spark PipedRDD 源码
spark PipedRDD 代码
文件路径:/core/src/main/scala/org/apache/spark/rdd/PipedRDD.scala
/*
* 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.rdd
import java.io.BufferedWriter
import java.io.File
import java.io.FilenameFilter
import java.io.IOException
import java.io.OutputStreamWriter
import java.io.PrintWriter
import java.util.StringTokenizer
import java.util.concurrent.atomic.AtomicReference
import scala.collection.JavaConverters._
import scala.collection.Map
import scala.collection.mutable.ArrayBuffer
import scala.io.Source
import scala.reflect.ClassTag
import org.apache.spark.{Partition, TaskContext}
import org.apache.spark.errors.SparkCoreErrors
import org.apache.spark.util.Utils
/**
* An RDD that pipes the contents of each parent partition through an external command
* (printing them one per line) and returns the output as a collection of strings.
*/
private[spark] class PipedRDD[T: ClassTag](
prev: RDD[T],
command: Seq[String],
envVars: Map[String, String],
printPipeContext: (String => Unit) => Unit,
printRDDElement: (T, String => Unit) => Unit,
separateWorkingDir: Boolean,
bufferSize: Int,
encoding: String)
extends RDD[String](prev) {
override def getPartitions: Array[Partition] = firstParent[T].partitions
/**
* A FilenameFilter that accepts anything that isn't equal to the name passed in.
* @param filterName of file or directory to leave out
*/
class NotEqualsFileNameFilter(filterName: String) extends FilenameFilter {
def accept(dir: File, name: String): Boolean = {
!name.equals(filterName)
}
}
override def compute(split: Partition, context: TaskContext): Iterator[String] = {
val pb = new ProcessBuilder(command.asJava)
// Add the environmental variables to the process.
val currentEnvVars = pb.environment()
envVars.foreach { case (variable, value) => currentEnvVars.put(variable, value) }
// for compatibility with Hadoop which sets these env variables
// so the user code can access the input filename
split match {
case hadoopSplit: HadoopPartition =>
currentEnvVars.putAll(hadoopSplit.getPipeEnvVars().asJava)
case _ => // do nothing
}
// When spark.worker.separated.working.directory option is turned on, each
// task will be run in separate directory. This should be resolve file
// access conflict issue
val taskDirectory = "tasks" + File.separator + java.util.UUID.randomUUID.toString
var workInTaskDirectory = false
logDebug("taskDirectory = " + taskDirectory)
if (separateWorkingDir) {
val currentDir = new File(".")
logDebug("currentDir = " + currentDir.getAbsolutePath())
val taskDirFile = new File(taskDirectory)
taskDirFile.mkdirs()
try {
val tasksDirFilter = new NotEqualsFileNameFilter("tasks")
// Need to add symlinks to jars, files, and directories. On Yarn we could have
// directories and other files not known to the SparkContext that were added via the
// Hadoop distributed cache. We also don't want to symlink to the /tasks directories we
// are creating here.
for (file <- currentDir.list(tasksDirFilter)) {
val fileWithDir = new File(currentDir, file)
Utils.symlink(new File(fileWithDir.getAbsolutePath()),
new File(taskDirectory + File.separator + fileWithDir.getName()))
}
pb.directory(taskDirFile)
workInTaskDirectory = true
} catch {
case e: Exception => logError("Unable to setup task working directory: " + e.getMessage +
" (" + taskDirectory + ")", e)
}
}
val proc = pb.start()
val childThreadException = new AtomicReference[Throwable](null)
// Start a thread to print the process's stderr to ours
val stderrReaderThread = new Thread(s"${PipedRDD.STDERR_READER_THREAD_PREFIX} $command") {
override def run(): Unit = {
val err = proc.getErrorStream
try {
for (line <- Source.fromInputStream(err)(encoding).getLines) {
// scalastyle:off println
System.err.println(line)
// scalastyle:on println
}
} catch {
case t: Throwable => childThreadException.set(t)
} finally {
err.close()
}
}
}
stderrReaderThread.start()
// Start a thread to feed the process input from our parent's iterator
val stdinWriterThread = new Thread(s"${PipedRDD.STDIN_WRITER_THREAD_PREFIX} $command") {
override def run(): Unit = {
TaskContext.setTaskContext(context)
val out = new PrintWriter(new BufferedWriter(
new OutputStreamWriter(proc.getOutputStream, encoding), bufferSize))
try {
// scalastyle:off println
// input the pipe context firstly
if (printPipeContext != null) {
printPipeContext(out.println)
}
for (elem <- firstParent[T].iterator(split, context)) {
if (printRDDElement != null) {
printRDDElement(elem, out.println)
} else {
out.println(elem)
}
}
// scalastyle:on println
} catch {
case t: Throwable => childThreadException.set(t)
} finally {
out.close()
}
}
}
stdinWriterThread.start()
// interrupts stdin writer and stderr reader threads when the corresponding task is finished.
// Otherwise, these threads could outlive the task's lifetime. For example:
// val pipeRDD = sc.range(1, 100).pipe(Seq("cat"))
// val abnormalRDD = pipeRDD.mapPartitions(_ => Iterator.empty)
// the iterator generated by PipedRDD is never involved. If the parent RDD's iterator takes a
// long time to generate(ShuffledRDD's shuffle operation for example), the stdin writer thread
// may consume significant memory and CPU time even if task is already finished.
context.addTaskCompletionListener[Unit] { _ =>
if (proc.isAlive) {
proc.destroy()
}
if (stdinWriterThread.isAlive) {
stdinWriterThread.interrupt()
}
if (stderrReaderThread.isAlive) {
stderrReaderThread.interrupt()
}
}
// Return an iterator that read lines from the process's stdout
val lines = Source.fromInputStream(proc.getInputStream)(encoding).getLines
new Iterator[String] {
def next(): String = {
if (!hasNext()) {
throw SparkCoreErrors.noSuchElementError()
}
lines.next()
}
def hasNext(): Boolean = {
val result = if (lines.hasNext) {
true
} else {
val exitStatus = proc.waitFor()
cleanup()
if (exitStatus != 0) {
throw new IllegalStateException(s"Subprocess exited with status $exitStatus. " +
s"Command ran: " + command.mkString(" "))
}
false
}
propagateChildException()
result
}
private def cleanup(): Unit = {
// cleanup task working directory if used
if (workInTaskDirectory) {
scala.util.control.Exception.ignoring(classOf[IOException]) {
Utils.deleteRecursively(new File(taskDirectory))
}
logDebug(s"Removed task working directory $taskDirectory")
}
}
private def propagateChildException(): Unit = {
val t = childThreadException.get()
if (t != null) {
val commandRan = command.mkString(" ")
logError(s"Caught exception while running pipe() operator. Command ran: $commandRan. " +
s"Exception: ${t.getMessage}")
proc.destroy()
cleanup()
throw t
}
}
}
}
}
private object PipedRDD {
// Split a string into words using a standard StringTokenizer
def tokenize(command: String): Seq[String] = {
val buf = new ArrayBuffer[String]
val tok = new StringTokenizer(command)
while(tok.hasMoreElements) {
buf += tok.nextToken()
}
buf.toSeq
}
val STDIN_WRITER_THREAD_PREFIX = "stdin writer for"
val STDERR_READER_THREAD_PREFIX = "stderr reader for"
}
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