Logging { value match { case value: java.lang.Integer => new Timestamp(value.asInstanceOf[Int].toLong) case value: java.lang.Long => new Timestamp(value) - case value: java.lang.String => toTimestamp(DataTypeConversions.stringToTime(value).getTime) + case value: java.lang.String => toTimestamp(DateUtils.stringToTime(value).getTime) } } @@ -399,7 +409,7 @@ private[sql] object JsonRDD extends Logging { null } else { desiredType match { - case StringType => toString(value) + case StringType => UTF8String(toString(value)) case _ if value == null || value == "" => null // guard the non string type case IntegerType => value.asInstanceOf[IntegerType.JvmType] case LongType => toLong(value) @@ -409,6 +419,9 @@ private[sql] object JsonRDD extends Logging { case NullType => null case ArrayType(elementType, _) => value.asInstanceOf[Seq[Any]].map(enforceCorrectType(_, elementType)) + case MapType(StringType, valueType, _) => + val map = value.asInstanceOf[Map[String, Any]] + map.mapValues(enforceCorrectType(_, valueType)).map(identity) case struct: StructType => asRow(value.asInstanceOf[Map[String, Any]], struct) case DateType => toDate(value) case TimestampType => toTimestamp(value) @@ -430,14 +443,11 @@ private[sql] object JsonRDD extends Logging { /** Transforms a single Row to JSON using Jackson * - * @param jsonFactory a JsonFactory object to construct a JsonGenerator * @param rowSchema the schema object used for conversion + * @param gen a JsonGenerator object * @param row The row to convert */ - private[sql] def rowToJSON(rowSchema: StructType, jsonFactory: JsonFactory)(row: Row): String = { - val writer = new StringWriter() - val gen = jsonFactory.createGenerator(writer) - + private[sql] def rowToJSON(rowSchema: StructType, gen: JsonGenerator)(row: Row) = { def valWriter: (DataType, Any) => Unit = { case (_, null) | (NullType, _) => gen.writeNull() case (StringType, v: String) => gen.writeString(v) @@ -479,8 +489,5 @@ private[sql] object JsonRDD extends Logging { } valWriter(rowSchema, row) - gen.close() - writer.toString } - } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/package.scala b/sql/core/src/main/scala/org/apache/spark/sql/package.scala index 02e5b015e8ec2..3f97a11ceb97d 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/package.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/package.scala @@ -34,10 +34,13 @@ import org.apache.spark.sql.execution.SparkPlan package object sql { /** - * Converts a logical plan into zero or more SparkPlans. + * Converts a logical plan into zero or more SparkPlans. This API is exposed for experimenting + * with the query planner and is not designed to be stable across spark releases. Developers + * writing libraries should instead consider using the stable APIs provided in + * [[org.apache.spark.sql.sources]] */ @DeveloperApi - protected[sql] type Strategy = org.apache.spark.sql.catalyst.planning.GenericStrategy[SparkPlan] + type Strategy = org.apache.spark.sql.catalyst.planning.GenericStrategy[SparkPlan] /** * Type alias for [[DataFrame]]. Kept here for backward source compatibility for Scala. diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala index 7d62f3728f036..bc108e37dfb0f 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala @@ -127,6 +127,12 @@ private[sql] object CatalystConverter { parent.updateByte(fieldIndex, value.asInstanceOf[ByteType.JvmType]) } } + case DateType => { + new CatalystPrimitiveConverter(parent, fieldIndex) { + override def addInt(value: Int): Unit = + parent.updateDate(fieldIndex, value.asInstanceOf[DateType.JvmType]) + } + } case d: DecimalType => { new CatalystPrimitiveConverter(parent, fieldIndex) { override def addBinary(value: Binary): Unit = @@ -192,6 +198,9 @@ private[parquet] abstract class CatalystConverter extends GroupConverter { protected[parquet] def updateInt(fieldIndex: Int, value: Int): Unit = updateField(fieldIndex, value) + protected[parquet] def updateDate(fieldIndex: Int, value: Int): Unit = + updateField(fieldIndex, value) + protected[parquet] def updateLong(fieldIndex: Int, value: Long): Unit = updateField(fieldIndex, value) @@ -210,8 +219,8 @@ private[parquet] abstract class CatalystConverter extends GroupConverter { protected[parquet] def updateBinary(fieldIndex: Int, value: Binary): Unit = updateField(fieldIndex, value.getBytes) - protected[parquet] def updateString(fieldIndex: Int, value: String): Unit = - updateField(fieldIndex, value) + protected[parquet] def updateString(fieldIndex: Int, value: Array[Byte]): Unit = + updateField(fieldIndex, UTF8String(value)) protected[parquet] def updateTimestamp(fieldIndex: Int, value: Binary): Unit = updateField(fieldIndex, readTimestamp(value)) @@ -388,6 +397,9 @@ private[parquet] class CatalystPrimitiveRowConverter( override protected[parquet] def updateInt(fieldIndex: Int, value: Int): Unit = current.setInt(fieldIndex, value) + override protected[parquet] def updateDate(fieldIndex: Int, value: Int): Unit = + current.update(fieldIndex, value) + override protected[parquet] def updateLong(fieldIndex: Int, value: Long): Unit = current.setLong(fieldIndex, value) @@ -406,8 +418,8 @@ private[parquet] class CatalystPrimitiveRowConverter( override protected[parquet] def updateBinary(fieldIndex: Int, value: Binary): Unit = current.update(fieldIndex, value.getBytes) - override protected[parquet] def updateString(fieldIndex: Int, value: String): Unit = - current.setString(fieldIndex, value) + override protected[parquet] def updateString(fieldIndex: Int, value: Array[Byte]): Unit = + current.update(fieldIndex, UTF8String(value)) override protected[parquet] def updateTimestamp(fieldIndex: Int, value: Binary): Unit = current.update(fieldIndex, readTimestamp(value)) @@ -463,19 +475,18 @@ private[parquet] class CatalystPrimitiveConverter( private[parquet] class CatalystPrimitiveStringConverter(parent: CatalystConverter, fieldIndex: Int) extends CatalystPrimitiveConverter(parent, fieldIndex) { - private[this] var dict: Array[String] = null + private[this] var dict: Array[Array[Byte]] = null override def hasDictionarySupport: Boolean = true override def setDictionary(dictionary: Dictionary):Unit = - dict = Array.tabulate(dictionary.getMaxId + 1) {dictionary.decodeToBinary(_).toStringUsingUTF8} - + dict = Array.tabulate(dictionary.getMaxId + 1) { dictionary.decodeToBinary(_).getBytes } override def addValueFromDictionary(dictionaryId: Int): Unit = parent.updateString(fieldIndex, dict(dictionaryId)) override def addBinary(value: Binary): Unit = - parent.updateString(fieldIndex, value.toStringUsingUTF8) + parent.updateString(fieldIndex, value.getBytes) } private[parquet] object CatalystArrayConverter { @@ -488,7 +499,7 @@ private[parquet] object CatalystTimestampConverter { // Also we use NanoTime and Int96Values from parquet-examples. // We utilize jodd to convert between NanoTime and Timestamp val parquetTsCalendar = new ThreadLocal[Calendar] - def getCalendar = { + def getCalendar: Calendar = { // this is a cache for the calendar instance. if (parquetTsCalendar.get == null) { parquetTsCalendar.set(Calendar.getInstance(TimeZone.getTimeZone("GMT"))) @@ -702,9 +713,9 @@ private[parquet] class CatalystNativeArrayConverter( elements += 1 } - override protected[parquet] def updateString(fieldIndex: Int, value: String): Unit = { + override protected[parquet] def updateString(fieldIndex: Int, value: Array[Byte]): Unit = { checkGrowBuffer() - buffer(elements) = value.asInstanceOf[NativeType] + buffer(elements) = UTF8String(value).asInstanceOf[NativeType] elements += 1 } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetFilters.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetFilters.scala index 0357dcc4688be..5eb1c6abc2432 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetFilters.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetFilters.scala @@ -55,7 +55,7 @@ private[sql] object ParquetFilters { case StringType => (n: String, v: Any) => FilterApi.eq( binaryColumn(n), - Option(v).map(s => Binary.fromString(s.asInstanceOf[String])).orNull) + Option(v).map(s => Binary.fromByteArray(s.asInstanceOf[UTF8String].getBytes)).orNull) case BinaryType => (n: String, v: Any) => FilterApi.eq( binaryColumn(n), @@ -76,7 +76,7 @@ private[sql] object ParquetFilters { case StringType => (n: String, v: Any) => FilterApi.notEq( binaryColumn(n), - Option(v).map(s => Binary.fromString(s.asInstanceOf[String])).orNull) + Option(v).map(s => Binary.fromByteArray(s.asInstanceOf[UTF8String].getBytes)).orNull) case BinaryType => (n: String, v: Any) => FilterApi.notEq( binaryColumn(n), @@ -94,7 +94,7 @@ private[sql] object ParquetFilters { (n: String, v: Any) => FilterApi.lt(doubleColumn(n), v.asInstanceOf[java.lang.Double]) case StringType => (n: String, v: Any) => - FilterApi.lt(binaryColumn(n), Binary.fromString(v.asInstanceOf[String])) + FilterApi.lt(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[UTF8String].getBytes)) case BinaryType => (n: String, v: Any) => FilterApi.lt(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[Array[Byte]])) @@ -111,7 +111,7 @@ private[sql] object ParquetFilters { (n: String, v: Any) => FilterApi.ltEq(doubleColumn(n), v.asInstanceOf[java.lang.Double]) case StringType => (n: String, v: Any) => - FilterApi.ltEq(binaryColumn(n), Binary.fromString(v.asInstanceOf[String])) + FilterApi.ltEq(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[UTF8String].getBytes)) case BinaryType => (n: String, v: Any) => FilterApi.ltEq(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[Array[Byte]])) @@ -128,7 +128,7 @@ private[sql] object ParquetFilters { (n: String, v: Any) => FilterApi.gt(doubleColumn(n), v.asInstanceOf[java.lang.Double]) case StringType => (n: String, v: Any) => - FilterApi.gt(binaryColumn(n), Binary.fromString(v.asInstanceOf[String])) + FilterApi.gt(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[UTF8String].getBytes)) case BinaryType => (n: String, v: Any) => FilterApi.gt(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[Array[Byte]])) @@ -145,7 +145,7 @@ private[sql] object ParquetFilters { (n: String, v: Any) => FilterApi.gtEq(doubleColumn(n), v.asInstanceOf[java.lang.Double]) case StringType => (n: String, v: Any) => - FilterApi.gtEq(binaryColumn(n), Binary.fromString(v.asInstanceOf[String])) + FilterApi.gtEq(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[UTF8String].getBytes)) case BinaryType => (n: String, v: Any) => FilterApi.gtEq(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[Array[Byte]])) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala index b0db9943a506c..fcb9513ab66f6 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala @@ -18,11 +18,13 @@ package org.apache.spark.sql.parquet import java.io.IOException +import java.util.logging.Level import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path import org.apache.hadoop.fs.permission.FsAction -import parquet.hadoop.ParquetOutputFormat +import org.apache.spark.sql.types.{StructType, DataType} +import parquet.hadoop.{ParquetOutputCommitter, ParquetOutputFormat} import parquet.hadoop.metadata.CompressionCodecName import parquet.schema.MessageType @@ -69,16 +71,22 @@ private[sql] case class ParquetRelation( sqlContext.conf.isParquetINT96AsTimestamp) lazy val attributeMap = AttributeMap(output.map(o => o -> o)) - override def newInstance() = ParquetRelation(path, conf, sqlContext).asInstanceOf[this.type] + override def newInstance(): this.type = { + ParquetRelation(path, conf, sqlContext).asInstanceOf[this.type] + } // Equals must also take into account the output attributes so that we can distinguish between // different instances of the same relation, - override def equals(other: Any) = other match { + override def equals(other: Any): Boolean = other match { case p: ParquetRelation => p.path == path && p.output == output case _ => false } + override def hashCode: Int = { + com.google.common.base.Objects.hashCode(path, output) + } + // TODO: Use data from the footers. override lazy val statistics = Statistics(sizeInBytes = sqlContext.conf.defaultSizeInBytes) } @@ -91,7 +99,7 @@ private[sql] object ParquetRelation { // checks first to see if there's any handlers already set // and if not it creates them. If this method executes prior // to that class being loaded then: - // 1) there's no handlers installed so there's none to + // 1) there's no handlers installed so there's none to // remove. But when it IS finally loaded the desired affect // of removing them is circumvented. // 2) The parquet.Log static initializer calls setUseParentHanders(false) @@ -99,7 +107,7 @@ private[sql] object ParquetRelation { // // Therefore we need to force the class to be loaded. // This should really be resolved by Parquet. - Class.forName(classOf[parquet.Log].getName()) + Class.forName(classOf[parquet.Log].getName) // Note: Logger.getLogger("parquet") has a default logger // that appends to Console which needs to be cleared. @@ -108,6 +116,11 @@ private[sql] object ParquetRelation { // TODO(witgo): Need to set the log level ? // if(parquetLogger.getLevel != null) parquetLogger.setLevel(null) if (!parquetLogger.getUseParentHandlers) parquetLogger.setUseParentHandlers(true) + + // Disables WARN log message in ParquetOutputCommitter. + // See https://issues.apache.org/jira/browse/SPARK-5968 for details + Class.forName(classOf[ParquetOutputCommitter].getName) + java.util.logging.Logger.getLogger(classOf[ParquetOutputCommitter].getName).setLevel(Level.OFF) } // The element type for the RDDs that this relation maps to. @@ -166,9 +179,13 @@ private[sql] object ParquetRelation { sqlContext.conf.parquetCompressionCodec.toUpperCase, CompressionCodecName.UNCOMPRESSED) .name()) ParquetRelation.enableLogForwarding() - ParquetTypesConverter.writeMetaData(attributes, path, conf) + // This is a hack. We always set nullable/containsNull/valueContainsNull to true + // for the schema of a parquet data. + val schema = StructType.fromAttributes(attributes).asNullable + val newAttributes = schema.toAttributes + ParquetTypesConverter.writeMetaData(newAttributes, path, conf) new ParquetRelation(path.toString, Some(conf), sqlContext) { - override val output = attributes + override val output = newAttributes } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala index 28cd17fde46ab..1c868da23e060 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala @@ -19,10 +19,9 @@ package org.apache.spark.sql.parquet import java.io.IOException import java.lang.{Long => JLong} -import java.text.SimpleDateFormat -import java.text.NumberFormat +import java.text.{NumberFormat, SimpleDateFormat} import java.util.concurrent.{Callable, TimeUnit} -import java.util.{ArrayList, Collections, Date, List => JList} +import java.util.{Date, List => JList} import scala.collection.JavaConversions._ import scala.collection.mutable @@ -43,11 +42,13 @@ import parquet.io.ParquetDecodingException import parquet.schema.MessageType import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.mapred.SparkHadoopMapRedUtil import org.apache.spark.mapreduce.SparkHadoopMapReduceUtil import org.apache.spark.rdd.RDD import org.apache.spark.sql.SQLConf import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression, Row, _} import org.apache.spark.sql.execution.{LeafNode, SparkPlan, UnaryNode} +import org.apache.spark.sql.types.StructType import org.apache.spark.{Logging, SerializableWritable, TaskContext} /** @@ -55,7 +56,7 @@ import org.apache.spark.{Logging, SerializableWritable, TaskContext} * Parquet table scan operator. Imports the file that backs the given * [[org.apache.spark.sql.parquet.ParquetRelation]] as a ``RDD[Row]``. */ -case class ParquetTableScan( +private[sql] case class ParquetTableScan( attributes: Seq[Attribute], relation: ParquetRelation, columnPruningPred: Seq[Expression]) @@ -125,6 +126,13 @@ case class ParquetTableScan( conf) if (requestedPartitionOrdinals.nonEmpty) { + // This check is based on CatalystConverter.createRootConverter. + val primitiveRow = output.forall(a => ParquetTypesConverter.isPrimitiveType(a.dataType)) + + // Uses temporary variable to avoid the whole `ParquetTableScan` object being captured into + // the `mapPartitionsWithInputSplit` closure below. + val outputSize = output.size + baseRDD.mapPartitionsWithInputSplit { case (split, iter) => val partValue = "([^=]+)=([^=]+)".r val partValues = @@ -142,19 +150,47 @@ case class ParquetTableScan( relation.partitioningAttributes .map(a => Cast(Literal(partValues(a.name)), a.dataType).eval(EmptyRow)) - new Iterator[Row] { - def hasNext = iter.hasNext - def next() = { - val row = iter.next()._2.asInstanceOf[SpecificMutableRow] - - // Parquet will leave partitioning columns empty, so we fill them in here. - var i = 0 - while (i < requestedPartitionOrdinals.size) { - row(requestedPartitionOrdinals(i)._2) = - partitionRowValues(requestedPartitionOrdinals(i)._1) - i += 1 + if (primitiveRow) { + new Iterator[Row] { + def hasNext: Boolean = iter.hasNext + def next(): Row = { + // We are using CatalystPrimitiveRowConverter and it returns a SpecificMutableRow. + val row = iter.next()._2.asInstanceOf[SpecificMutableRow] + + // Parquet will leave partitioning columns empty, so we fill them in here. + var i = 0 + while (i < requestedPartitionOrdinals.size) { + row(requestedPartitionOrdinals(i)._2) = + partitionRowValues(requestedPartitionOrdinals(i)._1) + i += 1 + } + row + } + } + } else { + // Create a mutable row since we need to fill in values from partition columns. + val mutableRow = new GenericMutableRow(outputSize) + new Iterator[Row] { + def hasNext: Boolean = iter.hasNext + def next(): Row = { + // We are using CatalystGroupConverter and it returns a GenericRow. + // Since GenericRow is not mutable, we just cast it to a Row. + val row = iter.next()._2.asInstanceOf[Row] + + var i = 0 + while (i < row.size) { + mutableRow(i) = row(i) + i += 1 + } + // Parquet will leave partitioning columns empty, so we fill them in here. + i = 0 + while (i < requestedPartitionOrdinals.size) { + mutableRow(requestedPartitionOrdinals(i)._2) = + partitionRowValues(requestedPartitionOrdinals(i)._1) + i += 1 + } + mutableRow } - row } } } @@ -210,7 +246,7 @@ case class ParquetTableScan( * (only detected via filename pattern so will not catch all cases). */ @DeveloperApi -case class InsertIntoParquetTable( +private[sql] case class InsertIntoParquetTable( relation: ParquetRelation, child: SparkPlan, overwrite: Boolean = false) @@ -219,7 +255,7 @@ case class InsertIntoParquetTable( /** * Inserts all rows into the Parquet file. */ - override def execute() = { + override def execute(): RDD[Row] = { // TODO: currently we do not check whether the "schema"s are compatible // That means if one first creates a table and then INSERTs data with // and incompatible schema the execution will fail. It would be nice @@ -242,7 +278,10 @@ case class InsertIntoParquetTable( ParquetOutputFormat.setWriteSupportClass(job, writeSupport) val conf = ContextUtil.getConfiguration(job) - RowWriteSupport.setSchema(relation.output, conf) + // This is a hack. We always set nullable/containsNull/valueContainsNull to true + // for the schema of a parquet data. + val schema = StructType.fromAttributes(relation.output).asNullable + RowWriteSupport.setSchema(schema.toAttributes, conf) val fspath = new Path(relation.path) val fs = fspath.getFileSystem(conf) @@ -263,7 +302,7 @@ case class InsertIntoParquetTable( childRdd } - override def output = child.output + override def output: Seq[Attribute] = child.output /** * Stores the given Row RDD as a Hadoop file. @@ -317,7 +356,7 @@ case class InsertIntoParquetTable( } finally { writer.close(hadoopContext) } - committer.commitTask(hadoopContext) + SparkHadoopMapRedUtil.commitTask(committer, hadoopContext, context) 1 } val jobFormat = new AppendingParquetOutputFormat(taskIdOffset) @@ -373,8 +412,6 @@ private[parquet] class AppendingParquetOutputFormat(offset: Int) private[parquet] class FilteringParquetRowInputFormat extends parquet.hadoop.ParquetInputFormat[Row] with Logging { - private var footers: JList[Footer] = _ - private var fileStatuses = Map.empty[Path, FileStatus] override def createRecordReader( @@ -395,46 +432,15 @@ private[parquet] class FilteringParquetRowInputFormat } } - override def getFooters(jobContext: JobContext): JList[Footer] = { - import org.apache.spark.sql.parquet.FilteringParquetRowInputFormat.footerCache - - if (footers eq null) { - val conf = ContextUtil.getConfiguration(jobContext) - val cacheMetadata = conf.getBoolean(SQLConf.PARQUET_CACHE_METADATA, true) - val statuses = listStatus(jobContext) - fileStatuses = statuses.map(file => file.getPath -> file).toMap - if (statuses.isEmpty) { - footers = Collections.emptyList[Footer] - } else if (!cacheMetadata) { - // Read the footers from HDFS - footers = getFooters(conf, statuses) - } else { - // Read only the footers that are not in the footerCache - val foundFooters = footerCache.getAllPresent(statuses) - val toFetch = new ArrayList[FileStatus] - for (s <- statuses) { - if (!foundFooters.containsKey(s)) { - toFetch.add(s) - } - } - val newFooters = new mutable.HashMap[FileStatus, Footer] - if (toFetch.size > 0) { - val startFetch = System.currentTimeMillis - val fetched = getFooters(conf, toFetch) - logInfo(s"Fetched $toFetch footers in ${System.currentTimeMillis - startFetch} ms") - for ((status, i) <- toFetch.zipWithIndex) { - newFooters(status) = fetched.get(i) - } - footerCache.putAll(newFooters) - } - footers = new ArrayList[Footer](statuses.size) - for (status <- statuses) { - footers.add(newFooters.getOrElse(status, foundFooters.get(status))) - } - } - } + // This is only a temporary solution sicne we need to use fileStatuses in + // both getClientSideSplits and getTaskSideSplits. It can be removed once we get rid of these + // two methods. + override def getSplits(jobContext: JobContext): JList[InputSplit] = { + // First set fileStatuses. + val statuses = listStatus(jobContext) + fileStatuses = statuses.map(file => file.getPath -> file).toMap - footers + super.getSplits(jobContext) } // TODO Remove this method and related code once PARQUET-16 is fixed @@ -459,13 +465,21 @@ private[parquet] class FilteringParquetRowInputFormat val getGlobalMetaData = classOf[ParquetFileWriter].getDeclaredMethod("getGlobalMetaData", classOf[JList[Footer]]) getGlobalMetaData.setAccessible(true) - val globalMetaData = getGlobalMetaData.invoke(null, footers).asInstanceOf[GlobalMetaData] + var globalMetaData = getGlobalMetaData.invoke(null, footers).asInstanceOf[GlobalMetaData] if (globalMetaData == null) { val splits = mutable.ArrayBuffer.empty[ParquetInputSplit] return splits } + val metadata = configuration.get(RowWriteSupport.SPARK_ROW_SCHEMA) + val mergedMetadata = globalMetaData + .getKeyValueMetaData + .updated(RowReadSupport.SPARK_METADATA_KEY, setAsJavaSet(Set(metadata))) + + globalMetaData = new GlobalMetaData(globalMetaData.getSchema, + mergedMetadata, globalMetaData.getCreatedBy) + val readContext = getReadSupport(configuration).init( new InitContext(configuration, globalMetaData.getKeyValueMetaData, @@ -498,6 +512,7 @@ private[parquet] class FilteringParquetRowInputFormat import parquet.filter2.compat.FilterCompat.Filter import parquet.filter2.compat.RowGroupFilter + import org.apache.spark.sql.parquet.FilteringParquetRowInputFormat.blockLocationCache val cacheMetadata = configuration.getBoolean(SQLConf.PARQUET_CACHE_METADATA, true) @@ -647,6 +662,6 @@ private[parquet] object FileSystemHelper { sys.error("ERROR: attempting to append to set of Parquet files and found file" + s"that does not match name pattern: $other") case _ => 0 - }.reduceLeft((a, b) => if (a < b) b else a) + }.reduceOption(_ max _).getOrElse(0) } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala index 19bfba34b8f4a..e05a4c20b0d41 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala @@ -198,10 +198,7 @@ private[parquet] class RowWriteSupport extends WriteSupport[Row] with Logging { if (value != null) { schema match { case StringType => writer.addBinary( - Binary.fromByteArray( - value.asInstanceOf[String].getBytes("utf-8") - ) - ) + Binary.fromByteArray(value.asInstanceOf[UTF8String].getBytes)) case BinaryType => writer.addBinary( Binary.fromByteArray(value.asInstanceOf[Array[Byte]])) case IntegerType => writer.addInteger(value.asInstanceOf[Int]) @@ -212,6 +209,7 @@ private[parquet] class RowWriteSupport extends WriteSupport[Row] with Logging { case DoubleType => writer.addDouble(value.asInstanceOf[Double]) case FloatType => writer.addFloat(value.asInstanceOf[Float]) case BooleanType => writer.addBoolean(value.asInstanceOf[Boolean]) + case DateType => writer.addInteger(value.asInstanceOf[Int]) case d: DecimalType => if (d.precisionInfo == None || d.precisionInfo.get.precision > 18) { sys.error(s"Unsupported datatype $d, cannot write to consumer") @@ -348,7 +346,7 @@ private[parquet] class MutableRowWriteSupport extends RowWriteSupport { index: Int): Unit = { ctype match { case StringType => writer.addBinary( - Binary.fromByteArray(record(index).asInstanceOf[String].getBytes("utf-8"))) + Binary.fromByteArray(record(index).asInstanceOf[UTF8String].getBytes)) case BinaryType => writer.addBinary( Binary.fromByteArray(record(index).asInstanceOf[Array[Byte]])) case IntegerType => writer.addInteger(record.getInt(index)) @@ -358,6 +356,7 @@ private[parquet] class MutableRowWriteSupport extends RowWriteSupport { case DoubleType => writer.addDouble(record.getDouble(index)) case FloatType => writer.addFloat(record.getFloat(index)) case BooleanType => writer.addBoolean(record.getBoolean(index)) + case DateType => writer.addInteger(record.getInt(index)) case TimestampType => writeTimestamp(record(index).asInstanceOf[java.sql.Timestamp]) case d: DecimalType => if (d.precisionInfo == None || d.precisionInfo.get.precision > 18) { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTest.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTest.scala index 8d3e094e3344d..9d17516e0ef7d 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTest.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTest.scala @@ -23,8 +23,7 @@ import scala.reflect.ClassTag import scala.reflect.runtime.universe.TypeTag import scala.util.Try -import org.apache.spark.sql.{DataFrame, SQLContext} -import org.apache.spark.sql.catalyst.util +import org.apache.spark.sql.{DataFrame, SQLContext, SaveMode} import org.apache.spark.util.Utils /** @@ -34,10 +33,11 @@ import org.apache.spark.util.Utils * convenient to use tuples rather than special case classes when writing test cases/suites. * Especially, `Tuple1.apply` can be used to easily wrap a single type/value. */ -trait ParquetTest { +private[sql] trait ParquetTest { val sqlContext: SQLContext - import sqlContext._ + import sqlContext.implicits.{localSeqToDataFrameHolder, rddToDataFrameHolder} + import sqlContext.{conf, sparkContext} protected def configuration = sparkContext.hadoopConfiguration @@ -49,11 +49,11 @@ trait ParquetTest { */ protected def withSQLConf(pairs: (String, String)*)(f: => Unit): Unit = { val (keys, values) = pairs.unzip - val currentValues = keys.map(key => Try(getConf(key)).toOption) - (keys, values).zipped.foreach(setConf) + val currentValues = keys.map(key => Try(conf.getConf(key)).toOption) + (keys, values).zipped.foreach(conf.setConf) try f finally { keys.zip(currentValues).foreach { - case (key, Some(value)) => setConf(key, value) + case (key, Some(value)) => conf.setConf(key, value) case (key, None) => conf.unsetConf(key) } } @@ -66,8 +66,9 @@ trait ParquetTest { * @todo Probably this method should be moved to a more general place */ protected def withTempPath(f: File => Unit): Unit = { - val file = util.getTempFilePath("parquetTest").getCanonicalFile - try f(file) finally if (file.exists()) Utils.deleteRecursively(file) + val path = Utils.createTempDir() + path.delete() + try f(path) finally Utils.deleteRecursively(path) } /** @@ -88,9 +89,8 @@ trait ParquetTest { protected def withParquetFile[T <: Product: ClassTag: TypeTag] (data: Seq[T]) (f: String => Unit): Unit = { - import sqlContext.implicits._ withTempPath { file => - sparkContext.parallelize(data).saveAsParquetFile(file.getCanonicalPath) + sparkContext.parallelize(data).toDF().saveAsParquetFile(file.getCanonicalPath) f(file.getCanonicalPath) } } @@ -99,17 +99,17 @@ trait ParquetTest { * Writes `data` to a Parquet file and reads it back as a [[DataFrame]], * which is then passed to `f`. The Parquet file will be deleted after `f` returns. */ - protected def withParquetRDD[T <: Product: ClassTag: TypeTag] + protected def withParquetDataFrame[T <: Product: ClassTag: TypeTag] (data: Seq[T]) (f: DataFrame => Unit): Unit = { - withParquetFile(data)(path => f(parquetFile(path))) + withParquetFile(data)(path => f(sqlContext.parquetFile(path))) } /** * Drops temporary table `tableName` after calling `f`. */ protected def withTempTable(tableName: String)(f: => Unit): Unit = { - try f finally dropTempTable(tableName) + try f finally sqlContext.dropTempTable(tableName) } /** @@ -120,9 +120,36 @@ trait ParquetTest { protected def withParquetTable[T <: Product: ClassTag: TypeTag] (data: Seq[T], tableName: String) (f: => Unit): Unit = { - withParquetRDD(data) { rdd => - sqlContext.registerRDDAsTable(rdd, tableName) + withParquetDataFrame(data) { df => + sqlContext.registerDataFrameAsTable(df, tableName) withTempTable(tableName)(f) } } + + protected def makeParquetFile[T <: Product: ClassTag: TypeTag]( + data: Seq[T], path: File): Unit = { + data.toDF().save(path.getCanonicalPath, "org.apache.spark.sql.parquet", SaveMode.Overwrite) + } + + protected def makeParquetFile[T <: Product: ClassTag: TypeTag]( + df: DataFrame, path: File): Unit = { + df.save(path.getCanonicalPath, "org.apache.spark.sql.parquet", SaveMode.Overwrite) + } + + protected def makePartitionDir( + basePath: File, + defaultPartitionName: String, + partitionCols: (String, Any)*): File = { + val partNames = partitionCols.map { case (k, v) => + val valueString = if (v == null || v == "") defaultPartitionName else v.toString + s"$k=$valueString" + } + + val partDir = partNames.foldLeft(basePath) { (parent, child) => + new File(parent, child) + } + + assert(partDir.mkdirs(), s"Couldn't create directory $partDir") + partDir + } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTestData.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTestData.scala deleted file mode 100644 index e4a10aa2ae6c3..0000000000000 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTestData.scala +++ /dev/null @@ -1,466 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.sql.parquet - -import java.io.File - -import org.apache.hadoop.conf.Configuration -import org.apache.hadoop.fs.{FileStatus, FileSystem, Path} -import org.apache.hadoop.mapreduce.Job -import org.apache.spark.sql.test.TestSQLContext - -import parquet.example.data.{GroupWriter, Group} -import parquet.example.data.simple.{NanoTime, SimpleGroup} -import parquet.hadoop.{ParquetReader, ParquetFileReader, ParquetWriter} -import parquet.hadoop.api.WriteSupport -import parquet.hadoop.api.WriteSupport.WriteContext -import parquet.hadoop.example.GroupReadSupport -import parquet.hadoop.util.ContextUtil -import parquet.io.api.RecordConsumer -import parquet.schema.{MessageType, MessageTypeParser} - -import org.apache.spark.util.Utils - -// Write support class for nested groups: ParquetWriter initializes GroupWriteSupport -// with an empty configuration (it is after all not intended to be used in this way?) -// and members are private so we need to make our own in order to pass the schema -// to the writer. -private class TestGroupWriteSupport(schema: MessageType) extends WriteSupport[Group] { - var groupWriter: GroupWriter = null - override def prepareForWrite(recordConsumer: RecordConsumer): Unit = { - groupWriter = new GroupWriter(recordConsumer, schema) - } - override def init(configuration: Configuration): WriteContext = { - new WriteContext(schema, new java.util.HashMap[String, String]()) - } - override def write(record: Group) { - groupWriter.write(record) - } -} - -private[sql] object ParquetTestData { - - val testSchema = - """message myrecord { - optional boolean myboolean; - optional int32 myint; - optional binary mystring (UTF8); - optional int64 mylong; - optional float myfloat; - optional double mydouble; - optional int96 mytimestamp; - }""" - - // field names for test assertion error messages - val testSchemaFieldNames = Seq( - "myboolean:Boolean", - "myint:Int", - "mystring:String", - "mylong:Long", - "myfloat:Float", - "mydouble:Double", - "mytimestamp:Timestamp" - ) - - val subTestSchema = - """ - message myrecord { - optional boolean myboolean; - optional int64 mylong; - } - """ - - val testFilterSchema = - """ - message myrecord { - required boolean myboolean; - required int32 myint; - required binary mystring (UTF8); - required int64 mylong; - required float myfloat; - required double mydouble; - optional boolean myoptboolean; - optional int32 myoptint; - optional binary myoptstring (UTF8); - optional int64 myoptlong; - optional float myoptfloat; - optional double myoptdouble; - optional int96 mytimestamp; - } - """ - - // field names for test assertion error messages - val subTestSchemaFieldNames = Seq( - "myboolean:Boolean", - "mylong:Long" - ) - - val testDir = Utils.createTempDir() - val testFilterDir = Utils.createTempDir() - - lazy val testData = new ParquetRelation(testDir.toURI.toString, None, TestSQLContext) - - val testNestedSchema1 = - // based on blogpost example, source: - // https://blog.twitter.com/2013/dremel-made-simple-with-parquet - // note: instead of string we have to use binary (?) otherwise - // Parquet gives us: - // IllegalArgumentException: expected one of [INT64, INT32, BOOLEAN, - // BINARY, FLOAT, DOUBLE, INT96, FIXED_LEN_BYTE_ARRAY] - // Also repeated primitives seem tricky to convert (AvroParquet - // only uses them in arrays?) so only use at most one in each group - // and nothing else in that group (-> is mapped to array)! - // The "values" inside ownerPhoneNumbers is a keyword currently - // so that array types can be translated correctly. - """ - message AddressBook { - required binary owner (UTF8); - optional group ownerPhoneNumbers { - repeated binary array (UTF8); - } - optional group contacts { - repeated group array { - required binary name (UTF8); - optional binary phoneNumber (UTF8); - } - } - } - """ - - - val testNestedSchema2 = - """ - message TestNested2 { - required int32 firstInt; - optional int32 secondInt; - optional group longs { - repeated int64 array; - } - required group entries { - repeated group array { - required double value; - optional boolean truth; - } - } - optional group outerouter { - repeated group array { - repeated group array { - repeated int32 array; - } - } - } - } - """ - - val testNestedSchema3 = - """ - message TestNested3 { - required int32 x; - optional group booleanNumberPairs { - repeated group array { - required int32 key; - optional group value { - repeated group array { - required double nestedValue; - optional boolean truth; - } - } - } - } - } - """ - - val testNestedSchema4 = - """ - message TestNested4 { - required int32 x; - optional group data1 { - repeated group map { - required binary key (UTF8); - required int32 value; - } - } - required group data2 { - repeated group map { - required binary key (UTF8); - required group value { - required int64 payload1; - optional binary payload2 (UTF8); - } - } - } - } - """ - - val testNestedDir1 = Utils.createTempDir() - val testNestedDir2 = Utils.createTempDir() - val testNestedDir3 = Utils.createTempDir() - val testNestedDir4 = Utils.createTempDir() - - lazy val testNestedData1 = - new ParquetRelation(testNestedDir1.toURI.toString, None, TestSQLContext) - lazy val testNestedData2 = - new ParquetRelation(testNestedDir2.toURI.toString, None, TestSQLContext) - - def writeFile() = { - testDir.delete() - val path: Path = new Path(new Path(testDir.toURI), new Path("part-r-0.parquet")) - val job = new Job() - val schema: MessageType = MessageTypeParser.parseMessageType(testSchema) - val writeSupport = new TestGroupWriteSupport(schema) - val writer = new ParquetWriter[Group](path, writeSupport) - - for(i <- 0 until 15) { - val record = new SimpleGroup(schema) - if (i % 3 == 0) { - record.add(0, true) - } else { - record.add(0, false) - } - if (i % 5 == 0) { - record.add(1, 5) - } - record.add(2, "abc") - record.add(3, i.toLong << 33) - record.add(4, 2.5F) - record.add(5, 4.5D) - record.add(6, new NanoTime(1,2)) - writer.write(record) - } - writer.close() - } - - def writeFilterFile(records: Int = 200) = { - // for microbenchmark use: records = 300000000 - testFilterDir.delete - val path: Path = new Path(new Path(testFilterDir.toURI), new Path("part-r-0.parquet")) - val schema: MessageType = MessageTypeParser.parseMessageType(testFilterSchema) - val writeSupport = new TestGroupWriteSupport(schema) - val writer = new ParquetWriter[Group](path, writeSupport) - - for(i <- 0 to records) { - val record = new SimpleGroup(schema) - if (i % 4 == 0) { - record.add(0, true) - } else { - record.add(0, false) - } - record.add(1, i) - record.add(2, i.toString) - record.add(3, i.toLong) - record.add(4, i.toFloat + 0.5f) - record.add(5, i.toDouble + 0.5d) - if (i % 2 == 0) { - if (i % 3 == 0) { - record.add(6, true) - } else { - record.add(6, false) - } - record.add(7, i) - record.add(8, i.toString) - record.add(9, i.toLong) - record.add(10, i.toFloat + 0.5f) - record.add(11, i.toDouble + 0.5d) - } - - writer.write(record) - } - writer.close() - } - - def writeNestedFile1() { - // example data from https://blog.twitter.com/2013/dremel-made-simple-with-parquet - testNestedDir1.delete() - val path: Path = new Path(new Path(testNestedDir1.toURI), new Path("part-r-0.parquet")) - val schema: MessageType = MessageTypeParser.parseMessageType(testNestedSchema1) - - val r1 = new SimpleGroup(schema) - r1.add(0, "Julien Le Dem") - r1.addGroup(1) - .append(CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME, "555 123 4567") - .append(CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME, "555 666 1337") - .append(CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME, "XXX XXX XXXX") - val contacts = r1.addGroup(2) - contacts.addGroup(0) - .append("name", "Dmitriy Ryaboy") - .append("phoneNumber", "555 987 6543") - contacts.addGroup(0) - .append("name", "Chris Aniszczyk") - - val r2 = new SimpleGroup(schema) - r2.add(0, "A. Nonymous") - - val writeSupport = new TestGroupWriteSupport(schema) - val writer = new ParquetWriter[Group](path, writeSupport) - writer.write(r1) - writer.write(r2) - writer.close() - } - - def writeNestedFile2() { - testNestedDir2.delete() - val path: Path = new Path(new Path(testNestedDir2.toURI), new Path("part-r-0.parquet")) - val schema: MessageType = MessageTypeParser.parseMessageType(testNestedSchema2) - - val r1 = new SimpleGroup(schema) - r1.add(0, 1) - r1.add(1, 7) - val longs = r1.addGroup(2) - longs.add(CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME , 1.toLong << 32) - longs.add(CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME, 1.toLong << 33) - longs.add(CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME, 1.toLong << 34) - val booleanNumberPair = r1.addGroup(3).addGroup(0) - booleanNumberPair.add("value", 2.5) - booleanNumberPair.add("truth", false) - val top_level = r1.addGroup(4) - val second_level_a = top_level.addGroup(0) - val second_level_b = top_level.addGroup(0) - val third_level_aa = second_level_a.addGroup(0) - val third_level_ab = second_level_a.addGroup(0) - val third_level_c = second_level_b.addGroup(0) - third_level_aa.add( - CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME, - 7) - third_level_ab.add( - CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME, - 8) - third_level_c.add( - CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME, - 9) - - val writeSupport = new TestGroupWriteSupport(schema) - val writer = new ParquetWriter[Group](path, writeSupport) - writer.write(r1) - writer.close() - } - - def writeNestedFile3() { - testNestedDir3.delete() - val path: Path = new Path(new Path(testNestedDir3.toURI), new Path("part-r-0.parquet")) - val schema: MessageType = MessageTypeParser.parseMessageType(testNestedSchema3) - - val r1 = new SimpleGroup(schema) - r1.add(0, 1) - val booleanNumberPairs = r1.addGroup(1) - val g1 = booleanNumberPairs.addGroup(0) - g1.add(0, 1) - val nested1 = g1.addGroup(1) - val ng1 = nested1.addGroup(0) - ng1.add(0, 1.5) - ng1.add(1, false) - val ng2 = nested1.addGroup(0) - ng2.add(0, 2.5) - ng2.add(1, true) - val g2 = booleanNumberPairs.addGroup(0) - g2.add(0, 2) - val ng3 = g2.addGroup(1) - .addGroup(0) - ng3.add(0, 3.5) - ng3.add(1, false) - - val writeSupport = new TestGroupWriteSupport(schema) - val writer = new ParquetWriter[Group](path, writeSupport) - writer.write(r1) - writer.close() - } - - def writeNestedFile4() { - testNestedDir4.delete() - val path: Path = new Path(new Path(testNestedDir4.toURI), new Path("part-r-0.parquet")) - val schema: MessageType = MessageTypeParser.parseMessageType(testNestedSchema4) - - val r1 = new SimpleGroup(schema) - r1.add(0, 7) - val map1 = r1.addGroup(1) - val keyValue1 = map1.addGroup(0) - keyValue1.add(0, "key1") - keyValue1.add(1, 1) - val keyValue2 = map1.addGroup(0) - keyValue2.add(0, "key2") - keyValue2.add(1, 2) - val map2 = r1.addGroup(2) - val keyValue3 = map2.addGroup(0) - // TODO: currently only string key type supported - keyValue3.add(0, "seven") - val valueGroup1 = keyValue3.addGroup(1) - valueGroup1.add(0, 42.toLong) - valueGroup1.add(1, "the answer") - val keyValue4 = map2.addGroup(0) - // TODO: currently only string key type supported - keyValue4.add(0, "eight") - val valueGroup2 = keyValue4.addGroup(1) - valueGroup2.add(0, 49.toLong) - - val writeSupport = new TestGroupWriteSupport(schema) - val writer = new ParquetWriter[Group](path, writeSupport) - writer.write(r1) - writer.close() - } - - // TODO: this is not actually used anywhere but useful for debugging - /* def readNestedFile(file: File, schemaString: String): Unit = { - val configuration = new Configuration() - val path = new Path(new Path(file.toURI), new Path("part-r-0.parquet")) - val fs: FileSystem = path.getFileSystem(configuration) - val schema: MessageType = MessageTypeParser.parseMessageType(schemaString) - assert(schema != null) - val outputStatus: FileStatus = fs.getFileStatus(new Path(path.toString)) - val footers = ParquetFileReader.readFooter(configuration, outputStatus) - assert(footers != null) - val reader = new ParquetReader(new Path(path.toString), new GroupReadSupport()) - val first = reader.read() - assert(first != null) - } */ - - // to test golb pattern (wild card pattern matching for parquetFile input - val testGlobDir = Utils.createTempDir() - val testGlobSubDir1 = Utils.createTempDir(testGlobDir.getPath) - val testGlobSubDir2 = Utils.createTempDir(testGlobDir.getPath) - val testGlobSubDir3 = Utils.createTempDir(testGlobDir.getPath) - - def writeGlobFiles() = { - val subDirs = Array(testGlobSubDir1, testGlobSubDir2, testGlobSubDir3) - - subDirs.foreach { dir => - val path: Path = new Path(new Path(dir.toURI), new Path("part-r-0.parquet")) - val job = new Job() - val schema: MessageType = MessageTypeParser.parseMessageType(testSchema) - val writeSupport = new TestGroupWriteSupport(schema) - val writer = new ParquetWriter[Group](path, writeSupport) - - for(i <- 0 until 15) { - val record = new SimpleGroup(schema) - if(i % 3 == 0) { - record.add(0, true) - } else { - record.add(0, false) - } - if(i % 5 == 0) { - record.add(1, 5) - } - record.add(2, "abc") - record.add(3, i.toLong << 33) - record.add(4, 2.5F) - record.add(5, 4.5D) - writer.write(record) - } - writer.close() - } - } -} - diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala index 5209581fa8357..60e1bec4db8e5 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala @@ -64,6 +64,8 @@ private[parquet] object ParquetTypesConverter extends Logging { case ParquetPrimitiveTypeName.BOOLEAN => BooleanType case ParquetPrimitiveTypeName.DOUBLE => DoubleType case ParquetPrimitiveTypeName.FLOAT => FloatType + case ParquetPrimitiveTypeName.INT32 + if originalType == ParquetOriginalType.DATE => DateType case ParquetPrimitiveTypeName.INT32 => IntegerType case ParquetPrimitiveTypeName.INT64 => LongType case ParquetPrimitiveTypeName.INT96 if int96AsTimestamp => TimestampType @@ -222,6 +224,8 @@ private[parquet] object ParquetTypesConverter extends Logging { // There is no type for Byte or Short so we promote them to INT32. case ShortType => Some(ParquetTypeInfo(ParquetPrimitiveTypeName.INT32)) case ByteType => Some(ParquetTypeInfo(ParquetPrimitiveTypeName.INT32)) + case DateType => Some(ParquetTypeInfo( + ParquetPrimitiveTypeName.INT32, Some(ParquetOriginalType.DATE))) case LongType => Some(ParquetTypeInfo(ParquetPrimitiveTypeName.INT64)) case TimestampType => Some(ParquetTypeInfo(ParquetPrimitiveTypeName.INT96)) case DecimalType.Fixed(precision, scale) if precision <= 18 => @@ -386,6 +390,7 @@ private[parquet] object ParquetTypesConverter extends Logging { def convertFromAttributes(attributes: Seq[Attribute], toThriftSchemaNames: Boolean = false): MessageType = { + checkSpecialCharacters(attributes) val fields = attributes.map( attribute => fromDataType(attribute.dataType, attribute.name, attribute.nullable, @@ -400,7 +405,20 @@ private[parquet] object ParquetTypesConverter extends Logging { } } + private def checkSpecialCharacters(schema: Seq[Attribute]) = { + // ,;{}()\n\t= and space character are special characters in Parquet schema + schema.map(_.name).foreach { name => + if (name.matches(".*[ ,;{}()\n\t=].*")) { + sys.error( + s"""Attribute name "$name" contains invalid character(s) among " ,;{}()\n\t=". + |Please use alias to rename it. + """.stripMargin.split("\n").mkString(" ")) + } + } + } + def convertToString(schema: Seq[Attribute]): String = { + checkSpecialCharacters(schema) StructType.fromAttributes(schema).json } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala index 49d46334b6525..af7b3c81ae7b2 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala @@ -19,8 +19,9 @@ package org.apache.spark.sql.parquet import java.io.IOException import java.lang.{Double => JDouble, Float => JFloat, Long => JLong} import java.math.{BigDecimal => JBigDecimal} +import java.net.URI import java.text.SimpleDateFormat -import java.util.{List => JList, Date} +import java.util.{Date, List => JList} import scala.collection.JavaConversions._ import scala.collection.mutable.ArrayBuffer @@ -34,32 +35,49 @@ import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat import org.apache.hadoop.mapreduce.{InputSplit, Job, JobContext} import parquet.filter2.predicate.FilterApi import parquet.format.converter.ParquetMetadataConverter -import parquet.hadoop.{ParquetInputFormat, _} +import parquet.hadoop.metadata.CompressionCodecName import parquet.hadoop.util.ContextUtil +import parquet.hadoop.{ParquetInputFormat, _} import org.apache.spark.annotation.DeveloperApi import org.apache.spark.deploy.SparkHadoopUtil +import org.apache.spark.mapred.SparkHadoopMapRedUtil import org.apache.spark.mapreduce.SparkHadoopMapReduceUtil import org.apache.spark.rdd.{NewHadoopPartition, NewHadoopRDD, RDD} import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.catalyst.{CatalystTypeConverters, expressions} import org.apache.spark.sql.parquet.ParquetTypesConverter._ import org.apache.spark.sql.sources._ import org.apache.spark.sql.types.{IntegerType, StructField, StructType, _} -import org.apache.spark.sql.types.StructType._ -import org.apache.spark.sql.{DataFrame, Row, SQLConf, SQLContext} -import org.apache.spark.{Partition => SparkPartition, TaskContext, SerializableWritable, Logging, SparkException} - +import org.apache.spark.sql.{DataFrame, Row, SQLConf, SQLContext, SaveMode} +import org.apache.spark.{Logging, SerializableWritable, SparkException, TaskContext, Partition => SparkPartition} /** - * Allows creation of parquet based tables using the syntax - * `CREATE TEMPORARY TABLE ... USING org.apache.spark.sql.parquet`. Currently the only option - * required is `path`, which should be the location of a collection of, optionally partitioned, - * parquet files. + * Allows creation of Parquet based tables using the syntax: + * {{{ + * CREATE TEMPORARY TABLE ... USING org.apache.spark.sql.parquet OPTIONS (...) + * }}} + * + * Supported options include: + * + * - `path`: Required. When reading Parquet files, `path` should point to the location of the + * Parquet file(s). It can be either a single raw Parquet file, or a directory of Parquet files. + * In the latter case, this data source tries to discover partitioning information if the the + * directory is structured in the same style of Hive partitioned tables. When writing Parquet + * file, `path` should point to the destination folder. + * + * - `mergeSchema`: Optional. Indicates whether we should merge potentially different (but + * compatible) schemas stored in all Parquet part-files. + * + * - `partition.defaultName`: Optional. Partition name used when a value of a partition column is + * null or empty string. This is similar to the `hive.exec.default.partition.name` configuration + * in Hive. */ -class DefaultSource +private[sql] class DefaultSource extends RelationProvider with SchemaRelationProvider with CreatableRelationProvider { + private def checkPath(parameters: Map[String, String]): String = { parameters.getOrElse("path", sys.error("'path' must be specified for parquet tables.")) } @@ -71,6 +89,7 @@ class DefaultSource ParquetRelation2(Seq(checkPath(parameters)), parameters, None)(sqlContext) } + /** Returns a new base relation with the given parameters and schema. */ override def createRelation( sqlContext: SQLContext, parameters: Map[String, String], @@ -78,56 +97,72 @@ class DefaultSource ParquetRelation2(Seq(checkPath(parameters)), parameters, Some(schema))(sqlContext) } + /** Returns a new base relation with the given parameters and save given data into it. */ override def createRelation( sqlContext: SQLContext, + mode: SaveMode, parameters: Map[String, String], data: DataFrame): BaseRelation = { val path = checkPath(parameters) - ParquetRelation.createEmpty( - path, - data.schema.toAttributes, - false, - sqlContext.sparkContext.hadoopConfiguration, - sqlContext) - - val relation = createRelation(sqlContext, parameters, data.schema) - relation.asInstanceOf[ParquetRelation2].insert(data, true) + val filesystemPath = new Path(path) + val fs = filesystemPath.getFileSystem(sqlContext.sparkContext.hadoopConfiguration) + val doInsertion = (mode, fs.exists(filesystemPath)) match { + case (SaveMode.ErrorIfExists, true) => + sys.error(s"path $path already exists.") + case (SaveMode.Append, _) | (SaveMode.Overwrite, _) | (SaveMode.ErrorIfExists, false) => + true + case (SaveMode.Ignore, exists) => + !exists + } + + val relation = if (doInsertion) { + // This is a hack. We always set nullable/containsNull/valueContainsNull to true + // for the schema of a parquet data. + val df = + sqlContext.createDataFrame( + data.queryExecution.toRdd, + data.schema.asNullable, + needsConversion = false) + val createdRelation = + createRelation(sqlContext, parameters, df.schema).asInstanceOf[ParquetRelation2] + createdRelation.insert(df, overwrite = mode == SaveMode.Overwrite) + createdRelation + } else { + // If the save mode is Ignore, we will just create the relation based on existing data. + createRelation(sqlContext, parameters) + } + relation } } -private[parquet] case class Partition(values: Row, path: String) +private[sql] case class Partition(values: Row, path: String) -private[parquet] case class PartitionSpec(partitionColumns: StructType, partitions: Seq[Partition]) +private[sql] case class PartitionSpec(partitionColumns: StructType, partitions: Seq[Partition]) /** * An alternative to [[ParquetRelation]] that plugs in using the data sources API. This class is - * currently not intended as a full replacement of the parquet support in Spark SQL though it is - * likely that it will eventually subsume the existing physical plan implementation. - * - * Compared with the current implementation, this class has the following notable differences: - * - * Partitioning: Partitions are auto discovered and must be in the form of directories `key=value/` - * located at `path`. Currently only a single partitioning column is supported and it must - * be an integer. This class supports both fully self-describing data, which contains the partition - * key, and data where the partition key is only present in the folder structure. The presence - * of the partitioning key in the data is also auto-detected. The `null` partition is not yet - * supported. + * intended as a full replacement of the Parquet support in Spark SQL. The old implementation will + * be deprecated and eventually removed once this version is proved to be stable enough. * - * Metadata: The metadata is automatically discovered by reading the first parquet file present. - * There is currently no support for working with files that have different schema. Additionally, - * when parquet metadata caching is turned on, the FileStatus objects for all data will be cached - * to improve the speed of interactive querying. When data is added to a table it must be dropped - * and recreated to pick up any changes. + * Compared with the old implementation, this class has the following notable differences: * - * Statistics: Statistics for the size of the table are automatically populated during metadata - * discovery. + * - Partitioning discovery: Hive style multi-level partitions are auto discovered. + * - Metadata discovery: Parquet is a format comes with schema evolving support. This data source + * can detect and merge schemas from all Parquet part-files as long as they are compatible. + * Also, metadata and [[FileStatus]]es are cached for better performance. + * - Statistics: Statistics for the size of the table are automatically populated during schema + * discovery. */ @DeveloperApi -case class ParquetRelation2 - (paths: Seq[String], parameters: Map[String, String], maybeSchema: Option[StructType] = None) - (@transient val sqlContext: SQLContext) - extends CatalystScan +private[sql] case class ParquetRelation2( + paths: Seq[String], + parameters: Map[String, String], + maybeSchema: Option[StructType] = None, + maybePartitionSpec: Option[PartitionSpec] = None)( + @transient val sqlContext: SQLContext) + extends BaseRelation + with CatalystScan with InsertableRelation with SparkHadoopMapReduceUtil with Logging { @@ -147,44 +182,110 @@ case class ParquetRelation2 private val defaultPartitionName = parameters.getOrElse( ParquetRelation2.DEFAULT_PARTITION_NAME, "__HIVE_DEFAULT_PARTITION__") - override def equals(other: Any) = other match { + override def equals(other: Any): Boolean = other match { case relation: ParquetRelation2 => + // If schema merging is required, we don't compare the actual schemas since they may evolve. + val schemaEquality = if (shouldMergeSchemas) { + shouldMergeSchemas == relation.shouldMergeSchemas + } else { + schema == relation.schema + } + paths.toSet == relation.paths.toSet && + schemaEquality && maybeMetastoreSchema == relation.maybeMetastoreSchema && - (shouldMergeSchemas == relation.shouldMergeSchemas || schema == relation.schema) + maybePartitionSpec == relation.maybePartitionSpec + + case _ => false } - private[sql] def sparkContext = sqlContext.sparkContext + override def hashCode(): Int = { + if (shouldMergeSchemas) { + com.google.common.base.Objects.hashCode( + shouldMergeSchemas: java.lang.Boolean, + paths.toSet, + maybeMetastoreSchema, + maybePartitionSpec) + } else { + com.google.common.base.Objects.hashCode( + shouldMergeSchemas: java.lang.Boolean, + schema, + paths.toSet, + maybeMetastoreSchema, + maybePartitionSpec) + } + } - @transient private val fs = FileSystem.get(sparkContext.hadoopConfiguration) + private[sql] def sparkContext = sqlContext.sparkContext private class MetadataCache { + // `FileStatus` objects of all "_metadata" files. private var metadataStatuses: Array[FileStatus] = _ + + // `FileStatus` objects of all "_common_metadata" files. private var commonMetadataStatuses: Array[FileStatus] = _ - private var footers: Map[FileStatus, Footer] = _ - private var parquetSchema: StructType = _ + // Parquet footer cache. + var footers: Map[FileStatus, Footer] = _ + + // `FileStatus` objects of all data files (Parquet part-files). var dataStatuses: Array[FileStatus] = _ + + // Partition spec of this table, including names, data types, and values of each partition + // column, and paths of each partition. var partitionSpec: PartitionSpec = _ + + // Schema of the actual Parquet files, without partition columns discovered from partition + // directory paths. + var parquetSchema: StructType = _ + + // Schema of the whole table, including partition columns. var schema: StructType = _ - var dataSchemaIncludesPartitionKeys: Boolean = _ + // Indicates whether partition columns are also included in Parquet data file schema. If not, + // we need to fill in partition column values into read rows when scanning the table. + var partitionKeysIncludedInParquetSchema: Boolean = _ + + def prepareMetadata(path: Path, schema: StructType, conf: Configuration): Unit = { + conf.set( + ParquetOutputFormat.COMPRESSION, + ParquetRelation + .shortParquetCompressionCodecNames + .getOrElse( + sqlContext.conf.parquetCompressionCodec.toUpperCase, + CompressionCodecName.UNCOMPRESSED).name()) + + ParquetRelation.enableLogForwarding() + ParquetTypesConverter.writeMetaData(schema.toAttributes, path, conf) + } + + /** + * Refreshes `FileStatus`es, footers, partition spec, and table schema. + */ def refresh(): Unit = { - val baseStatuses = { - val statuses = paths.distinct.map(p => fs.getFileStatus(fs.makeQualified(new Path(p)))) - // Support either reading a collection of raw Parquet part-files, or a collection of folders - // containing Parquet files (e.g. partitioned Parquet table). - assert(statuses.forall(!_.isDir) || statuses.forall(_.isDir)) - statuses.toArray - } + // Support either reading a collection of raw Parquet part-files, or a collection of folders + // containing Parquet files (e.g. partitioned Parquet table). + val baseStatuses = paths.distinct.map { p => + val fs = FileSystem.get(URI.create(p), sparkContext.hadoopConfiguration) + val path = new Path(p) + val qualified = path.makeQualified(fs.getUri, fs.getWorkingDirectory) + + if (!fs.exists(qualified) && maybeSchema.isDefined) { + fs.mkdirs(qualified) + prepareMetadata(qualified, maybeSchema.get, sparkContext.hadoopConfiguration) + } + + fs.getFileStatus(qualified) + }.toArray + assert(baseStatuses.forall(!_.isDir) || baseStatuses.forall(_.isDir)) + // Lists `FileStatus`es of all leaf nodes (files) under all base directories. val leaves = baseStatuses.flatMap { f => - val statuses = SparkHadoopUtil.get.listLeafStatuses(fs, f.getPath).filter { f => + val fs = FileSystem.get(f.getPath.toUri, sparkContext.hadoopConfiguration) + SparkHadoopUtil.get.listLeafStatuses(fs, f.getPath).filter { f => isSummaryFile(f.getPath) || !(f.getPath.getName.startsWith("_") || f.getPath.getName.startsWith(".")) } - assert(statuses.nonEmpty, s"${f.getPath} is an empty folder.") - statuses } dataStatuses = leaves.filterNot(f => isSummaryFile(f.getPath)) @@ -198,13 +299,14 @@ case class ParquetRelation2 f -> new Footer(f.getPath, parquetMetadata) }.seq.toMap - partitionSpec = { - val partitionDirs = dataStatuses + partitionSpec = maybePartitionSpec.getOrElse { + val partitionDirs = leaves .filterNot(baseStatuses.contains) .map(_.getPath.getParent) .distinct if (partitionDirs.nonEmpty) { + // Parses names and values of partition columns, and infer their data types. ParquetRelation2.parsePartitions(partitionDirs, defaultPartitionName) } else { // No partition directories found, makes an empty specification @@ -212,26 +314,37 @@ case class ParquetRelation2 } } - parquetSchema = maybeSchema.getOrElse(readSchema()) - - dataSchemaIncludesPartitionKeys = + // To get the schema. We first try to get the schema defined in maybeSchema. + // If maybeSchema is not defined, we will try to get the schema from existing parquet data + // (through readSchema). If data does not exist, we will try to get the schema defined in + // maybeMetastoreSchema (defined in the options of the data source). + // Finally, if we still could not get the schema. We throw an error. + parquetSchema = + maybeSchema + .orElse(readSchema()) + .orElse(maybeMetastoreSchema) + .getOrElse(sys.error("Failed to get the schema.")) + + partitionKeysIncludedInParquetSchema = isPartitioned && - partitionColumns.forall(f => metadataCache.parquetSchema.fieldNames.contains(f.name)) + partitionColumns.forall(f => parquetSchema.fieldNames.contains(f.name)) schema = { - val fullParquetSchema = if (dataSchemaIncludesPartitionKeys) { - metadataCache.parquetSchema + val fullRelationSchema = if (partitionKeysIncludedInParquetSchema) { + parquetSchema } else { - StructType(metadataCache.parquetSchema.fields ++ partitionColumns.fields) + StructType(parquetSchema.fields ++ partitionColumns.fields) } + // If this Parquet relation is converted from a Hive Metastore table, must reconcile case + // insensitivity issue and possible schema mismatch. maybeMetastoreSchema - .map(ParquetRelation2.mergeMetastoreParquetSchema(_, fullParquetSchema)) - .getOrElse(fullParquetSchema) + .map(ParquetRelation2.mergeMetastoreParquetSchema(_, fullRelationSchema)) + .getOrElse(fullRelationSchema) } } - private def readSchema(): StructType = { + private def readSchema(): Option[StructType] = { // Sees which file(s) we need to touch in order to figure out the schema. val filesToTouch = // Always tries the summary files first if users don't require a merged schema. In this case, @@ -276,21 +389,28 @@ case class ParquetRelation2 @transient private val metadataCache = new MetadataCache metadataCache.refresh() - private def partitionColumns = metadataCache.partitionSpec.partitionColumns + def partitionSpec: PartitionSpec = metadataCache.partitionSpec + + def partitionColumns: StructType = metadataCache.partitionSpec.partitionColumns - private def partitions = metadataCache.partitionSpec.partitions + def partitions: Seq[Partition] = metadataCache.partitionSpec.partitions - private def isPartitioned = partitionColumns.nonEmpty + def isPartitioned: Boolean = partitionColumns.nonEmpty - private def dataSchemaIncludesPartitionKeys = metadataCache.dataSchemaIncludesPartitionKeys + private def partitionKeysIncludedInDataSchema = metadataCache.partitionKeysIncludedInParquetSchema - override def schema = metadataCache.schema + private def parquetSchema = metadataCache.parquetSchema + + override def schema: StructType = metadataCache.schema private def isSummaryFile(file: Path): Boolean = { file.getName == ParquetFileWriter.PARQUET_COMMON_METADATA_FILE || file.getName == ParquetFileWriter.PARQUET_METADATA_FILE } + // Skip type conversion + override val needConversion: Boolean = false + // TODO Should calculate per scan size // It's common that a query only scans a fraction of a large Parquet file. Returning size of the // whole Parquet file disables some optimizations in this case (e.g. broadcast join). @@ -310,24 +430,38 @@ case class ParquetRelation2 } else { metadataCache.dataStatuses.toSeq } + val selectedFooters = selectedFiles.map(metadataCache.footers) // FileInputFormat cannot handle empty lists. if (selectedFiles.nonEmpty) { - FileInputFormat.setInputPaths(job, selectedFiles.map(_.getPath): _*) + // In order to encode the authority of a Path containning special characters such as /, + // we need to use the string retruned by the URI of the path to create a new Path. + val selectedPaths = selectedFiles.map(status => new Path(status.getPath.toUri.toString)) + FileInputFormat.setInputPaths(job, selectedPaths: _*) } - // Push down filters when possible. Notice that not all filters can be converted to Parquet - // filter predicate. Here we try to convert each individual predicate and only collect those - // convertible ones. - predicates - .flatMap(ParquetFilters.createFilter) - .reduceOption(FilterApi.and) - .filter(_ => sqlContext.conf.parquetFilterPushDown) - .foreach(ParquetInputFormat.setFilterPredicate(jobConf, _)) + // Try to push down filters when filter push-down is enabled. + if (sqlContext.conf.parquetFilterPushDown) { + val partitionColNames = partitionColumns.map(_.name).toSet + predicates + // Don't push down predicates which reference partition columns + .filter { pred => + val referencedColNames = pred.references.map(_.name).toSet + referencedColNames.intersect(partitionColNames).isEmpty + } + // Collects all converted Parquet filter predicates. Notice that not all predicates can be + // converted (`ParquetFilters.createFilter` returns an `Option`). That's why a `flatMap` + // is used here. + .flatMap(ParquetFilters.createFilter) + .reduceOption(FilterApi.and) + .foreach(ParquetInputFormat.setFilterPredicate(jobConf, _)) + } if (isPartitioned) { - def percentRead = selectedPartitions.size.toDouble / partitions.size.toDouble * 100 - logInfo(s"Reading $percentRead% of partitions") + logInfo { + val percentRead = selectedPartitions.size.toDouble / partitions.size.toDouble * 100 + s"Reading $percentRead% of partitions" + } } val requiredColumns = output.map(_.name) @@ -355,13 +489,39 @@ case class ParquetRelation2 val cacheMetadata = useCache @transient - val cachedStatus = selectedFiles + val cachedStatus = selectedFiles.map { st => + // In order to encode the authority of a Path containning special characters such as /, + // we need to use the string retruned by the URI of the path to create a new Path. + val newPath = new Path(st.getPath.toUri.toString) + + new FileStatus( + st.getLen, + st.isDir, + st.getReplication, + st.getBlockSize, + st.getModificationTime, + st.getAccessTime, + st.getPermission, + st.getOwner, + st.getGroup, + newPath) + } + + @transient + val cachedFooters = selectedFooters.map { f => + // In order to encode the authority of a Path containning special characters such as /, + // we need to use the string retruned by the URI of the path to create a new Path. + new Footer(new Path(f.getFile.toUri.toString), f.getParquetMetadata) + } + // Overridden so we can inject our own cached files statuses. override def getPartitions: Array[SparkPartition] = { val inputFormat = if (cacheMetadata) { new FilteringParquetRowInputFormat { override def listStatus(jobContext: JobContext): JList[FileStatus] = cachedStatus + + override def getFooters(jobContext: JobContext): JList[Footer] = cachedFooters } } else { new FilteringParquetRowInputFormat @@ -385,21 +545,55 @@ case class ParquetRelation2 // When the data does not include the key and the key is requested then we must fill it in // based on information from the input split. - if (!dataSchemaIncludesPartitionKeys && partitionKeyLocations.nonEmpty) { + if (!partitionKeysIncludedInDataSchema && partitionKeyLocations.nonEmpty) { + // This check is based on CatalystConverter.createRootConverter. + val primitiveRow = + requestedSchema.forall(a => ParquetTypesConverter.isPrimitiveType(a.dataType)) + baseRDD.mapPartitionsWithInputSplit { case (split: ParquetInputSplit, iterator) => val partValues = selectedPartitions.collectFirst { - case p if split.getPath.getParent.toString == p.path => p.values + case p if split.getPath.getParent.toString == p.path => + CatalystTypeConverters.convertToCatalyst(p.values).asInstanceOf[Row] }.get - iterator.map { pair => - val row = pair._2.asInstanceOf[SpecificMutableRow] - var i = 0 - while (i < partValues.size) { - // TODO Avoids boxing cost here! - row.update(partitionKeyLocations(i), partValues(i)) - i += 1 + val requiredPartOrdinal = partitionKeyLocations.keys.toSeq + + if (primitiveRow) { + iterator.map { pair => + // We are using CatalystPrimitiveRowConverter and it returns a SpecificMutableRow. + val row = pair._2.asInstanceOf[SpecificMutableRow] + var i = 0 + while (i < requiredPartOrdinal.size) { + // TODO Avoids boxing cost here! + val partOrdinal = requiredPartOrdinal(i) + row.update(partitionKeyLocations(partOrdinal), partValues(partOrdinal)) + i += 1 + } + row + } + } else { + // Create a mutable row since we need to fill in values from partition columns. + val mutableRow = new GenericMutableRow(requestedSchema.size) + iterator.map { pair => + // We are using CatalystGroupConverter and it returns a GenericRow. + // Since GenericRow is not mutable, we just cast it to a Row. + val row = pair._2.asInstanceOf[Row] + var i = 0 + while (i < row.size) { + // TODO Avoids boxing cost here! + mutableRow(i) = row(i) + i += 1 + } + + i = 0 + while (i < requiredPartOrdinal.size) { + // TODO Avoids boxing cost here! + val partOrdinal = requiredPartOrdinal(i) + mutableRow.update(partitionKeyLocations(partOrdinal), partValues(partOrdinal)) + i += 1 + } + mutableRow } - row } } } else { @@ -415,7 +609,8 @@ case class ParquetRelation2 _.references.map(_.name).toSet.subsetOf(partitionColumnNames) } - val rawPredicate = partitionPruningPredicates.reduceOption(And).getOrElse(Literal(true)) + val rawPredicate = + partitionPruningPredicates.reduceOption(expressions.And).getOrElse(Literal(true)) val boundPredicate = InterpretedPredicate(rawPredicate transform { case a: AttributeReference => val index = partitionColumns.indexWhere(a.name == _.name) @@ -430,6 +625,8 @@ case class ParquetRelation2 } override def insert(data: DataFrame, overwrite: Boolean): Unit = { + assert(paths.size == 1, s"Can't write to multiple destinations: ${paths.mkString(",")}") + // TODO: currently we do not check whether the "schema"s are compatible // That means if one first creates a table and then INSERTs data with // and incompatible schema the execution will fail. It would be nice @@ -437,7 +634,7 @@ case class ParquetRelation2 // before calling execute(). val job = new Job(sqlContext.sparkContext.hadoopConfiguration) - val writeSupport = if (schema.map(_.dataType).forall(_.isPrimitive)) { + val writeSupport = if (parquetSchema.map(_.dataType).forall(_.isPrimitive)) { log.debug("Initializing MutableRowWriteSupport") classOf[MutableRowWriteSupport] } else { @@ -447,18 +644,27 @@ case class ParquetRelation2 ParquetOutputFormat.setWriteSupportClass(job, writeSupport) val conf = ContextUtil.getConfiguration(job) - RowWriteSupport.setSchema(schema.toAttributes, conf) + RowWriteSupport.setSchema(data.schema.toAttributes, conf) val destinationPath = new Path(paths.head) if (overwrite) { - try { - destinationPath.getFileSystem(conf).delete(destinationPath, true) - } catch { - case e: IOException => + val fs = destinationPath.getFileSystem(conf) + if (fs.exists(destinationPath)) { + var success: Boolean = false + try { + success = fs.delete(destinationPath, true) + } catch { + case e: IOException => + throw new IOException( + s"Unable to clear output directory ${destinationPath.toString} prior" + + s" to writing to Parquet table:\n${e.toString}") + } + if (!success) { throw new IOException( s"Unable to clear output directory ${destinationPath.toString} prior" + - s" to writing to Parquet file:\n${e.toString}") + s" to writing to Parquet table.") + } } } @@ -494,7 +700,8 @@ case class ParquetRelation2 } finally { writer.close(hadoopContext) } - committer.commitTask(hadoopContext) + + SparkHadoopMapRedUtil.commitTask(committer, hadoopContext, context) } val jobFormat = new AppendingParquetOutputFormat(taskIdOffset) /* apparently we need a TaskAttemptID to construct an OutputCommitter; @@ -513,20 +720,19 @@ case class ParquetRelation2 } } -object ParquetRelation2 { +private[sql] object ParquetRelation2 extends Logging { // Whether we should merge schemas collected from all Parquet part-files. val MERGE_SCHEMA = "mergeSchema" - // Hive Metastore schema, passed in when the Parquet relation is converted from Metastore - val METASTORE_SCHEMA = "metastoreSchema" - - // Default partition name to use when the partition column value is null or empty string + // Default partition name to use when the partition column value is null or empty string. val DEFAULT_PARTITION_NAME = "partition.defaultName" - // When true, the Parquet data source caches Parquet metadata for performance - val CACHE_METADATA = "cacheMetadata" + // Hive Metastore schema, used when converting Metastore Parquet tables. This option is only used + // internally. + private[sql] val METASTORE_SCHEMA = "metastoreSchema" - private[parquet] def readSchema(footers: Seq[Footer], sqlContext: SQLContext): StructType = { + private[parquet] def readSchema( + footers: Seq[Footer], sqlContext: SQLContext): Option[StructType] = { footers.map { footer => val metadata = footer.getParquetMetadata.getFileMetaData val parquetSchema = metadata.getSchema @@ -534,7 +740,26 @@ object ParquetRelation2 { .getKeyValueMetaData .toMap .get(RowReadSupport.SPARK_METADATA_KEY) - .map(DataType.fromJson(_).asInstanceOf[StructType]) + .flatMap { serializedSchema => + // Don't throw even if we failed to parse the serialized Spark schema. Just fallback to + // whatever is available. + Try(DataType.fromJson(serializedSchema)) + .recover { case _: Throwable => + logInfo( + s"Serialized Spark schema in Parquet key-value metadata is not in JSON format, " + + "falling back to the deprecated DataType.fromCaseClassString parser.") + DataType.fromCaseClassString(serializedSchema) + } + .recover { case cause: Throwable => + logWarning( + s"""Failed to parse serialized Spark schema in Parquet key-value metadata: + |\t$serializedSchema + """.stripMargin, + cause) + } + .map(_.asInstanceOf[StructType]) + .toOption + } maybeSparkSchema.getOrElse { // Falls back to Parquet schema if Spark SQL schema is absent. @@ -545,17 +770,26 @@ object ParquetRelation2 { sqlContext.conf.isParquetBinaryAsString, sqlContext.conf.isParquetINT96AsTimestamp)) } - }.reduce { (left, right) => + }.reduceOption { (left, right) => try left.merge(right) catch { case e: Throwable => throw new SparkException(s"Failed to merge incompatible schemas $left and $right", e) } } } + /** + * Reconciles Hive Metastore case insensitivity issue and data type conflicts between Metastore + * schema and Parquet schema. + * + * Hive doesn't retain case information, while Parquet is case sensitive. On the other hand, the + * schema read from Parquet files may be incomplete (e.g. older versions of Parquet doesn't + * distinguish binary and string). This method generates a correct schema by merging Metastore + * schema data types and Parquet schema field names. + */ private[parquet] def mergeMetastoreParquetSchema( metastoreSchema: StructType, parquetSchema: StructType): StructType = { - def schemaConflictMessage = + def schemaConflictMessage: String = s"""Converting Hive Metastore Parquet, but detected conflicting schemas. Metastore schema: |${metastoreSchema.prettyJson} | @@ -563,12 +797,15 @@ object ParquetRelation2 { |${parquetSchema.prettyJson} """.stripMargin - assert(metastoreSchema.size == parquetSchema.size, schemaConflictMessage) + val mergedParquetSchema = mergeMissingNullableFields(metastoreSchema, parquetSchema) + + assert(metastoreSchema.size <= mergedParquetSchema.size, schemaConflictMessage) val ordinalMap = metastoreSchema.zipWithIndex.map { case (field, index) => field.name.toLowerCase -> index }.toMap - val reorderedParquetSchema = parquetSchema.sortBy(f => ordinalMap(f.name.toLowerCase)) + val reorderedParquetSchema = mergedParquetSchema.sortBy(f => + ordinalMap.getOrElse(f.name.toLowerCase, metastoreSchema.size + 1)) StructType(metastoreSchema.zip(reorderedParquetSchema).map { // Uses Parquet field names but retains Metastore data types. @@ -579,6 +816,32 @@ object ParquetRelation2 { }) } + /** + * Returns the original schema from the Parquet file with any missing nullable fields from the + * Hive Metastore schema merged in. + * + * When constructing a DataFrame from a collection of structured data, the resulting object has + * a schema corresponding to the union of the fields present in each element of the collection. + * Spark SQL simply assigns a null value to any field that isn't present for a particular row. + * In some cases, it is possible that a given table partition stored as a Parquet file doesn't + * contain a particular nullable field in its schema despite that field being present in the + * table schema obtained from the Hive Metastore. This method returns a schema representing the + * Parquet file schema along with any additional nullable fields from the Metastore schema + * merged in. + */ + private[parquet] def mergeMissingNullableFields( + metastoreSchema: StructType, + parquetSchema: StructType): StructType = { + val fieldMap = metastoreSchema.map(f => f.name.toLowerCase -> f).toMap + val missingFields = metastoreSchema + .map(_.name.toLowerCase) + .diff(parquetSchema.map(_.name.toLowerCase)) + .map(fieldMap(_)) + .filter(_.nullable) + StructType(parquetSchema ++ missingFields) + } + + // TODO Data source implementations shouldn't touch Catalyst types (`Literal`). // However, we are already using Catalyst expressions for partition pruning and predicate // push-down here... @@ -639,9 +902,9 @@ object ParquetRelation2 { * PartitionValues( * Seq("a", "b", "c"), * Seq( - * Literal(42, IntegerType), - * Literal("hello", StringType), - * Literal(3.14, FloatType))) + * Literal.create(42, IntegerType), + * Literal.create("hello", StringType), + * Literal.create(3.14, FloatType))) * }}} */ private[parquet] def parsePartition( @@ -692,16 +955,15 @@ object ParquetRelation2 { * }}} */ private[parquet] def resolvePartitions(values: Seq[PartitionValues]): Seq[PartitionValues] = { - val distinctColNamesOfPartitions = values.map(_.columnNames).distinct - val columnCount = values.head.columnNames.size - // Column names of all partitions must match - assert(distinctColNamesOfPartitions.size == 1, { - val list = distinctColNamesOfPartitions.mkString("\t", "\n", "") + val distinctPartitionsColNames = values.map(_.columnNames).distinct + assert(distinctPartitionsColNames.size == 1, { + val list = distinctPartitionsColNames.mkString("\t", "\n", "") s"Conflicting partition column names detected:\n$list" }) // Resolves possible type conflicts for each column + val columnCount = values.head.columnNames.size val resolvedValues = (0 until columnCount).map { i => resolveTypeConflicts(values.map(_.literals(i))) } @@ -721,15 +983,16 @@ object ParquetRelation2 { raw: String, defaultPartitionName: String): Literal = { // First tries integral types - Try(Literal(Integer.parseInt(raw), IntegerType)) - .orElse(Try(Literal(JLong.parseLong(raw), LongType))) + Try(Literal.create(Integer.parseInt(raw), IntegerType)) + .orElse(Try(Literal.create(JLong.parseLong(raw), LongType))) // Then falls back to fractional types - .orElse(Try(Literal(JFloat.parseFloat(raw), FloatType))) - .orElse(Try(Literal(JDouble.parseDouble(raw), DoubleType))) - .orElse(Try(Literal(new JBigDecimal(raw), DecimalType.Unlimited))) + .orElse(Try(Literal.create(JFloat.parseFloat(raw), FloatType))) + .orElse(Try(Literal.create(JDouble.parseDouble(raw), DoubleType))) + .orElse(Try(Literal.create(new JBigDecimal(raw), DecimalType.Unlimited))) // Then falls back to string .getOrElse { - if (raw == defaultPartitionName) Literal(null, NullType) else Literal(raw, StringType) + if (raw == defaultPartitionName) Literal.create(null, NullType) + else Literal.create(raw, StringType) } } @@ -748,7 +1011,7 @@ object ParquetRelation2 { } literals.map { case l @ Literal(_, dataType) => - Literal(Cast(l, desiredType).eval(), desiredType) + Literal.create(Cast(l, desiredType).eval(), desiredType) } } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/timestamp/NanoTime.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/timestamp/NanoTime.scala index 887161684429f..70bcca7526aae 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/timestamp/NanoTime.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/timestamp/NanoTime.scala @@ -26,7 +26,7 @@ private[parquet] class NanoTime extends Serializable { private var julianDay = 0 private var timeOfDayNanos = 0L - def set(julianDay: Int, timeOfDayNanos: Long) = { + def set(julianDay: Int, timeOfDayNanos: Long): this.type = { this.julianDay = julianDay this.timeOfDayNanos = timeOfDayNanos this @@ -45,15 +45,15 @@ private[parquet] class NanoTime extends Serializable { Binary.fromByteBuffer(buf) } - def writeValue(recordConsumer: RecordConsumer) { + def writeValue(recordConsumer: RecordConsumer): Unit = { recordConsumer.addBinary(toBinary) } - override def toString = + override def toString: String = "NanoTime{julianDay=" + julianDay + ", timeOfDayNanos=" + timeOfDayNanos + "}" } -object NanoTime { +private[sql] object NanoTime { def fromBinary(bytes: Binary): NanoTime = { Preconditions.checkArgument(bytes.length() == 12, "Must be 12 bytes") val buf = bytes.toByteBuffer diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/DataSourceStrategy.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/DataSourceStrategy.scala index 624369afe87b5..b3d71f687a60a 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/sources/DataSourceStrategy.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/DataSourceStrategy.scala @@ -19,11 +19,13 @@ package org.apache.spark.sql.sources import org.apache.spark.rdd.RDD import org.apache.spark.sql.catalyst.expressions -import org.apache.spark.sql.catalyst.expressions.{And, Attribute, AttributeReference, AttributeSet, Expression, NamedExpression} +import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.planning.PhysicalOperation import org.apache.spark.sql.catalyst.plans.logical import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan -import org.apache.spark.sql.{Row, Strategy, execution} +import org.apache.spark.sql.execution.SparkPlan +import org.apache.spark.sql.types.{UTF8String, StringType} +import org.apache.spark.sql.{Row, Strategy, execution, sources} /** * A Strategy for planning scans over data sources defined using the sources API. @@ -52,13 +54,10 @@ private[sql] object DataSourceStrategy extends Strategy { (a, _) => t.buildScan(a)) :: Nil case l @ LogicalRelation(t: TableScan) => - execution.PhysicalRDD(l.output, t.buildScan()) :: Nil + createPhysicalRDD(l.relation, l.output, t.buildScan()) :: Nil case i @ logical.InsertIntoTable( - l @ LogicalRelation(t: InsertableRelation), partition, query, overwrite) => - if (partition.nonEmpty) { - sys.error(s"Insert into a partition is not allowed because $l is not partitioned.") - } + l @ LogicalRelation(t: InsertableRelation), part, query, overwrite, false) if part.isEmpty => execution.ExecutedCommand(InsertIntoDataSource(l, query, overwrite)) :: Nil case _ => Nil @@ -88,7 +87,7 @@ private[sql] object DataSourceStrategy extends Strategy { val projectSet = AttributeSet(projectList.flatMap(_.references)) val filterSet = AttributeSet(filterPredicates.flatMap(_.references)) - val filterCondition = filterPredicates.reduceLeftOption(And) + val filterCondition = filterPredicates.reduceLeftOption(expressions.And) val pushedFilters = filterPredicates.map { _ transform { case a: AttributeReference => relation.attributeMap(a) // Match original case of attributes. @@ -104,41 +103,93 @@ private[sql] object DataSourceStrategy extends Strategy { projectList.asInstanceOf[Seq[Attribute]] // Safe due to if above. .map(relation.attributeMap) // Match original case of attributes. - val scan = - execution.PhysicalRDD( - projectList.map(_.toAttribute), + val scan = createPhysicalRDD(relation.relation, projectList.map(_.toAttribute), scanBuilder(requestedColumns, pushedFilters)) filterCondition.map(execution.Filter(_, scan)).getOrElse(scan) } else { val requestedColumns = (projectSet ++ filterSet).map(relation.attributeMap).toSeq - val scan = - execution.PhysicalRDD(requestedColumns, scanBuilder(requestedColumns, pushedFilters)) + val scan = createPhysicalRDD(relation.relation, requestedColumns, + scanBuilder(requestedColumns, pushedFilters)) execution.Project(projectList, filterCondition.map(execution.Filter(_, scan)).getOrElse(scan)) } } - /** Turn Catalyst [[Expression]]s into data source [[Filter]]s. */ - protected[sql] def selectFilters(filters: Seq[Expression]): Seq[Filter] = filters.collect { - case expressions.EqualTo(a: Attribute, expressions.Literal(v, _)) => EqualTo(a.name, v) - case expressions.EqualTo(expressions.Literal(v, _), a: Attribute) => EqualTo(a.name, v) - - case expressions.GreaterThan(a: Attribute, expressions.Literal(v, _)) => GreaterThan(a.name, v) - case expressions.GreaterThan(expressions.Literal(v, _), a: Attribute) => LessThan(a.name, v) - - case expressions.LessThan(a: Attribute, expressions.Literal(v, _)) => LessThan(a.name, v) - case expressions.LessThan(expressions.Literal(v, _), a: Attribute) => GreaterThan(a.name, v) - - case expressions.GreaterThanOrEqual(a: Attribute, expressions.Literal(v, _)) => - GreaterThanOrEqual(a.name, v) - case expressions.GreaterThanOrEqual(expressions.Literal(v, _), a: Attribute) => - LessThanOrEqual(a.name, v) + private[this] def createPhysicalRDD( + relation: BaseRelation, + output: Seq[Attribute], + rdd: RDD[Row]): SparkPlan = { + val converted = if (relation.needConversion) { + execution.RDDConversions.rowToRowRdd(rdd, relation.schema) + } else { + rdd + } + execution.PhysicalRDD(output, converted) + } - case expressions.LessThanOrEqual(a: Attribute, expressions.Literal(v, _)) => - LessThanOrEqual(a.name, v) - case expressions.LessThanOrEqual(expressions.Literal(v, _), a: Attribute) => - GreaterThanOrEqual(a.name, v) + /** + * Selects Catalyst predicate [[Expression]]s which are convertible into data source [[Filter]]s, + * and convert them. + */ + protected[sql] def selectFilters(filters: Seq[Expression]) = { + def translate(predicate: Expression): Option[Filter] = predicate match { + case expressions.EqualTo(a: Attribute, Literal(v, _)) => + Some(sources.EqualTo(a.name, v)) + case expressions.EqualTo(Literal(v, _), a: Attribute) => + Some(sources.EqualTo(a.name, v)) + + case expressions.GreaterThan(a: Attribute, Literal(v, _)) => + Some(sources.GreaterThan(a.name, v)) + case expressions.GreaterThan(Literal(v, _), a: Attribute) => + Some(sources.LessThan(a.name, v)) + + case expressions.LessThan(a: Attribute, Literal(v, _)) => + Some(sources.LessThan(a.name, v)) + case expressions.LessThan(Literal(v, _), a: Attribute) => + Some(sources.GreaterThan(a.name, v)) + + case expressions.GreaterThanOrEqual(a: Attribute, Literal(v, _)) => + Some(sources.GreaterThanOrEqual(a.name, v)) + case expressions.GreaterThanOrEqual(Literal(v, _), a: Attribute) => + Some(sources.LessThanOrEqual(a.name, v)) + + case expressions.LessThanOrEqual(a: Attribute, Literal(v, _)) => + Some(sources.LessThanOrEqual(a.name, v)) + case expressions.LessThanOrEqual(Literal(v, _), a: Attribute) => + Some(sources.GreaterThanOrEqual(a.name, v)) + + case expressions.InSet(a: Attribute, set) => + Some(sources.In(a.name, set.toArray)) + + case expressions.IsNull(a: Attribute) => + Some(sources.IsNull(a.name)) + case expressions.IsNotNull(a: Attribute) => + Some(sources.IsNotNull(a.name)) + + case expressions.And(left, right) => + (translate(left) ++ translate(right)).reduceOption(sources.And) + + case expressions.Or(left, right) => + for { + leftFilter <- translate(left) + rightFilter <- translate(right) + } yield sources.Or(leftFilter, rightFilter) + + case expressions.Not(child) => + translate(child).map(sources.Not) + + case expressions.StartsWith(a: Attribute, Literal(v: UTF8String, StringType)) => + Some(sources.StringStartsWith(a.name, v.toString)) + + case expressions.EndsWith(a: Attribute, Literal(v: UTF8String, StringType)) => + Some(sources.StringEndsWith(a.name, v.toString)) + + case expressions.Contains(a: Attribute, Literal(v: UTF8String, StringType)) => + Some(sources.StringContains(a.name, v.toString)) + + case _ => None + } - case expressions.InSet(a: Attribute, set) => In(a.name, set.toArray) + filters.flatMap(translate) } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/LogicalRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/LogicalRelation.scala index 12b59ba20bb10..f374abffdd505 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/sources/LogicalRelation.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/LogicalRelation.scala @@ -30,24 +30,28 @@ private[sql] case class LogicalRelation(relation: BaseRelation) override val output: Seq[AttributeReference] = relation.schema.toAttributes // Logical Relations are distinct if they have different output for the sake of transformations. - override def equals(other: Any) = other match { + override def equals(other: Any): Boolean = other match { case l @ LogicalRelation(otherRelation) => relation == otherRelation && output == l.output case _ => false } - override def sameResult(otherPlan: LogicalPlan) = otherPlan match { + override def hashCode: Int = { + com.google.common.base.Objects.hashCode(relation, output) + } + + override def sameResult(otherPlan: LogicalPlan): Boolean = otherPlan match { case LogicalRelation(otherRelation) => relation == otherRelation case _ => false } - @transient override lazy val statistics = Statistics( + @transient override lazy val statistics: Statistics = Statistics( sizeInBytes = BigInt(relation.sizeInBytes) ) /** Used to lookup original attribute capitalization */ - val attributeMap = AttributeMap(output.map(o => (o, o))) + val attributeMap: AttributeMap[AttributeReference] = AttributeMap(output.map(o => (o, o))) - def newInstance() = LogicalRelation(relation).asInstanceOf[this.type] + def newInstance(): this.type = LogicalRelation(relation).asInstanceOf[this.type] - override def simpleString = s"Relation[${output.mkString(",")}] $relation" + override def simpleString: String = s"Relation[${output.mkString(",")}] $relation" } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala index c9cd0e6e93829..dbdb0d39c26a1 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala @@ -19,7 +19,7 @@ package org.apache.spark.sql.sources import org.apache.spark.sql.{DataFrame, SQLContext} import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan -import org.apache.spark.sql.execution.{LogicalRDD, RunnableCommand} +import org.apache.spark.sql.execution.RunnableCommand private[sql] case class InsertIntoDataSource( logicalRelation: LogicalRelation, @@ -27,9 +27,13 @@ private[sql] case class InsertIntoDataSource( overwrite: Boolean) extends RunnableCommand { - override def run(sqlContext: SQLContext) = { + override def run(sqlContext: SQLContext): Seq[Row] = { val relation = logicalRelation.relation.asInstanceOf[InsertableRelation] - relation.insert(DataFrame(sqlContext, query), overwrite) + val data = DataFrame(sqlContext, query) + // Apply the schema of the existing table to the new data. + val df = sqlContext.createDataFrame( + data.queryExecution.toRdd, logicalRelation.schema, needsConversion = false) + relation.insert(df, overwrite) // Invalidate the cache. sqlContext.cacheManager.invalidateCache(logicalRelation) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala index a692ef51b31ed..2e861b84b7133 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala @@ -17,14 +17,16 @@ package org.apache.spark.sql.sources +import scala.language.existentials +import scala.util.matching.Regex import scala.language.implicitConversions import org.apache.spark.Logging -import org.apache.spark.sql.{DataFrame, SQLContext} +import org.apache.spark.sql.{AnalysisException, SaveMode, DataFrame, SQLContext} import org.apache.spark.sql.catalyst.plans.logical._ import org.apache.spark.sql.catalyst.AbstractSparkSQLParser import org.apache.spark.sql.catalyst.analysis.UnresolvedRelation -import org.apache.spark.sql.catalyst.expressions.AttributeReference +import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference, Row} import org.apache.spark.sql.execution.RunnableCommand import org.apache.spark.sql.types._ import org.apache.spark.util.Utils @@ -32,7 +34,9 @@ import org.apache.spark.util.Utils /** * A parser for foreign DDL commands. */ -private[sql] class DDLParser extends AbstractSparkSQLParser with Logging { +private[sql] class DDLParser( + parseQuery: String => LogicalPlan) + extends AbstractSparkSQLParser with DataTypeParser with Logging { def apply(input: String, exceptionOnError: Boolean): Option[LogicalPlan] = { try { @@ -44,14 +48,6 @@ private[sql] class DDLParser extends AbstractSparkSQLParser with Logging { } } - def parseType(input: String): DataType = { - lexical.initialize(reservedWords) - phrase(dataType)(new lexical.Scanner(input)) match { - case Success(r, x) => r - case x => throw new DDLException(s"Unsupported dataType: $x") - } - } - // Keyword is a convention with AbstractSparkSQLParser, which will scan all of the `Keyword` // properties via reflection the class in runtime for constructing the SqlLexical object protected val CREATE = Keyword("CREATE") @@ -66,26 +62,9 @@ private[sql] class DDLParser extends AbstractSparkSQLParser with Logging { protected val EXTENDED = Keyword("EXTENDED") protected val AS = Keyword("AS") protected val COMMENT = Keyword("COMMENT") + protected val REFRESH = Keyword("REFRESH") - // Data types. - protected val STRING = Keyword("STRING") - protected val BINARY = Keyword("BINARY") - protected val BOOLEAN = Keyword("BOOLEAN") - protected val TINYINT = Keyword("TINYINT") - protected val SMALLINT = Keyword("SMALLINT") - protected val INT = Keyword("INT") - protected val BIGINT = Keyword("BIGINT") - protected val FLOAT = Keyword("FLOAT") - protected val DOUBLE = Keyword("DOUBLE") - protected val DECIMAL = Keyword("DECIMAL") - protected val DATE = Keyword("DATE") - protected val TIMESTAMP = Keyword("TIMESTAMP") - protected val VARCHAR = Keyword("VARCHAR") - protected val ARRAY = Keyword("ARRAY") - protected val MAP = Keyword("MAP") - protected val STRUCT = Keyword("STRUCT") - - protected lazy val ddl: Parser[LogicalPlan] = createTable | describeTable + protected lazy val ddl: Parser[LogicalPlan] = createTable | describeTable | refreshTable protected def start: Parser[LogicalPlan] = ddl @@ -104,26 +83,37 @@ private[sql] class DDLParser extends AbstractSparkSQLParser with Logging { * AS SELECT ... */ protected lazy val createTable: Parser[LogicalPlan] = - ( - (CREATE ~> TEMPORARY.? <~ TABLE) ~ (IF ~> NOT <~ EXISTS).? ~ ident - ~ (tableCols).? ~ (USING ~> className) ~ (OPTIONS ~> options) ~ (AS ~> restInput).? ^^ { + // TODO: Support database.table. + (CREATE ~> TEMPORARY.? <~ TABLE) ~ (IF ~> NOT <~ EXISTS).? ~ ident ~ + tableCols.? ~ (USING ~> className) ~ (OPTIONS ~> options).? ~ (AS ~> restInput).? ^^ { case temp ~ allowExisting ~ tableName ~ columns ~ provider ~ opts ~ query => if (temp.isDefined && allowExisting.isDefined) { throw new DDLException( "a CREATE TEMPORARY TABLE statement does not allow IF NOT EXISTS clause.") } + val options = opts.getOrElse(Map.empty[String, String]) if (query.isDefined) { if (columns.isDefined) { throw new DDLException( "a CREATE TABLE AS SELECT statement does not allow column definitions.") } + // When IF NOT EXISTS clause appears in the query, the save mode will be ignore. + val mode = if (allowExisting.isDefined) { + SaveMode.Ignore + } else if (temp.isDefined) { + SaveMode.Overwrite + } else { + SaveMode.ErrorIfExists + } + + val queryPlan = parseQuery(query.get) CreateTableUsingAsSelect(tableName, provider, temp.isDefined, - opts, - allowExisting.isDefined, - query.get) + mode, + options, + queryPlan) } else { val userSpecifiedSchema = columns.flatMap(fields => Some(StructType(fields))) CreateTableUsing( @@ -131,17 +121,17 @@ private[sql] class DDLParser extends AbstractSparkSQLParser with Logging { userSpecifiedSchema, provider, temp.isDefined, - opts, - allowExisting.isDefined) + options, + allowExisting.isDefined, + managedIfNoPath = false) } - } - ) + } protected lazy val tableCols: Parser[Seq[StructField]] = "(" ~> repsep(column, ",") <~ ")" /* * describe [extended] table avroTable - * This will display all columns of table `avroTable` includes column_name,column_type,nullable + * This will display all columns of table `avroTable` includes column_name,column_type,comment */ protected lazy val describeTable: Parser[LogicalPlan] = (DESCRIBE ~> opt(EXTENDED)) ~ (ident <~ ".").? ~ ident ^^ { @@ -155,132 +145,116 @@ private[sql] class DDLParser extends AbstractSparkSQLParser with Logging { DescribeCommand(UnresolvedRelation(tblIdentifier, None), e.isDefined) } + protected lazy val refreshTable: Parser[LogicalPlan] = + REFRESH ~> TABLE ~> (ident <~ ".").? ~ ident ^^ { + case maybeDatabaseName ~ tableName => + RefreshTable(maybeDatabaseName.getOrElse("default"), tableName) + } + protected lazy val options: Parser[Map[String, String]] = "(" ~> repsep(pair, ",") <~ ")" ^^ { case s: Seq[(String, String)] => s.toMap } protected lazy val className: Parser[String] = repsep(ident, ".") ^^ { case s => s.mkString(".")} - protected lazy val pair: Parser[(String, String)] = ident ~ stringLit ^^ { case k ~ v => (k,v) } + override implicit def regexToParser(regex: Regex): Parser[String] = acceptMatch( + s"identifier matching regex ${regex}", { + case lexical.Identifier(str) if regex.unapplySeq(str).isDefined => str + case lexical.Keyword(str) if regex.unapplySeq(str).isDefined => str + } + ) + + protected lazy val optionName: Parser[String] = "[_a-zA-Z][a-zA-Z0-9]*".r ^^ { + case name => name + } + + protected lazy val pair: Parser[(String, String)] = + optionName ~ stringLit ^^ { case k ~ v => (k,v) } protected lazy val column: Parser[StructField] = ident ~ dataType ~ (COMMENT ~> stringLit).? ^^ { case columnName ~ typ ~ cm => val meta = cm match { case Some(comment) => - new MetadataBuilder().putString(COMMENT.str.toLowerCase(), comment).build() + new MetadataBuilder().putString(COMMENT.str.toLowerCase, comment).build() case None => Metadata.empty } - StructField(columnName, typ, true, meta) - } - protected lazy val primitiveType: Parser[DataType] = - STRING ^^^ StringType | - BINARY ^^^ BinaryType | - BOOLEAN ^^^ BooleanType | - TINYINT ^^^ ByteType | - SMALLINT ^^^ ShortType | - INT ^^^ IntegerType | - BIGINT ^^^ LongType | - FLOAT ^^^ FloatType | - DOUBLE ^^^ DoubleType | - fixedDecimalType | // decimal with precision/scale - DECIMAL ^^^ DecimalType.Unlimited | // decimal with no precision/scale - DATE ^^^ DateType | - TIMESTAMP ^^^ TimestampType | - VARCHAR ~ "(" ~ numericLit ~ ")" ^^^ StringType - - protected lazy val fixedDecimalType: Parser[DataType] = - (DECIMAL ~ "(" ~> numericLit) ~ ("," ~> numericLit <~ ")") ^^ { - case precision ~ scale => DecimalType(precision.toInt, scale.toInt) + StructField(columnName, typ, nullable = true, meta) } +} - protected lazy val arrayType: Parser[DataType] = - ARRAY ~> "<" ~> dataType <~ ">" ^^ { - case tpe => ArrayType(tpe) - } +private[sql] object ResolvedDataSource { - protected lazy val mapType: Parser[DataType] = - MAP ~> "<" ~> dataType ~ "," ~ dataType <~ ">" ^^ { - case t1 ~ _ ~ t2 => MapType(t1, t2) - } + private val builtinSources = Map( + "jdbc" -> classOf[org.apache.spark.sql.jdbc.DefaultSource], + "json" -> classOf[org.apache.spark.sql.json.DefaultSource], + "parquet" -> classOf[org.apache.spark.sql.parquet.DefaultSource] + ) - protected lazy val structField: Parser[StructField] = - ident ~ ":" ~ dataType ^^ { - case fieldName ~ _ ~ tpe => StructField(fieldName, tpe, nullable = true) + /** Given a provider name, look up the data source class definition. */ + def lookupDataSource(provider: String): Class[_] = { + if (builtinSources.contains(provider)) { + return builtinSources(provider) } - protected lazy val structType: Parser[DataType] = - (STRUCT ~> "<" ~> repsep(structField, ",") <~ ">" ^^ { - case fields => StructType(fields) - }) | - (STRUCT ~> "<>" ^^ { - case fields => StructType(Nil) - }) - - private[sql] lazy val dataType: Parser[DataType] = - arrayType | - mapType | - structType | - primitiveType -} - -object ResolvedDataSource { - def apply( - sqlContext: SQLContext, - userSpecifiedSchema: Option[StructType], - provider: String, - options: Map[String, String]): ResolvedDataSource = { val loader = Utils.getContextOrSparkClassLoader - val clazz: Class[_] = try loader.loadClass(provider) catch { + try { + loader.loadClass(provider) + } catch { case cnf: java.lang.ClassNotFoundException => - try loader.loadClass(provider + ".DefaultSource") catch { + try { + loader.loadClass(provider + ".DefaultSource") + } catch { case cnf: java.lang.ClassNotFoundException => sys.error(s"Failed to load class for data source: $provider") } } + } + /** Create a [[ResolvedDataSource]] for reading data in. */ + def apply( + sqlContext: SQLContext, + userSpecifiedSchema: Option[StructType], + provider: String, + options: Map[String, String]): ResolvedDataSource = { + val clazz: Class[_] = lookupDataSource(provider) + def className: String = clazz.getCanonicalName val relation = userSpecifiedSchema match { - case Some(schema: StructType) => { - clazz.newInstance match { - case dataSource: SchemaRelationProvider => - dataSource.createRelation(sqlContext, new CaseInsensitiveMap(options), schema) - case dataSource: org.apache.spark.sql.sources.RelationProvider => - sys.error(s"${clazz.getCanonicalName} does not allow user-specified schemas.") - } + case Some(schema: StructType) => clazz.newInstance() match { + case dataSource: SchemaRelationProvider => + dataSource.createRelation(sqlContext, new CaseInsensitiveMap(options), schema) + case dataSource: org.apache.spark.sql.sources.RelationProvider => + throw new AnalysisException(s"$className does not allow user-specified schemas.") + case _ => + throw new AnalysisException(s"$className is not a RelationProvider.") } - case None => { - clazz.newInstance match { - case dataSource: RelationProvider => - dataSource.createRelation(sqlContext, new CaseInsensitiveMap(options)) - case dataSource: org.apache.spark.sql.sources.SchemaRelationProvider => - sys.error(s"A schema needs to be specified when using ${clazz.getCanonicalName}.") - } + + case None => clazz.newInstance() match { + case dataSource: RelationProvider => + dataSource.createRelation(sqlContext, new CaseInsensitiveMap(options)) + case dataSource: org.apache.spark.sql.sources.SchemaRelationProvider => + throw new AnalysisException( + s"A schema needs to be specified when using $className.") + case _ => + throw new AnalysisException(s"$className is not a RelationProvider.") } } - new ResolvedDataSource(clazz, relation) } + /** Create a [[ResolvedDataSource]] for saving the content of the given [[DataFrame]]. */ def apply( sqlContext: SQLContext, provider: String, + mode: SaveMode, options: Map[String, String], data: DataFrame): ResolvedDataSource = { - val loader = Utils.getContextOrSparkClassLoader - val clazz: Class[_] = try loader.loadClass(provider) catch { - case cnf: java.lang.ClassNotFoundException => - try loader.loadClass(provider + ".DefaultSource") catch { - case cnf: java.lang.ClassNotFoundException => - sys.error(s"Failed to load class for data source: $provider") - } - } - - val relation = clazz.newInstance match { + val clazz: Class[_] = lookupDataSource(provider) + val relation = clazz.newInstance() match { case dataSource: CreatableRelationProvider => - dataSource.createRelation(sqlContext, options, data) + dataSource.createRelation(sqlContext, mode, options, data) case _ => sys.error(s"${clazz.getCanonicalName} does not allow create table as select.") } - new ResolvedDataSource(clazz, relation) } } @@ -298,72 +272,90 @@ private[sql] case class DescribeCommand( isExtended: Boolean) extends Command { override val output = Seq( // Column names are based on Hive. - AttributeReference("col_name", StringType, nullable = false, + AttributeReference("col_name", StringType, nullable = false, new MetadataBuilder().putString("comment", "name of the column").build())(), - AttributeReference("data_type", StringType, nullable = false, + AttributeReference("data_type", StringType, nullable = false, new MetadataBuilder().putString("comment", "data type of the column").build())(), - AttributeReference("comment", StringType, nullable = false, + AttributeReference("comment", StringType, nullable = false, new MetadataBuilder().putString("comment", "comment of the column").build())()) } +/** + * Used to represent the operation of create table using a data source. + * @param allowExisting If it is true, we will do nothing when the table already exists. + * If it is false, an exception will be thrown + */ private[sql] case class CreateTableUsing( tableName: String, userSpecifiedSchema: Option[StructType], provider: String, temporary: Boolean, options: Map[String, String], - allowExisting: Boolean) extends Command - -private[sql] case class CreateTableUsingAsSelect( - tableName: String, - provider: String, - temporary: Boolean, - options: Map[String, String], allowExisting: Boolean, - query: String) extends Command + managedIfNoPath: Boolean) extends Command -private[sql] case class CreateTableUsingAsLogicalPlan( +/** + * A node used to support CTAS statements and saveAsTable for the data source API. + * This node is a [[UnaryNode]] instead of a [[Command]] because we want the analyzer + * can analyze the logical plan that will be used to populate the table. + * So, [[PreWriteCheck]] can detect cases that are not allowed. + */ +private[sql] case class CreateTableUsingAsSelect( tableName: String, provider: String, temporary: Boolean, + mode: SaveMode, options: Map[String, String], - allowExisting: Boolean, - query: LogicalPlan) extends Command + child: LogicalPlan) extends UnaryNode { + override def output: Seq[Attribute] = Seq.empty[Attribute] + // TODO: Override resolved after we support databaseName. + // override lazy val resolved = databaseName != None && childrenResolved +} -private [sql] case class CreateTempTableUsing( +private[sql] case class CreateTempTableUsing( tableName: String, userSpecifiedSchema: Option[StructType], provider: String, options: Map[String, String]) extends RunnableCommand { - def run(sqlContext: SQLContext) = { + def run(sqlContext: SQLContext): Seq[Row] = { val resolved = ResolvedDataSource(sqlContext, userSpecifiedSchema, provider, options) - sqlContext.registerRDDAsTable( + sqlContext.registerDataFrameAsTable( DataFrame(sqlContext, LogicalRelation(resolved.relation)), tableName) Seq.empty } } -private [sql] case class CreateTempTableUsingAsSelect( +private[sql] case class CreateTempTableUsingAsSelect( tableName: String, provider: String, + mode: SaveMode, options: Map[String, String], query: LogicalPlan) extends RunnableCommand { - def run(sqlContext: SQLContext) = { + def run(sqlContext: SQLContext): Seq[Row] = { val df = DataFrame(sqlContext, query) - val resolved = ResolvedDataSource(sqlContext, provider, options, df) - sqlContext.registerRDDAsTable( + val resolved = ResolvedDataSource(sqlContext, provider, mode, options, df) + sqlContext.registerDataFrameAsTable( DataFrame(sqlContext, LogicalRelation(resolved.relation)), tableName) Seq.empty } } +private[sql] case class RefreshTable(databaseName: String, tableName: String) + extends RunnableCommand { + + override def run(sqlContext: SQLContext): Seq[Row] = { + sqlContext.catalog.refreshTable(databaseName, tableName) + Seq.empty[Row] + } +} + /** * Builds a map in which keys are case insensitive */ -protected class CaseInsensitiveMap(map: Map[String, String]) extends Map[String, String] +protected[sql] class CaseInsensitiveMap(map: Map[String, String]) extends Map[String, String] with Serializable { val baseMap = map.map(kv => kv.copy(_1 = kv._1.toLowerCase)) @@ -375,11 +367,10 @@ protected class CaseInsensitiveMap(map: Map[String, String]) extends Map[String, override def iterator: Iterator[(String, String)] = baseMap.iterator - override def -(key: String): Map[String, String] = baseMap - key.toLowerCase() + override def -(key: String): Map[String, String] = baseMap - key.toLowerCase } /** * The exception thrown from the DDL parser. - * @param message */ protected[sql] class DDLException(message: String) extends Exception(message) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/filters.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/filters.scala index 4a9fefc12b9ad..791046e0079d6 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/sources/filters.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/filters.scala @@ -17,11 +17,85 @@ package org.apache.spark.sql.sources +/** + * A filter predicate for data sources. + */ abstract class Filter +/** + * A filter that evaluates to `true` iff the attribute evaluates to a value + * equal to `value`. + */ case class EqualTo(attribute: String, value: Any) extends Filter + +/** + * A filter that evaluates to `true` iff the attribute evaluates to a value + * greater than `value`. + */ case class GreaterThan(attribute: String, value: Any) extends Filter + +/** + * A filter that evaluates to `true` iff the attribute evaluates to a value + * greater than or equal to `value`. + */ case class GreaterThanOrEqual(attribute: String, value: Any) extends Filter + +/** + * A filter that evaluates to `true` iff the attribute evaluates to a value + * less than `value`. + */ case class LessThan(attribute: String, value: Any) extends Filter + +/** + * A filter that evaluates to `true` iff the attribute evaluates to a value + * less than or equal to `value`. + */ case class LessThanOrEqual(attribute: String, value: Any) extends Filter + +/** + * A filter that evaluates to `true` iff the attribute evaluates to one of the values in the array. + */ case class In(attribute: String, values: Array[Any]) extends Filter + +/** + * A filter that evaluates to `true` iff the attribute evaluates to null. + */ +case class IsNull(attribute: String) extends Filter + +/** + * A filter that evaluates to `true` iff the attribute evaluates to a non-null value. + */ +case class IsNotNull(attribute: String) extends Filter + +/** + * A filter that evaluates to `true` iff both `left` or `right` evaluate to `true`. + */ +case class And(left: Filter, right: Filter) extends Filter + +/** + * A filter that evaluates to `true` iff at least one of `left` or `right` evaluates to `true`. + */ +case class Or(left: Filter, right: Filter) extends Filter + +/** + * A filter that evaluates to `true` iff `child` is evaluated to `false`. + */ +case class Not(child: Filter) extends Filter + +/** + * A filter that evaluates to `true` iff the attribute evaluates to + * a string that starts with `value`. + */ +case class StringStartsWith(attribute: String, value: String) extends Filter + +/** + * A filter that evaluates to `true` iff the attribute evaluates to + * a string that starts with `value`. + */ +case class StringEndsWith(attribute: String, value: String) extends Filter + +/** + * A filter that evaluates to `true` iff the attribute evaluates to + * a string that contains the string `value`. + */ +case class StringContains(attribute: String, value: String) extends Filter diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala index a640ba57e0885..ca53dcdb92c52 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala @@ -14,11 +14,12 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + package org.apache.spark.sql.sources import org.apache.spark.annotation.{Experimental, DeveloperApi} import org.apache.spark.rdd.RDD -import org.apache.spark.sql.{DataFrame, Row, SQLContext} +import org.apache.spark.sql.{SaveMode, DataFrame, Row, SQLContext} import org.apache.spark.sql.catalyst.expressions.{Expression, Attribute} import org.apache.spark.sql.types.StructType @@ -79,21 +80,34 @@ trait SchemaRelationProvider { @DeveloperApi trait CreatableRelationProvider { + /** + * Creates a relation with the given parameters based on the contents of the given + * DataFrame. The mode specifies the expected behavior of createRelation when + * data already exists. + * Right now, there are three modes, Append, Overwrite, and ErrorIfExists. + * Append mode means that when saving a DataFrame to a data source, if data already exists, + * contents of the DataFrame are expected to be appended to existing data. + * Overwrite mode means that when saving a DataFrame to a data source, if data already exists, + * existing data is expected to be overwritten by the contents of the DataFrame. + * ErrorIfExists mode means that when saving a DataFrame to a data source, + * if data already exists, an exception is expected to be thrown. + */ def createRelation( sqlContext: SQLContext, + mode: SaveMode, parameters: Map[String, String], data: DataFrame): BaseRelation } /** * ::DeveloperApi:: - * Represents a collection of tuples with a known schema. Classes that extend BaseRelation must - * be able to produce the schema of their data in the form of a [[StructType]] Concrete + * Represents a collection of tuples with a known schema. Classes that extend BaseRelation must + * be able to produce the schema of their data in the form of a [[StructType]]. Concrete * implementation should inherit from one of the descendant `Scan` classes, which define various * abstract methods for execution. * * BaseRelations must also define a equality function that only returns true when the two - * instances will return the same data. This equality function is used when determining when + * instances will return the same data. This equality function is used when determining when * it is safe to substitute cached results for a given relation. */ @DeveloperApi @@ -102,13 +116,26 @@ abstract class BaseRelation { def schema: StructType /** - * Returns an estimated size of this relation in bytes. This information is used by the planner + * Returns an estimated size of this relation in bytes. This information is used by the planner * to decided when it is safe to broadcast a relation and can be overridden by sources that * know the size ahead of time. By default, the system will assume that tables are too - * large to broadcast. This method will be called multiple times during query planning + * large to broadcast. This method will be called multiple times during query planning * and thus should not perform expensive operations for each invocation. + * + * Note that it is always better to overestimate size than underestimate, because underestimation + * could lead to execution plans that are suboptimal (i.e. broadcasting a very large table). + */ + def sizeInBytes: Long = sqlContext.conf.defaultSizeInBytes + + /** + * Whether does it need to convert the objects in Row to internal representation, for example: + * java.lang.String -> UTF8String + * java.lang.Decimal -> Decimal + * + * Note: The internal representation is not stable across releases and thus data sources outside + * of Spark SQL should leave this as true. */ - def sizeInBytes = sqlContext.conf.defaultSizeInBytes + def needConversion: Boolean = true } /** @@ -116,7 +143,7 @@ abstract class BaseRelation { * A BaseRelation that can produce all of its tuples as an RDD of Row objects. */ @DeveloperApi -trait TableScan extends BaseRelation { +trait TableScan { def buildScan(): RDD[Row] } @@ -126,7 +153,7 @@ trait TableScan extends BaseRelation { * containing all of its tuples as Row objects. */ @DeveloperApi -trait PrunedScan extends BaseRelation { +trait PrunedScan { def buildScan(requiredColumns: Array[String]): RDD[Row] } @@ -135,29 +162,18 @@ trait PrunedScan extends BaseRelation { * A BaseRelation that can eliminate unneeded columns and filter using selected * predicates before producing an RDD containing all matching tuples as Row objects. * + * The actual filter should be the conjunction of all `filters`, + * i.e. they should be "and" together. + * * The pushed down filters are currently purely an optimization as they will all be evaluated * again. This means it is safe to use them with methods that produce false positives such * as filtering partitions based on a bloom filter. */ @DeveloperApi -trait PrunedFilteredScan extends BaseRelation { +trait PrunedFilteredScan { def buildScan(requiredColumns: Array[String], filters: Array[Filter]): RDD[Row] } -/** - * ::Experimental:: - * An interface for experimenting with a more direct connection to the query planner. Compared to - * [[PrunedFilteredScan]], this operator receives the raw expressions from the - * [[org.apache.spark.sql.catalyst.plans.logical.LogicalPlan]]. Unlike the other APIs this - * interface is not designed to be binary compatible across releases and thus should only be used - * for experimentation. - */ -@Experimental -trait CatalystScan extends BaseRelation { - def buildScan(requiredColumns: Seq[Attribute], filters: Seq[Expression]): RDD[Row] -} - -@DeveloperApi /** * ::DeveloperApi:: * A BaseRelation that can be used to insert data into it through the insert method. @@ -174,6 +190,20 @@ trait CatalystScan extends BaseRelation { * If a data source needs to check the actual nullability of a field, it needs to do it in the * insert method. */ -trait InsertableRelation extends BaseRelation { +@DeveloperApi +trait InsertableRelation { def insert(data: DataFrame, overwrite: Boolean): Unit } + +/** + * ::Experimental:: + * An interface for experimenting with a more direct connection to the query planner. Compared to + * [[PrunedFilteredScan]], this operator receives the raw expressions from the + * [[org.apache.spark.sql.catalyst.plans.logical.LogicalPlan]]. Unlike the other APIs this + * interface is NOT designed to be binary compatible across releases and thus should only be used + * for experimentation. + */ +@Experimental +trait CatalystScan { + def buildScan(requiredColumns: Seq[Attribute], filters: Seq[Expression]): RDD[Row] +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/rules.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/rules.scala index 4ed22d363da5b..6ed68d179edc9 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/sources/rules.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/rules.scala @@ -17,7 +17,10 @@ package org.apache.spark.sql.sources +import org.apache.spark.sql.{SaveMode, AnalysisException} +import org.apache.spark.sql.catalyst.analysis.{EliminateSubQueries, Catalog} import org.apache.spark.sql.catalyst.expressions.{Attribute, Cast, Alias} +import org.apache.spark.sql.catalyst.plans.logical import org.apache.spark.sql.catalyst.plans.logical.{InsertIntoTable, LogicalPlan, Project} import org.apache.spark.sql.catalyst.rules.Rule import org.apache.spark.sql.types.DataType @@ -26,17 +29,15 @@ import org.apache.spark.sql.types.DataType * A rule to do pre-insert data type casting and field renaming. Before we insert into * an [[InsertableRelation]], we will use this rule to make sure that * the columns to be inserted have the correct data type and fields have the correct names. - * @param resolver The resolver used by the Analyzer. */ private[sql] object PreInsertCastAndRename extends Rule[LogicalPlan] { - def apply(plan: LogicalPlan): LogicalPlan = { - plan.transform { + def apply(plan: LogicalPlan): LogicalPlan = plan transform { // Wait until children are resolved. case p: LogicalPlan if !p.childrenResolved => p // We are inserting into an InsertableRelation. case i @ InsertIntoTable( - l @ LogicalRelation(r: InsertableRelation), partition, child, overwrite) => { + l @ LogicalRelation(r: InsertableRelation), partition, child, overwrite, ifNotExists) => { // First, make sure the data to be inserted have the same number of fields with the // schema of the relation. if (l.output.size != child.output.size) { @@ -46,17 +47,16 @@ private[sql] object PreInsertCastAndRename extends Rule[LogicalPlan] { } castAndRenameChildOutput(i, l.output, child) } - } } /** If necessary, cast data types and rename fields to the expected types and names. */ def castAndRenameChildOutput( insertInto: InsertIntoTable, expectedOutput: Seq[Attribute], - child: LogicalPlan) = { + child: LogicalPlan): InsertIntoTable = { val newChildOutput = expectedOutput.zip(child.output).map { case (expected, actual) => - val needCast = !DataType.equalsIgnoreNullability(expected.dataType, actual.dataType) + val needCast = !expected.dataType.sameType(actual.dataType) // We want to make sure the filed names in the data to be inserted exactly match // names in the schema. val needRename = expected.name != actual.name @@ -74,3 +74,66 @@ private[sql] object PreInsertCastAndRename extends Rule[LogicalPlan] { } } } + +/** + * A rule to do various checks before inserting into or writing to a data source table. + */ +private[sql] case class PreWriteCheck(catalog: Catalog) extends (LogicalPlan => Unit) { + def failAnalysis(msg: String): Unit = { throw new AnalysisException(msg) } + + def apply(plan: LogicalPlan): Unit = { + plan.foreach { + case i @ logical.InsertIntoTable( + l @ LogicalRelation(t: InsertableRelation), partition, query, overwrite, ifNotExists) => + // Right now, we do not support insert into a data source table with partition specs. + if (partition.nonEmpty) { + failAnalysis(s"Insert into a partition is not allowed because $l is not partitioned.") + } else { + // Get all input data source relations of the query. + val srcRelations = query.collect { + case LogicalRelation(src: BaseRelation) => src + } + if (srcRelations.contains(t)) { + failAnalysis( + "Cannot insert overwrite into table that is also being read from.") + } else { + // OK + } + } + + case i @ logical.InsertIntoTable( + l: LogicalRelation, partition, query, overwrite, ifNotExists) + if !l.isInstanceOf[InsertableRelation] => + // The relation in l is not an InsertableRelation. + failAnalysis(s"$l does not allow insertion.") + + case CreateTableUsingAsSelect(tableName, _, _, SaveMode.Overwrite, _, query) => + // When the SaveMode is Overwrite, we need to check if the table is an input table of + // the query. If so, we will throw an AnalysisException to let users know it is not allowed. + if (catalog.tableExists(Seq(tableName))) { + // Need to remove SubQuery operator. + EliminateSubQueries(catalog.lookupRelation(Seq(tableName))) match { + // Only do the check if the table is a data source table + // (the relation is a BaseRelation). + case l @ LogicalRelation(dest: BaseRelation) => + // Get all input data source relations of the query. + val srcRelations = query.collect { + case LogicalRelation(src: BaseRelation) => src + } + if (srcRelations.contains(dest)) { + failAnalysis( + s"Cannot overwrite table $tableName that is also being read from.") + } else { + // OK + } + + case _ => // OK + } + } else { + // OK + } + + case _ => // OK + } + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/test/ExamplePointUDT.scala b/sql/core/src/main/scala/org/apache/spark/sql/test/ExamplePointUDT.scala index e6f622e87f7a4..2fdd798b44bb6 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/test/ExamplePointUDT.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/test/ExamplePointUDT.scala @@ -28,7 +28,7 @@ import org.apache.spark.sql.types._ * @param y y coordinate */ @SQLUserDefinedType(udt = classOf[ExamplePointUDT]) -private[sql] class ExamplePoint(val x: Double, val y: Double) +private[sql] class ExamplePoint(val x: Double, val y: Double) extends Serializable /** * User-defined type for [[ExamplePoint]]. @@ -37,7 +37,7 @@ private[sql] class ExamplePointUDT extends UserDefinedType[ExamplePoint] { override def sqlType: DataType = ArrayType(DoubleType, false) - override def pyUDT: String = "pyspark.sql_tests.ExamplePointUDT" + override def pyUDT: String = "pyspark.sql.tests.ExamplePointUDT" override def serialize(obj: Any): Seq[Double] = { obj match { @@ -59,4 +59,6 @@ private[sql] class ExamplePointUDT extends UserDefinedType[ExamplePoint] { } override def userClass: Class[ExamplePoint] = classOf[ExamplePoint] + + private[spark] override def asNullable: ExamplePointUDT = this } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/test/TestSQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/test/TestSQLContext.scala index 4e1ec38bd0158..356a6100d2cf5 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/test/TestSQLContext.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/test/TestSQLContext.scala @@ -24,16 +24,22 @@ import org.apache.spark.sql.{DataFrame, SQLConf, SQLContext} import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan /** A SQLContext that can be used for local testing. */ -object TestSQLContext +class LocalSQLContext extends SQLContext( new SparkContext( "local[2]", "TestSQLContext", new SparkConf().set("spark.sql.testkey", "true"))) { - /** Fewer partitions to speed up testing. */ - protected[sql] override lazy val conf: SQLConf = new SQLConf { - override def numShufflePartitions: Int = this.getConf(SQLConf.SHUFFLE_PARTITIONS, "5").toInt + override protected[sql] def createSession(): SQLSession = { + new this.SQLSession() + } + + protected[sql] class SQLSession extends super.SQLSession { + protected[sql] override lazy val conf: SQLConf = new SQLConf { + /** Fewer partitions to speed up testing. */ + override def numShufflePartitions: Int = this.getConf(SQLConf.SHUFFLE_PARTITIONS, "5").toInt + } } /** @@ -45,3 +51,6 @@ object TestSQLContext } } + +object TestSQLContext extends LocalSQLContext + diff --git a/sql/core/src/test/java/org/apache/spark/sql/api/java/JavaDsl.java b/sql/core/src/test/java/org/apache/spark/sql/api/java/JavaDsl.java deleted file mode 100644 index 639436368c4a3..0000000000000 --- a/sql/core/src/test/java/org/apache/spark/sql/api/java/JavaDsl.java +++ /dev/null @@ -1,120 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.sql.api.java; - -import com.google.common.collect.ImmutableMap; - -import org.apache.spark.sql.Column; -import org.apache.spark.sql.DataFrame; -import org.apache.spark.sql.types.DataTypes; - -import static org.apache.spark.sql.Dsl.*; - -/** - * This test doesn't actually run anything. It is here to check the API compatibility for Java. - */ -public class JavaDsl { - - public static void testDataFrame(final DataFrame df) { - DataFrame df1 = df.select("colA"); - df1 = df.select("colA", "colB"); - - df1 = df.select(col("colA"), col("colB"), lit("literal value").$plus(1)); - - df1 = df.filter(col("colA")); - - java.util.Map aggExprs = ImmutableMap.builder() - .put("colA", "sum") - .put("colB", "avg") - .build(); - - df1 = df.agg(aggExprs); - - df1 = df.groupBy("groupCol").agg(aggExprs); - - df1 = df.join(df1, col("key1").$eq$eq$eq(col("key2")), "outer"); - - df.orderBy("colA"); - df.orderBy("colA", "colB", "colC"); - df.orderBy(col("colA").desc()); - df.orderBy(col("colA").desc(), col("colB").asc()); - - df.sort("colA"); - df.sort("colA", "colB", "colC"); - df.sort(col("colA").desc()); - df.sort(col("colA").desc(), col("colB").asc()); - - df.as("b"); - - df.limit(5); - - df.unionAll(df1); - df.intersect(df1); - df.except(df1); - - df.sample(true, 0.1, 234); - - df.head(); - df.head(5); - df.first(); - df.count(); - } - - public static void testColumn(final Column c) { - c.asc(); - c.desc(); - - c.endsWith("abcd"); - c.startsWith("afgasdf"); - - c.like("asdf%"); - c.rlike("wef%asdf"); - - c.as("newcol"); - - c.cast("int"); - c.cast(DataTypes.IntegerType); - } - - public static void testDsl() { - // Creating a column. - Column c = col("abcd"); - Column c1 = column("abcd"); - - // Literals - Column l1 = lit(1); - Column l2 = lit(1.0); - Column l3 = lit("abcd"); - - // Functions - Column a = upper(c); - a = lower(c); - a = sqrt(c); - a = abs(c); - - // Aggregates - a = min(c); - a = max(c); - a = sum(c); - a = sumDistinct(c); - a = countDistinct(c, a); - a = avg(c); - a = first(c); - a = last(c); - } -} diff --git a/sql/core/src/test/java/org/apache/spark/sql/jdbc/JavaJDBCTest.java b/sql/core/src/test/java/org/apache/spark/sql/jdbc/JavaJDBCTest.java deleted file mode 100644 index 80bd74f5b5525..0000000000000 --- a/sql/core/src/test/java/org/apache/spark/sql/jdbc/JavaJDBCTest.java +++ /dev/null @@ -1,102 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.sql.jdbc; - -import org.junit.*; -import static org.junit.Assert.*; -import java.sql.Connection; -import java.sql.DriverManager; - -import org.apache.spark.SparkEnv; -import org.apache.spark.api.java.JavaSparkContext; -import org.apache.spark.sql.SQLContext; -import org.apache.spark.sql.DataFrame; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.api.java.*; -import org.apache.spark.sql.test.TestSQLContext$; - -public class JavaJDBCTest { - static String url = "jdbc:h2:mem:testdb1"; - - static Connection conn = null; - - // This variable will always be null if TestSQLContext is intact when running - // these tests. Some Java tests do not play nicely with others, however; - // they create a SparkContext of their own at startup and stop it at exit. - // This renders TestSQLContext inoperable, meaning we have to do the same - // thing. If this variable is nonnull, that means we allocated a - // SparkContext of our own and that we need to stop it at teardown. - static JavaSparkContext localSparkContext = null; - - static SQLContext sql = TestSQLContext$.MODULE$; - - @Before - public void beforeTest() throws Exception { - if (SparkEnv.get() == null) { // A previous test destroyed TestSQLContext. - localSparkContext = new JavaSparkContext("local", "JavaAPISuite"); - sql = new SQLContext(localSparkContext); - } - Class.forName("org.h2.Driver"); - conn = DriverManager.getConnection(url); - conn.prepareStatement("create schema test").executeUpdate(); - conn.prepareStatement("create table test.people (name TEXT(32) NOT NULL, theid INTEGER NOT NULL)").executeUpdate(); - conn.prepareStatement("insert into test.people values ('fred', 1)").executeUpdate(); - conn.prepareStatement("insert into test.people values ('mary', 2)").executeUpdate(); - conn.prepareStatement("insert into test.people values ('joe', 3)").executeUpdate(); - conn.commit(); - } - - @After - public void afterTest() throws Exception { - if (localSparkContext != null) { - localSparkContext.stop(); - localSparkContext = null; - } - try { - conn.close(); - } finally { - conn = null; - } - } - - @Test - public void basicTest() throws Exception { - DataFrame rdd = JDBCUtils.jdbcRDD(sql, url, "TEST.PEOPLE"); - Row[] rows = rdd.collect(); - assertEquals(rows.length, 3); - } - - @Test - public void partitioningTest() throws Exception { - String[] parts = new String[2]; - parts[0] = "THEID < 2"; - parts[1] = "THEID = 2"; // Deliberately forget about one of them. - DataFrame rdd = JDBCUtils.jdbcRDD(sql, url, "TEST.PEOPLE", parts); - Row[] rows = rdd.collect(); - assertEquals(rows.length, 2); - } - - @Test - public void writeTest() throws Exception { - DataFrame rdd = JDBCUtils.jdbcRDD(sql, url, "TEST.PEOPLE"); - JDBCUtils.createJDBCTable(rdd, url, "TEST.PEOPLECOPY", false); - DataFrame rdd2 = JDBCUtils.jdbcRDD(sql, url, "TEST.PEOPLECOPY"); - Row[] rows = rdd2.collect(); - assertEquals(rows.length, 3); - } -} diff --git a/sql/core/src/test/java/org/apache/spark/sql/api/java/JavaApplySchemaSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaApplySchemaSuite.java similarity index 90% rename from sql/core/src/test/java/org/apache/spark/sql/api/java/JavaApplySchemaSuite.java rename to sql/core/src/test/java/test/org/apache/spark/sql/JavaApplySchemaSuite.java index 2e6e977fdc752..c344a9b095c52 100644 --- a/sql/core/src/test/java/org/apache/spark/sql/api/java/JavaApplySchemaSuite.java +++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaApplySchemaSuite.java @@ -15,7 +15,7 @@ * limitations under the License. */ -package org.apache.spark.sql.api.java; +package test.org.apache.spark.sql; import java.io.Serializable; import java.util.ArrayList; @@ -39,18 +39,18 @@ // see http://stackoverflow.com/questions/758570/. public class JavaApplySchemaSuite implements Serializable { private transient JavaSparkContext javaCtx; - private transient SQLContext javaSqlCtx; + private transient SQLContext sqlContext; @Before public void setUp() { - javaSqlCtx = TestSQLContext$.MODULE$; - javaCtx = new JavaSparkContext(javaSqlCtx.sparkContext()); + sqlContext = TestSQLContext$.MODULE$; + javaCtx = new JavaSparkContext(sqlContext.sparkContext()); } @After public void tearDown() { javaCtx = null; - javaSqlCtx = null; + sqlContext = null; } public static class Person implements Serializable { @@ -98,9 +98,9 @@ public Row call(Person person) throws Exception { fields.add(DataTypes.createStructField("age", DataTypes.IntegerType, false)); StructType schema = DataTypes.createStructType(fields); - DataFrame df = javaSqlCtx.applySchema(rowRDD, schema); + DataFrame df = sqlContext.applySchema(rowRDD, schema); df.registerTempTable("people"); - Row[] actual = javaSqlCtx.sql("SELECT * FROM people").collect(); + Row[] actual = sqlContext.sql("SELECT * FROM people").collect(); List expected = new ArrayList(2); expected.add(RowFactory.create("Michael", 29)); @@ -109,8 +109,6 @@ public Row call(Person person) throws Exception { Assert.assertEquals(expected, Arrays.asList(actual)); } - - @Test public void dataFrameRDDOperations() { List personList = new ArrayList(2); @@ -135,9 +133,9 @@ public Row call(Person person) throws Exception { fields.add(DataTypes.createStructField("age", DataTypes.IntegerType, false)); StructType schema = DataTypes.createStructType(fields); - DataFrame df = javaSqlCtx.applySchema(rowRDD, schema); + DataFrame df = sqlContext.applySchema(rowRDD, schema); df.registerTempTable("people"); - List actual = javaSqlCtx.sql("SELECT * FROM people").toJavaRDD().map(new Function() { + List actual = sqlContext.sql("SELECT * FROM people").toJavaRDD().map(new Function() { public String call(Row row) { return row.getString(0) + "_" + row.get(1).toString(); @@ -164,7 +162,7 @@ public void applySchemaToJSON() { fields.add(DataTypes.createStructField("bigInteger", DataTypes.createDecimalType(), true)); fields.add(DataTypes.createStructField("boolean", DataTypes.BooleanType, true)); fields.add(DataTypes.createStructField("double", DataTypes.DoubleType, true)); - fields.add(DataTypes.createStructField("integer", DataTypes.IntegerType, true)); + fields.add(DataTypes.createStructField("integer", DataTypes.LongType, true)); fields.add(DataTypes.createStructField("long", DataTypes.LongType, true)); fields.add(DataTypes.createStructField("null", DataTypes.StringType, true)); fields.add(DataTypes.createStructField("string", DataTypes.StringType, true)); @@ -189,18 +187,18 @@ public void applySchemaToJSON() { null, "this is another simple string.")); - DataFrame df1 = javaSqlCtx.jsonRDD(jsonRDD); + DataFrame df1 = sqlContext.jsonRDD(jsonRDD); StructType actualSchema1 = df1.schema(); Assert.assertEquals(expectedSchema, actualSchema1); df1.registerTempTable("jsonTable1"); - List actual1 = javaSqlCtx.sql("select * from jsonTable1").collectAsList(); + List actual1 = sqlContext.sql("select * from jsonTable1").collectAsList(); Assert.assertEquals(expectedResult, actual1); - DataFrame df2 = javaSqlCtx.jsonRDD(jsonRDD, expectedSchema); + DataFrame df2 = sqlContext.jsonRDD(jsonRDD, expectedSchema); StructType actualSchema2 = df2.schema(); Assert.assertEquals(expectedSchema, actualSchema2); df2.registerTempTable("jsonTable2"); - List actual2 = javaSqlCtx.sql("select * from jsonTable2").collectAsList(); + List actual2 = sqlContext.sql("select * from jsonTable2").collectAsList(); Assert.assertEquals(expectedResult, actual2); } } diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java new file mode 100644 index 0000000000000..6d0fbe83c2f36 --- /dev/null +++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java @@ -0,0 +1,140 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package test.org.apache.spark.sql; + +import java.io.Serializable; +import java.util.Arrays; + +import scala.collection.Seq; + +import org.junit.After; +import org.junit.Assert; +import org.junit.Before; +import org.junit.Ignore; +import org.junit.Test; + +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.sql.*; +import org.apache.spark.sql.test.TestSQLContext; +import org.apache.spark.sql.test.TestSQLContext$; +import org.apache.spark.sql.types.*; + +import static org.apache.spark.sql.functions.*; + +public class JavaDataFrameSuite { + private transient JavaSparkContext jsc; + private transient SQLContext context; + + @Before + public void setUp() { + // Trigger static initializer of TestData + TestData$.MODULE$.testData(); + jsc = new JavaSparkContext(TestSQLContext.sparkContext()); + context = TestSQLContext$.MODULE$; + } + + @After + public void tearDown() { + jsc = null; + context = null; + } + + @Test + public void testExecution() { + DataFrame df = context.table("testData").filter("key = 1"); + Assert.assertEquals(df.select("key").collect()[0].get(0), 1); + } + + /** + * See SPARK-5904. Abstract vararg methods defined in Scala do not work in Java. + */ + @Test + public void testVarargMethods() { + DataFrame df = context.table("testData"); + + df.toDF("key1", "value1"); + + df.select("key", "value"); + df.select(col("key"), col("value")); + df.selectExpr("key", "value + 1"); + + df.sort("key", "value"); + df.sort(col("key"), col("value")); + df.orderBy("key", "value"); + df.orderBy(col("key"), col("value")); + + df.groupBy("key", "value").agg(col("key"), col("value"), sum("value")); + df.groupBy(col("key"), col("value")).agg(col("key"), col("value"), sum("value")); + df.agg(first("key"), sum("value")); + + df.groupBy().avg("key"); + df.groupBy().mean("key"); + df.groupBy().max("key"); + df.groupBy().min("key"); + df.groupBy().sum("key"); + + // Varargs in column expressions + df.groupBy().agg(countDistinct("key", "value")); + df.groupBy().agg(countDistinct(col("key"), col("value"))); + df.select(coalesce(col("key"))); + } + + @Ignore + public void testShow() { + // This test case is intended ignored, but to make sure it compiles correctly + DataFrame df = context.table("testData"); + df.show(); + df.show(1000); + } + + public static class Bean implements Serializable { + private double a = 0.0; + private Integer[] b = new Integer[]{0, 1}; + + public double getA() { + return a; + } + + public Integer[] getB() { + return b; + } + } + + @Test + public void testCreateDataFrameFromJavaBeans() { + Bean bean = new Bean(); + JavaRDD rdd = jsc.parallelize(Arrays.asList(bean)); + DataFrame df = context.createDataFrame(rdd, Bean.class); + StructType schema = df.schema(); + Assert.assertEquals(new StructField("a", DoubleType$.MODULE$, false, Metadata.empty()), + schema.apply("a")); + Assert.assertEquals( + new StructField("b", new ArrayType(IntegerType$.MODULE$, true), true, Metadata.empty()), + schema.apply("b")); + Row first = df.select("a", "b").first(); + Assert.assertEquals(bean.getA(), first.getDouble(0), 0.0); + // Now Java lists and maps are converetd to Scala Seq's and Map's. Once we get a Seq below, + // verify that it has the expected length, and contains expected elements. + Seq result = first.getAs(1); + Assert.assertEquals(bean.getB().length, result.length()); + for (int i = 0; i < result.length(); i++) { + Assert.assertEquals(bean.getB()[i], result.apply(i)); + } + } +} diff --git a/sql/core/src/test/java/org/apache/spark/sql/api/java/JavaRowSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaRowSuite.java similarity index 99% rename from sql/core/src/test/java/org/apache/spark/sql/api/java/JavaRowSuite.java rename to sql/core/src/test/java/test/org/apache/spark/sql/JavaRowSuite.java index fbfcd3f59d910..4ce1d1dddb26a 100644 --- a/sql/core/src/test/java/org/apache/spark/sql/api/java/JavaRowSuite.java +++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaRowSuite.java @@ -15,7 +15,7 @@ * limitations under the License. */ -package org.apache.spark.sql.api.java; +package test.org.apache.spark.sql; import java.math.BigDecimal; import java.sql.Date; diff --git a/sql/core/src/test/java/org/apache/spark/sql/api/java/JavaAPISuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaUDFSuite.java similarity index 94% rename from sql/core/src/test/java/org/apache/spark/sql/api/java/JavaAPISuite.java rename to sql/core/src/test/java/test/org/apache/spark/sql/JavaUDFSuite.java index a21a15409080c..79d92734ff375 100644 --- a/sql/core/src/test/java/org/apache/spark/sql/api/java/JavaAPISuite.java +++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaUDFSuite.java @@ -15,24 +15,26 @@ * limitations under the License. */ -package org.apache.spark.sql.api.java; +package test.org.apache.spark.sql; import java.io.Serializable; -import org.apache.spark.sql.test.TestSQLContext$; import org.junit.After; import org.junit.Before; import org.junit.Test; -import org.apache.spark.api.java.JavaSparkContext; import org.apache.spark.sql.Row; import org.apache.spark.sql.SQLContext; +import org.apache.spark.sql.api.java.UDF1; +import org.apache.spark.sql.api.java.UDF2; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.sql.test.TestSQLContext$; import org.apache.spark.sql.types.DataTypes; // The test suite itself is Serializable so that anonymous Function implementations can be // serialized, as an alternative to converting these anonymous classes to static inner classes; // see http://stackoverflow.com/questions/758570/. -public class JavaAPISuite implements Serializable { +public class JavaUDFSuite implements Serializable { private transient JavaSparkContext sc; private transient SQLContext sqlContext; diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/sources/JavaSaveLoadSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/sources/JavaSaveLoadSuite.java new file mode 100644 index 0000000000000..b76f7d421f643 --- /dev/null +++ b/sql/core/src/test/java/test/org/apache/spark/sql/sources/JavaSaveLoadSuite.java @@ -0,0 +1,98 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package test.org.apache.spark.sql.sources; + +import java.io.File; +import java.io.IOException; +import java.util.*; + +import org.junit.Assert; +import org.junit.Before; +import org.junit.Test; + +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.sql.test.TestSQLContext$; +import org.apache.spark.sql.*; +import org.apache.spark.sql.types.DataTypes; +import org.apache.spark.sql.types.StructField; +import org.apache.spark.sql.types.StructType; +import org.apache.spark.util.Utils; + +public class JavaSaveLoadSuite { + + private transient JavaSparkContext sc; + private transient SQLContext sqlContext; + + String originalDefaultSource; + File path; + DataFrame df; + + private void checkAnswer(DataFrame actual, List expected) { + String errorMessage = QueryTest$.MODULE$.checkAnswer(actual, expected); + if (errorMessage != null) { + Assert.fail(errorMessage); + } + } + + @Before + public void setUp() throws IOException { + sqlContext = TestSQLContext$.MODULE$; + sc = new JavaSparkContext(sqlContext.sparkContext()); + + originalDefaultSource = sqlContext.conf().defaultDataSourceName(); + path = + Utils.createTempDir(System.getProperty("java.io.tmpdir"), "datasource").getCanonicalFile(); + if (path.exists()) { + path.delete(); + } + + List jsonObjects = new ArrayList(10); + for (int i = 0; i < 10; i++) { + jsonObjects.add("{\"a\":" + i + ", \"b\":\"str" + i + "\"}"); + } + JavaRDD rdd = sc.parallelize(jsonObjects); + df = sqlContext.jsonRDD(rdd); + df.registerTempTable("jsonTable"); + } + + @Test + public void saveAndLoad() { + Map options = new HashMap(); + options.put("path", path.toString()); + df.save("org.apache.spark.sql.json", SaveMode.ErrorIfExists, options); + + DataFrame loadedDF = sqlContext.load("org.apache.spark.sql.json", options); + + checkAnswer(loadedDF, df.collectAsList()); + } + + @Test + public void saveAndLoadWithSchema() { + Map options = new HashMap(); + options.put("path", path.toString()); + df.save("org.apache.spark.sql.json", SaveMode.ErrorIfExists, options); + + List fields = new ArrayList(); + fields.add(DataTypes.createStructField("b", DataTypes.StringType, true)); + StructType schema = DataTypes.createStructType(fields); + DataFrame loadedDF = sqlContext.load("org.apache.spark.sql.json", schema, options); + + checkAnswer(loadedDF, sqlContext.sql("SELECT b FROM jsonTable").collectAsList()); + } +} diff --git a/sql/core/src/test/resources/log4j.properties b/sql/core/src/test/resources/log4j.properties index fbed0a782dd3e..28e90b9520b2c 100644 --- a/sql/core/src/test/resources/log4j.properties +++ b/sql/core/src/test/resources/log4j.properties @@ -39,6 +39,9 @@ log4j.appender.FA.Threshold = INFO log4j.additivity.parquet.hadoop.ParquetRecordReader=false log4j.logger.parquet.hadoop.ParquetRecordReader=OFF +log4j.additivity.parquet.hadoop.ParquetOutputCommitter=false +log4j.logger.parquet.hadoop.ParquetOutputCommitter=OFF + log4j.additivity.org.apache.hadoop.hive.serde2.lazy.LazyStruct=false log4j.logger.org.apache.hadoop.hive.serde2.lazy.LazyStruct=OFF diff --git a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala index 1318750a4a3b0..01e3b8671071e 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala @@ -18,24 +18,22 @@ package org.apache.spark.sql import scala.concurrent.duration._ -import scala.language.implicitConversions -import scala.language.postfixOps +import scala.language.{implicitConversions, postfixOps} import org.scalatest.concurrent.Eventually._ +import org.apache.spark.Accumulators import org.apache.spark.sql.TestData._ import org.apache.spark.sql.columnar._ -import org.apache.spark.sql.Dsl._ import org.apache.spark.sql.test.TestSQLContext._ -import org.apache.spark.storage.{StorageLevel, RDDBlockId} +import org.apache.spark.sql.test.TestSQLContext.implicits._ +import org.apache.spark.storage.{RDDBlockId, StorageLevel} case class BigData(s: String) class CachedTableSuite extends QueryTest { TestData // Load test tables. - import org.apache.spark.sql.test.TestSQLContext.implicits._ - def rddIdOf(tableName: String): Int = { val executedPlan = table(tableName).queryExecution.executedPlan executedPlan.collect { @@ -60,15 +58,15 @@ class CachedTableSuite extends QueryTest { test("unpersist an uncached table will not raise exception") { assert(None == cacheManager.lookupCachedData(testData)) - testData.unpersist(true) + testData.unpersist(blocking = true) assert(None == cacheManager.lookupCachedData(testData)) - testData.unpersist(false) + testData.unpersist(blocking = false) assert(None == cacheManager.lookupCachedData(testData)) testData.persist() assert(None != cacheManager.lookupCachedData(testData)) - testData.unpersist(true) + testData.unpersist(blocking = true) assert(None == cacheManager.lookupCachedData(testData)) - testData.unpersist(false) + testData.unpersist(blocking = false) assert(None == cacheManager.lookupCachedData(testData)) } @@ -95,7 +93,8 @@ class CachedTableSuite extends QueryTest { test("too big for memory") { val data = "*" * 10000 - sparkContext.parallelize(1 to 200000, 1).map(_ => BigData(data)).registerTempTable("bigData") + sparkContext.parallelize(1 to 200000, 1).map(_ => BigData(data)).toDF() + .registerTempTable("bigData") table("bigData").persist(StorageLevel.MEMORY_AND_DISK) assert(table("bigData").count() === 200000L) table("bigData").unpersist(blocking = true) @@ -283,4 +282,37 @@ class CachedTableSuite extends QueryTest { assert(intercept[RuntimeException](table("t1")).getMessage.startsWith("Table Not Found")) assert(!isCached("t2")) } + + test("Clear all cache") { + sql("SELECT key FROM testData LIMIT 10").registerTempTable("t1") + sql("SELECT key FROM testData LIMIT 5").registerTempTable("t2") + cacheTable("t1") + cacheTable("t2") + clearCache() + assert(cacheManager.isEmpty) + + sql("SELECT key FROM testData LIMIT 10").registerTempTable("t1") + sql("SELECT key FROM testData LIMIT 5").registerTempTable("t2") + cacheTable("t1") + cacheTable("t2") + sql("Clear CACHE") + assert(cacheManager.isEmpty) + } + + test("Clear accumulators when uncacheTable to prevent memory leaking") { + val accsSize = Accumulators.originals.size + + sql("SELECT key FROM testData LIMIT 10").registerTempTable("t1") + sql("SELECT key FROM testData LIMIT 5").registerTempTable("t2") + cacheTable("t1") + cacheTable("t2") + sql("SELECT * FROM t1").count() + sql("SELECT * FROM t2").count() + sql("SELECT * FROM t1").count() + sql("SELECT * FROM t2").count() + uncacheTable("t1") + uncacheTable("t2") + + assert(accsSize >= Accumulators.originals.size) + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala index fa4cdecbcb340..bc8fae100db6a 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala @@ -17,9 +17,10 @@ package org.apache.spark.sql -import org.apache.spark.sql.Dsl._ +import org.apache.spark.sql.functions._ import org.apache.spark.sql.test.TestSQLContext -import org.apache.spark.sql.types.{BooleanType, IntegerType, StructField, StructType} +import org.apache.spark.sql.test.TestSQLContext.implicits._ +import org.apache.spark.sql.types._ class ColumnExpressionSuite extends QueryTest { @@ -27,43 +28,10 @@ class ColumnExpressionSuite extends QueryTest { // TODO: Add test cases for bitwise operations. - test("computability check") { - def shouldBeComputable(c: Column): Unit = assert(c.isComputable === true) - - def shouldNotBeComputable(c: Column): Unit = { - assert(c.isComputable === false) - intercept[UnsupportedOperationException] { c.head() } - } - - shouldBeComputable(testData2("a")) - shouldBeComputable(testData2("b")) - - shouldBeComputable(testData2("a") + testData2("b")) - shouldBeComputable(testData2("a") + testData2("b") + 1) - - shouldBeComputable(-testData2("a")) - shouldBeComputable(!testData2("a")) - - shouldBeComputable(testData2.select(($"a" + 1).as("c"))("c") + testData2("b")) - shouldBeComputable( - testData2.select(($"a" + 1).as("c"))("c") + testData2.select(($"b" / 2).as("d"))("d")) - shouldBeComputable( - testData2.select(($"a" + 1).as("c")).select(($"c" + 2).as("d"))("d") + testData2("b")) - - // Literals and unresolved columns should not be computable. - shouldNotBeComputable(col("1")) - shouldNotBeComputable(col("1") + 2) - shouldNotBeComputable(lit(100)) - shouldNotBeComputable(lit(100) + 10) - shouldNotBeComputable(-col("1")) - shouldNotBeComputable(!col("1")) - - // Getting data from different frames should not be computable. - shouldNotBeComputable(testData2("a") + testData("key")) - shouldNotBeComputable(testData2("a") + 1 + testData("key")) - - // Aggregate functions alone should not be computable. - shouldNotBeComputable(sum(testData2("a"))) + test("collect on column produced by a binary operator") { + val df = Seq((1, 2, 3)).toDF("a", "b", "c") + checkAnswer(df.select(df("a") + df("b")), Seq(Row(3))) + checkAnswer(df.select(df("a") + df("b").as("c")), Seq(Row(3))) } test("star") { @@ -71,8 +39,7 @@ class ColumnExpressionSuite extends QueryTest { } test("star qualified by data frame object") { - // This is not yet supported. - val df = testData.toDataFrame + val df = testData.toDF val goldAnswer = df.collect().toSeq checkAnswer(df.select(df("*")), goldAnswer) @@ -149,13 +116,13 @@ class ColumnExpressionSuite extends QueryTest { test("isNull") { checkAnswer( - nullStrings.toDataFrame.where($"s".isNull), + nullStrings.toDF.where($"s".isNull), nullStrings.collect().toSeq.filter(r => r.getString(1) eq null)) } test("isNotNull") { checkAnswer( - nullStrings.toDataFrame.where($"s".isNotNull), + nullStrings.toDF.where($"s".isNotNull), nullStrings.collect().toSeq.filter(r => r.getString(1) ne null)) } @@ -180,7 +147,7 @@ class ColumnExpressionSuite extends QueryTest { } test("!==") { - val nullData = TestSQLContext.applySchema(TestSQLContext.sparkContext.parallelize( + val nullData = TestSQLContext.createDataFrame(TestSQLContext.sparkContext.parallelize( Row(1, 1) :: Row(1, 2) :: Row(1, null) :: @@ -240,7 +207,7 @@ class ColumnExpressionSuite extends QueryTest { testData2.collect().toSeq.filter(r => r.getInt(0) <= r.getInt(1))) } - val booleanData = TestSQLContext.applySchema(TestSQLContext.sparkContext.parallelize( + val booleanData = TestSQLContext.createDataFrame(TestSQLContext.sparkContext.parallelize( Row(false, false) :: Row(false, true) :: Row(true, false) :: @@ -342,4 +309,26 @@ class ColumnExpressionSuite extends QueryTest { (1 to 100).map(n => Row(null)) ) } + + test("lift alias out of cast") { + compareExpressions( + col("1234").as("name").cast("int").expr, + col("1234").cast("int").as("name").expr) + } + + test("columns can be compared") { + assert('key.desc == 'key.desc) + assert('key.desc != 'key.asc) + } + + test("alias with metadata") { + val metadata = new MetadataBuilder() + .putString("originName", "value") + .build() + val schema = testData + .select($"*", col("value").as("abc", metadata)) + .schema + assert(schema("value").metadata === Metadata.empty) + assert(schema("abc").metadata === metadata) + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameImplicitsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameImplicitsSuite.scala new file mode 100644 index 0000000000000..2d2367d6e7292 --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameImplicitsSuite.scala @@ -0,0 +1,55 @@ +/* +* Licensed to the Apache Software Foundation (ASF) under one or more +* contributor license agreements. See the NOTICE file distributed with +* this work for additional information regarding copyright ownership. +* The ASF licenses this file to You under the Apache License, Version 2.0 +* (the "License"); you may not use this file except in compliance with +* the License. You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +*/ + +package org.apache.spark.sql + +import org.apache.spark.sql.test.TestSQLContext.{sparkContext => sc} +import org.apache.spark.sql.test.TestSQLContext.implicits._ + + +class DataFrameImplicitsSuite extends QueryTest { + + test("RDD of tuples") { + checkAnswer( + sc.parallelize(1 to 10).map(i => (i, i.toString)).toDF("intCol", "strCol"), + (1 to 10).map(i => Row(i, i.toString))) + } + + test("Seq of tuples") { + checkAnswer( + (1 to 10).map(i => (i, i.toString)).toDF("intCol", "strCol"), + (1 to 10).map(i => Row(i, i.toString))) + } + + test("RDD[Int]") { + checkAnswer( + sc.parallelize(1 to 10).toDF("intCol"), + (1 to 10).map(i => Row(i))) + } + + test("RDD[Long]") { + checkAnswer( + sc.parallelize(1L to 10L).toDF("longCol"), + (1L to 10L).map(i => Row(i))) + } + + test("RDD[String]") { + checkAnswer( + sc.parallelize(1 to 10).map(_.toString).toDF("stringCol"), + (1 to 10).map(i => Row(i.toString))) + } +} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala new file mode 100644 index 0000000000000..41b4f02e6a294 --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala @@ -0,0 +1,191 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql + +import scala.collection.JavaConversions._ + +import org.apache.spark.sql.test.TestSQLContext.implicits._ + + +class DataFrameNaFunctionsSuite extends QueryTest { + + def createDF(): DataFrame = { + Seq[(String, java.lang.Integer, java.lang.Double)]( + ("Bob", 16, 176.5), + ("Alice", null, 164.3), + ("David", 60, null), + ("Amy", null, null), + (null, null, null)).toDF("name", "age", "height") + } + + test("drop") { + val input = createDF() + val rows = input.collect() + + checkAnswer( + input.na.drop("name" :: Nil), + rows(0) :: rows(1) :: rows(2) :: rows(3) :: Nil) + + checkAnswer( + input.na.drop("age" :: Nil), + rows(0) :: rows(2) :: Nil) + + checkAnswer( + input.na.drop("age" :: "height" :: Nil), + rows(0) :: Nil) + + checkAnswer( + input.na.drop(), + rows(0)) + + // dropna on an a dataframe with no column should return an empty data frame. + val empty = input.sqlContext.emptyDataFrame.select() + assert(empty.na.drop().count() === 0L) + + // Make sure the columns are properly named. + assert(input.na.drop().columns.toSeq === input.columns.toSeq) + } + + test("drop with how") { + val input = createDF() + val rows = input.collect() + + checkAnswer( + input.na.drop("all"), + rows(0) :: rows(1) :: rows(2) :: rows(3) :: Nil) + + checkAnswer( + input.na.drop("any"), + rows(0) :: Nil) + + checkAnswer( + input.na.drop("any", Seq("age", "height")), + rows(0) :: Nil) + + checkAnswer( + input.na.drop("all", Seq("age", "height")), + rows(0) :: rows(1) :: rows(2) :: Nil) + } + + test("drop with threshold") { + val input = createDF() + val rows = input.collect() + + checkAnswer( + input.na.drop(2, Seq("age", "height")), + rows(0) :: Nil) + + checkAnswer( + input.na.drop(3, Seq("name", "age", "height")), + rows(0)) + + // Make sure the columns are properly named. + assert(input.na.drop(2, Seq("age", "height")).columns.toSeq === input.columns.toSeq) + } + + test("fill") { + val input = createDF() + + val fillNumeric = input.na.fill(50.6) + checkAnswer( + fillNumeric, + Row("Bob", 16, 176.5) :: + Row("Alice", 50, 164.3) :: + Row("David", 60, 50.6) :: + Row("Amy", 50, 50.6) :: + Row(null, 50, 50.6) :: Nil) + + // Make sure the columns are properly named. + assert(fillNumeric.columns.toSeq === input.columns.toSeq) + + // string + checkAnswer( + input.na.fill("unknown").select("name"), + Row("Bob") :: Row("Alice") :: Row("David") :: Row("Amy") :: Row("unknown") :: Nil) + assert(input.na.fill("unknown").columns.toSeq === input.columns.toSeq) + + // fill double with subset columns + checkAnswer( + input.na.fill(50.6, "age" :: Nil), + Row("Bob", 16, 176.5) :: + Row("Alice", 50, 164.3) :: + Row("David", 60, null) :: + Row("Amy", 50, null) :: + Row(null, 50, null) :: Nil) + + // fill string with subset columns + checkAnswer( + Seq[(String, String)]((null, null)).toDF("col1", "col2").na.fill("test", "col1" :: Nil), + Row("test", null)) + } + + test("fill with map") { + val df = Seq[(String, String, java.lang.Long, java.lang.Double)]( + (null, null, null, null)).toDF("a", "b", "c", "d") + checkAnswer( + df.na.fill(Map( + "a" -> "test", + "c" -> 1, + "d" -> 2.2 + )), + Row("test", null, 1, 2.2)) + + // Test Java version + checkAnswer( + df.na.fill(mapAsJavaMap(Map( + "a" -> "test", + "c" -> 1, + "d" -> 2.2 + ))), + Row("test", null, 1, 2.2)) + } + + test("replace") { + val input = createDF() + + // Replace two numeric columns: age and height + val out = input.na.replace(Seq("age", "height"), Map( + 16 -> 61, + 60 -> 6, + 164.3 -> 461.3 // Alice is really tall + )) + + checkAnswer( + out, + Row("Bob", 61, 176.5) :: + Row("Alice", null, 461.3) :: + Row("David", 6, null) :: + Row("Amy", null, null) :: + Row(null, null, null) :: Nil) + + // Replace only the age column + val out1 = input.na.replace("age", Map( + 16 -> 61, + 60 -> 6, + 164.3 -> 461.3 // Alice is really tall + )) + + checkAnswer( + out1, + Row("Bob", 61, 176.5) :: + Row("Alice", null, 164.3) :: + Row("David", 6, null) :: + Row("Amy", null, null) :: + Row(null, null, null) :: Nil) + } +} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala index 5aa3db720c886..3250ab476aeb4 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala @@ -19,11 +19,12 @@ package org.apache.spark.sql import scala.language.postfixOps -import org.apache.spark.sql.Dsl._ +import org.apache.spark.sql.functions._ import org.apache.spark.sql.types._ -import org.apache.spark.sql.test.TestSQLContext +import org.apache.spark.sql.test.{ExamplePointUDT, ExamplePoint, TestSQLContext} import org.apache.spark.sql.test.TestSQLContext.logicalPlanToSparkQuery import org.apache.spark.sql.test.TestSQLContext.implicits._ +import org.apache.spark.sql.test.TestSQLContext.sql class DataFrameSuite extends QueryTest { @@ -53,18 +54,118 @@ class DataFrameSuite extends QueryTest { TestSQLContext.setConf(SQLConf.DATAFRAME_EAGER_ANALYSIS, oldSetting.toString) } + test("dataframe toString") { + assert(testData.toString === "[key: int, value: string]") + assert(testData("key").toString === "key") + assert($"test".toString === "test") + } + + test("rename nested groupby") { + val df = Seq((1,(1,1))).toDF() + + checkAnswer( + df.groupBy("_1").agg(col("_1"), sum("_2._1")).toDF("key", "total"), + Row(1, 1) :: Nil) + } + + test("invalid plan toString, debug mode") { + val oldSetting = TestSQLContext.conf.dataFrameEagerAnalysis + TestSQLContext.setConf(SQLConf.DATAFRAME_EAGER_ANALYSIS, "true") + + // Turn on debug mode so we can see invalid query plans. + import org.apache.spark.sql.execution.debug._ + TestSQLContext.debug() + + val badPlan = testData.select('badColumn) + + assert(badPlan.toString contains badPlan.queryExecution.toString, + "toString on bad query plans should include the query execution but was:\n" + + badPlan.toString) + + // Set the flag back to original value before this test. + TestSQLContext.setConf(SQLConf.DATAFRAME_EAGER_ANALYSIS, oldSetting.toString) + } + + test("access complex data") { + assert(complexData.filter(complexData("a").getItem(0) === 2).count() == 1) + assert(complexData.filter(complexData("m").getItem("1") === 1).count() == 1) + assert(complexData.filter(complexData("s").getField("key") === 1).count() == 1) + } + test("table scan") { checkAnswer( testData, testData.collect().toSeq) } + test("empty data frame") { + assert(TestSQLContext.emptyDataFrame.columns.toSeq === Seq.empty[String]) + assert(TestSQLContext.emptyDataFrame.count() === 0) + } + + test("head and take") { + assert(testData.take(2) === testData.collect().take(2)) + assert(testData.head(2) === testData.collect().take(2)) + assert(testData.head(2).head.schema === testData.schema) + } + + test("self join") { + val df1 = testData.select(testData("key")).as('df1) + val df2 = testData.select(testData("key")).as('df2) + + checkAnswer( + df1.join(df2, $"df1.key" === $"df2.key"), + sql("SELECT a.key, b.key FROM testData a JOIN testData b ON a.key = b.key").collect().toSeq) + } + + test("simple explode") { + val df = Seq(Tuple1("a b c"), Tuple1("d e")).toDF("words") + + checkAnswer( + df.explode("words", "word") { word: String => word.split(" ").toSeq }.select('word), + Row("a") :: Row("b") :: Row("c") :: Row("d") ::Row("e") :: Nil + ) + } + + test("self join with aliases") { + val df = Seq(1,2,3).map(i => (i, i.toString)).toDF("int", "str") + checkAnswer( + df.as('x).join(df.as('y), $"x.str" === $"y.str").groupBy("x.str").count(), + Row("1", 1) :: Row("2", 1) :: Row("3", 1) :: Nil) + + checkAnswer( + df.as('x).join(df.as('y), $"x.str" === $"y.str").groupBy("y.str").count(), + Row("1", 1) :: Row("2", 1) :: Row("3", 1) :: Nil) + } + + test("explode") { + val df = Seq((1, "a b c"), (2, "a b"), (3, "a")).toDF("number", "letters") + val df2 = + df.explode('letters) { + case Row(letters: String) => letters.split(" ").map(Tuple1(_)).toSeq + } + + checkAnswer( + df2 + .select('_1 as 'letter, 'number) + .groupBy('letter) + .agg('letter, countDistinct('number)), + Row("a", 3) :: Row("b", 2) :: Row("c", 1) :: Nil + ) + } + test("selectExpr") { checkAnswer( testData.selectExpr("abs(key)", "value"), testData.collect().map(row => Row(math.abs(row.getInt(0)), row.getString(1))).toSeq) } + test("selectExpr with alias") { + checkAnswer( + testData.selectExpr("key as k").select("k"), + testData.select("key").collect().toSeq) + } + test("filterExpr") { checkAnswer( testData.filter("key > 90"), @@ -77,15 +178,50 @@ class DataFrameSuite extends QueryTest { testData.select('key).collect().toSeq) } - test("agg") { + test("coalesce") { + assert(testData.select('key).coalesce(1).rdd.partitions.size === 1) + + checkAnswer( + testData.select('key).coalesce(1).select('key), + testData.select('key).collect().toSeq) + } + + test("groupBy") { checkAnswer( testData2.groupBy("a").agg($"a", sum($"b")), - Seq(Row(1,3), Row(2,3), Row(3,3)) + Seq(Row(1, 3), Row(2, 3), Row(3, 3)) ) checkAnswer( testData2.groupBy("a").agg($"a", sum($"b").as("totB")).agg(sum('totB)), Row(9) ) + checkAnswer( + testData2.groupBy("a").agg(col("a"), count("*")), + Row(1, 2) :: Row(2, 2) :: Row(3, 2) :: Nil + ) + checkAnswer( + testData2.groupBy("a").agg(Map("*" -> "count")), + Row(1, 2) :: Row(2, 2) :: Row(3, 2) :: Nil + ) + checkAnswer( + testData2.groupBy("a").agg(Map("b" -> "sum")), + Row(1, 3) :: Row(2, 3) :: Row(3, 3) :: Nil + ) + + val df1 = Seq(("a", 1, 0, "b"), ("b", 2, 4, "c"), ("a", 2, 3, "d")) + .toDF("key", "value1", "value2", "rest") + + checkAnswer( + df1.groupBy("key").min(), + df1.groupBy("key").min("value1", "value2").collect() + ) + checkAnswer( + df1.groupBy("key").min("value2"), + Seq(Row("a", 0), Row("b", 4)) + ) + } + + test("agg without groups") { checkAnswer( testData2.agg(sum('b)), Row(9) @@ -141,6 +277,10 @@ class DataFrameSuite extends QueryTest { testData2.orderBy('a.asc, 'b.asc), Seq(Row(1,1), Row(1,2), Row(2,1), Row(2,2), Row(3,1), Row(3,2))) + checkAnswer( + testData2.orderBy(asc("a"), desc("b")), + Seq(Row(1,2), Row(1,1), Row(2,2), Row(2,1), Row(3,2), Row(3,1))) + checkAnswer( testData2.orderBy('a.asc, 'b.desc), Seq(Row(1,2), Row(1,1), Row(2,2), Row(2,1), Row(3,2), Row(3,1))) @@ -154,20 +294,20 @@ class DataFrameSuite extends QueryTest { Seq(Row(3,1), Row(3,2), Row(2,1), Row(2,2), Row(1,1), Row(1,2))) checkAnswer( - arrayData.orderBy('data.getItem(0).asc), - arrayData.toDataFrame.collect().sortBy(_.getAs[Seq[Int]](0)(0)).toSeq) + arrayData.toDF().orderBy('data.getItem(0).asc), + arrayData.toDF().collect().sortBy(_.getAs[Seq[Int]](0)(0)).toSeq) checkAnswer( - arrayData.orderBy('data.getItem(0).desc), - arrayData.toDataFrame.collect().sortBy(_.getAs[Seq[Int]](0)(0)).reverse.toSeq) + arrayData.toDF().orderBy('data.getItem(0).desc), + arrayData.toDF().collect().sortBy(_.getAs[Seq[Int]](0)(0)).reverse.toSeq) checkAnswer( - arrayData.orderBy('data.getItem(1).asc), - arrayData.toDataFrame.collect().sortBy(_.getAs[Seq[Int]](0)(1)).toSeq) + arrayData.toDF().orderBy('data.getItem(1).asc), + arrayData.toDF().collect().sortBy(_.getAs[Seq[Int]](0)(1)).toSeq) checkAnswer( - arrayData.orderBy('data.getItem(1).desc), - arrayData.toDataFrame.collect().sortBy(_.getAs[Seq[Int]](0)(1)).reverse.toSeq) + arrayData.toDF().orderBy('data.getItem(1).desc), + arrayData.toDF().collect().sortBy(_.getAs[Seq[Int]](0)(1)).reverse.toSeq) } test("limit") { @@ -176,11 +316,11 @@ class DataFrameSuite extends QueryTest { testData.take(10).toSeq) checkAnswer( - arrayData.limit(1), + arrayData.toDF().limit(1), arrayData.take(1).map(r => Row.fromSeq(r.productIterator.toSeq))) checkAnswer( - mapData.limit(1), + mapData.toDF().limit(1), mapData.take(1).map(r => Row.fromSeq(r.productIterator.toSeq))) } @@ -203,8 +343,9 @@ class DataFrameSuite extends QueryTest { checkAnswer( decimalData.agg(avg('a cast DecimalType(10, 2))), Row(new java.math.BigDecimal(2.0))) + // non-partial checkAnswer( - decimalData.agg(avg('a cast DecimalType(10, 2)), sumDistinct('a cast DecimalType(10, 2))), // non-partial + decimalData.agg(avg('a cast DecimalType(10, 2)), sumDistinct('a cast DecimalType(10, 2))), Row(new java.math.BigDecimal(2.0), new java.math.BigDecimal(6)) :: Nil) } @@ -313,8 +454,17 @@ class DataFrameSuite extends QueryTest { ) } - test("addColumn") { - val df = testData.toDataFrame.addColumn("newCol", col("key") + 1) + test("call udf in SQLContext") { + val df = Seq(("id1", 1), ("id2", 4), ("id3", 5)).toDF("id", "value") + val sqlctx = df.sqlContext + sqlctx.udf.register("simpleUdf", (v: Int) => v * v) + checkAnswer( + df.select($"id", callUdf("simpleUdf", $"value")), + Row("id1", 1) :: Row("id2", 16) :: Row("id3", 25) :: Nil) + } + + test("withColumn") { + val df = testData.toDF().withColumn("newCol", col("key") + 1) checkAnswer( df, testData.collect().map { case Row(key: Int, value: String) => @@ -323,9 +473,9 @@ class DataFrameSuite extends QueryTest { assert(df.schema.map(_.name).toSeq === Seq("key", "value", "newCol")) } - test("renameColumn") { - val df = testData.toDataFrame.addColumn("newCol", col("key") + 1) - .renameColumn("value", "valueRenamed") + test("withColumnRenamed") { + val df = testData.toDF().withColumn("newCol", col("key") + 1) + .withColumnRenamed("value", "valueRenamed") checkAnswer( df, testData.collect().map { case Row(key: Int, value: String) => @@ -334,9 +484,74 @@ class DataFrameSuite extends QueryTest { assert(df.schema.map(_.name).toSeq === Seq("key", "valueRenamed", "newCol")) } + test("describe") { + val describeTestData = Seq( + ("Bob", 16, 176), + ("Alice", 32, 164), + ("David", 60, 192), + ("Amy", 24, 180)).toDF("name", "age", "height") + + val describeResult = Seq( + Row("count", 4, 4), + Row("mean", 33.0, 178.0), + Row("stddev", 16.583123951777, 10.0), + Row("min", 16, 164), + Row("max", 60, 192)) + + val emptyDescribeResult = Seq( + Row("count", 0, 0), + Row("mean", null, null), + Row("stddev", null, null), + Row("min", null, null), + Row("max", null, null)) + + def getSchemaAsSeq(df: DataFrame): Seq[String] = df.schema.map(_.name) + + val describeTwoCols = describeTestData.describe("age", "height") + assert(getSchemaAsSeq(describeTwoCols) === Seq("summary", "age", "height")) + checkAnswer(describeTwoCols, describeResult) + + val describeAllCols = describeTestData.describe() + assert(getSchemaAsSeq(describeAllCols) === Seq("summary", "age", "height")) + checkAnswer(describeAllCols, describeResult) + + val describeOneCol = describeTestData.describe("age") + assert(getSchemaAsSeq(describeOneCol) === Seq("summary", "age")) + checkAnswer(describeOneCol, describeResult.map { case Row(s, d, _) => Row(s, d)} ) + + val describeNoCol = describeTestData.select("name").describe() + assert(getSchemaAsSeq(describeNoCol) === Seq("summary")) + checkAnswer(describeNoCol, describeResult.map { case Row(s, _, _) => Row(s)} ) + + val emptyDescription = describeTestData.limit(0).describe() + assert(getSchemaAsSeq(emptyDescription) === Seq("summary", "age", "height")) + checkAnswer(emptyDescription, emptyDescribeResult) + } + test("apply on query results (SPARK-5462)") { val df = testData.sqlContext.sql("select key from testData") - checkAnswer(df("key"), testData.select('key).collect().toSeq) + checkAnswer(df.select(df("key")), testData.select('key).collect().toSeq) + } + + ignore("show") { + // This test case is intended ignored, but to make sure it compiles correctly + testData.select($"*").show() + testData.select($"*").show(1000) + } + + test("createDataFrame(RDD[Row], StructType) should convert UDTs (SPARK-6672)") { + val rowRDD = TestSQLContext.sparkContext.parallelize(Seq(Row(new ExamplePoint(1.0, 2.0)))) + val schema = StructType(Array(StructField("point", new ExamplePointUDT(), false))) + val df = TestSQLContext.createDataFrame(rowRDD, schema) + df.rdd.collect() } + test("SPARK-6899") { + val originalValue = TestSQLContext.conf.codegenEnabled + TestSQLContext.setConf(SQLConf.CODEGEN_ENABLED, "true") + checkAnswer( + decimalData.agg(avg('a)), + Row(new java.math.BigDecimal(2.0))) + TestSQLContext.setConf(SQLConf.CODEGEN_ENABLED, originalValue.toString) + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala index f0c939dbb195f..037d392c1f929 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala @@ -20,10 +20,11 @@ package org.apache.spark.sql import org.scalatest.BeforeAndAfterEach import org.apache.spark.sql.TestData._ -import org.apache.spark.sql.Dsl._ +import org.apache.spark.sql.functions._ import org.apache.spark.sql.catalyst.analysis.UnresolvedRelation import org.apache.spark.sql.execution.joins._ import org.apache.spark.sql.test.TestSQLContext._ +import org.apache.spark.sql.test.TestSQLContext.implicits._ class JoinSuite extends QueryTest with BeforeAndAfterEach { @@ -33,14 +34,14 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach { test("equi-join is hash-join") { val x = testData2.as("x") val y = testData2.as("y") - val join = x.join(y, $"x.a" === $"y.a", "inner").queryExecution.analyzed + val join = x.join(y, $"x.a" === $"y.a", "inner").queryExecution.optimizedPlan val planned = planner.HashJoin(join) assert(planned.size === 1) } def assertJoin(sqlString: String, c: Class[_]): Any = { - val rdd = sql(sqlString) - val physical = rdd.queryExecution.sparkPlan + val df = sql(sqlString) + val physical = df.queryExecution.sparkPlan val operators = physical.collect { case j: ShuffledHashJoin => j case j: HashOuterJoin => j @@ -50,6 +51,7 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach { case j: CartesianProduct => j case j: BroadcastNestedLoopJoin => j case j: BroadcastLeftSemiJoinHash => j + case j: SortMergeJoin => j } assert(operators.size === 1) @@ -61,6 +63,7 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach { test("join operator selection") { cacheManager.clearCache() + val SORTMERGEJOIN_ENABLED: Boolean = conf.sortMergeJoinEnabled Seq( ("SELECT * FROM testData LEFT SEMI JOIN testData2 ON key = a", classOf[LeftSemiJoinHash]), ("SELECT * FROM testData LEFT SEMI JOIN testData2", classOf[LeftSemiJoinBNL]), @@ -90,17 +93,41 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach { ("SELECT * FROM testData full JOIN testData2 ON (key * a != key + a)", classOf[BroadcastNestedLoopJoin]) ).foreach { case (query, joinClass) => assertJoin(query, joinClass) } + try { + conf.setConf("spark.sql.planner.sortMergeJoin", "true") + Seq( + ("SELECT * FROM testData JOIN testData2 ON key = a", classOf[SortMergeJoin]), + ("SELECT * FROM testData JOIN testData2 ON key = a and key = 2", classOf[SortMergeJoin]), + ("SELECT * FROM testData JOIN testData2 ON key = a where key = 2", classOf[SortMergeJoin]) + ).foreach { case (query, joinClass) => assertJoin(query, joinClass) } + } finally { + conf.setConf("spark.sql.planner.sortMergeJoin", SORTMERGEJOIN_ENABLED.toString) + } } test("broadcasted hash join operator selection") { cacheManager.clearCache() sql("CACHE TABLE testData") + val SORTMERGEJOIN_ENABLED: Boolean = conf.sortMergeJoinEnabled Seq( ("SELECT * FROM testData join testData2 ON key = a", classOf[BroadcastHashJoin]), ("SELECT * FROM testData join testData2 ON key = a and key = 2", classOf[BroadcastHashJoin]), - ("SELECT * FROM testData join testData2 ON key = a where key = 2", classOf[BroadcastHashJoin]) + ("SELECT * FROM testData join testData2 ON key = a where key = 2", + classOf[BroadcastHashJoin]) ).foreach { case (query, joinClass) => assertJoin(query, joinClass) } + try { + conf.setConf("spark.sql.planner.sortMergeJoin", "true") + Seq( + ("SELECT * FROM testData join testData2 ON key = a", classOf[BroadcastHashJoin]), + ("SELECT * FROM testData join testData2 ON key = a and key = 2", + classOf[BroadcastHashJoin]), + ("SELECT * FROM testData join testData2 ON key = a where key = 2", + classOf[BroadcastHashJoin]) + ).foreach { case (query, joinClass) => assertJoin(query, joinClass) } + } finally { + conf.setConf("spark.sql.planner.sortMergeJoin", SORTMERGEJOIN_ENABLED.toString) + } sql("UNCACHE TABLE testData") } @@ -108,7 +135,7 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach { test("multiple-key equi-join is hash-join") { val x = testData2.as("x") val y = testData2.as("y") - val join = x.join(y, ($"x.a" === $"y.a") && ($"x.b" === $"y.b")).queryExecution.analyzed + val join = x.join(y, ($"x.a" === $"y.a") && ($"x.b" === $"y.b")).queryExecution.optimizedPlan val planned = planner.HashJoin(join) assert(planned.size === 1) } @@ -409,8 +436,8 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach { } test("left semi join") { - val rdd = sql("SELECT * FROM testData2 LEFT SEMI JOIN testData ON key = a") - checkAnswer(rdd, + val df = sql("SELECT * FROM testData2 LEFT SEMI JOIN testData ON key = a") + checkAnswer(df, Row(1, 1) :: Row(1, 2) :: Row(2, 1) :: diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ListTablesSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ListTablesSuite.scala new file mode 100644 index 0000000000000..f9f41eb358bd5 --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/ListTablesSuite.scala @@ -0,0 +1,87 @@ +/* +* Licensed to the Apache Software Foundation (ASF) under one or more +* contributor license agreements. See the NOTICE file distributed with +* this work for additional information regarding copyright ownership. +* The ASF licenses this file to You under the Apache License, Version 2.0 +* (the "License"); you may not use this file except in compliance with +* the License. You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +*/ + +package org.apache.spark.sql + +import org.scalatest.BeforeAndAfter + +import org.apache.spark.sql.test.TestSQLContext +import org.apache.spark.sql.test.TestSQLContext._ +import org.apache.spark.sql.types.{BooleanType, StringType, StructField, StructType} + +class ListTablesSuite extends QueryTest with BeforeAndAfter { + + import org.apache.spark.sql.test.TestSQLContext.implicits._ + + val df = + sparkContext.parallelize((1 to 10).map(i => (i,s"str$i"))).toDF("key", "value") + + before { + df.registerTempTable("ListTablesSuiteTable") + } + + after { + catalog.unregisterTable(Seq("ListTablesSuiteTable")) + } + + test("get all tables") { + checkAnswer( + tables().filter("tableName = 'ListTablesSuiteTable'"), + Row("ListTablesSuiteTable", true)) + + checkAnswer( + sql("SHOW tables").filter("tableName = 'ListTablesSuiteTable'"), + Row("ListTablesSuiteTable", true)) + + catalog.unregisterTable(Seq("ListTablesSuiteTable")) + assert(tables().filter("tableName = 'ListTablesSuiteTable'").count() === 0) + } + + test("getting all Tables with a database name has no impact on returned table names") { + checkAnswer( + tables("DB").filter("tableName = 'ListTablesSuiteTable'"), + Row("ListTablesSuiteTable", true)) + + checkAnswer( + sql("show TABLES in DB").filter("tableName = 'ListTablesSuiteTable'"), + Row("ListTablesSuiteTable", true)) + + catalog.unregisterTable(Seq("ListTablesSuiteTable")) + assert(tables().filter("tableName = 'ListTablesSuiteTable'").count() === 0) + } + + test("query the returned DataFrame of tables") { + val expectedSchema = StructType( + StructField("tableName", StringType, false) :: + StructField("isTemporary", BooleanType, false) :: Nil) + + Seq(tables(), sql("SHOW TABLes")).foreach { + case tableDF => + assert(expectedSchema === tableDF.schema) + + tableDF.registerTempTable("tables") + checkAnswer( + sql("SELECT isTemporary, tableName from tables WHERE tableName = 'ListTablesSuiteTable'"), + Row(true, "ListTablesSuiteTable") + ) + checkAnswer( + tables().filter("tableName = 'tables'").select("tableName", "isTemporary"), + Row("tables", true)) + dropTempTable("tables") + } + } +} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala index f9ddd2ca5c567..59f9508444f25 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala @@ -19,6 +19,8 @@ package org.apache.spark.sql import java.util.{Locale, TimeZone} +import scala.collection.JavaConversions._ + import org.apache.spark.sql.catalyst.plans._ import org.apache.spark.sql.catalyst.util._ import org.apache.spark.sql.columnar.InMemoryRelation @@ -33,94 +35,125 @@ class QueryTest extends PlanTest { /** * Runs the plan and makes sure the answer contains all of the keywords, or the * none of keywords are listed in the answer - * @param rdd the [[DataFrame]] to be executed + * @param df the [[DataFrame]] to be executed * @param exists true for make sure the keywords are listed in the output, otherwise * to make sure none of the keyword are not listed in the output * @param keywords keyword in string array */ - def checkExistence(rdd: DataFrame, exists: Boolean, keywords: String*) { - val outputs = rdd.collect().map(_.mkString).mkString + def checkExistence(df: DataFrame, exists: Boolean, keywords: String*) { + val outputs = df.collect().map(_.mkString).mkString for (key <- keywords) { if (exists) { - assert(outputs.contains(key), s"Failed for $rdd ($key doens't exist in result)") + assert(outputs.contains(key), s"Failed for $df ($key doesn't exist in result)") } else { - assert(!outputs.contains(key), s"Failed for $rdd ($key existed in the result)") + assert(!outputs.contains(key), s"Failed for $df ($key existed in the result)") } } } /** * Runs the plan and makes sure the answer matches the expected result. - * @param rdd the [[DataFrame]] to be executed - * @param expectedAnswer the expected result, can either be an Any, Seq[Product], or Seq[ Seq[Any] ]. + * @param df the [[DataFrame]] to be executed + * @param expectedAnswer the expected result in a [[Seq]] of [[Row]]s. */ - protected def checkAnswer(rdd: DataFrame, expectedAnswer: Seq[Row]): Unit = { - val isSorted = rdd.logicalPlan.collect { case s: logical.Sort => s }.nonEmpty + protected def checkAnswer(df: DataFrame, expectedAnswer: Seq[Row]): Unit = { + QueryTest.checkAnswer(df, expectedAnswer) match { + case Some(errorMessage) => fail(errorMessage) + case None => + } + } + + protected def checkAnswer(df: DataFrame, expectedAnswer: Row): Unit = { + checkAnswer(df, Seq(expectedAnswer)) + } + + def sqlTest(sqlString: String, expectedAnswer: Seq[Row])(implicit sqlContext: SQLContext) { + test(sqlString) { + checkAnswer(sqlContext.sql(sqlString), expectedAnswer) + } + } + + /** + * Asserts that a given [[DataFrame]] will be executed using the given number of cached results. + */ + def assertCached(query: DataFrame, numCachedTables: Int = 1): Unit = { + val planWithCaching = query.queryExecution.withCachedData + val cachedData = planWithCaching collect { + case cached: InMemoryRelation => cached + } + + assert( + cachedData.size == numCachedTables, + s"Expected query to contain $numCachedTables, but it actually had ${cachedData.size}\n" + + planWithCaching) + } +} + +object QueryTest { + /** + * Runs the plan and makes sure the answer matches the expected result. + * If there was exception during the execution or the contents of the DataFrame does not + * match the expected result, an error message will be returned. Otherwise, a [[None]] will + * be returned. + * @param df the [[DataFrame]] to be executed + * @param expectedAnswer the expected result in a [[Seq]] of [[Row]]s. + */ + def checkAnswer(df: DataFrame, expectedAnswer: Seq[Row]): Option[String] = { + val isSorted = df.logicalPlan.collect { case s: logical.Sort => s }.nonEmpty def prepareAnswer(answer: Seq[Row]): Seq[Row] = { // Converts data to types that we can do equality comparison using Scala collections. // For BigDecimal type, the Scala type has a better definition of equality test (similar to // Java's java.math.BigDecimal.compareTo). + // For binary arrays, we convert it to Seq to avoid of calling java.util.Arrays.equals for + // equality test. val converted: Seq[Row] = answer.map { s => Row.fromSeq(s.toSeq.map { case d: java.math.BigDecimal => BigDecimal(d) + case b: Array[Byte] => b.toSeq case o => o }) } - if (!isSorted) converted.sortBy(_.toString) else converted + if (!isSorted) converted.sortBy(_.toString()) else converted } - val sparkAnswer = try rdd.collect().toSeq catch { + val sparkAnswer = try df.collect().toSeq catch { case e: Exception => - fail( + val errorMessage = s""" |Exception thrown while executing query: - |${rdd.queryExecution} + |${df.queryExecution} |== Exception == |$e |${org.apache.spark.sql.catalyst.util.stackTraceToString(e)} - """.stripMargin) + """.stripMargin + return Some(errorMessage) } if (prepareAnswer(expectedAnswer) != prepareAnswer(sparkAnswer)) { - fail(s""" + val errorMessage = + s""" |Results do not match for query: - |${rdd.logicalPlan} + |${df.logicalPlan} |== Analyzed Plan == - |${rdd.queryExecution.analyzed} + |${df.queryExecution.analyzed} |== Physical Plan == - |${rdd.queryExecution.executedPlan} + |${df.queryExecution.executedPlan} |== Results == |${sideBySide( - s"== Correct Answer - ${expectedAnswer.size} ==" +: - prepareAnswer(expectedAnswer).map(_.toString), - s"== Spark Answer - ${sparkAnswer.size} ==" +: - prepareAnswer(sparkAnswer).map(_.toString)).mkString("\n")} - """.stripMargin) + s"== Correct Answer - ${expectedAnswer.size} ==" +: + prepareAnswer(expectedAnswer).map(_.toString()), + s"== Spark Answer - ${sparkAnswer.size} ==" +: + prepareAnswer(sparkAnswer).map(_.toString())).mkString("\n")} + """.stripMargin + return Some(errorMessage) } - } - protected def checkAnswer(rdd: DataFrame, expectedAnswer: Row): Unit = { - checkAnswer(rdd, Seq(expectedAnswer)) + return None } - def sqlTest(sqlString: String, expectedAnswer: Seq[Row])(implicit sqlContext: SQLContext): Unit = { - test(sqlString) { - checkAnswer(sqlContext.sql(sqlString), expectedAnswer) - } - } - - /** - * Asserts that a given [[DataFrame]] will be executed using the given number of cached results. - */ - def assertCached(query: DataFrame, numCachedTables: Int = 1): Unit = { - val planWithCaching = query.queryExecution.withCachedData - val cachedData = planWithCaching collect { - case cached: InMemoryRelation => cached + def checkAnswer(df: DataFrame, expectedAnswer: java.util.List[Row]): String = { + checkAnswer(df, expectedAnswer.toSeq) match { + case Some(errorMessage) => errorMessage + case None => null } - - assert( - cachedData.size == numCachedTables, - s"Expected query to contain $numCachedTables, but it actually had ${cachedData.size}\n" + - planWithCaching) } - } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala index f5b945f468dad..bf6cf1321a056 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala @@ -17,9 +17,12 @@ package org.apache.spark.sql +import org.apache.spark.sql.execution.SparkSqlSerializer import org.scalatest.FunSuite import org.apache.spark.sql.catalyst.expressions.{GenericMutableRow, SpecificMutableRow} +import org.apache.spark.sql.test.TestSQLContext +import org.apache.spark.sql.test.TestSQLContext.implicits._ import org.apache.spark.sql.types._ class RowSuite extends FunSuite { @@ -27,7 +30,7 @@ class RowSuite extends FunSuite { test("create row") { val expected = new GenericMutableRow(4) expected.update(0, 2147483647) - expected.update(1, "this is a string") + expected.setString(1, "this is a string") expected.update(2, false) expected.update(3, null) val actual1 = Row(2147483647, "this is a string", false, null) @@ -50,4 +53,13 @@ class RowSuite extends FunSuite { row(0) = null assert(row.isNullAt(0)) } + + test("serialize w/ kryo") { + val row = Seq((1, Seq(1), Map(1 -> 1), BigDecimal(1))).toDF().first() + val serializer = new SparkSqlSerializer(TestSQLContext.sparkContext.getConf) + val instance = serializer.newInstance() + val ser = instance.serialize(row) + val de = instance.deserialize(ser).asInstanceOf[Row] + assert(de === row) + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala index 11502edf972e9..9e02e69fda3f2 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala @@ -17,23 +17,52 @@ package org.apache.spark.sql -import org.apache.spark.sql.test.TestSQLContext import org.scalatest.BeforeAndAfterAll -import org.apache.spark.sql.Dsl._ -import org.apache.spark.sql.catalyst.errors.TreeNodeException -import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan -import org.apache.spark.sql.types._ - +import org.apache.spark.sql.execution.GeneratedAggregate +import org.apache.spark.sql.functions._ import org.apache.spark.sql.TestData._ +import org.apache.spark.sql.test.TestSQLContext import org.apache.spark.sql.test.TestSQLContext.{udf => _, _} - +import org.apache.spark.sql.types._ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { // Make sure the tables are loaded. TestData import org.apache.spark.sql.test.TestSQLContext.implicits._ + val sqlCtx = TestSQLContext + + test("self join with aliases") { + Seq(1,2,3).map(i => (i, i.toString)).toDF("int", "str").registerTempTable("df") + + checkAnswer( + sql( + """ + |SELECT x.str, COUNT(*) + |FROM df x JOIN df y ON x.str = y.str + |GROUP BY x.str + """.stripMargin), + Row("1", 1) :: Row("2", 1) :: Row("3", 1) :: Nil) + } + + test("self join with alias in agg") { + Seq(1,2,3) + .map(i => (i, i.toString)) + .toDF("int", "str") + .groupBy("str") + .agg($"str", count("str").as("strCount")) + .registerTempTable("df") + + checkAnswer( + sql( + """ + |SELECT x.str, SUM(x.strCount) + |FROM df x JOIN df y ON x.str = y.str + |GROUP BY x.str + """.stripMargin), + Row("1", 1) :: Row("2", 1) :: Row("3", 1) :: Nil) + } test("SPARK-4625 support SORT BY in SimpleSQLParser & DSL") { checkAnswer( @@ -70,11 +99,105 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { sql("SELECT ABS(2.5)"), Row(2.5)) } - + test("aggregation with codegen") { val originalValue = conf.codegenEnabled setConf(SQLConf.CODEGEN_ENABLED, "true") - sql("SELECT key FROM testData GROUP BY key").collect() + // Prepare a table that we can group some rows. + table("testData") + .unionAll(table("testData")) + .unionAll(table("testData")) + .registerTempTable("testData3x") + + def testCodeGen(sqlText: String, expectedResults: Seq[Row]): Unit = { + val df = sql(sqlText) + // First, check if we have GeneratedAggregate. + var hasGeneratedAgg = false + df.queryExecution.executedPlan.foreach { + case generatedAgg: GeneratedAggregate => hasGeneratedAgg = true + case _ => + } + if (!hasGeneratedAgg) { + fail( + s""" + |Codegen is enabled, but query $sqlText does not have GeneratedAggregate in the plan. + |${df.queryExecution.simpleString} + """.stripMargin) + } + // Then, check results. + checkAnswer(df, expectedResults) + } + + // Just to group rows. + testCodeGen( + "SELECT key FROM testData3x GROUP BY key", + (1 to 100).map(Row(_))) + // COUNT + testCodeGen( + "SELECT key, count(value) FROM testData3x GROUP BY key", + (1 to 100).map(i => Row(i, 3))) + testCodeGen( + "SELECT count(key) FROM testData3x", + Row(300) :: Nil) + // COUNT DISTINCT ON int + testCodeGen( + "SELECT value, count(distinct key) FROM testData3x GROUP BY value", + (1 to 100).map(i => Row(i.toString, 1))) + testCodeGen( + "SELECT count(distinct key) FROM testData3x", + Row(100) :: Nil) + // SUM + testCodeGen( + "SELECT value, sum(key) FROM testData3x GROUP BY value", + (1 to 100).map(i => Row(i.toString, 3 * i))) + testCodeGen( + "SELECT sum(key), SUM(CAST(key as Double)) FROM testData3x", + Row(5050 * 3, 5050 * 3.0) :: Nil) + // AVERAGE + testCodeGen( + "SELECT value, avg(key) FROM testData3x GROUP BY value", + (1 to 100).map(i => Row(i.toString, i))) + testCodeGen( + "SELECT avg(key) FROM testData3x", + Row(50.5) :: Nil) + // MAX + testCodeGen( + "SELECT value, max(key) FROM testData3x GROUP BY value", + (1 to 100).map(i => Row(i.toString, i))) + testCodeGen( + "SELECT max(key) FROM testData3x", + Row(100) :: Nil) + // MIN + testCodeGen( + "SELECT value, min(key) FROM testData3x GROUP BY value", + (1 to 100).map(i => Row(i.toString, i))) + testCodeGen( + "SELECT min(key) FROM testData3x", + Row(1) :: Nil) + // Some combinations. + testCodeGen( + """ + |SELECT + | value, + | sum(key), + | max(key), + | min(key), + | avg(key), + | count(key), + | count(distinct key) + |FROM testData3x + |GROUP BY value + """.stripMargin, + (1 to 100).map(i => Row(i.toString, i*3, i, i, i, 3, 1))) + testCodeGen( + "SELECT max(key), min(key), avg(key), count(key), count(distinct key) FROM testData3x", + Row(100, 1, 50.5, 300, 100) :: Nil) + // Aggregate with Code generation handling all null values + testCodeGen( + "SELECT sum('a'), avg('a'), count(null) FROM testData", + Row(0, null, 0) :: Nil) + + dropTempTable("testData3x") setConf(SQLConf.CODEGEN_ENABLED, originalValue.toString) } @@ -150,7 +273,10 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { Row(java.sql.Timestamp.valueOf("1969-12-31 16:00:00.002"))) checkAnswer(sql( - "SELECT time FROM timestamps WHERE time IN ('1969-12-31 16:00:00.001','1969-12-31 16:00:00.002')"), + """ + |SELECT time FROM timestamps + |WHERE time IN ('1969-12-31 16:00:00.001','1969-12-31 16:00:00.002') + """.stripMargin), Seq(Row(java.sql.Timestamp.valueOf("1969-12-31 16:00:00.001")), Row(java.sql.Timestamp.valueOf("1969-12-31 16:00:00.002")))) @@ -216,7 +342,7 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { Row("1")) } - def sortTest() = { + def sortTest(): Unit = { checkAnswer( sql("SELECT * FROM testData2 ORDER BY a ASC, b ASC"), Seq(Row(1,1), Row(1,2), Row(2,1), Row(2,2), Row(3,1), Row(3,2))) @@ -272,6 +398,26 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { setConf(SQLConf.EXTERNAL_SORT, before.toString) } + test("SPARK-6927 sorting with codegen on") { + val externalbefore = conf.externalSortEnabled + val codegenbefore = conf.codegenEnabled + setConf(SQLConf.EXTERNAL_SORT, "false") + setConf(SQLConf.CODEGEN_ENABLED, "true") + sortTest() + setConf(SQLConf.EXTERNAL_SORT, externalbefore.toString) + setConf(SQLConf.CODEGEN_ENABLED, codegenbefore.toString) + } + + test("SPARK-6927 external sorting with codegen on") { + val externalbefore = conf.externalSortEnabled + val codegenbefore = conf.codegenEnabled + setConf(SQLConf.CODEGEN_ENABLED, "true") + setConf(SQLConf.EXTERNAL_SORT, "true") + sortTest() + setConf(SQLConf.EXTERNAL_SORT, externalbefore.toString) + setConf(SQLConf.CODEGEN_ENABLED, codegenbefore.toString) + } + test("limit") { checkAnswer( sql("SELECT * FROM testData LIMIT 10"), @@ -286,6 +432,26 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { mapData.collect().take(1).map(Row.fromTuple).toSeq) } + test("CTE feature") { + checkAnswer( + sql("with q1 as (select * from testData limit 10) select * from q1"), + testData.take(10).toSeq) + + checkAnswer( + sql(""" + |with q1 as (select * from testData where key= '5'), + |q2 as (select * from testData where key = '4') + |select * from q1 union all select * from q2""".stripMargin), + Row(5, "5") :: Row(4, "4") :: Nil) + + } + + test("Allow only a single WITH clause per query") { + intercept[RuntimeException] { + sql("with q1 as (select * from testData) with q2 as (select * from q1) select * from q2") + } + } + test("date row") { checkAnswer(sql( """select cast("2015-01-28" as date) from testData limit 1"""), @@ -295,7 +461,10 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { test("from follow multiple brackets") { checkAnswer(sql( - "select key from ((select * from testData limit 1) union all (select * from testData limit 1)) x limit 1"), + """ + |select key from ((select * from testData limit 1) + | union all (select * from testData limit 1)) x limit 1 + """.stripMargin), Row(1) ) @@ -305,7 +474,11 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { ) checkAnswer(sql( - "select key from (select * from testData limit 1 union all select * from testData limit 1) x limit 1"), + """ + |select key from + | (select * from testData limit 1 union all select * from testData limit 1) x + | limit 1 + """.stripMargin), Row(1) ) } @@ -352,7 +525,10 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { Seq(Row(1, 0), Row(2, 1))) checkAnswer( - sql("SELECT COUNT(a), COUNT(b), COUNT(1), COUNT(DISTINCT a), COUNT(DISTINCT b) FROM testData3"), + sql( + """ + |SELECT COUNT(a), COUNT(b), COUNT(1), COUNT(DISTINCT a), COUNT(DISTINCT b) FROM testData3 + """.stripMargin), Row(2, 1, 2, 2, 1)) } @@ -589,7 +765,7 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { ("1" :: "2" :: "3" :: "4" :: "A" :: "B" :: "C" :: "D" :: "E" :: "F" :: Nil).map(Row(_))) // Column type mismatches where a coercion is not possible, in this case between integer // and array types, trigger a TreeNodeException. - intercept[TreeNodeException[_]] { + intercept[AnalysisException] { sql("SELECT data FROM arrayData UNION SELECT 1 FROM arrayData").collect() } } @@ -669,7 +845,7 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { Row(values(0).toInt, values(1), values(2).toBoolean, v4) } - val df1 = applySchema(rowRDD1, schema1) + val df1 = sqlCtx.createDataFrame(rowRDD1, schema1) df1.registerTempTable("applySchema1") checkAnswer( sql("SELECT * FROM applySchema1"), @@ -699,7 +875,7 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { Row(Row(values(0).toInt, values(2).toBoolean), Map(values(1) -> v4)) } - val df2 = applySchema(rowRDD2, schema2) + val df2 = sqlCtx.createDataFrame(rowRDD2, schema2) df2.registerTempTable("applySchema2") checkAnswer( sql("SELECT * FROM applySchema2"), @@ -724,7 +900,7 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { Row(Row(values(0).toInt, values(2).toBoolean), scala.collection.mutable.Map(values(1) -> v4)) } - val df3 = applySchema(rowRDD3, schema2) + val df3 = sqlCtx.createDataFrame(rowRDD3, schema2) df3.registerTempTable("applySchema3") checkAnswer( @@ -769,7 +945,7 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { .build() val schemaWithMeta = new StructType(Array( schema("id"), schema("name").copy(metadata = metadata), schema("age"))) - val personWithMeta = applySchema(person.rdd, schemaWithMeta) + val personWithMeta = sqlCtx.createDataFrame(person.rdd, schemaWithMeta) def validateMetadata(rdd: DataFrame): Unit = { assert(rdd.schema("name").metadata.getString(docKey) == docValue) } @@ -805,10 +981,8 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { test("throw errors for non-aggregate attributes with aggregation") { def checkAggregation(query: String, isInvalidQuery: Boolean = true) { if (isInvalidQuery) { - val e = intercept[TreeNodeException[LogicalPlan]](sql(query).queryExecution.analyzed) - assert( - e.getMessage.startsWith("Expression not in GROUP BY"), - "Non-aggregate attribute(s) not detected\n") + val e = intercept[AnalysisException](sql(query).queryExecution.analyzed) + assert(e.getMessage contains "group by") } else { // Should not throw sql(query).queryExecution.analyzed @@ -967,9 +1141,10 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { } test("SPARK-3483 Special chars in column names") { - val data = sparkContext.parallelize(Seq("""{"key?number1": "value1", "key.number2": "value2"}""")) + val data = sparkContext.parallelize( + Seq("""{"key?number1": "value1", "key.number2": "value2"}""")) jsonRDD(data).registerTempTable("records") - sql("SELECT `key?number1` FROM records") + sql("SELECT `key?number1`, `key.number2` FROM records") } test("SPARK-3814 Support Bitwise & operator") { @@ -1035,10 +1210,10 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { test("Supporting relational operator '<=>' in Spark SQL") { val nullCheckData1 = TestData(1,"1") :: TestData(2,null) :: Nil val rdd1 = sparkContext.parallelize((0 to 1).map(i => nullCheckData1(i))) - rdd1.registerTempTable("nulldata1") + rdd1.toDF().registerTempTable("nulldata1") val nullCheckData2 = TestData(1,"1") :: TestData(2,null) :: Nil val rdd2 = sparkContext.parallelize((0 to 1).map(i => nullCheckData2(i))) - rdd2.registerTempTable("nulldata2") + rdd2.toDF().registerTempTable("nulldata2") checkAnswer(sql("SELECT nulldata1.key FROM nulldata1 join " + "nulldata2 on nulldata1.value <=> nulldata2.value"), (1 to 2).map(i => Row(i))) @@ -1047,7 +1222,34 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { test("Multi-column COUNT(DISTINCT ...)") { val data = TestData(1,"val_1") :: TestData(2,"val_2") :: Nil val rdd = sparkContext.parallelize((0 to 1).map(i => data(i))) - rdd.registerTempTable("distinctData") + rdd.toDF().registerTempTable("distinctData") checkAnswer(sql("SELECT COUNT(DISTINCT key,value) FROM distinctData"), Row(2)) } + + test("SPARK-6145: ORDER BY test for nested fields") { + jsonRDD(sparkContext.makeRDD("""{"a": {"b": 1, "a": {"a": 1}}, "c": [{"d": 1}]}""" :: Nil)) + .registerTempTable("nestedOrder") + + checkAnswer(sql("SELECT 1 FROM nestedOrder ORDER BY a.b"), Row(1)) + checkAnswer(sql("SELECT a.b FROM nestedOrder ORDER BY a.b"), Row(1)) + checkAnswer(sql("SELECT 1 FROM nestedOrder ORDER BY a.a.a"), Row(1)) + checkAnswer(sql("SELECT a.a.a FROM nestedOrder ORDER BY a.a.a"), Row(1)) + checkAnswer(sql("SELECT 1 FROM nestedOrder ORDER BY c[0].d"), Row(1)) + checkAnswer(sql("SELECT c[0].d FROM nestedOrder ORDER BY c[0].d"), Row(1)) + } + + test("SPARK-6145: special cases") { + jsonRDD(sparkContext.makeRDD( + """{"a": {"b": [1]}, "b": [{"a": 1}], "c0": {"a": 1}}""" :: Nil)).registerTempTable("t") + checkAnswer(sql("SELECT a.b[0] FROM t ORDER BY c0.a"), Row(1)) + checkAnswer(sql("SELECT b[0].a FROM t ORDER BY c0.a"), Row(1)) + } + + test("SPARK-6898: complete support for special chars in column names") { + jsonRDD(sparkContext.makeRDD( + """{"a": {"c.b": 1}, "b.$q": [{"a@!.q": 1}], "q.w": {"w.i&": [1]}}""" :: Nil)) + .registerTempTable("t") + + checkAnswer(sql("SELECT a.`c.b`, `b.$q`[0].`a@!.q`, `q.w`.`w.i&`[0] FROM t"), Row(1, 1, 1)) + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala index 93782619826f0..3fa00fd9d0ccb 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala @@ -80,20 +80,20 @@ class ScalaReflectionRelationSuite extends FunSuite { test("query case class RDD") { val data = ReflectData("a", 1, 1L, 1.toFloat, 1.toDouble, 1.toShort, 1.toByte, true, - new java.math.BigDecimal(1), new Date(12345), new Timestamp(12345), Seq(1,2,3)) + new java.math.BigDecimal(1), new Date(12345), new Timestamp(12345), Seq(1,2,3)) val rdd = sparkContext.parallelize(data :: Nil) - rdd.registerTempTable("reflectData") + rdd.toDF().registerTempTable("reflectData") assert(sql("SELECT * FROM reflectData").collect().head === Row("a", 1, 1L, 1.toFloat, 1.toDouble, 1.toShort, 1.toByte, true, - new java.math.BigDecimal(1), new Date(70, 0, 1), // This is 1970-01-01 + new java.math.BigDecimal(1), Date.valueOf("1970-01-01"), new Timestamp(12345), Seq(1,2,3))) } test("query case class RDD with nulls") { val data = NullReflectData(null, null, null, null, null, null, null) val rdd = sparkContext.parallelize(data :: Nil) - rdd.registerTempTable("reflectNullData") + rdd.toDF().registerTempTable("reflectNullData") assert(sql("SELECT * FROM reflectNullData").collect().head === Row.fromSeq(Seq.fill(7)(null))) } @@ -101,15 +101,16 @@ class ScalaReflectionRelationSuite extends FunSuite { test("query case class RDD with Nones") { val data = OptionalReflectData(None, None, None, None, None, None, None) val rdd = sparkContext.parallelize(data :: Nil) - rdd.registerTempTable("reflectOptionalData") + rdd.toDF().registerTempTable("reflectOptionalData") - assert(sql("SELECT * FROM reflectOptionalData").collect().head === Row.fromSeq(Seq.fill(7)(null))) + assert(sql("SELECT * FROM reflectOptionalData").collect().head === + Row.fromSeq(Seq.fill(7)(null))) } // Equality is broken for Arrays, so we test that separately. test("query binary data") { val rdd = sparkContext.parallelize(ReflectBinary(Array[Byte](1)) :: Nil) - rdd.registerTempTable("reflectBinary") + rdd.toDF().registerTempTable("reflectBinary") val result = sql("SELECT data FROM reflectBinary").collect().head(0).asInstanceOf[Array[Byte]] assert(result.toSeq === Seq[Byte](1)) @@ -128,7 +129,7 @@ class ScalaReflectionRelationSuite extends FunSuite { Map(10 -> Some(100L), 20 -> Some(200L), 30 -> None), Nested(None, "abc"))) val rdd = sparkContext.parallelize(data :: Nil) - rdd.registerTempTable("reflectComplexData") + rdd.toDF().registerTempTable("reflectComplexData") assert(sql("SELECT * FROM reflectComplexData").collect().head === new GenericRow(Array[Any]( diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SerializationSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SerializationSuite.scala new file mode 100644 index 0000000000000..6f6d3c9c243d4 --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/SerializationSuite.scala @@ -0,0 +1,32 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql + +import org.scalatest.FunSuite + +import org.apache.spark.SparkConf +import org.apache.spark.serializer.JavaSerializer +import org.apache.spark.sql.test.TestSQLContext + +class SerializationSuite extends FunSuite { + + test("[SPARK-5235] SQLContext should be serializable") { + val sqlContext = new SQLContext(TestSQLContext.sparkContext) + new JavaSerializer(new SparkConf()).newInstance().serialize(sqlContext) + } +} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala index 0ed437edd05fd..225b51bd73d6c 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala @@ -20,20 +20,19 @@ package org.apache.spark.sql import java.sql.Timestamp import org.apache.spark.sql.catalyst.plans.logical -import org.apache.spark.sql.Dsl._ -import org.apache.spark.sql.test._ import org.apache.spark.sql.test.TestSQLContext.implicits._ +import org.apache.spark.sql.test._ case class TestData(key: Int, value: String) object TestData { val testData = TestSQLContext.sparkContext.parallelize( - (1 to 100).map(i => TestData(i, i.toString))).toDataFrame + (1 to 100).map(i => TestData(i, i.toString))).toDF() testData.registerTempTable("testData") val negativeData = TestSQLContext.sparkContext.parallelize( - (1 to 100).map(i => TestData(-i, (-i).toString))).toDataFrame + (1 to 100).map(i => TestData(-i, (-i).toString))).toDF() negativeData.registerTempTable("negativeData") case class LargeAndSmallInts(a: Int, b: Int) @@ -44,7 +43,7 @@ object TestData { LargeAndSmallInts(2147483645, 1) :: LargeAndSmallInts(2, 2) :: LargeAndSmallInts(2147483646, 1) :: - LargeAndSmallInts(3, 2) :: Nil).toDataFrame + LargeAndSmallInts(3, 2) :: Nil).toDF() largeAndSmallInts.registerTempTable("largeAndSmallInts") case class TestData2(a: Int, b: Int) @@ -55,7 +54,7 @@ object TestData { TestData2(2, 1) :: TestData2(2, 2) :: TestData2(3, 1) :: - TestData2(3, 2) :: Nil, 2).toDataFrame + TestData2(3, 2) :: Nil, 2).toDF() testData2.registerTempTable("testData2") case class DecimalData(a: BigDecimal, b: BigDecimal) @@ -67,7 +66,7 @@ object TestData { DecimalData(2, 1) :: DecimalData(2, 2) :: DecimalData(3, 1) :: - DecimalData(3, 2) :: Nil).toDataFrame + DecimalData(3, 2) :: Nil).toDF() decimalData.registerTempTable("decimalData") case class BinaryData(a: Array[Byte], b: Int) @@ -77,14 +76,14 @@ object TestData { BinaryData("22".getBytes(), 5) :: BinaryData("122".getBytes(), 3) :: BinaryData("121".getBytes(), 2) :: - BinaryData("123".getBytes(), 4) :: Nil).toDataFrame + BinaryData("123".getBytes(), 4) :: Nil).toDF() binaryData.registerTempTable("binaryData") case class TestData3(a: Int, b: Option[Int]) val testData3 = TestSQLContext.sparkContext.parallelize( TestData3(1, None) :: - TestData3(2, Some(2)) :: Nil).toDataFrame + TestData3(2, Some(2)) :: Nil).toDF() testData3.registerTempTable("testData3") val emptyTableData = logical.LocalRelation($"a".int, $"b".int) @@ -97,7 +96,7 @@ object TestData { UpperCaseData(3, "C") :: UpperCaseData(4, "D") :: UpperCaseData(5, "E") :: - UpperCaseData(6, "F") :: Nil).toDataFrame + UpperCaseData(6, "F") :: Nil).toDF() upperCaseData.registerTempTable("upperCaseData") case class LowerCaseData(n: Int, l: String) @@ -106,7 +105,7 @@ object TestData { LowerCaseData(1, "a") :: LowerCaseData(2, "b") :: LowerCaseData(3, "c") :: - LowerCaseData(4, "d") :: Nil).toDataFrame + LowerCaseData(4, "d") :: Nil).toDF() lowerCaseData.registerTempTable("lowerCaseData") case class ArrayData(data: Seq[Int], nestedData: Seq[Seq[Int]]) @@ -114,7 +113,7 @@ object TestData { TestSQLContext.sparkContext.parallelize( ArrayData(Seq(1,2,3), Seq(Seq(1,2,3))) :: ArrayData(Seq(2,3,4), Seq(Seq(2,3,4))) :: Nil) - arrayData.registerTempTable("arrayData") + arrayData.toDF().registerTempTable("arrayData") case class MapData(data: scala.collection.Map[Int, String]) val mapData = @@ -124,18 +123,18 @@ object TestData { MapData(Map(1 -> "a3", 2 -> "b3", 3 -> "c3")) :: MapData(Map(1 -> "a4", 2 -> "b4")) :: MapData(Map(1 -> "a5")) :: Nil) - mapData.registerTempTable("mapData") + mapData.toDF().registerTempTable("mapData") case class StringData(s: String) val repeatedData = TestSQLContext.sparkContext.parallelize(List.fill(2)(StringData("test"))) - repeatedData.registerTempTable("repeatedData") + repeatedData.toDF().registerTempTable("repeatedData") val nullableRepeatedData = TestSQLContext.sparkContext.parallelize( List.fill(2)(StringData(null)) ++ List.fill(2)(StringData("test"))) - nullableRepeatedData.registerTempTable("nullableRepeatedData") + nullableRepeatedData.toDF().registerTempTable("nullableRepeatedData") case class NullInts(a: Integer) val nullInts = @@ -144,7 +143,7 @@ object TestData { NullInts(2) :: NullInts(3) :: NullInts(null) :: Nil - ) + ).toDF() nullInts.registerTempTable("nullInts") val allNulls = @@ -152,7 +151,7 @@ object TestData { NullInts(null) :: NullInts(null) :: NullInts(null) :: - NullInts(null) :: Nil) + NullInts(null) :: Nil).toDF() allNulls.registerTempTable("allNulls") case class NullStrings(n: Int, s: String) @@ -160,11 +159,15 @@ object TestData { TestSQLContext.sparkContext.parallelize( NullStrings(1, "abc") :: NullStrings(2, "ABC") :: - NullStrings(3, null) :: Nil).toDataFrame + NullStrings(3, null) :: Nil).toDF() nullStrings.registerTempTable("nullStrings") case class TableName(tableName: String) - TestSQLContext.sparkContext.parallelize(TableName("test") :: Nil).registerTempTable("tableName") + TestSQLContext + .sparkContext + .parallelize(TableName("test") :: Nil) + .toDF() + .registerTempTable("tableName") val unparsedStrings = TestSQLContext.sparkContext.parallelize( @@ -177,29 +180,29 @@ object TestData { val timestamps = TestSQLContext.sparkContext.parallelize((1 to 3).map { i => TimestampField(new Timestamp(i)) }) - timestamps.registerTempTable("timestamps") + timestamps.toDF().registerTempTable("timestamps") case class IntField(i: Int) // An RDD with 4 elements and 8 partitions val withEmptyParts = TestSQLContext.sparkContext.parallelize((1 to 4).map(IntField), 8) - withEmptyParts.registerTempTable("withEmptyParts") + withEmptyParts.toDF().registerTempTable("withEmptyParts") case class Person(id: Int, name: String, age: Int) case class Salary(personId: Int, salary: Double) val person = TestSQLContext.sparkContext.parallelize( Person(0, "mike", 30) :: - Person(1, "jim", 20) :: Nil) + Person(1, "jim", 20) :: Nil).toDF() person.registerTempTable("person") val salary = TestSQLContext.sparkContext.parallelize( Salary(0, 2000.0) :: - Salary(1, 1000.0) :: Nil) + Salary(1, 1000.0) :: Nil).toDF() salary.registerTempTable("salary") - case class ComplexData(m: Map[Int, String], s: TestData, a: Seq[Int], b: Boolean) + case class ComplexData(m: Map[String, Int], s: TestData, a: Seq[Int], b: Boolean) val complexData = TestSQLContext.sparkContext.parallelize( - ComplexData(Map(1 -> "1"), TestData(1, "1"), Seq(1), true) - :: ComplexData(Map(2 -> "2"), TestData(2, "2"), Seq(2), false) - :: Nil).toDataFrame + ComplexData(Map("1" -> 1), TestData(1, "1"), Seq(1), true) + :: ComplexData(Map("2" -> 2), TestData(2, "2"), Seq(2), false) + :: Nil).toDF() complexData.registerTempTable("complexData") } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala index 95923f9aad931..d615542ab50a7 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala @@ -17,11 +17,11 @@ package org.apache.spark.sql -import org.apache.spark.sql.Dsl.StringToColumn import org.apache.spark.sql.test._ /* Implicits */ import TestSQLContext._ +import TestSQLContext.implicits._ case class FunctionResult(f1: String, f2: String) @@ -50,4 +50,10 @@ class UDFSuite extends QueryTest { .select($"ret.f1").head().getString(0) assert(result === "test") } + + test("udf that is transformed") { + udf.register("makeStruct", (x: Int, y: Int) => (x, y)) + // 1 + 1 is constant folded causing a transformation. + assert(sql("SELECT makeStruct(1 + 1, 2)").first().getAs[Row](0) === Row(2, 2)) + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala index 3c1657cd5fc3a..2672e20deadc5 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala @@ -17,15 +17,22 @@ package org.apache.spark.sql +import java.io.File + +import org.apache.spark.util.Utils + import scala.beans.{BeanInfo, BeanProperty} +import com.clearspring.analytics.stream.cardinality.HyperLogLog + import org.apache.spark.rdd.RDD -import org.apache.spark.sql.Dsl._ +import org.apache.spark.sql.catalyst.expressions.{OpenHashSetUDT, HyperLogLogUDT} +import org.apache.spark.sql.functions._ import org.apache.spark.sql.test.TestSQLContext import org.apache.spark.sql.test.TestSQLContext.{sparkContext, sql} import org.apache.spark.sql.test.TestSQLContext.implicits._ import org.apache.spark.sql.types._ - +import org.apache.spark.util.collection.OpenHashSet @SQLUserDefinedType(udt = classOf[MyDenseVectorUDT]) private[sql] class MyDenseVector(val data: Array[Double]) extends Serializable { @@ -59,14 +66,16 @@ private[sql] class MyDenseVectorUDT extends UserDefinedType[MyDenseVector] { } } - override def userClass = classOf[MyDenseVector] + override def userClass: Class[MyDenseVector] = classOf[MyDenseVector] + + private[spark] override def asNullable: MyDenseVectorUDT = this } class UserDefinedTypeSuite extends QueryTest { val points = Seq( MyLabeledPoint(1.0, new MyDenseVector(Array(0.1, 1.0))), MyLabeledPoint(0.0, new MyDenseVector(Array(0.2, 2.0)))) - val pointsRDD: RDD[MyLabeledPoint] = sparkContext.parallelize(points) + val pointsRDD = sparkContext.parallelize(points).toDF() test("register user type: MyDenseVector for MyLabeledPoint") { @@ -91,4 +100,45 @@ class UserDefinedTypeSuite extends QueryTest { sql("SELECT testType(features) from points"), Seq(Row(true), Row(true))) } + + + test("UDTs with Parquet") { + val tempDir = Utils.createTempDir() + tempDir.delete() + pointsRDD.saveAsParquetFile(tempDir.getCanonicalPath) + } + + test("Repartition UDTs with Parquet") { + val tempDir = Utils.createTempDir() + tempDir.delete() + pointsRDD.repartition(1).saveAsParquetFile(tempDir.getCanonicalPath) + } + + // Tests to make sure that all operators correctly convert types on the way out. + test("Local UDTs") { + val df = Seq((1, new MyDenseVector(Array(0.1, 1.0)))).toDF("int", "vec") + df.collect()(0).getAs[MyDenseVector](1) + df.take(1)(0).getAs[MyDenseVector](1) + df.limit(1).groupBy('int).agg(first('vec)).collect()(0).getAs[MyDenseVector](0) + df.orderBy('int).limit(1).groupBy('int).agg(first('vec)).collect()(0).getAs[MyDenseVector](0) + } + + test("HyperLogLogUDT") { + val hyperLogLogUDT = HyperLogLogUDT + val hyperLogLog = new HyperLogLog(0.4) + (1 to 10).foreach(i => hyperLogLog.offer(Row(i))) + + val actual = hyperLogLogUDT.deserialize(hyperLogLogUDT.serialize(hyperLogLog)) + assert(actual.cardinality() === hyperLogLog.cardinality()) + assert(java.util.Arrays.equals(actual.getBytes, hyperLogLog.getBytes)) + } + + test("OpenHashSetUDT") { + val openHashSetUDT = new OpenHashSetUDT(IntegerType) + val set = new OpenHashSet[Int] + (1 to 10).foreach(i => set.add(i)) + + val actual = openHashSetUDT.deserialize(openHashSetUDT.serialize(set)) + assert(actual.iterator.toSet === set.iterator.toSet) + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala index 581fccf8ee613..fec487f1d2c82 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala @@ -29,6 +29,7 @@ class ColumnStatsSuite extends FunSuite { testColumnStats(classOf[LongColumnStats], LONG, Row(Long.MaxValue, Long.MinValue, 0)) testColumnStats(classOf[FloatColumnStats], FLOAT, Row(Float.MaxValue, Float.MinValue, 0)) testColumnStats(classOf[DoubleColumnStats], DOUBLE, Row(Double.MaxValue, Double.MinValue, 0)) + testColumnStats(classOf[FixedDecimalColumnStats], FIXED_DECIMAL(15, 10), Row(null, null, 0)) testColumnStats(classOf[StringColumnStats], STRING, Row(null, null, 0)) testColumnStats(classOf[DateColumnStats], DATE, Row(Int.MaxValue, Int.MinValue, 0)) testColumnStats(classOf[TimestampColumnStats], TIMESTAMP, Row(null, null, 0)) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala index 9ce845912f1c7..b48bed1871c50 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala @@ -20,9 +20,12 @@ package org.apache.spark.sql.columnar import java.nio.ByteBuffer import java.sql.Timestamp +import com.esotericsoftware.kryo.{Serializer, Kryo} +import com.esotericsoftware.kryo.io.{Input, Output} +import org.apache.spark.serializer.KryoRegistrator import org.scalatest.FunSuite -import org.apache.spark.Logging +import org.apache.spark.{SparkConf, Logging} import org.apache.spark.sql.catalyst.expressions.GenericMutableRow import org.apache.spark.sql.columnar.ColumnarTestUtils._ import org.apache.spark.sql.execution.SparkSqlSerializer @@ -33,8 +36,9 @@ class ColumnTypeSuite extends FunSuite with Logging { test("defaultSize") { val checks = Map( - INT -> 4, SHORT -> 2, LONG -> 8, BYTE -> 1, DOUBLE -> 8, FLOAT -> 4, BOOLEAN -> 1, - STRING -> 8, DATE -> 4, TIMESTAMP -> 12, BINARY -> 16, GENERIC -> 16) + INT -> 4, SHORT -> 2, LONG -> 8, BYTE -> 1, DOUBLE -> 8, FLOAT -> 4, + FIXED_DECIMAL(15, 10) -> 8, BOOLEAN -> 1, STRING -> 8, DATE -> 4, TIMESTAMP -> 12, + BINARY -> 16, GENERIC -> 16) checks.foreach { case (columnType, expectedSize) => assertResult(expectedSize, s"Wrong defaultSize for $columnType") { @@ -56,22 +60,23 @@ class ColumnTypeSuite extends FunSuite with Logging { } } - checkActualSize(INT, Int.MaxValue, 4) - checkActualSize(SHORT, Short.MaxValue, 2) - checkActualSize(LONG, Long.MaxValue, 8) - checkActualSize(BYTE, Byte.MaxValue, 1) - checkActualSize(DOUBLE, Double.MaxValue, 8) - checkActualSize(FLOAT, Float.MaxValue, 4) - checkActualSize(BOOLEAN, true, 1) - checkActualSize(STRING, "hello", 4 + "hello".getBytes("utf-8").length) - checkActualSize(DATE, 0, 4) + checkActualSize(INT, Int.MaxValue, 4) + checkActualSize(SHORT, Short.MaxValue, 2) + checkActualSize(LONG, Long.MaxValue, 8) + checkActualSize(BYTE, Byte.MaxValue, 1) + checkActualSize(DOUBLE, Double.MaxValue, 8) + checkActualSize(FLOAT, Float.MaxValue, 4) + checkActualSize(FIXED_DECIMAL(15, 10), Decimal(0, 15, 10), 8) + checkActualSize(BOOLEAN, true, 1) + checkActualSize(STRING, UTF8String("hello"), 4 + "hello".getBytes("utf-8").length) + checkActualSize(DATE, 0, 4) checkActualSize(TIMESTAMP, new Timestamp(0L), 12) val binary = Array.fill[Byte](4)(0: Byte) checkActualSize(BINARY, binary, 4 + 4) val generic = Map(1 -> "a") - checkActualSize(GENERIC, SparkSqlSerializer.serialize(generic), 4 + 11) + checkActualSize(GENERIC, SparkSqlSerializer.serialize(generic), 4 + 8) } testNativeColumnType[BooleanType.type]( @@ -93,13 +98,21 @@ class ColumnTypeSuite extends FunSuite with Logging { testNativeColumnType[DoubleType.type](DOUBLE, _.putDouble(_), _.getDouble) + testNativeColumnType[DecimalType]( + FIXED_DECIMAL(15, 10), + (buffer: ByteBuffer, decimal: Decimal) => { + buffer.putLong(decimal.toUnscaledLong) + }, + (buffer: ByteBuffer) => { + Decimal(buffer.getLong(), 15, 10) + }) + testNativeColumnType[FloatType.type](FLOAT, _.putFloat(_), _.getFloat) testNativeColumnType[StringType.type]( STRING, - (buffer: ByteBuffer, string: String) => { - - val bytes = string.getBytes("utf-8") + (buffer: ByteBuffer, string: UTF8String) => { + val bytes = string.getBytes buffer.putInt(bytes.length) buffer.put(bytes) }, @@ -107,7 +120,7 @@ class ColumnTypeSuite extends FunSuite with Logging { val length = buffer.getInt() val bytes = new Array[Byte](length) buffer.get(bytes) - new String(bytes, "utf-8") + UTF8String(bytes) }) testColumnType[BinaryType.type, Array[Byte]]( @@ -148,6 +161,41 @@ class ColumnTypeSuite extends FunSuite with Logging { } } + test("CUSTOM") { + val conf = new SparkConf() + conf.set("spark.kryo.registrator", "org.apache.spark.sql.columnar.Registrator") + val serializer = new SparkSqlSerializer(conf).newInstance() + + val buffer = ByteBuffer.allocate(512) + val obj = CustomClass(Int.MaxValue,Long.MaxValue) + val serializedObj = serializer.serialize(obj).array() + + GENERIC.append(serializer.serialize(obj).array(), buffer) + buffer.rewind() + + val length = buffer.getInt + assert(length === serializedObj.length) + assert(13 == length) // id (1) + int (4) + long (8) + + val genericSerializedObj = SparkSqlSerializer.serialize(obj) + assert(length != genericSerializedObj.length) + assert(length < genericSerializedObj.length) + + assertResult(obj, "Custom deserialized object didn't equal the original object") { + val bytes = new Array[Byte](length) + buffer.get(bytes, 0, length) + serializer.deserialize(ByteBuffer.wrap(bytes)) + } + + buffer.rewind() + buffer.putInt(serializedObj.length).put(serializedObj) + + assertResult(obj, "Custom deserialized object didn't equal the original object") { + buffer.rewind() + serializer.deserialize(ByteBuffer.wrap(GENERIC.extract(buffer))) + } + } + def testNativeColumnType[T <: NativeType]( columnType: NativeColumnType[T], putter: (ByteBuffer, T#JvmType) => Unit, @@ -206,4 +254,36 @@ class ColumnTypeSuite extends FunSuite with Logging { if (sb.nonEmpty) sb.setLength(sb.length - 1) sb.toString() } + + test("column type for decimal types with different precision") { + (1 to 18).foreach { i => + assertResult(FIXED_DECIMAL(i, 0)) { + ColumnType(DecimalType(i, 0)) + } + } + + assertResult(GENERIC) { + ColumnType(DecimalType(19, 0)) + } + } +} + +private[columnar] final case class CustomClass(a: Int, b: Long) + +private[columnar] object CustomerSerializer extends Serializer[CustomClass] { + override def write(kryo: Kryo, output: Output, t: CustomClass) { + output.writeInt(t.a) + output.writeLong(t.b) + } + override def read(kryo: Kryo, input: Input, aClass: Class[CustomClass]): CustomClass = { + val a = input.readInt() + val b = input.readLong() + CustomClass(a,b) + } +} + +private[columnar] final class Registrator extends KryoRegistrator { + override def registerClasses(kryo: Kryo) { + kryo.register(classOf[CustomClass], CustomerSerializer) + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestUtils.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestUtils.scala index 60ed28cc97bf1..f76314b9dab5e 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestUtils.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestUtils.scala @@ -24,10 +24,10 @@ import scala.util.Random import org.apache.spark.sql.Row import org.apache.spark.sql.catalyst.expressions.GenericMutableRow -import org.apache.spark.sql.types.{DataType, NativeType} +import org.apache.spark.sql.types.{UTF8String, DataType, Decimal, NativeType} object ColumnarTestUtils { - def makeNullRow(length: Int) = { + def makeNullRow(length: Int): GenericMutableRow = { val row = new GenericMutableRow(length) (0 until length).foreach(row.setNullAt) row @@ -41,16 +41,17 @@ object ColumnarTestUtils { } (columnType match { - case BYTE => (Random.nextInt(Byte.MaxValue * 2) - Byte.MaxValue).toByte - case SHORT => (Random.nextInt(Short.MaxValue * 2) - Short.MaxValue).toShort - case INT => Random.nextInt() - case LONG => Random.nextLong() - case FLOAT => Random.nextFloat() - case DOUBLE => Random.nextDouble() - case STRING => Random.nextString(Random.nextInt(32)) - case BOOLEAN => Random.nextBoolean() - case BINARY => randomBytes(Random.nextInt(32)) - case DATE => Random.nextInt() + case BYTE => (Random.nextInt(Byte.MaxValue * 2) - Byte.MaxValue).toByte + case SHORT => (Random.nextInt(Short.MaxValue * 2) - Short.MaxValue).toShort + case INT => Random.nextInt() + case LONG => Random.nextLong() + case FLOAT => Random.nextFloat() + case DOUBLE => Random.nextDouble() + case FIXED_DECIMAL(precision, scale) => Decimal(Random.nextLong() % 100, precision, scale) + case STRING => UTF8String(Random.nextString(Random.nextInt(32))) + case BOOLEAN => Random.nextBoolean() + case BINARY => randomBytes(Random.nextInt(32)) + case DATE => Random.nextInt() case TIMESTAMP => val timestamp = new Timestamp(Random.nextLong()) timestamp.setNanos(Random.nextInt(999999999)) @@ -92,7 +93,7 @@ object ColumnarTestUtils { def makeUniqueValuesAndSingleValueRows[T <: NativeType]( columnType: NativeColumnType[T], - count: Int) = { + count: Int): (Seq[T#JvmType], Seq[GenericMutableRow]) = { val values = makeUniqueRandomValues(columnType, count) val rows = values.map { value => diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala index 86b1b5fda1c0f..56591d9dba29e 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala @@ -17,10 +17,13 @@ package org.apache.spark.sql.columnar -import org.apache.spark.sql.Dsl._ +import java.sql.{Date, Timestamp} + import org.apache.spark.sql.TestData._ import org.apache.spark.sql.catalyst.expressions.Row import org.apache.spark.sql.test.TestSQLContext._ +import org.apache.spark.sql.test.TestSQLContext.implicits._ +import org.apache.spark.sql.types._ import org.apache.spark.sql.{QueryTest, TestData} import org.apache.spark.storage.StorageLevel.MEMORY_ONLY @@ -28,8 +31,6 @@ class InMemoryColumnarQuerySuite extends QueryTest { // Make sure the tables are loaded. TestData - import org.apache.spark.sql.test.TestSQLContext.implicits._ - test("simple columnar query") { val plan = executePlan(testData.logicalPlan).executedPlan val scan = InMemoryRelation(useCompression = true, 5, MEMORY_ONLY, plan, None) @@ -39,10 +40,11 @@ class InMemoryColumnarQuerySuite extends QueryTest { test("default size avoids broadcast") { // TODO: Improve this test when we have better statistics - sparkContext.parallelize(1 to 10).map(i => TestData(i, i.toString)).registerTempTable("sizeTst") + sparkContext.parallelize(1 to 10).map(i => TestData(i, i.toString)) + .toDF().registerTempTable("sizeTst") cacheTable("sizeTst") assert( - table("sizeTst").queryExecution.logical.statistics.sizeInBytes > + table("sizeTst").queryExecution.analyzed.statistics.sizeInBytes > conf.autoBroadcastJoinThreshold) } @@ -117,4 +119,74 @@ class InMemoryColumnarQuerySuite extends QueryTest { complexData.count() complexData.unpersist() } + + test("decimal type") { + // Casting is required here because ScalaReflection can't capture decimal precision information. + val df = (1 to 10) + .map(i => Tuple1(Decimal(i, 15, 10))) + .toDF("dec") + .select($"dec" cast DecimalType(15, 10)) + + assert(df.schema.head.dataType === DecimalType(15, 10)) + + df.cache().registerTempTable("test_fixed_decimal") + checkAnswer( + sql("SELECT * FROM test_fixed_decimal"), + (1 to 10).map(i => Row(Decimal(i, 15, 10).toJavaBigDecimal))) + } + + test("test different data types") { + // Create the schema. + val struct = + StructType( + StructField("f1", FloatType, true) :: + StructField("f2", ArrayType(BooleanType), true) :: Nil) + val dataTypes = + Seq(StringType, BinaryType, NullType, BooleanType, + ByteType, ShortType, IntegerType, LongType, + FloatType, DoubleType, DecimalType.Unlimited, DecimalType(6, 5), + DateType, TimestampType, + ArrayType(IntegerType), MapType(StringType, LongType), struct) + val fields = dataTypes.zipWithIndex.map { case (dataType, index) => + StructField(s"col$index", dataType, true) + } + val allColumns = fields.map(_.name).mkString(",") + val schema = StructType(fields) + + // Create a RDD for the schema + val rdd = + sparkContext.parallelize((1 to 100), 10).map { i => + Row( + s"str${i}: test cache.", + s"binary${i}: test cache.".getBytes("UTF-8"), + null, + i % 2 == 0, + i.toByte, + i.toShort, + i, + Long.MaxValue - i.toLong, + (i + 0.25).toFloat, + (i + 0.75), + BigDecimal(Long.MaxValue.toString + ".12345"), + new java.math.BigDecimal(s"${i % 9 + 1}" + ".23456"), + new Date(i), + new Timestamp(i), + (1 to i).toSeq, + (0 to i).map(j => s"map_key_$j" -> (Long.MaxValue - j)).toMap, + Row((i - 0.25).toFloat, (1 to i).toSeq)) + } + createDataFrame(rdd, schema).registerTempTable("InMemoryCache_different_data_types") + // Cache the table. + sql("cache table InMemoryCache_different_data_types") + // Make sure the table is indeed cached. + val tableScan = table("InMemoryCache_different_data_types").queryExecution.executedPlan + assert( + isCached("InMemoryCache_different_data_types"), + "InMemoryCache_different_data_types should be cached.") + // Issue a query and check the results. + checkAnswer( + sql(s"SELECT DISTINCT ${allColumns} FROM InMemoryCache_different_data_types"), + table("InMemoryCache_different_data_types").collect()) + dropTempTable("InMemoryCache_different_data_types") + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnAccessorSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnAccessorSuite.scala index f95c895587f3f..a0702144f942c 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnAccessorSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnAccessorSuite.scala @@ -31,7 +31,8 @@ class TestNullableColumnAccessor[T <: DataType, JvmType]( with NullableColumnAccessor object TestNullableColumnAccessor { - def apply[T <: DataType, JvmType](buffer: ByteBuffer, columnType: ColumnType[T, JvmType]) = { + def apply[T <: DataType, JvmType](buffer: ByteBuffer, columnType: ColumnType[T, JvmType]) + : TestNullableColumnAccessor[T, JvmType] = { // Skips the column type ID buffer.getInt() new TestNullableColumnAccessor(buffer, columnType) @@ -42,7 +43,8 @@ class NullableColumnAccessorSuite extends FunSuite { import ColumnarTestUtils._ Seq( - INT, LONG, SHORT, BOOLEAN, BYTE, STRING, DOUBLE, FLOAT, BINARY, GENERIC, DATE, TIMESTAMP + INT, LONG, SHORT, BOOLEAN, BYTE, STRING, DOUBLE, FLOAT, FIXED_DECIMAL(15, 10), BINARY, GENERIC, + DATE, TIMESTAMP ).foreach { testNullableColumnAccessor(_) } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala index 80bd5c94570cb..3a5605d2335d7 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala @@ -27,7 +27,8 @@ class TestNullableColumnBuilder[T <: DataType, JvmType](columnType: ColumnType[T with NullableColumnBuilder object TestNullableColumnBuilder { - def apply[T <: DataType, JvmType](columnType: ColumnType[T, JvmType], initialSize: Int = 0) = { + def apply[T <: DataType, JvmType](columnType: ColumnType[T, JvmType], initialSize: Int = 0) + : TestNullableColumnBuilder[T, JvmType] = { val builder = new TestNullableColumnBuilder(columnType) builder.initialize(initialSize) builder @@ -38,7 +39,8 @@ class NullableColumnBuilderSuite extends FunSuite { import ColumnarTestUtils._ Seq( - INT, LONG, SHORT, BOOLEAN, BYTE, STRING, DOUBLE, FLOAT, BINARY, GENERIC, DATE, TIMESTAMP + INT, LONG, SHORT, BOOLEAN, BYTE, STRING, DOUBLE, FLOAT, FIXED_DECIMAL(15, 10), BINARY, GENERIC, + DATE, TIMESTAMP ).foreach { testNullableColumnBuilder(_) } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/PartitionBatchPruningSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/PartitionBatchPruningSuite.scala index 55a9f735b3506..2a0b701cad7fa 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/PartitionBatchPruningSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/PartitionBatchPruningSuite.scala @@ -21,13 +21,12 @@ import org.scalatest.{BeforeAndAfter, BeforeAndAfterAll, FunSuite} import org.apache.spark.sql._ import org.apache.spark.sql.test.TestSQLContext._ +import org.apache.spark.sql.test.TestSQLContext.implicits._ class PartitionBatchPruningSuite extends FunSuite with BeforeAndAfterAll with BeforeAndAfter { val originalColumnBatchSize = conf.columnBatchSize val originalInMemoryPartitionPruning = conf.inMemoryPartitionPruning - import org.apache.spark.sql.test.TestSQLContext.implicits._ - override protected def beforeAll(): Unit = { // Make a table with 5 partitions, 2 batches per partition, 10 elements per batch setConf(SQLConf.COLUMN_BATCH_SIZE, "10") @@ -35,11 +34,13 @@ class PartitionBatchPruningSuite extends FunSuite with BeforeAndAfterAll with Be val pruningData = sparkContext.makeRDD((1 to 100).map { key => val string = if (((key - 1) / 10) % 2 == 0) null else key.toString TestData(key, string) - }, 5) + }, 5).toDF() pruningData.registerTempTable("pruningData") // Enable in-memory partition pruning setConf(SQLConf.IN_MEMORY_PARTITION_PRUNING, "true") + // Enable in-memory table scan accumulators + setConf("spark.sql.inMemoryTableScanStatistics.enable", "true") } override protected def afterAll(): Unit = { diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/TestCompressibleColumnBuilder.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/TestCompressibleColumnBuilder.scala index 0b18b4119268f..fc8ff3b41d0e6 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/TestCompressibleColumnBuilder.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/TestCompressibleColumnBuilder.scala @@ -35,7 +35,7 @@ object TestCompressibleColumnBuilder { def apply[T <: NativeType]( columnStats: ColumnStats, columnType: NativeColumnType[T], - scheme: CompressionScheme) = { + scheme: CompressionScheme): TestCompressibleColumnBuilder[T] = { val builder = new TestCompressibleColumnBuilder(columnStats, columnType, Seq(scheme)) builder.initialize(0, "", useCompression = true) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala index df108a9d262bb..523be56df65ba 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala @@ -20,12 +20,13 @@ package org.apache.spark.sql.execution import org.scalatest.FunSuite import org.apache.spark.sql.{SQLConf, execution} -import org.apache.spark.sql.Dsl._ +import org.apache.spark.sql.functions._ import org.apache.spark.sql.TestData._ import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans._ import org.apache.spark.sql.execution.joins.{BroadcastHashJoin, ShuffledHashJoin} import org.apache.spark.sql.test.TestSQLContext._ +import org.apache.spark.sql.test.TestSQLContext.implicits._ import org.apache.spark.sql.test.TestSQLContext.planner._ import org.apache.spark.sql.types._ @@ -71,7 +72,7 @@ class PlannerSuite extends FunSuite { val schema = StructType(fields) val row = Row.fromSeq(Seq.fill(fields.size)(null)) val rowRDD = org.apache.spark.sql.test.TestSQLContext.sparkContext.parallelize(row :: Nil) - applySchema(rowRDD, schema).registerTempTable("testLimit") + createDataFrame(rowRDD, schema).registerTempTable("testLimit") val planned = sql( """ diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/debug/DebuggingSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/debug/DebuggingSuite.scala index 4e9472c60249e..358d8cf06e463 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/debug/DebuggingSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/debug/DebuggingSuite.scala @@ -30,4 +30,4 @@ class DebuggingSuite extends FunSuite { test("DataFrame.typeCheck()") { testData.typeCheck() } -} \ No newline at end of file +} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/DockerHacks.scala b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/DockerHacks.scala deleted file mode 100644 index f332cb389f339..0000000000000 --- a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/DockerHacks.scala +++ /dev/null @@ -1,51 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.sql.jdbc - -import scala.collection.mutable.MutableList - -import com.spotify.docker.client._ - -/** - * A factory and morgue for DockerClient objects. In the DockerClient we use, - * calling close() closes the desired DockerClient but also renders all other - * DockerClients inoperable. This is inconvenient if we have more than one - * open, such as during tests. - */ -object DockerClientFactory { - var numClients: Int = 0 - val zombies = new MutableList[DockerClient]() - - def get(): DockerClient = { - this.synchronized { - numClients = numClients + 1 - DefaultDockerClient.fromEnv.build() - } - } - - def close(dc: DockerClient) { - this.synchronized { - numClients = numClients - 1 - zombies += dc - if (numClients == 0) { - zombies.foreach(_.close()) - zombies.clear() - } - } - } -} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala index d25c1390db15c..3596b183d4328 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala @@ -18,40 +18,54 @@ package org.apache.spark.sql.jdbc import java.math.BigDecimal +import java.sql.DriverManager +import java.util.{Calendar, GregorianCalendar, Properties} + import org.apache.spark.sql.test._ +import org.h2.jdbc.JdbcSQLException import org.scalatest.{FunSuite, BeforeAndAfter} -import java.sql.DriverManager import TestSQLContext._ +import TestSQLContext.implicits._ class JDBCSuite extends FunSuite with BeforeAndAfter { val url = "jdbc:h2:mem:testdb0" + val urlWithUserAndPass = "jdbc:h2:mem:testdb0;user=testUser;password=testPass" var conn: java.sql.Connection = null val testBytes = Array[Byte](99.toByte, 134.toByte, 135.toByte, 200.toByte, 205.toByte) before { Class.forName("org.h2.Driver") - conn = DriverManager.getConnection(url) + // Extra properties that will be specified for our database. We need these to test + // usage of parameters from OPTIONS clause in queries. + val properties = new Properties() + properties.setProperty("user", "testUser") + properties.setProperty("password", "testPass") + properties.setProperty("rowId", "false") + + conn = DriverManager.getConnection(url, properties) conn.prepareStatement("create schema test").executeUpdate() - conn.prepareStatement("create table test.people (name TEXT(32) NOT NULL, theid INTEGER NOT NULL)").executeUpdate() + conn.prepareStatement( + "create table test.people (name TEXT(32) NOT NULL, theid INTEGER NOT NULL)").executeUpdate() conn.prepareStatement("insert into test.people values ('fred', 1)").executeUpdate() conn.prepareStatement("insert into test.people values ('mary', 2)").executeUpdate() - conn.prepareStatement("insert into test.people values ('joe', 3)").executeUpdate() + conn.prepareStatement( + "insert into test.people values ('joe ''foo'' \"bar\"', 3)").executeUpdate() conn.commit() sql( s""" |CREATE TEMPORARY TABLE foobar |USING org.apache.spark.sql.jdbc - |OPTIONS (url '$url', dbtable 'TEST.PEOPLE') + |OPTIONS (url '$url', dbtable 'TEST.PEOPLE', user 'testUser', password 'testPass') """.stripMargin.replaceAll("\n", " ")) sql( s""" |CREATE TEMPORARY TABLE parts |USING org.apache.spark.sql.jdbc - |OPTIONS (url '$url', dbtable 'TEST.PEOPLE', - |partitionColumn 'THEID', lowerBound '1', upperBound '4', numPartitions '3') + |OPTIONS (url '$url', dbtable 'TEST.PEOPLE', user 'testUser', password 'testPass', + | partitionColumn 'THEID', lowerBound '1', upperBound '4', numPartitions '3') """.stripMargin.replaceAll("\n", " ")) conn.prepareStatement("create table test.inttypes (a INT, b BOOLEAN, c TINYINT, " @@ -65,12 +79,12 @@ class JDBCSuite extends FunSuite with BeforeAndAfter { s""" |CREATE TEMPORARY TABLE inttypes |USING org.apache.spark.sql.jdbc - |OPTIONS (url '$url', dbtable 'TEST.INTTYPES') + |OPTIONS (url '$url', dbtable 'TEST.INTTYPES', user 'testUser', password 'testPass') """.stripMargin.replaceAll("\n", " ")) conn.prepareStatement("create table test.strtypes (a BINARY(20), b VARCHAR(20), " + "c VARCHAR_IGNORECASE(20), d CHAR(20), e BLOB, f CLOB)").executeUpdate() - var stmt = conn.prepareStatement("insert into test.strtypes values (?, ?, ?, ?, ?, ?)") + val stmt = conn.prepareStatement("insert into test.strtypes values (?, ?, ?, ?, ?, ?)") stmt.setBytes(1, testBytes) stmt.setString(2, "Sensitive") stmt.setString(3, "Insensitive") @@ -82,7 +96,7 @@ class JDBCSuite extends FunSuite with BeforeAndAfter { s""" |CREATE TEMPORARY TABLE strtypes |USING org.apache.spark.sql.jdbc - |OPTIONS (url '$url', dbtable 'TEST.STRTYPES') + |OPTIONS (url '$url', dbtable 'TEST.STRTYPES', user 'testUser', password 'testPass') """.stripMargin.replaceAll("\n", " ")) conn.prepareStatement("create table test.timetypes (a TIME, b DATE, c TIMESTAMP)" @@ -94,7 +108,7 @@ class JDBCSuite extends FunSuite with BeforeAndAfter { s""" |CREATE TEMPORARY TABLE timetypes |USING org.apache.spark.sql.jdbc - |OPTIONS (url '$url', dbtable 'TEST.TIMETYPES') + |OPTIONS (url '$url', dbtable 'TEST.TIMETYPES', user 'testUser', password 'testPass') """.stripMargin.replaceAll("\n", " ")) @@ -109,7 +123,7 @@ class JDBCSuite extends FunSuite with BeforeAndAfter { s""" |CREATE TEMPORARY TABLE flttypes |USING org.apache.spark.sql.jdbc - |OPTIONS (url '$url', dbtable 'TEST.FLTTYPES') + |OPTIONS (url '$url', dbtable 'TEST.FLTTYPES', user 'testUser', password 'testPass') """.stripMargin.replaceAll("\n", " ")) // Untested: IDENTITY, OTHER, UUID, ARRAY, and GEOMETRY types. @@ -120,29 +134,36 @@ class JDBCSuite extends FunSuite with BeforeAndAfter { } test("SELECT *") { - assert(sql("SELECT * FROM foobar").collect().size == 3) + assert(sql("SELECT * FROM foobar").collect().size === 3) } test("SELECT * WHERE (simple predicates)") { - assert(sql("SELECT * FROM foobar WHERE THEID < 1").collect().size == 0) - assert(sql("SELECT * FROM foobar WHERE THEID != 2").collect().size == 2) - assert(sql("SELECT * FROM foobar WHERE THEID = 1").collect().size == 1) + assert(sql("SELECT * FROM foobar WHERE THEID < 1").collect().size === 0) + assert(sql("SELECT * FROM foobar WHERE THEID != 2").collect().size === 2) + assert(sql("SELECT * FROM foobar WHERE THEID = 1").collect().size === 1) + assert(sql("SELECT * FROM foobar WHERE NAME = 'fred'").collect().size === 1) + assert(sql("SELECT * FROM foobar WHERE NAME > 'fred'").collect().size === 2) + assert(sql("SELECT * FROM foobar WHERE NAME != 'fred'").collect().size === 2) + } + + test("SELECT * WHERE (quoted strings)") { + assert(sql("select * from foobar").where('NAME === "joe 'foo' \"bar\"").collect().size === 1) } test("SELECT first field") { val names = sql("SELECT NAME FROM foobar").collect().map(x => x.getString(0)).sortWith(_ < _) - assert(names.size == 3) + assert(names.size === 3) assert(names(0).equals("fred")) - assert(names(1).equals("joe")) + assert(names(1).equals("joe 'foo' \"bar\"")) assert(names(2).equals("mary")) } test("SELECT second field") { val ids = sql("SELECT THEID FROM foobar").collect().map(x => x.getInt(0)).sortWith(_ < _) - assert(ids.size == 3) - assert(ids(0) == 1) - assert(ids(1) == 2) - assert(ids(2) == 3) + assert(ids.size === 3) + assert(ids(0) === 1) + assert(ids(1) === 2) + assert(ids(2) === 3) } test("SELECT * partitioned") { @@ -150,46 +171,46 @@ class JDBCSuite extends FunSuite with BeforeAndAfter { } test("SELECT WHERE (simple predicates) partitioned") { - assert(sql("SELECT * FROM parts WHERE THEID < 1").collect().size == 0) - assert(sql("SELECT * FROM parts WHERE THEID != 2").collect().size == 2) - assert(sql("SELECT THEID FROM parts WHERE THEID = 1").collect().size == 1) + assert(sql("SELECT * FROM parts WHERE THEID < 1").collect().size === 0) + assert(sql("SELECT * FROM parts WHERE THEID != 2").collect().size === 2) + assert(sql("SELECT THEID FROM parts WHERE THEID = 1").collect().size === 1) } test("SELECT second field partitioned") { val ids = sql("SELECT THEID FROM parts").collect().map(x => x.getInt(0)).sortWith(_ < _) - assert(ids.size == 3) - assert(ids(0) == 1) - assert(ids(1) == 2) - assert(ids(2) == 3) + assert(ids.size === 3) + assert(ids(0) === 1) + assert(ids(1) === 2) + assert(ids(2) === 3) } test("Basic API") { - assert(TestSQLContext.jdbcRDD(url, "TEST.PEOPLE").collect.size == 3) + assert(TestSQLContext.jdbc(urlWithUserAndPass, "TEST.PEOPLE").collect().size === 3) } test("Partitioning via JDBCPartitioningInfo API") { - val parts = JDBCPartitioningInfo("THEID", 0, 4, 3) - assert(TestSQLContext.jdbcRDD(url, "TEST.PEOPLE", parts).collect.size == 3) + assert(TestSQLContext.jdbc(urlWithUserAndPass, "TEST.PEOPLE", "THEID", 0, 4, 3) + .collect.size === 3) } test("Partitioning via list-of-where-clauses API") { val parts = Array[String]("THEID < 2", "THEID >= 2") - assert(TestSQLContext.jdbcRDD(url, "TEST.PEOPLE", parts).collect.size == 3) + assert(TestSQLContext.jdbc(urlWithUserAndPass, "TEST.PEOPLE", parts).collect().size === 3) } test("H2 integral types") { val rows = sql("SELECT * FROM inttypes WHERE A IS NOT NULL").collect() - assert(rows.size == 1) - assert(rows(0).getInt(0) == 1) - assert(rows(0).getBoolean(1) == false) - assert(rows(0).getInt(2) == 3) - assert(rows(0).getInt(3) == 4) - assert(rows(0).getLong(4) == 1234567890123L) + assert(rows.size === 1) + assert(rows(0).getInt(0) === 1) + assert(rows(0).getBoolean(1) === false) + assert(rows(0).getInt(2) === 3) + assert(rows(0).getInt(3) === 4) + assert(rows(0).getLong(4) === 1234567890123L) } test("H2 null entries") { val rows = sql("SELECT * FROM inttypes WHERE A IS NULL").collect() - assert(rows.size == 1) + assert(rows.size === 1) assert(rows(0).isNullAt(0)) assert(rows(0).isNullAt(1)) assert(rows(0).isNullAt(2)) @@ -209,40 +230,58 @@ class JDBCSuite extends FunSuite with BeforeAndAfter { test("H2 time types") { val rows = sql("SELECT * FROM timetypes").collect() - assert(rows(0).getAs[java.sql.Timestamp](0).getHours == 12) - assert(rows(0).getAs[java.sql.Timestamp](0).getMinutes == 34) - assert(rows(0).getAs[java.sql.Timestamp](0).getSeconds == 56) - assert(rows(0).getAs[java.sql.Date](1).getYear == 96) - assert(rows(0).getAs[java.sql.Date](1).getMonth == 0) - assert(rows(0).getAs[java.sql.Date](1).getDate == 1) - assert(rows(0).getAs[java.sql.Timestamp](2).getYear == 102) - assert(rows(0).getAs[java.sql.Timestamp](2).getMonth == 1) - assert(rows(0).getAs[java.sql.Timestamp](2).getDate == 20) - assert(rows(0).getAs[java.sql.Timestamp](2).getHours == 11) - assert(rows(0).getAs[java.sql.Timestamp](2).getMinutes == 22) - assert(rows(0).getAs[java.sql.Timestamp](2).getSeconds == 33) - assert(rows(0).getAs[java.sql.Timestamp](2).getNanos == 543543543) + val cal = new GregorianCalendar(java.util.Locale.ROOT) + cal.setTime(rows(0).getAs[java.sql.Timestamp](0)) + assert(cal.get(Calendar.HOUR_OF_DAY) === 12) + assert(cal.get(Calendar.MINUTE) === 34) + assert(cal.get(Calendar.SECOND) === 56) + cal.setTime(rows(0).getAs[java.sql.Timestamp](1)) + assert(cal.get(Calendar.YEAR) === 1996) + assert(cal.get(Calendar.MONTH) === 0) + assert(cal.get(Calendar.DAY_OF_MONTH) === 1) + cal.setTime(rows(0).getAs[java.sql.Timestamp](2)) + assert(cal.get(Calendar.YEAR) === 2002) + assert(cal.get(Calendar.MONTH) === 1) + assert(cal.get(Calendar.DAY_OF_MONTH) === 20) + assert(cal.get(Calendar.HOUR) === 11) + assert(cal.get(Calendar.MINUTE) === 22) + assert(cal.get(Calendar.SECOND) === 33) + assert(rows(0).getAs[java.sql.Timestamp](2).getNanos === 543543543) } test("H2 floating-point types") { val rows = sql("SELECT * FROM flttypes").collect() - assert(rows(0).getDouble(0) == 1.00000000000000022) // Yes, I meant ==. - assert(rows(0).getDouble(1) == 1.00000011920928955) // Yes, I meant ==. + assert(rows(0).getDouble(0) === 1.00000000000000022) // Yes, I meant ==. + assert(rows(0).getDouble(1) === 1.00000011920928955) // Yes, I meant ==. assert(rows(0).getAs[BigDecimal](2) .equals(new BigDecimal("123456789012345.54321543215432100000"))) } - test("SQL query as table name") { sql( s""" |CREATE TEMPORARY TABLE hack |USING org.apache.spark.sql.jdbc - |OPTIONS (url '$url', dbtable '(SELECT B, B*B FROM TEST.FLTTYPES)') + |OPTIONS (url '$url', dbtable '(SELECT B, B*B FROM TEST.FLTTYPES)', + | user 'testUser', password 'testPass') """.stripMargin.replaceAll("\n", " ")) val rows = sql("SELECT * FROM hack").collect() - assert(rows(0).getDouble(0) == 1.00000011920928955) // Yes, I meant ==. + assert(rows(0).getDouble(0) === 1.00000011920928955) // Yes, I meant ==. // For some reason, H2 computes this square incorrectly... assert(math.abs(rows(0).getDouble(1) - 1.00000023841859331) < 1e-12) } + + test("Pass extra properties via OPTIONS") { + // We set rowId to false during setup, which means that _ROWID_ column should be absent from + // all tables. If rowId is true (default), the query below doesn't throw an exception. + intercept[JdbcSQLException] { + sql( + s""" + |CREATE TEMPORARY TABLE abc + |USING org.apache.spark.sql.jdbc + |OPTIONS (url '$url', dbtable '(SELECT _ROWID_ FROM test.people)', + | user 'testUser', password 'testPass') + """.stripMargin.replaceAll("\n", " ")) + } + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCWriteSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCWriteSuite.scala index e581ac9b50c2b..ee5c7620d1a22 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCWriteSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCWriteSuite.scala @@ -17,13 +17,13 @@ package org.apache.spark.sql.jdbc -import java.math.BigDecimal +import java.sql.DriverManager + +import org.scalatest.{BeforeAndAfter, FunSuite} + import org.apache.spark.sql.Row -import org.apache.spark.sql.types._ import org.apache.spark.sql.test._ -import org.scalatest.{FunSuite, BeforeAndAfter} -import java.sql.DriverManager -import TestSQLContext._ +import org.apache.spark.sql.types._ class JDBCWriteSuite extends FunSuite with BeforeAndAfter { val url = "jdbc:h2:mem:testdb2" @@ -54,53 +54,53 @@ class JDBCWriteSuite extends FunSuite with BeforeAndAfter { StructField("seq", IntegerType) :: Nil) test("Basic CREATE") { - val srdd = TestSQLContext.applySchema(sc.parallelize(arr2x2), schema2) + val df = TestSQLContext.createDataFrame(sc.parallelize(arr2x2), schema2) - srdd.createJDBCTable(url, "TEST.BASICCREATETEST", false) - assert(2 == TestSQLContext.jdbcRDD(url, "TEST.BASICCREATETEST").count) - assert(2 == TestSQLContext.jdbcRDD(url, "TEST.BASICCREATETEST").collect()(0).length) + df.createJDBCTable(url, "TEST.BASICCREATETEST", false) + assert(2 == TestSQLContext.jdbc(url, "TEST.BASICCREATETEST").count) + assert(2 == TestSQLContext.jdbc(url, "TEST.BASICCREATETEST").collect()(0).length) } test("CREATE with overwrite") { - val srdd = TestSQLContext.applySchema(sc.parallelize(arr2x3), schema3) - val srdd2 = TestSQLContext.applySchema(sc.parallelize(arr1x2), schema2) + val df = TestSQLContext.createDataFrame(sc.parallelize(arr2x3), schema3) + val df2 = TestSQLContext.createDataFrame(sc.parallelize(arr1x2), schema2) - srdd.createJDBCTable(url, "TEST.DROPTEST", false) - assert(2 == TestSQLContext.jdbcRDD(url, "TEST.DROPTEST").count) - assert(3 == TestSQLContext.jdbcRDD(url, "TEST.DROPTEST").collect()(0).length) + df.createJDBCTable(url, "TEST.DROPTEST", false) + assert(2 == TestSQLContext.jdbc(url, "TEST.DROPTEST").count) + assert(3 == TestSQLContext.jdbc(url, "TEST.DROPTEST").collect()(0).length) - srdd2.createJDBCTable(url, "TEST.DROPTEST", true) - assert(1 == TestSQLContext.jdbcRDD(url, "TEST.DROPTEST").count) - assert(2 == TestSQLContext.jdbcRDD(url, "TEST.DROPTEST").collect()(0).length) + df2.createJDBCTable(url, "TEST.DROPTEST", true) + assert(1 == TestSQLContext.jdbc(url, "TEST.DROPTEST").count) + assert(2 == TestSQLContext.jdbc(url, "TEST.DROPTEST").collect()(0).length) } test("CREATE then INSERT to append") { - val srdd = TestSQLContext.applySchema(sc.parallelize(arr2x2), schema2) - val srdd2 = TestSQLContext.applySchema(sc.parallelize(arr1x2), schema2) + val df = TestSQLContext.createDataFrame(sc.parallelize(arr2x2), schema2) + val df2 = TestSQLContext.createDataFrame(sc.parallelize(arr1x2), schema2) - srdd.createJDBCTable(url, "TEST.APPENDTEST", false) - srdd2.insertIntoJDBC(url, "TEST.APPENDTEST", false) - assert(3 == TestSQLContext.jdbcRDD(url, "TEST.APPENDTEST").count) - assert(2 == TestSQLContext.jdbcRDD(url, "TEST.APPENDTEST").collect()(0).length) + df.createJDBCTable(url, "TEST.APPENDTEST", false) + df2.insertIntoJDBC(url, "TEST.APPENDTEST", false) + assert(3 == TestSQLContext.jdbc(url, "TEST.APPENDTEST").count) + assert(2 == TestSQLContext.jdbc(url, "TEST.APPENDTEST").collect()(0).length) } test("CREATE then INSERT to truncate") { - val srdd = TestSQLContext.applySchema(sc.parallelize(arr2x2), schema2) - val srdd2 = TestSQLContext.applySchema(sc.parallelize(arr1x2), schema2) + val df = TestSQLContext.createDataFrame(sc.parallelize(arr2x2), schema2) + val df2 = TestSQLContext.createDataFrame(sc.parallelize(arr1x2), schema2) - srdd.createJDBCTable(url, "TEST.TRUNCATETEST", false) - srdd2.insertIntoJDBC(url, "TEST.TRUNCATETEST", true) - assert(1 == TestSQLContext.jdbcRDD(url, "TEST.TRUNCATETEST").count) - assert(2 == TestSQLContext.jdbcRDD(url, "TEST.TRUNCATETEST").collect()(0).length) + df.createJDBCTable(url, "TEST.TRUNCATETEST", false) + df2.insertIntoJDBC(url, "TEST.TRUNCATETEST", true) + assert(1 == TestSQLContext.jdbc(url, "TEST.TRUNCATETEST").count) + assert(2 == TestSQLContext.jdbc(url, "TEST.TRUNCATETEST").collect()(0).length) } test("Incompatible INSERT to append") { - val srdd = TestSQLContext.applySchema(sc.parallelize(arr2x2), schema2) - val srdd2 = TestSQLContext.applySchema(sc.parallelize(arr2x3), schema3) + val df = TestSQLContext.createDataFrame(sc.parallelize(arr2x2), schema2) + val df2 = TestSQLContext.createDataFrame(sc.parallelize(arr2x3), schema3) - srdd.createJDBCTable(url, "TEST.INCOMPATIBLETEST", false) + df.createJDBCTable(url, "TEST.INCOMPATIBLETEST", false) intercept[org.apache.spark.SparkException] { - srdd2.insertIntoJDBC(url, "TEST.INCOMPATIBLETEST", true) + df2.insertIntoJDBC(url, "TEST.INCOMPATIBLETEST", true) } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/MySQLIntegration.scala b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/MySQLIntegration.scala deleted file mode 100644 index 89920f2650c3a..0000000000000 --- a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/MySQLIntegration.scala +++ /dev/null @@ -1,235 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.sql.jdbc - -import java.math.BigDecimal -import java.sql.{Date, DriverManager, Timestamp} -import com.spotify.docker.client.{DefaultDockerClient, DockerClient} -import com.spotify.docker.client.messages.ContainerConfig -import org.scalatest.{FunSuite, BeforeAndAfterAll, Ignore} - -import org.apache.spark.{SparkConf, SparkContext} -import org.apache.spark.SparkContext._ -import org.apache.spark.sql._ -import org.apache.spark.sql.test._ -import TestSQLContext._ - -import org.apache.spark.sql.jdbc._ - -class MySQLDatabase { - val docker: DockerClient = DockerClientFactory.get() - val containerId = { - println("Pulling mysql") - docker.pull("mysql") - println("Configuring container") - val config = (ContainerConfig.builder().image("mysql") - .env("MYSQL_ROOT_PASSWORD=rootpass") - .build()) - println("Creating container") - val id = docker.createContainer(config).id - println("Starting container " + id) - docker.startContainer(id) - id - } - val ip = docker.inspectContainer(containerId).networkSettings.ipAddress - - def close() { - try { - println("Killing container " + containerId) - docker.killContainer(containerId) - println("Removing container " + containerId) - docker.removeContainer(containerId) - println("Closing docker client") - DockerClientFactory.close(docker) - } catch { - case e: Exception => { - println(e) - println("You may need to clean this up manually.") - throw e - } - } - } -} - -@Ignore class MySQLIntegration extends FunSuite with BeforeAndAfterAll { - var ip: String = null - - def url(ip: String): String = url(ip, "mysql") - def url(ip: String, db: String): String = s"jdbc:mysql://$ip:3306/$db?user=root&password=rootpass" - - def waitForDatabase(ip: String, maxMillis: Long) { - println("Waiting for database to start up.") - val before = System.currentTimeMillis() - var lastException: java.sql.SQLException = null - while (true) { - if (System.currentTimeMillis() > before + maxMillis) { - throw new java.sql.SQLException(s"Database not up after $maxMillis ms.", lastException) - } - try { - val conn = java.sql.DriverManager.getConnection(url(ip)) - conn.close() - println("Database is up.") - return; - } catch { - case e: java.sql.SQLException => { - lastException = e - java.lang.Thread.sleep(250) - } - } - } - } - - def setupDatabase(ip: String) { - val conn = java.sql.DriverManager.getConnection(url(ip)) - try { - conn.prepareStatement("CREATE DATABASE foo").executeUpdate() - conn.prepareStatement("CREATE TABLE foo.tbl (x INTEGER, y TEXT(8))").executeUpdate() - conn.prepareStatement("INSERT INTO foo.tbl VALUES (42,'fred')").executeUpdate() - conn.prepareStatement("INSERT INTO foo.tbl VALUES (17,'dave')").executeUpdate() - - conn.prepareStatement("CREATE TABLE foo.numbers (onebit BIT(1), tenbits BIT(10), " - + "small SMALLINT, med MEDIUMINT, nor INT, big BIGINT, deci DECIMAL(40,20), flt FLOAT, " - + "dbl DOUBLE)").executeUpdate() - conn.prepareStatement("INSERT INTO foo.numbers VALUES (b'0', b'1000100101', " - + "17, 77777, 123456789, 123456789012345, 123456789012345.123456789012345, " - + "42.75, 1.0000000000000002)").executeUpdate() - - conn.prepareStatement("CREATE TABLE foo.dates (d DATE, t TIME, dt DATETIME, ts TIMESTAMP, " - + "yr YEAR)").executeUpdate() - conn.prepareStatement("INSERT INTO foo.dates VALUES ('1991-11-09', '13:31:24', " - + "'1996-01-01 01:23:45', '2009-02-13 23:31:30', '2001')").executeUpdate() - - // TODO: Test locale conversion for strings. - conn.prepareStatement("CREATE TABLE foo.strings (a CHAR(10), b VARCHAR(10), c TINYTEXT, " - + "d TEXT, e MEDIUMTEXT, f LONGTEXT, g BINARY(4), h VARBINARY(10), i BLOB)" - ).executeUpdate() - conn.prepareStatement("INSERT INTO foo.strings VALUES ('the', 'quick', 'brown', 'fox', 'jumps', 'over', 'the', 'lazy', 'dog')").executeUpdate() - } finally { - conn.close() - } - } - - var db: MySQLDatabase = null - - override def beforeAll() { - // If you load the MySQL driver here, DriverManager will deadlock. The - // MySQL driver gets loaded when its jar gets loaded, unlike the Postgres - // and H2 drivers. - //Class.forName("com.mysql.jdbc.Driver") - - db = new MySQLDatabase() - waitForDatabase(db.ip, 60000) - setupDatabase(db.ip) - ip = db.ip - } - - override def afterAll() { - db.close() - } - - test("Basic test") { - val rdd = TestSQLContext.jdbcRDD(url(ip, "foo"), "tbl") - val rows = rdd.collect - assert(rows.length == 2) - val types = rows(0).toSeq.map(x => x.getClass.toString) - assert(types.length == 2) - assert(types(0).equals("class java.lang.Integer")) - assert(types(1).equals("class java.lang.String")) - } - - test("Numeric types") { - val rdd = TestSQLContext.jdbcRDD(url(ip, "foo"), "numbers") - val rows = rdd.collect - assert(rows.length == 1) - val types = rows(0).toSeq.map(x => x.getClass.toString) - assert(types.length == 9) - println(types(1)) - assert(types(0).equals("class java.lang.Boolean")) - assert(types(1).equals("class java.lang.Long")) - assert(types(2).equals("class java.lang.Integer")) - assert(types(3).equals("class java.lang.Integer")) - assert(types(4).equals("class java.lang.Integer")) - assert(types(5).equals("class java.lang.Long")) - assert(types(6).equals("class java.math.BigDecimal")) - assert(types(7).equals("class java.lang.Double")) - assert(types(8).equals("class java.lang.Double")) - assert(rows(0).getBoolean(0) == false) - assert(rows(0).getLong(1) == 0x225) - assert(rows(0).getInt(2) == 17) - assert(rows(0).getInt(3) == 77777) - assert(rows(0).getInt(4) == 123456789) - assert(rows(0).getLong(5) == 123456789012345L) - val bd = new BigDecimal("123456789012345.12345678901234500000") - assert(rows(0).getAs[BigDecimal](6).equals(bd)) - assert(rows(0).getDouble(7) == 42.75) - assert(rows(0).getDouble(8) == 1.0000000000000002) - } - - test("Date types") { - val rdd = TestSQLContext.jdbcRDD(url(ip, "foo"), "dates") - val rows = rdd.collect - assert(rows.length == 1) - val types = rows(0).toSeq.map(x => x.getClass.toString) - assert(types.length == 5) - assert(types(0).equals("class java.sql.Date")) - assert(types(1).equals("class java.sql.Timestamp")) - assert(types(2).equals("class java.sql.Timestamp")) - assert(types(3).equals("class java.sql.Timestamp")) - assert(types(4).equals("class java.sql.Date")) - assert(rows(0).getAs[Date](0).equals(new Date(91, 10, 9))) - assert(rows(0).getAs[Timestamp](1).equals(new Timestamp(70, 0, 1, 13, 31, 24, 0))) - assert(rows(0).getAs[Timestamp](2).equals(new Timestamp(96, 0, 1, 1, 23, 45, 0))) - assert(rows(0).getAs[Timestamp](3).equals(new Timestamp(109, 1, 13, 23, 31, 30, 0))) - assert(rows(0).getAs[Date](4).equals(new Date(101, 0, 1))) - } - - test("String types") { - val rdd = TestSQLContext.jdbcRDD(url(ip, "foo"), "strings") - val rows = rdd.collect - assert(rows.length == 1) - val types = rows(0).toSeq.map(x => x.getClass.toString) - assert(types.length == 9) - assert(types(0).equals("class java.lang.String")) - assert(types(1).equals("class java.lang.String")) - assert(types(2).equals("class java.lang.String")) - assert(types(3).equals("class java.lang.String")) - assert(types(4).equals("class java.lang.String")) - assert(types(5).equals("class java.lang.String")) - assert(types(6).equals("class [B")) - assert(types(7).equals("class [B")) - assert(types(8).equals("class [B")) - assert(rows(0).getString(0).equals("the")) - assert(rows(0).getString(1).equals("quick")) - assert(rows(0).getString(2).equals("brown")) - assert(rows(0).getString(3).equals("fox")) - assert(rows(0).getString(4).equals("jumps")) - assert(rows(0).getString(5).equals("over")) - assert(java.util.Arrays.equals(rows(0).getAs[Array[Byte]](6), Array[Byte](116, 104, 101, 0))) - assert(java.util.Arrays.equals(rows(0).getAs[Array[Byte]](7), Array[Byte](108, 97, 122, 121))) - assert(java.util.Arrays.equals(rows(0).getAs[Array[Byte]](8), Array[Byte](100, 111, 103))) - } - - test("Basic write test") { - val rdd1 = TestSQLContext.jdbcRDD(url(ip, "foo"), "numbers") - val rdd2 = TestSQLContext.jdbcRDD(url(ip, "foo"), "dates") - val rdd3 = TestSQLContext.jdbcRDD(url(ip, "foo"), "strings") - rdd1.createJDBCTable(url(ip, "foo"), "numberscopy", false) - rdd2.createJDBCTable(url(ip, "foo"), "datescopy", false) - rdd3.createJDBCTable(url(ip, "foo"), "stringscopy", false) - } -} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/PostgresIntegration.scala b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/PostgresIntegration.scala deleted file mode 100644 index c174d7adb7204..0000000000000 --- a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/PostgresIntegration.scala +++ /dev/null @@ -1,149 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.sql.jdbc - -import java.math.BigDecimal -import org.apache.spark.sql.test._ -import org.scalatest.{FunSuite, BeforeAndAfterAll, Ignore} -import java.sql.DriverManager -import TestSQLContext._ -import com.spotify.docker.client.{DefaultDockerClient, DockerClient} -import com.spotify.docker.client.messages.ContainerConfig - -class PostgresDatabase { - val docker: DockerClient = DockerClientFactory.get() - val containerId = { - println("Pulling postgres") - docker.pull("postgres") - println("Configuring container") - val config = (ContainerConfig.builder().image("postgres") - .env("POSTGRES_PASSWORD=rootpass") - .build()) - println("Creating container") - val id = docker.createContainer(config).id - println("Starting container " + id) - docker.startContainer(id) - id - } - val ip = docker.inspectContainer(containerId).networkSettings.ipAddress - - def close() { - try { - println("Killing container " + containerId) - docker.killContainer(containerId) - println("Removing container " + containerId) - docker.removeContainer(containerId) - println("Closing docker client") - DockerClientFactory.close(docker) - } catch { - case e: Exception => { - println(e) - println("You may need to clean this up manually.") - throw e - } - } - } -} - -@Ignore class PostgresIntegration extends FunSuite with BeforeAndAfterAll { - lazy val db = new PostgresDatabase() - - def url(ip: String) = s"jdbc:postgresql://$ip:5432/postgres?user=postgres&password=rootpass" - - def waitForDatabase(ip: String, maxMillis: Long) { - val before = System.currentTimeMillis() - var lastException: java.sql.SQLException = null - while (true) { - if (System.currentTimeMillis() > before + maxMillis) { - throw new java.sql.SQLException(s"Database not up after $maxMillis ms.", - lastException) - } - try { - val conn = java.sql.DriverManager.getConnection(url(ip)) - conn.close() - println("Database is up.") - return; - } catch { - case e: java.sql.SQLException => { - lastException = e - java.lang.Thread.sleep(250) - } - } - } - } - - def setupDatabase(ip: String) { - val conn = DriverManager.getConnection(url(ip)) - try { - conn.prepareStatement("CREATE DATABASE foo").executeUpdate() - conn.setCatalog("foo") - conn.prepareStatement("CREATE TABLE bar (a text, b integer, c double precision, d bigint, " - + "e bit(1), f bit(10), g bytea, h boolean, i inet, j cidr)").executeUpdate() - conn.prepareStatement("INSERT INTO bar VALUES ('hello', 42, 1.25, 123456789012345, B'0', " - + "B'1000100101', E'\\\\xDEADBEEF', true, '172.16.0.42', '192.168.0.0/16')").executeUpdate() - } finally { - conn.close() - } - } - - override def beforeAll() { - println("Waiting for database to start up.") - waitForDatabase(db.ip, 60000) - println("Setting up database.") - setupDatabase(db.ip) - } - - override def afterAll() { - db.close() - } - - test("Type mapping for various types") { - val rdd = TestSQLContext.jdbcRDD(url(db.ip), "public.bar") - val rows = rdd.collect - assert(rows.length == 1) - val types = rows(0).toSeq.map(x => x.getClass.toString) - assert(types.length == 10) - assert(types(0).equals("class java.lang.String")) - assert(types(1).equals("class java.lang.Integer")) - assert(types(2).equals("class java.lang.Double")) - assert(types(3).equals("class java.lang.Long")) - assert(types(4).equals("class java.lang.Boolean")) - assert(types(5).equals("class [B")) - assert(types(6).equals("class [B")) - assert(types(7).equals("class java.lang.Boolean")) - assert(types(8).equals("class java.lang.String")) - assert(types(9).equals("class java.lang.String")) - assert(rows(0).getString(0).equals("hello")) - assert(rows(0).getInt(1) == 42) - assert(rows(0).getDouble(2) == 1.25) - assert(rows(0).getLong(3) == 123456789012345L) - assert(rows(0).getBoolean(4) == false) - // BIT(10)'s come back as ASCII strings of ten ASCII 0's and 1's... - assert(java.util.Arrays.equals(rows(0).getAs[Array[Byte]](5), Array[Byte](49,48,48,48,49,48,48,49,48,49))) - assert(java.util.Arrays.equals(rows(0).getAs[Array[Byte]](6), Array[Byte](0xDE.toByte, 0xAD.toByte, 0xBE.toByte, 0xEF.toByte))) - assert(rows(0).getBoolean(7) == true) - assert(rows(0).getString(8) == "172.16.0.42") - assert(rows(0).getString(9) == "192.168.0.0/16") - } - - test("Basic write test") { - val rdd = TestSQLContext.jdbcRDD(url(db.ip), "public.bar") - rdd.createJDBCTable(url(db.ip), "public.barcopy", false) - // Test only that it doesn't bomb out. - } -} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala index 926ba68828ee8..fd0e2746dc045 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala @@ -19,15 +19,18 @@ package org.apache.spark.sql.json import java.sql.{Date, Timestamp} +import org.scalactic.Tolerance._ + import org.apache.spark.sql.TestData._ -import org.apache.spark.sql.catalyst.util._ -import org.apache.spark.sql.Dsl._ +import org.apache.spark.sql.functions._ import org.apache.spark.sql.json.JsonRDD.{compatibleType, enforceCorrectType} import org.apache.spark.sql.sources.LogicalRelation import org.apache.spark.sql.test.TestSQLContext import org.apache.spark.sql.test.TestSQLContext._ +import org.apache.spark.sql.test.TestSQLContext.implicits._ import org.apache.spark.sql.types._ import org.apache.spark.sql.{QueryTest, Row, SQLConf} +import org.apache.spark.util.Utils class JsonSuite extends QueryTest { import org.apache.spark.sql.json.TestJsonData._ @@ -222,7 +225,7 @@ class JsonSuite extends QueryTest { StructField("bigInteger", DecimalType.Unlimited, true) :: StructField("boolean", BooleanType, true) :: StructField("double", DoubleType, true) :: - StructField("integer", IntegerType, true) :: + StructField("integer", LongType, true) :: StructField("long", LongType, true) :: StructField("null", StringType, true) :: StructField("string", StringType, true) :: Nil) @@ -247,26 +250,26 @@ class JsonSuite extends QueryTest { val jsonDF = jsonRDD(complexFieldAndType1) val expectedSchema = StructType( - StructField("arrayOfArray1", ArrayType(ArrayType(StringType, false), false), true) :: - StructField("arrayOfArray2", ArrayType(ArrayType(DoubleType, false), false), true) :: - StructField("arrayOfBigInteger", ArrayType(DecimalType.Unlimited, false), true) :: - StructField("arrayOfBoolean", ArrayType(BooleanType, false), true) :: - StructField("arrayOfDouble", ArrayType(DoubleType, false), true) :: - StructField("arrayOfInteger", ArrayType(IntegerType, false), true) :: - StructField("arrayOfLong", ArrayType(LongType, false), true) :: + StructField("arrayOfArray1", ArrayType(ArrayType(StringType, true), true), true) :: + StructField("arrayOfArray2", ArrayType(ArrayType(DoubleType, true), true), true) :: + StructField("arrayOfBigInteger", ArrayType(DecimalType.Unlimited, true), true) :: + StructField("arrayOfBoolean", ArrayType(BooleanType, true), true) :: + StructField("arrayOfDouble", ArrayType(DoubleType, true), true) :: + StructField("arrayOfInteger", ArrayType(LongType, true), true) :: + StructField("arrayOfLong", ArrayType(LongType, true), true) :: StructField("arrayOfNull", ArrayType(StringType, true), true) :: - StructField("arrayOfString", ArrayType(StringType, false), true) :: + StructField("arrayOfString", ArrayType(StringType, true), true) :: StructField("arrayOfStruct", ArrayType( StructType( StructField("field1", BooleanType, true) :: StructField("field2", StringType, true) :: - StructField("field3", StringType, true) :: Nil), false), true) :: + StructField("field3", StringType, true) :: Nil), true), true) :: StructField("struct", StructType( StructField("field1", BooleanType, true) :: StructField("field2", DecimalType.Unlimited, true) :: Nil), true) :: StructField("structWithArrayFields", StructType( - StructField("field1", ArrayType(IntegerType, false), true) :: - StructField("field2", ArrayType(StringType, false), true) :: Nil), true) :: Nil) + StructField("field1", ArrayType(LongType, true), true) :: + StructField("field2", ArrayType(StringType, true), true) :: Nil), true) :: Nil) assert(expectedSchema === jsonDF.schema) @@ -342,21 +345,19 @@ class JsonSuite extends QueryTest { ) } - ignore("Complex field and type inferring (Ignored)") { + test("GetField operation on complex data type") { val jsonDF = jsonRDD(complexFieldAndType1) jsonDF.registerTempTable("jsonTable") - // Right now, "field1" and "field2" are treated as aliases. We should fix it. checkAnswer( sql("select arrayOfStruct[0].field1, arrayOfStruct[0].field2 from jsonTable"), Row(true, "str1") ) - // Right now, the analyzer cannot resolve arrayOfStruct.field1 and arrayOfStruct.field2. // Getting all values of a specific field from an array of structs. checkAnswer( sql("select arrayOfStruct.field1, arrayOfStruct.field2 from jsonTable"), - Row(Seq(true, false), Seq("str1", null)) + Row(Seq(true, false, null), Seq("str1", null, null)) ) } @@ -379,8 +380,10 @@ class JsonSuite extends QueryTest { sql("select * from jsonTable"), Row("true", 11L, null, 1.1, "13.1", "str1") :: Row("12", null, new java.math.BigDecimal("21474836470.9"), null, null, "true") :: - Row("false", 21474836470L, new java.math.BigDecimal("92233720368547758070"), 100, "str1", "false") :: - Row(null, 21474836570L, new java.math.BigDecimal("1.1"), 21474836470L, "92233720368547758070", null) :: Nil + Row("false", 21474836470L, + new java.math.BigDecimal("92233720368547758070"), 100, "str1", "false") :: + Row(null, 21474836570L, + new java.math.BigDecimal("1.1"), 21474836470L, "92233720368547758070", null) :: Nil ) // Number and Boolean conflict: resolve the type as number in this query. @@ -403,7 +406,8 @@ class JsonSuite extends QueryTest { // Widening to DecimalType checkAnswer( sql("select num_num_2 + 1.2 from jsonTable where num_num_2 > 1.1"), - Row(new java.math.BigDecimal("21474836472.1")) :: Row(new java.math.BigDecimal("92233720368547758071.2")) :: Nil + Row(new java.math.BigDecimal("21474836472.1")) :: + Row(new java.math.BigDecimal("92233720368547758071.2")) :: Nil ) // Widening to DoubleType @@ -488,7 +492,7 @@ class JsonSuite extends QueryTest { val jsonDF = jsonRDD(complexFieldValueTypeConflict) val expectedSchema = StructType( - StructField("array", ArrayType(IntegerType, false), true) :: + StructField("array", ArrayType(LongType, true), true) :: StructField("num_struct", StringType, true) :: StructField("str_array", StringType, true) :: StructField("struct", StructType( @@ -514,8 +518,8 @@ class JsonSuite extends QueryTest { val expectedSchema = StructType( StructField("array1", ArrayType(StringType, true), true) :: StructField("array2", ArrayType(StructType( - StructField("field", LongType, true) :: Nil), false), true) :: - StructField("array3", ArrayType(StringType, false), true) :: Nil) + StructField("field", LongType, true) :: Nil), true), true) :: + StructField("array3", ArrayType(StringType, true), true) :: Nil) assert(expectedSchema === jsonDF.schema) @@ -542,7 +546,7 @@ class JsonSuite extends QueryTest { val expectedSchema = StructType( StructField("a", BooleanType, true) :: StructField("b", LongType, true) :: - StructField("c", ArrayType(IntegerType, false), true) :: + StructField("c", ArrayType(LongType, true), true) :: StructField("d", StructType( StructField("field", BooleanType, true) :: Nil), true) :: StructField("e", StringType, true) :: Nil) @@ -552,9 +556,37 @@ class JsonSuite extends QueryTest { jsonDF.registerTempTable("jsonTable") } + test("jsonFile should be based on JSONRelation") { + val dir = Utils.createTempDir() + dir.delete() + val path = dir.getCanonicalPath + sparkContext.parallelize(1 to 100).map(i => s"""{"a": 1, "b": "str$i"}""").saveAsTextFile(path) + val jsonDF = jsonFile(path, 0.49) + + val analyzed = jsonDF.queryExecution.analyzed + assert( + analyzed.isInstanceOf[LogicalRelation], + "The DataFrame returned by jsonFile should be based on JSONRelation.") + val relation = analyzed.asInstanceOf[LogicalRelation].relation + assert( + relation.isInstanceOf[JSONRelation], + "The DataFrame returned by jsonFile should be based on JSONRelation.") + assert(relation.asInstanceOf[JSONRelation].path === path) + assert(relation.asInstanceOf[JSONRelation].samplingRatio === (0.49 +- 0.001)) + + val schema = StructType(StructField("a", LongType, true) :: Nil) + val logicalRelation = + jsonFile(path, schema).queryExecution.analyzed.asInstanceOf[LogicalRelation] + val relationWithSchema = logicalRelation.relation.asInstanceOf[JSONRelation] + assert(relationWithSchema.path === path) + assert(relationWithSchema.schema === schema) + assert(relationWithSchema.samplingRatio > 0.99) + } + test("Loading a JSON dataset from a text file") { - val file = getTempFilePath("json") - val path = file.toString + val dir = Utils.createTempDir() + dir.delete() + val path = dir.getCanonicalPath primitiveFieldAndType.map(record => record.replaceAll("\n", " ")).saveAsTextFile(path) val jsonDF = jsonFile(path) @@ -562,7 +594,7 @@ class JsonSuite extends QueryTest { StructField("bigInteger", DecimalType.Unlimited, true) :: StructField("boolean", BooleanType, true) :: StructField("double", DoubleType, true) :: - StructField("integer", IntegerType, true) :: + StructField("integer", LongType, true) :: StructField("long", LongType, true) :: StructField("null", StringType, true) :: StructField("string", StringType, true) :: Nil) @@ -584,8 +616,9 @@ class JsonSuite extends QueryTest { } test("Loading a JSON dataset from a text file with SQL") { - val file = getTempFilePath("json") - val path = file.toString + val dir = Utils.createTempDir() + dir.delete() + val path = dir.getCanonicalPath primitiveFieldAndType.map(record => record.replaceAll("\n", " ")).saveAsTextFile(path) sql( @@ -610,8 +643,9 @@ class JsonSuite extends QueryTest { } test("Applying schemas") { - val file = getTempFilePath("json") - val path = file.toString + val dir = Utils.createTempDir() + dir.delete() + val path = dir.getCanonicalPath primitiveFieldAndType.map(record => record.replaceAll("\n", " ")).saveAsTextFile(path) val schema = StructType( @@ -658,6 +692,62 @@ class JsonSuite extends QueryTest { ) } + test("Applying schemas with MapType") { + val schemaWithSimpleMap = StructType( + StructField("map", MapType(StringType, IntegerType, true), false) :: Nil) + val jsonWithSimpleMap = jsonRDD(mapType1, schemaWithSimpleMap) + + jsonWithSimpleMap.registerTempTable("jsonWithSimpleMap") + + checkAnswer( + sql("select map from jsonWithSimpleMap"), + Row(Map("a" -> 1)) :: + Row(Map("b" -> 2)) :: + Row(Map("c" -> 3)) :: + Row(Map("c" -> 1, "d" -> 4)) :: + Row(Map("e" -> null)) :: Nil + ) + + checkAnswer( + sql("select map['c'] from jsonWithSimpleMap"), + Row(null) :: + Row(null) :: + Row(3) :: + Row(1) :: + Row(null) :: Nil + ) + + val innerStruct = StructType( + StructField("field1", ArrayType(IntegerType, true), true) :: + StructField("field2", IntegerType, true) :: Nil) + val schemaWithComplexMap = StructType( + StructField("map", MapType(StringType, innerStruct, true), false) :: Nil) + + val jsonWithComplexMap = jsonRDD(mapType2, schemaWithComplexMap) + + jsonWithComplexMap.registerTempTable("jsonWithComplexMap") + + checkAnswer( + sql("select map from jsonWithComplexMap"), + Row(Map("a" -> Row(Seq(1, 2, 3, null), null))) :: + Row(Map("b" -> Row(null, 2))) :: + Row(Map("c" -> Row(Seq(), 4))) :: + Row(Map("c" -> Row(null, 3), "d" -> Row(Seq(null), null))) :: + Row(Map("e" -> null)) :: + Row(Map("f" -> Row(null, null))) :: Nil + ) + + checkAnswer( + sql("select map['a'].field1, map['c'].field2 from jsonWithComplexMap"), + Row(Seq(1, 2, 3, null), null) :: + Row(null, null) :: + Row(null, 4) :: + Row(null, 3) :: + Row(null, null) :: + Row(null, null) :: Nil + ) + } + test("SPARK-2096 Correctly parse dot notations") { val jsonDF = jsonRDD(complexFieldAndType2) jsonDF.registerTempTable("jsonTable") @@ -780,15 +870,15 @@ class JsonSuite extends QueryTest { val schema = StructType( StructField("field1", - ArrayType(ArrayType(ArrayType(ArrayType(StringType, false), false), true), false), true) :: + ArrayType(ArrayType(ArrayType(ArrayType(StringType, true), true), true), true), true) :: StructField("field2", ArrayType(ArrayType( - StructType(StructField("Test", IntegerType, true) :: Nil), false), true), true) :: + StructType(StructField("Test", LongType, true) :: Nil), true), true), true) :: StructField("field3", ArrayType(ArrayType( - StructType(StructField("Test", StringType, true) :: Nil), true), false), true) :: + StructType(StructField("Test", StringType, true) :: Nil), true), true), true) :: StructField("field4", - ArrayType(ArrayType(ArrayType(IntegerType, false), true), false), true) :: Nil) + ArrayType(ArrayType(ArrayType(LongType, true), true), true), true) :: Nil) assert(schema === jsonDF.schema) @@ -805,8 +895,7 @@ class JsonSuite extends QueryTest { ) } - test("SPARK-4228 DataFrame to JSON") - { + test("SPARK-4228 DataFrame to JSON") { val schema1 = StructType( StructField("f1", IntegerType, false) :: StructField("f2", StringType, false) :: @@ -822,12 +911,14 @@ class JsonSuite extends QueryTest { Row(values(0).toInt, values(1), values(2).toBoolean, r.split(",").toList, v5) } - val df1 = applySchema(rowRDD1, schema1) + val df1 = createDataFrame(rowRDD1, schema1) df1.registerTempTable("applySchema1") - val df2 = df1.toDataFrame + val df2 = df1.toDF val result = df2.toJSON.collect() - assert(result(0) == "{\"f1\":1,\"f2\":\"A1\",\"f3\":true,\"f4\":[\"1\",\" A1\",\" true\",\" null\"]}") - assert(result(3) == "{\"f1\":4,\"f2\":\"D4\",\"f3\":true,\"f4\":[\"4\",\" D4\",\" true\",\" 2147483644\"],\"f5\":2147483644}") + // scalastyle:off + assert(result(0) === "{\"f1\":1,\"f2\":\"A1\",\"f3\":true,\"f4\":[\"1\",\" A1\",\" true\",\" null\"]}") + assert(result(3) === "{\"f1\":4,\"f2\":\"D4\",\"f3\":true,\"f4\":[\"4\",\" D4\",\" true\",\" 2147483644\"],\"f5\":2147483644}") + // scalastyle:on val schema2 = StructType( StructField("f1", StructType( @@ -843,13 +934,13 @@ class JsonSuite extends QueryTest { Row(Row(values(0).toInt, values(2).toBoolean), Map(values(1) -> v4)) } - val df3 = applySchema(rowRDD2, schema2) + val df3 = createDataFrame(rowRDD2, schema2) df3.registerTempTable("applySchema2") - val df4 = df3.toDataFrame + val df4 = df3.toDF val result2 = df4.toJSON.collect() - assert(result2(1) == "{\"f1\":{\"f11\":2,\"f12\":false},\"f2\":{\"B2\":null}}") - assert(result2(3) == "{\"f1\":{\"f11\":4,\"f12\":true},\"f2\":{\"D4\":2147483644}}") + assert(result2(1) === "{\"f1\":{\"f11\":2,\"f12\":false},\"f2\":{\"B2\":null}}") + assert(result2(3) === "{\"f1\":{\"f11\":4,\"f12\":true},\"f2\":{\"D4\":2147483644}}") val jsonDF = jsonRDD(primitiveFieldAndType) val primTable = jsonRDD(jsonDF.toJSON) @@ -881,7 +972,8 @@ class JsonSuite extends QueryTest { // Access elements of a BigInteger array (we use DecimalType internally). checkAnswer( - sql("select arrayOfBigInteger[0], arrayOfBigInteger[1], arrayOfBigInteger[2] from complexTable"), + sql("select arrayOfBigInteger[0], arrayOfBigInteger[1], arrayOfBigInteger[2] " + + " from complexTable"), Row(new java.math.BigDecimal("922337203685477580700"), new java.math.BigDecimal("-922337203685477580800"), null) ) @@ -921,7 +1013,8 @@ class JsonSuite extends QueryTest { // Access elements of an array field of a struct. checkAnswer( - sql("select structWithArrayFields.field1[1], structWithArrayFields.field2[3] from complexTable"), + sql("select structWithArrayFields.field1[1], structWithArrayFields.field2[3] " + + "from complexTable"), Row(5, null) ) } @@ -950,4 +1043,11 @@ class JsonSuite extends QueryTest { assert(!logicalRelation2.sameResult(logicalRelation3), s"$logicalRelation2 and $logicalRelation3 should be considered not having the same result.") } + + test("SPARK-6245 JsonRDD.inferSchema on empty RDD") { + // This is really a test that it doesn't throw an exception + val emptySchema = JsonRDD.inferSchema(empty, 1.0, "") + assert(StructType(Seq()) === emptySchema) + } + } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala b/sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala index 3370b3c98b4be..47a97a49daabb 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala @@ -146,6 +146,23 @@ object TestJsonData { ]] }""" :: Nil) + val mapType1 = + TestSQLContext.sparkContext.parallelize( + """{"map": {"a": 1}}""" :: + """{"map": {"b": 2}}""" :: + """{"map": {"c": 3}}""" :: + """{"map": {"c": 1, "d": 4}}""" :: + """{"map": {"e": null}}""" :: Nil) + + val mapType2 = + TestSQLContext.sparkContext.parallelize( + """{"map": {"a": {"field1": [1, 2, 3, null]}}}""" :: + """{"map": {"b": {"field2": 2}}}""" :: + """{"map": {"c": {"field1": [], "field2": 4}}}""" :: + """{"map": {"c": {"field2": 3}, "d": {"field1": [null]}}}""" :: + """{"map": {"e": null}}""" :: + """{"map": {"f": {"field1": null}}}""" :: Nil) + val nullsInArrays = TestSQLContext.sparkContext.parallelize( """{"field1":[[null], [[["Test"]]]]}""" :: @@ -168,4 +185,7 @@ object TestJsonData { """{"a":{, b:3}""" :: """{"b":"str_b_4", "a":"str_a_4", "c":"str_c_4"}""" :: """]""" :: Nil) + + val empty = + TestSQLContext.sparkContext.parallelize(Seq[String]()) } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetFilterSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetFilterSuite.scala index f8117c21773ae..10d0ede4dc0dc 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetFilterSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetFilterSuite.scala @@ -17,11 +17,12 @@ package org.apache.spark.sql.parquet +import org.scalatest.BeforeAndAfterAll import parquet.filter2.predicate.Operators._ import parquet.filter2.predicate.{FilterPredicate, Operators} import org.apache.spark.sql.catalyst.dsl.expressions._ -import org.apache.spark.sql.catalyst.expressions.{Attribute, Cast, Literal, Predicate, Row} +import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.planning.PhysicalOperation import org.apache.spark.sql.sources.LogicalRelation import org.apache.spark.sql.test.TestSQLContext @@ -40,11 +41,11 @@ import org.apache.spark.sql.{Column, DataFrame, QueryTest, SQLConf} * 2. `Tuple1(Option(x))` is used together with `AnyVal` types like `Int` to ensure the inferred * data type is nullable. */ -class ParquetFilterSuite extends QueryTest with ParquetTest { +class ParquetFilterSuiteBase extends QueryTest with ParquetTest { val sqlContext = TestSQLContext private def checkFilterPredicate( - rdd: DataFrame, + df: DataFrame, predicate: Predicate, filterClass: Class[_ <: FilterPredicate], checker: (DataFrame, Seq[Row]) => Unit, @@ -52,7 +53,7 @@ class ParquetFilterSuite extends QueryTest with ParquetTest { val output = predicate.collect { case a: Attribute => a }.distinct withSQLConf(SQLConf.PARQUET_FILTER_PUSHDOWN_ENABLED -> "true") { - val query = rdd + val query = df .select(output.map(e => Column(e)): _*) .where(Column(predicate)) @@ -84,238 +85,291 @@ class ParquetFilterSuite extends QueryTest with ParquetTest { private def checkFilterPredicate (predicate: Predicate, filterClass: Class[_ <: FilterPredicate], expected: Seq[Row]) - (implicit rdd: DataFrame): Unit = { - checkFilterPredicate(rdd, predicate, filterClass, checkAnswer(_, _: Seq[Row]), expected) + (implicit df: DataFrame): Unit = { + checkFilterPredicate(df, predicate, filterClass, checkAnswer(_, _: Seq[Row]), expected) } private def checkFilterPredicate[T] (predicate: Predicate, filterClass: Class[_ <: FilterPredicate], expected: T) - (implicit rdd: DataFrame): Unit = { - checkFilterPredicate(predicate, filterClass, Seq(Row(expected)))(rdd) + (implicit df: DataFrame): Unit = { + checkFilterPredicate(predicate, filterClass, Seq(Row(expected)))(df) } private def checkBinaryFilterPredicate (predicate: Predicate, filterClass: Class[_ <: FilterPredicate], expected: Seq[Row]) - (implicit rdd: DataFrame): Unit = { - def checkBinaryAnswer(rdd: DataFrame, expected: Seq[Row]) = { + (implicit df: DataFrame): Unit = { + def checkBinaryAnswer(df: DataFrame, expected: Seq[Row]) = { assertResult(expected.map(_.getAs[Array[Byte]](0).mkString(",")).toSeq.sorted) { - rdd.map(_.getAs[Array[Byte]](0).mkString(",")).collect().toSeq.sorted + df.map(_.getAs[Array[Byte]](0).mkString(",")).collect().toSeq.sorted } } - checkFilterPredicate(rdd, predicate, filterClass, checkBinaryAnswer _, expected) + checkFilterPredicate(df, predicate, filterClass, checkBinaryAnswer _, expected) } private def checkBinaryFilterPredicate (predicate: Predicate, filterClass: Class[_ <: FilterPredicate], expected: Array[Byte]) - (implicit rdd: DataFrame): Unit = { - checkBinaryFilterPredicate(predicate, filterClass, Seq(Row(expected)))(rdd) + (implicit df: DataFrame): Unit = { + checkBinaryFilterPredicate(predicate, filterClass, Seq(Row(expected)))(df) } - def run(prefix: String): Unit = { - test(s"$prefix: filter pushdown - boolean") { - withParquetRDD((true :: false :: Nil).map(b => Tuple1.apply(Option(b)))) { implicit rdd => - checkFilterPredicate('_1.isNull, classOf[Eq[_]], Seq.empty[Row]) - checkFilterPredicate('_1.isNotNull, classOf[NotEq[_]], Seq(Row(true), Row(false))) + test("filter pushdown - boolean") { + withParquetDataFrame((true :: false :: Nil).map(b => Tuple1.apply(Option(b)))) { implicit df => + checkFilterPredicate('_1.isNull, classOf[Eq[_]], Seq.empty[Row]) + checkFilterPredicate('_1.isNotNull, classOf[NotEq[_]], Seq(Row(true), Row(false))) - checkFilterPredicate('_1 === true, classOf[Eq[_]], true) - checkFilterPredicate('_1 !== true, classOf[NotEq[_]], false) - } + checkFilterPredicate('_1 === true, classOf[Eq[_]], true) + checkFilterPredicate('_1 !== true, classOf[NotEq[_]], false) } + } - test(s"$prefix: filter pushdown - short") { - withParquetRDD((1 to 4).map(i => Tuple1(Option(i.toShort)))) { implicit rdd => - checkFilterPredicate(Cast('_1, IntegerType) === 1, classOf[Eq[_]], 1) - checkFilterPredicate( - Cast('_1, IntegerType) !== 1, classOf[NotEq[_]], (2 to 4).map(Row.apply(_))) - - checkFilterPredicate(Cast('_1, IntegerType) < 2, classOf[Lt[_]], 1) - checkFilterPredicate(Cast('_1, IntegerType) > 3, classOf[Gt[_]], 4) - checkFilterPredicate(Cast('_1, IntegerType) <= 1, classOf[LtEq[_]], 1) - checkFilterPredicate(Cast('_1, IntegerType) >= 4, classOf[GtEq[_]], 4) - - checkFilterPredicate(Literal(1) === Cast('_1, IntegerType), classOf[Eq[_]], 1) - checkFilterPredicate(Literal(2) > Cast('_1, IntegerType), classOf[Lt[_]], 1) - checkFilterPredicate(Literal(3) < Cast('_1, IntegerType), classOf[Gt[_]], 4) - checkFilterPredicate(Literal(1) >= Cast('_1, IntegerType), classOf[LtEq[_]], 1) - checkFilterPredicate(Literal(4) <= Cast('_1, IntegerType), classOf[GtEq[_]], 4) - - checkFilterPredicate(!(Cast('_1, IntegerType) < 4), classOf[GtEq[_]], 4) - checkFilterPredicate( - Cast('_1, IntegerType) > 2 && Cast('_1, IntegerType) < 4, classOf[Operators.And], 3) - checkFilterPredicate( - Cast('_1, IntegerType) < 2 || Cast('_1, IntegerType) > 3, - classOf[Operators.Or], - Seq(Row(1), Row(4))) - } + test("filter pushdown - short") { + withParquetDataFrame((1 to 4).map(i => Tuple1(Option(i.toShort)))) { implicit df => + checkFilterPredicate(Cast('_1, IntegerType) === 1, classOf[Eq[_]], 1) + checkFilterPredicate( + Cast('_1, IntegerType) !== 1, classOf[NotEq[_]], (2 to 4).map(Row.apply(_))) + + checkFilterPredicate(Cast('_1, IntegerType) < 2, classOf[Lt[_]], 1) + checkFilterPredicate(Cast('_1, IntegerType) > 3, classOf[Gt[_]], 4) + checkFilterPredicate(Cast('_1, IntegerType) <= 1, classOf[LtEq[_]], 1) + checkFilterPredicate(Cast('_1, IntegerType) >= 4, classOf[GtEq[_]], 4) + + checkFilterPredicate(Literal(1) === Cast('_1, IntegerType), classOf[Eq[_]], 1) + checkFilterPredicate(Literal(2) > Cast('_1, IntegerType), classOf[Lt[_]], 1) + checkFilterPredicate(Literal(3) < Cast('_1, IntegerType), classOf[Gt[_]], 4) + checkFilterPredicate(Literal(1) >= Cast('_1, IntegerType), classOf[LtEq[_]], 1) + checkFilterPredicate(Literal(4) <= Cast('_1, IntegerType), classOf[GtEq[_]], 4) + + checkFilterPredicate(!(Cast('_1, IntegerType) < 4), classOf[GtEq[_]], 4) + checkFilterPredicate( + Cast('_1, IntegerType) > 2 && Cast('_1, IntegerType) < 4, classOf[Operators.And], 3) + checkFilterPredicate( + Cast('_1, IntegerType) < 2 || Cast('_1, IntegerType) > 3, + classOf[Operators.Or], + Seq(Row(1), Row(4))) } + } - test(s"$prefix: filter pushdown - integer") { - withParquetRDD((1 to 4).map(i => Tuple1(Option(i)))) { implicit rdd => - checkFilterPredicate('_1.isNull, classOf[Eq[_]], Seq.empty[Row]) - checkFilterPredicate('_1.isNotNull, classOf[NotEq[_]], (1 to 4).map(Row.apply(_))) + test("filter pushdown - integer") { + withParquetDataFrame((1 to 4).map(i => Tuple1(Option(i)))) { implicit df => + checkFilterPredicate('_1.isNull, classOf[Eq[_]], Seq.empty[Row]) + checkFilterPredicate('_1.isNotNull, classOf[NotEq[_]], (1 to 4).map(Row.apply(_))) - checkFilterPredicate('_1 === 1, classOf[Eq[_]], 1) - checkFilterPredicate('_1 !== 1, classOf[NotEq[_]], (2 to 4).map(Row.apply(_))) + checkFilterPredicate('_1 === 1, classOf[Eq[_]], 1) + checkFilterPredicate('_1 !== 1, classOf[NotEq[_]], (2 to 4).map(Row.apply(_))) - checkFilterPredicate('_1 < 2, classOf[Lt[_]], 1) - checkFilterPredicate('_1 > 3, classOf[Gt[_]], 4) - checkFilterPredicate('_1 <= 1, classOf[LtEq[_]], 1) - checkFilterPredicate('_1 >= 4, classOf[GtEq[_]], 4) + checkFilterPredicate('_1 < 2, classOf[Lt[_]], 1) + checkFilterPredicate('_1 > 3, classOf[Gt[_]], 4) + checkFilterPredicate('_1 <= 1, classOf[LtEq[_]], 1) + checkFilterPredicate('_1 >= 4, classOf[GtEq[_]], 4) - checkFilterPredicate(Literal(1) === '_1, classOf[Eq[_]], 1) - checkFilterPredicate(Literal(2) > '_1, classOf[Lt[_]], 1) - checkFilterPredicate(Literal(3) < '_1, classOf[Gt[_]], 4) - checkFilterPredicate(Literal(1) >= '_1, classOf[LtEq[_]], 1) - checkFilterPredicate(Literal(4) <= '_1, classOf[GtEq[_]], 4) + checkFilterPredicate(Literal(1) === '_1, classOf[Eq[_]], 1) + checkFilterPredicate(Literal(2) > '_1, classOf[Lt[_]], 1) + checkFilterPredicate(Literal(3) < '_1, classOf[Gt[_]], 4) + checkFilterPredicate(Literal(1) >= '_1, classOf[LtEq[_]], 1) + checkFilterPredicate(Literal(4) <= '_1, classOf[GtEq[_]], 4) - checkFilterPredicate(!('_1 < 4), classOf[GtEq[_]], 4) - checkFilterPredicate('_1 > 2 && '_1 < 4, classOf[Operators.And], 3) - checkFilterPredicate('_1 < 2 || '_1 > 3, classOf[Operators.Or], Seq(Row(1), Row(4))) - } + checkFilterPredicate(!('_1 < 4), classOf[GtEq[_]], 4) + checkFilterPredicate('_1 > 2 && '_1 < 4, classOf[Operators.And], 3) + checkFilterPredicate('_1 < 2 || '_1 > 3, classOf[Operators.Or], Seq(Row(1), Row(4))) } + } - test(s"$prefix: filter pushdown - long") { - withParquetRDD((1 to 4).map(i => Tuple1(Option(i.toLong)))) { implicit rdd => - checkFilterPredicate('_1.isNull, classOf[Eq[_]], Seq.empty[Row]) - checkFilterPredicate('_1.isNotNull, classOf[NotEq[_]], (1 to 4).map(Row.apply(_))) + test("filter pushdown - long") { + withParquetDataFrame((1 to 4).map(i => Tuple1(Option(i.toLong)))) { implicit df => + checkFilterPredicate('_1.isNull, classOf[Eq[_]], Seq.empty[Row]) + checkFilterPredicate('_1.isNotNull, classOf[NotEq[_]], (1 to 4).map(Row.apply(_))) - checkFilterPredicate('_1 === 1, classOf[Eq[_]], 1) - checkFilterPredicate('_1 !== 1, classOf[NotEq[_]], (2 to 4).map(Row.apply(_))) + checkFilterPredicate('_1 === 1, classOf[Eq[_]], 1) + checkFilterPredicate('_1 !== 1, classOf[NotEq[_]], (2 to 4).map(Row.apply(_))) - checkFilterPredicate('_1 < 2, classOf[Lt[_]], 1) - checkFilterPredicate('_1 > 3, classOf[Gt[_]], 4) - checkFilterPredicate('_1 <= 1, classOf[LtEq[_]], 1) - checkFilterPredicate('_1 >= 4, classOf[GtEq[_]], 4) + checkFilterPredicate('_1 < 2, classOf[Lt[_]], 1) + checkFilterPredicate('_1 > 3, classOf[Gt[_]], 4) + checkFilterPredicate('_1 <= 1, classOf[LtEq[_]], 1) + checkFilterPredicate('_1 >= 4, classOf[GtEq[_]], 4) - checkFilterPredicate(Literal(1) === '_1, classOf[Eq[_]], 1) - checkFilterPredicate(Literal(2) > '_1, classOf[Lt[_]], 1) - checkFilterPredicate(Literal(3) < '_1, classOf[Gt[_]], 4) - checkFilterPredicate(Literal(1) >= '_1, classOf[LtEq[_]], 1) - checkFilterPredicate(Literal(4) <= '_1, classOf[GtEq[_]], 4) + checkFilterPredicate(Literal(1) === '_1, classOf[Eq[_]], 1) + checkFilterPredicate(Literal(2) > '_1, classOf[Lt[_]], 1) + checkFilterPredicate(Literal(3) < '_1, classOf[Gt[_]], 4) + checkFilterPredicate(Literal(1) >= '_1, classOf[LtEq[_]], 1) + checkFilterPredicate(Literal(4) <= '_1, classOf[GtEq[_]], 4) - checkFilterPredicate(!('_1 < 4), classOf[GtEq[_]], 4) - checkFilterPredicate('_1 > 2 && '_1 < 4, classOf[Operators.And], 3) - checkFilterPredicate('_1 < 2 || '_1 > 3, classOf[Operators.Or], Seq(Row(1), Row(4))) - } + checkFilterPredicate(!('_1 < 4), classOf[GtEq[_]], 4) + checkFilterPredicate('_1 > 2 && '_1 < 4, classOf[Operators.And], 3) + checkFilterPredicate('_1 < 2 || '_1 > 3, classOf[Operators.Or], Seq(Row(1), Row(4))) } + } - test(s"$prefix: filter pushdown - float") { - withParquetRDD((1 to 4).map(i => Tuple1(Option(i.toFloat)))) { implicit rdd => - checkFilterPredicate('_1.isNull, classOf[Eq[_]], Seq.empty[Row]) - checkFilterPredicate('_1.isNotNull, classOf[NotEq[_]], (1 to 4).map(Row.apply(_))) + test("filter pushdown - float") { + withParquetDataFrame((1 to 4).map(i => Tuple1(Option(i.toFloat)))) { implicit df => + checkFilterPredicate('_1.isNull, classOf[Eq[_]], Seq.empty[Row]) + checkFilterPredicate('_1.isNotNull, classOf[NotEq[_]], (1 to 4).map(Row.apply(_))) - checkFilterPredicate('_1 === 1, classOf[Eq[_]], 1) - checkFilterPredicate('_1 !== 1, classOf[NotEq[_]], (2 to 4).map(Row.apply(_))) + checkFilterPredicate('_1 === 1, classOf[Eq[_]], 1) + checkFilterPredicate('_1 !== 1, classOf[NotEq[_]], (2 to 4).map(Row.apply(_))) - checkFilterPredicate('_1 < 2, classOf[Lt[_]], 1) - checkFilterPredicate('_1 > 3, classOf[Gt[_]], 4) - checkFilterPredicate('_1 <= 1, classOf[LtEq[_]], 1) - checkFilterPredicate('_1 >= 4, classOf[GtEq[_]], 4) + checkFilterPredicate('_1 < 2, classOf[Lt[_]], 1) + checkFilterPredicate('_1 > 3, classOf[Gt[_]], 4) + checkFilterPredicate('_1 <= 1, classOf[LtEq[_]], 1) + checkFilterPredicate('_1 >= 4, classOf[GtEq[_]], 4) - checkFilterPredicate(Literal(1) === '_1, classOf[Eq[_]], 1) - checkFilterPredicate(Literal(2) > '_1, classOf[Lt[_]], 1) - checkFilterPredicate(Literal(3) < '_1, classOf[Gt[_]], 4) - checkFilterPredicate(Literal(1) >= '_1, classOf[LtEq[_]], 1) - checkFilterPredicate(Literal(4) <= '_1, classOf[GtEq[_]], 4) + checkFilterPredicate(Literal(1) === '_1, classOf[Eq[_]], 1) + checkFilterPredicate(Literal(2) > '_1, classOf[Lt[_]], 1) + checkFilterPredicate(Literal(3) < '_1, classOf[Gt[_]], 4) + checkFilterPredicate(Literal(1) >= '_1, classOf[LtEq[_]], 1) + checkFilterPredicate(Literal(4) <= '_1, classOf[GtEq[_]], 4) - checkFilterPredicate(!('_1 < 4), classOf[GtEq[_]], 4) - checkFilterPredicate('_1 > 2 && '_1 < 4, classOf[Operators.And], 3) - checkFilterPredicate('_1 < 2 || '_1 > 3, classOf[Operators.Or], Seq(Row(1), Row(4))) - } + checkFilterPredicate(!('_1 < 4), classOf[GtEq[_]], 4) + checkFilterPredicate('_1 > 2 && '_1 < 4, classOf[Operators.And], 3) + checkFilterPredicate('_1 < 2 || '_1 > 3, classOf[Operators.Or], Seq(Row(1), Row(4))) } + } - test(s"$prefix: filter pushdown - double") { - withParquetRDD((1 to 4).map(i => Tuple1(Option(i.toDouble)))) { implicit rdd => - checkFilterPredicate('_1.isNull, classOf[Eq[_]], Seq.empty[Row]) - checkFilterPredicate('_1.isNotNull, classOf[NotEq[_]], (1 to 4).map(Row.apply(_))) + test("filter pushdown - double") { + withParquetDataFrame((1 to 4).map(i => Tuple1(Option(i.toDouble)))) { implicit df => + checkFilterPredicate('_1.isNull, classOf[Eq[_]], Seq.empty[Row]) + checkFilterPredicate('_1.isNotNull, classOf[NotEq[_]], (1 to 4).map(Row.apply(_))) - checkFilterPredicate('_1 === 1, classOf[Eq[_]], 1) - checkFilterPredicate('_1 !== 1, classOf[NotEq[_]], (2 to 4).map(Row.apply(_))) + checkFilterPredicate('_1 === 1, classOf[Eq[_]], 1) + checkFilterPredicate('_1 !== 1, classOf[NotEq[_]], (2 to 4).map(Row.apply(_))) - checkFilterPredicate('_1 < 2, classOf[Lt[_]], 1) - checkFilterPredicate('_1 > 3, classOf[Gt[_]], 4) - checkFilterPredicate('_1 <= 1, classOf[LtEq[_]], 1) - checkFilterPredicate('_1 >= 4, classOf[GtEq[_]], 4) + checkFilterPredicate('_1 < 2, classOf[Lt[_]], 1) + checkFilterPredicate('_1 > 3, classOf[Gt[_]], 4) + checkFilterPredicate('_1 <= 1, classOf[LtEq[_]], 1) + checkFilterPredicate('_1 >= 4, classOf[GtEq[_]], 4) - checkFilterPredicate(Literal(1) === '_1, classOf[Eq[_]], 1) - checkFilterPredicate(Literal(2) > '_1, classOf[Lt[_]], 1) - checkFilterPredicate(Literal(3) < '_1, classOf[Gt[_]], 4) - checkFilterPredicate(Literal(1) >= '_1, classOf[LtEq[_]], 1) - checkFilterPredicate(Literal(4) <= '_1, classOf[GtEq[_]], 4) + checkFilterPredicate(Literal(1) === '_1, classOf[Eq[_]], 1) + checkFilterPredicate(Literal(2) > '_1, classOf[Lt[_]], 1) + checkFilterPredicate(Literal(3) < '_1, classOf[Gt[_]], 4) + checkFilterPredicate(Literal(1) >= '_1, classOf[LtEq[_]], 1) + checkFilterPredicate(Literal(4) <= '_1, classOf[GtEq[_]], 4) - checkFilterPredicate(!('_1 < 4), classOf[GtEq[_]], 4) - checkFilterPredicate('_1 > 2 && '_1 < 4, classOf[Operators.And], 3) - checkFilterPredicate('_1 < 2 || '_1 > 3, classOf[Operators.Or], Seq(Row(1), Row(4))) - } + checkFilterPredicate(!('_1 < 4), classOf[GtEq[_]], 4) + checkFilterPredicate('_1 > 2 && '_1 < 4, classOf[Operators.And], 3) + checkFilterPredicate('_1 < 2 || '_1 > 3, classOf[Operators.Or], Seq(Row(1), Row(4))) } + } - test(s"$prefix: filter pushdown - string") { - withParquetRDD((1 to 4).map(i => Tuple1(i.toString))) { implicit rdd => - checkFilterPredicate('_1.isNull, classOf[Eq[_]], Seq.empty[Row]) - checkFilterPredicate( - '_1.isNotNull, classOf[NotEq[_]], (1 to 4).map(i => Row.apply(i.toString))) - - checkFilterPredicate('_1 === "1", classOf[Eq[_]], "1") - checkFilterPredicate( - '_1 !== "1", classOf[NotEq[_]], (2 to 4).map(i => Row.apply(i.toString))) - - checkFilterPredicate('_1 < "2", classOf[Lt[_]], "1") - checkFilterPredicate('_1 > "3", classOf[Gt[_]], "4") - checkFilterPredicate('_1 <= "1", classOf[LtEq[_]], "1") - checkFilterPredicate('_1 >= "4", classOf[GtEq[_]], "4") - - checkFilterPredicate(Literal("1") === '_1, classOf[Eq[_]], "1") - checkFilterPredicate(Literal("2") > '_1, classOf[Lt[_]], "1") - checkFilterPredicate(Literal("3") < '_1, classOf[Gt[_]], "4") - checkFilterPredicate(Literal("1") >= '_1, classOf[LtEq[_]], "1") - checkFilterPredicate(Literal("4") <= '_1, classOf[GtEq[_]], "4") - - checkFilterPredicate(!('_1 < "4"), classOf[GtEq[_]], "4") - checkFilterPredicate('_1 > "2" && '_1 < "4", classOf[Operators.And], "3") - checkFilterPredicate('_1 < "2" || '_1 > "3", classOf[Operators.Or], Seq(Row("1"), Row("4"))) - } + test("filter pushdown - string") { + withParquetDataFrame((1 to 4).map(i => Tuple1(i.toString))) { implicit df => + checkFilterPredicate('_1.isNull, classOf[Eq[_]], Seq.empty[Row]) + checkFilterPredicate( + '_1.isNotNull, classOf[NotEq[_]], (1 to 4).map(i => Row.apply(i.toString))) + + checkFilterPredicate('_1 === "1", classOf[Eq[_]], "1") + checkFilterPredicate( + '_1 !== "1", classOf[NotEq[_]], (2 to 4).map(i => Row.apply(i.toString))) + + checkFilterPredicate('_1 < "2", classOf[Lt[_]], "1") + checkFilterPredicate('_1 > "3", classOf[Gt[_]], "4") + checkFilterPredicate('_1 <= "1", classOf[LtEq[_]], "1") + checkFilterPredicate('_1 >= "4", classOf[GtEq[_]], "4") + + checkFilterPredicate(Literal("1") === '_1, classOf[Eq[_]], "1") + checkFilterPredicate(Literal("2") > '_1, classOf[Lt[_]], "1") + checkFilterPredicate(Literal("3") < '_1, classOf[Gt[_]], "4") + checkFilterPredicate(Literal("1") >= '_1, classOf[LtEq[_]], "1") + checkFilterPredicate(Literal("4") <= '_1, classOf[GtEq[_]], "4") + + checkFilterPredicate(!('_1 < "4"), classOf[GtEq[_]], "4") + checkFilterPredicate('_1 > "2" && '_1 < "4", classOf[Operators.And], "3") + checkFilterPredicate('_1 < "2" || '_1 > "3", classOf[Operators.Or], Seq(Row("1"), Row("4"))) } + } - test(s"$prefix: filter pushdown - binary") { - implicit class IntToBinary(int: Int) { - def b: Array[Byte] = int.toString.getBytes("UTF-8") - } + test("filter pushdown - binary") { + implicit class IntToBinary(int: Int) { + def b: Array[Byte] = int.toString.getBytes("UTF-8") + } + + withParquetDataFrame((1 to 4).map(i => Tuple1(i.b))) { implicit df => + checkBinaryFilterPredicate('_1 === 1.b, classOf[Eq[_]], 1.b) - withParquetRDD((1 to 4).map(i => Tuple1(i.b))) { implicit rdd => - checkBinaryFilterPredicate('_1 === 1.b, classOf[Eq[_]], 1.b) + checkBinaryFilterPredicate('_1.isNull, classOf[Eq[_]], Seq.empty[Row]) + checkBinaryFilterPredicate( + '_1.isNotNull, classOf[NotEq[_]], (1 to 4).map(i => Row.apply(i.b)).toSeq) - checkBinaryFilterPredicate('_1.isNull, classOf[Eq[_]], Seq.empty[Row]) - checkBinaryFilterPredicate( - '_1.isNotNull, classOf[NotEq[_]], (1 to 4).map(i => Row.apply(i.b)).toSeq) + checkBinaryFilterPredicate( + '_1 !== 1.b, classOf[NotEq[_]], (2 to 4).map(i => Row.apply(i.b)).toSeq) - checkBinaryFilterPredicate( - '_1 !== 1.b, classOf[NotEq[_]], (2 to 4).map(i => Row.apply(i.b)).toSeq) + checkBinaryFilterPredicate('_1 < 2.b, classOf[Lt[_]], 1.b) + checkBinaryFilterPredicate('_1 > 3.b, classOf[Gt[_]], 4.b) + checkBinaryFilterPredicate('_1 <= 1.b, classOf[LtEq[_]], 1.b) + checkBinaryFilterPredicate('_1 >= 4.b, classOf[GtEq[_]], 4.b) - checkBinaryFilterPredicate('_1 < 2.b, classOf[Lt[_]], 1.b) - checkBinaryFilterPredicate('_1 > 3.b, classOf[Gt[_]], 4.b) - checkBinaryFilterPredicate('_1 <= 1.b, classOf[LtEq[_]], 1.b) - checkBinaryFilterPredicate('_1 >= 4.b, classOf[GtEq[_]], 4.b) + checkBinaryFilterPredicate(Literal(1.b) === '_1, classOf[Eq[_]], 1.b) + checkBinaryFilterPredicate(Literal(2.b) > '_1, classOf[Lt[_]], 1.b) + checkBinaryFilterPredicate(Literal(3.b) < '_1, classOf[Gt[_]], 4.b) + checkBinaryFilterPredicate(Literal(1.b) >= '_1, classOf[LtEq[_]], 1.b) + checkBinaryFilterPredicate(Literal(4.b) <= '_1, classOf[GtEq[_]], 4.b) - checkBinaryFilterPredicate(Literal(1.b) === '_1, classOf[Eq[_]], 1.b) - checkBinaryFilterPredicate(Literal(2.b) > '_1, classOf[Lt[_]], 1.b) - checkBinaryFilterPredicate(Literal(3.b) < '_1, classOf[Gt[_]], 4.b) - checkBinaryFilterPredicate(Literal(1.b) >= '_1, classOf[LtEq[_]], 1.b) - checkBinaryFilterPredicate(Literal(4.b) <= '_1, classOf[GtEq[_]], 4.b) + checkBinaryFilterPredicate(!('_1 < 4.b), classOf[GtEq[_]], 4.b) + checkBinaryFilterPredicate('_1 > 2.b && '_1 < 4.b, classOf[Operators.And], 3.b) + checkBinaryFilterPredicate( + '_1 < 2.b || '_1 > 3.b, classOf[Operators.Or], Seq(Row(1.b), Row(4.b))) + } + } +} + +class ParquetDataSourceOnFilterSuite extends ParquetFilterSuiteBase with BeforeAndAfterAll { + val originalConf = sqlContext.conf.parquetUseDataSourceApi + + override protected def beforeAll(): Unit = { + sqlContext.conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, "true") + } + + override protected def afterAll(): Unit = { + sqlContext.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf.toString) + } + + test("SPARK-6554: don't push down predicates which reference partition columns") { + import sqlContext.implicits._ - checkBinaryFilterPredicate(!('_1 < 4.b), classOf[GtEq[_]], 4.b) - checkBinaryFilterPredicate('_1 > 2.b && '_1 < 4.b, classOf[Operators.And], 3.b) - checkBinaryFilterPredicate( - '_1 < 2.b || '_1 > 3.b, classOf[Operators.Or], Seq(Row(1.b), Row(4.b))) + withSQLConf(SQLConf.PARQUET_FILTER_PUSHDOWN_ENABLED -> "true") { + withTempPath { dir => + val path = s"${dir.getCanonicalPath}/part=1" + (1 to 3).map(i => (i, i.toString)).toDF("a", "b").saveAsParquetFile(path) + + // If the "part = 1" filter gets pushed down, this query will throw an exception since + // "part" is not a valid column in the actual Parquet file + checkAnswer( + sqlContext.parquetFile(path).filter("part = 1"), + (1 to 3).map(i => Row(i, i.toString, 1))) } } } +} + +class ParquetDataSourceOffFilterSuite extends ParquetFilterSuiteBase with BeforeAndAfterAll { + val originalConf = sqlContext.conf.parquetUseDataSourceApi + + override protected def beforeAll(): Unit = { + sqlContext.conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, "false") + } - withSQLConf(SQLConf.PARQUET_USE_DATA_SOURCE_API -> "true") { - run("Parquet data source enabled") + override protected def afterAll(): Unit = { + sqlContext.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf.toString) } + + test("SPARK-6742: don't push down predicates which reference partition columns") { + import sqlContext.implicits._ - withSQLConf(SQLConf.PARQUET_USE_DATA_SOURCE_API -> "false") { - run("Parquet data source disabled") + withSQLConf(SQLConf.PARQUET_FILTER_PUSHDOWN_ENABLED -> "true") { + withTempPath { dir => + val path = s"${dir.getCanonicalPath}/part=1" + (1 to 3).map(i => (i, i.toString)).toDF("a", "b").saveAsParquetFile(path) + + // If the "part = 1" filter gets pushed down, this query will throw an exception since + // "part" is not a valid column in the actual Parquet file + val df = DataFrame(sqlContext, org.apache.spark.sql.parquet.ParquetRelation( + path, + Some(sqlContext.sparkContext.hadoopConfiguration), sqlContext, + Seq(AttributeReference("part", IntegerType, false)()) )) + + checkAnswer( + df.filter("a = 1 or part = 1"), + (1 to 3).map(i => Row(1, i, i.toString))) + } + } } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala index c8ebbbc7d2eac..97c0f439acf13 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala @@ -21,24 +21,25 @@ import scala.collection.JavaConversions._ import scala.reflect.ClassTag import scala.reflect.runtime.universe.TypeTag +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs.{FileSystem, Path} +import org.scalatest.BeforeAndAfterAll import parquet.example.data.simple.SimpleGroup import parquet.example.data.{Group, GroupWriter} import parquet.hadoop.api.WriteSupport import parquet.hadoop.api.WriteSupport.WriteContext -import parquet.hadoop.metadata.CompressionCodecName -import parquet.hadoop.{ParquetFileWriter, ParquetWriter} +import parquet.hadoop.metadata.{ParquetMetadata, FileMetaData, CompressionCodecName} +import parquet.hadoop.{Footer, ParquetFileWriter, ParquetWriter} import parquet.io.api.RecordConsumer import parquet.schema.{MessageType, MessageTypeParser} -import org.apache.hadoop.conf.Configuration -import org.apache.hadoop.fs.{FileSystem, Path} -import org.apache.spark.sql.{DataFrame, QueryTest, SQLConf} -import org.apache.spark.sql.Dsl._ import org.apache.spark.sql.catalyst.ScalaReflection import org.apache.spark.sql.catalyst.expressions.Row import org.apache.spark.sql.test.TestSQLContext import org.apache.spark.sql.test.TestSQLContext._ -import org.apache.spark.sql.types.DecimalType +import org.apache.spark.sql.test.TestSQLContext.implicits._ +import org.apache.spark.sql.types._ +import org.apache.spark.sql.{DataFrame, QueryTest, SQLConf, SaveMode} // Write support class for nested groups: ParquetWriter initializes GroupWriteSupport // with an empty configuration (it is after all not intended to be used in this way?) @@ -63,239 +64,357 @@ private[parquet] class TestGroupWriteSupport(schema: MessageType) extends WriteS /** * A test suite that tests basic Parquet I/O. */ -class ParquetIOSuite extends QueryTest with ParquetTest { +class ParquetIOSuiteBase extends QueryTest with ParquetTest { val sqlContext = TestSQLContext + import sqlContext.implicits.localSeqToDataFrameHolder + /** * Writes `data` to a Parquet file, reads it back and check file contents. */ protected def checkParquetFile[T <: Product : ClassTag: TypeTag](data: Seq[T]): Unit = { - withParquetRDD(data)(r => checkAnswer(r, data.map(Row.fromTuple))) + withParquetDataFrame(data)(r => checkAnswer(r, data.map(Row.fromTuple))) } - def run(prefix: String): Unit = { - test(s"$prefix: basic data types (without binary)") { - val data = (1 to 4).map { i => - (i % 2 == 0, i, i.toLong, i.toFloat, i.toDouble) - } - checkParquetFile(data) + test("basic data types (without binary)") { + val data = (1 to 4).map { i => + (i % 2 == 0, i, i.toLong, i.toFloat, i.toDouble) } + checkParquetFile(data) + } - test(s"$prefix: raw binary") { - val data = (1 to 4).map(i => Tuple1(Array.fill(3)(i.toByte))) - withParquetRDD(data) { rdd => - assertResult(data.map(_._1.mkString(",")).sorted) { - rdd.collect().map(_.getAs[Array[Byte]](0).mkString(",")).sorted - } + test("raw binary") { + val data = (1 to 4).map(i => Tuple1(Array.fill(3)(i.toByte))) + withParquetDataFrame(data) { df => + assertResult(data.map(_._1.mkString(",")).sorted) { + df.collect().map(_.getAs[Array[Byte]](0).mkString(",")).sorted } } + } - test(s"$prefix: string") { - val data = (1 to 4).map(i => Tuple1(i.toString)) - // Property spark.sql.parquet.binaryAsString shouldn't affect Parquet files written by Spark SQL - // as we store Spark SQL schema in the extra metadata. - withSQLConf(SQLConf.PARQUET_BINARY_AS_STRING -> "false")(checkParquetFile(data)) - withSQLConf(SQLConf.PARQUET_BINARY_AS_STRING -> "true")(checkParquetFile(data)) - } + test("string") { + val data = (1 to 4).map(i => Tuple1(i.toString)) + // Property spark.sql.parquet.binaryAsString shouldn't affect Parquet files written by Spark SQL + // as we store Spark SQL schema in the extra metadata. + withSQLConf(SQLConf.PARQUET_BINARY_AS_STRING -> "false")(checkParquetFile(data)) + withSQLConf(SQLConf.PARQUET_BINARY_AS_STRING -> "true")(checkParquetFile(data)) + } - test(s"$prefix: fixed-length decimals") { - import org.apache.spark.sql.test.TestSQLContext.implicits._ - - def makeDecimalRDD(decimal: DecimalType): DataFrame = - sparkContext - .parallelize(0 to 1000) - .map(i => Tuple1(i / 100.0)) - // Parquet doesn't allow column names with spaces, have to add an alias here - .select($"_1" cast decimal as "dec") - - for ((precision, scale) <- Seq((5, 2), (1, 0), (1, 1), (18, 10), (18, 17))) { - withTempPath { dir => - val data = makeDecimalRDD(DecimalType(precision, scale)) - data.saveAsParquetFile(dir.getCanonicalPath) - checkAnswer(parquetFile(dir.getCanonicalPath), data.collect().toSeq) - } + test("fixed-length decimals") { + + def makeDecimalRDD(decimal: DecimalType): DataFrame = + sparkContext + .parallelize(0 to 1000) + .map(i => Tuple1(i / 100.0)) + .toDF() + // Parquet doesn't allow column names with spaces, have to add an alias here + .select($"_1" cast decimal as "dec") + + for ((precision, scale) <- Seq((5, 2), (1, 0), (1, 1), (18, 10), (18, 17))) { + withTempPath { dir => + val data = makeDecimalRDD(DecimalType(precision, scale)) + data.saveAsParquetFile(dir.getCanonicalPath) + checkAnswer(parquetFile(dir.getCanonicalPath), data.collect().toSeq) } + } - // Decimals with precision above 18 are not yet supported - intercept[RuntimeException] { - withTempPath { dir => - makeDecimalRDD(DecimalType(19, 10)).saveAsParquetFile(dir.getCanonicalPath) - parquetFile(dir.getCanonicalPath).collect() - } + // Decimals with precision above 18 are not yet supported + intercept[RuntimeException] { + withTempPath { dir => + makeDecimalRDD(DecimalType(19, 10)).saveAsParquetFile(dir.getCanonicalPath) + parquetFile(dir.getCanonicalPath).collect() } + } - // Unlimited-length decimals are not yet supported - intercept[RuntimeException] { - withTempPath { dir => - makeDecimalRDD(DecimalType.Unlimited).saveAsParquetFile(dir.getCanonicalPath) - parquetFile(dir.getCanonicalPath).collect() - } + // Unlimited-length decimals are not yet supported + intercept[RuntimeException] { + withTempPath { dir => + makeDecimalRDD(DecimalType.Unlimited).saveAsParquetFile(dir.getCanonicalPath) + parquetFile(dir.getCanonicalPath).collect() } } + } - test(s"$prefix: map") { - val data = (1 to 4).map(i => Tuple1(Map(i -> s"val_$i"))) - checkParquetFile(data) + test("date type") { + def makeDateRDD(): DataFrame = + sparkContext + .parallelize(0 to 1000) + .map(i => Tuple1(DateUtils.toJavaDate(i))) + .toDF() + .select($"_1") + + withTempPath { dir => + val data = makeDateRDD() + data.saveAsParquetFile(dir.getCanonicalPath) + checkAnswer(parquetFile(dir.getCanonicalPath), data.collect().toSeq) } + } - test(s"$prefix: array") { - val data = (1 to 4).map(i => Tuple1(Seq(i, i + 1))) - checkParquetFile(data) - } + test("map") { + val data = (1 to 4).map(i => Tuple1(Map(i -> s"val_$i"))) + checkParquetFile(data) + } - test(s"$prefix: struct") { - val data = (1 to 4).map(i => Tuple1((i, s"val_$i"))) - withParquetRDD(data) { rdd => - // Structs are converted to `Row`s - checkAnswer(rdd, data.map { case Tuple1(struct) => - Row(Row(struct.productIterator.toSeq: _*)) - }) - } + test("array") { + val data = (1 to 4).map(i => Tuple1(Seq(i, i + 1))) + checkParquetFile(data) + } + + test("struct") { + val data = (1 to 4).map(i => Tuple1((i, s"val_$i"))) + withParquetDataFrame(data) { df => + // Structs are converted to `Row`s + checkAnswer(df, data.map { case Tuple1(struct) => + Row(Row(struct.productIterator.toSeq: _*)) + }) } + } - test(s"$prefix: nested struct with array of array as field") { - val data = (1 to 4).map(i => Tuple1((i, Seq(Seq(s"val_$i"))))) - withParquetRDD(data) { rdd => - // Structs are converted to `Row`s - checkAnswer(rdd, data.map { case Tuple1(struct) => - Row(Row(struct.productIterator.toSeq: _*)) - }) - } + test("nested struct with array of array as field") { + val data = (1 to 4).map(i => Tuple1((i, Seq(Seq(s"val_$i"))))) + withParquetDataFrame(data) { df => + // Structs are converted to `Row`s + checkAnswer(df, data.map { case Tuple1(struct) => + Row(Row(struct.productIterator.toSeq: _*)) + }) } + } - test(s"$prefix: nested map with struct as value type") { - val data = (1 to 4).map(i => Tuple1(Map(i -> (i, s"val_$i")))) - withParquetRDD(data) { rdd => - checkAnswer(rdd, data.map { case Tuple1(m) => - Row(m.mapValues(struct => Row(struct.productIterator.toSeq: _*))) - }) - } + test("nested map with struct as value type") { + val data = (1 to 4).map(i => Tuple1(Map(i -> (i, s"val_$i")))) + withParquetDataFrame(data) { df => + checkAnswer(df, data.map { case Tuple1(m) => + Row(m.mapValues(struct => Row(struct.productIterator.toSeq: _*))) + }) } + } - test(s"$prefix: nulls") { - val allNulls = ( - null.asInstanceOf[java.lang.Boolean], - null.asInstanceOf[Integer], - null.asInstanceOf[java.lang.Long], - null.asInstanceOf[java.lang.Float], - null.asInstanceOf[java.lang.Double]) - - withParquetRDD(allNulls :: Nil) { rdd => - val rows = rdd.collect() - assert(rows.size === 1) - assert(rows.head === Row(Seq.fill(5)(null): _*)) - } + test("nulls") { + val allNulls = ( + null.asInstanceOf[java.lang.Boolean], + null.asInstanceOf[Integer], + null.asInstanceOf[java.lang.Long], + null.asInstanceOf[java.lang.Float], + null.asInstanceOf[java.lang.Double]) + + withParquetDataFrame(allNulls :: Nil) { df => + val rows = df.collect() + assert(rows.size === 1) + assert(rows.head === Row(Seq.fill(5)(null): _*)) } + } - test(s"$prefix: nones") { - val allNones = ( - None.asInstanceOf[Option[Int]], - None.asInstanceOf[Option[Long]], - None.asInstanceOf[Option[String]]) + test("nones") { + val allNones = ( + None.asInstanceOf[Option[Int]], + None.asInstanceOf[Option[Long]], + None.asInstanceOf[Option[String]]) - withParquetRDD(allNones :: Nil) { rdd => - val rows = rdd.collect() - assert(rows.size === 1) - assert(rows.head === Row(Seq.fill(3)(null): _*)) - } + withParquetDataFrame(allNones :: Nil) { df => + val rows = df.collect() + assert(rows.size === 1) + assert(rows.head === Row(Seq.fill(3)(null): _*)) } + } - test(s"$prefix: compression codec") { - def compressionCodecFor(path: String) = { - val codecs = ParquetTypesConverter - .readMetaData(new Path(path), Some(configuration)) - .getBlocks - .flatMap(_.getColumns) - .map(_.getCodec.name()) - .distinct - - assert(codecs.size === 1) - codecs.head - } + test("compression codec") { + def compressionCodecFor(path: String): String = { + val codecs = ParquetTypesConverter + .readMetaData(new Path(path), Some(configuration)) + .getBlocks + .flatMap(_.getColumns) + .map(_.getCodec.name()) + .distinct + + assert(codecs.size === 1) + codecs.head + } - val data = (0 until 10).map(i => (i, i.toString)) + val data = (0 until 10).map(i => (i, i.toString)) - def checkCompressionCodec(codec: CompressionCodecName): Unit = { - withSQLConf(SQLConf.PARQUET_COMPRESSION -> codec.name()) { - withParquetFile(data) { path => - assertResult(conf.parquetCompressionCodec.toUpperCase) { - compressionCodecFor(path) - } + def checkCompressionCodec(codec: CompressionCodecName): Unit = { + withSQLConf(SQLConf.PARQUET_COMPRESSION -> codec.name()) { + withParquetFile(data) { path => + assertResult(conf.parquetCompressionCodec.toUpperCase) { + compressionCodecFor(path) } } } + } + + // Checks default compression codec + checkCompressionCodec(CompressionCodecName.fromConf(conf.parquetCompressionCodec)) - // Checks default compression codec - checkCompressionCodec(CompressionCodecName.fromConf(conf.parquetCompressionCodec)) + checkCompressionCodec(CompressionCodecName.UNCOMPRESSED) + checkCompressionCodec(CompressionCodecName.GZIP) + checkCompressionCodec(CompressionCodecName.SNAPPY) + } + + test("read raw Parquet file") { + def makeRawParquetFile(path: Path): Unit = { + val schema = MessageTypeParser.parseMessageType( + """ + |message root { + | required boolean _1; + | required int32 _2; + | required int64 _3; + | required float _4; + | required double _5; + |} + """.stripMargin) + + val writeSupport = new TestGroupWriteSupport(schema) + val writer = new ParquetWriter[Group](path, writeSupport) + + (0 until 10).foreach { i => + val record = new SimpleGroup(schema) + record.add(0, i % 2 == 0) + record.add(1, i) + record.add(2, i.toLong) + record.add(3, i.toFloat) + record.add(4, i.toDouble) + writer.write(record) + } - checkCompressionCodec(CompressionCodecName.UNCOMPRESSED) - checkCompressionCodec(CompressionCodecName.GZIP) - checkCompressionCodec(CompressionCodecName.SNAPPY) + writer.close() } - test(s"$prefix: read raw Parquet file") { - def makeRawParquetFile(path: Path): Unit = { - val schema = MessageTypeParser.parseMessageType( - """ - |message root { - | required boolean _1; - | required int32 _2; - | required int64 _3; - | required float _4; - | required double _5; - |} - """.stripMargin) - - val writeSupport = new TestGroupWriteSupport(schema) - val writer = new ParquetWriter[Group](path, writeSupport) - - (0 until 10).foreach { i => - val record = new SimpleGroup(schema) - record.add(0, i % 2 == 0) - record.add(1, i) - record.add(2, i.toLong) - record.add(3, i.toFloat) - record.add(4, i.toDouble) - writer.write(record) - } + withTempDir { dir => + val path = new Path(dir.toURI.toString, "part-r-0.parquet") + makeRawParquetFile(path) + checkAnswer(parquetFile(path.toString), (0 until 10).map { i => + Row(i % 2 == 0, i, i.toLong, i.toFloat, i.toDouble) + }) + } + } - writer.close() - } + test("write metadata") { + withTempPath { file => + val path = new Path(file.toURI.toString) + val fs = FileSystem.getLocal(configuration) + val attributes = ScalaReflection.attributesFor[(Int, String)] + ParquetTypesConverter.writeMetaData(attributes, path, configuration) - withTempDir { dir => - val path = new Path(dir.toURI.toString, "part-r-0.parquet") - makeRawParquetFile(path) - checkAnswer(parquetFile(path.toString), (0 until 10).map { i => - Row(i % 2 == 0, i, i.toLong, i.toFloat, i.toDouble) - }) - } + assert(fs.exists(new Path(path, ParquetFileWriter.PARQUET_COMMON_METADATA_FILE))) + assert(fs.exists(new Path(path, ParquetFileWriter.PARQUET_METADATA_FILE))) + + val metaData = ParquetTypesConverter.readMetaData(path, Some(configuration)) + val actualSchema = metaData.getFileMetaData.getSchema + val expectedSchema = ParquetTypesConverter.convertFromAttributes(attributes) + + actualSchema.checkContains(expectedSchema) + expectedSchema.checkContains(actualSchema) } + } - test(s"$prefix: write metadata") { - withTempPath { file => - val path = new Path(file.toURI.toString) - val fs = FileSystem.getLocal(configuration) - val attributes = ScalaReflection.attributesFor[(Int, String)] - ParquetTypesConverter.writeMetaData(attributes, path, configuration) + test("save - overwrite") { + withParquetFile((1 to 10).map(i => (i, i.toString))) { file => + val newData = (11 to 20).map(i => (i, i.toString)) + newData.toDF().save("org.apache.spark.sql.parquet", SaveMode.Overwrite, Map("path" -> file)) + checkAnswer(parquetFile(file), newData.map(Row.fromTuple)) + } + } - assert(fs.exists(new Path(path, ParquetFileWriter.PARQUET_COMMON_METADATA_FILE))) - assert(fs.exists(new Path(path, ParquetFileWriter.PARQUET_METADATA_FILE))) + test("save - ignore") { + val data = (1 to 10).map(i => (i, i.toString)) + withParquetFile(data) { file => + val newData = (11 to 20).map(i => (i, i.toString)) + newData.toDF().save("org.apache.spark.sql.parquet", SaveMode.Ignore, Map("path" -> file)) + checkAnswer(parquetFile(file), data.map(Row.fromTuple)) + } + } - val metaData = ParquetTypesConverter.readMetaData(path, Some(configuration)) - val actualSchema = metaData.getFileMetaData.getSchema - val expectedSchema = ParquetTypesConverter.convertFromAttributes(attributes) + test("save - throw") { + val data = (1 to 10).map(i => (i, i.toString)) + withParquetFile(data) { file => + val newData = (11 to 20).map(i => (i, i.toString)) + val errorMessage = intercept[Throwable] { + newData.toDF().save( + "org.apache.spark.sql.parquet", SaveMode.ErrorIfExists, Map("path" -> file)) + }.getMessage + assert(errorMessage.contains("already exists")) + } + } + + test("save - append") { + val data = (1 to 10).map(i => (i, i.toString)) + withParquetFile(data) { file => + val newData = (11 to 20).map(i => (i, i.toString)) + newData.toDF().save("org.apache.spark.sql.parquet", SaveMode.Append, Map("path" -> file)) + checkAnswer(parquetFile(file), (data ++ newData).map(Row.fromTuple)) + } + } - actualSchema.checkContains(expectedSchema) - expectedSchema.checkContains(actualSchema) + test("SPARK-6315 regression test") { + // Spark 1.1 and prior versions write Spark schema as case class string into Parquet metadata. + // This has been deprecated by JSON format since 1.2. Notice that, 1.3 further refactored data + // types API, and made StructType.fields an array. This makes the result of StructType.toString + // different from prior versions: there's no "Seq" wrapping the fields part in the string now. + val sparkSchema = + "StructType(Seq(StructField(a,BooleanType,false),StructField(b,IntegerType,false)))" + + // The Parquet schema is intentionally made different from the Spark schema. Because the new + // Parquet data source simply falls back to the Parquet schema once it fails to parse the Spark + // schema. By making these two different, we are able to assert the old style case class string + // is parsed successfully. + val parquetSchema = MessageTypeParser.parseMessageType( + """message root { + | required int32 c; + |} + """.stripMargin) + + withTempPath { location => + val extraMetadata = Map(RowReadSupport.SPARK_METADATA_KEY -> sparkSchema.toString) + val fileMetadata = new FileMetaData(parquetSchema, extraMetadata, "Spark") + val path = new Path(location.getCanonicalPath) + + ParquetFileWriter.writeMetadataFile( + sparkContext.hadoopConfiguration, + path, + new Footer(path, new ParquetMetadata(fileMetadata, Nil)) :: Nil) + + assertResult(parquetFile(path.toString).schema) { + StructType( + StructField("a", BooleanType, nullable = false) :: + StructField("b", IntegerType, nullable = false) :: + Nil) } } } +} + +class ParquetDataSourceOnIOSuite extends ParquetIOSuiteBase with BeforeAndAfterAll { + val originalConf = sqlContext.conf.parquetUseDataSourceApi + + override protected def beforeAll(): Unit = { + sqlContext.conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, "true") + } + + override protected def afterAll(): Unit = { + sqlContext.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf.toString) + } + + test("SPARK-6330 regression test") { + // In 1.3.0, save to fs other than file: without configuring core-site.xml would get: + // IllegalArgumentException: Wrong FS: hdfs://..., expected: file:/// + intercept[java.io.FileNotFoundException] { + sqlContext.parquetFile("file:///nonexistent") + } + val errorMessage = intercept[Throwable] { + sqlContext.parquetFile("hdfs://nonexistent") + }.toString + assert(errorMessage.contains("UnknownHostException")) + } +} + +class ParquetDataSourceOffIOSuite extends ParquetIOSuiteBase with BeforeAndAfterAll { + val originalConf = sqlContext.conf.parquetUseDataSourceApi - withSQLConf(SQLConf.PARQUET_USE_DATA_SOURCE_API -> "true") { - run("Parquet data source enabled") + override protected def beforeAll(): Unit = { + sqlContext.conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, "false") } - withSQLConf(SQLConf.PARQUET_USE_DATA_SOURCE_API -> "false") { - run("Parquet data source disabled") + override protected def afterAll(): Unit = { + sqlContext.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf.toString) } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala index ae606d11a8f68..b7561ce7298cb 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala @@ -19,17 +19,25 @@ package org.apache.spark.sql.parquet import scala.collection.mutable.ArrayBuffer import org.apache.hadoop.fs.Path -import org.scalatest.FunSuite import org.apache.spark.sql.catalyst.expressions.Literal import org.apache.spark.sql.parquet.ParquetRelation2._ import org.apache.spark.sql.test.TestSQLContext import org.apache.spark.sql.types._ -import org.apache.spark.sql.{Row, SQLContext} +import org.apache.spark.sql.{QueryTest, Row, SQLContext} -class ParquetPartitionDiscoverySuite extends FunSuite with ParquetTest { +// The data where the partitioning key exists only in the directory structure. +case class ParquetData(intField: Int, stringField: String) + +// The data that also includes the partitioning key +case class ParquetDataWithKey(intField: Int, pi: Int, stringField: String, ps: String) + +class ParquetPartitionDiscoverySuite extends QueryTest with ParquetTest { override val sqlContext: SQLContext = TestSQLContext + import sqlContext._ + import sqlContext.implicits._ + val defaultPartitionName = "__NULL__" test("column type inference") { @@ -37,11 +45,11 @@ class ParquetPartitionDiscoverySuite extends FunSuite with ParquetTest { assert(inferPartitionColumnValue(raw, defaultPartitionName) === literal) } - check("10", Literal(10, IntegerType)) - check("1000000000000000", Literal(1000000000000000L, LongType)) - check("1.5", Literal(1.5, FloatType)) - check("hello", Literal("hello", StringType)) - check(defaultPartitionName, Literal(null, NullType)) + check("10", Literal.create(10, IntegerType)) + check("1000000000000000", Literal.create(1000000000000000L, LongType)) + check("1.5", Literal.create(1.5, FloatType)) + check("hello", Literal.create("hello", StringType)) + check(defaultPartitionName, Literal.create(null, NullType)) } test("parse partition") { @@ -67,22 +75,22 @@ class ParquetPartitionDiscoverySuite extends FunSuite with ParquetTest { "file://path/a=10", PartitionValues( ArrayBuffer("a"), - ArrayBuffer(Literal(10, IntegerType)))) + ArrayBuffer(Literal.create(10, IntegerType)))) check( "file://path/a=10/b=hello/c=1.5", PartitionValues( ArrayBuffer("a", "b", "c"), ArrayBuffer( - Literal(10, IntegerType), - Literal("hello", StringType), - Literal(1.5, FloatType)))) + Literal.create(10, IntegerType), + Literal.create("hello", StringType), + Literal.create(1.5, FloatType)))) check( "file://path/a=10/b_hello/c=1.5", PartitionValues( ArrayBuffer("c"), - ArrayBuffer(Literal(1.5, FloatType)))) + ArrayBuffer(Literal.create(1.5, FloatType)))) checkThrows[AssertionError]("file://path/=10", "Empty partition column name") checkThrows[AssertionError]("file://path/a=", "Empty partition column value") @@ -112,6 +120,17 @@ class ParquetPartitionDiscoverySuite extends FunSuite with ParquetTest { Partition(Row(10, "20"), "hdfs://host:9000/path/a=10/b=20"), Partition(Row(10.5, "hello"), "hdfs://host:9000/path/a=10.5/b=hello")))) + check(Seq( + s"hdfs://host:9000/path/a=10/b=20", + s"hdfs://host:9000/path/a=$defaultPartitionName/b=hello"), + PartitionSpec( + StructType(Seq( + StructField("a", IntegerType), + StructField("b", StringType))), + Seq( + Partition(Row(10, "20"), s"hdfs://host:9000/path/a=10/b=20"), + Partition(Row(null, "hello"), s"hdfs://host:9000/path/a=$defaultPartitionName/b=hello")))) + check(Seq( s"hdfs://host:9000/path/a=10/b=$defaultPartitionName", s"hdfs://host:9000/path/a=10.5/b=$defaultPartitionName"), @@ -123,4 +142,202 @@ class ParquetPartitionDiscoverySuite extends FunSuite with ParquetTest { Partition(Row(10, null), s"hdfs://host:9000/path/a=10/b=$defaultPartitionName"), Partition(Row(10.5, null), s"hdfs://host:9000/path/a=10.5/b=$defaultPartitionName")))) } + + test("read partitioned table - normal case") { + withTempDir { base => + for { + pi <- Seq(1, 2) + ps <- Seq("foo", "bar") + } { + makeParquetFile( + (1 to 10).map(i => ParquetData(i, i.toString)), + makePartitionDir(base, defaultPartitionName, "pi" -> pi, "ps" -> ps)) + } + + parquetFile(base.getCanonicalPath).registerTempTable("t") + + withTempTable("t") { + checkAnswer( + sql("SELECT * FROM t"), + for { + i <- 1 to 10 + pi <- Seq(1, 2) + ps <- Seq("foo", "bar") + } yield Row(i, i.toString, pi, ps)) + + checkAnswer( + sql("SELECT intField, pi FROM t"), + for { + i <- 1 to 10 + pi <- Seq(1, 2) + _ <- Seq("foo", "bar") + } yield Row(i, pi)) + + checkAnswer( + sql("SELECT * FROM t WHERE pi = 1"), + for { + i <- 1 to 10 + ps <- Seq("foo", "bar") + } yield Row(i, i.toString, 1, ps)) + + checkAnswer( + sql("SELECT * FROM t WHERE ps = 'foo'"), + for { + i <- 1 to 10 + pi <- Seq(1, 2) + } yield Row(i, i.toString, pi, "foo")) + } + } + } + + test("read partitioned table - partition key included in Parquet file") { + withTempDir { base => + for { + pi <- Seq(1, 2) + ps <- Seq("foo", "bar") + } { + makeParquetFile( + (1 to 10).map(i => ParquetDataWithKey(i, pi, i.toString, ps)), + makePartitionDir(base, defaultPartitionName, "pi" -> pi, "ps" -> ps)) + } + + parquetFile(base.getCanonicalPath).registerTempTable("t") + + withTempTable("t") { + checkAnswer( + sql("SELECT * FROM t"), + for { + i <- 1 to 10 + pi <- Seq(1, 2) + ps <- Seq("foo", "bar") + } yield Row(i, pi, i.toString, ps)) + + checkAnswer( + sql("SELECT intField, pi FROM t"), + for { + i <- 1 to 10 + pi <- Seq(1, 2) + _ <- Seq("foo", "bar") + } yield Row(i, pi)) + + checkAnswer( + sql("SELECT * FROM t WHERE pi = 1"), + for { + i <- 1 to 10 + ps <- Seq("foo", "bar") + } yield Row(i, 1, i.toString, ps)) + + checkAnswer( + sql("SELECT * FROM t WHERE ps = 'foo'"), + for { + i <- 1 to 10 + pi <- Seq(1, 2) + } yield Row(i, pi, i.toString, "foo")) + } + } + } + + test("read partitioned table - with nulls") { + withTempDir { base => + for { + // Must be `Integer` rather than `Int` here. `null.asInstanceOf[Int]` results in a zero... + pi <- Seq(1, null.asInstanceOf[Integer]) + ps <- Seq("foo", null.asInstanceOf[String]) + } { + makeParquetFile( + (1 to 10).map(i => ParquetData(i, i.toString)), + makePartitionDir(base, defaultPartitionName, "pi" -> pi, "ps" -> ps)) + } + + val parquetRelation = load( + "org.apache.spark.sql.parquet", + Map( + "path" -> base.getCanonicalPath, + ParquetRelation2.DEFAULT_PARTITION_NAME -> defaultPartitionName)) + + parquetRelation.registerTempTable("t") + + withTempTable("t") { + checkAnswer( + sql("SELECT * FROM t"), + for { + i <- 1 to 10 + pi <- Seq(1, null.asInstanceOf[Integer]) + ps <- Seq("foo", null.asInstanceOf[String]) + } yield Row(i, i.toString, pi, ps)) + + checkAnswer( + sql("SELECT * FROM t WHERE pi IS NULL"), + for { + i <- 1 to 10 + ps <- Seq("foo", null.asInstanceOf[String]) + } yield Row(i, i.toString, null, ps)) + + checkAnswer( + sql("SELECT * FROM t WHERE ps IS NULL"), + for { + i <- 1 to 10 + pi <- Seq(1, null.asInstanceOf[Integer]) + } yield Row(i, i.toString, pi, null)) + } + } + } + + test("read partitioned table - with nulls and partition keys are included in Parquet file") { + withTempDir { base => + for { + pi <- Seq(1, 2) + ps <- Seq("foo", null.asInstanceOf[String]) + } { + makeParquetFile( + (1 to 10).map(i => ParquetDataWithKey(i, pi, i.toString, ps)), + makePartitionDir(base, defaultPartitionName, "pi" -> pi, "ps" -> ps)) + } + + val parquetRelation = load( + "org.apache.spark.sql.parquet", + Map( + "path" -> base.getCanonicalPath, + ParquetRelation2.DEFAULT_PARTITION_NAME -> defaultPartitionName)) + + parquetRelation.registerTempTable("t") + + withTempTable("t") { + checkAnswer( + sql("SELECT * FROM t"), + for { + i <- 1 to 10 + pi <- Seq(1, 2) + ps <- Seq("foo", null.asInstanceOf[String]) + } yield Row(i, pi, i.toString, ps)) + + checkAnswer( + sql("SELECT * FROM t WHERE ps IS NULL"), + for { + i <- 1 to 10 + pi <- Seq(1, 2) + } yield Row(i, pi, i.toString, null)) + } + } + } + + test("read partitioned table - merging compatible schemas") { + withTempDir { base => + makeParquetFile( + (1 to 10).map(i => Tuple1(i)).toDF("intField"), + makePartitionDir(base, defaultPartitionName, "pi" -> 1)) + + makeParquetFile( + (1 to 10).map(i => (i, i.toString)).toDF("intField", "stringField"), + makePartitionDir(base, defaultPartitionName, "pi" -> 2)) + + load(base.getCanonicalPath, "org.apache.spark.sql.parquet").registerTempTable("t") + + withTempTable("t") { + checkAnswer( + sql("SELECT * FROM t"), + (1 to 10).map(i => Row(i, null, 1)) ++ (1 to 10).map(i => Row(i, i.toString, 2))) + } + } + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala index cba06835f9a61..b98ba09ccfc2d 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala @@ -17,103 +17,122 @@ package org.apache.spark.sql.parquet +import org.scalatest.BeforeAndAfterAll + +import org.apache.spark.sql.{SQLConf, QueryTest} import org.apache.spark.sql.catalyst.expressions.Row import org.apache.spark.sql.test.TestSQLContext import org.apache.spark.sql.test.TestSQLContext._ -import org.apache.spark.sql.{QueryTest, SQLConf} /** * A test suite that tests various Parquet queries. */ -class ParquetQuerySuite extends QueryTest with ParquetTest { +class ParquetQuerySuiteBase extends QueryTest with ParquetTest { val sqlContext = TestSQLContext - def run(prefix: String): Unit = { - test(s"$prefix: simple projection") { - withParquetTable((0 until 10).map(i => (i, i.toString)), "t") { - checkAnswer(sql("SELECT _1 FROM t"), (0 until 10).map(Row.apply(_))) - } + test("simple select queries") { + withParquetTable((0 until 10).map(i => (i, i.toString)), "t") { + checkAnswer(sql("SELECT _1 FROM t where t._1 > 5"), (6 until 10).map(Row.apply(_))) + checkAnswer(sql("SELECT _1 FROM t as tmp where tmp._1 < 5"), (0 until 5).map(Row.apply(_))) } + } - test(s"$prefix: appending") { - val data = (0 until 10).map(i => (i, i.toString)) - withParquetTable(data, "t") { - sql("INSERT INTO TABLE t SELECT * FROM t") - checkAnswer(table("t"), (data ++ data).map(Row.fromTuple)) - } + test("appending") { + val data = (0 until 10).map(i => (i, i.toString)) + createDataFrame(data).toDF("c1", "c2").registerTempTable("tmp") + withParquetTable(data, "t") { + sql("INSERT INTO TABLE t SELECT * FROM tmp") + checkAnswer(table("t"), (data ++ data).map(Row.fromTuple)) } + catalog.unregisterTable(Seq("tmp")) + } - // This test case will trigger the NPE mentioned in - // https://issues.apache.org/jira/browse/PARQUET-151. - ignore(s"$prefix: overwriting") { - val data = (0 until 10).map(i => (i, i.toString)) - withParquetTable(data, "t") { - sql("INSERT OVERWRITE TABLE t SELECT * FROM t") - checkAnswer(table("t"), data.map(Row.fromTuple)) - } + test("overwriting") { + val data = (0 until 10).map(i => (i, i.toString)) + createDataFrame(data).toDF("c1", "c2").registerTempTable("tmp") + withParquetTable(data, "t") { + sql("INSERT OVERWRITE TABLE t SELECT * FROM tmp") + checkAnswer(table("t"), data.map(Row.fromTuple)) } + catalog.unregisterTable(Seq("tmp")) + } - test(s"$prefix: self-join") { - // 4 rows, cells of column 1 of row 2 and row 4 are null - val data = (1 to 4).map { i => - val maybeInt = if (i % 2 == 0) None else Some(i) - (maybeInt, i.toString) - } - - withParquetTable(data, "t") { - val selfJoin = sql("SELECT * FROM t x JOIN t y WHERE x._1 = y._1") - val queryOutput = selfJoin.queryExecution.analyzed.output + test("self-join") { + // 4 rows, cells of column 1 of row 2 and row 4 are null + val data = (1 to 4).map { i => + val maybeInt = if (i % 2 == 0) None else Some(i) + (maybeInt, i.toString) + } - assertResult(4, s"Field count mismatches")(queryOutput.size) - assertResult(2, s"Duplicated expression ID in query plan:\n $selfJoin") { - queryOutput.filter(_.name == "_1").map(_.exprId).size - } + withParquetTable(data, "t") { + val selfJoin = sql("SELECT * FROM t x JOIN t y WHERE x._1 = y._1") + val queryOutput = selfJoin.queryExecution.analyzed.output - checkAnswer(selfJoin, List(Row(1, "1", 1, "1"), Row(3, "3", 3, "3"))) + assertResult(4, "Field count mismatches")(queryOutput.size) + assertResult(2, "Duplicated expression ID in query plan:\n $selfJoin") { + queryOutput.filter(_.name == "_1").map(_.exprId).size } + + checkAnswer(selfJoin, List(Row(1, "1", 1, "1"), Row(3, "3", 3, "3"))) } + } - test(s"$prefix: nested data - struct with array field") { - val data = (1 to 10).map(i => Tuple1((i, Seq(s"val_$i")))) - withParquetTable(data, "t") { - checkAnswer(sql("SELECT _1._2[0] FROM t"), data.map { - case Tuple1((_, Seq(string))) => Row(string) - }) - } + test("nested data - struct with array field") { + val data = (1 to 10).map(i => Tuple1((i, Seq("val_$i")))) + withParquetTable(data, "t") { + checkAnswer(sql("SELECT _1._2[0] FROM t"), data.map { + case Tuple1((_, Seq(string))) => Row(string) + }) } + } - test(s"$prefix: nested data - array of struct") { - val data = (1 to 10).map(i => Tuple1(Seq(i -> s"val_$i"))) - withParquetTable(data, "t") { - checkAnswer(sql("SELECT _1[0]._2 FROM t"), data.map { - case Tuple1(Seq((_, string))) => Row(string) - }) - } + test("nested data - array of struct") { + val data = (1 to 10).map(i => Tuple1(Seq(i -> "val_$i"))) + withParquetTable(data, "t") { + checkAnswer(sql("SELECT _1[0]._2 FROM t"), data.map { + case Tuple1(Seq((_, string))) => Row(string) + }) } + } - test(s"$prefix: SPARK-1913 regression: columns only referenced by pushed down filters should remain") { - withParquetTable((1 to 10).map(Tuple1.apply), "t") { - checkAnswer(sql(s"SELECT _1 FROM t WHERE _1 < 10"), (1 to 9).map(Row.apply(_))) - } + test("SPARK-1913 regression: columns only referenced by pushed down filters should remain") { + withParquetTable((1 to 10).map(Tuple1.apply), "t") { + checkAnswer(sql("SELECT _1 FROM t WHERE _1 < 10"), (1 to 9).map(Row.apply(_))) } + } - test(s"$prefix: SPARK-5309 strings stored using dictionary compression in parquet") { - withParquetTable((0 until 1000).map(i => ("same", "run_" + i /100, 1)), "t") { + test("SPARK-5309 strings stored using dictionary compression in parquet") { + withParquetTable((0 until 1000).map(i => ("same", "run_" + i /100, 1)), "t") { - checkAnswer(sql(s"SELECT _1, _2, SUM(_3) FROM t GROUP BY _1, _2"), - (0 until 10).map(i => Row("same", "run_" + i, 100))) + checkAnswer(sql("SELECT _1, _2, SUM(_3) FROM t GROUP BY _1, _2"), + (0 until 10).map(i => Row("same", "run_" + i, 100))) - checkAnswer(sql(s"SELECT _1, _2, SUM(_3) FROM t WHERE _2 = 'run_5' GROUP BY _1, _2"), - List(Row("same", "run_5", 100))) - } + checkAnswer(sql("SELECT _1, _2, SUM(_3) FROM t WHERE _2 = 'run_5' GROUP BY _1, _2"), + List(Row("same", "run_5", 100))) } } +} + +class ParquetDataSourceOnQuerySuite extends ParquetQuerySuiteBase with BeforeAndAfterAll { + val originalConf = sqlContext.conf.parquetUseDataSourceApi + + override protected def beforeAll(): Unit = { + sqlContext.conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, "true") + } + + override protected def afterAll(): Unit = { + sqlContext.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf.toString) + } +} + +class ParquetDataSourceOffQuerySuite extends ParquetQuerySuiteBase with BeforeAndAfterAll { + val originalConf = sqlContext.conf.parquetUseDataSourceApi - withSQLConf(SQLConf.PARQUET_USE_DATA_SOURCE_API -> "true") { - run("Parquet data source enabled") + override protected def beforeAll(): Unit = { + sqlContext.conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, "false") } - withSQLConf(SQLConf.PARQUET_USE_DATA_SOURCE_API -> "false") { - run("Parquet data source disabled") + override protected def afterAll(): Unit = { + sqlContext.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf.toString) } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala index 2e6c2d5f9ab55..c964b6d984557 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala @@ -36,8 +36,8 @@ class ParquetSchemaSuite extends FunSuite with ParquetTest { private def testSchema[T <: Product: ClassTag: TypeTag]( testName: String, messageType: String, isThriftDerived: Boolean = false): Unit = { test(testName) { - val actual = ParquetTypesConverter.convertFromAttributes(ScalaReflection.attributesFor[T], - isThriftDerived) + val actual = ParquetTypesConverter.convertFromAttributes( + ScalaReflection.attributesFor[T], isThriftDerived) val expected = MessageTypeParser.parseMessageType(messageType) actual.checkContains(expected) expected.checkContains(actual) @@ -57,7 +57,7 @@ class ParquetSchemaSuite extends FunSuite with ParquetTest { |} """.stripMargin) - testSchema[(Byte, Short, Int, Long)]( + testSchema[(Byte, Short, Int, Long, java.sql.Date)]( "logical integral types", """ |message root { @@ -65,6 +65,7 @@ class ParquetSchemaSuite extends FunSuite with ParquetTest { | required int32 _2 (INT_16); | required int32 _3 (INT_32); | required int64 _4 (INT_64); + | optional int32 _5 (DATE); |} """.stripMargin) @@ -179,10 +180,12 @@ class ParquetSchemaSuite extends FunSuite with ParquetTest { val caseClassString = "StructType(List(StructField(c1,IntegerType,false), StructField(c2,BinaryType,true)))" + // scalastyle:off val jsonString = """ |{"type":"struct","fields":[{"name":"c1","type":"integer","nullable":false,"metadata":{}},{"name":"c2","type":"binary","nullable":true,"metadata":{}}]} """.stripMargin + // scalastyle:on val fromCaseClassString = ParquetTypesConverter.convertFromString(caseClassString) val fromJson = ParquetTypesConverter.convertFromString(jsonString) @@ -211,8 +214,11 @@ class ParquetSchemaSuite extends FunSuite with ParquetTest { StructField("UPPERCase", IntegerType, nullable = true)))) } - // Conflicting field count - assert(intercept[Throwable] { + // MetaStore schema is subset of parquet schema + assertResult( + StructType(Seq( + StructField("UPPERCase", DoubleType, nullable = false)))) { + ParquetRelation2.mergeMetastoreParquetSchema( StructType(Seq( StructField("uppercase", DoubleType, nullable = false))), @@ -220,13 +226,56 @@ class ParquetSchemaSuite extends FunSuite with ParquetTest { StructType(Seq( StructField("lowerCase", BinaryType), StructField("UPPERCase", IntegerType, nullable = true)))) + } + + // Metastore schema contains additional non-nullable fields. + assert(intercept[Throwable] { + ParquetRelation2.mergeMetastoreParquetSchema( + StructType(Seq( + StructField("uppercase", DoubleType, nullable = false), + StructField("lowerCase", BinaryType, nullable = false))), + + StructType(Seq( + StructField("UPPERCase", IntegerType, nullable = true)))) }.getMessage.contains("detected conflicting schemas")) - // Conflicting field names + // Conflicting non-nullable field names intercept[Throwable] { ParquetRelation2.mergeMetastoreParquetSchema( - StructType(Seq(StructField("lower", StringType))), + StructType(Seq(StructField("lower", StringType, nullable = false))), StructType(Seq(StructField("lowerCase", BinaryType)))) } } + + test("merge missing nullable fields from Metastore schema") { + // Standard case: Metastore schema contains additional nullable fields not present + // in the Parquet file schema. + assertResult( + StructType(Seq( + StructField("firstField", StringType, nullable = true), + StructField("secondField", StringType, nullable = true), + StructField("thirdfield", StringType, nullable = true)))) { + ParquetRelation2.mergeMetastoreParquetSchema( + StructType(Seq( + StructField("firstfield", StringType, nullable = true), + StructField("secondfield", StringType, nullable = true), + StructField("thirdfield", StringType, nullable = true))), + StructType(Seq( + StructField("firstField", StringType, nullable = true), + StructField("secondField", StringType, nullable = true)))) + } + + // Merge should fail if the Metastore contains any additional fields that are not + // nullable. + assert(intercept[Throwable] { + ParquetRelation2.mergeMetastoreParquetSchema( + StructType(Seq( + StructField("firstfield", StringType, nullable = true), + StructField("secondfield", StringType, nullable = true), + StructField("thirdfield", StringType, nullable = false))), + StructType(Seq( + StructField("firstField", StringType, nullable = true), + StructField("secondField", StringType, nullable = true)))) + }.getMessage.contains("detected conflicting schemas")) + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/CreateTableAsSelectSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/CreateTableAsSelectSuite.scala index b02389978b625..20a23b3bd6aa9 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/sources/CreateTableAsSelectSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/CreateTableAsSelectSuite.scala @@ -17,11 +17,11 @@ package org.apache.spark.sql.sources -import java.io.File +import java.io.{IOException, File} +import org.apache.spark.sql.AnalysisException import org.scalatest.BeforeAndAfterAll -import org.apache.spark.sql.catalyst.util import org.apache.spark.util.Utils class CreateTableAsSelectSuite extends DataSourceTest with BeforeAndAfterAll { @@ -31,7 +31,7 @@ class CreateTableAsSelectSuite extends DataSourceTest with BeforeAndAfterAll { var path: File = null override def beforeAll(): Unit = { - path = util.getTempFilePath("jsonCTAS").getCanonicalFile + path = Utils.createTempDir() val rdd = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str${i}"}""")) jsonRDD(rdd).registerTempTable("jt") } @@ -41,7 +41,7 @@ class CreateTableAsSelectSuite extends DataSourceTest with BeforeAndAfterAll { } after { - if (path.exists()) Utils.deleteRecursively(path) + Utils.deleteRecursively(path) } test("CREATE TEMPORARY TABLE AS SELECT") { @@ -62,6 +62,29 @@ class CreateTableAsSelectSuite extends DataSourceTest with BeforeAndAfterAll { dropTempTable("jsonTable") } + test("CREATE TEMPORARY TABLE AS SELECT based on the file without write permission") { + val childPath = new File(path.toString, "child") + path.mkdir() + childPath.createNewFile() + path.setWritable(false) + + val e = intercept[IOException] { + sql( + s""" + |CREATE TEMPORARY TABLE jsonTable + |USING org.apache.spark.sql.json.DefaultSource + |OPTIONS ( + | path '${path.toString}' + |) AS + |SELECT a, b FROM jt + """.stripMargin) + sql("SELECT a, b FROM jsonTable").collect() + } + assert(e.getMessage().contains("Unable to clear output directory")) + + path.setWritable(true) + } + test("create a table, drop it and create another one with the same name") { sql( s""" @@ -77,12 +100,10 @@ class CreateTableAsSelectSuite extends DataSourceTest with BeforeAndAfterAll { sql("SELECT a, b FROM jsonTable"), sql("SELECT a, b FROM jt").collect()) - dropTempTable("jsonTable") - - val message = intercept[RuntimeException]{ + val message = intercept[DDLException]{ sql( s""" - |CREATE TEMPORARY TABLE jsonTable + |CREATE TEMPORARY TABLE IF NOT EXISTS jsonTable |USING org.apache.spark.sql.json.DefaultSource |OPTIONS ( | path '${path.toString}' @@ -91,10 +112,25 @@ class CreateTableAsSelectSuite extends DataSourceTest with BeforeAndAfterAll { """.stripMargin) }.getMessage assert( - message.contains(s"path ${path.toString} already exists."), + message.contains(s"a CREATE TEMPORARY TABLE statement does not allow IF NOT EXISTS clause."), "CREATE TEMPORARY TABLE IF NOT EXISTS should not be allowed.") - // Explicitly delete it. + // Overwrite the temporary table. + sql( + s""" + |CREATE TEMPORARY TABLE jsonTable + |USING org.apache.spark.sql.json.DefaultSource + |OPTIONS ( + | path '${path.toString}' + |) AS + |SELECT a * 4 FROM jt + """.stripMargin) + checkAnswer( + sql("SELECT * FROM jsonTable"), + sql("SELECT a * 4 FROM jt").collect()) + + dropTempTable("jsonTable") + // Explicitly delete the data. if (path.exists()) Utils.deleteRecursively(path) sql( @@ -104,12 +140,12 @@ class CreateTableAsSelectSuite extends DataSourceTest with BeforeAndAfterAll { |OPTIONS ( | path '${path.toString}' |) AS - |SELECT a * 4 FROM jt + |SELECT b FROM jt """.stripMargin) checkAnswer( sql("SELECT * FROM jsonTable"), - sql("SELECT a * 4 FROM jt").collect()) + sql("SELECT b FROM jt").collect()) dropTempTable("jsonTable") } @@ -144,4 +180,31 @@ class CreateTableAsSelectSuite extends DataSourceTest with BeforeAndAfterAll { """.stripMargin) } } + + test("it is not allowed to write to a table while querying it.") { + sql( + s""" + |CREATE TEMPORARY TABLE jsonTable + |USING org.apache.spark.sql.json.DefaultSource + |OPTIONS ( + | path '${path.toString}' + |) AS + |SELECT a, b FROM jt + """.stripMargin) + + val message = intercept[AnalysisException] { + sql( + s""" + |CREATE TEMPORARY TABLE jsonTable + |USING org.apache.spark.sql.json.DefaultSource + |OPTIONS ( + | path '${path.toString}' + |) AS + |SELECT a, b FROM jsonTable + """.stripMargin) + }.getMessage + assert( + message.contains("Cannot overwrite table "), + "Writing to a table while querying it should not be allowed.") + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLTestSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLTestSuite.scala index 0ec756bfeb7ef..ca25751b9583d 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLTestSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLTestSuite.scala @@ -17,6 +17,7 @@ package org.apache.spark.sql.sources +import org.apache.spark.rdd.RDD import org.apache.spark.sql._ import org.apache.spark.sql.types._ @@ -24,17 +25,17 @@ class DDLScanSource extends RelationProvider { override def createRelation( sqlContext: SQLContext, parameters: Map[String, String]): BaseRelation = { - SimpleDDLScan(parameters("from").toInt, parameters("TO").toInt)(sqlContext) + SimpleDDLScan(parameters("from").toInt, parameters("TO").toInt, parameters("Table"))(sqlContext) } } -case class SimpleDDLScan(from: Int, to: Int)(@transient val sqlContext: SQLContext) - extends TableScan { +case class SimpleDDLScan(from: Int, to: Int, table: String)(@transient val sqlContext: SQLContext) + extends BaseRelation with TableScan { - override def schema = + override def schema: StructType = StructType(Seq( StructField("intType", IntegerType, nullable = false, - new MetadataBuilder().putString("comment", "test comment").build()), + new MetadataBuilder().putString("comment", s"test comment $table").build()), StructField("stringType", StringType, nullable = false), StructField("dateType", DateType, nullable = false), StructField("timestampType", TimestampType, nullable = false), @@ -57,8 +58,9 @@ case class SimpleDDLScan(from: Int, to: Int)(@transient val sqlContext: SQLConte )) - override def buildScan() = sqlContext.sparkContext.parallelize(from to to). - map(e => Row(s"people$e", e * 2)) + override def buildScan(): RDD[Row] = { + sqlContext.sparkContext.parallelize(from to to).map(e => Row(s"people$e", e * 2)) + } } class DDLTestSuite extends DataSourceTest { @@ -71,7 +73,8 @@ class DDLTestSuite extends DataSourceTest { |USING org.apache.spark.sql.sources.DDLScanSource |OPTIONS ( | From '1', - | To '10' + | To '10', + | Table 'test1' |) """.stripMargin) } @@ -79,7 +82,7 @@ class DDLTestSuite extends DataSourceTest { sqlTest( "describe ddlPeople", Seq( - Row("intType", "int", "test comment"), + Row("intType", "int", "test comment test1"), Row("stringType", "string", ""), Row("dateType", "date", ""), Row("timestampType", "timestamp", ""), diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/DataSourceTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/DataSourceTest.scala index 53f5f7426e9e6..33c67355967dd 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/sources/DataSourceTest.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/DataSourceTest.scala @@ -29,9 +29,13 @@ abstract class DataSourceTest extends QueryTest with BeforeAndAfter { @transient override protected[sql] lazy val analyzer: Analyzer = new Analyzer(catalog, functionRegistry, caseSensitive = false) { - override val extendedRules = + override val extendedResolutionRules = PreInsertCastAndRename :: Nil + + override val extendedCheckRules = Seq( + sources.PreWriteCheck(catalog) + ) } } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala index 390538d35a348..cb5e5147ff189 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala @@ -19,6 +19,7 @@ package org.apache.spark.sql.sources import scala.language.existentials +import org.apache.spark.rdd.RDD import org.apache.spark.sql._ import org.apache.spark.sql.types._ @@ -32,31 +33,57 @@ class FilteredScanSource extends RelationProvider { } case class SimpleFilteredScan(from: Int, to: Int)(@transient val sqlContext: SQLContext) - extends PrunedFilteredScan { + extends BaseRelation + with PrunedFilteredScan { - override def schema = + override def schema: StructType = StructType( StructField("a", IntegerType, nullable = false) :: - StructField("b", IntegerType, nullable = false) :: Nil) + StructField("b", IntegerType, nullable = false) :: + StructField("c", StringType, nullable = false) :: Nil) - override def buildScan(requiredColumns: Array[String], filters: Array[Filter]) = { + override def buildScan(requiredColumns: Array[String], filters: Array[Filter]): RDD[Row] = { val rowBuilders = requiredColumns.map { case "a" => (i: Int) => Seq(i) case "b" => (i: Int) => Seq(i * 2) + case "c" => (i: Int) => + val c = (i - 1 + 'a').toChar.toString + Seq(c * 5 + c.toUpperCase() * 5) } FiltersPushed.list = filters - val filterFunctions = filters.collect { + // Predicate test on integer column + def translateFilterOnA(filter: Filter): Int => Boolean = filter match { case EqualTo("a", v) => (a: Int) => a == v case LessThan("a", v: Int) => (a: Int) => a < v case LessThanOrEqual("a", v: Int) => (a: Int) => a <= v case GreaterThan("a", v: Int) => (a: Int) => a > v case GreaterThanOrEqual("a", v: Int) => (a: Int) => a >= v case In("a", values) => (a: Int) => values.map(_.asInstanceOf[Int]).toSet.contains(a) + case IsNull("a") => (a: Int) => false // Int can't be null + case IsNotNull("a") => (a: Int) => true + case Not(pred) => (a: Int) => !translateFilterOnA(pred)(a) + case And(left, right) => (a: Int) => + translateFilterOnA(left)(a) && translateFilterOnA(right)(a) + case Or(left, right) => (a: Int) => + translateFilterOnA(left)(a) || translateFilterOnA(right)(a) + case _ => (a: Int) => true } - def eval(a: Int) = !filterFunctions.map(_(a)).contains(false) + // Predicate test on string column + def translateFilterOnC(filter: Filter): String => Boolean = filter match { + case StringStartsWith("c", v) => _.startsWith(v) + case StringEndsWith("c", v) => _.endsWith(v) + case StringContains("c", v) => _.contains(v) + case _ => (c: String) => true + } + + def eval(a: Int) = { + val c = (a - 1 + 'a').toChar.toString * 5 + (a - 1 + 'a').toChar.toString.toUpperCase() * 5 + !filters.map(translateFilterOnA(_)(a)).contains(false) && + !filters.map(translateFilterOnC(_)(c)).contains(false) + } sqlContext.sparkContext.parallelize(from to to).filter(eval).map(i => Row.fromSeq(rowBuilders.map(_(i)).reduceOption(_ ++ _).getOrElse(Seq.empty))) @@ -86,7 +113,8 @@ class FilteredScanSuite extends DataSourceTest { sqlTest( "SELECT * FROM oneToTenFiltered", - (1 to 10).map(i => Row(i, i * 2)).toSeq) + (1 to 10).map(i => Row(i, i * 2, (i - 1 + 'a').toChar.toString * 5 + + (i - 1 + 'a').toChar.toString.toUpperCase() * 5)).toSeq) sqlTest( "SELECT a, b FROM oneToTenFiltered", @@ -121,20 +149,52 @@ class FilteredScanSuite extends DataSourceTest { (2 to 10 by 2).map(i => Row(i, i)).toSeq) sqlTest( - "SELECT * FROM oneToTenFiltered WHERE a = 1", - Seq(1).map(i => Row(i, i * 2)).toSeq) + "SELECT a, b FROM oneToTenFiltered WHERE a = 1", + Seq(1).map(i => Row(i, i * 2))) sqlTest( - "SELECT * FROM oneToTenFiltered WHERE a IN (1,3,5)", - Seq(1,3,5).map(i => Row(i, i * 2)).toSeq) + "SELECT a, b FROM oneToTenFiltered WHERE a IN (1,3,5)", + Seq(1,3,5).map(i => Row(i, i * 2))) + + sqlTest( + "SELECT a, b FROM oneToTenFiltered WHERE A = 1", + Seq(1).map(i => Row(i, i * 2))) + + sqlTest( + "SELECT a, b FROM oneToTenFiltered WHERE b = 2", + Seq(1).map(i => Row(i, i * 2))) + + sqlTest( + "SELECT a, b FROM oneToTenFiltered WHERE a IS NULL", + Seq.empty[Row]) + + sqlTest( + "SELECT a, b FROM oneToTenFiltered WHERE a IS NOT NULL", + (1 to 10).map(i => Row(i, i * 2)).toSeq) sqlTest( - "SELECT * FROM oneToTenFiltered WHERE A = 1", - Seq(1).map(i => Row(i, i * 2)).toSeq) + "SELECT a, b FROM oneToTenFiltered WHERE a < 5 AND a > 1", + (2 to 4).map(i => Row(i, i * 2)).toSeq) sqlTest( - "SELECT * FROM oneToTenFiltered WHERE b = 2", - Seq(1).map(i => Row(i, i * 2)).toSeq) + "SELECT a, b FROM oneToTenFiltered WHERE a < 3 OR a > 8", + Seq(1, 2, 9, 10).map(i => Row(i, i * 2))) + + sqlTest( + "SELECT a, b FROM oneToTenFiltered WHERE NOT (a < 6)", + (6 to 10).map(i => Row(i, i * 2)).toSeq) + + sqlTest( + "SELECT a, b, c FROM oneToTenFiltered WHERE c like 'c%'", + Seq(Row(3, 3 * 2, "c" * 5 + "C" * 5))) + + sqlTest( + "SELECT a, b, c FROM oneToTenFiltered WHERE c like '%D'", + Seq(Row(4, 4 * 2, "d" * 5 + "D" * 5))) + + sqlTest( + "SELECT a, b, c FROM oneToTenFiltered WHERE c like '%eE%'", + Seq(Row(5, 5 * 2, "e" * 5 + "E" * 5))) testPushDown("SELECT * FROM oneToTenFiltered WHERE A = 1", 1) testPushDown("SELECT a FROM oneToTenFiltered WHERE A = 1", 1) @@ -162,6 +222,19 @@ class FilteredScanSuite extends DataSourceTest { testPushDown("SELECT * FROM oneToTenFiltered WHERE a = 20", 0) testPushDown("SELECT * FROM oneToTenFiltered WHERE b = 1", 10) + testPushDown("SELECT * FROM oneToTenFiltered WHERE a < 5 AND a > 1", 3) + testPushDown("SELECT * FROM oneToTenFiltered WHERE a < 3 OR a > 8", 4) + testPushDown("SELECT * FROM oneToTenFiltered WHERE NOT (a < 6)", 5) + + testPushDown("SELECT a, b, c FROM oneToTenFiltered WHERE c like 'c%'", 1) + testPushDown("SELECT a, b, c FROM oneToTenFiltered WHERE c like 'C%'", 0) + + testPushDown("SELECT a, b, c FROM oneToTenFiltered WHERE c like '%D'", 1) + testPushDown("SELECT a, b, c FROM oneToTenFiltered WHERE c like '%d'", 0) + + testPushDown("SELECT a, b, c FROM oneToTenFiltered WHERE c like '%eE%'", 1) + testPushDown("SELECT a, b, c FROM oneToTenFiltered WHERE c like '%Ee%'", 0) + def testPushDown(sqlString: String, expectedCount: Int): Unit = { test(s"PushDown Returns $expectedCount: $sqlString") { val queryExecution = sql(sqlString).queryExecution diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertIntoSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala similarity index 77% rename from sql/core/src/test/scala/org/apache/spark/sql/sources/InsertIntoSuite.scala rename to sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala index 36e504e759152..80efe9728fbc2 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertIntoSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala @@ -21,18 +21,17 @@ import java.io.File import org.scalatest.BeforeAndAfterAll -import org.apache.spark.sql.Row -import org.apache.spark.sql.catalyst.util +import org.apache.spark.sql.{AnalysisException, Row} import org.apache.spark.util.Utils -class InsertIntoSuite extends DataSourceTest with BeforeAndAfterAll { +class InsertSuite extends DataSourceTest with BeforeAndAfterAll { import caseInsensisitiveContext._ var path: File = null override def beforeAll: Unit = { - path = util.getTempFilePath("jsonCTAS").getCanonicalFile + path = Utils.createTempDir() val rdd = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str${i}"}""")) jsonRDD(rdd).registerTempTable("jt") sql( @@ -48,7 +47,7 @@ class InsertIntoSuite extends DataSourceTest with BeforeAndAfterAll { override def afterAll: Unit = { dropTempTable("jsonTable") dropTempTable("jt") - if (path.exists()) Utils.deleteRecursively(path) + Utils.deleteRecursively(path) } test("Simple INSERT OVERWRITE a JSONRelation") { @@ -129,6 +128,18 @@ class InsertIntoSuite extends DataSourceTest with BeforeAndAfterAll { } } + test("it is not allowed to write to a table while querying it.") { + val message = intercept[AnalysisException] { + sql( + s""" + |INSERT OVERWRITE TABLE jsonTable SELECT a, b FROM jsonTable + """.stripMargin) + }.getMessage + assert( + message.contains("Cannot insert overwrite into table that is also being read from."), + "INSERT OVERWRITE to a table while querying it should not be allowed.") + } + test("Caching") { // Cached Query Execution cacheTable("jsonTable") @@ -173,4 +184,34 @@ class InsertIntoSuite extends DataSourceTest with BeforeAndAfterAll { uncacheTable("jsonTable") assertCached(sql("SELECT * FROM jsonTable"), 0) } + + test("it's not allowed to insert into a relation that is not an InsertableRelation") { + sql( + """ + |CREATE TEMPORARY TABLE oneToTen + |USING org.apache.spark.sql.sources.SimpleScanSource + |OPTIONS ( + | From '1', + | To '10' + |) + """.stripMargin) + + checkAnswer( + sql("SELECT * FROM oneToTen"), + (1 to 10).map(Row(_)).toSeq + ) + + val message = intercept[AnalysisException] { + sql( + s""" + |INSERT OVERWRITE TABLE oneToTen SELECT CAST(a AS INT) FROM jt + """.stripMargin) + }.getMessage + assert( + message.contains("does not allow insertion."), + "It is not allowed to insert into a table that is not an InsertableRelation." + ) + + dropTempTable("oneToTen") + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/PrunedScanSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/PrunedScanSuite.scala index a33cf1172cac9..6a1ddf2f8e98b 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/sources/PrunedScanSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/PrunedScanSuite.scala @@ -19,6 +19,7 @@ package org.apache.spark.sql.sources import scala.language.existentials +import org.apache.spark.rdd.RDD import org.apache.spark.sql._ import org.apache.spark.sql.types._ @@ -31,14 +32,15 @@ class PrunedScanSource extends RelationProvider { } case class SimplePrunedScan(from: Int, to: Int)(@transient val sqlContext: SQLContext) - extends PrunedScan { + extends BaseRelation + with PrunedScan { - override def schema = + override def schema: StructType = StructType( StructField("a", IntegerType, nullable = false) :: StructField("b", IntegerType, nullable = false) :: Nil) - override def buildScan(requiredColumns: Array[String]) = { + override def buildScan(requiredColumns: Array[String]): RDD[Row] = { val rowBuilders = requiredColumns.map { case "a" => (i: Int) => Seq(i) case "b" => (i: Int) => Seq(i * 2) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/ResolvedDataSourceSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/ResolvedDataSourceSuite.scala new file mode 100644 index 0000000000000..8331a14c9295c --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/ResolvedDataSourceSuite.scala @@ -0,0 +1,34 @@ +/* +* Licensed to the Apache Software Foundation (ASF) under one or more +* contributor license agreements. See the NOTICE file distributed with +* this work for additional information regarding copyright ownership. +* The ASF licenses this file to You under the Apache License, Version 2.0 +* (the "License"); you may not use this file except in compliance with +* the License. You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +*/ + +package org.apache.spark.sql.sources + +import org.scalatest.FunSuite + +class ResolvedDataSourceSuite extends FunSuite { + + test("builtin sources") { + assert(ResolvedDataSource.lookupDataSource("jdbc") === + classOf[org.apache.spark.sql.jdbc.DefaultSource]) + + assert(ResolvedDataSource.lookupDataSource("json") === + classOf[org.apache.spark.sql.json.DefaultSource]) + + assert(ResolvedDataSource.lookupDataSource("parquet") === + classOf[org.apache.spark.sql.parquet.DefaultSource]) + } +} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/SaveLoadSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/SaveLoadSuite.scala index fe2f76cc397f5..cb287ba85c1f8 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/sources/SaveLoadSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/SaveLoadSuite.scala @@ -21,11 +21,10 @@ import java.io.File import org.scalatest.BeforeAndAfterAll -import org.apache.spark.sql.DataFrame +import org.apache.spark.sql.{SaveMode, SQLConf, DataFrame} +import org.apache.spark.sql.types._ import org.apache.spark.util.Utils -import org.apache.spark.sql.catalyst.util - class SaveLoadSuite extends DataSourceTest with BeforeAndAfterAll { import caseInsensisitiveContext._ @@ -38,42 +37,61 @@ class SaveLoadSuite extends DataSourceTest with BeforeAndAfterAll { override def beforeAll(): Unit = { originalDefaultSource = conf.defaultDataSourceName - conf.setConf("spark.sql.default.datasource", "org.apache.spark.sql.json") - path = util.getTempFilePath("datasource").getCanonicalFile + path = Utils.createTempDir() + path.delete() val rdd = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str${i}"}""")) df = jsonRDD(rdd) + df.registerTempTable("jsonTable") } override def afterAll(): Unit = { - conf.setConf("spark.sql.default.datasource", originalDefaultSource) + conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, originalDefaultSource) } after { - if (path.exists()) Utils.deleteRecursively(path) + conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, originalDefaultSource) + Utils.deleteRecursively(path) } def checkLoad(): Unit = { + conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, "org.apache.spark.sql.json") checkAnswer(load(path.toString), df.collect()) - checkAnswer(load("org.apache.spark.sql.json", ("path", path.toString)), df.collect()) + + // Test if we can pick up the data source name passed in load. + conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, "not a source name") + checkAnswer(load(path.toString, "org.apache.spark.sql.json"), df.collect()) + checkAnswer(load("org.apache.spark.sql.json", Map("path" -> path.toString)), df.collect()) + val schema = StructType(StructField("b", StringType, true) :: Nil) + checkAnswer( + load("org.apache.spark.sql.json", schema, Map("path" -> path.toString)), + sql("SELECT b FROM jsonTable").collect()) } - test("save with overwrite and load") { + test("save with path and load") { + conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, "org.apache.spark.sql.json") df.save(path.toString) - checkLoad + checkLoad() + } + + test("save with path and datasource, and load") { + conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, "not a source name") + df.save(path.toString, "org.apache.spark.sql.json") + checkLoad() } test("save with data source and options, and load") { - df.save("org.apache.spark.sql.json", ("path", path.toString)) - checkLoad + conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, "not a source name") + df.save("org.apache.spark.sql.json", SaveMode.ErrorIfExists, Map("path" -> path.toString)) + checkLoad() } test("save and save again") { - df.save(path.toString) + df.save(path.toString, "org.apache.spark.sql.json") - val message = intercept[RuntimeException] { - df.save(path.toString) + var message = intercept[RuntimeException] { + df.save(path.toString, "org.apache.spark.sql.json") }.getMessage assert( @@ -82,7 +100,18 @@ class SaveLoadSuite extends DataSourceTest with BeforeAndAfterAll { if (path.exists()) Utils.deleteRecursively(path) - df.save(path.toString) - checkLoad + df.save(path.toString, "org.apache.spark.sql.json") + checkLoad() + + df.save("org.apache.spark.sql.json", SaveMode.Overwrite, Map("path" -> path.toString)) + checkLoad() + + message = intercept[RuntimeException] { + df.save("org.apache.spark.sql.json", SaveMode.Append, Map("path" -> path.toString)) + }.getMessage + + assert( + message.contains("Append mode is not supported"), + "We should complain that 'Append mode is not supported' for JSON source.") } -} \ No newline at end of file +} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala index 0a4d4b6342d4f..3b47b8adf313b 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala @@ -19,6 +19,7 @@ package org.apache.spark.sql.sources import java.sql.{Timestamp, Date} +import org.apache.spark.rdd.RDD import org.apache.spark.sql._ import org.apache.spark.sql.types._ @@ -33,12 +34,12 @@ class SimpleScanSource extends RelationProvider { } case class SimpleScan(from: Int, to: Int)(@transient val sqlContext: SQLContext) - extends TableScan { + extends BaseRelation with TableScan { - override def schema = + override def schema: StructType = StructType(StructField("i", IntegerType, nullable = false) :: Nil) - override def buildScan() = sqlContext.sparkContext.parallelize(from to to).map(Row(_)) + override def buildScan(): RDD[Row] = sqlContext.sparkContext.parallelize(from to to).map(Row(_)) } class AllDataTypesScanSource extends SchemaRelationProvider { @@ -51,14 +52,15 @@ class AllDataTypesScanSource extends SchemaRelationProvider { } case class AllDataTypesScan( - from: Int, - to: Int, - userSpecifiedSchema: StructType)(@transient val sqlContext: SQLContext) - extends TableScan { + from: Int, + to: Int, + userSpecifiedSchema: StructType)(@transient val sqlContext: SQLContext) + extends BaseRelation + with TableScan { - override def schema = userSpecifiedSchema + override def schema: StructType = userSpecifiedSchema - override def buildScan() = { + override def buildScan(): RDD[Row] = { sqlContext.sparkContext.parallelize(from to to).map { i => Row( s"str_$i", @@ -72,7 +74,7 @@ case class AllDataTypesScan( i.toDouble, new java.math.BigDecimal(i), new java.math.BigDecimal(i), - new Date((i + 1) * 8640000), + new Date(1970, 1, 1), new Timestamp(20000 + i), s"varchar_$i", Seq(i, i + 1), @@ -80,7 +82,7 @@ case class AllDataTypesScan( Map(i -> i.toString), Map(Map(s"str_$i" -> i.toFloat) -> Row(i.toLong)), Row(i, i.toString), - Row(Seq(s"str_$i", s"str_${i + 1}"), Row(Seq(new Date((i + 2) * 8640000))))) + Row(Seq(s"str_$i", s"str_${i + 1}"), Row(Seq(new Date(1970, 1, i + 1))))) } } } @@ -101,7 +103,7 @@ class TableScanSuite extends DataSourceTest { i.toDouble, new java.math.BigDecimal(i), new java.math.BigDecimal(i), - new Date((i + 1) * 8640000), + new Date(1970, 1, 1), new Timestamp(20000 + i), s"varchar_$i", Seq(i, i + 1), @@ -109,7 +111,7 @@ class TableScanSuite extends DataSourceTest { Map(i -> i.toString), Map(Map(s"str_$i" -> i.toFloat) -> Row(i.toLong)), Row(i, i.toString), - Row(Seq(s"str_$i", s"str_${i + 1}"), Row(Seq(new Date((i + 2) * 8640000))))) + Row(Seq(s"str_$i", s"str_${i + 1}"), Row(Seq(new Date(1970, 1, i + 1))))) }.toSeq before { @@ -264,7 +266,7 @@ class TableScanSuite extends DataSourceTest { sqlTest( "SELECT structFieldComplex.Value.`value_(2)` FROM tableWithSchema", - (1 to 10).map(i => Row(Seq(new Date((i + 2) * 8640000)))).toSeq) + (1 to 10).map(i => Row(Seq(new Date(1970, 1, i + 1)))).toSeq) test("Caching") { // Cached Query Execution diff --git a/sql/hive-thriftserver/pom.xml b/sql/hive-thriftserver/pom.xml index 123a1f629ab1c..f38c796241df1 100644 --- a/sql/hive-thriftserver/pom.xml +++ b/sql/hive-thriftserver/pom.xml @@ -21,8 +21,8 @@ 4.0.0 org.apache.spark - spark-parent - 1.3.0-SNAPSHOT + spark-parent_2.10 + 1.4.0-SNAPSHOT ../../pom.xml @@ -41,6 +41,10 @@ spark-hive_${scala.binary.version} ${project.version} + + com.google.guava + guava + ${hive.group} hive-cli diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala index 6e07df18b0e15..c3a3f8c0f41df 100644 --- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala +++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala @@ -98,16 +98,14 @@ private[hive] class HiveThriftServer2(hiveContext: HiveContext) setSuperField(this, "cliService", sparkSqlCliService) addService(sparkSqlCliService) - if (isHTTPTransportMode(hiveConf)) { - val thriftCliService = new ThriftHttpCLIService(sparkSqlCliService) - setSuperField(this, "thriftCLIService", thriftCliService) - addService(thriftCliService) + val thriftCliService = if (isHTTPTransportMode(hiveConf)) { + new ThriftHttpCLIService(sparkSqlCliService) } else { - val thriftCliService = new ThriftBinaryCLIService(sparkSqlCliService) - setSuperField(this, "thriftCLIService", thriftCliService) - addService(thriftCliService) + new ThriftBinaryCLIService(sparkSqlCliService) } + setSuperField(this, "thriftCLIService", thriftCliService) + addService(thriftCliService) initCompositeService(hiveConf) } diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala old mode 100755 new mode 100644 index bb19ac232fcbe..85281c6d73a3b --- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala +++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala @@ -33,7 +33,7 @@ import org.apache.hadoop.hive.common.{HiveInterruptCallback, HiveInterruptUtils, import org.apache.hadoop.hive.conf.HiveConf import org.apache.hadoop.hive.ql.Driver import org.apache.hadoop.hive.ql.exec.Utilities -import org.apache.hadoop.hive.ql.processors.{SetProcessor, CommandProcessor, CommandProcessorFactory} +import org.apache.hadoop.hive.ql.processors.{AddResourceProcessor, SetProcessor, CommandProcessor, CommandProcessorFactory} import org.apache.hadoop.hive.ql.session.SessionState import org.apache.hadoop.hive.shims.ShimLoader import org.apache.thrift.transport.TSocket @@ -145,6 +145,9 @@ private[hive] object SparkSQLCLIDriver { case e: UnsupportedEncodingException => System.exit(3) } + // use the specified database if specified + cli.processSelectDatabase(sessionState); + // Execute -i init files (always in silent mode) cli.processInitFiles(sessionState) @@ -194,28 +197,29 @@ private[hive] object SparkSQLCLIDriver { val currentDB = ReflectionUtils.invokeStatic(classOf[CliDriver], "getFormattedDb", classOf[HiveConf] -> conf, classOf[CliSessionState] -> sessionState) - def promptWithCurrentDB = s"$prompt$currentDB" - def continuedPromptWithDBSpaces = continuedPrompt + ReflectionUtils.invokeStatic( + def promptWithCurrentDB: String = s"$prompt$currentDB" + def continuedPromptWithDBSpaces: String = continuedPrompt + ReflectionUtils.invokeStatic( classOf[CliDriver], "spacesForString", classOf[String] -> currentDB) var currentPrompt = promptWithCurrentDB var line = reader.readLine(currentPrompt + "> ") while (line != null) { - if (prefix.nonEmpty) { - prefix += '\n' - } + if (!line.startsWith("--")) { + if (prefix.nonEmpty) { + prefix += '\n' + } - if (line.trim().endsWith(";") && !line.trim().endsWith("\\;")) { - line = prefix + line - ret = cli.processLine(line, true) - prefix = "" - currentPrompt = promptWithCurrentDB - } else { - prefix = prefix + line - currentPrompt = continuedPromptWithDBSpaces + if (line.trim().endsWith(";") && !line.trim().endsWith("\\;")) { + line = prefix + line + ret = cli.processLine(line, true) + prefix = "" + currentPrompt = promptWithCurrentDB + } else { + prefix = prefix + line + currentPrompt = continuedPromptWithDBSpaces + } } - line = reader.readLine(currentPrompt + "> ") } @@ -263,7 +267,8 @@ private[hive] class SparkSQLCLIDriver extends CliDriver with Logging { val proc: CommandProcessor = HiveShim.getCommandProcessor(Array(tokens(0)), hconf) if (proc != null) { - if (proc.isInstanceOf[Driver] || proc.isInstanceOf[SetProcessor]) { + if (proc.isInstanceOf[Driver] || proc.isInstanceOf[SetProcessor] || + proc.isInstanceOf[AddResourceProcessor]) { val driver = new SparkSQLDriver driver.init() @@ -292,9 +297,13 @@ private[hive] class SparkSQLCLIDriver extends CliDriver with Logging { } } + var counter = 0 try { while (!out.checkError() && driver.getResults(res)) { - res.foreach(out.println) + res.foreach{ l => + counter += 1 + out.println(l) + } res.clear() } } catch { @@ -311,7 +320,11 @@ private[hive] class SparkSQLCLIDriver extends CliDriver with Logging { ret = cret } - console.printInfo(s"Time taken: $timeTaken seconds", null) + var responseMsg = s"Time taken: $timeTaken seconds" + if (counter != 0) { + responseMsg += s", Fetched $counter row(s)" + } + console.printInfo(responseMsg , null) // Destroy the driver to release all the locks. driver.destroy() } else { diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala index 158c225159720..97b46a01ba5b4 100644 --- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala +++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala @@ -22,6 +22,7 @@ import scala.collection.JavaConversions._ import org.apache.spark.scheduler.StatsReportListener import org.apache.spark.sql.hive.{HiveShim, HiveContext} import org.apache.spark.{Logging, SparkConf, SparkContext} +import org.apache.spark.util.Utils /** A singleton object for the master program. The slaves should not access this. */ private[hive] object SparkSQLEnv extends Logging { @@ -37,7 +38,7 @@ private[hive] object SparkSQLEnv extends Logging { val maybeKryoReferenceTracking = sparkConf.getOption("spark.kryo.referenceTracking") sparkConf - .setAppName(s"SparkSQL::${java.net.InetAddress.getLocalHost.getHostName}") + .setAppName(s"SparkSQL::${Utils.localHostName()}") .set("spark.sql.hive.version", HiveShim.version) .set( "spark.serializer", diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLSessionManager.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLSessionManager.scala deleted file mode 100644 index 89e9ede7261c9..0000000000000 --- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLSessionManager.scala +++ /dev/null @@ -1,56 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.sql.hive.thriftserver - -import java.util.concurrent.Executors - -import org.apache.commons.logging.Log -import org.apache.hadoop.hive.conf.HiveConf -import org.apache.hadoop.hive.conf.HiveConf.ConfVars -import org.apache.hive.service.cli.session.SessionManager - -import org.apache.spark.sql.hive.HiveContext -import org.apache.spark.sql.hive.thriftserver.ReflectionUtils._ -import org.apache.spark.sql.hive.thriftserver.server.SparkSQLOperationManager -import org.apache.hive.service.cli.SessionHandle - -private[hive] class SparkSQLSessionManager(hiveContext: HiveContext) - extends SessionManager - with ReflectedCompositeService { - - private lazy val sparkSqlOperationManager = new SparkSQLOperationManager(hiveContext) - - override def init(hiveConf: HiveConf) { - setSuperField(this, "hiveConf", hiveConf) - - val backgroundPoolSize = hiveConf.getIntVar(ConfVars.HIVE_SERVER2_ASYNC_EXEC_THREADS) - setSuperField(this, "backgroundOperationPool", Executors.newFixedThreadPool(backgroundPoolSize)) - getAncestorField[Log](this, 3, "LOG").info( - s"HiveServer2: Async execution pool size $backgroundPoolSize") - - setSuperField(this, "operationManager", sparkSqlOperationManager) - addService(sparkSqlOperationManager) - - initCompositeService(hiveConf) - } - - override def closeSession(sessionHandle: SessionHandle) { - super.closeSession(sessionHandle) - sparkSqlOperationManager.sessionToActivePool -= sessionHandle - } -} diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala index 60953576d0e37..b070fa8eaa469 100644 --- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala +++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala @@ -1,13 +1,12 @@ /* - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at * - * http://www.apache.org/licenses/LICENSE-2.0 + * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, @@ -26,20 +25,31 @@ import scala.concurrent.{Await, Promise} import scala.sys.process.{Process, ProcessLogger} import org.apache.hadoop.hive.conf.HiveConf.ConfVars -import org.scalatest.{BeforeAndAfterAll, FunSuite} +import org.scalatest.{BeforeAndAfter, BeforeAndAfterAll, FunSuite} import org.apache.spark.Logging -import org.apache.spark.sql.catalyst.util.getTempFilePath +import org.apache.spark.util.Utils + +class CliSuite extends FunSuite with BeforeAndAfter with Logging { + val warehousePath = Utils.createTempDir() + val metastorePath = Utils.createTempDir() + + before { + warehousePath.delete() + metastorePath.delete() + } + + after { + warehousePath.delete() + metastorePath.delete() + } -class CliSuite extends FunSuite with BeforeAndAfterAll with Logging { def runCliWithin( timeout: FiniteDuration, extraArgs: Seq[String] = Seq.empty)( - queriesAndExpectedAnswers: (String, String)*) { + queriesAndExpectedAnswers: (String, String)*): Unit = { val (queries, expectedAnswers) = queriesAndExpectedAnswers.unzip - val warehousePath = getTempFilePath("warehouse") - val metastorePath = getTempFilePath("metastore") val cliScript = "../../bin/spark-sql".split("/").mkString(File.separator) val command = { @@ -94,8 +104,6 @@ class CliSuite extends FunSuite with BeforeAndAfterAll with Logging { """.stripMargin, cause) throw cause } finally { - warehousePath.delete() - metastorePath.delete() process.destroy() } } @@ -121,6 +129,26 @@ class CliSuite extends FunSuite with BeforeAndAfterAll with Logging { } test("Single command with -e") { - runCliWithin(1.minute, Seq("-e", "SHOW TABLES;"))("" -> "OK") + runCliWithin(1.minute, Seq("-e", "SHOW DATABASES;"))("" -> "OK") + } + + test("Single command with --database") { + runCliWithin(1.minute)( + "CREATE DATABASE hive_test_db;" + -> "OK", + "USE hive_test_db;" + -> "OK", + "CREATE TABLE hive_test(key INT, val STRING);" + -> "OK", + "SHOW TABLES;" + -> "Time taken: " + ) + + runCliWithin(1.minute, Seq("--database", "hive_test_db", "-e", "SHOW TABLES;"))( + "" + -> "OK", + "" + -> "hive_test" + ) } } diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suite.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suite.scala deleted file mode 100644 index b52a51d11e4ad..0000000000000 --- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suite.scala +++ /dev/null @@ -1,387 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.sql.hive.thriftserver - -import java.io.File -import java.net.ServerSocket -import java.sql.{Date, DriverManager, Statement} -import java.util.concurrent.TimeoutException - -import scala.collection.JavaConversions._ -import scala.collection.mutable.ArrayBuffer -import scala.concurrent.duration._ -import scala.concurrent.{Await, Promise} -import scala.sys.process.{Process, ProcessLogger} -import scala.util.Try - -import org.apache.hadoop.hive.conf.HiveConf.ConfVars -import org.apache.hive.jdbc.HiveDriver -import org.apache.hive.service.auth.PlainSaslHelper -import org.apache.hive.service.cli.GetInfoType -import org.apache.hive.service.cli.thrift.TCLIService.Client -import org.apache.hive.service.cli.thrift._ -import org.apache.thrift.protocol.TBinaryProtocol -import org.apache.thrift.transport.TSocket -import org.scalatest.FunSuite - -import org.apache.spark.Logging -import org.apache.spark.sql.catalyst.util.getTempFilePath -import org.apache.spark.sql.hive.HiveShim - -/** - * Tests for the HiveThriftServer2 using JDBC. - * - * NOTE: SPARK_PREPEND_CLASSES is explicitly disabled in this test suite. Assembly jar must be - * rebuilt after changing HiveThriftServer2 related code. - */ -class HiveThriftServer2Suite extends FunSuite with Logging { - Class.forName(classOf[HiveDriver].getCanonicalName) - - object TestData { - def getTestDataFilePath(name: String) = { - Thread.currentThread().getContextClassLoader.getResource(s"data/files/$name") - } - - val smallKv = getTestDataFilePath("small_kv.txt") - val smallKvWithNull = getTestDataFilePath("small_kv_with_null.txt") - } - - def randomListeningPort = { - // Let the system to choose a random available port to avoid collision with other parallel - // builds. - val socket = new ServerSocket(0) - val port = socket.getLocalPort - socket.close() - port - } - - def withJdbcStatement( - serverStartTimeout: FiniteDuration = 1.minute, - httpMode: Boolean = false)( - f: Statement => Unit) { - val port = randomListeningPort - - startThriftServer(port, serverStartTimeout, httpMode) { - val jdbcUri = if (httpMode) { - s"jdbc:hive2://${"localhost"}:$port/" + - "default?hive.server2.transport.mode=http;hive.server2.thrift.http.path=cliservice" - } else { - s"jdbc:hive2://${"localhost"}:$port/" - } - - val user = System.getProperty("user.name") - val connection = DriverManager.getConnection(jdbcUri, user, "") - val statement = connection.createStatement() - - try { - f(statement) - } finally { - statement.close() - connection.close() - } - } - } - - def withCLIServiceClient( - serverStartTimeout: FiniteDuration = 1.minute)( - f: ThriftCLIServiceClient => Unit) { - val port = randomListeningPort - - startThriftServer(port) { - // Transport creation logics below mimics HiveConnection.createBinaryTransport - val rawTransport = new TSocket("localhost", port) - val user = System.getProperty("user.name") - val transport = PlainSaslHelper.getPlainTransport(user, "anonymous", rawTransport) - val protocol = new TBinaryProtocol(transport) - val client = new ThriftCLIServiceClient(new Client(protocol)) - - transport.open() - - try { - f(client) - } finally { - transport.close() - } - } - } - - def startThriftServer( - port: Int, - serverStartTimeout: FiniteDuration = 1.minute, - httpMode: Boolean = false)( - f: => Unit) { - val startScript = "../../sbin/start-thriftserver.sh".split("/").mkString(File.separator) - val stopScript = "../../sbin/stop-thriftserver.sh".split("/").mkString(File.separator) - - val warehousePath = getTempFilePath("warehouse") - val metastorePath = getTempFilePath("metastore") - val metastoreJdbcUri = s"jdbc:derby:;databaseName=$metastorePath;create=true" - - val command = - if (httpMode) { - s"""$startScript - | --master local - | --hiveconf hive.root.logger=INFO,console - | --hiveconf ${ConfVars.METASTORECONNECTURLKEY}=$metastoreJdbcUri - | --hiveconf ${ConfVars.METASTOREWAREHOUSE}=$warehousePath - | --hiveconf ${ConfVars.HIVE_SERVER2_THRIFT_BIND_HOST}=localhost - | --hiveconf ${ConfVars.HIVE_SERVER2_TRANSPORT_MODE}=http - | --hiveconf ${ConfVars.HIVE_SERVER2_THRIFT_HTTP_PORT}=$port - | --driver-class-path ${sys.props("java.class.path")} - | --conf spark.ui.enabled=false - """.stripMargin.split("\\s+").toSeq - } else { - s"""$startScript - | --master local - | --hiveconf hive.root.logger=INFO,console - | --hiveconf ${ConfVars.METASTORECONNECTURLKEY}=$metastoreJdbcUri - | --hiveconf ${ConfVars.METASTOREWAREHOUSE}=$warehousePath - | --hiveconf ${ConfVars.HIVE_SERVER2_THRIFT_BIND_HOST}=localhost - | --hiveconf ${ConfVars.HIVE_SERVER2_THRIFT_PORT}=$port - | --driver-class-path ${sys.props("java.class.path")} - | --conf spark.ui.enabled=false - """.stripMargin.split("\\s+").toSeq - } - - val serverRunning = Promise[Unit]() - val buffer = new ArrayBuffer[String]() - val LOGGING_MARK = - s"starting ${HiveThriftServer2.getClass.getCanonicalName.stripSuffix("$")}, logging to " - var logTailingProcess: Process = null - var logFilePath: String = null - - def captureLogOutput(line: String): Unit = { - buffer += line - if (line.contains("ThriftBinaryCLIService listening on") || - line.contains("Started ThriftHttpCLIService in http")) { - serverRunning.success(()) - } - } - - def captureThriftServerOutput(source: String)(line: String): Unit = { - if (line.startsWith(LOGGING_MARK)) { - logFilePath = line.drop(LOGGING_MARK.length).trim - // Ensure that the log file is created so that the `tail' command won't fail - Try(new File(logFilePath).createNewFile()) - logTailingProcess = Process(s"/usr/bin/env tail -f $logFilePath") - .run(ProcessLogger(captureLogOutput, _ => ())) - } - } - - val env = Seq( - // Resets SPARK_TESTING to avoid loading Log4J configurations in testing class paths - "SPARK_TESTING" -> "0") - - Process(command, None, env: _*).run(ProcessLogger( - captureThriftServerOutput("stdout"), - captureThriftServerOutput("stderr"))) - - try { - Await.result(serverRunning.future, serverStartTimeout) - f - } catch { - case cause: Exception => - cause match { - case _: TimeoutException => - logError(s"Failed to start Hive Thrift server within $serverStartTimeout", cause) - case _ => - } - logError( - s""" - |===================================== - |HiveThriftServer2Suite failure output - |===================================== - |HiveThriftServer2 command line: ${command.mkString(" ")} - |Binding port: $port - |System user: ${System.getProperty("user.name")} - | - |${buffer.mkString("\n")} - |========================================= - |End HiveThriftServer2Suite failure output - |========================================= - """.stripMargin, cause) - throw cause - } finally { - warehousePath.delete() - metastorePath.delete() - Process(stopScript, None, env: _*).run().exitValue() - // The `spark-daemon.sh' script uses kill, which is not synchronous, have to wait for a while. - Thread.sleep(3.seconds.toMillis) - Option(logTailingProcess).map(_.destroy()) - Option(logFilePath).map(new File(_).delete()) - } - } - - test("Test JDBC query execution") { - withJdbcStatement() { statement => - val queries = Seq( - "SET spark.sql.shuffle.partitions=3", - "DROP TABLE IF EXISTS test", - "CREATE TABLE test(key INT, val STRING)", - s"LOAD DATA LOCAL INPATH '${TestData.smallKv}' OVERWRITE INTO TABLE test", - "CACHE TABLE test") - - queries.foreach(statement.execute) - - assertResult(5, "Row count mismatch") { - val resultSet = statement.executeQuery("SELECT COUNT(*) FROM test") - resultSet.next() - resultSet.getInt(1) - } - } - } - - test("Test JDBC query execution in Http Mode") { - withJdbcStatement(httpMode = true) { statement => - val queries = Seq( - "SET spark.sql.shuffle.partitions=3", - "DROP TABLE IF EXISTS test", - "CREATE TABLE test(key INT, val STRING)", - s"LOAD DATA LOCAL INPATH '${TestData.smallKv}' OVERWRITE INTO TABLE test", - "CACHE TABLE test") - - queries.foreach(statement.execute) - - assertResult(5, "Row count mismatch") { - val resultSet = statement.executeQuery("SELECT COUNT(*) FROM test") - resultSet.next() - resultSet.getInt(1) - } - } - } - - test("SPARK-3004 regression: result set containing NULL") { - withJdbcStatement() { statement => - val queries = Seq( - "DROP TABLE IF EXISTS test_null", - "CREATE TABLE test_null(key INT, val STRING)", - s"LOAD DATA LOCAL INPATH '${TestData.smallKvWithNull}' OVERWRITE INTO TABLE test_null") - - queries.foreach(statement.execute) - - val resultSet = statement.executeQuery("SELECT * FROM test_null WHERE key IS NULL") - - (0 until 5).foreach { _ => - resultSet.next() - assert(resultSet.getInt(1) === 0) - assert(resultSet.wasNull()) - } - - assert(!resultSet.next()) - } - } - - test("GetInfo Thrift API") { - withCLIServiceClient() { client => - val user = System.getProperty("user.name") - val sessionHandle = client.openSession(user, "") - - assertResult("Spark SQL", "Wrong GetInfo(CLI_DBMS_NAME) result") { - client.getInfo(sessionHandle, GetInfoType.CLI_DBMS_NAME).getStringValue - } - - assertResult("Spark SQL", "Wrong GetInfo(CLI_SERVER_NAME) result") { - client.getInfo(sessionHandle, GetInfoType.CLI_SERVER_NAME).getStringValue - } - - assertResult(true, "Spark version shouldn't be \"Unknown\"") { - val version = client.getInfo(sessionHandle, GetInfoType.CLI_DBMS_VER).getStringValue - logInfo(s"Spark version: $version") - version != "Unknown" - } - } - } - - test("Checks Hive version") { - withJdbcStatement() { statement => - val resultSet = statement.executeQuery("SET spark.sql.hive.version") - resultSet.next() - assert(resultSet.getString(1) === s"spark.sql.hive.version=${HiveShim.version}") - } - } - - test("Checks Hive version in Http Mode") { - withJdbcStatement(httpMode = true) { statement => - val resultSet = statement.executeQuery("SET spark.sql.hive.version") - resultSet.next() - assert(resultSet.getString(1) === s"spark.sql.hive.version=${HiveShim.version}") - } - } - - test("SPARK-4292 regression: result set iterator issue") { - withJdbcStatement() { statement => - val queries = Seq( - "DROP TABLE IF EXISTS test_4292", - "CREATE TABLE test_4292(key INT, val STRING)", - s"LOAD DATA LOCAL INPATH '${TestData.smallKv}' OVERWRITE INTO TABLE test_4292") - - queries.foreach(statement.execute) - - val resultSet = statement.executeQuery("SELECT key FROM test_4292") - - Seq(238, 86, 311, 27, 165).foreach { key => - resultSet.next() - assert(resultSet.getInt(1) === key) - } - - statement.executeQuery("DROP TABLE IF EXISTS test_4292") - } - } - - test("SPARK-4309 regression: Date type support") { - withJdbcStatement() { statement => - val queries = Seq( - "DROP TABLE IF EXISTS test_date", - "CREATE TABLE test_date(key INT, value STRING)", - s"LOAD DATA LOCAL INPATH '${TestData.smallKv}' OVERWRITE INTO TABLE test_date") - - queries.foreach(statement.execute) - - assertResult(Date.valueOf("2011-01-01")) { - val resultSet = statement.executeQuery( - "SELECT CAST('2011-01-01' as date) FROM test_date LIMIT 1") - resultSet.next() - resultSet.getDate(1) - } - } - } - - test("SPARK-4407 regression: Complex type support") { - withJdbcStatement() { statement => - val queries = Seq( - "DROP TABLE IF EXISTS test_map", - "CREATE TABLE test_map(key INT, value STRING)", - s"LOAD DATA LOCAL INPATH '${TestData.smallKv}' OVERWRITE INTO TABLE test_map") - - queries.foreach(statement.execute) - - assertResult("""{238:"val_238"}""") { - val resultSet = statement.executeQuery("SELECT MAP(key, value) FROM test_map LIMIT 1") - resultSet.next() - resultSet.getString(1) - } - - assertResult("""["238","val_238"]""") { - val resultSet = statement.executeQuery( - "SELECT ARRAY(CAST(key AS STRING), value) FROM test_map LIMIT 1") - resultSet.next() - resultSet.getString(1) - } - } - } -} diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala new file mode 100644 index 0000000000000..4cf95e7bdfb2b --- /dev/null +++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala @@ -0,0 +1,561 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.thriftserver + +import java.io.File +import java.net.URL +import java.sql.{Date, DriverManager, Statement} + +import scala.collection.mutable.ArrayBuffer +import scala.concurrent.duration._ +import scala.concurrent.{Await, Promise} +import scala.sys.process.{Process, ProcessLogger} +import scala.util.{Random, Try} + +import org.apache.hadoop.hive.conf.HiveConf.ConfVars +import org.apache.hive.jdbc.HiveDriver +import org.apache.hive.service.auth.PlainSaslHelper +import org.apache.hive.service.cli.GetInfoType +import org.apache.hive.service.cli.thrift.TCLIService.Client +import org.apache.hive.service.cli.thrift.ThriftCLIServiceClient +import org.apache.thrift.protocol.TBinaryProtocol +import org.apache.thrift.transport.TSocket +import org.scalatest.{BeforeAndAfterAll, FunSuite} + +import org.apache.spark.Logging +import org.apache.spark.sql.hive.HiveShim +import org.apache.spark.util.Utils + +object TestData { + def getTestDataFilePath(name: String): URL = { + Thread.currentThread().getContextClassLoader.getResource(s"data/files/$name") + } + + val smallKv = getTestDataFilePath("small_kv.txt") + val smallKvWithNull = getTestDataFilePath("small_kv_with_null.txt") +} + +class HiveThriftBinaryServerSuite extends HiveThriftJdbcTest { + override def mode: ServerMode.Value = ServerMode.binary + + private def withCLIServiceClient(f: ThriftCLIServiceClient => Unit): Unit = { + // Transport creation logics below mimics HiveConnection.createBinaryTransport + val rawTransport = new TSocket("localhost", serverPort) + val user = System.getProperty("user.name") + val transport = PlainSaslHelper.getPlainTransport(user, "anonymous", rawTransport) + val protocol = new TBinaryProtocol(transport) + val client = new ThriftCLIServiceClient(new Client(protocol)) + + transport.open() + try f(client) finally transport.close() + } + + test("GetInfo Thrift API") { + withCLIServiceClient { client => + val user = System.getProperty("user.name") + val sessionHandle = client.openSession(user, "") + + assertResult("Spark SQL", "Wrong GetInfo(CLI_DBMS_NAME) result") { + client.getInfo(sessionHandle, GetInfoType.CLI_DBMS_NAME).getStringValue + } + + assertResult("Spark SQL", "Wrong GetInfo(CLI_SERVER_NAME) result") { + client.getInfo(sessionHandle, GetInfoType.CLI_SERVER_NAME).getStringValue + } + + assertResult(true, "Spark version shouldn't be \"Unknown\"") { + val version = client.getInfo(sessionHandle, GetInfoType.CLI_DBMS_VER).getStringValue + logInfo(s"Spark version: $version") + version != "Unknown" + } + } + } + + test("JDBC query execution") { + withJdbcStatement { statement => + val queries = Seq( + "SET spark.sql.shuffle.partitions=3", + "DROP TABLE IF EXISTS test", + "CREATE TABLE test(key INT, val STRING)", + s"LOAD DATA LOCAL INPATH '${TestData.smallKv}' OVERWRITE INTO TABLE test", + "CACHE TABLE test") + + queries.foreach(statement.execute) + + assertResult(5, "Row count mismatch") { + val resultSet = statement.executeQuery("SELECT COUNT(*) FROM test") + resultSet.next() + resultSet.getInt(1) + } + } + } + + test("Checks Hive version") { + withJdbcStatement { statement => + val resultSet = statement.executeQuery("SET spark.sql.hive.version") + resultSet.next() + assert(resultSet.getString(1) === s"spark.sql.hive.version=${HiveShim.version}") + } + } + + test("SPARK-3004 regression: result set containing NULL") { + withJdbcStatement { statement => + val queries = Seq( + "DROP TABLE IF EXISTS test_null", + "CREATE TABLE test_null(key INT, val STRING)", + s"LOAD DATA LOCAL INPATH '${TestData.smallKvWithNull}' OVERWRITE INTO TABLE test_null") + + queries.foreach(statement.execute) + + val resultSet = statement.executeQuery("SELECT * FROM test_null WHERE key IS NULL") + + (0 until 5).foreach { _ => + resultSet.next() + assert(resultSet.getInt(1) === 0) + assert(resultSet.wasNull()) + } + + assert(!resultSet.next()) + } + } + + test("SPARK-4292 regression: result set iterator issue") { + withJdbcStatement { statement => + val queries = Seq( + "DROP TABLE IF EXISTS test_4292", + "CREATE TABLE test_4292(key INT, val STRING)", + s"LOAD DATA LOCAL INPATH '${TestData.smallKv}' OVERWRITE INTO TABLE test_4292") + + queries.foreach(statement.execute) + + val resultSet = statement.executeQuery("SELECT key FROM test_4292") + + Seq(238, 86, 311, 27, 165).foreach { key => + resultSet.next() + assert(resultSet.getInt(1) === key) + } + + statement.executeQuery("DROP TABLE IF EXISTS test_4292") + } + } + + test("SPARK-4309 regression: Date type support") { + withJdbcStatement { statement => + val queries = Seq( + "DROP TABLE IF EXISTS test_date", + "CREATE TABLE test_date(key INT, value STRING)", + s"LOAD DATA LOCAL INPATH '${TestData.smallKv}' OVERWRITE INTO TABLE test_date") + + queries.foreach(statement.execute) + + assertResult(Date.valueOf("2011-01-01")) { + val resultSet = statement.executeQuery( + "SELECT CAST('2011-01-01' as date) FROM test_date LIMIT 1") + resultSet.next() + resultSet.getDate(1) + } + } + } + + test("SPARK-4407 regression: Complex type support") { + withJdbcStatement { statement => + val queries = Seq( + "DROP TABLE IF EXISTS test_map", + "CREATE TABLE test_map(key INT, value STRING)", + s"LOAD DATA LOCAL INPATH '${TestData.smallKv}' OVERWRITE INTO TABLE test_map") + + queries.foreach(statement.execute) + + assertResult("""{238:"val_238"}""") { + val resultSet = statement.executeQuery("SELECT MAP(key, value) FROM test_map LIMIT 1") + resultSet.next() + resultSet.getString(1) + } + + assertResult("""["238","val_238"]""") { + val resultSet = statement.executeQuery( + "SELECT ARRAY(CAST(key AS STRING), value) FROM test_map LIMIT 1") + resultSet.next() + resultSet.getString(1) + } + } + } + + test("test multiple session") { + import org.apache.spark.sql.SQLConf + var defaultV1: String = null + var defaultV2: String = null + + withMultipleConnectionJdbcStatement( + // create table + { statement => + + val queries = Seq( + "DROP TABLE IF EXISTS test_map", + "CREATE TABLE test_map(key INT, value STRING)", + s"LOAD DATA LOCAL INPATH '${TestData.smallKv}' OVERWRITE INTO TABLE test_map", + "CACHE TABLE test_table AS SELECT key FROM test_map ORDER BY key DESC") + + queries.foreach(statement.execute) + + val rs1 = statement.executeQuery("SELECT key FROM test_table ORDER BY KEY DESC") + val buf1 = new collection.mutable.ArrayBuffer[Int]() + while (rs1.next()) { + buf1 += rs1.getInt(1) + } + rs1.close() + + val rs2 = statement.executeQuery("SELECT key FROM test_map ORDER BY KEY DESC") + val buf2 = new collection.mutable.ArrayBuffer[Int]() + while (rs2.next()) { + buf2 += rs2.getInt(1) + } + rs2.close() + + assert(buf1 === buf2) + }, + + // first session, we get the default value of the session status + { statement => + + val rs1 = statement.executeQuery(s"SET ${SQLConf.SHUFFLE_PARTITIONS}") + rs1.next() + defaultV1 = rs1.getString(1) + assert(defaultV1 != "200") + rs1.close() + + val rs2 = statement.executeQuery("SET hive.cli.print.header") + rs2.next() + + defaultV2 = rs2.getString(1) + assert(defaultV1 != "true") + rs2.close() + }, + + // second session, we update the session status + { statement => + + val queries = Seq( + s"SET ${SQLConf.SHUFFLE_PARTITIONS}=291", + "SET hive.cli.print.header=true" + ) + + queries.map(statement.execute) + val rs1 = statement.executeQuery(s"SET ${SQLConf.SHUFFLE_PARTITIONS}") + rs1.next() + assert("spark.sql.shuffle.partitions=291" === rs1.getString(1)) + rs1.close() + + val rs2 = statement.executeQuery("SET hive.cli.print.header") + rs2.next() + assert("hive.cli.print.header=true" === rs2.getString(1)) + rs2.close() + }, + + // third session, we get the latest session status, supposed to be the + // default value + { statement => + + val rs1 = statement.executeQuery(s"SET ${SQLConf.SHUFFLE_PARTITIONS}") + rs1.next() + assert(defaultV1 === rs1.getString(1)) + rs1.close() + + val rs2 = statement.executeQuery("SET hive.cli.print.header") + rs2.next() + assert(defaultV2 === rs2.getString(1)) + rs2.close() + }, + + // accessing the cached data in another session + { statement => + + val rs1 = statement.executeQuery("SELECT key FROM test_table ORDER BY KEY DESC") + val buf1 = new collection.mutable.ArrayBuffer[Int]() + while (rs1.next()) { + buf1 += rs1.getInt(1) + } + rs1.close() + + val rs2 = statement.executeQuery("SELECT key FROM test_map ORDER BY KEY DESC") + val buf2 = new collection.mutable.ArrayBuffer[Int]() + while (rs2.next()) { + buf2 += rs2.getInt(1) + } + rs2.close() + + assert(buf1 === buf2) + statement.executeQuery("UNCACHE TABLE test_table") + + // TODO need to figure out how to determine if the data loaded from cache + val rs3 = statement.executeQuery("SELECT key FROM test_map ORDER BY KEY DESC") + val buf3 = new collection.mutable.ArrayBuffer[Int]() + while (rs3.next()) { + buf3 += rs3.getInt(1) + } + rs3.close() + + assert(buf1 === buf3) + }, + + // accessing the uncached table + { statement => + + // TODO need to figure out how to determine if the data loaded from cache + val rs1 = statement.executeQuery("SELECT key FROM test_table ORDER BY KEY DESC") + val buf1 = new collection.mutable.ArrayBuffer[Int]() + while (rs1.next()) { + buf1 += rs1.getInt(1) + } + rs1.close() + + val rs2 = statement.executeQuery("SELECT key FROM test_map ORDER BY KEY DESC") + val buf2 = new collection.mutable.ArrayBuffer[Int]() + while (rs2.next()) { + buf2 += rs2.getInt(1) + } + rs2.close() + + assert(buf1 === buf2) + } + ) + } +} + +class HiveThriftHttpServerSuite extends HiveThriftJdbcTest { + override def mode: ServerMode.Value = ServerMode.http + + test("JDBC query execution") { + withJdbcStatement { statement => + val queries = Seq( + "SET spark.sql.shuffle.partitions=3", + "DROP TABLE IF EXISTS test", + "CREATE TABLE test(key INT, val STRING)", + s"LOAD DATA LOCAL INPATH '${TestData.smallKv}' OVERWRITE INTO TABLE test", + "CACHE TABLE test") + + queries.foreach(statement.execute) + + assertResult(5, "Row count mismatch") { + val resultSet = statement.executeQuery("SELECT COUNT(*) FROM test") + resultSet.next() + resultSet.getInt(1) + } + } + } + + test("Checks Hive version") { + withJdbcStatement { statement => + val resultSet = statement.executeQuery("SET spark.sql.hive.version") + resultSet.next() + assert(resultSet.getString(1) === s"spark.sql.hive.version=${HiveShim.version}") + } + } +} + +object ServerMode extends Enumeration { + val binary, http = Value +} + +abstract class HiveThriftJdbcTest extends HiveThriftServer2Test { + Class.forName(classOf[HiveDriver].getCanonicalName) + + private def jdbcUri = if (mode == ServerMode.http) { + s"""jdbc:hive2://localhost:$serverPort/ + |default? + |hive.server2.transport.mode=http; + |hive.server2.thrift.http.path=cliservice + """.stripMargin.split("\n").mkString.trim + } else { + s"jdbc:hive2://localhost:$serverPort/" + } + + def withMultipleConnectionJdbcStatement(fs: (Statement => Unit)*) { + val user = System.getProperty("user.name") + val connections = fs.map { _ => DriverManager.getConnection(jdbcUri, user, "") } + val statements = connections.map(_.createStatement()) + + try { + statements.zip(fs).map { case (s, f) => f(s) } + } finally { + statements.map(_.close()) + connections.map(_.close()) + } + } + + def withJdbcStatement(f: Statement => Unit) { + withMultipleConnectionJdbcStatement(f) + } +} + +abstract class HiveThriftServer2Test extends FunSuite with BeforeAndAfterAll with Logging { + def mode: ServerMode.Value + + private val CLASS_NAME = HiveThriftServer2.getClass.getCanonicalName.stripSuffix("$") + private val LOG_FILE_MARK = s"starting $CLASS_NAME, logging to " + + private val startScript = "../../sbin/start-thriftserver.sh".split("/").mkString(File.separator) + private val stopScript = "../../sbin/stop-thriftserver.sh".split("/").mkString(File.separator) + + private var listeningPort: Int = _ + protected def serverPort: Int = listeningPort + + protected def user = System.getProperty("user.name") + + private var warehousePath: File = _ + private var metastorePath: File = _ + private def metastoreJdbcUri = s"jdbc:derby:;databaseName=$metastorePath;create=true" + + private val pidDir: File = Utils.createTempDir("thriftserver-pid") + private var logPath: File = _ + private var logTailingProcess: Process = _ + private var diagnosisBuffer: ArrayBuffer[String] = ArrayBuffer.empty[String] + + private def serverStartCommand(port: Int) = { + val portConf = if (mode == ServerMode.binary) { + ConfVars.HIVE_SERVER2_THRIFT_PORT + } else { + ConfVars.HIVE_SERVER2_THRIFT_HTTP_PORT + } + + s"""$startScript + | --master local + | --hiveconf hive.root.logger=INFO,console + | --hiveconf ${ConfVars.METASTORECONNECTURLKEY}=$metastoreJdbcUri + | --hiveconf ${ConfVars.METASTOREWAREHOUSE}=$warehousePath + | --hiveconf ${ConfVars.HIVE_SERVER2_THRIFT_BIND_HOST}=localhost + | --hiveconf ${ConfVars.HIVE_SERVER2_TRANSPORT_MODE}=$mode + | --hiveconf $portConf=$port + | --driver-class-path ${sys.props("java.class.path")} + | --conf spark.ui.enabled=false + """.stripMargin.split("\\s+").toSeq + } + + private def startThriftServer(port: Int, attempt: Int) = { + warehousePath = Utils.createTempDir() + warehousePath.delete() + metastorePath = Utils.createTempDir() + metastorePath.delete() + logPath = null + logTailingProcess = null + + val command = serverStartCommand(port) + + diagnosisBuffer ++= + s""" + |### Attempt $attempt ### + |HiveThriftServer2 command line: $command + |Listening port: $port + |System user: $user + """.stripMargin.split("\n") + + logInfo(s"Trying to start HiveThriftServer2: port=$port, mode=$mode, attempt=$attempt") + + val env = Seq( + // Disables SPARK_TESTING to exclude log4j.properties in test directories. + "SPARK_TESTING" -> "0", + // Points SPARK_PID_DIR to SPARK_HOME, otherwise only 1 Thrift server instance can be started + // at a time, which is not Jenkins friendly. + "SPARK_PID_DIR" -> pidDir.getCanonicalPath) + + logPath = Process(command, None, env: _*).lines.collectFirst { + case line if line.contains(LOG_FILE_MARK) => new File(line.drop(LOG_FILE_MARK.length)) + }.getOrElse { + throw new RuntimeException("Failed to find HiveThriftServer2 log file.") + } + + val serverStarted = Promise[Unit]() + + // Ensures that the following "tail" command won't fail. + logPath.createNewFile() + logTailingProcess = + // Using "-n +0" to make sure all lines in the log file are checked. + Process(s"/usr/bin/env tail -n +0 -f ${logPath.getCanonicalPath}").run(ProcessLogger( + (line: String) => { + diagnosisBuffer += line + + if (line.contains("ThriftBinaryCLIService listening on") || + line.contains("Started ThriftHttpCLIService in http")) { + serverStarted.trySuccess(()) + } else if (line.contains("HiveServer2 is stopped")) { + // This log line appears when the server fails to start and terminates gracefully (e.g. + // because of port contention). + serverStarted.tryFailure(new RuntimeException("Failed to start HiveThriftServer2")) + } + })) + + Await.result(serverStarted.future, 2.minute) + } + + private def stopThriftServer(): Unit = { + // The `spark-daemon.sh' script uses kill, which is not synchronous, have to wait for a while. + Process(stopScript, None, "SPARK_PID_DIR" -> pidDir.getCanonicalPath).run().exitValue() + Thread.sleep(3.seconds.toMillis) + + warehousePath.delete() + warehousePath = null + + metastorePath.delete() + metastorePath = null + + Option(logPath).foreach(_.delete()) + logPath = null + + Option(logTailingProcess).foreach(_.destroy()) + logTailingProcess = null + } + + private def dumpLogs(): Unit = { + logError( + s""" + |===================================== + |HiveThriftServer2Suite failure output + |===================================== + |${diagnosisBuffer.mkString("\n")} + |========================================= + |End HiveThriftServer2Suite failure output + |========================================= + """.stripMargin) + } + + override protected def beforeAll(): Unit = { + // Chooses a random port between 10000 and 19999 + listeningPort = 10000 + Random.nextInt(10000) + diagnosisBuffer.clear() + + // Retries up to 3 times with different port numbers if the server fails to start + (1 to 3).foldLeft(Try(startThriftServer(listeningPort, 0))) { case (started, attempt) => + started.orElse { + listeningPort += 1 + stopThriftServer() + Try(startThriftServer(listeningPort, attempt)) + } + }.recover { + case cause: Throwable => + dumpLogs() + throw cause + }.get + + logInfo(s"HiveThriftServer2 started successfully") + } + + override protected def afterAll(): Unit = { + stopThriftServer() + logInfo("HiveThriftServer2 stopped") + } +} diff --git a/sql/hive-thriftserver/v0.12.0/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim12.scala b/sql/hive-thriftserver/v0.12.0/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim12.scala index ea9d61d8d0f5e..95a6e86d0546d 100644 --- a/sql/hive-thriftserver/v0.12.0/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim12.scala +++ b/sql/hive-thriftserver/v0.12.0/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim12.scala @@ -18,8 +18,15 @@ package org.apache.spark.sql.hive.thriftserver import java.sql.{Date, Timestamp} +import java.util.concurrent.Executors import java.util.{ArrayList => JArrayList, Map => JMap} +import org.apache.commons.logging.Log +import org.apache.hadoop.hive.conf.HiveConf +import org.apache.hadoop.hive.conf.HiveConf.ConfVars +import org.apache.hive.service.cli.thrift.TProtocolVersion +import org.apache.spark.sql.hive.thriftserver.server.SparkSQLOperationManager + import scala.collection.JavaConversions._ import scala.collection.mutable.{ArrayBuffer, Map => SMap} @@ -29,7 +36,7 @@ import org.apache.hadoop.hive.shims.ShimLoader import org.apache.hadoop.security.UserGroupInformation import org.apache.hive.service.cli._ import org.apache.hive.service.cli.operation.ExecuteStatementOperation -import org.apache.hive.service.cli.session.HiveSession +import org.apache.hive.service.cli.session.{SessionManager, HiveSession} import org.apache.spark.Logging import org.apache.spark.sql.{DataFrame, SQLConf, Row => SparkRow} @@ -185,6 +192,10 @@ private[hive] class SparkExecuteStatementOperation( def run(): Unit = { logInfo(s"Running query '$statement'") setState(OperationState.RUNNING) + hiveContext.sparkContext.setJobDescription(statement) + sessionToActivePool.get(parentSession.getSessionHandle).foreach { pool => + hiveContext.sparkContext.setLocalProperty("spark.scheduler.pool", pool) + } try { result = hiveContext.sql(statement) logDebug(result.queryExecution.toString()) @@ -194,10 +205,6 @@ private[hive] class SparkExecuteStatementOperation( logInfo(s"Setting spark.scheduler.pool=$value for future statements in this session.") case _ => } - hiveContext.sparkContext.setJobDescription(statement) - sessionToActivePool.get(parentSession.getSessionHandle).foreach { pool => - hiveContext.sparkContext.setLocalProperty("spark.scheduler.pool", pool) - } iter = { val useIncrementalCollect = hiveContext.getConf("spark.sql.thriftServer.incrementalCollect", "false").toBoolean @@ -220,3 +227,42 @@ private[hive] class SparkExecuteStatementOperation( setState(OperationState.FINISHED) } } + +private[hive] class SparkSQLSessionManager(hiveContext: HiveContext) + extends SessionManager + with ReflectedCompositeService { + + private lazy val sparkSqlOperationManager = new SparkSQLOperationManager(hiveContext) + + override def init(hiveConf: HiveConf) { + setSuperField(this, "hiveConf", hiveConf) + + val backgroundPoolSize = hiveConf.getIntVar(ConfVars.HIVE_SERVER2_ASYNC_EXEC_THREADS) + setSuperField(this, "backgroundOperationPool", Executors.newFixedThreadPool(backgroundPoolSize)) + getAncestorField[Log](this, 3, "LOG").info( + s"HiveServer2: Async execution pool size $backgroundPoolSize") + + setSuperField(this, "operationManager", sparkSqlOperationManager) + addService(sparkSqlOperationManager) + + initCompositeService(hiveConf) + } + + override def openSession( + username: String, + passwd: String, + sessionConf: java.util.Map[String, String], + withImpersonation: Boolean, + delegationToken: String): SessionHandle = { + hiveContext.openSession() + + super.openSession(username, passwd, sessionConf, withImpersonation, delegationToken) + } + + override def closeSession(sessionHandle: SessionHandle) { + super.closeSession(sessionHandle) + sparkSqlOperationManager.sessionToActivePool -= sessionHandle + + hiveContext.detachSession() + } +} diff --git a/sql/hive-thriftserver/v0.13.1/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim13.scala b/sql/hive-thriftserver/v0.13.1/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim13.scala index 71e3954b2c7ac..178eb1af7cdcd 100644 --- a/sql/hive-thriftserver/v0.13.1/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim13.scala +++ b/sql/hive-thriftserver/v0.13.1/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim13.scala @@ -18,8 +18,15 @@ package org.apache.spark.sql.hive.thriftserver import java.sql.{Date, Timestamp} +import java.util.concurrent.Executors import java.util.{ArrayList => JArrayList, List => JList, Map => JMap} +import org.apache.commons.logging.Log +import org.apache.hadoop.hive.conf.HiveConf +import org.apache.hadoop.hive.conf.HiveConf.ConfVars +import org.apache.hive.service.cli.thrift.TProtocolVersion +import org.apache.spark.sql.hive.thriftserver.server.SparkSQLOperationManager + import scala.collection.JavaConversions._ import scala.collection.mutable.{ArrayBuffer, Map => SMap} @@ -27,7 +34,7 @@ import org.apache.hadoop.hive.metastore.api.FieldSchema import org.apache.hadoop.security.UserGroupInformation import org.apache.hive.service.cli._ import org.apache.hive.service.cli.operation.ExecuteStatementOperation -import org.apache.hive.service.cli.session.HiveSession +import org.apache.hive.service.cli.session.{SessionManager, HiveSession} import org.apache.spark.Logging import org.apache.spark.sql.{DataFrame, Row => SparkRow, SQLConf} @@ -156,6 +163,10 @@ private[hive] class SparkExecuteStatementOperation( def run(): Unit = { logInfo(s"Running query '$statement'") setState(OperationState.RUNNING) + hiveContext.sparkContext.setJobDescription(statement) + sessionToActivePool.get(parentSession.getSessionHandle).foreach { pool => + hiveContext.sparkContext.setLocalProperty("spark.scheduler.pool", pool) + } try { result = hiveContext.sql(statement) logDebug(result.queryExecution.toString()) @@ -165,10 +176,6 @@ private[hive] class SparkExecuteStatementOperation( logInfo(s"Setting spark.scheduler.pool=$value for future statements in this session.") case _ => } - hiveContext.sparkContext.setJobDescription(statement) - sessionToActivePool.get(parentSession.getSessionHandle).foreach { pool => - hiveContext.sparkContext.setLocalProperty("spark.scheduler.pool", pool) - } iter = { val useIncrementalCollect = hiveContext.getConf("spark.sql.thriftServer.incrementalCollect", "false").toBoolean @@ -191,3 +198,43 @@ private[hive] class SparkExecuteStatementOperation( setState(OperationState.FINISHED) } } + +private[hive] class SparkSQLSessionManager(hiveContext: HiveContext) + extends SessionManager + with ReflectedCompositeService { + + private lazy val sparkSqlOperationManager = new SparkSQLOperationManager(hiveContext) + + override def init(hiveConf: HiveConf) { + setSuperField(this, "hiveConf", hiveConf) + + val backgroundPoolSize = hiveConf.getIntVar(ConfVars.HIVE_SERVER2_ASYNC_EXEC_THREADS) + setSuperField(this, "backgroundOperationPool", Executors.newFixedThreadPool(backgroundPoolSize)) + getAncestorField[Log](this, 3, "LOG").info( + s"HiveServer2: Async execution pool size $backgroundPoolSize") + + setSuperField(this, "operationManager", sparkSqlOperationManager) + addService(sparkSqlOperationManager) + + initCompositeService(hiveConf) + } + + override def openSession( + protocol: TProtocolVersion, + username: String, + passwd: String, + sessionConf: java.util.Map[String, String], + withImpersonation: Boolean, + delegationToken: String): SessionHandle = { + hiveContext.openSession() + + super.openSession(protocol, username, passwd, sessionConf, withImpersonation, delegationToken) + } + + override def closeSession(sessionHandle: SessionHandle) { + super.closeSession(sessionHandle) + sparkSqlOperationManager.sessionToActivePool -= sessionHandle + + hiveContext.detachSession() + } +} diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala index a6266f611c219..81ee48ef4152f 100644 --- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala +++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala @@ -225,13 +225,22 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter { // Needs constant object inspectors "udf_round", + // the table src(key INT, value STRING) is not the same as HIVE unittest. In Hive + // is src(key STRING, value STRING), and in the reflect.q, it failed in + // Integer.valueOf, which expect the first argument passed as STRING type not INT. + "udf_reflect", + // Sort with Limit clause causes failure. "ctas", "ctas_hadoop20", // timestamp in array, the output format of Hive contains double quotes, while // Spark SQL doesn't - "udf_sort_array" + "udf_sort_array", + + // It has a bug and it has been fixed by + // https://issues.apache.org/jira/browse/HIVE-7673 (in Hive 0.14 and trunk). + "input46" ) ++ HiveShim.compatibilityBlackList /** @@ -518,10 +527,12 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter { "inputddl2", "inputddl3", "inputddl4", + "inputddl5", "inputddl6", "inputddl7", "inputddl8", "insert1", + "insert1_overwrite_partitions", "insert2_overwrite_partitions", "insert_compressed", "join0", @@ -626,6 +637,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter { "mapreduce8", "merge1", "merge2", + "merge4", "mergejoins", "multiMapJoin1", "multiMapJoin2", @@ -639,6 +651,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter { "nonblock_op_deduplicate", "notable_alias1", "notable_alias2", + "nullformatCTAS", "nullgroup", "nullgroup2", "nullgroup3", @@ -718,6 +731,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter { "select_unquote_and", "select_unquote_not", "select_unquote_or", + "semicolon", "semijoin", "serde_regex", "serde_reported_schema", @@ -787,6 +801,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter { "udaf_covar_pop", "udaf_covar_samp", "udaf_histogram_numeric", + "udaf_number_format", "udf2", "udf5", "udf6", @@ -884,6 +899,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter { "udf_power", "udf_radians", "udf_rand", + "udf_reflect2", "udf_regexp", "udf_regexp_extract", "udf_regexp_replace", diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/SortMergeCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/SortMergeCompatibilitySuite.scala new file mode 100644 index 0000000000000..65d070bd3cbde --- /dev/null +++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/SortMergeCompatibilitySuite.scala @@ -0,0 +1,162 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.execution + +import org.apache.spark.sql.SQLConf +import org.apache.spark.sql.hive.test.TestHive + +/** + * Runs the test cases that are included in the hive distribution with sort merge join is true. + */ +class SortMergeCompatibilitySuite extends HiveCompatibilitySuite { + override def beforeAll() { + super.beforeAll() + TestHive.setConf(SQLConf.SORTMERGE_JOIN, "true") + } + + override def afterAll() { + TestHive.setConf(SQLConf.SORTMERGE_JOIN, "false") + super.afterAll() + } + + override def whiteList = Seq( + "auto_join0", + "auto_join1", + "auto_join10", + "auto_join11", + "auto_join12", + "auto_join13", + "auto_join14", + "auto_join14_hadoop20", + "auto_join15", + "auto_join17", + "auto_join18", + "auto_join19", + "auto_join2", + "auto_join20", + "auto_join21", + "auto_join22", + "auto_join23", + "auto_join24", + "auto_join25", + "auto_join26", + "auto_join27", + "auto_join28", + "auto_join3", + "auto_join30", + "auto_join31", + "auto_join32", + "auto_join4", + "auto_join5", + "auto_join6", + "auto_join7", + "auto_join8", + "auto_join9", + "auto_join_filters", + "auto_join_nulls", + "auto_join_reordering_values", + "auto_smb_mapjoin_14", + "auto_sortmerge_join_1", + "auto_sortmerge_join_10", + "auto_sortmerge_join_11", + "auto_sortmerge_join_12", + "auto_sortmerge_join_13", + "auto_sortmerge_join_14", + "auto_sortmerge_join_15", + "auto_sortmerge_join_16", + "auto_sortmerge_join_2", + "auto_sortmerge_join_3", + "auto_sortmerge_join_4", + "auto_sortmerge_join_5", + "auto_sortmerge_join_6", + "auto_sortmerge_join_7", + "auto_sortmerge_join_8", + "auto_sortmerge_join_9", + "correlationoptimizer1", + "correlationoptimizer10", + "correlationoptimizer11", + "correlationoptimizer13", + "correlationoptimizer14", + "correlationoptimizer15", + "correlationoptimizer2", + "correlationoptimizer3", + "correlationoptimizer4", + "correlationoptimizer6", + "correlationoptimizer7", + "correlationoptimizer8", + "correlationoptimizer9", + "join0", + "join1", + "join10", + "join11", + "join12", + "join13", + "join14", + "join14_hadoop20", + "join15", + "join16", + "join17", + "join18", + "join19", + "join2", + "join20", + "join21", + "join22", + "join23", + "join24", + "join25", + "join26", + "join27", + "join28", + "join29", + "join3", + "join30", + "join31", + "join32", + "join32_lessSize", + "join33", + "join34", + "join35", + "join36", + "join37", + "join38", + "join39", + "join4", + "join40", + "join41", + "join5", + "join6", + "join7", + "join8", + "join9", + "join_1to1", + "join_array", + "join_casesensitive", + "join_empty", + "join_filters", + "join_hive_626", + "join_map_ppr", + "join_nulls", + "join_nullsafe", + "join_rc", + "join_reorder2", + "join_reorder3", + "join_reorder4", + "join_star" + ) +} diff --git a/sql/hive/pom.xml b/sql/hive/pom.xml index 58b0722464be8..04440076a26a3 100644 --- a/sql/hive/pom.xml +++ b/sql/hive/pom.xml @@ -21,8 +21,8 @@ 4.0.0 org.apache.spark - spark-parent - 1.3.0-SNAPSHOT + spark-parent_2.10 + 1.4.0-SNAPSHOT ../../pom.xml @@ -84,6 +84,25 @@ scalacheck_${scala.binary.version} test + + junit + junit + test + + + org.apache.spark + spark-sql_${scala.binary.version} + test-jar + ${project.version} + test + + + org.apache.spark + spark-catalyst_${scala.binary.version} + test-jar + ${project.version} + test + diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala index ad37b7d0e6f59..7c6a7df2bd01e 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala @@ -22,26 +22,24 @@ import java.sql.Timestamp import scala.collection.JavaConversions._ import scala.language.implicitConversions -import scala.reflect.runtime.universe.TypeTag import org.apache.hadoop.fs.{FileSystem, Path} import org.apache.hadoop.hive.conf.HiveConf import org.apache.hadoop.hive.ql.Driver import org.apache.hadoop.hive.ql.metadata.Table -import org.apache.hadoop.hive.ql.processors._ import org.apache.hadoop.hive.ql.parse.VariableSubstitution +import org.apache.hadoop.hive.ql.processors._ import org.apache.hadoop.hive.ql.session.SessionState import org.apache.hadoop.hive.serde2.io.{DateWritable, TimestampWritable} import org.apache.spark.SparkContext import org.apache.spark.annotation.Experimental import org.apache.spark.sql._ -import org.apache.spark.sql.catalyst.ScalaReflection -import org.apache.spark.sql.catalyst.analysis.{Analyzer, EliminateAnalysisOperators, OverrideCatalog, OverrideFunctionRegistry} +import org.apache.spark.sql.catalyst.analysis.{Analyzer, EliminateSubQueries, OverrideCatalog, OverrideFunctionRegistry} import org.apache.spark.sql.catalyst.plans.logical._ -import org.apache.spark.sql.execution.{ExecutedCommand, ExtractPythonUdfs, SetCommand, QueryExecutionException} -import org.apache.spark.sql.hive.execution.{HiveNativeCommand, DescribeHiveTableCommand} -import org.apache.spark.sql.sources.{CreateTableUsing, DataSourceStrategy} +import org.apache.spark.sql.execution.{ExecutedCommand, ExtractPythonUdfs, QueryExecutionException, SetCommand} +import org.apache.spark.sql.hive.execution.{DescribeHiveTableCommand, HiveNativeCommand} +import org.apache.spark.sql.sources.{DDLParser, DataSourceStrategy} import org.apache.spark.sql.types._ /** @@ -51,10 +49,6 @@ import org.apache.spark.sql.types._ class HiveContext(sc: SparkContext) extends SQLContext(sc) { self => - protected[sql] override lazy val conf: SQLConf = new SQLConf { - override def dialect: String = getConf(SQLConf.DIALECT, "hiveql") - } - /** * When true, enables an experimental feature where metastore tables that use the parquet SerDe * are automatically converted to use the Spark SQL parquet table scan, instead of the Hive @@ -63,34 +57,49 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { protected[sql] def convertMetastoreParquet: Boolean = getConf("spark.sql.hive.convertMetastoreParquet", "true") == "true" + /** + * When true, also tries to merge possibly different but compatible Parquet schemas in different + * Parquet data files. + * + * This configuration is only effective when "spark.sql.hive.convertMetastoreParquet" is true. + */ + protected[sql] def convertMetastoreParquetWithSchemaMerging: Boolean = + getConf("spark.sql.hive.convertMetastoreParquet.mergeSchema", "false") == "true" + + /** + * When true, a table created by a Hive CTAS statement (no USING clause) will be + * converted to a data source table, using the data source set by spark.sql.sources.default. + * The table in CTAS statement will be converted when it meets any of the following conditions: + * - The CTAS does not specify any of a SerDe (ROW FORMAT SERDE), a File Format (STORED AS), or + * a Storage Hanlder (STORED BY), and the value of hive.default.fileformat in hive-site.xml + * is either TextFile or SequenceFile. + * - The CTAS statement specifies TextFile (STORED AS TEXTFILE) as the file format and no SerDe + * is specified (no ROW FORMAT SERDE clause). + * - The CTAS statement specifies SequenceFile (STORED AS SEQUENCEFILE) as the file format + * and no SerDe is specified (no ROW FORMAT SERDE clause). + */ + protected[sql] def convertCTAS: Boolean = + getConf("spark.sql.hive.convertCTAS", "false").toBoolean + override protected[sql] def executePlan(plan: LogicalPlan): this.QueryExecution = new this.QueryExecution(plan) + @transient + protected[sql] val ddlParserWithHiveQL = new DDLParser(HiveQl.parseSql(_)) + override def sql(sqlText: String): DataFrame = { val substituted = new VariableSubstitution().substitute(hiveconf, sqlText) // TODO: Create a framework for registering parsers instead of just hardcoding if statements. if (conf.dialect == "sql") { super.sql(substituted) } else if (conf.dialect == "hiveql") { - DataFrame(this, - ddlParser(sqlText, exceptionOnError = false).getOrElse(HiveQl.parseSql(substituted))) + val ddlPlan = ddlParserWithHiveQL(sqlText, exceptionOnError = false) + DataFrame(this, ddlPlan.getOrElse(HiveQl.parseSql(substituted))) } else { sys.error(s"Unsupported SQL dialect: ${conf.dialect}. Try 'sql' or 'hiveql'") } } - /** - * Creates a table using the schema of the given class. - * - * @param tableName The name of the table to create. - * @param allowExisting When false, an exception will be thrown if the table already exists. - * @tparam A A case class that is used to describe the schema of the table to be created. - */ - @Deprecated - def createTable[A <: Product : TypeTag](tableName: String, allowExisting: Boolean = true) { - catalog.createTable("default", tableName, ScalaReflection.attributesFor[A], allowExisting) - } - /** * Invalidate and refresh all the cached the metadata of the given table. For performance reasons, * Spark SQL or the external data source library it uses might cache certain metadata about a @@ -107,70 +116,6 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { catalog.invalidateTable("default", tableName) } - @Experimental - def createTable(tableName: String, path: String, allowExisting: Boolean): Unit = { - val dataSourceName = conf.defaultDataSourceName - createTable(tableName, dataSourceName, allowExisting, ("path", path)) - } - - @Experimental - def createTable( - tableName: String, - dataSourceName: String, - allowExisting: Boolean, - option: (String, String), - options: (String, String)*): Unit = { - val cmd = - CreateTableUsing( - tableName, - userSpecifiedSchema = None, - dataSourceName, - temporary = false, - (option +: options).toMap, - allowExisting) - executePlan(cmd).toRdd - } - - @Experimental - def createTable( - tableName: String, - dataSourceName: String, - schema: StructType, - allowExisting: Boolean, - option: (String, String), - options: (String, String)*): Unit = { - val cmd = - CreateTableUsing( - tableName, - userSpecifiedSchema = Some(schema), - dataSourceName, - temporary = false, - (option +: options).toMap, - allowExisting) - executePlan(cmd).toRdd - } - - @Experimental - def createTable( - tableName: String, - dataSourceName: String, - allowExisting: Boolean, - options: java.util.Map[String, String]): Unit = { - val opts = options.toSeq - createTable(tableName, dataSourceName, allowExisting, opts.head, opts.tail:_*) - } - - @Experimental - def createTable( - tableName: String, - dataSourceName: String, - schema: StructType, - allowExisting: Boolean, - options: java.util.Map[String, String]): Unit = { - val opts = options.toSeq - createTable(tableName, dataSourceName, schema, allowExisting, opts.head, opts.tail:_*) - } - /** * Analyzes the given table in the current database to generate statistics, which will be * used in query optimizations. @@ -180,7 +125,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { */ @Experimental def analyze(tableName: String) { - val relation = EliminateAnalysisOperators(catalog.lookupRelation(Seq(tableName))) + val relation = EliminateSubQueries(catalog.lookupRelation(Seq(tableName))) relation match { case relation: MetastoreRelation => @@ -236,18 +181,19 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { val tableFullName = relation.hiveQlTable.getDbName + "." + relation.hiveQlTable.getTableName - catalog.client.alterTable(tableFullName, new Table(hiveTTable)) + catalog.synchronized { + catalog.client.alterTable(tableFullName, new Table(hiveTTable)) + } } case otherRelation => - throw new NotImplementedError( - s"Analyze has only implemented for Hive tables, " + - s"but $tableName is a ${otherRelation.nodeName}") + throw new UnsupportedOperationException( + s"Analyze only works for Hive tables, but $tableName is a ${otherRelation.nodeName}") } } // Circular buffer to hold what hive prints to STDOUT and ERR. Only printed when failures occur. @transient - protected lazy val outputBuffer = new java.io.OutputStream { + protected lazy val outputBuffer = new java.io.OutputStream { var pos: Int = 0 var buffer = new Array[Int](10240) def write(i: Int): Unit = { @@ -255,7 +201,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { pos = (pos + 1) % buffer.size } - override def toString = { + override def toString: String = { val (end, start) = buffer.splitAt(pos) val input = new java.io.InputStream { val iterator = (start ++ end).iterator @@ -274,30 +220,9 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { } } - /** - * SQLConf and HiveConf contracts: - * - * 1. reuse existing started SessionState if any - * 2. when the Hive session is first initialized, params in HiveConf will get picked up by the - * SQLConf. Additionally, any properties set by set() or a SET command inside sql() will be - * set in the SQLConf *as well as* in the HiveConf. - */ - @transient protected[hive] lazy val (hiveconf, sessionState) = - Option(SessionState.get()) - .orElse { - val newState = new SessionState(new HiveConf(classOf[SessionState])) - // Only starts newly created `SessionState` instance. Any existing `SessionState` instance - // returned by `SessionState.get()` must be the most recently started one. - SessionState.start(newState) - Some(newState) - } - .map { state => - setConf(state.getConf.getAllProperties) - if (state.out == null) state.out = new PrintStream(outputBuffer, true, "UTF-8") - if (state.err == null) state.err = new PrintStream(outputBuffer, true, "UTF-8") - (state.getConf, state) - } - .get + protected[hive] def sessionState = tlSession.get().asInstanceOf[this.SQLSession].sessionState + + protected[hive] def hiveconf = tlSession.get().asInstanceOf[this.SQLSession].hiveconf override def setConf(key: String, value: String): Unit = { super.setConf(key, value) @@ -312,14 +237,15 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { @transient override protected[sql] lazy val functionRegistry = new HiveFunctionRegistry with OverrideFunctionRegistry { - def caseSensitive = false + def caseSensitive: Boolean = false } /* An analyzer that uses the Hive metastore. */ @transient override protected[sql] lazy val analyzer = new Analyzer(catalog, functionRegistry, caseSensitive = false) { - override val extendedRules = + override val extendedResolutionRules = + catalog.ParquetConversions :: catalog.CreateTables :: catalog.PreInsertionCasts :: ExtractPythonUdfs :: @@ -328,6 +254,44 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { Nil } + override protected[sql] def createSession(): SQLSession = { + new this.SQLSession() + } + + protected[hive] class SQLSession extends super.SQLSession { + protected[sql] override lazy val conf: SQLConf = new SQLConf { + override def dialect: String = getConf(SQLConf.DIALECT, "hiveql") + } + + protected[hive] lazy val hiveconf: HiveConf = { + setConf(sessionState.getConf.getAllProperties) + sessionState.getConf + } + + /** + * SQLConf and HiveConf contracts: + * + * 1. reuse existing started SessionState if any + * 2. when the Hive session is first initialized, params in HiveConf will get picked up by the + * SQLConf. Additionally, any properties set by set() or a SET command inside sql() will be + * set in the SQLConf *as well as* in the HiveConf. + */ + protected[hive] lazy val sessionState: SessionState = { + var state = SessionState.get() + if (state == null) { + state = new SessionState(new HiveConf(classOf[SessionState])) + SessionState.start(state) + } + if (state.out == null) { + state.out = new PrintStream(outputBuffer, true, "UTF-8") + } + if (state.err == null) { + state.err = new PrintStream(outputBuffer, true, "UTF-8") + } + state + } + } + /** * Runs the specified SQL query using Hive. */ @@ -424,6 +388,11 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { /** Extends QueryExecution with hive specific features. */ protected[sql] class QueryExecution(logicalPlan: LogicalPlan) extends super.QueryExecution(logicalPlan) { + // Like what we do in runHive, makes sure the session represented by the + // `sessionState` field is activated. + if (SessionState.get() != sessionState) { + SessionState.start(sessionState) + } /** * Returns the result as a hive compatible sequence of strings. For native commands, the diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala index 4afa2e71d77cc..74ae984f34866 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala @@ -34,7 +34,7 @@ import scala.collection.JavaConversions._ * 1. The Underlying data type in catalyst and in Hive * In catalyst: * Primitive => - * java.lang.String + * UTF8String * int / scala.Int * boolean / scala.Boolean * float / scala.Float @@ -239,9 +239,10 @@ private[hive] trait HiveInspectors { */ def unwrap(data: Any, oi: ObjectInspector): Any = oi match { case coi: ConstantObjectInspector if coi.getWritableConstantValue == null => null - case poi: WritableConstantStringObjectInspector => poi.getWritableConstantValue.toString + case poi: WritableConstantStringObjectInspector => + UTF8String(poi.getWritableConstantValue.toString) case poi: WritableConstantHiveVarcharObjectInspector => - poi.getWritableConstantValue.getHiveVarchar.getValue + UTF8String(poi.getWritableConstantValue.getHiveVarchar.getValue) case poi: WritableConstantHiveDecimalObjectInspector => HiveShim.toCatalystDecimal( PrimitiveObjectInspectorFactory.javaHiveDecimalObjectInspector, @@ -284,10 +285,13 @@ private[hive] trait HiveInspectors { case pi: PrimitiveObjectInspector => pi match { // We think HiveVarchar is also a String case hvoi: HiveVarcharObjectInspector if hvoi.preferWritable() => - hvoi.getPrimitiveWritableObject(data).getHiveVarchar.getValue - case hvoi: HiveVarcharObjectInspector => hvoi.getPrimitiveJavaObject(data).getValue + UTF8String(hvoi.getPrimitiveWritableObject(data).getHiveVarchar.getValue) + case hvoi: HiveVarcharObjectInspector => + UTF8String(hvoi.getPrimitiveJavaObject(data).getValue) case x: StringObjectInspector if x.preferWritable() => - x.getPrimitiveWritableObject(data).toString + UTF8String(x.getPrimitiveWritableObject(data).toString) + case x: StringObjectInspector => + UTF8String(x.getPrimitiveJavaObject(data)) case x: IntObjectInspector if x.preferWritable() => x.get(data) case x: BooleanObjectInspector if x.preferWritable() => x.get(data) case x: FloatObjectInspector if x.preferWritable() => x.get(data) @@ -340,7 +344,9 @@ private[hive] trait HiveInspectors { */ protected def wrapperFor(oi: ObjectInspector): Any => Any = oi match { case _: JavaHiveVarcharObjectInspector => - (o: Any) => new HiveVarchar(o.asInstanceOf[String], o.asInstanceOf[String].size) + (o: Any) => + val s = o.asInstanceOf[UTF8String].toString + new HiveVarchar(s, s.size) case _: JavaHiveDecimalObjectInspector => (o: Any) => HiveShim.createDecimal(o.asInstanceOf[Decimal].toJavaBigDecimal) @@ -409,7 +415,7 @@ private[hive] trait HiveInspectors { case x: PrimitiveObjectInspector => x match { // TODO we don't support the HiveVarcharObjectInspector yet. case _: StringObjectInspector if x.preferWritable() => HiveShim.getStringWritable(a) - case _: StringObjectInspector => a.asInstanceOf[java.lang.String] + case _: StringObjectInspector => a.asInstanceOf[UTF8String].toString() case _: IntObjectInspector if x.preferWritable() => HiveShim.getIntWritable(a) case _: IntObjectInspector => a.asInstanceOf[java.lang.Integer] case _: BooleanObjectInspector if x.preferWritable() => HiveShim.getBooleanWritable(a) @@ -593,7 +599,7 @@ private[hive] trait HiveInspectors { case Literal(_, dt) => sys.error(s"Hive doesn't support the constant type [$dt].") // ideally, we don't test the foldable here(but in optimizer), however, some of the // Hive UDF / UDAF requires its argument to be constant objectinspector, we do it eagerly. - case _ if expr.foldable => toInspector(Literal(expr.eval(), expr.dataType)) + case _ if expr.foldable => toInspector(Literal.create(expr.eval(), expr.dataType)) // For those non constant expression, map to object inspector according to its data type case _ => toInspector(expr.dataType) } diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala index c78369d12cf55..f1c0bd92aa23d 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala @@ -20,26 +20,27 @@ package org.apache.spark.sql.hive import java.io.IOException import java.util.{List => JList} -import com.google.common.cache.{LoadingCache, CacheLoader, CacheBuilder} - -import org.apache.hadoop.util.ReflectionUtils -import org.apache.hadoop.hive.metastore.{Warehouse, TableType} -import org.apache.hadoop.hive.metastore.api.{Table => TTable, Partition => TPartition, FieldSchema} +import com.google.common.base.Objects +import com.google.common.cache.{CacheBuilder, CacheLoader, LoadingCache} +import org.apache.hadoop.hive.metastore.api.{FieldSchema, Partition => TPartition, Table => TTable} +import org.apache.hadoop.hive.metastore.{TableType, Warehouse} import org.apache.hadoop.hive.ql.metadata._ import org.apache.hadoop.hive.ql.plan.CreateTableDesc import org.apache.hadoop.hive.serde.serdeConstants -import org.apache.hadoop.hive.serde2.{Deserializer, SerDeException} import org.apache.hadoop.hive.serde2.`lazy`.LazySimpleSerDe +import org.apache.hadoop.hive.serde2.{Deserializer, SerDeException} +import org.apache.hadoop.util.ReflectionUtils import org.apache.spark.Logging -import org.apache.spark.sql.SQLContext -import org.apache.spark.sql.catalyst.analysis.{Catalog, OverrideCatalog} +import org.apache.spark.sql.{SaveMode, AnalysisException, SQLContext} +import org.apache.spark.sql.catalyst.analysis.{MultiInstanceRelation, NoSuchTableException, Catalog, OverrideCatalog} import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.catalyst.planning.PhysicalOperation import org.apache.spark.sql.catalyst.plans.logical import org.apache.spark.sql.catalyst.plans.logical._ import org.apache.spark.sql.catalyst.rules._ -import org.apache.spark.sql.parquet.ParquetRelation2 -import org.apache.spark.sql.sources.{DDLParser, LogicalRelation, ResolvedDataSource} +import org.apache.spark.sql.parquet.{ParquetRelation2, Partition => ParquetPartition, PartitionSpec} +import org.apache.spark.sql.sources.{CreateTableUsingAsSelect, DDLParser, LogicalRelation, ResolvedDataSource} import org.apache.spark.sql.types._ import org.apache.spark.util.Utils @@ -52,12 +53,13 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with /** Connection to hive metastore. Usages should lock on `this`. */ protected[hive] val client = Hive.get(hive.hiveconf) + /** Usages should lock on `this`. */ protected[hive] lazy val hiveWarehouse = new Warehouse(hive.hiveconf) // TODO: Use this everywhere instead of tuples or databaseName, tableName,. /** A fully qualified identifier for a table (i.e., database.tableName) */ case class QualifiedTableName(database: String, name: String) { - def toLowerCase = QualifiedTableName(database.toLowerCase, name.toLowerCase) + def toLowerCase: QualifiedTableName = QualifiedTableName(database.toLowerCase, name.toLowerCase) } /** A cache of Spark SQL data source tables that have been accessed. */ @@ -65,14 +67,36 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with val cacheLoader = new CacheLoader[QualifiedTableName, LogicalPlan]() { override def load(in: QualifiedTableName): LogicalPlan = { logDebug(s"Creating new cached data source for $in") - val table = client.getTable(in.database, in.name) - val schemaString = table.getProperty("spark.sql.sources.schema") - val userSpecifiedSchema = - if (schemaString == null) { - None - } else { - Some(DataType.fromJson(schemaString).asInstanceOf[StructType]) + val table = HiveMetastoreCatalog.this.synchronized { + client.getTable(in.database, in.name) + } + + def schemaStringFromParts: Option[String] = { + Option(table.getProperty("spark.sql.sources.schema.numParts")).map { numParts => + val parts = (0 until numParts.toInt).map { index => + val part = table.getProperty(s"spark.sql.sources.schema.part.${index}") + if (part == null) { + throw new AnalysisException( + s"Could not read schema from the metastore because it is corrupted " + + s"(missing part ${index} of the schema).") + } + + part + } + // Stick all parts back to a single schema string. + parts.mkString } + } + + // Originally, we used spark.sql.sources.schema to store the schema of a data source table. + // After SPARK-6024, we removed this flag. + // Although we are not using spark.sql.sources.schema any more, we need to still support. + val schemaString = + Option(table.getProperty("spark.sql.sources.schema")).orElse(schemaStringFromParts) + + val userSpecifiedSchema = + schemaString.map(s => DataType.fromJson(s).asInstanceOf[StructType]) + // It does not appear that the ql client for the metastore has a way to enumerate all the // SerDe properties directly... val options = table.getTTable.getSd.getSerdeInfo.getParameters.toMap @@ -91,8 +115,16 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with CacheBuilder.newBuilder().maximumSize(1000).build(cacheLoader) } - def refreshTable(databaseName: String, tableName: String): Unit = { - cachedDataSourceTables.refresh(QualifiedTableName(databaseName, tableName).toLowerCase) + override def refreshTable(databaseName: String, tableName: String): Unit = { + // refreshTable does not eagerly reload the cache. It just invalidate the cache. + // Next time when we use the table, it will be populated in the cache. + // Since we also cache ParquetRealtions converted from Hive Parquet tables and + // adding converted ParquetRealtions into the cache is not defined in the load function + // of the cache (instead, we add the cache entry in convertToParquetRelation), + // it is better at here to invalidate the cache to avoid confusing waring logs from the + // cache loader (e.g. cannot find data source provider, which is only defined for + // data source table.). + invalidateTable(databaseName, tableName) } def invalidateTable(databaseName: String, tableName: String): Unit = { @@ -101,16 +133,10 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with val caseSensitive: Boolean = false - /** * - * Creates a data source table (a table created with USING clause) in Hive's metastore. - * Returns true when the table has been created. Otherwise, false. - * @param tableName - * @param userSpecifiedSchema - * @param provider - * @param options - * @param isExternal - * @return - */ + /** + * Creates a data source table (a table created with USING clause) in Hive's metastore. + * Returns true when the table has been created. Otherwise, false. + */ def createDataSourceTable( tableName: String, userSpecifiedSchema: Option[StructType], @@ -122,7 +148,14 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with tbl.setProperty("spark.sql.sources.provider", provider) if (userSpecifiedSchema.isDefined) { - tbl.setProperty("spark.sql.sources.schema", userSpecifiedSchema.get.json) + val threshold = hive.conf.schemaStringLengthThreshold + val schemaJsonString = userSpecifiedSchema.get.json + // Split the JSON string. + val parts = schemaJsonString.grouped(threshold).toSeq + tbl.setProperty("spark.sql.sources.schema.numParts", parts.size.toString) + parts.zipWithIndex.foreach { case (part, index) => + tbl.setProperty(s"spark.sql.sources.schema.part.${index}", part) + } } options.foreach { case (key, value) => tbl.setSerdeParam(key, value) } @@ -140,30 +173,48 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with } } - def hiveDefaultTableFilePath(tableName: String): String = { - val currentDatabase = client.getDatabase(hive.sessionState.getCurrentDatabase()) + def hiveDefaultTableFilePath(tableName: String): String = synchronized { + val currentDatabase = client.getDatabase(hive.sessionState.getCurrentDatabase) + hiveWarehouse.getTablePath(currentDatabase, tableName).toString } - def tableExists(tableIdentifier: Seq[String]): Boolean = { + def tableExists(tableIdentifier: Seq[String]): Boolean = synchronized { val tableIdent = processTableIdentifier(tableIdentifier) - val databaseName = tableIdent.lift(tableIdent.size - 2).getOrElse( - hive.sessionState.getCurrentDatabase) + val databaseName = + tableIdent + .lift(tableIdent.size - 2) + .getOrElse(hive.sessionState.getCurrentDatabase) val tblName = tableIdent.last client.getTable(databaseName, tblName, false) != null } def lookupRelation( tableIdentifier: Seq[String], - alias: Option[String]): LogicalPlan = synchronized { + alias: Option[String]): LogicalPlan = { val tableIdent = processTableIdentifier(tableIdentifier) val databaseName = tableIdent.lift(tableIdent.size - 2).getOrElse( hive.sessionState.getCurrentDatabase) val tblName = tableIdent.last - val table = client.getTable(databaseName, tblName) + val table = try { + synchronized { + client.getTable(databaseName, tblName) + } + } catch { + case te: org.apache.hadoop.hive.ql.metadata.InvalidTableException => + throw new NoSuchTableException + } if (table.getProperty("spark.sql.sources.provider") != null) { - cachedDataSourceTables(QualifiedTableName(databaseName, tblName).toLowerCase) + val dataSourceTable = + cachedDataSourceTables(QualifiedTableName(databaseName, tblName).toLowerCase) + // Then, if alias is specified, wrap the table with a Subquery using the alias. + // Othersie, wrap the table with a Subquery using the table name. + val withAlias = + alias.map(a => Subquery(a, dataSourceTable)).getOrElse( + Subquery(tableIdent.last, dataSourceTable)) + + withAlias } else if (table.isView) { // if the unresolved relation is from hive view // parse the text into logic node. @@ -171,31 +222,111 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with } else { val partitions: Seq[Partition] = if (table.isPartitioned) { - HiveShim.getAllPartitionsOf(client, table).toSeq + synchronized { + HiveShim.getAllPartitionsOf(client, table).toSeq + } } else { Nil } - val relation = MetastoreRelation( - databaseName, tblName, alias)( - table.getTTable, partitions.map(part => part.getTPartition))(hive) + MetastoreRelation(databaseName, tblName, alias)( + table.getTTable, partitions.map(part => part.getTPartition))(hive) + } + } - if (hive.convertMetastoreParquet && - hive.conf.parquetUseDataSourceApi && - relation.tableDesc.getSerdeClassName.toLowerCase.contains("parquet")) { - val metastoreSchema = StructType.fromAttributes(relation.output) - val paths = if (relation.hiveQlTable.isPartitioned) { - relation.hiveQlPartitions.map(p => p.getLocation) - } else { - Seq(relation.hiveQlTable.getDataLocation.toString) - } + private def convertToParquetRelation(metastoreRelation: MetastoreRelation): LogicalRelation = { + val metastoreSchema = StructType.fromAttributes(metastoreRelation.output) + val mergeSchema = hive.convertMetastoreParquetWithSchemaMerging + + // NOTE: Instead of passing Metastore schema directly to `ParquetRelation2`, we have to + // serialize the Metastore schema to JSON and pass it as a data source option because of the + // evil case insensitivity issue, which is reconciled within `ParquetRelation2`. + val parquetOptions = Map( + ParquetRelation2.METASTORE_SCHEMA -> metastoreSchema.json, + ParquetRelation2.MERGE_SCHEMA -> mergeSchema.toString) + val tableIdentifier = + QualifiedTableName(metastoreRelation.databaseName, metastoreRelation.tableName) + + def getCached( + tableIdentifier: QualifiedTableName, + pathsInMetastore: Seq[String], + schemaInMetastore: StructType, + partitionSpecInMetastore: Option[PartitionSpec]): Option[LogicalRelation] = { + cachedDataSourceTables.getIfPresent(tableIdentifier) match { + case null => None // Cache miss + case logical@LogicalRelation(parquetRelation: ParquetRelation2) => + // If we have the same paths, same schema, and same partition spec, + // we will use the cached Parquet Relation. + val useCached = + parquetRelation.paths.toSet == pathsInMetastore.toSet && + logical.schema.sameType(metastoreSchema) && + parquetRelation.maybePartitionSpec == partitionSpecInMetastore + + if (useCached) { + Some(logical) + } else { + // If the cached relation is not updated, we invalidate it right away. + cachedDataSourceTables.invalidate(tableIdentifier) + None + } + case other => + logWarning( + s"${metastoreRelation.databaseName}.${metastoreRelation.tableName} should be stored " + + s"as Parquet. However, we are getting a ${other} from the metastore cache. " + + s"This cached entry will be invalidated.") + cachedDataSourceTables.invalidate(tableIdentifier) + None + } + } - LogicalRelation(ParquetRelation2( - paths, Map(ParquetRelation2.METASTORE_SCHEMA -> metastoreSchema.json))(hive)) - } else { - relation + val result = if (metastoreRelation.hiveQlTable.isPartitioned) { + val partitionSchema = StructType.fromAttributes(metastoreRelation.partitionKeys) + val partitionColumnDataTypes = partitionSchema.map(_.dataType) + val partitions = metastoreRelation.hiveQlPartitions.map { p => + val location = p.getLocation + val values = Row.fromSeq(p.getValues.zip(partitionColumnDataTypes).map { + case (rawValue, dataType) => Cast(Literal(rawValue), dataType).eval(null) + }) + ParquetPartition(values, location) + } + val partitionSpec = PartitionSpec(partitionSchema, partitions) + val paths = partitions.map(_.path) + + val cached = getCached(tableIdentifier, paths, metastoreSchema, Some(partitionSpec)) + val parquetRelation = cached.getOrElse { + val created = + LogicalRelation(ParquetRelation2(paths, parquetOptions, None, Some(partitionSpec))(hive)) + cachedDataSourceTables.put(tableIdentifier, created) + created } + + parquetRelation + } else { + val paths = Seq(metastoreRelation.hiveQlTable.getDataLocation.toString) + + val cached = getCached(tableIdentifier, paths, metastoreSchema, None) + val parquetRelation = cached.getOrElse { + val created = + LogicalRelation(ParquetRelation2(paths, parquetOptions)(hive)) + cachedDataSourceTables.put(tableIdentifier, created) + created + } + + parquetRelation + } + + result.newInstance() + } + + override def getTables(databaseName: Option[String]): Seq[(String, Boolean)] = synchronized { + val dbName = if (!caseSensitive) { + if (databaseName.isDefined) Some(databaseName.get.toLowerCase) else None + } else { + databaseName } + val db = dbName.getOrElse(hive.sessionState.getCurrentDatabase) + + client.getAllTables(db).map(tableName => (tableName, false)) } /** @@ -225,7 +356,7 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with val hiveSchema: JList[FieldSchema] = if (schema == null || schema.isEmpty) { crtTbl.getCols } else { - schema.map(attr => new FieldSchema(attr.name, toMetastoreType(attr.dataType), "")) + schema.map(attr => new FieldSchema(attr.name, toMetastoreType(attr.dataType), null)) } tbl.setFields(hiveSchema) @@ -256,9 +387,9 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with logInfo(s"Default to LazySimpleSerDe for table $dbName.$tblName") tbl.setSerializationLib(classOf[LazySimpleSerDe].getName()) - import org.apache.hadoop.mapred.TextInputFormat import org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat import org.apache.hadoop.io.Text + import org.apache.hadoop.mapred.TextInputFormat tbl.setInputFormatClass(classOf[TextInputFormat]) tbl.setOutputFormatClass(classOf[HiveIgnoreKeyTextOutputFormat[Text, Text]]) @@ -299,6 +430,7 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with if (crtTbl != null && crtTbl.getLineDelim() != null) { tbl.setSerdeParam(serdeConstants.LINE_DELIM, crtTbl.getLineDelim()) } + HiveShim.setTblNullFormat(crtTbl, tbl) if (crtTbl != null && crtTbl.getSerdeProps() != null) { val iter = crtTbl.getSerdeProps().entrySet().iterator() @@ -380,13 +512,87 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with } } + /** + * When scanning or writing to non-partitioned Metastore Parquet tables, convert them to Parquet + * data source relations for better performance. + * + * This rule can be considered as [[HiveStrategies.ParquetConversion]] done right. + */ + object ParquetConversions extends Rule[LogicalPlan] { + override def apply(plan: LogicalPlan): LogicalPlan = { + if (!plan.resolved) { + return plan + } + + // Collects all `MetastoreRelation`s which should be replaced + val toBeReplaced = plan.collect { + // Write path + case InsertIntoTable(relation: MetastoreRelation, _, _, _, _) + // Inserting into partitioned table is not supported in Parquet data source (yet). + if !relation.hiveQlTable.isPartitioned && + hive.convertMetastoreParquet && + hive.conf.parquetUseDataSourceApi && + relation.tableDesc.getSerdeClassName.toLowerCase.contains("parquet") => + val parquetRelation = convertToParquetRelation(relation) + val attributedRewrites = relation.output.zip(parquetRelation.output) + (relation, parquetRelation, attributedRewrites) + + // Write path + case InsertIntoHiveTable(relation: MetastoreRelation, _, _, _, _) + // Inserting into partitioned table is not supported in Parquet data source (yet). + if !relation.hiveQlTable.isPartitioned && + hive.convertMetastoreParquet && + hive.conf.parquetUseDataSourceApi && + relation.tableDesc.getSerdeClassName.toLowerCase.contains("parquet") => + val parquetRelation = convertToParquetRelation(relation) + val attributedRewrites = relation.output.zip(parquetRelation.output) + (relation, parquetRelation, attributedRewrites) + + // Read path + case p @ PhysicalOperation(_, _, relation: MetastoreRelation) + if hive.convertMetastoreParquet && + hive.conf.parquetUseDataSourceApi && + relation.tableDesc.getSerdeClassName.toLowerCase.contains("parquet") => + val parquetRelation = convertToParquetRelation(relation) + val attributedRewrites = relation.output.zip(parquetRelation.output) + (relation, parquetRelation, attributedRewrites) + } + + val relationMap = toBeReplaced.map(r => (r._1, r._2)).toMap + val attributedRewrites = AttributeMap(toBeReplaced.map(_._3).fold(Nil)(_ ++: _)) + + // Replaces all `MetastoreRelation`s with corresponding `ParquetRelation2`s, and fixes + // attribute IDs referenced in other nodes. + plan.transformUp { + case r: MetastoreRelation if relationMap.contains(r) => + val parquetRelation = relationMap(r) + val alias = r.alias.getOrElse(r.tableName) + Subquery(alias, parquetRelation) + + case InsertIntoTable(r: MetastoreRelation, partition, child, overwrite, ifNotExists) + if relationMap.contains(r) => + val parquetRelation = relationMap(r) + InsertIntoTable(parquetRelation, partition, child, overwrite, ifNotExists) + + case InsertIntoHiveTable(r: MetastoreRelation, partition, child, overwrite, ifNotExists) + if relationMap.contains(r) => + val parquetRelation = relationMap(r) + InsertIntoTable(parquetRelation, partition, child, overwrite, ifNotExists) + + case other => other.transformExpressions { + case a: Attribute if a.resolved => attributedRewrites.getOrElse(a, a) + } + } + } + } + /** * Creates any tables required for query execution. * For example, because of a CREATE TABLE X AS statement. */ object CreateTables extends Rule[LogicalPlan] { import org.apache.hadoop.hive.ql.Context - import org.apache.hadoop.hive.ql.parse.{QB, ASTNode, SemanticAnalyzer} + import org.apache.hadoop.hive.ql.parse.{ASTNode, QB, SemanticAnalyzer} def apply(plan: LogicalPlan): LogicalPlan = plan transform { // Wait until children are resolved. @@ -417,24 +623,69 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with Some(sa.getQB().getTableDesc) } - execution.CreateTableAsSelect( - databaseName, - tableName, - child, - allowExisting, - desc) + // Check if the query specifies file format or storage handler. + val hasStorageSpec = desc match { + case Some(crtTbl) => + crtTbl != null && (crtTbl.getSerName != null || crtTbl.getStorageHandler != null) + case None => false + } + + if (hive.convertCTAS && !hasStorageSpec) { + // Do the conversion when spark.sql.hive.convertCTAS is true and the query + // does not specify any storage format (file format and storage handler). + if (dbName.isDefined) { + throw new AnalysisException( + "Cannot specify database name in a CTAS statement " + + "when spark.sql.hive.convertCTAS is set to true.") + } + + val mode = if (allowExisting) SaveMode.Ignore else SaveMode.ErrorIfExists + CreateTableUsingAsSelect( + tblName, + hive.conf.defaultDataSourceName, + temporary = false, + mode, + options = Map.empty[String, String], + child + ) + } else { + execution.CreateTableAsSelect( + databaseName, + tableName, + child, + allowExisting, + desc) + } case p: LogicalPlan if p.resolved => p case p @ CreateTableAsSelect(db, tableName, child, allowExisting, None) => val (dbName, tblName) = processDatabaseAndTableName(db, tableName) - val databaseName = dbName.getOrElse(hive.sessionState.getCurrentDatabase) - execution.CreateTableAsSelect( - databaseName, - tableName, - child, - allowExisting, - None) + if (hive.convertCTAS) { + if (dbName.isDefined) { + throw new AnalysisException( + "Cannot specify database name in a CTAS statement " + + "when spark.sql.hive.convertCTAS is set to true.") + } + + val mode = if (allowExisting) SaveMode.Ignore else SaveMode.ErrorIfExists + CreateTableUsingAsSelect( + tblName, + hive.conf.defaultDataSourceName, + temporary = false, + mode, + options = Map.empty[String, String], + child + ) + } else { + val databaseName = dbName.getOrElse(hive.sessionState.getCurrentDatabase) + execution.CreateTableAsSelect( + databaseName, + tableName, + child, + allowExisting, + None) + } } } @@ -447,11 +698,12 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with // Wait until children are resolved. case p: LogicalPlan if !p.childrenResolved => p - case p @ InsertIntoTable(table: MetastoreRelation, _, child, _) => + case p @ InsertIntoTable(table: MetastoreRelation, _, child, _, _) => castChildOutput(p, table, child) } - def castChildOutput(p: InsertIntoTable, table: MetastoreRelation, child: LogicalPlan) = { + def castChildOutput(p: InsertIntoTable, table: MetastoreRelation, child: LogicalPlan) + : LogicalPlan = { val childOutputDataTypes = child.output.map(_.dataType) val tableOutputDataTypes = (table.attributes ++ table.partitionKeys).take(child.output.length).map(_.dataType) @@ -460,10 +712,10 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with p } else if (childOutputDataTypes.size == tableOutputDataTypes.size && childOutputDataTypes.zip(tableOutputDataTypes) - .forall { case (left, right) => DataType.equalsIgnoreNullability(left, right) }) { + .forall { case (left, right) => left.sameType(right) }) { // If both types ignoring nullability of ArrayType, MapType, StructType are the same, // use InsertIntoHiveTable instead of InsertIntoTable. - InsertIntoHiveTable(p.table, p.partition, p.child, p.overwrite) + InsertIntoHiveTable(p.table, p.partition, p.child, p.overwrite, p.ifNotExists) } else { // Only do the casting when child output data types differ from table output data types. val castedChildOutput = child.output.zip(table.output).map { @@ -489,7 +741,7 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with */ override def unregisterTable(tableIdentifier: Seq[String]): Unit = ??? - override def unregisterAllTables() = {} + override def unregisterAllTables(): Unit = {} } /** @@ -501,15 +753,15 @@ private[hive] case class InsertIntoHiveTable( table: LogicalPlan, partition: Map[String, Option[String]], child: LogicalPlan, - overwrite: Boolean) + overwrite: Boolean, + ifNotExists: Boolean) extends LogicalPlan { - override def children = child :: Nil - override def output = child.output + override def children: Seq[LogicalPlan] = child :: Nil + override def output: Seq[Attribute] = child.output - override lazy val resolved = childrenResolved && child.output.zip(table.output).forall { - case (childAttr, tableAttr) => - DataType.equalsIgnoreNullability(childAttr.dataType, tableAttr.dataType) + override lazy val resolved: Boolean = childrenResolved && child.output.zip(table.output).forall { + case (childAttr, tableAttr) => childAttr.dataType.sameType(tableAttr.dataType) } } @@ -517,23 +769,36 @@ private[hive] case class MetastoreRelation (databaseName: String, tableName: String, alias: Option[String]) (val table: TTable, val partitions: Seq[TPartition]) (@transient sqlContext: SQLContext) - extends LeafNode { + extends LeafNode with MultiInstanceRelation { self: Product => + override def equals(other: scala.Any): Boolean = other match { + case relation: MetastoreRelation => + databaseName == relation.databaseName && + tableName == relation.tableName && + alias == relation.alias && + output == relation.output + case _ => false + } + + override def hashCode(): Int = { + Objects.hashCode(databaseName, tableName, alias, output) + } + // TODO: Can we use org.apache.hadoop.hive.ql.metadata.Table as the type of table and // use org.apache.hadoop.hive.ql.metadata.Partition as the type of elements of partitions. // Right now, using org.apache.hadoop.hive.ql.metadata.Table and // org.apache.hadoop.hive.ql.metadata.Partition will cause a NotSerializableException // which indicates the SerDe we used is not Serializable. - @transient val hiveQlTable = new Table(table) + @transient val hiveQlTable: Table = new Table(table) - @transient val hiveQlPartitions = partitions.map { p => + @transient val hiveQlPartitions: Seq[Partition] = partitions.map { p => new Partition(hiveQlTable, p) } - @transient override lazy val statistics = Statistics( + @transient override lazy val statistics: Statistics = Statistics( sizeInBytes = { val totalSize = hiveQlTable.getParameters.get(HiveShim.getStatsSetupConstTotalSize) val rawDataSize = hiveQlTable.getParameters.get(HiveShim.getStatsSetupConstRawDataSize) @@ -577,9 +842,9 @@ private[hive] case class MetastoreRelation ) implicit class SchemaAttribute(f: FieldSchema) { - def toAttribute = AttributeReference( + def toAttribute: AttributeReference = AttributeReference( f.getName, - sqlContext.ddlParser.parseType(f.getType), + HiveMetastoreTypes.toDataType(f.getType), // Since data can be dumped in randomly with no validation, everything is nullable. nullable = true )(qualifiers = Seq(alias.getOrElse(tableName))) @@ -598,14 +863,15 @@ private[hive] case class MetastoreRelation /** An attribute map for determining the ordinal for non-partition columns. */ val columnOrdinals = AttributeMap(attributes.zipWithIndex) + + override def newInstance(): MetastoreRelation = { + MetastoreRelation(databaseName, tableName, alias)(table, partitions)(sqlContext) + } } -object HiveMetastoreTypes { - protected val ddlParser = new DDLParser - def toDataType(metastoreType: String): DataType = synchronized { - ddlParser.parseType(metastoreType) - } +private[hive] object HiveMetastoreTypes { + def toDataType(metastoreType: String): DataType = DataTypeParser(metastoreType) def toMetastoreType(dt: DataType): String = dt match { case ArrayType(elementType, _) => s"array<${toMetastoreType(elementType)}>" diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala index 2a4b88092179f..fd305eb480e63 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala @@ -18,6 +18,10 @@ package org.apache.spark.sql.hive import java.sql.Date + + +import org.apache.hadoop.hive.ql.exec.{FunctionRegistry, FunctionInfo} + import scala.collection.mutable.ArrayBuffer import org.apache.hadoop.hive.conf.HiveConf @@ -26,17 +30,19 @@ import org.apache.hadoop.hive.ql.lib.Node import org.apache.hadoop.hive.ql.metadata.Table import org.apache.hadoop.hive.ql.parse._ import org.apache.hadoop.hive.ql.plan.PlanUtils -import org.apache.spark.sql.SparkSQLParser +import org.apache.spark.sql.{AnalysisException, SparkSQLParser} import org.apache.spark.sql.catalyst.analysis._ import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans._ import org.apache.spark.sql.catalyst.plans.logical import org.apache.spark.sql.catalyst.plans.logical._ +import org.apache.spark.sql.catalyst.trees.CurrentOrigin import org.apache.spark.sql.execution.ExplainCommand import org.apache.spark.sql.sources.DescribeCommand import org.apache.spark.sql.hive.execution.{HiveNativeCommand, DropTable, AnalyzeTable, HiveScriptIOSchema} import org.apache.spark.sql.types._ +import org.apache.spark.util.random.RandomSampler /* Implicit conversions */ import scala.collection.JavaConversions._ @@ -51,37 +57,8 @@ private[hive] case object NativePlaceholder extends Command /** Provides a mapping from HiveQL statements to catalyst logical plans and expression trees. */ private[hive] object HiveQl { protected val nativeCommands = Seq( - "TOK_DESCFUNCTION", - "TOK_DESCDATABASE", - "TOK_SHOW_CREATETABLE", - "TOK_SHOWCOLUMNS", - "TOK_SHOW_TABLESTATUS", - "TOK_SHOWDATABASES", - "TOK_SHOWFUNCTIONS", - "TOK_SHOWINDEXES", - "TOK_SHOWINDEXES", - "TOK_SHOWPARTITIONS", - "TOK_SHOWTABLES", - "TOK_SHOW_TBLPROPERTIES", - - "TOK_LOCKTABLE", - "TOK_SHOWLOCKS", - "TOK_UNLOCKTABLE", - - "TOK_SHOW_ROLES", - "TOK_CREATEROLE", - "TOK_DROPROLE", - "TOK_GRANT", - "TOK_GRANT_ROLE", - "TOK_REVOKE", - "TOK_SHOW_GRANT", - "TOK_SHOW_ROLE_GRANT", - - "TOK_CREATEFUNCTION", - "TOK_DROPFUNCTION", - - "TOK_ALTERDATABASE_PROPERTIES", "TOK_ALTERDATABASE_OWNER", + "TOK_ALTERDATABASE_PROPERTIES", "TOK_ALTERINDEX_PROPERTIES", "TOK_ALTERINDEX_REBUILD", "TOK_ALTERTABLE_ADDCOLS", @@ -98,31 +75,70 @@ private[hive] object HiveQl { "TOK_ALTERTABLE_SKEWED", "TOK_ALTERTABLE_TOUCH", "TOK_ALTERTABLE_UNARCHIVE", - "TOK_CREATEDATABASE", - "TOK_CREATEFUNCTION", - "TOK_CREATEINDEX", - "TOK_DROPDATABASE", - "TOK_DROPINDEX", - "TOK_MSCK", - "TOK_ALTERVIEW_ADDPARTS", "TOK_ALTERVIEW_AS", "TOK_ALTERVIEW_DROPPARTS", "TOK_ALTERVIEW_PROPERTIES", "TOK_ALTERVIEW_RENAME", + + "TOK_CREATEDATABASE", + "TOK_CREATEFUNCTION", + "TOK_CREATEINDEX", + "TOK_CREATEROLE", "TOK_CREATEVIEW", + + "TOK_DESCDATABASE", + "TOK_DESCFUNCTION", + + "TOK_DROPDATABASE", + "TOK_DROPFUNCTION", + "TOK_DROPINDEX", + "TOK_DROPROLE", + "TOK_DROPTABLE_PROPERTIES", "TOK_DROPVIEW", - + "TOK_DROPVIEW_PROPERTIES", + "TOK_EXPORT", + + "TOK_GRANT", + "TOK_GRANT_ROLE", + "TOK_IMPORT", + "TOK_LOAD", - - "TOK_SWITCHDATABASE" + + "TOK_LOCKTABLE", + + "TOK_MSCK", + + "TOK_REVOKE", + + "TOK_SHOW_COMPACTIONS", + "TOK_SHOW_CREATETABLE", + "TOK_SHOW_GRANT", + "TOK_SHOW_ROLE_GRANT", + "TOK_SHOW_ROLE_PRINCIPALS", + "TOK_SHOW_ROLES", + "TOK_SHOW_SET_ROLE", + "TOK_SHOW_TABLESTATUS", + "TOK_SHOW_TBLPROPERTIES", + "TOK_SHOW_TRANSACTIONS", + "TOK_SHOWCOLUMNS", + "TOK_SHOWDATABASES", + "TOK_SHOWFUNCTIONS", + "TOK_SHOWINDEXES", + "TOK_SHOWLOCKS", + "TOK_SHOWPARTITIONS", + + "TOK_SWITCHDATABASE", + + "TOK_UNLOCKTABLE" ) // Commands that we do not need to explain. protected val noExplainCommands = Seq( "TOK_DESCTABLE", + "TOK_SHOWTABLES", "TOK_TRUNCATETABLE" // truncate table" is a NativeCommand, does not need to explain. ) ++ nativeCommands @@ -189,8 +205,8 @@ private[hive] object HiveQl { * Right now this function only checks the name, type, text and children of the node * for equality. */ - def checkEquals(other: ASTNode) { - def check(field: String, f: ASTNode => Any) = if (f(n) != f(other)) { + def checkEquals(other: ASTNode): Unit = { + def check(field: String, f: ASTNode => Any): Unit = if (f(n) != f(other)) { sys.error(s"$field does not match for trees. " + s"'${f(n)}' != '${f(other)}' left: ${dumpTree(n)}, right: ${dumpTree(other)}") } @@ -202,17 +218,11 @@ private[hive] object HiveQl { val leftChildren = nilIfEmpty(n.getChildren).asInstanceOf[Seq[ASTNode]] val rightChildren = nilIfEmpty(other.getChildren).asInstanceOf[Seq[ASTNode]] leftChildren zip rightChildren foreach { - case (l,r) => l checkEquals r + case (l, r) => l checkEquals r } } } - class ParseException(sql: String, cause: Throwable) - extends Exception(s"Failed to parse: $sql", cause) - - class SemanticException(msg: String) - extends Exception(s"Error in semantic analysis: $msg") - /** * Returns the AST for the given SQL string. */ @@ -232,8 +242,10 @@ private[hive] object HiveQl { /** Returns a LogicalPlan for a given HiveQL string. */ def parseSql(sql: String): LogicalPlan = hqlParser(sql) + val errorRegEx = "line (\\d+):(\\d+) (.*)".r + /** Creates LogicalPlan for a given HiveQL string. */ - def createPlan(sql: String) = { + def createPlan(sql: String): LogicalPlan = { try { val tree = getAst(sql) if (nativeCommands contains tree.getText) { @@ -245,19 +257,28 @@ private[hive] object HiveQl { } } } catch { - case e: Exception => throw new ParseException(sql, e) - case e: NotImplementedError => sys.error( - s""" - |Unsupported language features in query: $sql - |${dumpTree(getAst(sql))} - |$e - |${e.getStackTrace.head} - """.stripMargin) + case pe: org.apache.hadoop.hive.ql.parse.ParseException => + pe.getMessage match { + case errorRegEx(line, start, message) => + throw new AnalysisException(message, Some(line.toInt), Some(start.toInt)) + case otherMessage => + throw new AnalysisException(otherMessage) + } + case e: Exception => + throw new AnalysisException(e.getMessage) + case e: NotImplementedError => + throw new AnalysisException( + s""" + |Unsupported language features in query: $sql + |${dumpTree(getAst(sql))} + |$e + |${e.getStackTrace.head} + """.stripMargin) } } /** Creates LogicalPlan for a given VIEW */ - def createPlanForView(view: Table, alias: Option[String]) = alias match { + def createPlanForView(view: Table, alias: Option[String]): Subquery = alias match { // because hive use things like `_c0` to build the expanded text // currently we cannot support view from "create view v1(c1) as ..." case None => Subquery(view.getTableName, createPlan(view.getViewExpandedText)) @@ -288,6 +309,7 @@ private[hive] object HiveQl { /** @return matches of the form (tokenName, children). */ def unapply(t: Any): Option[(String, Seq[ASTNode])] = t match { case t: ASTNode => + CurrentOrigin.setPosition(t.getLine, t.getCharPositionInLine) Some((t.getText, Option(t.getChildren).map(_.toList).getOrElse(Nil).asInstanceOf[Seq[ASTNode]])) case _ => None @@ -310,7 +332,7 @@ private[hive] object HiveQl { clauses } - def getClause(clauseName: String, nodeList: Seq[Node]) = + def getClause(clauseName: String, nodeList: Seq[Node]): Node = getClauseOption(clauseName, nodeList).getOrElse(sys.error( s"Expected clause $clauseName missing from ${nodeList.map(dumpTree(_)).mkString("\n")}")) @@ -462,23 +484,21 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C // Just fake explain for any of the native commands. case Token("TOK_EXPLAIN", explainArgs) if noExplainCommands.contains(explainArgs.head.getText) => - ExplainCommand(NoRelation, Seq(AttributeReference("plan", StringType, nullable = false)())) + ExplainCommand(OneRowRelation) case Token("TOK_EXPLAIN", explainArgs) if "TOK_CREATETABLE" == explainArgs.head.getText => val Some(crtTbl) :: _ :: extended :: Nil = getClauses(Seq("TOK_CREATETABLE", "FORMATTED", "EXTENDED"), explainArgs) ExplainCommand( nodeToPlan(crtTbl), - Seq(AttributeReference("plan", StringType,nullable = false)()), - extended != None) + extended = extended.isDefined) case Token("TOK_EXPLAIN", explainArgs) => // Ignore FORMATTED if present. val Some(query) :: _ :: extended :: Nil = getClauses(Seq("TOK_QUERY", "FORMATTED", "EXTENDED"), explainArgs) ExplainCommand( nodeToPlan(query), - Seq(AttributeReference("plan", StringType, nullable = false)()), - extended != None) + extended = extended.isDefined) case Token("TOK_DESCTABLE", describeArgs) => // Reference: https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL @@ -559,11 +579,23 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C case Token("TOK_QUERY", queryArgs) if Seq("TOK_FROM", "TOK_INSERT").contains(queryArgs.head.getText) => - val (fromClause: Option[ASTNode], insertClauses) = queryArgs match { - case Token("TOK_FROM", args: Seq[ASTNode]) :: insertClauses => - (Some(args.head), insertClauses) - case Token("TOK_INSERT", _) :: Nil => (None, queryArgs) - } + val (fromClause: Option[ASTNode], insertClauses, cteRelations) = + queryArgs match { + case Token("TOK_FROM", args: Seq[ASTNode]) :: insertClauses => + // check if has CTE + insertClauses.last match { + case Token("TOK_CTE", cteClauses) => + val cteRelations = cteClauses.map(node => { + val relation = nodeToRelation(node).asInstanceOf[Subquery] + (relation.alias, relation) + }).toMap + (Some(args.head), insertClauses.init, Some(cteRelations)) + + case _ => (Some(args.head), insertClauses, None) + } + + case Token("TOK_INSERT", _) :: Nil => (None, queryArgs, None) + } // Return one query for each insert clause. val queries = insertClauses.map { case Token("TOK_INSERT", singleInsert) => @@ -607,7 +639,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C val relations = fromClause match { case Some(f) => nodeToRelation(f) - case None => NoRelation + case None => OneRowRelation } val withWhere = whereClause.map { whereNode => @@ -644,7 +676,8 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C AttributeReference("value", StringType)()), true) } - def matchSerDe(clause: Seq[ASTNode]) = clause match { + def matchSerDe(clause: Seq[ASTNode]) + : (Seq[(String, String)], String, Seq[(String, String)]) = clause match { case Token("TOK_SERDEPROPS", propsClause) :: Nil => val rowFormat = propsClause.map { case Token(name, Token(value, Nil) :: Nil) => (name, value) @@ -776,7 +809,10 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C } // If there are multiple INSERTS just UNION them together into on query. - queries.reduceLeft(Union) + val query = queries.reduceLeft(Union) + + // return With plan if there is CTE + cteRelations.map(With(query, _)).getOrElse(query) case Token("TOK_UNION", left :: right :: Nil) => Union(nodeToPlan(left), nodeToPlan(right)) @@ -840,7 +876,15 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C case Token("TOK_TABLESPLITSAMPLE", Token("TOK_PERCENT", Nil) :: Token(fraction, Nil) :: Nil) => - Sample(fraction.toDouble, withReplacement = false, (math.random * 1000).toInt, relation) + // The range of fraction accepted by Sample is [0, 1]. Because Hive's block sampling + // function takes X PERCENT as the input and the range of X is [0, 100], we need to + // adjust the fraction. + require( + fraction.toDouble >= (0.0 - RandomSampler.roundingEpsilon) + && fraction.toDouble <= (100.0 + RandomSampler.roundingEpsilon), + s"Sampling fraction ($fraction) must be on interval [0, 100]") + Sample(fraction.toDouble / 100, withReplacement = false, (math.random * 1000).toInt, + relation) case Token("TOK_TABLEBUCKETSAMPLE", Token(numerator, Nil) :: Token(denominator, Nil) :: Nil) => @@ -958,7 +1002,27 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C cleanIdentifier(key.toLowerCase) -> None }.toMap).getOrElse(Map.empty) - InsertIntoTable(UnresolvedRelation(tableIdent, None), partitionKeys, query, overwrite) + InsertIntoTable(UnresolvedRelation(tableIdent, None), partitionKeys, query, overwrite, false) + + case Token(destinationToken(), + Token("TOK_TAB", + tableArgs) :: + Token("TOK_IFNOTEXISTS", + ifNotExists) :: Nil) => + val Some(tableNameParts) :: partitionClause :: Nil = + getClauses(Seq("TOK_TABNAME", "TOK_PARTSPEC"), tableArgs) + + val tableIdent = extractTableIdent(tableNameParts) + + val partitionKeys = partitionClause.map(_.getChildren.map { + // Parse partitions. We also make keys case insensitive. + case Token("TOK_PARTVAL", Token(key, Nil) :: Token(value, Nil) :: Nil) => + cleanIdentifier(key.toLowerCase) -> Some(PlanUtils.stripQuotes(value)) + case Token("TOK_PARTVAL", Token(key, Nil) :: Nil) => + cleanIdentifier(key.toLowerCase) -> None + }.toMap).getOrElse(Map.empty) + + InsertIntoTable(UnresolvedRelation(tableIdent, None), partitionKeys, query, overwrite, true) case a: ASTNode => throw new NotImplementedError(s"No parse rules for:\n ${dumpTree(a).toString} ") @@ -1037,7 +1101,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C case Token(".", qualifier :: Token(attr, Nil) :: Nil) => nodeToExpr(qualifier) match { case UnresolvedAttribute(qualifierName) => - UnresolvedAttribute(qualifierName + "." + cleanIdentifier(attr)) + UnresolvedAttribute(qualifierName :+ cleanIdentifier(attr)) case other => UnresolvedGetField(other, attr) } @@ -1097,7 +1161,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C Cast(nodeToExpr(arg), DateType) /* Arithmetic */ - case Token("+", child :: Nil) => Add(Literal(0), nodeToExpr(child)) + case Token("+", child :: Nil) => nodeToExpr(child) case Token("-", child :: Nil) => UnaryMinus(nodeToExpr(child)) case Token("~", child :: Nil) => BitwiseNot(nodeToExpr(child)) case Token("+", left :: right:: Nil) => Add(nodeToExpr(left), nodeToExpr(right)) @@ -1178,7 +1242,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C CreateArray(children.map(nodeToExpr)) case Token("TOK_FUNCTION", Token(RAND(), Nil) :: Nil) => Rand case Token("TOK_FUNCTION", Token(SUBSTR(), Nil) :: string :: pos :: Nil) => - Substring(nodeToExpr(string), nodeToExpr(pos), Literal(Integer.MAX_VALUE, IntegerType)) + Substring(nodeToExpr(string), nodeToExpr(pos), Literal.create(Integer.MAX_VALUE, IntegerType)) case Token("TOK_FUNCTION", Token(SUBSTR(), Nil) :: string :: pos :: length :: Nil) => Substring(nodeToExpr(string), nodeToExpr(pos), nodeToExpr(length)) case Token("TOK_FUNCTION", Token(COALESCE(), Nil) :: list) => Coalesce(list.map(nodeToExpr)) @@ -1190,9 +1254,9 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C UnresolvedFunction(name, UnresolvedStar(None) :: Nil) /* Literals */ - case Token("TOK_NULL", Nil) => Literal(null, NullType) - case Token(TRUE(), Nil) => Literal(true, BooleanType) - case Token(FALSE(), Nil) => Literal(false, BooleanType) + case Token("TOK_NULL", Nil) => Literal.create(null, NullType) + case Token(TRUE(), Nil) => Literal.create(true, BooleanType) + case Token(FALSE(), Nil) => Literal.create(false, BooleanType) case Token("TOK_STRINGLITERALSEQUENCE", strings) => Literal(strings.map(s => BaseSemanticAnalyzer.unescapeSQLString(s.getText)).mkString) @@ -1203,21 +1267,21 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C try { if (ast.getText.endsWith("L")) { // Literal bigint. - v = Literal(ast.getText.substring(0, ast.getText.length() - 1).toLong, LongType) + v = Literal.create(ast.getText.substring(0, ast.getText.length() - 1).toLong, LongType) } else if (ast.getText.endsWith("S")) { // Literal smallint. - v = Literal(ast.getText.substring(0, ast.getText.length() - 1).toShort, ShortType) + v = Literal.create(ast.getText.substring(0, ast.getText.length() - 1).toShort, ShortType) } else if (ast.getText.endsWith("Y")) { // Literal tinyint. - v = Literal(ast.getText.substring(0, ast.getText.length() - 1).toByte, ByteType) + v = Literal.create(ast.getText.substring(0, ast.getText.length() - 1).toByte, ByteType) } else if (ast.getText.endsWith("BD") || ast.getText.endsWith("D")) { // Literal decimal val strVal = ast.getText.stripSuffix("D").stripSuffix("B") v = Literal(Decimal(strVal)) } else { - v = Literal(ast.getText.toDouble, DoubleType) - v = Literal(ast.getText.toLong, LongType) - v = Literal(ast.getText.toInt, IntegerType) + v = Literal.create(ast.getText.toDouble, DoubleType) + v = Literal.create(ast.getText.toLong, LongType) + v = Literal.create(ast.getText.toInt, IntegerType) } } catch { case nfe: NumberFormatException => // Do nothing @@ -1235,6 +1299,9 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C case ast: ASTNode if ast.getType == HiveParser.TOK_DATELITERAL => Literal(Date.valueOf(ast.getText.substring(1, ast.getText.length - 1))) + case ast: ASTNode if ast.getType == HiveParser.TOK_CHARSETLITERAL => + Literal(BaseSemanticAnalyzer.charSetString(ast.getChild(0).getText, ast.getChild(1).getText)) + case a: ASTNode => throw new NotImplementedError( s"""No parse rules for ASTNode type: ${a.getType}, text: ${a.getText} : @@ -1257,8 +1324,13 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C Explode(attributes, nodeToExpr(child)) case Token("TOK_FUNCTION", Token(functionName, Nil) :: children) => + val functionInfo: FunctionInfo = + Option(FunctionRegistry.getFunctionInfo(functionName.toLowerCase)).getOrElse( + sys.error(s"Couldn't find function $functionName")) + val functionClassName = functionInfo.getFunctionClass.getName + HiveGenericUdtf( - new HiveFunctionWrapper(functionName), + new HiveFunctionWrapper(functionClassName), attributes, children.map(nodeToExpr)) @@ -1273,7 +1345,12 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C def dumpTree(node: Node, builder: StringBuilder = new StringBuilder, indent: Int = 0) : StringBuilder = { node match { - case a: ASTNode => builder.append((" " * indent) + a.getText + "\n") + case a: ASTNode => builder.append( + (" " * indent) + a.getText + " " + + a.getLine + ", " + + a.getTokenStartIndex + "," + + a.getTokenStopIndex + ", " + + a.getCharPositionInLine + "\n") case other => sys.error(s"Non ASTNode encountered: $other") } diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala index 95abc363ae767..a6f4fbe8aba06 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala @@ -17,24 +17,21 @@ package org.apache.spark.sql.hive -import org.apache.spark.sql.catalyst.expressions.Row - import scala.collection.JavaConversions._ import org.apache.spark.annotation.Experimental import org.apache.spark.sql._ +import org.apache.spark.sql.catalyst.CatalystTypeConverters import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute -import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.expressions.codegen.GeneratePredicate +import org.apache.spark.sql.catalyst.expressions.{Row, _} import org.apache.spark.sql.catalyst.planning._ import org.apache.spark.sql.catalyst.plans._ import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan -import org.apache.spark.sql.sources.DescribeCommand -import org.apache.spark.sql.execution.{DescribeCommand => RunnableDescribeCommand} -import org.apache.spark.sql.execution._ +import org.apache.spark.sql.execution.{DescribeCommand => RunnableDescribeCommand, _} import org.apache.spark.sql.hive.execution._ import org.apache.spark.sql.parquet.ParquetRelation -import org.apache.spark.sql.sources.{CreateTableUsingAsLogicalPlan, CreateTableUsingAsSelect, CreateTableUsing} +import org.apache.spark.sql.sources.{CreateTableUsing, CreateTableUsingAsSelect, DescribeCommand} import org.apache.spark.sql.types.StringType @@ -58,9 +55,9 @@ private[hive] trait HiveStrategies { @Experimental object ParquetConversion extends Strategy { implicit class LogicalPlanHacks(s: DataFrame) { - def lowerCase = DataFrame(s.sqlContext, s.logicalPlan) + def lowerCase: DataFrame = DataFrame(s.sqlContext, s.logicalPlan) - def addPartitioningAttributes(attrs: Seq[Attribute]) = { + def addPartitioningAttributes(attrs: Seq[Attribute]): DataFrame = { // Don't add the partitioning key if its already present in the data. if (attrs.map(_.name).toSet.subsetOf(s.logicalPlan.output.map(_.name).toSet)) { s @@ -75,7 +72,7 @@ private[hive] trait HiveStrategies { } implicit class PhysicalPlanHacks(originalPlan: SparkPlan) { - def fakeOutput(newOutput: Seq[Attribute]) = + def fakeOutput(newOutput: Seq[Attribute]): OutputFaker = OutputFaker( originalPlan.output.map(a => newOutput.find(a.name.toLowerCase == _.name.toLowerCase) @@ -131,7 +128,7 @@ private[hive] trait HiveStrategies { val partitionValues = part.getValues var i = 0 while (i < partitionValues.size()) { - inputData(i) = partitionValues(i) + inputData(i) = CatalystTypeConverters.convertToCatalyst(partitionValues(i)) i += 1 } pruningCondition(inputData) @@ -139,15 +136,20 @@ private[hive] trait HiveStrategies { val partitionLocations = partitions.map(_.getLocation) - hiveContext - .parquetFile(partitionLocations.head, partitionLocations.tail: _*) - .addPartitioningAttributes(relation.partitionKeys) - .lowerCase - .where(unresolvedOtherPredicates) - .select(unresolvedProjection: _*) - .queryExecution - .executedPlan - .fakeOutput(projectList.map(_.toAttribute)) :: Nil + if (partitionLocations.isEmpty) { + PhysicalRDD(plan.output, sparkContext.emptyRDD[Row]) :: Nil + } else { + hiveContext + .parquetFile(partitionLocations: _*) + .addPartitioningAttributes(relation.partitionKeys) + .lowerCase + .where(unresolvedOtherPredicates) + .select(unresolvedProjection: _*) + .queryExecution + .executedPlan + .fakeOutput(projectList.map(_.toAttribute)) :: Nil + } + } else { hiveContext .parquetFile(relation.hiveQlTable.getDataLocation.toString) @@ -179,12 +181,14 @@ private[hive] trait HiveStrategies { object DataSinks extends Strategy { def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match { - case logical.InsertIntoTable(table: MetastoreRelation, partition, child, overwrite) => + case logical.InsertIntoTable( + table: MetastoreRelation, partition, child, overwrite, ifNotExists) => execution.InsertIntoHiveTable( - table, partition, planLater(child), overwrite) :: Nil - case hive.InsertIntoHiveTable(table: MetastoreRelation, partition, child, overwrite) => + table, partition, planLater(child), overwrite, ifNotExists) :: Nil + case hive.InsertIntoHiveTable( + table: MetastoreRelation, partition, child, overwrite, ifNotExists) => execution.InsertIntoHiveTable( - table, partition, planLater(child), overwrite) :: Nil + table, partition, planLater(child), overwrite, ifNotExists) :: Nil case _ => Nil } } @@ -216,20 +220,15 @@ private[hive] trait HiveStrategies { object HiveDDLStrategy extends Strategy { def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match { - case CreateTableUsing(tableName, userSpecifiedSchema, provider, false, opts, allowExisting) => + case CreateTableUsing( + tableName, userSpecifiedSchema, provider, false, opts, allowExisting, managedIfNoPath) => ExecutedCommand( CreateMetastoreDataSource( - tableName, userSpecifiedSchema, provider, opts, allowExisting)) :: Nil - - case CreateTableUsingAsSelect(tableName, provider, false, opts, allowExisting, query) => - val logicalPlan = hiveContext.parseSql(query) - val cmd = - CreateMetastoreDataSourceAsSelect(tableName, provider, opts, allowExisting, logicalPlan) - ExecutedCommand(cmd) :: Nil + tableName, userSpecifiedSchema, provider, opts, allowExisting, managedIfNoPath)) :: Nil - case CreateTableUsingAsLogicalPlan(tableName, provider, false, opts, allowExisting, query) => + case CreateTableUsingAsSelect(tableName, provider, false, mode, opts, query) => val cmd = - CreateMetastoreDataSourceAsSelect(tableName, provider, opts, allowExisting, query) + CreateMetastoreDataSourceAsSelect(tableName, provider, mode, opts, query) ExecutedCommand(cmd) :: Nil case _ => Nil diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala index effaa5a443512..e556c74ffb015 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala @@ -25,7 +25,7 @@ import org.apache.hadoop.hive.ql.exec.Utilities import org.apache.hadoop.hive.ql.metadata.{Partition => HivePartition, Table => HiveTable} import org.apache.hadoop.hive.ql.plan.{PlanUtils, TableDesc} import org.apache.hadoop.hive.serde2.Deserializer -import org.apache.hadoop.hive.serde2.objectinspector.StructObjectInspector +import org.apache.hadoop.hive.serde2.objectinspector.{ObjectInspectorConverters, StructObjectInspector} import org.apache.hadoop.hive.serde2.objectinspector.primitive._ import org.apache.hadoop.io.Writable import org.apache.hadoop.mapred.{FileInputFormat, InputFormat, JobConf} @@ -35,6 +35,7 @@ import org.apache.spark.broadcast.Broadcast import org.apache.spark.rdd.{EmptyRDD, HadoopRDD, RDD, UnionRDD} import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.types.DateUtils +import org.apache.spark.util.Utils /** * A trait for subclasses that handle table scans. @@ -76,7 +77,9 @@ class HadoopTableReader( override def makeRDDForTable(hiveTable: HiveTable): RDD[Row] = makeRDDForTable( hiveTable, - relation.tableDesc.getDeserializerClass.asInstanceOf[Class[Deserializer]], + Class.forName( + relation.tableDesc.getSerdeClassName, true, sc.sessionState.getConf.getClassLoader) + .asInstanceOf[Class[Deserializer]], filterOpt = None) /** @@ -116,7 +119,7 @@ class HadoopTableReader( val hconf = broadcastedHiveConf.value.value val deserializer = deserializerClass.newInstance() deserializer.initialize(hconf, tableDesc.getProperties) - HadoopTableReader.fillObject(iter, deserializer, attrsWithIndex, mutableRow) + HadoopTableReader.fillObject(iter, deserializer, attrsWithIndex, mutableRow, deserializer) } deserializedHadoopRDD @@ -142,7 +145,46 @@ class HadoopTableReader( partitionToDeserializer: Map[HivePartition, Class[_ <: Deserializer]], filterOpt: Option[PathFilter]): RDD[Row] = { - val hivePartitionRDDs = partitionToDeserializer.map { case (partition, partDeserializer) => + + // SPARK-5068:get FileStatus and do the filtering locally when the path is not exists + def verifyPartitionPath( + partitionToDeserializer: Map[HivePartition, Class[_ <: Deserializer]]): + Map[HivePartition, Class[_ <: Deserializer]] = { + if (!sc.conf.verifyPartitionPath) { + partitionToDeserializer + } else { + var existPathSet = collection.mutable.Set[String]() + var pathPatternSet = collection.mutable.Set[String]() + partitionToDeserializer.filter { + case (partition, partDeserializer) => + def updateExistPathSetByPathPattern(pathPatternStr: String) { + val pathPattern = new Path(pathPatternStr) + val fs = pathPattern.getFileSystem(sc.hiveconf) + val matches = fs.globStatus(pathPattern) + matches.foreach(fileStatus => existPathSet += fileStatus.getPath.toString) + } + // convert /demo/data/year/month/day to /demo/data/*/*/*/ + def getPathPatternByPath(parNum: Int, tempPath: Path): String = { + var path = tempPath + for (i <- (1 to parNum)) path = path.getParent + val tails = (1 to parNum).map(_ => "*").mkString("/", "/", "/") + path.toString + tails + } + + val partPath = HiveShim.getDataLocationPath(partition) + val partNum = Utilities.getPartitionDesc(partition).getPartSpec.size(); + var pathPatternStr = getPathPatternByPath(partNum, partPath) + if (!pathPatternSet.contains(pathPatternStr)) { + pathPatternSet += pathPatternStr + updateExistPathSetByPathPattern(pathPatternStr) + } + existPathSet.contains(partPath.toString) + } + } + } + + val hivePartitionRDDs = verifyPartitionPath(partitionToDeserializer) + .map { case (partition, partDeserializer) => val partDesc = Utilities.getPartitionDesc(partition) val partPath = HiveShim.getDataLocationPath(partition) val inputPathStr = applyFilterIfNeeded(partPath, filterOpt) @@ -175,7 +217,7 @@ class HadoopTableReader( relation.partitionKeys.contains(attr) } - def fillPartitionKeys(rawPartValues: Array[String], row: MutableRow) = { + def fillPartitionKeys(rawPartValues: Array[String], row: MutableRow): Unit = { partitionKeyAttrs.foreach { case (attr, ordinal) => val partOrdinal = relation.partitionKeys.indexOf(attr) row(ordinal) = Cast(Literal(rawPartValues(partOrdinal)), attr.dataType).eval(null) @@ -189,9 +231,13 @@ class HadoopTableReader( val hconf = broadcastedHiveConf.value.value val deserializer = localDeserializer.newInstance() deserializer.initialize(hconf, partProps) + // get the table deserializer + val tableSerDe = tableDesc.getDeserializerClass.newInstance() + tableSerDe.initialize(hconf, tableDesc.getProperties) // fill the non partition key attributes - HadoopTableReader.fillObject(iter, deserializer, nonPartitionKeyAttrs, mutableRow) + HadoopTableReader.fillObject(iter, deserializer, nonPartitionKeyAttrs, + mutableRow, tableSerDe) } }.toSeq @@ -248,7 +294,7 @@ private[hive] object HadoopTableReader extends HiveInspectors { * instantiate a HadoopRDD. */ def initializeLocalJobConfFunc(path: String, tableDesc: TableDesc)(jobConf: JobConf) { - FileInputFormat.setInputPaths(jobConf, path) + FileInputFormat.setInputPaths(jobConf, Seq[Path](new Path(path)): _*) if (tableDesc != null) { PlanUtils.configureInputJobPropertiesForStorageHandler(tableDesc) Utilities.copyTableJobPropertiesToConf(tableDesc, jobConf) @@ -261,25 +307,36 @@ private[hive] object HadoopTableReader extends HiveInspectors { * Transform all given raw `Writable`s into `Row`s. * * @param iterator Iterator of all `Writable`s to be transformed - * @param deserializer The `Deserializer` associated with the input `Writable` + * @param rawDeser The `Deserializer` associated with the input `Writable` * @param nonPartitionKeyAttrs Attributes that should be filled together with their corresponding * positions in the output schema * @param mutableRow A reusable `MutableRow` that should be filled + * @param tableDeser Table Deserializer * @return An `Iterator[Row]` transformed from `iterator` */ def fillObject( iterator: Iterator[Writable], - deserializer: Deserializer, + rawDeser: Deserializer, nonPartitionKeyAttrs: Seq[(Attribute, Int)], - mutableRow: MutableRow): Iterator[Row] = { + mutableRow: MutableRow, + tableDeser: Deserializer): Iterator[Row] = { + + val soi = if (rawDeser.getObjectInspector.equals(tableDeser.getObjectInspector)) { + rawDeser.getObjectInspector.asInstanceOf[StructObjectInspector] + } else { + HiveShim.getConvertedOI( + rawDeser.getObjectInspector, + tableDeser.getObjectInspector).asInstanceOf[StructObjectInspector] + } - val soi = deserializer.getObjectInspector().asInstanceOf[StructObjectInspector] val (fieldRefs, fieldOrdinals) = nonPartitionKeyAttrs.map { case (attr, ordinal) => soi.getStructFieldRef(attr.name) -> ordinal }.unzip - // Builds specific unwrappers ahead of time according to object inspector types to avoid pattern - // matching and branching costs per row. + /** + * Builds specific unwrappers ahead of time according to object inspector + * types to avoid pattern matching and branching costs per row. + */ val unwrappers: Seq[(Any, MutableRow, Int) => Unit] = fieldRefs.map { _.getFieldObjectInspector match { case oi: BooleanObjectInspector => @@ -316,9 +373,11 @@ private[hive] object HadoopTableReader extends HiveInspectors { } } + val converter = ObjectInspectorConverters.getConverter(rawDeser.getObjectInspector, soi) + // Map each tuple to a row object iterator.map { value => - val raw = deserializer.deserialize(value) + val raw = converter.convert(rawDeser.deserialize(value)) var i = 0 while (i < fieldRefs.length) { val fieldValue = soi.getStructFieldData(raw, fieldRefs(i)) diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/CreateTableAsSelect.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/CreateTableAsSelect.scala index a547babcebfff..76a1965f3cb25 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/CreateTableAsSelect.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/CreateTableAsSelect.scala @@ -28,7 +28,6 @@ import org.apache.spark.sql.hive.HiveContext import org.apache.spark.sql.hive.MetastoreRelation /** - * :: Experimental :: * Create table and insert the query result into it. * @param database the database name of the new relation * @param tableName the table name of the new relation @@ -38,7 +37,7 @@ import org.apache.spark.sql.hive.MetastoreRelation * @param desc the CreateTableDesc, which may contains serde, storage handler etc. */ -@Experimental +private[hive] case class CreateTableAsSelect( database: String, tableName: String, @@ -46,7 +45,7 @@ case class CreateTableAsSelect( allowExisting: Boolean, desc: Option[CreateTableDesc]) extends RunnableCommand { - override def run(sqlContext: SQLContext) = { + override def run(sqlContext: SQLContext): Seq[Row] = { val hiveContext = sqlContext.asInstanceOf[HiveContext] lazy val metastoreRelation: MetastoreRelation = { // Create Hive Table @@ -68,7 +67,7 @@ case class CreateTableAsSelect( new org.apache.hadoop.hive.metastore.api.AlreadyExistsException(s"$database.$tableName") } } else { - hiveContext.executePlan(InsertIntoTable(metastoreRelation, Map(), query, true)).toRdd + hiveContext.executePlan(InsertIntoTable(metastoreRelation, Map(), query, true, false)).toRdd } Seq.empty[Row] diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/DescribeHiveTableCommand.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/DescribeHiveTableCommand.scala index bfacc51ef57ab..6fce69b58b85e 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/DescribeHiveTableCommand.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/DescribeHiveTableCommand.scala @@ -30,16 +30,14 @@ import org.apache.spark.sql.SQLContext /** * Implementation for "describe [extended] table". - * - * :: DeveloperApi :: */ -@DeveloperApi +private[hive] case class DescribeHiveTableCommand( table: MetastoreRelation, override val output: Seq[Attribute], isExtended: Boolean) extends RunnableCommand { - override def run(sqlContext: SQLContext) = { + override def run(sqlContext: SQLContext): Seq[Row] = { // Trying to mimic the format of Hive's output. But not exactly the same. var results: Seq[(String, String, String)] = Nil diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveNativeCommand.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveNativeCommand.scala index 781a2e9164c82..60a9bb630d0d9 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveNativeCommand.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveNativeCommand.scala @@ -17,22 +17,18 @@ package org.apache.spark.sql.hive.execution -import org.apache.spark.annotation.DeveloperApi import org.apache.spark.sql.catalyst.expressions.{AttributeReference, Row} import org.apache.spark.sql.execution.RunnableCommand import org.apache.spark.sql.hive.HiveContext import org.apache.spark.sql.SQLContext import org.apache.spark.sql.types.StringType -/** - * :: DeveloperApi :: - */ -@DeveloperApi +private[hive] case class HiveNativeCommand(sql: String) extends RunnableCommand { - override def output = + override def output: Seq[AttributeReference] = Seq(AttributeReference("result", StringType, nullable = false)()) - override def run(sqlContext: SQLContext) = + override def run(sqlContext: SQLContext): Seq[Row] = sqlContext.asInstanceOf[HiveContext].runSqlHive(sql).map(Row(_)) } diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala index b56175fe76376..0a5f19eee7105 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala @@ -26,21 +26,20 @@ import org.apache.hadoop.hive.serde2.objectinspector._ import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorUtils.ObjectInspectorCopyOption import org.apache.hadoop.hive.serde2.typeinfo.TypeInfoUtils -import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.rdd.RDD import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.execution._ import org.apache.spark.sql.hive._ import org.apache.spark.sql.types.{BooleanType, DataType} /** - * :: DeveloperApi :: * The Hive table scan operator. Column and partition pruning are both handled. * * @param requestedAttributes Attributes to be fetched from the Hive table. * @param relation The Hive table be be scanned. * @param partitionPruningPred An optional partition pruning predicate for partitioned table. */ -@DeveloperApi +private[hive] case class HiveTableScan( requestedAttributes: Seq[Attribute], relation: MetastoreRelation, @@ -130,11 +129,11 @@ case class HiveTableScan( } } - override def execute() = if (!relation.hiveQlTable.isPartitioned) { + override def execute(): RDD[Row] = if (!relation.hiveQlTable.isPartitioned) { hadoopReader.makeRDDForTable(relation.hiveQlTable) } else { hadoopReader.makeRDDForPartitionedTable(prunePartitions(relation.hiveQlPartitions)) } - override def output = attributes + override def output: Seq[Attribute] = attributes } diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala index 91af35f0965c0..89995a91b1a92 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala @@ -32,29 +32,26 @@ import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorUtils.Object import org.apache.hadoop.hive.serde2.objectinspector._ import org.apache.hadoop.mapred.{FileOutputCommitter, FileOutputFormat, JobConf} -import org.apache.spark.annotation.DeveloperApi import org.apache.spark.rdd.RDD -import org.apache.spark.sql.catalyst.expressions.Row +import org.apache.spark.sql.catalyst.expressions.{Attribute, Row} import org.apache.spark.sql.execution.{UnaryNode, SparkPlan} import org.apache.spark.sql.hive._ import org.apache.spark.sql.hive.{ ShimFileSinkDesc => FileSinkDesc} import org.apache.spark.sql.hive.HiveShim._ import org.apache.spark.{SerializableWritable, SparkException, TaskContext} -/** - * :: DeveloperApi :: - */ -@DeveloperApi +private[hive] case class InsertIntoHiveTable( table: MetastoreRelation, partition: Map[String, Option[String]], child: SparkPlan, - overwrite: Boolean) extends UnaryNode with HiveInspectors { + overwrite: Boolean, + ifNotExists: Boolean) extends UnaryNode with HiveInspectors { @transient val sc: HiveContext = sqlContext.asInstanceOf[HiveContext] @transient lazy val outputClass = newSerializer(table.tableDesc).getSerializedClass @transient private lazy val hiveContext = new Context(sc.hiveconf) - @transient private lazy val db = Hive.get(sc.hiveconf) + @transient private lazy val catalog = sc.catalog private def newSerializer(tableDesc: TableDesc): Serializer = { val serializer = tableDesc.getDeserializerClass.newInstance().asInstanceOf[Serializer] @@ -62,7 +59,7 @@ case class InsertIntoHiveTable( serializer } - def output = child.output + def output: Seq[Attribute] = child.output def saveAsHiveFile( rdd: RDD[Row], @@ -76,7 +73,6 @@ case class InsertIntoHiveTable( val outputFileFormatClassName = fileSinkConf.getTableInfo.getOutputFileFormatClassName assert(outputFileFormatClassName != null, "Output format class not set") conf.value.set("mapred.output.format.class", outputFileFormatClassName) - conf.value.setOutputCommitter(classOf[FileOutputCommitter]) FileOutputFormat.setOutputPath( conf.value, @@ -204,38 +200,55 @@ case class InsertIntoHiveTable( orderedPartitionSpec.put(entry.getName,partitionSpec.get(entry.getName).getOrElse("")) } val partVals = MetaStoreUtils.getPvals(table.hiveQlTable.getPartCols, partitionSpec) - db.validatePartitionNameCharacters(partVals) + catalog.synchronized { + catalog.client.validatePartitionNameCharacters(partVals) + } // inheritTableSpecs is set to true. It should be set to false for a IMPORT query // which is currently considered as a Hive native command. val inheritTableSpecs = true // TODO: Correctly set isSkewedStoreAsSubdir. val isSkewedStoreAsSubdir = false if (numDynamicPartitions > 0) { - db.loadDynamicPartitions( - outputPath, - qualifiedTableName, - orderedPartitionSpec, - overwrite, - numDynamicPartitions, - holdDDLTime, - isSkewedStoreAsSubdir - ) + catalog.synchronized { + catalog.client.loadDynamicPartitions( + outputPath, + qualifiedTableName, + orderedPartitionSpec, + overwrite, + numDynamicPartitions, + holdDDLTime, + isSkewedStoreAsSubdir) + } } else { - db.loadPartition( + // scalastyle:off + // ifNotExists is only valid with static partition, refer to + // https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DML#LanguageManualDML-InsertingdataintoHiveTablesfromqueries + // scalastyle:on + val oldPart = catalog.synchronized { + catalog.client.getPartition( + catalog.client.getTable(qualifiedTableName), partitionSpec, false) + } + if (oldPart == null || !ifNotExists) { + catalog.synchronized { + catalog.client.loadPartition( + outputPath, + qualifiedTableName, + orderedPartitionSpec, + overwrite, + holdDDLTime, + inheritTableSpecs, + isSkewedStoreAsSubdir) + } + } + } + } else { + catalog.synchronized { + catalog.client.loadTable( outputPath, qualifiedTableName, - orderedPartitionSpec, overwrite, - holdDDLTime, - inheritTableSpecs, - isSkewedStoreAsSubdir) + holdDDLTime) } - } else { - db.loadTable( - outputPath, - qualifiedTableName, - overwrite, - holdDDLTime) } // Invalidate the cache. diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala index c54fbb6e24690..cab0fdd35723a 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala @@ -17,40 +17,33 @@ package org.apache.spark.sql.hive.execution -import java.io.{BufferedReader, InputStreamReader} -import java.io.{DataInputStream, DataOutputStream, EOFException} +import java.io.{BufferedReader, DataInputStream, DataOutputStream, EOFException, InputStreamReader} import java.util.Properties +import scala.collection.JavaConversions._ + import org.apache.hadoop.hive.serde.serdeConstants import org.apache.hadoop.hive.serde2.AbstractSerDe -import org.apache.hadoop.hive.serde2.Serializer -import org.apache.hadoop.hive.serde2.Deserializer import org.apache.hadoop.hive.serde2.objectinspector._ -import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorFactory.ObjectInspectorOptions -import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorUtils.ObjectInspectorCopyOption -import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.catalyst.CatalystTypeConverters import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.logical.ScriptInputOutputSchema import org.apache.spark.sql.execution._ -import org.apache.spark.sql.types.DataType -import org.apache.spark.sql.hive.{HiveContext, HiveInspectors} import org.apache.spark.sql.hive.HiveShim._ +import org.apache.spark.sql.hive.{HiveContext, HiveInspectors} +import org.apache.spark.sql.types.DataType import org.apache.spark.util.Utils - -/* Implicit conversions */ -import scala.collection.JavaConversions._ - /** - * :: DeveloperApi :: * Transforms the input by forking and running the specified script. * * @param input the set of expression that should be passed to the script. * @param script the command that should be executed. * @param output the attributes that are produced by the script. */ -@DeveloperApi +private[hive] case class ScriptTransformation( input: Seq[Expression], script: String, @@ -59,9 +52,9 @@ case class ScriptTransformation( ioschema: HiveScriptIOSchema)(@transient sc: HiveContext) extends UnaryNode { - override def otherCopyArgs = sc :: Nil + override def otherCopyArgs: Seq[HiveContext] = sc :: Nil - def execute() = { + def execute(): RDD[Row] = { child.execute().mapPartitions { iter => val cmd = List("/bin/bash", "-c", script) val builder = new ProcessBuilder(cmd) @@ -128,14 +121,13 @@ case class ScriptTransformation( if (outputSerde == null) { val prevLine = curLine curLine = reader.readLine() - if (!ioschema.schemaLess) { - new GenericRow( - prevLine.split(ioschema.outputRowFormatMap("TOK_TABLEROWFORMATFIELD")) + new GenericRow(CatalystTypeConverters.convertToCatalyst( + prevLine.split(ioschema.outputRowFormatMap("TOK_TABLEROWFORMATFIELD"))) .asInstanceOf[Array[Any]]) } else { - new GenericRow( - prevLine.split(ioschema.outputRowFormatMap("TOK_TABLEROWFORMATFIELD"), 2) + new GenericRow(CatalystTypeConverters.convertToCatalyst( + prevLine.split(ioschema.outputRowFormatMap("TOK_TABLEROWFORMATFIELD"), 2)) .asInstanceOf[Array[Any]]) } } else { @@ -175,6 +167,7 @@ case class ScriptTransformation( /** * The wrapper class of Hive input and output schema properties */ +private[hive] case class HiveScriptIOSchema ( inputRowFormat: Seq[(String, String)], outputRowFormat: Seq[(String, String)], diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala index 95dcaccefdc54..a40a1e53117cd 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala @@ -17,53 +17,54 @@ package org.apache.spark.sql.hive.execution -import org.apache.spark.annotation.DeveloperApi -import org.apache.spark.sql.sources.ResolvedDataSource -import org.apache.spark.sql.{DataFrame, SQLContext} -import org.apache.spark.sql.catalyst.expressions.Row +import org.apache.spark.sql.AnalysisException +import org.apache.spark.sql.catalyst.analysis.EliminateSubQueries +import org.apache.spark.sql.catalyst.util._ +import org.apache.spark.sql.sources._ +import org.apache.spark.sql.{SaveMode, DataFrame, SQLContext} +import org.apache.spark.sql.catalyst.expressions.{Attribute, Row} import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan import org.apache.spark.sql.execution.RunnableCommand import org.apache.spark.sql.hive.HiveContext -import org.apache.spark.sql.types.StructType +import org.apache.spark.sql.types._ /** - * :: DeveloperApi :: * Analyzes the given table in the current database to generate statistics, which will be * used in query optimizations. * * Right now, it only supports Hive tables and it only updates the size of a Hive table * in the Hive metastore. */ -@DeveloperApi +private[hive] case class AnalyzeTable(tableName: String) extends RunnableCommand { - override def run(sqlContext: SQLContext) = { + override def run(sqlContext: SQLContext): Seq[Row] = { sqlContext.asInstanceOf[HiveContext].analyze(tableName) Seq.empty[Row] } } /** - * :: DeveloperApi :: * Drops a table from the metastore and removes it if it is cached. */ -@DeveloperApi +private[hive] case class DropTable( tableName: String, ifExists: Boolean) extends RunnableCommand { - override def run(sqlContext: SQLContext) = { + override def run(sqlContext: SQLContext): Seq[Row] = { val hiveContext = sqlContext.asInstanceOf[HiveContext] val ifExistsClause = if (ifExists) "IF EXISTS " else "" try { hiveContext.cacheManager.tryUncacheQuery(hiveContext.table(tableName)) } catch { - // This table's metadata is not in + // This table's metadata is not in Hive metastore (e.g. the table does not exist). case _: org.apache.hadoop.hive.ql.metadata.InvalidTableException => - // Other exceptions can be caused by users providing wrong parameters in OPTIONS + case _: org.apache.spark.sql.catalyst.analysis.NoSuchTableException => + // Other Throwables can be caused by users providing wrong parameters in OPTIONS // (e.g. invalid paths). We catch it and log a warning message. - // Users should be able to drop such kinds of tables regardless if there is an exception. - case e: Exception => log.warn(s"${e.getMessage}") + // Users should be able to drop such kinds of tables regardless if there is an error. + case e: Throwable => log.warn(s"${e.getMessage}", e) } hiveContext.invalidateTable(tableName) hiveContext.runSqlHive(s"DROP TABLE $ifExistsClause$tableName") @@ -72,27 +73,27 @@ case class DropTable( } } -/** - * :: DeveloperApi :: - */ -@DeveloperApi +private[hive] case class AddJar(path: String) extends RunnableCommand { - override def run(sqlContext: SQLContext) = { + override val output: Seq[Attribute] = { + val schema = StructType( + StructField("result", IntegerType, false) :: Nil) + schema.toAttributes + } + + override def run(sqlContext: SQLContext): Seq[Row] = { val hiveContext = sqlContext.asInstanceOf[HiveContext] hiveContext.runSqlHive(s"ADD JAR $path") hiveContext.sparkContext.addJar(path) - Seq.empty[Row] + Seq(Row(0)) } } -/** - * :: DeveloperApi :: - */ -@DeveloperApi +private[hive] case class AddFile(path: String) extends RunnableCommand { - override def run(sqlContext: SQLContext) = { + override def run(sqlContext: SQLContext): Seq[Row] = { val hiveContext = sqlContext.asInstanceOf[HiveContext] hiveContext.runSqlHive(s"ADD FILE $path") hiveContext.sparkContext.addFile(path) @@ -100,12 +101,14 @@ case class AddFile(path: String) extends RunnableCommand { } } +private[hive] case class CreateMetastoreDataSource( tableName: String, userSpecifiedSchema: Option[StructType], provider: String, options: Map[String, String], - allowExisting: Boolean) extends RunnableCommand { + allowExisting: Boolean, + managedIfNoPath: Boolean) extends RunnableCommand { override def run(sqlContext: SQLContext): Seq[Row] = { val hiveContext = sqlContext.asInstanceOf[HiveContext] @@ -114,13 +117,13 @@ case class CreateMetastoreDataSource( if (allowExisting) { return Seq.empty[Row] } else { - sys.error(s"Table $tableName already exists.") + throw new AnalysisException(s"Table $tableName already exists.") } } var isExternal = true val optionsWithPath = - if (!options.contains("path")) { + if (!options.contains("path") && managedIfNoPath) { isExternal = false options + ("path" -> hiveContext.catalog.hiveDefaultTableFilePath(tableName)) } else { @@ -138,25 +141,17 @@ case class CreateMetastoreDataSource( } } +private[hive] case class CreateMetastoreDataSourceAsSelect( tableName: String, provider: String, + mode: SaveMode, options: Map[String, String], - allowExisting: Boolean, query: LogicalPlan) extends RunnableCommand { override def run(sqlContext: SQLContext): Seq[Row] = { val hiveContext = sqlContext.asInstanceOf[HiveContext] - - if (hiveContext.catalog.tableExists(tableName :: Nil)) { - if (allowExisting) { - return Seq.empty[Row] - } else { - sys.error(s"Table $tableName already exists.") - } - } - - val df = DataFrame(hiveContext, query) + var createMetastoreTable = false var isExternal = true val optionsWithPath = if (!options.contains("path")) { @@ -166,16 +161,82 @@ case class CreateMetastoreDataSourceAsSelect( options } - // Create the relation based on the data of df. - ResolvedDataSource(sqlContext, provider, optionsWithPath, df) + var existingSchema = None: Option[StructType] + if (sqlContext.catalog.tableExists(Seq(tableName))) { + // Check if we need to throw an exception or just return. + mode match { + case SaveMode.ErrorIfExists => + throw new AnalysisException(s"Table $tableName already exists. " + + s"If you are using saveAsTable, you can set SaveMode to SaveMode.Append to " + + s"insert data into the table or set SaveMode to SaveMode.Overwrite to overwrite" + + s"the existing data. " + + s"Or, if you are using SQL CREATE TABLE, you need to drop $tableName first.") + case SaveMode.Ignore => + // Since the table already exists and the save mode is Ignore, we will just return. + return Seq.empty[Row] + case SaveMode.Append => + // Check if the specified data source match the data source of the existing table. + val resolved = + ResolvedDataSource(sqlContext, Some(query.schema), provider, optionsWithPath) + val createdRelation = LogicalRelation(resolved.relation) + EliminateSubQueries(sqlContext.table(tableName).logicalPlan) match { + case l @ LogicalRelation(i: InsertableRelation) => + if (i != createdRelation.relation) { + val errorDescription = + s"Cannot append to table $tableName because the resolved relation does not " + + s"match the existing relation of $tableName. " + + s"You can use insertInto($tableName, false) to append this DataFrame to the " + + s"table $tableName and using its data source and options." + val errorMessage = + s""" + |$errorDescription + |== Relations == + |${sideBySide( + s"== Expected Relation ==" :: + l.toString :: Nil, + s"== Actual Relation ==" :: + createdRelation.toString :: Nil).mkString("\n")} + """.stripMargin + throw new AnalysisException(errorMessage) + } + existingSchema = Some(l.schema) + case o => + throw new AnalysisException(s"Saving data in ${o.toString} is not supported.") + } + case SaveMode.Overwrite => + hiveContext.sql(s"DROP TABLE IF EXISTS $tableName") + // Need to create the table again. + createMetastoreTable = true + } + } else { + // The table does not exist. We need to create it in metastore. + createMetastoreTable = true + } - hiveContext.catalog.createDataSourceTable( - tableName, - None, - provider, - optionsWithPath, - isExternal) + val data = DataFrame(hiveContext, query) + val df = existingSchema match { + // If we are inserting into an existing table, just use the existing schema. + case Some(schema) => sqlContext.createDataFrame(data.queryExecution.toRdd, schema) + case None => data + } + + // Create the relation based on the data of df. + val resolved = ResolvedDataSource(sqlContext, provider, mode, optionsWithPath, df) + + if (createMetastoreTable) { + // We will use the schema of resolved.relation as the schema of the table (instead of + // the schema of df). It is important since the nullability may be changed by the relation + // provider (for example, see org.apache.spark.sql.parquet.DefaultSource). + hiveContext.catalog.createDataSourceTable( + tableName, + Some(resolved.relation.schema), + provider, + optionsWithPath, + isExternal) + } + // Refresh the cache of the table in the catalog. + hiveContext.refreshTable(tableName) Seq.empty[Row] } } diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala index 34c21c11761ae..47305571e579e 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala @@ -45,7 +45,7 @@ import scala.collection.JavaConversions._ private[hive] abstract class HiveFunctionRegistry extends analysis.FunctionRegistry with HiveInspectors { - def getFunctionInfo(name: String) = FunctionRegistry.getFunctionInfo(name) + def getFunctionInfo(name: String): FunctionInfo = FunctionRegistry.getFunctionInfo(name) def lookupFunction(name: String, children: Seq[Expression]): Expression = { // We only look it up to see if it exists, but do not include it in the HiveUDF since it is @@ -78,7 +78,7 @@ private[hive] case class HiveSimpleUdf(funcWrapper: HiveFunctionWrapper, childre type EvaluatedType = Any type UDFType = UDF - def nullable = true + override def nullable: Boolean = true @transient lazy val function = funcWrapper.createFunction[UDFType]() @@ -96,7 +96,7 @@ private[hive] case class HiveSimpleUdf(funcWrapper: HiveFunctionWrapper, childre udfType != null && udfType.deterministic() } - override def foldable = isUDFDeterministic && children.forall(_.foldable) + override def foldable: Boolean = isUDFDeterministic && children.forall(_.foldable) // Create parameter converters @transient @@ -110,7 +110,7 @@ private[hive] case class HiveSimpleUdf(funcWrapper: HiveFunctionWrapper, childre method.getGenericReturnType(), ObjectInspectorOptions.JAVA) @transient - protected lazy val cached = new Array[AnyRef](children.length) + protected lazy val cached: Array[AnyRef] = new Array[AnyRef](children.length) // TODO: Finish input output types. override def eval(input: Row): Any = { @@ -120,17 +120,19 @@ private[hive] case class HiveSimpleUdf(funcWrapper: HiveFunctionWrapper, childre returnInspector) } - override def toString = s"$nodeName#${funcWrapper.functionClassName}(${children.mkString(",")})" + override def toString: String = { + s"$nodeName#${funcWrapper.functionClassName}(${children.mkString(",")})" + } } // Adapter from Catalyst ExpressionResult to Hive DeferredObject private[hive] class DeferredObjectAdapter(oi: ObjectInspector) extends DeferredObject with HiveInspectors { private var func: () => Any = _ - def set(func: () => Any) { + def set(func: () => Any): Unit = { this.func = func } - override def prepare(i: Int) = {} + override def prepare(i: Int): Unit = {} override def get(): AnyRef = wrap(func(), oi) } @@ -139,7 +141,7 @@ private[hive] case class HiveGenericUdf(funcWrapper: HiveFunctionWrapper, childr type UDFType = GenericUDF type EvaluatedType = Any - def nullable = true + override def nullable: Boolean = true @transient lazy val function = funcWrapper.createFunction[UDFType]() @@ -158,7 +160,7 @@ private[hive] case class HiveGenericUdf(funcWrapper: HiveFunctionWrapper, childr (udfType != null && udfType.deterministic()) } - override def foldable = + override def foldable: Boolean = isUDFDeterministic && returnInspector.isInstanceOf[ConstantObjectInspector] @transient @@ -182,7 +184,9 @@ private[hive] case class HiveGenericUdf(funcWrapper: HiveFunctionWrapper, childr unwrap(function.evaluate(deferedObjects), returnInspector) } - override def toString = s"$nodeName#${funcWrapper.functionClassName}(${children.mkString(",")})" + override def toString: String = { + s"$nodeName#${funcWrapper.functionClassName}(${children.mkString(",")})" + } } private[hive] case class HiveGenericUdaf( @@ -209,9 +213,11 @@ private[hive] case class HiveGenericUdaf( def nullable: Boolean = true - override def toString = s"$nodeName#${funcWrapper.functionClassName}(${children.mkString(",")})" + override def toString: String = { + s"$nodeName#${funcWrapper.functionClassName}(${children.mkString(",")})" + } - def newInstance() = new HiveUdafFunction(funcWrapper, children, this) + def newInstance(): HiveUdafFunction = new HiveUdafFunction(funcWrapper, children, this) } /** It is used as a wrapper for the hive functions which uses UDAF interface */ @@ -240,10 +246,11 @@ private[hive] case class HiveUdaf( def nullable: Boolean = true - override def toString = s"$nodeName#${funcWrapper.functionClassName}(${children.mkString(",")})" + override def toString: String = { + s"$nodeName#${funcWrapper.functionClassName}(${children.mkString(",")})" + } - def newInstance() = - new HiveUdafFunction(funcWrapper, children, this, true) + def newInstance(): HiveUdafFunction = new HiveUdafFunction(funcWrapper, children, this, true) } /** @@ -314,25 +321,29 @@ private[hive] case class HiveGenericUdtf( collected += unwrap(input, outputInspector).asInstanceOf[Row] } - def collectRows() = { + def collectRows(): Seq[Row] = { val toCollect = collected collected = new ArrayBuffer[Row] toCollect } } - override def toString = s"$nodeName#${funcWrapper.functionClassName}(${children.mkString(",")})" + override def toString: String = { + s"$nodeName#${funcWrapper.functionClassName}(${children.mkString(",")})" + } } /** * Resolve Udtfs Alias. */ private[spark] object ResolveUdtfsAlias extends Rule[LogicalPlan] { - def apply(plan: LogicalPlan) = plan transform { + def apply(plan: LogicalPlan): LogicalPlan = plan transform { case p @ Project(projectList, _) if projectList.exists(_.isInstanceOf[MultiAlias]) && projectList.size != 1 => throw new TreeNodeException(p, "only single Generator supported for SELECT clause") + case Project(Seq(Alias(udtf @ HiveGenericUdtf(_, _, _), name)), child) => + Generate(udtf.copy(aliasNames = Seq(name)), join = false, outer = false, None, child) case Project(Seq(MultiAlias(udtf @ HiveGenericUdtf(_, _, _), names)), child) => Generate(udtf.copy(aliasNames = names), join = false, outer = false, None, child) } @@ -364,9 +375,8 @@ private[hive] case class HiveUdafFunction( private val returnInspector = function.init(GenericUDAFEvaluator.Mode.COMPLETE, inspectors) - // Cast required to avoid type inference selecting a deprecated Hive API. private val buffer = - function.getNewAggregationBuffer.asInstanceOf[GenericUDAFEvaluator.AbstractAggregationBuffer] + function.getNewAggregationBuffer override def eval(input: Row): Any = unwrap(function.evaluate(buffer), returnInspector) diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala index aae175e426ade..8398da268174d 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala @@ -17,7 +17,6 @@ package org.apache.spark.sql.hive -import java.io.IOException import java.text.NumberFormat import java.util.Date @@ -30,6 +29,7 @@ import org.apache.hadoop.hive.ql.io.{HiveFileFormatUtils, HiveOutputFormat} import org.apache.hadoop.hive.ql.plan.{PlanUtils, TableDesc} import org.apache.hadoop.io.Writable import org.apache.hadoop.mapred._ +import org.apache.hadoop.hive.common.FileUtils import org.apache.spark.mapred.SparkHadoopMapRedUtil import org.apache.spark.sql.Row @@ -117,19 +117,7 @@ private[hive] class SparkHiveWriterContainer( } protected def commit() { - if (committer.needsTaskCommit(taskContext)) { - try { - committer.commitTask(taskContext) - logInfo (taID + ": Committed") - } catch { - case e: IOException => - logError("Error committing the output of task: " + taID.value, e) - committer.abortTask(taskContext) - throw e - } - } else { - logInfo("No need to commit output of task: " + taID.value) - } + SparkHadoopMapRedUtil.commitTask(committer, taskContext, jobID, splitID, attemptID) } private def setIDs(jobId: Int, splitId: Int, attemptId: Int) { @@ -212,11 +200,16 @@ private[spark] class SparkHiveDynamicPartitionWriterContainer( .zip(row.toSeq.takeRight(dynamicPartColNames.length)) .map { case (col, rawVal) => val string = if (rawVal == null) null else String.valueOf(rawVal) - s"/$col=${if (string == null || string.isEmpty) defaultPartName else string}" - } - .mkString - - def newWriter = { + val colString = + if (string == null || string.isEmpty) { + defaultPartName + } else { + FileUtils.escapePathName(string) + } + s"/$col=$colString" + }.mkString + + def newWriter(): FileSinkOperator.RecordWriter = { val newFileSinkDesc = new FileSinkDesc( fileSinkConf.getDirName + dynamicPartPath, fileSinkConf.getTableInfo, @@ -240,6 +233,6 @@ private[spark] class SparkHiveDynamicPartitionWriterContainer( Reporter.NULL) } - writers.getOrElseUpdate(dynamicPartPath, newWriter) + writers.getOrElseUpdate(dynamicPartPath, newWriter()) } } diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/package.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/package.scala index a6c8ed4f7e866..db074361ef03c 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/package.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/package.scala @@ -17,4 +17,14 @@ package org.apache.spark.sql +/** + * Support for running Spark SQL queries using functionality from Apache Hive (does not require an + * existing Hive installation). Supported Hive features include: + * - Using HiveQL to express queries. + * - Reading metadata from the Hive Metastore using HiveSerDes. + * - Hive UDFs, UDAs, UDTs + * + * Users that would like access to this functionality should create a + * [[hive.HiveContext HiveContext]] instead of a [[SQLContext]]. + */ package object hive diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/parquet/FakeParquetSerDe.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/parquet/FakeParquetSerDe.scala deleted file mode 100644 index 2a16c9d1a27c9..0000000000000 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/parquet/FakeParquetSerDe.scala +++ /dev/null @@ -1,56 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.sql.hive.parquet - -import java.util.Properties - -import org.apache.hadoop.conf.Configuration -import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector.Category -import org.apache.hadoop.hive.serde2.{SerDeStats, SerDe} -import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector -import org.apache.hadoop.io.Writable - -/** - * A placeholder that allows Spark SQL users to create metastore tables that are stored as - * parquet files. It is only intended to pass the checks that the serde is valid and exists - * when a CREATE TABLE is run. The actual work of decoding will be done by ParquetTableScan - * when "spark.sql.hive.convertMetastoreParquet" is set to true. - */ -@deprecated("No code should depend on FakeParquetHiveSerDe as it is only intended as a " + - "placeholder in the Hive MetaStore", "1.2.0") -class FakeParquetSerDe extends SerDe { - override def getObjectInspector: ObjectInspector = new ObjectInspector { - override def getCategory: Category = Category.PRIMITIVE - - override def getTypeName: String = "string" - } - - override def deserialize(p1: Writable): AnyRef = throwError - - override def initialize(p1: Configuration, p2: Properties): Unit = {} - - override def getSerializedClass: Class[_ <: Writable] = throwError - - override def getSerDeStats: SerDeStats = throwError - - override def serialize(p1: scala.Any, p2: ObjectInspector): Writable = throwError - - private def throwError = - sys.error( - "spark.sql.hive.convertMetastoreParquet must be set to true to use FakeParquetSerDe") -} diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala similarity index 91% rename from sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala rename to sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala index 7c1d1133c3425..6570fa1043900 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala @@ -20,26 +20,25 @@ package org.apache.spark.sql.hive.test import java.io.File import java.util.{Set => JavaSet} -import scala.collection.mutable -import scala.language.implicitConversions - import org.apache.hadoop.hive.ql.exec.FunctionRegistry import org.apache.hadoop.hive.ql.io.avro.{AvroContainerInputFormat, AvroContainerOutputFormat} import org.apache.hadoop.hive.ql.metadata.Table +import org.apache.hadoop.hive.ql.parse.VariableSubstitution import org.apache.hadoop.hive.ql.processors._ import org.apache.hadoop.hive.serde2.RegexSerDe import org.apache.hadoop.hive.serde2.`lazy`.LazySimpleSerDe import org.apache.hadoop.hive.serde2.avro.AvroSerDe - -import org.apache.spark.{SparkConf, SparkContext} -import org.apache.spark.util.Utils +import org.apache.spark.sql.SQLConf import org.apache.spark.sql.catalyst.analysis._ import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan -import org.apache.spark.sql.catalyst.util._ import org.apache.spark.sql.execution.CacheTableCommand import org.apache.spark.sql.hive._ -import org.apache.spark.sql.SQLConf import org.apache.spark.sql.hive.execution.HiveNativeCommand +import org.apache.spark.util.Utils +import org.apache.spark.{SparkConf, SparkContext} + +import scala.collection.mutable +import scala.language.implicitConversions /* Implicit conversions */ import scala.collection.JavaConversions._ @@ -70,22 +69,19 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) { hiveconf.set("hive.plan.serialization.format", "javaXML") - lazy val warehousePath = getTempFilePath("sparkHiveWarehouse").getCanonicalPath - lazy val metastorePath = getTempFilePath("sparkHiveMetastore").getCanonicalPath + lazy val warehousePath = Utils.createTempDir() + lazy val metastorePath = Utils.createTempDir() /** Sets up the system initially or after a RESET command */ protected def configure(): Unit = { + warehousePath.delete() + metastorePath.delete() setConf("javax.jdo.option.ConnectionURL", s"jdbc:derby:;databaseName=$metastorePath;create=true") - setConf("hive.metastore.warehouse.dir", warehousePath) - Utils.registerShutdownDeleteDir(new File(warehousePath)) - Utils.registerShutdownDeleteDir(new File(metastorePath)) + setConf("hive.metastore.warehouse.dir", warehousePath.toString) } - val testTempDir = File.createTempFile("testTempFiles", "spark.hive.tmp") - testTempDir.delete() - testTempDir.mkdir() - Utils.registerShutdownDeleteDir(testTempDir) + val testTempDir = Utils.createTempDir() // For some hive test case which contain ${system:test.tmp.dir} System.setProperty("test.tmp.dir", testTempDir.getCanonicalPath) @@ -103,10 +99,16 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) { override def executePlan(plan: LogicalPlan): this.QueryExecution = new this.QueryExecution(plan) - /** Fewer partitions to speed up testing. */ - protected[sql] override lazy val conf: SQLConf = new SQLConf { - override def numShufflePartitions: Int = getConf(SQLConf.SHUFFLE_PARTITIONS, "5").toInt - override def dialect: String = getConf(SQLConf.DIALECT, "hiveql") + override protected[sql] def createSession(): SQLSession = { + new this.SQLSession() + } + + protected[hive] class SQLSession extends super.SQLSession { + /** Fewer partitions to speed up testing. */ + protected[sql] override lazy val conf: SQLConf = new SQLConf { + override def numShufflePartitions: Int = getConf(SQLConf.SHUFFLE_PARTITIONS, "5").toInt + override def dialect: String = getConf(SQLConf.DIALECT, "hiveql") + } } /** @@ -152,10 +154,15 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) { val describedTable = "DESCRIBE (\\w+)".r + val vs = new VariableSubstitution() + + // we should substitute variables in hql to pass the text to parseSql() as a parameter. + // Hive parser need substituted text. HiveContext.sql() does this but return a DataFrame, + // while we need a logicalPlan so we cannot reuse that. protected[hive] class HiveQLQueryExecution(hql: String) - extends this.QueryExecution(HiveQl.parseSql(hql)) { - def hiveExec() = runSqlHive(hql) - override def toString = hql + "\n" + super.toString + extends this.QueryExecution(HiveQl.parseSql(vs.substitute(hiveconf, hql))) { + def hiveExec(): Seq[String] = runSqlHive(hql) + override def toString: String = hql + "\n" + super.toString } /** @@ -185,7 +192,9 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) { case class TestTable(name: String, commands: (()=>Unit)*) protected[hive] implicit class SqlCmd(sql: String) { - def cmd = () => new HiveQLQueryExecution(sql).stringResult(): Unit + def cmd: () => Unit = { + () => new HiveQLQueryExecution(sql).stringResult(): Unit + } } /** @@ -193,10 +202,14 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) { * demand when a query are run against it. */ lazy val testTables = new mutable.HashMap[String, TestTable]() - def registerTestTable(testTable: TestTable) = testTables += (testTable.name -> testTable) + + def registerTestTable(testTable: TestTable): Unit = { + testTables += (testTable.name -> testTable) + } // The test tables that are defined in the Hive QTestUtil. // /itests/util/src/main/java/org/apache/hadoop/hive/ql/QTestUtil.java + // https://github.com/apache/hive/blob/branch-0.13/data/scripts/q_test_init.sql val hiveQTestUtilTables = Seq( TestTable("src", "CREATE TABLE src (key INT, value STRING)".cmd, @@ -224,11 +237,10 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) { } }), TestTable("src_thrift", () => { - import org.apache.thrift.protocol.TBinaryProtocol - import org.apache.hadoop.hive.serde2.thrift.test.Complex import org.apache.hadoop.hive.serde2.thrift.ThriftDeserializer - import org.apache.hadoop.mapred.SequenceFileInputFormat - import org.apache.hadoop.mapred.SequenceFileOutputFormat + import org.apache.hadoop.hive.serde2.thrift.test.Complex + import org.apache.hadoop.mapred.{SequenceFileInputFormat, SequenceFileOutputFormat} + import org.apache.thrift.protocol.TBinaryProtocol val srcThrift = new Table("default", "src_thrift") srcThrift.setFields(Nil) @@ -250,12 +262,6 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) { |WITH SERDEPROPERTIES ('field.delim'='\\t') """.stripMargin.cmd, "INSERT OVERWRITE TABLE serdeins SELECT * FROM src".cmd), - TestTable("sales", - s"""CREATE TABLE IF NOT EXISTS sales (key STRING, value INT) - |ROW FORMAT SERDE '${classOf[RegexSerDe].getCanonicalName}' - |WITH SERDEPROPERTIES ("input.regex" = "([^ ]*)\t([^ ]*)") - """.stripMargin.cmd, - s"LOAD DATA LOCAL INPATH '${getHiveFile("data/files/sales.txt")}' INTO TABLE sales".cmd), TestTable("episodes", s"""CREATE TABLE episodes (title STRING, air_date STRING, doctor INT) |ROW FORMAT SERDE '${classOf[AvroSerDe].getCanonicalName}' diff --git a/sql/hive/src/test/java/org/apache/spark/sql/hive/JavaMetastoreDataSourcesSuite.java b/sql/hive/src/test/java/org/apache/spark/sql/hive/JavaMetastoreDataSourcesSuite.java new file mode 100644 index 0000000000000..53ddecf57958b --- /dev/null +++ b/sql/hive/src/test/java/org/apache/spark/sql/hive/JavaMetastoreDataSourcesSuite.java @@ -0,0 +1,147 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package org.apache.spark.sql.hive; + +import java.io.File; +import java.io.IOException; +import java.util.ArrayList; +import java.util.HashMap; +import java.util.List; +import java.util.Map; + +import org.apache.hadoop.fs.FileSystem; +import org.apache.hadoop.fs.Path; +import org.apache.spark.sql.SaveMode; +import org.junit.After; +import org.junit.Assert; +import org.junit.Before; +import org.junit.Test; + +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.sql.DataFrame; +import org.apache.spark.sql.QueryTest$; +import org.apache.spark.sql.Row; +import org.apache.spark.sql.hive.test.TestHive$; +import org.apache.spark.sql.types.DataTypes; +import org.apache.spark.sql.types.StructField; +import org.apache.spark.sql.types.StructType; +import org.apache.spark.util.Utils; + +public class JavaMetastoreDataSourcesSuite { + private transient JavaSparkContext sc; + private transient HiveContext sqlContext; + + String originalDefaultSource; + File path; + Path hiveManagedPath; + FileSystem fs; + DataFrame df; + + private void checkAnswer(DataFrame actual, List expected) { + String errorMessage = QueryTest$.MODULE$.checkAnswer(actual, expected); + if (errorMessage != null) { + Assert.fail(errorMessage); + } + } + + @Before + public void setUp() throws IOException { + sqlContext = TestHive$.MODULE$; + sc = new JavaSparkContext(sqlContext.sparkContext()); + + originalDefaultSource = sqlContext.conf().defaultDataSourceName(); + path = + Utils.createTempDir(System.getProperty("java.io.tmpdir"), "datasource").getCanonicalFile(); + if (path.exists()) { + path.delete(); + } + hiveManagedPath = new Path(sqlContext.catalog().hiveDefaultTableFilePath("javaSavedTable")); + fs = hiveManagedPath.getFileSystem(sc.hadoopConfiguration()); + if (fs.exists(hiveManagedPath)){ + fs.delete(hiveManagedPath, true); + } + + List jsonObjects = new ArrayList(10); + for (int i = 0; i < 10; i++) { + jsonObjects.add("{\"a\":" + i + ", \"b\":\"str" + i + "\"}"); + } + JavaRDD rdd = sc.parallelize(jsonObjects); + df = sqlContext.jsonRDD(rdd); + df.registerTempTable("jsonTable"); + } + + @After + public void tearDown() throws IOException { + // Clean up tables. + sqlContext.sql("DROP TABLE IF EXISTS javaSavedTable"); + sqlContext.sql("DROP TABLE IF EXISTS externalTable"); + } + + @Test + public void saveExternalTableAndQueryIt() { + Map options = new HashMap(); + options.put("path", path.toString()); + df.saveAsTable("javaSavedTable", "org.apache.spark.sql.json", SaveMode.Append, options); + + checkAnswer( + sqlContext.sql("SELECT * FROM javaSavedTable"), + df.collectAsList()); + + DataFrame loadedDF = + sqlContext.createExternalTable("externalTable", "org.apache.spark.sql.json", options); + + checkAnswer(loadedDF, df.collectAsList()); + checkAnswer( + sqlContext.sql("SELECT * FROM externalTable"), + df.collectAsList()); + } + + @Test + public void saveExternalTableWithSchemaAndQueryIt() { + Map options = new HashMap(); + options.put("path", path.toString()); + df.saveAsTable("javaSavedTable", "org.apache.spark.sql.json", SaveMode.Append, options); + + checkAnswer( + sqlContext.sql("SELECT * FROM javaSavedTable"), + df.collectAsList()); + + List fields = new ArrayList(); + fields.add(DataTypes.createStructField("b", DataTypes.StringType, true)); + StructType schema = DataTypes.createStructType(fields); + DataFrame loadedDF = + sqlContext.createExternalTable("externalTable", "org.apache.spark.sql.json", schema, options); + + checkAnswer( + loadedDF, + sqlContext.sql("SELECT b FROM javaSavedTable").collectAsList()); + checkAnswer( + sqlContext.sql("SELECT * FROM externalTable"), + sqlContext.sql("SELECT b FROM javaSavedTable").collectAsList()); + } + + @Test + public void saveTableAndQueryIt() { + Map options = new HashMap(); + df.saveAsTable("javaSavedTable", "org.apache.spark.sql.json", SaveMode.Append, options); + + checkAnswer( + sqlContext.sql("SELECT * FROM javaSavedTable"), + df.collectAsList()); + } +} diff --git a/sql/hive/src/test/resources/golden/CTE feature #1-0-eedabbfe6ba8799f7b7782fb47a82768 b/sql/hive/src/test/resources/golden/CTE feature #1-0-eedabbfe6ba8799f7b7782fb47a82768 new file mode 100644 index 0000000000000..f6ba75da254ca --- /dev/null +++ b/sql/hive/src/test/resources/golden/CTE feature #1-0-eedabbfe6ba8799f7b7782fb47a82768 @@ -0,0 +1,3 @@ +5 +5 +5 diff --git a/sql/hive/src/test/resources/golden/CTE feature #2-0-aa03d104251f97e36bc52279cb9931c9 b/sql/hive/src/test/resources/golden/CTE feature #2-0-aa03d104251f97e36bc52279cb9931c9 new file mode 100644 index 0000000000000..ca7b591095e28 --- /dev/null +++ b/sql/hive/src/test/resources/golden/CTE feature #2-0-aa03d104251f97e36bc52279cb9931c9 @@ -0,0 +1,4 @@ +val_4 +val_5 +val_5 +val_5 diff --git a/sql/hive/src/test/resources/golden/CTE feature #3-0-b5d4bf3c0ee92b2fda0ca24f422383f2 b/sql/hive/src/test/resources/golden/CTE feature #3-0-b5d4bf3c0ee92b2fda0ca24f422383f2 new file mode 100644 index 0000000000000..b8626c4cff284 --- /dev/null +++ b/sql/hive/src/test/resources/golden/CTE feature #3-0-b5d4bf3c0ee92b2fda0ca24f422383f2 @@ -0,0 +1 @@ +4 diff --git a/sql/hive/src/test/resources/golden/create table as with db name within backticks-0-a253b1ed35dbf503d1b8902dacbe23ac b/sql/hive/src/test/resources/golden/create table as with db name within backticks-0-a253b1ed35dbf503d1b8902dacbe23ac new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/create table as with db name within backticks-1-61dc640dfeaff471f3d2b730f9cbf959 b/sql/hive/src/test/resources/golden/create table as with db name within backticks-1-61dc640dfeaff471f3d2b730f9cbf959 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/create table as with db name within backticks-2-ce780d068b8d24786e639e361101a0c7 b/sql/hive/src/test/resources/golden/create table as with db name within backticks-2-ce780d068b8d24786e639e361101a0c7 new file mode 100644 index 0000000000000..7aae61e5eb82f --- /dev/null +++ b/sql/hive/src/test/resources/golden/create table as with db name within backticks-2-ce780d068b8d24786e639e361101a0c7 @@ -0,0 +1,500 @@ +238 val_238 +86 val_86 +311 val_311 +27 val_27 +165 val_165 +409 val_409 +255 val_255 +278 val_278 +98 val_98 +484 val_484 +265 val_265 +193 val_193 +401 val_401 +150 val_150 +273 val_273 +224 val_224 +369 val_369 +66 val_66 +128 val_128 +213 val_213 +146 val_146 +406 val_406 +429 val_429 +374 val_374 +152 val_152 +469 val_469 +145 val_145 +495 val_495 +37 val_37 +327 val_327 +281 val_281 +277 val_277 +209 val_209 +15 val_15 +82 val_82 +403 val_403 +166 val_166 +417 val_417 +430 val_430 +252 val_252 +292 val_292 +219 val_219 +287 val_287 +153 val_153 +193 val_193 +338 val_338 +446 val_446 +459 val_459 +394 val_394 +237 val_237 +482 val_482 +174 val_174 +413 val_413 +494 val_494 +207 val_207 +199 val_199 +466 val_466 +208 val_208 +174 val_174 +399 val_399 +396 val_396 +247 val_247 +417 val_417 +489 val_489 +162 val_162 +377 val_377 +397 val_397 +309 val_309 +365 val_365 +266 val_266 +439 val_439 +342 val_342 +367 val_367 +325 val_325 +167 val_167 +195 val_195 +475 val_475 +17 val_17 +113 val_113 +155 val_155 +203 val_203 +339 val_339 +0 val_0 +455 val_455 +128 val_128 +311 val_311 +316 val_316 +57 val_57 +302 val_302 +205 val_205 +149 val_149 +438 val_438 +345 val_345 +129 val_129 +170 val_170 +20 val_20 +489 val_489 +157 val_157 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b/sql/hive/src/test/resources/golden/nullformatCTAS-10-7f4f04b87c7ef9653b4646949b24cf0b new file mode 100644 index 0000000000000..e74deff51c9ba --- /dev/null +++ b/sql/hive/src/test/resources/golden/nullformatCTAS-10-7f4f04b87c7ef9653b4646949b24cf0b @@ -0,0 +1,10 @@ +1.0 1 +1.0 1 +1.0 1 +1.0 1 +1.0 1 +NULL 1 +NULL NULL +1.0 NULL +1.0 1 +1.0 1 diff --git a/sql/hive/src/test/resources/golden/nullformatCTAS-11-4a4c16b53c612d00012d338c97bf5281 b/sql/hive/src/test/resources/golden/nullformatCTAS-11-4a4c16b53c612d00012d338c97bf5281 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/nullformatCTAS-12-7f4f04b87c7ef9653b4646949b24cf0b b/sql/hive/src/test/resources/golden/nullformatCTAS-12-7f4f04b87c7ef9653b4646949b24cf0b new file mode 100644 index 0000000000000..00ebb521970dd --- /dev/null +++ b/sql/hive/src/test/resources/golden/nullformatCTAS-12-7f4f04b87c7ef9653b4646949b24cf0b @@ -0,0 +1,10 @@ +1.0 1 +1.0 1 +1.0 1 +1.0 1 +1.0 1 +fooNull 1 +fooNull fooNull +1.0 fooNull +1.0 1 +1.0 1 diff --git a/sql/hive/src/test/resources/golden/nullformatCTAS-13-2e59caa113585495d8684fee69d88bc0 b/sql/hive/src/test/resources/golden/nullformatCTAS-13-2e59caa113585495d8684fee69d88bc0 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/nullformatCTAS-14-ad9fe9d68c2cf492259af4f6167c1b12 b/sql/hive/src/test/resources/golden/nullformatCTAS-14-ad9fe9d68c2cf492259af4f6167c1b12 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/nullformatCTAS-2-aa2bdbd93668dceae43d1a02f2ede68d b/sql/hive/src/test/resources/golden/nullformatCTAS-2-aa2bdbd93668dceae43d1a02f2ede68d new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/nullformatCTAS-3-b0057150f237050f38c1efa1f2d6b273 b/sql/hive/src/test/resources/golden/nullformatCTAS-3-b0057150f237050f38c1efa1f2d6b273 new file mode 100644 index 0000000000000..b00bcb3624532 --- /dev/null +++ b/sql/hive/src/test/resources/golden/nullformatCTAS-3-b0057150f237050f38c1efa1f2d6b273 @@ -0,0 +1,6 @@ +a string +b string +c string +d string + +Detailed Table Information Table(tableName:base_tab, dbName:default, owner:animal, createTime:1423973915, lastAccessTime:0, retention:0, sd:StorageDescriptor(cols:[FieldSchema(name:a, type:string, comment:null), FieldSchema(name:b, type:string, comment:null), FieldSchema(name:c, type:string, comment:null), FieldSchema(name:d, type:string, comment:null)], location:file:/tmp/sparkHiveWarehouse2573474017665704744/base_tab, inputFormat:org.apache.hadoop.mapred.TextInputFormat, outputFormat:org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat, compressed:false, numBuckets:-1, serdeInfo:SerDeInfo(name:null, serializationLib:org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, parameters:{serialization.format=1}), bucketCols:[], sortCols:[], parameters:{}, skewedInfo:SkewedInfo(skewedColNames:[], skewedColValues:[], skewedColValueLocationMaps:{}), storedAsSubDirectories:false), partitionKeys:[], parameters:{numFiles=1, transient_lastDdlTime=1423973915, COLUMN_STATS_ACCURATE=true, totalSize=130, numRows=0, rawDataSize=0}, viewOriginalText:null, viewExpandedText:null, tableType:MANAGED_TABLE) diff --git a/sql/hive/src/test/resources/golden/nullformatCTAS-4-16c7086f39d6458b6c5cf2479f0473bd b/sql/hive/src/test/resources/golden/nullformatCTAS-4-16c7086f39d6458b6c5cf2479f0473bd new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/nullformatCTAS-5-183d77b734ce6a373de5b3ebe1cd04c9 b/sql/hive/src/test/resources/golden/nullformatCTAS-5-183d77b734ce6a373de5b3ebe1cd04c9 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/nullformatCTAS-6-159fff36b548e00ee952d1df8ef19833 b/sql/hive/src/test/resources/golden/nullformatCTAS-6-159fff36b548e00ee952d1df8ef19833 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/nullformatCTAS-7-46900b082b02ce3e58087d1f41128f65 b/sql/hive/src/test/resources/golden/nullformatCTAS-7-46900b082b02ce3e58087d1f41128f65 new file mode 100644 index 0000000000000..264c973ff7af1 --- /dev/null +++ b/sql/hive/src/test/resources/golden/nullformatCTAS-7-46900b082b02ce3e58087d1f41128f65 @@ -0,0 +1,4 @@ +a string +b string + +Detailed Table Information Table(tableName:null_tab3, dbName:default, owner:animal, createTime:1423973928, lastAccessTime:0, retention:0, sd:StorageDescriptor(cols:[FieldSchema(name:a, type:string, comment:null), FieldSchema(name:b, type:string, comment:null)], location:file:/tmp/sparkHiveWarehouse2573474017665704744/null_tab3, inputFormat:org.apache.hadoop.mapred.TextInputFormat, outputFormat:org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat, compressed:false, numBuckets:-1, serdeInfo:SerDeInfo(name:null, serializationLib:org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, parameters:{serialization.null.format=fooNull, serialization.format=1}), bucketCols:[], sortCols:[], parameters:{}, skewedInfo:SkewedInfo(skewedColNames:[], skewedColValues:[], skewedColValueLocationMaps:{}), storedAsSubDirectories:false), partitionKeys:[], parameters:{numFiles=1, transient_lastDdlTime=1423973928, COLUMN_STATS_ACCURATE=true, totalSize=80, numRows=10, rawDataSize=70}, viewOriginalText:null, viewExpandedText:null, tableType:MANAGED_TABLE) diff --git a/sql/hive/src/test/resources/golden/nullformatCTAS-8-7f26cbd6be5631a3acce26f667d1c5d8 b/sql/hive/src/test/resources/golden/nullformatCTAS-8-7f26cbd6be5631a3acce26f667d1c5d8 new file mode 100644 index 0000000000000..881917bcf1c69 --- /dev/null +++ b/sql/hive/src/test/resources/golden/nullformatCTAS-8-7f26cbd6be5631a3acce26f667d1c5d8 @@ -0,0 +1,18 @@ +CREATE TABLE `null_tab3`( + `a` string, + `b` string) +ROW FORMAT DELIMITED + NULL DEFINED AS 'fooNull' +STORED AS INPUTFORMAT + 'org.apache.hadoop.mapred.TextInputFormat' +OUTPUTFORMAT + 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat' +LOCATION + 'file:/tmp/sparkHiveWarehouse2573474017665704744/null_tab3' +TBLPROPERTIES ( + 'numFiles'='1', + 'transient_lastDdlTime'='1423973928', + 'COLUMN_STATS_ACCURATE'='true', + 'totalSize'='80', + 'numRows'='10', + 'rawDataSize'='70') diff --git a/sql/hive/src/test/resources/golden/nullformatCTAS-9-22e1b3899de7087b39c24d9d8f643b47 b/sql/hive/src/test/resources/golden/nullformatCTAS-9-22e1b3899de7087b39c24d9d8f643b47 new file mode 100644 index 0000000000000..3a2e3f4984a0e --- /dev/null +++ b/sql/hive/src/test/resources/golden/nullformatCTAS-9-22e1b3899de7087b39c24d9d8f643b47 @@ -0,0 +1 @@ +-1 diff --git a/sql/hive/src/test/resources/golden/semicolon-0-f104632770dc96b81f00ccdac51fe5a8 b/sql/hive/src/test/resources/golden/semicolon-0-f104632770dc96b81f00ccdac51fe5a8 new file mode 100644 index 0000000000000..1b79f38e25b24 --- /dev/null +++ b/sql/hive/src/test/resources/golden/semicolon-0-f104632770dc96b81f00ccdac51fe5a8 @@ -0,0 +1 @@ +500 diff --git a/sql/hive/src/test/resources/golden/test ambiguousReferences resolved as hive-0-c6d02549aec166e16bfc44d5905fa33a b/sql/hive/src/test/resources/golden/test ambiguousReferences resolved as hive-0-c6d02549aec166e16bfc44d5905fa33a new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/test ambiguousReferences resolved as hive-1-a8987ff8c7b9ca95bf8b32314694ed1f b/sql/hive/src/test/resources/golden/test ambiguousReferences resolved as hive-1-a8987ff8c7b9ca95bf8b32314694ed1f new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/test ambiguousReferences resolved as hive-2-26f54240cf5b909086fc34a34d7fdb56 b/sql/hive/src/test/resources/golden/test ambiguousReferences resolved as hive-2-26f54240cf5b909086fc34a34d7fdb56 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/test ambiguousReferences resolved as hive-3-d08d5280027adea681001ad82a5a6974 b/sql/hive/src/test/resources/golden/test ambiguousReferences resolved as hive-3-d08d5280027adea681001ad82a5a6974 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/test ambiguousReferences resolved as hive-4-22eb25b5be6daf72a6649adfe5041749 b/sql/hive/src/test/resources/golden/test ambiguousReferences resolved as hive-4-22eb25b5be6daf72a6649adfe5041749 new file mode 100644 index 0000000000000..d00491fd7e5bb --- /dev/null +++ b/sql/hive/src/test/resources/golden/test ambiguousReferences resolved as hive-4-22eb25b5be6daf72a6649adfe5041749 @@ -0,0 +1 @@ +1 diff --git a/sql/hive/src/test/resources/golden/udaf_number_format-0-eff4ef3c207d14d5121368f294697964 b/sql/hive/src/test/resources/golden/udaf_number_format-0-eff4ef3c207d14d5121368f294697964 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/udaf_number_format-1-4a03c4328565c60ca99689239f07fb16 b/sql/hive/src/test/resources/golden/udaf_number_format-1-4a03c4328565c60ca99689239f07fb16 new file mode 100644 index 0000000000000..c6f275a0db131 --- /dev/null +++ b/sql/hive/src/test/resources/golden/udaf_number_format-1-4a03c4328565c60ca99689239f07fb16 @@ -0,0 +1 @@ +0.0 NULL NULL NULL diff --git a/sql/hive/src/test/resources/golden/udf_reflect2-0-50131c0ba7b7a6b65c789a5a8497bada b/sql/hive/src/test/resources/golden/udf_reflect2-0-50131c0ba7b7a6b65c789a5a8497bada new file mode 100644 index 0000000000000..573541ac9702d --- /dev/null +++ b/sql/hive/src/test/resources/golden/udf_reflect2-0-50131c0ba7b7a6b65c789a5a8497bada @@ -0,0 +1 @@ +0 diff --git a/sql/hive/src/test/resources/golden/udf_reflect2-1-7bec330c7bc6f71cbaf9bf1883d1b184 b/sql/hive/src/test/resources/golden/udf_reflect2-1-7bec330c7bc6f71cbaf9bf1883d1b184 new file mode 100644 index 0000000000000..cd35e5b290db5 --- /dev/null +++ b/sql/hive/src/test/resources/golden/udf_reflect2-1-7bec330c7bc6f71cbaf9bf1883d1b184 @@ -0,0 +1 @@ +reflect2(arg0,method[,arg1[,arg2..]]) calls method of arg0 with reflection diff --git a/sql/hive/src/test/resources/golden/udf_reflect2-2-c5a05379f482215a5a484bed0299bf19 b/sql/hive/src/test/resources/golden/udf_reflect2-2-c5a05379f482215a5a484bed0299bf19 new file mode 100644 index 0000000000000..48ef97292ab62 --- /dev/null +++ b/sql/hive/src/test/resources/golden/udf_reflect2-2-c5a05379f482215a5a484bed0299bf19 @@ -0,0 +1,3 @@ +reflect2(arg0,method[,arg1[,arg2..]]) calls method of arg0 with reflection +Use this UDF to call Java methods by matching the argument signature + diff --git a/sql/hive/src/test/resources/golden/udf_reflect2-3-effc057c78c00b0af26a4ac0f5f116ca b/sql/hive/src/test/resources/golden/udf_reflect2-3-effc057c78c00b0af26a4ac0f5f116ca new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/udf_reflect2-4-73d466e70e96e9e5f0cd373b37d4e1f4 b/sql/hive/src/test/resources/golden/udf_reflect2-4-73d466e70e96e9e5f0cd373b37d4e1f4 new file mode 100644 index 0000000000000..176ea0358d7ea --- /dev/null +++ b/sql/hive/src/test/resources/golden/udf_reflect2-4-73d466e70e96e9e5f0cd373b37d4e1f4 @@ -0,0 +1,5 @@ +238 -18 238 238 238 238.0 238.0 238 val_238 val_238_concat false true false false false val_238 -1 -1 VALUE_238 al_238 al_2 VAL_238 val_238 2013-02-15 19:41:20 113 1 5 19 41 20 1360986080000 +86 86 86 86 86 86.0 86.0 86 val_86 val_86_concat true true true true true val_86 -1 -1 VALUE_86 al_86 al_8 VAL_86 val_86 2013-02-15 19:41:20 113 1 5 19 41 20 1360986080000 +311 55 311 311 311 311.0 311.0 311 val_311 val_311_concat false true false false false val_311 5 6 VALUE_311 al_311 al_3 VAL_311 val_311 2013-02-15 19:41:20 113 1 5 19 41 20 1360986080000 +27 27 27 27 27 27.0 27.0 27 val_27 val_27_concat false true false false false val_27 -1 -1 VALUE_27 al_27 al_2 VAL_27 val_27 2013-02-15 19:41:20 113 1 5 19 41 20 1360986080000 +165 -91 165 165 165 165.0 165.0 165 val_165 val_165_concat false true false false false val_165 4 4 VALUE_165 al_165 al_1 VAL_165 val_165 2013-02-15 19:41:20 113 1 5 19 41 20 1360986080000 diff --git a/sql/hive/src/test/resources/hive-hcatalog-core-0.13.1.jar b/sql/hive/src/test/resources/hive-hcatalog-core-0.13.1.jar new file mode 100644 index 0000000000000..37af9aafad8a4 Binary files /dev/null and b/sql/hive/src/test/resources/hive-hcatalog-core-0.13.1.jar differ diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/QueryTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/QueryTest.scala deleted file mode 100644 index ba391293884bd..0000000000000 --- a/sql/hive/src/test/scala/org/apache/spark/sql/QueryTest.scala +++ /dev/null @@ -1,114 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.sql - -import org.scalatest.FunSuite - -import org.apache.spark.sql.catalyst.expressions.{ExprId, AttributeReference} -import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan -import org.apache.spark.sql.catalyst.plans._ -import org.apache.spark.sql.catalyst.util._ - - -/** - * *** DUPLICATED FROM sql/core. *** - * - * It is hard to have maven allow one subproject depend on another subprojects test code. - * So, we duplicate this code here. - */ -class QueryTest extends PlanTest { - - /** - * Runs the plan and makes sure the answer contains all of the keywords, or the - * none of keywords are listed in the answer - * @param rdd the [[DataFrame]] to be executed - * @param exists true for make sure the keywords are listed in the output, otherwise - * to make sure none of the keyword are not listed in the output - * @param keywords keyword in string array - */ - def checkExistence(rdd: DataFrame, exists: Boolean, keywords: String*) { - val outputs = rdd.collect().map(_.mkString).mkString - for (key <- keywords) { - if (exists) { - assert(outputs.contains(key), s"Failed for $rdd ($key doens't exist in result)") - } else { - assert(!outputs.contains(key), s"Failed for $rdd ($key existed in the result)") - } - } - } - - /** - * Runs the plan and makes sure the answer matches the expected result. - * @param rdd the [[DataFrame]] to be executed - * @param expectedAnswer the expected result, can either be an Any, Seq[Product], or Seq[ Seq[Any] ]. - */ - protected def checkAnswer(rdd: DataFrame, expectedAnswer: Seq[Row]): Unit = { - val isSorted = rdd.logicalPlan.collect { case s: logical.Sort => s }.nonEmpty - def prepareAnswer(answer: Seq[Row]): Seq[Row] = { - // Converts data to types that we can do equality comparison using Scala collections. - // For BigDecimal type, the Scala type has a better definition of equality test (similar to - // Java's java.math.BigDecimal.compareTo). - val converted: Seq[Row] = answer.map { s => - Row.fromSeq(s.toSeq.map { - case d: java.math.BigDecimal => BigDecimal(d) - case o => o - }) - } - if (!isSorted) converted.sortBy(_.toString) else converted - } - val sparkAnswer = try rdd.collect().toSeq catch { - case e: Exception => - fail( - s""" - |Exception thrown while executing query: - |${rdd.queryExecution} - |== Exception == - |$e - |${org.apache.spark.sql.catalyst.util.stackTraceToString(e)} - """.stripMargin) - } - - if (prepareAnswer(expectedAnswer) != prepareAnswer(sparkAnswer)) { - fail(s""" - |Results do not match for query: - |${rdd.logicalPlan} - |== Analyzed Plan == - |${rdd.queryExecution.analyzed} - |== Physical Plan == - |${rdd.queryExecution.executedPlan} - |== Results == - |${sideBySide( - s"== Correct Answer - ${expectedAnswer.size} ==" +: - prepareAnswer(expectedAnswer).map(_.toString), - s"== Spark Answer - ${sparkAnswer.size} ==" +: - prepareAnswer(sparkAnswer).map(_.toString)).mkString("\n")} - """.stripMargin) - } - } - - protected def checkAnswer(rdd: DataFrame, expectedAnswer: Row): Unit = { - checkAnswer(rdd, Seq(expectedAnswer)) - } - - def sqlTest(sqlString: String, expectedAnswer: Seq[Row])(implicit sqlContext: SQLContext): Unit = { - test(sqlString) { - checkAnswer(sqlContext.sql(sqlString), expectedAnswer) - } - } - -} diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala deleted file mode 100644 index 081d94b6fc020..0000000000000 --- a/sql/hive/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala +++ /dev/null @@ -1,57 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.sql.catalyst.plans - -import org.apache.spark.sql.catalyst.expressions.{AttributeReference, ExprId} -import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan -import org.apache.spark.sql.catalyst.util._ -import org.scalatest.FunSuite - -/** - * *** DUPLICATED FROM sql/catalyst/plans. *** - * - * It is hard to have maven allow one subproject depend on another subprojects test code. - * So, we duplicate this code here. - */ -class PlanTest extends FunSuite { - - /** - * Since attribute references are given globally unique ids during analysis, - * we must normalize them to check if two different queries are identical. - */ - protected def normalizeExprIds(plan: LogicalPlan) = { - val list = plan.flatMap(_.expressions.flatMap(_.references).map(_.exprId.id)) - val minId = if (list.isEmpty) 0 else list.min - plan transformAllExpressions { - case a: AttributeReference => - AttributeReference(a.name, a.dataType, a.nullable)(exprId = ExprId(a.exprId.id - minId)) - } - } - - /** Fails the test if the two plans do not match */ - protected def comparePlans(plan1: LogicalPlan, plan2: LogicalPlan) { - val normalized1 = normalizeExprIds(plan1) - val normalized2 = normalizeExprIds(plan2) - if (normalized1 != normalized2) - fail( - s""" - |== FAIL: Plans do not match === - |${sideBySide(normalized1.treeString, normalized2.treeString).mkString("\n")} - """.stripMargin) - } -} diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala index 7c8b5205e239e..c188264072a84 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala @@ -20,25 +20,10 @@ package org.apache.spark.sql.hive import org.apache.spark.sql.columnar.{InMemoryColumnarTableScan, InMemoryRelation} import org.apache.spark.sql.hive.test.TestHive import org.apache.spark.sql.hive.test.TestHive._ -import org.apache.spark.sql.{DataFrame, QueryTest} +import org.apache.spark.sql.{AnalysisException, DataFrame, QueryTest} import org.apache.spark.storage.RDDBlockId class CachedTableSuite extends QueryTest { - /** - * Throws a test failed exception when the number of cached tables differs from the expected - * number. - */ - def assertCached(query: DataFrame, numCachedTables: Int = 1): Unit = { - val planWithCaching = query.queryExecution.withCachedData - val cachedData = planWithCaching collect { - case cached: InMemoryRelation => cached - } - - assert( - cachedData.size == numCachedTables, - s"Expected query to contain $numCachedTables, but it actually had ${cachedData.size}\n" + - planWithCaching) - } def rddIdOf(tableName: String): Int = { val executedPlan = table(tableName).queryExecution.executedPlan @@ -92,12 +77,12 @@ class CachedTableSuite extends QueryTest { } test("Drop cached table") { - sql("CREATE TABLE test(a INT)") - cacheTable("test") - sql("SELECT * FROM test").collect() - sql("DROP TABLE test") - intercept[org.apache.hadoop.hive.ql.metadata.InvalidTableException] { - sql("SELECT * FROM test").collect() + sql("CREATE TABLE cachedTableTest(a INT)") + cacheTable("cachedTableTest") + sql("SELECT * FROM cachedTableTest").collect() + sql("DROP TABLE cachedTableTest") + intercept[AnalysisException] { + sql("SELECT * FROM cachedTableTest").collect() } } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ErrorPositionSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ErrorPositionSuite.scala new file mode 100644 index 0000000000000..d960a30e00738 --- /dev/null +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ErrorPositionSuite.scala @@ -0,0 +1,184 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive + +import java.io.{OutputStream, PrintStream} + +import scala.util.Try + +import org.scalatest.BeforeAndAfter + +import org.apache.spark.sql.hive.test.TestHive._ +import org.apache.spark.sql.hive.test.TestHive.implicits._ +import org.apache.spark.sql.{AnalysisException, QueryTest} + + +class ErrorPositionSuite extends QueryTest with BeforeAndAfter { + + before { + Seq((1, 1, 1)).toDF("a", "a", "b").registerTempTable("dupAttributes") + } + + positionTest("ambiguous attribute reference 1", + "SELECT a from dupAttributes", "a") + + positionTest("ambiguous attribute reference 2", + "SELECT a, b from dupAttributes", "a") + + positionTest("ambiguous attribute reference 3", + "SELECT b, a from dupAttributes", "a") + + positionTest("unresolved attribute 1", + "SELECT x FROM src", "x") + + positionTest("unresolved attribute 2", + "SELECT x FROM src", "x") + + positionTest("unresolved attribute 3", + "SELECT key, x FROM src", "x") + + positionTest("unresolved attribute 4", + """SELECT key, + |x FROM src + """.stripMargin, "x") + + positionTest("unresolved attribute 5", + """SELECT key, + | x FROM src + """.stripMargin, "x") + + positionTest("unresolved attribute 6", + """SELECT key, + | + | 1 + x FROM src + """.stripMargin, "x") + + positionTest("unresolved attribute 7", + """SELECT key, + | + | 1 + x + 1 FROM src + """.stripMargin, "x") + + positionTest("multi-char unresolved attribute", + """SELECT key, + | + | 1 + abcd + 1 FROM src + """.stripMargin, "abcd") + + positionTest("unresolved attribute group by", + """SELECT key FROM src GROUP BY + |x + """.stripMargin, "x") + + positionTest("unresolved attribute order by", + """SELECT key FROM src ORDER BY + |x + """.stripMargin, "x") + + positionTest("unresolved attribute where", + """SELECT key FROM src + |WHERE x = true + """.stripMargin, "x") + + positionTest("unresolved attribute backticks", + "SELECT `x` FROM src", "`x`") + + positionTest("parse error", + "SELECT WHERE", "WHERE") + + positionTest("bad relation", + "SELECT * FROM badTable", "badTable") + + ignore("other expressions") { + positionTest("bad addition", + "SELECT 1 + array(1)", "1 + array") + } + + /** Hive can be very noisy, messing up the output of our tests. */ + private def quietly[A](f: => A): A = { + val origErr = System.err + val origOut = System.out + try { + System.setErr(new PrintStream(new OutputStream { + def write(b: Int) = {} + })) + System.setOut(new PrintStream(new OutputStream { + def write(b: Int) = {} + })) + + f + } finally { + System.setErr(origErr) + System.setOut(origOut) + } + } + + /** + * Creates a test that checks to see if the error thrown when analyzing a given query includes + * the location of the given token in the query string. + * + * @param name the name of the test + * @param query the query to analyze + * @param token a unique token in the string that should be indicated by the exception + */ + def positionTest(name: String, query: String, token: String): Unit = { + def parseTree = + Try(quietly(HiveQl.dumpTree(HiveQl.getAst(query)))).getOrElse("") + + test(name) { + val error = intercept[AnalysisException] { + quietly(sql(query)) + } + + assert(!error.getMessage.contains("Seq(")) + assert(!error.getMessage.contains("List(")) + + val (line, expectedLineNum) = query.split("\n").zipWithIndex.collect { + case (l, i) if l.contains(token) => (l, i + 1) + }.headOption.getOrElse(sys.error(s"Invalid test. Token $token not in $query")) + val actualLine = error.line.getOrElse { + fail( + s"line not returned for error '${error.getMessage}' on token $token\n$parseTree" + ) + } + assert(actualLine === expectedLineNum, "wrong line") + + val expectedStart = line.indexOf(token) + val actualStart = error.startPosition.getOrElse { + fail( + s"start not returned for error on token $token\n" + + HiveQl.dumpTree(HiveQl.getAst(query)) + ) + } + assert(expectedStart === actualStart, + s"""Incorrect start position. + |== QUERY == + |$query + | + |== AST == + |$parseTree + | + |Actual: $actualStart, Expected: $expectedStart + |$line + |${" " * actualStart}^ + |0123456789 123456789 1234567890 + | 2 3 + """.stripMargin) + } + } +} diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala index 09bbd5c867e4e..2a7374cc172b7 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala @@ -75,13 +75,13 @@ class HiveInspectorSuite extends FunSuite with HiveInspectors { Literal(0.asInstanceOf[Float]) :: Literal(0.asInstanceOf[Double]) :: Literal("0") :: - Literal(new java.sql.Date(114, 8, 23)) :: + Literal(java.sql.Date.valueOf("2014-09-23")) :: Literal(Decimal(BigDecimal(123.123))) :: Literal(new java.sql.Timestamp(123123)) :: Literal(Array[Byte](1,2,3)) :: - Literal(Seq[Int](1,2,3), ArrayType(IntegerType)) :: - Literal(Map[Int, Int](1->2, 2->1), MapType(IntegerType, IntegerType)) :: - Literal(Row(1,2.0d,3.0f), + Literal.create(Seq[Int](1,2,3), ArrayType(IntegerType)) :: + Literal.create(Map[Int, Int](1->2, 2->1), MapType(IntegerType, IntegerType)) :: + Literal.create(Row(1,2.0d,3.0f), StructType(StructField("c1", IntegerType) :: StructField("c2", DoubleType) :: StructField("c3", FloatType) :: Nil)) :: @@ -116,21 +116,20 @@ class HiveInspectorSuite extends FunSuite with HiveInspectors { } def checkDataType(dt1: Seq[DataType], dt2: Seq[DataType]): Unit = { - dt1.zip(dt2).map { - case (dd1, dd2) => - assert(dd1.getClass === dd2.getClass) // DecimalType doesn't has the default precision info + dt1.zip(dt2).foreach { case (dd1, dd2) => + assert(dd1.getClass === dd2.getClass) // DecimalType doesn't has the default precision info } } def checkValues(row1: Seq[Any], row2: Seq[Any]): Unit = { - row1.zip(row2).map { - case (r1, r2) => checkValue(r1, r2) + row1.zip(row2).foreach { case (r1, r2) => + checkValue(r1, r2) } } def checkValues(row1: Seq[Any], row2: Row): Unit = { - row1.zip(row2.toSeq).map { - case (r1, r2) => checkValue(r1, r2) + row1.zip(row2.toSeq).foreach { case (r1, r2) => + checkValue(r1, r2) } } @@ -141,7 +140,7 @@ class HiveInspectorSuite extends FunSuite with HiveInspectors { assert(r1.compare(r2) === 0) case (r1: Array[Byte], r2: Array[Byte]) if r1 != null && r2 != null && r1.length == r2.length => - r1.zip(r2).map { case (b1, b2) => assert(b1 === b2) } + r1.zip(r2).foreach { case (b1, b2) => assert(b1 === b2) } case (r1, r2) => assert(r1 === r2) } } @@ -166,7 +165,8 @@ class HiveInspectorSuite extends FunSuite with HiveInspectors { val constantData = constantExprs.map(_.eval()) val constantNullData = constantData.map(_ => null) val constantWritableOIs = constantExprs.map(e => toWritableInspector(e.dataType)) - val constantNullWritableOIs = constantExprs.map(e => toInspector(Literal(null, e.dataType))) + val constantNullWritableOIs = + constantExprs.map(e => toInspector(Literal.create(null, e.dataType))) checkValues(constantData, constantData.zip(constantWritableOIs).map { case (d, oi) => unwrap(wrap(d, oi), oi) @@ -202,7 +202,8 @@ class HiveInspectorSuite extends FunSuite with HiveInspectors { case (t, idx) => StructField(s"c_$idx", t) }) - checkValues(row, unwrap(wrap(Row.fromSeq(row), toInspector(dt)), toInspector(dt)).asInstanceOf[Row]) + checkValues(row, + unwrap(wrap(Row.fromSeq(row), toInspector(dt)), toInspector(dt)).asInstanceOf[Row]) checkValue(null, unwrap(wrap(null, toInspector(dt)), toInspector(dt))) } @@ -212,8 +213,10 @@ class HiveInspectorSuite extends FunSuite with HiveInspectors { val d = row(0) :: row(0) :: Nil checkValue(d, unwrap(wrap(d, toInspector(dt)), toInspector(dt))) checkValue(null, unwrap(wrap(null, toInspector(dt)), toInspector(dt))) - checkValue(d, unwrap(wrap(d, toInspector(Literal(d, dt))), toInspector(Literal(d, dt)))) - checkValue(d, unwrap(wrap(null, toInspector(Literal(d, dt))), toInspector(Literal(d, dt)))) + checkValue(d, + unwrap(wrap(d, toInspector(Literal.create(d, dt))), toInspector(Literal.create(d, dt)))) + checkValue(d, + unwrap(wrap(null, toInspector(Literal.create(d, dt))), toInspector(Literal.create(d, dt)))) } test("wrap / unwrap Map Type") { @@ -222,7 +225,9 @@ class HiveInspectorSuite extends FunSuite with HiveInspectors { val d = Map(row(0) -> row(1)) checkValue(d, unwrap(wrap(d, toInspector(dt)), toInspector(dt))) checkValue(null, unwrap(wrap(null, toInspector(dt)), toInspector(dt))) - checkValue(d, unwrap(wrap(d, toInspector(Literal(d, dt))), toInspector(Literal(d, dt)))) - checkValue(d, unwrap(wrap(null, toInspector(Literal(d, dt))), toInspector(Literal(d, dt)))) + checkValue(d, + unwrap(wrap(d, toInspector(Literal.create(d, dt))), toInspector(Literal.create(d, dt)))) + checkValue(d, + unwrap(wrap(null, toInspector(Literal.create(d, dt))), toInspector(Literal.create(d, dt)))) } } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala index aad48ada52642..fa8e11ffec2b4 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala @@ -17,6 +17,7 @@ package org.apache.spark.sql.hive +import org.apache.spark.sql.hive.test.TestHive import org.scalatest.FunSuite import org.apache.spark.sql.test.ExamplePointUDT @@ -36,4 +37,11 @@ class HiveMetastoreCatalogSuite extends FunSuite { assert(HiveMetastoreTypes.toMetastoreType(udt) === HiveMetastoreTypes.toMetastoreType(udt.sqlType)) } + + test("duplicated metastore relations") { + import TestHive.implicits._ + val df = TestHive.sql("SELECT * FROM src") + println(df.queryExecution) + df.as('a).join(df.as('b), $"a.key" === $"b.key") + } } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/parquet/HiveParquetSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveParquetSuite.scala similarity index 96% rename from sql/hive/src/test/scala/org/apache/spark/sql/parquet/HiveParquetSuite.scala rename to sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveParquetSuite.scala index e89b4489f15d1..7ff5719adb3ab 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/parquet/HiveParquetSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveParquetSuite.scala @@ -15,11 +15,12 @@ * limitations under the License. */ -package org.apache.spark.sql.parquet +package org.apache.spark.sql.hive -import org.apache.spark.sql.{SQLConf, QueryTest} import org.apache.spark.sql.catalyst.expressions.Row import org.apache.spark.sql.hive.test.TestHive +import org.apache.spark.sql.parquet.ParquetTest +import org.apache.spark.sql.{QueryTest, SQLConf} case class Cases(lower: String, UPPER: String) diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala index 869d01eb398c5..ecb990e8aac91 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala @@ -19,25 +19,38 @@ package org.apache.spark.sql.hive import java.io.File -import com.google.common.io.Files +import org.scalatest.BeforeAndAfter + +import org.apache.spark.sql.execution.QueryExecutionException import org.apache.spark.sql.{QueryTest, _} import org.apache.spark.sql.hive.test.TestHive import org.apache.spark.sql.types._ +import org.apache.spark.util.Utils /* Implicits */ import org.apache.spark.sql.hive.test.TestHive._ case class TestData(key: Int, value: String) -class InsertIntoHiveTableSuite extends QueryTest { +case class ThreeCloumntable(key: Int, value: String, key1: String) + +class InsertIntoHiveTableSuite extends QueryTest with BeforeAndAfter { import org.apache.spark.sql.hive.test.TestHive.implicits._ + val testData = TestHive.sparkContext.parallelize( - (1 to 100).map(i => TestData(i, i.toString))) - testData.registerTempTable("testData") + (1 to 100).map(i => TestData(i, i.toString))).toDF() + + before { + // Since every we are doing tests for DDL statements, + // it is better to reset before every test. + TestHive.reset() + // Register the testData, which will be used in every test. + testData.registerTempTable("testData") + } test("insertInto() HiveTable") { - createTable[TestData]("createAndInsertTest") + sql("CREATE TABLE createAndInsertTest (key int, value string)") // Add some data. testData.insertInto("createAndInsertTest") @@ -45,7 +58,7 @@ class InsertIntoHiveTableSuite extends QueryTest { // Make sure the table has also been updated. checkAnswer( sql("SELECT * FROM createAndInsertTest"), - testData.collect().toSeq.map(Row.fromTuple) + testData.collect().toSeq ) // Add more data. @@ -54,7 +67,7 @@ class InsertIntoHiveTableSuite extends QueryTest { // Make sure the table has been updated. checkAnswer( sql("SELECT * FROM createAndInsertTest"), - testData.toDataFrame.collect().toSeq ++ testData.toDataFrame.collect().toSeq + testData.toDF().collect().toSeq ++ testData.toDF().collect().toSeq ) // Now overwrite. @@ -63,28 +76,30 @@ class InsertIntoHiveTableSuite extends QueryTest { // Make sure the registered table has also been updated. checkAnswer( sql("SELECT * FROM createAndInsertTest"), - testData.collect().toSeq.map(Row.fromTuple) + testData.collect().toSeq ) } test("Double create fails when allowExisting = false") { - createTable[TestData]("doubleCreateAndInsertTest") + sql("CREATE TABLE doubleCreateAndInsertTest (key int, value string)") - intercept[org.apache.hadoop.hive.ql.metadata.HiveException] { - createTable[TestData]("doubleCreateAndInsertTest", allowExisting = false) - } + val message = intercept[QueryExecutionException] { + sql("CREATE TABLE doubleCreateAndInsertTest (key int, value string)") + }.getMessage + + println("message!!!!" + message) } test("Double create does not fail when allowExisting = true") { - createTable[TestData]("createAndInsertTest") - createTable[TestData]("createAndInsertTest") + sql("CREATE TABLE doubleCreateAndInsertTest (key int, value string)") + sql("CREATE TABLE IF NOT EXISTS doubleCreateAndInsertTest (key int, value string)") } test("SPARK-4052: scala.collection.Map as value type of MapType") { val schema = StructType(StructField("m", MapType(StringType, StringType), true) :: Nil) val rowRDD = TestHive.sparkContext.parallelize( (1 to 100).map(i => Row(scala.collection.mutable.HashMap(s"key$i" -> s"value$i")))) - val df = applySchema(rowRDD, schema) + val df = TestHive.createDataFrame(rowRDD, schema) df.registerTempTable("tableWithMapValue") sql("CREATE TABLE hiveTableWithMapValue(m MAP )") sql("INSERT OVERWRITE TABLE hiveTableWithMapValue SELECT m FROM tableWithMapValue") @@ -98,13 +113,38 @@ class InsertIntoHiveTableSuite extends QueryTest { } test("SPARK-4203:random partition directory order") { - createTable[TestData]("tmp_table") - val tmpDir = Files.createTempDir() - sql(s"CREATE TABLE table_with_partition(c1 string) PARTITIONED by (p1 string,p2 string,p3 string,p4 string,p5 string) location '${tmpDir.toURI.toString}' ") - sql("INSERT OVERWRITE TABLE table_with_partition partition (p1='a',p2='b',p3='c',p4='c',p5='1') SELECT 'blarr' FROM tmp_table") - sql("INSERT OVERWRITE TABLE table_with_partition partition (p1='a',p2='b',p3='c',p4='c',p5='2') SELECT 'blarr' FROM tmp_table") - sql("INSERT OVERWRITE TABLE table_with_partition partition (p1='a',p2='b',p3='c',p4='c',p5='3') SELECT 'blarr' FROM tmp_table") - sql("INSERT OVERWRITE TABLE table_with_partition partition (p1='a',p2='b',p3='c',p4='c',p5='4') SELECT 'blarr' FROM tmp_table") + sql("CREATE TABLE tmp_table (key int, value string)") + val tmpDir = Utils.createTempDir() + sql( + s""" + |CREATE TABLE table_with_partition(c1 string) + |PARTITIONED by (p1 string,p2 string,p3 string,p4 string,p5 string) + |location '${tmpDir.toURI.toString}' + """.stripMargin) + sql( + """ + |INSERT OVERWRITE TABLE table_with_partition + |partition (p1='a',p2='b',p3='c',p4='c',p5='1') + |SELECT 'blarr' FROM tmp_table + """.stripMargin) + sql( + """ + |INSERT OVERWRITE TABLE table_with_partition + |partition (p1='a',p2='b',p3='c',p4='c',p5='2') + |SELECT 'blarr' FROM tmp_table + """.stripMargin) + sql( + """ + |INSERT OVERWRITE TABLE table_with_partition + |partition (p1='a',p2='b',p3='c',p4='c',p5='3') + |SELECT 'blarr' FROM tmp_table + """.stripMargin) + sql( + """ + |INSERT OVERWRITE TABLE table_with_partition + |partition (p1='a',p2='b',p3='c',p4='c',p5='4') + |SELECT 'blarr' FROM tmp_table + """.stripMargin) def listFolders(path: File, acc: List[String]): List[List[String]] = { val dir = path.listFiles() val folders = dir.filter(_.isDirectory).toList @@ -129,7 +169,7 @@ class InsertIntoHiveTableSuite extends QueryTest { val schema = StructType(Seq( StructField("a", ArrayType(StringType, containsNull = false)))) val rowRDD = TestHive.sparkContext.parallelize((1 to 100).map(i => Row(Seq(s"value$i")))) - val df = applySchema(rowRDD, schema) + val df = TestHive.createDataFrame(rowRDD, schema) df.registerTempTable("tableWithArrayValue") sql("CREATE TABLE hiveTableWithArrayValue(a Array )") sql("INSERT OVERWRITE TABLE hiveTableWithArrayValue SELECT a FROM tableWithArrayValue") @@ -146,7 +186,7 @@ class InsertIntoHiveTableSuite extends QueryTest { StructField("m", MapType(StringType, StringType, valueContainsNull = false)))) val rowRDD = TestHive.sparkContext.parallelize( (1 to 100).map(i => Row(Map(s"key$i" -> s"value$i")))) - val df = applySchema(rowRDD, schema) + val df = TestHive.createDataFrame(rowRDD, schema) df.registerTempTable("tableWithMapValue") sql("CREATE TABLE hiveTableWithMapValue(m Map )") sql("INSERT OVERWRITE TABLE hiveTableWithMapValue SELECT m FROM tableWithMapValue") @@ -163,7 +203,7 @@ class InsertIntoHiveTableSuite extends QueryTest { StructField("s", StructType(Seq(StructField("f", StringType, nullable = false)))))) val rowRDD = TestHive.sparkContext.parallelize( (1 to 100).map(i => Row(Row(s"value$i")))) - val df = applySchema(rowRDD, schema) + val df = TestHive.createDataFrame(rowRDD, schema) df.registerTempTable("tableWithStructValue") sql("CREATE TABLE hiveTableWithStructValue(s Struct )") sql("INSERT OVERWRITE TABLE hiveTableWithStructValue SELECT s FROM tableWithStructValue") @@ -174,4 +214,51 @@ class InsertIntoHiveTableSuite extends QueryTest { sql("DROP TABLE hiveTableWithStructValue") } + + test("SPARK-5498:partition schema does not match table schema") { + val testData = TestHive.sparkContext.parallelize( + (1 to 10).map(i => TestData(i, i.toString))).toDF() + testData.registerTempTable("testData") + + val testDatawithNull = TestHive.sparkContext.parallelize( + (1 to 10).map(i => ThreeCloumntable(i, i.toString, null))).toDF() + + val tmpDir = Utils.createTempDir() + sql( + s""" + |CREATE TABLE table_with_partition(key int,value string) + |PARTITIONED by (ds string) location '${tmpDir.toURI.toString}' + """.stripMargin) + sql( + """ + |INSERT OVERWRITE TABLE table_with_partition + |partition (ds='1') SELECT key,value FROM testData + """.stripMargin) + + // test schema the same between partition and table + sql("ALTER TABLE table_with_partition CHANGE COLUMN key key BIGINT") + checkAnswer(sql("select key,value from table_with_partition where ds='1' "), + testData.collect().toSeq + ) + + // test difference type of field + sql("ALTER TABLE table_with_partition CHANGE COLUMN key key BIGINT") + checkAnswer(sql("select key,value from table_with_partition where ds='1' "), + testData.collect().toSeq + ) + + // add column to table + sql("ALTER TABLE table_with_partition ADD COLUMNS(key1 string)") + checkAnswer(sql("select key,value,key1 from table_with_partition where ds='1' "), + testDatawithNull.collect().toSeq + ) + + // change column name to table + sql("ALTER TABLE table_with_partition CHANGE COLUMN key keynew BIGINT") + checkAnswer(sql("select keynew,value from table_with_partition where ds='1' "), + testData.collect().toSeq + ) + + sql("DROP TABLE table_with_partition") + } } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ListTablesSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ListTablesSuite.scala new file mode 100644 index 0000000000000..e12a6c21ccac4 --- /dev/null +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ListTablesSuite.scala @@ -0,0 +1,81 @@ +/* +* Licensed to the Apache Software Foundation (ASF) under one or more +* contributor license agreements. See the NOTICE file distributed with +* this work for additional information regarding copyright ownership. +* The ASF licenses this file to You under the Apache License, Version 2.0 +* (the "License"); you may not use this file except in compliance with +* the License. You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +*/ + +package org.apache.spark.sql.hive + +import org.scalatest.BeforeAndAfterAll + +import org.apache.spark.sql.hive.test.TestHive +import org.apache.spark.sql.hive.test.TestHive._ +import org.apache.spark.sql.QueryTest +import org.apache.spark.sql.Row + +class ListTablesSuite extends QueryTest with BeforeAndAfterAll { + + import org.apache.spark.sql.hive.test.TestHive.implicits._ + + val df = + sparkContext.parallelize((1 to 10).map(i => (i,s"str$i"))).toDF("key", "value") + + override def beforeAll(): Unit = { + // The catalog in HiveContext is a case insensitive one. + catalog.registerTable(Seq("ListTablesSuiteTable"), df.logicalPlan) + catalog.registerTable(Seq("ListTablesSuiteDB", "InDBListTablesSuiteTable"), df.logicalPlan) + sql("CREATE TABLE HiveListTablesSuiteTable (key int, value string)") + sql("CREATE DATABASE IF NOT EXISTS ListTablesSuiteDB") + sql("CREATE TABLE ListTablesSuiteDB.HiveInDBListTablesSuiteTable (key int, value string)") + } + + override def afterAll(): Unit = { + catalog.unregisterTable(Seq("ListTablesSuiteTable")) + catalog.unregisterTable(Seq("ListTablesSuiteDB", "InDBListTablesSuiteTable")) + sql("DROP TABLE IF EXISTS HiveListTablesSuiteTable") + sql("DROP TABLE IF EXISTS ListTablesSuiteDB.HiveInDBListTablesSuiteTable") + sql("DROP DATABASE IF EXISTS ListTablesSuiteDB") + } + + test("get all tables of current database") { + Seq(tables(), sql("SHOW TABLes")).foreach { + case allTables => + // We are using default DB. + checkAnswer( + allTables.filter("tableName = 'listtablessuitetable'"), + Row("listtablessuitetable", true)) + assert(allTables.filter("tableName = 'indblisttablessuitetable'").count() === 0) + checkAnswer( + allTables.filter("tableName = 'hivelisttablessuitetable'"), + Row("hivelisttablessuitetable", false)) + assert(allTables.filter("tableName = 'hiveindblisttablessuitetable'").count() === 0) + } + } + + test("getting all tables with a database name") { + Seq(tables("listtablessuiteDb"), sql("SHOW TABLes in listTablesSuitedb")).foreach { + case allTables => + checkAnswer( + allTables.filter("tableName = 'listtablessuitetable'"), + Row("listtablessuitetable", true)) + checkAnswer( + allTables.filter("tableName = 'indblisttablessuitetable'"), + Row("indblisttablessuitetable", true)) + assert(allTables.filter("tableName = 'hivelisttablessuitetable'").count() === 0) + checkAnswer( + allTables.filter("tableName = 'hiveindblisttablessuitetable'"), + Row("hiveindblisttablessuitetable", false)) + } + } +} diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala index c23575fe96898..e09c702c8969e 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala @@ -19,33 +19,37 @@ package org.apache.spark.sql.hive import java.io.File +import scala.collection.mutable.ArrayBuffer + import org.scalatest.BeforeAndAfterEach import org.apache.commons.io.FileUtils import org.apache.hadoop.fs.Path +import org.apache.hadoop.hive.metastore.TableType +import org.apache.hadoop.hive.ql.metadata.Table +import org.apache.hadoop.mapred.InvalidInputException -import org.apache.spark.sql.catalyst.util import org.apache.spark.sql._ import org.apache.spark.util.Utils import org.apache.spark.sql.types._ - -/* Implicits */ import org.apache.spark.sql.hive.test.TestHive._ +import org.apache.spark.sql.hive.test.TestHive.implicits._ +import org.apache.spark.sql.parquet.ParquetRelation2 +import org.apache.spark.sql.sources.LogicalRelation /** * Tests for persisting tables created though the data sources API into the metastore. */ class MetastoreDataSourcesSuite extends QueryTest with BeforeAndAfterEach { - import org.apache.spark.sql.hive.test.TestHive.implicits._ - override def afterEach(): Unit = { reset() - if (ctasPath.exists()) Utils.deleteRecursively(ctasPath) + Utils.deleteRecursively(tempPath) } val filePath = Utils.getSparkClassLoader.getResource("sample.json").getFile - var ctasPath: File = util.getTempFilePath("jsonCTAS").getCanonicalFile + var tempPath: File = Utils.createTempDir() + tempPath.delete() test ("persistent JSON table") { sql( @@ -152,9 +156,10 @@ class MetastoreDataSourcesSuite extends QueryTest with BeforeAndAfterEach { } test("check change without refresh") { - val tempDir = File.createTempFile("sparksql", "json") + val tempDir = File.createTempFile("sparksql", "json", Utils.createTempDir()) tempDir.delete() - sparkContext.parallelize(("a", "b") :: Nil).toJSON.saveAsTextFile(tempDir.getCanonicalPath) + sparkContext.parallelize(("a", "b") :: Nil).toDF() + .toJSON.saveAsTextFile(tempDir.getCanonicalPath) sql( s""" @@ -170,7 +175,8 @@ class MetastoreDataSourcesSuite extends QueryTest with BeforeAndAfterEach { Row("a", "b")) FileUtils.deleteDirectory(tempDir) - sparkContext.parallelize(("a1", "b1", "c1") :: Nil).toJSON.saveAsTextFile(tempDir.getCanonicalPath) + sparkContext.parallelize(("a1", "b1", "c1") :: Nil).toDF() + .toJSON.saveAsTextFile(tempDir.getCanonicalPath) // Schema is cached so the new column does not show. The updated values in existing columns // will show. @@ -178,7 +184,7 @@ class MetastoreDataSourcesSuite extends QueryTest with BeforeAndAfterEach { sql("SELECT * FROM jsonTable"), Row("a1", "b1")) - refreshTable("jsonTable") + sql("REFRESH TABLE jsonTable") // Check that the refresh worked checkAnswer( @@ -188,9 +194,10 @@ class MetastoreDataSourcesSuite extends QueryTest with BeforeAndAfterEach { } test("drop, change, recreate") { - val tempDir = File.createTempFile("sparksql", "json") + val tempDir = File.createTempFile("sparksql", "json", Utils.createTempDir()) tempDir.delete() - sparkContext.parallelize(("a", "b") :: Nil).toJSON.saveAsTextFile(tempDir.getCanonicalPath) + sparkContext.parallelize(("a", "b") :: Nil).toDF() + .toJSON.saveAsTextFile(tempDir.getCanonicalPath) sql( s""" @@ -206,7 +213,8 @@ class MetastoreDataSourcesSuite extends QueryTest with BeforeAndAfterEach { Row("a", "b")) FileUtils.deleteDirectory(tempDir) - sparkContext.parallelize(("a", "b", "c") :: Nil).toJSON.saveAsTextFile(tempDir.getCanonicalPath) + sparkContext.parallelize(("a", "b", "c") :: Nil).toDF() + .toJSON.saveAsTextFile(tempDir.getCanonicalPath) sql("DROP TABLE jsonTable") @@ -270,7 +278,7 @@ class MetastoreDataSourcesSuite extends QueryTest with BeforeAndAfterEach { |CREATE TABLE ctasJsonTable |USING org.apache.spark.sql.json.DefaultSource |OPTIONS ( - | path '${ctasPath}' + | path '${tempPath}' |) AS |SELECT * FROM jsonTable """.stripMargin) @@ -297,19 +305,19 @@ class MetastoreDataSourcesSuite extends QueryTest with BeforeAndAfterEach { |CREATE TABLE ctasJsonTable |USING org.apache.spark.sql.json.DefaultSource |OPTIONS ( - | path '${ctasPath}' + | path '${tempPath}' |) AS |SELECT * FROM jsonTable """.stripMargin) - // Create the table again should trigger a AlreadyExistsException. - val message = intercept[RuntimeException] { + // Create the table again should trigger a AnalysisException. + val message = intercept[AnalysisException] { sql( s""" |CREATE TABLE ctasJsonTable |USING org.apache.spark.sql.json.DefaultSource |OPTIONS ( - | path '${ctasPath}' + | path '${tempPath}' |) AS |SELECT * FROM jsonTable """.stripMargin) @@ -325,7 +333,7 @@ class MetastoreDataSourcesSuite extends QueryTest with BeforeAndAfterEach { |CREATE TABLE IF NOT EXISTS ctasJsonTable |USING org.apache.spark.sql.json.DefaultSource |OPTIONS ( - | path '${ctasPath}' + | path '${tempPath}' |) AS |SELECT a FROM jsonTable """.stripMargin) @@ -351,9 +359,6 @@ class MetastoreDataSourcesSuite extends QueryTest with BeforeAndAfterEach { |) """.stripMargin) - new Path("/Users/yhuai/Desktop/whatever") - - val expectedPath = catalog.hiveDefaultTableFilePath("ctasJsonTable") val filesystemPath = new Path(expectedPath) val fs = filesystemPath.getFileSystem(sparkContext.hadoopConfiguration) @@ -364,9 +369,7 @@ class MetastoreDataSourcesSuite extends QueryTest with BeforeAndAfterEach { s""" |CREATE TABLE ctasJsonTable |USING org.apache.spark.sql.json.DefaultSource - |OPTIONS ( - | - |) AS + |AS |SELECT * FROM jsonTable """.stripMargin) @@ -405,38 +408,352 @@ class MetastoreDataSourcesSuite extends QueryTest with BeforeAndAfterEach { sql("DROP TABLE jsonTable").collect().foreach(println) } - test("save and load table") { + test("SPARK-5839 HiveMetastoreCatalog does not recognize table aliases of data source tables.") { val originalDefaultSource = conf.defaultDataSourceName - conf.setConf("spark.sql.default.datasource", "org.apache.spark.sql.json") val rdd = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str${i}"}""")) val df = jsonRDD(rdd) + conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, "org.apache.spark.sql.json") + // Save the df as a managed table (by not specifiying the path). + df.saveAsTable("savedJsonTable") + + checkAnswer( + sql("SELECT * FROM savedJsonTable where savedJsonTable.a < 5"), + (1 to 4).map(i => Row(i, s"str${i}"))) + + checkAnswer( + sql("SELECT * FROM savedJsonTable tmp where tmp.a > 5"), + (6 to 10).map(i => Row(i, s"str${i}"))) + + invalidateTable("savedJsonTable") + + checkAnswer( + sql("SELECT * FROM savedJsonTable where savedJsonTable.a < 5"), + (1 to 4).map(i => Row(i, s"str${i}"))) + + checkAnswer( + sql("SELECT * FROM savedJsonTable tmp where tmp.a > 5"), + (6 to 10).map(i => Row(i, s"str${i}"))) + + // Drop table will also delete the data. + sql("DROP TABLE savedJsonTable") + + conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, originalDefaultSource) + } + + test("save table") { + val originalDefaultSource = conf.defaultDataSourceName + + val rdd = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str${i}"}""")) + val df = jsonRDD(rdd) + + conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, "org.apache.spark.sql.json") + // Save the df as a managed table (by not specifiying the path). df.saveAsTable("savedJsonTable") checkAnswer( sql("SELECT * FROM savedJsonTable"), df.collect()) - createTable("createdJsonTable", catalog.hiveDefaultTableFilePath("savedJsonTable"), false) + // Right now, we cannot append to an existing JSON table. + intercept[RuntimeException] { + df.saveAsTable("savedJsonTable", SaveMode.Append) + } + + // We can overwrite it. + df.saveAsTable("savedJsonTable", SaveMode.Overwrite) + checkAnswer( + sql("SELECT * FROM savedJsonTable"), + df.collect()) + + // When the save mode is Ignore, we will do nothing when the table already exists. + df.select("b").saveAsTable("savedJsonTable", SaveMode.Ignore) + assert(df.schema === table("savedJsonTable").schema) + checkAnswer( + sql("SELECT * FROM savedJsonTable"), + df.collect()) + + // Drop table will also delete the data. + sql("DROP TABLE savedJsonTable") + intercept[InvalidInputException] { + jsonFile(catalog.hiveDefaultTableFilePath("savedJsonTable")) + } + + // Create an external table by specifying the path. + conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, "not a source name") + df.saveAsTable( + "savedJsonTable", + "org.apache.spark.sql.json", + SaveMode.Append, + Map("path" -> tempPath.toString)) + checkAnswer( + sql("SELECT * FROM savedJsonTable"), + df.collect()) + + // Data should not be deleted after we drop the table. + sql("DROP TABLE savedJsonTable") + checkAnswer( + jsonFile(tempPath.toString), + df.collect()) + + conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, originalDefaultSource) + } + + test("create external table") { + val originalDefaultSource = conf.defaultDataSourceName + + val rdd = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str${i}"}""")) + val df = jsonRDD(rdd) + + conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, "not a source name") + df.saveAsTable( + "savedJsonTable", + "org.apache.spark.sql.json", + SaveMode.Append, + Map("path" -> tempPath.toString)) + + conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, "org.apache.spark.sql.json") + createExternalTable("createdJsonTable", tempPath.toString) assert(table("createdJsonTable").schema === df.schema) checkAnswer( sql("SELECT * FROM createdJsonTable"), df.collect()) - val message = intercept[RuntimeException] { - createTable("createdJsonTable", filePath.toString, false) + var message = intercept[AnalysisException] { + createExternalTable("createdJsonTable", filePath.toString) }.getMessage assert(message.contains("Table createdJsonTable already exists."), "We should complain that ctasJsonTable already exists") - createTable("createdJsonTable", filePath.toString, true) - // createdJsonTable should be not changed. - assert(table("createdJsonTable").schema === df.schema) + // Data should not be deleted. + sql("DROP TABLE createdJsonTable") checkAnswer( - sql("SELECT * FROM createdJsonTable"), + jsonFile(tempPath.toString), df.collect()) - conf.setConf("spark.sql.default.datasource", originalDefaultSource) + // Try to specify the schema. + conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, "not a source name") + val schema = StructType(StructField("b", StringType, true) :: Nil) + createExternalTable( + "createdJsonTable", + "org.apache.spark.sql.json", + schema, + Map("path" -> tempPath.toString)) + checkAnswer( + sql("SELECT * FROM createdJsonTable"), + sql("SELECT b FROM savedJsonTable").collect()) + + sql("DROP TABLE createdJsonTable") + + message = intercept[RuntimeException] { + createExternalTable( + "createdJsonTable", + "org.apache.spark.sql.json", + schema, + Map.empty[String, String]) + }.getMessage + assert( + message.contains("'path' must be specified for json data."), + "We should complain that path is not specified.") + + sql("DROP TABLE savedJsonTable") + conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, originalDefaultSource) + } + + if (HiveShim.version == "0.13.1") { + test("scan a parquet table created through a CTAS statement") { + val originalConvertMetastore = getConf("spark.sql.hive.convertMetastoreParquet", "true") + val originalUseDataSource = getConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, "true") + setConf("spark.sql.hive.convertMetastoreParquet", "true") + setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, "true") + + val rdd = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str${i}"}""")) + jsonRDD(rdd).registerTempTable("jt") + sql( + """ + |create table test_parquet_ctas STORED AS parquET + |AS select tmp.a from jt tmp where tmp.a < 5 + """.stripMargin) + + checkAnswer( + sql(s"SELECT a FROM test_parquet_ctas WHERE a > 2 "), + Row(3) :: Row(4) :: Nil + ) + + table("test_parquet_ctas").queryExecution.optimizedPlan match { + case LogicalRelation(p: ParquetRelation2) => // OK + case _ => + fail( + "test_parquet_ctas should be converted to " + + s"${classOf[ParquetRelation2].getCanonicalName}") + } + + // Clenup and reset confs. + sql("DROP TABLE IF EXISTS jt") + sql("DROP TABLE IF EXISTS test_parquet_ctas") + setConf("spark.sql.hive.convertMetastoreParquet", originalConvertMetastore) + setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalUseDataSource) + } + } + + test("Pre insert nullability check (ArrayType)") { + val df1 = + createDataFrame(Tuple1(Seq(Int.box(1), null.asInstanceOf[Integer])) :: Nil).toDF("a") + val expectedSchema1 = + StructType( + StructField("a", ArrayType(IntegerType, containsNull = true), nullable = true) :: Nil) + assert(df1.schema === expectedSchema1) + df1.saveAsTable("arrayInParquet", "parquet", SaveMode.Overwrite) + + val df2 = + createDataFrame(Tuple1(Seq(2, 3)) :: Nil).toDF("a") + val expectedSchema2 = + StructType( + StructField("a", ArrayType(IntegerType, containsNull = false), nullable = true) :: Nil) + assert(df2.schema === expectedSchema2) + df2.insertInto("arrayInParquet", overwrite = false) + createDataFrame(Tuple1(Seq(4, 5)) :: Nil).toDF("a") + .saveAsTable("arrayInParquet", SaveMode.Append) // This one internally calls df2.insertInto. + createDataFrame(Tuple1(Seq(Int.box(6), null.asInstanceOf[Integer])) :: Nil).toDF("a") + .saveAsTable("arrayInParquet", "parquet", SaveMode.Append) + refreshTable("arrayInParquet") + + checkAnswer( + sql("SELECT a FROM arrayInParquet"), + Row(ArrayBuffer(1, null)) :: + Row(ArrayBuffer(2, 3)) :: + Row(ArrayBuffer(4, 5)) :: + Row(ArrayBuffer(6, null)) :: Nil) + + sql("DROP TABLE arrayInParquet") + } + + test("Pre insert nullability check (MapType)") { + val df1 = + createDataFrame(Tuple1(Map(1 -> null.asInstanceOf[Integer])) :: Nil).toDF("a") + val mapType1 = MapType(IntegerType, IntegerType, valueContainsNull = true) + val expectedSchema1 = + StructType( + StructField("a", mapType1, nullable = true) :: Nil) + assert(df1.schema === expectedSchema1) + df1.saveAsTable("mapInParquet", "parquet", SaveMode.Overwrite) + + val df2 = + createDataFrame(Tuple1(Map(2 -> 3)) :: Nil).toDF("a") + val mapType2 = MapType(IntegerType, IntegerType, valueContainsNull = false) + val expectedSchema2 = + StructType( + StructField("a", mapType2, nullable = true) :: Nil) + assert(df2.schema === expectedSchema2) + df2.insertInto("mapInParquet", overwrite = false) + createDataFrame(Tuple1(Map(4 -> 5)) :: Nil).toDF("a") + .saveAsTable("mapInParquet", SaveMode.Append) // This one internally calls df2.insertInto. + createDataFrame(Tuple1(Map(6 -> null.asInstanceOf[Integer])) :: Nil).toDF("a") + .saveAsTable("mapInParquet", "parquet", SaveMode.Append) + refreshTable("mapInParquet") + + checkAnswer( + sql("SELECT a FROM mapInParquet"), + Row(Map(1 -> null)) :: + Row(Map(2 -> 3)) :: + Row(Map(4 -> 5)) :: + Row(Map(6 -> null)) :: Nil) + + sql("DROP TABLE mapInParquet") + } + + test("SPARK-6024 wide schema support") { + // We will need 80 splits for this schema if the threshold is 4000. + val schema = StructType((1 to 5000).map(i => StructField(s"c_${i}", StringType, true))) + assert( + schema.json.size > conf.schemaStringLengthThreshold, + "To correctly test the fix of SPARK-6024, the value of " + + s"spark.sql.sources.schemaStringLengthThreshold needs to be less than ${schema.json.size}") + // Manually create a metastore data source table. + catalog.createDataSourceTable( + tableName = "wide_schema", + userSpecifiedSchema = Some(schema), + provider = "json", + options = Map("path" -> "just a dummy path"), + isExternal = false) + + invalidateTable("wide_schema") + + val actualSchema = table("wide_schema").schema + assert(schema === actualSchema) + } + + test("SPARK-6655 still support a schema stored in spark.sql.sources.schema") { + val tableName = "spark6655" + val schema = StructType(StructField("int", IntegerType, true) :: Nil) + // Manually create the metadata in metastore. + val tbl = new Table("default", tableName) + tbl.setProperty("spark.sql.sources.provider", "json") + tbl.setProperty("spark.sql.sources.schema", schema.json) + tbl.setProperty("EXTERNAL", "FALSE") + tbl.setTableType(TableType.MANAGED_TABLE) + tbl.setSerdeParam("path", catalog.hiveDefaultTableFilePath(tableName)) + catalog.synchronized { + catalog.client.createTable(tbl) + } + + invalidateTable(tableName) + val actualSchema = table(tableName).schema + assert(schema === actualSchema) + sql(s"drop table $tableName") + } + + + test("insert into a table") { + def createDF(from: Int, to: Int): DataFrame = + createDataFrame((from to to).map(i => Tuple2(i, s"str$i"))).toDF("c1", "c2") + + createDF(0, 9).saveAsTable("insertParquet", "parquet") + checkAnswer( + sql("SELECT p.c1, p.c2 FROM insertParquet p WHERE p.c1 > 5"), + (6 to 9).map(i => Row(i, s"str$i"))) + + intercept[AnalysisException] { + createDF(10, 19).saveAsTable("insertParquet", "parquet") + } + + createDF(10, 19).saveAsTable("insertParquet", "parquet", SaveMode.Append) + checkAnswer( + sql("SELECT p.c1, p.c2 FROM insertParquet p WHERE p.c1 > 5"), + (6 to 19).map(i => Row(i, s"str$i"))) + + createDF(20, 29).saveAsTable("insertParquet", "parquet", SaveMode.Append) + checkAnswer( + sql("SELECT p.c1, c2 FROM insertParquet p WHERE p.c1 > 5 AND p.c1 < 25"), + (6 to 24).map(i => Row(i, s"str$i"))) + + intercept[AnalysisException] { + createDF(30, 39).saveAsTable("insertParquet") + } + + createDF(30, 39).saveAsTable("insertParquet", SaveMode.Append) + checkAnswer( + sql("SELECT p.c1, c2 FROM insertParquet p WHERE p.c1 > 5 AND p.c1 < 35"), + (6 to 34).map(i => Row(i, s"str$i"))) + + createDF(40, 49).insertInto("insertParquet") + checkAnswer( + sql("SELECT p.c1, c2 FROM insertParquet p WHERE p.c1 > 5 AND p.c1 < 45"), + (6 to 44).map(i => Row(i, s"str$i"))) + + createDF(50, 59).saveAsTable("insertParquet", SaveMode.Overwrite) + checkAnswer( + sql("SELECT p.c1, c2 FROM insertParquet p WHERE p.c1 > 51 AND p.c1 < 55"), + (52 to 54).map(i => Row(i, s"str$i"))) + createDF(60, 69).saveAsTable("insertParquet", SaveMode.Ignore) + checkAnswer( + sql("SELECT p.c1, c2 FROM insertParquet p"), + (50 to 59).map(i => Row(i, s"str$i"))) + + createDF(70, 79).insertInto("insertParquet", overwrite = true) + checkAnswer( + sql("SELECT p.c1, c2 FROM insertParquet p"), + (70 to 79).map(i => Row(i, s"str$i"))) } } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala new file mode 100644 index 0000000000000..a787fa5546e76 --- /dev/null +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala @@ -0,0 +1,66 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive + +import com.google.common.io.Files + +import org.apache.spark.sql.{QueryTest, _} +import org.apache.spark.sql.hive.test.TestHive +import org.apache.spark.sql.hive.test.TestHive._ +import org.apache.spark.util.Utils + + +class QueryPartitionSuite extends QueryTest { + import org.apache.spark.sql.hive.test.TestHive.implicits._ + + test("SPARK-5068: query data when path doesn't exists"){ + val testData = TestHive.sparkContext.parallelize( + (1 to 10).map(i => TestData(i, i.toString))).toDF() + testData.registerTempTable("testData") + + val tmpDir = Files.createTempDir() + // create the table for test + sql(s"CREATE TABLE table_with_partition(key int,value string) " + + s"PARTITIONED by (ds string) location '${tmpDir.toURI.toString}' ") + sql("INSERT OVERWRITE TABLE table_with_partition partition (ds='1') " + + "SELECT key,value FROM testData") + sql("INSERT OVERWRITE TABLE table_with_partition partition (ds='2') " + + "SELECT key,value FROM testData") + sql("INSERT OVERWRITE TABLE table_with_partition partition (ds='3') " + + "SELECT key,value FROM testData") + sql("INSERT OVERWRITE TABLE table_with_partition partition (ds='4') " + + "SELECT key,value FROM testData") + + // test for the exist path + checkAnswer(sql("select key,value from table_with_partition"), + testData.toSchemaRDD.collect ++ testData.toSchemaRDD.collect + ++ testData.toSchemaRDD.collect ++ testData.toSchemaRDD.collect) + + // delete the path of one partition + val folders = tmpDir.listFiles.filter(_.isDirectory) + Utils.deleteRecursively(folders(0)) + + // test for after delete the path + checkAnswer(sql("select key,value from table_with_partition"), + testData.toSchemaRDD.collect ++ testData.toSchemaRDD.collect + ++ testData.toSchemaRDD.collect) + + sql("DROP TABLE table_with_partition") + sql("DROP TABLE createAndInsertTest") + } +} diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/SerializationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/SerializationSuite.scala new file mode 100644 index 0000000000000..d6ddd539d159d --- /dev/null +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/SerializationSuite.scala @@ -0,0 +1,33 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive + +import org.scalatest.FunSuite + +import org.apache.spark.SparkConf +import org.apache.spark.serializer.JavaSerializer +import org.apache.spark.sql.hive.test.TestHive + +class SerializationSuite extends FunSuite { + + test("[SPARK-5840] HiveContext should be serializable") { + val hiveContext = new HiveContext(TestHive.sparkContext) + hiveContext.hiveconf + new JavaSerializer(new SparkConf()).newInstance().serialize(hiveContext) + } +} diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala index 6f07fd5a879c0..00a69de9e4262 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala @@ -120,18 +120,18 @@ class StatisticsSuite extends QueryTest with BeforeAndAfterAll { // Try to analyze a temp table sql("""SELECT * FROM src""").registerTempTable("tempTable") - intercept[NotImplementedError] { + intercept[UnsupportedOperationException] { analyze("tempTable") } catalog.unregisterTable(Seq("tempTable")) } test("estimates the size of a test MetastoreRelation") { - val rdd = sql("""SELECT * FROM src""") - val sizes = rdd.queryExecution.analyzed.collect { case mr: MetastoreRelation => + val df = sql("""SELECT * FROM src""") + val sizes = df.queryExecution.analyzed.collect { case mr: MetastoreRelation => mr.statistics.sizeInBytes } - assert(sizes.size === 1, s"Size wrong for:\n ${rdd.queryExecution}") + assert(sizes.size === 1, s"Size wrong for:\n ${df.queryExecution}") assert(sizes(0).equals(BigInt(5812)), s"expected exact size 5812 for test table 'src', got: ${sizes(0)}") } @@ -142,13 +142,13 @@ class StatisticsSuite extends QueryTest with BeforeAndAfterAll { after: () => Unit, query: String, expectedAnswer: Seq[Row], - ct: ClassTag[_]) = { + ct: ClassTag[_]): Unit = { before() - var rdd = sql(query) + var df = sql(query) // Assert src has a size smaller than the threshold. - val sizes = rdd.queryExecution.analyzed.collect { + val sizes = df.queryExecution.analyzed.collect { case r if ct.runtimeClass.isAssignableFrom(r.getClass) => r.statistics.sizeInBytes } assert(sizes.size === 2 && sizes(0) <= conf.autoBroadcastJoinThreshold @@ -157,21 +157,21 @@ class StatisticsSuite extends QueryTest with BeforeAndAfterAll { // Using `sparkPlan` because for relevant patterns in HashJoin to be // matched, other strategies need to be applied. - var bhj = rdd.queryExecution.sparkPlan.collect { case j: BroadcastHashJoin => j } + var bhj = df.queryExecution.sparkPlan.collect { case j: BroadcastHashJoin => j } assert(bhj.size === 1, - s"actual query plans do not contain broadcast join: ${rdd.queryExecution}") + s"actual query plans do not contain broadcast join: ${df.queryExecution}") - checkAnswer(rdd, expectedAnswer) // check correctness of output + checkAnswer(df, expectedAnswer) // check correctness of output TestHive.conf.settings.synchronized { val tmp = conf.autoBroadcastJoinThreshold sql(s"""SET ${SQLConf.AUTO_BROADCASTJOIN_THRESHOLD}=-1""") - rdd = sql(query) - bhj = rdd.queryExecution.sparkPlan.collect { case j: BroadcastHashJoin => j } + df = sql(query) + bhj = df.queryExecution.sparkPlan.collect { case j: BroadcastHashJoin => j } assert(bhj.isEmpty, "BroadcastHashJoin still planned even though it is switched off") - val shj = rdd.queryExecution.sparkPlan.collect { case j: ShuffledHashJoin => j } + val shj = df.queryExecution.sparkPlan.collect { case j: ShuffledHashJoin => j } assert(shj.size === 1, "ShuffledHashJoin should be planned when BroadcastHashJoin is turned off") @@ -199,10 +199,10 @@ class StatisticsSuite extends QueryTest with BeforeAndAfterAll { |left semi JOIN src b ON a.key=86 and a.key = b.key""".stripMargin val answer = Row(86, "val_86") - var rdd = sql(leftSemiJoinQuery) + var df = sql(leftSemiJoinQuery) // Assert src has a size smaller than the threshold. - val sizes = rdd.queryExecution.analyzed.collect { + val sizes = df.queryExecution.analyzed.collect { case r if implicitly[ClassTag[MetastoreRelation]].runtimeClass .isAssignableFrom(r.getClass) => r.statistics.sizeInBytes @@ -213,25 +213,25 @@ class StatisticsSuite extends QueryTest with BeforeAndAfterAll { // Using `sparkPlan` because for relevant patterns in HashJoin to be // matched, other strategies need to be applied. - var bhj = rdd.queryExecution.sparkPlan.collect { + var bhj = df.queryExecution.sparkPlan.collect { case j: BroadcastLeftSemiJoinHash => j } assert(bhj.size === 1, - s"actual query plans do not contain broadcast join: ${rdd.queryExecution}") + s"actual query plans do not contain broadcast join: ${df.queryExecution}") - checkAnswer(rdd, answer) // check correctness of output + checkAnswer(df, answer) // check correctness of output TestHive.conf.settings.synchronized { val tmp = conf.autoBroadcastJoinThreshold sql(s"SET ${SQLConf.AUTO_BROADCASTJOIN_THRESHOLD}=-1") - rdd = sql(leftSemiJoinQuery) - bhj = rdd.queryExecution.sparkPlan.collect { + df = sql(leftSemiJoinQuery) + bhj = df.queryExecution.sparkPlan.collect { case j: BroadcastLeftSemiJoinHash => j } assert(bhj.isEmpty, "BroadcastHashJoin still planned even though it is switched off") - val shj = rdd.queryExecution.sparkPlan.collect { + val shj = df.queryExecution.sparkPlan.collect { case j: LeftSemiJoinHash => j } assert(shj.size === 1, diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/BigDataBenchmarkSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/BigDataBenchmarkSuite.scala index 42a82c1fbf5c7..a3f5921a0cb23 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/BigDataBenchmarkSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/BigDataBenchmarkSuite.scala @@ -28,6 +28,7 @@ import org.apache.spark.sql.hive.test.TestHive._ class BigDataBenchmarkSuite extends HiveComparisonTest { val testDataDirectory = new File("target" + File.separator + "big-data-benchmark-testdata") + val userVisitPath = new File(testDataDirectory, "uservisits").getCanonicalPath val testTables = Seq( TestTable( "rankings", @@ -63,7 +64,7 @@ class BigDataBenchmarkSuite extends HiveComparisonTest { | searchWord STRING, | duration INT) | ROW FORMAT DELIMITED FIELDS TERMINATED BY "," - | STORED AS TEXTFILE LOCATION "${new File(testDataDirectory, "uservisits").getCanonicalPath}" + | STORED AS TEXTFILE LOCATION "$userVisitPath" """.stripMargin.cmd), TestTable( "documents", @@ -83,7 +84,10 @@ class BigDataBenchmarkSuite extends HiveComparisonTest { "SELECT pageURL, pageRank FROM rankings WHERE pageRank > 1") createQueryTest("query2", - "SELECT SUBSTR(sourceIP, 1, 10), SUM(adRevenue) FROM uservisits GROUP BY SUBSTR(sourceIP, 1, 10)") + """ + |SELECT SUBSTR(sourceIP, 1, 10), SUM(adRevenue) FROM uservisits + |GROUP BY SUBSTR(sourceIP, 1, 10) + """.stripMargin) createQueryTest("query3", """ @@ -113,8 +117,8 @@ class BigDataBenchmarkSuite extends HiveComparisonTest { |CREATE TABLE url_counts_total AS | SELECT SUM(count) AS totalCount, destpage | FROM url_counts_partial GROUP BY destpage - |-- The following queries run, but generate different results in HIVE likely because the UDF is not deterministic - |-- given different input splits. + |-- The following queries run, but generate different results in HIVE + |-- likely because the UDF is not deterministic given different input splits. |-- SELECT CAST(SUM(count) AS INT) FROM url_counts_partial |-- SELECT COUNT(*) FROM url_counts_partial |-- SELECT * FROM url_counts_partial diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala index a90bd1e257ade..027056d4b865f 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala @@ -138,7 +138,7 @@ abstract class HiveComparisonTest case _ => plan.children.iterator.exists(isSorted) } - val orderedAnswer = hiveQuery.logical match { + val orderedAnswer = hiveQuery.analyzed match { // Clean out non-deterministic time schema info. // Hack: Hive simply prints the result of a SET command to screen, // and does not return it as a query answer. @@ -241,7 +241,10 @@ abstract class HiveComparisonTest // Clear old output for this testcase. outputDirectories.map(new File(_, testCaseName)).filter(_.exists()).foreach(_.delete()) - val allQueries = sql.split("(?<=[^\\\\]);").map(_.trim).filterNot(q => q == "").toSeq + val sqlWithoutComment = + sql.split("\n").filterNot(l => l.matches("--.*(?<=[^\\\\]);")).mkString("\n") + val allQueries = + sqlWithoutComment.split("(?<=[^\\\\]);").map(_.trim).filterNot(q => q == "").toSeq // TODO: DOCUMENT UNSUPPORTED val queryList = @@ -252,8 +255,9 @@ abstract class HiveComparisonTest .filterNot(_ contains "hive.outerjoin.supports.filters") .filterNot(_ contains "hive.exec.post.hooks") - if (allQueries != queryList) + if (allQueries != queryList) { logWarning(s"Simplifications made on unsupported operations for test $testCaseName") + } lazy val consoleTestCase = { val quotes = "\"\"\"" @@ -296,19 +300,22 @@ abstract class HiveComparisonTest val hiveQueries = queryList.map(new TestHive.HiveQLQueryExecution(_)) // Make sure we can at least parse everything before attempting hive execution. - hiveQueries.foreach(_.logical) + hiveQueries.foreach(_.analyzed) val computedResults = (queryList.zipWithIndex, hiveQueries, hiveCacheFiles).zipped.map { case ((queryString, i), hiveQuery, cachedAnswerFile)=> try { // Hooks often break the harness and don't really affect our test anyway, don't // even try running them. - if (installHooksCommand.findAllMatchIn(queryString).nonEmpty) + if (installHooksCommand.findAllMatchIn(queryString).nonEmpty) { sys.error("hive exec hooks not supported for tests.") + } - logWarning(s"Running query ${i+1}/${queryList.size} with hive.") + logWarning(s"Running query ${i + 1}/${queryList.size} with hive.") // Analyze the query with catalyst to ensure test tables are loaded. val answer = hiveQuery.analyzed match { - case _: ExplainCommand => Nil // No need to execute EXPLAIN queries as we don't check the output. + case _: ExplainCommand => + // No need to execute EXPLAIN queries as we don't check the output. + Nil case _ => TestHive.runSqlHive(queryString) } @@ -391,21 +398,24 @@ abstract class HiveComparisonTest case tf: org.scalatest.exceptions.TestFailedException => throw tf case originalException: Exception => if (System.getProperty("spark.hive.canarytest") != null) { - // When we encounter an error we check to see if the environment is still okay by running a simple query. - // If this fails then we halt testing since something must have gone seriously wrong. + // When we encounter an error we check to see if the environment is still + // okay by running a simple query. If this fails then we halt testing since + // something must have gone seriously wrong. try { new TestHive.HiveQLQueryExecution("SELECT key FROM src").stringResult() TestHive.runSqlHive("SELECT key FROM src") } catch { case e: Exception => - logError(s"FATAL ERROR: Canary query threw $e This implies that the testing environment has likely been corrupted.") - // The testing setup traps exits so wait here for a long time so the developer can see when things started - // to go wrong. + logError(s"FATAL ERROR: Canary query threw $e This implies that the " + + "testing environment has likely been corrupted.") + // The testing setup traps exits so wait here for a long time so the developer + // can see when things started to go wrong. Thread.sleep(1000000) } } - // If the canary query didn't fail then the environment is still okay, so just throw the original exception. + // If the canary query didn't fail then the environment is still okay, + // so just throw the original exception. throw originalException } } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HivePlanTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HivePlanTest.scala index c939e6e99d28a..bdb53ddf59c19 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HivePlanTest.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HivePlanTest.scala @@ -22,10 +22,12 @@ import org.apache.spark.sql.hive.test.TestHive class HivePlanTest extends QueryTest { import TestHive._ + import TestHive.implicits._ test("udf constant folding") { - val optimized = sql("SELECT cos(null) FROM src").queryExecution.optimizedPlan - val correctAnswer = sql("SELECT cast(null as double) FROM src").queryExecution.optimizedPlan + Seq.empty[Tuple1[Int]].toDF("a").registerTempTable("t") + val optimized = sql("SELECT cos(null) FROM t").queryExecution.optimizedPlan + val correctAnswer = sql("SELECT cast(null as double) FROM t").queryExecution.optimizedPlan comparePlans(optimized, correctAnswer) } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQueryFileTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQueryFileTest.scala index 02518d516261b..f7b37dae0a5f3 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQueryFileTest.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQueryFileTest.scala @@ -24,8 +24,9 @@ import org.apache.spark.sql.catalyst.util._ /** * A framework for running the query tests that are listed as a set of text files. * - * TestSuites that derive from this class must provide a map of testCaseName -> testCaseFiles that should be included. - * Additionally, there is support for whitelisting and blacklisting tests as development progresses. + * TestSuites that derive from this class must provide a map of testCaseName -> testCaseFiles + * that should be included. Additionally, there is support for whitelisting and blacklisting + * tests as development progresses. */ abstract class HiveQueryFileTest extends HiveComparisonTest { /** A list of tests deemed out of scope and thus completely disregarded */ @@ -54,15 +55,17 @@ abstract class HiveQueryFileTest extends HiveComparisonTest { case (testCaseName, testCaseFile) => if (blackList.map(_.r.pattern.matcher(testCaseName).matches()).reduceLeft(_||_)) { logDebug(s"Blacklisted test skipped $testCaseName") - } else if (realWhiteList.map(_.r.pattern.matcher(testCaseName).matches()).reduceLeft(_||_) || runAll) { + } else if (realWhiteList.map(_.r.pattern.matcher(testCaseName).matches()).reduceLeft(_||_) || + runAll) { // Build a test case and submit it to scala test framework... val queriesString = fileToString(testCaseFile) createQueryTest(testCaseName, queriesString) } else { // Only output warnings for the built in whitelist as this clutters the output when the user // trying to execute a single test from the commandline. - if(System.getProperty(whiteListProperty) == null && !runAll) + if (System.getProperty(whiteListProperty) == null && !runAll) { ignore(testCaseName) {} + } } } } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala index 27047ce4b1b0b..300b1f7920473 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala @@ -29,7 +29,7 @@ import org.apache.hadoop.hive.conf.HiveConf.ConfVars import org.apache.spark.{SparkFiles, SparkException} import org.apache.spark.sql.{DataFrame, Row} import org.apache.spark.sql.catalyst.plans.logical.Project -import org.apache.spark.sql.Dsl._ +import org.apache.spark.sql.functions._ import org.apache.spark.sql.hive._ import org.apache.spark.sql.hive.test.TestHive import org.apache.spark.sql.hive.test.TestHive._ @@ -37,7 +37,8 @@ import org.apache.spark.sql.hive.test.TestHive._ case class TestData(a: Int, b: String) /** - * A set of test cases expressed in Hive QL that are not covered by the tests included in the hive distribution. + * A set of test cases expressed in Hive QL that are not covered by the tests + * included in the hive distribution. */ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter { private val originalTimeZone = TimeZone.getDefault @@ -62,7 +63,7 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter { test("SPARK-4908: concurrent hive native commands") { (1 to 100).par.map { _ => sql("USE default") - sql("SHOW TABLES") + sql("SHOW DATABASES") } } @@ -237,7 +238,8 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter { } createQueryTest("modulus", - "SELECT 11 % 10, IF((101.1 % 100.0) BETWEEN 1.01 AND 1.11, \"true\", \"false\"), (101 / 2) % 10 FROM src LIMIT 1") + "SELECT 11 % 10, IF((101.1 % 100.0) BETWEEN 1.01 AND 1.11, \"true\", \"false\"), " + + "(101 / 2) % 10 FROM src LIMIT 1") test("Query expressed in SQL") { setConf("spark.sql.dialect", "sql") @@ -309,7 +311,8 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter { "SELECT * FROM src a JOIN src b ON a.key = b.key") createQueryTest("small.cartesian", - "SELECT a.key, b.key FROM (SELECT key FROM src WHERE key < 1) a JOIN (SELECT key FROM src WHERE key = 2) b") + "SELECT a.key, b.key FROM (SELECT key FROM src WHERE key < 1) a JOIN " + + "(SELECT key FROM src WHERE key = 2) b") createQueryTest("length.udf", "SELECT length(\"test\") FROM src LIMIT 1") @@ -334,6 +337,14 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter { |DROP DATABASE IF EXISTS testdb CASCADE """.stripMargin) + createQueryTest("create table as with db name within backticks", + """ + |CREATE DATABASE IF NOT EXISTS testdb; + |CREATE TABLE `testdb`.`createdtable` AS SELECT * FROM default.src; + |SELECT * FROM testdb.createdtable; + |DROP DATABASE IF EXISTS testdb CASCADE + """.stripMargin) + createQueryTest("insert table with db name", """ |CREATE DATABASE IF NOT EXISTS testdb; @@ -429,7 +440,7 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter { |'org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe' WITH SERDEPROPERTIES |('serialization.last.column.takes.rest'='true') FROM src; """.stripMargin.replaceAll("\n", " ")) - + createQueryTest("LIKE", "SELECT * FROM src WHERE value LIKE '%1%'") @@ -449,6 +460,7 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter { createQueryTest("lateral view3", "FROM src SELECT key, D.* lateral view explode(array(key+3, key+4)) D as CX") + // scalastyle:off createQueryTest("lateral view4", """ |create table src_lv1 (key string, value string); @@ -458,6 +470,7 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter { |insert overwrite table src_lv1 SELECT key, D.* lateral view explode(array(key+3, key+4)) D as CX |insert overwrite table src_lv2 SELECT key, D.* lateral view explode(array(key+3, key+4)) D as CX """.stripMargin) + // scalastyle:on createQueryTest("lateral view5", "FROM src SELECT explode(array(key+3, key+4))") @@ -467,6 +480,7 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter { test("sampling") { sql("SELECT * FROM src TABLESAMPLE(0.1 PERCENT) s") + sql("SELECT * FROM src TABLESAMPLE(100 PERCENT) s") } test("DataFrame toString") { @@ -528,6 +542,21 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter { createQueryTest("select null from table", "SELECT null FROM src LIMIT 1") + createQueryTest("CTE feature #1", + "with q1 as (select key from src) select * from q1 where key = 5") + + createQueryTest("CTE feature #2", + """with q1 as (select * from src where key= 5), + |q2 as (select * from src s2 where key = 4) + |select value from q1 union all select value from q2 + """.stripMargin) + + createQueryTest("CTE feature #3", + """with q1 as (select key from src) + |from q1 + |select * where key = 4 + """.stripMargin) + test("predicates contains an empty AttributeSet() references") { sql( """ @@ -567,7 +596,7 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter { TestHive.sparkContext.parallelize( TestData(1, "str1") :: TestData(2, "str2") :: Nil) - testData.registerTempTable("REGisteredTABle") + testData.toDF().registerTempTable("REGisteredTABle") assertResult(Array(Row(2, "str2"))) { sql("SELECT tablealias.A, TABLEALIAS.b FROM reGisteredTABle TableAlias " + @@ -575,7 +604,7 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter { } } - def isExplanation(result: DataFrame) = { + def isExplanation(result: DataFrame): Boolean = { val explanation = result.select('plan).collect().map { case Row(plan: String) => plan } explanation.contains("== Physical Plan ==") } @@ -583,8 +612,8 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter { test("SPARK-1704: Explain commands as a DataFrame") { sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)") - val rdd = sql("explain select key, count(value) from src group by key") - assert(isExplanation(rdd)) + val df = sql("explain select key, count(value) from src group by key") + assert(isExplanation(df)) TestHive.reset() } @@ -592,7 +621,7 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter { test("SPARK-2180: HAVING support in GROUP BY clauses (positive)") { val fixture = List(("foo", 2), ("bar", 1), ("foo", 4), ("bar", 3)) .zipWithIndex.map {case Pair(Pair(value, attr), key) => HavingRow(key, value, attr)} - TestHive.sparkContext.parallelize(fixture).registerTempTable("having_test") + TestHive.sparkContext.parallelize(fixture).toDF().registerTempTable("having_test") val results = sql("SELECT value, max(attr) AS attr FROM having_test GROUP BY value HAVING attr > 3") .collect() @@ -630,24 +659,24 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter { } test("Query Hive native command execution result") { - val tableName = "test_native_commands" + val databaseName = "test_native_commands" assertResult(0) { - sql(s"DROP TABLE IF EXISTS $tableName").count() + sql(s"DROP DATABASE IF EXISTS $databaseName").count() } assertResult(0) { - sql(s"CREATE TABLE $tableName(key INT, value STRING)").count() + sql(s"CREATE DATABASE $databaseName").count() } assert( - sql("SHOW TABLES") + sql("SHOW DATABASES") .select('result) .collect() .map(_.getString(0)) - .contains(tableName)) + .contains(databaseName)) - assert(isExplanation(sql(s"EXPLAIN SELECT key, COUNT(*) FROM $tableName GROUP BY key"))) + assert(isExplanation(sql(s"EXPLAIN SELECT key, COUNT(*) FROM src GROUP BY key"))) TestHive.reset() } @@ -740,7 +769,7 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter { TestHive.sparkContext.parallelize( TestData(1, "str1") :: TestData(1, "str2") :: Nil) - testData.registerTempTable("test_describe_commands2") + testData.toDF().registerTempTable("test_describe_commands2") assertResult( Array( @@ -784,6 +813,21 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter { sql("DROP TABLE alter1") } + test("ADD JAR command 2") { + // this is a test case from mapjoin_addjar.q + val testJar = TestHive.getHiveFile("hive-hcatalog-core-0.13.1.jar").getCanonicalPath + val testData = TestHive.getHiveFile("data/files/sample.json").getCanonicalPath + if (HiveShim.version == "0.13.1") { + sql(s"ADD JAR $testJar") + sql( + """CREATE TABLE t1(a string, b string) + |ROW FORMAT SERDE 'org.apache.hive.hcatalog.data.JsonSerDe'""".stripMargin) + sql(s"""LOAD DATA LOCAL INPATH "$testData" INTO TABLE t1""") + sql("select * from src join t1 on src.key = t1.a") + sql("DROP TABLE t1") + } + } + test("ADD FILE command") { val testFile = TestHive.getHiveFile("data/files/v1.txt").getCanonicalFile sql(s"ADD FILE $testFile") @@ -859,6 +903,22 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter { } } + test("SPARK-5592: get java.net.URISyntaxException when dynamic partitioning") { + sql(""" + |create table sc as select * + |from (select '2011-01-11', '2011-01-11+14:18:26' from src tablesample (1 rows) + |union all + |select '2011-01-11', '2011-01-11+15:18:26' from src tablesample (1 rows) + |union all + |select '2011-01-11', '2011-01-11+16:18:26' from src tablesample (1 rows) ) s + """.stripMargin) + sql("create table sc_part (key string) partitioned by (ts string) stored as rcfile") + sql("set hive.exec.dynamic.partition=true") + sql("set hive.exec.dynamic.partition.mode=nonstrict") + sql("insert overwrite table sc_part partition(ts) select * from sc") + sql("drop table sc_part") + } + test("Partition spec validation") { sql("DROP TABLE IF EXISTS dp_test") sql("CREATE TABLE dp_test(key INT, value STRING) PARTITIONED BY (dp INT, sp INT)") @@ -884,8 +944,8 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter { } test("SPARK-3414 regression: should store analyzed logical plan when registering a temp table") { - sparkContext.makeRDD(Seq.empty[LogEntry]).registerTempTable("rawLogs") - sparkContext.makeRDD(Seq.empty[LogFile]).registerTempTable("logFiles") + sparkContext.makeRDD(Seq.empty[LogEntry]).toDF().registerTempTable("rawLogs") + sparkContext.makeRDD(Seq.empty[LogFile]).toDF().registerTempTable("logFiles") sql( """ @@ -963,8 +1023,8 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter { val testVal = "test.val.0" val nonexistentKey = "nonexistent" val KV = "([^=]+)=([^=]*)".r - def collectResults(rdd: DataFrame): Set[(String, String)] = - rdd.collect().map { + def collectResults(df: DataFrame): Set[(String, String)] = + df.collect().map { case Row(key: String, value: String) => key -> value case Row(KV(key, value)) => key -> value }.toSet diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveResolutionSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveResolutionSuite.scala index ff8130ae5f6bc..8ad3627504229 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveResolutionSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveResolutionSuite.scala @@ -17,6 +17,7 @@ package org.apache.spark.sql.hive.execution +import org.apache.spark.sql.AnalysisException import org.apache.spark.sql.hive.test.TestHive.{sparkContext, jsonRDD, sql} import org.apache.spark.sql.hive.test.TestHive.implicits._ @@ -24,7 +25,8 @@ case class Nested(a: Int, B: Int) case class Data(a: Int, B: Int, n: Nested, nestedArray: Seq[Nested]) /** - * A set of test cases expressed in Hive QL that are not covered by the tests included in the hive distribution. + * A set of test cases expressed in Hive QL that are not covered by the tests + * included in the hive distribution. */ class HiveResolutionSuite extends HiveComparisonTest { @@ -40,7 +42,7 @@ class HiveResolutionSuite extends HiveComparisonTest { """{"a": [{"b": 1, "B": 2}]}""" :: Nil)).registerTempTable("nested") // there are 2 filed matching field name "b", we should report Ambiguous reference error - val exception = intercept[RuntimeException] { + val exception = intercept[AnalysisException] { sql("SELECT a[0].b from nested").queryExecution.analyzed } assert(exception.getMessage.contains("Ambiguous reference to fields")) @@ -76,7 +78,7 @@ class HiveResolutionSuite extends HiveComparisonTest { test("case insensitivity with scala reflection") { // Test resolution with Scala Reflection sparkContext.parallelize(Data(1, 2, Nested(1,2), Seq(Nested(1,2))) :: Nil) - .registerTempTable("caseSensitivityTest") + .toDF().registerTempTable("caseSensitivityTest") val query = sql("SELECT a, b, A, B, n.a, n.b, n.A, n.B FROM caseSensitivityTest") assert(query.schema.fields.map(_.name) === Seq("a", "b", "A", "B", "a", "b", "A", "B"), @@ -87,17 +89,26 @@ class HiveResolutionSuite extends HiveComparisonTest { ignore("case insensitivity with scala reflection joins") { // Test resolution with Scala Reflection sparkContext.parallelize(Data(1, 2, Nested(1,2), Seq(Nested(1,2))) :: Nil) - .registerTempTable("caseSensitivityTest") + .toDF().registerTempTable("caseSensitivityTest") sql("SELECT * FROM casesensitivitytest a JOIN casesensitivitytest b ON a.a = b.a").collect() } test("nested repeated resolution") { sparkContext.parallelize(Data(1, 2, Nested(1,2), Seq(Nested(1,2))) :: Nil) - .registerTempTable("nestedRepeatedTest") + .toDF().registerTempTable("nestedRepeatedTest") assert(sql("SELECT nestedArray[0].a FROM nestedRepeatedTest").collect().head(0) === 1) } + createQueryTest("test ambiguousReferences resolved as hive", + """ + |CREATE TABLE t1(x INT); + |CREATE TABLE t2(a STRUCT, k INT); + |INSERT OVERWRITE TABLE t1 SELECT 1 FROM src LIMIT 1; + |INSERT OVERWRITE TABLE t2 SELECT named_struct("x",1),1 FROM src LIMIT 1; + |SELECT a.x FROM t1 a JOIN t2 b ON a.x = b.k; + """.stripMargin) + /** * Negative examples. Currently only left here for documentation purposes. * TODO(marmbrus): Test that catalyst fails on these queries. diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveSerDeSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveSerDeSuite.scala index 7486bfa82b00b..5586a793618bd 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveSerDeSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveSerDeSuite.scala @@ -25,17 +25,25 @@ import org.apache.spark.sql.hive.test.TestHive * A set of tests that validates support for Hive SerDe. */ class HiveSerDeSuite extends HiveComparisonTest with BeforeAndAfterAll { - - override def beforeAll() = { + override def beforeAll(): Unit = { + import TestHive._ + import org.apache.hadoop.hive.serde2.RegexSerDe + super.beforeAll() TestHive.cacheTables = false + sql(s"""CREATE TABLE IF NOT EXISTS sales (key STRING, value INT) + |ROW FORMAT SERDE '${classOf[RegexSerDe].getCanonicalName}' + |WITH SERDEPROPERTIES ("input.regex" = "([^ ]*)\t([^ ]*)") + """.stripMargin) + sql(s"LOAD DATA LOCAL INPATH '${getHiveFile("data/files/sales.txt")}' INTO TABLE sales") } + // table sales is not a cache table, and will be clear after reset + createQueryTest("Read with RegexSerDe", "SELECT * FROM sales", false) + createQueryTest( "Read and write with LazySimpleSerDe (tab separated)", "SELECT * from serdeins") - createQueryTest("Read with RegexSerDe", "SELECT * FROM sales") - createQueryTest("Read with AvroSerDe", "SELECT * FROM episodes") createQueryTest("Read Partitioned with AvroSerDe", "SELECT * FROM episodes_part") diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTableScanSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTableScanSuite.scala index 8fb5e050a237a..ab53c6309e089 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTableScanSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTableScanSuite.scala @@ -18,9 +18,10 @@ package org.apache.spark.sql.hive.execution import org.apache.spark.sql.Row -import org.apache.spark.sql.Dsl._ +import org.apache.spark.sql.functions._ import org.apache.spark.sql.hive.test.TestHive import org.apache.spark.sql.hive.test.TestHive._ +import org.apache.spark.sql.hive.test.TestHive.implicits._ import org.apache.spark.util.Utils diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTypeCoercionSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTypeCoercionSuite.scala index ab0e0443c7faa..f0f04f8c73fb4 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTypeCoercionSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTypeCoercionSuite.scala @@ -35,8 +35,10 @@ class HiveTypeCoercionSuite extends HiveComparisonTest { val nullVal = "null" baseTypes.init.foreach { i => - createQueryTest(s"case when then $i else $nullVal end ", s"SELECT case when true then $i else $nullVal end FROM src limit 1") - createQueryTest(s"case when then $nullVal else $i end ", s"SELECT case when true then $nullVal else $i end FROM src limit 1") + createQueryTest(s"case when then $i else $nullVal end ", + s"SELECT case when true then $i else $nullVal end FROM src limit 1") + createQueryTest(s"case when then $nullVal else $i end ", + s"SELECT case when true then $nullVal else $i end FROM src limit 1") } test("[SPARK-2210] boolean cast on boolean value should be removed") { diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUdfSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUdfSuite.scala index 1e99003d3e9b5..7f49eac490572 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUdfSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUdfSuite.scala @@ -22,7 +22,7 @@ import java.util import java.util.Properties import org.apache.hadoop.conf.Configuration -import org.apache.hadoop.hive.ql.udf.generic.GenericUDF +import org.apache.hadoop.hive.ql.udf.generic.{GenericUDAFAverage, GenericUDF} import org.apache.hadoop.hive.ql.udf.generic.GenericUDF.DeferredObject import org.apache.hadoop.hive.serde2.objectinspector.primitive.PrimitiveObjectInspectorFactory import org.apache.hadoop.hive.serde2.objectinspector.{ObjectInspector, ObjectInspectorFactory} @@ -62,7 +62,7 @@ class HiveUdfSuite extends QueryTest { | getStruct(1).f5 FROM src LIMIT 1 """.stripMargin).head() === Row(1, 2, 3, 4, 5)) } - + test("SPARK-4785 When called with arguments referring column fields, PMOD throws NPE") { checkAnswer( sql("SELECT PMOD(CAST(key as INT), 10) FROM src LIMIT 1"), @@ -93,10 +93,19 @@ class HiveUdfSuite extends QueryTest { sql("DROP TEMPORARY FUNCTION IF EXISTS testUdf") } + test("SPARK-6409 UDAFAverage test") { + sql(s"CREATE TEMPORARY FUNCTION test_avg AS '${classOf[GenericUDAFAverage].getName}'") + checkAnswer( + sql("SELECT test_avg(1), test_avg(substr(value,5)) FROM src"), + Seq(Row(1.0, 260.182))) + sql("DROP TEMPORARY FUNCTION IF EXISTS test_avg") + TestHive.reset() + } + test("SPARK-2693 udaf aggregates test") { checkAnswer(sql("SELECT percentile(key, 1) FROM src LIMIT 1"), sql("SELECT max(key) FROM src").collect().toSeq) - + checkAnswer(sql("SELECT percentile(key, array(1, 1)) FROM src LIMIT 1"), sql("SELECT array(max(key), max(key)) FROM src").collect().toSeq) } @@ -104,19 +113,20 @@ class HiveUdfSuite extends QueryTest { test("Generic UDAF aggregates") { checkAnswer(sql("SELECT ceiling(percentile_approx(key, 0.99999)) FROM src LIMIT 1"), sql("SELECT max(key) FROM src LIMIT 1").collect().toSeq) - + checkAnswer(sql("SELECT percentile_approx(100.0, array(0.9, 0.9)) FROM src LIMIT 1"), sql("SELECT array(100, 100) FROM src LIMIT 1").collect().toSeq) } - + test("UDFIntegerToString") { val testData = TestHive.sparkContext.parallelize( - IntegerCaseClass(1) :: IntegerCaseClass(2) :: Nil) + IntegerCaseClass(1) :: IntegerCaseClass(2) :: Nil).toDF() testData.registerTempTable("integerTable") - sql(s"CREATE TEMPORARY FUNCTION testUDFIntegerToString AS '${classOf[UDFIntegerToString].getName}'") + val udfName = classOf[UDFIntegerToString].getName + sql(s"CREATE TEMPORARY FUNCTION testUDFIntegerToString AS '$udfName'") checkAnswer( - sql("SELECT testUDFIntegerToString(i) FROM integerTable"), //.collect(), + sql("SELECT testUDFIntegerToString(i) FROM integerTable"), Seq(Row("1"), Row("2"))) sql("DROP TEMPORARY FUNCTION IF EXISTS testUDFIntegerToString") @@ -127,12 +137,12 @@ class HiveUdfSuite extends QueryTest { val testData = TestHive.sparkContext.parallelize( ListListIntCaseClass(Nil) :: ListListIntCaseClass(Seq((1, 2, 3))) :: - ListListIntCaseClass(Seq((4, 5, 6), (7, 8, 9))) :: Nil) + ListListIntCaseClass(Seq((4, 5, 6), (7, 8, 9))) :: Nil).toDF() testData.registerTempTable("listListIntTable") sql(s"CREATE TEMPORARY FUNCTION testUDFListListInt AS '${classOf[UDFListListInt].getName}'") checkAnswer( - sql("SELECT testUDFListListInt(lli) FROM listListIntTable"), //.collect(), + sql("SELECT testUDFListListInt(lli) FROM listListIntTable"), Seq(Row(0), Row(2), Row(13))) sql("DROP TEMPORARY FUNCTION IF EXISTS testUDFListListInt") @@ -142,12 +152,12 @@ class HiveUdfSuite extends QueryTest { test("UDFListString") { val testData = TestHive.sparkContext.parallelize( ListStringCaseClass(Seq("a", "b", "c")) :: - ListStringCaseClass(Seq("d", "e")) :: Nil) + ListStringCaseClass(Seq("d", "e")) :: Nil).toDF() testData.registerTempTable("listStringTable") sql(s"CREATE TEMPORARY FUNCTION testUDFListString AS '${classOf[UDFListString].getName}'") checkAnswer( - sql("SELECT testUDFListString(l) FROM listStringTable"), //.collect(), + sql("SELECT testUDFListString(l) FROM listStringTable"), Seq(Row("a,b,c"), Row("d,e"))) sql("DROP TEMPORARY FUNCTION IF EXISTS testUDFListString") @@ -156,12 +166,12 @@ class HiveUdfSuite extends QueryTest { test("UDFStringString") { val testData = TestHive.sparkContext.parallelize( - StringCaseClass("world") :: StringCaseClass("goodbye") :: Nil) + StringCaseClass("world") :: StringCaseClass("goodbye") :: Nil).toDF() testData.registerTempTable("stringTable") sql(s"CREATE TEMPORARY FUNCTION testStringStringUdf AS '${classOf[UDFStringString].getName}'") checkAnswer( - sql("SELECT testStringStringUdf(\"hello\", s) FROM stringTable"), //.collect(), + sql("SELECT testStringStringUdf(\"hello\", s) FROM stringTable"), Seq(Row("hello world"), Row("hello goodbye"))) sql("DROP TEMPORARY FUNCTION IF EXISTS testStringStringUdf") @@ -173,12 +183,12 @@ class HiveUdfSuite extends QueryTest { ListListIntCaseClass(Nil) :: ListListIntCaseClass(Seq((1, 2, 3))) :: ListListIntCaseClass(Seq((4, 5, 6), (7, 8, 9))) :: - Nil) + Nil).toDF() testData.registerTempTable("TwoListTable") sql(s"CREATE TEMPORARY FUNCTION testUDFTwoListList AS '${classOf[UDFTwoListList].getName}'") checkAnswer( - sql("SELECT testUDFTwoListList(lli, lli) FROM TwoListTable"), //.collect(), + sql("SELECT testUDFTwoListList(lli, lli) FROM TwoListTable"), Seq(Row("0, 0"), Row("2, 2"), Row("13, 13"))) sql("DROP TEMPORARY FUNCTION IF EXISTS testUDFTwoListList") @@ -238,7 +248,8 @@ class PairUdf extends GenericUDF { override def initialize(p1: Array[ObjectInspector]): ObjectInspector = ObjectInspectorFactory.getStandardStructObjectInspector( Seq("id", "value"), - Seq(PrimitiveObjectInspectorFactory.javaIntObjectInspector, PrimitiveObjectInspectorFactory.javaIntObjectInspector) + Seq(PrimitiveObjectInspectorFactory.javaIntObjectInspector, + PrimitiveObjectInspectorFactory.javaIntObjectInspector) ) override def evaluate(args: Array[DeferredObject]): AnyRef = { diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala index 8474d850c9c6c..067b577f1560e 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala @@ -143,7 +143,7 @@ class PruningSuite extends HiveComparisonTest with BeforeAndAfter { sql: String, expectedOutputColumns: Seq[String], expectedScannedColumns: Seq[String], - expectedPartValues: Seq[Seq[String]]) = { + expectedPartValues: Seq[Seq[String]]): Unit = { test(s"$testCaseName - pruning test") { val plan = new TestHive.HiveQLQueryExecution(sql).executedPlan val actualOutputColumns = plan.output.map(_.name) diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala index 49fe79d989259..47b4cb9ca61ff 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala @@ -17,23 +17,143 @@ package org.apache.spark.sql.hive.execution -import org.apache.spark.sql.hive.HiveShim +import org.apache.spark.sql.catalyst.analysis.EliminateSubQueries +import org.apache.spark.sql.hive.{MetastoreRelation, HiveShim} +import org.apache.spark.sql.hive.test.TestHive import org.apache.spark.sql.hive.test.TestHive._ +import org.apache.spark.sql.hive.test.TestHive.implicits._ +import org.apache.spark.sql.parquet.ParquetRelation2 +import org.apache.spark.sql.sources.LogicalRelation import org.apache.spark.sql.types._ -import org.apache.spark.sql.{QueryTest, Row, SQLConf} +import org.apache.spark.sql.{AnalysisException, QueryTest, Row, SQLConf} case class Nested1(f1: Nested2) case class Nested2(f2: Nested3) case class Nested3(f3: Int) +case class NestedArray2(b: Seq[Int]) +case class NestedArray1(a: NestedArray2) + +case class Order( + id: Int, + make: String, + `type`: String, + price: Int, + pdate: String, + customer: String, + city: String, + state: String, + month: Int) + /** * A collection of hive query tests where we generate the answers ourselves instead of depending on * Hive to generate them (in contrast to HiveQuerySuite). Often this is because the query is * valid, but Hive currently cannot execute it. */ class SQLQuerySuite extends QueryTest { + test("SPARK-6835: udtf in lateral view") { + val df = Seq((1, 1)).toDF("c1", "c2") + df.registerTempTable("table1") + val query = sql("SELECT c1, v FROM table1 LATERAL VIEW stack(3, 1, c1 + 1, c1 + 2) d AS v") + checkAnswer(query, Row(1, 1) :: Row(1, 2) :: Row(1, 3) :: Nil) + } - import org.apache.spark.sql.hive.test.TestHive.implicits._ + test("SPARK-6851: Self-joined converted parquet tables") { + val orders = Seq( + Order(1, "Atlas", "MTB", 234, "2015-01-07", "John D", "Pacifica", "CA", 20151), + Order(3, "Swift", "MTB", 285, "2015-01-17", "John S", "Redwood City", "CA", 20151), + Order(4, "Atlas", "Hybrid", 303, "2015-01-23", "Jones S", "San Mateo", "CA", 20151), + Order(7, "Next", "MTB", 356, "2015-01-04", "Jane D", "Daly City", "CA", 20151), + Order(10, "Next", "YFlikr", 187, "2015-01-09", "John D", "Fremont", "CA", 20151), + Order(11, "Swift", "YFlikr", 187, "2015-01-23", "John D", "Hayward", "CA", 20151), + Order(2, "Next", "Hybrid", 324, "2015-02-03", "Jane D", "Daly City", "CA", 20152), + Order(5, "Next", "Street", 187, "2015-02-08", "John D", "Fremont", "CA", 20152), + Order(6, "Atlas", "Street", 154, "2015-02-09", "John D", "Pacifica", "CA", 20152), + Order(8, "Swift", "Hybrid", 485, "2015-02-19", "John S", "Redwood City", "CA", 20152), + Order(9, "Atlas", "Split", 303, "2015-02-28", "Jones S", "San Mateo", "CA", 20152)) + + val orderUpdates = Seq( + Order(1, "Atlas", "MTB", 434, "2015-01-07", "John D", "Pacifica", "CA", 20151), + Order(11, "Swift", "YFlikr", 137, "2015-01-23", "John D", "Hayward", "CA", 20151)) + + orders.toDF.registerTempTable("orders1") + orderUpdates.toDF.registerTempTable("orderupdates1") + + sql( + """CREATE TABLE orders( + | id INT, + | make String, + | type String, + | price INT, + | pdate String, + | customer String, + | city String) + |PARTITIONED BY (state STRING, month INT) + |STORED AS PARQUET + """.stripMargin) + + sql( + """CREATE TABLE orderupdates( + | id INT, + | make String, + | type String, + | price INT, + | pdate String, + | customer String, + | city String) + |PARTITIONED BY (state STRING, month INT) + |STORED AS PARQUET + """.stripMargin) + + sql("set hive.exec.dynamic.partition.mode=nonstrict") + sql("INSERT INTO TABLE orders PARTITION(state, month) SELECT * FROM orders1") + sql("INSERT INTO TABLE orderupdates PARTITION(state, month) SELECT * FROM orderupdates1") + + checkAnswer( + sql( + """ + |select orders.state, orders.month + |from orders + |join ( + | select distinct orders.state,orders.month + | from orders + | join orderupdates + | on orderupdates.id = orders.id) ao + | on ao.state = orders.state and ao.month = orders.month + """.stripMargin), + (1 to 6).map(_ => Row("CA", 20151))) + } + + test("SPARK-5371: union with null and sum") { + val df = Seq((1, 1)).toDF("c1", "c2") + df.registerTempTable("table1") + + val query = sql( + """ + |SELECT + | MIN(c1), + | MIN(c2) + |FROM ( + | SELECT + | SUM(c1) c1, + | NULL c2 + | FROM table1 + | UNION ALL + | SELECT + | NULL c1, + | SUM(c2) c2 + | FROM table1 + |) a + """.stripMargin) + checkAnswer(query, Row(1, 1) :: Nil) + } + + test("explode nested Field") { + Seq(NestedArray1(NestedArray2(Seq(1, 2, 3)))).toDF.registerTempTable("nestedArray") + checkAnswer( + sql("SELECT ints FROM nestedArray LATERAL VIEW explode(a.b) a AS ints"), + Row(1) :: Row(2) :: Row(3) :: Nil) + } test("SPARK-4512 Fix attribute reference resolution error when using SORT BY") { checkAnswer( @@ -42,6 +162,73 @@ class SQLQuerySuite extends QueryTest { ) } + test("CTAS without serde") { + def checkRelation(tableName: String, isDataSourceParquet: Boolean): Unit = { + val relation = EliminateSubQueries(catalog.lookupRelation(Seq(tableName))) + relation match { + case LogicalRelation(r: ParquetRelation2) => + if (!isDataSourceParquet) { + fail( + s"${classOf[MetastoreRelation].getCanonicalName} is expected, but found " + + s"${ParquetRelation2.getClass.getCanonicalName}.") + } + + case r: MetastoreRelation => + if (isDataSourceParquet) { + fail( + s"${ParquetRelation2.getClass.getCanonicalName} is expected, but found " + + s"${classOf[MetastoreRelation].getCanonicalName}.") + } + } + } + + val originalConf = getConf("spark.sql.hive.convertCTAS", "false") + + setConf("spark.sql.hive.convertCTAS", "true") + + sql("CREATE TABLE ctas1 AS SELECT key k, value FROM src ORDER BY k, value") + sql("CREATE TABLE IF NOT EXISTS ctas1 AS SELECT key k, value FROM src ORDER BY k, value") + var message = intercept[AnalysisException] { + sql("CREATE TABLE ctas1 AS SELECT key k, value FROM src ORDER BY k, value") + }.getMessage + assert(message.contains("Table ctas1 already exists")) + checkRelation("ctas1", true) + sql("DROP TABLE ctas1") + + // Specifying database name for query can be converted to data source write path + // is not allowed right now. + message = intercept[AnalysisException] { + sql("CREATE TABLE default.ctas1 AS SELECT key k, value FROM src ORDER BY k, value") + }.getMessage + assert( + message.contains("Cannot specify database name in a CTAS statement"), + "When spark.sql.hive.convertCTAS is true, we should not allow " + + "database name specified.") + + sql("CREATE TABLE ctas1 stored as textfile AS SELECT key k, value FROM src ORDER BY k, value") + checkRelation("ctas1", true) + sql("DROP TABLE ctas1") + + sql( + "CREATE TABLE ctas1 stored as sequencefile AS SELECT key k, value FROM src ORDER BY k, value") + checkRelation("ctas1", true) + sql("DROP TABLE ctas1") + + sql("CREATE TABLE ctas1 stored as rcfile AS SELECT key k, value FROM src ORDER BY k, value") + checkRelation("ctas1", false) + sql("DROP TABLE ctas1") + + sql("CREATE TABLE ctas1 stored as orc AS SELECT key k, value FROM src ORDER BY k, value") + checkRelation("ctas1", false) + sql("DROP TABLE ctas1") + + sql("CREATE TABLE ctas1 stored as parquet AS SELECT key k, value FROM src ORDER BY k, value") + checkRelation("ctas1", false) + sql("DROP TABLE ctas1") + + setConf("spark.sql.hive.convertCTAS", originalConf) + } + test("CTAS with serde") { sql("CREATE TABLE ctas1 AS SELECT key k, value FROM src ORDER BY k, value").collect() sql( @@ -174,7 +361,8 @@ class SQLQuerySuite extends QueryTest { } test("double nested data") { - sparkContext.parallelize(Nested1(Nested2(Nested3(1))) :: Nil).registerTempTable("nested") + sparkContext.parallelize(Nested1(Nested2(Nested3(1))) :: Nil) + .toDF().registerTempTable("nested") checkAnswer( sql("SELECT f1.f2.f3 FROM nested"), Row(1)) @@ -184,7 +372,7 @@ class SQLQuerySuite extends QueryTest { sql("SELECT * FROM test_ctas_1234"), sql("SELECT * FROM nested").collect().toSeq) - intercept[org.apache.hadoop.hive.ql.metadata.InvalidTableException] { + intercept[AnalysisException] { sql("CREATE TABLE test_ctas_12345 AS SELECT * from notexists").collect() } } @@ -197,7 +385,7 @@ class SQLQuerySuite extends QueryTest { } test("SPARK-4825 save join to table") { - val testData = sparkContext.parallelize(1 to 10).map(i => TestData(i, i.toString)) + val testData = sparkContext.parallelize(1 to 10).map(i => TestData(i, i.toString)).toDF() sql("CREATE TABLE test1 (key INT, value STRING)") testData.insertInto("test1") sql("CREATE TABLE test2 (key INT, value STRING)") @@ -277,7 +465,7 @@ class SQLQuerySuite extends QueryTest { val rowRdd = sparkContext.parallelize(row :: Nil) - applySchema(rowRdd, schema).registerTempTable("testTable") + TestHive.createDataFrame(rowRdd, schema).registerTempTable("testTable") sql( """CREATE TABLE nullValuesInInnerComplexTypes @@ -315,4 +503,62 @@ class SQLQuerySuite extends QueryTest { dropTempTable("data") } + + test("resolve udtf with single alias") { + val rdd = sparkContext.makeRDD((1 to 5).map(i => s"""{"a":[$i, ${i + 1}]}""")) + jsonRDD(rdd).registerTempTable("data") + val df = sql("SELECT explode(a) AS val FROM data") + val col = df("val") + } + + test("logical.Project should not be resolved if it contains aggregates or generators") { + // This test is used to test the fix of SPARK-5875. + // The original issue was that Project's resolved will be true when it contains + // AggregateExpressions or Generators. However, in this case, the Project + // is not in a valid state (cannot be executed). Because of this bug, the analysis rule of + // PreInsertionCasts will actually start to work before ImplicitGenerate and then + // generates an invalid query plan. + val rdd = sparkContext.makeRDD((1 to 5).map(i => s"""{"a":[$i, ${i + 1}]}""")) + jsonRDD(rdd).registerTempTable("data") + val originalConf = getConf("spark.sql.hive.convertCTAS", "false") + setConf("spark.sql.hive.convertCTAS", "false") + + sql("CREATE TABLE explodeTest (key bigInt)") + table("explodeTest").queryExecution.analyzed match { + case metastoreRelation: MetastoreRelation => // OK + case _ => + fail("To correctly test the fix of SPARK-5875, explodeTest should be a MetastoreRelation") + } + + sql(s"INSERT OVERWRITE TABLE explodeTest SELECT explode(a) AS val FROM data") + checkAnswer( + sql("SELECT key from explodeTest"), + (1 to 5).flatMap(i => Row(i) :: Row(i + 1) :: Nil) + ) + + sql("DROP TABLE explodeTest") + dropTempTable("data") + setConf("spark.sql.hive.convertCTAS", originalConf) + } + + test("sanity test for SPARK-6618") { + (1 to 100).par.map { i => + val tableName = s"SPARK_6618_table_$i" + sql(s"CREATE TABLE $tableName (col1 string)") + catalog.lookupRelation(Seq(tableName)) + table(tableName) + tables() + sql(s"DROP TABLE $tableName") + } + } + + test("SPARK-5203 union with different decimal precision") { + Seq.empty[(Decimal, Decimal)] + .toDF("d1", "d2") + .select($"d1".cast(DecimalType(10, 15)).as("d")) + .registerTempTable("dn") + + sql("select d from dn union all select d * 2 from dn") + .queryExecution.analyzed + } } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala new file mode 100644 index 0000000000000..d5dd0bf58e702 --- /dev/null +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala @@ -0,0 +1,911 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive + +import java.io.File + +import org.scalatest.BeforeAndAfterAll + +import org.apache.spark.sql.{QueryTest, SQLConf, SaveMode} +import org.apache.spark.sql.catalyst.expressions.Row +import org.apache.spark.sql.execution.{ExecutedCommand, PhysicalRDD} +import org.apache.spark.sql.hive.execution.HiveTableScan +import org.apache.spark.sql.hive.test.TestHive +import org.apache.spark.sql.hive.test.TestHive._ +import org.apache.spark.sql.hive.test.TestHive.implicits._ +import org.apache.spark.sql.json.JSONRelation +import org.apache.spark.sql.sources.{InsertIntoDataSource, LogicalRelation} +import org.apache.spark.sql.parquet.{ParquetRelation2, ParquetTableScan} +import org.apache.spark.sql.SaveMode +import org.apache.spark.sql.types._ +import org.apache.spark.util.Utils + +// The data where the partitioning key exists only in the directory structure. +case class ParquetData(intField: Int, stringField: String) +// The data that also includes the partitioning key +case class ParquetDataWithKey(p: Int, intField: Int, stringField: String) + +case class StructContainer(intStructField :Int, stringStructField: String) + +case class ParquetDataWithComplexTypes( + intField: Int, + stringField: String, + structField: StructContainer, + arrayField: Seq[Int]) + +case class ParquetDataWithKeyAndComplexTypes( + p: Int, + intField: Int, + stringField: String, + structField: StructContainer, + arrayField: Seq[Int]) + +/** + * A suite to test the automatic conversion of metastore tables with parquet data to use the + * built in parquet support. + */ +class ParquetMetastoreSuiteBase extends ParquetPartitioningTest { + override def beforeAll(): Unit = { + super.beforeAll() + + sql(s""" + create external table partitioned_parquet + ( + intField INT, + stringField STRING + ) + PARTITIONED BY (p int) + ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe' + STORED AS + INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat' + OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat' + location '${partitionedTableDir.getCanonicalPath}' + """) + + sql(s""" + create external table partitioned_parquet_with_key + ( + intField INT, + stringField STRING + ) + PARTITIONED BY (p int) + ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe' + STORED AS + INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat' + OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat' + location '${partitionedTableDirWithKey.getCanonicalPath}' + """) + + sql(s""" + create external table normal_parquet + ( + intField INT, + stringField STRING + ) + ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe' + STORED AS + INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat' + OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat' + location '${new File(normalTableDir, "normal").getCanonicalPath}' + """) + + sql(s""" + CREATE EXTERNAL TABLE partitioned_parquet_with_complextypes + ( + intField INT, + stringField STRING, + structField STRUCT, + arrayField ARRAY + ) + PARTITIONED BY (p int) + ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe' + STORED AS + INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat' + OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat' + LOCATION '${partitionedTableDirWithComplexTypes.getCanonicalPath}' + """) + + sql(s""" + CREATE EXTERNAL TABLE partitioned_parquet_with_key_and_complextypes + ( + intField INT, + stringField STRING, + structField STRUCT, + arrayField ARRAY + ) + PARTITIONED BY (p int) + ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe' + STORED AS + INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat' + OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat' + LOCATION '${partitionedTableDirWithKeyAndComplexTypes.getCanonicalPath}' + """) + + (1 to 10).foreach { p => + sql(s"ALTER TABLE partitioned_parquet ADD PARTITION (p=$p)") + } + + (1 to 10).foreach { p => + sql(s"ALTER TABLE partitioned_parquet_with_key ADD PARTITION (p=$p)") + } + + (1 to 10).foreach { p => + sql(s"ALTER TABLE partitioned_parquet_with_key_and_complextypes ADD PARTITION (p=$p)") + } + + (1 to 10).foreach { p => + sql(s"ALTER TABLE partitioned_parquet_with_complextypes ADD PARTITION (p=$p)") + } + + val rdd1 = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str$i"}""")) + jsonRDD(rdd1).registerTempTable("jt") + val rdd2 = sparkContext.parallelize((1 to 10).map(i => s"""{"a":[$i, null]}""")) + jsonRDD(rdd2).registerTempTable("jt_array") + + setConf("spark.sql.hive.convertMetastoreParquet", "true") + } + + override def afterAll(): Unit = { + sql("DROP TABLE partitioned_parquet") + sql("DROP TABLE partitioned_parquet_with_key") + sql("DROP TABLE partitioned_parquet_with_complextypes") + sql("DROP TABLE partitioned_parquet_with_key_and_complextypes") + sql("DROP TABLE normal_parquet") + sql("DROP TABLE IF EXISTS jt") + sql("DROP TABLE IF EXISTS jt_array") + setConf("spark.sql.hive.convertMetastoreParquet", "false") + } + + test(s"conversion is working") { + assert( + sql("SELECT * FROM normal_parquet").queryExecution.executedPlan.collect { + case _: HiveTableScan => true + }.isEmpty) + assert( + sql("SELECT * FROM normal_parquet").queryExecution.executedPlan.collect { + case _: ParquetTableScan => true + case _: PhysicalRDD => true + }.nonEmpty) + } +} + +class ParquetDataSourceOnMetastoreSuite extends ParquetMetastoreSuiteBase { + val originalConf = conf.parquetUseDataSourceApi + + override def beforeAll(): Unit = { + super.beforeAll() + + sql( + """ + |create table test_parquet + |( + | intField INT, + | stringField STRING + |) + |ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe' + |STORED AS + | INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat' + | OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat' + """.stripMargin) + + conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, "true") + } + + override def afterAll(): Unit = { + super.afterAll() + sql("DROP TABLE IF EXISTS test_parquet") + + setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf.toString) + } + + test("scan an empty parquet table") { + checkAnswer(sql("SELECT count(*) FROM test_parquet"), Row(0)) + } + + test("scan an empty parquet table with upper case") { + checkAnswer(sql("SELECT count(INTFIELD) FROM TEST_parquet"), Row(0)) + } + + test("insert into an empty parquet table") { + sql( + """ + |create table test_insert_parquet + |( + | intField INT, + | stringField STRING + |) + |ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe' + |STORED AS + | INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat' + | OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat' + """.stripMargin) + + // Insert into am empty table. + sql("insert into table test_insert_parquet select a, b from jt where jt.a > 5") + checkAnswer( + sql(s"SELECT intField, stringField FROM test_insert_parquet WHERE intField < 8"), + Row(6, "str6") :: Row(7, "str7") :: Nil + ) + // Insert overwrite. + sql("insert overwrite table test_insert_parquet select a, b from jt where jt.a < 5") + checkAnswer( + sql(s"SELECT intField, stringField FROM test_insert_parquet WHERE intField > 2"), + Row(3, "str3") :: Row(4, "str4") :: Nil + ) + sql("DROP TABLE IF EXISTS test_insert_parquet") + + // Create it again. + sql( + """ + |create table test_insert_parquet + |( + | intField INT, + | stringField STRING + |) + |ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe' + |STORED AS + | INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat' + | OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat' + """.stripMargin) + // Insert overwrite an empty table. + sql("insert overwrite table test_insert_parquet select a, b from jt where jt.a < 5") + checkAnswer( + sql(s"SELECT intField, stringField FROM test_insert_parquet WHERE intField > 2"), + Row(3, "str3") :: Row(4, "str4") :: Nil + ) + // Insert into the table. + sql("insert into table test_insert_parquet select a, b from jt") + checkAnswer( + sql(s"SELECT intField, stringField FROM test_insert_parquet"), + (1 to 10).map(i => Row(i, s"str$i")) ++ (1 to 4).map(i => Row(i, s"str$i")) + ) + sql("DROP TABLE IF EXISTS test_insert_parquet") + } + + test("scan a parquet table created through a CTAS statement") { + sql( + """ + |create table test_parquet_ctas ROW FORMAT + |SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe' + |STORED AS + | INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat' + | OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat' + |AS select * from jt + """.stripMargin) + + checkAnswer( + sql(s"SELECT a, b FROM test_parquet_ctas WHERE a = 1"), + Seq(Row(1, "str1")) + ) + + table("test_parquet_ctas").queryExecution.optimizedPlan match { + case LogicalRelation(p: ParquetRelation2) => // OK + case _ => + fail( + s"test_parquet_ctas should be converted to ${classOf[ParquetRelation2].getCanonicalName}") + } + + sql("DROP TABLE IF EXISTS test_parquet_ctas") + } + + test("MetastoreRelation in InsertIntoTable will be converted") { + sql( + """ + |create table test_insert_parquet + |( + | intField INT + |) + |ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe' + |STORED AS + | INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat' + | OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat' + """.stripMargin) + + val df = sql("INSERT INTO TABLE test_insert_parquet SELECT a FROM jt") + df.queryExecution.executedPlan match { + case ExecutedCommand( + InsertIntoDataSource( + LogicalRelation(r: ParquetRelation2), query, overwrite)) => // OK + case o => fail("test_insert_parquet should be converted to a " + + s"${classOf[ParquetRelation2].getCanonicalName} and " + + s"${classOf[InsertIntoDataSource].getCanonicalName} is expcted as the SparkPlan." + + s"However, found a ${o.toString} ") + } + + checkAnswer( + sql("SELECT intField FROM test_insert_parquet WHERE test_insert_parquet.intField > 5"), + sql("SELECT a FROM jt WHERE jt.a > 5").collect() + ) + + sql("DROP TABLE IF EXISTS test_insert_parquet") + } + + test("MetastoreRelation in InsertIntoHiveTable will be converted") { + sql( + """ + |create table test_insert_parquet + |( + | int_array array + |) + |ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe' + |STORED AS + | INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat' + | OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat' + """.stripMargin) + + val df = sql("INSERT INTO TABLE test_insert_parquet SELECT a FROM jt_array") + df.queryExecution.executedPlan match { + case ExecutedCommand( + InsertIntoDataSource( + LogicalRelation(r: ParquetRelation2), query, overwrite)) => // OK + case o => fail("test_insert_parquet should be converted to a " + + s"${classOf[ParquetRelation2].getCanonicalName} and " + + s"${classOf[InsertIntoDataSource].getCanonicalName} is expcted as the SparkPlan." + + s"However, found a ${o.toString} ") + } + + checkAnswer( + sql("SELECT int_array FROM test_insert_parquet"), + sql("SELECT a FROM jt_array").collect() + ) + + sql("DROP TABLE IF EXISTS test_insert_parquet") + } + + test("SPARK-6450 regression test") { + sql( + """CREATE TABLE IF NOT EXISTS ms_convert (key INT) + |ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe' + |STORED AS + | INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat' + | OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat' + """.stripMargin) + + // This shouldn't throw AnalysisException + val analyzed = sql( + """SELECT key FROM ms_convert + |UNION ALL + |SELECT key FROM ms_convert + """.stripMargin).queryExecution.analyzed + + assertResult(2) { + analyzed.collect { + case r @ LogicalRelation(_: ParquetRelation2) => r + }.size + } + + sql("DROP TABLE ms_convert") + } + + test("Caching converted data source Parquet Relations") { + def checkCached(tableIdentifer: catalog.QualifiedTableName): Unit = { + // Converted test_parquet should be cached. + catalog.cachedDataSourceTables.getIfPresent(tableIdentifer) match { + case null => fail("Converted test_parquet should be cached in the cache.") + case logical @ LogicalRelation(parquetRelation: ParquetRelation2) => // OK + case other => + fail( + "The cached test_parquet should be a Parquet Relation. " + + s"However, $other is returned form the cache.") + } + } + + sql("DROP TABLE IF EXISTS test_insert_parquet") + sql("DROP TABLE IF EXISTS test_parquet_partitioned_cache_test") + + sql( + """ + |create table test_insert_parquet + |( + | intField INT, + | stringField STRING + |) + |ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe' + |STORED AS + | INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat' + | OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat' + """.stripMargin) + + var tableIdentifer = catalog.QualifiedTableName("default", "test_insert_parquet") + + // First, make sure the converted test_parquet is not cached. + assert(catalog.cachedDataSourceTables.getIfPresent(tableIdentifer) === null) + // Table lookup will make the table cached. + table("test_insert_parquet") + checkCached(tableIdentifer) + // For insert into non-partitioned table, we will do the conversion, + // so the converted test_insert_parquet should be cached. + invalidateTable("test_insert_parquet") + assert(catalog.cachedDataSourceTables.getIfPresent(tableIdentifer) === null) + sql( + """ + |INSERT INTO TABLE test_insert_parquet + |select a, b from jt + """.stripMargin) + checkCached(tableIdentifer) + // Make sure we can read the data. + checkAnswer( + sql("select * from test_insert_parquet"), + sql("select a, b from jt").collect()) + // Invalidate the cache. + invalidateTable("test_insert_parquet") + assert(catalog.cachedDataSourceTables.getIfPresent(tableIdentifer) === null) + + // Create a partitioned table. + sql( + """ + |create table test_parquet_partitioned_cache_test + |( + | intField INT, + | stringField STRING + |) + |PARTITIONED BY (date string) + |ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe' + |STORED AS + | INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat' + | OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat' + """.stripMargin) + + tableIdentifer = catalog.QualifiedTableName("default", "test_parquet_partitioned_cache_test") + assert(catalog.cachedDataSourceTables.getIfPresent(tableIdentifer) === null) + sql( + """ + |INSERT INTO TABLE test_parquet_partitioned_cache_test + |PARTITION (date='2015-04-01') + |select a, b from jt + """.stripMargin) + // Right now, insert into a partitioned Parquet is not supported in data source Parquet. + // So, we expect it is not cached. + assert(catalog.cachedDataSourceTables.getIfPresent(tableIdentifer) === null) + sql( + """ + |INSERT INTO TABLE test_parquet_partitioned_cache_test + |PARTITION (date='2015-04-02') + |select a, b from jt + """.stripMargin) + assert(catalog.cachedDataSourceTables.getIfPresent(tableIdentifer) === null) + + // Make sure we can cache the partitioned table. + table("test_parquet_partitioned_cache_test") + checkCached(tableIdentifer) + // Make sure we can read the data. + checkAnswer( + sql("select STRINGField, date, intField from test_parquet_partitioned_cache_test"), + sql( + """ + |select b, '2015-04-01', a FROM jt + |UNION ALL + |select b, '2015-04-02', a FROM jt + """.stripMargin).collect()) + + invalidateTable("test_parquet_partitioned_cache_test") + assert(catalog.cachedDataSourceTables.getIfPresent(tableIdentifer) === null) + + sql("DROP TABLE test_insert_parquet") + sql("DROP TABLE test_parquet_partitioned_cache_test") + } +} + +class ParquetDataSourceOffMetastoreSuite extends ParquetMetastoreSuiteBase { + val originalConf = conf.parquetUseDataSourceApi + + override def beforeAll(): Unit = { + super.beforeAll() + conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, "false") + } + + override def afterAll(): Unit = { + super.afterAll() + setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf.toString) + } + + test("MetastoreRelation in InsertIntoTable will not be converted") { + sql( + """ + |create table test_insert_parquet + |( + | intField INT + |) + |ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe' + |STORED AS + | INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat' + | OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat' + """.stripMargin) + + val df = sql("INSERT INTO TABLE test_insert_parquet SELECT a FROM jt") + df.queryExecution.executedPlan match { + case insert: execution.InsertIntoHiveTable => // OK + case o => fail(s"The SparkPlan should be ${classOf[InsertIntoHiveTable].getCanonicalName}. " + + s"However, found ${o.toString}.") + } + + checkAnswer( + sql("SELECT intField FROM test_insert_parquet WHERE test_insert_parquet.intField > 5"), + sql("SELECT a FROM jt WHERE jt.a > 5").collect() + ) + + sql("DROP TABLE IF EXISTS test_insert_parquet") + } + + // TODO: enable it after the fix of SPARK-5950. + ignore("MetastoreRelation in InsertIntoHiveTable will not be converted") { + sql( + """ + |create table test_insert_parquet + |( + | int_array array + |) + |ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe' + |STORED AS + | INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat' + | OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat' + """.stripMargin) + + val df = sql("INSERT INTO TABLE test_insert_parquet SELECT a FROM jt_array") + df.queryExecution.executedPlan match { + case insert: execution.InsertIntoHiveTable => // OK + case o => fail(s"The SparkPlan should be ${classOf[InsertIntoHiveTable].getCanonicalName}. " + + s"However, found ${o.toString}.") + } + + checkAnswer( + sql("SELECT int_array FROM test_insert_parquet"), + sql("SELECT a FROM jt_array").collect() + ) + + sql("DROP TABLE IF EXISTS test_insert_parquet") + } +} + +/** + * A suite of tests for the Parquet support through the data sources API. + */ +class ParquetSourceSuiteBase extends ParquetPartitioningTest { + override def beforeAll(): Unit = { + super.beforeAll() + + sql( s""" + create temporary table partitioned_parquet + USING org.apache.spark.sql.parquet + OPTIONS ( + path '${partitionedTableDir.getCanonicalPath}' + ) + """) + + sql( s""" + create temporary table partitioned_parquet_with_key + USING org.apache.spark.sql.parquet + OPTIONS ( + path '${partitionedTableDirWithKey.getCanonicalPath}' + ) + """) + + sql( s""" + create temporary table normal_parquet + USING org.apache.spark.sql.parquet + OPTIONS ( + path '${new File(partitionedTableDir, "p=1").getCanonicalPath}' + ) + """) + + sql( s""" + CREATE TEMPORARY TABLE partitioned_parquet_with_key_and_complextypes + USING org.apache.spark.sql.parquet + OPTIONS ( + path '${partitionedTableDirWithKeyAndComplexTypes.getCanonicalPath}' + ) + """) + + sql( s""" + CREATE TEMPORARY TABLE partitioned_parquet_with_complextypes + USING org.apache.spark.sql.parquet + OPTIONS ( + path '${partitionedTableDirWithComplexTypes.getCanonicalPath}' + ) + """) + } + + test("SPARK-6016 make sure to use the latest footers") { + sql("drop table if exists spark_6016_fix") + + // Create a DataFrame with two partitions. So, the created table will have two parquet files. + val df1 = jsonRDD(sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i}"""), 2)) + df1.saveAsTable("spark_6016_fix", "parquet", SaveMode.Overwrite) + checkAnswer( + sql("select * from spark_6016_fix"), + (1 to 10).map(i => Row(i)) + ) + + // Create a DataFrame with four partitions. So, the created table will have four parquet files. + val df2 = jsonRDD(sparkContext.parallelize((1 to 10).map(i => s"""{"b":$i}"""), 4)) + df2.saveAsTable("spark_6016_fix", "parquet", SaveMode.Overwrite) + // For the bug of SPARK-6016, we are caching two outdated footers for df1. Then, + // since the new table has four parquet files, we are trying to read new footers from two files + // and then merge metadata in footers of these four (two outdated ones and two latest one), + // which will cause an error. + checkAnswer( + sql("select * from spark_6016_fix"), + (1 to 10).map(i => Row(i)) + ) + + sql("drop table spark_6016_fix") + } +} + +class ParquetDataSourceOnSourceSuite extends ParquetSourceSuiteBase { + val originalConf = conf.parquetUseDataSourceApi + + override def beforeAll(): Unit = { + super.beforeAll() + conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, "true") + } + + override def afterAll(): Unit = { + super.afterAll() + setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf.toString) + } + + test("values in arrays and maps stored in parquet are always nullable") { + val df = createDataFrame(Tuple2(Map(2 -> 3), Seq(4, 5, 6)) :: Nil).toDF("m", "a") + val mapType1 = MapType(IntegerType, IntegerType, valueContainsNull = false) + val arrayType1 = ArrayType(IntegerType, containsNull = false) + val expectedSchema1 = + StructType( + StructField("m", mapType1, nullable = true) :: + StructField("a", arrayType1, nullable = true) :: Nil) + assert(df.schema === expectedSchema1) + + df.saveAsTable("alwaysNullable", "parquet") + + val mapType2 = MapType(IntegerType, IntegerType, valueContainsNull = true) + val arrayType2 = ArrayType(IntegerType, containsNull = true) + val expectedSchema2 = + StructType( + StructField("m", mapType2, nullable = true) :: + StructField("a", arrayType2, nullable = true) :: Nil) + + assert(table("alwaysNullable").schema === expectedSchema2) + + checkAnswer( + sql("SELECT m, a FROM alwaysNullable"), + Row(Map(2 -> 3), Seq(4, 5, 6))) + + sql("DROP TABLE alwaysNullable") + } + + test("Aggregation attribute names can't contain special chars \" ,;{}()\\n\\t=\"") { + val tempDir = Utils.createTempDir() + val filePath = new File(tempDir, "testParquet").getCanonicalPath + val filePath2 = new File(tempDir, "testParquet2").getCanonicalPath + + val df = Seq(1,2,3).map(i => (i, i.toString)).toDF("int", "str") + val df2 = df.as('x).join(df.as('y), $"x.str" === $"y.str").groupBy("y.str").max("y.int") + intercept[RuntimeException](df2.saveAsParquetFile(filePath)) + + val df3 = df2.toDF("str", "max_int") + df3.saveAsParquetFile(filePath2) + val df4 = parquetFile(filePath2) + checkAnswer(df4, Row("1", 1) :: Row("2", 2) :: Row("3", 3) :: Nil) + assert(df4.columns === Array("str", "max_int")) + } +} + +class ParquetDataSourceOffSourceSuite extends ParquetSourceSuiteBase { + val originalConf = conf.parquetUseDataSourceApi + + override def beforeAll(): Unit = { + super.beforeAll() + conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, "false") + } + + override def afterAll(): Unit = { + super.afterAll() + setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf.toString) + } +} + +/** + * A collection of tests for parquet data with various forms of partitioning. + */ +abstract class ParquetPartitioningTest extends QueryTest with BeforeAndAfterAll { + var partitionedTableDir: File = null + var normalTableDir: File = null + var partitionedTableDirWithKey: File = null + var partitionedTableDirWithComplexTypes: File = null + var partitionedTableDirWithKeyAndComplexTypes: File = null + + override def beforeAll(): Unit = { + partitionedTableDir = Utils.createTempDir() + normalTableDir = Utils.createTempDir() + + (1 to 10).foreach { p => + val partDir = new File(partitionedTableDir, s"p=$p") + sparkContext.makeRDD(1 to 10) + .map(i => ParquetData(i, s"part-$p")) + .toDF() + .saveAsParquetFile(partDir.getCanonicalPath) + } + + sparkContext + .makeRDD(1 to 10) + .map(i => ParquetData(i, s"part-1")) + .toDF() + .saveAsParquetFile(new File(normalTableDir, "normal").getCanonicalPath) + + partitionedTableDirWithKey = Utils.createTempDir() + + (1 to 10).foreach { p => + val partDir = new File(partitionedTableDirWithKey, s"p=$p") + sparkContext.makeRDD(1 to 10) + .map(i => ParquetDataWithKey(p, i, s"part-$p")) + .toDF() + .saveAsParquetFile(partDir.getCanonicalPath) + } + + partitionedTableDirWithKeyAndComplexTypes = Utils.createTempDir() + + (1 to 10).foreach { p => + val partDir = new File(partitionedTableDirWithKeyAndComplexTypes, s"p=$p") + sparkContext.makeRDD(1 to 10).map { i => + ParquetDataWithKeyAndComplexTypes( + p, i, s"part-$p", StructContainer(i, f"${i}_string"), 1 to i) + }.toDF().saveAsParquetFile(partDir.getCanonicalPath) + } + + partitionedTableDirWithComplexTypes = Utils.createTempDir() + + (1 to 10).foreach { p => + val partDir = new File(partitionedTableDirWithComplexTypes, s"p=$p") + sparkContext.makeRDD(1 to 10).map { i => + ParquetDataWithComplexTypes(i, s"part-$p", StructContainer(i, f"${i}_string"), 1 to i) + }.toDF().saveAsParquetFile(partDir.getCanonicalPath) + } + } + + override protected def afterAll(): Unit = { + partitionedTableDir.delete() + normalTableDir.delete() + partitionedTableDirWithKey.delete() + partitionedTableDirWithComplexTypes.delete() + partitionedTableDirWithKeyAndComplexTypes.delete() + } + + Seq( + "partitioned_parquet", + "partitioned_parquet_with_key", + "partitioned_parquet_with_complextypes", + "partitioned_parquet_with_key_and_complextypes").foreach { table => + + test(s"ordering of the partitioning columns $table") { + checkAnswer( + sql(s"SELECT p, stringField FROM $table WHERE p = 1"), + Seq.fill(10)(Row(1, "part-1")) + ) + + checkAnswer( + sql(s"SELECT stringField, p FROM $table WHERE p = 1"), + Seq.fill(10)(Row("part-1", 1)) + ) + } + + test(s"project the partitioning column $table") { + checkAnswer( + sql(s"SELECT p, count(*) FROM $table group by p"), + Row(1, 10) :: + Row(2, 10) :: + Row(3, 10) :: + Row(4, 10) :: + Row(5, 10) :: + Row(6, 10) :: + Row(7, 10) :: + Row(8, 10) :: + Row(9, 10) :: + Row(10, 10) :: Nil + ) + } + + test(s"project partitioning and non-partitioning columns $table") { + checkAnswer( + sql(s"SELECT stringField, p, count(intField) FROM $table GROUP BY p, stringField"), + Row("part-1", 1, 10) :: + Row("part-2", 2, 10) :: + Row("part-3", 3, 10) :: + Row("part-4", 4, 10) :: + Row("part-5", 5, 10) :: + Row("part-6", 6, 10) :: + Row("part-7", 7, 10) :: + Row("part-8", 8, 10) :: + Row("part-9", 9, 10) :: + Row("part-10", 10, 10) :: Nil + ) + } + + test(s"simple count $table") { + checkAnswer( + sql(s"SELECT COUNT(*) FROM $table"), + Row(100)) + } + + test(s"pruned count $table") { + checkAnswer( + sql(s"SELECT COUNT(*) FROM $table WHERE p = 1"), + Row(10)) + } + + test(s"non-existent partition $table") { + checkAnswer( + sql(s"SELECT COUNT(*) FROM $table WHERE p = 1000"), + Row(0)) + } + + test(s"multi-partition pruned count $table") { + checkAnswer( + sql(s"SELECT COUNT(*) FROM $table WHERE p IN (1,2,3)"), + Row(30)) + } + + test(s"non-partition predicates $table") { + checkAnswer( + sql(s"SELECT COUNT(*) FROM $table WHERE intField IN (1,2,3)"), + Row(30)) + } + + test(s"sum $table") { + checkAnswer( + sql(s"SELECT SUM(intField) FROM $table WHERE intField IN (1,2,3) AND p = 1"), + Row(1 + 2 + 3)) + } + + test(s"hive udfs $table") { + checkAnswer( + sql(s"SELECT concat(stringField, stringField) FROM $table"), + sql(s"SELECT stringField FROM $table").map { + case Row(s: String) => Row(s + s) + }.collect().toSeq) + } + } + + Seq( + "partitioned_parquet_with_key_and_complextypes", + "partitioned_parquet_with_complextypes").foreach { table => + + test(s"SPARK-5775 read struct from $table") { + checkAnswer( + sql( + s""" + |SELECT p, structField.intStructField, structField.stringStructField + |FROM $table WHERE p = 1 + """.stripMargin), + (1 to 10).map(i => Row(1, i, f"${i}_string"))) + } + + // Re-enable this after SPARK-5508 is fixed + ignore(s"SPARK-5775 read array from $table") { + checkAnswer( + sql(s"SELECT arrayField, p FROM $table WHERE p = 1"), + (1 to 10).map(i => Row(1 to i, 1))) + } + } + + + test("non-part select(*)") { + checkAnswer( + sql("SELECT COUNT(*) FROM normal_parquet"), + Row(10)) + } +} diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/parquet/parquetSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/parquet/parquetSuites.scala deleted file mode 100644 index a7479a5b95864..0000000000000 --- a/sql/hive/src/test/scala/org/apache/spark/sql/parquet/parquetSuites.scala +++ /dev/null @@ -1,293 +0,0 @@ - -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.sql.parquet - -import java.io.File - -import org.apache.spark.sql.catalyst.expressions.Row -import org.scalatest.BeforeAndAfterAll - -import org.apache.spark.sql.{SQLConf, QueryTest} -import org.apache.spark.sql.execution.PhysicalRDD -import org.apache.spark.sql.hive.execution.HiveTableScan -import org.apache.spark.sql.hive.test.TestHive._ - -// The data where the partitioning key exists only in the directory structure. -case class ParquetData(intField: Int, stringField: String) -// The data that also includes the partitioning key -case class ParquetDataWithKey(p: Int, intField: Int, stringField: String) - - -/** - * A suite to test the automatic conversion of metastore tables with parquet data to use the - * built in parquet support. - */ -class ParquetMetastoreSuite extends ParquetPartitioningTest { - override def beforeAll(): Unit = { - super.beforeAll() - - sql(s""" - create external table partitioned_parquet - ( - intField INT, - stringField STRING - ) - PARTITIONED BY (p int) - ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe' - STORED AS - INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat' - OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat' - location '${partitionedTableDir.getCanonicalPath}' - """) - - sql(s""" - create external table partitioned_parquet_with_key - ( - intField INT, - stringField STRING - ) - PARTITIONED BY (p int) - ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe' - STORED AS - INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat' - OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat' - location '${partitionedTableDirWithKey.getCanonicalPath}' - """) - - sql(s""" - create external table normal_parquet - ( - intField INT, - stringField STRING - ) - ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe' - STORED AS - INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat' - OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat' - location '${new File(normalTableDir, "normal").getCanonicalPath}' - """) - - (1 to 10).foreach { p => - sql(s"ALTER TABLE partitioned_parquet ADD PARTITION (p=$p)") - } - - (1 to 10).foreach { p => - sql(s"ALTER TABLE partitioned_parquet_with_key ADD PARTITION (p=$p)") - } - - setConf("spark.sql.hive.convertMetastoreParquet", "true") - } - - override def afterAll(): Unit = { - setConf("spark.sql.hive.convertMetastoreParquet", "false") - } - - test(s"conversion is working") { - assert( - sql("SELECT * FROM normal_parquet").queryExecution.executedPlan.collect { - case _: HiveTableScan => true - }.isEmpty) - assert( - sql("SELECT * FROM normal_parquet").queryExecution.executedPlan.collect { - case _: ParquetTableScan => true - case _: PhysicalRDD => true - }.nonEmpty) - } -} - -/** - * A suite of tests for the Parquet support through the data sources API. - */ -class ParquetSourceSuite extends ParquetPartitioningTest { - override def beforeAll(): Unit = { - super.beforeAll() - - sql( s""" - create temporary table partitioned_parquet - USING org.apache.spark.sql.parquet - OPTIONS ( - path '${partitionedTableDir.getCanonicalPath}' - ) - """) - - sql( s""" - create temporary table partitioned_parquet_with_key - USING org.apache.spark.sql.parquet - OPTIONS ( - path '${partitionedTableDirWithKey.getCanonicalPath}' - ) - """) - - sql( s""" - create temporary table normal_parquet - USING org.apache.spark.sql.parquet - OPTIONS ( - path '${new File(partitionedTableDir, "p=1").getCanonicalPath}' - ) - """) - } -} - -/** - * A collection of tests for parquet data with various forms of partitioning. - */ -abstract class ParquetPartitioningTest extends QueryTest with BeforeAndAfterAll { - var partitionedTableDir: File = null - var normalTableDir: File = null - var partitionedTableDirWithKey: File = null - - import org.apache.spark.sql.hive.test.TestHive.implicits._ - - override def beforeAll(): Unit = { - partitionedTableDir = File.createTempFile("parquettests", "sparksql") - partitionedTableDir.delete() - partitionedTableDir.mkdir() - - normalTableDir = File.createTempFile("parquettests", "sparksql") - normalTableDir.delete() - normalTableDir.mkdir() - - (1 to 10).foreach { p => - val partDir = new File(partitionedTableDir, s"p=$p") - sparkContext.makeRDD(1 to 10) - .map(i => ParquetData(i, s"part-$p")) - .saveAsParquetFile(partDir.getCanonicalPath) - } - - sparkContext - .makeRDD(1 to 10) - .map(i => ParquetData(i, s"part-1")) - .saveAsParquetFile(new File(normalTableDir, "normal").getCanonicalPath) - - partitionedTableDirWithKey = File.createTempFile("parquettests", "sparksql") - partitionedTableDirWithKey.delete() - partitionedTableDirWithKey.mkdir() - - (1 to 10).foreach { p => - val partDir = new File(partitionedTableDirWithKey, s"p=$p") - sparkContext.makeRDD(1 to 10) - .map(i => ParquetDataWithKey(p, i, s"part-$p")) - .saveAsParquetFile(partDir.getCanonicalPath) - } - } - - def run(prefix: String): Unit = { - Seq("partitioned_parquet", "partitioned_parquet_with_key").foreach { table => - test(s"$prefix: ordering of the partitioning columns $table") { - checkAnswer( - sql(s"SELECT p, stringField FROM $table WHERE p = 1"), - Seq.fill(10)(Row(1, "part-1")) - ) - - checkAnswer( - sql(s"SELECT stringField, p FROM $table WHERE p = 1"), - Seq.fill(10)(Row("part-1", 1)) - ) - } - - test(s"$prefix: project the partitioning column $table") { - checkAnswer( - sql(s"SELECT p, count(*) FROM $table group by p"), - Row(1, 10) :: - Row(2, 10) :: - Row(3, 10) :: - Row(4, 10) :: - Row(5, 10) :: - Row(6, 10) :: - Row(7, 10) :: - Row(8, 10) :: - Row(9, 10) :: - Row(10, 10) :: Nil - ) - } - - test(s"$prefix: project partitioning and non-partitioning columns $table") { - checkAnswer( - sql(s"SELECT stringField, p, count(intField) FROM $table GROUP BY p, stringField"), - Row("part-1", 1, 10) :: - Row("part-2", 2, 10) :: - Row("part-3", 3, 10) :: - Row("part-4", 4, 10) :: - Row("part-5", 5, 10) :: - Row("part-6", 6, 10) :: - Row("part-7", 7, 10) :: - Row("part-8", 8, 10) :: - Row("part-9", 9, 10) :: - Row("part-10", 10, 10) :: Nil - ) - } - - test(s"$prefix: simple count $table") { - checkAnswer( - sql(s"SELECT COUNT(*) FROM $table"), - Row(100)) - } - - test(s"$prefix: pruned count $table") { - checkAnswer( - sql(s"SELECT COUNT(*) FROM $table WHERE p = 1"), - Row(10)) - } - - test(s"$prefix: non-existent partition $table") { - checkAnswer( - sql(s"SELECT COUNT(*) FROM $table WHERE p = 1000"), - Row(0)) - } - - test(s"$prefix: multi-partition pruned count $table") { - checkAnswer( - sql(s"SELECT COUNT(*) FROM $table WHERE p IN (1,2,3)"), - Row(30)) - } - - test(s"$prefix: non-partition predicates $table") { - checkAnswer( - sql(s"SELECT COUNT(*) FROM $table WHERE intField IN (1,2,3)"), - Row(30)) - } - - test(s"$prefix: sum $table") { - checkAnswer( - sql(s"SELECT SUM(intField) FROM $table WHERE intField IN (1,2,3) AND p = 1"), - Row(1 + 2 + 3)) - } - - test(s"$prefix: hive udfs $table") { - checkAnswer( - sql(s"SELECT concat(stringField, stringField) FROM $table"), - sql(s"SELECT stringField FROM $table").map { - case Row(s: String) => Row(s + s) - }.collect().toSeq) - } - } - - test(s"$prefix: $prefix: non-part select(*)") { - checkAnswer( - sql("SELECT COUNT(*) FROM normal_parquet"), - Row(10)) - } - } - - setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, "false") - run("Parquet data source enabled") - - setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, "true") - run("Parquet data source disabled") -} diff --git a/sql/hive/v0.12.0/src/main/scala/org/apache/spark/sql/hive/Shim12.scala b/sql/hive/v0.12.0/src/main/scala/org/apache/spark/sql/hive/Shim12.scala index b5a0754ff61f9..33e96eaabfbf6 100644 --- a/sql/hive/v0.12.0/src/main/scala/org/apache/spark/sql/hive/Shim12.scala +++ b/sql/hive/v0.12.0/src/main/scala/org/apache/spark/sql/hive/Shim12.scala @@ -34,16 +34,18 @@ import org.apache.hadoop.hive.ql.plan.{CreateTableDesc, FileSinkDesc, TableDesc} import org.apache.hadoop.hive.ql.processors._ import org.apache.hadoop.hive.ql.stats.StatsSetupConst import org.apache.hadoop.hive.serde2.{ColumnProjectionUtils, Deserializer, io => hiveIo} -import org.apache.hadoop.hive.serde2.objectinspector.{ObjectInspector, PrimitiveObjectInspector} +import org.apache.hadoop.hive.serde2.objectinspector.{ObjectInspectorConverters, ObjectInspector, PrimitiveObjectInspector} import org.apache.hadoop.hive.serde2.objectinspector.PrimitiveObjectInspector.PrimitiveCategory import org.apache.hadoop.hive.serde2.objectinspector.primitive.{HiveDecimalObjectInspector, PrimitiveObjectInspectorFactory} import org.apache.hadoop.hive.serde2.typeinfo.{TypeInfo, TypeInfoFactory} import org.apache.hadoop.io.{NullWritable, Writable} import org.apache.hadoop.mapred.InputFormat -import org.apache.spark.sql.types.{Decimal, DecimalType} +import org.apache.spark.sql.types.{UTF8String, Decimal, DecimalType} + +private[hive] case class HiveFunctionWrapper(functionClassName: String) + extends java.io.Serializable { -case class HiveFunctionWrapper(functionClassName: String) extends java.io.Serializable { // for Serialization def this() = this(null) @@ -133,7 +135,7 @@ private[hive] object HiveShim { PrimitiveCategory.VOID, null) def getStringWritable(value: Any): hadoopIo.Text = - if (value == null) null else new hadoopIo.Text(value.asInstanceOf[String]) + if (value == null) null else new hadoopIo.Text(value.asInstanceOf[UTF8String].toString) def getIntWritable(value: Any): hadoopIo.IntWritable = if (value == null) null else new hadoopIo.IntWritable(value.asInstanceOf[Int]) @@ -208,7 +210,7 @@ private[hive] object HiveShim { def getDataLocationPath(p: Partition) = p.getPartitionPath - def getAllPartitionsOf(client: Hive, tbl: Table) = client.getAllPartitionsForPruner(tbl) + def getAllPartitionsOf(client: Hive, tbl: Table) = client.getAllPartitionsForPruner(tbl) def compatibilityBlackList = Seq( "decimal_.*", @@ -242,11 +244,22 @@ private[hive] object HiveShim { } } + def getConvertedOI( + inputOI: ObjectInspector, + outputOI: ObjectInspector): ObjectInspector = { + ObjectInspectorConverters.getConvertedOI(inputOI, outputOI, true) + } + def prepareWritable(w: Writable): Writable = { w } + + def setTblNullFormat(crtTbl: CreateTableDesc, tbl: Table) = {} } -class ShimFileSinkDesc(var dir: String, var tableInfo: TableDesc, var compressed: Boolean) +private[hive] class ShimFileSinkDesc( + var dir: String, + var tableInfo: TableDesc, + var compressed: Boolean) extends FileSinkDesc(dir, tableInfo, compressed) { } diff --git a/sql/hive/v0.13.1/src/main/scala/org/apache/spark/sql/hive/Shim13.scala b/sql/hive/v0.13.1/src/main/scala/org/apache/spark/sql/hive/Shim13.scala index e4c1809c8bb21..d331c210e8939 100644 --- a/sql/hive/v0.13.1/src/main/scala/org/apache/spark/sql/hive/Shim13.scala +++ b/sql/hive/v0.13.1/src/main/scala/org/apache/spark/sql/hive/Shim13.scala @@ -17,35 +17,35 @@ package org.apache.spark.sql.hive -import java.util.{ArrayList => JArrayList} -import java.util.Properties import java.rmi.server.UID +import java.util.{Properties, ArrayList => JArrayList} import scala.collection.JavaConversions._ import scala.language.implicitConversions +import com.esotericsoftware.kryo.Kryo import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path -import org.apache.hadoop.io.{NullWritable, Writable} -import org.apache.hadoop.mapred.InputFormat import org.apache.hadoop.hive.common.StatsSetupConst -import org.apache.hadoop.hive.common.`type`.{HiveDecimal} +import org.apache.hadoop.hive.common.`type`.HiveDecimal import org.apache.hadoop.hive.conf.HiveConf import org.apache.hadoop.hive.ql.Context -import org.apache.hadoop.hive.ql.metadata.{Table, Hive, Partition} +import org.apache.hadoop.hive.ql.exec.{UDF, Utilities} +import org.apache.hadoop.hive.ql.metadata.{Hive, Partition, Table} import org.apache.hadoop.hive.ql.plan.{CreateTableDesc, FileSinkDesc, TableDesc} import org.apache.hadoop.hive.ql.processors.CommandProcessorFactory -import org.apache.hadoop.hive.serde2.typeinfo.{TypeInfo, DecimalTypeInfo, TypeInfoFactory} -import org.apache.hadoop.hive.serde2.objectinspector.primitive.{HiveDecimalObjectInspector, PrimitiveObjectInspectorFactory} -import org.apache.hadoop.hive.serde2.objectinspector.{PrimitiveObjectInspector, ObjectInspector} -import org.apache.hadoop.hive.serde2.{Deserializer, ColumnProjectionUtils} -import org.apache.hadoop.hive.serde2.{io => hiveIo} +import org.apache.hadoop.hive.serde.serdeConstants import org.apache.hadoop.hive.serde2.avro.AvroGenericRecordWritable +import org.apache.hadoop.hive.serde2.objectinspector.primitive.{HiveDecimalObjectInspector, PrimitiveObjectInspectorFactory} +import org.apache.hadoop.hive.serde2.objectinspector.{ObjectInspector, ObjectInspectorConverters, PrimitiveObjectInspector} +import org.apache.hadoop.hive.serde2.typeinfo.{DecimalTypeInfo, TypeInfo, TypeInfoFactory} +import org.apache.hadoop.hive.serde2.{ColumnProjectionUtils, Deserializer, io => hiveIo} +import org.apache.hadoop.io.{NullWritable, Writable} +import org.apache.hadoop.mapred.InputFormat import org.apache.hadoop.{io => hadoopIo} import org.apache.spark.Logging -import org.apache.spark.sql.types.{Decimal, DecimalType} - +import org.apache.spark.sql.types.{Decimal, DecimalType, UTF8String} /** * This class provides the UDF creation and also the UDF instance serialization and @@ -55,22 +55,20 @@ import org.apache.spark.sql.types.{Decimal, DecimalType} * * @param functionClassName UDF class name */ -case class HiveFunctionWrapper(var functionClassName: String) extends java.io.Externalizable { +private[hive] case class HiveFunctionWrapper(var functionClassName: String) + extends java.io.Externalizable { + // for Serialization def this() = this(null) - import java.io.{OutputStream, InputStream} - import com.esotericsoftware.kryo.Kryo import org.apache.spark.util.Utils._ - import org.apache.hadoop.hive.ql.exec.Utilities - import org.apache.hadoop.hive.ql.exec.UDF @transient private val methodDeSerialize = { val method = classOf[Utilities].getDeclaredMethod( "deserializeObjectByKryo", classOf[Kryo], - classOf[InputStream], + classOf[java.io.InputStream], classOf[Class[_]]) method.setAccessible(true) @@ -83,7 +81,7 @@ case class HiveFunctionWrapper(var functionClassName: String) extends java.io.Ex "serializeObjectByKryo", classOf[Kryo], classOf[Object], - classOf[OutputStream]) + classOf[java.io.OutputStream]) method.setAccessible(true) method @@ -220,7 +218,7 @@ private[hive] object HiveShim { TypeInfoFactory.voidTypeInfo, null) def getStringWritable(value: Any): hadoopIo.Text = - if (value == null) null else new hadoopIo.Text(value.asInstanceOf[String]) + if (value == null) null else new hadoopIo.Text(value.asInstanceOf[UTF8String].toString) def getIntWritable(value: Any): hadoopIo.IntWritable = if (value == null) null else new hadoopIo.IntWritable(value.asInstanceOf[Int]) @@ -397,7 +395,11 @@ private[hive] object HiveShim { Decimal(hdoi.getPrimitiveJavaObject(data).bigDecimalValue(), hdoi.precision(), hdoi.scale()) } } - + + def getConvertedOI(inputOI: ObjectInspector, outputOI: ObjectInspector): ObjectInspector = { + ObjectInspectorConverters.getConvertedOI(inputOI, outputOI) + } + /* * Bug introduced in hive-0.13. AvroGenericRecordWritable has a member recordReaderID that * is needed to initialize before serialization. @@ -410,13 +412,22 @@ private[hive] object HiveShim { } w } + + def setTblNullFormat(crtTbl: CreateTableDesc, tbl: Table) = { + if (crtTbl != null && crtTbl.getNullFormat() != null) { + tbl.setSerdeParam(serdeConstants.SERIALIZATION_NULL_FORMAT, crtTbl.getNullFormat()) + } + } } /* * Bug introduced in hive-0.13. FileSinkDesc is serilizable, but its member path is not. * Fix it through wrapper. */ -class ShimFileSinkDesc(var dir: String, var tableInfo: TableDesc, var compressed: Boolean) +private[hive] class ShimFileSinkDesc( + var dir: String, + var tableInfo: TableDesc, + var compressed: Boolean) extends Serializable with Logging { var compressCodec: String = _ var compressType: String = _ diff --git a/streaming/pom.xml b/streaming/pom.xml index 1e92ba686a57d..5ca55a4f680bb 100644 --- a/streaming/pom.xml +++ b/streaming/pom.xml @@ -20,8 +20,8 @@ 4.0.0 org.apache.spark - spark-parent - 1.3.0-SNAPSHOT + spark-parent_2.10 + 1.4.0-SNAPSHOT ../pom.xml @@ -82,6 +82,11 @@ junit test + + org.seleniumhq.selenium + selenium-java + test + com.novocode junit-interface @@ -92,34 +97,6 @@ target/scala-${scala.binary.version}/classes target/scala-${scala.binary.version}/test-classes - - - org.apache.maven.plugins - maven-jar-plugin - - - - test-jar - - - - test-jar-on-test-compile - test-compile - - test-jar - - - - - org.apache.maven.plugins maven-shade-plugin diff --git a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala index b780282bdac37..0a50485118588 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala @@ -26,7 +26,7 @@ import org.apache.hadoop.conf.Configuration import org.apache.spark.{SparkException, SparkConf, Logging} import org.apache.spark.io.CompressionCodec -import org.apache.spark.util.MetadataCleaner +import org.apache.spark.util.{MetadataCleaner, Utils} import org.apache.spark.streaming.scheduler.JobGenerator @@ -43,10 +43,13 @@ class Checkpoint(@transient ssc: StreamingContext, val checkpointTime: Time) val delaySeconds = MetadataCleaner.getDelaySeconds(ssc.conf) val sparkConfPairs = ssc.conf.getAll - def sparkConf = { - new SparkConf(false).setAll(sparkConfPairs) + def createSparkConf(): SparkConf = { + val newSparkConf = new SparkConf(loadDefaults = false).setAll(sparkConfPairs) .remove("spark.driver.host") .remove("spark.driver.port") + val newMasterOption = new SparkConf(loadDefaults = true).getOption("spark.master") + newMasterOption.foreach { newMaster => newSparkConf.setMaster(newMaster) } + newSparkConf } def validate() { @@ -64,12 +67,12 @@ object Checkpoint extends Logging { val REGEX = (PREFIX + """([\d]+)([\w\.]*)""").r /** Get the checkpoint file for the given checkpoint time */ - def checkpointFile(checkpointDir: String, checkpointTime: Time) = { + def checkpointFile(checkpointDir: String, checkpointTime: Time): Path = { new Path(checkpointDir, PREFIX + checkpointTime.milliseconds) } /** Get the checkpoint backup file for the given checkpoint time */ - def checkpointBackupFile(checkpointDir: String, checkpointTime: Time) = { + def checkpointBackupFile(checkpointDir: String, checkpointTime: Time): Path = { new Path(checkpointDir, PREFIX + checkpointTime.milliseconds + ".bk") } @@ -116,7 +119,10 @@ class CheckpointWriter( private var stopped = false private var fs_ : FileSystem = _ - class CheckpointWriteHandler(checkpointTime: Time, bytes: Array[Byte]) extends Runnable { + class CheckpointWriteHandler( + checkpointTime: Time, + bytes: Array[Byte], + clearCheckpointDataLater: Boolean) extends Runnable { def run() { var attempts = 0 val startTime = System.currentTimeMillis() @@ -133,8 +139,11 @@ class CheckpointWriter( // Write checkpoint to temp file fs.delete(tempFile, true) // just in case it exists val fos = fs.create(tempFile) - fos.write(bytes) - fos.close() + Utils.tryWithSafeFinally { + fos.write(bytes) + } { + fos.close() + } // If the checkpoint file exists, back it up // If the backup exists as well, just delete it, otherwise rename will fail @@ -152,7 +161,7 @@ class CheckpointWriter( // Delete old checkpoint files val allCheckpointFiles = Checkpoint.getCheckpointFiles(checkpointDir, fs) - if (allCheckpointFiles.size > 4) { + if (allCheckpointFiles.size > 10) { allCheckpointFiles.take(allCheckpointFiles.size - 10).foreach(file => { logInfo("Deleting " + file) fs.delete(file, true) @@ -163,7 +172,7 @@ class CheckpointWriter( val finishTime = System.currentTimeMillis() logInfo("Checkpoint for time " + checkpointTime + " saved to file '" + checkpointFile + "', took " + bytes.length + " bytes and " + (finishTime - startTime) + " ms") - jobGenerator.onCheckpointCompletion(checkpointTime) + jobGenerator.onCheckpointCompletion(checkpointTime, clearCheckpointDataLater) return } catch { case ioe: IOException => @@ -177,15 +186,18 @@ class CheckpointWriter( } } - def write(checkpoint: Checkpoint) { + def write(checkpoint: Checkpoint, clearCheckpointDataLater: Boolean) { val bos = new ByteArrayOutputStream() val zos = compressionCodec.compressedOutputStream(bos) val oos = new ObjectOutputStream(zos) - oos.writeObject(checkpoint) - oos.close() - bos.close() + Utils.tryWithSafeFinally { + oos.writeObject(checkpoint) + } { + oos.close() + } try { - executor.execute(new CheckpointWriteHandler(checkpoint.checkpointTime, bos.toByteArray)) + executor.execute(new CheckpointWriteHandler( + checkpoint.checkpointTime, bos.toByteArray, clearCheckpointDataLater)) logDebug("Submitted checkpoint of time " + checkpoint.checkpointTime + " writer queue") } catch { case rej: RejectedExecutionException => @@ -225,7 +237,9 @@ object CheckpointReader extends Logging { def read(checkpointDir: String, conf: SparkConf, hadoopConf: Configuration): Option[Checkpoint] = { val checkpointPath = new Path(checkpointDir) - def fs = checkpointPath.getFileSystem(hadoopConf) + + // TODO(rxin): Why is this a def?! + def fs: FileSystem = checkpointPath.getFileSystem(hadoopConf) // Try to find the checkpoint files val checkpointFiles = Checkpoint.getCheckpointFiles(checkpointDir, fs).reverse @@ -239,18 +253,24 @@ object CheckpointReader extends Logging { checkpointFiles.foreach(file => { logInfo("Attempting to load checkpoint from file " + file) try { - val fis = fs.open(file) - // ObjectInputStream uses the last defined user-defined class loader in the stack - // to find classes, which maybe the wrong class loader. Hence, a inherited version - // of ObjectInputStream is used to explicitly use the current thread's default class - // loader to find and load classes. This is a well know Java issue and has popped up - // in other places (e.g., http://jira.codehaus.org/browse/GROOVY-1627) - val zis = compressionCodec.compressedInputStream(fis) - val ois = new ObjectInputStreamWithLoader(zis, - Thread.currentThread().getContextClassLoader) - val cp = ois.readObject.asInstanceOf[Checkpoint] - ois.close() - fs.close() + var ois: ObjectInputStreamWithLoader = null + var cp: Checkpoint = null + Utils.tryWithSafeFinally { + val fis = fs.open(file) + // ObjectInputStream uses the last defined user-defined class loader in the stack + // to find classes, which maybe the wrong class loader. Hence, a inherited version + // of ObjectInputStream is used to explicitly use the current thread's default class + // loader to find and load classes. This is a well know Java issue and has popped up + // in other places (e.g., http://jira.codehaus.org/browse/GROOVY-1627) + val zis = compressionCodec.compressedInputStream(fis) + ois = new ObjectInputStreamWithLoader(zis, + Thread.currentThread().getContextClassLoader) + cp = ois.readObject.asInstanceOf[Checkpoint] + } { + if (ois != null) { + ois.close() + } + } cp.validate() logInfo("Checkpoint successfully loaded from file " + file) logInfo("Checkpoint was generated at time " + cp.checkpointTime) diff --git a/streaming/src/main/scala/org/apache/spark/streaming/DStreamGraph.scala b/streaming/src/main/scala/org/apache/spark/streaming/DStreamGraph.scala index 0e285d6088ec1..175140481e5ae 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/DStreamGraph.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/DStreamGraph.scala @@ -100,11 +100,11 @@ final private[streaming] class DStreamGraph extends Serializable with Logging { } } - def getInputStreams() = this.synchronized { inputStreams.toArray } + def getInputStreams(): Array[InputDStream[_]] = this.synchronized { inputStreams.toArray } - def getOutputStreams() = this.synchronized { outputStreams.toArray } + def getOutputStreams(): Array[DStream[_]] = this.synchronized { outputStreams.toArray } - def getReceiverInputStreams() = this.synchronized { + def getReceiverInputStreams(): Array[ReceiverInputDStream[_]] = this.synchronized { inputStreams.filter(_.isInstanceOf[ReceiverInputDStream[_]]) .map(_.asInstanceOf[ReceiverInputDStream[_]]) .toArray diff --git a/streaming/src/main/scala/org/apache/spark/streaming/Duration.scala b/streaming/src/main/scala/org/apache/spark/streaming/Duration.scala index a0d8fb5ab93ec..3249bb348981f 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/Duration.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/Duration.scala @@ -55,7 +55,6 @@ case class Duration (private val millis: Long) { def div(that: Duration): Double = this / that - def isMultipleOf(that: Duration): Boolean = (this.millis % that.millis == 0) @@ -71,7 +70,7 @@ case class Duration (private val millis: Long) { def milliseconds: Long = millis - def prettyPrint = Utils.msDurationToString(millis) + def prettyPrint: String = Utils.msDurationToString(millis) } @@ -80,7 +79,7 @@ case class Duration (private val millis: Long) { * a given number of milliseconds. */ object Milliseconds { - def apply(milliseconds: Long) = new Duration(milliseconds) + def apply(milliseconds: Long): Duration = new Duration(milliseconds) } /** @@ -88,7 +87,7 @@ object Milliseconds { * a given number of seconds. */ object Seconds { - def apply(seconds: Long) = new Duration(seconds * 1000) + def apply(seconds: Long): Duration = new Duration(seconds * 1000) } /** @@ -96,7 +95,7 @@ object Seconds { * a given number of minutes. */ object Minutes { - def apply(minutes: Long) = new Duration(minutes * 60000) + def apply(minutes: Long): Duration = new Duration(minutes * 60000) } // Java-friendlier versions of the objects above. @@ -107,16 +106,16 @@ object Durations { /** * @return [[org.apache.spark.streaming.Duration]] representing given number of milliseconds. */ - def milliseconds(milliseconds: Long) = Milliseconds(milliseconds) + def milliseconds(milliseconds: Long): Duration = Milliseconds(milliseconds) /** * @return [[org.apache.spark.streaming.Duration]] representing given number of seconds. */ - def seconds(seconds: Long) = Seconds(seconds) + def seconds(seconds: Long): Duration = Seconds(seconds) /** * @return [[org.apache.spark.streaming.Duration]] representing given number of minutes. */ - def minutes(minutes: Long) = Minutes(minutes) + def minutes(minutes: Long): Duration = Minutes(minutes) } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/Interval.scala b/streaming/src/main/scala/org/apache/spark/streaming/Interval.scala index ad4f3fdd14ad6..3f5be785e1b1a 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/Interval.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/Interval.scala @@ -39,18 +39,18 @@ class Interval(val beginTime: Time, val endTime: Time) { this.endTime < that.endTime } - def <= (that: Interval) = (this < that || this == that) + def <= (that: Interval): Boolean = (this < that || this == that) - def > (that: Interval) = !(this <= that) + def > (that: Interval): Boolean = !(this <= that) - def >= (that: Interval) = !(this < that) + def >= (that: Interval): Boolean = !(this < that) - override def toString = "[" + beginTime + ", " + endTime + "]" + override def toString: String = "[" + beginTime + ", " + endTime + "]" } private[streaming] object Interval { - def currentInterval(duration: Duration): Interval = { + def currentInterval(duration: Duration): Interval = { val time = new Time(System.currentTimeMillis) val intervalBegin = time.floor(duration) new Interval(intervalBegin, intervalBegin + duration) diff --git a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala index ba3f23434f24c..f57f295874645 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala @@ -116,7 +116,7 @@ class StreamingContext private[streaming] ( private[streaming] val sc: SparkContext = { if (isCheckpointPresent) { - new SparkContext(cp_.sparkConf) + new SparkContext(cp_.createSparkConf()) } else { sc_ } @@ -188,7 +188,7 @@ class StreamingContext private[streaming] ( /** * Return the associated Spark context */ - def sparkContext = sc + def sparkContext: SparkContext = sc /** * Set each DStreams in this context to remember RDDs it generated in the last given duration. @@ -578,6 +578,7 @@ class StreamingContext private[streaming] ( // Even if we have already stopped, we still need to attempt to stop the SparkContext because // a user might stop(stopSparkContext = false) and then call stop(stopSparkContext = true). if (stopSparkContext) sc.stop() + uiTab.foreach(_.detach()) // The state should always be Stopped after calling `stop()`, even if we haven't started yet: state = Stopped } @@ -595,7 +596,8 @@ object StreamingContext extends Logging { @deprecated("Replaced by implicit functions in the DStream companion object. This is " + "kept here only for backward compatibility.", "1.3.0") def toPairDStreamFunctions[K, V](stream: DStream[(K, V)]) - (implicit kt: ClassTag[K], vt: ClassTag[V], ord: Ordering[K] = null) = { + (implicit kt: ClassTag[K], vt: ClassTag[V], ord: Ordering[K] = null) + : PairDStreamFunctions[K, V] = { DStream.toPairDStreamFunctions(stream)(kt, vt, ord) } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStream.scala index 505e4431e4350..01cdcb0574040 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStream.scala @@ -36,7 +36,7 @@ import org.apache.spark.streaming.dstream.DStream * [[org.apache.spark.streaming.api.java.JavaPairDStream]]. */ class JavaDStream[T](val dstream: DStream[T])(implicit val classTag: ClassTag[T]) - extends JavaDStreamLike[T, JavaDStream[T], JavaRDD[T]] { + extends AbstractJavaDStreamLike[T, JavaDStream[T], JavaRDD[T]] { override def wrapRDD(rdd: RDD[T]): JavaRDD[T] = JavaRDD.fromRDD(rdd) diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala index c382a12f4d099..808dcc174cf9a 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala @@ -34,6 +34,15 @@ import org.apache.spark.streaming._ import org.apache.spark.streaming.api.java.JavaDStream._ import org.apache.spark.streaming.dstream.DStream +/** + * As a workaround for https://issues.scala-lang.org/browse/SI-8905, implementations + * of JavaDStreamLike should extend this dummy abstract class instead of directly inheriting + * from the trait. See SPARK-3266 for additional details. + */ +private[streaming] +abstract class AbstractJavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], + R <: JavaRDDLike[T, R]] extends JavaDStreamLike[T, This, R] + trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T, R]] extends Serializable { implicit val classTag: ClassTag[T] @@ -160,7 +169,7 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T */ def flatMap[U](f: FlatMapFunction[T, U]): JavaDStream[U] = { import scala.collection.JavaConverters._ - def fn = (x: T) => f.call(x).asScala + def fn: (T) => Iterable[U] = (x: T) => f.call(x).asScala new JavaDStream(dstream.flatMap(fn)(fakeClassTag[U]))(fakeClassTag[U]) } @@ -170,7 +179,7 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T */ def flatMapToPair[K2, V2](f: PairFlatMapFunction[T, K2, V2]): JavaPairDStream[K2, V2] = { import scala.collection.JavaConverters._ - def fn = (x: T) => f.call(x).asScala + def fn: (T) => Iterable[(K2, V2)] = (x: T) => f.call(x).asScala def cm: ClassTag[(K2, V2)] = fakeClassTag new JavaPairDStream(dstream.flatMap(fn)(cm))(fakeClassTag[K2], fakeClassTag[V2]) } @@ -181,7 +190,9 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T * of the RDD. */ def mapPartitions[U](f: FlatMapFunction[java.util.Iterator[T], U]): JavaDStream[U] = { - def fn = (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator()) + def fn: (Iterator[T]) => Iterator[U] = { + (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator()) + } new JavaDStream(dstream.mapPartitions(fn)(fakeClassTag[U]))(fakeClassTag[U]) } @@ -192,7 +203,9 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T */ def mapPartitionsToPair[K2, V2](f: PairFlatMapFunction[java.util.Iterator[T], K2, V2]) : JavaPairDStream[K2, V2] = { - def fn = (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator()) + def fn: (Iterator[T]) => Iterator[(K2, V2)] = { + (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator()) + } new JavaPairDStream(dstream.mapPartitions(fn))(fakeClassTag[K2], fakeClassTag[V2]) } @@ -406,8 +419,9 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T implicit val cmv2: ClassTag[V2] = fakeClassTag implicit val cmw: ClassTag[W] = fakeClassTag - def scalaTransform (inThis: RDD[T], inThat: RDD[(K2, V2)], time: Time): RDD[W] = + def scalaTransform (inThis: RDD[T], inThat: RDD[(K2, V2)], time: Time): RDD[W] = { transformFunc.call(wrapRDD(inThis), other.wrapRDD(inThat), time).rdd + } dstream.transformWith[(K2, V2), W](other.dstream, scalaTransform(_, _, _)) } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala index de124cf40eff1..93baad19e3ee1 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala @@ -45,7 +45,7 @@ import org.apache.spark.streaming.dstream.DStream class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( implicit val kManifest: ClassTag[K], implicit val vManifest: ClassTag[V]) - extends JavaDStreamLike[(K, V), JavaPairDStream[K, V], JavaPairRDD[K, V]] { + extends AbstractJavaDStreamLike[(K, V), JavaPairDStream[K, V], JavaPairRDD[K, V]] { override def wrapRDD(rdd: RDD[(K, V)]): JavaPairRDD[K, V] = JavaPairRDD.fromRDD(rdd) @@ -526,7 +526,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( */ def flatMapValues[U](f: JFunction[V, java.lang.Iterable[U]]): JavaPairDStream[K, U] = { import scala.collection.JavaConverters._ - def fn = (x: V) => f.apply(x).asScala + def fn: (V) => Iterable[U] = (x: V) => f.apply(x).asScala implicit val cm: ClassTag[U] = implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[U]] dstream.flatMapValues(fn) @@ -726,7 +726,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( * Save each RDD in `this` DStream as a Hadoop file. The file name at each batch interval is * generated based on `prefix` and `suffix`: "prefix-TIME_IN_MS.suffix". */ - def saveAsHadoopFiles[F <: OutputFormat[K, V]](prefix: String, suffix: String) { + def saveAsHadoopFiles(prefix: String, suffix: String) { dstream.saveAsHadoopFiles(prefix, suffix) } @@ -734,12 +734,12 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( * Save each RDD in `this` DStream as a Hadoop file. The file name at each batch interval is * generated based on `prefix` and `suffix`: "prefix-TIME_IN_MS.suffix". */ - def saveAsHadoopFiles( + def saveAsHadoopFiles[F <: OutputFormat[_, _]]( prefix: String, suffix: String, keyClass: Class[_], valueClass: Class[_], - outputFormatClass: Class[_ <: OutputFormat[_, _]]) { + outputFormatClass: Class[F]) { dstream.saveAsHadoopFiles(prefix, suffix, keyClass, valueClass, outputFormatClass) } @@ -747,12 +747,12 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( * Save each RDD in `this` DStream as a Hadoop file. The file name at each batch interval is * generated based on `prefix` and `suffix`: "prefix-TIME_IN_MS.suffix". */ - def saveAsHadoopFiles( + def saveAsHadoopFiles[F <: OutputFormat[_, _]]( prefix: String, suffix: String, keyClass: Class[_], valueClass: Class[_], - outputFormatClass: Class[_ <: OutputFormat[_, _]], + outputFormatClass: Class[F], conf: JobConf) { dstream.saveAsHadoopFiles(prefix, suffix, keyClass, valueClass, outputFormatClass, conf) } @@ -761,7 +761,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( * Save each RDD in `this` DStream as a Hadoop file. The file name at each batch interval is * generated based on `prefix` and `suffix`: "prefix-TIME_IN_MS.suffix". */ - def saveAsNewAPIHadoopFiles[F <: NewOutputFormat[K, V]](prefix: String, suffix: String) { + def saveAsNewAPIHadoopFiles(prefix: String, suffix: String) { dstream.saveAsNewAPIHadoopFiles(prefix, suffix) } @@ -769,12 +769,12 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( * Save each RDD in `this` DStream as a Hadoop file. The file name at each batch interval is * generated based on `prefix` and `suffix`: "prefix-TIME_IN_MS.suffix". */ - def saveAsNewAPIHadoopFiles( + def saveAsNewAPIHadoopFiles[F <: NewOutputFormat[_, _]]( prefix: String, suffix: String, keyClass: Class[_], valueClass: Class[_], - outputFormatClass: Class[_ <: NewOutputFormat[_, _]]) { + outputFormatClass: Class[F]) { dstream.saveAsNewAPIHadoopFiles(prefix, suffix, keyClass, valueClass, outputFormatClass) } @@ -782,12 +782,12 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( * Save each RDD in `this` DStream as a Hadoop file. The file name at each batch interval is * generated based on `prefix` and `suffix`: "prefix-TIME_IN_MS.suffix". */ - def saveAsNewAPIHadoopFiles( + def saveAsNewAPIHadoopFiles[F <: NewOutputFormat[_, _]]( prefix: String, suffix: String, keyClass: Class[_], valueClass: Class[_], - outputFormatClass: Class[_ <: NewOutputFormat[_, _]], + outputFormatClass: Class[F], conf: Configuration = new Configuration) { dstream.saveAsNewAPIHadoopFiles(prefix, suffix, keyClass, valueClass, outputFormatClass, conf) } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala index e3db01c1e12c6..4095a7cc84946 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala @@ -192,7 +192,7 @@ class JavaStreamingContext(val ssc: StreamingContext) extends Closeable { converter: JFunction[InputStream, java.lang.Iterable[T]], storageLevel: StorageLevel) : JavaReceiverInputDStream[T] = { - def fn = (x: InputStream) => converter.call(x).toIterator + def fn: (InputStream) => Iterator[T] = (x: InputStream) => converter.call(x).toIterator implicit val cmt: ClassTag[T] = implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]] ssc.socketStream(hostname, port, fn, storageLevel) @@ -313,7 +313,7 @@ class JavaStreamingContext(val ssc: StreamingContext) extends Closeable { implicit val cmk: ClassTag[K] = ClassTag(kClass) implicit val cmv: ClassTag[V] = ClassTag(vClass) implicit val cmf: ClassTag[F] = ClassTag(fClass) - def fn = (x: Path) => filter.call(x).booleanValue() + def fn: (Path) => Boolean = (x: Path) => filter.call(x).booleanValue() ssc.fileStream[K, V, F](directory, fn, newFilesOnly) } @@ -344,7 +344,7 @@ class JavaStreamingContext(val ssc: StreamingContext) extends Closeable { implicit val cmk: ClassTag[K] = ClassTag(kClass) implicit val cmv: ClassTag[V] = ClassTag(vClass) implicit val cmf: ClassTag[F] = ClassTag(fClass) - def fn = (x: Path) => filter.call(x).booleanValue() + def fn: (Path) => Boolean = (x: Path) => filter.call(x).booleanValue() ssc.fileStream[K, V, F](directory, fn, newFilesOnly, conf) } @@ -625,7 +625,7 @@ class JavaStreamingContext(val ssc: StreamingContext) extends Closeable { * Stop the execution of the streams. * @param stopSparkContext Stop the associated SparkContext or not */ - def stop(stopSparkContext: Boolean) = ssc.stop(stopSparkContext) + def stop(stopSparkContext: Boolean): Unit = ssc.stop(stopSparkContext) /** * Stop the execution of the streams. @@ -633,7 +633,7 @@ class JavaStreamingContext(val ssc: StreamingContext) extends Closeable { * @param stopGracefully Stop gracefully by waiting for the processing of all * received data to be completed */ - def stop(stopSparkContext: Boolean, stopGracefully: Boolean) = { + def stop(stopSparkContext: Boolean, stopGracefully: Boolean): Unit = { ssc.stop(stopSparkContext, stopGracefully) } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala index 7053f47ec69a2..4c28654ef6413 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala @@ -176,11 +176,11 @@ private[python] abstract class PythonDStream( val func = new TransformFunction(pfunc) - override def dependencies = List(parent) + override def dependencies: List[DStream[_]] = List(parent) override def slideDuration: Duration = parent.slideDuration - val asJavaDStream = JavaDStream.fromDStream(this) + val asJavaDStream: JavaDStream[Array[Byte]] = JavaDStream.fromDStream(this) } /** @@ -212,7 +212,7 @@ private[python] class PythonTransformed2DStream( val func = new TransformFunction(pfunc) - override def dependencies = List(parent, parent2) + override def dependencies: List[DStream[_]] = List(parent, parent2) override def slideDuration: Duration = parent.slideDuration @@ -223,7 +223,7 @@ private[python] class PythonTransformed2DStream( func(Some(rdd1), Some(rdd2), validTime) } - val asJavaDStream = JavaDStream.fromDStream(this) + val asJavaDStream: JavaDStream[Array[Byte]] = JavaDStream.fromDStream(this) } /** @@ -260,12 +260,15 @@ private[python] class PythonReducedWindowedDStream( extends PythonDStream(parent, preduceFunc) { super.persist(StorageLevel.MEMORY_ONLY) - override val mustCheckpoint = true - val invReduceFunc = new TransformFunction(pinvReduceFunc) + override val mustCheckpoint: Boolean = true + + val invReduceFunc: TransformFunction = new TransformFunction(pinvReduceFunc) def windowDuration: Duration = _windowDuration + override def slideDuration: Duration = _slideDuration + override def parentRememberDuration: Duration = rememberDuration + windowDuration override def compute(validTime: Time): Option[RDD[Array[Byte]]] = { diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala index b874f561c12eb..24f99a2b929f5 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala @@ -104,7 +104,7 @@ abstract class DStream[T: ClassTag] ( private[streaming] def parentRememberDuration = rememberDuration /** Return the StreamingContext associated with this DStream */ - def context = ssc + def context: StreamingContext = ssc /* Set the creation call site */ private[streaming] val creationSite = DStream.getCreationSite() @@ -619,14 +619,16 @@ abstract class DStream[T: ClassTag] ( * operator, so this DStream will be registered as an output stream and there materialized. */ def print(num: Int) { - def foreachFunc = (rdd: RDD[T], time: Time) => { - val firstNum = rdd.take(num + 1) - println ("-------------------------------------------") - println ("Time: " + time) - println ("-------------------------------------------") - firstNum.take(num).foreach(println) - if (firstNum.size > num) println("...") - println() + def foreachFunc: (RDD[T], Time) => Unit = { + (rdd: RDD[T], time: Time) => { + val firstNum = rdd.take(num + 1) + println("-------------------------------------------") + println("Time: " + time) + println("-------------------------------------------") + firstNum.take(num).foreach(println) + if (firstNum.size > num) println("...") + println() + } } new ForEachDStream(this, context.sparkContext.clean(foreachFunc)).register() } @@ -837,7 +839,7 @@ object DStream { /** Filtering function that excludes non-user classes for a streaming application */ def streamingExclustionFunction(className: String): Boolean = { - def doesMatch(r: Regex) = r.findFirstIn(className).isDefined + def doesMatch(r: Regex): Boolean = r.findFirstIn(className).isDefined val isSparkClass = doesMatch(SPARK_CLASS_REGEX) val isSparkExampleClass = doesMatch(SPARK_EXAMPLES_CLASS_REGEX) val isSparkStreamingTestClass = doesMatch(SPARK_STREAMING_TESTCLASS_REGEX) diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStreamCheckpointData.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStreamCheckpointData.scala index 0dc72790fbdbd..39fd21342813e 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStreamCheckpointData.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStreamCheckpointData.scala @@ -114,7 +114,7 @@ class DStreamCheckpointData[T: ClassTag] (dstream: DStream[T]) } } - override def toString() = { + override def toString: String = { "[\n" + currentCheckpointFiles.size + " checkpoint files \n" + currentCheckpointFiles.mkString("\n") + "\n]" } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala index 6379b88527ec8..66d519171fd76 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala @@ -18,7 +18,6 @@ package org.apache.spark.streaming.dstream import java.io.{IOException, ObjectInputStream} -import java.util.concurrent.ConcurrentHashMap import scala.collection.mutable import scala.reflect.ClassTag @@ -27,6 +26,7 @@ import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.{FileSystem, Path, PathFilter} import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat} +import org.apache.spark.SerializableWritable import org.apache.spark.rdd.{RDD, UnionRDD} import org.apache.spark.streaming._ import org.apache.spark.util.{TimeStampedHashMap, Utils} @@ -78,6 +78,8 @@ class FileInputDStream[K, V, F <: NewInputFormat[K,V]]( (implicit km: ClassTag[K], vm: ClassTag[V], fm: ClassTag[F]) extends InputDStream[(K, V)](ssc_) { + private val serializableConfOpt = conf.map(new SerializableWritable(_)) + // This is a def so that it works during checkpoint recovery: private def clock = ssc.scheduler.clock @@ -86,7 +88,7 @@ class FileInputDStream[K, V, F <: NewInputFormat[K,V]]( // Initial ignore threshold based on which old, existing files in the directory (at the time of // starting the streaming application) will be ignored or considered - private val initialModTimeIgnoreThreshold = if (newFilesOnly) clock.currentTime() else 0L + private val initialModTimeIgnoreThreshold = if (newFilesOnly) clock.getTimeMillis() else 0L /* * Make sure that the information of files selected in the last few batches are remembered. @@ -159,7 +161,7 @@ class FileInputDStream[K, V, F <: NewInputFormat[K,V]]( */ private def findNewFiles(currentTime: Long): Array[String] = { try { - lastNewFileFindingTime = clock.currentTime() + lastNewFileFindingTime = clock.getTimeMillis() // Calculate ignore threshold val modTimeIgnoreThreshold = math.max( @@ -172,7 +174,7 @@ class FileInputDStream[K, V, F <: NewInputFormat[K,V]]( def accept(path: Path): Boolean = isNewFile(path, currentTime, modTimeIgnoreThreshold) } val newFiles = fs.listStatus(directoryPath, filter).map(_.getPath.toString) - val timeTaken = clock.currentTime() - lastNewFileFindingTime + val timeTaken = clock.getTimeMillis() - lastNewFileFindingTime logInfo("Finding new files took " + timeTaken + " ms") logDebug("# cached file times = " + fileToModTime.size) if (timeTaken > slideDuration.milliseconds) { @@ -240,7 +242,7 @@ class FileInputDStream[K, V, F <: NewInputFormat[K,V]]( /** Generate one RDD from an array of files */ private def filesToRDD(files: Seq[String]): RDD[(K, V)] = { val fileRDDs = files.map(file =>{ - val rdd = conf match { + val rdd = serializableConfOpt.map(_.value) match { case Some(config) => context.sparkContext.newAPIHadoopFile( file, fm.runtimeClass.asInstanceOf[Class[F]], @@ -296,7 +298,7 @@ class FileInputDStream[K, V, F <: NewInputFormat[K,V]]( private[streaming] class FileInputDStreamCheckpointData extends DStreamCheckpointData(this) { - def hadoopFiles = data.asInstanceOf[mutable.HashMap[Time, Array[String]]] + private def hadoopFiles = data.asInstanceOf[mutable.HashMap[Time, Array[String]]] override def update(time: Time) { hadoopFiles.clear() @@ -318,7 +320,7 @@ class FileInputDStream[K, V, F <: NewInputFormat[K,V]]( } } - override def toString() = { + override def toString: String = { "[\n" + hadoopFiles.size + " file sets\n" + hadoopFiles.map(p => (p._1, p._2.mkString(", "))).mkString("\n") + "\n]" } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FilteredDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FilteredDStream.scala index c81534ae584ea..fcd5216f101af 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FilteredDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FilteredDStream.scala @@ -27,7 +27,7 @@ class FilteredDStream[T: ClassTag]( filterFunc: T => Boolean ) extends DStream[T](parent.ssc) { - override def dependencies = List(parent) + override def dependencies: List[DStream[_]] = List(parent) override def slideDuration: Duration = parent.slideDuration diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FlatMapValuedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FlatMapValuedDStream.scala index 658623455498c..9d09a3baf37ca 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FlatMapValuedDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FlatMapValuedDStream.scala @@ -28,7 +28,7 @@ class FlatMapValuedDStream[K: ClassTag, V: ClassTag, U: ClassTag]( flatMapValueFunc: V => TraversableOnce[U] ) extends DStream[(K, U)](parent.ssc) { - override def dependencies = List(parent) + override def dependencies: List[DStream[_]] = List(parent) override def slideDuration: Duration = parent.slideDuration diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FlatMappedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FlatMappedDStream.scala index c7bb2833eabb8..475ea2d2d4f38 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FlatMappedDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FlatMappedDStream.scala @@ -27,7 +27,7 @@ class FlatMappedDStream[T: ClassTag, U: ClassTag]( flatMapFunc: T => Traversable[U] ) extends DStream[U](parent.ssc) { - override def dependencies = List(parent) + override def dependencies: List[DStream[_]] = List(parent) override def slideDuration: Duration = parent.slideDuration diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ForEachDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ForEachDStream.scala index 1361c30395b57..685a32e1d280d 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ForEachDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ForEachDStream.scala @@ -28,7 +28,7 @@ class ForEachDStream[T: ClassTag] ( foreachFunc: (RDD[T], Time) => Unit ) extends DStream[Unit](parent.ssc) { - override def dependencies = List(parent) + override def dependencies: List[DStream[_]] = List(parent) override def slideDuration: Duration = parent.slideDuration diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/GlommedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/GlommedDStream.scala index a9bb51f054048..dbb295fe54f71 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/GlommedDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/GlommedDStream.scala @@ -25,7 +25,7 @@ private[streaming] class GlommedDStream[T: ClassTag](parent: DStream[T]) extends DStream[Array[T]](parent.ssc) { - override def dependencies = List(parent) + override def dependencies: List[DStream[_]] = List(parent) override def slideDuration: Duration = parent.slideDuration diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala index aa1993f0580a8..e652702e213ef 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala @@ -61,7 +61,7 @@ abstract class InputDStream[T: ClassTag] (@transient ssc_ : StreamingContext) } } - override def dependencies = List() + override def dependencies: List[DStream[_]] = List() override def slideDuration: Duration = { if (ssc == null) throw new Exception("ssc is null") diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/MapPartitionedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/MapPartitionedDStream.scala index 3d8ee29df1e82..5994bc1e23f2b 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/MapPartitionedDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/MapPartitionedDStream.scala @@ -28,7 +28,7 @@ class MapPartitionedDStream[T: ClassTag, U: ClassTag]( preservePartitioning: Boolean ) extends DStream[U](parent.ssc) { - override def dependencies = List(parent) + override def dependencies: List[DStream[_]] = List(parent) override def slideDuration: Duration = parent.slideDuration diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/MapValuedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/MapValuedDStream.scala index 7aea1f945d9db..954d2eb4a7b00 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/MapValuedDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/MapValuedDStream.scala @@ -28,7 +28,7 @@ class MapValuedDStream[K: ClassTag, V: ClassTag, U: ClassTag]( mapValueFunc: V => U ) extends DStream[(K, U)](parent.ssc) { - override def dependencies = List(parent) + override def dependencies: List[DStream[_]] = List(parent) override def slideDuration: Duration = parent.slideDuration diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/MappedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/MappedDStream.scala index 02704a8d1c2e0..fa14b2e897c3e 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/MappedDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/MappedDStream.scala @@ -27,7 +27,7 @@ class MappedDStream[T: ClassTag, U: ClassTag] ( mapFunc: T => U ) extends DStream[U](parent.ssc) { - override def dependencies = List(parent) + override def dependencies: List[DStream[_]] = List(parent) override def slideDuration: Duration = parent.slideDuration diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReducedWindowedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReducedWindowedDStream.scala index c0a5af0b65cc3..1385ccbf56ee5 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReducedWindowedDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReducedWindowedDStream.scala @@ -52,7 +52,7 @@ class ReducedWindowedDStream[K: ClassTag, V: ClassTag]( // Reduce each batch of data using reduceByKey which will be further reduced by window // by ReducedWindowedDStream - val reducedStream = parent.reduceByKey(reduceFunc, partitioner) + private val reducedStream = parent.reduceByKey(reduceFunc, partitioner) // Persist RDDs to memory by default as these RDDs are going to be reused. super.persist(StorageLevel.MEMORY_ONLY_SER) @@ -60,7 +60,7 @@ class ReducedWindowedDStream[K: ClassTag, V: ClassTag]( def windowDuration: Duration = _windowDuration - override def dependencies = List(reducedStream) + override def dependencies: List[DStream[_]] = List(reducedStream) override def slideDuration: Duration = _slideDuration diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ShuffledDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ShuffledDStream.scala index 880a89bc36895..7757ccac09a58 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ShuffledDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ShuffledDStream.scala @@ -33,7 +33,7 @@ class ShuffledDStream[K: ClassTag, V: ClassTag, C: ClassTag]( mapSideCombine: Boolean = true ) extends DStream[(K,C)] (parent.ssc) { - override def dependencies = List(parent) + override def dependencies: List[DStream[_]] = List(parent) override def slideDuration: Duration = parent.slideDuration diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/StateDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/StateDStream.scala index ebb04dd35b9a2..de8718d0a80fe 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/StateDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/StateDStream.scala @@ -36,7 +36,7 @@ class StateDStream[K: ClassTag, V: ClassTag, S: ClassTag]( super.persist(StorageLevel.MEMORY_ONLY_SER) - override def dependencies = List(parent) + override def dependencies: List[DStream[_]] = List(parent) override def slideDuration: Duration = parent.slideDuration diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/TransformedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/TransformedDStream.scala index 71b61856e23c0..5d46ca0715ffd 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/TransformedDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/TransformedDStream.scala @@ -32,7 +32,7 @@ class TransformedDStream[U: ClassTag] ( require(parents.map(_.slideDuration).distinct.size == 1, "Some of the DStreams have different slide durations") - override def dependencies = parents.toList + override def dependencies: List[DStream[_]] = parents.toList override def slideDuration: Duration = parents.head.slideDuration diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/UnionDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/UnionDStream.scala index abbc40befa95b..9405dbaa12329 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/UnionDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/UnionDStream.scala @@ -33,17 +33,17 @@ class UnionDStream[T: ClassTag](parents: Array[DStream[T]]) require(parents.map(_.slideDuration).distinct.size == 1, "Some of the DStreams have different slide durations") - override def dependencies = parents.toList + override def dependencies: List[DStream[_]] = parents.toList override def slideDuration: Duration = parents.head.slideDuration override def compute(validTime: Time): Option[RDD[T]] = { val rdds = new ArrayBuffer[RDD[T]]() - parents.map(_.getOrCompute(validTime)).foreach(_ match { + parents.map(_.getOrCompute(validTime)).foreach { case Some(rdd) => rdds += rdd case None => throw new Exception("Could not generate RDD from a parent for unifying at time " + validTime) - }) + } if (rdds.size > 0) { Some(new UnionRDD(ssc.sc, rdds)) } else { diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/WindowedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/WindowedDStream.scala index 775b6bfd065c0..899865a906c27 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/WindowedDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/WindowedDStream.scala @@ -46,7 +46,7 @@ class WindowedDStream[T: ClassTag]( def windowDuration: Duration = _windowDuration - override def dependencies = List(parent) + override def dependencies: List[DStream[_]] = List(parent) override def slideDuration: Duration = _slideDuration diff --git a/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala b/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala index dd1e96334952f..93caa4ba35c7f 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala @@ -117,8 +117,8 @@ class WriteAheadLogBackedBlockRDD[T: ClassTag]( override def getPreferredLocations(split: Partition): Seq[String] = { val partition = split.asInstanceOf[WriteAheadLogBackedBlockRDDPartition] val blockLocations = getBlockIdLocations().get(partition.blockId) - def segmentLocations = HdfsUtils.getFileSegmentLocations( - partition.segment.path, partition.segment.offset, partition.segment.length, hadoopConfig) - blockLocations.getOrElse(segmentLocations) + blockLocations.getOrElse( + HdfsUtils.getFileSegmentLocations( + partition.segment.path, partition.segment.offset, partition.segment.length, hadoopConfig)) } } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ActorReceiver.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ActorReceiver.scala index a7d63bd4f2dbf..cd309788a7717 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ActorReceiver.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ActorReceiver.scala @@ -17,6 +17,7 @@ package org.apache.spark.streaming.receiver +import java.nio.ByteBuffer import java.util.concurrent.atomic.AtomicInteger import scala.concurrent.duration._ @@ -25,10 +26,10 @@ import scala.reflect.ClassTag import akka.actor._ import akka.actor.SupervisorStrategy.{Escalate, Restart} + import org.apache.spark.{Logging, SparkEnv} -import org.apache.spark.storage.StorageLevel -import java.nio.ByteBuffer import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.storage.StorageLevel /** * :: DeveloperApi :: @@ -149,13 +150,13 @@ private[streaming] class ActorReceiver[T: ClassTag]( class Supervisor extends Actor { override val supervisorStrategy = receiverSupervisorStrategy - val worker = context.actorOf(props, name) + private val worker = context.actorOf(props, name) logInfo("Started receiver worker at:" + worker.path) - val n: AtomicInteger = new AtomicInteger(0) - val hiccups: AtomicInteger = new AtomicInteger(0) + private val n: AtomicInteger = new AtomicInteger(0) + private val hiccups: AtomicInteger = new AtomicInteger(0) - def receive = { + override def receive: PartialFunction[Any, Unit] = { case IteratorData(iterator) => logDebug("received iterator") @@ -189,13 +190,12 @@ private[streaming] class ActorReceiver[T: ClassTag]( } } - def onStart() = { + def onStart(): Unit = { supervisor logInfo("Supervision tree for receivers initialized at:" + supervisor.path) - } - def onStop() = { + def onStop(): Unit = { supervisor ! PoisonPill } } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala index 79263a7183977..f4963a78e1d18 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala @@ -23,7 +23,8 @@ import scala.collection.mutable.ArrayBuffer import org.apache.spark.{Logging, SparkConf} import org.apache.spark.storage.StreamBlockId -import org.apache.spark.streaming.util.{RecurringTimer, SystemClock} +import org.apache.spark.streaming.util.RecurringTimer +import org.apache.spark.util.{SystemClock, Utils} /** Listener object for BlockGenerator events */ private[streaming] trait BlockGeneratorListener { @@ -78,9 +79,9 @@ private[streaming] class BlockGenerator( private case class Block(id: StreamBlockId, buffer: ArrayBuffer[Any]) private val clock = new SystemClock() - private val blockInterval = conf.getLong("spark.streaming.blockInterval", 200) + private val blockIntervalMs = conf.getTimeAsMs("spark.streaming.blockInterval", "200ms") private val blockIntervalTimer = - new RecurringTimer(clock, blockInterval, updateCurrentBuffer, "BlockGenerator") + new RecurringTimer(clock, blockIntervalMs, updateCurrentBuffer, "BlockGenerator") private val blockQueueSize = conf.getInt("spark.streaming.blockQueueSize", 10) private val blocksForPushing = new ArrayBlockingQueue[Block](blockQueueSize) private val blockPushingThread = new Thread() { override def run() { keepPushingBlocks() } } @@ -119,7 +120,7 @@ private[streaming] class BlockGenerator( * `BlockGeneratorListener.onAddData` callback will be called. All received data items * will be periodically pushed into BlockManager. */ - def addDataWithCallback(data: Any, metadata: Any) = synchronized { + def addDataWithCallback(data: Any, metadata: Any): Unit = synchronized { waitToPush() currentBuffer += data listener.onAddData(data, metadata) @@ -131,7 +132,7 @@ private[streaming] class BlockGenerator( val newBlockBuffer = currentBuffer currentBuffer = new ArrayBuffer[Any] if (newBlockBuffer.size > 0) { - val blockId = StreamBlockId(receiverId, time - blockInterval) + val blockId = StreamBlockId(receiverId, time - blockIntervalMs) val newBlock = new Block(blockId, newBlockBuffer) listener.onGenerateBlock(blockId) blocksForPushing.put(newBlock) // put is blocking when queue is full diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceivedBlockHandler.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceivedBlockHandler.scala index f7a8ebee8a544..dcdc27d29c270 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceivedBlockHandler.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceivedBlockHandler.scala @@ -27,8 +27,8 @@ import org.apache.hadoop.fs.Path import org.apache.spark.{Logging, SparkConf, SparkException} import org.apache.spark.storage._ -import org.apache.spark.streaming.util.{Clock, SystemClock, WriteAheadLogFileSegment, WriteAheadLogManager} -import org.apache.spark.util.Utils +import org.apache.spark.streaming.util.{WriteAheadLogFileSegment, WriteAheadLogManager} +import org.apache.spark.util.{Clock, SystemClock, Utils} /** Trait that represents the metadata related to storage of blocks */ private[streaming] trait ReceivedBlockStoreResult { diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala index 5acf8a9a811ee..5b5a3fe648602 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala @@ -245,7 +245,7 @@ abstract class Receiver[T](val storageLevel: StorageLevel) extends Serializable * Get the unique identifier the receiver input stream that this * receiver is associated with. */ - def streamId = id + def streamId: Int = id /* * ================= diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala index 1f0244c251eba..4943f29395d12 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala @@ -162,13 +162,13 @@ private[streaming] abstract class ReceiverSupervisor( } /** Check if receiver has been marked for stopping */ - def isReceiverStarted() = { + def isReceiverStarted(): Boolean = { logDebug("state = " + receiverState) receiverState == Started } /** Check if receiver has been marked for stopping */ - def isReceiverStopped() = { + def isReceiverStopped(): Boolean = { logDebug("state = " + receiverState) receiverState == Stopped } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala index 7d29ed88cfcb4..8f2f1fef76874 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala @@ -23,7 +23,7 @@ import java.util.concurrent.atomic.AtomicLong import scala.collection.mutable.ArrayBuffer import scala.concurrent.Await -import akka.actor.{Actor, Props} +import akka.actor.{ActorRef, Actor, Props} import akka.pattern.ask import com.google.common.base.Throwables import org.apache.hadoop.conf.Configuration @@ -83,7 +83,7 @@ private[streaming] class ReceiverSupervisorImpl( private val actor = env.actorSystem.actorOf( Props(new Actor { - override def receive() = { + override def receive: PartialFunction[Any, Unit] = { case StopReceiver => logInfo("Received stop signal") stop("Stopped by driver", None) @@ -92,7 +92,7 @@ private[streaming] class ReceiverSupervisorImpl( cleanupOldBlocks(threshTime) } - def ref = self + def ref: ActorRef = self }), "Receiver-" + streamId + "-" + System.currentTimeMillis()) /** Unique block ids if one wants to add blocks directly */ diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/Job.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/Job.scala index 7e0f6b2cdfc08..30cf87f5b7dd1 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/Job.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/Job.scala @@ -36,5 +36,5 @@ class Job(val time: Time, func: () => _) { id = "streaming job " + time + "." + number } - override def toString = id + override def toString: String = id } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala index 8632c94349bf9..58e56638a2dca 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala @@ -23,13 +23,15 @@ import akka.actor.{ActorRef, Props, Actor} import org.apache.spark.{SparkEnv, Logging} import org.apache.spark.streaming.{Checkpoint, CheckpointWriter, Time} -import org.apache.spark.streaming.util.{Clock, ManualClock, RecurringTimer} +import org.apache.spark.streaming.util.RecurringTimer +import org.apache.spark.util.{Clock, ManualClock, Utils} /** Event classes for JobGenerator */ private[scheduler] sealed trait JobGeneratorEvent private[scheduler] case class GenerateJobs(time: Time) extends JobGeneratorEvent private[scheduler] case class ClearMetadata(time: Time) extends JobGeneratorEvent -private[scheduler] case class DoCheckpoint(time: Time) extends JobGeneratorEvent +private[scheduler] case class DoCheckpoint( + time: Time, clearCheckpointDataLater: Boolean) extends JobGeneratorEvent private[scheduler] case class ClearCheckpointData(time: Time) extends JobGeneratorEvent /** @@ -45,8 +47,14 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging { val clock = { val clockClass = ssc.sc.conf.get( - "spark.streaming.clock", "org.apache.spark.streaming.util.SystemClock") - Class.forName(clockClass).newInstance().asInstanceOf[Clock] + "spark.streaming.clock", "org.apache.spark.util.SystemClock") + try { + Class.forName(clockClass).newInstance().asInstanceOf[Clock] + } catch { + case e: ClassNotFoundException if clockClass.startsWith("org.apache.spark.streaming") => + val newClockClass = clockClass.replace("org.apache.spark.streaming", "org.apache.spark") + Class.forName(newClockClass).newInstance().asInstanceOf[Clock] + } } private val timer = new RecurringTimer(clock, ssc.graph.batchDuration.milliseconds, @@ -74,7 +82,7 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging { if (eventActor != null) return // generator has already been started eventActor = ssc.env.actorSystem.actorOf(Props(new Actor { - def receive = { + override def receive: PartialFunction[Any, Unit] = { case event: JobGeneratorEvent => processEvent(event) } }), "JobGenerator") @@ -96,17 +104,15 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging { if (processReceivedData) { logInfo("Stopping JobGenerator gracefully") val timeWhenStopStarted = System.currentTimeMillis() - val stopTimeout = conf.getLong( - "spark.streaming.gracefulStopTimeout", - 10 * ssc.graph.batchDuration.milliseconds - ) + val stopTimeoutMs = conf.getTimeAsMs( + "spark.streaming.gracefulStopTimeout", s"${10 * ssc.graph.batchDuration.milliseconds}ms") val pollTime = 100 // To prevent graceful stop to get stuck permanently - def hasTimedOut = { - val timedOut = System.currentTimeMillis() - timeWhenStopStarted > stopTimeout + def hasTimedOut: Boolean = { + val timedOut = (System.currentTimeMillis() - timeWhenStopStarted) > stopTimeoutMs if (timedOut) { - logWarning("Timed out while stopping the job generator (timeout = " + stopTimeout + ")") + logWarning("Timed out while stopping the job generator (timeout = " + stopTimeoutMs + ")") } timedOut } @@ -125,7 +131,7 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging { logInfo("Stopped generation timer") // Wait for the jobs to complete and checkpoints to be written - def haveAllBatchesBeenProcessed = { + def haveAllBatchesBeenProcessed: Boolean = { lastProcessedBatch != null && lastProcessedBatch.milliseconds == stopTime } logInfo("Waiting for jobs to be processed and checkpoints to be written") @@ -156,8 +162,10 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging { /** * Callback called when the checkpoint of a batch has been written. */ - def onCheckpointCompletion(time: Time) { - eventActor ! ClearCheckpointData(time) + def onCheckpointCompletion(time: Time, clearCheckpointDataLater: Boolean) { + if (clearCheckpointDataLater) { + eventActor ! ClearCheckpointData(time) + } } /** Processes all events */ @@ -166,7 +174,8 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging { event match { case GenerateJobs(time) => generateJobs(time) case ClearMetadata(time) => clearMetadata(time) - case DoCheckpoint(time) => doCheckpoint(time) + case DoCheckpoint(time, clearCheckpointDataLater) => + doCheckpoint(time, clearCheckpointDataLater) case ClearCheckpointData(time) => clearCheckpointData(time) } } @@ -238,7 +247,7 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging { case Failure(e) => jobScheduler.reportError("Error generating jobs for time " + time, e) } - eventActor ! DoCheckpoint(time) + eventActor ! DoCheckpoint(time, clearCheckpointDataLater = false) } /** Clear DStream metadata for the given `time`. */ @@ -248,7 +257,7 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging { // If checkpointing is enabled, then checkpoint, // else mark batch to be fully processed if (shouldCheckpoint) { - eventActor ! DoCheckpoint(time) + eventActor ! DoCheckpoint(time, clearCheckpointDataLater = true) } else { // If checkpointing is not enabled, then delete metadata information about // received blocks (block data not saved in any case). Otherwise, wait for @@ -271,11 +280,11 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging { } /** Perform checkpoint for the give `time`. */ - private def doCheckpoint(time: Time) { + private def doCheckpoint(time: Time, clearCheckpointDataLater: Boolean) { if (shouldCheckpoint && (time - graph.zeroTime).isMultipleOf(ssc.checkpointDuration)) { logInfo("Checkpointing graph for time " + time) ssc.graph.updateCheckpointData(time) - checkpointWriter.write(new Checkpoint(ssc, time)) + checkpointWriter.write(new Checkpoint(ssc, time), clearCheckpointDataLater) } } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala index b3ffc71904c76..95f1857b4c377 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala @@ -56,12 +56,12 @@ class JobScheduler(val ssc: StreamingContext) extends Logging { logDebug("Starting JobScheduler") eventActor = ssc.env.actorSystem.actorOf(Props(new Actor { - def receive = { + override def receive: PartialFunction[Any, Unit] = { case event: JobSchedulerEvent => processEvent(event) } }), "JobScheduler") - listenerBus.start() + listenerBus.start(ssc.sparkContext) receiverTracker = new ReceiverTracker(ssc) receiverTracker.start() jobGenerator.start() @@ -105,6 +105,7 @@ class JobScheduler(val ssc: StreamingContext) extends Logging { if (jobSet.jobs.isEmpty) { logInfo("No jobs added for time " + jobSet.time) } else { + listenerBus.post(StreamingListenerBatchSubmitted(jobSet.toBatchInfo)) jobSets.put(jobSet.time, jobSet) jobSet.jobs.foreach(job => jobExecutor.execute(new JobHandler(job))) logInfo("Added jobs for time " + jobSet.time) @@ -134,10 +135,13 @@ class JobScheduler(val ssc: StreamingContext) extends Logging { private def handleJobStart(job: Job) { val jobSet = jobSets.get(job.time) - if (!jobSet.hasStarted) { + val isFirstJobOfJobSet = !jobSet.hasStarted + jobSet.handleJobStart(job) + if (isFirstJobOfJobSet) { + // "StreamingListenerBatchStarted" should be posted after calling "handleJobStart" to get the + // correct "jobSet.processingStartTime". listenerBus.post(StreamingListenerBatchStarted(jobSet.toBatchInfo)) } - jobSet.handleJobStart(job) logInfo("Starting job " + job.id + " from job set of time " + jobSet.time) } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobSet.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobSet.scala index 8c15a75b1b0e0..5b134877d0b2d 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobSet.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobSet.scala @@ -28,8 +28,7 @@ private[streaming] case class JobSet( time: Time, jobs: Seq[Job], - receivedBlockInfo: Map[Int, Array[ReceivedBlockInfo]] = Map.empty - ) { + receivedBlockInfo: Map[Int, Array[ReceivedBlockInfo]] = Map.empty) { private val incompleteJobs = new HashSet[Job]() private val submissionTime = System.currentTimeMillis() // when this jobset was submitted @@ -48,17 +47,17 @@ case class JobSet( if (hasCompleted) processingEndTime = System.currentTimeMillis() } - def hasStarted = processingStartTime > 0 + def hasStarted: Boolean = processingStartTime > 0 - def hasCompleted = incompleteJobs.isEmpty + def hasCompleted: Boolean = incompleteJobs.isEmpty // Time taken to process all the jobs from the time they started processing // (i.e. not including the time they wait in the streaming scheduler queue) - def processingDelay = processingEndTime - processingStartTime + def processingDelay: Long = processingEndTime - processingStartTime // Time taken to process all the jobs from the time they were submitted // (i.e. including the time they wait in the streaming scheduler queue) - def totalDelay = { + def totalDelay: Long = { processingEndTime - time.milliseconds } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala index e19ac939f9ac5..200cf4ef4b0f1 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala @@ -27,8 +27,8 @@ import org.apache.hadoop.fs.Path import org.apache.spark.{SparkException, Logging, SparkConf} import org.apache.spark.streaming.Time -import org.apache.spark.streaming.util.{Clock, WriteAheadLogManager} -import org.apache.spark.util.Utils +import org.apache.spark.streaming.util.WriteAheadLogManager +import org.apache.spark.util.{Clock, Utils} /** Trait representing any event in the ReceivedBlockTracker that updates its state. */ private[streaming] sealed trait ReceivedBlockTrackerLogEvent @@ -150,7 +150,7 @@ private[streaming] class ReceivedBlockTracker( * returns only after the files are cleaned up. */ def cleanupOldBatches(cleanupThreshTime: Time, waitForCompletion: Boolean): Unit = synchronized { - assert(cleanupThreshTime.milliseconds < clock.currentTime()) + assert(cleanupThreshTime.milliseconds < clock.getTimeMillis()) val timesToCleanup = timeToAllocatedBlocks.keys.filter { _ < cleanupThreshTime }.toSeq logInfo("Deleting batches " + timesToCleanup) writeToLog(BatchCleanupEvent(timesToCleanup)) diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala index b36aeb341d25e..98900473138fe 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala @@ -72,7 +72,7 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false private var actor: ActorRef = null /** Start the actor and receiver execution thread. */ - def start() = synchronized { + def start(): Unit = synchronized { if (actor != null) { throw new SparkException("ReceiverTracker already started") } @@ -86,7 +86,7 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false } /** Stop the receiver execution thread. */ - def stop(graceful: Boolean) = synchronized { + def stop(graceful: Boolean): Unit = synchronized { if (!receiverInputStreams.isEmpty && actor != null) { // First, stop the receivers if (!skipReceiverLaunch) receiverExecutor.stop(graceful) @@ -201,7 +201,7 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false /** Actor to receive messages from the receivers. */ private class ReceiverTrackerActor extends Actor { - def receive = { + override def receive: PartialFunction[Any, Unit] = { case RegisterReceiver(streamId, typ, host, receiverActor) => registerReceiver(streamId, typ, host, receiverActor, sender) sender ! true @@ -244,16 +244,15 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false if (graceful) { val pollTime = 100 - def done = { receiverInfo.isEmpty && !running } logInfo("Waiting for receiver job to terminate gracefully") - while(!done) { + while (receiverInfo.nonEmpty || running) { Thread.sleep(pollTime) } logInfo("Waited for receiver job to terminate gracefully") } // Check if all the receivers have been deregistered or not - if (!receiverInfo.isEmpty) { + if (receiverInfo.nonEmpty) { logWarning("Not all of the receivers have deregistered, " + receiverInfo) } else { logInfo("All of the receivers have deregistered successfully") diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/AllBatchesTable.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/AllBatchesTable.scala new file mode 100644 index 0000000000000..df1c0a10704c3 --- /dev/null +++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/AllBatchesTable.scala @@ -0,0 +1,114 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming.ui + +import scala.xml.Node + +import org.apache.spark.streaming.scheduler.BatchInfo +import org.apache.spark.ui.UIUtils + +private[ui] abstract class BatchTableBase(tableId: String) { + + protected def columns: Seq[Node] = { +     + + + + } + + protected def baseRow(batch: BatchInfo): Seq[Node] = { + val batchTime = batch.batchTime.milliseconds + val formattedBatchTime = UIUtils.formatDate(batch.batchTime.milliseconds) + val eventCount = batch.receivedBlockInfo.values.map { + receivers => receivers.map(_.numRecords).sum + }.sum + val schedulingDelay = batch.schedulingDelay + val formattedSchedulingDelay = schedulingDelay.map(UIUtils.formatDuration).getOrElse("-") + val processingTime = batch.processingDelay + val formattedProcessingTime = processingTime.map(UIUtils.formatDuration).getOrElse("-") + + + + + + } + + private def batchTable: Seq[Node] = { +
    Batch TimeInput SizeScheduling DelayProcessing Time{formattedBatchTime}{eventCount.toString} events + {formattedSchedulingDelay} + + {formattedProcessingTime} +
    + + {columns} + + + {renderRows} + +
    + } + + def toNodeSeq: Seq[Node] = { + batchTable + } + + /** + * Return HTML for all rows of this table. + */ + protected def renderRows: Seq[Node] +} + +private[ui] class ActiveBatchTable(runningBatches: Seq[BatchInfo], waitingBatches: Seq[BatchInfo]) + extends BatchTableBase("active-batches-table") { + + override protected def columns: Seq[Node] = super.columns ++ Status + + override protected def renderRows: Seq[Node] = { + // The "batchTime"s of "waitingBatches" must be greater than "runningBatches"'s, so display + // waiting batches before running batches + waitingBatches.flatMap(batch => {waitingBatchRow(batch)}) ++ + runningBatches.flatMap(batch => {runningBatchRow(batch)}) + } + + private def runningBatchRow(batch: BatchInfo): Seq[Node] = { + baseRow(batch) ++ processing + } + + private def waitingBatchRow(batch: BatchInfo): Seq[Node] = { + baseRow(batch) ++ queued + } +} + +private[ui] class CompletedBatchTable(batches: Seq[BatchInfo]) + extends BatchTableBase("completed-batches-table") { + + override protected def columns: Seq[Node] = super.columns ++ Total Delay + + override protected def renderRows: Seq[Node] = { + batches.flatMap(batch => {completedBatchRow(batch)}) + } + + private def completedBatchRow(batch: BatchInfo): Seq[Node] = { + val totalDelay = batch.totalDelay + val formattedTotalDelay = totalDelay.map(UIUtils.formatDuration).getOrElse("-") + baseRow(batch) ++ + + {formattedTotalDelay} + + } +} diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala index 5ee53a5c5f561..be1e8686cf9fa 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala @@ -17,9 +17,10 @@ package org.apache.spark.streaming.ui +import scala.collection.mutable.{Queue, HashMap} + import org.apache.spark.streaming.{Time, StreamingContext} import org.apache.spark.streaming.scheduler._ -import scala.collection.mutable.{Queue, HashMap} import org.apache.spark.streaming.scheduler.StreamingListenerReceiverStarted import org.apache.spark.streaming.scheduler.StreamingListenerBatchStarted import org.apache.spark.streaming.scheduler.BatchInfo @@ -32,7 +33,7 @@ private[streaming] class StreamingJobProgressListener(ssc: StreamingContext) private val waitingBatchInfos = new HashMap[Time, BatchInfo] private val runningBatchInfos = new HashMap[Time, BatchInfo] - private val completedaBatchInfos = new Queue[BatchInfo] + private val completedBatchInfos = new Queue[BatchInfo] private val batchInfoLimit = ssc.conf.getInt("spark.streaming.ui.retainedBatches", 100) private var totalCompletedBatches = 0L private var totalReceivedRecords = 0L @@ -59,11 +60,13 @@ private[streaming] class StreamingJobProgressListener(ssc: StreamingContext) } } - override def onBatchSubmitted(batchSubmitted: StreamingListenerBatchSubmitted) = synchronized { - runningBatchInfos(batchSubmitted.batchInfo.batchTime) = batchSubmitted.batchInfo + override def onBatchSubmitted(batchSubmitted: StreamingListenerBatchSubmitted): Unit = { + synchronized { + waitingBatchInfos(batchSubmitted.batchInfo.batchTime) = batchSubmitted.batchInfo + } } - override def onBatchStarted(batchStarted: StreamingListenerBatchStarted) = synchronized { + override def onBatchStarted(batchStarted: StreamingListenerBatchStarted): Unit = synchronized { runningBatchInfos(batchStarted.batchInfo.batchTime) = batchStarted.batchInfo waitingBatchInfos.remove(batchStarted.batchInfo.batchTime) @@ -72,19 +75,21 @@ private[streaming] class StreamingJobProgressListener(ssc: StreamingContext) } } - override def onBatchCompleted(batchCompleted: StreamingListenerBatchCompleted) = synchronized { - waitingBatchInfos.remove(batchCompleted.batchInfo.batchTime) - runningBatchInfos.remove(batchCompleted.batchInfo.batchTime) - completedaBatchInfos.enqueue(batchCompleted.batchInfo) - if (completedaBatchInfos.size > batchInfoLimit) completedaBatchInfos.dequeue() - totalCompletedBatches += 1L - - batchCompleted.batchInfo.receivedBlockInfo.foreach { case (_, infos) => - totalProcessedRecords += infos.map(_.numRecords).sum + override def onBatchCompleted(batchCompleted: StreamingListenerBatchCompleted): Unit = { + synchronized { + waitingBatchInfos.remove(batchCompleted.batchInfo.batchTime) + runningBatchInfos.remove(batchCompleted.batchInfo.batchTime) + completedBatchInfos.enqueue(batchCompleted.batchInfo) + if (completedBatchInfos.size > batchInfoLimit) completedBatchInfos.dequeue() + totalCompletedBatches += 1L + + batchCompleted.batchInfo.receivedBlockInfo.foreach { case (_, infos) => + totalProcessedRecords += infos.map(_.numRecords).sum + } } } - def numReceivers = synchronized { + def numReceivers: Int = synchronized { ssc.graph.getReceiverInputStreams().size } @@ -113,7 +118,7 @@ private[streaming] class StreamingJobProgressListener(ssc: StreamingContext) } def retainedCompletedBatches: Seq[BatchInfo] = synchronized { - completedaBatchInfos.toSeq + completedBatchInfos.toSeq } def processingDelayDistribution: Option[Distribution] = synchronized { @@ -144,7 +149,7 @@ private[streaming] class StreamingJobProgressListener(ssc: StreamingContext) }.toMap } - def lastReceivedBatchRecords: Map[Int, Long] = { + def lastReceivedBatchRecords: Map[Int, Long] = synchronized { val lastReceivedBlockInfoOption = lastReceivedBatch.map(_.receivedBlockInfo) lastReceivedBlockInfoOption.map { lastReceivedBlockInfo => (0 until numReceivers).map { receiverId => @@ -155,24 +160,24 @@ private[streaming] class StreamingJobProgressListener(ssc: StreamingContext) } } - def receiverInfo(receiverId: Int): Option[ReceiverInfo] = { + def receiverInfo(receiverId: Int): Option[ReceiverInfo] = synchronized { receiverInfos.get(receiverId) } - def lastCompletedBatch: Option[BatchInfo] = { - completedaBatchInfos.sortBy(_.batchTime)(Time.ordering).lastOption + def lastCompletedBatch: Option[BatchInfo] = synchronized { + completedBatchInfos.sortBy(_.batchTime)(Time.ordering).lastOption } - def lastReceivedBatch: Option[BatchInfo] = { + def lastReceivedBatch: Option[BatchInfo] = synchronized { retainedBatches.lastOption } - private def retainedBatches: Seq[BatchInfo] = synchronized { + private def retainedBatches: Seq[BatchInfo] = { (waitingBatchInfos.values.toSeq ++ - runningBatchInfos.values.toSeq ++ completedaBatchInfos).sortBy(_.batchTime)(Time.ordering) + runningBatchInfos.values.toSeq ++ completedBatchInfos).sortBy(_.batchTime)(Time.ordering) } private def extractDistribution(getMetric: BatchInfo => Option[Long]): Option[Distribution] = { - Distribution(completedaBatchInfos.flatMap(getMetric(_)).map(_.toDouble)) + Distribution(completedBatchInfos.flatMap(getMetric(_)).map(_.toDouble)) } } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala index 98e9a2e639e25..07fa285642eec 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala @@ -32,25 +32,28 @@ private[ui] class StreamingPage(parent: StreamingTab) extends WebUIPage("") with Logging { private val listener = parent.listener - private val startTime = Calendar.getInstance().getTime() + private val startTime = System.currentTimeMillis() private val emptyCell = "-" /** Render the page */ def render(request: HttpServletRequest): Seq[Node] = { - val content = + val content = listener.synchronized { generateBasicStats() ++

    ++

    Statistics over last {listener.retainedCompletedBatches.size} processed batches

    ++ generateReceiverStats() ++ - generateBatchStatsTable() + generateBatchStatsTable() ++ + generateBatchListTables() + } UIUtils.headerSparkPage("Streaming", content, parent, Some(5000)) } /** Generate basic stats of the streaming program */ private def generateBasicStats(): Seq[Node] = { - val timeSinceStart = System.currentTimeMillis() - startTime.getTime + val timeSinceStart = System.currentTimeMillis() - startTime + // scalastyle:off
    • - Started at: {startTime.toString} + Started at: {UIUtils.formatDate(startTime)}
    • Time since start: {formatDurationVerbose(timeSinceStart)} @@ -62,18 +65,19 @@ private[ui] class StreamingPage(parent: StreamingTab) Batch interval: {formatDurationVerbose(listener.batchDuration)}
    • - Processed batches: {listener.numTotalCompletedBatches} + Completed batches: {listener.numTotalCompletedBatches}
    • - Waiting batches: {listener.numUnprocessedBatches} + Active batches: {listener.numUnprocessedBatches}
    • - Received records: {listener.numTotalReceivedRecords} + Received events: {listener.numTotalReceivedRecords}
    • - Processed records: {listener.numTotalProcessedRecords} + Processed events: {listener.numTotalProcessedRecords}
    + // scalastyle:on } /** Generate stats of data received by the receivers in the streaming program */ @@ -85,10 +89,10 @@ private[ui] class StreamingPage(parent: StreamingTab) "Receiver", "Status", "Location", - "Records in last batch\n[" + formatDate(Calendar.getInstance().getTime()) + "]", - "Minimum rate\n[records/sec]", - "Median rate\n[records/sec]", - "Maximum rate\n[records/sec]", + "Events in last batch\n[" + formatDate(Calendar.getInstance().getTime()) + "]", + "Minimum rate\n[events/sec]", + "Median rate\n[events/sec]", + "Maximum rate\n[events/sec]", "Last Error" ) val dataRows = (0 until listener.numReceivers).map { receiverId => @@ -189,5 +193,26 @@ private[ui] class StreamingPage(parent: StreamingTab) } UIUtils.listingTable(headers, generateDataRow, data, fixedWidth = true) } + + private def generateBatchListTables(): Seq[Node] = { + val runningBatches = listener.runningBatches.sortBy(_.batchTime.milliseconds).reverse + val waitingBatches = listener.waitingBatches.sortBy(_.batchTime.milliseconds).reverse + val completedBatches = listener.retainedCompletedBatches. + sortBy(_.batchTime.milliseconds).reverse + + val activeBatchesContent = { +

    Active Batches ({runningBatches.size + waitingBatches.size})

    ++ + new ActiveBatchTable(runningBatches, waitingBatches).toNodeSeq + } + + val completedBatchesContent = { +

    + Completed Batches (last {completedBatches.size} out of {listener.numTotalCompletedBatches}) +

    ++ + new CompletedBatchTable(completedBatches).toNodeSeq + } + + activeBatchesContent ++ completedBatchesContent + } } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingTab.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingTab.scala index d9d04cd706a04..9a860ea4a6c68 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingTab.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingTab.scala @@ -36,6 +36,10 @@ private[spark] class StreamingTab(ssc: StreamingContext) ssc.addStreamingListener(listener) attachPage(new StreamingPage(this)) parent.attachTab(this) + + def detach() { + getSparkUI(ssc).detachTab(this) + } } private object StreamingTab { diff --git a/streaming/src/main/scala/org/apache/spark/streaming/util/Clock.scala b/streaming/src/main/scala/org/apache/spark/streaming/util/Clock.scala deleted file mode 100644 index d6d96d7ba00fd..0000000000000 --- a/streaming/src/main/scala/org/apache/spark/streaming/util/Clock.scala +++ /dev/null @@ -1,89 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.streaming.util - -private[streaming] -trait Clock { - def currentTime(): Long - def waitTillTime(targetTime: Long): Long -} - -private[streaming] -class SystemClock() extends Clock { - - val minPollTime = 25L - - def currentTime(): Long = { - System.currentTimeMillis() - } - - def waitTillTime(targetTime: Long): Long = { - var currentTime = 0L - currentTime = System.currentTimeMillis() - - var waitTime = targetTime - currentTime - if (waitTime <= 0) { - return currentTime - } - - val pollTime = math.max(waitTime / 10.0, minPollTime).toLong - - while (true) { - currentTime = System.currentTimeMillis() - waitTime = targetTime - currentTime - if (waitTime <= 0) { - return currentTime - } - val sleepTime = math.min(waitTime, pollTime) - Thread.sleep(sleepTime) - } - -1 - } -} - -private[streaming] -class ManualClock() extends Clock { - - private var time = 0L - - def currentTime() = this.synchronized { - time - } - - def setTime(timeToSet: Long) = { - this.synchronized { - time = timeToSet - this.notifyAll() - } - } - - def addToTime(timeToAdd: Long) = { - this.synchronized { - time += timeToAdd - this.notifyAll() - } - } - def waitTillTime(targetTime: Long): Long = { - this.synchronized { - while (time < targetTime) { - this.wait(100) - } - } - currentTime() - } -} diff --git a/streaming/src/main/scala/org/apache/spark/streaming/util/RawTextHelper.scala b/streaming/src/main/scala/org/apache/spark/streaming/util/RawTextHelper.scala index a73d6f3bf0661..4d968f8bfa7a8 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/util/RawTextHelper.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/util/RawTextHelper.scala @@ -18,9 +18,7 @@ package org.apache.spark.streaming.util import org.apache.spark.SparkContext -import org.apache.spark.SparkContext._ import org.apache.spark.util.collection.OpenHashMap -import scala.collection.JavaConversions.mapAsScalaMap private[streaming] object RawTextHelper { @@ -71,7 +69,7 @@ object RawTextHelper { var count = 0 while(data.hasNext) { - value = data.next + value = data.next() if (value != null) { count += 1 if (len == 0) { @@ -108,9 +106,13 @@ object RawTextHelper { } } - def add(v1: Long, v2: Long) = (v1 + v2) + def add(v1: Long, v2: Long): Long = { + v1 + v2 + } - def subtract(v1: Long, v2: Long) = (v1 - v2) + def subtract(v1: Long, v2: Long): Long = { + v1 - v2 + } - def max(v1: Long, v2: Long) = math.max(v1, v2) + def max(v1: Long, v2: Long): Long = math.max(v1, v2) } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/util/RawTextSender.scala b/streaming/src/main/scala/org/apache/spark/streaming/util/RawTextSender.scala index a7850812bd612..ca2f319f174a2 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/util/RawTextSender.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/util/RawTextSender.scala @@ -72,7 +72,8 @@ object RawTextSender extends Logging { } catch { case e: IOException => logError("Client disconnected") - socket.close() + } finally { + socket.close() } } } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/util/RecurringTimer.scala b/streaming/src/main/scala/org/apache/spark/streaming/util/RecurringTimer.scala index 1a616a0434f2c..c8eef833eb431 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/util/RecurringTimer.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/util/RecurringTimer.scala @@ -18,6 +18,7 @@ package org.apache.spark.streaming.util import org.apache.spark.Logging +import org.apache.spark.util.{Clock, SystemClock} private[streaming] class RecurringTimer(clock: Clock, period: Long, callback: (Long) => Unit, name: String) @@ -38,7 +39,7 @@ class RecurringTimer(clock: Clock, period: Long, callback: (Long) => Unit, name: * current system time. */ def getStartTime(): Long = { - (math.floor(clock.currentTime.toDouble / period) + 1).toLong * period + (math.floor(clock.getTimeMillis().toDouble / period) + 1).toLong * period } /** @@ -48,7 +49,7 @@ class RecurringTimer(clock: Clock, period: Long, callback: (Long) => Unit, name: * more than current time. */ def getRestartTime(originalStartTime: Long): Long = { - val gap = clock.currentTime - originalStartTime + val gap = clock.getTimeMillis() - originalStartTime (math.floor(gap.toDouble / period).toLong + 1) * period + originalStartTime } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogManager.scala b/streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogManager.scala index 166661b7496df..6bdfe45dc7f83 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogManager.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogManager.scala @@ -19,13 +19,13 @@ package org.apache.spark.streaming.util import java.nio.ByteBuffer import scala.collection.mutable.ArrayBuffer -import scala.concurrent.duration.Duration import scala.concurrent.{Await, ExecutionContext, Future} +import scala.language.postfixOps import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path import org.apache.spark.Logging -import org.apache.spark.util.Utils +import org.apache.spark.util.{Clock, SystemClock, Utils} import WriteAheadLogManager._ /** @@ -82,7 +82,7 @@ private[streaming] class WriteAheadLogManager( var succeeded = false while (!succeeded && failures < maxFailures) { try { - fileSegment = getLogWriter(clock.currentTime).write(byteBuffer) + fileSegment = getLogWriter(clock.getTimeMillis()).write(byteBuffer) succeeded = true } catch { case ex: Exception => diff --git a/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java b/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java index 2df8cf6a8a3df..90340753a4eed 100644 --- a/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java +++ b/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java @@ -316,6 +316,7 @@ public void testReduceByWindowWithoutInverse() { testReduceByWindow(false); } + @SuppressWarnings("unchecked") private void testReduceByWindow(boolean withInverse) { List> inputData = Arrays.asList( Arrays.asList(1,2,3), @@ -684,6 +685,7 @@ public void testStreamingContextTransform(){ JavaDStream transformed1 = ssc.transform( listOfDStreams1, new Function2>, Time, JavaRDD>() { + @Override public JavaRDD call(List> listOfRDDs, Time time) { Assert.assertEquals(2, listOfRDDs.size()); return null; @@ -697,6 +699,7 @@ public JavaRDD call(List> listOfRDDs, Time time) { JavaPairDStream> transformed2 = ssc.transformToPair( listOfDStreams2, new Function2>, Time, JavaPairRDD>>() { + @Override public JavaPairRDD> call(List> listOfRDDs, Time time) { Assert.assertEquals(3, listOfRDDs.size()); JavaRDD rdd1 = (JavaRDD)listOfRDDs.get(0); @@ -1828,4 +1831,23 @@ private List> fileTestPrepare(File testDir) throws IOException { return expected; } + + @SuppressWarnings("unchecked") + // SPARK-5795: no logic assertions, just testing that intended API invocations compile + private void compileSaveAsJavaAPI(JavaPairDStream pds) { + pds.saveAsNewAPIHadoopFiles( + "", "", LongWritable.class, Text.class, + org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat.class); + pds.saveAsHadoopFiles( + "", "", LongWritable.class, Text.class, + org.apache.hadoop.mapred.SequenceFileOutputFormat.class); + // Checks that a previous common workaround for this API still compiles + pds.saveAsNewAPIHadoopFiles( + "", "", LongWritable.class, Text.class, + (Class) org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat.class); + pds.saveAsHadoopFiles( + "", "", LongWritable.class, Text.class, + (Class) org.apache.hadoop.mapred.SequenceFileOutputFormat.class); + } + } diff --git a/streaming/src/test/java/org/apache/spark/streaming/LocalJavaStreamingContext.java b/streaming/src/test/java/org/apache/spark/streaming/LocalJavaStreamingContext.java index 1e24da7f5f60c..cfedb5a042a35 100644 --- a/streaming/src/test/java/org/apache/spark/streaming/LocalJavaStreamingContext.java +++ b/streaming/src/test/java/org/apache/spark/streaming/LocalJavaStreamingContext.java @@ -31,7 +31,7 @@ public void setUp() { SparkConf conf = new SparkConf() .setMaster("local[2]") .setAppName("test") - .set("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock"); + .set("spark.streaming.clock", "org.apache.spark.util.ManualClock"); ssc = new JavaStreamingContext(conf, new Duration(1000)); ssc.checkpoint("checkpoint"); } diff --git a/streaming/src/test/resources/log4j.properties b/streaming/src/test/resources/log4j.properties index 9697237bfa1a3..75e3b53a093f6 100644 --- a/streaming/src/test/resources/log4j.properties +++ b/streaming/src/test/resources/log4j.properties @@ -24,5 +24,5 @@ log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n # Ignore messages below warning level from Jetty, because it's a bit verbose -log4j.logger.org.eclipse.jetty=WARN +log4j.logger.org.spark-project.jetty=WARN diff --git a/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala index e8f4a7779ec21..87bc20f79c3cd 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala @@ -22,13 +22,12 @@ import scala.collection.mutable.{ArrayBuffer, SynchronizedBuffer} import scala.language.existentials import scala.reflect.ClassTag -import util.ManualClock - import org.apache.spark.{SparkConf, SparkException} import org.apache.spark.SparkContext._ import org.apache.spark.rdd.{BlockRDD, RDD} import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming.dstream.{DStream, WindowedDStream} +import org.apache.spark.util.{Clock, ManualClock} import org.apache.spark.HashPartitioner class BasicOperationsSuite extends TestSuiteBase { @@ -172,7 +171,9 @@ class BasicOperationsSuite extends TestSuiteBase { test("flatMapValues") { testOperation( Seq( Seq("a", "a", "b"), Seq("", ""), Seq() ), - (s: DStream[String]) => s.map(x => (x, 1)).reduceByKey(_ + _).flatMapValues(x => Seq(x, x + 10)), + (s: DStream[String]) => { + s.map(x => (x, 1)).reduceByKey(_ + _).flatMapValues(x => Seq(x, x + 10)) + }, Seq( Seq(("a", 2), ("a", 12), ("b", 1), ("b", 11)), Seq(("", 2), ("", 12)), Seq() ), true ) @@ -475,7 +476,7 @@ class BasicOperationsSuite extends TestSuiteBase { stream.foreachRDD(_ => {}) // Dummy output stream ssc.start() Thread.sleep(2000) - def getInputFromSlice(fromMillis: Long, toMillis: Long) = { + def getInputFromSlice(fromMillis: Long, toMillis: Long): Set[Int] = { stream.slice(new Time(fromMillis), new Time(toMillis)).flatMap(_.collect()).toSet } @@ -586,7 +587,7 @@ class BasicOperationsSuite extends TestSuiteBase { for (i <- 0 until input.size) { testServer.send(input(i).toString + "\n") Thread.sleep(200) - clock.addToTime(batchDuration.milliseconds) + clock.advance(batchDuration.milliseconds) collectRddInfo() } @@ -637,8 +638,8 @@ class BasicOperationsSuite extends TestSuiteBase { ssc.graph.getOutputStreams().head.dependencies.head.asInstanceOf[DStream[T]] if (rememberDuration != null) ssc.remember(rememberDuration) val output = runStreams[(Int, Int)](ssc, cleanupTestInput.size, numExpectedOutput) - val clock = ssc.scheduler.clock.asInstanceOf[ManualClock] - assert(clock.currentTime() === Seconds(10).milliseconds) + val clock = ssc.scheduler.clock.asInstanceOf[Clock] + assert(clock.getTimeMillis() === Seconds(10).milliseconds) assert(output.size === numExpectedOutput) operatedStream } diff --git a/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala index 8f8bc61437ba5..54c30440a6e8d 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala @@ -32,8 +32,7 @@ import org.apache.hadoop.mapreduce.lib.output.{TextOutputFormat => NewTextOutput import org.scalatest.concurrent.Eventually._ import org.apache.spark.streaming.dstream.{DStream, FileInputDStream} -import org.apache.spark.streaming.util.ManualClock -import org.apache.spark.util.Utils +import org.apache.spark.util.{Clock, ManualClock, Utils} /** * This test suites tests the checkpointing functionality of DStreams - @@ -44,7 +43,7 @@ class CheckpointSuite extends TestSuiteBase { var ssc: StreamingContext = null - override def batchDuration = Milliseconds(500) + override def batchDuration: Duration = Milliseconds(500) override def beforeFunction() { super.beforeFunction() @@ -61,7 +60,7 @@ class CheckpointSuite extends TestSuiteBase { assert(batchDuration === Milliseconds(500), "batchDuration for this test must be 1 second") - conf.set("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock") + conf.set("spark.streaming.clock", "org.apache.spark.util.ManualClock") val stateStreamCheckpointInterval = Seconds(1) val fs = FileSystem.getLocal(new Configuration()) @@ -73,7 +72,7 @@ class CheckpointSuite extends TestSuiteBase { val input = (1 to 10).map(_ => Seq("a")).toSeq val operation = (st: DStream[String]) => { val updateFunc = (values: Seq[Int], state: Option[Int]) => { - Some((values.sum + state.getOrElse(0))) + Some(values.sum + state.getOrElse(0)) } st.map(x => (x, 1)) .updateStateByKey(updateFunc) @@ -147,7 +146,7 @@ class CheckpointSuite extends TestSuiteBase { // This tests whether spark conf persists through checkpoints, and certain // configs gets scrubbed - test("persistence of conf through checkpoints") { + test("recovery of conf through checkpoints") { val key = "spark.mykey" val value = "myvalue" System.setProperty(key, value) @@ -155,7 +154,7 @@ class CheckpointSuite extends TestSuiteBase { val originalConf = ssc.conf val cp = new Checkpoint(ssc, Time(1000)) - val cpConf = cp.sparkConf + val cpConf = cp.createSparkConf() assert(cpConf.get("spark.master") === originalConf.get("spark.master")) assert(cpConf.get("spark.app.name") === originalConf.get("spark.app.name")) assert(cpConf.get(key) === value) @@ -164,7 +163,8 @@ class CheckpointSuite extends TestSuiteBase { // Serialize/deserialize to simulate write to storage and reading it back val newCp = Utils.deserialize[Checkpoint](Utils.serialize(cp)) - val newCpConf = newCp.sparkConf + // Verify new SparkConf has all the previous properties + val newCpConf = newCp.createSparkConf() assert(newCpConf.get("spark.master") === originalConf.get("spark.master")) assert(newCpConf.get("spark.app.name") === originalConf.get("spark.app.name")) assert(newCpConf.get(key) === value) @@ -175,6 +175,20 @@ class CheckpointSuite extends TestSuiteBase { ssc = new StreamingContext(null, newCp, null) val restoredConf = ssc.conf assert(restoredConf.get(key) === value) + ssc.stop() + + // Verify new SparkConf picks up new master url if it is set in the properties. See SPARK-6331. + try { + val newMaster = "local[100]" + System.setProperty("spark.master", newMaster) + val newCpConf = newCp.createSparkConf() + assert(newCpConf.get("spark.master") === newMaster) + assert(newCpConf.get("spark.app.name") === originalConf.get("spark.app.name")) + ssc = new StreamingContext(null, newCp, null) + assert(ssc.sparkContext.master === newMaster) + } finally { + System.clearProperty("spark.master") + } } @@ -185,7 +199,12 @@ class CheckpointSuite extends TestSuiteBase { testCheckpointedOperation( Seq( Seq("a", "a", "b"), Seq("", ""), Seq(), Seq("a", "a", "b"), Seq("", ""), Seq() ), (s: DStream[String]) => s.map(x => (x, 1)).reduceByKey(_ + _), - Seq( Seq(("a", 2), ("b", 1)), Seq(("", 2)), Seq(), Seq(("a", 2), ("b", 1)), Seq(("", 2)), Seq() ), + Seq( + Seq(("a", 2), ("b", 1)), + Seq(("", 2)), + Seq(), + Seq(("a", 2), ("b", 1)), + Seq(("", 2)), Seq() ), 3 ) } @@ -198,7 +217,8 @@ class CheckpointSuite extends TestSuiteBase { val n = 10 val w = 4 val input = (1 to n).map(_ => Seq("a")).toSeq - val output = Seq(Seq(("a", 1)), Seq(("a", 2)), Seq(("a", 3))) ++ (1 to (n - w + 1)).map(x => Seq(("a", 4))) + val output = Seq( + Seq(("a", 1)), Seq(("a", 2)), Seq(("a", 3))) ++ (1 to (n - w + 1)).map(x => Seq(("a", 4))) val operation = (st: DStream[String]) => { st.map(x => (x, 1)) .reduceByKeyAndWindow(_ + _, _ - _, batchDuration * w, batchDuration) @@ -208,7 +228,7 @@ class CheckpointSuite extends TestSuiteBase { } test("recovery with saveAsHadoopFiles operation") { - val tempDir = Files.createTempDir() + val tempDir = Utils.createTempDir() try { testCheckpointedOperation( Seq(Seq("a", "a", "b"), Seq("", ""), Seq(), Seq("a", "a", "b"), Seq("", ""), Seq()), @@ -222,7 +242,13 @@ class CheckpointSuite extends TestSuiteBase { classOf[TextOutputFormat[Text, IntWritable]]) output }, - Seq(Seq(("a", 2), ("b", 1)), Seq(("", 2)), Seq(), Seq(("a", 2), ("b", 1)), Seq(("", 2)), Seq()), + Seq( + Seq(("a", 2), ("b", 1)), + Seq(("", 2)), + Seq(), + Seq(("a", 2), ("b", 1)), + Seq(("", 2)), + Seq()), 3 ) } finally { @@ -231,7 +257,7 @@ class CheckpointSuite extends TestSuiteBase { } test("recovery with saveAsNewAPIHadoopFiles operation") { - val tempDir = Files.createTempDir() + val tempDir = Utils.createTempDir() try { testCheckpointedOperation( Seq(Seq("a", "a", "b"), Seq("", ""), Seq(), Seq("a", "a", "b"), Seq("", ""), Seq()), @@ -245,7 +271,13 @@ class CheckpointSuite extends TestSuiteBase { classOf[NewTextOutputFormat[Text, IntWritable]]) output }, - Seq(Seq(("a", 2), ("b", 1)), Seq(("", 2)), Seq(), Seq(("a", 2), ("b", 1)), Seq(("", 2)), Seq()), + Seq( + Seq(("a", 2), ("b", 1)), + Seq(("", 2)), + Seq(), + Seq(("a", 2), ("b", 1)), + Seq(("", 2)), + Seq()), 3 ) } finally { @@ -269,7 +301,7 @@ class CheckpointSuite extends TestSuiteBase { // // After SPARK-5079 is addressed, should be able to remove this test since a strengthened // version of the other saveAsHadoopFile* tests would prevent regressions for this issue. - val tempDir = Files.createTempDir() + val tempDir = Utils.createTempDir() try { testCheckpointedOperation( Seq(Seq("a", "a", "b"), Seq("", ""), Seq(), Seq("a", "a", "b"), Seq("", ""), Seq()), @@ -284,7 +316,13 @@ class CheckpointSuite extends TestSuiteBase { output } }, - Seq(Seq(("a", 2), ("b", 1)), Seq(("", 2)), Seq(), Seq(("a", 2), ("b", 1)), Seq(("", 2)), Seq()), + Seq( + Seq(("a", 2), ("b", 1)), + Seq(("", 2)), + Seq(), + Seq(("a", 2), ("b", 1)), + Seq(("", 2)), + Seq()), 3 ) } finally { @@ -324,13 +362,13 @@ class CheckpointSuite extends TestSuiteBase { * Writes a file named `i` (which contains the number `i`) to the test directory and sets its * modification time to `clock`'s current time. */ - def writeFile(i: Int, clock: ManualClock): Unit = { + def writeFile(i: Int, clock: Clock): Unit = { val file = new File(testDir, i.toString) Files.write(i + "\n", file, Charsets.UTF_8) - assert(file.setLastModified(clock.currentTime())) + assert(file.setLastModified(clock.getTimeMillis())) // Check that the file's modification date is actually the value we wrote, since rounding or // truncation will break the test: - assert(file.lastModified() === clock.currentTime()) + assert(file.lastModified() === clock.getTimeMillis()) } /** @@ -372,13 +410,13 @@ class CheckpointSuite extends TestSuiteBase { ssc.start() // Advance half a batch so that the first file is created after the StreamingContext starts - clock.addToTime(batchDuration.milliseconds / 2) + clock.advance(batchDuration.milliseconds / 2) // Create files and advance manual clock to process them for (i <- Seq(1, 2, 3)) { writeFile(i, clock) // Advance the clock after creating the file to avoid a race when // setting its modification time - clock.addToTime(batchDuration.milliseconds) + clock.advance(batchDuration.milliseconds) if (i != 3) { // Since we want to shut down while the 3rd batch is processing eventually(eventuallyTimeout) { @@ -386,7 +424,7 @@ class CheckpointSuite extends TestSuiteBase { } } } - clock.addToTime(batchDuration.milliseconds) + clock.advance(batchDuration.milliseconds) eventually(eventuallyTimeout) { // Wait until all files have been recorded and all batches have started assert(recordedFiles(ssc) === Seq(1, 2, 3) && batchCounter.getNumStartedBatches === 3) @@ -410,7 +448,7 @@ class CheckpointSuite extends TestSuiteBase { writeFile(i, clock) // Advance the clock after creating the file to avoid a race when // setting its modification time - clock.addToTime(batchDuration.milliseconds) + clock.advance(batchDuration.milliseconds) } // Recover context from checkpoint file and verify whether the files that were @@ -419,7 +457,7 @@ class CheckpointSuite extends TestSuiteBase { withStreamingContext(new StreamingContext(checkpointDir)) { ssc => // So that the restarted StreamingContext's clock has gone forward in time since failure ssc.conf.set("spark.streaming.manualClock.jump", (batchDuration * 3).milliseconds.toString) - val oldClockTime = clock.currentTime() + val oldClockTime = clock.getTimeMillis() clock = ssc.scheduler.clock.asInstanceOf[ManualClock] val batchCounter = new BatchCounter(ssc) val outputStream = ssc.graph.getOutputStreams().head.asInstanceOf[TestOutputStream[Int]] @@ -430,7 +468,7 @@ class CheckpointSuite extends TestSuiteBase { ssc.start() // Verify that the clock has traveled forward to the expected time eventually(eventuallyTimeout) { - clock.currentTime() === oldClockTime + clock.getTimeMillis() === oldClockTime } // Wait for pre-failure batch to be recomputed (3 while SSC was down plus last batch) val numBatchesAfterRestart = 4 @@ -441,12 +479,12 @@ class CheckpointSuite extends TestSuiteBase { writeFile(i, clock) // Advance the clock after creating the file to avoid a race when // setting its modification time - clock.addToTime(batchDuration.milliseconds) + clock.advance(batchDuration.milliseconds) eventually(eventuallyTimeout) { assert(batchCounter.getNumCompletedBatches === index + numBatchesAfterRestart + 1) } } - clock.addToTime(batchDuration.milliseconds) + clock.advance(batchDuration.milliseconds) logInfo("Output after restart = " + outputStream.output.mkString("[", ", ", "]")) assert(outputStream.output.size > 0, "No files processed after restart") ssc.stop() @@ -519,17 +557,18 @@ class CheckpointSuite extends TestSuiteBase { * Advances the manual clock on the streaming scheduler by given number of batches. * It also waits for the expected amount of time for each batch. */ - def advanceTimeWithRealDelay[V: ClassTag](ssc: StreamingContext, numBatches: Long): Seq[Seq[V]] = { + def advanceTimeWithRealDelay[V: ClassTag](ssc: StreamingContext, numBatches: Long): Seq[Seq[V]] = + { val clock = ssc.scheduler.clock.asInstanceOf[ManualClock] - logInfo("Manual clock before advancing = " + clock.currentTime()) + logInfo("Manual clock before advancing = " + clock.getTimeMillis()) for (i <- 1 to numBatches.toInt) { - clock.addToTime(batchDuration.milliseconds) + clock.advance(batchDuration.milliseconds) Thread.sleep(batchDuration.milliseconds) } - logInfo("Manual clock after advancing = " + clock.currentTime()) + logInfo("Manual clock after advancing = " + clock.getTimeMillis()) Thread.sleep(batchDuration.milliseconds) - val outputStream = ssc.graph.getOutputStreams.filter { dstream => + val outputStream = ssc.graph.getOutputStreams().filter { dstream => dstream.isInstanceOf[TestOutputStreamWithPartitions[V]] }.head.asInstanceOf[TestOutputStreamWithPartitions[V]] outputStream.output.map(_.flatten) @@ -538,4 +577,4 @@ class CheckpointSuite extends TestSuiteBase { private object CheckpointSuite extends Serializable { var batchThreeShouldBlockIndefinitely: Boolean = true -} \ No newline at end of file +} diff --git a/streaming/src/test/scala/org/apache/spark/streaming/FailureSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/FailureSuite.scala index 6500608bba87c..0c4c06534a693 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/FailureSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/FailureSuite.scala @@ -20,32 +20,30 @@ package org.apache.spark.streaming import org.apache.spark.Logging import org.apache.spark.util.Utils -import java.io.File - /** * This testsuite tests master failures at random times while the stream is running using * the real clock. */ class FailureSuite extends TestSuiteBase with Logging { - val directory = Utils.createTempDir().getAbsolutePath + val directory = Utils.createTempDir() val numBatches = 30 - override def batchDuration = Milliseconds(1000) + override def batchDuration: Duration = Milliseconds(1000) - override def useManualClock = false + override def useManualClock: Boolean = false override def afterFunction() { - Utils.deleteRecursively(new File(directory)) + Utils.deleteRecursively(directory) super.afterFunction() } test("multiple failures with map") { - MasterFailureTest.testMap(directory, numBatches, batchDuration) + MasterFailureTest.testMap(directory.getAbsolutePath, numBatches, batchDuration) } test("multiple failures with updateStateByKey") { - MasterFailureTest.testUpdateStateByKey(directory, numBatches, batchDuration) + MasterFailureTest.testUpdateStateByKey(directory.getAbsolutePath, numBatches, batchDuration) } } diff --git a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala index 01084a457db4f..e6ac4975c5e68 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala @@ -17,12 +17,8 @@ package org.apache.spark.streaming -import akka.actor.Actor -import akka.actor.Props -import akka.util.ByteString - import java.io.{File, BufferedWriter, OutputStreamWriter} -import java.net.{InetSocketAddress, SocketException, ServerSocket} +import java.net.{SocketException, ServerSocket} import java.nio.charset.Charset import java.util.concurrent.{Executors, TimeUnit, ArrayBlockingQueue} import java.util.concurrent.atomic.AtomicInteger @@ -36,9 +32,8 @@ import org.scalatest.concurrent.Eventually._ import org.apache.spark.Logging import org.apache.spark.storage.StorageLevel -import org.apache.spark.streaming.util.ManualClock -import org.apache.spark.util.Utils -import org.apache.spark.streaming.receiver.{ActorHelper, Receiver} +import org.apache.spark.util.{ManualClock, Utils} +import org.apache.spark.streaming.receiver.Receiver import org.apache.spark.rdd.RDD import org.apache.hadoop.io.{Text, LongWritable} import org.apache.hadoop.mapreduce.lib.input.TextInputFormat @@ -57,7 +52,7 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { "localhost", testServer.port, StorageLevel.MEMORY_AND_DISK) val outputBuffer = new ArrayBuffer[Seq[String]] with SynchronizedBuffer[Seq[String]] val outputStream = new TestOutputStream(networkStream, outputBuffer) - def output = outputBuffer.flatMap(x => x) + def output: ArrayBuffer[String] = outputBuffer.flatMap(x => x) outputStream.register() ssc.start() @@ -69,7 +64,7 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { for (i <- 0 until input.size) { testServer.send(input(i).toString + "\n") Thread.sleep(500) - clock.addToTime(batchDuration.milliseconds) + clock.advance(batchDuration.milliseconds) } Thread.sleep(1000) logInfo("Stopping server") @@ -120,19 +115,19 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { // Advance the clock so that the files are created after StreamingContext starts, but // not enough to trigger a batch - clock.addToTime(batchDuration.milliseconds / 2) + clock.advance(batchDuration.milliseconds / 2) val input = Seq(1, 2, 3, 4, 5) input.foreach { i => Thread.sleep(batchDuration.milliseconds) val file = new File(testDir, i.toString) Files.write(Array[Byte](i.toByte), file) - assert(file.setLastModified(clock.currentTime())) - assert(file.lastModified === clock.currentTime) + assert(file.setLastModified(clock.getTimeMillis())) + assert(file.lastModified === clock.getTimeMillis()) logInfo("Created file " + file) // Advance the clock after creating the file to avoid a race when // setting its modification time - clock.addToTime(batchDuration.milliseconds) + clock.advance(batchDuration.milliseconds) eventually(eventuallyTimeout) { assert(batchCounter.getNumCompletedBatches === i) } @@ -169,7 +164,7 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { val countStream = networkStream.count val outputBuffer = new ArrayBuffer[Seq[Long]] with SynchronizedBuffer[Seq[Long]] val outputStream = new TestOutputStream(countStream, outputBuffer) - def output = outputBuffer.flatMap(x => x) + def output: ArrayBuffer[Long] = outputBuffer.flatMap(x => x) outputStream.register() ssc.start() @@ -179,7 +174,7 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { while((!MultiThreadTestReceiver.haveAllThreadsFinished || output.sum < numTotalRecords) && System.currentTimeMillis() - startTime < 5000) { Thread.sleep(100) - clock.addToTime(batchDuration.milliseconds) + clock.advance(batchDuration.milliseconds) } Thread.sleep(1000) logInfo("Stopping context") @@ -201,7 +196,7 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { val queueStream = ssc.queueStream(queue, oneAtATime = true) val outputBuffer = new ArrayBuffer[Seq[String]] with SynchronizedBuffer[Seq[String]] val outputStream = new TestOutputStream(queueStream, outputBuffer) - def output = outputBuffer.filter(_.size > 0) + def output: ArrayBuffer[Seq[String]] = outputBuffer.filter(_.size > 0) outputStream.register() ssc.start() @@ -209,12 +204,12 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { val clock = ssc.scheduler.clock.asInstanceOf[ManualClock] val input = Seq("1", "2", "3", "4", "5") val expectedOutput = input.map(Seq(_)) - //Thread.sleep(1000) + val inputIterator = input.toIterator for (i <- 0 until input.size) { // Enqueue more than 1 item per tick but they should dequeue one at a time inputIterator.take(2).foreach(i => queue += ssc.sparkContext.makeRDD(Seq(i))) - clock.addToTime(batchDuration.milliseconds) + clock.advance(batchDuration.milliseconds) } Thread.sleep(1000) logInfo("Stopping context") @@ -244,7 +239,7 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { val queueStream = ssc.queueStream(queue, oneAtATime = false) val outputBuffer = new ArrayBuffer[Seq[String]] with SynchronizedBuffer[Seq[String]] val outputStream = new TestOutputStream(queueStream, outputBuffer) - def output = outputBuffer.filter(_.size > 0) + def output: ArrayBuffer[Seq[String]] = outputBuffer.filter(_.size > 0) outputStream.register() ssc.start() @@ -256,12 +251,12 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { // Enqueue the first 3 items (one by one), they should be merged in the next batch val inputIterator = input.toIterator inputIterator.take(3).foreach(i => queue += ssc.sparkContext.makeRDD(Seq(i))) - clock.addToTime(batchDuration.milliseconds) + clock.advance(batchDuration.milliseconds) Thread.sleep(1000) // Enqueue the remaining items (again one by one), merged in the final batch inputIterator.foreach(i => queue += ssc.sparkContext.makeRDD(Seq(i))) - clock.addToTime(batchDuration.milliseconds) + clock.advance(batchDuration.milliseconds) Thread.sleep(1000) logInfo("Stopping context") ssc.stop() @@ -308,19 +303,19 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { // Advance the clock so that the files are created after StreamingContext starts, but // not enough to trigger a batch - clock.addToTime(batchDuration.milliseconds / 2) + clock.advance(batchDuration.milliseconds / 2) // Over time, create files in the directory val input = Seq(1, 2, 3, 4, 5) input.foreach { i => val file = new File(testDir, i.toString) Files.write(i + "\n", file, Charset.forName("UTF-8")) - assert(file.setLastModified(clock.currentTime())) - assert(file.lastModified === clock.currentTime) + assert(file.setLastModified(clock.getTimeMillis())) + assert(file.lastModified === clock.getTimeMillis()) logInfo("Created file " + file) // Advance the clock after creating the file to avoid a race when // setting its modification time - clock.addToTime(batchDuration.milliseconds) + clock.advance(batchDuration.milliseconds) eventually(eventuallyTimeout) { assert(batchCounter.getNumCompletedBatches === i) } @@ -357,7 +352,8 @@ class TestServer(portToBind: Int = 0) extends Logging { logInfo("New connection") try { clientSocket.setTcpNoDelay(true) - val outputStream = new BufferedWriter(new OutputStreamWriter(clientSocket.getOutputStream)) + val outputStream = new BufferedWriter( + new OutputStreamWriter(clientSocket.getOutputStream)) while(clientSocket.isConnected) { val msg = queue.poll(100, TimeUnit.MILLISECONDS) @@ -389,7 +385,7 @@ class TestServer(portToBind: Int = 0) extends Logging { def stop() { servingThread.interrupt() } - def port = serverSocket.getLocalPort + def port: Int = serverSocket.getLocalPort } /** This is a receiver to test multiple threads inserting data using block generator */ diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockHandlerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockHandlerSuite.scala index 132ff2443fc0f..c090eaec2928d 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockHandlerSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockHandlerSuite.scala @@ -24,22 +24,20 @@ import scala.collection.mutable.ArrayBuffer import scala.concurrent.duration._ import scala.language.postfixOps -import akka.actor.{ActorSystem, Props} -import com.google.common.io.Files -import org.apache.commons.io.FileUtils import org.apache.hadoop.conf.Configuration import org.scalatest.{BeforeAndAfter, FunSuite, Matchers} import org.scalatest.concurrent.Eventually._ import org.apache.spark._ import org.apache.spark.network.nio.NioBlockTransferService +import org.apache.spark.rpc.RpcEnv import org.apache.spark.scheduler.LiveListenerBus import org.apache.spark.serializer.KryoSerializer import org.apache.spark.shuffle.hash.HashShuffleManager import org.apache.spark.storage._ import org.apache.spark.streaming.receiver._ import org.apache.spark.streaming.util._ -import org.apache.spark.util.AkkaUtils +import org.apache.spark.util.{ManualClock, Utils} import WriteAheadLogBasedBlockHandler._ import WriteAheadLogSuite._ @@ -56,27 +54,24 @@ class ReceivedBlockHandlerSuite extends FunSuite with BeforeAndAfter with Matche val manualClock = new ManualClock val blockManagerSize = 10000000 - var actorSystem: ActorSystem = null + var rpcEnv: RpcEnv = null var blockManagerMaster: BlockManagerMaster = null var blockManager: BlockManager = null var tempDirectory: File = null before { - val (actorSystem, boundPort) = AkkaUtils.createActorSystem( - "test", "localhost", 0, conf = conf, securityManager = securityMgr) - this.actorSystem = actorSystem - conf.set("spark.driver.port", boundPort.toString) + rpcEnv = RpcEnv.create("test", "localhost", 0, conf, securityMgr) + conf.set("spark.driver.port", rpcEnv.address.port.toString) - blockManagerMaster = new BlockManagerMaster( - actorSystem.actorOf(Props(new BlockManagerMasterActor(true, conf, new LiveListenerBus))), - conf, true) + blockManagerMaster = new BlockManagerMaster(rpcEnv.setupEndpoint("blockmanager", + new BlockManagerMasterEndpoint(rpcEnv, true, conf, new LiveListenerBus)), conf, true) - blockManager = new BlockManager("bm", actorSystem, blockManagerMaster, serializer, + blockManager = new BlockManager("bm", rpcEnv, blockManagerMaster, serializer, blockManagerSize, conf, mapOutputTracker, shuffleManager, new NioBlockTransferService(conf, securityMgr), securityMgr, 0) blockManager.initialize("app-id") - tempDirectory = Files.createTempDir() + tempDirectory = Utils.createTempDir() manualClock.setTime(0) } @@ -89,14 +84,11 @@ class ReceivedBlockHandlerSuite extends FunSuite with BeforeAndAfter with Matche blockManagerMaster.stop() blockManagerMaster = null } - actorSystem.shutdown() - actorSystem.awaitTermination() - actorSystem = null + rpcEnv.shutdown() + rpcEnv.awaitTermination() + rpcEnv = null - if (tempDirectory != null && tempDirectory.exists()) { - FileUtils.deleteDirectory(tempDirectory) - tempDirectory = null - } + Utils.deleteRecursively(tempDirectory) } test("BlockManagerBasedBlockHandler - store blocks") { @@ -104,7 +96,7 @@ class ReceivedBlockHandlerSuite extends FunSuite with BeforeAndAfter with Matche testBlockStoring(handler) { case (data, blockIds, storeResults) => // Verify the data in block manager is correct val storedData = blockIds.flatMap { blockId => - blockManager.getLocal(blockId).map { _.data.map {_.toString}.toList }.getOrElse(List.empty) + blockManager.getLocal(blockId).map(_.data.map(_.toString).toList).getOrElse(List.empty) }.toList storedData shouldEqual data @@ -128,7 +120,7 @@ class ReceivedBlockHandlerSuite extends FunSuite with BeforeAndAfter with Matche testBlockStoring(handler) { case (data, blockIds, storeResults) => // Verify the data in block manager is correct val storedData = blockIds.flatMap { blockId => - blockManager.getLocal(blockId).map { _.data.map {_.toString}.toList }.getOrElse(List.empty) + blockManager.getLocal(blockId).map(_.data.map(_.toString).toList).getOrElse(List.empty) }.toList storedData shouldEqual data @@ -165,7 +157,7 @@ class ReceivedBlockHandlerSuite extends FunSuite with BeforeAndAfter with Matche preCleanupLogFiles.size should be > 1 // this depends on the number of blocks inserted using generateAndStoreData() - manualClock.currentTime() shouldEqual 5000L + manualClock.getTimeMillis() shouldEqual 5000L val cleanupThreshTime = 3000L handler.cleanupOldBlocks(cleanupThreshTime) @@ -243,7 +235,7 @@ class ReceivedBlockHandlerSuite extends FunSuite with BeforeAndAfter with Matche val blockIds = Seq.fill(blocks.size)(generateBlockId()) val storeResults = blocks.zip(blockIds).map { case (block, id) => - manualClock.addToTime(500) // log rolling interval set to 1000 ms through SparkConf + manualClock.advance(500) // log rolling interval set to 1000 ms through SparkConf logDebug("Inserting block " + id) receivedBlockHandler.storeBlock(id, block) }.toList diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockTrackerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockTrackerSuite.scala index fbb7b0bfebafc..b63b37d9f9cef 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockTrackerSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockTrackerSuite.scala @@ -24,8 +24,6 @@ import scala.concurrent.duration._ import scala.language.{implicitConversions, postfixOps} import scala.util.Random -import com.google.common.io.Files -import org.apache.commons.io.FileUtils import org.apache.hadoop.conf.Configuration import org.scalatest.{BeforeAndAfter, FunSuite, Matchers} import org.scalatest.concurrent.Eventually._ @@ -34,9 +32,9 @@ import org.apache.spark.{Logging, SparkConf, SparkException} import org.apache.spark.storage.StreamBlockId import org.apache.spark.streaming.receiver.BlockManagerBasedStoreResult import org.apache.spark.streaming.scheduler._ -import org.apache.spark.streaming.util.{Clock, ManualClock, SystemClock, WriteAheadLogReader} +import org.apache.spark.streaming.util.WriteAheadLogReader import org.apache.spark.streaming.util.WriteAheadLogSuite._ -import org.apache.spark.util.Utils +import org.apache.spark.util.{Clock, ManualClock, SystemClock, Utils} class ReceivedBlockTrackerSuite extends FunSuite with BeforeAndAfter with Matchers with Logging { @@ -51,15 +49,12 @@ class ReceivedBlockTrackerSuite before { conf = new SparkConf().setMaster("local[2]").setAppName("ReceivedBlockTrackerSuite") - checkpointDirectory = Files.createTempDir() + checkpointDirectory = Utils.createTempDir() } after { allReceivedBlockTrackers.foreach { _.stop() } - if (checkpointDirectory != null && checkpointDirectory.exists()) { - FileUtils.deleteDirectory(checkpointDirectory) - checkpointDirectory = null - } + Utils.deleteRecursively(checkpointDirectory) } test("block addition, and block to batch allocation") { @@ -100,7 +95,7 @@ class ReceivedBlockTrackerSuite def incrementTime() { val timeIncrementMillis = 2000L - manualClock.addToTime(timeIncrementMillis) + manualClock.advance(timeIncrementMillis) } // Generate and add blocks to the given tracker @@ -138,13 +133,13 @@ class ReceivedBlockTrackerSuite tracker2.getUnallocatedBlocks(streamId).toList shouldEqual blockInfos1 // Allocate blocks to batch and verify whether the unallocated blocks got allocated - val batchTime1 = manualClock.currentTime + val batchTime1 = manualClock.getTimeMillis() tracker2.allocateBlocksToBatch(batchTime1) tracker2.getBlocksOfBatchAndStream(batchTime1, streamId) shouldEqual blockInfos1 // Add more blocks and allocate to another batch incrementTime() - val batchTime2 = manualClock.currentTime + val batchTime2 = manualClock.getTimeMillis() val blockInfos2 = addBlockInfos(tracker2) tracker2.allocateBlocksToBatch(batchTime2) tracker2.getBlocksOfBatchAndStream(batchTime2, streamId) shouldEqual blockInfos2 @@ -233,7 +228,8 @@ class ReceivedBlockTrackerSuite * Get all the data written in the given write ahead log files. By default, it will read all * files in the test log directory. */ - def getWrittenLogData(logFiles: Seq[String] = getWriteAheadLogFiles): Seq[ReceivedBlockTrackerLogEvent] = { + def getWrittenLogData(logFiles: Seq[String] = getWriteAheadLogFiles) + : Seq[ReceivedBlockTrackerLogEvent] = { logFiles.flatMap { file => new WriteAheadLogReader(file, hadoopConf).toSeq }.map { byteBuffer => @@ -249,7 +245,8 @@ class ReceivedBlockTrackerSuite } /** Create batch allocation object from the given info */ - def createBatchAllocation(time: Long, blockInfos: Seq[ReceivedBlockInfo]): BatchAllocationEvent = { + def createBatchAllocation(time: Long, blockInfos: Seq[ReceivedBlockInfo]) + : BatchAllocationEvent = { BatchAllocationEvent(time, AllocatedBlocks(Map((streamId -> blockInfos)))) } diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala index e8c34a9ee40b9..91261a9db7360 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala @@ -24,7 +24,6 @@ import java.util.concurrent.Semaphore import scala.collection.mutable import scala.collection.mutable.ArrayBuffer -import com.google.common.io.Files import org.scalatest.concurrent.Timeouts import org.scalatest.concurrent.Eventually._ import org.scalatest.time.SpanSugar._ @@ -34,6 +33,7 @@ import org.apache.spark.storage.StorageLevel import org.apache.spark.storage.StreamBlockId import org.apache.spark.streaming.receiver._ import org.apache.spark.streaming.receiver.WriteAheadLogBasedBlockHandler._ +import org.apache.spark.util.Utils /** Testsuite for testing the network receiver behavior */ class ReceiverSuite extends TestSuiteBase with Timeouts with Serializable { @@ -131,11 +131,11 @@ class ReceiverSuite extends TestSuiteBase with Timeouts with Serializable { test("block generator") { val blockGeneratorListener = new FakeBlockGeneratorListener - val blockInterval = 200 - val conf = new SparkConf().set("spark.streaming.blockInterval", blockInterval.toString) + val blockIntervalMs = 200 + val conf = new SparkConf().set("spark.streaming.blockInterval", s"${blockIntervalMs}ms") val blockGenerator = new BlockGenerator(blockGeneratorListener, 1, conf) val expectedBlocks = 5 - val waitTime = expectedBlocks * blockInterval + (blockInterval / 2) + val waitTime = expectedBlocks * blockIntervalMs + (blockIntervalMs / 2) val generatedData = new ArrayBuffer[Int] // Generate blocks @@ -157,15 +157,15 @@ class ReceiverSuite extends TestSuiteBase with Timeouts with Serializable { test("block generator throttling") { val blockGeneratorListener = new FakeBlockGeneratorListener - val blockInterval = 100 + val blockIntervalMs = 100 val maxRate = 100 - val conf = new SparkConf().set("spark.streaming.blockInterval", blockInterval.toString). + val conf = new SparkConf().set("spark.streaming.blockInterval", s"${blockIntervalMs}ms"). set("spark.streaming.receiver.maxRate", maxRate.toString) val blockGenerator = new BlockGenerator(blockGeneratorListener, 1, conf) val expectedBlocks = 20 - val waitTime = expectedBlocks * blockInterval + val waitTime = expectedBlocks * blockIntervalMs val expectedMessages = maxRate * waitTime / 1000 - val expectedMessagesPerBlock = maxRate * blockInterval / 1000 + val expectedMessagesPerBlock = maxRate * blockIntervalMs / 1000 val generatedData = new ArrayBuffer[Int] // Generate blocks @@ -222,7 +222,7 @@ class ReceiverSuite extends TestSuiteBase with Timeouts with Serializable { .set("spark.streaming.receiver.writeAheadLog.enable", "true") .set("spark.streaming.receiver.writeAheadLog.rollingInterval", "1") val batchDuration = Milliseconds(500) - val tempDirectory = Files.createTempDir() + val tempDirectory = Utils.createTempDir() val logDirectory1 = new File(checkpointDirToLogDir(tempDirectory.getAbsolutePath, 0)) val logDirectory2 = new File(checkpointDirToLogDir(tempDirectory.getAbsolutePath, 1)) val allLogFiles1 = new mutable.HashSet[String]() @@ -251,7 +251,6 @@ class ReceiverSuite extends TestSuiteBase with Timeouts with Serializable { } withStreamingContext(new StreamingContext(sparkConf, batchDuration)) { ssc => - tempDirectory.deleteOnExit() val receiver1 = ssc.sparkContext.clean(new FakeReceiver(sendData = true)) val receiver2 = ssc.sparkContext.clean(new FakeReceiver(sendData = true)) val receiverStream1 = ssc.receiverStream(receiver1) @@ -309,7 +308,7 @@ class ReceiverSuite extends TestSuiteBase with Timeouts with Serializable { val errors = new ArrayBuffer[Throwable] /** Check if all data structures are clean */ - def isAllEmpty = { + def isAllEmpty: Boolean = { singles.isEmpty && byteBuffers.isEmpty && iterators.isEmpty && arrayBuffers.isEmpty && errors.isEmpty } @@ -321,24 +320,21 @@ class ReceiverSuite extends TestSuiteBase with Timeouts with Serializable { def pushBytes( bytes: ByteBuffer, optionalMetadata: Option[Any], - optionalBlockId: Option[StreamBlockId] - ) { + optionalBlockId: Option[StreamBlockId]) { byteBuffers += bytes } def pushIterator( iterator: Iterator[_], optionalMetadata: Option[Any], - optionalBlockId: Option[StreamBlockId] - ) { + optionalBlockId: Option[StreamBlockId]) { iterators += iterator } def pushArrayBuffer( arrayBuffer: ArrayBuffer[_], optionalMetadata: Option[Any], - optionalBlockId: Option[StreamBlockId] - ) { + optionalBlockId: Option[StreamBlockId]) { arrayBuffers += arrayBuffer } diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala index 2aa5e0876b6e0..58353a5f97c8a 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala @@ -73,9 +73,9 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts w test("from conf with settings") { val myConf = SparkContext.updatedConf(new SparkConf(false), master, appName) - myConf.set("spark.cleaner.ttl", "10") + myConf.set("spark.cleaner.ttl", "10s") ssc = new StreamingContext(myConf, batchDuration) - assert(ssc.conf.getInt("spark.cleaner.ttl", -1) === 10) + assert(ssc.conf.getTimeAsSeconds("spark.cleaner.ttl", "-1") === 10) } test("from existing SparkContext") { @@ -85,24 +85,26 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts w test("from existing SparkContext with settings") { val myConf = SparkContext.updatedConf(new SparkConf(false), master, appName) - myConf.set("spark.cleaner.ttl", "10") + myConf.set("spark.cleaner.ttl", "10s") ssc = new StreamingContext(myConf, batchDuration) - assert(ssc.conf.getInt("spark.cleaner.ttl", -1) === 10) + assert(ssc.conf.getTimeAsSeconds("spark.cleaner.ttl", "-1") === 10) } test("from checkpoint") { val myConf = SparkContext.updatedConf(new SparkConf(false), master, appName) - myConf.set("spark.cleaner.ttl", "10") + myConf.set("spark.cleaner.ttl", "10s") val ssc1 = new StreamingContext(myConf, batchDuration) addInputStream(ssc1).register() ssc1.start() val cp = new Checkpoint(ssc1, Time(1000)) - assert(cp.sparkConfPairs.toMap.getOrElse("spark.cleaner.ttl", "-1") === "10") + assert( + Utils.timeStringAsSeconds(cp.sparkConfPairs + .toMap.getOrElse("spark.cleaner.ttl", "-1")) === 10) ssc1.stop() val newCp = Utils.deserialize[Checkpoint](Utils.serialize(cp)) - assert(newCp.sparkConf.getInt("spark.cleaner.ttl", -1) === 10) + assert(newCp.createSparkConf().getTimeAsSeconds("spark.cleaner.ttl", "-1") === 10) ssc = new StreamingContext(null, newCp, null) - assert(ssc.conf.getInt("spark.cleaner.ttl", -1) === 10) + assert(ssc.conf.getTimeAsSeconds("spark.cleaner.ttl", "-1") === 10) } test("start and stop state check") { @@ -176,7 +178,7 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts w test("stop gracefully") { val conf = new SparkConf().setMaster(master).setAppName(appName) - conf.set("spark.cleaner.ttl", "3600") + conf.set("spark.cleaner.ttl", "3600s") sc = new SparkContext(conf) for (i <- 1 to 4) { logInfo("==================================\n\n\n") @@ -190,7 +192,7 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts w logInfo("Count = " + count + ", Running count = " + runningCount) } ssc.start() - ssc.awaitTermination(500) + ssc.awaitTerminationOrTimeout(500) ssc.stop(stopSparkContext = false, stopGracefully = true) logInfo("Running count = " + runningCount) logInfo("TestReceiver.counter = " + TestReceiver.counter.get()) @@ -207,13 +209,13 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts w test("stop slow receiver gracefully") { val conf = new SparkConf().setMaster(master).setAppName(appName) - conf.set("spark.streaming.gracefulStopTimeout", "20000") + conf.set("spark.streaming.gracefulStopTimeout", "20000s") sc = new SparkContext(conf) logInfo("==================================\n\n\n") ssc = new StreamingContext(sc, Milliseconds(100)) var runningCount = 0 SlowTestReceiver.receivedAllRecords = false - //Create test receiver that sleeps in onStop() + // Create test receiver that sleeps in onStop() val totalNumRecords = 15 val recordsPerSecond = 1 val input = ssc.receiverStream(new SlowTestReceiver(totalNumRecords, recordsPerSecond)) @@ -223,7 +225,7 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts w logInfo("Count = " + count + ", Running count = " + runningCount) } ssc.start() - ssc.awaitTermination(500) + ssc.awaitTerminationOrTimeout(500) ssc.stop(stopSparkContext = false, stopGracefully = true) logInfo("Running count = " + runningCount) assert(runningCount > 0) @@ -243,7 +245,7 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts w // test whether awaitTermination() exits after give amount of time failAfter(1000 millis) { - ssc.awaitTermination(500) + ssc.awaitTerminationOrTimeout(500) } // test whether awaitTermination() does not exit if not time is given @@ -288,7 +290,7 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts w val exception = intercept[Exception] { ssc.start() - ssc.awaitTermination(5000) + ssc.awaitTerminationOrTimeout(5000) } assert(exception.getMessage.contains("map task"), "Expected exception not thrown") } @@ -299,7 +301,7 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts w inputStream.transform { rdd => throw new TestException("error in transform"); rdd }.register() val exception = intercept[TestException] { ssc.start() - ssc.awaitTermination(5000) + ssc.awaitTerminationOrTimeout(5000) } assert(exception.getMessage.contains("transform"), "Expected exception not thrown") } @@ -370,7 +372,8 @@ object TestReceiver { } /** Custom receiver for testing whether a slow receiver can be shutdown gracefully or not */ -class SlowTestReceiver(totalRecords: Int, recordsPerSecond: Int) extends Receiver[Int](StorageLevel.MEMORY_ONLY) with Logging { +class SlowTestReceiver(totalRecords: Int, recordsPerSecond: Int) + extends Receiver[Int](StorageLevel.MEMORY_ONLY) with Logging { var receivingThreadOption: Option[Thread] = None diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala index f52562b0a0f73..7210439509541 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala @@ -38,18 +38,46 @@ class StreamingListenerSuite extends TestSuiteBase with Matchers { // To make sure that the processing start and end times in collected // information are different for successive batches - override def batchDuration = Milliseconds(100) - override def actuallyWait = true + override def batchDuration: Duration = Milliseconds(100) + override def actuallyWait: Boolean = true test("batch info reporting") { val ssc = setupStreams(input, operation) val collector = new BatchInfoCollector ssc.addStreamingListener(collector) runStreams(ssc, input.size, input.size) - val batchInfos = collector.batchInfos - batchInfos should have size 4 - batchInfos.foreach(info => { + // SPARK-6766: batch info should be submitted + val batchInfosSubmitted = collector.batchInfosSubmitted + batchInfosSubmitted should have size 4 + + batchInfosSubmitted.foreach(info => { + info.schedulingDelay should be (None) + info.processingDelay should be (None) + info.totalDelay should be (None) + }) + + isInIncreasingOrder(batchInfosSubmitted.map(_.submissionTime)) should be (true) + + // SPARK-6766: processingStartTime of batch info should not be None when starting + val batchInfosStarted = collector.batchInfosStarted + batchInfosStarted should have size 4 + + batchInfosStarted.foreach(info => { + info.schedulingDelay should not be None + info.schedulingDelay.get should be >= 0L + info.processingDelay should be (None) + info.totalDelay should be (None) + }) + + isInIncreasingOrder(batchInfosStarted.map(_.submissionTime)) should be (true) + isInIncreasingOrder(batchInfosStarted.map(_.processingStartTime.get)) should be (true) + + // test onBatchCompleted + val batchInfosCompleted = collector.batchInfosCompleted + batchInfosCompleted should have size 4 + + batchInfosCompleted.foreach(info => { info.schedulingDelay should not be None info.processingDelay should not be None info.totalDelay should not be None @@ -58,9 +86,9 @@ class StreamingListenerSuite extends TestSuiteBase with Matchers { info.totalDelay.get should be >= 0L }) - isInIncreasingOrder(batchInfos.map(_.submissionTime)) should be (true) - isInIncreasingOrder(batchInfos.map(_.processingStartTime.get)) should be (true) - isInIncreasingOrder(batchInfos.map(_.processingEndTime.get)) should be (true) + isInIncreasingOrder(batchInfosCompleted.map(_.submissionTime)) should be (true) + isInIncreasingOrder(batchInfosCompleted.map(_.processingStartTime.get)) should be (true) + isInIncreasingOrder(batchInfosCompleted.map(_.processingEndTime.get)) should be (true) } test("receiver info reporting") { @@ -99,9 +127,20 @@ class StreamingListenerSuite extends TestSuiteBase with Matchers { /** Listener that collects information on processed batches */ class BatchInfoCollector extends StreamingListener { - val batchInfos = new ArrayBuffer[BatchInfo] + val batchInfosCompleted = new ArrayBuffer[BatchInfo] + val batchInfosStarted = new ArrayBuffer[BatchInfo] + val batchInfosSubmitted = new ArrayBuffer[BatchInfo] + + override def onBatchSubmitted(batchSubmitted: StreamingListenerBatchSubmitted) { + batchInfosSubmitted += batchSubmitted.batchInfo + } + + override def onBatchStarted(batchStarted: StreamingListenerBatchStarted) { + batchInfosStarted += batchStarted.batchInfo + } + override def onBatchCompleted(batchCompleted: StreamingListenerBatchCompleted) { - batchInfos += batchCompleted.batchInfo + batchInfosCompleted += batchCompleted.batchInfo } } diff --git a/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala b/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala index 7d82c3e4aadcf..c3cae8aeb6d15 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala @@ -31,10 +31,9 @@ import org.scalatest.concurrent.PatienceConfiguration import org.apache.spark.streaming.dstream.{DStream, InputDStream, ForEachDStream} import org.apache.spark.streaming.scheduler.{StreamingListenerBatchStarted, StreamingListenerBatchCompleted, StreamingListener} -import org.apache.spark.streaming.util.ManualClock import org.apache.spark.{SparkConf, Logging} import org.apache.spark.rdd.RDD -import org.apache.spark.util.Utils +import org.apache.spark.util.{ManualClock, Utils} /** * This is a input stream just for the testsuites. This is equivalent to a checkpointable, @@ -54,8 +53,9 @@ class TestInputStream[T: ClassTag](ssc_ : StreamingContext, input: Seq[Seq[T]], val selectedInput = if (index < input.size) input(index) else Seq[T]() // lets us test cases where RDDs are not created - if (selectedInput == null) + if (selectedInput == null) { return None + } val rdd = ssc.sc.makeRDD(selectedInput, numPartitions) logInfo("Created RDD " + rdd.id + " with " + selectedInput) @@ -105,7 +105,9 @@ class TestOutputStreamWithPartitions[T: ClassTag](parent: DStream[T], output.clear() } - def toTestOutputStream = new TestOutputStream[T](this.parent, this.output.map(_.flatten)) + def toTestOutputStream: TestOutputStream[T] = { + new TestOutputStream[T](this.parent, this.output.map(_.flatten)) + } } /** @@ -149,34 +151,34 @@ class BatchCounter(ssc: StreamingContext) { trait TestSuiteBase extends FunSuite with BeforeAndAfter with Logging { // Name of the framework for Spark context - def framework = this.getClass.getSimpleName + def framework: String = this.getClass.getSimpleName // Master for Spark context - def master = "local[2]" + def master: String = "local[2]" // Batch duration - def batchDuration = Seconds(1) + def batchDuration: Duration = Seconds(1) // Directory where the checkpoint data will be saved - lazy val checkpointDir = { + lazy val checkpointDir: String = { val dir = Utils.createTempDir() logDebug(s"checkpointDir: $dir") dir.toString } // Number of partitions of the input parallel collections created for testing - def numInputPartitions = 2 + def numInputPartitions: Int = 2 // Maximum time to wait before the test times out - def maxWaitTimeMillis = 10000 + def maxWaitTimeMillis: Int = 10000 // Whether to use manual clock or not - def useManualClock = true + def useManualClock: Boolean = true // Whether to actually wait in real time before changing manual clock - def actuallyWait = false + def actuallyWait: Boolean = false - //// A SparkConf to use in tests. Can be modified before calling setupStreams to configure things. + // A SparkConf to use in tests. Can be modified before calling setupStreams to configure things. val conf = new SparkConf() .setMaster(master) .setAppName(framework) @@ -189,10 +191,10 @@ trait TestSuiteBase extends FunSuite with BeforeAndAfter with Logging { def beforeFunction() { if (useManualClock) { logInfo("Using manual clock") - conf.set("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock") + conf.set("spark.streaming.clock", "org.apache.spark.util.ManualClock") } else { logInfo("Using real clock") - conf.set("spark.streaming.clock", "org.apache.spark.streaming.util.SystemClock") + conf.set("spark.streaming.clock", "org.apache.spark.util.SystemClock") } } @@ -333,23 +335,24 @@ trait TestSuiteBase extends FunSuite with BeforeAndAfter with Logging { // Advance manual clock val clock = ssc.scheduler.clock.asInstanceOf[ManualClock] - logInfo("Manual clock before advancing = " + clock.currentTime()) + logInfo("Manual clock before advancing = " + clock.getTimeMillis()) if (actuallyWait) { for (i <- 1 to numBatches) { logInfo("Actually waiting for " + batchDuration) - clock.addToTime(batchDuration.milliseconds) + clock.advance(batchDuration.milliseconds) Thread.sleep(batchDuration.milliseconds) } } else { - clock.addToTime(numBatches * batchDuration.milliseconds) + clock.advance(numBatches * batchDuration.milliseconds) } - logInfo("Manual clock after advancing = " + clock.currentTime()) + logInfo("Manual clock after advancing = " + clock.getTimeMillis()) // Wait until expected number of output items have been generated val startTime = System.currentTimeMillis() - while (output.size < numExpectedOutput && System.currentTimeMillis() - startTime < maxWaitTimeMillis) { + while (output.size < numExpectedOutput && + System.currentTimeMillis() - startTime < maxWaitTimeMillis) { logInfo("output.size = " + output.size + ", numExpectedOutput = " + numExpectedOutput) - ssc.awaitTermination(50) + ssc.awaitTerminationOrTimeout(50) } val timeTaken = System.currentTimeMillis() - startTime logInfo("Output generated in " + timeTaken + " milliseconds") diff --git a/streaming/src/test/scala/org/apache/spark/streaming/UISeleniumSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/UISeleniumSuite.scala new file mode 100644 index 0000000000000..205ddf6dbe9b0 --- /dev/null +++ b/streaming/src/test/scala/org/apache/spark/streaming/UISeleniumSuite.scala @@ -0,0 +1,107 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming + +import org.openqa.selenium.WebDriver +import org.openqa.selenium.htmlunit.HtmlUnitDriver +import org.scalatest._ +import org.scalatest.concurrent.Eventually._ +import org.scalatest.selenium.WebBrowser +import org.scalatest.time.SpanSugar._ + +import org.apache.spark._ + + + + +/** + * Selenium tests for the Spark Web UI. + */ +class UISeleniumSuite + extends FunSuite with WebBrowser with Matchers with BeforeAndAfterAll with TestSuiteBase { + + implicit var webDriver: WebDriver = _ + + override def beforeAll(): Unit = { + webDriver = new HtmlUnitDriver + } + + override def afterAll(): Unit = { + if (webDriver != null) { + webDriver.quit() + } + } + + /** + * Create a test SparkStreamingContext with the SparkUI enabled. + */ + private def newSparkStreamingContext(): StreamingContext = { + val conf = new SparkConf() + .setMaster("local") + .setAppName("test") + .set("spark.ui.enabled", "true") + val ssc = new StreamingContext(conf, Seconds(1)) + assert(ssc.sc.ui.isDefined, "Spark UI is not started!") + ssc + } + + test("attaching and detaching a Streaming tab") { + withStreamingContext(newSparkStreamingContext()) { ssc => + val sparkUI = ssc.sparkContext.ui.get + + eventually(timeout(10 seconds), interval(50 milliseconds)) { + go to (sparkUI.appUIAddress.stripSuffix("/")) + find(cssSelector( """ul li a[href*="streaming"]""")) should not be (None) + } + + eventually(timeout(10 seconds), interval(50 milliseconds)) { + // check whether streaming page exists + go to (sparkUI.appUIAddress.stripSuffix("/") + "/streaming") + val statisticText = findAll(cssSelector("li strong")).map(_.text).toSeq + statisticText should contain("Network receivers:") + statisticText should contain("Batch interval:") + + val h4Text = findAll(cssSelector("h4")).map(_.text).toSeq + h4Text should contain("Active Batches (0)") + h4Text should contain("Completed Batches (last 0 out of 0)") + + findAll(cssSelector("""#active-batches-table th""")).map(_.text).toSeq should be { + List("Batch Time", "Input Size", "Scheduling Delay", "Processing Time", "Status") + } + findAll(cssSelector("""#completed-batches-table th""")).map(_.text).toSeq should be { + List("Batch Time", "Input Size", "Scheduling Delay", "Processing Time", "Total Delay") + } + } + + ssc.stop(false) + + eventually(timeout(10 seconds), interval(50 milliseconds)) { + go to (sparkUI.appUIAddress.stripSuffix("/")) + find(cssSelector( """ul li a[href*="streaming"]""")) should be(None) + } + + eventually(timeout(10 seconds), interval(50 milliseconds)) { + go to (sparkUI.appUIAddress.stripSuffix("/") + "/streaming") + val statisticText = findAll(cssSelector("li strong")).map(_.text).toSeq + statisticText should not contain ("Network receivers:") + statisticText should not contain ("Batch interval:") + } + } + } +} + diff --git a/streaming/src/test/scala/org/apache/spark/streaming/UISuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/UISuite.scala deleted file mode 100644 index 8e30118266855..0000000000000 --- a/streaming/src/test/scala/org/apache/spark/streaming/UISuite.scala +++ /dev/null @@ -1,55 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.streaming - -import scala.io.Source - -import org.scalatest.FunSuite -import org.scalatest.concurrent.Eventually._ -import org.scalatest.time.SpanSugar._ - -import org.apache.spark.SparkConf - -class UISuite extends FunSuite { - - // Ignored: See SPARK-1530 - ignore("streaming tab in spark UI") { - val conf = new SparkConf() - .setMaster("local") - .setAppName("test") - .set("spark.ui.enabled", "true") - val ssc = new StreamingContext(conf, Seconds(1)) - assert(ssc.sc.ui.isDefined, "Spark UI is not started!") - val ui = ssc.sc.ui.get - - eventually(timeout(10 seconds), interval(50 milliseconds)) { - val html = Source.fromURL(ui.appUIAddress).mkString - assert(!html.contains("random data that should not be present")) - // test if streaming tab exist - assert(html.toLowerCase.contains("streaming")) - // test if other Spark tabs still exist - assert(html.toLowerCase.contains("stages")) - } - - eventually(timeout(10 seconds), interval(50 milliseconds)) { - val html = Source.fromURL(ui.appUIAddress.stripSuffix("/") + "/streaming").mkString - assert(html.toLowerCase.contains("batch")) - assert(html.toLowerCase.contains("network")) - } - } -} diff --git a/streaming/src/test/scala/org/apache/spark/streaming/WindowOperationsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/WindowOperationsSuite.scala index a5d2bb2fde16c..c39ad05f41520 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/WindowOperationsSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/WindowOperationsSuite.scala @@ -22,9 +22,9 @@ import org.apache.spark.storage.StorageLevel class WindowOperationsSuite extends TestSuiteBase { - override def maxWaitTimeMillis = 20000 // large window tests can sometimes take longer + override def maxWaitTimeMillis: Int = 20000 // large window tests can sometimes take longer - override def batchDuration = Seconds(1) // making sure its visible in this class + override def batchDuration: Duration = Seconds(1) // making sure its visible in this class val largerSlideInput = Seq( Seq(("a", 1)), diff --git a/streaming/src/test/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDDSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDDSuite.scala index 7a6a2f3e577dd..c3602a5b73732 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDDSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDDSuite.scala @@ -28,10 +28,13 @@ import org.apache.spark.storage.{BlockId, BlockManager, StorageLevel, StreamBloc import org.apache.spark.streaming.util.{WriteAheadLogFileSegment, WriteAheadLogWriter} import org.apache.spark.util.Utils -class WriteAheadLogBackedBlockRDDSuite extends FunSuite with BeforeAndAfterAll with BeforeAndAfterEach { +class WriteAheadLogBackedBlockRDDSuite + extends FunSuite with BeforeAndAfterAll with BeforeAndAfterEach { + val conf = new SparkConf() .setMaster("local[2]") .setAppName(this.getClass.getSimpleName) + val hadoopConf = new Configuration() var sparkContext: SparkContext = null @@ -86,7 +89,8 @@ class WriteAheadLogBackedBlockRDDSuite extends FunSuite with BeforeAndAfterAll w * @param numPartitionsInWAL Number of partitions to write to the Write Ahead Log * @param testStoreInBM Test whether blocks read from log are stored back into block manager */ - private def testRDD(numPartitionsInBM: Int, numPartitionsInWAL: Int, testStoreInBM: Boolean = false) { + private def testRDD( + numPartitionsInBM: Int, numPartitionsInWAL: Int, testStoreInBM: Boolean = false) { val numBlocks = numPartitionsInBM + numPartitionsInWAL val data = Seq.fill(numBlocks, 10)(scala.util.Random.nextString(50)) @@ -110,7 +114,7 @@ class WriteAheadLogBackedBlockRDDSuite extends FunSuite with BeforeAndAfterAll w "Unexpected blocks in BlockManager" ) - // Make sure that the right `numPartitionsInWAL` blocks are in write ahead logs, and other are not + // Make sure that the right `numPartitionsInWAL` blocks are in WALs, and other are not require( segments.takeRight(numPartitionsInWAL).forall(s => new File(s.path.stripPrefix("file://")).exists()), @@ -152,6 +156,6 @@ class WriteAheadLogBackedBlockRDDSuite extends FunSuite with BeforeAndAfterAll w } private def generateFakeSegments(count: Int): Seq[WriteAheadLogFileSegment] = { - Array.fill(count)(new WriteAheadLogFileSegment("random", 0l, 0)) + Array.fill(count)(new WriteAheadLogFileSegment("random", 0L, 0)) } } diff --git a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/JobGeneratorSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/JobGeneratorSuite.scala new file mode 100644 index 0000000000000..7865b06c2e3c2 --- /dev/null +++ b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/JobGeneratorSuite.scala @@ -0,0 +1,133 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming.scheduler + +import java.util.concurrent.CountDownLatch + +import scala.concurrent.duration._ +import scala.language.postfixOps + +import org.scalatest.concurrent.Eventually._ + +import org.apache.spark.rdd.RDD +import org.apache.spark.streaming._ +import org.apache.spark.util.{ManualClock, Utils} + +class JobGeneratorSuite extends TestSuiteBase { + + // SPARK-6222 is a tricky regression bug which causes received block metadata + // to be deleted before the corresponding batch has completed. This occurs when + // the following conditions are met. + // 1. streaming checkpointing is enabled by setting streamingContext.checkpoint(dir) + // 2. input data is received through a receiver as blocks + // 3. a batch processing a set of blocks takes a long time, such that a few subsequent + // batches have been generated and submitted for processing. + // + // The JobGenerator (as of Mar 16, 2015) checkpoints twice per batch, once after generation + // of a batch, and another time after the completion of a batch. The cleanup of + // checkpoint data (including block metadata, etc.) from DStream must be done only after the + // 2nd checkpoint has completed, that is, after the batch has been completely processed. + // However, the issue is that the checkpoint data and along with it received block data is + // cleaned even in the case of the 1st checkpoint, causing pre-mature deletion of received block + // data. For example, if the 3rd batch is still being process, the 7th batch may get generated, + // and the corresponding "1st checkpoint" will delete received block metadata of batch older + // than 6th batch. That, is 3rd batch's block metadata gets deleted even before 3rd batch has + // been completely processed. + // + // This test tries to create that scenario by the following. + // 1. enable checkpointing + // 2. generate batches with received blocks + // 3. make the 3rd batch never complete + // 4. allow subsequent batches to be generated (to allow premature deletion of 3rd batch metadata) + // 5. verify whether 3rd batch's block metadata still exists + // + test("SPARK-6222: Do not clear received block data too soon") { + import JobGeneratorSuite._ + val checkpointDir = Utils.createTempDir() + val testConf = conf + testConf.set("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock") + testConf.set("spark.streaming.receiver.writeAheadLog.rollingInterval", "1") + + withStreamingContext(new StreamingContext(testConf, batchDuration)) { ssc => + val clock = ssc.scheduler.clock.asInstanceOf[ManualClock] + val numBatches = 10 + val longBatchNumber = 3 // 3rd batch will take a long time + val longBatchTime = longBatchNumber * batchDuration.milliseconds + + val testTimeout = timeout(10 seconds) + val inputStream = ssc.receiverStream(new TestReceiver) + + inputStream.foreachRDD((rdd: RDD[Int], time: Time) => { + if (time.milliseconds == longBatchTime) { + while (waitLatch.getCount() > 0) { + waitLatch.await() + println("Await over") + } + } + }) + + val batchCounter = new BatchCounter(ssc) + ssc.checkpoint(checkpointDir.getAbsolutePath) + ssc.start() + + // Make sure the only 1 batch of information is to be remembered + assert(inputStream.rememberDuration === batchDuration) + val receiverTracker = ssc.scheduler.receiverTracker + + // Get the blocks belonging to a batch + def getBlocksOfBatch(batchTime: Long): Seq[ReceivedBlockInfo] = { + receiverTracker.getBlocksOfBatchAndStream(Time(batchTime), inputStream.id) + } + + // Wait for new blocks to be received + def waitForNewReceivedBlocks() { + eventually(testTimeout) { + assert(receiverTracker.hasUnallocatedBlocks) + } + } + + // Wait for received blocks to be allocated to a batch + def waitForBlocksToBeAllocatedToBatch(batchTime: Long) { + eventually(testTimeout) { + assert(getBlocksOfBatch(batchTime).nonEmpty) + } + } + + // Generate a large number of batches with blocks in them + for (batchNum <- 1 to numBatches) { + waitForNewReceivedBlocks() + clock.advance(batchDuration.milliseconds) + waitForBlocksToBeAllocatedToBatch(clock.getTimeMillis()) + } + + // Wait for 3rd batch to start + eventually(testTimeout) { + ssc.scheduler.getPendingTimes().contains(Time(numBatches * batchDuration.milliseconds)) + } + + // Verify that the 3rd batch's block data is still present while the 3rd batch is incomplete + assert(getBlocksOfBatch(longBatchTime).nonEmpty, "blocks of incomplete batch already deleted") + assert(batchCounter.getNumCompletedBatches < longBatchNumber) + waitLatch.countDown() + } + } +} + +object JobGeneratorSuite { + val waitLatch = new CountDownLatch(1) +} diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala new file mode 100644 index 0000000000000..94b1985116feb --- /dev/null +++ b/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala @@ -0,0 +1,119 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming.ui + +import org.scalatest.Matchers + +import org.apache.spark.streaming.dstream.DStream +import org.apache.spark.streaming.scheduler._ +import org.apache.spark.streaming.{Duration, Time, Milliseconds, TestSuiteBase} + +class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers { + + val input = (1 to 4).map(Seq(_)).toSeq + val operation = (d: DStream[Int]) => d.map(x => x) + + override def batchDuration: Duration = Milliseconds(100) + + test("onBatchSubmitted, onBatchStarted, onBatchCompleted, " + + "onReceiverStarted, onReceiverError, onReceiverStopped") { + val ssc = setupStreams(input, operation) + val listener = new StreamingJobProgressListener(ssc) + + val receivedBlockInfo = Map( + 0 -> Array(ReceivedBlockInfo(0, 100, null), ReceivedBlockInfo(0, 200, null)), + 1 -> Array(ReceivedBlockInfo(1, 300, null)) + ) + + // onBatchSubmitted + val batchInfoSubmitted = BatchInfo(Time(1000), receivedBlockInfo, 1000, None, None) + listener.onBatchSubmitted(StreamingListenerBatchSubmitted(batchInfoSubmitted)) + listener.waitingBatches should be (List(batchInfoSubmitted)) + listener.runningBatches should be (Nil) + listener.retainedCompletedBatches should be (Nil) + listener.lastCompletedBatch should be (None) + listener.numUnprocessedBatches should be (1) + listener.numTotalCompletedBatches should be (0) + listener.numTotalProcessedRecords should be (0) + listener.numTotalReceivedRecords should be (0) + + // onBatchStarted + val batchInfoStarted = BatchInfo(Time(1000), receivedBlockInfo, 1000, Some(2000), None) + listener.onBatchStarted(StreamingListenerBatchStarted(batchInfoStarted)) + listener.waitingBatches should be (Nil) + listener.runningBatches should be (List(batchInfoStarted)) + listener.retainedCompletedBatches should be (Nil) + listener.lastCompletedBatch should be (None) + listener.numUnprocessedBatches should be (1) + listener.numTotalCompletedBatches should be (0) + listener.numTotalProcessedRecords should be (0) + listener.numTotalReceivedRecords should be (600) + + // onBatchCompleted + val batchInfoCompleted = BatchInfo(Time(1000), receivedBlockInfo, 1000, Some(2000), None) + listener.onBatchCompleted(StreamingListenerBatchCompleted(batchInfoCompleted)) + listener.waitingBatches should be (Nil) + listener.runningBatches should be (Nil) + listener.retainedCompletedBatches should be (List(batchInfoCompleted)) + listener.lastCompletedBatch should be (Some(batchInfoCompleted)) + listener.numUnprocessedBatches should be (0) + listener.numTotalCompletedBatches should be (1) + listener.numTotalProcessedRecords should be (600) + listener.numTotalReceivedRecords should be (600) + + // onReceiverStarted + val receiverInfoStarted = ReceiverInfo(0, "test", null, true, "localhost") + listener.onReceiverStarted(StreamingListenerReceiverStarted(receiverInfoStarted)) + listener.receiverInfo(0) should be (Some(receiverInfoStarted)) + listener.receiverInfo(1) should be (None) + + // onReceiverError + val receiverInfoError = ReceiverInfo(1, "test", null, true, "localhost") + listener.onReceiverError(StreamingListenerReceiverError(receiverInfoError)) + listener.receiverInfo(0) should be (Some(receiverInfoStarted)) + listener.receiverInfo(1) should be (Some(receiverInfoError)) + listener.receiverInfo(2) should be (None) + + // onReceiverStopped + val receiverInfoStopped = ReceiverInfo(2, "test", null, true, "localhost") + listener.onReceiverStopped(StreamingListenerReceiverStopped(receiverInfoStopped)) + listener.receiverInfo(0) should be (Some(receiverInfoStarted)) + listener.receiverInfo(1) should be (Some(receiverInfoError)) + listener.receiverInfo(2) should be (Some(receiverInfoStopped)) + listener.receiverInfo(3) should be (None) + } + + test("Remove the old completed batches when exceeding the limit") { + val ssc = setupStreams(input, operation) + val limit = ssc.conf.getInt("spark.streaming.ui.retainedBatches", 100) + val listener = new StreamingJobProgressListener(ssc) + + val receivedBlockInfo = Map( + 0 -> Array(ReceivedBlockInfo(0, 100, null), ReceivedBlockInfo(0, 200, null)), + 1 -> Array(ReceivedBlockInfo(1, 300, null)) + ) + val batchInfoCompleted = BatchInfo(Time(1000), receivedBlockInfo, 1000, Some(2000), None) + + for(_ <- 0 until (limit + 10)) { + listener.onBatchCompleted(StreamingListenerBatchCompleted(batchInfoCompleted)) + } + + listener.retainedCompletedBatches.size should be (limit) + listener.numTotalCompletedBatches should be(limit + 10) + } +} diff --git a/streaming/src/test/scala/org/apache/spark/streaming/util/WriteAheadLogSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/util/WriteAheadLogSuite.scala index 7ce9499dc614d..a3919c43b95b4 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/util/WriteAheadLogSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/util/WriteAheadLogSuite.scala @@ -26,7 +26,7 @@ import scala.language.{implicitConversions, postfixOps} import WriteAheadLogSuite._ import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path -import org.apache.spark.util.Utils +import org.apache.spark.util.{ManualClock, Utils} import org.scalatest.{BeforeAndAfter, FunSuite} import org.scalatest.concurrent.Eventually._ @@ -197,7 +197,7 @@ class WriteAheadLogSuite extends FunSuite with BeforeAndAfter { val logFiles = getLogFilesInDirectory(testDir) assert(logFiles.size > 1) - manager.cleanupOldLogs(manualClock.currentTime() / 2, waitForCompletion) + manager.cleanupOldLogs(manualClock.getTimeMillis() / 2, waitForCompletion) if (waitForCompletion) { assert(getLogFilesInDirectory(testDir).size < logFiles.size) @@ -219,7 +219,7 @@ class WriteAheadLogSuite extends FunSuite with BeforeAndAfter { // Recover old files and generate a second set of log files val dataToWrite2 = generateRandomData() - manualClock.addToTime(100000) + manualClock.advance(100000) writeDataUsingManager(testDir, dataToWrite2, manualClock) val logFiles2 = getLogFilesInDirectory(testDir) assert(logFiles2.size > logFiles1.size) @@ -279,19 +279,19 @@ object WriteAheadLogSuite { manualClock: ManualClock = new ManualClock, stopManager: Boolean = true ): WriteAheadLogManager = { - if (manualClock.currentTime < 100000) manualClock.setTime(10000) + if (manualClock.getTimeMillis() < 100000) manualClock.setTime(10000) val manager = new WriteAheadLogManager(logDirectory, hadoopConf, rollingIntervalSecs = 1, callerName = "WriteAheadLogSuite", clock = manualClock) // Ensure that 500 does not get sorted after 2000, so put a high base value. data.foreach { item => - manualClock.addToTime(500) + manualClock.advance(500) manager.writeToLog(item) } if (stopManager) manager.stop() manager } - /** Read data from a segments of a log file directly and return the list of byte buffers.*/ + /** Read data from a segments of a log file directly and return the list of byte buffers. */ def readDataManually(segments: Seq[WriteAheadLogFileSegment]): Seq[String] = { segments.map { segment => val reader = HdfsUtils.getInputStream(segment.path, hadoopConf) diff --git a/streaming/src/test/scala/org/apache/spark/streamingtest/ImplicitSuite.scala b/streaming/src/test/scala/org/apache/spark/streamingtest/ImplicitSuite.scala index d0bf328f2b74d..d66750463033a 100644 --- a/streaming/src/test/scala/org/apache/spark/streamingtest/ImplicitSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streamingtest/ImplicitSuite.scala @@ -25,7 +25,8 @@ package org.apache.spark.streamingtest */ class ImplicitSuite { - // We only want to test if `implict` works well with the compiler, so we don't need a real DStream. + // We only want to test if `implicit` works well with the compiler, + // so we don't need a real DStream. def mockDStream[T]: org.apache.spark.streaming.dstream.DStream[T] = null def testToPairDStreamFunctions(): Unit = { diff --git a/tools/pom.xml b/tools/pom.xml index e7419ed2c607a..1c6f3e83a1819 100644 --- a/tools/pom.xml +++ b/tools/pom.xml @@ -19,8 +19,8 @@ 4.0.0 org.apache.spark - spark-parent - 1.3.0-SNAPSHOT + spark-parent_2.10 + 1.4.0-SNAPSHOT ../pom.xml diff --git a/tools/src/main/scala/org/apache/spark/tools/JavaAPICompletenessChecker.scala b/tools/src/main/scala/org/apache/spark/tools/JavaAPICompletenessChecker.scala index 8d0f09933c8d3..583823c90c5c6 100644 --- a/tools/src/main/scala/org/apache/spark/tools/JavaAPICompletenessChecker.scala +++ b/tools/src/main/scala/org/apache/spark/tools/JavaAPICompletenessChecker.scala @@ -17,7 +17,7 @@ package org.apache.spark.tools -import java.lang.reflect.Method +import java.lang.reflect.{Type, Method} import scala.collection.mutable.ArrayBuffer import scala.language.existentials @@ -302,7 +302,7 @@ object JavaAPICompletenessChecker { private def isExcludedByInterface(method: Method): Boolean = { val excludedInterfaces = Set("org.apache.spark.Logging", "org.apache.hadoop.mapreduce.HadoopMapReduceUtil") - def toComparisionKey(method: Method) = + def toComparisionKey(method: Method): (Class[_], String, Type) = (method.getReturnType, method.getName, method.getGenericReturnType) val interfaces = method.getDeclaringClass.getInterfaces.filter { i => excludedInterfaces.contains(i.getName) diff --git a/tools/src/main/scala/org/apache/spark/tools/StoragePerfTester.scala b/tools/src/main/scala/org/apache/spark/tools/StoragePerfTester.scala index 15ee95070a3d3..f2d135397ce2f 100644 --- a/tools/src/main/scala/org/apache/spark/tools/StoragePerfTester.scala +++ b/tools/src/main/scala/org/apache/spark/tools/StoragePerfTester.scala @@ -32,7 +32,7 @@ import org.apache.spark.util.Utils * Writes simulated shuffle output from several threads and records the observed throughput. */ object StoragePerfTester { - def main(args: Array[String]) = { + def main(args: Array[String]): Unit = { /** Total amount of data to generate. Distributed evenly amongst maps and reduce splits. */ val dataSizeMb = Utils.memoryStringToMb(sys.env.getOrElse("OUTPUT_DATA", "1g")) @@ -58,8 +58,8 @@ object StoragePerfTester { val sc = new SparkContext("local[4]", "Write Tester", conf) val hashShuffleManager = sc.env.shuffleManager.asInstanceOf[HashShuffleManager] - def writeOutputBytes(mapId: Int, total: AtomicLong) = { - val shuffle = hashShuffleManager.shuffleBlockManager.forMapTask(1, mapId, numOutputSplits, + def writeOutputBytes(mapId: Int, total: AtomicLong): Unit = { + val shuffle = hashShuffleManager.shuffleBlockResolver.forMapTask(1, mapId, numOutputSplits, new KryoSerializer(sc.conf), new ShuffleWriteMetrics()) val writers = shuffle.writers for (i <- 1 to recordsPerMap) { @@ -78,7 +78,7 @@ object StoragePerfTester { val totalBytes = new AtomicLong() for (task <- 1 to numMaps) { executor.submit(new Runnable() { - override def run() = { + override def run(): Unit = { try { writeOutputBytes(task, totalBytes) latch.countDown() diff --git a/yarn/pom.xml b/yarn/pom.xml index 65344aa8738e0..7c8c3613e7a05 100644 --- a/yarn/pom.xml +++ b/yarn/pom.xml @@ -19,8 +19,8 @@ 4.0.0 org.apache.spark - spark-parent - 1.3.0-SNAPSHOT + spark-parent_2.10 + 1.4.0-SNAPSHOT ../pom.xml diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala index 4cc320c5d59b5..f7a84207e9da6 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala @@ -19,26 +19,25 @@ package org.apache.spark.deploy.yarn import scala.util.control.NonFatal -import java.io.IOException +import java.io.{File, IOException} import java.lang.reflect.InvocationTargetException -import java.net.Socket +import java.net.{Socket, URL} import java.util.concurrent.atomic.AtomicReference -import akka.actor._ -import akka.remote._ import org.apache.hadoop.fs.{FileSystem, Path} import org.apache.hadoop.util.ShutdownHookManager import org.apache.hadoop.yarn.api._ import org.apache.hadoop.yarn.api.records._ import org.apache.hadoop.yarn.conf.YarnConfiguration +import org.apache.spark.rpc._ import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkContext, SparkEnv} import org.apache.spark.SparkException import org.apache.spark.deploy.{PythonRunner, SparkHadoopUtil} import org.apache.spark.deploy.history.HistoryServer import org.apache.spark.scheduler.cluster.YarnSchedulerBackend import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._ -import org.apache.spark.util.{AkkaUtils, SignalLogger, Utils} +import org.apache.spark.util._ /** * Common application master functionality for Spark on Yarn. @@ -67,12 +66,12 @@ private[spark] class ApplicationMaster( @volatile private var finalMsg: String = "" @volatile private var userClassThread: Thread = _ - private var reporterThread: Thread = _ - private var allocator: YarnAllocator = _ + @volatile private var reporterThread: Thread = _ + @volatile private var allocator: YarnAllocator = _ // Fields used in client mode. - private var actorSystem: ActorSystem = null - private var actor: ActorRef = _ + private var rpcEnv: RpcEnv = null + private var amEndpoint: RpcEndpointRef = _ // Fields used in cluster mode. private val sparkContextRef = new AtomicReference[SparkContext](null) @@ -150,7 +149,7 @@ private[spark] class ApplicationMaster( logError("Uncaught exception: ", e) finish(FinalApplicationStatus.FAILED, ApplicationMaster.EXIT_UNCAUGHT_EXCEPTION, - "Uncaught exception: " + e.getMessage()) + "Uncaught exception: " + e) } exitCode } @@ -161,7 +160,7 @@ private[spark] class ApplicationMaster( * status to SUCCEEDED in cluster mode to handle if the user calls System.exit * from the application code. */ - final def getDefaultFinalStatus() = { + final def getDefaultFinalStatus(): FinalApplicationStatus = { if (isClusterMode) { FinalApplicationStatus.SUCCEEDED } else { @@ -174,31 +173,35 @@ private[spark] class ApplicationMaster( * This means the ResourceManager will not retry the application attempt on your behalf if * a failure occurred. */ - final def unregister(status: FinalApplicationStatus, diagnostics: String = null) = synchronized { - if (!unregistered) { - logInfo(s"Unregistering ApplicationMaster with $status" + - Option(diagnostics).map(msg => s" (diag message: $msg)").getOrElse("")) - unregistered = true - client.unregister(status, Option(diagnostics).getOrElse("")) + final def unregister(status: FinalApplicationStatus, diagnostics: String = null): Unit = { + synchronized { + if (!unregistered) { + logInfo(s"Unregistering ApplicationMaster with $status" + + Option(diagnostics).map(msg => s" (diag message: $msg)").getOrElse("")) + unregistered = true + client.unregister(status, Option(diagnostics).getOrElse("")) + } } } - final def finish(status: FinalApplicationStatus, code: Int, msg: String = null) = synchronized { - if (!finished) { - val inShutdown = Utils.inShutdown() - logInfo(s"Final app status: ${status}, exitCode: ${code}" + - Option(msg).map(msg => s", (reason: $msg)").getOrElse("")) - exitCode = code - finalStatus = status - finalMsg = msg - finished = true - if (!inShutdown && Thread.currentThread() != reporterThread && reporterThread != null) { - logDebug("shutting down reporter thread") - reporterThread.interrupt() - } - if (!inShutdown && Thread.currentThread() != userClassThread && userClassThread != null) { - logDebug("shutting down user thread") - userClassThread.interrupt() + final def finish(status: FinalApplicationStatus, code: Int, msg: String = null): Unit = { + synchronized { + if (!finished) { + val inShutdown = Utils.inShutdown() + logInfo(s"Final app status: $status, exitCode: $code" + + Option(msg).map(msg => s", (reason: $msg)").getOrElse("")) + exitCode = code + finalStatus = status + finalMsg = msg + finished = true + if (!inShutdown && Thread.currentThread() != reporterThread && reporterThread != null) { + logDebug("shutting down reporter thread") + reporterThread.interrupt() + } + if (!inShutdown && Thread.currentThread() != userClassThread && userClassThread != null) { + logDebug("shutting down user thread") + userClassThread.interrupt() + } } } } @@ -220,6 +223,7 @@ private[spark] class ApplicationMaster( val appId = client.getAttemptId().getApplicationId().toString() val historyAddress = sparkConf.getOption("spark.yarn.historyServer.address") + .map { text => SparkHadoopUtil.get.substituteHadoopVariables(text, yarnConf) } .map { address => s"${address}${HistoryServer.UI_PATH_PREFIX}/${appId}" } .getOrElse("") @@ -235,23 +239,21 @@ private[spark] class ApplicationMaster( } /** - * Create an actor that communicates with the driver. + * Create an [[RpcEndpoint]] that communicates with the driver. * * In cluster mode, the AM and the driver belong to same process - * so the AM actor need not monitor lifecycle of the driver. + * so the AMEndpoint need not monitor lifecycle of the driver. */ - private def runAMActor( + private def runAMEndpoint( host: String, port: String, isClusterMode: Boolean): Unit = { - - val driverUrl = AkkaUtils.address( - AkkaUtils.protocol(actorSystem), + val driverEndpont = rpcEnv.setupEndpointRef( SparkEnv.driverActorSystemName, - host, - port, - YarnSchedulerBackend.ACTOR_NAME) - actor = actorSystem.actorOf(Props(new AMActor(driverUrl, isClusterMode)), name = "YarnAM") + RpcAddress(host, port.toInt), + YarnSchedulerBackend.ENDPOINT_NAME) + amEndpoint = + rpcEnv.setupEndpoint("YarnAM", new AMEndpoint(rpcEnv, driverEndpont, isClusterMode)) } private def runDriver(securityMgr: SecurityManager): Unit = { @@ -268,8 +270,8 @@ private[spark] class ApplicationMaster( ApplicationMaster.EXIT_SC_NOT_INITED, "Timed out waiting for SparkContext.") } else { - actorSystem = sc.env.actorSystem - runAMActor( + rpcEnv = sc.env.rpcEnv + runAMEndpoint( sc.getConf.get("spark.driver.host"), sc.getConf.get("spark.driver.port"), isClusterMode = true) @@ -279,8 +281,7 @@ private[spark] class ApplicationMaster( } private def runExecutorLauncher(securityMgr: SecurityManager): Unit = { - actorSystem = AkkaUtils.createActorSystem("sparkYarnAM", Utils.localHostName, 0, - conf = sparkConf, securityManager = securityMgr)._1 + rpcEnv = RpcEnv.create("sparkYarnAM", Utils.localHostName, 0, sparkConf, securityMgr) waitForSparkDriver() addAmIpFilter() registerAM(sparkConf.get("spark.driver.appUIAddress", ""), securityMgr) @@ -295,7 +296,7 @@ private[spark] class ApplicationMaster( // we want to be reasonably responsive without causing too many requests to RM. val schedulerInterval = - sparkConf.getLong("spark.yarn.scheduler.heartbeat.interval-ms", 5000) + sparkConf.getTimeAsMs("spark.yarn.scheduler.heartbeat.interval-ms", "5s") // must be <= expiryInterval / 2. val interval = math.max(0, math.min(expiryInterval / 2, schedulerInterval)) @@ -372,13 +373,7 @@ private[spark] class ApplicationMaster( private def waitForSparkContextInitialized(): SparkContext = { logInfo("Waiting for spark context initialization") sparkContextRef.synchronized { - val waitTries = sparkConf.getOption("spark.yarn.applicationMaster.waitTries") - .map(_.toLong * 10000L) - if (waitTries.isDefined) { - logWarning( - "spark.yarn.applicationMaster.waitTries is deprecated, use spark.yarn.am.waitTime") - } - val totalWaitTime = sparkConf.getLong("spark.yarn.am.waitTime", waitTries.getOrElse(100000L)) + val totalWaitTime = sparkConf.getTimeAsMs("spark.yarn.am.waitTime", "100s") val deadline = System.currentTimeMillis() + totalWaitTime while (sparkContextRef.get() == null && System.currentTimeMillis < deadline && !finished) { @@ -403,8 +398,8 @@ private[spark] class ApplicationMaster( // Spark driver should already be up since it launched us, but we don't want to // wait forever, so wait 100 seconds max to match the cluster mode setting. - val totalWaitTime = sparkConf.getLong("spark.yarn.am.waitTime", 100000L) - val deadline = System.currentTimeMillis + totalWaitTime + val totalWaitTimeMs = sparkConf.getTimeAsMs("spark.yarn.am.waitTime", "100s") + val deadline = System.currentTimeMillis + totalWaitTimeMs while (!driverUp && !finished && System.currentTimeMillis < deadline) { try { @@ -427,7 +422,7 @@ private[spark] class ApplicationMaster( sparkConf.set("spark.driver.host", driverHost) sparkConf.set("spark.driver.port", driverPort.toString) - runAMActor(driverHost, driverPort.toString, isClusterMode = false) + runAMEndpoint(driverHost, driverPort.toString, isClusterMode = false) } /** Add the Yarn IP filter that is required for properly securing the UI. */ @@ -439,7 +434,7 @@ private[spark] class ApplicationMaster( System.setProperty("spark.ui.filters", amFilter) params.foreach { case (k, v) => System.setProperty(s"spark.$amFilter.param.$k", v) } } else { - actor ! AddWebUIFilter(amFilter, params.toMap, proxyBase) + amEndpoint.send(AddWebUIFilter(amFilter, params.toMap, proxyBase)) } } @@ -453,12 +448,27 @@ private[spark] class ApplicationMaster( private def startUserApplication(): Thread = { logInfo("Starting the user application in a separate Thread") System.setProperty("spark.executor.instances", args.numExecutors.toString) + + val classpath = Client.getUserClasspath(sparkConf) + val urls = classpath.map { entry => + new URL("file:" + new File(entry.getPath()).getAbsolutePath()) + } + val userClassLoader = + if (Client.isUserClassPathFirst(sparkConf, isDriver = true)) { + new ChildFirstURLClassLoader(urls, Utils.getContextOrSparkClassLoader) + } else { + new MutableURLClassLoader(urls, Utils.getContextOrSparkClassLoader) + } + if (args.primaryPyFile != null && args.primaryPyFile.endsWith(".py")) { System.setProperty("spark.submit.pyFiles", PythonRunner.formatPaths(args.pyFiles).mkString(",")) } - val mainMethod = Class.forName(args.userClass, false, - Thread.currentThread.getContextClassLoader).getMethod("main", classOf[Array[String]]) + if (args.primaryRFile != null && args.primaryRFile.endsWith(".R")) { + // TODO(davies): add R dependencies here + } + val mainMethod = userClassLoader.loadClass(args.userClass) + .getMethod("main", classOf[Array[String]]) val userThread = new Thread { override def run() { @@ -473,61 +483,45 @@ private[spark] class ApplicationMaster( e.getCause match { case _: InterruptedException => // Reporter thread can interrupt to stop user class - case e: Exception => + case cause: Throwable => + logError("User class threw exception: " + cause, cause) finish(FinalApplicationStatus.FAILED, ApplicationMaster.EXIT_EXCEPTION_USER_CLASS, - "User class threw exception: " + e.getMessage) - // re-throw to get it logged - throw e + "User class threw exception: " + cause) } } } } + userThread.setContextClassLoader(userClassLoader) userThread.setName("Driver") userThread.start() userThread } /** - * An actor that communicates with the driver's scheduler backend. + * An [[RpcEndpoint]] that communicates with the driver's scheduler backend. */ - private class AMActor(driverUrl: String, isClusterMode: Boolean) extends Actor { - var driver: ActorSelection = _ - - override def preStart() = { - logInfo("Listen to driver: " + driverUrl) - driver = context.actorSelection(driverUrl) - // Send a hello message to establish the connection, after which - // we can monitor Lifecycle Events. - driver ! "Hello" - driver ! RegisterClusterManager - // In cluster mode, the AM can directly monitor the driver status instead - // of trying to deduce it from the lifecycle of the driver's actor - if (!isClusterMode) { - context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent]) - } - } + private class AMEndpoint( + override val rpcEnv: RpcEnv, driver: RpcEndpointRef, isClusterMode: Boolean) + extends RpcEndpoint with Logging { - override def receive = { - case x: DisassociatedEvent => - logInfo(s"Driver terminated or disconnected! Shutting down. $x") - // In cluster mode, do not rely on the disassociated event to exit - // This avoids potentially reporting incorrect exit codes if the driver fails - if (!isClusterMode) { - finish(FinalApplicationStatus.SUCCEEDED, ApplicationMaster.EXIT_SUCCESS) - } + override def onStart(): Unit = { + driver.send(RegisterClusterManager(self)) + } + override def receive: PartialFunction[Any, Unit] = { case x: AddWebUIFilter => logInfo(s"Add WebUI Filter. $x") - driver ! x + driver.send(x) + } + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { case RequestExecutors(requestedTotal) => - logInfo(s"Driver requested a total number of $requestedTotal executor(s).") Option(allocator) match { case Some(a) => a.requestTotalExecutors(requestedTotal) case None => logWarning("Container allocator is not ready to request executors yet.") } - sender ! true + context.reply(true) case KillExecutors(executorIds) => logInfo(s"Driver requested to kill executor(s) ${executorIds.mkString(", ")}.") @@ -535,7 +529,16 @@ private[spark] class ApplicationMaster( case Some(a) => executorIds.foreach(a.killExecutor) case None => logWarning("Container allocator is not ready to kill executors yet.") } - sender ! true + context.reply(true) + } + + override def onDisconnected(remoteAddress: RpcAddress): Unit = { + logInfo(s"Driver terminated or disconnected! Shutting down. $remoteAddress") + // In cluster mode, do not rely on the disassociated event to exit + // This avoids potentially reporting incorrect exit codes if the driver fails + if (!isClusterMode) { + finish(FinalApplicationStatus.SUCCEEDED, ApplicationMaster.EXIT_SUCCESS) + } } } @@ -556,7 +559,7 @@ object ApplicationMaster extends Logging { private var master: ApplicationMaster = _ - def main(args: Array[String]) = { + def main(args: Array[String]): Unit = { SignalLogger.register(log) val amArgs = new ApplicationMasterArguments(args) SparkHadoopUtil.get.runAsSparkUser { () => @@ -565,11 +568,11 @@ object ApplicationMaster extends Logging { } } - private[spark] def sparkContextInitialized(sc: SparkContext) = { + private[spark] def sparkContextInitialized(sc: SparkContext): Unit = { master.sparkContextInitialized(sc) } - private[spark] def sparkContextStopped(sc: SparkContext) = { + private[spark] def sparkContextStopped(sc: SparkContext): Boolean = { master.sparkContextStopped(sc) } @@ -581,7 +584,7 @@ object ApplicationMaster extends Logging { */ object ExecutorLauncher { - def main(args: Array[String]) = { + def main(args: Array[String]): Unit = { ApplicationMaster.main(args) } diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala index e1a992af3aae7..ae6dc1094d724 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala @@ -25,6 +25,7 @@ class ApplicationMasterArguments(val args: Array[String]) { var userJar: String = null var userClass: String = null var primaryPyFile: String = null + var primaryRFile: String = null var pyFiles: String = null var userArgs: Seq[String] = Seq[String]() var executorMemory = 1024 @@ -54,6 +55,10 @@ class ApplicationMasterArguments(val args: Array[String]) { primaryPyFile = value args = tail + case ("--primary-r-file") :: value :: tail => + primaryRFile = value + args = tail + case ("--py-files") :: value :: tail => pyFiles = value args = tail @@ -79,6 +84,11 @@ class ApplicationMasterArguments(val args: Array[String]) { } } + if (primaryPyFile != null && primaryRFile != null) { + System.err.println("Cannot have primary-py-file and primary-r-file at the same time") + System.exit(-1) + } + userArgs = userArgsBuffer.readOnly } @@ -92,6 +102,7 @@ class ApplicationMasterArguments(val args: Array[String]) { | --jar JAR_PATH Path to your application's JAR file | --class CLASS_NAME Name of your application's main class | --primary-py-file A main Python file + | --primary-r-file A main R file | --py-files PY_FILES Comma-separated list of .zip, .egg, or .py files to | place on the PYTHONPATH for Python apps. | --args ARGS Arguments to be passed to your application's main class. diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala index e7005094b5f3c..019afbd1a1743 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala @@ -17,22 +17,29 @@ package org.apache.spark.deploy.yarn +import java.io.{File, FileOutputStream} import java.net.{InetAddress, UnknownHostException, URI, URISyntaxException} import java.nio.ByteBuffer +import java.util.zip.{ZipEntry, ZipOutputStream} import scala.collection.JavaConversions._ -import scala.collection.mutable.{ArrayBuffer, HashMap, ListBuffer, Map} +import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet, ListBuffer, Map} +import scala.reflect.runtime.universe import scala.util.{Try, Success, Failure} import com.google.common.base.Objects +import com.google.common.io.Files import org.apache.hadoop.io.DataOutputBuffer import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.hdfs.security.token.delegation.DelegationTokenIdentifier import org.apache.hadoop.fs._ import org.apache.hadoop.fs.permission.FsPermission +import org.apache.hadoop.io.Text import org.apache.hadoop.mapred.Master import org.apache.hadoop.mapreduce.MRJobConfig import org.apache.hadoop.security.{Credentials, UserGroupInformation} +import org.apache.hadoop.security.token.Token import org.apache.hadoop.util.StringUtils import org.apache.hadoop.yarn.api._ import org.apache.hadoop.yarn.api.ApplicationConstants.Environment @@ -40,6 +47,7 @@ import org.apache.hadoop.yarn.api.protocolrecords._ import org.apache.hadoop.yarn.api.records._ import org.apache.hadoop.yarn.client.api.{YarnClient, YarnClientApplication} import org.apache.hadoop.yarn.conf.YarnConfiguration +import org.apache.hadoop.yarn.exceptions.ApplicationNotFoundException import org.apache.hadoop.yarn.util.Records import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkContext, SparkException} @@ -66,16 +74,12 @@ private[spark] class Client( private val executorMemoryOverhead = args.executorMemoryOverhead // MB private val distCacheMgr = new ClientDistributedCacheManager() private val isClusterMode = args.isClusterMode + private val fireAndForget = isClusterMode && + !sparkConf.getBoolean("spark.yarn.submit.waitAppCompletion", true) def stop(): Unit = yarnClient.stop() - /* ------------------------------------------------------------------------------------- * - | The following methods have much in common in the stable and alpha versions of Client, | - | but cannot be implemented in the parent trait due to subtle API differences across | - | hadoop versions. | - * ------------------------------------------------------------------------------------- */ - /** * Submit an application running our ApplicationMaster to the ResourceManager. * @@ -183,8 +187,7 @@ private[spark] class Client( private[yarn] def copyFileToRemote( destDir: Path, srcPath: Path, - replication: Short, - setPerms: Boolean = false): Path = { + replication: Short): Path = { val destFs = destDir.getFileSystem(hadoopConf) val srcFs = srcPath.getFileSystem(hadoopConf) var destPath = srcPath @@ -193,9 +196,7 @@ private[spark] class Client( logInfo(s"Uploading resource $srcPath -> $destPath") FileUtil.copy(srcFs, srcPath, destFs, destPath, false, hadoopConf) destFs.setReplication(destPath, replication) - if (setPerms) { - destFs.setPermission(destPath, new FsPermission(APP_FILE_PERMISSION)) - } + destFs.setPermission(destPath, new FsPermission(APP_FILE_PERMISSION)) } else { logInfo(s"Source and destination file systems are the same. Not copying $srcPath") } @@ -219,7 +220,12 @@ private[spark] class Client( val fs = FileSystem.get(hadoopConf) val dst = new Path(fs.getHomeDirectory(), appStagingDir) val nns = getNameNodesToAccess(sparkConf) + dst + // Used to keep track of URIs added to the distributed cache. If the same URI is added + // multiple times, YARN will fail to launch containers for the app with an internal + // error. + val distributedUris = new HashSet[String] obtainTokensForNamenodes(nns, hadoopConf, credentials) + obtainTokenForHiveMetastore(hadoopConf, credentials) val replication = sparkConf.getInt("spark.yarn.submit.file.replication", fs.getDefaultReplication(dst)).toShort @@ -236,29 +242,41 @@ private[spark] class Client( "for alternatives.") } + def addDistributedUri(uri: URI): Boolean = { + val uriStr = uri.toString() + if (distributedUris.contains(uriStr)) { + logWarning(s"Resource $uri added multiple times to distributed cache.") + false + } else { + distributedUris += uriStr + true + } + } + /** * Copy the given main resource to the distributed cache if the scheme is not "local". * Otherwise, set the corresponding key in our SparkConf to handle it downstream. - * Each resource is represented by a 4-tuple of: + * Each resource is represented by a 3-tuple of: * (1) destination resource name, * (2) local path to the resource, - * (3) Spark property key to set if the scheme is not local, and - * (4) whether to set permissions for this resource + * (3) Spark property key to set if the scheme is not local */ List( - (SPARK_JAR, sparkJar(sparkConf), CONF_SPARK_JAR, false), - (APP_JAR, args.userJar, CONF_SPARK_USER_JAR, true), - ("log4j.properties", oldLog4jConf.orNull, null, false) - ).foreach { case (destName, _localPath, confKey, setPermissions) => + (SPARK_JAR, sparkJar(sparkConf), CONF_SPARK_JAR), + (APP_JAR, args.userJar, CONF_SPARK_USER_JAR), + ("log4j.properties", oldLog4jConf.orNull, null) + ).foreach { case (destName, _localPath, confKey) => val localPath: String = if (_localPath != null) _localPath.trim() else "" if (!localPath.isEmpty()) { val localURI = new URI(localPath) if (localURI.getScheme != LOCAL_SCHEME) { - val src = getQualifiedLocalPath(localURI, hadoopConf) - val destPath = copyFileToRemote(dst, src, replication, setPermissions) - val destFs = FileSystem.get(destPath.toUri(), hadoopConf) - distCacheMgr.addResource(destFs, hadoopConf, destPath, - localResources, LocalResourceType.FILE, destName, statCache) + if (addDistributedUri(localURI)) { + val src = getQualifiedLocalPath(localURI, hadoopConf) + val destPath = copyFileToRemote(dst, src, replication) + val destFs = FileSystem.get(destPath.toUri(), hadoopConf) + distCacheMgr.addResource(destFs, hadoopConf, destPath, + localResources, LocalResourceType.FILE, destName, statCache) + } } else if (confKey != null) { // If the resource is intended for local use only, handle this downstream // by setting the appropriate property @@ -267,6 +285,13 @@ private[spark] class Client( } } + createConfArchive().foreach { file => + require(addDistributedUri(file.toURI())) + val destPath = copyFileToRemote(dst, new Path(file.toURI()), replication) + distCacheMgr.addResource(fs, hadoopConf, destPath, localResources, LocalResourceType.ARCHIVE, + LOCALIZED_HADOOP_CONF_DIR, statCache, appMasterOnly = true) + } + /** * Do the same for any additional resources passed in through ClientArguments. * Each resource category is represented by a 3-tuple of: @@ -284,13 +309,15 @@ private[spark] class Client( flist.split(',').foreach { file => val localURI = new URI(file.trim()) if (localURI.getScheme != LOCAL_SCHEME) { - val localPath = new Path(localURI) - val linkname = Option(localURI.getFragment()).getOrElse(localPath.getName()) - val destPath = copyFileToRemote(dst, localPath, replication) - distCacheMgr.addResource( - fs, hadoopConf, destPath, localResources, resType, linkname, statCache) - if (addToClasspath) { - cachedSecondaryJarLinks += linkname + if (addDistributedUri(localURI)) { + val localPath = new Path(localURI) + val linkname = Option(localURI.getFragment()).getOrElse(localPath.getName()) + val destPath = copyFileToRemote(dst, localPath, replication) + distCacheMgr.addResource( + fs, hadoopConf, destPath, localResources, resType, linkname, statCache) + if (addToClasspath) { + cachedSecondaryJarLinks += linkname + } } } else if (addToClasspath) { // Resource is intended for local use only and should be added to the class path @@ -306,6 +333,57 @@ private[spark] class Client( localResources } + /** + * Create an archive with the Hadoop config files for distribution. + * + * These are only used by the AM, since executors will use the configuration object broadcast by + * the driver. The files are zipped and added to the job as an archive, so that YARN will explode + * it when distributing to the AM. This directory is then added to the classpath of the AM + * process, just to make sure that everybody is using the same default config. + * + * This follows the order of precedence set by the startup scripts, in which HADOOP_CONF_DIR + * shows up in the classpath before YARN_CONF_DIR. + * + * Currently this makes a shallow copy of the conf directory. If there are cases where a + * Hadoop config directory contains subdirectories, this code will have to be fixed. + */ + private def createConfArchive(): Option[File] = { + val hadoopConfFiles = new HashMap[String, File]() + Seq("HADOOP_CONF_DIR", "YARN_CONF_DIR").foreach { envKey => + sys.env.get(envKey).foreach { path => + val dir = new File(path) + if (dir.isDirectory()) { + dir.listFiles().foreach { file => + if (!hadoopConfFiles.contains(file.getName())) { + hadoopConfFiles(file.getName()) = file + } + } + } + } + } + + if (!hadoopConfFiles.isEmpty) { + val hadoopConfArchive = File.createTempFile(LOCALIZED_HADOOP_CONF_DIR, ".zip", + new File(Utils.getLocalDir(sparkConf))) + + val hadoopConfStream = new ZipOutputStream(new FileOutputStream(hadoopConfArchive)) + try { + hadoopConfStream.setLevel(0) + hadoopConfFiles.foreach { case (name, file) => + hadoopConfStream.putNextEntry(new ZipEntry(name)) + Files.copy(file, hadoopConfStream) + hadoopConfStream.closeEntry() + } + } finally { + hadoopConfStream.close() + } + + Some(hadoopConfArchive) + } else { + None + } + } + /** * Set up the environment for launching our ApplicationMaster container. */ @@ -313,7 +391,7 @@ private[spark] class Client( logInfo("Setting up the launch environment for our AM container") val env = new HashMap[String, String]() val extraCp = sparkConf.getOption("spark.driver.extraClassPath") - populateClasspath(args, yarnConf, sparkConf, env, extraCp) + populateClasspath(args, yarnConf, sparkConf, env, true, extraCp) env("SPARK_YARN_MODE") = "true" env("SPARK_YARN_STAGING_DIR") = stagingDir env("SPARK_USER") = UserGroupInformation.getCurrentUser().getShortUserName() @@ -435,10 +513,11 @@ private[spark] class Client( // Include driver-specific java options if we are launching a driver if (isClusterMode) { - sparkConf.getOption("spark.driver.extraJavaOptions") + val driverOpts = sparkConf.getOption("spark.driver.extraJavaOptions") .orElse(sys.env.get("SPARK_JAVA_OPTS")) - .map(Utils.splitCommandString).getOrElse(Seq.empty) - .foreach(opts => javaOpts += opts) + driverOpts.foreach { opts => + javaOpts ++= Utils.splitCommandString(opts).map(YarnSparkHadoopUtil.escapeForShell) + } val libraryPaths = Seq(sys.props.get("spark.driver.extraLibraryPath"), sys.props.get("spark.driver.libraryPath")).flatten if (libraryPaths.nonEmpty) { @@ -460,7 +539,7 @@ private[spark] class Client( val msg = s"$amOptsKey is not allowed to alter memory settings (was '$opts')." throw new SparkException(msg) } - javaOpts ++= Utils.splitCommandString(opts) + javaOpts ++= Utils.splitCommandString(opts).map(YarnSparkHadoopUtil.escapeForShell) } } @@ -491,6 +570,12 @@ private[spark] class Client( } else { Nil } + val primaryRFile = + if (args.primaryRFile != null) { + Seq("--primary-r-file", args.primaryRFile) + } else { + Nil + } val amClass = if (isClusterMode) { Class.forName("org.apache.spark.deploy.yarn.ApplicationMaster").getName @@ -500,12 +585,15 @@ private[spark] class Client( if (args.primaryPyFile != null && args.primaryPyFile.endsWith(".py")) { args.userArgs = ArrayBuffer(args.primaryPyFile, args.pyFiles) ++ args.userArgs } + if (args.primaryRFile != null && args.primaryRFile.endsWith(".R")) { + args.userArgs = ArrayBuffer(args.primaryRFile) ++ args.userArgs + } val userArgs = args.userArgs.flatMap { arg => Seq("--arg", YarnSparkHadoopUtil.escapeForShell(arg)) } val amArgs = - Seq(amClass) ++ userClass ++ userJar ++ primaryPyFile ++ pyFiles ++ userArgs ++ - Seq( + Seq(amClass) ++ userClass ++ userJar ++ primaryPyFile ++ pyFiles ++ primaryRFile ++ + userArgs ++ Seq( "--executor-memory", args.executorMemory.toString + "m", "--executor-cores", args.executorCores.toString, "--num-executors ", args.numExecutors.toString) @@ -562,36 +650,25 @@ private[spark] class Client( var lastState: YarnApplicationState = null while (true) { Thread.sleep(interval) - val report = getApplicationReport(appId) + val report: ApplicationReport = + try { + getApplicationReport(appId) + } catch { + case e: ApplicationNotFoundException => + logError(s"Application $appId not found.") + return (YarnApplicationState.KILLED, FinalApplicationStatus.KILLED) + } val state = report.getYarnApplicationState if (logApplicationReport) { logInfo(s"Application report for $appId (state: $state)") - val details = Seq[(String, String)]( - ("client token", getClientToken(report)), - ("diagnostics", report.getDiagnostics), - ("ApplicationMaster host", report.getHost), - ("ApplicationMaster RPC port", report.getRpcPort.toString), - ("queue", report.getQueue), - ("start time", report.getStartTime.toString), - ("final status", report.getFinalApplicationStatus.toString), - ("tracking URL", report.getTrackingUrl), - ("user", report.getUser) - ) - - // Use more loggable format if value is null or empty - val formattedDetails = details - .map { case (k, v) => - val newValue = Option(v).filter(_.nonEmpty).getOrElse("N/A") - s"\n\t $k: $newValue" } - .mkString("") // If DEBUG is enabled, log report details every iteration // Otherwise, log them every time the application changes state if (log.isDebugEnabled) { - logDebug(formattedDetails) + logDebug(formatReportDetails(report)) } else if (lastState != state) { - logInfo(formattedDetails) + logInfo(formatReportDetails(report)) } } @@ -612,24 +689,57 @@ private[spark] class Client( throw new SparkException("While loop is depleted! This should never happen...") } + private def formatReportDetails(report: ApplicationReport): String = { + val details = Seq[(String, String)]( + ("client token", getClientToken(report)), + ("diagnostics", report.getDiagnostics), + ("ApplicationMaster host", report.getHost), + ("ApplicationMaster RPC port", report.getRpcPort.toString), + ("queue", report.getQueue), + ("start time", report.getStartTime.toString), + ("final status", report.getFinalApplicationStatus.toString), + ("tracking URL", report.getTrackingUrl), + ("user", report.getUser) + ) + + // Use more loggable format if value is null or empty + details.map { case (k, v) => + val newValue = Option(v).filter(_.nonEmpty).getOrElse("N/A") + s"\n\t $k: $newValue" + }.mkString("") + } + /** - * Submit an application to the ResourceManager and monitor its state. - * This continues until the application has exited for any reason. + * Submit an application to the ResourceManager. + * If set spark.yarn.submit.waitAppCompletion to true, it will stay alive + * reporting the application's status until the application has exited for any reason. + * Otherwise, the client process will exit after submission. * If the application finishes with a failed, killed, or undefined status, * throw an appropriate SparkException. */ def run(): Unit = { - val (yarnApplicationState, finalApplicationStatus) = monitorApplication(submitApplication()) - if (yarnApplicationState == YarnApplicationState.FAILED || - finalApplicationStatus == FinalApplicationStatus.FAILED) { - throw new SparkException("Application finished with failed status") - } - if (yarnApplicationState == YarnApplicationState.KILLED || - finalApplicationStatus == FinalApplicationStatus.KILLED) { - throw new SparkException("Application is killed") - } - if (finalApplicationStatus == FinalApplicationStatus.UNDEFINED) { - throw new SparkException("The final status of application is undefined") + val appId = submitApplication() + if (fireAndForget) { + val report = getApplicationReport(appId) + val state = report.getYarnApplicationState + logInfo(s"Application report for $appId (state: $state)") + logInfo(formatReportDetails(report)) + if (state == YarnApplicationState.FAILED || state == YarnApplicationState.KILLED) { + throw new SparkException(s"Application $appId finished with status: $state") + } + } else { + val (yarnApplicationState, finalApplicationStatus) = monitorApplication(appId) + if (yarnApplicationState == YarnApplicationState.FAILED || + finalApplicationStatus == FinalApplicationStatus.FAILED) { + throw new SparkException(s"Application $appId finished with failed status") + } + if (yarnApplicationState == YarnApplicationState.KILLED || + finalApplicationStatus == FinalApplicationStatus.KILLED) { + throw new SparkException(s"Application $appId is killed") + } + if (finalApplicationStatus == FinalApplicationStatus.UNDEFINED) { + throw new SparkException(s"The final status of application $appId is undefined") + } } } } @@ -682,6 +792,9 @@ object Client extends Logging { // Distribution-defined classpath to add to processes val ENV_DIST_CLASSPATH = "SPARK_DIST_CLASSPATH" + // Subdirectory where the user's hadoop config files will be placed. + val LOCALIZED_HADOOP_CONF_DIR = "__hadoop_conf__" + /** * Find the user-defined Spark jar if configured, or return the jar containing this * class if not. @@ -706,7 +819,7 @@ object Client extends Logging { * Return the path to the given application's staging directory. */ private def getAppStagingDir(appId: ApplicationId): String = { - SPARK_STAGING + Path.SEPARATOR + appId.toString() + Path.SEPARATOR + buildPath(SPARK_STAGING, appId.toString()) } /** @@ -782,60 +895,64 @@ object Client extends Logging { /** * Populate the classpath entry in the given environment map. - * This includes the user jar, Spark jar, and any extra application jars. + * + * User jars are generally not added to the JVM's system classpath; those are handled by the AM + * and executor backend. When the deprecated `spark.yarn.user.classpath.first` is used, user jars + * are included in the system classpath, though. The extra class path and other uploaded files are + * always made available through the system class path. + * + * @param args Client arguments (when starting the AM) or null (when starting executors). */ private[yarn] def populateClasspath( args: ClientArguments, conf: Configuration, sparkConf: SparkConf, env: HashMap[String, String], + isAM: Boolean, extraClassPath: Option[String] = None): Unit = { extraClassPath.foreach(addClasspathEntry(_, env)) addClasspathEntry( YarnSparkHadoopUtil.expandEnvironment(Environment.PWD), env ) - // Normally the users app.jar is last in case conflicts with spark jars - if (sparkConf.getBoolean("spark.yarn.user.classpath.first", false)) { - addUserClasspath(args, sparkConf, env) - addFileToClasspath(sparkJar(sparkConf), SPARK_JAR, env) - populateHadoopClasspath(conf, env) - } else { - addFileToClasspath(sparkJar(sparkConf), SPARK_JAR, env) - populateHadoopClasspath(conf, env) - addUserClasspath(args, sparkConf, env) + if (isAM) { + addClasspathEntry( + YarnSparkHadoopUtil.expandEnvironment(Environment.PWD) + Path.SEPARATOR + + LOCALIZED_HADOOP_CONF_DIR, env) } - // Append all jar files under the working directory to the classpath. - addClasspathEntry( - YarnSparkHadoopUtil.expandEnvironment(Environment.PWD) + Path.SEPARATOR + "*", env - ) + if (sparkConf.getBoolean("spark.yarn.user.classpath.first", false)) { + val userClassPath = + if (args != null) { + getUserClasspath(Option(args.userJar), Option(args.addJars)) + } else { + getUserClasspath(sparkConf) + } + userClassPath.foreach { x => + addFileToClasspath(x, null, env) + } + } + addFileToClasspath(new URI(sparkJar(sparkConf)), SPARK_JAR, env) + populateHadoopClasspath(conf, env) + sys.env.get(ENV_DIST_CLASSPATH).foreach(addClasspathEntry(_, env)) } /** - * Adds the user jars which have local: URIs (or alternate names, such as APP_JAR) explicitly - * to the classpath. + * Returns a list of URIs representing the user classpath. + * + * @param conf Spark configuration. */ - private def addUserClasspath( - args: ClientArguments, - conf: SparkConf, - env: HashMap[String, String]): Unit = { - - // If `args` is not null, we are launching an AM container. - // Otherwise, we are launching executor containers. - val (mainJar, secondaryJars) = - if (args != null) { - (args.userJar, args.addJars) - } else { - (conf.get(CONF_SPARK_USER_JAR, null), conf.get(CONF_SPARK_YARN_SECONDARY_JARS, null)) - } + def getUserClasspath(conf: SparkConf): Array[URI] = { + getUserClasspath(conf.getOption(CONF_SPARK_USER_JAR), + conf.getOption(CONF_SPARK_YARN_SECONDARY_JARS)) + } - addFileToClasspath(mainJar, APP_JAR, env) - if (secondaryJars != null) { - secondaryJars.split(",").filter(_.nonEmpty).foreach { jar => - addFileToClasspath(jar, null, env) - } - } + private def getUserClasspath( + mainJar: Option[String], + secondaryJars: Option[String]): Array[URI] = { + val mainUri = mainJar.orElse(Some(APP_JAR)).map(new URI(_)) + val secondaryUris = secondaryJars.map(_.split(",")).toSeq.flatten.map(new URI(_)) + (mainUri ++ secondaryUris).toArray } /** @@ -846,27 +963,19 @@ object Client extends Logging { * * If not a "local:" file and no alternate name, the environment is not modified. * - * @param path Path to add to classpath (optional). + * @param uri URI to add to classpath (optional). * @param fileName Alternate name for the file (optional). * @param env Map holding the environment variables. */ private def addFileToClasspath( - path: String, + uri: URI, fileName: String, env: HashMap[String, String]): Unit = { - if (path != null) { - scala.util.control.Exception.ignoring(classOf[URISyntaxException]) { - val uri = new URI(path) - if (uri.getScheme == LOCAL_SCHEME) { - addClasspathEntry(uri.getPath, env) - return - } - } - } - if (fileName != null) { - addClasspathEntry( - YarnSparkHadoopUtil.expandEnvironment(Environment.PWD) + Path.SEPARATOR + fileName, env - ) + if (uri != null && uri.getScheme == LOCAL_SCHEME) { + addClasspathEntry(uri.getPath, env) + } else if (fileName != null) { + addClasspathEntry(buildPath( + YarnSparkHadoopUtil.expandEnvironment(Environment.PWD), fileName), env) } } @@ -917,6 +1026,64 @@ object Client extends Logging { } } + /** + * Obtains token for the Hive metastore and adds them to the credentials. + */ + private def obtainTokenForHiveMetastore(conf: Configuration, credentials: Credentials) { + if (UserGroupInformation.isSecurityEnabled) { + val mirror = universe.runtimeMirror(getClass.getClassLoader) + + try { + val hiveClass = mirror.classLoader.loadClass("org.apache.hadoop.hive.ql.metadata.Hive") + val hive = hiveClass.getMethod("get").invoke(null) + + val hiveConf = hiveClass.getMethod("getConf").invoke(hive) + val hiveConfClass = mirror.classLoader.loadClass("org.apache.hadoop.hive.conf.HiveConf") + + val hiveConfGet = (param:String) => Option(hiveConfClass + .getMethod("get", classOf[java.lang.String]) + .invoke(hiveConf, param)) + + val metastore_uri = hiveConfGet("hive.metastore.uris") + + // Check for local metastore + if (metastore_uri != None && metastore_uri.get.toString.size > 0) { + val metastore_kerberos_principal_conf_var = mirror.classLoader + .loadClass("org.apache.hadoop.hive.conf.HiveConf$ConfVars") + .getField("METASTORE_KERBEROS_PRINCIPAL").get("varname").toString + + val principal = hiveConfGet(metastore_kerberos_principal_conf_var) + + val username = Option(UserGroupInformation.getCurrentUser().getUserName) + if (principal != None && username != None) { + val tokenStr = hiveClass.getMethod("getDelegationToken", + classOf[java.lang.String], classOf[java.lang.String]) + .invoke(hive, username.get, principal.get).asInstanceOf[java.lang.String] + + val hive2Token = new Token[DelegationTokenIdentifier]() + hive2Token.decodeFromUrlString(tokenStr) + credentials.addToken(new Text("hive.server2.delegation.token"),hive2Token) + logDebug("Added hive.Server2.delegation.token to conf.") + hiveClass.getMethod("closeCurrent").invoke(null) + } else { + logError("Username or principal == NULL") + logError(s"""username=${username.getOrElse("(NULL)")}""") + logError(s"""principal=${principal.getOrElse("(NULL)")}""") + throw new IllegalArgumentException("username and/or principal is equal to null!") + } + } else { + logDebug("HiveMetaStore configured in localmode") + } + } catch { + case e:java.lang.NoSuchMethodException => { logInfo("Hive Method not found " + e); return } + case e:java.lang.ClassNotFoundException => { logInfo("Hive Class not found " + e); return } + case e:Exception => { logError("Unexpected Exception " + e) + throw new RuntimeException("Unexpected exception", e) + } + } + } + } + /** * Return whether the two file systems are the same. */ @@ -962,4 +1129,23 @@ object Client extends Logging { new Path(qualifiedURI) } + /** + * Whether to consider jars provided by the user to have precedence over the Spark jars when + * loading user classes. + */ + def isUserClassPathFirst(conf: SparkConf, isDriver: Boolean): Boolean = { + if (isDriver) { + conf.getBoolean("spark.driver.userClassPathFirst", false) + } else { + conf.getBoolean("spark.executor.userClassPathFirst", false) + } + } + + /** + * Joins all the path components using Path.SEPARATOR. + */ + def buildPath(components: String*): String = { + components.mkString(Path.SEPARATOR) + } + } diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala index 3bc7eb1abf341..1423533470fc0 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala @@ -32,6 +32,7 @@ private[spark] class ClientArguments(args: Array[String], sparkConf: SparkConf) var userClass: String = null var pyFiles: String = null var primaryPyFile: String = null + var primaryRFile: String = null var userArgs: ArrayBuffer[String] = new ArrayBuffer[String]() var executorMemory = 1024 // MB var executorCores = 1 @@ -102,9 +103,13 @@ private[spark] class ClientArguments(args: Array[String], sparkConf: SparkConf) * This is intended to be called only after the provided arguments have been parsed. */ private def validateArgs(): Unit = { - if (numExecutors <= 0) { + if (numExecutors < 0 || (!isDynamicAllocationEnabled && numExecutors == 0)) { throw new IllegalArgumentException( - "You must specify at least 1 executor!\n" + getUsageMessage()) + s""" + |Number of executors was $numExecutors, but must be at least 1 + |(or 0 if dynamic executor allocation is enabled). + |${getUsageMessage()} + """.stripMargin) } if (executorCores < sparkConf.getInt("spark.task.cpus", 1)) { throw new SparkException("Executor cores must not be less than " + @@ -150,6 +155,10 @@ private[spark] class ClientArguments(args: Array[String], sparkConf: SparkConf) primaryPyFile = value args = tail + case ("--primary-r-file") :: value :: tail => + primaryRFile = value + args = tail + case ("--args" | "--arg") :: value :: tail => if (args(0) == "--args") { println("--args is deprecated. Use --arg instead.") @@ -228,6 +237,11 @@ private[spark] class ClientArguments(args: Array[String], sparkConf: SparkConf) throw new IllegalArgumentException(getUsageMessage(args)) } } + + if (primaryPyFile != null && primaryRFile != null) { + throw new IllegalArgumentException("Cannot have primary-py-file and primary-r-file" + + " at the same time") + } } private def getUsageMessage(unknownParam: List[String] = null): String = { @@ -240,6 +254,7 @@ private[spark] class ClientArguments(args: Array[String], sparkConf: SparkConf) | mode) | --class CLASS_NAME Name of your application's main class (required) | --primary-py-file A main Python file + | --primary-r-file A main R file | --arg ARG Argument to be passed to your application's main class. | Multiple invocations are possible, each will be passed in order. | --num-executors NUM Number of executors to start (Default: 2) diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala index 408cf09b9bdfa..9d04d241dae9e 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala @@ -17,6 +17,7 @@ package org.apache.spark.deploy.yarn +import java.io.File import java.net.URI import java.nio.ByteBuffer @@ -37,7 +38,7 @@ import org.apache.hadoop.yarn.conf.YarnConfiguration import org.apache.hadoop.yarn.ipc.YarnRPC import org.apache.hadoop.yarn.util.{ConverterUtils, Records} -import org.apache.spark.{SecurityManager, SparkConf, Logging} +import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkException} import org.apache.spark.network.util.JavaUtils class ExecutorRunnable( @@ -57,16 +58,16 @@ class ExecutorRunnable( var nmClient: NMClient = _ val yarnConf: YarnConfiguration = new YarnConfiguration(conf) lazy val env = prepareEnvironment(container) - - def run = { + + override def run(): Unit = { logInfo("Starting Executor Container") nmClient = NMClient.createNMClient() nmClient.init(yarnConf) nmClient.start() - startContainer + startContainer() } - def startContainer = { + def startContainer(): java.util.Map[String, ByteBuffer] = { logInfo("Setting up ContainerLaunchContext") val ctx = Records.newRecord(classOf[ContainerLaunchContext]) @@ -108,7 +109,13 @@ class ExecutorRunnable( } // Send the start request to the ContainerManager - nmClient.startContainer(container, ctx) + try { + nmClient.startContainer(container, ctx) + } catch { + case ex: Exception => + throw new SparkException(s"Exception while starting container ${container.getId}" + + s" on host $hostname", ex) + } } private def prepareCommand( @@ -128,14 +135,15 @@ class ExecutorRunnable( // Set the JVM memory val executorMemoryString = executorMemory + "m" - javaOpts += "-Xms" + executorMemoryString + " -Xmx" + executorMemoryString + " " + javaOpts += "-Xms" + executorMemoryString + javaOpts += "-Xmx" + executorMemoryString // Set extra Java options for the executor, if defined sys.props.get("spark.executor.extraJavaOptions").foreach { opts => - javaOpts += opts + javaOpts ++= Utils.splitCommandString(opts).map(YarnSparkHadoopUtil.escapeForShell) } sys.env.get("SPARK_JAVA_OPTS").foreach { opts => - javaOpts += opts + javaOpts ++= Utils.splitCommandString(opts).map(YarnSparkHadoopUtil.escapeForShell) } sys.props.get("spark.executor.extraLibraryPath").foreach { p => prefixEnv = Some(Utils.libraryPathEnvPrefix(Seq(p))) @@ -173,17 +181,27 @@ class ExecutorRunnable( // The options are based on // http://www.oracle.com/technetwork/java/gc-tuning-5-138395.html#0.0.0.%20When%20to%20Use // %20the%20Concurrent%20Low%20Pause%20Collector|outline - javaOpts += " -XX:+UseConcMarkSweepGC " - javaOpts += " -XX:+CMSIncrementalMode " - javaOpts += " -XX:+CMSIncrementalPacing " - javaOpts += " -XX:CMSIncrementalDutyCycleMin=0 " - javaOpts += " -XX:CMSIncrementalDutyCycle=10 " + javaOpts += "-XX:+UseConcMarkSweepGC" + javaOpts += "-XX:+CMSIncrementalMode" + javaOpts += "-XX:+CMSIncrementalPacing" + javaOpts += "-XX:CMSIncrementalDutyCycleMin=0" + javaOpts += "-XX:CMSIncrementalDutyCycle=10" } */ // For log4j configuration to reference javaOpts += ("-Dspark.yarn.app.container.log.dir=" + ApplicationConstants.LOG_DIR_EXPANSION_VAR) + val userClassPath = Client.getUserClasspath(sparkConf).flatMap { uri => + val absPath = + if (new File(uri.getPath()).isAbsolute()) { + uri.getPath() + } else { + Client.buildPath(Environment.PWD.$(), uri.getPath()) + } + Seq("--user-class-path", "file:" + absPath) + }.toSeq + val commands = prefixEnv ++ Seq( YarnSparkHadoopUtil.expandEnvironment(Environment.JAVA_HOME) + "/bin/java", "-server", @@ -195,11 +213,13 @@ class ExecutorRunnable( "-XX:OnOutOfMemoryError='kill %p'") ++ javaOpts ++ Seq("org.apache.spark.executor.CoarseGrainedExecutorBackend", - masterAddress.toString, - slaveId.toString, - hostname.toString, - executorCores.toString, - appId, + "--driver-url", masterAddress.toString, + "--executor-id", slaveId.toString, + "--hostname", hostname.toString, + "--cores", executorCores.toString, + "--app-id", appId) ++ + userClassPath ++ + Seq( "1>", ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stdout", "2>", ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stderr") @@ -257,7 +277,7 @@ class ExecutorRunnable( private def prepareEnvironment(container: Container): HashMap[String, String] = { val env = new HashMap[String, String]() val extraCp = sparkConf.getOption("spark.executor.extraClassPath") - Client.populateClasspath(null, yarnConf, sparkConf, env, extraCp) + Client.populateClasspath(null, yarnConf, sparkConf, env, false, extraCp) sparkConf.getExecutorEnv.foreach { case (key, value) => // This assumes each executor environment variable set here is a path @@ -270,10 +290,19 @@ class ExecutorRunnable( YarnSparkHadoopUtil.setEnvFromInputString(env, userEnvs) } + // lookup appropriate http scheme for container log urls + val yarnHttpPolicy = yarnConf.get( + YarnConfiguration.YARN_HTTP_POLICY_KEY, + YarnConfiguration.YARN_HTTP_POLICY_DEFAULT + ) + val httpScheme = if (yarnHttpPolicy == "HTTPS_ONLY") "https://" else "http://" + // Add log urls sys.env.get("SPARK_USER").foreach { user => - val baseUrl = "http://%s/node/containerlogs/%s/%s" - .format(container.getNodeHttpAddress, ConverterUtils.toString(container.getId), user) + val containerId = ConverterUtils.toString(container.getId) + val address = container.getNodeHttpAddress + val baseUrl = s"$httpScheme$address/node/containerlogs/$containerId/$user" + env("SPARK_LOG_URL_STDERR") = s"$baseUrl/stderr?start=0" env("SPARK_LOG_URL_STDOUT") = s"$baseUrl/stdout?start=0" } diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala index 12c62a659d799..b8f42dadcb464 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala @@ -86,7 +86,8 @@ private[yarn] class YarnAllocator( @volatile private var targetNumExecutors = args.numExecutors // Keep track of which container is running which executor to remove the executors later - private val executorIdToContainer = new HashMap[String, Container] + // Visible for testing. + private[yarn] val executorIdToContainer = new HashMap[String, Container] // Executor memory in MB. protected val executorMemory = args.executorMemory @@ -111,7 +112,7 @@ private[yarn] class YarnAllocator( SparkEnv.driverActorSystemName, sparkConf.get("spark.driver.host"), sparkConf.get("spark.driver.port"), - CoarseGrainedSchedulerBackend.ACTOR_NAME) + CoarseGrainedSchedulerBackend.ENDPOINT_NAME) // For testing private val launchContainers = sparkConf.getBoolean("spark.yarn.launchContainers", true) @@ -137,7 +138,10 @@ private[yarn] class YarnAllocator( * be killed. */ def requestTotalExecutors(requestedTotal: Int): Unit = synchronized { - targetNumExecutors = requestedTotal + if (requestedTotal != targetNumExecutors) { + logInfo(s"Driver requested a total number of $requestedTotal executor(s).") + targetNumExecutors = requestedTotal + } } /** @@ -148,8 +152,6 @@ private[yarn] class YarnAllocator( val container = executorIdToContainer.remove(executorId).get internalReleaseContainer(container) numExecutorsRunning -= 1 - targetNumExecutors -= 1 - assert(targetNumExecutors >= 0, "Allocator killed more executors than are allocated!") } else { logWarning(s"Attempted to kill unknown executor $executorId!") } @@ -290,8 +292,14 @@ private[yarn] class YarnAllocator( location: String, containersToUse: ArrayBuffer[Container], remaining: ArrayBuffer[Container]): Unit = { + // SPARK-6050: certain Yarn configurations return a virtual core count that doesn't match the + // request; for example, capacity scheduler + DefaultResourceCalculator. So match on requested + // memory, but use the asked vcore count for matching, effectively disabling matching on vcore + // count. + val matchingResource = Resource.newInstance(allocatedContainer.getResource.getMemory, + resource.getVirtualCores) val matchingRequests = amClient.getMatchingRequests(allocatedContainer.getPriority, location, - allocatedContainer.getResource) + matchingResource) // Match the allocation to a request if (!matchingRequests.isEmpty) { @@ -318,7 +326,7 @@ private[yarn] class YarnAllocator( assert(container.getResource.getMemory >= resource.getMemory) logInfo("Launching container %s for on host %s".format(containerId, executorHostname)) - executorIdToContainer(executorId) = container + executorIdToContainer(executorId) = container val containerSet = allocatedHostToContainersMap.getOrElseUpdate(executorHostname, new HashSet[ContainerId]) @@ -345,7 +353,8 @@ private[yarn] class YarnAllocator( } } - private def processCompletedContainers(completedContainers: Seq[ContainerStatus]): Unit = { + // Visible for testing. + private[yarn] def processCompletedContainers(completedContainers: Seq[ContainerStatus]): Unit = { for (completedContainer <- completedContainers) { val containerId = completedContainer.getContainerId diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala index 146b2c0f1a302..5881dc5ffa3ad 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala @@ -86,10 +86,10 @@ class YarnSparkHadoopUtil extends SparkHadoopUtil { object YarnSparkHadoopUtil { // Additional memory overhead - // 7% was arrived at experimentally. In the interest of minimizing memory waste while covering + // 10% was arrived at experimentally. In the interest of minimizing memory waste while covering // the common cases. Memory overhead tends to grow with container size. - val MEMORY_OVERHEAD_FACTOR = 0.07 + val MEMORY_OVERHEAD_FACTOR = 0.10 val MEMORY_OVERHEAD_MIN = 384 val ANY_HOST = "*" diff --git a/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala b/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala index f1b5aafac4066..99c05329b4d73 100644 --- a/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala +++ b/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala @@ -34,7 +34,7 @@ private[spark] class YarnClientSchedulerBackend( private var client: Client = null private var appId: ApplicationId = null - @volatile private var stopping: Boolean = false + private var monitorThread: Thread = null /** * Create a Yarn client to submit an application to the ResourceManager. @@ -57,7 +57,8 @@ private[spark] class YarnClientSchedulerBackend( client = new Client(args, conf) appId = client.submitApplication() waitForApplication() - asyncMonitorApplication() + monitorThread = asyncMonitorApplication() + monitorThread.start() } /** @@ -79,18 +80,12 @@ private[spark] class YarnClientSchedulerBackend( ) // Warn against the following deprecated environment variables: env var -> suggestion val deprecatedEnvVars = Map( - "SPARK_MASTER_MEMORY" -> "SPARK_DRIVER_MEMORY or --driver-memory through spark-submit", "SPARK_WORKER_INSTANCES" -> "SPARK_WORKER_INSTANCES or --num-executors through spark-submit", "SPARK_WORKER_MEMORY" -> "SPARK_EXECUTOR_MEMORY or --executor-memory through spark-submit", "SPARK_WORKER_CORES" -> "SPARK_EXECUTOR_CORES or --executor-cores through spark-submit") - // Do the same for deprecated properties: property -> suggestion - val deprecatedProps = Map("spark.master.memory" -> "--driver-memory through spark-submit") optionTuples.foreach { case (optionName, envVar, sparkProp) => if (sc.getConf.contains(sparkProp)) { extraArgs += (optionName, sc.getConf.get(sparkProp)) - if (deprecatedProps.contains(sparkProp)) { - logWarning(s"NOTE: $sparkProp is deprecated. Use ${deprecatedProps(sparkProp)} instead.") - } } else if (System.getenv(envVar) != null) { extraArgs += (optionName, System.getenv(envVar)) if (deprecatedEnvVars.contains(envVar)) { @@ -129,34 +124,22 @@ private[spark] class YarnClientSchedulerBackend( * If the application has exited for any reason, stop the SparkContext. * This assumes both `client` and `appId` have already been set. */ - private def asyncMonitorApplication(): Unit = { + private def asyncMonitorApplication(): Thread = { assert(client != null && appId != null, "Application has not been submitted yet!") val t = new Thread { override def run() { - while (!stopping) { - var state: YarnApplicationState = null - try { - val report = client.getApplicationReport(appId) - state = report.getYarnApplicationState() - } catch { - case e: ApplicationNotFoundException => - state = YarnApplicationState.KILLED - } - if (state == YarnApplicationState.FINISHED || - state == YarnApplicationState.KILLED || - state == YarnApplicationState.FAILED) { - logError(s"Yarn application has already exited with state $state!") - sc.stop() - stopping = true - } - Thread.sleep(1000L) + try { + val (state, _) = client.monitorApplication(appId, logApplicationReport = false) + logError(s"Yarn application has already exited with state $state!") + sc.stop() + } catch { + case e: InterruptedException => logInfo("Interrupting monitor thread") } - Thread.currentThread().interrupt() } } t.setName("Yarn application state monitor") t.setDaemon(true) - t.start() + t } /** @@ -164,7 +147,7 @@ private[spark] class YarnClientSchedulerBackend( */ override def stop() { assert(client != null, "Attempted to stop this scheduler before starting it!") - stopping = true + monitorThread.interrupt() super.stop() client.stop() logInfo("Stopped") diff --git a/yarn/src/test/resources/log4j.properties b/yarn/src/test/resources/log4j.properties index 287c8e3563503..6b8a5dbf6373e 100644 --- a/yarn/src/test/resources/log4j.properties +++ b/yarn/src/test/resources/log4j.properties @@ -16,7 +16,7 @@ # # Set everything to be logged to the file target/unit-tests.log -log4j.rootCategory=INFO, file +log4j.rootCategory=DEBUG, file log4j.appender.file=org.apache.log4j.FileAppender log4j.appender.file.append=true log4j.appender.file.file=target/unit-tests.log @@ -24,5 +24,5 @@ log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n # Ignore messages below warning level from Jetty, because it's a bit verbose -log4j.logger.org.eclipse.jetty=WARN -org.eclipse.jetty.LEVEL=WARN +log4j.logger.org.spark-project.jetty=WARN +log4j.logger.org.apache.hadoop=WARN diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala index 2bb3dcffd61d9..a51c2005cb472 100644 --- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala +++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala @@ -20,6 +20,11 @@ package org.apache.spark.deploy.yarn import java.io.File import java.net.URI +import scala.collection.JavaConversions._ +import scala.collection.mutable.{ HashMap => MutableHashMap } +import scala.reflect.ClassTag +import scala.util.Try + import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path import org.apache.hadoop.mapreduce.MRJobConfig @@ -28,18 +33,20 @@ import org.apache.hadoop.yarn.api.records._ import org.apache.hadoop.yarn.conf.YarnConfiguration import org.mockito.Matchers._ import org.mockito.Mockito._ -import org.scalatest.FunSuite -import org.scalatest.Matchers - -import scala.collection.JavaConversions._ -import scala.collection.mutable.{ HashMap => MutableHashMap } -import scala.reflect.ClassTag -import scala.util.Try +import org.scalatest.{BeforeAndAfterAll, FunSuite, Matchers} import org.apache.spark.{SparkException, SparkConf} import org.apache.spark.util.Utils -class ClientSuite extends FunSuite with Matchers { +class ClientSuite extends FunSuite with Matchers with BeforeAndAfterAll { + + override def beforeAll(): Unit = { + System.setProperty("SPARK_YARN_MODE", "true") + } + + override def afterAll(): Unit = { + System.clearProperty("SPARK_YARN_MODE") + } test("default Yarn application classpath") { Client.getDefaultYarnApplicationClasspath should be(Some(Fixtures.knownDefYarnAppCP)) @@ -82,10 +89,11 @@ class ClientSuite extends FunSuite with Matchers { test("Local jar URIs") { val conf = new Configuration() val sparkConf = new SparkConf().set(Client.CONF_SPARK_JAR, SPARK) + .set("spark.yarn.user.classpath.first", "true") val env = new MutableHashMap[String, String]() val args = new ClientArguments(Array("--jar", USER, "--addJars", ADDED), sparkConf) - Client.populateClasspath(args, conf, sparkConf, env) + Client.populateClasspath(args, conf, sparkConf, env, true) val cp = env("CLASSPATH").split(":|;|") s"$SPARK,$USER,$ADDED".split(",").foreach({ entry => @@ -96,16 +104,16 @@ class ClientSuite extends FunSuite with Matchers { cp should not contain (uri.getPath()) } }) - if (classOf[Environment].getMethods().exists(_.getName == "$$")) { - cp should contain("{{PWD}}") - cp should contain(s"{{PWD}}${Path.SEPARATOR}*") - } else if (Utils.isWindows) { - cp should contain("%PWD%") - cp should contain(s"%PWD%${Path.SEPARATOR}*") - } else { - cp should contain(Environment.PWD.$()) - cp should contain(s"${Environment.PWD.$()}${File.separator}*") - } + val pwdVar = + if (classOf[Environment].getMethods().exists(_.getName == "$$")) { + "{{PWD}}" + } else if (Utils.isWindows) { + "%PWD%" + } else { + Environment.PWD.$() + } + cp should contain(pwdVar) + cp should contain (s"$pwdVar${Path.SEPARATOR}${Client.LOCALIZED_HADOOP_CONF_DIR}") cp should not contain (Client.SPARK_JAR) cp should not contain (Client.APP_JAR) } @@ -117,7 +125,7 @@ class ClientSuite extends FunSuite with Matchers { val client = spy(new Client(args, conf, sparkConf)) doReturn(new Path("/")).when(client).copyFileToRemote(any(classOf[Path]), - any(classOf[Path]), anyShort(), anyBoolean()) + any(classOf[Path]), anyShort()) val tempDir = Utils.createTempDir() try { @@ -227,19 +235,26 @@ class ClientSuite extends FunSuite with Matchers { testCode(conf) } - def newEnv = MutableHashMap[String, String]() + def newEnv: MutableHashMap[String, String] = MutableHashMap[String, String]() - def classpath(env: MutableHashMap[String, String]) = env(Environment.CLASSPATH.name).split(":|;|") + def classpath(env: MutableHashMap[String, String]): Array[String] = + env(Environment.CLASSPATH.name).split(":|;|") - def flatten(a: Option[Seq[String]], b: Option[Seq[String]]) = (a ++ b).flatten.toArray + def flatten(a: Option[Seq[String]], b: Option[Seq[String]]): Array[String] = + (a ++ b).flatten.toArray - def getFieldValue[A, B](clazz: Class[_], field: String, defaults: => B)(mapTo: A => B): B = - Try(clazz.getField(field)).map(_.get(null).asInstanceOf[A]).toOption.map(mapTo).getOrElse(defaults) + def getFieldValue[A, B](clazz: Class[_], field: String, defaults: => B)(mapTo: A => B): B = { + Try(clazz.getField(field)) + .map(_.get(null).asInstanceOf[A]) + .toOption + .map(mapTo) + .getOrElse(defaults) + } def getFieldValue2[A: ClassTag, A1: ClassTag, B]( clazz: Class[_], field: String, - defaults: => B)(mapTo: A => B)(mapTo1: A1 => B) : B = { + defaults: => B)(mapTo: A => B)(mapTo1: A1 => B): B = { Try(clazz.getField(field)).map(_.get(null)).map { case v: A => mapTo(v) case v1: A1 => mapTo1(v1) diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnAllocatorSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnAllocatorSuite.scala index 3c224f148802e..455f1019d86dd 100644 --- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnAllocatorSuite.scala +++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnAllocatorSuite.scala @@ -79,7 +79,7 @@ class YarnAllocatorSuite extends FunSuite with Matchers with BeforeAndAfterEach } class MockSplitInfo(host: String) extends SplitInfo(null, host, null, 1, null) { - override def equals(other: Any) = false + override def equals(other: Any): Boolean = false } def createAllocator(maxExecutors: Int = 5): YarnAllocator = { @@ -118,7 +118,9 @@ class YarnAllocatorSuite extends FunSuite with Matchers with BeforeAndAfterEach handler.getNumExecutorsRunning should be (1) handler.allocatedContainerToHostMap.get(container.getId).get should be ("host1") handler.allocatedHostToContainersMap.get("host1").get should contain (container.getId) - rmClient.getMatchingRequests(container.getPriority, "host1", containerResource).size should be (0) + + val size = rmClient.getMatchingRequests(container.getPriority, "host1", containerResource).size + size should be (0) } test("some containers allocated") { @@ -206,6 +208,28 @@ class YarnAllocatorSuite extends FunSuite with Matchers with BeforeAndAfterEach handler.getNumExecutorsRunning should be (2) } + test("kill executors") { + val handler = createAllocator(4) + handler.updateResourceRequests() + handler.getNumExecutorsRunning should be (0) + handler.getNumPendingAllocate should be (4) + + val container1 = createContainer("host1") + val container2 = createContainer("host2") + handler.handleAllocatedContainers(Array(container1, container2)) + + handler.requestTotalExecutors(1) + handler.executorIdToContainer.keys.foreach { id => handler.killExecutor(id ) } + + val statuses = Seq(container1, container2).map { c => + ContainerStatus.newInstance(c.getId(), ContainerState.COMPLETE, "Finished", 0) + } + handler.updateResourceRequests() + handler.processCompletedContainers(statuses.toSeq) + handler.getNumExecutorsRunning should be (0) + handler.getNumPendingAllocate should be (1) + } + test("memory exceeded diagnostic regexes") { val diagnostics = "Container [pid=12465,containerID=container_1412887393566_0003_01_000002] is running " + diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala index eda40efc4c77f..3877da4120e7c 100644 --- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala +++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala @@ -17,27 +17,34 @@ package org.apache.spark.deploy.yarn -import java.io.File +import java.io.{File, FileOutputStream, OutputStreamWriter} +import java.util.Properties import java.util.concurrent.TimeUnit import scala.collection.JavaConversions._ import scala.collection.mutable -import com.google.common.base.Charsets +import com.google.common.base.Charsets.UTF_8 +import com.google.common.io.ByteStreams import com.google.common.io.Files import org.apache.hadoop.yarn.conf.YarnConfiguration import org.apache.hadoop.yarn.server.MiniYARNCluster import org.scalatest.{BeforeAndAfterAll, FunSuite, Matchers} -import org.apache.spark.{Logging, SparkConf, SparkContext, SparkException} +import org.apache.spark.{Logging, SparkConf, SparkContext, SparkException, TestUtils} import org.apache.spark.scheduler.cluster.ExecutorInfo -import org.apache.spark.scheduler.{SparkListener, SparkListenerExecutorAdded} +import org.apache.spark.scheduler.{SparkListenerJobStart, SparkListener, SparkListenerExecutorAdded} import org.apache.spark.util.Utils +/** + * Integration tests for YARN; these tests use a mini Yarn cluster to run Spark-on-YARN + * applications, and require the Spark assembly to be built before they can be successfully + * run. + */ class YarnClusterSuite extends FunSuite with BeforeAndAfterAll with Matchers with Logging { - // log4j configuration for the Yarn containers, so that their output is collected - // by Yarn instead of trying to overwrite unit-tests.log. + // log4j configuration for the YARN containers, so that their output is collected + // by YARN instead of trying to overwrite unit-tests.log. private val LOG4J_CONF = """ |log4j.rootCategory=DEBUG, console |log4j.appender.console=org.apache.log4j.ConsoleAppender @@ -52,13 +59,11 @@ class YarnClusterSuite extends FunSuite with BeforeAndAfterAll with Matchers wit | |from pyspark import SparkConf , SparkContext |if __name__ == "__main__": - | if len(sys.argv) != 3: - | print >> sys.stderr, "Usage: test.py [master] [result file]" + | if len(sys.argv) != 2: + | print >> sys.stderr, "Usage: test.py [result file]" | exit(-1) - | conf = SparkConf() - | conf.setMaster(sys.argv[1]).setAppName("python test in yarn cluster mode") - | sc = SparkContext(conf=conf) - | status = open(sys.argv[2],'w') + | sc = SparkContext(conf=SparkConf()) + | status = open(sys.argv[1],'w') | result = "failure" | rdd = sc.parallelize(range(10)) | cnt = rdd.count() @@ -72,21 +77,18 @@ class YarnClusterSuite extends FunSuite with BeforeAndAfterAll with Matchers wit private var yarnCluster: MiniYARNCluster = _ private var tempDir: File = _ private var fakeSparkJar: File = _ - private var oldConf: Map[String, String] = _ + private var hadoopConfDir: File = _ + private var logConfDir: File = _ override def beforeAll() { - tempDir = Utils.createTempDir() + super.beforeAll() - val logConfDir = new File(tempDir, "log4j") + tempDir = Utils.createTempDir() + logConfDir = new File(tempDir, "log4j") logConfDir.mkdir() val logConfFile = new File(logConfDir, "log4j.properties") - Files.write(LOG4J_CONF, logConfFile, Charsets.UTF_8) - - val childClasspath = logConfDir.getAbsolutePath() + File.pathSeparator + - sys.props("java.class.path") - - oldConf = sys.props.filter { case (k, v) => k.startsWith("spark.") }.toMap + Files.write(LOG4J_CONF, logConfFile, UTF_8) yarnCluster = new MiniYARNCluster(getClass().getName(), 1, 1, 1) yarnCluster.init(new YarnConfiguration()) @@ -117,107 +119,177 @@ class YarnClusterSuite extends FunSuite with BeforeAndAfterAll with Matchers wit } logInfo(s"RM address in configuration is ${config.get(YarnConfiguration.RM_ADDRESS)}") - config.foreach { e => - sys.props += ("spark.hadoop." + e.getKey() -> e.getValue()) - } fakeSparkJar = File.createTempFile("sparkJar", null, tempDir) - val sparkHome = sys.props.getOrElse("spark.test.home", fail("spark.test.home is not set!")) - sys.props += ("spark.yarn.appMasterEnv.SPARK_HOME" -> sparkHome) - sys.props += ("spark.executorEnv.SPARK_HOME" -> sparkHome) - sys.props += ("spark.yarn.jar" -> ("local:" + fakeSparkJar.getAbsolutePath())) - sys.props += ("spark.executor.instances" -> "1") - sys.props += ("spark.driver.extraClassPath" -> childClasspath) - sys.props += ("spark.executor.extraClassPath" -> childClasspath) - - super.beforeAll() + hadoopConfDir = new File(tempDir, Client.LOCALIZED_HADOOP_CONF_DIR) + assert(hadoopConfDir.mkdir()) + File.createTempFile("token", ".txt", hadoopConfDir) } override def afterAll() { yarnCluster.stop() - sys.props.retain { case (k, v) => !k.startsWith("spark.") } - sys.props ++= oldConf super.afterAll() } test("run Spark in yarn-client mode") { - var result = File.createTempFile("result", null, tempDir) - YarnClusterDriver.main(Array("yarn-client", result.getAbsolutePath())) - checkResult(result) - - // verify log urls are present - YarnClusterDriver.listener.addedExecutorInfos.values.foreach { info => - assert(info.logUrlMap.nonEmpty) - } + testBasicYarnApp(true) } test("run Spark in yarn-cluster mode") { - val main = YarnClusterDriver.getClass.getName().stripSuffix("$") - var result = File.createTempFile("result", null, tempDir) - - val args = Array("--class", main, - "--jar", "file:" + fakeSparkJar.getAbsolutePath(), - "--arg", "yarn-cluster", - "--arg", result.getAbsolutePath(), - "--num-executors", "1") - Client.main(args) - checkResult(result) - - // verify log urls are present. - YarnClusterDriver.listener.addedExecutorInfos.values.foreach { info => - assert(info.logUrlMap.nonEmpty) - } + testBasicYarnApp(false) } test("run Spark in yarn-cluster mode unsuccessfully") { - val main = YarnClusterDriver.getClass.getName().stripSuffix("$") - - // Use only one argument so the driver will fail - val args = Array("--class", main, - "--jar", "file:" + fakeSparkJar.getAbsolutePath(), - "--arg", "yarn-cluster", - "--num-executors", "1") + // Don't provide arguments so the driver will fail. val exception = intercept[SparkException] { - Client.main(args) + runSpark(false, mainClassName(YarnClusterDriver.getClass)) + fail("Spark application should have failed.") } - assert(Utils.exceptionString(exception).contains("Application finished with failed status")) } + // Enable this once fix SPARK-6700 test("run Python application in yarn-cluster mode") { val primaryPyFile = new File(tempDir, "test.py") - Files.write(TEST_PYFILE, primaryPyFile, Charsets.UTF_8) + Files.write(TEST_PYFILE, primaryPyFile, UTF_8) val pyFile = new File(tempDir, "test2.py") - Files.write(TEST_PYFILE, pyFile, Charsets.UTF_8) + Files.write(TEST_PYFILE, pyFile, UTF_8) var result = File.createTempFile("result", null, tempDir) - val args = Array("--class", "org.apache.spark.deploy.PythonRunner", - "--primary-py-file", primaryPyFile.getAbsolutePath(), - "--py-files", pyFile.getAbsolutePath(), - "--arg", "yarn-cluster", - "--arg", result.getAbsolutePath(), - "--name", "python test in yarn-cluster mode", - "--num-executors", "1") - Client.main(args) + // The sbt assembly does not include pyspark / py4j python dependencies, so we need to + // propagate SPARK_HOME so that those are added to PYTHONPATH. See PythonUtils.scala. + val sparkHome = sys.props("spark.test.home") + val extraConf = Map( + "spark.executorEnv.SPARK_HOME" -> sparkHome, + "spark.yarn.appMasterEnv.SPARK_HOME" -> sparkHome) + + runSpark(false, primaryPyFile.getAbsolutePath(), + sparkArgs = Seq("--py-files", pyFile.getAbsolutePath()), + appArgs = Seq(result.getAbsolutePath()), + extraConf = extraConf) checkResult(result) } + test("user class path first in client mode") { + testUseClassPathFirst(true) + } + + test("user class path first in cluster mode") { + testUseClassPathFirst(false) + } + + private def testBasicYarnApp(clientMode: Boolean): Unit = { + var result = File.createTempFile("result", null, tempDir) + runSpark(clientMode, mainClassName(YarnClusterDriver.getClass), + appArgs = Seq(result.getAbsolutePath())) + checkResult(result) + } + + private def testUseClassPathFirst(clientMode: Boolean): Unit = { + // Create a jar file that contains a different version of "test.resource". + val originalJar = TestUtils.createJarWithFiles(Map("test.resource" -> "ORIGINAL"), tempDir) + val userJar = TestUtils.createJarWithFiles(Map("test.resource" -> "OVERRIDDEN"), tempDir) + val driverResult = File.createTempFile("driver", null, tempDir) + val executorResult = File.createTempFile("executor", null, tempDir) + runSpark(clientMode, mainClassName(YarnClasspathTest.getClass), + appArgs = Seq(driverResult.getAbsolutePath(), executorResult.getAbsolutePath()), + extraClassPath = Seq(originalJar.getPath()), + extraJars = Seq("local:" + userJar.getPath()), + extraConf = Map( + "spark.driver.userClassPathFirst" -> "true", + "spark.executor.userClassPathFirst" -> "true")) + checkResult(driverResult, "OVERRIDDEN") + checkResult(executorResult, "OVERRIDDEN") + } + + private def runSpark( + clientMode: Boolean, + klass: String, + appArgs: Seq[String] = Nil, + sparkArgs: Seq[String] = Nil, + extraClassPath: Seq[String] = Nil, + extraJars: Seq[String] = Nil, + extraConf: Map[String, String] = Map()): Unit = { + val master = if (clientMode) "yarn-client" else "yarn-cluster" + val props = new Properties() + + props.setProperty("spark.yarn.jar", "local:" + fakeSparkJar.getAbsolutePath()) + + val childClasspath = logConfDir.getAbsolutePath() + + File.pathSeparator + + sys.props("java.class.path") + + File.pathSeparator + + extraClassPath.mkString(File.pathSeparator) + props.setProperty("spark.driver.extraClassPath", childClasspath) + props.setProperty("spark.executor.extraClassPath", childClasspath) + + // SPARK-4267: make sure java options are propagated correctly. + props.setProperty("spark.driver.extraJavaOptions", "-Dfoo=\"one two three\"") + props.setProperty("spark.executor.extraJavaOptions", "-Dfoo=\"one two three\"") + + yarnCluster.getConfig().foreach { e => + props.setProperty("spark.hadoop." + e.getKey(), e.getValue()) + } + + sys.props.foreach { case (k, v) => + if (k.startsWith("spark.")) { + props.setProperty(k, v) + } + } + + extraConf.foreach { case (k, v) => props.setProperty(k, v) } + + val propsFile = File.createTempFile("spark", ".properties", tempDir) + val writer = new OutputStreamWriter(new FileOutputStream(propsFile), UTF_8) + props.store(writer, "Spark properties.") + writer.close() + + val extraJarArgs = if (!extraJars.isEmpty()) Seq("--jars", extraJars.mkString(",")) else Nil + val mainArgs = + if (klass.endsWith(".py")) { + Seq(klass) + } else { + Seq("--class", klass, fakeSparkJar.getAbsolutePath()) + } + val argv = + Seq( + new File(sys.props("spark.test.home"), "bin/spark-submit").getAbsolutePath(), + "--master", master, + "--num-executors", "1", + "--properties-file", propsFile.getAbsolutePath()) ++ + extraJarArgs ++ + sparkArgs ++ + mainArgs ++ + appArgs + + Utils.executeAndGetOutput(argv, + extraEnvironment = Map("YARN_CONF_DIR" -> hadoopConfDir.getAbsolutePath())) + } + /** * This is a workaround for an issue with yarn-cluster mode: the Client class will not provide * any sort of error when the job process finishes successfully, but the job itself fails. So * the tests enforce that something is written to a file after everything is ok to indicate * that the job succeeded. */ - private def checkResult(result: File) = { - var resultString = Files.toString(result, Charsets.UTF_8) - resultString should be ("success") + private def checkResult(result: File): Unit = { + checkResult(result, "success") + } + + private def checkResult(result: File, expected: String): Unit = { + var resultString = Files.toString(result, UTF_8) + resultString should be (expected) + } + + private def mainClassName(klass: Class[_]): String = { + klass.getName().stripSuffix("$") } } -private class SaveExecutorInfo extends SparkListener { +private[spark] class SaveExecutorInfo extends SparkListener { val addedExecutorInfos = mutable.Map[String, ExecutorInfo]() - override def onExecutorAdded(executor : SparkListenerExecutorAdded) { + override def onExecutorAdded(executor: SparkListenerExecutorAdded) { addedExecutorInfos(executor.executorId) = executor.executorInfo } } @@ -225,24 +297,22 @@ private class SaveExecutorInfo extends SparkListener { private object YarnClusterDriver extends Logging with Matchers { val WAIT_TIMEOUT_MILLIS = 10000 - var listener: SaveExecutorInfo = null - def main(args: Array[String]) = { - if (args.length != 2) { + def main(args: Array[String]): Unit = { + if (args.length != 1) { System.err.println( s""" |Invalid command line: ${args.mkString(" ")} | - |Usage: YarnClusterDriver [master] [result file] + |Usage: YarnClusterDriver [result file] """.stripMargin) System.exit(1) } - listener = new SaveExecutorInfo - val sc = new SparkContext(new SparkConf().setMaster(args(0)) + val sc = new SparkContext(new SparkConf() + .set("spark.extraListeners", classOf[SaveExecutorInfo].getName) .setAppName("yarn \"test app\" 'with quotes' and \\back\\slashes and $dollarSigns")) - sc.addSparkListener(listener) - val status = new File(args(1)) + val status = new File(args(0)) var result = "failure" try { val data = sc.parallelize(1 to 4, 4).collect().toSet @@ -251,7 +321,53 @@ private object YarnClusterDriver extends Logging with Matchers { result = "success" } finally { sc.stop() - Files.write(result, status, Charsets.UTF_8) + Files.write(result, status, UTF_8) + } + + // verify log urls are present + val listeners = sc.listenerBus.findListenersByClass[SaveExecutorInfo] + assert(listeners.size === 1) + val listener = listeners(0) + val executorInfos = listener.addedExecutorInfos.values + assert(executorInfos.nonEmpty) + executorInfos.foreach { info => + assert(info.logUrlMap.nonEmpty) + } + } + +} + +private object YarnClasspathTest { + + def main(args: Array[String]): Unit = { + if (args.length != 2) { + System.err.println( + s""" + |Invalid command line: ${args.mkString(" ")} + | + |Usage: YarnClasspathTest [driver result file] [executor result file] + """.stripMargin) + System.exit(1) + } + + readResource(args(0)) + val sc = new SparkContext(new SparkConf()) + try { + sc.parallelize(Seq(1)).foreach { x => readResource(args(1)) } + } finally { + sc.stop() + } + } + + private def readResource(resultPath: String): Unit = { + var result = "failure" + try { + val ccl = Thread.currentThread().getContextClassLoader() + val resource = ccl.getResourceAsStream("test.resource") + val bytes = ByteStreams.toByteArray(resource) + result = new String(bytes, 0, bytes.length, UTF_8) + } finally { + Files.write(result, new File(resultPath), UTF_8) } } diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala index b5a2db8f6225c..9395316b71ff4 100644 --- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala +++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala @@ -46,11 +46,11 @@ class YarnSparkHadoopUtilSuite extends FunSuite with Matchers with Logging { logWarning("Cannot execute bash, skipping bash tests.") } - def bashTest(name: String)(fn: => Unit) = + def bashTest(name: String)(fn: => Unit): Unit = if (hasBash) test(name)(fn) else ignore(name)(fn) bashTest("shell script escaping") { - val scriptFile = File.createTempFile("script.", ".sh") + val scriptFile = File.createTempFile("script.", ".sh", Utils.createTempDir()) val args = Array("arg1", "${arg.2}", "\"arg3\"", "'arg4'", "$arg5", "\\arg6") try { val argLine = args.map(a => YarnSparkHadoopUtil.escapeForShell(a)).mkString(" ")