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[SPARK-14078] Streaming Parquet Based FileSink
This PR adds a new `Sink` implementation that writes out Parquet files. In order to correctly handle partial failures while maintaining exactly once semantics, the files for each batch are written out to a unique directory and then atomically appended to a metadata log. When a parquet based `DataSource` is initialized for reading, we first check for this log directory and use it instead of file listing when present. Unit tests are added, as well as a stress test that checks the answer after non-deterministic injected failures. Author: Michael Armbrust <[email protected]> Closes #11897 from marmbrus/fileSink.
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81 changes: 81 additions & 0 deletions
81
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/FileStreamSink.scala
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| /* | ||
| * 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. | ||
| */ | ||
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| package org.apache.spark.sql.execution.streaming | ||
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| import java.util.UUID | ||
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| import org.apache.hadoop.fs.Path | ||
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| import org.apache.spark.internal.Logging | ||
| import org.apache.spark.sql.{DataFrame, SQLContext} | ||
| import org.apache.spark.sql.sources.FileFormat | ||
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| object FileStreamSink { | ||
| // The name of the subdirectory that is used to store metadata about which files are valid. | ||
| val metadataDir = "_spark_metadata" | ||
| } | ||
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| /** | ||
| * A sink that writes out results to parquet files. Each batch is written out to a unique | ||
| * directory. After all of the files in a batch have been succesfully written, the list of | ||
| * file paths is appended to the log atomically. In the case of partial failures, some duplicate | ||
| * data may be present in the target directory, but only one copy of each file will be present | ||
| * in the log. | ||
| */ | ||
| class FileStreamSink( | ||
| sqlContext: SQLContext, | ||
| path: String, | ||
| fileFormat: FileFormat) extends Sink with Logging { | ||
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| private val basePath = new Path(path) | ||
| private val logPath = new Path(basePath, FileStreamSink.metadataDir) | ||
| private val fileLog = new HDFSMetadataLog[Seq[String]](sqlContext, logPath.toUri.toString) | ||
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| override def addBatch(batchId: Long, data: DataFrame): Unit = { | ||
| if (fileLog.get(batchId).isDefined) { | ||
| logInfo(s"Skipping already committed batch $batchId") | ||
| } else { | ||
| val files = writeFiles(data) | ||
| if (fileLog.add(batchId, files)) { | ||
| logInfo(s"Committed batch $batchId") | ||
| } else { | ||
| logWarning(s"Race while writing batch $batchId") | ||
| } | ||
| } | ||
| } | ||
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| /** Writes the [[DataFrame]] to a UUID-named dir, returning the list of files paths. */ | ||
| private def writeFiles(data: DataFrame): Seq[String] = { | ||
| val ctx = sqlContext | ||
| val outputDir = path | ||
| val format = fileFormat | ||
| val schema = data.schema | ||
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| val file = new Path(basePath, UUID.randomUUID().toString).toUri.toString | ||
| data.write.parquet(file) | ||
| sqlContext.read | ||
| .schema(data.schema) | ||
| .parquet(file) | ||
| .inputFiles | ||
| .map(new Path(_)) | ||
| .filterNot(_.getName.startsWith("_")) | ||
| .map(_.toUri.toString) | ||
| } | ||
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| override def toString: String = s"FileSink[$path]" | ||
| } |
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59 changes: 59 additions & 0 deletions
59
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamFileCatalog.scala
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,59 @@ | ||
| /* | ||
| * 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. | ||
| */ | ||
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| package org.apache.spark.sql.execution.streaming | ||
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| import org.apache.hadoop.fs.{FileStatus, Path} | ||
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| import org.apache.spark.internal.Logging | ||
| import org.apache.spark.sql.SQLContext | ||
| import org.apache.spark.sql.catalyst.InternalRow | ||
| import org.apache.spark.sql.catalyst.expressions.Expression | ||
| import org.apache.spark.sql.execution.datasources.PartitionSpec | ||
| import org.apache.spark.sql.sources.{FileCatalog, Partition} | ||
| import org.apache.spark.sql.types.StructType | ||
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| class StreamFileCatalog(sqlContext: SQLContext, path: Path) extends FileCatalog with Logging { | ||
| val metadataDirectory = new Path(path, FileStreamSink.metadataDir) | ||
| logInfo(s"Reading streaming file log from $metadataDirectory") | ||
| val metadataLog = new HDFSMetadataLog[Seq[String]](sqlContext, metadataDirectory.toUri.toString) | ||
| val fs = path.getFileSystem(sqlContext.sparkContext.hadoopConfiguration) | ||
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| override def paths: Seq[Path] = path :: Nil | ||
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| override def partitionSpec(): PartitionSpec = PartitionSpec(StructType(Nil), Nil) | ||
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| /** | ||
| * Returns all valid files grouped into partitions when the data is partitioned. If the data is | ||
| * unpartitioned, this will return a single partition with not partition values. | ||
| * | ||
| * @param filters the filters used to prune which partitions are returned. These filters must | ||
| * only refer to partition columns and this method will only return files | ||
| * where these predicates are guaranteed to evaluate to `true`. Thus, these | ||
| * filters will not need to be evaluated again on the returned data. | ||
| */ | ||
| override def listFiles(filters: Seq[Expression]): Seq[Partition] = | ||
| Partition(InternalRow.empty, allFiles()) :: Nil | ||
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| override def getStatus(path: Path): Array[FileStatus] = fs.listStatus(path) | ||
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| override def refresh(): Unit = {} | ||
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| override def allFiles(): Seq[FileStatus] = { | ||
| fs.listStatus(metadataLog.get(None, None).flatMap(_._2).map(new Path(_))) | ||
| } | ||
| } |
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