From a80a6a139e729ee3f81ec4f0028e084d2d9f7e82 Mon Sep 17 00:00:00 2001 From: Michael Armbrust Date: Fri, 16 May 2014 20:25:10 -0700 Subject: [PATCH 001/221] SPARK-1864 Look in spark conf instead of system properties when propagating configuration to executors. Author: Michael Armbrust Closes #808 from marmbrus/confClasspath and squashes the following commits: 4c31d57 [Michael Armbrust] Look in spark conf instead of system properties when propagating configuration to executors. --- .../scheduler/cluster/SparkDeploySchedulerBackend.scala | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) 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 933f6e0571518..9768670855f80 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 @@ -46,12 +46,13 @@ private[spark] class SparkDeploySchedulerBackend( CoarseGrainedSchedulerBackend.ACTOR_NAME) val args = Seq(driverUrl, "{{EXECUTOR_ID}}", "{{HOSTNAME}}", "{{CORES}}", "{{WORKER_URL}}") val extraJavaOpts = sc.conf.getOption("spark.executor.extraJavaOptions") - val classPathEntries = sys.props.get("spark.executor.extraClassPath").toSeq.flatMap { cp => - cp.split(java.io.File.pathSeparator) - } - val libraryPathEntries = sys.props.get("spark.executor.extraLibraryPath").toSeq.flatMap { cp => + val classPathEntries = sc.conf.getOption("spark.executor.extraClassPath").toSeq.flatMap { cp => cp.split(java.io.File.pathSeparator) } + val libraryPathEntries = + sc.conf.getOption("spark.executor.extraLibraryPath").toSeq.flatMap { cp => + cp.split(java.io.File.pathSeparator) + } val command = Command( "org.apache.spark.executor.CoarseGrainedExecutorBackend", args, sc.executorEnvs, From c0ab85d7320cea90e6331fb03a70349bc804c1b1 Mon Sep 17 00:00:00 2001 From: Patrick Wendell Date: Fri, 16 May 2014 21:42:14 -0700 Subject: [PATCH 002/221] Version bump of spark-ec2 scripts This will allow us to change things in spark-ec2 related to the 1.0 release. Author: Patrick Wendell Closes #809 from pwendell/spark-ec2 and squashes the following commits: 59117fb [Patrick Wendell] Version bump of spark-ec2 scripts --- ec2/spark_ec2.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py index 0a8d6ca5d7519..4c5a99f5f6b2e 100755 --- a/ec2/spark_ec2.py +++ b/ec2/spark_ec2.py @@ -443,7 +443,7 @@ 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 - ssh(master, opts, "rm -rf spark-ec2 && git clone https://github.com/mesos/spark-ec2.git -b v2") + ssh(master, opts, "rm -rf spark-ec2 && git clone https://github.com/mesos/spark-ec2.git -b v3") print "Deploying files to master..." deploy_files(conn, "deploy.generic", opts, master_nodes, slave_nodes, modules) From 4b8ec6fcfd7a7ef0857d5b21917183c181301c95 Mon Sep 17 00:00:00 2001 From: Andrew Or Date: Fri, 16 May 2014 22:34:38 -0700 Subject: [PATCH 003/221] [SPARK-1808] Route bin/pyspark through Spark submit **Problem.** For `bin/pyspark`, there is currently no other way to specify Spark configuration properties other than through `SPARK_JAVA_OPTS` in `conf/spark-env.sh`. However, this mechanism is supposedly deprecated. Instead, it needs to pick up configurations explicitly specified in `conf/spark-defaults.conf`. **Solution.** Have `bin/pyspark` invoke `bin/spark-submit`, like all of its counterparts in Scala land (i.e. `bin/spark-shell`, `bin/run-example`). This has the additional benefit of making the invocation of all the user facing Spark scripts consistent. **Details.** `bin/pyspark` inherently handles two cases: (1) running python applications and (2) running the python shell. For (1), Spark submit already handles running python applications. For cases in which `bin/pyspark` is given a python file, we can simply call pass the file directly to Spark submit and let it handle the rest. For case (2), `bin/pyspark` starts a python process as before, which launches the JVM as a sub-process. The existing code already provides a code path to do this. All we needed to change is to use `bin/spark-submit` instead of `spark-class` to launch the JVM. This requires modifications to Spark submit to handle the pyspark shell as a special case. This has been tested locally (OSX and Windows 7), on a standalone cluster, and on a YARN cluster. Running IPython also works as before, except now it takes in Spark submit arguments too. Author: Andrew Or Closes #799 from andrewor14/pyspark-submit and squashes the following commits: bf37e36 [Andrew Or] Minor changes 01066fa [Andrew Or] bin/pyspark for Windows c8cb3bf [Andrew Or] Handle perverse app names (with escaped quotes) 1866f85 [Andrew Or] Windows is not cooperating 456d844 [Andrew Or] Guard against shlex hanging if PYSPARK_SUBMIT_ARGS is not set 7eebda8 [Andrew Or] Merge branch 'master' of github.com:apache/spark into pyspark-submit b7ba0d8 [Andrew Or] Address a few comments (minor) 06eb138 [Andrew Or] Use shlex instead of writing our own parser 05879fa [Andrew Or] Merge branch 'master' of github.com:apache/spark into pyspark-submit a823661 [Andrew Or] Fix --die-on-broken-pipe not propagated properly 6fba412 [Andrew Or] Deal with quotes + address various comments fe4c8a7 [Andrew Or] Update --help for bin/pyspark afe47bf [Andrew Or] Fix spark shell f04aaa4 [Andrew Or] Merge branch 'master' of github.com:apache/spark into pyspark-submit a371d26 [Andrew Or] Route bin/pyspark through Spark submit --- bin/pyspark | 35 ++++++++++-- bin/pyspark2.cmd | 21 ++++++- bin/spark-shell | 6 +- bin/spark-shell.cmd | 2 +- .../apache/spark/deploy/PythonRunner.scala | 2 +- .../org/apache/spark/deploy/SparkSubmit.scala | 55 ++++++++++++++----- .../spark/deploy/SparkSubmitArguments.scala | 6 +- .../scala/org/apache/spark/util/Utils.scala | 2 +- python/pyspark/java_gateway.py | 10 ++-- python/pyspark/shell.py | 2 +- 10 files changed, 107 insertions(+), 34 deletions(-) diff --git a/bin/pyspark b/bin/pyspark index 10e35e0f1734e..9e1364e44c8c4 100755 --- a/bin/pyspark +++ b/bin/pyspark @@ -25,6 +25,12 @@ export SPARK_HOME="$FWDIR" SCALA_VERSION=2.10 +if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then + echo "Usage: ./bin/pyspark [options]" + ./bin/spark-submit --help 2>&1 | grep -v Usage 1>&2 + exit 0 +fi + # Exit if the user hasn't compiled Spark if [ ! -f "$FWDIR/RELEASE" ]; then # Exit if the user hasn't compiled Spark @@ -52,13 +58,34 @@ export PYTHONPATH=$SPARK_HOME/python/lib/py4j-0.8.1-src.zip:$PYTHONPATH export OLD_PYTHONSTARTUP=$PYTHONSTARTUP export PYTHONSTARTUP=$FWDIR/python/pyspark/shell.py +# If IPython options are specified, assume user wants to run IPython if [ -n "$IPYTHON_OPTS" ]; then IPYTHON=1 fi -# Only use ipython if no command line arguments were provided [SPARK-1134] -if [[ "$IPYTHON" = "1" && $# = 0 ]] ; then - exec ipython $IPYTHON_OPTS +# Build up arguments list manually to preserve quotes and backslashes. +# We export Spark submit arguments as an environment variable because shell.py must run as a +# PYTHONSTARTUP script, which does not take in arguments. This is required for IPython notebooks. + +PYSPARK_SUBMIT_ARGS="" +whitespace="[[:space:]]" +for i in "$@"; 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 + +# 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 + exec $FWDIR/bin/spark-submit "$@" else - exec "$PYSPARK_PYTHON" "$@" + # Only use ipython if no command line arguments were provided [SPARK-1134] + if [[ "$IPYTHON" = "1" ]]; then + exec ipython $IPYTHON_OPTS + else + exec "$PYSPARK_PYTHON" + fi fi diff --git a/bin/pyspark2.cmd b/bin/pyspark2.cmd index d7cfd5eec501c..0ef9eea95342e 100644 --- a/bin/pyspark2.cmd +++ b/bin/pyspark2.cmd @@ -31,7 +31,7 @@ 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" ( +if [%FOUND_JAR%] == [0] ( echo Failed to find Spark assembly JAR. echo You need to build Spark with sbt\sbt assembly before running this program. goto exit @@ -42,15 +42,30 @@ 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 Figure out which Python to use. -if "x%PYSPARK_PYTHON%"=="x" set PYSPARK_PYTHON=python +if [%PYSPARK_PYTHON%] == [] set PYSPARK_PYTHON=python set PYTHONPATH=%FWDIR%python;%PYTHONPATH% set PYTHONPATH=%FWDIR%python\lib\py4j-0.8.1-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% -"%PYSPARK_PYTHON%" %* +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%] == [] ( + %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% +) + :exit diff --git a/bin/spark-shell b/bin/spark-shell index 7f03349c5e910..c158683ab3f99 100755 --- a/bin/spark-shell +++ b/bin/spark-shell @@ -28,7 +28,7 @@ esac # Enter posix mode for bash set -o posix -if [[ "$@" == *--help* ]]; then +if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then echo "Usage: ./bin/spark-shell [options]" ./bin/spark-submit --help 2>&1 | grep -v Usage 1>&2 exit 0 @@ -46,11 +46,11 @@ function main(){ # (see https://github.com/sbt/sbt/issues/562). stty -icanon min 1 -echo > /dev/null 2>&1 export SPARK_SUBMIT_OPTS="$SPARK_SUBMIT_OPTS -Djline.terminal=unix" - $FWDIR/bin/spark-submit spark-internal "$@" --class org.apache.spark.repl.Main + $FWDIR/bin/spark-submit spark-shell "$@" --class org.apache.spark.repl.Main stty icanon echo > /dev/null 2>&1 else export SPARK_SUBMIT_OPTS - $FWDIR/bin/spark-submit spark-internal "$@" --class org.apache.spark.repl.Main + $FWDIR/bin/spark-submit spark-shell "$@" --class org.apache.spark.repl.Main fi } diff --git a/bin/spark-shell.cmd b/bin/spark-shell.cmd index ca0c722c926f5..4b9708a8c03f3 100755 --- a/bin/spark-shell.cmd +++ b/bin/spark-shell.cmd @@ -19,4 +19,4 @@ rem set SPARK_HOME=%~dp0.. -cmd /V /E /C %SPARK_HOME%\bin\spark-submit.cmd spark-internal %* --class org.apache.spark.repl.Main +cmd /V /E /C %SPARK_HOME%\bin\spark-submit.cmd spark-shell %* --class org.apache.spark.repl.Main diff --git a/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala b/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala index e20d4486c8f0c..2dfa02bd26f13 100644 --- a/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala +++ b/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala @@ -42,7 +42,7 @@ object PythonRunner { // Build up a PYTHONPATH that includes the Spark assembly JAR (where this class is), the // python directories in SPARK_HOME (if set), and any files in the pyFiles argument val pathElements = new ArrayBuffer[String] - pathElements ++= pyFiles.split(",") + pathElements ++= Option(pyFiles).getOrElse("").split(",") pathElements += PythonUtils.sparkPythonPath pathElements += sys.env.getOrElse("PYTHONPATH", "") val pythonPath = PythonUtils.mergePythonPaths(pathElements: _*) 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 e86182e4c56ce..a99b2176e2b5e 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala @@ -41,10 +41,10 @@ object SparkSubmit { private var clusterManager: Int = LOCAL /** - * A special jar name that indicates the class being run is inside of Spark itself, - * and therefore no user jar is needed. + * Special primary resource names that represent shells rather than application jars. */ - private val RESERVED_JAR_NAME = "spark-internal" + private val SPARK_SHELL = "spark-shell" + private val PYSPARK_SHELL = "pyspark-shell" def main(args: Array[String]) { val appArgs = new SparkSubmitArguments(args) @@ -71,8 +71,8 @@ object SparkSubmit { * entries for the child, a list of system properties, a list of env vars * and the main class for the child */ - private[spark] def createLaunchEnv(args: SparkSubmitArguments): (ArrayBuffer[String], - ArrayBuffer[String], Map[String, String], String) = { + private[spark] def createLaunchEnv(args: SparkSubmitArguments) + : (ArrayBuffer[String], ArrayBuffer[String], Map[String, String], String) = { if (args.master.startsWith("local")) { clusterManager = LOCAL } else if (args.master.startsWith("yarn")) { @@ -121,24 +121,30 @@ object SparkSubmit { printErrorAndExit("Cannot currently run driver on the cluster in Mesos") } - // If we're running a Python app, set the Java class to run to be our PythonRunner, add - // Python files to deployment list, and pass the main file and Python path to PythonRunner + // If we're running a python app, set the main class to our specific python runner if (isPython) { if (deployOnCluster) { printErrorAndExit("Cannot currently run Python driver programs on cluster") } - args.mainClass = "org.apache.spark.deploy.PythonRunner" - args.files = mergeFileLists(args.files, args.pyFiles, args.primaryResource) + if (args.primaryResource == PYSPARK_SHELL) { + args.mainClass = "py4j.GatewayServer" + args.childArgs = ArrayBuffer("--die-on-broken-pipe", "0") + } else { + // If a python file is provided, add it to the child arguments and list of files to deploy. + // Usage: PythonAppRunner
[app arguments] + args.mainClass = "org.apache.spark.deploy.PythonRunner" + args.childArgs = ArrayBuffer(args.primaryResource, args.pyFiles) ++ args.childArgs + args.files = mergeFileLists(args.files, args.primaryResource) + } val pyFiles = Option(args.pyFiles).getOrElse("") - args.childArgs = ArrayBuffer(args.primaryResource, pyFiles) ++ args.childArgs - args.primaryResource = RESERVED_JAR_NAME + args.files = mergeFileLists(args.files, pyFiles) sysProps("spark.submit.pyFiles") = pyFiles } // If we're deploying into YARN, use yarn.Client as a wrapper around the user class if (!deployOnCluster) { childMainClass = args.mainClass - if (args.primaryResource != RESERVED_JAR_NAME) { + if (isUserJar(args.primaryResource)) { childClasspath += args.primaryResource } } else if (clusterManager == YARN) { @@ -219,7 +225,7 @@ object SparkSubmit { // For python files, the primary resource is already distributed as a regular file if (!isYarnCluster && !isPython) { var jars = sysProps.get("spark.jars").map(x => x.split(",").toSeq).getOrElse(Seq()) - if (args.primaryResource != RESERVED_JAR_NAME) { + if (isUserJar(args.primaryResource)) { jars = jars ++ Seq(args.primaryResource) } sysProps.put("spark.jars", jars.mkString(",")) @@ -293,7 +299,7 @@ object SparkSubmit { } private def addJarToClasspath(localJar: String, loader: ExecutorURLClassLoader) { - val localJarFile = new File(new URI(localJar).getPath()) + val localJarFile = new File(new URI(localJar).getPath) if (!localJarFile.exists()) { printWarning(s"Jar $localJar does not exist, skipping.") } @@ -302,6 +308,27 @@ object SparkSubmit { loader.addURL(url) } + /** + * Return whether the given primary resource represents a user jar. + */ + private def isUserJar(primaryResource: String): Boolean = { + !isShell(primaryResource) && !isPython(primaryResource) + } + + /** + * Return whether the given primary resource represents a shell. + */ + private def isShell(primaryResource: String): Boolean = { + primaryResource == SPARK_SHELL || primaryResource == PYSPARK_SHELL + } + + /** + * Return whether the given primary resource requires running python. + */ + private[spark] def isPython(primaryResource: String): Boolean = { + primaryResource.endsWith(".py") || primaryResource == PYSPARK_SHELL + } + /** * Merge a sequence of comma-separated file lists, some of which may be null to indicate * no files, into a single comma-separated string. 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 2d327aa3fb27f..264d4544cd31c 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala @@ -298,11 +298,13 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) { case v => primaryResource = v inSparkOpts = false - isPython = v.endsWith(".py") + isPython = SparkSubmit.isPython(v) parse(tail) } } else { - childArgs += value + if (!value.isEmpty) { + childArgs += value + } parse(tail) } 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 388f7222428db..0c7cff019fce1 100644 --- a/core/src/main/scala/org/apache/spark/util/Utils.scala +++ b/core/src/main/scala/org/apache/spark/util/Utils.scala @@ -1101,7 +1101,7 @@ private[spark] object Utils extends Logging { * Strip the directory from a path name */ def stripDirectory(path: String): String = { - path.split(File.separator).last + new File(path).getName } /** diff --git a/python/pyspark/java_gateway.py b/python/pyspark/java_gateway.py index 3d0936fdca911..91ae8263f66b8 100644 --- a/python/pyspark/java_gateway.py +++ b/python/pyspark/java_gateway.py @@ -18,12 +18,12 @@ import os import sys import signal +import shlex import platform from subprocess import Popen, PIPE from threading import Thread from py4j.java_gateway import java_import, JavaGateway, GatewayClient - def launch_gateway(): SPARK_HOME = os.environ["SPARK_HOME"] @@ -34,9 +34,11 @@ def launch_gateway(): # 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-class.cmd" if on_windows else "./bin/spark-class" - command = [os.path.join(SPARK_HOME, script), "py4j.GatewayServer", - "--die-on-broken-pipe", "0"] + 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), "pyspark-shell"] + submit_args if not on_windows: # Don't send ctrl-c / SIGINT to the Java gateway: def preexec_func(): diff --git a/python/pyspark/shell.py b/python/pyspark/shell.py index d172d588bfbd8..ebd714db7a918 100644 --- a/python/pyspark/shell.py +++ b/python/pyspark/shell.py @@ -40,7 +40,7 @@ if os.environ.get("SPARK_EXECUTOR_URI"): SparkContext.setSystemProperty("spark.executor.uri", os.environ["SPARK_EXECUTOR_URI"]) -sc = SparkContext(os.environ.get("MASTER", "local[*]"), "PySparkShell", pyFiles=add_files) +sc = SparkContext(appName="PySparkShell", pyFiles=add_files) print("""Welcome to ____ __ From cf6cbe9f76c3b322a968c836d039fc5b70d4ce43 Mon Sep 17 00:00:00 2001 From: Andrew Or Date: Fri, 16 May 2014 22:36:23 -0700 Subject: [PATCH 004/221] [SPARK-1824] Remove from Python examples A recent PR (#552) fixed this for all Scala / Java examples. We need to do it for python too. Note that this blocks on #799, which makes `bin/pyspark` go through Spark submit. With only the changes in this PR, the only way to run these examples is through Spark submit. Once #799 goes in, you can use `bin/pyspark` to run them too. For example, ``` bin/pyspark examples/src/main/python/pi.py 100 --master local-cluster[4,1,512] ``` Author: Andrew Or Closes #802 from andrewor14/python-examples and squashes the following commits: cf50b9f [Andrew Or] De-indent python comments (minor) 50f80b1 [Andrew Or] Remove pyFiles from SparkContext construction c362f69 [Andrew Or] Update docs to use spark-submit for python applications 7072c6a [Andrew Or] Merge branch 'master' of github.com:apache/spark into python-examples 427a5f0 [Andrew Or] Update docs d32072c [Andrew Or] Remove from examples + update usages --- docs/index.md | 11 ++++--- docs/python-programming-guide.md | 32 ++++++++++--------- examples/src/main/python/als.py | 18 +++++------ examples/src/main/python/kmeans.py | 12 +++---- .../src/main/python/logistic_regression.py | 10 +++--- examples/src/main/python/mllib/kmeans.py | 10 +++--- .../main/python/mllib/logistic_regression.py | 10 +++--- examples/src/main/python/pagerank.py | 10 +++--- examples/src/main/python/pi.py | 10 +++--- examples/src/main/python/sort.py | 8 ++--- .../src/main/python/transitive_closure.py | 10 +++--- examples/src/main/python/wordcount.py | 8 ++--- 12 files changed, 77 insertions(+), 72 deletions(-) diff --git a/docs/index.md b/docs/index.md index 48182a27d28ae..c9b10376cc809 100644 --- a/docs/index.md +++ b/docs/index.md @@ -43,12 +43,15 @@ The `--master` option specifies the locally with one thread, or `local[N]` to run locally with N threads. You should start by using `local` for testing. For a full list of options, run Spark shell with the `--help` option. -Spark also provides a Python interface. To run an example Spark application written in Python, use -`bin/pyspark [params]`. For example, +Spark also provides a Python interface. To run Spark interactively in a Python interpreter, use +`bin/pyspark`. As in Spark shell, you can also pass in the `--master` option to configure your +master URL. - ./bin/pyspark examples/src/main/python/pi.py local[2] 10 + ./bin/pyspark --master local[2] -or simply `bin/pyspark` without any arguments to run Spark interactively in a python interpreter. +Example applications are also provided in Python. For example, + + ./bin/spark-submit examples/src/main/python/pi.py 10 # Launching on a Cluster diff --git a/docs/python-programming-guide.md b/docs/python-programming-guide.md index 17675acba6bb8..b686bee1aebe2 100644 --- a/docs/python-programming-guide.md +++ b/docs/python-programming-guide.md @@ -60,13 +60,9 @@ By default, PySpark requires `python` to be available on the system `PATH` and u All of PySpark's library dependencies, including [Py4J](http://py4j.sourceforge.net/), are bundled with PySpark and automatically imported. -Standalone PySpark applications should be run using the `bin/spark-submit` script, which automatically -configures the Java and Python environment for running Spark. - - # Interactive Use -The `bin/pyspark` script launches a Python interpreter that is configured to run PySpark applications. To use `pyspark` interactively, first build Spark, then launch it directly from the command line without any options: +The `bin/pyspark` script launches a Python interpreter that is configured to run PySpark applications. To use `pyspark` interactively, first build Spark, then launch it directly from the command line: {% highlight bash %} $ sbt/sbt assembly @@ -83,20 +79,24 @@ The Python shell can be used explore data interactively and is a simple way to l {% endhighlight %} By default, the `bin/pyspark` shell creates SparkContext that runs applications locally on all of -your machine's logical cores. -To connect to a non-local cluster, or to specify a number of cores, set the `MASTER` environment variable. -For example, to use the `bin/pyspark` shell with a [standalone Spark cluster](spark-standalone.html): +your machine's logical cores. To connect to a non-local cluster, or to specify a number of cores, +set the `--master` flag. For example, to use the `bin/pyspark` shell with a +[standalone Spark cluster](spark-standalone.html): {% highlight bash %} -$ MASTER=spark://IP:PORT ./bin/pyspark +$ ./bin/pyspark --master spark://1.2.3.4:7077 {% endhighlight %} Or, to use exactly four cores on the local machine: {% highlight bash %} -$ MASTER=local[4] ./bin/pyspark +$ ./bin/pyspark --master local[4] {% endhighlight %} +Under the hood `bin/pyspark` is a wrapper around the +[Spark submit script](cluster-overview.html#launching-applications-with-spark-submit), so these +two scripts share the same list of options. For a complete list of options, run `bin/pyspark` with +the `--help` option. ## IPython @@ -115,13 +115,14 @@ the [IPython Notebook](http://ipython.org/notebook.html) with PyLab graphing sup $ IPYTHON_OPTS="notebook --pylab inline" ./bin/pyspark {% endhighlight %} -IPython also works on a cluster or on multiple cores if you set the `MASTER` environment variable. +IPython also works on a cluster or on multiple cores if you set the `--master` flag. # Standalone Programs -PySpark can also be used from standalone Python scripts by creating a SparkContext in your script and running the script using `bin/spark-submit`. -The Quick Start guide includes a [complete example](quick-start.html#standalone-applications) of a standalone Python application. +PySpark can also be used from standalone Python scripts by creating a SparkContext in your script +and running the script using `bin/spark-submit`. The Quick Start guide includes a +[complete example](quick-start.html#standalone-applications) of a standalone Python application. Code dependencies can be deployed by passing .zip or .egg files in the `--py-files` option of `spark-submit`: @@ -138,6 +139,7 @@ You can set [configuration properties](configuration.html#spark-properties) by p {% highlight python %} from pyspark import SparkConf, SparkContext conf = (SparkConf() + .setMaster("local") .setAppName("My app") .set("spark.executor.memory", "1g")) sc = SparkContext(conf = conf) @@ -164,6 +166,6 @@ some example applications. PySpark also includes several sample programs in the [`examples/src/main/python` folder](https://github.com/apache/spark/tree/master/examples/src/main/python). You can run them by passing the files to `pyspark`; e.g.: - ./bin/spark-submit examples/src/main/python/wordcount.py local[2] README.md + ./bin/spark-submit examples/src/main/python/wordcount.py README.md -Each program prints usage help when run without arguments. +Each program prints usage help when run without the sufficient arguments. diff --git a/examples/src/main/python/als.py b/examples/src/main/python/als.py index 01552dc1d449e..f0b46cd28b7aa 100755 --- a/examples/src/main/python/als.py +++ b/examples/src/main/python/als.py @@ -46,15 +46,15 @@ def update(i, vec, mat, ratings): return np.linalg.solve(XtX, Xty) if __name__ == "__main__": - if len(sys.argv) < 2: - print >> sys.stderr, "Usage: als " - exit(-1) - sc = SparkContext(sys.argv[1], "PythonALS", pyFiles=[realpath(__file__)]) - M = int(sys.argv[2]) if len(sys.argv) > 2 else 100 - U = int(sys.argv[3]) if len(sys.argv) > 3 else 500 - F = int(sys.argv[4]) if len(sys.argv) > 4 else 10 - ITERATIONS = int(sys.argv[5]) if len(sys.argv) > 5 else 5 - slices = int(sys.argv[6]) if len(sys.argv) > 6 else 2 + """ + Usage: als [M] [U] [F] [iterations] [slices]" + """ + sc = SparkContext(appName="PythonALS") + M = int(sys.argv[1]) if len(sys.argv) > 1 else 100 + U = int(sys.argv[2]) if len(sys.argv) > 2 else 500 + F = int(sys.argv[3]) if len(sys.argv) > 3 else 10 + ITERATIONS = int(sys.argv[4]) if len(sys.argv) > 4 else 5 + slices = int(sys.argv[5]) if len(sys.argv) > 5 else 2 print "Running ALS with M=%d, U=%d, F=%d, iters=%d, slices=%d\n" % \ (M, U, F, ITERATIONS, slices) diff --git a/examples/src/main/python/kmeans.py b/examples/src/main/python/kmeans.py index e3596488faf9e..fc16586c28a46 100755 --- a/examples/src/main/python/kmeans.py +++ b/examples/src/main/python/kmeans.py @@ -45,14 +45,14 @@ def closestPoint(p, centers): if __name__ == "__main__": - if len(sys.argv) < 5: - print >> sys.stderr, "Usage: kmeans " + if len(sys.argv) != 4: + print >> sys.stderr, "Usage: kmeans " exit(-1) - sc = SparkContext(sys.argv[1], "PythonKMeans") - lines = sc.textFile(sys.argv[2]) + sc = SparkContext(appName="PythonKMeans") + lines = sc.textFile(sys.argv[1]) data = lines.map(parseVector).cache() - K = int(sys.argv[3]) - convergeDist = float(sys.argv[4]) + K = int(sys.argv[2]) + convergeDist = float(sys.argv[3]) kPoints = data.takeSample(False, K, 1) tempDist = 1.0 diff --git a/examples/src/main/python/logistic_regression.py b/examples/src/main/python/logistic_regression.py index fe5373cf799b1..0f22d0b32319e 100755 --- a/examples/src/main/python/logistic_regression.py +++ b/examples/src/main/python/logistic_regression.py @@ -47,12 +47,12 @@ def readPointBatch(iterator): return [matrix] if __name__ == "__main__": - if len(sys.argv) != 4: - print >> sys.stderr, "Usage: logistic_regression " + if len(sys.argv) != 3: + print >> sys.stderr, "Usage: logistic_regression " exit(-1) - sc = SparkContext(sys.argv[1], "PythonLR", pyFiles=[realpath(__file__)]) - points = sc.textFile(sys.argv[2]).mapPartitions(readPointBatch).cache() - iterations = int(sys.argv[3]) + sc = SparkContext(appName="PythonLR") + points = sc.textFile(sys.argv[1]).mapPartitions(readPointBatch).cache() + iterations = int(sys.argv[2]) # Initialize w to a random value w = 2 * np.random.ranf(size=D) - 1 diff --git a/examples/src/main/python/mllib/kmeans.py b/examples/src/main/python/mllib/kmeans.py index dec82ff34fbac..b308132c9aeeb 100755 --- a/examples/src/main/python/mllib/kmeans.py +++ b/examples/src/main/python/mllib/kmeans.py @@ -33,12 +33,12 @@ def parseVector(line): if __name__ == "__main__": - if len(sys.argv) < 4: - print >> sys.stderr, "Usage: kmeans " + if len(sys.argv) != 3: + print >> sys.stderr, "Usage: kmeans " exit(-1) - sc = SparkContext(sys.argv[1], "KMeans") - lines = sc.textFile(sys.argv[2]) + sc = SparkContext(appName="KMeans") + lines = sc.textFile(sys.argv[1]) data = lines.map(parseVector) - k = int(sys.argv[3]) + k = int(sys.argv[2]) model = KMeans.train(data, k) print "Final centers: " + str(model.clusterCenters) diff --git a/examples/src/main/python/mllib/logistic_regression.py b/examples/src/main/python/mllib/logistic_regression.py index 8631051d00ff2..6e0f7a4ee5a81 100755 --- a/examples/src/main/python/mllib/logistic_regression.py +++ b/examples/src/main/python/mllib/logistic_regression.py @@ -39,12 +39,12 @@ def parsePoint(line): if __name__ == "__main__": - if len(sys.argv) != 4: - print >> sys.stderr, "Usage: logistic_regression " + if len(sys.argv) != 3: + print >> sys.stderr, "Usage: logistic_regression " exit(-1) - sc = SparkContext(sys.argv[1], "PythonLR") - points = sc.textFile(sys.argv[2]).map(parsePoint) - iterations = int(sys.argv[3]) + 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) diff --git a/examples/src/main/python/pagerank.py b/examples/src/main/python/pagerank.py index cd774cf3a319f..d350fa46fa49a 100755 --- a/examples/src/main/python/pagerank.py +++ b/examples/src/main/python/pagerank.py @@ -36,19 +36,19 @@ def parseNeighbors(urls): if __name__ == "__main__": - if len(sys.argv) < 3: - print >> sys.stderr, "Usage: pagerank " + if len(sys.argv) != 3: + print >> sys.stderr, "Usage: pagerank " exit(-1) # Initialize the spark context. - sc = SparkContext(sys.argv[1], "PythonPageRank") + sc = SparkContext(appName="PythonPageRank") # Loads in input file. It should be in format of: # URL neighbor URL # URL neighbor URL # URL neighbor URL # ... - lines = sc.textFile(sys.argv[2], 1) + lines = sc.textFile(sys.argv[1], 1) # Loads all URLs from input file and initialize their neighbors. links = lines.map(lambda urls: parseNeighbors(urls)).distinct().groupByKey().cache() @@ -57,7 +57,7 @@ def parseNeighbors(urls): ranks = links.map(lambda (url, neighbors): (url, 1.0)) # Calculates and updates URL ranks continuously using PageRank algorithm. - for iteration in xrange(int(sys.argv[3])): + for iteration in xrange(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)) diff --git a/examples/src/main/python/pi.py b/examples/src/main/python/pi.py index ab0645fc2f326..234720b55fa49 100755 --- a/examples/src/main/python/pi.py +++ b/examples/src/main/python/pi.py @@ -23,11 +23,11 @@ if __name__ == "__main__": - if len(sys.argv) == 1: - print >> sys.stderr, "Usage: pi []" - exit(-1) - sc = SparkContext(sys.argv[1], "PythonPi") - slices = int(sys.argv[2]) if len(sys.argv) > 2 else 2 + """ + Usage: pi [slices] + """ + sc = SparkContext(appName="PythonPi") + slices = int(sys.argv[1]) if len(sys.argv) > 1 else 2 n = 100000 * slices def f(_): x = random() * 2 - 1 diff --git a/examples/src/main/python/sort.py b/examples/src/main/python/sort.py index 5de20a6d98f43..4913ee926aa03 100755 --- a/examples/src/main/python/sort.py +++ b/examples/src/main/python/sort.py @@ -21,11 +21,11 @@ if __name__ == "__main__": - if len(sys.argv) < 3: - print >> sys.stderr, "Usage: sort " + if len(sys.argv) != 2: + print >> sys.stderr, "Usage: sort " exit(-1) - sc = SparkContext(sys.argv[1], "PythonSort") - lines = sc.textFile(sys.argv[2], 1) + sc = SparkContext(appName="PythonSort") + lines = sc.textFile(sys.argv[1], 1) sortedCount = lines.flatMap(lambda x: x.split(' ')) \ .map(lambda x: (int(x), 1)) \ .sortByKey(lambda x: x) diff --git a/examples/src/main/python/transitive_closure.py b/examples/src/main/python/transitive_closure.py index 744cce6651607..8698369b13d84 100755 --- a/examples/src/main/python/transitive_closure.py +++ b/examples/src/main/python/transitive_closure.py @@ -36,11 +36,11 @@ def generateGraph(): if __name__ == "__main__": - if len(sys.argv) == 1: - print >> sys.stderr, "Usage: transitive_closure []" - exit(-1) - sc = SparkContext(sys.argv[1], "PythonTransitiveClosure") - slices = int(sys.argv[2]) if len(sys.argv) > 2 else 2 + """ + Usage: transitive_closure [slices] + """ + sc = SparkContext(appName="PythonTransitiveClosure") + slices = int(sys.argv[1]) if len(sys.argv) > 1 else 2 tc = sc.parallelize(generateGraph(), slices).cache() # Linear transitive closure: each round grows paths by one edge, diff --git a/examples/src/main/python/wordcount.py b/examples/src/main/python/wordcount.py index b9139b9d76520..dcc095fdd0ed9 100755 --- a/examples/src/main/python/wordcount.py +++ b/examples/src/main/python/wordcount.py @@ -22,11 +22,11 @@ if __name__ == "__main__": - if len(sys.argv) < 3: - print >> sys.stderr, "Usage: wordcount " + if len(sys.argv) != 2: + print >> sys.stderr, "Usage: wordcount " exit(-1) - sc = SparkContext(sys.argv[1], "PythonWordCount") - lines = sc.textFile(sys.argv[2], 1) + sc = SparkContext(appName="PythonWordCount") + lines = sc.textFile(sys.argv[1], 1) counts = lines.flatMap(lambda x: x.split(' ')) \ .map(lambda x: (x, 1)) \ .reduceByKey(add) From 442808a7482b81c8de887c901b424683da62022e Mon Sep 17 00:00:00 2001 From: Patrick Wendell Date: Fri, 16 May 2014 22:58:47 -0700 Subject: [PATCH 005/221] Make deprecation warning less severe Just a small change. I think it's good not to scare people who are using the old options. Author: Patrick Wendell Closes #810 from pwendell/warnings and squashes the following commits: cb8a311 [Patrick Wendell] Make deprecation warning less severe --- core/src/main/scala/org/apache/spark/SparkConf.scala | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/SparkConf.scala b/core/src/main/scala/org/apache/spark/SparkConf.scala index 800616622d7bf..8ce4b91cae8ae 100644 --- a/core/src/main/scala/org/apache/spark/SparkConf.scala +++ b/core/src/main/scala/org/apache/spark/SparkConf.scala @@ -238,10 +238,10 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging { // Check for legacy configs sys.env.get("SPARK_JAVA_OPTS").foreach { value => - val error = + val warning = s""" |SPARK_JAVA_OPTS was detected (set to '$value'). - |This has undefined behavior when running on a cluster and is deprecated in Spark 1.0+. + |This is deprecated in Spark 1.0+. | |Please instead use: | - ./spark-submit with conf/spark-defaults.conf to set defaults for an application @@ -249,7 +249,7 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging { | - spark.executor.extraJavaOptions to set -X options for executors | - SPARK_DAEMON_JAVA_OPTS to set java options for standalone daemons (master or worker) """.stripMargin - logError(error) + logWarning(warning) for (key <- Seq(executorOptsKey, driverOptsKey)) { if (getOption(key).isDefined) { @@ -262,16 +262,16 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging { } sys.env.get("SPARK_CLASSPATH").foreach { value => - val error = + val warning = s""" |SPARK_CLASSPATH was detected (set to '$value'). - | This has undefined behavior when running on a cluster and is deprecated in Spark 1.0+. + |This is deprecated in Spark 1.0+. | |Please instead use: | - ./spark-submit with --driver-class-path to augment the driver classpath | - spark.executor.extraClassPath to augment the executor classpath """.stripMargin - logError(error) + logWarning(warning) for (key <- Seq(executorClasspathKey, driverClassPathKey)) { if (getOption(key).isDefined) { From ebcd2d68897f423614d0629758240da97dca4a12 Mon Sep 17 00:00:00 2001 From: Neville Li Date: Sun, 18 May 2014 13:31:23 -0700 Subject: [PATCH 006/221] Fix spark-submit path in spark-shell & pyspark Author: Neville Li Closes #812 from nevillelyh/neville/v1.0 and squashes the following commits: 0dc33ed [Neville Li] Fix spark-submit path in pyspark becec64 [Neville Li] Fix spark-submit path in spark-shell --- bin/pyspark | 2 +- bin/spark-shell | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/bin/pyspark b/bin/pyspark index 9e1364e44c8c4..3908ffe79939b 100755 --- a/bin/pyspark +++ b/bin/pyspark @@ -27,7 +27,7 @@ SCALA_VERSION=2.10 if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then echo "Usage: ./bin/pyspark [options]" - ./bin/spark-submit --help 2>&1 | grep -v Usage 1>&2 + $FWDIR/bin/spark-submit --help 2>&1 | grep -v Usage 1>&2 exit 0 fi diff --git a/bin/spark-shell b/bin/spark-shell index c158683ab3f99..850e9507ec38f 100755 --- a/bin/spark-shell +++ b/bin/spark-shell @@ -28,15 +28,15 @@ esac # Enter posix mode for bash set -o posix +## Global script variables +FWDIR="$(cd `dirname $0`/..; pwd)" + if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then echo "Usage: ./bin/spark-shell [options]" - ./bin/spark-submit --help 2>&1 | grep -v Usage 1>&2 + $FWDIR/bin/spark-submit --help 2>&1 | grep -v Usage 1>&2 exit 0 fi -## Global script variables -FWDIR="$(cd `dirname $0`/..; pwd)" - function main(){ if $cygwin; then # Workaround for issue involving JLine and Cygwin From 4ce479324bdcf603806fc90b5b0f4968c6de690e Mon Sep 17 00:00:00 2001 From: Patrick Wendell Date: Sun, 18 May 2014 16:51:53 -0700 Subject: [PATCH 007/221] SPARK-1873: Add README.md file when making distributions Author: Patrick Wendell Closes #818 from pwendell/reamde and squashes the following commits: 4020b11 [Patrick Wendell] SPARK-1873: Add README.md file when making distributions --- make-distribution.sh | 1 + 1 file changed, 1 insertion(+) diff --git a/make-distribution.sh b/make-distribution.sh index 7a08d6b9151c4..1c89027d68bed 100755 --- a/make-distribution.sh +++ b/make-distribution.sh @@ -191,6 +191,7 @@ fi mkdir "$DISTDIR"/conf cp "$FWDIR"/conf/*.template "$DISTDIR"/conf cp "$FWDIR"/conf/slaves "$DISTDIR"/conf +cp "$FWDIR/README.md" "$DISTDIR" cp -r "$FWDIR/bin" "$DISTDIR" cp -r "$FWDIR/python" "$DISTDIR" cp -r "$FWDIR/sbin" "$DISTDIR" From df0aa8353ab6d3b19d838c6fa95a93a64948309f Mon Sep 17 00:00:00 2001 From: Xiangrui Meng Date: Sun, 18 May 2014 17:00:57 -0700 Subject: [PATCH 008/221] [WIP][SPARK-1871][MLLIB] Improve MLlib guide for v1.0 Some improvements to MLlib guide: 1. [SPARK-1872] Update API links for unidoc. 2. [SPARK-1783] Added `page.displayTitle` to the global layout. If it is defined, use it instead of `page.title` for title display. 3. Add more Java/Python examples. Author: Xiangrui Meng Closes #816 from mengxr/mllib-doc and squashes the following commits: ec2e407 [Xiangrui Meng] format scala example for ALS cd9f40b [Xiangrui Meng] add a paragraph to summarize distributed matrix types 4617f04 [Xiangrui Meng] add python example to loadLibSVMFile and fix Java example d6509c2 [Xiangrui Meng] [SPARK-1783] update mllib titles 561fdc0 [Xiangrui Meng] add a displayTitle option to global layout 195d06f [Xiangrui Meng] add Java example for summary stats and minor fix 9f1ff89 [Xiangrui Meng] update java api links in mllib-basics 7dad18e [Xiangrui Meng] update java api links in NB 3a0f4a6 [Xiangrui Meng] api/pyspark -> api/python 35bdeb9 [Xiangrui Meng] api/mllib -> api/scala e4afaa8 [Xiangrui Meng] explicity state what might change --- docs/_layouts/global.html | 6 +- docs/mllib-basics.md | 125 +++++++++++++++++-------- docs/mllib-clustering.md | 5 +- docs/mllib-collaborative-filtering.md | 29 +++--- docs/mllib-decision-tree.md | 3 +- docs/mllib-dimensionality-reduction.md | 3 +- docs/mllib-guide.md | 19 ++-- docs/mllib-linear-methods.md | 21 +++-- docs/mllib-naive-bayes.md | 21 +++-- docs/mllib-optimization.md | 11 ++- 10 files changed, 153 insertions(+), 90 deletions(-) diff --git a/docs/_layouts/global.html b/docs/_layouts/global.html index 8b543de574622..fb808129bb65d 100755 --- a/docs/_layouts/global.html +++ b/docs/_layouts/global.html @@ -114,7 +114,11 @@
-

{{ page.title }}

+ {% if page.displayTitle %} +

{{ page.displayTitle }}

+ {% else %} +

{{ page.title }}

+ {% endif %} {{ content }} diff --git a/docs/mllib-basics.md b/docs/mllib-basics.md index aa9321a547097..5796e16e8f99c 100644 --- a/docs/mllib-basics.md +++ b/docs/mllib-basics.md @@ -1,6 +1,7 @@ --- layout: global -title: MLlib - Basics +title: Basics - MLlib +displayTitle: MLlib - Basics --- * Table of contents @@ -26,11 +27,11 @@ of the vector.
The base class of local vectors is -[`Vector`](api/mllib/index.html#org.apache.spark.mllib.linalg.Vector), and we provide two -implementations: [`DenseVector`](api/mllib/index.html#org.apache.spark.mllib.linalg.DenseVector) and -[`SparseVector`](api/mllib/index.html#org.apache.spark.mllib.linalg.SparseVector). We recommend +[`Vector`](api/scala/index.html#org.apache.spark.mllib.linalg.Vector), and we provide two +implementations: [`DenseVector`](api/scala/index.html#org.apache.spark.mllib.linalg.DenseVector) and +[`SparseVector`](api/scala/index.html#org.apache.spark.mllib.linalg.SparseVector). We recommend using the factory methods implemented in -[`Vectors`](api/mllib/index.html#org.apache.spark.mllib.linalg.Vector) to create local vectors. +[`Vectors`](api/scala/index.html#org.apache.spark.mllib.linalg.Vector) to create local vectors. {% highlight scala %} import org.apache.spark.mllib.linalg.{Vector, Vectors} @@ -53,11 +54,11 @@ Scala imports `scala.collection.immutable.Vector` by default, so you have to imp
The base class of local vectors is -[`Vector`](api/mllib/index.html#org.apache.spark.mllib.linalg.Vector), and we provide two -implementations: [`DenseVector`](api/mllib/index.html#org.apache.spark.mllib.linalg.DenseVector) and -[`SparseVector`](api/mllib/index.html#org.apache.spark.mllib.linalg.SparseVector). We recommend +[`Vector`](api/java/org/apache/spark/mllib/linalg/Vector.html), and we provide two +implementations: [`DenseVector`](api/java/org/apache/spark/mllib/linalg/DenseVector.html) and +[`SparseVector`](api/java/org/apache/spark/mllib/linalg/SparseVector.html). We recommend using the factory methods implemented in -[`Vectors`](api/mllib/index.html#org.apache.spark.mllib.linalg.Vector) to create local vectors. +[`Vectors`](api/java/org/apache/spark/mllib/linalg/Vector.html) to create local vectors. {% highlight java %} import org.apache.spark.mllib.linalg.Vector; @@ -78,13 +79,13 @@ MLlib recognizes the following types as dense vectors: and the following as sparse vectors: -* MLlib's [`SparseVector`](api/pyspark/pyspark.mllib.linalg.SparseVector-class.html). +* MLlib's [`SparseVector`](api/python/pyspark.mllib.linalg.SparseVector-class.html). * 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/pyspark/pyspark.mllib.linalg.Vectors-class.html) to create sparse vectors. +in [`Vectors`](api/python/pyspark.mllib.linalg.Vectors-class.html) to create sparse vectors. {% highlight python %} import numpy as np @@ -117,7 +118,7 @@ For multiclass classification, labels should be class indices staring from zero:
A labeled point is represented by the case class -[`LabeledPoint`](api/mllib/index.html#org.apache.spark.mllib.regression.LabeledPoint). +[`LabeledPoint`](api/scala/index.html#org.apache.spark.mllib.regression.LabeledPoint). {% highlight scala %} import org.apache.spark.mllib.linalg.Vectors @@ -134,7 +135,7 @@ val neg = LabeledPoint(0.0, Vectors.sparse(3, Array(0, 2), Array(1.0, 3.0)))
A labeled point is represented by -[`LabeledPoint`](api/mllib/index.html#org.apache.spark.mllib.regression.LabeledPoint). +[`LabeledPoint`](api/java/org/apache/spark/mllib/regression/LabeledPoint.html). {% highlight java %} import org.apache.spark.mllib.linalg.Vectors; @@ -151,7 +152,7 @@ LabeledPoint neg = new LabeledPoint(1.0, Vectors.sparse(3, new int[] {0, 2}, new
A labeled point is represented by -[`LabeledPoint`](api/pyspark/pyspark.mllib.regression.LabeledPoint-class.html). +[`LabeledPoint`](api/python/pyspark.mllib.regression.LabeledPoint-class.html). {% highlight python %} from pyspark.mllib.linalg import SparseVector @@ -184,7 +185,7 @@ After loading, the feature indices are converted to zero-based.
-[`MLUtils.loadLibSVMFile`](api/mllib/index.html#org.apache.spark.mllib.util.MLUtils$) reads training +[`MLUtils.loadLibSVMFile`](api/scala/index.html#org.apache.spark.mllib.util.MLUtils$) reads training examples stored in LIBSVM format. {% highlight scala %} @@ -192,20 +193,32 @@ import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.util.MLUtils import org.apache.spark.rdd.RDD -val training: RDD[LabeledPoint] = MLUtils.loadLibSVMFile(sc, "mllib/data/sample_libsvm_data.txt") +val examples: RDD[LabeledPoint] = MLUtils.loadLibSVMFile(sc, "mllib/data/sample_libsvm_data.txt") {% endhighlight %}
-[`MLUtils.loadLibSVMFile`](api/mllib/index.html#org.apache.spark.mllib.util.MLUtils$) reads training +[`MLUtils.loadLibSVMFile`](api/java/org/apache/spark/mllib/util/MLUtils.html) reads training examples stored in LIBSVM format. {% highlight java %} import org.apache.spark.mllib.regression.LabeledPoint; import org.apache.spark.mllib.util.MLUtils; -import org.apache.spark.rdd.RDDimport; +import org.apache.spark.api.java.JavaRDD; + +JavaRDD examples = + MLUtils.loadLibSVMFile(jsc.sc(), "mllib/data/sample_libsvm_data.txt").toJavaRDD(); +{% endhighlight %} +
+ +
+[`MLUtils.loadLibSVMFile`](api/python/pyspark.mllib.util.MLUtils-class.html) reads training +examples stored in LIBSVM format. -RDD training = MLUtils.loadLibSVMFile(jsc, "mllib/data/sample_libsvm_data.txt"); +{% highlight python %} +from pyspark.mllib.util import MLUtils + +examples = MLUtils.loadLibSVMFile(sc, "mllib/data/sample_libsvm_data.txt") {% endhighlight %}
@@ -227,10 +240,10 @@ We are going to add sparse matrix in the next release.
The base class of local matrices is -[`Matrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.Matrix), and we provide one -implementation: [`DenseMatrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.DenseMatrix). +[`Matrix`](api/scala/index.html#org.apache.spark.mllib.linalg.Matrix), and we provide one +implementation: [`DenseMatrix`](api/scala/index.html#org.apache.spark.mllib.linalg.DenseMatrix). Sparse matrix will be added in the next release. We recommend using the factory methods implemented -in [`Matrices`](api/mllib/index.html#org.apache.spark.mllib.linalg.Matrices) to create local +in [`Matrices`](api/scala/index.html#org.apache.spark.mllib.linalg.Matrices) to create local matrices. {% highlight scala %} @@ -244,10 +257,10 @@ val dm: Matrix = Matrices.dense(3, 2, Array(1.0, 3.0, 5.0, 2.0, 4.0, 6.0))
The base class of local matrices is -[`Matrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.Matrix), and we provide one -implementation: [`DenseMatrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.DenseMatrix). +[`Matrix`](api/java/org/apache/spark/mllib/linalg/Matrix.html), and we provide one +implementation: [`DenseMatrix`](api/java/org/apache/spark/mllib/linalg/DenseMatrix.html). Sparse matrix will be added in the next release. We recommend using the factory methods implemented -in [`Matrices`](api/mllib/index.html#org.apache.spark.mllib.linalg.Matrices) to create local +in [`Matrices`](api/java/org/apache/spark/mllib/linalg/Matrices.html) to create local matrices. {% highlight java %} @@ -269,6 +282,15 @@ and distributed matrices. Converting a distributed matrix to a different format global shuffle, which is quite expensive. We implemented three types of distributed matrices in this release and will add more types in the future. +The basic type is called `RowMatrix`. A `RowMatrix` is a row-oriented distributed +matrix without meaningful row indices, e.g., a collection of feature vectors. +It is backed by an RDD of its rows, where each row is a local vector. +We assume that the number of columns is not huge for a `RowMatrix`. +An `IndexedRowMatrix` is similar to a `RowMatrix` but with row indices, +which can be used for identifying rows and joins. +A `CoordinateMatrix` is a distributed matrix stored in [coordinate list (COO)](https://en.wikipedia.org/wiki/Sparse_matrix) format, +backed by an RDD of its entries. + ***Note*** The underlying RDDs of a distributed matrix must be deterministic, because we cache the matrix size. @@ -284,7 +306,7 @@ limited by the integer range but it should be much smaller in practice.
-A [`RowMatrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.RowMatrix) can be +A [`RowMatrix`](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.RowMatrix) can be created from an `RDD[Vector]` instance. Then we can compute its column summary statistics. {% highlight scala %} @@ -303,7 +325,7 @@ val n = mat.numCols()
-A [`RowMatrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.RowMatrix) can be +A [`RowMatrix`](api/java/org/apache/spark/mllib/linalg/distributed/RowMatrix.html) can be created from a `JavaRDD` instance. Then we can compute its column summary statistics. {% highlight java %} @@ -333,8 +355,8 @@ which could be faster if the rows are sparse.
-`RowMatrix#computeColumnSummaryStatistics` returns an instance of -[`MultivariateStatisticalSummary`](api/mllib/index.html#org.apache.spark.mllib.stat.MultivariateStatisticalSummary), +[`RowMatrix#computeColumnSummaryStatistics`](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.RowMatrix) returns an instance of +[`MultivariateStatisticalSummary`](api/scala/index.html#org.apache.spark.mllib.stat.MultivariateStatisticalSummary), which contains the column-wise max, min, mean, variance, and number of nonzeros, as well as the total count. @@ -355,6 +377,31 @@ println(summary.numNonzeros) // number of nonzeros in each column val cov: Matrix = mat.computeCovariance() {% endhighlight %}
+ +
+ +[`RowMatrix#computeColumnSummaryStatistics`](api/java/org/apache/spark/mllib/linalg/distributed/RowMatrix.html#computeColumnSummaryStatistics()) returns an instance of +[`MultivariateStatisticalSummary`](api/java/org/apache/spark/mllib/stat/MultivariateStatisticalSummary.html), +which contains the column-wise max, min, mean, variance, and number of nonzeros, as well as the +total count. + +{% highlight java %} +import org.apache.spark.mllib.linalg.Matrix; +import org.apache.spark.mllib.linalg.distributed.RowMatrix; +import org.apache.spark.mllib.stat.MultivariateStatisticalSummary; + +RowMatrix mat = ... // a RowMatrix + +// Compute column summary statistics. +MultivariateStatisticalSummary summary = mat.computeColumnSummaryStatistics(); +System.out.println(summary.mean()); // a dense vector containing the mean value for each column +System.out.println(summary.variance()); // column-wise variance +System.out.println(summary.numNonzeros()); // number of nonzeros in each column + +// Compute the covariance matrix. +Matrix cov = mat.computeCovariance(); +{% endhighlight %} +
### IndexedRowMatrix @@ -366,9 +413,9 @@ an RDD of indexed rows, which each row is represented by its index (long-typed)
An -[`IndexedRowMatrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix) +[`IndexedRowMatrix`](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix) can be created from an `RDD[IndexedRow]` instance, where -[`IndexedRow`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.IndexedRow) is a +[`IndexedRow`](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.IndexedRow) is a wrapper over `(Long, Vector)`. An `IndexedRowMatrix` can be converted to a `RowMatrix` by dropping its row indices. @@ -391,9 +438,9 @@ val rowMat: RowMatrix = mat.toRowMatrix()
An -[`IndexedRowMatrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix) +[`IndexedRowMatrix`](api/java/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.html) can be created from an `JavaRDD` instance, where -[`IndexedRow`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.IndexedRow) is a +[`IndexedRow`](api/java/org/apache/spark/mllib/linalg/distributed/IndexedRow.html) is a wrapper over `(long, Vector)`. An `IndexedRowMatrix` can be converted to a `RowMatrix` by dropping its row indices. @@ -427,9 +474,9 @@ dimensions of the matrix are huge and the matrix is very sparse.
A -[`CoordinateMatrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.CoordinateMatrix) +[`CoordinateMatrix`](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.CoordinateMatrix) can be created from an `RDD[MatrixEntry]` instance, where -[`MatrixEntry`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.MatrixEntry) is a +[`MatrixEntry`](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.MatrixEntry) is a wrapper over `(Long, Long, Double)`. A `CoordinateMatrix` can be converted to a `IndexedRowMatrix` with sparse rows by calling `toIndexedRowMatrix`. In this release, we do not provide other computation for `CoordinateMatrix`. @@ -453,13 +500,13 @@ val indexedRowMatrix = mat.toIndexedRowMatrix()
A -[`CoordinateMatrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.CoordinateMatrix) +[`CoordinateMatrix`](api/java/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.html) can be created from a `JavaRDD` instance, where -[`MatrixEntry`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.MatrixEntry) is a +[`MatrixEntry`](api/java/org/apache/spark/mllib/linalg/distributed/MatrixEntry.html) is a wrapper over `(long, long, double)`. A `CoordinateMatrix` can be converted to a `IndexedRowMatrix` with sparse rows by calling `toIndexedRowMatrix`. -{% highlight scala %} +{% highlight java %} import org.apache.spark.api.java.JavaRDD; import org.apache.spark.mllib.linalg.distributed.CoordinateMatrix; import org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix; @@ -467,7 +514,7 @@ import org.apache.spark.mllib.linalg.distributed.MatrixEntry; JavaRDD entries = ... // a JavaRDD of matrix entries // Create a CoordinateMatrix from a JavaRDD. -CoordinateMatrix mat = new CoordinateMatrix(entries); +CoordinateMatrix mat = new CoordinateMatrix(entries.rdd()); // Get its size. long m = mat.numRows(); diff --git a/docs/mllib-clustering.md b/docs/mllib-clustering.md index 276868fa8490d..429cdf8d40cec 100644 --- a/docs/mllib-clustering.md +++ b/docs/mllib-clustering.md @@ -1,6 +1,7 @@ --- layout: global -title: MLlib - Clustering +title: Clustering - MLlib +displayTitle: MLlib - Clustering --- * Table of contents @@ -40,7 +41,7 @@ a given dataset, the algorithm returns the best clustering result). Following code snippets can be executed in `spark-shell`. In the following example after loading and parsing data, we use the -[`KMeans`](api/mllib/index.html#org.apache.spark.mllib.clustering.KMeans) object to cluster the data +[`KMeans`](api/scala/index.html#org.apache.spark.mllib.clustering.KMeans) object to cluster the data into two clusters. The number of desired clusters is passed to the algorithm. We then compute Within Set Sum of Squared Error (WSSSE). You can reduce this error measure by increasing *k*. In fact the optimal *k* is usually one where there is an "elbow" in the WSSSE graph. diff --git a/docs/mllib-collaborative-filtering.md b/docs/mllib-collaborative-filtering.md index f486c56e55907..d51002f015670 100644 --- a/docs/mllib-collaborative-filtering.md +++ b/docs/mllib-collaborative-filtering.md @@ -1,6 +1,7 @@ --- layout: global -title: MLlib - Collaborative Filtering +title: Collaborative Filtering - MLlib +displayTitle: MLlib - Collaborative Filtering --- * Table of contents @@ -48,7 +49,7 @@ user for an item.
In the following example we load rating data. Each row consists of a user, a product and a rating. -We use the default [ALS.train()](api/mllib/index.html#org.apache.spark.mllib.recommendation.ALS$) +We use the default [ALS.train()](api/scala/index.html#org.apache.spark.mllib.recommendation.ALS$) method which assumes ratings are explicit. We evaluate the recommendation model by measuring the Mean Squared Error of rating prediction. @@ -58,9 +59,9 @@ import org.apache.spark.mllib.recommendation.Rating // Load and parse the data val data = sc.textFile("mllib/data/als/test.data") -val ratings = data.map(_.split(',') match { - case Array(user, item, rate) => Rating(user.toInt, item.toInt, rate.toDouble) -}) +val ratings = data.map(_.split(',') match { case Array(user, item, rate) => + Rating(user.toInt, item.toInt, rate.toDouble) + }) // Build the recommendation model using ALS val rank = 10 @@ -68,15 +69,19 @@ val numIterations = 20 val model = ALS.train(ratings, rank, numIterations, 0.01) // Evaluate the model on rating data -val usersProducts = ratings.map{ case Rating(user, product, rate) => (user, product)} -val predictions = model.predict(usersProducts).map{ - case Rating(user, product, rate) => ((user, product), rate) +val usersProducts = ratings.map { case Rating(user, product, rate) => + (user, product) } -val ratesAndPreds = ratings.map{ - case Rating(user, product, rate) => ((user, product), rate) +val predictions = + model.predict(usersProducts).map { case Rating(user, product, rate) => + ((user, product), rate) + } +val ratesAndPreds = ratings.map { case Rating(user, product, rate) => + ((user, product), rate) }.join(predictions) -val MSE = ratesAndPreds.map{ - case ((user, product), (r1, r2)) => math.pow((r1- r2), 2) +val MSE = ratesAndPreds.map { case ((user, product), (r1, r2)) => + val err = (r1 - r2) + err * err }.mean() println("Mean Squared Error = " + MSE) {% endhighlight %} diff --git a/docs/mllib-decision-tree.md b/docs/mllib-decision-tree.md index acf0feff42a8d..3002a66a4fdb3 100644 --- a/docs/mllib-decision-tree.md +++ b/docs/mllib-decision-tree.md @@ -1,6 +1,7 @@ --- layout: global -title: MLlib - Decision Tree +title: Decision Tree - MLlib +displayTitle: MLlib - Decision Tree --- * Table of contents diff --git a/docs/mllib-dimensionality-reduction.md b/docs/mllib-dimensionality-reduction.md index ab24663cfe258..e3608075fbb13 100644 --- a/docs/mllib-dimensionality-reduction.md +++ b/docs/mllib-dimensionality-reduction.md @@ -1,6 +1,7 @@ --- layout: global -title: MLlib - Dimensionality Reduction +title: Dimensionality Reduction - MLlib +displayTitle: MLlib - Dimensionality Reduction --- * Table of contents diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md index 842ca5c8c6d8a..640ca83085387 100644 --- a/docs/mllib-guide.md +++ b/docs/mllib-guide.md @@ -27,8 +27,9 @@ filtering, dimensionality reduction, as well as underlying optimization primitiv * stochastic gradient descent * limited-memory BFGS (L-BFGS) -MLlib is currently a *beta* component under active development. -The APIs may change in the future releases, and we will provide migration guide between releases. +MLlib is a new component under active development. +The APIs marked `Experimental`/`DeveloperApi` may change in future releases, +and we will provide migration guide between releases. ## Dependencies @@ -61,9 +62,9 @@ take advantage of sparsity in both storage and computation.
We used to represent a feature vector by `Array[Double]`, which is replaced by -[`Vector`](api/mllib/index.html#org.apache.spark.mllib.linalg.Vector) in v1.0. Algorithms that used +[`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/mllib/index.html#org.apache.spark.mllib.regression.LabeledPoint) +`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: @@ -74,7 +75,7 @@ val array: Array[Double] = ... // a double array val vector: Vector = Vectors.dense(array) // a dense vector {% endhighlight %} -[`Vectors`](api/mllib/index.html#org.apache.spark.mllib.linalg.Vectors$) provides factory methods to create sparse vectors. +[`Vectors`](api/scala/index.html#org.apache.spark.mllib.linalg.Vectors$) provides factory methods to create sparse vectors. *Note*. Scala imports `scala.collection.immutable.Vector` by default, so you have to import `org.apache.spark.mllib.linalg.Vector` explicitly to use MLlib's `Vector`. @@ -83,9 +84,9 @@ val vector: Vector = Vectors.dense(array) // a dense vector
We used to represent a feature vector by `double[]`, which is replaced by -[`Vector`](api/mllib/index.html#org.apache.spark.mllib.linalg.Vector) in v1.0. Algorithms that used +[`Vector`](api/scala/index.html#org.apache.spark.mllib.linalg.Vector) in v1.0. Algorithms that used to accept `RDD` now take -`RDD`. [`LabeledPoint`](api/mllib/index.html#org.apache.spark.mllib.regression.LabeledPoint) +`RDD`. [`LabeledPoint`](api/scala/index.html#org.apache.spark.mllib.regression.LabeledPoint) is now a wrapper of `(double, Vector)` instead of `(double, double[])`. Converting `double[]` to `Vector` is straightforward: @@ -97,7 +98,7 @@ double[] array = ... // a double array Vector vector = Vectors.dense(array); // a dense vector {% endhighlight %} -[`Vectors`](api/mllib/index.html#org.apache.spark.mllib.linalg.Vectors$) provides factory methods to +[`Vectors`](api/scala/index.html#org.apache.spark.mllib.linalg.Vectors$) provides factory methods to create sparse vectors.
@@ -106,7 +107,7 @@ create sparse vectors. 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/pyspark/pyspark.mllib.regression.LabeledPoint-class.html), which takes both +[`LabeledPoint`](api/python/pyspark.mllib.regression.LabeledPoint-class.html), which takes both dense and sparse feature vectors. {% highlight python %} diff --git a/docs/mllib-linear-methods.md b/docs/mllib-linear-methods.md index eff617d8641e2..4dfbebbcd04b7 100644 --- a/docs/mllib-linear-methods.md +++ b/docs/mllib-linear-methods.md @@ -1,6 +1,7 @@ --- layout: global -title: MLlib - Linear Methods +title: Linear Methods - MLlib +displayTitle: MLlib - Linear Methods --- * Table of contents @@ -233,7 +234,7 @@ val modelL1 = svmAlg.run(training) {% endhighlight %} Similarly, you can use replace `SVMWithSGD` by -[`LogisticRegressionWithSGD`](api/mllib/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithSGD). +[`LogisticRegressionWithSGD`](api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithSGD).
@@ -328,8 +329,8 @@ println("training Mean Squared Error = " + MSE) {% endhighlight %} Similarly you can use -[`RidgeRegressionWithSGD`](api/mllib/index.html#org.apache.spark.mllib.regression.RidgeRegressionWithSGD) -and [`LassoWithSGD`](api/mllib/index.html#org.apache.spark.mllib.regression.LassoWithSGD). +[`RidgeRegressionWithSGD`](api/scala/index.html#org.apache.spark.mllib.regression.RidgeRegressionWithSGD) +and [`LassoWithSGD`](api/scala/index.html#org.apache.spark.mllib.regression.LassoWithSGD).
@@ -380,11 +381,11 @@ all three possible regularizations (none, L1 or L2). Algorithms are all implemented in Scala: -* [SVMWithSGD](api/mllib/index.html#org.apache.spark.mllib.classification.SVMWithSGD) -* [LogisticRegressionWithSGD](api/mllib/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithSGD) -* [LinearRegressionWithSGD](api/mllib/index.html#org.apache.spark.mllib.regression.LinearRegressionWithSGD) -* [RidgeRegressionWithSGD](api/mllib/index.html#org.apache.spark.mllib.regression.RidgeRegressionWithSGD) -* [LassoWithSGD](api/mllib/index.html#org.apache.spark.mllib.regression.LassoWithSGD) +* [SVMWithSGD](api/scala/index.html#org.apache.spark.mllib.classification.SVMWithSGD) +* [LogisticRegressionWithSGD](api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithSGD) +* [LinearRegressionWithSGD](api/scala/index.html#org.apache.spark.mllib.regression.LinearRegressionWithSGD) +* [RidgeRegressionWithSGD](api/scala/index.html#org.apache.spark.mllib.regression.RidgeRegressionWithSGD) +* [LassoWithSGD](api/scala/index.html#org.apache.spark.mllib.regression.LassoWithSGD) Python calls the Scala implementation via -[PythonMLLibAPI](api/mllib/index.html#org.apache.spark.mllib.api.python.PythonMLLibAPI). +[PythonMLLibAPI](api/scala/index.html#org.apache.spark.mllib.api.python.PythonMLLibAPI). diff --git a/docs/mllib-naive-bayes.md b/docs/mllib-naive-bayes.md index c47508b7daa2d..4b3a7cab32118 100644 --- a/docs/mllib-naive-bayes.md +++ b/docs/mllib-naive-bayes.md @@ -1,6 +1,7 @@ --- layout: global -title: MLlib - Naive Bayes +title: Naive Bayes - MLlib +displayTitle: MLlib - Naive Bayes --- Naive Bayes is a simple multiclass classification algorithm with the assumption of independence @@ -27,11 +28,11 @@ sparsity. Since the training data is only used once, it is not necessary to cach
-[NaiveBayes](api/mllib/index.html#org.apache.spark.mllib.classification.NaiveBayes$) implements +[NaiveBayes](api/scala/index.html#org.apache.spark.mllib.classification.NaiveBayes$) implements multinomial naive Bayes. It takes an RDD of -[LabeledPoint](api/mllib/index.html#org.apache.spark.mllib.regression.LabeledPoint) and an optional +[LabeledPoint](api/scala/index.html#org.apache.spark.mllib.regression.LabeledPoint) and an optional smoothing parameter `lambda` as input, and output a -[NaiveBayesModel](api/mllib/index.html#org.apache.spark.mllib.classification.NaiveBayesModel), which +[NaiveBayesModel](api/scala/index.html#org.apache.spark.mllib.classification.NaiveBayesModel), which can be used for evaluation and prediction. {% highlight scala %} @@ -59,11 +60,11 @@ val accuracy = 1.0 * predictionAndLabel.filter(x => x._1 == x._2).count() / test
-[NaiveBayes](api/mllib/index.html#org.apache.spark.mllib.classification.NaiveBayes$) implements +[NaiveBayes](api/java/org/apache/spark/mllib/classification/NaiveBayes.html) implements multinomial naive Bayes. It takes a Scala RDD of -[LabeledPoint](api/mllib/index.html#org.apache.spark.mllib.regression.LabeledPoint) and an +[LabeledPoint](api/java/org/apache/spark/mllib/regression/LabeledPoint.html) and an optionally smoothing parameter `lambda` as input, and output a -[NaiveBayesModel](api/mllib/index.html#org.apache.spark.mllib.classification.NaiveBayesModel), which +[NaiveBayesModel](api/java/org/apache/spark/mllib/classification/NaiveBayesModel.html), which can be used for evaluation and prediction. {% highlight java %} @@ -102,11 +103,11 @@ double accuracy = 1.0 * predictionAndLabel.filter(new Function -[NaiveBayes](api/pyspark/pyspark.mllib.classification.NaiveBayes-class.html) implements multinomial +[NaiveBayes](api/python/pyspark.mllib.classification.NaiveBayes-class.html) implements multinomial naive Bayes. It takes an RDD of -[LabeledPoint](api/pyspark/pyspark.mllib.regression.LabeledPoint-class.html) and an optionally +[LabeledPoint](api/python/pyspark.mllib.regression.LabeledPoint-class.html) and an optionally smoothing parameter `lambda` as input, and output a -[NaiveBayesModel](api/pyspark/pyspark.mllib.classification.NaiveBayesModel-class.html), which can be +[NaiveBayesModel](api/python/pyspark.mllib.classification.NaiveBayesModel-class.html), which can be used for evaluation and prediction. diff --git a/docs/mllib-optimization.md b/docs/mllib-optimization.md index aa0dec2130593..a22980d03a2f0 100644 --- a/docs/mllib-optimization.md +++ b/docs/mllib-optimization.md @@ -1,6 +1,7 @@ --- layout: global -title: MLlib - Optimization +title: Optimization - MLlib +displayTitle: MLlib - Optimization --- * Table of contents @@ -170,17 +171,17 @@ each iteration, to compute the gradient direction. Available algorithms for gradient descent: -* [GradientDescent.runMiniBatchSGD](api/mllib/index.html#org.apache.spark.mllib.optimization.GradientDescent) +* [GradientDescent.runMiniBatchSGD](api/scala/index.html#org.apache.spark.mllib.optimization.GradientDescent) ### L-BFGS L-BFGS is currently only a low-level optimization primitive in `MLlib`. If you want to use L-BFGS in various ML algorithms such as Linear Regression, and Logistic Regression, you have to pass the gradient of objective function, and updater into optimizer yourself instead of using the training APIs like -[LogisticRegressionWithSGD](api/mllib/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithSGD). +[LogisticRegressionWithSGD](api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithSGD). See the example below. It will be addressed in the next release. The L1 regularization by using -[L1Updater](api/mllib/index.html#org.apache.spark.mllib.optimization.L1Updater) will not work since the +[L1Updater](api/scala/index.html#org.apache.spark.mllib.optimization.L1Updater) will not work since the soft-thresholding logic in L1Updater is designed for gradient descent. See the developer's note. The L-BFGS method @@ -274,4 +275,4 @@ the actual gradient descent step. However, we're able to take the gradient and loss of objective function of regularization for L-BFGS by ignoring the part of logic only for gradient decent such as adaptive step size stuff. We will refactorize this into regularizer to replace updater to separate the logic between -regularization and step update later. \ No newline at end of file +regularization and step update later. From 7b70a7071894dd90ea1d0091542b3e13e7ef8d3a Mon Sep 17 00:00:00 2001 From: Matei Zaharia Date: Mon, 19 May 2014 15:02:35 -0700 Subject: [PATCH 009/221] [SPARK-1876] Windows fixes to deal with latest distribution layout changes - Look for JARs in the right place - Launch examples the same way as on Unix - Load datanucleus JARs if they exist - Don't attempt to parse local paths as URIs in SparkSubmit, since paths with C:\ are not valid URIs - Also fixed POM exclusion rules for datanucleus (it wasn't properly excluding it, whereas SBT was) Author: Matei Zaharia Closes #819 from mateiz/win-fixes and squashes the following commits: d558f96 [Matei Zaharia] Fix comment 228577b [Matei Zaharia] Review comments d3b71c7 [Matei Zaharia] Properly exclude datanucleus files in Maven assembly 144af84 [Matei Zaharia] Update Windows scripts to match latest binary package layout --- README.md | 7 +-- assembly/pom.xml | 2 +- bin/compute-classpath.cmd | 24 ++++++++- bin/run-example | 23 ++++----- bin/run-example2.cmd | 51 ++++++++++++++----- bin/spark-class2.cmd | 2 + .../org/apache/spark/deploy/SparkSubmit.scala | 2 +- 7 files changed, 81 insertions(+), 30 deletions(-) diff --git a/README.md b/README.md index 9c2e32b90f162..6211a5889a3f5 100644 --- a/README.md +++ b/README.md @@ -9,13 +9,14 @@ You can find the latest Spark documentation, including a programming guide, on the project webpage at . This README file only contains basic setup instructions. - ## Building Spark Spark is built on Scala 2.10. To build Spark and its example programs, run: ./sbt/sbt assembly +(You do not need to do this if you downloaded a pre-built package.) + ## Interactive Scala Shell The easiest way to start using Spark is through the Scala shell: @@ -41,9 +42,9 @@ And run the following command, which should also return 1000: Spark also comes with several sample programs in the `examples` directory. To run one of them, use `./bin/run-example [params]`. For example: - ./bin/run-example org.apache.spark.examples.SparkLR + ./bin/run-example SparkPi -will run the Logistic Regression example locally. +will run the Pi example locally. You can set the MASTER environment variable when running examples to submit examples to a cluster. This can be a mesos:// or spark:// URL, diff --git a/assembly/pom.xml b/assembly/pom.xml index abd8935339992..963357b9ab167 100644 --- a/assembly/pom.xml +++ b/assembly/pom.xml @@ -96,7 +96,7 @@ *:* - org.datanucleus:* + org/datanucleus/** META-INF/*.SF META-INF/*.DSA META-INF/*.RSA diff --git a/bin/compute-classpath.cmd b/bin/compute-classpath.cmd index 065553eb31939..58710cd1bd548 100644 --- a/bin/compute-classpath.cmd +++ b/bin/compute-classpath.cmd @@ -20,6 +20,13 @@ 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 @@ -31,7 +38,7 @@ if exist "%FWDIR%conf\spark-env.cmd" call "%FWDIR%conf\spark-env.cmd" rem Build up classpath set CLASSPATH=%FWDIR%conf if exist "%FWDIR%RELEASE" ( - for %%d in ("%FWDIR%jars\spark-assembly*.jar") do ( + for %%d in ("%FWDIR%lib\spark-assembly*.jar") do ( set ASSEMBLY_JAR=%%d ) ) else ( @@ -42,6 +49,21 @@ if exist "%FWDIR%RELEASE" ( 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 diff --git a/bin/run-example b/bin/run-example index 146951ac0ee56..7caab31daef39 100755 --- a/bin/run-example +++ b/bin/run-example @@ -23,6 +23,16 @@ FWDIR="$(cd `dirname $0`/..; pwd)" export SPARK_HOME="$FWDIR" EXAMPLES_DIR="$FWDIR"/examples +if [ -n "$1" ]; then + EXAMPLE_CLASS="$1" + shift +else + echo "Usage: ./bin/run-example [example-args]" + echo " - set MASTER=XX to use a specific master" + echo " - can use abbreviated example class name (e.g. SparkPi, mllib.LinearRegression)" + exit 1 +fi + if [ -f "$FWDIR/RELEASE" ]; then export SPARK_EXAMPLES_JAR=`ls "$FWDIR"/lib/spark-examples-*hadoop*.jar` elif [ -e "$EXAMPLES_DIR"/target/scala-$SCALA_VERSION/spark-examples-*hadoop*.jar ]; then @@ -37,17 +47,6 @@ fi EXAMPLE_MASTER=${MASTER:-"local[*]"} -if [ -n "$1" ]; then - EXAMPLE_CLASS="$1" - shift -else - echo "usage: ./bin/run-example [example-args]" - echo " - set MASTER=XX to use a specific master" - echo " - can use abbreviated example class name (e.g. SparkPi, mllib.MovieLensALS)" - echo - exit -1 -fi - if [[ ! $EXAMPLE_CLASS == org.apache.spark.examples* ]]; then EXAMPLE_CLASS="org.apache.spark.examples.$EXAMPLE_CLASS" fi @@ -55,5 +54,5 @@ fi ./bin/spark-submit \ --master $EXAMPLE_MASTER \ --class $EXAMPLE_CLASS \ - $SPARK_EXAMPLES_JAR \ + "$SPARK_EXAMPLES_JAR" \ "$@" diff --git a/bin/run-example2.cmd b/bin/run-example2.cmd index 40abb9af74246..eadedd7fa61ff 100644 --- a/bin/run-example2.cmd +++ b/bin/run-example2.cmd @@ -30,7 +30,9 @@ if exist "%FWDIR%conf\spark-env.cmd" call "%FWDIR%conf\spark-env.cmd" rem Test that an argument was given if not "x%1"=="x" goto arg_given - echo Usage: run-example ^ [^] + echo Usage: run-example ^ [example-args] + echo - set MASTER=XX to use a specific master + echo - can use abbreviated example class name (e.g. SparkPi, mllib.LinearRegression) goto exit :arg_given @@ -38,8 +40,14 @@ set EXAMPLES_DIR=%FWDIR%examples rem Figure out the JAR file that our examples were packaged into. set SPARK_EXAMPLES_JAR= -for %%d in ("%EXAMPLES_DIR%\target\scala-%SCALA_VERSION%\spark-examples*assembly*.jar") do ( - set SPARK_EXAMPLES_JAR=%%d +if exist "%FWDIR%RELEASE" ( + for %%d in ("%FWDIR%lib\spark-examples*.jar") do ( + set SPARK_EXAMPLES_JAR=%%d + ) +) else ( + for %%d in ("%EXAMPLES_DIR%\target\scala-%SCALA_VERSION%\spark-examples*.jar") do ( + set SPARK_EXAMPLES_JAR=%%d + ) ) if "x%SPARK_EXAMPLES_JAR%"=="x" ( echo Failed to find Spark examples assembly JAR. @@ -47,15 +55,34 @@ if "x%SPARK_EXAMPLES_JAR%"=="x" ( goto exit ) -rem Compute Spark classpath using external script -set DONT_PRINT_CLASSPATH=1 -call "%FWDIR%bin\compute-classpath.cmd" -set DONT_PRINT_CLASSPATH=0 -set CLASSPATH=%SPARK_EXAMPLES_JAR%;%CLASSPATH% +rem Set master from MASTER environment variable if given +if "x%MASTER%"=="x" ( + set EXAMPLE_MASTER=local[*] +) else ( + set EXAMPLE_MASTER=%MASTER% +) + +rem If the EXAMPLE_CLASS does not start with org.apache.spark.examples, add that +set EXAMPLE_CLASS=%1 +set PREFIX=%EXAMPLE_CLASS:~0,25% +if not %PREFIX%==org.apache.spark.examples ( + set EXAMPLE_CLASS=org.apache.spark.examples.%EXAMPLE_CLASS% +) + +rem Get the tail of the argument list, to skip the first one. This is surprisingly +rem complicated on Windows. +set "ARGS=" +:top +shift +if "%~1" neq "" ( + set ARGS=%ARGS% "%~1" + goto :top +) +if defined ARGS set ARGS=%ARGS:~1% -rem Figure out where java is. -set RUNNER=java -if not "x%JAVA_HOME%"=="x" set RUNNER=%JAVA_HOME%\bin\java +call "%FWDIR%bin\spark-submit.cmd" ^ + --master %EXAMPLE_MASTER% ^ + --class %EXAMPLE_CLASS% ^ + "%SPARK_EXAMPLES_JAR%" %ARGS% -"%RUNNER%" -cp "%CLASSPATH%" %JAVA_OPTS% %* :exit diff --git a/bin/spark-class2.cmd b/bin/spark-class2.cmd index 4302c1b6b7ff4..266edd9fa9835 100755 --- a/bin/spark-class2.cmd +++ b/bin/spark-class2.cmd @@ -17,6 +17,8 @@ rem See the License for the specific language governing permissions and rem limitations under the License. rem +setlocal enabledelayedexpansion + set SCALA_VERSION=2.10 rem Figure out where the Spark framework is installed 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 a99b2176e2b5e..c54331c00fab8 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala @@ -299,7 +299,7 @@ object SparkSubmit { } private def addJarToClasspath(localJar: String, loader: ExecutorURLClassLoader) { - val localJarFile = new File(new URI(localJar).getPath) + val localJarFile = new File(localJar) if (!localJarFile.exists()) { printWarning(s"Jar $localJar does not exist, skipping.") } From 1811ba8ccb580979aa2e12019e6a82805f09ab53 Mon Sep 17 00:00:00 2001 From: zsxwing Date: Mon, 19 May 2014 16:41:31 -0700 Subject: [PATCH 010/221] SPARK-1878: Fix the incorrect initialization order JIRA: https://issues.apache.org/jira/browse/SPARK-1878 Author: zsxwing Closes #822 from zsxwing/SPARK-1878 and squashes the following commits: 4a47e27 [zsxwing] SPARK-1878: Fix the incorrect initialization order --- .../spark/streaming/api/java/JavaStreamingContext.scala | 6 +++--- .../test/java/org/apache/spark/streaming/JavaAPISuite.java | 4 ++++ 2 files changed, 7 insertions(+), 3 deletions(-) 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 75a3e9334e6d5..18605cac7006c 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 @@ -142,12 +142,12 @@ class JavaStreamingContext(val ssc: StreamingContext) { */ def this(path: String, hadoopConf: Configuration) = this(new StreamingContext(path, hadoopConf)) - @deprecated("use sparkContext", "0.9.0") - val sc: JavaSparkContext = sparkContext - /** The underlying SparkContext */ val sparkContext = new JavaSparkContext(ssc.sc) + @deprecated("use sparkContext", "0.9.0") + val sc: JavaSparkContext = sparkContext + /** * Create an input stream from network source hostname:port. Data is received using * a TCP socket and the receive bytes is interpreted as UTF8 encoded \n delimited 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 ce58cb12a4564..4efeb8dfbe1ad 100644 --- a/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java +++ b/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java @@ -55,6 +55,10 @@ public void equalIterable(Iterable a, Iterable b) { equalIterator(a.iterator(), b.iterator()); } + @Test + public void testInitialization() { + Assert.assertNotNull(ssc.sc()); + } @SuppressWarnings("unchecked") @Test From 5af99d7617ba3b9fbfdb345ef9571b7dd41f45a1 Mon Sep 17 00:00:00 2001 From: Matei Zaharia Date: Mon, 19 May 2014 18:42:28 -0700 Subject: [PATCH 011/221] SPARK-1879. Increase MaxPermSize since some of our builds have many classes See https://issues.apache.org/jira/browse/SPARK-1879 -- builds with Hadoop2 and Hive ran out of PermGen space in spark-shell, when those things added up with the Scala compiler. Note that users can still override it by setting their own Java options with this change. Their options will come later in the command string than the -XX:MaxPermSize=128m. Author: Matei Zaharia Closes #823 from mateiz/spark-1879 and squashes the following commits: 6bc0ee8 [Matei Zaharia] Increase MaxPermSize to 128m since some of our builds have lots of classes --- bin/spark-class | 4 ++-- bin/spark-class2.cmd | 4 ++-- .../scala/org/apache/spark/deploy/worker/CommandUtils.scala | 4 +++- 3 files changed, 7 insertions(+), 5 deletions(-) diff --git a/bin/spark-class b/bin/spark-class index 6480ccb58d6aa..2e57295fd0234 100755 --- a/bin/spark-class +++ b/bin/spark-class @@ -99,14 +99,14 @@ else fi # Set JAVA_OPTS to be able to load native libraries and to set heap size -JAVA_OPTS="$OUR_JAVA_OPTS" +JAVA_OPTS="-XX:MaxPermSize=128m $OUR_JAVA_OPTS" JAVA_OPTS="$JAVA_OPTS -Xms$OUR_JAVA_MEM -Xmx$OUR_JAVA_MEM" # Load extra JAVA_OPTS from conf/java-opts, if it exists if [ -e "$FWDIR/conf/java-opts" ] ; then JAVA_OPTS="$JAVA_OPTS `cat $FWDIR/conf/java-opts`" fi export JAVA_OPTS -# Attention: when changing the way the JAVA_OPTS are assembled, the change must be reflected in ExecutorRunner.scala! +# Attention: when changing the way the JAVA_OPTS are assembled, the change must be reflected in CommandUtils.scala! if [ ! -f "$FWDIR/RELEASE" ]; then # Exit if the user hasn't compiled Spark diff --git a/bin/spark-class2.cmd b/bin/spark-class2.cmd index 266edd9fa9835..e420eb409e529 100755 --- a/bin/spark-class2.cmd +++ b/bin/spark-class2.cmd @@ -77,8 +77,8 @@ rem All drivers use SPARK_JAVA_OPTS + SPARK_DRIVER_MEMORY. The repl also uses SP ) rem Set JAVA_OPTS to be able to load native libraries and to set heap size -set JAVA_OPTS=%OUR_JAVA_OPTS% -Djava.library.path=%SPARK_LIBRARY_PATH% -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 ExecutorRunner.scala! +set JAVA_OPTS=-XX:MaxPermSize=128m %OUR_JAVA_OPTS% -Djava.library.path=%SPARK_LIBRARY_PATH% -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 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 c7f0f244ea2fc..4af5bc3afad6c 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 @@ -65,6 +65,8 @@ object CommandUtils extends Logging { Seq() } + val permGenOpt = Seq("-XX:MaxPermSize=128m") + // 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( @@ -73,7 +75,7 @@ object CommandUtils extends Logging { val userClassPath = command.classPathEntries ++ Seq(classPath) Seq("-cp", userClassPath.filterNot(_.isEmpty).mkString(File.pathSeparator)) ++ - libraryOpts ++ extraOpts ++ workerLocalOpts ++ memoryOpts + permGenOpt ++ libraryOpts ++ extraOpts ++ workerLocalOpts ++ memoryOpts } /** Spawn a thread that will redirect a given stream to a file */ From 6a2c5c610c259f62cb12d8cfc18bf59cdb334bb2 Mon Sep 17 00:00:00 2001 From: witgo Date: Mon, 19 May 2014 19:40:29 -0700 Subject: [PATCH 012/221] [SPARK-1875]NoClassDefFoundError: StringUtils when building with hadoop 1.x and hive Author: witgo Closes #824 from witgo/SPARK-1875_commons-lang-2.6 and squashes the following commits: ef7231d [witgo] review commit ead3c3b [witgo] SPARK-1875:NoClassDefFoundError: StringUtils when building against Hadoop 1 --- project/SparkBuild.scala | 3 +-- sql/hive/pom.xml | 8 -------- 2 files changed, 1 insertion(+), 10 deletions(-) diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala index 29dcd8678b476..b9d92340ff75b 100644 --- a/project/SparkBuild.scala +++ b/project/SparkBuild.scala @@ -318,7 +318,6 @@ object SparkBuild extends Build { val excludeFastutil = ExclusionRule(organization = "it.unimi.dsi") val excludeJruby = ExclusionRule(organization = "org.jruby") val excludeThrift = ExclusionRule(organization = "org.apache.thrift") - val excludeCommonsLang = ExclusionRule(organization = "commons-lang") val excludeServletApi = ExclusionRule(organization = "javax.servlet", artifact = "servlet-api") def sparkPreviousArtifact(id: String, organization: String = "org.apache.spark", @@ -493,7 +492,7 @@ object SparkBuild extends Build { javaOptions += "-XX:MaxPermSize=1g", libraryDependencies ++= Seq( "org.spark-project.hive" % "hive-metastore" % hiveVersion, - "org.spark-project.hive" % "hive-exec" % hiveVersion excludeAll(excludeCommonsLang, excludeCommonsLogging), + "org.spark-project.hive" % "hive-exec" % hiveVersion excludeAll(excludeCommonsLogging), "org.spark-project.hive" % "hive-serde" % hiveVersion ), // Multiple queries rely on the TestHive singleton. See comments there for more details. diff --git a/sql/hive/pom.xml b/sql/hive/pom.xml index 4d0b2fa1452a2..9254b70e64a08 100644 --- a/sql/hive/pom.xml +++ b/sql/hive/pom.xml @@ -57,10 +57,6 @@ commons-logging commons-logging - - commons-lang - commons-lang - @@ -80,10 +76,6 @@ commons-logging commons-logging-api - - commons-lang - commons-lang - From b0ce22e071da4cc62ec5e29abf7b1299b8e4a6b0 Mon Sep 17 00:00:00 2001 From: Aaron Davidson Date: Mon, 19 May 2014 20:55:26 -0700 Subject: [PATCH 013/221] SPARK-1689: Spark application should die when removed by Master scheduler.error() will mask the error if there are active tasks. Being removed is a cataclysmic event for Spark applications, and should probably be treated as such. Author: Aaron Davidson Closes #832 from aarondav/i-love-u and squashes the following commits: 9f1200f [Aaron Davidson] SPARK-1689: Spark application should die when removed by Master --- .../spark/scheduler/cluster/SparkDeploySchedulerBackend.scala | 2 ++ 1 file changed, 2 insertions(+) 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 9768670855f80..9c07b3f7b695a 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 @@ -88,6 +88,8 @@ private[spark] class SparkDeploySchedulerBackend( 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() } } From bcb9dce6f444a977c714117811bce0c54b417650 Mon Sep 17 00:00:00 2001 From: Xiangrui Meng Date: Mon, 19 May 2014 21:29:33 -0700 Subject: [PATCH 014/221] [SPARK-1874][MLLIB] Clean up MLlib sample data 1. Added synthetic datasets for `MovieLensALS`, `LinearRegression`, `BinaryClassification`. 2. Embedded instructions in the help message of those example apps. Per discussion with Matei on the JIRA page, new example data is under `data/mllib`. Author: Xiangrui Meng Closes #833 from mengxr/mllib-sample-data and squashes the following commits: 59f0a18 [Xiangrui Meng] add sample binary classification data 3c2f92f [Xiangrui Meng] add linear regression data 050f1ca [Xiangrui Meng] add a sample dataset for MovieLensALS example --- .../sample_binary_classification_data.txt | 100 ++ data/mllib/sample_linear_regression_data.txt | 501 ++++++ data/mllib/sample_movielens_data.txt | 1501 +++++++++++++++++ .../examples/mllib/BinaryClassification.scala | 12 +- .../examples/mllib/LinearRegression.scala | 11 +- .../spark/examples/mllib/MovieLensALS.scala | 15 + 6 files changed, 2138 insertions(+), 2 deletions(-) create mode 100644 data/mllib/sample_binary_classification_data.txt create mode 100755 data/mllib/sample_linear_regression_data.txt create mode 100644 data/mllib/sample_movielens_data.txt diff --git a/data/mllib/sample_binary_classification_data.txt b/data/mllib/sample_binary_classification_data.txt new file mode 100644 index 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+29::80::1 +29::81::2 +29::83::1 +29::85::1 +29::86::1 +29::90::4 +29::93::1 +29::94::4 +29::97::1 +29::99::1 diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala index 4001908c98015..56b02b65d8724 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala @@ -29,8 +29,9 @@ import org.apache.spark.mllib.optimization.{SquaredL2Updater, L1Updater} /** * An example app for binary classification. Run with * {{{ - * ./bin/run-example org.apache.spark.examples.mllib.BinaryClassification + * bin/run-example org.apache.spark.examples.mllib.BinaryClassification * }}} + * A synthetic dataset is located at `data/mllib/sample_binary_classification_data.txt`. * If you use it as a template to create your own app, please use `spark-submit` to submit your app. */ object BinaryClassification { @@ -81,6 +82,15 @@ object BinaryClassification { .required() .text("input paths to labeled examples in LIBSVM format") .action((x, c) => c.copy(input = x)) + note( + """ + |For example, the following command runs this app on a synthetic dataset: + | + | bin/spark-submit --class org.apache.spark.examples.mllib.BinaryClassification \ + | examples/target/scala-*/spark-examples-*.jar \ + | --algorithm LR --regType L2 --regParam 1.0 \ + | data/mllib/sample_binary_classification_data.txt + """.stripMargin) } parser.parse(args, defaultParams).map { params => diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala index 658d370f8656e..4811bb70e4b28 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala @@ -28,8 +28,9 @@ import org.apache.spark.mllib.optimization.{SimpleUpdater, SquaredL2Updater, L1U /** * An example app for linear regression. Run with * {{{ - * ./bin/run-example org.apache.spark.examples.mllib.LinearRegression + * bin/run-example org.apache.spark.examples.mllib.LinearRegression * }}} + * A synthetic dataset can be found at `data/mllib/sample_linear_regression_data.txt`. * If you use it as a template to create your own app, please use `spark-submit` to submit your app. */ object LinearRegression extends App { @@ -68,6 +69,14 @@ object LinearRegression extends App { .required() .text("input paths to labeled examples in LIBSVM format") .action((x, c) => c.copy(input = x)) + note( + """ + |For example, the following command runs this app on a synthetic dataset: + | + | bin/spark-submit --class org.apache.spark.examples.mllib.LinearRegression \ + | examples/target/scala-*/spark-examples-*.jar \ + | data/mllib/sample_linear_regression_data.txt + """.stripMargin) } parser.parse(args, defaultParams).map { params => 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 0e4447e0de24f..6eb41e7ba36fb 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 @@ -29,6 +29,12 @@ import org.apache.spark.serializer.{KryoSerializer, KryoRegistrator} /** * An example app for ALS on MovieLens data (http://grouplens.org/datasets/movielens/). + * Run with + * {{{ + * bin/run-example org.apache.spark.examples.mllib.MovieLensALS + * }}} + * A synthetic dataset in MovieLens format can be found at `data/mllib/sample_movielens_data.txt`. + * If you use it as a template to create your own app, please use `spark-submit` to submit your app. */ object MovieLensALS { @@ -70,6 +76,15 @@ object MovieLensALS { .required() .text("input paths to a MovieLens dataset of ratings") .action((x, c) => c.copy(input = x)) + note( + """ + |For example, the following command runs this app on a synthetic dataset: + | + | bin/spark-submit --class org.apache.spark.examples.mllib.MovieLensALS \ + | examples/target/scala-*/spark-examples-*.jar \ + | --rank 5 --numIterations 20 --lambda 1.0 --kryo \ + | data/mllib/sample_movielens_data.txt + """.stripMargin) } parser.parse(args, defaultParams).map { params => From 52eb54d02403a3c37d84b9da7cc1cdb261048cf8 Mon Sep 17 00:00:00 2001 From: Tathagata Das Date: Mon, 19 May 2014 22:36:24 -0700 Subject: [PATCH 015/221] [Spark 1877] ClassNotFoundException when loading RDD with serialized objects Updated version of #821 Author: Tathagata Das Author: Ghidireac Closes #835 from tdas/SPARK-1877 and squashes the following commits: f346f71 [Tathagata Das] Addressed Patrick's comments. fee0c5d [Ghidireac] SPARK-1877: ClassNotFoundException when loading RDD with serialized objects --- core/src/main/scala/org/apache/spark/SparkContext.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala index 634c10c33fc86..49737fa4be56b 100644 --- a/core/src/main/scala/org/apache/spark/SparkContext.scala +++ b/core/src/main/scala/org/apache/spark/SparkContext.scala @@ -718,7 +718,7 @@ class SparkContext(config: SparkConf) extends Logging { minPartitions: Int = defaultMinPartitions ): RDD[T] = { sequenceFile(path, classOf[NullWritable], classOf[BytesWritable], minPartitions) - .flatMap(x => Utils.deserialize[Array[T]](x._2.getBytes)) + .flatMap(x => Utils.deserialize[Array[T]](x._2.getBytes, Utils.getContextOrSparkClassLoader)) } protected[spark] def checkpointFile[T: ClassTag]( From 7f0cfe47f4709843d70ceccc25dee7551206ce0d Mon Sep 17 00:00:00 2001 From: Tathagata Das Date: Tue, 20 May 2014 10:27:12 -0700 Subject: [PATCH 016/221] [Hotfix] Blacklisted flaky HiveCompatibility test `lateral_view_outer` query sometimes returns a different set of 10 rows. Author: Tathagata Das Closes #838 from tdas/hive-test-fix2 and squashes the following commits: 9128a0d [Tathagata Das] Blacklisted flaky HiveCompatibility test. --- .../spark/sql/hive/execution/HiveCompatibilitySuite.scala | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala index d83732b51e9c2..9031abf733cd4 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala @@ -169,7 +169,10 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter { "archive_corrupt", // No support for case sensitivity is resolution using hive properties atm. - "case_sensitivity" + "case_sensitivity", + + // Flaky test, Hive sometimes returns different set of 10 rows. + "lateral_view_outer" ) /** @@ -476,7 +479,6 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter { "join_view", "lateral_view", "lateral_view_cp", - "lateral_view_outer", "lateral_view_ppd", "lineage1", "literal_double", From 6e337380fc47071fc7fb28d744e8209c729fe1e9 Mon Sep 17 00:00:00 2001 From: Sumedh Mungee Date: Wed, 21 May 2014 01:22:25 -0700 Subject: [PATCH 017/221] [SPARK-1250] Fixed misleading comments in bin/pyspark, bin/spark-class Fixed a couple of misleading comments in bin/pyspark and bin/spark-class. The comments make it seem like the script is looking for the Scala installation when in fact it is looking for Spark. Author: Sumedh Mungee Closes #843 from smungee/spark-1250-fix-comments and squashes the following commits: 26870f3 [Sumedh Mungee] [SPARK-1250] Fixed misleading comments in bin/pyspark and bin/spark-class --- bin/pyspark | 2 +- bin/spark-class | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/bin/pyspark b/bin/pyspark index 3908ffe79939b..d0fa56f31913f 100755 --- a/bin/pyspark +++ b/bin/pyspark @@ -17,7 +17,7 @@ # limitations under the License. # -# Figure out where the Scala framework is installed +# Figure out where Spark is installed FWDIR="$(cd `dirname $0`/..; pwd)" # Export this as SPARK_HOME diff --git a/bin/spark-class b/bin/spark-class index 2e57295fd0234..e884511010c6c 100755 --- a/bin/spark-class +++ b/bin/spark-class @@ -24,7 +24,7 @@ esac SCALA_VERSION=2.10 -# Figure out where the Scala framework is installed +# Figure out where Spark is installed FWDIR="$(cd `dirname $0`/..; pwd)" # Export this as SPARK_HOME From 1014668f2727863fe46f9c75201ee459d093bf0c Mon Sep 17 00:00:00 2001 From: Andrew Or Date: Wed, 21 May 2014 01:23:34 -0700 Subject: [PATCH 018/221] [Docs] Correct example of creating a new SparkConf The example code on the configuration page currently does not compile. Author: Andrew Or Closes #842 from andrewor14/conf-docs and squashes the following commits: aabff57 [Andrew Or] Correct example of creating a new SparkConf --- docs/configuration.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/configuration.md b/docs/configuration.md index f89040d714773..462a9d9013d98 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -23,7 +23,7 @@ cluster (e.g. master URL and application name), as well as arbitrary key-value p `set()` method. For example, we could initialize an application as follows: {% highlight scala %} -val conf = new SparkConf +val conf = new SparkConf() .setMaster("local") .setAppName("CountingSheep") .set("spark.executor.memory", "1g") From 7c79ef7d43de258ad9a5de15c590132bd78ce8dd Mon Sep 17 00:00:00 2001 From: Andrew Or Date: Wed, 21 May 2014 01:25:10 -0700 Subject: [PATCH 019/221] [Minor] Move JdbcRDDSuite to the correct package It was in the wrong package Author: Andrew Or Closes #839 from andrewor14/jdbc-suite and squashes the following commits: f948c5a [Andrew Or] cache -> cache() b215279 [Andrew Or] Move JdbcRDDSuite to the correct package --- .../scala/org/apache/spark/rdd/JdbcRDDSuite.scala | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) 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 7c7f69b261a0a..76e317d754ba3 100644 --- a/core/src/test/scala/org/apache/spark/rdd/JdbcRDDSuite.scala +++ b/core/src/test/scala/org/apache/spark/rdd/JdbcRDDSuite.scala @@ -15,13 +15,13 @@ * limitations under the License. */ -package org.apache.spark +package org.apache.spark.rdd import java.sql._ import org.scalatest.{BeforeAndAfter, FunSuite} -import org.apache.spark.rdd.JdbcRDD +import org.apache.spark.{LocalSparkContext, SparkContext} class JdbcRDDSuite extends FunSuite with BeforeAndAfter with LocalSparkContext { @@ -35,18 +35,18 @@ class JdbcRDDSuite extends FunSuite with BeforeAndAfter with LocalSparkContext { ID INTEGER NOT NULL GENERATED ALWAYS AS IDENTITY (START WITH 1, INCREMENT BY 1), DATA INTEGER )""") - create.close + 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 + insert.close() } catch { case e: SQLException if e.getSQLState == "X0Y32" => // table exists } finally { - conn.close + conn.close() } } @@ -57,7 +57,7 @@ class JdbcRDDSuite extends FunSuite with BeforeAndAfter with LocalSparkContext { () => { DriverManager.getConnection("jdbc:derby:target/JdbcRDDSuiteDb") }, "SELECT DATA FROM FOO WHERE ? <= ID AND ID <= ?", 1, 100, 3, - (r: ResultSet) => { r.getInt(1) } ).cache + (r: ResultSet) => { r.getInt(1) } ).cache() assert(rdd.count === 100) assert(rdd.reduce(_+_) === 10100) From ba5d4a99425a2083fea2a9759050c5e770197e23 Mon Sep 17 00:00:00 2001 From: Andrew Or Date: Wed, 21 May 2014 11:59:05 -0700 Subject: [PATCH 020/221] [Typo] Stoped -> Stopped Author: Andrew Or Closes #847 from andrewor14/yarn-typo and squashes the following commits: c1906af [Andrew Or] Stoped -> Stopped --- .../spark/scheduler/cluster/YarnClientSchedulerBackend.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala index 2924189077b7d..0ac162538fc4b 100644 --- a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala +++ b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala @@ -113,7 +113,7 @@ private[spark] class YarnClientSchedulerBackend( override def stop() { super.stop() client.stop() - logInfo("Stoped") + logInfo("Stopped") } } From f18fd05b513b136363c94adb3e5b841f8bf48134 Mon Sep 17 00:00:00 2001 From: Kan Zhang Date: Wed, 21 May 2014 13:26:53 -0700 Subject: [PATCH 021/221] [SPARK-1519] Support minPartitions param of wholeTextFiles() in PySpark Author: Kan Zhang Closes #697 from kanzhang/SPARK-1519 and squashes the following commits: 4f8d1ed [Kan Zhang] [SPARK-1519] Support minPartitions param of wholeTextFiles() in PySpark --- python/pyspark/context.py | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/python/pyspark/context.py b/python/pyspark/context.py index cac133d0fcf6c..c9ff82d23b3cf 100644 --- a/python/pyspark/context.py +++ b/python/pyspark/context.py @@ -211,6 +211,13 @@ def defaultParallelism(self): """ return self._jsc.sc().defaultParallelism() + @property + def defaultMinPartitions(self): + """ + Default min number of partitions for Hadoop RDDs when not given by user + """ + return self._jsc.sc().defaultMinPartitions() + def __del__(self): self.stop() @@ -264,7 +271,7 @@ def textFile(self, name, minPartitions=None): return RDD(self._jsc.textFile(name, minPartitions), self, UTF8Deserializer()) - def wholeTextFiles(self, path): + def wholeTextFiles(self, path, minPartitions=None): """ Read a directory of text files from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system @@ -300,7 +307,8 @@ def wholeTextFiles(self, path): >>> sorted(textFiles.collect()) [(u'.../1.txt', u'1'), (u'.../2.txt', u'2')] """ - return RDD(self._jsc.wholeTextFiles(path), self, + minPartitions = minPartitions or self.defaultMinPartitions + return RDD(self._jsc.wholeTextFiles(path, minPartitions), self, PairDeserializer(UTF8Deserializer(), UTF8Deserializer())) def _checkpointFile(self, name, input_deserializer): From bb88875ad52e8209c25e8350af1fe4b7159086ae Mon Sep 17 00:00:00 2001 From: Takuya UESHIN Date: Wed, 21 May 2014 15:37:47 -0700 Subject: [PATCH 022/221] [SPARK-1889] [SQL] Apply splitConjunctivePredicates to join condition while finding join ke... ...ys. When tables are equi-joined by multiple-keys `HashJoin` should be used, but `CartesianProduct` and then `Filter` are used. The join keys are paired by `And` expression so we need to apply `splitConjunctivePredicates` to join condition while finding join keys. Author: Takuya UESHIN Closes #836 from ueshin/issues/SPARK-1889 and squashes the following commits: fe1c387 [Takuya UESHIN] Apply splitConjunctivePredicates to join condition while finding join keys. --- .../sql/catalyst/planning/patterns.scala | 11 ++++++----- .../spark/sql/execution/PlannerSuite.scala | 19 ++++++++++++++++++- 2 files changed, 24 insertions(+), 6 deletions(-) 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 0e3a8a6bd30a8..4544b32958c7e 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 @@ -129,11 +129,12 @@ object HashFilteredJoin extends Logging with PredicateHelper { // as join keys. def splitPredicates(allPredicates: Seq[Expression], join: Join): Option[ReturnType] = { val Join(left, right, joinType, _) = join - val (joinPredicates, otherPredicates) = allPredicates.partition { - case Equals(l, r) if (canEvaluate(l, left) && canEvaluate(r, right)) || - (canEvaluate(l, right) && canEvaluate(r, left)) => true - case _ => false - } + val (joinPredicates, otherPredicates) = + allPredicates.flatMap(splitConjunctivePredicates).partition { + case Equals(l, r) if (canEvaluate(l, left) && canEvaluate(r, right)) || + (canEvaluate(l, right) && canEvaluate(r, left)) => true + case _ => false + } val joinKeys = joinPredicates.map { case Equals(l, r) if canEvaluate(l, left) && canEvaluate(r, right) => (l, r) 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 e24c74a7a5572..c563d63627544 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 @@ -21,7 +21,7 @@ import org.scalatest.FunSuite import org.apache.spark.sql.TestData._ import org.apache.spark.sql.catalyst.expressions._ -import org.apache.spark.sql.catalyst.plans.logical +import org.apache.spark.sql.catalyst.plans._ import org.apache.spark.sql.execution import org.apache.spark.sql.test.TestSQLContext._ import org.apache.spark.sql.test.TestSQLContext.planner._ @@ -57,4 +57,21 @@ class PlannerSuite extends FunSuite { val planned = PartialAggregation(query) assert(planned.isEmpty) } + + test("equi-join is hash-join") { + val x = testData2.as('x) + val y = testData2.as('y) + val join = x.join(y, Inner, Some("x.a".attr === "y.a".attr)).queryExecution.analyzed + val planned = planner.HashJoin(join) + assert(planned.size === 1) + } + + test("multiple-key equi-join is hash-join") { + val x = testData2.as('x) + val y = testData2.as('y) + val join = x.join(y, Inner, + Some("x.a".attr === "y.a".attr && "x.b".attr === "y.b".attr)).queryExecution.analyzed + val planned = planner.HashJoin(join) + assert(planned.size === 1) + } } From 2a948e7e1a345ae4e3d89ea24123410819d799d1 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Wed, 21 May 2014 18:49:12 -0700 Subject: [PATCH 023/221] Configuration documentation updates 1. Add < code > to configuration options 2. List env variables in tabular format to be consistent with other pages. 3. Moved Viewing Spark Properties section up. This is against branch-1.0, but should be cherry picked into master as well. Author: Reynold Xin Closes #851 from rxin/doc-config and squashes the following commits: 28ac0d3 [Reynold Xin] Add to configuration options, and list env variables in a table. (cherry picked from commit 75af8bd3336d09e8c691e54ae9d2358fe1bf3723) Signed-off-by: Reynold Xin --- docs/configuration.md | 194 +++++++++++++++++++++++------------------- 1 file changed, 105 insertions(+), 89 deletions(-) diff --git a/docs/configuration.md b/docs/configuration.md index 462a9d9013d98..4d41c36e38e26 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -47,6 +47,13 @@ Any values specified in the file will be passed on to the application, and merge specified through SparkConf. If the same configuration property exists in both `spark-defaults.conf` and SparkConf, then the latter will take precedence as it is the most application-specific. +## Viewing Spark Properties + +The application web UI at `http://:4040` lists Spark properties in the "Environment" tab. +This is a useful place to check to make sure that your properties have been set correctly. Note +that only values explicitly specified through either `spark-defaults.conf` or SparkConf will +appear. For all other configuration properties, you can assume the default value is used. + ## All Configuration Properties Most of the properties that control internal settings have reasonable default values. However, @@ -55,14 +62,14 @@ there are at least five properties that you will commonly want to control: - + - + - + - + - + @@ -93,9 +89,9 @@ there are at least five properties that you will commonly want to control: @@ -167,8 +163,9 @@ Apart from these, the following properties are also available, and may be useful @@ -182,9 +179,9 @@ Apart from these, the following properties are also available, and may be useful @@ -271,15 +268,17 @@ Apart from these, the following properties are also available, and may be useful @@ -296,7 +295,8 @@ Apart from these, the following properties are also available, and may be useful @@ -304,8 +304,8 @@ Apart from these, the following properties are also available, and may be useful @@ -329,9 +329,10 @@ Apart from these, the following properties are also available, and may be useful @@ -399,9 +400,9 @@ Apart from these, the following properties are also available, and may be useful @@ -416,10 +417,10 @@ Apart from these, the following properties are also available, and may be useful @@ -450,21 +451,36 @@ Apart from these, the following properties are also available, and may be useful @@ -485,10 +501,11 @@ Apart from these, the following properties are also available, and may be useful @@ -523,7 +540,8 @@ Apart from these, the following properties are also available, and may be useful @@ -531,7 +549,10 @@ Apart from these, the following properties are also available, and may be useful @@ -546,15 +567,16 @@ Apart from these, the following properties are also available, and may be useful @@ -589,7 +611,8 @@ Apart from these, the following properties are also available, and may be useful @@ -604,39 +627,40 @@ Apart from these, the following properties are also available, and may be useful @@ -660,8 +684,8 @@ Apart from these, the following properties are also available, and may be useful @@ -719,13 +743,13 @@ Apart from these, the following properties are also available, and may be useful # Environment Variables -Certain Spark settings can be configured through environment variables, which are read from the `conf/spark-env.sh` -script in the directory where Spark is installed (or `conf/spark-env.cmd` on Windows). In Standalone and Mesos modes, -this file can give machine specific information such as hostnames. It is also sourced when running local -Spark applications or submission scripts. +Certain Spark settings can be configured through environment variables, which are read from the +`conf/spark-env.sh` script in the directory where Spark is installed (or `conf/spark-env.cmd` on +Windows). In Standalone and Mesos modes, this file can give machine specific information such as +hostnames. It is also sourced when running local Spark applications or submission scripts. -Note that `conf/spark-env.sh` does not exist by default when Spark is installed. However, you can copy -`conf/spark-env.sh.template` to create it. Make sure you make the copy executable. +Note that `conf/spark-env.sh` does not exist by default when Spark is installed. However, you can +copy `conf/spark-env.sh.template` to create it. Make sure you make the copy executable. The following variables can be set in `spark-env.sh`: @@ -750,12 +774,104 @@ The following variables can be set in `spark-env.sh`:
Property NameDefaultMeaning
spark.executor.memoryspark.executor.memory 512m Amount of memory to use per executor process, in the same format as JVM memory strings (e.g. 512m, 2g).
spark.serializerspark.serializer org.apache.spark.serializer.
JavaSerializer		 Class to use for serializing objects that will be sent over the network or need to be cached @@ -73,7 +80,7 @@ there are at least five properties that you will commonly want to control:
spark.kryo.registratorspark.kryo.registrator (none) If you use Kryo serialization, set this class to register your custom classes with Kryo. @@ -83,7 +90,7 @@ there are at least five properties that you will commonly want to control:
spark.local.dirspark.local.dir /tmp Directory to use for "scratch" space in Spark, including map output files and RDDs that get stored @@ -95,7 +102,7 @@ there are at least five properties that you will commonly want to control:
spark.cores.maxspark.cores.max (not set) When running on a standalone deploy cluster or a @@ -114,7 +121,7 @@ Apart from these, the following properties are also available, and may be useful - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - +
Property NameDefaultMeaning
spark.default.parallelismspark.default.parallelism
  • Local mode: number of cores on the local machine
    • @@ -128,7 +135,7 @@ Apart from these, the following properties are also available, and may be useful
spark.storage.memoryFractionspark.storage.memoryFraction 0.6 Fraction of Java heap to use for Spark's memory cache. This should not be larger than the "old" @@ -137,7 +144,7 @@ Apart from these, the following properties are also available, and may be useful
spark.shuffle.memoryFractionspark.shuffle.memoryFraction 0.3 Fraction of Java heap to use for aggregation and cogroups during shuffles, if @@ -148,7 +155,7 @@ Apart from these, the following properties are also available, and may be useful
spark.storage.memoryMapThresholdspark.storage.memoryMapThreshold 8192 Size of a block, in bytes, above which Spark memory maps when reading a block from disk. @@ -157,7 +164,7 @@ Apart from these, the following properties are also available, and may be useful
spark.tachyonStore.baseDirspark.tachyonStore.baseDir System.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. @@ -165,14 +172,14 @@ Apart from these, the following properties are also available, and may be useful
spark.tachyonStore.urlspark.tachyonStore.url tachyon://localhost:19998 The URL of the underlying Tachyon file system in the TachyonStore.
spark.mesos.coarsespark.mesos.coarse false If set to "true", runs over Mesos clusters in @@ -183,21 +190,21 @@ Apart from these, the following properties are also available, and may be useful
spark.ui.portspark.ui.port 4040 Port for your application's dashboard, which shows memory and workload data
spark.ui.retainedStagesspark.ui.retainedStages 1000 How many stages the Spark UI remembers before garbage collecting.
spark.ui.filtersspark.ui.filters None Comma separated list of filter class names to apply to the Spark web ui. The filter should be a @@ -207,7 +214,7 @@ Apart from these, the following properties are also available, and may be useful
spark.ui.acls.enablespark.ui.acls.enable false Whether spark web ui acls should are enabled. If enabled, this checks to see if the user has @@ -217,7 +224,7 @@ Apart from these, the following properties are also available, and may be useful
spark.ui.view.aclsspark.ui.view.acls Empty Comma separated list of users that have view access to the spark web ui. By default only the @@ -225,35 +232,35 @@ Apart from these, the following properties are also available, and may be useful
spark.ui.killEnabledspark.ui.killEnabled true Allows stages and corresponding jobs to be killed from the web ui.
spark.shuffle.compressspark.shuffle.compress true Whether to compress map output files. Generally a good idea.
spark.shuffle.spill.compressspark.shuffle.spill.compress true Whether to compress data spilled during shuffles.
spark.broadcast.compressspark.broadcast.compress true Whether to compress broadcast variables before sending them. Generally a good idea.
spark.rdd.compressspark.rdd.compress false Whether to compress serialized RDD partitions (e.g. for StorageLevel.MEMORY_ONLY_SER). @@ -261,7 +268,7 @@ Apart from these, the following properties are also available, and may be useful
spark.io.compression.codecspark.io.compression.codec org.apache.spark.io.
LZFCompressionCodec		 The codec used to compress internal data such as RDD partitions and shuffle outputs. By default, Spark provides two @@ -269,14 +276,14 @@ Apart from these, the following properties are also available, and may be useful
spark.io.compression.snappy.block.sizespark.io.compression.snappy.block.size 32768 Block size (in bytes) used in Snappy compression, in the case when Snappy compression codec is used.
spark.scheduler.modespark.scheduler.mode FIFO The scheduling mode between @@ -286,14 +293,14 @@ Apart from these, the following properties are also available, and may be useful
spark.scheduler.revive.intervalspark.scheduler.revive.interval 1000 The interval length for the scheduler to revive the worker resource offers to run tasks. (in milliseconds)
spark.reducer.maxMbInFlightspark.reducer.maxMbInFlight 48 Maximum size (in megabytes) of map outputs to fetch simultaneously from each reduce task. Since @@ -302,14 +309,14 @@ Apart from these, the following properties are also available, and may be useful
spark.closure.serializerspark.closure.serializer org.apache.spark.serializer.
JavaSerializer		 Serializer class to use for closures. Currently only the Java serializer is supported.
spark.kryo.referenceTrackingspark.kryo.referenceTracking true Whether to track references to the same object when serializing data with Kryo, which is @@ -319,7 +326,7 @@ Apart from these, the following properties are also available, and may be useful
spark.kryoserializer.buffer.mbspark.kryoserializer.buffer.mb 2 Maximum object size to allow within Kryo (the library needs to create a buffer at least as @@ -328,7 +335,7 @@ Apart from these, the following properties are also available, and may be useful
spark.serializer.objectStreamResetspark.serializer.objectStreamReset 10000 When serializing using org.apache.spark.serializer.JavaSerializer, the serializer caches @@ -339,14 +346,14 @@ Apart from these, the following properties are also available, and may be useful
spark.broadcast.factoryspark.broadcast.factory org.apache.spark.broadcast.
HttpBroadcastFactory		 Which broadcast implementation to use.
spark.locality.waitspark.locality.wait 3000 Number of milliseconds to wait to launch a data-local task before giving up and launching it @@ -358,7 +365,7 @@ Apart from these, the following properties are also available, and may be useful
spark.locality.wait.processspark.locality.wait.process spark.locality.wait Customize the locality wait for process locality. This affects tasks that attempt to access @@ -366,7 +373,7 @@ Apart from these, the following properties are also available, and may be useful
spark.locality.wait.nodespark.locality.wait.node spark.locality.wait Customize the locality wait for node locality. For example, you can set this to 0 to skip @@ -374,14 +381,14 @@ Apart from these, the following properties are also available, and may be useful
spark.locality.wait.rackspark.locality.wait.rack spark.locality.wait Customize the locality wait for rack locality.
spark.worker.timeoutspark.worker.timeout 60 Number of seconds after which the standalone deploy master considers a worker lost if it @@ -389,7 +396,7 @@ Apart from these, the following properties are also available, and may be useful
spark.worker.cleanup.enabledspark.worker.cleanup.enabled false Enable periodic cleanup of worker / application directories. Note that this only affects standalone @@ -398,7 +405,7 @@ Apart from these, the following properties are also available, and may be useful
spark.worker.cleanup.intervalspark.worker.cleanup.interval 1800 (30 minutes) Controls the interval, in seconds, at which the worker cleans up old application work dirs @@ -406,7 +413,7 @@ Apart from these, the following properties are also available, and may be useful
spark.worker.cleanup.appDataTtlspark.worker.cleanup.appDataTtl 7 * 24 * 3600 (7 days) The number of seconds to retain application work directories on each worker. This is a Time To Live @@ -416,7 +423,7 @@ Apart from these, the following properties are also available, and may be useful
spark.akka.frameSizespark.akka.frameSize 10 Maximum message size to allow in "control plane" communication (for serialized tasks and task @@ -425,7 +432,7 @@ Apart from these, the following properties are also available, and may be useful
spark.akka.threadsspark.akka.threads 4 Number of actor threads to use for communication. Can be useful to increase on large clusters @@ -433,49 +440,49 @@ Apart from these, the following properties are also available, and may be useful
spark.akka.timeoutspark.akka.timeout 100 Communication timeout between Spark nodes, in seconds.
spark.akka.heartbeat.pausesspark.akka.heartbeat.pauses 600 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.
spark.akka.failure-detector.thresholdspark.akka.failure-detector.threshold 300.0 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.heartbeat.intervalspark.akka.heartbeat.interval 1000 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.
spark.driver.hostspark.driver.host (local hostname) Hostname or IP address for the driver to listen on.
spark.driver.portspark.driver.port (random) Port for the driver to listen on.
spark.cleaner.ttlspark.cleaner.ttl (infinite) Duration (seconds) of how long Spark will remember any metadata (stages generated, tasks generated, etc.). @@ -485,7 +492,7 @@ Apart from these, the following properties are also available, and may be useful
spark.streaming.blockIntervalspark.streaming.blockInterval 200 Interval (milliseconds) at which data received by Spark Streaming receivers is coalesced @@ -493,7 +500,7 @@ Apart from these, the following properties are also available, and may be useful
spark.streaming.unpersistspark.streaming.unpersist true Force RDDs generated and persisted by Spark Streaming to be automatically unpersisted from @@ -504,7 +511,7 @@ Apart from these, the following properties are also available, and may be useful
spark.task.maxFailuresspark.task.maxFailures 4 Number of individual task failures before giving up on the job. @@ -512,7 +519,7 @@ Apart from these, the following properties are also available, and may be useful
spark.broadcast.blockSizespark.broadcast.blockSize 4096 Size of each piece of a block in kilobytes for TorrentBroadcastFactory. @@ -521,14 +528,14 @@ Apart from these, the following properties are also available, and may be useful
spark.shuffle.consolidateFilesspark.shuffle.consolidateFiles false If set to "true", consolidates intermediate files created during a shuffle. Creating fewer files can improve filesystem performance for shuffles with large numbers of reduce tasks. It is recommended to set this to "true" when using ext4 or xfs filesystems. On ext3, this option might degrade performance on machines with many (>8) cores due to filesystem limitations.
spark.shuffle.file.buffer.kbspark.shuffle.file.buffer.kb 100 Size of the in-memory buffer for each shuffle file output stream, in kilobytes. These buffers @@ -536,7 +543,7 @@ Apart from these, the following properties are also available, and may be useful
spark.shuffle.spillspark.shuffle.spill true If set to "true", limits the amount of memory used during reduces by spilling data out to disk. This spilling @@ -544,56 +551,56 @@ Apart from these, the following properties are also available, and may be useful
spark.speculationspark.speculation 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.intervalspark.speculation.interval 100 How often Spark will check for tasks to speculate, in milliseconds.
spark.speculation.quantilespark.speculation.quantile 0.75 Percentage of tasks which must be complete before speculation is enabled for a particular stage.
spark.speculation.multiplierspark.speculation.multiplier 1.5 How many times slower a task is than the median to be considered for speculation.
spark.logConfspark.logConf false Whether to log the supplied SparkConf as INFO at start of spark context.
spark.eventLog.enabledspark.eventLog.enabled false Whether to log spark events, useful for reconstructing the Web UI after the application has finished.
spark.eventLog.compressspark.eventLog.compress false Whether to compress logged events, if spark.eventLog.enabled is true.
spark.eventLog.dirspark.eventLog.dir file:///tmp/spark-events Base directory in which spark events are logged, if spark.eventLog.enabled is true. @@ -602,7 +609,7 @@ Apart from these, the following properties are also available, and may be useful
spark.deploy.spreadOutspark.deploy.spreadOut true Whether the standalone cluster manager should spread applications out across nodes or try @@ -613,7 +620,7 @@ Apart from these, the following properties are also available, and may be useful
spark.deploy.defaultCoresspark.deploy.defaultCores (infinite) Default number of cores to give to applications in Spark's standalone mode if they don't @@ -626,14 +633,14 @@ Apart from these, the following properties are also available, and may be useful
spark.files.overwritespark.files.overwrite false Whether to overwrite files added through SparkContext.addFile() when the target file exists and its contents do not match those of the source.
spark.files.fetchTimeoutspark.files.fetchTimeout false Communication timeout to use when fetching files added through SparkContext.addFile() from @@ -641,7 +648,7 @@ Apart from these, the following properties are also available, and may be useful
spark.files.userClassPathFirstspark.files.userClassPathFirst false (Experimental) Whether to give user-added jars precedence over Spark's own jars when @@ -650,7 +657,7 @@ Apart from these, the following properties are also available, and may be useful
spark.authenticatespark.authenticate false Whether spark authenticates its internal connections. See spark.authenticate.secret if not @@ -658,7 +665,7 @@ Apart from these, the following properties are also available, and may be useful
spark.authenticate.secretspark.authenticate.secret None Set the secret key used for Spark to authenticate between components. This needs to be set if @@ -666,7 +673,7 @@ Apart from these, the following properties are also available, and may be useful
spark.core.connection.auth.wait.timeoutspark.core.connection.auth.wait.timeout 30 Number of seconds for the connection to wait for authentication to occur before timing @@ -674,14 +681,14 @@ Apart from these, the following properties are also available, and may be useful
spark.task.cpusspark.task.cpus 1 Number of cores to allocate for each task.
spark.executor.extraJavaOptionsspark.executor.extraJavaOptions (none) A string of extra JVM options to pass to executors. For instance, GC settings or other @@ -692,7 +699,7 @@ Apart from these, the following properties are also available, and may be useful
spark.executor.extraClassPathspark.executor.extraClassPath (none) Extra classpath entries to append to the classpath of executors. This exists primarily @@ -701,7 +708,7 @@ Apart from these, the following properties are also available, and may be useful
spark.executor.extraLibraryPathspark.executor.extraLibraryPath (none) Set a special library path to use when launching executor JVM's. @@ -710,13 +717,6 @@ Apart from these, the following properties are also available, and may be useful
-## Viewing Spark Properties - -The application web UI at `http://:4040` lists Spark properties in the "Environment" tab. -This is a useful place to check to make sure that your properties have been set correctly. Note -that only values explicitly specified through either `spark-defaults.conf` or SparkConf will -appear. For all other configuration properties, you can assume the default value is used. - # Environment Variables Certain Spark settings can be configured through environment variables, which are read from the `conf/spark-env.sh` @@ -729,12 +729,28 @@ Note that `conf/spark-env.sh` does not exist by default when Spark is installed. The following variables can be set in `spark-env.sh`: -* `JAVA_HOME`, the location where Java is installed (if it's not on your default `PATH`) -* `PYSPARK_PYTHON`, the Python binary to use for PySpark -* `SPARK_LOCAL_IP`, to configure which IP address of the machine to bind to. -* `SPARK_PUBLIC_DNS`, the hostname your Spark program will advertise to other machines. -* Options for the Spark [standalone cluster scripts](spark-standalone.html#cluster-launch-scripts), - such as number of cores to use on each machine and maximum memory. + + + + + + + + + + + + + + + + + + + +
Environment VariableMeaning
JAVA_HOMELocation where Java is installed (if it's not on your default `PATH`).
PYSPARK_PYTHONPython binary executable to use for PySpark.
SPARK_LOCAL_IPIP address of the machine to bind to.
SPARK_PUBLIC_DNSHostname your Spark program will advertise to other machines.
+ +In addition to the above, there are also options for setting up the Spark [standalone cluster scripts](spark-standalone.html#cluster-launch-scripts), such as number of cores to use on each machine and maximum memory. Since `spark-env.sh` is a shell script, some of these can be set programmatically -- for example, you might compute `SPARK_LOCAL_IP` by looking up the IP of a specific network interface. From dba314029b4c9d72d7e48a2093b39edd01931f57 Mon Sep 17 00:00:00 2001 From: Xiangrui Meng Date: Thu, 22 May 2014 01:52:50 -0700 Subject: [PATCH 024/221] [SPARK-1870] Make spark-submit --jars work in yarn-cluster mode. Sent secondary jars to distributed cache of all containers and add the cached jars to classpath before executors start. Tested on a YARN cluster (CDH-5.0). `spark-submit --jars` also works in standalone server and `yarn-client`. Thanks for @andrewor14 for testing! I removed "Doesn't work for drivers in standalone mode with "cluster" deploy mode." from `spark-submit`'s help message, though we haven't tested mesos yet. CC: @dbtsai @sryza Author: Xiangrui Meng Closes #848 from mengxr/yarn-classpath and squashes the following commits: 23e7df4 [Xiangrui Meng] rename spark.jar to __spark__.jar and app.jar to __app__.jar to avoid confliction apped $CWD/ and $CWD/* to the classpath remove unused methods a40f6ed [Xiangrui Meng] standalone -> cluster 65e04ad [Xiangrui Meng] update spark-submit help message and add a comment for yarn-client 11e5354 [Xiangrui Meng] minor changes 3e7e1c4 [Xiangrui Meng] use sparkConf instead of hadoop conf dc3c825 [Xiangrui Meng] add secondary jars to classpath in yarn --- .../spark/deploy/SparkSubmitArguments.scala | 3 +- .../apache/spark/deploy/yarn/ClientBase.scala | 69 +++++-------------- .../cluster/YarnClientSchedulerBackend.scala | 2 +- 3 files changed, 19 insertions(+), 55 deletions(-) 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 264d4544cd31c..0cc05fb95aef0 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala @@ -326,8 +326,7 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) { | --class CLASS_NAME Your application's main class (for Java / Scala apps). | --name NAME A name of your application. | --jars JARS Comma-separated list of local jars to include on the driver - | and executor classpaths. Doesn't work for drivers in - | standalone mode with "cluster" deploy mode. + | and executor classpaths. | --py-files PY_FILES Comma-separated list of .zip or .egg files to place on the | PYTHONPATH for Python apps. | --files FILES Comma-separated list of files to be placed in the working diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala index 27a518ccda459..aeb3f0062df3b 100644 --- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala +++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala @@ -44,7 +44,7 @@ import org.apache.spark.{Logging, SparkConf, SparkContext} * Client submits an application to the YARN ResourceManager. * * Depending on the deployment mode this will launch one of two application master classes: - * 1. In standalone mode, it will launch an [[org.apache.spark.deploy.yarn.ApplicationMaster]] + * 1. In cluster mode, it will launch an [[org.apache.spark.deploy.yarn.ApplicationMaster]] * which launches a driver program inside of the cluster. * 2. In client mode, it will launch an [[org.apache.spark.deploy.yarn.ExecutorLauncher]] to * request executors on behalf of a driver running outside of the cluster. @@ -220,10 +220,11 @@ trait ClientBase extends Logging { } } + var cachedSecondaryJarLinks = ListBuffer.empty[String] val fileLists = List( (args.addJars, LocalResourceType.FILE, true), (args.files, LocalResourceType.FILE, false), (args.archives, LocalResourceType.ARCHIVE, false) ) - fileLists.foreach { case (flist, resType, appMasterOnly) => + fileLists.foreach { case (flist, resType, addToClasspath) => if (flist != null && !flist.isEmpty()) { flist.split(',').foreach { case file: String => val localURI = new URI(file.trim()) @@ -232,11 +233,15 @@ trait ClientBase extends Logging { val linkname = Option(localURI.getFragment()).getOrElse(localPath.getName()) val destPath = copyRemoteFile(dst, localPath, replication) distCacheMgr.addResource(fs, conf, destPath, localResources, resType, - linkname, statCache, appMasterOnly) + linkname, statCache) + if (addToClasspath) { + cachedSecondaryJarLinks += linkname + } } } } } + sparkConf.set(ClientBase.CONF_SPARK_YARN_SECONDARY_JARS, cachedSecondaryJarLinks.mkString(",")) UserGroupInformation.getCurrentUser().addCredentials(credentials) localResources @@ -374,11 +379,12 @@ trait ClientBase extends Logging { } object ClientBase { - val SPARK_JAR: String = "spark.jar" - val APP_JAR: String = "app.jar" + val SPARK_JAR: String = "__spark__.jar" + val APP_JAR: String = "__app__.jar" val LOG4J_PROP: String = "log4j.properties" val LOG4J_CONF_ENV_KEY: String = "SPARK_LOG4J_CONF" val LOCAL_SCHEME = "local" + val CONF_SPARK_YARN_SECONDARY_JARS = "spark.yarn.secondary.jars" def getSparkJar = sys.env.get("SPARK_JAR").getOrElse(SparkContext.jarOfClass(this.getClass).head) @@ -479,66 +485,25 @@ object ClientBase { extraClassPath.foreach(addClasspathEntry) - addClasspathEntry(Environment.PWD.$()) + val cachedSecondaryJarLinks = + sparkConf.getOption(CONF_SPARK_YARN_SECONDARY_JARS).getOrElse("").split(",") // Normally the users app.jar is last in case conflicts with spark jars if (sparkConf.get("spark.yarn.user.classpath.first", "false").toBoolean) { addPwdClasspathEntry(APP_JAR) + cachedSecondaryJarLinks.foreach(addPwdClasspathEntry) addPwdClasspathEntry(SPARK_JAR) ClientBase.populateHadoopClasspath(conf, env) } else { addPwdClasspathEntry(SPARK_JAR) ClientBase.populateHadoopClasspath(conf, env) addPwdClasspathEntry(APP_JAR) + cachedSecondaryJarLinks.foreach(addPwdClasspathEntry) } + // Append all class files and jar files under the working directory to the classpath. + addClasspathEntry(Environment.PWD.$()) addPwdClasspathEntry("*") } - /** - * Adds the user jars which have local: URIs (or alternate names, such as APP_JAR) explicitly - * to the classpath. - */ - private def addUserClasspath(args: ClientArguments, env: HashMap[String, String]) = { - if (args != null) { - addClasspathEntry(args.userJar, APP_JAR, env) - } - - if (args != null && args.addJars != null) { - args.addJars.split(",").foreach { case file: String => - addClasspathEntry(file, null, env) - } - } - } - - /** - * Adds the given path to the classpath, handling "local:" URIs correctly. - * - * If an alternate name for the file is given, and it's not a "local:" file, the alternate - * name will be added to the classpath (relative to the job's work directory). - * - * If not a "local:" file and no alternate name, the environment is not modified. - * - * @param path Path to add to classpath (optional). - * @param fileName Alternate name for the file (optional). - * @param env Map holding the environment variables. - */ - private def addClasspathEntry(path: String, fileName: String, - env: HashMap[String, String]) : Unit = { - if (path != null) { - scala.util.control.Exception.ignoring(classOf[URISyntaxException]) { - val localPath = getLocalPath(path) - if (localPath != null) { - YarnSparkHadoopUtil.addToEnvironment(env, Environment.CLASSPATH.name, localPath, - File.pathSeparator) - return - } - } - } - if (fileName != null) { - YarnSparkHadoopUtil.addToEnvironment(env, Environment.CLASSPATH.name, - Environment.PWD.$() + Path.SEPARATOR + fileName, File.pathSeparator); - } - } - /** * Returns the local path if the URI is a "local:" URI, or null otherwise. */ diff --git a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala index 0ac162538fc4b..e01ed5a57d697 100644 --- a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala +++ b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala @@ -52,7 +52,7 @@ private[spark] class YarnClientSchedulerBackend( val argsArrayBuf = new ArrayBuffer[String]() argsArrayBuf += ( "--class", "notused", - "--jar", null, + "--jar", null, // The primary jar will be added dynamically in SparkContext. "--args", hostport, "--am-class", classOf[ExecutorLauncher].getName ) From f9f5fd5f4e81828a3e0c391892e0f28751568843 Mon Sep 17 00:00:00 2001 From: Aaron Davidson Date: Thu, 22 May 2014 15:11:05 -0700 Subject: [PATCH 025/221] Fix UISuite unit test that fails under Jenkins contention Due to perhaps zombie processes on Jenkins, it seems that at least 10 Spark ports are in use. It also doesn't matter that the port increases when used, it could in fact go down -- the only part that matters is that it selects a different port rather than failing to bind. Changed test to match this. Thanks to @andrewor14 for helping diagnose this. Author: Aaron Davidson Closes #857 from aarondav/tiny and squashes the following commits: c199ec8 [Aaron Davidson] Fix UISuite unit test that fails under Jenkins contention --- core/src/test/scala/org/apache/spark/ui/UISuite.scala | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) 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 fff8020ade6d1..038746d2eda4b 100644 --- a/core/src/test/scala/org/apache/spark/ui/UISuite.scala +++ b/core/src/test/scala/org/apache/spark/ui/UISuite.scala @@ -94,7 +94,7 @@ class UISuite extends FunSuite { } } - test("jetty port increases under contention") { + test("jetty selects different port under contention") { val startPort = 4040 val server = new Server(startPort) @@ -110,8 +110,9 @@ class UISuite extends FunSuite { // Allow some wiggle room in case ports on the machine are under contention val boundPort1 = serverInfo1.boundPort val boundPort2 = serverInfo2.boundPort - assert(boundPort1 > startPort && boundPort1 < startPort + 10) - assert(boundPort2 > boundPort1 && boundPort2 < boundPort1 + 10) + assert(boundPort1 != startPort) + assert(boundPort2 != startPort) + assert(boundPort1 != boundPort2) } test("jetty binds to port 0 correctly") { From 8edbee7d1b4afc192d97ba192a5526affc464205 Mon Sep 17 00:00:00 2001 From: Andrew Or Date: Thu, 22 May 2014 20:25:41 -0700 Subject: [PATCH 026/221] [SPARK-1897] Respect spark.jars (and --jars) in spark-shell Spark shell currently overwrites `spark.jars` with `ADD_JARS`. In all modes except yarn-cluster, this means the `--jar` flag passed to `bin/spark-shell` is also discarded. However, in the [docs](http://people.apache.org/~pwendell/spark-1.0.0-rc7-docs/scala-programming-guide.html#initializing-spark), we explicitly tell the users to add the jars this way. Author: Andrew Or Closes #849 from andrewor14/shell-jars and squashes the following commits: 928a7e6 [Andrew Or] ',' -> "," (minor) afc357c [Andrew Or] Handle spark.jars == "" in SparkILoop, not SparkSubmit c6da113 [Andrew Or] Do not set spark.jars to "" d8549f7 [Andrew Or] Respect spark.jars and --jars in spark-shell --- .../src/main/scala/org/apache/spark/repl/SparkILoop.scala | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala b/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala index 296da740687ec..55684e94bd614 100644 --- a/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala +++ b/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala @@ -993,7 +993,13 @@ object SparkILoop { implicit def loopToInterpreter(repl: SparkILoop): SparkIMain = repl.intp private def echo(msg: String) = Console println msg - def getAddedJars: Array[String] = Option(System.getenv("ADD_JARS")).map(_.split(',')).getOrElse(new Array[String](0)) + 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) + } + propJars.orElse(envJars).map(_.split(",")).getOrElse(Array.empty) + } // Designed primarily for use by test code: take a String with a // bunch of code, and prints out a transcript of what it would look From cce77457e00aa5f1f4db3d50454cf257efb156ed Mon Sep 17 00:00:00 2001 From: Andrew Or Date: Thu, 22 May 2014 20:32:27 -0700 Subject: [PATCH 027/221] [SPARK-1896] Respect spark.master (and --master) before MASTER in spark-shell The hierarchy for configuring the Spark master in the shell is as follows: ``` MASTER > --master > spark.master (spark-defaults.conf) ``` This is inconsistent with the way we run normal applications, which is: ``` --master > spark.master (spark-defaults.conf) > MASTER ``` I was trying to run a shell locally on a standalone cluster launched through the ec2 scripts, which automatically set `MASTER` in spark-env.sh. It was surprising to me that `--master` didn't take effect, considering that this is the way we tell users to set their masters [here](http://people.apache.org/~pwendell/spark-1.0.0-rc7-docs/scala-programming-guide.html#initializing-spark). Author: Andrew Or Closes #846 from andrewor14/shell-master and squashes the following commits: 2cb81c9 [Andrew Or] Respect spark.master before MASTER in REPL --- repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala b/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala index 55684e94bd614..5f34362ccd973 100644 --- a/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala +++ b/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala @@ -962,11 +962,10 @@ class SparkILoop(in0: Option[BufferedReader], protected val out: JPrintWriter, private def getMaster(): String = { val master = this.master match { case Some(m) => m - case None => { + case None => val envMaster = sys.env.get("MASTER") val propMaster = sys.props.get("spark.master") - envMaster.orElse(propMaster).getOrElse("local[*]") - } + propMaster.orElse(envMaster).getOrElse("local[*]") } master } From b2bdd0e505f1ae3d39c46139f17bd43779ece635 Mon Sep 17 00:00:00 2001 From: Tathagata Das Date: Thu, 22 May 2014 20:48:55 -0700 Subject: [PATCH 028/221] Updated scripts for auditing releases - Added script to automatically generate change list CHANGES.txt - Added test for verifying linking against maven distributions of `spark-sql` and `spark-hive` - Added SBT projects for testing functionality of `spark-sql` and `spark-hive` - Fixed issues in existing tests that might have come up because of changes in Spark 1.0 Author: Tathagata Das Closes #844 from tdas/update-dev-scripts and squashes the following commits: 25090ba [Tathagata Das] Added missing license e2e20b3 [Tathagata Das] Updated tests for auditing releases. --- dev/audit-release/audit_release.py | 11 +- .../src/main/java/SimpleApp.java | 1 + .../src/main/scala/SparkApp.scala | 2 +- dev/audit-release/sbt_app_hive/build.sbt | 29 +++ dev/audit-release/sbt_app_hive/data.txt | 9 + .../src/main/resources/hive-site.xml | 213 ++++++++++++++++++ .../sbt_app_hive/src/main/scala/HiveApp.scala | 57 +++++ dev/audit-release/sbt_app_sql/build.sbt | 29 +++ .../sbt_app_sql/src/main/scala/SqlApp.scala | 57 +++++ .../src/main/scala/StreamingApp.scala | 1 - dev/create-release/generate-changelist.py | 144 ++++++++++++ 11 files changed, 547 insertions(+), 6 deletions(-) create mode 100644 dev/audit-release/sbt_app_hive/build.sbt create mode 100644 dev/audit-release/sbt_app_hive/data.txt create mode 100644 dev/audit-release/sbt_app_hive/src/main/resources/hive-site.xml create mode 100644 dev/audit-release/sbt_app_hive/src/main/scala/HiveApp.scala create mode 100644 dev/audit-release/sbt_app_sql/build.sbt create mode 100644 dev/audit-release/sbt_app_sql/src/main/scala/SqlApp.scala create mode 100755 dev/create-release/generate-changelist.py diff --git a/dev/audit-release/audit_release.py b/dev/audit-release/audit_release.py index 4a816d4101e57..8c7573b91f688 100755 --- a/dev/audit-release/audit_release.py +++ b/dev/audit-release/audit_release.py @@ -93,9 +93,12 @@ def get_url(url): # For each of these modules, we'll test an 'empty' application in sbt and # maven that links against them. This will catch issues with messed up # dependencies within those projects. -modules = ["spark-core", "spark-bagel", "spark-mllib", "spark-streaming", "spark-repl", - "spark-graphx", "spark-streaming-flume", "spark-streaming-kafka", - "spark-streaming-mqtt", "spark-streaming-twitter", "spark-streaming-zeromq"] +modules = [ + "spark-core", "spark-bagel", "spark-mllib", "spark-streaming", "spark-repl", + "spark-graphx", "spark-streaming-flume", "spark-streaming-kafka", + "spark-streaming-mqtt", "spark-streaming-twitter", "spark-streaming-zeromq", + "spark-catalyst", "spark-sql", "spark-hive" +] modules = map(lambda m: "%s_%s" % (m, SCALA_BINARY_VERSION), modules) # Check for directories that might interfere with tests @@ -122,7 +125,7 @@ def ensure_path_not_present(x): os.chdir(original_dir) # SBT application tests -for app in ["sbt_app_core", "sbt_app_graphx", "sbt_app_streaming"]: +for app in ["sbt_app_core", "sbt_app_graphx", "sbt_app_streaming", "sbt_app_sql", "sbt_app_hive"]: os.chdir(app) ret = run_cmd("sbt clean run", exit_on_failure=False) test(ret == 0, "sbt application (%s)" % app) diff --git a/dev/audit-release/maven_app_core/src/main/java/SimpleApp.java b/dev/audit-release/maven_app_core/src/main/java/SimpleApp.java index 6b65dda39b1a2..5217689e7c092 100644 --- a/dev/audit-release/maven_app_core/src/main/java/SimpleApp.java +++ b/dev/audit-release/maven_app_core/src/main/java/SimpleApp.java @@ -37,5 +37,6 @@ public static void main(String[] args) { System.exit(-1); } System.out.println("Test succeeded"); + sc.stop(); } } diff --git a/dev/audit-release/sbt_app_core/src/main/scala/SparkApp.scala b/dev/audit-release/sbt_app_core/src/main/scala/SparkApp.scala index a89b0d7d38bf1..77bbd167b199a 100644 --- a/dev/audit-release/sbt_app_core/src/main/scala/SparkApp.scala +++ b/dev/audit-release/sbt_app_core/src/main/scala/SparkApp.scala @@ -19,6 +19,7 @@ package main.scala import scala.util.Try +import org.apache.spark.SparkConf import org.apache.spark.SparkContext import org.apache.spark.SparkContext._ @@ -30,7 +31,6 @@ object SimpleApp { } val logFile = "input.txt" val sc = new SparkContext(conf) - SparkContext.jarOfClass(this.getClass).foreach(sc.addJar) val logData = sc.textFile(logFile, 2).cache() val numAs = logData.filter(line => line.contains("a")).count() val numBs = logData.filter(line => line.contains("b")).count() diff --git a/dev/audit-release/sbt_app_hive/build.sbt b/dev/audit-release/sbt_app_hive/build.sbt new file mode 100644 index 0000000000000..7ac1be729c561 --- /dev/null +++ b/dev/audit-release/sbt_app_hive/build.sbt @@ -0,0 +1,29 @@ +// +// 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. +// + +name := "Simple Project" + +version := "1.0" + +scalaVersion := System.getenv.get("SCALA_VERSION") + +libraryDependencies += "org.apache.spark" %% "spark-hive" % System.getenv.get("SPARK_VERSION") + +resolvers ++= Seq( + "Spark Release Repository" at System.getenv.get("SPARK_RELEASE_REPOSITORY"), + "Akka Repository" at "http://repo.akka.io/releases/", + "Spray Repository" at "http://repo.spray.cc/") diff --git a/dev/audit-release/sbt_app_hive/data.txt b/dev/audit-release/sbt_app_hive/data.txt new file mode 100644 index 0000000000000..0229e67f51e01 --- /dev/null +++ b/dev/audit-release/sbt_app_hive/data.txt @@ -0,0 +1,9 @@ +0val_0 +1val_1 +2val_2 +3val_3 +4val_4 +5val_5 +6val_6 +7val_7 +9val_9 diff --git a/dev/audit-release/sbt_app_hive/src/main/resources/hive-site.xml b/dev/audit-release/sbt_app_hive/src/main/resources/hive-site.xml new file mode 100644 index 0000000000000..93b835813d535 --- /dev/null +++ b/dev/audit-release/sbt_app_hive/src/main/resources/hive-site.xml @@ -0,0 +1,213 @@ + + + + + + + + + + + + + + + + + + build.dir + ${user.dir}/build + + + + build.dir.hive + ${build.dir}/hive + + + + hadoop.tmp.dir + ${build.dir.hive}/test/hadoop-${user.name} + A base for other temporary directories. + + + + + + hive.exec.scratchdir + ${build.dir}/scratchdir + Scratch space for Hive jobs + + + + hive.exec.local.scratchdir + ${build.dir}/localscratchdir/ + Local scratch space for Hive jobs + + + + javax.jdo.option.ConnectionURL + + jdbc:derby:;databaseName=../build/test/junit_metastore_db;create=true + + + + javax.jdo.option.ConnectionDriverName + org.apache.derby.jdbc.EmbeddedDriver + + + + javax.jdo.option.ConnectionUserName + APP + + + + javax.jdo.option.ConnectionPassword + mine + + + + + hive.metastore.warehouse.dir + ${test.warehouse.dir} + + + + + hive.metastore.metadb.dir + ${build.dir}/test/data/metadb/ + + Required by metastore server or if the uris argument below is not supplied + + + + + test.log.dir + ${build.dir}/test/logs + + + + + test.src.dir + ${build.dir}/src/test + + + + + + + hive.jar.path + ${build.dir.hive}/ql/hive-exec-${version}.jar + + + + + hive.metastore.rawstore.impl + org.apache.hadoop.hive.metastore.ObjectStore + Name of the class that implements org.apache.hadoop.hive.metastore.rawstore interface. This class is used to store and retrieval of raw metadata objects such as table, database + + + + hive.querylog.location + ${build.dir}/tmp + Location of the structured hive logs + + + + + + hive.task.progress + false + Track progress of a task + + + + hive.support.concurrency + false + Whether hive supports concurrency or not. A zookeeper instance must be up and running for the default hive lock manager to support read-write locks. + + + + fs.pfile.impl + org.apache.hadoop.fs.ProxyLocalFileSystem + A proxy for local file system used for cross file system testing + + + + hive.exec.mode.local.auto + false + + Let hive determine whether to run in local mode automatically + Disabling this for tests so that minimr is not affected + + + + + hive.auto.convert.join + false + Whether Hive enable the optimization about converting common join into mapjoin based on the input file size + + + + hive.ignore.mapjoin.hint + false + Whether Hive ignores the mapjoin hint + + + + hive.input.format + org.apache.hadoop.hive.ql.io.CombineHiveInputFormat + The default input format, if it is not specified, the system assigns it. It is set to HiveInputFormat for hadoop versions 17, 18 and 19, whereas it is set to CombineHiveInputFormat for hadoop 20. The user can always overwrite it - if there is a bug in CombineHiveInputFormat, it can always be manually set to HiveInputFormat. + + + + hive.default.rcfile.serde + org.apache.hadoop.hive.serde2.columnar.ColumnarSerDe + The default SerDe hive will use for the rcfile format + + + diff --git a/dev/audit-release/sbt_app_hive/src/main/scala/HiveApp.scala b/dev/audit-release/sbt_app_hive/src/main/scala/HiveApp.scala new file mode 100644 index 0000000000000..7257d17d10116 --- /dev/null +++ b/dev/audit-release/sbt_app_hive/src/main/scala/HiveApp.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 main.scala + +import scala.collection.mutable.{ListBuffer, Queue} + +import org.apache.spark.SparkConf +import org.apache.spark.SparkContext +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.hive.LocalHiveContext + +case class Person(name: String, age: Int) + +object SparkSqlExample { + + def main(args: Array[String]) { + val conf = sys.env.get("SPARK_AUDIT_MASTER") match { + case Some(master) => new SparkConf().setAppName("Simple Sql App").setMaster(master) + case None => new SparkConf().setAppName("Simple Sql App") + } + val sc = new SparkContext(conf) + val hiveContext = new LocalHiveContext(sc) + + import hiveContext._ + hql("DROP TABLE IF EXISTS src") + hql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)") + hql("LOAD DATA LOCAL INPATH 'data.txt' INTO TABLE src") + val results = hql("FROM src SELECT key, value WHERE key >= 0 AND KEY < 5").collect() + results.foreach(println) + + def test(f: => Boolean, failureMsg: String) = { + if (!f) { + println(failureMsg) + System.exit(-1) + } + } + + test(results.size == 5, "Unexpected number of selected elements: " + results) + println("Test succeeded") + sc.stop() + } +} diff --git a/dev/audit-release/sbt_app_sql/build.sbt b/dev/audit-release/sbt_app_sql/build.sbt new file mode 100644 index 0000000000000..6e0ad3b4b2960 --- /dev/null +++ b/dev/audit-release/sbt_app_sql/build.sbt @@ -0,0 +1,29 @@ +// +// 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. +// + +name := "Simple Project" + +version := "1.0" + +scalaVersion := System.getenv.get("SCALA_VERSION") + +libraryDependencies += "org.apache.spark" %% "spark-sql" % System.getenv.get("SPARK_VERSION") + +resolvers ++= Seq( + "Spark Release Repository" at System.getenv.get("SPARK_RELEASE_REPOSITORY"), + "Akka Repository" at "http://repo.akka.io/releases/", + "Spray Repository" at "http://repo.spray.cc/") 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 new file mode 100644 index 0000000000000..50af90c213b5a --- /dev/null +++ b/dev/audit-release/sbt_app_sql/src/main/scala/SqlApp.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 main.scala + +import scala.collection.mutable.{ListBuffer, Queue} + +import org.apache.spark.SparkConf +import org.apache.spark.SparkContext +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.SQLContext + +case class Person(name: String, age: Int) + +object SparkSqlExample { + + def main(args: Array[String]) { + val conf = sys.env.get("SPARK_AUDIT_MASTER") match { + case Some(master) => new SparkConf().setAppName("Simple Sql App").setMaster(master) + case None => new SparkConf().setAppName("Simple Sql App") + } + val sc = new SparkContext(conf) + val sqlContext = new SQLContext(sc) + + import sqlContext._ + val people = sc.makeRDD(1 to 100, 10).map(x => Person(s"Name$x", x)) + people.registerAsTable("people") + val teenagers = sql("SELECT name FROM people WHERE age >= 13 AND age <= 19") + val teenagerNames = teenagers.map(t => "Name: " + t(0)).collect() + teenagerNames.foreach(println) + + def test(f: => Boolean, failureMsg: String) = { + if (!f) { + println(failureMsg) + System.exit(-1) + } + } + + test(teenagerNames.size == 7, "Unexpected number of selected elements: " + teenagerNames) + println("Test succeeded") + sc.stop() + } +} diff --git a/dev/audit-release/sbt_app_streaming/src/main/scala/StreamingApp.scala b/dev/audit-release/sbt_app_streaming/src/main/scala/StreamingApp.scala index a1d8971abe9a4..58a662bd9b2e8 100644 --- a/dev/audit-release/sbt_app_streaming/src/main/scala/StreamingApp.scala +++ b/dev/audit-release/sbt_app_streaming/src/main/scala/StreamingApp.scala @@ -32,7 +32,6 @@ object SparkStreamingExample { case None => new SparkConf().setAppName("Simple Streaming App") } val ssc = new StreamingContext(conf, Seconds(1)) - SparkContext.jarOfClass(this.getClass).foreach(ssc.sparkContext.addJar) val seen = ListBuffer[RDD[Int]]() val rdd1 = ssc.sparkContext.makeRDD(1 to 100, 10) diff --git a/dev/create-release/generate-changelist.py b/dev/create-release/generate-changelist.py new file mode 100755 index 0000000000000..13b744ec1b37e --- /dev/null +++ b/dev/create-release/generate-changelist.py @@ -0,0 +1,144 @@ +#!/usr/bin/python + +# +# 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. +# +# Creates CHANGES.txt from git history. +# +# Usage: +# First set the new release version and old CHANGES.txt version in this file. +# Make sure you have SPARK_HOME set. +# $ python generate-changelist.py + + +import os +import sys +import subprocess +import time +import traceback + +SPARK_HOME = os.environ["SPARK_HOME"] +NEW_RELEASE_VERSION = "1.0.0" +PREV_RELEASE_GIT_TAG = "v0.9.1" + +CHANGELIST = "CHANGES.txt" +OLD_CHANGELIST = "%s.old" % (CHANGELIST) +NEW_CHANGELIST = "%s.new" % (CHANGELIST) +TMP_CHANGELIST = "%s.tmp" % (CHANGELIST) + +# date before first PR in TLP Spark repo +SPARK_REPO_CHANGE_DATE1 = time.strptime("2014-02-26", "%Y-%m-%d") +# date after last PR in incubator Spark repo +SPARK_REPO_CHANGE_DATE2 = time.strptime("2014-03-01", "%Y-%m-%d") +# Threshold PR number that differentiates PRs to TLP +# and incubator repos +SPARK_REPO_PR_NUM_THRESH = 200 + +LOG_FILE_NAME = "changes_%s" % time.strftime("%h_%m_%Y_%I_%M_%S") +LOG_FILE = open(LOG_FILE_NAME, 'w') + +def run_cmd(cmd): + try: + print >> LOG_FILE, "Running command: %s" % cmd + output = subprocess.check_output(cmd, shell=True, stderr=LOG_FILE) + print >> LOG_FILE, "Output: %s" % output + return output + except: + traceback.print_exc() + cleanup() + sys.exit(1) + +def append_to_changelist(string): + with open(TMP_CHANGELIST, "a") as f: + print >> f, string + +def cleanup(ask = True): + if ask == True: + print "OK to delete temporary and log files? (y/N): " + response = raw_input() + if ask == False or (ask == True and response == "y"): + if os.path.isfile(TMP_CHANGELIST): + os.remove(TMP_CHANGELIST) + if os.path.isfile(OLD_CHANGELIST): + os.remove(OLD_CHANGELIST) + LOG_FILE.close() + os.remove(LOG_FILE_NAME) + +print "Generating new %s for Spark release %s" % (CHANGELIST, NEW_RELEASE_VERSION) +os.chdir(SPARK_HOME) +if os.path.isfile(TMP_CHANGELIST): + os.remove(TMP_CHANGELIST) +if os.path.isfile(OLD_CHANGELIST): + os.remove(OLD_CHANGELIST) + +append_to_changelist("Spark Change Log") +append_to_changelist("----------------") +append_to_changelist("") +append_to_changelist("Release %s" % NEW_RELEASE_VERSION) +append_to_changelist("") + +print "Getting commits between tag %s and HEAD" % PREV_RELEASE_GIT_TAG +hashes = run_cmd("git log %s..HEAD --pretty='%%h'" % PREV_RELEASE_GIT_TAG).split() + +print "Getting details of %s commits" % len(hashes) +for h in hashes: + date = run_cmd("git log %s -1 --pretty='%%ad' --date=iso | head -1" % h).strip() + subject = run_cmd("git log %s -1 --pretty='%%s' | head -1" % h).strip() + body = run_cmd("git log %s -1 --pretty='%%b'" % h) + committer = run_cmd("git log %s -1 --pretty='%%cn <%%ce>' | head -1" % h).strip() + body_lines = body.split("\n") + + if "Merge pull" in subject: + ## Parse old format commit message + append_to_changelist(" %s %s" % (h, date)) + append_to_changelist(" %s" % subject) + append_to_changelist(" [%s]" % body_lines[0]) + append_to_changelist("") + + elif "maven-release" not in subject: + ## Parse new format commit message + # Get authors from commit message, committer otherwise + authors = [committer] + if "Author:" in body: + authors = [line.split(":")[1].strip() for line in body_lines if "Author:" in line] + + # Generate GitHub PR URL for easy access if possible + github_url = "" + if "Closes #" in body: + pr_num = [line.split()[1].lstrip("#") for line in body_lines if "Closes #" in line][0] + github_url = "github.com/apache/spark/pull/%s" % pr_num + day = time.strptime(date.split()[0], "%Y-%m-%d") + if day < SPARK_REPO_CHANGE_DATE1 or (day < SPARK_REPO_CHANGE_DATE2 and pr_num < SPARK_REPO_PR_NUM_THRESH): + github_url = "github.com/apache/incubator-spark/pull/%s" % pr_num + + append_to_changelist(" %s" % subject) + append_to_changelist(" %s" % ', '.join(authors)) + # for author in authors: + # append_to_changelist(" %s" % author) + append_to_changelist(" %s" % date) + if len(github_url) > 0: + append_to_changelist(" Commit: %s, %s" % (h, github_url)) + else: + append_to_changelist(" Commit: %s" % h) + append_to_changelist("") + +# Append old change list +print "Appending changelist from tag %s" % PREV_RELEASE_GIT_TAG +run_cmd("git show %s:%s | tail -n +3 >> %s" % (PREV_RELEASE_GIT_TAG, CHANGELIST, TMP_CHANGELIST)) +run_cmd("cp %s %s" % (TMP_CHANGELIST, NEW_CHANGELIST)) +print "New change list generated as %s" % NEW_CHANGELIST +cleanup(False) + From a08262d8769808dd3a8ee1b1e80fbf6ac13a557c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?baishuo=28=E7=99=BD=E7=A1=95=29?= Date: Fri, 23 May 2014 13:02:40 -0700 Subject: [PATCH 029/221] Update LBFGSSuite.scala MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit the same reason as https://github.com/apache/spark/pull/588 Author: baishuo(白硕) Closes #815 from baishuo/master and squashes the following commits: 6876c1e [baishuo(白硕)] Update LBFGSSuite.scala --- .../org/apache/spark/mllib/optimization/LBFGSSuite.scala | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala index 6af1b502eb4dd..820eca9b1bf65 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala @@ -43,7 +43,7 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with ShouldMatchers { // Add an extra variable consisting of all 1.0's for the intercept. val testData = GradientDescentSuite.generateGDInput(A, B, nPoints, 42) val data = testData.map { case LabeledPoint(label, features) => - label -> Vectors.dense(1.0, features.toArray: _*) + label -> Vectors.dense(1.0 +: features.toArray) } lazy val dataRDD = sc.parallelize(data, 2).cache() @@ -55,7 +55,7 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with ShouldMatchers { test("LBFGS loss should be decreasing and match the result of Gradient Descent.") { val regParam = 0 - val initialWeightsWithIntercept = Vectors.dense(1.0, initialWeights: _*) + val initialWeightsWithIntercept = Vectors.dense(1.0 +: initialWeights.toArray) val convergenceTol = 1e-12 val maxNumIterations = 10 From 5081a0a9d47ca31900ea4de570de2cbb0e063105 Mon Sep 17 00:00:00 2001 From: Andrew Or Date: Sat, 24 May 2014 18:01:49 -0700 Subject: [PATCH 030/221] [SPARK-1900 / 1918] PySpark on YARN is broken If I run the following on a YARN cluster ``` bin/spark-submit sheep.py --master yarn-client ``` it fails because of a mismatch in paths: `spark-submit` thinks that `sheep.py` resides on HDFS, and balks when it can't find the file there. A natural workaround is to add the `file:` prefix to the file: ``` bin/spark-submit file:/path/to/sheep.py --master yarn-client ``` However, this also fails. This time it is because python does not understand URI schemes. This PR fixes this by automatically resolving all paths passed as command line argument to `spark-submit` properly. This has the added benefit of keeping file and jar paths consistent across different cluster modes. For python, we strip the URI scheme before we actually try to run it. Much of the code is originally written by @mengxr. Tested on YARN cluster. More tests pending. Author: Andrew Or Closes #853 from andrewor14/submit-paths and squashes the following commits: 0bb097a [Andrew Or] Format path correctly before adding it to PYTHONPATH 323b45c [Andrew Or] Include --py-files on PYTHONPATH for pyspark shell 3c36587 [Andrew Or] Improve error messages (minor) 854aa6a [Andrew Or] Guard against NPE if user gives pathological paths 6638a6b [Andrew Or] Fix spark-shell jar paths after #849 went in 3bb0359 [Andrew Or] Update more comments (minor) 2a1f8a0 [Andrew Or] Update comments (minor) 6af2c77 [Andrew Or] Merge branch 'master' of github.com:apache/spark into submit-paths a68c4d1 [Andrew Or] Handle Windows python file path correctly 427a250 [Andrew Or] Resolve paths properly for Windows a591a4a [Andrew Or] Update tests for resolving URIs 6c8621c [Andrew Or] Move resolveURIs to Utils db8255e [Andrew Or] Merge branch 'master' of github.com:apache/spark into submit-paths f542dce [Andrew Or] Fix outdated tests 691c4ce [Andrew Or] Ignore special primary resource names 5342ac7 [Andrew Or] Add missing space in error message 02f77f3 [Andrew Or] Resolve command line arguments to spark-submit properly --- .../apache/spark/deploy/PythonRunner.scala | 60 ++++++++++++++-- .../org/apache/spark/deploy/SparkSubmit.scala | 25 ++++--- .../spark/deploy/SparkSubmitArguments.scala | 33 ++++++--- .../scala/org/apache/spark/util/Utils.scala | 71 ++++++++++++++++++- .../spark/deploy/PythonRunnerSuite.scala | 61 ++++++++++++++++ .../spark/deploy/SparkSubmitSuite.scala | 41 ++++++----- .../org/apache/spark/util/UtilsSuite.scala | 66 ++++++++++++++++- python/pyspark/context.py | 8 ++- .../org/apache/spark/repl/SparkILoop.scala | 5 +- 9 files changed, 323 insertions(+), 47 deletions(-) create mode 100644 core/src/test/scala/org/apache/spark/deploy/PythonRunnerSuite.scala diff --git a/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala b/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala index 2dfa02bd26f13..0d6751f3fa6d2 100644 --- a/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala +++ b/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala @@ -17,10 +17,13 @@ package org.apache.spark.deploy +import java.net.URI + import scala.collection.mutable.ArrayBuffer import scala.collection.JavaConversions._ import org.apache.spark.api.python.{PythonUtils, RedirectThread} +import org.apache.spark.util.Utils /** * A main class used by spark-submit to launch Python applications. It executes python as a @@ -28,12 +31,15 @@ import org.apache.spark.api.python.{PythonUtils, RedirectThread} */ object PythonRunner { def main(args: Array[String]) { - val primaryResource = args(0) + val pythonFile = args(0) val pyFiles = args(1) val otherArgs = args.slice(2, args.length) - val pythonExec = sys.env.get("PYSPARK_PYTHON").getOrElse("python") // TODO: get this from conf + // Format python file paths before adding them to the PYTHONPATH + val formattedPythonFile = formatPath(pythonFile) + val formattedPyFiles = formatPaths(pyFiles) + // Launch a Py4J gateway server for the process to connect to; this will let it see our // Java system properties and such val gatewayServer = new py4j.GatewayServer(null, 0) @@ -42,13 +48,13 @@ object PythonRunner { // Build up a PYTHONPATH that includes the Spark assembly JAR (where this class is), the // python directories in SPARK_HOME (if set), and any files in the pyFiles argument val pathElements = new ArrayBuffer[String] - pathElements ++= Option(pyFiles).getOrElse("").split(",") + pathElements ++= formattedPyFiles pathElements += PythonUtils.sparkPythonPath pathElements += sys.env.getOrElse("PYTHONPATH", "") val pythonPath = PythonUtils.mergePythonPaths(pathElements: _*) // Launch Python process - val builder = new ProcessBuilder(Seq(pythonExec, "-u", primaryResource) ++ otherArgs) + val builder = new ProcessBuilder(Seq(pythonExec, "-u", formattedPythonFile) ++ otherArgs) val env = builder.environment() env.put("PYTHONPATH", pythonPath) env.put("PYSPARK_GATEWAY_PORT", "" + gatewayServer.getListeningPort) @@ -59,4 +65,50 @@ object PythonRunner { System.exit(process.waitFor()) } + + /** + * Format the python file path so that it can be added to the PYTHONPATH correctly. + * + * Python does not understand URI schemes in paths. Before adding python files to the + * PYTHONPATH, we need to extract the path from the URI. This is safe to do because we + * currently only support local python files. + */ + def formatPath(path: String, testWindows: Boolean = false): String = { + if (Utils.nonLocalPaths(path, testWindows).nonEmpty) { + throw new IllegalArgumentException("Launching Python applications through " + + s"spark-submit is currently only supported for local files: $path") + } + val windows = Utils.isWindows || testWindows + var formattedPath = if (windows) Utils.formatWindowsPath(path) else path + + // Strip the URI scheme from the path + formattedPath = + new URI(formattedPath).getScheme match { + case Utils.windowsDrive(d) if windows => formattedPath + case null => formattedPath + case _ => new URI(formattedPath).getPath + } + + // Guard against malformed paths potentially throwing NPE + if (formattedPath == null) { + throw new IllegalArgumentException(s"Python file path is malformed: $path") + } + + // In Windows, the drive should not be prefixed with "/" + // For instance, python does not understand "/C:/path/to/sheep.py" + formattedPath = if (windows) formattedPath.stripPrefix("/") else formattedPath + formattedPath + } + + /** + * Format each python file path in the comma-delimited list of paths, so it can be + * added to the PYTHONPATH correctly. + */ + def formatPaths(paths: String, testWindows: Boolean = false): Array[String] = { + Option(paths).getOrElse("") + .split(",") + .filter(_.nonEmpty) + .map { p => formatPath(p, testWindows) } + } + } 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 c54331c00fab8..7e9a9344e61f9 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala @@ -136,9 +136,9 @@ object SparkSubmit { args.childArgs = ArrayBuffer(args.primaryResource, args.pyFiles) ++ args.childArgs args.files = mergeFileLists(args.files, args.primaryResource) } - val pyFiles = Option(args.pyFiles).getOrElse("") - args.files = mergeFileLists(args.files, pyFiles) - sysProps("spark.submit.pyFiles") = pyFiles + args.files = mergeFileLists(args.files, args.pyFiles) + // Format python file paths properly before adding them to the PYTHONPATH + sysProps("spark.submit.pyFiles") = PythonRunner.formatPaths(args.pyFiles).mkString(",") } // If we're deploying into YARN, use yarn.Client as a wrapper around the user class @@ -299,13 +299,18 @@ object SparkSubmit { } private def addJarToClasspath(localJar: String, loader: ExecutorURLClassLoader) { - val localJarFile = new File(localJar) - if (!localJarFile.exists()) { - printWarning(s"Jar $localJar does not exist, skipping.") + val uri = Utils.resolveURI(localJar) + uri.getScheme match { + case "file" | "local" => + val file = new File(uri.getPath) + if (file.exists()) { + loader.addURL(file.toURI.toURL) + } else { + printWarning(s"Local jar $file does not exist, skipping.") + } + case _ => + printWarning(s"Skip remote jar $uri.") } - - val url = localJarFile.getAbsoluteFile.toURI.toURL - loader.addURL(url) } /** @@ -318,7 +323,7 @@ object SparkSubmit { /** * Return whether the given primary resource represents a shell. */ - private def isShell(primaryResource: String): Boolean = { + private[spark] def isShell(primaryResource: String): Boolean = { primaryResource == SPARK_SHELL || primaryResource == PYSPARK_SHELL } 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 0cc05fb95aef0..bf449afae695f 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala @@ -118,7 +118,7 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) { mainClass = jar.getManifest.getMainAttributes.getValue("Main-Class") } catch { case e: Exception => - SparkSubmit.printErrorAndExit("Failed to read JAR: " + primaryResource) + SparkSubmit.printErrorAndExit("Cannot load main class from JAR: " + primaryResource) return } } @@ -148,6 +148,18 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) { SparkSubmit.printErrorAndExit("--py-files given but primary resource is not a Python script") } + // Require all python files to be local, so we can add them to the PYTHONPATH + if (isPython) { + if (Utils.nonLocalPaths(primaryResource).nonEmpty) { + SparkSubmit.printErrorAndExit(s"Only local python files are supported: $primaryResource") + } + val nonLocalPyFiles = Utils.nonLocalPaths(pyFiles).mkString(",") + if (nonLocalPyFiles.nonEmpty) { + SparkSubmit.printErrorAndExit( + s"Only local additional python files are supported: $nonLocalPyFiles") + } + } + if (master.startsWith("yarn")) { val hasHadoopEnv = sys.env.contains("HADOOP_CONF_DIR") || sys.env.contains("YARN_CONF_DIR") if (!hasHadoopEnv && !Utils.isTesting) { @@ -263,19 +275,19 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) { parse(tail) case ("--files") :: value :: tail => - files = value + files = Utils.resolveURIs(value) parse(tail) case ("--py-files") :: value :: tail => - pyFiles = value + pyFiles = Utils.resolveURIs(value) parse(tail) case ("--archives") :: value :: tail => - archives = value + archives = Utils.resolveURIs(value) parse(tail) case ("--jars") :: value :: tail => - jars = value + jars = Utils.resolveURIs(value) parse(tail) case ("--help" | "-h") :: tail => @@ -296,7 +308,12 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) { val errMessage = s"Unrecognized option '$value'." SparkSubmit.printErrorAndExit(errMessage) case v => - primaryResource = v + primaryResource = + if (!SparkSubmit.isShell(v)) { + Utils.resolveURI(v).toString + } else { + v + } inSparkOpts = false isPython = SparkSubmit.isPython(v) parse(tail) @@ -327,8 +344,8 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) { | --name NAME A name of your application. | --jars JARS Comma-separated list of local jars to include on the driver | and executor classpaths. - | --py-files PY_FILES Comma-separated list of .zip or .egg files to place on the - | PYTHONPATH for Python apps. + | --py-files PY_FILES Comma-separated list of .zip, .egg, or .py files to place + | on the PYTHONPATH for Python apps. | --files FILES Comma-separated list of files to be placed in the working | directory of each executor. | --properties-file FILE Path to a file from which to load extra properties. If not 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 0c7cff019fce1..3b1b6df089b8e 100644 --- a/core/src/main/scala/org/apache/spark/util/Utils.scala +++ b/core/src/main/scala/org/apache/spark/util/Utils.scala @@ -1086,9 +1086,19 @@ private[spark] object Utils extends Logging { } /** - * Return true if this is Windows. + * Whether the underlying operating system is Windows. */ - def isWindows = SystemUtils.IS_OS_WINDOWS + val isWindows = SystemUtils.IS_OS_WINDOWS + + /** + * Pattern for matching a Windows drive, which contains only a single alphabet character. + */ + val windowsDrive = "([a-zA-Z])".r + + /** + * Format a Windows path such that it can be safely passed to a URI. + */ + def formatWindowsPath(path: String): String = path.replace("\\", "/") /** * Indicates whether Spark is currently running unit tests. @@ -1166,4 +1176,61 @@ private[spark] object Utils extends Logging { true } } + + /** + * Return a well-formed URI for the file described by a user input string. + * + * If the supplied path does not contain a scheme, or is a relative path, it will be + * converted into an absolute path with a file:// scheme. + */ + def resolveURI(path: String, testWindows: Boolean = false): URI = { + + // In Windows, the file separator is a backslash, but this is inconsistent with the URI format + val windows = isWindows || testWindows + val formattedPath = if (windows) formatWindowsPath(path) else path + + val uri = new URI(formattedPath) + if (uri.getPath == null) { + throw new IllegalArgumentException(s"Given path is malformed: $uri") + } + uri.getScheme match { + case windowsDrive(d) if windows => + new URI("file:/" + uri.toString.stripPrefix("/")) + case null => + // Preserve fragments for HDFS file name substitution (denoted by "#") + // For instance, in "abc.py#xyz.py", "xyz.py" is the name observed by the application + val fragment = uri.getFragment + val part = new File(uri.getPath).toURI + new URI(part.getScheme, part.getPath, fragment) + case _ => + uri + } + } + + /** Resolve a comma-separated list of paths. */ + def resolveURIs(paths: String, testWindows: Boolean = false): String = { + if (paths == null || paths.trim.isEmpty) { + "" + } else { + paths.split(",").map { p => Utils.resolveURI(p, testWindows) }.mkString(",") + } + } + + /** Return all non-local paths from a comma-separated list of paths. */ + def nonLocalPaths(paths: String, testWindows: Boolean = false): Array[String] = { + val windows = isWindows || testWindows + if (paths == null || paths.trim.isEmpty) { + Array.empty + } else { + paths.split(",").filter { p => + val formattedPath = if (windows) formatWindowsPath(p) else p + new URI(formattedPath).getScheme match { + case windowsDrive(d) if windows => false + case "local" | "file" | null => false + case _ => true + } + } + } + } + } diff --git a/core/src/test/scala/org/apache/spark/deploy/PythonRunnerSuite.scala b/core/src/test/scala/org/apache/spark/deploy/PythonRunnerSuite.scala new file mode 100644 index 0000000000000..bb6251fb4bfbe --- /dev/null +++ b/core/src/test/scala/org/apache/spark/deploy/PythonRunnerSuite.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.deploy + +import org.scalatest.FunSuite + +class PythonRunnerSuite extends FunSuite { + + // Test formatting a single path to be added to the PYTHONPATH + test("format path") { + assert(PythonRunner.formatPath("spark.py") === "spark.py") + assert(PythonRunner.formatPath("file:/spark.py") === "/spark.py") + assert(PythonRunner.formatPath("file:///spark.py") === "/spark.py") + assert(PythonRunner.formatPath("local:/spark.py") === "/spark.py") + assert(PythonRunner.formatPath("local:///spark.py") === "/spark.py") + assert(PythonRunner.formatPath("C:/a/b/spark.py", testWindows = true) === "C:/a/b/spark.py") + assert(PythonRunner.formatPath("/C:/a/b/spark.py", testWindows = true) === "C:/a/b/spark.py") + assert(PythonRunner.formatPath("file:/C:/a/b/spark.py", testWindows = true) === + "C:/a/b/spark.py") + intercept[IllegalArgumentException] { PythonRunner.formatPath("one:two") } + intercept[IllegalArgumentException] { PythonRunner.formatPath("hdfs:s3:xtremeFS") } + intercept[IllegalArgumentException] { PythonRunner.formatPath("hdfs:/path/to/some.py") } + } + + // Test formatting multiple comma-separated paths to be added to the PYTHONPATH + test("format paths") { + assert(PythonRunner.formatPaths("spark.py") === Array("spark.py")) + assert(PythonRunner.formatPaths("file:/spark.py") === Array("/spark.py")) + assert(PythonRunner.formatPaths("file:/app.py,local:/spark.py") === + Array("/app.py", "/spark.py")) + assert(PythonRunner.formatPaths("me.py,file:/you.py,local:/we.py") === + Array("me.py", "/you.py", "/we.py")) + assert(PythonRunner.formatPaths("C:/a/b/spark.py", testWindows = true) === + Array("C:/a/b/spark.py")) + assert(PythonRunner.formatPaths("/C:/a/b/spark.py", testWindows = true) === + Array("C:/a/b/spark.py")) + assert(PythonRunner.formatPaths("C:/free.py,pie.py", testWindows = true) === + Array("C:/free.py", "pie.py")) + assert(PythonRunner.formatPaths("lovely.py,C:/free.py,file:/d:/fry.py", testWindows = true) === + Array("lovely.py", "C:/free.py", "d:/fry.py")) + intercept[IllegalArgumentException] { PythonRunner.formatPaths("one:two,three") } + intercept[IllegalArgumentException] { PythonRunner.formatPaths("two,three,four:five:six") } + intercept[IllegalArgumentException] { PythonRunner.formatPaths("hdfs:/some.py,foo.py") } + intercept[IllegalArgumentException] { PythonRunner.formatPaths("foo.py,hdfs:/some.py") } + } +} 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 6c0deede53784..02427a4a83506 100644 --- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala @@ -91,7 +91,7 @@ class SparkSubmitSuite extends FunSuite with ShouldMatchers { "--jars=one.jar,two.jar,three.jar", "--name=myApp") val appArgs = new SparkSubmitArguments(clArgs) - appArgs.jars should be ("one.jar,two.jar,three.jar") + appArgs.jars should include regex (".*one.jar,.*two.jar,.*three.jar") appArgs.name should be ("myApp") } @@ -125,17 +125,17 @@ class SparkSubmitSuite extends FunSuite with ShouldMatchers { val appArgs = new SparkSubmitArguments(clArgs) val (childArgs, classpath, sysProps, mainClass) = createLaunchEnv(appArgs) val childArgsStr = childArgs.mkString(" ") - childArgsStr should include ("--jar thejar.jar") childArgsStr should include ("--class org.SomeClass") - childArgsStr should include ("--addJars one.jar,two.jar,three.jar") childArgsStr should include ("--executor-memory 5g") childArgsStr should include ("--driver-memory 4g") childArgsStr should include ("--executor-cores 5") childArgsStr should include ("--arg arg1 --arg arg2") childArgsStr should include ("--queue thequeue") - childArgsStr should include ("--files file1.txt,file2.txt") - childArgsStr should include ("--archives archive1.txt,archive2.txt") childArgsStr should include ("--num-executors 6") + childArgsStr should include regex ("--jar .*thejar.jar") + childArgsStr should include regex ("--addJars .*one.jar,.*two.jar,.*three.jar") + childArgsStr should include regex ("--files .*file1.txt,.*file2.txt") + childArgsStr should include regex ("--archives .*archive1.txt,.*archive2.txt") mainClass should be ("org.apache.spark.deploy.yarn.Client") classpath should have length (0) sysProps("spark.app.name") should be ("beauty") @@ -162,18 +162,19 @@ class SparkSubmitSuite extends FunSuite with ShouldMatchers { val (childArgs, classpath, sysProps, mainClass) = createLaunchEnv(appArgs) childArgs.mkString(" ") should be ("arg1 arg2") mainClass should be ("org.SomeClass") - classpath should contain ("thejar.jar") - classpath should contain ("one.jar") - classpath should contain ("two.jar") - classpath should contain ("three.jar") + classpath should have length (4) + classpath(0) should endWith ("thejar.jar") + classpath(1) should endWith ("one.jar") + classpath(2) should endWith ("two.jar") + classpath(3) should endWith ("three.jar") sysProps("spark.app.name") should be ("trill") - sysProps("spark.jars") should be ("one.jar,two.jar,three.jar,thejar.jar") sysProps("spark.executor.memory") should be ("5g") sysProps("spark.executor.cores") should be ("5") sysProps("spark.yarn.queue") should be ("thequeue") - sysProps("spark.yarn.dist.files") should be ("file1.txt,file2.txt") - sysProps("spark.yarn.dist.archives") should be ("archive1.txt,archive2.txt") sysProps("spark.executor.instances") should be ("6") + sysProps("spark.yarn.dist.files") should include regex (".*file1.txt,.*file2.txt") + sysProps("spark.yarn.dist.archives") should include regex (".*archive1.txt,.*archive2.txt") + sysProps("spark.jars") should include regex (".*one.jar,.*two.jar,.*three.jar,.*thejar.jar") sysProps("SPARK_SUBMIT") should be ("true") } @@ -190,11 +191,13 @@ class SparkSubmitSuite extends FunSuite with ShouldMatchers { val appArgs = new SparkSubmitArguments(clArgs) val (childArgs, classpath, sysProps, mainClass) = createLaunchEnv(appArgs) val childArgsStr = childArgs.mkString(" ") - childArgsStr.startsWith("--memory 4g --cores 5 --supervise") should be (true) - childArgsStr should include ("launch spark://h:p thejar.jar org.SomeClass arg1 arg2") + childArgsStr should startWith ("--memory 4g --cores 5 --supervise") + childArgsStr should include regex ("launch spark://h:p .*thejar.jar org.SomeClass arg1 arg2") mainClass should be ("org.apache.spark.deploy.Client") - classpath should have length (0) - sysProps should have size (2) // contains --jar entry and SPARK_SUBMIT + classpath should have size (0) + sysProps should have size (2) + sysProps.keys should contain ("spark.jars") + sysProps.keys should contain ("SPARK_SUBMIT") } test("handles standalone client mode") { @@ -211,7 +214,8 @@ class SparkSubmitSuite extends FunSuite with ShouldMatchers { val (childArgs, classpath, sysProps, mainClass) = createLaunchEnv(appArgs) childArgs.mkString(" ") should be ("arg1 arg2") mainClass should be ("org.SomeClass") - classpath should contain ("thejar.jar") + classpath should have length (1) + classpath(0) should endWith ("thejar.jar") sysProps("spark.executor.memory") should be ("5g") sysProps("spark.cores.max") should be ("5") } @@ -230,7 +234,8 @@ class SparkSubmitSuite extends FunSuite with ShouldMatchers { val (childArgs, classpath, sysProps, mainClass) = createLaunchEnv(appArgs) childArgs.mkString(" ") should be ("arg1 arg2") mainClass should be ("org.SomeClass") - classpath should contain ("thejar.jar") + classpath should have length (1) + classpath(0) should endWith ("thejar.jar") sysProps("spark.executor.memory") should be ("5g") sysProps("spark.cores.max") should be ("5") } 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 cf9e20d347ddd..0aad882ed76a8 100644 --- a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala @@ -20,6 +20,7 @@ package org.apache.spark.util import scala.util.Random import java.io.{File, ByteArrayOutputStream, ByteArrayInputStream, FileOutputStream} +import java.net.URI import java.nio.{ByteBuffer, ByteOrder} import com.google.common.base.Charsets @@ -168,5 +169,68 @@ class UtilsSuite extends FunSuite { assert(result.size.equals(1)) assert(result(0).getCanonicalPath.equals(child1.getCanonicalPath)) } -} + test("resolveURI") { + def assertResolves(before: String, after: String, testWindows: Boolean = false): Unit = { + assume(before.split(",").length == 1) + assert(Utils.resolveURI(before, testWindows) === new URI(after)) + assert(Utils.resolveURI(after, testWindows) === new URI(after)) + assert(new URI(Utils.resolveURIs(before, testWindows)) === new URI(after)) + assert(new URI(Utils.resolveURIs(after, testWindows)) === new URI(after)) + } + val cwd = System.getProperty("user.dir") + assertResolves("hdfs:/root/spark.jar", "hdfs:/root/spark.jar") + assertResolves("hdfs:///root/spark.jar#app.jar", "hdfs:/root/spark.jar#app.jar") + assertResolves("spark.jar", s"file:$cwd/spark.jar") + assertResolves("spark.jar#app.jar", s"file:$cwd/spark.jar#app.jar") + assertResolves("C:/path/to/file.txt", "file:/C:/path/to/file.txt", testWindows = true) + assertResolves("C:\\path\\to\\file.txt", "file:/C:/path/to/file.txt", testWindows = true) + assertResolves("file:/C:/path/to/file.txt", "file:/C:/path/to/file.txt", testWindows = true) + assertResolves("file:///C:/path/to/file.txt", "file:/C:/path/to/file.txt", testWindows = true) + assertResolves("file:/C:/file.txt#alias.txt", "file:/C:/file.txt#alias.txt", testWindows = true) + intercept[IllegalArgumentException] { Utils.resolveURI("file:foo") } + intercept[IllegalArgumentException] { Utils.resolveURI("file:foo:baby") } + + // Test resolving comma-delimited paths + assert(Utils.resolveURIs("jar1,jar2") === s"file:$cwd/jar1,file:$cwd/jar2") + assert(Utils.resolveURIs("file:/jar1,file:/jar2") === "file:/jar1,file:/jar2") + assert(Utils.resolveURIs("hdfs:/jar1,file:/jar2,jar3") === + s"hdfs:/jar1,file:/jar2,file:$cwd/jar3") + assert(Utils.resolveURIs("hdfs:/jar1,file:/jar2,jar3,jar4#jar5") === + s"hdfs:/jar1,file:/jar2,file:$cwd/jar3,file:$cwd/jar4#jar5") + assert(Utils.resolveURIs("hdfs:/jar1,file:/jar2,jar3,C:\\pi.py#py.pi", testWindows = true) === + s"hdfs:/jar1,file:/jar2,file:$cwd/jar3,file:/C:/pi.py#py.pi") + } + + test("nonLocalPaths") { + assert(Utils.nonLocalPaths("spark.jar") === Array.empty) + assert(Utils.nonLocalPaths("file:/spark.jar") === Array.empty) + assert(Utils.nonLocalPaths("file:///spark.jar") === Array.empty) + assert(Utils.nonLocalPaths("local:/spark.jar") === Array.empty) + assert(Utils.nonLocalPaths("local:///spark.jar") === Array.empty) + assert(Utils.nonLocalPaths("hdfs:/spark.jar") === Array("hdfs:/spark.jar")) + assert(Utils.nonLocalPaths("hdfs:///spark.jar") === Array("hdfs:///spark.jar")) + assert(Utils.nonLocalPaths("file:/spark.jar,local:/smart.jar,family.py") === Array.empty) + assert(Utils.nonLocalPaths("local:/spark.jar,file:/smart.jar,family.py") === Array.empty) + assert(Utils.nonLocalPaths("hdfs:/spark.jar,s3:/smart.jar") === + Array("hdfs:/spark.jar", "s3:/smart.jar")) + assert(Utils.nonLocalPaths("hdfs:/spark.jar,s3:/smart.jar,local.py,file:/hello/pi.py") === + Array("hdfs:/spark.jar", "s3:/smart.jar")) + assert(Utils.nonLocalPaths("local.py,hdfs:/spark.jar,file:/hello/pi.py,s3:/smart.jar") === + Array("hdfs:/spark.jar", "s3:/smart.jar")) + + // Test Windows paths + assert(Utils.nonLocalPaths("C:/some/path.jar", testWindows = true) === Array.empty) + assert(Utils.nonLocalPaths("file:/C:/some/path.jar", testWindows = true) === Array.empty) + assert(Utils.nonLocalPaths("file:///C:/some/path.jar", testWindows = true) === Array.empty) + assert(Utils.nonLocalPaths("local:/C:/some/path.jar", testWindows = true) === Array.empty) + assert(Utils.nonLocalPaths("local:///C:/some/path.jar", testWindows = true) === Array.empty) + assert(Utils.nonLocalPaths("hdfs:/a.jar,C:/my.jar,s3:/another.jar", testWindows = true) === + Array("hdfs:/a.jar", "s3:/another.jar")) + assert(Utils.nonLocalPaths("D:/your.jar,hdfs:/a.jar,s3:/another.jar", testWindows = true) === + Array("hdfs:/a.jar", "s3:/another.jar")) + assert(Utils.nonLocalPaths("hdfs:/a.jar,s3:/another.jar,e:/our.jar", testWindows = true) === + Array("hdfs:/a.jar", "s3:/another.jar")) + } + +} diff --git a/python/pyspark/context.py b/python/pyspark/context.py index c9ff82d23b3cf..27b440d73bdc3 100644 --- a/python/pyspark/context.py +++ b/python/pyspark/context.py @@ -159,10 +159,14 @@ def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None, self.addPyFile(path) # Deploy code dependencies set by spark-submit; these will already have been added - # with SparkContext.addFile, so we just need to add them + # with SparkContext.addFile, so we just need to add them to the PYTHONPATH for path in self._conf.get("spark.submit.pyFiles", "").split(","): if path != "": - self._python_includes.append(os.path.basename(path)) + (dirname, filename) = os.path.split(path) + self._python_includes.append(filename) + sys.path.append(path) + if not dirname in sys.path: + sys.path.append(dirname) # Create a temporary directory inside spark.local.dir: local_dir = self._jvm.org.apache.spark.util.Utils.getLocalDir(self._jsc.sc().conf()) diff --git a/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala b/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala index 5f34362ccd973..e1db4d5395ab9 100644 --- a/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala +++ b/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala @@ -942,7 +942,7 @@ class SparkILoop(in0: Option[BufferedReader], protected val out: JPrintWriter, def createSparkContext(): SparkContext = { val execUri = System.getenv("SPARK_EXECUTOR_URI") - val jars = SparkILoop.getAddedJars.map(new java.io.File(_).getAbsolutePath) + val jars = SparkILoop.getAddedJars val conf = new SparkConf() .setMaster(getMaster()) .setAppName("Spark shell") @@ -997,7 +997,8 @@ object SparkILoop { val propJars = sys.props.get("spark.jars").flatMap { p => if (p == "") None else Some(p) } - propJars.orElse(envJars).map(_.split(",")).getOrElse(Array.empty) + val jars = propJars.orElse(envJars).getOrElse("") + Utils.resolveURIs(jars).split(",").filter(_.nonEmpty) } // Designed primarily for use by test code: take a String with a From 75a03277704f8618a0f1c41aecfb1ebd24a8ac1a Mon Sep 17 00:00:00 2001 From: Patrick Wendell Date: Sat, 24 May 2014 18:27:00 -0700 Subject: [PATCH 031/221] SPARK-1911: Emphasize that Spark jars should be built with Java 6. This commit requires the user to manually say "yes" when buiding Spark without Java 6. The prompt can be bypassed with a flag (e.g. if the user is scripting around make-distribution). Author: Patrick Wendell Closes #859 from pwendell/java6 and squashes the following commits: 4921133 [Patrick Wendell] Adding Pyspark Notice fee8c9e [Patrick Wendell] SPARK-1911: Emphasize that Spark jars should be built with Java 6. --- make-distribution.sh | 52 ++++++++++++++++++++++++++------------------ 1 file changed, 31 insertions(+), 21 deletions(-) diff --git a/make-distribution.sh b/make-distribution.sh index 1c89027d68bed..ae52b4976dc25 100755 --- a/make-distribution.sh +++ b/make-distribution.sh @@ -46,27 +46,6 @@ set -e FWDIR="$(cd `dirname $0`; pwd)" DISTDIR="$FWDIR/dist" -if [ -z "$JAVA_HOME" ]; then - echo "Error: JAVA_HOME is not set, cannot proceed." - exit -1 -fi - -JAVA_CMD="$JAVA_HOME"/bin/java -JAVA_VERSION=$("$JAVA_CMD" -version 2>&1) -if ! [[ "$JAVA_VERSION" =~ "1.6" ]]; then - echo "***NOTE***: JAVA_HOME is not set to a JDK 6 installation. The resulting" - echo " distribution will not support Java 6. See SPARK-1703." - echo "Output from 'java -version' was:" - echo "$JAVA_VERSION" -fi - -VERSION=$(mvn help:evaluate -Dexpression=project.version 2>/dev/null | grep -v "INFO" | tail -n 1) -if [ $? != 0 ]; then - echo -e "You need Maven installed to build Spark." - echo -e "Download Maven from https://maven.apache.org/" - exit -1; -fi - # Initialize defaults SPARK_HADOOP_VERSION=1.0.4 SPARK_YARN=false @@ -88,6 +67,9 @@ while (( "$#" )); do --with-hive) SPARK_HIVE=true ;; + --skip-java-test) + SKIP_JAVA_TEST=true + ;; --with-tachyon) SPARK_TACHYON=true ;; @@ -102,6 +84,34 @@ while (( "$#" )); do shift done +if [ -z "$JAVA_HOME" ]; then + echo "Error: JAVA_HOME is not set, cannot proceed." + exit -1 +fi + +VERSION=$(mvn help:evaluate -Dexpression=project.version 2>/dev/null | grep -v "INFO" | tail -n 1) +if [ $? != 0 ]; then + echo -e "You need Maven installed to build Spark." + echo -e "Download Maven from https://maven.apache.org/" + exit -1; +fi + +JAVA_CMD="$JAVA_HOME"/bin/java +JAVA_VERSION=$("$JAVA_CMD" -version 2>&1) +if [[ ! "$JAVA_VERSION" =~ "1.6" && -z "$SKIP_JAVA_TEST" ]]; then + echo "***NOTE***: JAVA_HOME is not set to a JDK 6 installation. The resulting" + echo " distribution may not work well with PySpark and will not run" + echo " with Java 6 (See SPARK-1703 and SPARK-1911)." + echo " This test can be disabled by adding --skip-java-test." + echo "Output from 'java -version' was:" + echo "$JAVA_VERSION" + read -p "Would you like to continue anyways? [y,n]: " -r + if [[ ! $REPLY =~ ^[Yy]$ ]]; then + echo "Okay, exiting." + exit 1 + fi +fi + if [ "$NAME" == "none" ]; then NAME=$SPARK_HADOOP_VERSION fi From 4e4831b8facc186cda6ef31040ccdeab48acbbb7 Mon Sep 17 00:00:00 2001 From: Zhen Peng Date: Sat, 24 May 2014 20:40:19 -0700 Subject: [PATCH 032/221] [SPARK-1886] check executor id existence when executor exit Author: Zhen Peng Closes #827 from zhpengg/bugfix-executor-id-not-found and squashes the following commits: cd8bb65 [Zhen Peng] bugfix: check executor id existence when executor exit --- .../apache/spark/deploy/worker/Worker.scala | 22 ++++++++++++------- 1 file changed, 14 insertions(+), 8 deletions(-) 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 fb9cc116cd08b..8b6747977eb87 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 @@ -263,14 +263,20 @@ private[spark] class Worker( } val fullId = appId + "/" + execId if (ExecutorState.isFinished(state)) { - val executor = executors(fullId) - logInfo("Executor " + fullId + " finished with state " + state + - message.map(" message " + _).getOrElse("") + - exitStatus.map(" exitStatus " + _).getOrElse("")) - executors -= fullId - finishedExecutors(fullId) = executor - coresUsed -= executor.cores - memoryUsed -= executor.memory + executors.get(fullId) match { + case Some(executor) => + logInfo("Executor " + fullId + " finished with state " + state + + message.map(" message " + _).getOrElse("") + + exitStatus.map(" exitStatus " + _).getOrElse("")) + executors -= fullId + finishedExecutors(fullId) = executor + coresUsed -= executor.cores + memoryUsed -= executor.memory + case None => + logInfo("Unknown Executor " + fullId + " finished with state " + state + + message.map(" message " + _).getOrElse("") + + exitStatus.map(" exitStatus " + _).getOrElse("")) + } } case KillExecutor(masterUrl, appId, execId) => From 5afe6af0b192ce7e908634992e8752537b1c4ed1 Mon Sep 17 00:00:00 2001 From: Cheng Lian Date: Sat, 24 May 2014 20:42:01 -0700 Subject: [PATCH 033/221] [SPARK-1913][SQL] Bug fix: column pruning error in Parquet support JIRA issue: [SPARK-1913](https://issues.apache.org/jira/browse/SPARK-1913) When scanning Parquet tables, attributes referenced only in predicates that are pushed down are not passed to the `ParquetTableScan` operator and causes exception. Author: Cheng Lian Closes #863 from liancheng/spark-1913 and squashes the following commits: f976b73 [Cheng Lian] Addessed the readability issue commented by @rxin f5b257d [Cheng Lian] Added back comments deleted by mistake ae60ab3 [Cheng Lian] [SPARK-1913] Attributes referenced only in predicates pushed down should remain in ParquetTableScan operator --- .../org/apache/spark/sql/SQLContext.scala | 6 +++++- .../spark/sql/execution/SparkStrategies.scala | 20 ++++++++++--------- .../spark/sql/parquet/ParquetQuerySuite.scala | 6 +++++- .../spark/sql/hive/HiveStrategies.scala | 1 + 4 files changed, 22 insertions(+), 11 deletions(-) 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 bfebfa0c28c52..043be58edc91b 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 @@ -206,17 +206,21 @@ class SQLContext(@transient val sparkContext: SparkContext) * final desired output requires complex expressions to be evaluated or when columns can be * further eliminated out after filtering has been done. * + * The `prunePushedDownFilters` parameter is used to remove those filters that can be optimized + * away by the filter pushdown optimization. + * * The required attributes for both filtering and expression evaluation are passed to the * provided `scanBuilder` function so that it can avoid unnecessary column materialization. */ def pruneFilterProject( projectList: Seq[NamedExpression], filterPredicates: Seq[Expression], + prunePushedDownFilters: Seq[Expression] => Seq[Expression], scanBuilder: Seq[Attribute] => SparkPlan): SparkPlan = { val projectSet = projectList.flatMap(_.references).toSet val filterSet = filterPredicates.flatMap(_.references).toSet - val filterCondition = filterPredicates.reduceLeftOption(And) + val filterCondition = prunePushedDownFilters(filterPredicates).reduceLeftOption(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 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 394a59700dbaf..cfa8bdae58b11 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 @@ -141,14 +141,14 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] { case logical.InsertIntoTable(table: ParquetRelation, partition, child, overwrite) => InsertIntoParquetTable(table, planLater(child), overwrite)(sparkContext) :: Nil case PhysicalOperation(projectList, filters: Seq[Expression], relation: ParquetRelation) => { - val remainingFilters = + val prunePushedDownFilters = if (sparkContext.conf.getBoolean(ParquetFilters.PARQUET_FILTER_PUSHDOWN_ENABLED, true)) { - filters.filter { - // Note: filters cannot be pushed down to Parquet if they contain more complex - // expressions than simple "Attribute cmp Literal" comparisons. Here we remove - // all filters that have been pushed down. Note that a predicate such as - // "(A AND B) OR C" can result in "A OR C" being pushed down. - filter => + (filters: Seq[Expression]) => { + filters.filter { filter => + // Note: filters cannot be pushed down to Parquet if they contain more complex + // expressions than simple "Attribute cmp Literal" comparisons. Here we remove + // all filters that have been pushed down. Note that a predicate such as + // "(A AND B) OR C" can result in "A OR C" being pushed down. val recordFilter = ParquetFilters.createFilter(filter) if (!recordFilter.isDefined) { // First case: the pushdown did not result in any record filter. @@ -159,13 +159,15 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] { // still want to keep "A AND B" in the higher-level filter, not just "B". !ParquetFilters.findExpression(recordFilter.get, filter).isDefined } + } } } else { - filters + identity[Seq[Expression]] _ } pruneFilterProject( projectList, - remainingFilters, + filters, + prunePushedDownFilters, ParquetTableScan(_, relation, filters)(sparkContext)) :: Nil } 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 65f4c17aeee3a..f9731e82e4924 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 @@ -358,5 +358,9 @@ class ParquetQuerySuite extends QueryTest with FunSuite with BeforeAndAfterAll { assert(stringResult(0).getString(2) == "100", "stringvalue incorrect") assert(stringResult(0).getInt(1) === 100) } -} + test("SPARK-1913 regression: columns only referenced by pushed down filters should remain") { + val query = sql(s"SELECT mystring FROM testfiltersource WHERE myint < 10") + assert(query.collect().size === 10) + } +} 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 b2157074a41bf..8b51957162e04 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 @@ -69,6 +69,7 @@ private[hive] trait HiveStrategies { pruneFilterProject( projectList, otherPredicates, + identity[Seq[Expression]], HiveTableScan(_, relation, pruningPredicates.reduceLeftOption(And))(hiveContext)) :: Nil case _ => Nil From 6e9fb6320bec3371bc9c010ccbc1b915f500486b Mon Sep 17 00:00:00 2001 From: Colin Patrick Mccabe Date: Sat, 24 May 2014 22:39:27 -0700 Subject: [PATCH 034/221] spark-submit: add exec at the end of the script Add an 'exec' at the end of the spark-submit script, to avoid keeping a bash process hanging around while it runs. This makes ps look a little bit nicer. Author: Colin Patrick Mccabe Closes #858 from cmccabe/SPARK-1907 and squashes the following commits: 7023b64 [Colin Patrick Mccabe] spark-submit: add exec at the end of the script --- bin/spark-submit | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bin/spark-submit b/bin/spark-submit index 63903b17a2902..9e7cecedd0325 100755 --- a/bin/spark-submit +++ b/bin/spark-submit @@ -41,5 +41,5 @@ if [ -n "$DRIVER_MEMORY" ] && [ $DEPLOY_MODE == "client" ]; then export SPARK_DRIVER_MEMORY=$DRIVER_MEMORY fi -$SPARK_HOME/bin/spark-class org.apache.spark.deploy.SparkSubmit "${ORIG_ARGS[@]}" +exec $SPARK_HOME/bin/spark-class org.apache.spark.deploy.SparkSubmit "${ORIG_ARGS[@]}" From 6052db9dc10c996215658485e805200e4f0cf549 Mon Sep 17 00:00:00 2001 From: Kan Zhang Date: Sun, 25 May 2014 00:06:42 -0700 Subject: [PATCH 035/221] [SPARK-1822] SchemaRDD.count() should use query optimizer Author: Kan Zhang Closes #841 from kanzhang/SPARK-1822 and squashes the following commits: 2f8072a [Kan Zhang] [SPARK-1822] Minor style update cf4baa4 [Kan Zhang] [SPARK-1822] Adding Scaladoc e67c910 [Kan Zhang] [SPARK-1822] SchemaRDD.count() should use optimizer --- python/pyspark/sql.py | 14 +++++++++++++- .../sql/catalyst/expressions/aggregates.scala | 6 +++--- .../scala/org/apache/spark/sql/SchemaRDD.scala | 9 +++++++++ .../scala/org/apache/spark/sql/DslQuerySuite.scala | 9 +++++---- .../test/scala/org/apache/spark/sql/TestData.scala | 2 ++ 5 files changed, 32 insertions(+), 8 deletions(-) diff --git a/python/pyspark/sql.py b/python/pyspark/sql.py index bbe69e7d8f89b..f2001afae4ee5 100644 --- a/python/pyspark/sql.py +++ b/python/pyspark/sql.py @@ -268,7 +268,7 @@ def __init__(self, jschema_rdd, sql_ctx): def _jrdd(self): """ Lazy evaluation of PythonRDD object. Only done when a user calls methods defined by the - L{pyspark.rdd.RDD} super class (map, count, etc.). + L{pyspark.rdd.RDD} super class (map, filter, etc.). """ if not hasattr(self, '_lazy_jrdd'): self._lazy_jrdd = self._toPython()._jrdd @@ -321,6 +321,18 @@ def saveAsTable(self, tableName): """ self._jschema_rdd.saveAsTable(tableName) + def count(self): + """ + Return the number of elements in this RDD. + + >>> srdd = sqlCtx.inferSchema(rdd) + >>> srdd.count() + 3L + >>> srdd.count() == srdd.map(lambda x: x).count() + True + """ + return self._jschema_rdd.count() + def _toPython(self): # We have to import the Row class explicitly, so that the reference Pickler has is # pyspark.sql.Row instead of __main__.Row 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 index 5dbaaa3b0ce35..1bcd4e22766a9 100644 --- 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 @@ -151,7 +151,7 @@ case class MaxFunction(expr: Expression, base: AggregateExpression) extends Aggr case class Count(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] { override def references = child.references override def nullable = false - override def dataType = IntegerType + override def dataType = LongType override def toString = s"COUNT($child)" override def asPartial: SplitEvaluation = { @@ -295,12 +295,12 @@ case class AverageFunction(expr: Expression, base: AggregateExpression) case class CountFunction(expr: Expression, base: AggregateExpression) extends AggregateFunction { def this() = this(null, null) // Required for serialization. - var count: Int = _ + var count: Long = _ override def update(input: Row): Unit = { val evaluatedExpr = expr.map(_.eval(input)) if (evaluatedExpr.map(_ != null).reduceLeft(_ || _)) { - count += 1 + count += 1L } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala index 2569815ebb209..452da3d02310d 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala @@ -274,6 +274,15 @@ class SchemaRDD( seed: Long) = new SchemaRDD(sqlContext, Sample(fraction, withReplacement, seed, logicalPlan)) + /** + * :: Experimental :: + * Overriding base RDD implementation to leverage query optimizer + */ + @Experimental + override def count(): Long = { + groupBy()(Count(Literal(1))).collect().head.getLong(0) + } + /** * :: Experimental :: * Applies the given Generator, or table generating function, to this relation. diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala index f43e98d614094..233132a2fec69 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala @@ -108,10 +108,7 @@ class DslQuerySuite extends QueryTest { } test("count") { - checkAnswer( - testData2.groupBy()(Count(1)), - testData2.count() - ) + assert(testData2.count() === testData2.map(_ => 1).count()) } test("null count") { @@ -126,6 +123,10 @@ class DslQuerySuite extends QueryTest { ) } + test("zero count") { + assert(testData4.count() === 0) + } + test("inner join where, one match per row") { checkAnswer( upperCaseData.join(lowerCaseData, Inner).where('n === 'N), 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 1aca3872524d7..b1eecb4dd3be4 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 @@ -47,6 +47,8 @@ object TestData { (1, null) :: (2, 2) :: Nil) + val testData4 = logical.LocalRelation('a.int, 'b.int) + case class UpperCaseData(N: Int, L: String) val upperCaseData = TestSQLContext.sparkContext.parallelize( From 5c7faecd75ea59454ad3209390ac078e6cf6e4a6 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Sun, 25 May 2014 01:32:15 -0700 Subject: [PATCH 036/221] Added PEP8 style configuration file. This sets the max line length to 100 as a PEP8 exception. Author: Reynold Xin Closes #872 from rxin/pep8 and squashes the following commits: 2f26029 [Reynold Xin] Added PEP8 style configuration file. --- tox.ini | 2 ++ 1 file changed, 2 insertions(+) create mode 100644 tox.ini diff --git a/tox.ini b/tox.ini new file mode 100644 index 0000000000000..2d94b09a90a46 --- /dev/null +++ b/tox.ini @@ -0,0 +1,2 @@ +[pep8] +max-line-length=100 From d66642e3978a76977414c2fdaedebaad35662667 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Sun, 25 May 2014 01:44:49 -0700 Subject: [PATCH 037/221] SPARK-1822: Some minor cleanup work on SchemaRDD.count() Minor cleanup following #841. Author: Reynold Xin Closes #868 from rxin/schema-count and squashes the following commits: 5442651 [Reynold Xin] SPARK-1822: Some minor cleanup work on SchemaRDD.count() --- python/pyspark/sql.py | 5 ++++- .../src/main/scala/org/apache/spark/sql/SchemaRDD.scala | 8 ++++---- .../test/scala/org/apache/spark/sql/DslQuerySuite.scala | 2 +- .../src/test/scala/org/apache/spark/sql/TestData.scala | 2 +- 4 files changed, 10 insertions(+), 7 deletions(-) diff --git a/python/pyspark/sql.py b/python/pyspark/sql.py index f2001afae4ee5..fa4b9c7b688ea 100644 --- a/python/pyspark/sql.py +++ b/python/pyspark/sql.py @@ -323,7 +323,10 @@ def saveAsTable(self, tableName): def count(self): """ - Return the number of elements in this RDD. + 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 SchemaRDD, which supports + features such as filter pushdown. >>> srdd = sqlCtx.inferSchema(rdd) >>> srdd.count() diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala index 452da3d02310d..9883ebc0b3c62 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala @@ -276,12 +276,12 @@ class SchemaRDD( /** * :: Experimental :: - * Overriding base RDD implementation to leverage query optimizer + * Return the number of elements in the RDD. Unlike the base RDD implementation of count, this + * implementation leverages the query optimizer to compute the count on the SchemaRDD, which + * supports features such as filter pushdown. */ @Experimental - override def count(): Long = { - groupBy()(Count(Literal(1))).collect().head.getLong(0) - } + override def count(): Long = groupBy()(Count(Literal(1))).collect().head.getLong(0) /** * :: Experimental :: diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala index 233132a2fec69..94ba13b14b33d 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala @@ -124,7 +124,7 @@ class DslQuerySuite extends QueryTest { } test("zero count") { - assert(testData4.count() === 0) + assert(emptyTableData.count() === 0) } test("inner join where, one match per row") { 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 b1eecb4dd3be4..944f520e43515 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 @@ -47,7 +47,7 @@ object TestData { (1, null) :: (2, 2) :: Nil) - val testData4 = logical.LocalRelation('a.int, 'b.int) + val emptyTableData = logical.LocalRelation('a.int, 'b.int) case class UpperCaseData(N: Int, L: String) val upperCaseData = From 55fddf9cc0fe420d5396b0e730c8413b2f23d636 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Sun, 25 May 2014 01:47:08 -0700 Subject: [PATCH 038/221] Added license header for tox.ini. (cherry picked from commit fa541f32c5b92e6868a9c99cbb2c87115d624d23) Signed-off-by: Reynold Xin --- tox.ini | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/tox.ini b/tox.ini index 2d94b09a90a46..44766e529bf7f 100644 --- a/tox.ini +++ b/tox.ini @@ -1,2 +1,17 @@ +# 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. + [pep8] max-line-length=100 From d79c2b28e17ec0b15198aaedd2e1f403d81f717e Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Sun, 25 May 2014 14:48:27 -0700 Subject: [PATCH 039/221] Fix PEP8 violations in examples/src/main/python. Author: Reynold Xin Closes #870 from rxin/examples-python-pep8 and squashes the following commits: 2829e84 [Reynold Xin] Fix PEP8 violations in examples/src/main/python. --- examples/src/main/python/als.py | 20 +++++++++++-------- examples/src/main/python/kmeans.py | 2 +- .../src/main/python/logistic_regression.py | 4 ++-- examples/src/main/python/pagerank.py | 12 +++++------ examples/src/main/python/pi.py | 2 ++ examples/src/main/python/sort.py | 4 ++-- 6 files changed, 25 insertions(+), 19 deletions(-) diff --git a/examples/src/main/python/als.py b/examples/src/main/python/als.py index f0b46cd28b7aa..1a7c4c51f48cd 100755 --- a/examples/src/main/python/als.py +++ b/examples/src/main/python/als.py @@ -29,22 +29,25 @@ LAMBDA = 0.01 # regularization np.random.seed(42) + def rmse(R, ms, us): diff = R - ms * us.T return np.sqrt(np.sum(np.power(diff, 2)) / M * U) + def update(i, vec, mat, ratings): uu = mat.shape[0] ff = mat.shape[1] - + XtX = mat.T * mat Xty = mat.T * ratings[i, :].T - + for j in range(ff): - XtX[j,j] += LAMBDA * uu - + XtX[j, j] += LAMBDA * uu + return np.linalg.solve(XtX, Xty) + if __name__ == "__main__": """ Usage: als [M] [U] [F] [iterations] [slices]" @@ -57,10 +60,10 @@ def update(i, vec, mat, ratings): slices = int(sys.argv[5]) if len(sys.argv) > 5 else 2 print "Running ALS with M=%d, U=%d, F=%d, iters=%d, slices=%d\n" % \ - (M, U, F, ITERATIONS, slices) + (M, U, F, ITERATIONS, slices) R = matrix(rand(M, F)) * matrix(rand(U, F).T) - ms = matrix(rand(M ,F)) + ms = matrix(rand(M, F)) us = matrix(rand(U, F)) Rb = sc.broadcast(R) @@ -71,8 +74,9 @@ def update(i, vec, mat, ratings): ms = sc.parallelize(range(M), slices) \ .map(lambda x: update(x, msb.value[x, :], usb.value, Rb.value)) \ .collect() - ms = matrix(np.array(ms)[:, :, 0]) # collect() returns a list, so array ends up being - # a 3-d array, we take the first 2 dims for the matrix + # collect() returns a list, so array ends up being + # a 3-d array, we take the first 2 dims for the matrix + ms = matrix(np.array(ms)[:, :, 0]) msb = sc.broadcast(ms) us = sc.parallelize(range(U), slices) \ diff --git a/examples/src/main/python/kmeans.py b/examples/src/main/python/kmeans.py index fc16586c28a46..988fc45baf3bc 100755 --- a/examples/src/main/python/kmeans.py +++ b/examples/src/main/python/kmeans.py @@ -59,7 +59,7 @@ def closestPoint(p, centers): while tempDist > convergeDist: closest = data.map( - lambda p : (closestPoint(p, kPoints), (p, 1))) + lambda p: (closestPoint(p, kPoints), (p, 1))) pointStats = closest.reduceByKey( lambda (x1, y1), (x2, y2): (x1 + x2, y1 + y2)) newPoints = pointStats.map( diff --git a/examples/src/main/python/logistic_regression.py b/examples/src/main/python/logistic_regression.py index 0f22d0b32319e..6c33deabfd6ea 100755 --- a/examples/src/main/python/logistic_regression.py +++ b/examples/src/main/python/logistic_regression.py @@ -60,8 +60,8 @@ def readPointBatch(iterator): # Compute logistic regression gradient for a matrix of data points def gradient(matrix, w): - Y = matrix[:,0] # point labels (first column of input file) - X = matrix[:,1:] # point coordinates + Y = matrix[:, 0] # point labels (first column of input file) + X = matrix[:, 1:] # point coordinates # For each point (x, y), compute gradient function, then sum these up return ((1.0 / (1.0 + np.exp(-Y * X.dot(w))) - 1.0) * Y * X.T).sum(1) diff --git a/examples/src/main/python/pagerank.py b/examples/src/main/python/pagerank.py index d350fa46fa49a..0b96343158d44 100755 --- a/examples/src/main/python/pagerank.py +++ b/examples/src/main/python/pagerank.py @@ -15,9 +15,8 @@ # limitations under the License. # -#!/usr/bin/env python - -import re, sys +import re +import sys from operator import add from pyspark import SparkContext @@ -26,7 +25,8 @@ def computeContribs(urls, rank): """Calculates URL contributions to the rank of other URLs.""" num_urls = len(urls) - for url in urls: yield (url, rank / num_urls) + for url in urls: + yield (url, rank / num_urls) def parseNeighbors(urls): @@ -59,8 +59,8 @@ def parseNeighbors(urls): # Calculates and updates URL ranks continuously using PageRank algorithm. for iteration in xrange(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)) + contribs = links.join(ranks).flatMap( + lambda (url, (urls, rank)): computeContribs(urls, rank)) # Re-calculates URL ranks based on neighbor contributions. ranks = contribs.reduceByKey(add).mapValues(lambda rank: rank * 0.85 + 0.15) diff --git a/examples/src/main/python/pi.py b/examples/src/main/python/pi.py index 234720b55fa49..21d94a2cd4b64 100755 --- a/examples/src/main/python/pi.py +++ b/examples/src/main/python/pi.py @@ -29,9 +29,11 @@ sc = SparkContext(appName="PythonPi") slices = int(sys.argv[1]) if len(sys.argv) > 1 else 2 n = 100000 * slices + def f(_): x = random() * 2 - 1 y = random() * 2 - 1 return 1 if x ** 2 + y ** 2 < 1 else 0 + count = sc.parallelize(xrange(1, n+1), slices).map(f).reduce(add) print "Pi is roughly %f" % (4.0 * count / n) diff --git a/examples/src/main/python/sort.py b/examples/src/main/python/sort.py index 4913ee926aa03..41d00c1b79133 100755 --- a/examples/src/main/python/sort.py +++ b/examples/src/main/python/sort.py @@ -27,8 +27,8 @@ sc = SparkContext(appName="PythonSort") lines = sc.textFile(sys.argv[1], 1) sortedCount = lines.flatMap(lambda x: x.split(' ')) \ - .map(lambda x: (int(x), 1)) \ - .sortByKey(lambda x: x) + .map(lambda x: (int(x), 1)) \ + .sortByKey(lambda x: x) # This is just a demo on how to bring all the sorted data back to a single node. # In reality, we wouldn't want to collect all the data to the driver node. output = sortedCount.collect() From 14f0358b2a0a9b92526bdad6d501ab753459eaa0 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Sun, 25 May 2014 16:04:17 -0700 Subject: [PATCH 040/221] Python docstring update for sql.py. Mostly related to the following two rules in PEP8 and PEP257: - Line length < 72 chars. - First line should be a concise description of the function/class. Author: Reynold Xin Closes #869 from rxin/docstring-schemardd and squashes the following commits: 7cf0cbc [Reynold Xin] Updated sql.py for pep8 docstring. 0a4aef9 [Reynold Xin] Merge branch 'master' into docstring-schemardd 6678937 [Reynold Xin] Python docstring update for sql.py. --- python/pyspark/sql.py | 124 +++++++++++++++++++++--------------------- 1 file changed, 63 insertions(+), 61 deletions(-) diff --git a/python/pyspark/sql.py b/python/pyspark/sql.py index fa4b9c7b688ea..b4e9618cc25b5 100644 --- a/python/pyspark/sql.py +++ b/python/pyspark/sql.py @@ -23,14 +23,14 @@ class SQLContext: - """ - Main entry point for SparkSQL functionality. A SQLContext can be used create L{SchemaRDD}s, - register L{SchemaRDD}s as tables, execute sql over tables, cache tables, and read parquet files. + """Main entry point for SparkSQL functionality. + + A SQLContext can be used create L{SchemaRDD}s, register L{SchemaRDD}s as + tables, execute SQL over tables, cache tables, and read parquet files. """ def __init__(self, sparkContext, sqlContext = None): - """ - Create a new SQLContext. + """Create a new SQLContext. @param sparkContext: The SparkContext to wrap. @@ -63,18 +63,20 @@ def __init__(self, sparkContext, sqlContext = None): @property def _ssql_ctx(self): - """ - Accessor for the JVM SparkSQL context. Subclasses can override this property to provide - their own JVM Contexts. + """Accessor for the JVM SparkSQL context. + + Subclasses can override this property to provide their own + JVM Contexts. """ if not hasattr(self, '_scala_SQLContext'): self._scala_SQLContext = self._jvm.SQLContext(self._jsc.sc()) return self._scala_SQLContext def inferSchema(self, rdd): - """ - Infer and apply a schema to an RDD of L{dict}s. We peek at the first row of the RDD to - determine the fields names and types, and then use that to extract all the dictionaries. + """Infer and apply a schema to an RDD of L{dict}s. + + We peek at the first row of the RDD to determine the fields names + and types, and then use that to extract all the dictionaries. >>> srdd = sqlCtx.inferSchema(rdd) >>> srdd.collect() == [{"field1" : 1, "field2" : "row1"}, {"field1" : 2, "field2": "row2"}, @@ -92,9 +94,10 @@ def inferSchema(self, rdd): return SchemaRDD(srdd, 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. + """Registers the given RDD as a temporary table in the catalog. + + Temporary tables exist only during the lifetime of this instance of + SQLContext. >>> srdd = sqlCtx.inferSchema(rdd) >>> sqlCtx.registerRDDAsTable(srdd, "table1") @@ -106,8 +109,7 @@ def registerRDDAsTable(self, rdd, tableName): raise ValueError("Can only register SchemaRDD as table") def parquetFile(self, path): - """ - Loads a Parquet file, returning the result as a L{SchemaRDD}. + """Loads a Parquet file, returning the result as a L{SchemaRDD}. >>> import tempfile, shutil >>> parquetFile = tempfile.mkdtemp() @@ -122,8 +124,7 @@ def parquetFile(self, path): return SchemaRDD(jschema_rdd, self) def sql(self, sqlQuery): - """ - Executes a SQL query using Spark, returning the result as a L{SchemaRDD}. + """Return a L{SchemaRDD} representing the result of the given query. >>> srdd = sqlCtx.inferSchema(rdd) >>> sqlCtx.registerRDDAsTable(srdd, "table1") @@ -135,8 +136,7 @@ def sql(self, sqlQuery): return SchemaRDD(self._ssql_ctx.sql(sqlQuery), self) def table(self, tableName): - """ - Returns the specified table as a L{SchemaRDD}. + """Returns the specified table as a L{SchemaRDD}. >>> srdd = sqlCtx.inferSchema(rdd) >>> sqlCtx.registerRDDAsTable(srdd, "table1") @@ -147,23 +147,19 @@ def table(self, tableName): return SchemaRDD(self._ssql_ctx.table(tableName), self) def cacheTable(self, tableName): - """ - Caches the specified table in-memory. - """ + """Caches the specified table in-memory.""" self._ssql_ctx.cacheTable(tableName) def uncacheTable(self, tableName): - """ - Removes the specified table from the in-memory cache. - """ + """Removes the specified table from the in-memory cache.""" self._ssql_ctx.uncacheTable(tableName) class HiveContext(SQLContext): - """ - An instance of the Spark SQL execution engine 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. + """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. """ @property @@ -193,9 +189,10 @@ def hql(self, hqlQuery): class LocalHiveContext(HiveContext): - """ - Starts up an instance of hive where metadata is stored locally. An in-process metadata data is - created with data stored in ./metadata. Warehouse data is stored in in ./warehouse. + """Starts up an instance of hive where metadata is stored locally. + + An in-process metadata data is created with data stored in ./metadata. + Warehouse data is stored in in ./warehouse. >>> import os >>> hiveCtx = LocalHiveContext(sc) @@ -228,8 +225,10 @@ def _get_hive_ctx(self): # TODO: Investigate if it is more efficient to use a namedtuple. One problem is that named tuples # are custom classes that must be generated per Schema. class Row(dict): - """ - An extended L{dict} that takes a L{dict} in its constructor, and exposes those items as fields. + """A row in L{SchemaRDD}. + + An extended L{dict} that takes a L{dict} in its constructor, and + exposes those items as fields. >>> r = Row({"hello" : "world", "foo" : "bar"}) >>> r.hello @@ -245,13 +244,16 @@ def __init__(self, d): class SchemaRDD(RDD): - """ - An RDD of L{Row} objects that has an associated schema. The underlying JVM object is a SchemaRDD, - not a PythonRDD, so we can utilize the relational query api exposed by SparkSQL. + """An RDD of L{Row} objects that has an associated schema. - For normal L{pyspark.rdd.RDD} operations (map, count, etc.) the L{SchemaRDD} is not operated on - directly, as it's underlying implementation is a RDD composed of Java objects. Instead it is - converted to a PythonRDD in the JVM, on which Python operations can be done. + The underlying JVM object is a SchemaRDD, not a PythonRDD, so we can + utilize the relational query api exposed by SparkSQL. + + For normal L{pyspark.rdd.RDD} operations (map, count, etc.) the + L{SchemaRDD} is not operated on directly, as it's underlying + implementation is a RDD composed of Java objects. Instead it is + converted to a PythonRDD in the JVM, on which Python operations can + be done. """ def __init__(self, jschema_rdd, sql_ctx): @@ -266,8 +268,9 @@ def __init__(self, jschema_rdd, sql_ctx): @property def _jrdd(self): - """ - Lazy evaluation of PythonRDD object. Only done when a user calls methods defined by the + """Lazy evaluation of PythonRDD object. + + Only done when a user calls methods defined by the L{pyspark.rdd.RDD} super class (map, filter, etc.). """ if not hasattr(self, '_lazy_jrdd'): @@ -279,10 +282,10 @@ def _id(self): return self._jrdd.id() def saveAsParquetFile(self, path): - """ - Saves the contents of this L{SchemaRDD} as a parquet file, preserving the schema. Files - that are written out using this method can be read back in as a SchemaRDD using the - L{SQLContext.parquetFile} method. + """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 SchemaRDD using the L{SQLContext.parquetFile} method. >>> import tempfile, shutil >>> parquetFile = tempfile.mkdtemp() @@ -296,9 +299,10 @@ def saveAsParquetFile(self, path): self._jschema_rdd.saveAsParquetFile(path) def registerAsTable(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 SchemaRDD. + """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 SchemaRDD. >>> srdd = sqlCtx.inferSchema(rdd) >>> srdd.registerAsTable("test") @@ -309,24 +313,22 @@ def registerAsTable(self, name): self._jschema_rdd.registerAsTable(name) def insertInto(self, tableName, overwrite = False): - """ - Inserts the contents of this SchemaRDD into the specified table, - optionally overwriting any existing data. + """Inserts the contents of this SchemaRDD into the specified table. + + Optionally overwriting any existing data. """ self._jschema_rdd.insertInto(tableName, overwrite) def saveAsTable(self, tableName): - """ - Creates a new table with the contents of this SchemaRDD. - """ + """Creates a new table with the contents of this SchemaRDD.""" self._jschema_rdd.saveAsTable(tableName) 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 SchemaRDD, which supports - features such as filter pushdown. + """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 SchemaRDD, + which supports features such as filter pushdown. >>> srdd = sqlCtx.inferSchema(rdd) >>> srdd.count() From d33d3c61ae9e4551aed0217e525a109e678298f2 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Sun, 25 May 2014 17:15:01 -0700 Subject: [PATCH 041/221] Fix PEP8 violations in Python mllib. Author: Reynold Xin Closes #871 from rxin/mllib-pep8 and squashes the following commits: 848416f [Reynold Xin] Fixed a typo in the previous cleanup (c -> sc). a8db4cd [Reynold Xin] Fix PEP8 violations in Python mllib. --- python/pyspark/mllib/_common.py | 42 +++++++++++++------------- python/pyspark/mllib/classification.py | 26 ++++++++-------- python/pyspark/mllib/clustering.py | 15 +++++---- python/pyspark/mllib/linalg.py | 13 ++++---- python/pyspark/mllib/recommendation.py | 15 +++++---- python/pyspark/mllib/regression.py | 24 ++++++--------- python/pyspark/mllib/tests.py | 27 ++++++----------- python/pyspark/mllib/util.py | 4 +-- 8 files changed, 78 insertions(+), 88 deletions(-) diff --git a/python/pyspark/mllib/_common.py b/python/pyspark/mllib/_common.py index e6f0953810ed7..802a27a8da14d 100644 --- a/python/pyspark/mllib/_common.py +++ b/python/pyspark/mllib/_common.py @@ -56,7 +56,8 @@ # # Sparse double vector format: # -# [1-byte 2] [4-byte length] [4-byte nonzeros] [nonzeros*4 bytes of indices] [nonzeros*8 bytes of values] +# [1-byte 2] [4-byte length] [4-byte nonzeros] [nonzeros*4 bytes of indices] \ +# [nonzeros*8 bytes of values] # # Double matrix format: # @@ -110,18 +111,18 @@ def _serialize_double_vector(v): return _serialize_sparse_vector(v) else: raise TypeError("_serialize_double_vector called on a %s; " - "wanted ndarray or SparseVector" % type(v)) + "wanted ndarray or SparseVector" % type(v)) def _serialize_dense_vector(v): """Serialize a dense vector given as a NumPy array.""" if v.ndim != 1: raise TypeError("_serialize_double_vector called on a %ddarray; " - "wanted a 1darray" % v.ndim) + "wanted a 1darray" % v.ndim) if v.dtype != float64: if numpy.issubdtype(v.dtype, numpy.complex): raise TypeError("_serialize_double_vector called on an ndarray of %s; " - "wanted ndarray of float64" % v.dtype) + "wanted ndarray of float64" % v.dtype) v = v.astype(float64) length = v.shape[0] ba = bytearray(5 + 8 * length) @@ -158,10 +159,10 @@ def _deserialize_double_vector(ba): """ if type(ba) != bytearray: raise TypeError("_deserialize_double_vector called on a %s; " - "wanted bytearray" % type(ba)) + "wanted bytearray" % type(ba)) if len(ba) < 5: raise TypeError("_deserialize_double_vector called on a %d-byte array, " - "which is too short" % len(ba)) + "which is too short" % len(ba)) if ba[0] == DENSE_VECTOR_MAGIC: return _deserialize_dense_vector(ba) elif ba[0] == SPARSE_VECTOR_MAGIC: @@ -175,7 +176,7 @@ def _deserialize_dense_vector(ba): """Deserialize a dense vector into a numpy array.""" if len(ba) < 5: raise TypeError("_deserialize_dense_vector called on a %d-byte array, " - "which is too short" % len(ba)) + "which is too short" % len(ba)) length = ndarray(shape=[1], buffer=ba, offset=1, dtype=int32)[0] if len(ba) != 8 * length + 5: raise TypeError("_deserialize_dense_vector called on bytearray " @@ -187,7 +188,7 @@ def _deserialize_sparse_vector(ba): """Deserialize a sparse vector into a MLlib SparseVector object.""" if len(ba) < 9: raise TypeError("_deserialize_sparse_vector called on a %d-byte array, " - "which is too short" % len(ba)) + "which is too short" % len(ba)) header = ndarray(shape=[2], buffer=ba, offset=1, dtype=int32) size = header[0] nonzeros = header[1] @@ -205,7 +206,7 @@ def _serialize_double_matrix(m): if m.dtype != float64: if numpy.issubdtype(m.dtype, numpy.complex): raise TypeError("_serialize_double_matrix called on an ndarray of %s; " - "wanted ndarray of float64" % m.dtype) + "wanted ndarray of float64" % m.dtype) m = m.astype(float64) rows = m.shape[0] cols = m.shape[1] @@ -225,10 +226,10 @@ def _deserialize_double_matrix(ba): """Deserialize a double matrix from a mutually understood format.""" if type(ba) != bytearray: raise TypeError("_deserialize_double_matrix called on a %s; " - "wanted bytearray" % type(ba)) + "wanted bytearray" % type(ba)) if len(ba) < 9: raise TypeError("_deserialize_double_matrix called on a %d-byte array, " - "which is too short" % len(ba)) + "which is too short" % len(ba)) if ba[0] != DENSE_MATRIX_MAGIC: raise TypeError("_deserialize_double_matrix called on bytearray " "with wrong magic") @@ -267,7 +268,7 @@ def _copyto(array, buffer, offset, shape, dtype): def _get_unmangled_rdd(data, serializer): dataBytes = data.map(serializer) dataBytes._bypass_serializer = True - dataBytes.cache() # TODO: users should unpersist() this later! + dataBytes.cache() # TODO: users should unpersist() this later! return dataBytes @@ -293,14 +294,14 @@ def _linear_predictor_typecheck(x, coeffs): if type(x) == ndarray: if x.ndim == 1: if x.shape != coeffs.shape: - raise RuntimeError("Got array of %d elements; wanted %d" - % (numpy.shape(x)[0], coeffs.shape[0])) + raise RuntimeError("Got array of %d elements; wanted %d" % ( + numpy.shape(x)[0], coeffs.shape[0])) else: raise RuntimeError("Bulk predict not yet supported.") elif type(x) == SparseVector: if x.size != coeffs.shape[0]: - raise RuntimeError("Got sparse vector of size %d; wanted %d" - % (x.size, coeffs.shape[0])) + raise RuntimeError("Got sparse vector of size %d; wanted %d" % ( + x.size, coeffs.shape[0])) elif (type(x) == RDD): raise RuntimeError("Bulk predict not yet supported.") else: @@ -315,7 +316,7 @@ def _get_initial_weights(initial_weights, data): if type(initial_weights) == ndarray: if initial_weights.ndim != 1: raise TypeError("At least one data element has " - + initial_weights.ndim + " dimensions, which is not 1") + + initial_weights.ndim + " dimensions, which is not 1") initial_weights = numpy.zeros([initial_weights.shape[0]]) elif type(initial_weights) == SparseVector: initial_weights = numpy.zeros([initial_weights.size]) @@ -333,10 +334,10 @@ def _regression_train_wrapper(sc, train_func, klass, data, initial_weights): raise RuntimeError("JVM call result had unexpected length") elif type(ans[0]) != bytearray: raise RuntimeError("JVM call result had first element of type " - + type(ans[0]).__name__ + " which is not bytearray") + + type(ans[0]).__name__ + " which is not bytearray") elif type(ans[1]) != float: raise RuntimeError("JVM call result had second element of type " - + type(ans[0]).__name__ + " which is not float") + + type(ans[0]).__name__ + " which is not float") return klass(_deserialize_double_vector(ans[0]), ans[1]) @@ -450,8 +451,7 @@ def _test(): import doctest globs = globals().copy() globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2) - (failure_count, test_count) = doctest.testmod(globs=globs, - optionflags=doctest.ELLIPSIS) + (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS) globs['sc'].stop() if failure_count: exit(-1) diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.py index 6772e4337ef39..1c0c536c4fb3d 100644 --- a/python/pyspark/mllib/classification.py +++ b/python/pyspark/mllib/classification.py @@ -29,6 +29,7 @@ from pyspark.mllib.regression import LabeledPoint, LinearModel from math import exp, log + class LogisticRegressionModel(LinearModel): """A linear binary classification model derived from logistic regression. @@ -68,14 +69,14 @@ def predict(self, x): class LogisticRegressionWithSGD(object): @classmethod - def train(cls, data, iterations=100, step=1.0, - miniBatchFraction=1.0, initialWeights=None): + def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0, initialWeights=None): """Train a logistic regression model on the given data.""" sc = data.context - return _regression_train_wrapper(sc, lambda d, i: - sc._jvm.PythonMLLibAPI().trainLogisticRegressionModelWithSGD(d._jrdd, - iterations, step, miniBatchFraction, i), - LogisticRegressionModel, data, initialWeights) + train_func = lambda d, i: sc._jvm.PythonMLLibAPI().trainLogisticRegressionModelWithSGD( + d._jrdd, iterations, step, miniBatchFraction, i) + return _regression_train_wrapper(sc, train_func, LogisticRegressionModel, data, + initialWeights) + class SVMModel(LinearModel): """A support vector machine. @@ -106,16 +107,17 @@ def predict(self, x): margin = _dot(x, self._coeff) + self._intercept return 1 if margin >= 0 else 0 + class SVMWithSGD(object): @classmethod def train(cls, data, iterations=100, step=1.0, regParam=1.0, miniBatchFraction=1.0, initialWeights=None): """Train a support vector machine on the given data.""" sc = data.context - return _regression_train_wrapper(sc, lambda d, i: - sc._jvm.PythonMLLibAPI().trainSVMModelWithSGD(d._jrdd, - iterations, step, regParam, miniBatchFraction, i), - SVMModel, data, initialWeights) + train_func = lambda d, i: sc._jvm.PythonMLLibAPI().trainSVMModelWithSGD( + d._jrdd, iterations, step, regParam, miniBatchFraction, i) + return _regression_train_wrapper(sc, train_func, SVMModel, data, initialWeights) + class NaiveBayesModel(object): """ @@ -156,6 +158,7 @@ def predict(self, x): """Return the most likely class for a data vector x""" return self.labels[numpy.argmax(self.pi + _dot(x, self.theta.transpose()))] + class NaiveBayes(object): @classmethod def train(cls, data, lambda_=1.0): @@ -186,8 +189,7 @@ def _test(): import doctest globs = globals().copy() globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2) - (failure_count, test_count) = doctest.testmod(globs=globs, - optionflags=doctest.ELLIPSIS) + (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS) globs['sc'].stop() if failure_count: exit(-1) diff --git a/python/pyspark/mllib/clustering.py b/python/pyspark/mllib/clustering.py index f65088c9170e0..b380e8f6c8725 100644 --- a/python/pyspark/mllib/clustering.py +++ b/python/pyspark/mllib/clustering.py @@ -30,7 +30,8 @@ class KMeansModel(object): """A clustering model derived from the k-means method. >>> data = array([0.0,0.0, 1.0,1.0, 9.0,8.0, 8.0,9.0]).reshape(4,2) - >>> model = KMeans.train(sc.parallelize(data), 2, maxIterations=10, runs=30, initializationMode="random") + >>> model = KMeans.train( + ... sc.parallelize(data), 2, maxIterations=10, runs=30, initializationMode="random") >>> model.predict(array([0.0, 0.0])) == model.predict(array([1.0, 1.0])) True >>> model.predict(array([8.0, 9.0])) == model.predict(array([9.0, 8.0])) @@ -76,18 +77,17 @@ def predict(self, x): class KMeans(object): @classmethod - def train(cls, data, k, maxIterations=100, runs=1, - initializationMode="k-means||"): + def train(cls, data, k, maxIterations=100, runs=1, initializationMode="k-means||"): """Train a k-means clustering model.""" sc = data.context dataBytes = _get_unmangled_double_vector_rdd(data) - ans = sc._jvm.PythonMLLibAPI().trainKMeansModel(dataBytes._jrdd, - k, maxIterations, runs, initializationMode) + ans = sc._jvm.PythonMLLibAPI().trainKMeansModel( + dataBytes._jrdd, k, maxIterations, runs, initializationMode) if len(ans) != 1: raise RuntimeError("JVM call result had unexpected length") elif type(ans[0]) != bytearray: raise RuntimeError("JVM call result had first element of type " - + type(ans[0]) + " which is not bytearray") + + type(ans[0]) + " which is not bytearray") matrix = _deserialize_double_matrix(ans[0]) return KMeansModel([row for row in matrix]) @@ -96,8 +96,7 @@ def _test(): import doctest globs = globals().copy() globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2) - (failure_count, test_count) = doctest.testmod(globs=globs, - optionflags=doctest.ELLIPSIS) + (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS) globs['sc'].stop() if failure_count: exit(-1) diff --git a/python/pyspark/mllib/linalg.py b/python/pyspark/mllib/linalg.py index 7511ca7573ddb..276684272068b 100644 --- a/python/pyspark/mllib/linalg.py +++ b/python/pyspark/mllib/linalg.py @@ -54,7 +54,7 @@ def __init__(self, size, *args): if len(args) == 1: pairs = args[0] if type(pairs) == dict: - pairs = pairs.items() + pairs = pairs.items() pairs = sorted(pairs) self.indices = array([p[0] for p in pairs], dtype=int32) self.values = array([p[1] for p in pairs], dtype=float64) @@ -88,7 +88,7 @@ def dot(self, other): result += self.values[i] * other[self.indices[i]] return result elif other.ndim == 2: - results = [self.dot(other[:,i]) for i in xrange(other.shape[1])] + results = [self.dot(other[:, i]) for i in xrange(other.shape[1])] return array(results) else: raise Exception("Cannot call dot with %d-dimensional array" % other.ndim) @@ -135,7 +135,7 @@ def squared_distance(self, other): return result else: raise Exception("Cannot call squared_distance with %d-dimensional array" % - other.ndim) + other.ndim) else: result = 0.0 i, j = 0, 0 @@ -184,15 +184,14 @@ def __eq__(self, other): """ return (isinstance(other, self.__class__) - and other.size == self.size - and array_equal(other.indices, self.indices) - and array_equal(other.values, self.values)) + and other.size == self.size + and array_equal(other.indices, self.indices) + and array_equal(other.values, self.values)) def __ne__(self, other): return not self.__eq__(other) - class Vectors(object): """ Factory methods for working with vectors. Note that dense vectors diff --git a/python/pyspark/mllib/recommendation.py b/python/pyspark/mllib/recommendation.py index f4a83f0209e27..6c385042ffa5f 100644 --- a/python/pyspark/mllib/recommendation.py +++ b/python/pyspark/mllib/recommendation.py @@ -24,6 +24,7 @@ _serialize_tuple, RatingDeserializer from pyspark.rdd import RDD + class MatrixFactorizationModel(object): """A matrix factorisation model trained by regularized alternating least-squares. @@ -55,32 +56,34 @@ def predictAll(self, usersProducts): return RDD(self._java_model.predict(usersProductsJRDD._jrdd), self._context, RatingDeserializer()) + class ALS(object): @classmethod def train(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1): sc = ratings.context ratingBytes = _get_unmangled_rdd(ratings, _serialize_rating) - mod = sc._jvm.PythonMLLibAPI().trainALSModel(ratingBytes._jrdd, - rank, iterations, lambda_, blocks) + mod = sc._jvm.PythonMLLibAPI().trainALSModel( + ratingBytes._jrdd, rank, iterations, lambda_, blocks) return MatrixFactorizationModel(sc, mod) @classmethod def trainImplicit(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1, alpha=0.01): sc = ratings.context ratingBytes = _get_unmangled_rdd(ratings, _serialize_rating) - mod = sc._jvm.PythonMLLibAPI().trainImplicitALSModel(ratingBytes._jrdd, - rank, iterations, lambda_, blocks, alpha) + mod = sc._jvm.PythonMLLibAPI().trainImplicitALSModel( + ratingBytes._jrdd, rank, iterations, lambda_, blocks, alpha) return MatrixFactorizationModel(sc, mod) + def _test(): import doctest globs = globals().copy() globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2) - (failure_count, test_count) = doctest.testmod(globs=globs, - optionflags=doctest.ELLIPSIS) + (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/regression.py b/python/pyspark/mllib/regression.py index 266b31d3fab0e..bc7de6d2e8958 100644 --- a/python/pyspark/mllib/regression.py +++ b/python/pyspark/mllib/regression.py @@ -113,10 +113,9 @@ def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0, initialWeights=None): """Train a linear regression model on the given data.""" sc = data.context - return _regression_train_wrapper(sc, lambda d, i: - sc._jvm.PythonMLLibAPI().trainLinearRegressionModelWithSGD( - d._jrdd, iterations, step, miniBatchFraction, i), - LinearRegressionModel, data, initialWeights) + train_f = lambda d, i: sc._jvm.PythonMLLibAPI().trainLinearRegressionModelWithSGD( + d._jrdd, iterations, step, miniBatchFraction, i) + return _regression_train_wrapper(sc, train_f, LinearRegressionModel, data, initialWeights) class LassoModel(LinearRegressionModelBase): @@ -157,10 +156,9 @@ def train(cls, data, iterations=100, step=1.0, regParam=1.0, miniBatchFraction=1.0, initialWeights=None): """Train a Lasso regression model on the given data.""" sc = data.context - return _regression_train_wrapper(sc, lambda d, i: - sc._jvm.PythonMLLibAPI().trainLassoModelWithSGD(d._jrdd, - iterations, step, regParam, miniBatchFraction, i), - LassoModel, data, initialWeights) + train_f = lambda d, i: sc._jvm.PythonMLLibAPI().trainLassoModelWithSGD( + d._jrdd, iterations, step, regParam, miniBatchFraction, i) + return _regression_train_wrapper(sc, train_f, LassoModel, data, initialWeights) class RidgeRegressionModel(LinearRegressionModelBase): @@ -201,18 +199,16 @@ def train(cls, data, iterations=100, step=1.0, regParam=1.0, miniBatchFraction=1.0, initialWeights=None): """Train a ridge regression model on the given data.""" sc = data.context - return _regression_train_wrapper(sc, lambda d, i: - sc._jvm.PythonMLLibAPI().trainRidgeModelWithSGD(d._jrdd, - iterations, step, regParam, miniBatchFraction, i), - RidgeRegressionModel, data, initialWeights) + train_func = lambda d, i: sc._jvm.PythonMLLibAPI().trainRidgeModelWithSGD( + d._jrdd, iterations, step, regParam, miniBatchFraction, i) + return _regression_train_wrapper(sc, train_func, RidgeRegressionModel, data, initialWeights) def _test(): import doctest globs = globals().copy() globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2) - (failure_count, test_count) = doctest.testmod(globs=globs, - optionflags=doctest.ELLIPSIS) + (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS) globs['sc'].stop() if failure_count: exit(-1) diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py index 1ee96bb4af37b..37ccf1d590743 100644 --- a/python/pyspark/mllib/tests.py +++ b/python/pyspark/mllib/tests.py @@ -23,7 +23,7 @@ import unittest from pyspark.mllib._common import _convert_vector, _serialize_double_vector, \ - _deserialize_double_vector, _dot, _squared_distance + _deserialize_double_vector, _dot, _squared_distance from pyspark.mllib.linalg import SparseVector from pyspark.mllib.regression import LabeledPoint from pyspark.tests import PySparkTestCase @@ -46,12 +46,9 @@ def test_serialize(self): self.assertTrue(sv is _convert_vector(sv)) self.assertTrue(dv is _convert_vector(dv)) self.assertTrue(array_equal(dv, _convert_vector(lst))) - self.assertEquals(sv, - _deserialize_double_vector(_serialize_double_vector(sv))) - self.assertTrue(array_equal(dv, - _deserialize_double_vector(_serialize_double_vector(dv)))) - self.assertTrue(array_equal(dv, - _deserialize_double_vector(_serialize_double_vector(lst)))) + self.assertEquals(sv, _deserialize_double_vector(_serialize_double_vector(sv))) + self.assertTrue(array_equal(dv, _deserialize_double_vector(_serialize_double_vector(dv)))) + self.assertTrue(array_equal(dv, _deserialize_double_vector(_serialize_double_vector(lst)))) def test_dot(self): sv = SparseVector(4, {1: 1, 3: 2}) @@ -132,7 +129,7 @@ def test_classification(self): def test_regression(self): from pyspark.mllib.regression import LinearRegressionWithSGD, LassoWithSGD, \ - RidgeRegressionWithSGD + RidgeRegressionWithSGD data = [ LabeledPoint(-1.0, [0, -1]), LabeledPoint(1.0, [0, 1]), @@ -179,14 +176,10 @@ def test_serialize(self): self.assertEquals(sv, _convert_vector(lil.tocoo())) self.assertEquals(sv, _convert_vector(lil.tocsr())) self.assertEquals(sv, _convert_vector(lil.todok())) - self.assertEquals(sv, - _deserialize_double_vector(_serialize_double_vector(lil))) - self.assertEquals(sv, - _deserialize_double_vector(_serialize_double_vector(lil.tocsc()))) - self.assertEquals(sv, - _deserialize_double_vector(_serialize_double_vector(lil.tocsr()))) - self.assertEquals(sv, - _deserialize_double_vector(_serialize_double_vector(lil.todok()))) + self.assertEquals(sv, _deserialize_double_vector(_serialize_double_vector(lil))) + self.assertEquals(sv, _deserialize_double_vector(_serialize_double_vector(lil.tocsc()))) + self.assertEquals(sv, _deserialize_double_vector(_serialize_double_vector(lil.tocsr()))) + self.assertEquals(sv, _deserialize_double_vector(_serialize_double_vector(lil.todok()))) def test_dot(self): from scipy.sparse import lil_matrix @@ -265,7 +258,7 @@ def test_classification(self): def test_regression(self): from pyspark.mllib.regression import LinearRegressionWithSGD, LassoWithSGD, \ - RidgeRegressionWithSGD + RidgeRegressionWithSGD data = [ LabeledPoint(-1.0, self.scipy_matrix(2, {1: -1.0})), LabeledPoint(1.0, self.scipy_matrix(2, {1: 1.0})), diff --git a/python/pyspark/mllib/util.py b/python/pyspark/mllib/util.py index 50d0cdd087625..0e5f4520b9402 100644 --- a/python/pyspark/mllib/util.py +++ b/python/pyspark/mllib/util.py @@ -21,6 +21,7 @@ from pyspark.mllib.regression import LabeledPoint from pyspark.mllib._common import _convert_vector + class MLUtils: """ Helper methods to load, save and pre-process data used in MLlib. @@ -44,7 +45,6 @@ def _parse_libsvm_line(line, multiclass): values[i] = float(value) return label, indices, values - @staticmethod def _convert_labeled_point_to_libsvm(p): """Converts a LabeledPoint to a string in LIBSVM format.""" @@ -62,7 +62,6 @@ def _convert_labeled_point_to_libsvm(p): " but got " % type(v)) return " ".join(items) - @staticmethod def loadLibSVMFile(sc, path, multiclass=False, numFeatures=-1, minPartitions=None): """ @@ -135,7 +134,6 @@ def loadLibSVMFile(sc, path, multiclass=False, numFeatures=-1, minPartitions=Non numFeatures = parsed.map(lambda x: 0 if x[1].size == 0 else x[1][-1]).reduce(max) + 1 return parsed.map(lambda x: LabeledPoint(x[0], Vectors.sparse(numFeatures, x[1], x[2]))) - @staticmethod def saveAsLibSVMFile(data, dir): """ From 0659529614c804e0c04efc59cb67dab3a6cdc9d9 Mon Sep 17 00:00:00 2001 From: Andrew Ash Date: Sun, 25 May 2014 17:15:47 -0700 Subject: [PATCH 042/221] SPARK-1903 Document Spark's network connections https://issues.apache.org/jira/browse/SPARK-1903 Author: Andrew Ash Closes #856 from ash211/SPARK-1903 and squashes the following commits: 6e7782a [Andrew Ash] Add the technology used on each port 1d9b5d3 [Andrew Ash] Document port for history server 56193ee [Andrew Ash] spark.ui.port becomes worker.ui.port and master.ui.port a774c07 [Andrew Ash] Wording in network section 90e8237 [Andrew Ash] Use real :toc instead of the hand-written one edaa337 [Andrew Ash] Master -> Standalone Cluster Master 57e8869 [Andrew Ash] Port -> Default Port 3d4d289 [Andrew Ash] Title to title case c7d42d9 [Andrew Ash] [WIP] SPARK-1903 Add initial port listing for documentation a416ae9 [Andrew Ash] Word wrap to 100 lines --- docs/README.md | 43 +++++-- docs/configuration.md | 268 ++++++++++++++++++++++++++++++------------ 2 files changed, 222 insertions(+), 89 deletions(-) diff --git a/docs/README.md b/docs/README.md index f1eb644f93406..fd7ba4e0d72ea 100644 --- a/docs/README.md +++ b/docs/README.md @@ -1,23 +1,31 @@ Welcome to the Spark documentation! -This readme will walk you through navigating and building the Spark documentation, which is included here with the Spark source code. You can also find documentation specific to release versions of Spark at http://spark.apache.org/documentation.html. +This readme will walk you through navigating and building the Spark documentation, which is included +here with the Spark source code. You can also find documentation specific to release versions of +Spark at http://spark.apache.org/documentation.html. -Read on to learn more about viewing documentation in plain text (i.e., markdown) or building the documentation yourself. Why build it yourself? So that you have the docs that corresponds to whichever version of Spark you currently have checked out of revision control. +Read on to learn more about viewing documentation in plain text (i.e., markdown) or building the +documentation yourself. Why build it yourself? So that you have the docs that corresponds to +whichever version of Spark you currently have checked out of revision control. ## Generating the Documentation HTML -We include the Spark documentation as part of the source (as opposed to using a hosted wiki, such as the github wiki, as the definitive documentation) to enable the documentation to evolve along with the source code and be captured by revision control (currently git). This way the code automatically includes the version of the documentation that is relevant regardless of which version or release you have checked out or downloaded. +We include the Spark documentation as part of the source (as opposed to using a hosted wiki, such as +the github wiki, as the definitive documentation) to enable the documentation to evolve along with +the source code and be captured by revision control (currently git). This way the code automatically +includes the version of the documentation that is relevant regardless of which version or release +you have checked out or downloaded. -In this directory you will find textfiles formatted using Markdown, with an ".md" suffix. You can read those text files directly if you want. Start with index.md. +In this directory you will find textfiles formatted using Markdown, with an ".md" suffix. You can +read those text files directly if you want. Start with index.md. -The markdown code can be compiled to HTML using the -[Jekyll tool](http://jekyllrb.com). +The markdown code can be compiled to HTML using the [Jekyll tool](http://jekyllrb.com). To use the `jekyll` command, you will need to have Jekyll installed. The easiest way to do this is via a Ruby Gem, see the [jekyll installation instructions](http://jekyllrb.com/docs/installation). If not already installed, you need to install `kramdown` with `sudo gem install kramdown`. -Execute `jekyll` from the `docs/` directory. Compiling the site with Jekyll will create a directory called -`_site` containing index.html as well as the rest of the compiled files. +Execute `jekyll` from the `docs/` directory. Compiling the site with Jekyll will create a directory +called `_site` containing index.html as well as the rest of the compiled files. You can modify the default Jekyll build as follows: @@ -30,9 +38,11 @@ You can modify the default Jekyll build as follows: ## Pygments -We also use pygments (http://pygments.org) for syntax highlighting in documentation markdown pages, so you will also need to install that (it requires Python) by running `sudo easy_install Pygments`. +We also use pygments (http://pygments.org) for syntax highlighting in documentation markdown pages, +so you will also need to install that (it requires Python) by running `sudo easy_install Pygments`. -To mark a block of code in your markdown to be syntax highlighted by jekyll during the compile phase, use the following sytax: +To mark a block of code in your markdown to be syntax highlighted by jekyll during the compile +phase, use the following sytax: {% highlight scala %} // Your scala code goes here, you can replace scala with many other @@ -43,8 +53,15 @@ To mark a block of code in your markdown to be syntax highlighted by jekyll duri You can build just the Spark scaladoc by running `sbt/sbt doc` from the SPARK_PROJECT_ROOT directory. -Similarly, you can build just the PySpark epydoc by running `epydoc --config epydoc.conf` from the SPARK_PROJECT_ROOT/pyspark directory. Documentation is only generated for classes that are listed as public in `__init__.py`. +Similarly, you can build just the PySpark epydoc by running `epydoc --config epydoc.conf` from the +SPARK_PROJECT_ROOT/pyspark directory. Documentation is only generated for classes that are listed as +public in `__init__.py`. -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 `sbt/sbt doc` 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 using [epydoc](http://epydoc.sourceforge.net/). +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 `sbt/sbt doc` 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 using [epydoc](http://epydoc.sourceforge.net/). -NOTE: To skip the step of building and copying over the Scala and Python API docs, run `SKIP_API=1 jekyll`. +NOTE: To skip the step of building and copying over the Scala and Python API docs, run `SKIP_API=1 +jekyll`. diff --git a/docs/configuration.md b/docs/configuration.md index 4d41c36e38e26..e5d955f23fe32 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -3,15 +3,10 @@ layout: global title: Spark Configuration --- -Spark provides three locations to configure the system: - -* [Spark properties](#spark-properties) control most application parameters and can be set by - passing a [SparkConf](api/scala/index.html#org.apache.spark.SparkConf) object to SparkContext, - or through the `conf/spark-defaults.conf` properties file. -* [Environment variables](#environment-variables) can be used to set per-machine settings, such as - the IP address, through the `conf/spark-env.sh` script on each node. -* [Logging](#configuring-logging) can be configured through `log4j.properties`. +* This will become a table of contents (this text will be scraped). +{:toc} +Spark provides several locations to configure the system: # Spark Properties @@ -65,7 +60,8 @@ there are at least five properties that you will commonly want to control: 		spark.executor.memory 512m - Amount of memory to use per executor process, in the same format as JVM memory strings (e.g. 512m, 2g). + Amount of memory to use per executor process, in the same format as JVM memory strings (e.g. + 512m, 2g).
spark.local.dir /tmp - Directory to use for "scratch" space in Spark, including map output files and RDDs that get stored - on disk. This should be on a fast, local disk in your system. It can also be a comma-separated - list of multiple directories on different disks. + Directory to use for "scratch" space in Spark, including map output files and RDDs that get + stored on disk. This should be on a fast, local disk in your system. It can also be a + comma-separated list of multiple directories on different disks. NOTE: In Spark 1.0 and later this will be overriden by SPARK_LOCAL_DIRS (Standalone, Mesos) or LOCAL_DIRS (YARN) envrionment variables set by the cluster manager. @@ -130,8 +126,8 @@ Apart from these, the following properties are also available, and may be useful - Default number of tasks to use across the cluster for distributed shuffle operations (groupByKey, - reduceByKey, etc) when not set by user. + Default number of tasks to use across the cluster for distributed shuffle operations + (groupByKey, reduceByKey, etc) when not set by user.
spark.tachyonStore.baseDir System.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. + 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.mesos.coarse false - 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. + 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.io.compression.codec org.apache.spark.io.
LZFCompressionCodec		 - The codec used to compress internal data such as RDD partitions and shuffle outputs. By default, Spark provides two - codecs: org.apache.spark.io.LZFCompressionCodec and org.apache.spark.io.SnappyCompressionCodec. + The codec used to compress internal data such as RDD partitions and shuffle outputs. By default, + Spark provides two codecs: org.apache.spark.io.LZFCompressionCodec 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. + Block size (in bytes) used in Snappy compression, in the case when Snappy compression codec is + used.
spark.scheduler.revive.interval 1000 - The interval length for the scheduler to revive the worker resource offers to run tasks. (in milliseconds) + The interval length for the scheduler to revive the worker resource offers to run tasks. (in + milliseconds)
48 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. + 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.kryoserializer.buffer.mb 2 - Maximum object size to allow within Kryo (the library needs to create a buffer at least as - large as the largest single object you'll serialize). Increase this if you get a "buffer limit - exceeded" exception inside Kryo. Note that there will be one buffer per core on each worker. + Maximum object size to allow within Kryo (the library needs to create a buffer at least as large + as the largest single object you'll serialize). Increase this if you get a "buffer limit + exceeded" exception inside Kryo. Note that there will be one buffer per core on each + worker.
spark.worker.cleanup.enabled 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. + 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.
spark.worker.cleanup.appDataTtl 7 * 24 * 3600 (7 days) - The number of seconds to retain application work directories on each worker. This is a Time To Live - and should depend on the amount of available disk space you have. Application logs and jars are - downloaded to each application work dir. Over time, the work dirs can quickly fill up disk space, - especially if you run jobs very frequently. + The number of seconds to retain application work directories on each worker. This is a Time To + Live and should depend on the amount of available disk space you have. Application logs and + jars are downloaded to each application work dir. Over time, the work dirs can quickly fill up + disk space, especially if you run jobs very frequently.
spark.akka.heartbeat.pauses 600 - 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. + 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.
spark.akka.failure-detector.threshold 300.0 - 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. + 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.heartbeat.interval 1000 - 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. + 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.
spark.cleaner.ttl (infinite) - 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. + 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.
4096 Size of each piece of a block in kilobytes for TorrentBroadcastFactory. - Too large a value decreases parallelism during broadcast (makes it slower); however, if it is too small, BlockManager might take a performance hit. + Too large a value decreases parallelism during broadcast (makes it slower); however, if it is + too small, BlockManager might take a performance hit.
spark.shuffle.consolidateFiles false - If set to "true", consolidates intermediate files created during a shuffle. Creating fewer files can improve filesystem performance for shuffles with large numbers of reduce tasks. It is recommended to set this to "true" when using ext4 or xfs filesystems. On ext3, this option might degrade performance on machines with many (>8) cores due to filesystem limitations. + If set to "true", consolidates intermediate files created during a shuffle. Creating fewer files + can improve filesystem performance for shuffles with large numbers of reduce tasks. It is + recommended to set this to "true" when using ext4 or xfs filesystems. On ext3, this option might + degrade performance on machines with many (>8) cores due to filesystem limitations.
spark.shuffle.spill true - 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. + 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.speculation 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. + 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.eventLog.enabled false - Whether to log spark events, useful for reconstructing the Web UI after the application has finished. + Whether to log spark events, useful for reconstructing the Web UI after the application has + finished.
file:///tmp/spark-events 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. + Within this base directory, Spark creates a sub-directory for each application, and logs the + events specific to the application in this directory.
spark.deploy.spreadOut true - Whether the standalone cluster manager should spread applications out across nodes or try - to consolidate them onto as few nodes as possible. Spreading out is usually better for - data locality in HDFS, but consolidating is more efficient for compute-intensive workloads.
- Note: this setting needs to be configured in the standalone cluster master, not in individual - applications; you can set it through SPARK_MASTER_OPTS in spark-env.sh. + Whether the standalone cluster manager should spread applications out across nodes or try to + consolidate them onto as few nodes as possible. Spreading out is usually better for data + locality in HDFS, but consolidating is more efficient for compute-intensive workloads.
+ Note: this setting needs to be configured in the standalone cluster master, not in + individual applications; you can set it through SPARK_MASTER_OPTS in + spark-env.sh.
spark.deploy.defaultCores (infinite) - Default number of cores to give to applications in Spark's standalone mode if they don't - set spark.cores.max. If not set, applications always get all available - cores unless they configure spark.cores.max themselves. - Set this lower on a shared cluster to prevent users from grabbing - the whole cluster by default.
- Note: this setting needs to be configured in the standalone cluster master, not in individual - applications; you can set it through SPARK_MASTER_OPTS in spark-env.sh. + Default number of cores to give to applications in Spark's standalone mode if they don't set + spark.cores.max. If not set, applications always get all available cores unless + they configure spark.cores.max themselves. Set this lower on a shared cluster to + prevent users from grabbing the whole cluster by default.
Note: this setting needs + to be configured in the standalone cluster master, not in individual applications; you can set + it through SPARK_MASTER_OPTS in spark-env.sh.
spark.files.overwrite false - Whether to overwrite files added through SparkContext.addFile() when the target file exists and its contents do not match those of the source. + Whether to overwrite files added through SparkContext.addFile() when the target file exists and + its contents do not match those of the source.
spark.authenticate false - Whether spark authenticates its internal connections. See spark.authenticate.secret if not - running on Yarn. + Whether spark authenticates its internal connections. See spark.authenticate.secret + if not running on Yarn.
-In addition to the above, there are also options for setting up the Spark [standalone cluster scripts](spark-standalone.html#cluster-launch-scripts), such as number of cores to use on each machine and maximum memory. +In addition to the above, there are also options for setting up the Spark [standalone cluster +scripts](spark-standalone.html#cluster-launch-scripts), such as number of cores to use on each +machine and maximum memory. -Since `spark-env.sh` is a shell script, some of these can be set programmatically -- for example, you might -compute `SPARK_LOCAL_IP` by looking up the IP of a specific network interface. +Since `spark-env.sh` is a shell script, some of these can be set programmatically -- for example, +you might compute `SPARK_LOCAL_IP` by looking up the IP of a specific network interface. # Configuring Logging -Spark uses [log4j](http://logging.apache.org/log4j/) for logging. You can configure it by adding a `log4j.properties` -file in the `conf` directory. One way to start is to copy the existing `log4j.properties.template` located there. +Spark uses [log4j](http://logging.apache.org/log4j/) for logging. You can configure it by adding a +`log4j.properties` file in the `conf` directory. One way to start is to copy the existing +`log4j.properties.template` located there. + +# Configuring Ports for Network Security + +Spark makes heavy use of the network, and some environments have strict requirements for using tight +firewall settings. Below are the primary ports that Spark uses for its communication and how to +configure those ports. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
FromToDefault PortPurposeConfiguration + SettingNotes
BrowserStandalone Cluster Master8080Web UImaster.ui.portJetty-based
BrowserWorker8081Web UIworker.ui.portJetty-based
BrowserDriver4040Web UIspark.ui.portJetty-based
BrowserHistory Server18080Web UIspark.history.ui.portJetty-based
ApplicationStandalone Cluster Master7077Submit job to clusterspark.driver.portAkka-based. Set to "0" to choose a port randomly
WorkerStandalone Cluster Master7077Join clusterspark.driver.portAkka-based. Set to "0" to choose a port randomly
ApplicationWorker(random)Join clusterSPARK_WORKER_PORT (standalone cluster)Akka-based
Driver and other WorkersWorker(random) +
    +
  • File server for file and jars
    • +
  • Http Broadcast
    • +
  • Class file server (Spark Shell only)
    • +
+ 		NoneJetty-based. Each of these services starts on a random port that cannot be configured
From c3576ffcd7910e38928f233a824dd9e037cde05f Mon Sep 17 00:00:00 2001 From: Aaron Davidson Date: Sun, 25 May 2014 18:37:44 -0700 Subject: [PATCH 043/221] [SQL] Minor: Introduce SchemaRDD#aggregate() for simple aggregations ```scala rdd.aggregate(Sum('val)) ``` is just shorthand for ```scala rdd.groupBy()(Sum('val)) ``` but seems be more natural than doing a groupBy with no grouping expressions when you really just want an aggregation over all rows. Did not add a JavaSchemaRDD or Python API, as these seem to be lacking several other methods like groupBy() already -- leaving that cleanup for future patches. Author: Aaron Davidson Closes #874 from aarondav/schemardd and squashes the following commits: e9e68ee [Aaron Davidson] Add comment db6afe2 [Aaron Davidson] Introduce SchemaRDD#aggregate() for simple aggregations --- .../scala/org/apache/spark/sql/SchemaRDD.scala | 18 ++++++++++++++++-- .../org/apache/spark/sql/DslQuerySuite.scala | 8 ++++++++ 2 files changed, 24 insertions(+), 2 deletions(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala index 9883ebc0b3c62..e855f36256bc5 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala @@ -59,7 +59,7 @@ import java.util.{Map => JMap} * // Importing the SQL context gives access to all the SQL functions and implicit conversions. * import sqlContext._ * - * val rdd = sc.parallelize((1 to 100).map(i => Record(i, s"val_\$i"))) + * val rdd = sc.parallelize((1 to 100).map(i => Record(i, s"val_$i"))) * // Any RDD containing case classes can be registered as a table. The schema of the table is * // automatically inferred using scala reflection. * rdd.registerAsTable("records") @@ -204,6 +204,20 @@ class SchemaRDD( new SchemaRDD(sqlContext, Aggregate(groupingExprs, aliasedExprs, logicalPlan)) } + /** + * Performs an aggregation over all Rows in this RDD. + * This is equivalent to a groupBy with no grouping expressions. + * + * {{{ + * schemaRDD.aggregate(Sum('sales) as 'totalSales) + * }}} + * + * @group Query + */ + def aggregate(aggregateExprs: Expression*): SchemaRDD = { + groupBy()(aggregateExprs: _*) + } + /** * Applies a qualifier to the attributes of this relation. Can be used to disambiguate attributes * with the same name, for example, when performing self-joins. @@ -281,7 +295,7 @@ class SchemaRDD( * supports features such as filter pushdown. */ @Experimental - override def count(): Long = groupBy()(Count(Literal(1))).collect().head.getLong(0) + override def count(): Long = aggregate(Count(Literal(1))).collect().head.getLong(0) /** * :: Experimental :: diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala index 94ba13b14b33d..692569a73ffcf 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala @@ -39,6 +39,14 @@ class DslQuerySuite extends QueryTest { testData2.groupBy('a)('a, Sum('b)), Seq((1,3),(2,3),(3,3)) ) + checkAnswer( + testData2.groupBy('a)('a, Sum('b) as 'totB).aggregate(Sum('totB)), + 9 + ) + checkAnswer( + testData2.aggregate(Sum('b)), + 9 + ) } test("select *") { From b6d22af040073cd611b0fcfdf8a5259c0dfd854c Mon Sep 17 00:00:00 2001 From: Patrick Wendell Date: Sun, 25 May 2014 20:13:32 -0700 Subject: [PATCH 044/221] HOTFIX: Add no-arg SparkContext constructor in Java Self explanatory. Author: Patrick Wendell Closes #878 from pwendell/java-constructor and squashes the following commits: 2cc1605 [Patrick Wendell] HOTFIX: Add no-arg SparkContext constructor in Java --- .../scala/org/apache/spark/api/java/JavaSparkContext.scala | 6 ++++++ 1 file changed, 6 insertions(+) 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 a7cfee6d01711..1e0493c4855e0 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 @@ -41,6 +41,12 @@ import org.apache.spark.rdd.RDD * [[org.apache.spark.api.java.JavaRDD]]s and works with Java collections instead of Scala ones. */ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWorkaround { + /** + * Create a JavaSparkContext that loads settings from system properties (for instance, when + * launching with ./bin/spark-submit). + */ + def this() = this(new SparkContext()) + /** * @param conf a [[org.apache.spark.SparkConf]] object specifying Spark parameters */ From d6395d86f90d1c47c5b6ad17c618b56e00b7fc85 Mon Sep 17 00:00:00 2001 From: Takuya UESHIN Date: Mon, 26 May 2014 00:17:20 -0700 Subject: [PATCH 045/221] [SPARK-1914] [SQL] Simplify CountFunction not to traverse to evaluate all child expressions. `CountFunction` should count up only if the child's evaluated value is not null. Because it traverses to evaluate all child expressions, even if the child is null, it counts up if one of the all children is not null. Author: Takuya UESHIN Closes #861 from ueshin/issues/SPARK-1914 and squashes the following commits: 3b37315 [Takuya UESHIN] Merge branch 'master' into issues/SPARK-1914 2afa238 [Takuya UESHIN] Simplify CountFunction not to traverse to evaluate all child expressions. --- .../apache/spark/sql/catalyst/expressions/aggregates.scala | 4 ++-- .../src/test/scala/org/apache/spark/sql/DslQuerySuite.scala | 5 +++++ 2 files changed, 7 insertions(+), 2 deletions(-) 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 index 1bcd4e22766a9..79937b129aeae 100644 --- 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 @@ -298,8 +298,8 @@ case class CountFunction(expr: Expression, base: AggregateExpression) extends Ag var count: Long = _ override def update(input: Row): Unit = { - val evaluatedExpr = expr.map(_.eval(input)) - if (evaluatedExpr.map(_ != null).reduceLeft(_ || _)) { + val evaluatedExpr = expr.eval(input) + if (evaluatedExpr != null) { count += 1L } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala index 692569a73ffcf..8197e8a18d447 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala @@ -125,6 +125,11 @@ class DslQuerySuite extends QueryTest { Seq((1,0), (2, 1)) ) + checkAnswer( + testData3.groupBy('a)('a, Count('a + 'b)), + Seq((1,0), (2, 1)) + ) + checkAnswer( testData3.groupBy()(Count('a), Count('b), Count(1), CountDistinct('a :: Nil), CountDistinct('b :: Nil)), (2, 1, 2, 2, 1) :: Nil From bee6c4f4a155f625495212c17b58dc76f525f312 Mon Sep 17 00:00:00 2001 From: witgo Date: Mon, 26 May 2014 13:16:35 -0700 Subject: [PATCH 046/221] Fix scalastyle warnings in yarn alpha Author: witgo Closes #884 from witgo/scalastyle and squashes the following commits: 4b08ae4 [witgo] Fix scalastyle warnings in yarn alpha --- dev/scalastyle | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/dev/scalastyle b/dev/scalastyle index a972811ba8ed6..0e8fd5cc8d64c 100755 --- a/dev/scalastyle +++ b/dev/scalastyle @@ -19,7 +19,8 @@ echo -e "q\n" | SPARK_HIVE=true sbt/sbt scalastyle > scalastyle.txt # Check style with YARN alpha built too -echo -e "q\n" | SPARK_YARN=true sbt/sbt yarn/scalastyle >> scalastyle.txt +echo -e "q\n" | SPARK_HADOOP_VERSION=0.23.9 SPARK_YARN=true sbt/sbt yarn-alpha/scalastyle \ + >> scalastyle.txt # Check style with YARN built too echo -e "q\n" | SPARK_HADOOP_VERSION=2.2.0 SPARK_YARN=true sbt/sbt yarn/scalastyle \ >> scalastyle.txt From cb7fe5034826844f1b50fbe8b92646317b66f21c Mon Sep 17 00:00:00 2001 From: zsxwing Date: Mon, 26 May 2014 14:34:58 -0700 Subject: [PATCH 047/221] SPARK-1925: Replace '&' with '&&' JIRA: https://issues.apache.org/jira/browse/SPARK-1925 Author: zsxwing Closes #879 from zsxwing/SPARK-1925 and squashes the following commits: 5cf5a6d [zsxwing] SPARK-1925: Replace '&' with '&&' --- .../main/scala/org/apache/spark/mllib/tree/DecisionTree.scala | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) 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 0fe30a3e7040b..3b13e52a7b445 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 @@ -401,7 +401,7 @@ object DecisionTree extends Serializable with Logging { */ def isSampleValid(parentFilters: List[Filter], labeledPoint: LabeledPoint): Boolean = { // leaf - if ((level > 0) & (parentFilters.length == 0)) { + if ((level > 0) && (parentFilters.length == 0)) { return false } @@ -454,7 +454,7 @@ object DecisionTree extends Serializable with Logging { val bin = binForFeatures(mid) val lowThreshold = bin.lowSplit.threshold val highThreshold = bin.highSplit.threshold - if ((lowThreshold < feature) & (highThreshold >= feature)){ + if ((lowThreshold < feature) && (highThreshold >= feature)){ return mid } else if (lowThreshold >= feature) { From 56c771cb2d00a5843c391ae6561536ee46e535d4 Mon Sep 17 00:00:00 2001 From: Ankur Dave Date: Mon, 26 May 2014 16:10:22 -0700 Subject: [PATCH 048/221] [SPARK-1931] Reconstruct routing tables in Graph.partitionBy 905173df57b90f90ebafb22e43f55164445330e6 introduced a bug in partitionBy where, after repartitioning the edges, it reuses the VertexRDD without updating the routing tables to reflect the new edge layout. Subsequent accesses of the triplets contain nulls for many vertex properties. This commit adds a test for this bug and fixes it by introducing `VertexRDD#withEdges` and calling it in `partitionBy`. Author: Ankur Dave Closes #885 from ankurdave/SPARK-1931 and squashes the following commits: 3930cdd [Ankur Dave] Note how to set up VertexRDD for efficient joins 9bdbaa4 [Ankur Dave] [SPARK-1931] Reconstruct routing tables in Graph.partitionBy --- .../scala/org/apache/spark/graphx/VertexRDD.scala | 12 ++++++++++++ .../org/apache/spark/graphx/impl/GraphImpl.scala | 13 +++++++++---- .../scala/org/apache/spark/graphx/GraphSuite.scala | 10 ++++++++++ 3 files changed, 31 insertions(+), 4 deletions(-) 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 8c62897037b6d..8b910fbc5a423 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala @@ -300,6 +300,18 @@ class VertexRDD[@specialized VD: ClassTag]( def reverseRoutingTables(): VertexRDD[VD] = this.mapVertexPartitions(vPart => vPart.withRoutingTable(vPart.routingTable.reverse)) + /** Prepares this VertexRDD for efficient joins with the given EdgeRDD. */ + def withEdges(edges: EdgeRDD[_, _]): VertexRDD[VD] = { + val routingTables = VertexRDD.createRoutingTables(edges, this.partitioner.get) + val vertexPartitions = partitionsRDD.zipPartitions(routingTables, true) { + (partIter, routingTableIter) => + val routingTable = + if (routingTableIter.hasNext) routingTableIter.next() else RoutingTablePartition.empty + partIter.map(_.withRoutingTable(routingTable)) + } + new VertexRDD(vertexPartitions) + } + /** Generates an RDD of vertex attributes suitable for shipping to the edge partitions. */ private[graphx] def shipVertexAttributes( shipSrc: Boolean, shipDst: Boolean): RDD[(PartitionID, VertexAttributeBlock[VD])] = { diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/GraphImpl.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/GraphImpl.scala index 2f2d0e03fd7b5..1649b244d2881 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/impl/GraphImpl.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/GraphImpl.scala @@ -88,8 +88,8 @@ class GraphImpl[VD: ClassTag, ED: ClassTag] protected ( } val edgePartition = builder.toEdgePartition Iterator((pid, edgePartition)) - }, preservesPartitioning = true)) - GraphImpl.fromExistingRDDs(vertices, newEdges) + }, preservesPartitioning = true)).cache() + GraphImpl.fromExistingRDDs(vertices.withEdges(newEdges), newEdges) } override def reverse: Graph[VD, ED] = { @@ -277,7 +277,11 @@ object GraphImpl { GraphImpl(vertexRDD, edgeRDD) } - /** Create a graph from a VertexRDD and an EdgeRDD with arbitrary replicated vertices. */ + /** + * Create a graph from a VertexRDD and an EdgeRDD with arbitrary replicated vertices. The + * VertexRDD must already be set up for efficient joins with the EdgeRDD by calling + * `VertexRDD.withEdges` or an appropriate VertexRDD constructor. + */ def apply[VD: ClassTag, ED: ClassTag]( vertices: VertexRDD[VD], edges: EdgeRDD[ED, _]): GraphImpl[VD, ED] = { @@ -290,7 +294,8 @@ object GraphImpl { /** * Create a graph from a VertexRDD and an EdgeRDD with the same replicated vertex type as the - * vertices. + * vertices. The VertexRDD must already be set up for efficient joins with the EdgeRDD by calling + * `VertexRDD.withEdges` or an appropriate VertexRDD constructor. */ def fromExistingRDDs[VD: ClassTag, ED: ClassTag]( vertices: VertexRDD[VD], 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 7b9bac5d9c8ea..abc25d0671133 100644 --- a/graphx/src/test/scala/org/apache/spark/graphx/GraphSuite.scala +++ b/graphx/src/test/scala/org/apache/spark/graphx/GraphSuite.scala @@ -133,6 +133,16 @@ class GraphSuite extends FunSuite with LocalSparkContext { Iterator((part.srcIds ++ part.dstIds).toSet) }.collect assert(verts.exists(id => partitionSetsUnpartitioned.count(_.contains(id)) > bound)) + + // Forming triplets view + 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 === + Set(((0L, "a"), (1L, "b"), 1), ((0L, "a"), (2L, "c"), 1))) + val gPart = g.partitionBy(EdgePartition2D) + assert(gPart.triplets.collect.map(_.toTuple).toSet === + Set(((0L, "a"), (1L, "b"), 1), ((0L, "a"), (2L, "c"), 1))) } } From 8d271c90fa496cb24e2b7362ef0497563591b97d Mon Sep 17 00:00:00 2001 From: Zhen Peng Date: Mon, 26 May 2014 21:30:25 -0700 Subject: [PATCH 049/221] SPARK-1929 DAGScheduler suspended by local task OOM DAGScheduler does not handle local task OOM properly, and will wait for the job result forever. Author: Zhen Peng Closes #883 from zhpengg/bugfix-dag-scheduler-oom and squashes the following commits: 76f7eda [Zhen Peng] remove redundant memory allocations aa63161 [Zhen Peng] SPARK-1929 DAGScheduler suspended by local task OOM --- .../org/apache/spark/scheduler/DAGScheduler.scala | 6 +++++- .../apache/spark/scheduler/DAGSchedulerSuite.scala | 14 ++++++++++++++ 2 files changed, 19 insertions(+), 1 deletion(-) 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 ff411e24a3d85..c70aa0e6e4523 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala @@ -17,7 +17,7 @@ package org.apache.spark.scheduler -import java.io.NotSerializableException +import java.io.{NotSerializableException, PrintWriter, StringWriter} import java.util.Properties import java.util.concurrent.atomic.AtomicInteger @@ -580,6 +580,10 @@ class DAGScheduler( case e: Exception => jobResult = JobFailed(e) job.listener.jobFailed(e) + case oom: OutOfMemoryError => + val exception = new SparkException("job failed for Out of memory exception", oom) + jobResult = JobFailed(exception) + job.listener.jobFailed(exception) } finally { val s = job.finalStage stageIdToJobIds -= s.id // clean up data structures that were populated for a local job, 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 d172dd1ac8e1b..81e64c1846ed5 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala @@ -256,6 +256,20 @@ class DAGSchedulerSuite extends TestKit(ActorSystem("DAGSchedulerSuite")) with F assertDataStructuresEmpty } + test("local job oom") { + val rdd = new MyRDD(sc, Nil) { + override def compute(split: Partition, context: TaskContext): Iterator[(Int, Int)] = + throw new java.lang.OutOfMemoryError("test local job oom") + override def getPartitions = Array( new Partition { override def index = 0 } ) + override def getPreferredLocations(split: Partition) = Nil + override def toString = "DAGSchedulerSuite Local RDD" + } + val jobId = scheduler.nextJobId.getAndIncrement() + runEvent(JobSubmitted(jobId, rdd, jobComputeFunc, Array(0), true, null, jobListener)) + assert(results.size == 0) + assertDataStructuresEmpty + } + test("run trivial job w/ dependency") { val baseRdd = makeRdd(1, Nil) val finalRdd = makeRdd(1, List(new OneToOneDependency(baseRdd))) From ef690e1f69cb8e2e03bb0c43e3ccb2c54c995df7 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Mon, 26 May 2014 21:31:27 -0700 Subject: [PATCH 050/221] Fixed the error message for OutOfMemoryError in DAGScheduler. --- .../main/scala/org/apache/spark/scheduler/DAGScheduler.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) 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 c70aa0e6e4523..ccff6a3d1aebc 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala @@ -581,7 +581,7 @@ class DAGScheduler( jobResult = JobFailed(e) job.listener.jobFailed(e) case oom: OutOfMemoryError => - val exception = new SparkException("job failed for Out of memory exception", oom) + val exception = new SparkException("Local job aborted due to out of memory error", oom) jobResult = JobFailed(exception) job.listener.jobFailed(exception) } finally { From 9ed37190f45fd9e6aa0f2c73b66d317732a53eb8 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Mon, 26 May 2014 21:40:52 -0700 Subject: [PATCH 051/221] Updated dev Python scripts to make them PEP8 compliant. Author: Reynold Xin Closes #875 from rxin/pep8-dev-scripts and squashes the following commits: 04b084f [Reynold Xin] Made dev Python scripts PEP8 compliant. --- dev/audit-release/audit_release.py | 225 ++++++------ dev/create-release/generate-changelist.py | 160 ++++----- dev/merge_spark_pr.py | 402 +++++++++++----------- 3 files changed, 408 insertions(+), 379 deletions(-) diff --git a/dev/audit-release/audit_release.py b/dev/audit-release/audit_release.py index 8c7573b91f688..230e900ecd4de 100755 --- a/dev/audit-release/audit_release.py +++ b/dev/audit-release/audit_release.py @@ -30,18 +30,18 @@ import time import urllib2 -## Fill in release details here: +# Fill in release details here: RELEASE_URL = "http://people.apache.org/~pwendell/spark-1.0.0-rc1/" RELEASE_KEY = "9E4FE3AF" RELEASE_REPOSITORY = "https://repository.apache.org/content/repositories/orgapachespark-1006/" RELEASE_VERSION = "1.0.0" SCALA_VERSION = "2.10.4" SCALA_BINARY_VERSION = "2.10" -## +# LOG_FILE_NAME = "spark_audit_%s" % time.strftime("%h_%m_%Y_%I_%M_%S") LOG_FILE = open(LOG_FILE_NAME, 'w') -WORK_DIR = "/tmp/audit_%s" % int(time.time()) +WORK_DIR = "/tmp/audit_%s" % int(time.time()) MAVEN_CMD = "mvn" GPG_CMD = "gpg" @@ -50,54 +50,62 @@ # Track failures failures = [] + def clean_work_files(): - print "OK to delete scratch directory '%s'? (y/N): " % WORK_DIR - response = raw_input() - if response == "y": - shutil.rmtree(WORK_DIR) - print "Should I delete the log output file '%s'? (y/N): " % LOG_FILE_NAME - response = raw_input() - if response == "y": - os.unlink(LOG_FILE_NAME) + print "OK to delete scratch directory '%s'? (y/N): " % WORK_DIR + response = raw_input() + if response == "y": + shutil.rmtree(WORK_DIR) + print "Should I delete the log output file '%s'? (y/N): " % LOG_FILE_NAME + response = raw_input() + if response == "y": + os.unlink(LOG_FILE_NAME) + def run_cmd(cmd, exit_on_failure=True): - print >> LOG_FILE, "Running command: %s" % cmd - ret = subprocess.call(cmd, shell=True, stdout=LOG_FILE, stderr=LOG_FILE) - if ret != 0 and exit_on_failure: - print "Command failed: %s" % cmd - clean_work_files() - sys.exit(-1) - return ret + print >> LOG_FILE, "Running command: %s" % cmd + ret = subprocess.call(cmd, shell=True, stdout=LOG_FILE, stderr=LOG_FILE) + if ret != 0 and exit_on_failure: + print "Command failed: %s" % cmd + clean_work_files() + sys.exit(-1) + return ret + def run_cmd_with_output(cmd): - print >> sys.stderr, "Running command: %s" % cmd - return subprocess.check_output(cmd, shell=True, stderr=LOG_FILE) + print >> sys.stderr, "Running command: %s" % cmd + return subprocess.check_output(cmd, shell=True, stderr=LOG_FILE) + def test(bool, str): - if bool: - return passed(str) - failed(str) + if bool: + return passed(str) + failed(str) + def passed(str): - print "[PASSED] %s" % str + print "[PASSED] %s" % str + def failed(str): - failures.append(str) - print "[**FAILED**] %s" % str + failures.append(str) + print "[**FAILED**] %s" % str + def get_url(url): - return urllib2.urlopen(url).read() + return urllib2.urlopen(url).read() + original_dir = os.getcwd() -# For each of these modules, we'll test an 'empty' application in sbt and +# For each of these modules, we'll test an 'empty' application in sbt and # maven that links against them. This will catch issues with messed up # dependencies within those projects. modules = [ - "spark-core", "spark-bagel", "spark-mllib", "spark-streaming", "spark-repl", - "spark-graphx", "spark-streaming-flume", "spark-streaming-kafka", - "spark-streaming-mqtt", "spark-streaming-twitter", "spark-streaming-zeromq", - "spark-catalyst", "spark-sql", "spark-hive" + "spark-core", "spark-bagel", "spark-mllib", "spark-streaming", "spark-repl", + "spark-graphx", "spark-streaming-flume", "spark-streaming-kafka", + "spark-streaming-mqtt", "spark-streaming-twitter", "spark-streaming-zeromq", + "spark-catalyst", "spark-sql", "spark-hive" ] modules = map(lambda m: "%s_%s" % (m, SCALA_BINARY_VERSION), modules) @@ -106,54 +114,57 @@ def get_url(url): cache_ivy_spark = "~/.ivy2/cache/org.apache.spark" local_maven_kafka = "~/.m2/repository/org/apache/kafka" local_maven_kafka = "~/.m2/repository/org/apache/spark" + + def ensure_path_not_present(x): - if os.path.exists(os.path.expanduser(x)): - print "Please remove %s, it can interfere with testing published artifacts." % x - sys.exit(-1) + if os.path.exists(os.path.expanduser(x)): + print "Please remove %s, it can interfere with testing published artifacts." % x + sys.exit(-1) + map(ensure_path_not_present, [local_ivy_spark, cache_ivy_spark, local_maven_kafka]) -# SBT build tests +# SBT build tests os.chdir("blank_sbt_build") os.environ["SPARK_VERSION"] = RELEASE_VERSION os.environ["SCALA_VERSION"] = SCALA_VERSION os.environ["SPARK_RELEASE_REPOSITORY"] = RELEASE_REPOSITORY os.environ["SPARK_AUDIT_MASTER"] = "local" for module in modules: - os.environ["SPARK_MODULE"] = module - ret = run_cmd("sbt clean update", exit_on_failure=False) - test(ret == 0, "sbt build against '%s' module" % module) + os.environ["SPARK_MODULE"] = module + ret = run_cmd("sbt clean update", exit_on_failure=False) + test(ret == 0, "sbt build against '%s' module" % module) os.chdir(original_dir) # SBT application tests for app in ["sbt_app_core", "sbt_app_graphx", "sbt_app_streaming", "sbt_app_sql", "sbt_app_hive"]: - os.chdir(app) - ret = run_cmd("sbt clean run", exit_on_failure=False) - test(ret == 0, "sbt application (%s)" % app) - os.chdir(original_dir) + os.chdir(app) + ret = run_cmd("sbt clean run", exit_on_failure=False) + test(ret == 0, "sbt application (%s)" % app) + os.chdir(original_dir) # Maven build tests os.chdir("blank_maven_build") for module in modules: - cmd = ('%s --update-snapshots -Dspark.release.repository="%s" -Dspark.version="%s" ' - '-Dspark.module="%s" clean compile' % - (MAVEN_CMD, RELEASE_REPOSITORY, RELEASE_VERSION, module)) - ret = run_cmd(cmd, exit_on_failure=False) - test(ret == 0, "maven build against '%s' module" % module) + cmd = ('%s --update-snapshots -Dspark.release.repository="%s" -Dspark.version="%s" ' + '-Dspark.module="%s" clean compile' % + (MAVEN_CMD, RELEASE_REPOSITORY, RELEASE_VERSION, module)) + ret = run_cmd(cmd, exit_on_failure=False) + test(ret == 0, "maven build against '%s' module" % module) os.chdir(original_dir) os.chdir("maven_app_core") mvn_exec_cmd = ('%s --update-snapshots -Dspark.release.repository="%s" -Dspark.version="%s" ' '-Dscala.binary.version="%s" clean compile ' - 'exec:java -Dexec.mainClass="SimpleApp"' % - (MAVEN_CMD, RELEASE_REPOSITORY, RELEASE_VERSION, SCALA_BINARY_VERSION)) + 'exec:java -Dexec.mainClass="SimpleApp"' % + (MAVEN_CMD, RELEASE_REPOSITORY, RELEASE_VERSION, SCALA_BINARY_VERSION)) ret = run_cmd(mvn_exec_cmd, exit_on_failure=False) test(ret == 0, "maven application (core)") os.chdir(original_dir) # Binary artifact tests if os.path.exists(WORK_DIR): - print "Working directory '%s' already exists" % WORK_DIR - sys.exit(-1) + print "Working directory '%s' already exists" % WORK_DIR + sys.exit(-1) os.mkdir(WORK_DIR) os.chdir(WORK_DIR) @@ -162,66 +173,66 @@ def ensure_path_not_present(x): artifacts = r.findall(index_page) for artifact in artifacts: - print "==== Verifying download integrity for artifact: %s ====" % artifact - - artifact_url = "%s/%s" % (RELEASE_URL, artifact) - run_cmd("wget %s" % artifact_url) - - key_file = "%s.asc" % artifact - run_cmd("wget %s/%s" % (RELEASE_URL, key_file)) - - run_cmd("wget %s%s" % (artifact_url, ".sha")) - - # Verify signature - run_cmd("%s --keyserver pgp.mit.edu --recv-key %s" % (GPG_CMD, RELEASE_KEY)) - run_cmd("%s %s" % (GPG_CMD, key_file)) - passed("Artifact signature verified.") - - # Verify md5 - my_md5 = run_cmd_with_output("%s --print-md MD5 %s" % (GPG_CMD, artifact)).strip() - release_md5 = get_url("%s.md5" % artifact_url).strip() - test(my_md5 == release_md5, "Artifact MD5 verified.") - - # Verify sha - my_sha = run_cmd_with_output("%s --print-md SHA512 %s" % (GPG_CMD, artifact)).strip() - release_sha = get_url("%s.sha" % artifact_url).strip() - test(my_sha == release_sha, "Artifact SHA verified.") - - # Verify Apache required files - dir_name = artifact.replace(".tgz", "") - run_cmd("tar xvzf %s" % artifact) - base_files = os.listdir(dir_name) - test("CHANGES.txt" in base_files, "Tarball contains CHANGES.txt file") - test("NOTICE" in base_files, "Tarball contains NOTICE file") - test("LICENSE" in base_files, "Tarball contains LICENSE file") - - os.chdir(WORK_DIR) - + print "==== Verifying download integrity for artifact: %s ====" % artifact + + artifact_url = "%s/%s" % (RELEASE_URL, artifact) + run_cmd("wget %s" % artifact_url) + + key_file = "%s.asc" % artifact + run_cmd("wget %s/%s" % (RELEASE_URL, key_file)) + + run_cmd("wget %s%s" % (artifact_url, ".sha")) + + # Verify signature + run_cmd("%s --keyserver pgp.mit.edu --recv-key %s" % (GPG_CMD, RELEASE_KEY)) + run_cmd("%s %s" % (GPG_CMD, key_file)) + passed("Artifact signature verified.") + + # Verify md5 + my_md5 = run_cmd_with_output("%s --print-md MD5 %s" % (GPG_CMD, artifact)).strip() + release_md5 = get_url("%s.md5" % artifact_url).strip() + test(my_md5 == release_md5, "Artifact MD5 verified.") + + # Verify sha + my_sha = run_cmd_with_output("%s --print-md SHA512 %s" % (GPG_CMD, artifact)).strip() + release_sha = get_url("%s.sha" % artifact_url).strip() + test(my_sha == release_sha, "Artifact SHA verified.") + + # Verify Apache required files + dir_name = artifact.replace(".tgz", "") + run_cmd("tar xvzf %s" % artifact) + base_files = os.listdir(dir_name) + test("CHANGES.txt" in base_files, "Tarball contains CHANGES.txt file") + test("NOTICE" in base_files, "Tarball contains NOTICE file") + test("LICENSE" in base_files, "Tarball contains LICENSE file") + + os.chdir(WORK_DIR) + for artifact in artifacts: - print "==== Verifying build and tests for artifact: %s ====" % artifact - os.chdir(os.path.join(WORK_DIR, dir_name)) - - os.environ["MAVEN_OPTS"] = "-Xmx3g -XX:MaxPermSize=1g -XX:ReservedCodeCacheSize=1g" - # Verify build - print "==> Running build" - run_cmd("sbt assembly") - passed("sbt build successful") - run_cmd("%s package -DskipTests" % MAVEN_CMD) - passed("Maven build successful") - - # Verify tests - print "==> Performing unit tests" - run_cmd("%s test" % MAVEN_CMD) - passed("Tests successful") - os.chdir(WORK_DIR) + print "==== Verifying build and tests for artifact: %s ====" % artifact + os.chdir(os.path.join(WORK_DIR, dir_name)) + + os.environ["MAVEN_OPTS"] = "-Xmx3g -XX:MaxPermSize=1g -XX:ReservedCodeCacheSize=1g" + # Verify build + print "==> Running build" + run_cmd("sbt assembly") + passed("sbt build successful") + run_cmd("%s package -DskipTests" % MAVEN_CMD) + passed("Maven build successful") + + # Verify tests + print "==> Performing unit tests" + run_cmd("%s test" % MAVEN_CMD) + passed("Tests successful") + os.chdir(WORK_DIR) clean_work_files() if len(failures) == 0: - print "ALL TESTS PASSED" + print "ALL TESTS PASSED" else: - print "SOME TESTS DID NOT PASS" - for f in failures: - print f + print "SOME TESTS DID NOT PASS" + for f in failures: + print f os.chdir(original_dir) diff --git a/dev/create-release/generate-changelist.py b/dev/create-release/generate-changelist.py index 13b744ec1b37e..de1b5d4ae1314 100755 --- a/dev/create-release/generate-changelist.py +++ b/dev/create-release/generate-changelist.py @@ -29,16 +29,16 @@ import subprocess import time import traceback - + SPARK_HOME = os.environ["SPARK_HOME"] NEW_RELEASE_VERSION = "1.0.0" PREV_RELEASE_GIT_TAG = "v0.9.1" - -CHANGELIST = "CHANGES.txt" + +CHANGELIST = "CHANGES.txt" OLD_CHANGELIST = "%s.old" % (CHANGELIST) NEW_CHANGELIST = "%s.new" % (CHANGELIST) TMP_CHANGELIST = "%s.tmp" % (CHANGELIST) - + # date before first PR in TLP Spark repo SPARK_REPO_CHANGE_DATE1 = time.strptime("2014-02-26", "%Y-%m-%d") # date after last PR in incubator Spark repo @@ -46,99 +46,103 @@ # Threshold PR number that differentiates PRs to TLP # and incubator repos SPARK_REPO_PR_NUM_THRESH = 200 - + LOG_FILE_NAME = "changes_%s" % time.strftime("%h_%m_%Y_%I_%M_%S") LOG_FILE = open(LOG_FILE_NAME, 'w') - + + def run_cmd(cmd): - try: - print >> LOG_FILE, "Running command: %s" % cmd - output = subprocess.check_output(cmd, shell=True, stderr=LOG_FILE) - print >> LOG_FILE, "Output: %s" % output - return output - except: - traceback.print_exc() - cleanup() - sys.exit(1) - + try: + print >> LOG_FILE, "Running command: %s" % cmd + output = subprocess.check_output(cmd, shell=True, stderr=LOG_FILE) + print >> LOG_FILE, "Output: %s" % output + return output + except: + traceback.print_exc() + cleanup() + sys.exit(1) + + def append_to_changelist(string): - with open(TMP_CHANGELIST, "a") as f: - print >> f, string - -def cleanup(ask = True): - if ask == True: - print "OK to delete temporary and log files? (y/N): " - response = raw_input() - if ask == False or (ask == True and response == "y"): - if os.path.isfile(TMP_CHANGELIST): - os.remove(TMP_CHANGELIST) - if os.path.isfile(OLD_CHANGELIST): - os.remove(OLD_CHANGELIST) - LOG_FILE.close() - os.remove(LOG_FILE_NAME) - + with open(TMP_CHANGELIST, "a") as f: + print >> f, string + + +def cleanup(ask=True): + if ask is True: + print "OK to delete temporary and log files? (y/N): " + response = raw_input() + if ask is False or (ask is True and response == "y"): + if os.path.isfile(TMP_CHANGELIST): + os.remove(TMP_CHANGELIST) + if os.path.isfile(OLD_CHANGELIST): + os.remove(OLD_CHANGELIST) + LOG_FILE.close() + os.remove(LOG_FILE_NAME) + + print "Generating new %s for Spark release %s" % (CHANGELIST, NEW_RELEASE_VERSION) os.chdir(SPARK_HOME) if os.path.isfile(TMP_CHANGELIST): - os.remove(TMP_CHANGELIST) + os.remove(TMP_CHANGELIST) if os.path.isfile(OLD_CHANGELIST): - os.remove(OLD_CHANGELIST) - + os.remove(OLD_CHANGELIST) + append_to_changelist("Spark Change Log") append_to_changelist("----------------") append_to_changelist("") append_to_changelist("Release %s" % NEW_RELEASE_VERSION) append_to_changelist("") - + print "Getting commits between tag %s and HEAD" % PREV_RELEASE_GIT_TAG hashes = run_cmd("git log %s..HEAD --pretty='%%h'" % PREV_RELEASE_GIT_TAG).split() - + print "Getting details of %s commits" % len(hashes) for h in hashes: - date = run_cmd("git log %s -1 --pretty='%%ad' --date=iso | head -1" % h).strip() - subject = run_cmd("git log %s -1 --pretty='%%s' | head -1" % h).strip() - body = run_cmd("git log %s -1 --pretty='%%b'" % h) - committer = run_cmd("git log %s -1 --pretty='%%cn <%%ce>' | head -1" % h).strip() - body_lines = body.split("\n") - - if "Merge pull" in subject: - ## Parse old format commit message - append_to_changelist(" %s %s" % (h, date)) - append_to_changelist(" %s" % subject) - append_to_changelist(" [%s]" % body_lines[0]) - append_to_changelist("") - - elif "maven-release" not in subject: - ## Parse new format commit message - # Get authors from commit message, committer otherwise - authors = [committer] - if "Author:" in body: - authors = [line.split(":")[1].strip() for line in body_lines if "Author:" in line] - - # Generate GitHub PR URL for easy access if possible - github_url = "" - if "Closes #" in body: - pr_num = [line.split()[1].lstrip("#") for line in body_lines if "Closes #" in line][0] - github_url = "github.com/apache/spark/pull/%s" % pr_num - day = time.strptime(date.split()[0], "%Y-%m-%d") - if day < SPARK_REPO_CHANGE_DATE1 or (day < SPARK_REPO_CHANGE_DATE2 and pr_num < SPARK_REPO_PR_NUM_THRESH): - github_url = "github.com/apache/incubator-spark/pull/%s" % pr_num - - append_to_changelist(" %s" % subject) - append_to_changelist(" %s" % ', '.join(authors)) - # for author in authors: - # append_to_changelist(" %s" % author) - append_to_changelist(" %s" % date) - if len(github_url) > 0: - append_to_changelist(" Commit: %s, %s" % (h, github_url)) - else: - append_to_changelist(" Commit: %s" % h) - append_to_changelist("") - + date = run_cmd("git log %s -1 --pretty='%%ad' --date=iso | head -1" % h).strip() + subject = run_cmd("git log %s -1 --pretty='%%s' | head -1" % h).strip() + body = run_cmd("git log %s -1 --pretty='%%b'" % h) + committer = run_cmd("git log %s -1 --pretty='%%cn <%%ce>' | head -1" % h).strip() + body_lines = body.split("\n") + + if "Merge pull" in subject: + # Parse old format commit message + append_to_changelist(" %s %s" % (h, date)) + append_to_changelist(" %s" % subject) + append_to_changelist(" [%s]" % body_lines[0]) + append_to_changelist("") + + elif "maven-release" not in subject: + # Parse new format commit message + # Get authors from commit message, committer otherwise + authors = [committer] + if "Author:" in body: + authors = [line.split(":")[1].strip() for line in body_lines if "Author:" in line] + + # Generate GitHub PR URL for easy access if possible + github_url = "" + if "Closes #" in body: + pr_num = [line.split()[1].lstrip("#") for line in body_lines if "Closes #" in line][0] + github_url = "github.com/apache/spark/pull/%s" % pr_num + day = time.strptime(date.split()[0], "%Y-%m-%d") + if day < SPARK_REPO_CHANGE_DATE1 or + (day < SPARK_REPO_CHANGE_DATE2 and pr_num < SPARK_REPO_PR_NUM_THRESH): + github_url = "github.com/apache/incubator-spark/pull/%s" % pr_num + + append_to_changelist(" %s" % subject) + append_to_changelist(" %s" % ', '.join(authors)) + # for author in authors: + # append_to_changelist(" %s" % author) + append_to_changelist(" %s" % date) + if len(github_url) > 0: + append_to_changelist(" Commit: %s, %s" % (h, github_url)) + else: + append_to_changelist(" Commit: %s" % h) + append_to_changelist("") + # Append old change list -print "Appending changelist from tag %s" % PREV_RELEASE_GIT_TAG +print "Appending changelist from tag %s" % PREV_RELEASE_GIT_TAG run_cmd("git show %s:%s | tail -n +3 >> %s" % (PREV_RELEASE_GIT_TAG, CHANGELIST, TMP_CHANGELIST)) run_cmd("cp %s %s" % (TMP_CHANGELIST, NEW_CHANGELIST)) print "New change list generated as %s" % NEW_CHANGELIST cleanup(False) - diff --git a/dev/merge_spark_pr.py b/dev/merge_spark_pr.py index 83618c8068d35..7f744d5589ef7 100755 --- a/dev/merge_spark_pr.py +++ b/dev/merge_spark_pr.py @@ -21,7 +21,7 @@ # usage: ./apache-pr-merge.py (see config env vars below) # # This utility assumes you already have local a Spark git folder and that you -# have added remotes corresponding to both (i) the github apache Spark +# have added remotes corresponding to both (i) the github apache Spark # mirror and (ii) the apache git repo. import json @@ -33,10 +33,10 @@ import urllib2 try: - import jira.client - JIRA_IMPORTED=True + import jira.client + JIRA_IMPORTED = True except ImportError: - JIRA_IMPORTED=False + JIRA_IMPORTED = False # Location of your Spark git development area SPARK_HOME = os.environ.get("SPARK_HOME", "/home/patrick/Documents/spark") @@ -58,204 +58,217 @@ os.chdir(SPARK_HOME) + def get_json(url): - try: - return json.load(urllib2.urlopen(url)) - except urllib2.HTTPError as e: - print "Unable to fetch URL, exiting: %s" % url - sys.exit(-1) + try: + return json.load(urllib2.urlopen(url)) + except urllib2.HTTPError as e: + print "Unable to fetch URL, exiting: %s" % url + sys.exit(-1) + def fail(msg): - print msg - clean_up() - sys.exit(-1) + print msg + clean_up() + sys.exit(-1) + def run_cmd(cmd): - if isinstance(cmd, list): - return subprocess.check_output(cmd) - else: - return subprocess.check_output(cmd.split(" ")) + if isinstance(cmd, list): + return subprocess.check_output(cmd) + else: + return subprocess.check_output(cmd.split(" ")) + def continue_maybe(prompt): - result = raw_input("\n%s (y/n): " % prompt) - if result.lower() != "y": - fail("Okay, exiting") + result = raw_input("\n%s (y/n): " % prompt) + if result.lower() != "y": + fail("Okay, exiting") + original_head = run_cmd("git rev-parse HEAD")[:8] + def clean_up(): - print "Restoring head pointer to %s" % original_head - run_cmd("git checkout %s" % original_head) + print "Restoring head pointer to %s" % original_head + run_cmd("git checkout %s" % original_head) + + branches = run_cmd("git branch").replace(" ", "").split("\n") - branches = run_cmd("git branch").replace(" ", "").split("\n") + for branch in filter(lambda x: x.startswith(BRANCH_PREFIX), branches): + print "Deleting local branch %s" % branch + run_cmd("git branch -D %s" % branch) - for branch in filter(lambda x: x.startswith(BRANCH_PREFIX), branches): - print "Deleting local branch %s" % branch - run_cmd("git branch -D %s" % branch) # merge the requested PR and return the merge hash def merge_pr(pr_num, target_ref): - pr_branch_name = "%s_MERGE_PR_%s" % (BRANCH_PREFIX, pr_num) - target_branch_name = "%s_MERGE_PR_%s_%s" % (BRANCH_PREFIX, pr_num, target_ref.upper()) - run_cmd("git fetch %s pull/%s/head:%s" % (PR_REMOTE_NAME, pr_num, pr_branch_name)) - run_cmd("git fetch %s %s:%s" % (PUSH_REMOTE_NAME, target_ref, target_branch_name)) - run_cmd("git checkout %s" % target_branch_name) - - had_conflicts = False - try: - run_cmd(['git', 'merge', pr_branch_name, '--squash']) - except Exception as e: - msg = "Error merging: %s\nWould you like to manually fix-up this merge?" % e - continue_maybe(msg) - msg = "Okay, please fix any conflicts and 'git add' conflicting files... Finished?" - continue_maybe(msg) - had_conflicts = True - - commit_authors = run_cmd(['git', 'log', 'HEAD..%s' % pr_branch_name, - '--pretty=format:%an <%ae>']).split("\n") - distinct_authors = sorted(set(commit_authors), key=lambda x: commit_authors.count(x), - reverse=True) - primary_author = distinct_authors[0] - commits = run_cmd(['git', 'log', 'HEAD..%s' % pr_branch_name, - '--pretty=format:%h [%an] %s']).split("\n\n") - - merge_message_flags = [] - - for p in [title, body]: - merge_message_flags += ["-m", p] - - authors = "\n".join(["Author: %s" % a for a in distinct_authors]) - - merge_message_flags += ["-m", authors] + pr_branch_name = "%s_MERGE_PR_%s" % (BRANCH_PREFIX, pr_num) + target_branch_name = "%s_MERGE_PR_%s_%s" % (BRANCH_PREFIX, pr_num, target_ref.upper()) + run_cmd("git fetch %s pull/%s/head:%s" % (PR_REMOTE_NAME, pr_num, pr_branch_name)) + run_cmd("git fetch %s %s:%s" % (PUSH_REMOTE_NAME, target_ref, target_branch_name)) + run_cmd("git checkout %s" % target_branch_name) + + had_conflicts = False + try: + run_cmd(['git', 'merge', pr_branch_name, '--squash']) + except Exception as e: + msg = "Error merging: %s\nWould you like to manually fix-up this merge?" % e + continue_maybe(msg) + msg = "Okay, please fix any conflicts and 'git add' conflicting files... Finished?" + continue_maybe(msg) + had_conflicts = True + + commit_authors = run_cmd(['git', 'log', 'HEAD..%s' % pr_branch_name, + '--pretty=format:%an <%ae>']).split("\n") + distinct_authors = sorted(set(commit_authors), + key=lambda x: commit_authors.count(x), reverse=True) + primary_author = distinct_authors[0] + commits = run_cmd(['git', 'log', 'HEAD..%s' % pr_branch_name, + '--pretty=format:%h [%an] %s']).split("\n\n") + + merge_message_flags = [] + + for p in [title, body]: + merge_message_flags += ["-m", p] + + authors = "\n".join(["Author: %s" % a for a in distinct_authors]) + + merge_message_flags += ["-m", authors] + + if had_conflicts: + committer_name = run_cmd("git config --get user.name").strip() + committer_email = run_cmd("git config --get user.email").strip() + message = "This patch had conflicts when merged, resolved by\nCommitter: %s <%s>" % ( + committer_name, committer_email) + merge_message_flags += ["-m", message] + + # The string "Closes #%s" string is required for GitHub to correctly close the PR + merge_message_flags += [ + "-m", + "Closes #%s from %s and squashes the following commits:" % (pr_num, pr_repo_desc)] + for c in commits: + merge_message_flags += ["-m", c] + + run_cmd(['git', 'commit', '--author="%s"' % primary_author] + merge_message_flags) + + continue_maybe("Merge complete (local ref %s). Push to %s?" % ( + target_branch_name, PUSH_REMOTE_NAME)) + + try: + run_cmd('git push %s %s:%s' % (PUSH_REMOTE_NAME, target_branch_name, target_ref)) + except Exception as e: + clean_up() + fail("Exception while pushing: %s" % e) + + merge_hash = run_cmd("git rev-parse %s" % target_branch_name)[:8] + clean_up() + print("Pull request #%s merged!" % pr_num) + print("Merge hash: %s" % merge_hash) + return merge_hash - if had_conflicts: - committer_name = run_cmd("git config --get user.name").strip() - committer_email = run_cmd("git config --get user.email").strip() - message = "This patch had conflicts when merged, resolved by\nCommitter: %s <%s>" % ( - committer_name, committer_email) - merge_message_flags += ["-m", message] - # The string "Closes #%s" string is required for GitHub to correctly close the PR - merge_message_flags += ["-m", - "Closes #%s from %s and squashes the following commits:" % (pr_num, pr_repo_desc)] - for c in commits: - merge_message_flags += ["-m", c] +def cherry_pick(pr_num, merge_hash, default_branch): + pick_ref = raw_input("Enter a branch name [%s]: " % default_branch) + if pick_ref == "": + pick_ref = default_branch - run_cmd(['git', 'commit', '--author="%s"' % primary_author] + merge_message_flags) + pick_branch_name = "%s_PICK_PR_%s_%s" % (BRANCH_PREFIX, pr_num, pick_ref.upper()) - continue_maybe("Merge complete (local ref %s). Push to %s?" % ( - target_branch_name, PUSH_REMOTE_NAME)) + run_cmd("git fetch %s %s:%s" % (PUSH_REMOTE_NAME, pick_ref, pick_branch_name)) + run_cmd("git checkout %s" % pick_branch_name) + run_cmd("git cherry-pick -sx %s" % merge_hash) - try: - run_cmd('git push %s %s:%s' % (PUSH_REMOTE_NAME, target_branch_name, target_ref)) - except Exception as e: - clean_up() - fail("Exception while pushing: %s" % e) - - merge_hash = run_cmd("git rev-parse %s" % target_branch_name)[:8] - clean_up() - print("Pull request #%s merged!" % pr_num) - print("Merge hash: %s" % merge_hash) - return merge_hash + continue_maybe("Pick complete (local ref %s). Push to %s?" % ( + pick_branch_name, PUSH_REMOTE_NAME)) + try: + run_cmd('git push %s %s:%s' % (PUSH_REMOTE_NAME, pick_branch_name, pick_ref)) + except Exception as e: + clean_up() + fail("Exception while pushing: %s" % e) -def cherry_pick(pr_num, merge_hash, default_branch): - pick_ref = raw_input("Enter a branch name [%s]: " % default_branch) - if pick_ref == "": - pick_ref = default_branch - - pick_branch_name = "%s_PICK_PR_%s_%s" % (BRANCH_PREFIX, pr_num, pick_ref.upper()) - - run_cmd("git fetch %s %s:%s" % (PUSH_REMOTE_NAME, pick_ref, pick_branch_name)) - run_cmd("git checkout %s" % pick_branch_name) - run_cmd("git cherry-pick -sx %s" % merge_hash) - - continue_maybe("Pick complete (local ref %s). Push to %s?" % ( - pick_branch_name, PUSH_REMOTE_NAME)) - - try: - run_cmd('git push %s %s:%s' % (PUSH_REMOTE_NAME, pick_branch_name, pick_ref)) - except Exception as e: + pick_hash = run_cmd("git rev-parse %s" % pick_branch_name)[:8] clean_up() - fail("Exception while pushing: %s" % e) - pick_hash = run_cmd("git rev-parse %s" % pick_branch_name)[:8] - clean_up() + print("Pull request #%s picked into %s!" % (pr_num, pick_ref)) + print("Pick hash: %s" % pick_hash) + return pick_ref - print("Pull request #%s picked into %s!" % (pr_num, pick_ref)) - print("Pick hash: %s" % pick_hash) - return pick_ref def fix_version_from_branch(branch, versions): - # Note: Assumes this is a sorted (newest->oldest) list of un-released versions - if branch == "master": - return versions[0] - else: - branch_ver = branch.replace("branch-", "") - return filter(lambda x: x.name.startswith(branch_ver), versions)[-1] + # Note: Assumes this is a sorted (newest->oldest) list of un-released versions + if branch == "master": + return versions[0] + else: + branch_ver = branch.replace("branch-", "") + return filter(lambda x: x.name.startswith(branch_ver), versions)[-1] + def resolve_jira(title, merge_branches, comment): - asf_jira = jira.client.JIRA({'server': JIRA_API_BASE}, - basic_auth=(JIRA_USERNAME, JIRA_PASSWORD)) - - default_jira_id = "" - search = re.findall("SPARK-[0-9]{4,5}", title) - if len(search) > 0: - default_jira_id = search[0] - - jira_id = raw_input("Enter a JIRA id [%s]: " % default_jira_id) - if jira_id == "": - jira_id = default_jira_id - - try: - issue = asf_jira.issue(jira_id) - except Exception as e: - fail("ASF JIRA could not find %s\n%s" % (jira_id, e)) - - cur_status = issue.fields.status.name - cur_summary = issue.fields.summary - cur_assignee = issue.fields.assignee - if cur_assignee == None: - cur_assignee = "NOT ASSIGNED!!!" - else: - cur_assignee = cur_assignee.displayName - - if cur_status == "Resolved" or cur_status == "Closed": - fail("JIRA issue %s already has status '%s'" % (jira_id, cur_status)) - print ("=== JIRA %s ===" % jira_id) - print ("summary\t\t%s\nassignee\t%s\nstatus\t\t%s\nurl\t\t%s/%s\n" % ( - cur_summary, cur_assignee, cur_status, JIRA_BASE, 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'] == False, versions) - - default_fix_versions = map(lambda x: fix_version_from_branch(x, versions).name, merge_branches) - for v in default_fix_versions: - # Handles the case where we have forked a release branch but not yet made the release. - # In this case, if the PR is committed to the master branch and the release branch, we - # only consider the release branch to be the fix version. E.g. it is not valid to have - # both 1.1.0 and 1.0.0 as fix versions. - (major, minor, patch) = v.split(".") - if patch == "0": - previous = "%s.%s.%s" % (major, int(minor) - 1, 0) - if previous in default_fix_versions: - default_fix_versions = filter(lambda x: x != v, default_fix_versions) - default_fix_versions = ",".join(default_fix_versions) - - fix_versions = raw_input("Enter comma-separated fix version(s) [%s]: " % default_fix_versions) - if fix_versions == "": - fix_versions = default_fix_versions - fix_versions = fix_versions.replace(" ", "").split(",") - - def get_version_json(version_str): - return filter(lambda v: v.name == version_str, versions)[0].raw - jira_fix_versions = map(lambda v: get_version_json(v), fix_versions) - - resolve = filter(lambda a: a['name'] == "Resolve Issue", asf_jira.transitions(jira_id))[0] - asf_jira.transition_issue(jira_id, resolve["id"], fixVersions=jira_fix_versions, comment=comment) - - print "Succesfully resolved %s with fixVersions=%s!" % (jira_id, fix_versions) + asf_jira = jira.client.JIRA({'server': JIRA_API_BASE}, + basic_auth=(JIRA_USERNAME, JIRA_PASSWORD)) + + default_jira_id = "" + search = re.findall("SPARK-[0-9]{4,5}", title) + if len(search) > 0: + default_jira_id = search[0] + + jira_id = raw_input("Enter a JIRA id [%s]: " % default_jira_id) + if jira_id == "": + jira_id = default_jira_id + + try: + issue = asf_jira.issue(jira_id) + except Exception as e: + fail("ASF JIRA could not find %s\n%s" % (jira_id, e)) + + cur_status = issue.fields.status.name + cur_summary = issue.fields.summary + cur_assignee = issue.fields.assignee + if cur_assignee is None: + cur_assignee = "NOT ASSIGNED!!!" + else: + cur_assignee = cur_assignee.displayName + + if cur_status == "Resolved" or cur_status == "Closed": + fail("JIRA issue %s already has status '%s'" % (jira_id, cur_status)) + print ("=== JIRA %s ===" % jira_id) + print ("summary\t\t%s\nassignee\t%s\nstatus\t\t%s\nurl\t\t%s/%s\n" % ( + cur_summary, cur_assignee, cur_status, JIRA_BASE, 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) + + default_fix_versions = map(lambda x: fix_version_from_branch(x, versions).name, merge_branches) + for v in default_fix_versions: + # Handles the case where we have forked a release branch but not yet made the release. + # In this case, if the PR is committed to the master branch and the release branch, we + # only consider the release branch to be the fix version. E.g. it is not valid to have + # both 1.1.0 and 1.0.0 as fix versions. + (major, minor, patch) = v.split(".") + if patch == "0": + previous = "%s.%s.%s" % (major, int(minor) - 1, 0) + if previous in default_fix_versions: + default_fix_versions = filter(lambda x: x != v, default_fix_versions) + default_fix_versions = ",".join(default_fix_versions) + + fix_versions = raw_input("Enter comma-separated fix version(s) [%s]: " % default_fix_versions) + if fix_versions == "": + fix_versions = default_fix_versions + fix_versions = fix_versions.replace(" ", "").split(",") + + def get_version_json(version_str): + return filter(lambda v: v.name == version_str, versions)[0].raw + + jira_fix_versions = map(lambda v: get_version_json(v), fix_versions) + + resolve = filter(lambda a: a['name'] == "Resolve Issue", asf_jira.transitions(jira_id))[0] + asf_jira.transition_issue( + jira_id, resolve["id"], fixVersions=jira_fix_versions, comment=comment) + + print "Succesfully resolved %s with fixVersions=%s!" % (jira_id, fix_versions) + branches = get_json("%s/branches" % GITHUB_API_BASE) branch_names = filter(lambda x: x.startswith("branch-"), [x['name'] for x in branches]) @@ -273,28 +286,29 @@ def get_version_json(version_str): base_ref = pr["head"]["ref"] pr_repo_desc = "%s/%s" % (user_login, base_ref) -if pr["merged"] == True: - print "Pull request %s has already been merged, assuming you want to backport" % pr_num - merge_commit_desc = run_cmd(['git', 'log', '--merges', '--first-parent', - '--grep=pull request #%s' % pr_num, '--oneline']).split("\n")[0] - if merge_commit_desc == "": - fail("Couldn't find any merge commit for #%s, you may need to update HEAD." % pr_num) +if pr["merged"] is True: + print "Pull request %s has already been merged, assuming you want to backport" % pr_num + merge_commit_desc = run_cmd([ + 'git', 'log', '--merges', '--first-parent', + '--grep=pull request #%s' % pr_num, '--oneline']).split("\n")[0] + if merge_commit_desc == "": + fail("Couldn't find any merge commit for #%s, you may need to update HEAD." % pr_num) + + merge_hash = merge_commit_desc[:7] + message = merge_commit_desc[8:] - merge_hash = merge_commit_desc[:7] - message = merge_commit_desc[8:] - - print "Found: %s" % message - maybe_cherry_pick(pr_num, merge_hash, latest_branch) - sys.exit(0) + print "Found: %s" % message + maybe_cherry_pick(pr_num, merge_hash, latest_branch) + sys.exit(0) if not bool(pr["mergeable"]): - msg = "Pull request %s is not mergeable in its current form.\n" % pr_num + \ - "Continue? (experts only!)" - continue_maybe(msg) + msg = "Pull request %s is not mergeable in its current form.\n" % pr_num + \ + "Continue? (experts only!)" + continue_maybe(msg) print ("\n=== Pull Request #%s ===" % pr_num) print ("title\t%s\nsource\t%s\ntarget\t%s\nurl\t%s" % ( - title, pr_repo_desc, target_ref, url)) + title, pr_repo_desc, target_ref, url)) continue_maybe("Proceed with merging pull request #%s?" % pr_num) merged_refs = [target_ref] @@ -303,12 +317,12 @@ def get_version_json(version_str): pick_prompt = "Would you like to pick %s into another branch?" % merge_hash while raw_input("\n%s (y/n): " % pick_prompt).lower() == "y": - merged_refs = merged_refs + [cherry_pick(pr_num, merge_hash, latest_branch)] + merged_refs = merged_refs + [cherry_pick(pr_num, merge_hash, latest_branch)] if JIRA_IMPORTED: - continue_maybe("Would you like to update an associated JIRA?") - jira_comment = "Issue resolved by pull request %s\n[%s/%s]" % (pr_num, GITHUB_BASE, pr_num) - resolve_jira(title, merged_refs, jira_comment) + continue_maybe("Would you like to update an associated JIRA?") + jira_comment = "Issue resolved by pull request %s\n[%s/%s]" % (pr_num, GITHUB_BASE, pr_num) + resolve_jira(title, merged_refs, jira_comment) else: - print "Could not find jira-python library. Run 'sudo pip install jira-python' to install." - print "Exiting without trying to close the associated JIRA." + print "Could not find jira-python library. Run 'sudo pip install jira-python' to install." + print "Exiting without trying to close the associated JIRA." From 90e281b55aecbfbe4431ac582311d5790fe7aad3 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Mon, 26 May 2014 22:05:23 -0700 Subject: [PATCH 052/221] SPARK-1933: Throw a more meaningful exception when a directory is passed to addJar/addFile. https://issues.apache.org/jira/browse/SPARK-1933 Author: Reynold Xin Closes #888 from rxin/addfile and squashes the following commits: 8c402a3 [Reynold Xin] Updated comment. ff6c162 [Reynold Xin] SPARK-1933: Throw a more meaningful exception when a directory is passed to addJar/addFile. --- core/src/main/scala/org/apache/spark/HttpFileServer.scala | 7 +++++++ core/src/main/scala/org/apache/spark/SparkContext.scala | 5 ++--- 2 files changed, 9 insertions(+), 3 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/HttpFileServer.scala b/core/src/main/scala/org/apache/spark/HttpFileServer.scala index a6e300d345786..0e3750fdde415 100644 --- a/core/src/main/scala/org/apache/spark/HttpFileServer.scala +++ b/core/src/main/scala/org/apache/spark/HttpFileServer.scala @@ -59,6 +59,13 @@ private[spark] class HttpFileServer(securityManager: SecurityManager) extends Lo } def addFileToDir(file: File, dir: File) : String = { + // Check whether the file is a directory. If it is, throw a more meaningful exception. + // If we don't catch this, Guava throws a very confusing error message: + // java.io.FileNotFoundException: [file] (No such file or directory) + // even though the directory ([file]) exists. + if (file.isDirectory) { + throw new IllegalArgumentException(s"$file cannot be a directory.") + } Files.copy(file, new File(dir, file.getName)) dir + "/" + file.getName } diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala index 49737fa4be56b..03ceff8bf1fb0 100644 --- a/core/src/main/scala/org/apache/spark/SparkContext.scala +++ b/core/src/main/scala/org/apache/spark/SparkContext.scala @@ -794,7 +794,7 @@ class SparkContext(config: SparkConf) extends Logging { addedFiles(key) = System.currentTimeMillis // Fetch the file locally in case a job is executed using DAGScheduler.runLocally(). - Utils.fetchFile(path, new File(SparkFiles.getRootDirectory), conf, env.securityManager) + Utils.fetchFile(path, new File(SparkFiles.getRootDirectory()), conf, env.securityManager) logInfo("Added file " + path + " at " + key + " with timestamp " + addedFiles(key)) postEnvironmentUpdate() @@ -932,13 +932,12 @@ class SparkContext(config: SparkConf) extends Logging { try { env.httpFileServer.addJar(new File(fileName)) } catch { - case e: Exception => { + case e: Exception => // For now just log an error but allow to go through so spark examples work. // The spark examples don't really need the jar distributed since its also // the app jar. logError("Error adding jar (" + e + "), was the --addJars option used?") null - } } } else { env.httpFileServer.addJar(new File(uri.getPath)) From 549830b0db2c8b069391224f3a73bb0d7f397f71 Mon Sep 17 00:00:00 2001 From: zsxwing Date: Mon, 26 May 2014 23:17:39 -0700 Subject: [PATCH 053/221] SPARK-1932: Fix race conditions in onReceiveCallback and cachedPeers `var cachedPeers: Seq[BlockManagerId] = null` is used in `def replicate(blockId: BlockId, data: ByteBuffer, level: StorageLevel)` without proper protection. There are two place will call `replicate(blockId, bytesAfterPut, level)` * https://github.com/apache/spark/blob/17f3075bc4aa8cbed165f7b367f70e84b1bc8db9/core/src/main/scala/org/apache/spark/storage/BlockManager.scala#L644 runs in `connectionManager.futureExecContext` * https://github.com/apache/spark/blob/17f3075bc4aa8cbed165f7b367f70e84b1bc8db9/core/src/main/scala/org/apache/spark/storage/BlockManager.scala#L752 `doPut` runs in `connectionManager.handleMessageExecutor`. `org.apache.spark.storage.BlockManagerWorker` calls `blockManager.putBytes` in `connectionManager.handleMessageExecutor`. As they run in different `Executor`s, this is a race condition which may cause the memory pointed by `cachedPeers` is not correct even if `cachedPeers != null`. The race condition of `onReceiveCallback` is that it's set in `BlockManagerWorker` but read in a different thread in `ConnectionManager.handleMessageExecutor`. Author: zsxwing Closes #887 from zsxwing/SPARK-1932 and squashes the following commits: 524f69c [zsxwing] SPARK-1932: Fix race conditions in onReceiveCallback and cachedPeers --- .../scala/org/apache/spark/network/ConnectionManager.scala | 3 ++- .../src/main/scala/org/apache/spark/storage/BlockManager.scala | 2 +- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala b/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala index dcbbc1853186b..5dd5fd0047c0d 100644 --- a/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala +++ b/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala @@ -93,7 +93,8 @@ private[spark] class ConnectionManager(port: Int, conf: SparkConf, implicit val futureExecContext = ExecutionContext.fromExecutor( Utils.newDaemonCachedThreadPool("Connection manager future execution context")) - private var onReceiveCallback: (BufferMessage, ConnectionManagerId) => Option[Message]= null + @volatile + private var onReceiveCallback: (BufferMessage, ConnectionManagerId) => Option[Message] = null private val authEnabled = securityManager.isAuthenticationEnabled() 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 6534095811907..6e450081dcb11 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala @@ -772,7 +772,7 @@ private[spark] class BlockManager( /** * Replicate block to another node. */ - var cachedPeers: Seq[BlockManagerId] = null + @volatile var cachedPeers: Seq[BlockManagerId] = null private def replicate(blockId: BlockId, data: ByteBuffer, level: StorageLevel) { val tLevel = StorageLevel( level.useDisk, level.useMemory, level.useOffHeap, level.deserialized, 1) From 95e4c9c6fb153b7f0aa4c442c4bdb6552d326640 Mon Sep 17 00:00:00 2001 From: lianhuiwang Date: Tue, 27 May 2014 11:53:38 -0700 Subject: [PATCH 054/221] bugfix worker DriverStateChanged state should match DriverState.FAILED bugfix worker DriverStateChanged state should match DriverState.FAILED Author: lianhuiwang Closes #864 from lianhuiwang/master and squashes the following commits: 480ce94 [lianhuiwang] address aarondav comments f2b5970 [lianhuiwang] bugfix worker DriverStateChanged state should match DriverState.FAILED --- .../main/scala/org/apache/spark/deploy/worker/Worker.scala | 4 ++++ 1 file changed, 4 insertions(+) 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 8b6747977eb87..100de26170a50 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 @@ -317,10 +317,14 @@ private[spark] class Worker( state match { case DriverState.ERROR => logWarning(s"Driver $driverId failed with unrecoverable exception: ${exception.get}") + case DriverState.FAILED => + logWarning(s"Driver $driverId exited with failure") case DriverState.FINISHED => logInfo(s"Driver $driverId exited successfully") case DriverState.KILLED => logInfo(s"Driver $driverId was killed by user") + case _ => + logDebug(s"Driver $driverId changed state to $state") } masterLock.synchronized { master ! DriverStateChanged(driverId, state, exception) From d1375a2bff846f2c4274e14545924646852895f9 Mon Sep 17 00:00:00 2001 From: Takuya UESHIN Date: Tue, 27 May 2014 14:53:57 -0700 Subject: [PATCH 055/221] [SPARK-1926] [SQL] Nullability of Max/Min/First should be true. Nullability of `Max`/`Min`/`First` should be `true` because they return `null` if there are no rows. Author: Takuya UESHIN Closes #881 from ueshin/issues/SPARK-1926 and squashes the following commits: 322610f [Takuya UESHIN] Fix nullability of Min/Max/First. --- .../apache/spark/sql/catalyst/expressions/aggregates.scala | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala index 79937b129aeae..b49a4614eacab 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala @@ -90,7 +90,7 @@ abstract class AggregateFunction case class Min(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] { override def references = child.references - override def nullable = child.nullable + override def nullable = true override def dataType = child.dataType override def toString = s"MIN($child)" @@ -120,7 +120,7 @@ case class MinFunction(expr: Expression, base: AggregateExpression) extends Aggr case class Max(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] { override def references = child.references - override def nullable = child.nullable + override def nullable = true override def dataType = child.dataType override def toString = s"MAX($child)" @@ -257,7 +257,7 @@ case class SumDistinct(child: Expression) case class First(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] { override def references = child.references - override def nullable = child.nullable + override def nullable = true override def dataType = child.dataType override def toString = s"FIRST($child)" From 3b0babad1f0856ee16f9d58e1ead30779a4a6310 Mon Sep 17 00:00:00 2001 From: Takuya UESHIN Date: Tue, 27 May 2014 14:55:23 -0700 Subject: [PATCH 056/221] [SPARK-1915] [SQL] AverageFunction should not count if the evaluated value is null. Average values are difference between the calculation is done partially or not partially. Because `AverageFunction` (in not-partially calculation) counts even if the evaluated value is null. Author: Takuya UESHIN Closes #862 from ueshin/issues/SPARK-1915 and squashes the following commits: b1ff3c0 [Takuya UESHIN] Modify AverageFunction not to count if the evaluated value is null. --- .../spark/sql/catalyst/expressions/aggregates.scala | 9 ++++++--- .../scala/org/apache/spark/sql/DslQuerySuite.scala | 10 ++++++++++ 2 files changed, 16 insertions(+), 3 deletions(-) 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 index b49a4614eacab..c902433688943 100644 --- 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 @@ -281,14 +281,17 @@ case class AverageFunction(expr: Expression, base: AggregateExpression) private val sum = MutableLiteral(zero.eval(EmptyRow)) private val sumAsDouble = Cast(sum, DoubleType) - private val addFunction = Add(sum, Coalesce(Seq(expr, zero))) + private def addFunction(value: Any) = Add(sum, Literal(value)) override def eval(input: Row): Any = sumAsDouble.eval(EmptyRow).asInstanceOf[Double] / count.toDouble override def update(input: Row): Unit = { - count += 1 - sum.update(addFunction, input) + val evaluatedExpr = expr.eval(input) + if (evaluatedExpr != null) { + count += 1 + sum.update(addFunction(evaluatedExpr), input) + } } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala index 8197e8a18d447..fb599e1e01e73 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala @@ -115,6 +115,16 @@ class DslQuerySuite extends QueryTest { 2.0) } + test("null average") { + checkAnswer( + testData3.groupBy()(Average('b)), + 2.0) + + checkAnswer( + testData3.groupBy()(Average('b), CountDistinct('b :: Nil)), + (2.0, 1) :: Nil) + } + test("count") { assert(testData2.count() === testData2.map(_ => 1).count()) } From 068256745052b0aa947dd8c16b1f1d73d8e4631e Mon Sep 17 00:00:00 2001 From: LY Lai Date: Tue, 27 May 2014 16:08:38 -0700 Subject: [PATCH 057/221] [SQL] SPARK-1922 Allow underscore in column name of a struct field https://issues.apache.org/jira/browse/SPARK-1922 . Author: LY Lai Closes #873 from lyuanlai/master and squashes the following commits: 2253263 [LY Lai] Allow underscore in struct field column name --- .../spark/sql/hive/HiveMetastoreCatalog.scala | 2 +- .../sql/hive/HiveMetastoreCatalogSuite.scala | 32 +++++++++++++++++++ 2 files changed, 33 insertions(+), 1 deletion(-) create mode 100644 sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala 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 636c4f7b93190..9f74e0334f727 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 @@ -201,7 +201,7 @@ object HiveMetastoreTypes extends RegexParsers { } protected lazy val structField: Parser[StructField] = - "[a-zA-Z0-9]*".r ~ ":" ~ dataType ^^ { + "[a-zA-Z0-9_]*".r ~ ":" ~ dataType ^^ { case name ~ _ ~ tpe => StructField(name, tpe, nullable = true) } 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 new file mode 100644 index 0000000000000..4a64b5f5eb1b4 --- /dev/null +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.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.hive + +import org.scalatest.FunSuite + +import org.apache.spark.sql.catalyst.types.{DataType, StructType} + +class HiveMetastoreCatalogSuite extends FunSuite { + + test("struct field should accept underscore in sub-column name") { + val metastr = "struct" + + val datatype = HiveMetastoreTypes.toDataType(metastr) + assert(datatype.isInstanceOf[StructType]) + } +} From 9df86835b60ce587c8b9bd4ad7410eebf59a179d Mon Sep 17 00:00:00 2001 From: Takuya UESHIN Date: Tue, 27 May 2014 22:17:50 -0700 Subject: [PATCH 058/221] [SPARK-1938] [SQL] ApproxCountDistinctMergeFunction should return Int value. `ApproxCountDistinctMergeFunction` should return `Int` value because the `dataType` of `ApproxCountDistinct` is `IntegerType`. Author: Takuya UESHIN Closes #893 from ueshin/issues/SPARK-1938 and squashes the following commits: 3970e88 [Takuya UESHIN] Remove a superfluous line. 5ad7ec1 [Takuya UESHIN] Make dataType for each of CountDistinct, ApproxCountDistinctMerge and ApproxCountDistinct LongType. cbe7c71 [Takuya UESHIN] Revert a change. fc3ac0f [Takuya UESHIN] Fix evaluated value type of ApproxCountDistinctMergeFunction to Int. --- .../spark/sql/catalyst/expressions/aggregates.scala | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) 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 index c902433688943..01947273b6ccc 100644 --- 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 @@ -82,7 +82,6 @@ abstract class AggregateFunction override def dataType = base.dataType def update(input: Row): Unit - override def eval(input: Row): Any // Do we really need this? override def newInstance() = makeCopy(productIterator.map { case a: AnyRef => a }.toArray) @@ -166,7 +165,7 @@ case class CountDistinct(expressions: Seq[Expression]) extends AggregateExpressi override def children = expressions override def references = expressions.flatMap(_.references).toSet override def nullable = false - override def dataType = IntegerType + override def dataType = LongType override def toString = s"COUNT(DISTINCT ${expressions.mkString(",")})" override def newInstance() = new CountDistinctFunction(expressions, this) } @@ -184,7 +183,7 @@ case class ApproxCountDistinctMerge(child: Expression, relativeSD: Double) extends AggregateExpression with trees.UnaryNode[Expression] { override def references = child.references override def nullable = false - override def dataType = IntegerType + override def dataType = LongType override def toString = s"APPROXIMATE COUNT(DISTINCT $child)" override def newInstance() = new ApproxCountDistinctMergeFunction(child, this, relativeSD) } @@ -193,7 +192,7 @@ case class ApproxCountDistinct(child: Expression, relativeSD: Double = 0.05) extends PartialAggregate with trees.UnaryNode[Expression] { override def references = child.references override def nullable = false - override def dataType = IntegerType + override def dataType = LongType override def toString = s"APPROXIMATE COUNT(DISTINCT $child)" override def asPartial: SplitEvaluation = { @@ -394,7 +393,7 @@ case class CountDistinctFunction(expr: Seq[Expression], base: AggregateExpressio } } - override def eval(input: Row): Any = seen.size + override def eval(input: Row): Any = seen.size.toLong } case class FirstFunction(expr: Expression, base: AggregateExpression) extends AggregateFunction { From 82eadc3b07d3f00eebd30811f981016e68cf60bf Mon Sep 17 00:00:00 2001 From: jmu Date: Tue, 27 May 2014 22:41:47 -0700 Subject: [PATCH 059/221] Fix doc about NetworkWordCount/JavaNetworkWordCount usage of spark streaming Usage: NetworkWordCount --> Usage: NetworkWordCount Usage: JavaNetworkWordCount --> Usage: JavaNetworkWordCount Author: jmu Closes #826 from jmu/master and squashes the following commits: 9fb7980 [jmu] Merge branch 'master' of https://github.com/jmu/spark b9a6b02 [jmu] Fix doc for NetworkWordCount/JavaNetworkWordCount Usage: NetworkWordCount --> Usage: NetworkWordCount --- docs/streaming-programming-guide.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md index 0c125eb693a8e..972b660262d14 100644 --- a/docs/streaming-programming-guide.md +++ b/docs/streaming-programming-guide.md @@ -234,12 +234,12 @@ Then, in a different terminal, you can start the example by using
{% highlight bash %} -$ ./bin/run-example org.apache.spark.examples.streaming.NetworkWordCount local[2] localhost 9999 +$ ./bin/run-example org.apache.spark.examples.streaming.NetworkWordCount localhost 9999 {% endhighlight %}
{% highlight bash %} -$ ./bin/run-example org.apache.spark.examples.streaming.JavaNetworkWordCount local[2] localhost 9999 +$ ./bin/run-example org.apache.spark.examples.streaming.JavaNetworkWordCount localhost 9999 {% endhighlight %}
@@ -268,7 +268,7 @@ hello world {% highlight bash %} # TERMINAL 2: RUNNING NetworkWordCount or JavaNetworkWordCount -$ ./bin/run-example org.apache.spark.examples.streaming.NetworkWordCount local[2] localhost 9999 +$ ./bin/run-example org.apache.spark.examples.streaming.NetworkWordCount localhost 9999 ... ------------------------------------------- Time: 1357008430000 ms From 7801d44fd3bcf4d82e6db12574cc42bef15bf0e1 Mon Sep 17 00:00:00 2001 From: Patrick Wendell Date: Wed, 28 May 2014 15:49:54 -0700 Subject: [PATCH 060/221] Organize configuration docs This PR improves and organizes the config option page and makes a few other changes to config docs. See a preview here: http://people.apache.org/~pwendell/config-improvements/configuration.html The biggest changes are: 1. The configs for the standalone master/workers were moved to the standalone page and out of the general config doc. 2. SPARK_LOCAL_DIRS was missing from the standalone docs. 3. Expanded discussion of injecting configs with spark-submit, including an example. 4. Config options were organized into the following categories: - Runtime Environment - Shuffle Behavior - Spark UI - Compression and Serialization - Execution Behavior - Networking - Scheduling - Security - Spark Streaming Author: Patrick Wendell Closes #880 from pwendell/config-cleanup and squashes the following commits: 93f56c3 [Patrick Wendell] Feedback from Matei 6f66efc [Patrick Wendell] More feedback 16ae776 [Patrick Wendell] Adding back header section d9c264f [Patrick Wendell] Small fix e0c1728 [Patrick Wendell] Response to Matei's review 27d57db [Patrick Wendell] Reverting changes to index.html (covered in #896) e230ef9 [Patrick Wendell] Merge remote-tracking branch 'apache/master' into config-cleanup a374369 [Patrick Wendell] Line wrapping fixes fdff7fc [Patrick Wendell] Merge remote-tracking branch 'apache/master' into config-cleanup 3289ea4 [Patrick Wendell] Pulling in changes from #856 106ee31 [Patrick Wendell] Small link fix f7e79bc [Patrick Wendell] Re-organizing config options. 54b184d [Patrick Wendell] Adding standalone configs to the standalone page 592e94a [Patrick Wendell] Stash 29b5446 [Patrick Wendell] Better discussion of spark-submit in configuration docs 2d719ef [Patrick Wendell] Small fix 4af9e07 [Patrick Wendell] Adding SPARK_LOCAL_DIRS docs 204b248 [Patrick Wendell] Small fixes --- docs/configuration.md | 815 ++++++++++++++++++--------------------- docs/quick-start.md | 8 +- docs/spark-standalone.md | 167 +++++++- 3 files changed, 554 insertions(+), 436 deletions(-) diff --git a/docs/configuration.md b/docs/configuration.md index e5d955f23fe32..b6e7fd34eae68 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -2,19 +2,25 @@ layout: global title: Spark Configuration --- - * This will become a table of contents (this text will be scraped). {:toc} -Spark provides several locations to configure the system: +Spark provides three locations to configure the system: + +* [Spark properties](#spark-properties) control most application parameters and can be set by passing + a [SparkConf](api/core/index.html#org.apache.spark.SparkConf) object to SparkContext, or through Java + system properties. +* [Environment variables](#environment-variables) can be used to set per-machine settings, such as + the IP address, through the `conf/spark-env.sh` script on each node. +* [Logging](#configuring-logging) can be configured through `log4j.properties`. # Spark Properties Spark properties control most application settings and are configured separately for each -application. The preferred way is to set them through -[SparkConf](api/scala/index.html#org.apache.spark.SparkConf) and passing it as an argument to your -SparkContext. SparkConf allows you to configure most of the common properties to initialize a -cluster (e.g. master URL and application name), as well as arbitrary key-value pairs through the +application. These properties can be set directly on a +[SparkConf](api/scala/index.html#org.apache.spark.SparkConf) and passed as an argument to your +SparkContext. SparkConf allows you to configure some of the common properties +(e.g. master URL and application name), as well as arbitrary key-value pairs through the `set()` method. For example, we could initialize an application as follows: {% highlight scala %} @@ -25,22 +31,37 @@ val conf = new SparkConf() val sc = new SparkContext(conf) {% endhighlight %} -## Loading Default Configurations +## 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 +amounts of memory. Spark allows you to simply create an empty conf: -In the case of `spark-shell`, a SparkContext has already been created for you, so you cannot control -the configuration properties through SparkConf. However, you can still set configuration properties -through a default configuration file. By default, `spark-shell` (and more generally `spark-submit`) -will read configuration options from `conf/spark-defaults.conf`, in which each line consists of a -key and a value separated by whitespace. For example, +{% highlight scala %} +val sc = new SparkContext(new SparkConf()) +{% endhighlight %} + +Then, you can supply configuration values at runtime: +{% highlight bash %} +./bin/spark-submit --name "My fancy app" --master local[4] myApp.jar +{% endhighlight %} + +The Spark shell and [`spark-submit`](cluster-overview.html#launching-applications-with-spark-submit) +tool support two ways to load configurations dynamically. The first are command line options, +such as `--master`, as shown above. Running `./bin/spark-submit --help` will show the entire list +of options. + +`bin/spark-submit` will also read configuration options from `conf/spark-defaults.conf`, in which +each line consists of a key and a value separated by whitespace. For example: spark.master spark://5.6.7.8:7077 spark.executor.memory 512m spark.eventLog.enabled true spark.serializer org.apache.spark.serializer.KryoSerializer -Any values specified in the file will be passed on to the application, and merged with those -specified through SparkConf. If the same configuration property exists in both `spark-defaults.conf` -and SparkConf, then the latter will take precedence as it is the most application-specific. +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. ## Viewing Spark Properties @@ -49,19 +70,34 @@ This is a useful place to check to make sure that your properties have been set that only values explicitly specified through either `spark-defaults.conf` or SparkConf will appear. For all other configuration properties, you can assume the default value is used. -## All Configuration Properties +## Available Properties -Most of the properties that control internal settings have reasonable default values. However, -there are at least five properties that you will commonly want to control: +Most of the properties that control internal settings have reasonable default values. Some +of the most common options to set are: + + + + + + + + + + @@ -69,10 +105,12 @@ there are at least five properties that you will commonly want to control: @@ -81,7 +119,8 @@ there are at least five properties that you will commonly want to control: @@ -94,138 +133,151 @@ there are at least five properties that you will commonly want to control: comma-separated list of multiple directories on different disks. NOTE: In Spark 1.0 and later this will be overriden by SPARK_LOCAL_DIRS (Standalone, Mesos) or - LOCAL_DIRS (YARN) envrionment variables set by the cluster manager. + LOCAL_DIRS (YARN) environment variables set by the cluster manager. - - + +
Property NameDefaultMeaning
spark.app.name(none) + 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.executor.memory 512m - Amount of memory to use per executor process, in the same format as JVM memory strings (e.g. - 512m, 2g). + Amount of memory to use per executor process, in the same format as JVM memory strings + (e.g. 512m, 2g).
org.apache.spark.serializer.
JavaSerializer		 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. + 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.
If you use Kryo serialization, set this class to register your custom classes with Kryo. It should be set to a class that extends - KryoRegistrator. + + KryoRegistrator. See the tuning guide for more details.
spark.cores.max(not set)spark.logConffalse - When running on a standalone deploy cluster or a - Mesos cluster in "coarse-grained" - sharing mode, the maximum amount of CPU cores to request for the application from - across the cluster (not from each machine). If not set, the default will be - spark.deploy.defaultCores on Spark's standalone cluster manager, or - infinite (all available cores) on Mesos. + Logs the effective SparkConf as INFO when a SparkContext is started.
- Apart from these, the following properties are also available, and may be useful in some situations: +#### Runtime Environment - + + + + + + - - + + - - + + - - + + +
Property NameDefaultMeaning
spark.default.parallelismspark.executor.memory512m -
    -
  • 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
    • -
+ Amount of memory to use per executor process, in the same format as JVM memory strings + (e.g. 512m, 2g).
spark.executor.extraJavaOptions(none) - Default number of tasks to use across the cluster for distributed shuffle operations - (groupByKey, reduceByKey, etc) when not set by user. + 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.storage.memoryFraction0.6spark.executor.extraClassPath(none) - 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. + 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.shuffle.memoryFraction0.3spark.executor.extraLibraryPath(none) - 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. + Set a special library path to use when launching executor JVM's.
spark.storage.memoryMapThreshold8192spark.files.userClassPathFirstfalse - 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. + (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.
+ +#### Shuffle Behavior + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + + +
Property NameDefaultMeaning
spark.tachyonStore.baseDirSystem.getProperty("java.io.tmpdir")spark.shuffle.consolidateFilesfalse - 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. + If set to "true", consolidates intermediate files created during a shuffle. Creating fewer + files can improve filesystem performance for shuffles with large numbers of reduce tasks. It + is recommended to set this to "true" when using ext4 or xfs filesystems. On ext3, this option + might degrade performance on machines with many (>8) cores due to filesystem limitations.
spark.tachyonStore.urltachyon://localhost:19998spark.shuffle.spilltrue - The URL of the underlying Tachyon file system in the TachyonStore. + 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.mesos.coarsefalsespark.shuffle.spill.compresstrue - 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. + Whether to compress data spilled during shuffles. Compression will use + spark.io.compression.codec.
spark.ui.port4040spark.shuffle.memoryFraction0.3 - Port for your application's dashboard, which shows memory and workload data + 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.ui.retainedStages1000spark.shuffle.compresstrue - How many stages the Spark UI remembers before garbage collecting. + Whether to compress map output files. Generally a good idea. Compression will use + spark.io.compression.codec.
spark.ui.filtersNonespark.shuffle.file.buffer.kb100 - Comma separated list of filter class names to apply to the Spark web ui. The filter should be a - standard javax servlet Filter. Parameters to each filter can also be specified by setting a - java system property of spark.<class name of filter>.params='param1=value1,param2=value2' - (e.g. -Dspark.ui.filters=com.test.filter1 -Dspark.com.test.filter1.params='param1=foo,param2=testing') + 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.ui.acls.enablefalsespark.reducer.maxMbInFlight48 - Whether spark web ui acls should are enabled. If enabled, this checks to see if the user has - access permissions to view the web ui. See spark.ui.view.acls for more details. - Also note this requires the user to be known, if the user comes across as null no checks - are done. Filters can be used to authenticate and set the user. + 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 UI + + + + + + - - + + @@ -236,19 +288,35 @@ Apart from these, the following properties are also available, and may be useful - - + + - - + + + + + + + +
Property NameDefaultMeaning
spark.ui.port4040 + Port for your application's dashboard, which shows memory and workload data
spark.ui.view.aclsEmptyspark.ui.retainedStages1000 - Comma separated list of users that have view access to the spark web ui. By default only the - user that started the Spark job has view access. + How many stages the Spark UI remembers before garbage collecting.
spark.shuffle.compresstruespark.eventLog.enabledfalse - Whether to compress map output files. Generally a good idea. + Whether to log Spark events, useful for reconstructing the Web UI after the application has + finished.
spark.shuffle.spill.compresstruespark.eventLog.compressfalse - Whether to compress data spilled during shuffles. + Whether to compress logged events, if spark.eventLog.enabled is true.
spark.eventLog.dirfile:///tmp/spark-events + 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 + and HDFS directory so that history files can be read by the history server. +
+ +#### Compression and Serialization + + @@ -260,59 +328,46 @@ Apart from these, the following properties are also available, and may be useful - - - - - - - - - - - - + + - - + + @@ -329,21 +384,29 @@ Apart from these, the following properties are also available, and may be useful +
Property NameDefaultMeaning
spark.broadcast.compress truespark.rdd.compress false - 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. + 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.io.compression.codec org.apache.spark.io.
LZFCompressionCodec		 - The codec used to compress internal data such as RDD partitions and shuffle outputs. By default, - Spark provides two codecs: org.apache.spark.io.LZFCompressionCodec and - org.apache.spark.io.SnappyCompressionCodec. + The codec used to compress internal data such as RDD partitions and shuffle outputs. + By default, Spark provides two codecs: org.apache.spark.io.LZFCompressionCodec + and org.apache.spark.io.SnappyCompressionCodec. Of these two choices, + Snappy offers faster compression and decompression, while LZF offers a better compression + ratio.
spark.io.compression.snappy.block.size 32768 - Block size (in bytes) used in Snappy compression, in the case when Snappy compression codec is - used. -
spark.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.interval1000 - The interval length for the scheduler to revive the worker resource offers to run tasks. (in - milliseconds) + Block size (in bytes) used in Snappy compression, in the case when Snappy compression codec + is used.
spark.reducer.maxMbInFlight48spark.closure.serializerorg.apache.spark.serializer.
JavaSerializer		 - 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. + Serializer class to use for closures. Currently only the Java serializer is supported.
spark.closure.serializerorg.apache.spark.serializer.
JavaSerializer		spark.serializer.objectStreamReset10000 - Serializer class to use for closures. Currently only the Java serializer is supported. + 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 a value <= 0. + By default it will reset the serializer every 10,000 objects.
spark.kryoserializer.buffer.mb 2 - Maximum object size to allow within Kryo (the library needs to create a buffer at least as large - as the largest single object you'll serialize). Increase this if you get a "buffer limit + Maximum object size to allow within Kryo (the library needs to create a buffer at least as + large as the largest single object you'll serialize). Increase this if you get a "buffer limit exceeded" exception inside Kryo. Note that there will be one buffer per core on each worker.
+ +#### Execution Behavior + + - - + + @@ -354,73 +417,92 @@ Apart from these, the following properties are also available, and may be useful - - + + - - + + - - + + + + + + + + + + + + - - + + - - + + - - + + +
Property NameDefaultMeaning
spark.serializer.objectStreamReset10000spark.default.parallelism - 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 a value <= 0. - By default it will reset the serializer every 10,000 objects. +
    +
  • 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
    • +
+ 		+ Default number of tasks to use across the cluster for distributed shuffle operations + (groupByKey, reduceByKey, etc) when not set by user.
spark.locality.wait3000spark.broadcast.blockSize4096 - Number of milliseconds 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. - You should increase this setting if your tasks are long and see poor locality, but the - default usually works well. + Size of each piece of a block in kilobytes for TorrentBroadcastFactory. + Too large a value decreases parallelism during broadcast (makes it slower); however, if it is + too small, BlockManager might take a performance hit.
spark.locality.wait.processspark.locality.waitspark.files.overwritefalse - Customize the locality wait for process locality. This affects tasks that attempt to access - cached data in a particular executor process. + Whether to overwrite files added through SparkContext.addFile() when the target file exists and + its contents do not match those of the source.
spark.locality.wait.nodespark.locality.waitspark.files.fetchTimeoutfalse + Communication timeout to use when fetching files added through SparkContext.addFile() from + the driver. +
spark.storage.memoryFraction0.6 + 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.tachyonStore.baseDirSystem.getProperty("java.io.tmpdir") - 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). + 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.locality.wait.rackspark.locality.waitspark.storage.memoryMapThreshold8192 - Customize the locality wait for rack locality. + 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.worker.timeout60spark.tachyonStore.urltachyon://localhost:19998 - Number of seconds after which the standalone deploy master considers a worker lost if it - receives no heartbeats. + The URL of the underlying Tachyon file system in the TachyonStore.
spark.worker.cleanup.enabledfalsespark.cleaner.ttl(infinite) - 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. + 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.
+ +#### Networking + + - - + + - - + + @@ -454,8 +536,8 @@ Apart from these, the following properties are also available, and may be useful 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. + combination of `spark.akka.heartbeat.interval` and `spark.akka.failure-detector.threshold` + if you need to. @@ -476,55 +558,23 @@ Apart from these, the following properties are also available, and may be useful 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. - - - - - - - - - - - - - - - - - - - - - +
Property NameDefaultMeaning
spark.worker.cleanup.interval1800 (30 minutes)spark.driver.host(local hostname) - Controls the interval, in seconds, at which the worker cleans up old application work dirs - on the local machine. + Hostname or IP address for the driver to listen on.
spark.worker.cleanup.appDataTtl7 * 24 * 3600 (7 days)spark.driver.port(random) - The number of seconds to retain application work directories on each worker. This is a Time To - Live and should depend on the amount of available disk space you have. Application logs and - jars are downloaded to each application work dir. Over time, the work dirs can quickly fill up - disk space, especially if you run jobs very frequently. + Port for the driver to listen on.
spark.driver.host(local hostname) - Hostname or IP address for the driver to listen on. -
spark.driver.port(random) - Port for the driver to listen on. -
spark.cleaner.ttl(infinite) - 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.streaming.blockInterval200 - Interval (milliseconds) at which data received by Spark Streaming receivers is coalesced - into blocks of data before storing them in Spark. + `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.
+ +#### Scheduling + + - - + + @@ -536,39 +586,36 @@ Apart from these, the following properties are also available, and may be useful - - - - - - - - + + - - + + - - + + @@ -601,91 +648,59 @@ Apart from these, the following properties are also available, and may be useful - - - - - - - - - - - - - - - - - - - - - - + + - - + + - - + + - - + + - - + + +
Property NameDefaultMeaning
spark.streaming.unpersisttruespark.task.cpus1 - Force RDDs generated and persisted by Spark Streaming to be automatically unpersisted from - Spark's memory. The raw input data received by Spark Streaming is also automatically cleared. - Setting this to false will allow the raw data and persisted RDDs to be accessible outside the - streaming application as they will not be cleared automatically. But it comes at the cost of - higher memory usage in Spark. + Number of cores to allocate for each task.
spark.broadcast.blockSize4096 - Size of each piece of a block in kilobytes for TorrentBroadcastFactory. - Too large a value decreases parallelism during broadcast (makes it slower); however, if it is - too small, BlockManager might take a performance hit. -
spark.shuffle.consolidateFilesfalsespark.scheduler.modeFIFO - If set to "true", consolidates intermediate files created during a shuffle. Creating fewer files - can improve filesystem performance for shuffles with large numbers of reduce tasks. It is - recommended to set this to "true" when using ext4 or xfs filesystems. On ext3, this option might - degrade performance on machines with many (>8) cores due to filesystem limitations. + 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.shuffle.file.buffer.kb100spark.cores.max(not set) - 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. + When running on a standalone deploy cluster or a + Mesos cluster in "coarse-grained" + sharing mode, the maximum amount of CPU cores to request for the application from + across the cluster (not from each machine). If not set, the default will be + spark.deploy.defaultCores on Spark's standalone cluster manager, or + infinite (all available cores) on Mesos.
spark.shuffle.spilltruespark.mesos.coarsefalse - 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. + 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.logConffalse - Whether to log the supplied SparkConf as INFO at start of spark context. -
spark.eventLog.enabledfalse - Whether to log spark events, useful for reconstructing the Web UI after the application has - finished. -
spark.eventLog.compressfalse - Whether to compress logged events, if spark.eventLog.enabled is true. -
spark.eventLog.dirfile:///tmp/spark-events - 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. -
spark.deploy.spreadOuttruespark.locality.wait3000 - Whether the standalone cluster manager should spread applications out across nodes or try to - consolidate them onto as few nodes as possible. Spreading out is usually better for data - locality in HDFS, but consolidating is more efficient for compute-intensive workloads.
- Note: this setting needs to be configured in the standalone cluster master, not in - individual applications; you can set it through SPARK_MASTER_OPTS in - spark-env.sh. + Number of milliseconds 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. + You should increase this setting if your tasks are long and see poor locality, but the + default usually works well.
spark.deploy.defaultCores(infinite)spark.locality.wait.processspark.locality.wait - Default number of cores to give to applications in Spark's standalone mode if they don't set - spark.cores.max. If not set, applications always get all available cores unless - they configure spark.cores.max themselves. Set this lower on a shared cluster to - prevent users from grabbing the whole cluster by default.
Note: this setting needs - to be configured in the standalone cluster master, not in individual applications; you can set - it through SPARK_MASTER_OPTS in spark-env.sh. + Customize the locality wait for process locality. This affects tasks that attempt to access + cached data in a particular executor process.
spark.files.overwritefalsespark.locality.wait.nodespark.locality.wait - Whether to overwrite files added through SparkContext.addFile() when the target file exists and - its contents do not match those of the source. + 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.files.fetchTimeoutfalsespark.locality.wait.rackspark.locality.wait - Communication timeout to use when fetching files added through SparkContext.addFile() from - the driver. + Customize the locality wait for rack locality.
spark.files.userClassPathFirstfalsespark.scheduler.revive.interval1000 - (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. + The interval length for the scheduler to revive the worker resource offers to run tasks. + (in milliseconds)
+ +#### Security + + @@ -693,7 +708,7 @@ Apart from these, the following properties are also available, and may be useful @@ -705,42 +720,71 @@ Apart from these, the following properties are also available, and may be useful - - + + - - + + - - + + +
Property NameDefaultMeaning
spark.authenticate false - Whether spark authenticates its internal connections. See spark.authenticate.secret - if not running on Yarn. + Whether Spark authenticates its internal connections. See + spark.authenticate.secret if not running on YARN.
None Set the secret key used for Spark to authenticate between components. This needs to be set if - not running on Yarn and authentication is enabled. + not running on YARN and authentication is enabled.
spark.task.cpus1spark.ui.filtersNone - Number of cores to allocate for each task. + Comma separated list of filter class names to apply to the Spark web UI. The filter should be a + standard + javax servlet Filter. Parameters to each filter can also be specified by setting a + java system property of:
+ spark.<class name of filter>.params='param1=value1,param2=value2'
+ For example:
+ -Dspark.ui.filters=com.test.filter1
+ -Dspark.com.test.filter1.params='param1=foo,param2=testing'
spark.executor.extraJavaOptions(none)spark.ui.acls.enablefalse - 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. + Whether Spark web ui acls should are enabled. If enabled, this checks to see if the user has + access permissions to view the web ui. See spark.ui.view.acls for more details. + Also note this requires the user to be known, if the user comes across as null no checks + are done. Filters can be used to authenticate and set the user.
spark.executor.extraClassPath(none)spark.ui.view.aclsEmpty - 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. + Comma separated list of users that have view access to the Spark web ui. By default only the + user that started the Spark job has view access.
+ +#### Spark Streaming + + - - + + + + + + + -
Property NameDefaultMeaning
spark.executor.extraLibraryPath(none)spark.streaming.blockInterval200 - Set a special library path to use when launching executor JVM's. + Interval (milliseconds) at which data received by Spark Streaming receivers is coalesced + into blocks of data before storing them in Spark. +
spark.streaming.unpersisttrue + Force RDDs generated and persisted by Spark Streaming to be automatically unpersisted from + Spark's memory. The raw input data received by Spark Streaming is also automatically cleared. + Setting this to false will allow the raw data and persisted RDDs to be accessible outside the + streaming application as they will not be cleared automatically. But it comes at the cost of + higher memory usage in Spark.
+#### Cluster Managers (YARN, Mesos, Standalone) +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) + # Environment Variables Certain Spark settings can be configured through environment variables, which are read from the @@ -774,104 +818,15 @@ The following variables can be set in `spark-env.sh`: -In addition to the above, there are also options for setting up the Spark [standalone cluster -scripts](spark-standalone.html#cluster-launch-scripts), such as number of cores to use on each -machine and maximum memory. +In addition to the above, there are also options for setting up the Spark +[standalone cluster scripts](spark-standalone.html#cluster-launch-scripts), such as number of cores +to use on each machine and maximum memory. -Since `spark-env.sh` is a shell script, some of these can be set programmatically -- for example, -you might compute `SPARK_LOCAL_IP` by looking up the IP of a specific network interface. +Since `spark-env.sh` is a shell script, some of these can be set programmatically -- for example, you might +compute `SPARK_LOCAL_IP` by looking up the IP of a specific network interface. # Configuring Logging Spark uses [log4j](http://logging.apache.org/log4j/) for logging. You can configure it by adding a `log4j.properties` file in the `conf` directory. One way to start is to copy the existing `log4j.properties.template` located there. - -# Configuring Ports for Network Security - -Spark makes heavy use of the network, and some environments have strict requirements for using tight -firewall settings. Below are the primary ports that Spark uses for its communication and how to -configure those ports. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
FromToDefault PortPurposeConfiguration - SettingNotes
BrowserStandalone Cluster Master8080Web UImaster.ui.portJetty-based
BrowserWorker8081Web UIworker.ui.portJetty-based
BrowserDriver4040Web UIspark.ui.portJetty-based
BrowserHistory Server18080Web UIspark.history.ui.portJetty-based
ApplicationStandalone Cluster Master7077Submit job to clusterspark.driver.portAkka-based. Set to "0" to choose a port randomly
WorkerStandalone Cluster Master7077Join clusterspark.driver.portAkka-based. Set to "0" to choose a port randomly
ApplicationWorker(random)Join clusterSPARK_WORKER_PORT (standalone cluster)Akka-based
Driver and other WorkersWorker(random) -
    -
  • File server for file and jars
    • -
  • Http Broadcast
    • -
  • Class file server (Spark Shell only)
    • -
- 		NoneJetty-based. Each of these services starts on a random port that cannot be configured
diff --git a/docs/quick-start.md b/docs/quick-start.md index 33a0df1036424..20e17ebf703fc 100644 --- a/docs/quick-start.md +++ b/docs/quick-start.md @@ -252,11 +252,11 @@ we initialize a SparkContext as part of the program. We pass the SparkContext constructor a [SparkConf](api/scala/index.html#org.apache.spark.SparkConf) object which contains information about our -application. We also call sc.addJar to make sure that when our application is launched in cluster -mode, the jar file containing it will be shipped automatically to worker nodes. +application. -This file depends on the Spark API, so we'll also include an sbt configuration file, `simple.sbt` -which explains that Spark is a dependency. This file also adds a repository that Spark depends on: +Our application depends on the Spark API, so we'll also include an sbt configuration file, +`simple.sbt` which explains that Spark is a dependency. This file also adds a repository that +Spark depends on: {% highlight scala %} name := "Simple Project" diff --git a/docs/spark-standalone.md b/docs/spark-standalone.md index eb3211b6b0e4e..dca80a9a69614 100644 --- a/docs/spark-standalone.md +++ b/docs/spark-standalone.md @@ -93,7 +93,15 @@ You can optionally configure the cluster further by setting environment variable SPARK_MASTER_OPTS - Configuration properties that apply only to the master in the form "-Dx=y" (default: none). + Configuration properties that apply only to the master in the form "-Dx=y" (default: none). See below for a list of possible options. + + + SPARK_LOCAL_DIRS + + Directory to use for "scratch" space in Spark, including map output files and RDDs that get + stored on disk. This should be on a fast, local disk in your system. It can also be a + comma-separated list of multiple directories on different disks. + SPARK_WORKER_CORES @@ -126,7 +134,7 @@ You can optionally configure the cluster further by setting environment variable SPARK_WORKER_OPTS - Configuration properties that apply only to the worker in the form "-Dx=y" (default: none). + Configuration properties that apply only to the worker in the form "-Dx=y" (default: none). See below for a list of possible options. SPARK_DAEMON_MEMORY @@ -144,6 +152,73 @@ You can optionally configure the cluster further by setting environment variable **Note:** The launch scripts do not currently support Windows. To run a Spark cluster on Windows, start the master and workers by hand. +SPARK_MASTER_OPTS supports the following system properties: + + + + + + + + + + + + + + + + + + +
Property NameDefaultMeaning
spark.deploy.spreadOuttrue + Whether the standalone cluster manager should spread applications out across nodes or try + to consolidate them onto as few nodes as possible. Spreading out is usually better for + data locality in HDFS, but consolidating is more efficient for compute-intensive workloads.
+
spark.deploy.defaultCores(infinite) + Default number of cores to give to applications in Spark's standalone mode if they don't + set spark.cores.max. If not set, applications always get all available + cores unless they configure spark.cores.max themselves. + Set this lower on a shared cluster to prevent users from grabbing + the whole cluster by default.
+
spark.worker.timeout60 + Number of seconds after which the standalone deploy master considers a worker lost if it + receives no heartbeats. +
+ +SPARK_WORKER_OPTS supports the following system properties: + + + + + + + + + + + + + + + + + + +
Property NameDefaultMeaning
spark.worker.cleanup.enabledfalse + 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. +
spark.worker.cleanup.interval1800 (30 minutes) + Controls the interval, in seconds, at which the worker cleans up old application work dirs + on the local machine. +
spark.worker.cleanup.appDataTtl7 * 24 * 3600 (7 days) + The number of seconds to retain application work directories on each worker. This is a Time To Live + and should depend on the amount of available disk space you have. Application logs and jars are + downloaded to each application work dir. Over time, the work dirs can quickly fill up disk space, + especially if you run jobs very frequently. +
+ # Connecting an Application to the Cluster To run an application on the Spark cluster, simply pass the `spark://IP:PORT` URL of the master as to the [`SparkContext` @@ -212,6 +287,94 @@ In addition, detailed log output for each job is also written to the work direct You can run Spark alongside your existing Hadoop cluster by just launching it as a separate service on the same machines. To access Hadoop data from Spark, just use a hdfs:// URL (typically `hdfs://:9000/path`, but you can find the right URL on your Hadoop Namenode's web UI). Alternatively, you can set up a separate cluster for Spark, and still have it access HDFS over the network; this will be slower than disk-local access, but may not be a concern if you are still running in the same local area network (e.g. you place a few Spark machines on each rack that you have Hadoop on). +# Configuring Ports for Network Security + +Spark makes heavy use of the network, and some environments have strict requirements for using tight +firewall settings. Below are the primary ports that Spark uses for its communication and how to +configure those ports. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
FromToDefault PortPurposeConfiguration + SettingNotes
BrowserStandalone Cluster Master8080Web UImaster.ui.portJetty-based
BrowserDriver4040Web UIspark.ui.portJetty-based
BrowserHistory Server18080Web UIspark.history.ui.portJetty-based
BrowserWorker8081Web UIworker.ui.portJetty-based
ApplicationStandalone Cluster Master7077Submit job to clusterspark.driver.portAkka-based. Set to "0" to choose a port randomly
WorkerStandalone Cluster Master7077Join clusterspark.driver.portAkka-based. Set to "0" to choose a port randomly
ApplicationWorker(random)Join clusterSPARK_WORKER_PORT (standalone cluster)Akka-based
Driver and other WorkersWorker(random) +
    +
  • File server for file and jars
    • +
  • Http Broadcast
    • +
  • Class file server (Spark Shell only)
    • +
+ 		NoneJetty-based. Each of these services starts on a random port that cannot be configured
+ # High Availability By default, standalone scheduling clusters are resilient to Worker failures (insofar as Spark itself is resilient to losing work by moving it to other workers). However, the scheduler uses a Master to make scheduling decisions, and this (by default) creates a single point of failure: if the Master crashes, no new applications can be created. In order to circumvent this, we have two high availability schemes, detailed below. From 4312cf0bade82d4b54eef80e637953e1633edc3e Mon Sep 17 00:00:00 2001 From: David Lemieux Date: Wed, 28 May 2014 15:50:35 -0700 Subject: [PATCH 061/221] Spark 1916 The changes could be ported back to 0.9 as well. Changing in.read to in.readFully to read the whole input stream rather than the first 1020 bytes. This should ok considering that Flume caps the body size to 32K by default. Author: David Lemieux Closes #865 from lemieud/SPARK-1916 and squashes the following commits: a265673 [David Lemieux] Updated SparkFlumeEvent to read the whole stream rather than the first X bytes. (cherry picked from commit 0b769b73fb7ae314325857138a2d3138ed157908) Signed-off-by: Patrick Wendell --- .../org/apache/spark/streaming/flume/FlumeInputDStream.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) 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 df7605fe579f8..5be33f1d5c428 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 @@ -63,7 +63,7 @@ class SparkFlumeEvent() extends Externalizable { def readExternal(in: ObjectInput) { val bodyLength = in.readInt() val bodyBuff = new Array[Byte](bodyLength) - in.read(bodyBuff) + in.readFully(bodyBuff) val numHeaders = in.readInt() val headers = new java.util.HashMap[CharSequence, CharSequence] From 4dbb27b0cf4eb67c92aad2c1158616312f5a54e6 Mon Sep 17 00:00:00 2001 From: witgo Date: Wed, 28 May 2014 15:57:05 -0700 Subject: [PATCH 062/221] [SPARK-1712]: TaskDescription instance is too big causes Spark to hang Author: witgo Closes #694 from witgo/SPARK-1712_new and squashes the following commits: 0f52483 [witgo] review commit 83ce29b [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1712_new 52e6752 [witgo] reset test SparkContext 63636b6 [witgo] review commit 44a59ee [witgo] review commit 3b6d48c [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1712_new 926bd6a [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1712_new 9a5cfad [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1712_new 03cc562 [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1712_new b0930b0 [witgo] review commit b1174bd [witgo] merge master f76679b [witgo] merge master 689495d [witgo] fix scala style bug 1d35c3c [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1712_new 062c182 [witgo] fix small bug for code style 0a428cf [witgo] add unit tests 158b2dc [witgo] review commit 4afe71d [witgo] review commit 9e4ffa7 [witgo] review commit 1d35c7d [witgo] fix hang 7965580 [witgo] fix Statement order 0e29eac [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1712_new 3ea1ca1 [witgo] remove duplicate serialize 743a7ad [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1712_new 86e2048 [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1712_new 2a89adc [witgo] SPARK-1712: TaskDescription instance is too big causes Spark to hang --- .../CoarseGrainedExecutorBackend.scala | 9 ++-- .../cluster/CoarseGrainedClusterMessage.scala | 2 +- .../CoarseGrainedSchedulerBackend.scala | 27 ++++++++++-- .../CoarseGrainedSchedulerBackendSuite.scala | 43 +++++++++++++++++++ 4 files changed, 73 insertions(+), 8 deletions(-) create mode 100644 core/src/test/scala/org/apache/spark/scheduler/CoarseGrainedSchedulerBackendSuite.scala 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 84aec65b7765d..2279d77c91c89 100644 --- a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala +++ b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala @@ -22,11 +22,12 @@ import java.nio.ByteBuffer import akka.actor._ import akka.remote._ -import org.apache.spark.{Logging, SecurityManager, SparkConf} +import org.apache.spark.{SparkEnv, Logging, SecurityManager, SparkConf} import org.apache.spark.TaskState.TaskState import org.apache.spark.deploy.SparkHadoopUtil import org.apache.spark.deploy.worker.WorkerWatcher import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._ +import org.apache.spark.scheduler.TaskDescription import org.apache.spark.util.{AkkaUtils, Utils} private[spark] class CoarseGrainedExecutorBackend( @@ -61,12 +62,14 @@ private[spark] class CoarseGrainedExecutorBackend( logError("Slave registration failed: " + message) System.exit(1) - case LaunchTask(taskDesc) => - logInfo("Got assigned task " + taskDesc.taskId) + case LaunchTask(data) => if (executor == null) { logError("Received LaunchTask command but executor was null") System.exit(1) } else { + val ser = SparkEnv.get.closureSerializer.newInstance() + val taskDesc = ser.deserialize[TaskDescription](data.value) + logInfo("Got assigned task " + taskDesc.taskId) executor.launchTask(this, taskDesc.taskId, taskDesc.serializedTask) } 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 ddbc74e82ac49..ca74069ef885c 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 @@ -28,7 +28,7 @@ private[spark] sealed trait CoarseGrainedClusterMessage extends Serializable private[spark] object CoarseGrainedClusterMessages { // Driver to executors - case class LaunchTask(task: TaskDescription) extends CoarseGrainedClusterMessage + case class LaunchTask(data: SerializableBuffer) extends CoarseGrainedClusterMessage case class KillTask(taskId: Long, executor: String, interruptThread: Boolean) extends CoarseGrainedClusterMessage 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 a6d6b3d26a3c6..e47a060683a2d 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 @@ -27,10 +27,10 @@ import akka.actor._ import akka.pattern.ask import akka.remote.{DisassociatedEvent, RemotingLifecycleEvent} -import org.apache.spark.{Logging, SparkException, TaskState} +import org.apache.spark.{SparkEnv, Logging, SparkException, TaskState} import org.apache.spark.scheduler.{SchedulerBackend, SlaveLost, TaskDescription, TaskSchedulerImpl, WorkerOffer} import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._ -import org.apache.spark.util.{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. @@ -48,6 +48,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, actorSystem: A var totalCoreCount = new AtomicInteger(0) val conf = scheduler.sc.conf private val timeout = AkkaUtils.askTimeout(conf) + private val akkaFrameSize = AkkaUtils.maxFrameSizeBytes(conf) class DriverActor(sparkProperties: Seq[(String, String)]) extends Actor { private val executorActor = new HashMap[String, ActorRef] @@ -140,8 +141,26 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, actorSystem: A // Launch tasks returned by a set of resource offers def launchTasks(tasks: Seq[Seq[TaskDescription]]) { for (task <- tasks.flatten) { - freeCores(task.executorId) -= scheduler.CPUS_PER_TASK - executorActor(task.executorId) ! LaunchTask(task) + val ser = SparkEnv.get.closureSerializer.newInstance() + val serializedTask = ser.serialize(task) + if (serializedTask.limit >= akkaFrameSize - 1024) { + val taskSetId = scheduler.taskIdToTaskSetId(task.taskId) + scheduler.activeTaskSets.get(taskSetId).foreach { taskSet => + try { + var msg = "Serialized task %s:%d was %d bytes which " + + "exceeds spark.akka.frameSize (%d bytes). " + + "Consider using broadcast variables for large values." + msg = msg.format(task.taskId, task.index, serializedTask.limit, akkaFrameSize) + taskSet.abort(msg) + } catch { + case e: Exception => logError("Exception in error callback", e) + } + } + } + else { + freeCores(task.executorId) -= scheduler.CPUS_PER_TASK + executorActor(task.executorId) ! LaunchTask(new SerializableBuffer(serializedTask)) + } } } diff --git a/core/src/test/scala/org/apache/spark/scheduler/CoarseGrainedSchedulerBackendSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/CoarseGrainedSchedulerBackendSuite.scala new file mode 100644 index 0000000000000..efef9d26dadca --- /dev/null +++ b/core/src/test/scala/org/apache/spark/scheduler/CoarseGrainedSchedulerBackendSuite.scala @@ -0,0 +1,43 @@ +/* + * 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.{LocalSparkContext, SparkConf, SparkException, SparkContext} +import org.apache.spark.util.{SerializableBuffer, AkkaUtils} + +import org.scalatest.FunSuite + +class CoarseGrainedSchedulerBackendSuite extends FunSuite with LocalSparkContext { + + test("serialized task larger than akka frame size") { + val conf = new SparkConf + conf.set("spark.akka.frameSize","1") + conf.set("spark.default.parallelism","1") + sc = new SparkContext("local-cluster[2 , 1 , 512]", "test", conf) + val frameSize = AkkaUtils.maxFrameSizeBytes(sc.conf) + val buffer = new SerializableBuffer(java.nio.ByteBuffer.allocate(2 * frameSize)) + val larger = sc.parallelize(Seq(buffer)) + val thrown = intercept[SparkException] { + larger.collect() + } + assert(thrown.getMessage.contains("Consider using broadcast variables for large values")) + val smaller = sc.parallelize(1 to 4).collect() + assert(smaller.size === 4) + } + +} From 9cff1dd25abc5e848720d853172ed42e35376fd0 Mon Sep 17 00:00:00 2001 From: Jyotiska NK Date: Wed, 28 May 2014 23:08:39 -0700 Subject: [PATCH 063/221] Added doctest and method description in context.py Added doctest for method textFile and description for methods _initialize_context and _ensure_initialized in context.py Author: Jyotiska NK Closes #187 from jyotiska/pyspark_context and squashes the following commits: 356f945 [Jyotiska NK] Added doctest for textFile method in context.py 5b23686 [Jyotiska NK] Updated context.py with method descriptions --- python/pyspark/context.py | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/python/pyspark/context.py b/python/pyspark/context.py index 27b440d73bdc3..56746cb7aab3d 100644 --- a/python/pyspark/context.py +++ b/python/pyspark/context.py @@ -173,12 +173,18 @@ def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None, self._temp_dir = \ self._jvm.org.apache.spark.util.Utils.createTempDir(local_dir).getAbsolutePath() - # Initialize SparkContext in function to allow subclass specific initialization def _initialize_context(self, jconf): + """ + Initialize SparkContext in function to allow subclass specific initialization + """ return self._jvm.JavaSparkContext(jconf) @classmethod def _ensure_initialized(cls, instance=None, gateway=None): + """ + Checks whether a SparkContext is initialized or not. + Throws error if a SparkContext is already running. + """ with SparkContext._lock: if not SparkContext._gateway: SparkContext._gateway = gateway or launch_gateway() @@ -270,6 +276,13 @@ def textFile(self, name, minPartitions=None): Read a text file from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI, and return it as an RDD of Strings. + + >>> path = os.path.join(tempdir, "sample-text.txt") + >>> with open(path, "w") as testFile: + ... testFile.write("Hello world!") + >>> textFile = sc.textFile(path) + >>> textFile.collect() + [u'Hello world!'] """ minPartitions = minPartitions or min(self.defaultParallelism, 2) return RDD(self._jsc.textFile(name, minPartitions), self, From 60b89fe6b09ff896a30d74204876da883e307de7 Mon Sep 17 00:00:00 2001 From: Yin Huai Date: Thu, 29 May 2014 09:07:39 -0700 Subject: [PATCH 064/221] SPARK-1935: Explicitly add commons-codec 1.5 as a dependency. Author: Yin Huai Closes #889 from yhuai/SPARK-1935 and squashes the following commits: 7d50ef1 [Yin Huai] Explicitly add commons-codec 1.5 as a dependency. --- pom.xml | 5 +++++ project/SparkBuild.scala | 1 + 2 files changed, 6 insertions(+) diff --git a/pom.xml b/pom.xml index 86264d1132ec4..7bf9f135fd340 100644 --- a/pom.xml +++ b/pom.xml @@ -240,6 +240,11 @@ commons-lang3 3.3.2 + + commons-codec + commons-codec + 1.5 + com.google.code.findbugs jsr305 diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala index b9d92340ff75b..8ef1e91f609fb 100644 --- a/project/SparkBuild.scala +++ b/project/SparkBuild.scala @@ -348,6 +348,7 @@ object SparkBuild extends Build { "org.apache.mesos" % "mesos" % "0.18.1" classifier("shaded-protobuf") exclude("com.google.protobuf", "protobuf-java"), "commons-net" % "commons-net" % "2.2", "net.java.dev.jets3t" % "jets3t" % jets3tVersion excludeAll(excludeCommonsLogging), + "commons-codec" % "commons-codec" % "1.5", // Prevent jets3t from including the older version of commons-codec "org.apache.derby" % "derby" % "10.4.2.0" % "test", "org.apache.hadoop" % hadoopClient % hadoopVersion excludeAll(excludeJBossNetty, excludeAsm, excludeCommonsLogging, excludeSLF4J, excludeOldAsm), "org.apache.curator" % "curator-recipes" % "2.4.0" excludeAll(excludeJBossNetty), From 8f7141fbc015addb314e1d5801085587b5cbb171 Mon Sep 17 00:00:00 2001 From: Cheng Lian Date: Thu, 29 May 2014 15:24:03 -0700 Subject: [PATCH 065/221] [SPARK-1368][SQL] Optimized HiveTableScan JIRA issue: [SPARK-1368](https://issues.apache.org/jira/browse/SPARK-1368) This PR introduces two major updates: - Replaced FP style code with `while` loop and reusable `GenericMutableRow` object in critical path of `HiveTableScan`. - Using `ColumnProjectionUtils` to help optimizing RCFile and ORC column pruning. My quick micro benchmark suggests these two optimizations made the optimized version 2x and 2.5x faster when scanning CSV table and RCFile table respectively: ``` Original: [info] CSV: 27676 ms, RCFile: 26415 ms [info] CSV: 27703 ms, RCFile: 26029 ms [info] CSV: 27511 ms, RCFile: 25962 ms Optimized: [info] CSV: 13820 ms, RCFile: 10402 ms [info] CSV: 14158 ms, RCFile: 10691 ms [info] CSV: 13606 ms, RCFile: 10346 ms ``` The micro benchmark loads a 609MB CVS file (structurally similar to the `src` test table) into a normal Hive table with `LazySimpleSerDe` and a RCFile table, then scans these tables respectively. Preparation code: ```scala package org.apache.spark.examples.sql.hive import org.apache.spark.sql.hive.LocalHiveContext import org.apache.spark.{SparkConf, SparkContext} object HiveTableScanPrepare extends App { val sparkContext = new SparkContext( new SparkConf() .setMaster("local") .setAppName(getClass.getSimpleName.stripSuffix("$"))) val hiveContext = new LocalHiveContext(sparkContext) import hiveContext._ hql("drop table scan_csv") hql("drop table scan_rcfile") hql("""create table scan_csv (key int, value string) | row format serde 'org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe' | with serdeproperties ('field.delim'=',') """.stripMargin) hql(s"""load data local inpath "${args(0)}" into table scan_csv""") hql("""create table scan_rcfile (key int, value string) | row format serde 'org.apache.hadoop.hive.serde2.columnar.ColumnarSerDe' |stored as | inputformat 'org.apache.hadoop.hive.ql.io.RCFileInputFormat' | outputformat 'org.apache.hadoop.hive.ql.io.RCFileOutputFormat' """.stripMargin) hql( """ |from scan_csv |insert overwrite table scan_rcfile |select scan_csv.key, scan_csv.value """.stripMargin) } ``` Benchmark code: ```scala package org.apache.spark.examples.sql.hive import org.apache.spark.sql.hive.LocalHiveContext import org.apache.spark.{SparkConf, SparkContext} object HiveTableScanBenchmark extends App { val sparkContext = new SparkContext( new SparkConf() .setMaster("local") .setAppName(getClass.getSimpleName.stripSuffix("$"))) val hiveContext = new LocalHiveContext(sparkContext) import hiveContext._ val scanCsv = hql("select key from scan_csv") val scanRcfile = hql("select key from scan_rcfile") val csvDuration = benchmark(scanCsv.count()) val rcfileDuration = benchmark(scanRcfile.count()) println(s"CSV: $csvDuration ms, RCFile: $rcfileDuration ms") def benchmark(f: => Unit) = { val begin = System.currentTimeMillis() f val end = System.currentTimeMillis() end - begin } } ``` @marmbrus Please help review, thanks! Author: Cheng Lian Closes #758 from liancheng/fastHiveTableScan and squashes the following commits: 4241a19 [Cheng Lian] Distinguishes sorted and possibly not sorted operations more accurately in HiveComparisonTest cf640d8 [Cheng Lian] More HiveTableScan optimisations: bf0e7dc [Cheng Lian] Added SortedOperation pattern to match *some* definitely sorted operations and avoid some sorting cost in HiveComparisonTest. 6d1c642 [Cheng Lian] Using ColumnProjectionUtils to optimise RCFile and ORC column pruning eb62fd3 [Cheng Lian] [SPARK-1368] Optimized HiveTableScan --- .../spark/sql/execution/Aggregate.scala | 2 +- .../apache/spark/sql/hive/hiveOperators.scala | 97 ++++++++++++++++--- .../hive/execution/HiveComparisonTest.scala | 25 ++--- 3 files changed, 96 insertions(+), 28 deletions(-) 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 index 36b3b956da96c..604914e547790 100644 --- 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 @@ -116,7 +116,7 @@ case class Aggregate( */ @transient private[this] lazy val resultMap = - (computedAggregates.map { agg => agg.unbound -> agg.resultAttribute} ++ namedGroups).toMap + (computedAggregates.map { agg => agg.unbound -> agg.resultAttribute } ++ namedGroups).toMap /** * Substituted version of aggregateExpressions expressions which are used to compute final diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveOperators.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveOperators.scala index 96faebc5a8687..f141139ef46a8 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveOperators.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveOperators.scala @@ -18,15 +18,18 @@ package org.apache.spark.sql.hive.execution import org.apache.hadoop.hive.common.`type`.{HiveDecimal, HiveVarchar} +import org.apache.hadoop.hive.conf.HiveConf import org.apache.hadoop.hive.metastore.MetaStoreUtils import org.apache.hadoop.hive.ql.Context import org.apache.hadoop.hive.ql.metadata.{Partition => HivePartition, Hive} import org.apache.hadoop.hive.ql.plan.{TableDesc, FileSinkDesc} -import org.apache.hadoop.hive.serde2.Serializer +import org.apache.hadoop.hive.serde.serdeConstants import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorUtils.ObjectInspectorCopyOption import org.apache.hadoop.hive.serde2.objectinspector._ import org.apache.hadoop.hive.serde2.objectinspector.primitive.JavaHiveDecimalObjectInspector import org.apache.hadoop.hive.serde2.objectinspector.primitive.JavaHiveVarcharObjectInspector +import org.apache.hadoop.hive.serde2.typeinfo.TypeInfoUtils +import org.apache.hadoop.hive.serde2.{ColumnProjectionUtils, Serializer} import org.apache.hadoop.io.Writable import org.apache.hadoop.mapred._ @@ -37,6 +40,7 @@ import org.apache.spark.sql.catalyst.types.{BooleanType, DataType} import org.apache.spark.sql.execution._ import org.apache.spark.sql.hive._ import org.apache.spark.{TaskContext, SparkException} +import org.apache.spark.util.MutablePair /* Implicits */ import scala.collection.JavaConversions._ @@ -94,7 +98,7 @@ case class HiveTableScan( (_: Any, partitionKeys: Array[String]) => { val value = partitionKeys(ordinal) val dataType = relation.partitionKeys(ordinal).dataType - castFromString(value, dataType) + unwrapHiveData(castFromString(value, dataType)) } } else { val ref = objectInspector.getAllStructFieldRefs @@ -102,16 +106,55 @@ case class HiveTableScan( .getOrElse(sys.error(s"Can't find attribute $a")) (row: Any, _: Array[String]) => { val data = objectInspector.getStructFieldData(row, ref) - unwrapData(data, ref.getFieldObjectInspector) + unwrapHiveData(unwrapData(data, ref.getFieldObjectInspector)) } } } } + private def unwrapHiveData(value: Any) = value match { + case maybeNull: String if maybeNull.toLowerCase == "null" => null + case varchar: HiveVarchar => varchar.getValue + case decimal: HiveDecimal => BigDecimal(decimal.bigDecimalValue) + case other => other + } + private def castFromString(value: String, dataType: DataType) = { Cast(Literal(value), dataType).eval(null) } + private def addColumnMetadataToConf(hiveConf: HiveConf) { + // Specifies IDs and internal names of columns to be scanned. + val neededColumnIDs = attributes.map(a => relation.output.indexWhere(_.name == a.name): Integer) + val columnInternalNames = neededColumnIDs.map(HiveConf.getColumnInternalName(_)).mkString(",") + + if (attributes.size == relation.output.size) { + ColumnProjectionUtils.setFullyReadColumns(hiveConf) + } else { + ColumnProjectionUtils.appendReadColumnIDs(hiveConf, neededColumnIDs) + } + + ColumnProjectionUtils.appendReadColumnNames(hiveConf, attributes.map(_.name)) + + // Specifies types and object inspectors of columns to be scanned. + val structOI = ObjectInspectorUtils + .getStandardObjectInspector( + relation.tableDesc.getDeserializer.getObjectInspector, + ObjectInspectorCopyOption.JAVA) + .asInstanceOf[StructObjectInspector] + + val columnTypeNames = structOI + .getAllStructFieldRefs + .map(_.getFieldObjectInspector) + .map(TypeInfoUtils.getTypeInfoFromObjectInspector(_).getTypeName) + .mkString(",") + + hiveConf.set(serdeConstants.LIST_COLUMN_TYPES, columnTypeNames) + hiveConf.set(serdeConstants.LIST_COLUMNS, columnInternalNames) + } + + addColumnMetadataToConf(sc.hiveconf) + @transient def inputRdd = if (!relation.hiveQlTable.isPartitioned) { hadoopReader.makeRDDForTable(relation.hiveQlTable) @@ -143,20 +186,42 @@ case class HiveTableScan( } def execute() = { - inputRdd.map { row => - val values = row match { - case Array(deserializedRow: AnyRef, partitionKeys: Array[String]) => - attributeFunctions.map(_(deserializedRow, partitionKeys)) - case deserializedRow: AnyRef => - attributeFunctions.map(_(deserializedRow, Array.empty)) + inputRdd.mapPartitions { iterator => + if (iterator.isEmpty) { + Iterator.empty + } else { + val mutableRow = new GenericMutableRow(attributes.length) + val mutablePair = new MutablePair[Any, Array[String]]() + val buffered = iterator.buffered + + // NOTE (lian): Critical path of Hive table scan, unnecessary FP style code and pattern + // matching are avoided intentionally. + val rowsAndPartitionKeys = buffered.head match { + // With partition keys + case _: Array[Any] => + buffered.map { case array: Array[Any] => + val deserializedRow = array(0) + val partitionKeys = array(1).asInstanceOf[Array[String]] + mutablePair.update(deserializedRow, partitionKeys) + } + + // Without partition keys + case _ => + val emptyPartitionKeys = Array.empty[String] + buffered.map { deserializedRow => + mutablePair.update(deserializedRow, emptyPartitionKeys) + } + } + + rowsAndPartitionKeys.map { pair => + var i = 0 + while (i < attributes.length) { + mutableRow(i) = attributeFunctions(i)(pair._1, pair._2) + i += 1 + } + mutableRow: Row + } } - buildRow(values.map { - case n: String if n.toLowerCase == "null" => null - case varchar: org.apache.hadoop.hive.common.`type`.HiveVarchar => varchar.getValue - case decimal: org.apache.hadoop.hive.common.`type`.HiveDecimal => - BigDecimal(decimal.bigDecimalValue) - case other => other - }) } } 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 edff38b901073..1b5a132f9665d 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 @@ -19,11 +19,12 @@ package org.apache.spark.sql.hive.execution import java.io._ +import org.scalatest.{BeforeAndAfterAll, FunSuite, GivenWhenThen} + import org.apache.spark.sql.Logging -import org.apache.spark.sql.catalyst.plans.logical.{ExplainCommand, NativeCommand} +import org.apache.spark.sql.catalyst.planning.PhysicalOperation +import org.apache.spark.sql.catalyst.plans.logical._ import org.apache.spark.sql.catalyst.util._ -import org.apache.spark.sql.execution.Sort -import org.scalatest.{BeforeAndAfterAll, FunSuite, GivenWhenThen} import org.apache.spark.sql.hive.test.TestHive /** @@ -128,17 +129,19 @@ abstract class HiveComparisonTest protected def prepareAnswer( hiveQuery: TestHive.type#HiveQLQueryExecution, answer: Seq[String]): Seq[String] = { + + def isSorted(plan: LogicalPlan): Boolean = plan match { + case _: Join | _: Aggregate | _: BaseRelation | _: Generate | _: Sample | _: Distinct => false + case PhysicalOperation(_, _, Sort(_, _)) => true + case _ => plan.children.iterator.map(isSorted).exists(_ == true) + } + val orderedAnswer = hiveQuery.logical match { // Clean out non-deterministic time schema info. case _: NativeCommand => answer.filterNot(nonDeterministicLine).filterNot(_ == "") case _: ExplainCommand => answer - case _ => - // TODO: Really we only care about the final total ordering here... - val isOrdered = hiveQuery.executedPlan.collect { - case s @ Sort(_, global, _) if global => s - }.nonEmpty - // If the query results aren't sorted, then sort them to ensure deterministic answers. - if (!isOrdered) answer.sorted else answer + case plan if isSorted(plan) => answer + case _ => answer.sorted } orderedAnswer.map(cleanPaths) } @@ -161,7 +164,7 @@ abstract class HiveComparisonTest "minFileSize" ) protected def nonDeterministicLine(line: String) = - nonDeterministicLineIndicators.map(line contains _).reduceLeft(_||_) + nonDeterministicLineIndicators.exists(line contains _) /** * Removes non-deterministic paths from `str` so cached answers will compare correctly. From b7e28fa451511b3b0f849c3d2919ac9c2e4231a1 Mon Sep 17 00:00:00 2001 From: Ankur Dave Date: Thu, 29 May 2014 15:39:25 -0700 Subject: [PATCH 066/221] initial version of LPA A straightforward implementation of LPA algorithm for detecting graph communities using the Pregel framework. Amongst the growing literature on community detection algorithms in networks, LPA is perhaps the most elementary, and despite its flaws it remains a nice and simple approach. Author: Ankur Dave Author: haroldsultan Author: Harold Sultan Closes #905 from haroldsultan/master and squashes the following commits: 327aee0 [haroldsultan] Merge pull request #2 from ankurdave/label-propagation 227a4d0 [Ankur Dave] Untabify 0ac574c [haroldsultan] Merge pull request #1 from ankurdave/label-propagation 0e24303 [Ankur Dave] Add LabelPropagationSuite 84aa061 [Ankur Dave] LabelPropagation: Fix compile errors and style; rename from LPA 9830342 [Harold Sultan] initial version of LPA --- .../spark/graphx/lib/LabelPropagation.scala | 66 +++++++++++++++++++ .../graphx/lib/LabelPropagationSuite.scala | 45 +++++++++++++ 2 files changed, 111 insertions(+) create mode 100644 graphx/src/main/scala/org/apache/spark/graphx/lib/LabelPropagation.scala create mode 100644 graphx/src/test/scala/org/apache/spark/graphx/lib/LabelPropagationSuite.scala 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 new file mode 100644 index 0000000000000..776bfb8dd6bfa --- /dev/null +++ b/graphx/src/main/scala/org/apache/spark/graphx/lib/LabelPropagation.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.graphx.lib + +import scala.reflect.ClassTag +import org.apache.spark.graphx._ + +/** Label Propagation algorithm. */ +object LabelPropagation { + /** + * Run static Label Propagation for detecting communities in networks. + * + * Each node in the network is initially assigned to its own community. At every superstep, nodes + * send their community affiliation to all neighbors and update their state to the mode community + * affiliation of incoming messages. + * + * LPA is a standard community detection algorithm for graphs. It is very inexpensive + * computationally, although (1) convergence is not guaranteed and (2) one can end up with + * trivial solutions (all nodes are identified into a single community). + * + * @tparam ED the edge attribute type (not used in the computation) + * + * @param graph the graph for which to compute the community affiliation + * @param maxSteps the number of supersteps of LPA to be performed. Because this is a static + * implementation, the algorithm will run for exactly this many supersteps. + * + * @return a graph with vertex attributes containing the label of community affiliation + */ + def run[ED: ClassTag](graph: Graph[_, ED], maxSteps: Int): Graph[VertexId, ED] = { + val lpaGraph = graph.mapVertices { case (vid, _) => vid } + def sendMessage(e: EdgeTriplet[VertexId, ED]) = { + Iterator((e.srcId, Map(e.dstAttr -> 1L)), (e.dstId, Map(e.srcAttr -> 1L))) + } + def mergeMessage(count1: Map[VertexId, Long], count2: Map[VertexId, Long]) + : Map[VertexId, Long] = { + (count1.keySet ++ count2.keySet).map { i => + val count1Val = count1.getOrElse(i, 0L) + val count2Val = count2.getOrElse(i, 0L) + i -> (count1Val + count2Val) + }.toMap + } + def vertexProgram(vid: VertexId, attr: Long, message: Map[VertexId, Long]) = { + if (message.isEmpty) attr else message.maxBy(_._2)._1 + } + val initialMessage = Map[VertexId, Long]() + Pregel(lpaGraph, initialMessage, maxIterations = maxSteps)( + vprog = vertexProgram, + sendMsg = sendMessage, + mergeMsg = mergeMessage) + } +} diff --git a/graphx/src/test/scala/org/apache/spark/graphx/lib/LabelPropagationSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/lib/LabelPropagationSuite.scala new file mode 100644 index 0000000000000..61fd0c4605568 --- /dev/null +++ b/graphx/src/test/scala/org/apache/spark/graphx/lib/LabelPropagationSuite.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.graphx.lib + +import org.scalatest.FunSuite + +import org.apache.spark.graphx._ + +class LabelPropagationSuite extends FunSuite with LocalSparkContext { + test("Label Propagation") { + withSpark { sc => + // Construct a graph with two cliques connected by a single edge + val n = 5 + val clique1 = for (u <- 0L until n; v <- 0L until n) yield Edge(u, v, 1) + val clique2 = for (u <- 0L to n; v <- 0L to n) yield Edge(u + n, v + n, 1) + val twoCliques = sc.parallelize(clique1 ++ clique2 :+ Edge(0L, n, 1)) + val graph = Graph.fromEdges(twoCliques, 1) + // Run label propagation + val labels = LabelPropagation.run(graph, n * 4).cache() + + // All vertices within a clique should have the same label + val clique1Labels = labels.vertices.filter(_._1 < n).map(_._2).collect.toArray + assert(clique1Labels.forall(_ == clique1Labels(0))) + val clique2Labels = labels.vertices.filter(_._1 >= n).map(_._2).collect.toArray + assert(clique2Labels.forall(_ == clique2Labels(0))) + // The two cliques should have different labels + assert(clique1Labels(0) != clique2Labels(0)) + } + } +} From eeee978a348ec2a35cc27865cea6357f9db75b74 Mon Sep 17 00:00:00 2001 From: Prashant Sharma Date: Thu, 29 May 2014 23:20:20 -0700 Subject: [PATCH 067/221] [SPARK-1820] Make GenerateMimaIgnore @DeveloperApi annotation aware. We add all the classes annotated as `DeveloperApi` to `~/.mima-excludes`. Author: Prashant Sharma Author: nikhil7sh Closes #904 from ScrapCodes/SPARK-1820/ignore-Developer-Api and squashes the following commits: de944f9 [Prashant Sharma] Code review. e3c5215 [Prashant Sharma] Incorporated patrick's suggestions and fixed the scalastyle build. 9983a42 [nikhil7sh] [SPARK-1820] Make GenerateMimaIgnore @DeveloperApi annotation aware --- .../spark/tools/GenerateMIMAIgnore.scala | 22 ++++++++++++++++--- 1 file changed, 19 insertions(+), 3 deletions(-) diff --git a/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala b/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala index a433e8e2e89f4..011db50b7d568 100644 --- a/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala +++ b/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala @@ -23,6 +23,7 @@ import java.util.jar.JarFile import scala.collection.mutable import scala.collection.JavaConversions._ import scala.reflect.runtime.universe.runtimeMirror +import scala.reflect.runtime.{universe => unv} /** * A tool for generating classes to be excluded during binary checking with MIMA. It is expected @@ -42,7 +43,7 @@ object GenerateMIMAIgnore { private def classesPrivateWithin(packageName: String): Set[String] = { val classes = getClasses(packageName) - val privateClasses = mutable.HashSet[String]() + val ignoredClasses = mutable.HashSet[String]() def isPackagePrivate(className: String) = { try { @@ -70,8 +71,21 @@ object GenerateMIMAIgnore { } } + def isDeveloperApi(className: String) = { + try { + val clazz = mirror.classSymbol(Class.forName(className, false, classLoader)) + clazz.annotations.exists(_.tpe =:= unv.typeOf[org.apache.spark.annotation.DeveloperApi]) + } catch { + case _: Throwable => { + println("Error determining Annotations: " + className) + false + } + } + } + for (className <- classes) { val directlyPrivateSpark = isPackagePrivate(className) + val developerApi = isDeveloperApi(className) /* Inner classes defined within a private[spark] class or object are effectively invisible, so we account for them as package private. */ @@ -83,9 +97,11 @@ object GenerateMIMAIgnore { false } } - if (directlyPrivateSpark || indirectlyPrivateSpark) privateClasses += className + if (directlyPrivateSpark || indirectlyPrivateSpark || developerApi) { + ignoredClasses += className + } } - privateClasses.flatMap(c => Seq(c, c.replace("$", "#"))).toSet + ignoredClasses.flatMap(c => Seq(c, c.replace("$", "#"))).toSet } def main(args: Array[String]) { From c8bf4131bc2a2e147e977159fc90e94b85738830 Mon Sep 17 00:00:00 2001 From: Matei Zaharia Date: Fri, 30 May 2014 00:34:33 -0700 Subject: [PATCH 068/221] [SPARK-1566] consolidate programming guide, and general doc updates This is a fairly large PR to clean up and update the docs for 1.0. The major changes are: * A unified programming guide for all languages replaces language-specific ones and shows language-specific info in tabs * New programming guide sections on key-value pairs, unit testing, input formats beyond text, migrating from 0.9, and passing functions to Spark * Spark-submit guide moved to a separate page and expanded slightly * Various cleanups of the menu system, security docs, and others * Updated look of title bar to differentiate the docs from previous Spark versions You can find the updated docs at http://people.apache.org/~matei/1.0-docs/_site/ and in particular http://people.apache.org/~matei/1.0-docs/_site/programming-guide.html. Author: Matei Zaharia Closes #896 from mateiz/1.0-docs and squashes the following commits: 03e6853 [Matei Zaharia] Some tweaks to configuration and YARN docs 0779508 [Matei Zaharia] tweak ef671d4 [Matei Zaharia] Keep frames in JavaDoc links, and other small tweaks 1bf4112 [Matei Zaharia] Review comments 4414f88 [Matei Zaharia] tweaks d04e979 [Matei Zaharia] Fix some old links to Java guide a34ed33 [Matei Zaharia] tweak 541bb3b [Matei Zaharia] miscellaneous changes fcefdec [Matei Zaharia] Moved submitting apps to separate doc 61d72b4 [Matei Zaharia] stuff 181f217 [Matei Zaharia] migration guide, remove old language guides e11a0da [Matei Zaharia] Add more API functions 6a030a9 [Matei Zaharia] tweaks 8db0ae3 [Matei Zaharia] Added key-value pairs section 318d2c9 [Matei Zaharia] tweaks 1c81477 [Matei Zaharia] New section on basics and function syntax e38f559 [Matei Zaharia] Actually added programming guide to Git a33d6fe [Matei Zaharia] First pass at updating programming guide to support all languages, plus other tweaks throughout 3b6a876 [Matei Zaharia] More CSS tweaks 01ec8bf [Matei Zaharia] More CSS tweaks e6d252e [Matei Zaharia] Change color of doc title bar to differentiate from 0.9.0 --- docs/_layouts/global.html | 18 +- docs/bagel-programming-guide.md | 2 +- docs/building-with-maven.md | 90 +- docs/cluster-overview.md | 108 +- docs/configuration.md | 11 +- docs/css/bootstrap.min.css | 2 +- docs/graphx-programming-guide.md | 8 +- docs/hadoop-third-party-distributions.md | 2 +- docs/index.md | 79 +- docs/java-programming-guide.md | 215 +--- docs/js/api-docs.js | 23 +- docs/js/main.js | 21 + docs/mllib-guide.md | 10 +- docs/mllib-optimization.md | 2 +- docs/monitoring.md | 2 +- docs/programming-guide.md | 1294 ++++++++++++++++++++++ docs/python-programming-guide.md | 168 +-- docs/quick-start.md | 39 +- docs/running-on-mesos.md | 7 +- docs/running-on-yarn.md | 91 +- docs/scala-programming-guide.md | 445 +------- docs/security.md | 18 +- docs/spark-standalone.md | 4 +- docs/sql-programming-guide.md | 29 +- docs/streaming-programming-guide.md | 42 +- docs/submitting-applications.md | 153 +++ docs/tuning.md | 6 +- 27 files changed, 1767 insertions(+), 1122 deletions(-) create mode 100644 docs/programming-guide.md create mode 100644 docs/submitting-applications.md diff --git a/docs/_layouts/global.html b/docs/_layouts/global.html index fb808129bb65d..4ba20e590f2c2 100755 --- a/docs/_layouts/global.html +++ b/docs/_layouts/global.html @@ -9,6 +9,11 @@ {{ page.title }} - Spark {{site.SPARK_VERSION_SHORT}} Documentation + {% if page.redirect %} + + + {% endif %} +