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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.
*/
package org.apache.spark.scheduler
import scala.language.existentials
import java.io._
import java.util.zip.{GZIPInputStream, GZIPOutputStream}
import scala.collection.mutable.HashMap
import org.apache.spark._
import org.apache.spark.rdd.{RDD, RDDCheckpointData}
import org.apache.spark.shuffle.ShuffleWriter
private[spark] object ShuffleMapTask {
// A simple map between the stage id to the serialized byte array of a task.
// Served as a cache for task serialization because serialization can be
// expensive on the master node if it needs to launch thousands of tasks.
private val serializedInfoCache = new HashMap[Int, Array[Byte]]
def serializeInfo(stageId: Int, rdd: RDD[_], dep: ShuffleDependency[_, _, _]): Array[Byte] = {
synchronized {
val old = serializedInfoCache.get(stageId).orNull
if (old != null) {
return old
} else {
val out = new ByteArrayOutputStream
val ser = SparkEnv.get.closureSerializer.newInstance()
val objOut = ser.serializeStream(new GZIPOutputStream(out))
objOut.writeObject(rdd)
objOut.writeObject(dep)
objOut.close()
val bytes = out.toByteArray
serializedInfoCache.put(stageId, bytes)
bytes
}
}
}
def deserializeInfo(stageId: Int, bytes: Array[Byte]): (RDD[_], ShuffleDependency[_, _, _]) = {
val in = new GZIPInputStream(new ByteArrayInputStream(bytes))
val ser = SparkEnv.get.closureSerializer.newInstance()
val objIn = ser.deserializeStream(in)
val rdd = objIn.readObject().asInstanceOf[RDD[_]]
val dep = objIn.readObject().asInstanceOf[ShuffleDependency[_, _, _]]
(rdd, dep)
}
// Since both the JarSet and FileSet have the same format this is used for both.
def deserializeFileSet(bytes: Array[Byte]): HashMap[String, Long] = {
val in = new GZIPInputStream(new ByteArrayInputStream(bytes))
val objIn = new ObjectInputStream(in)
val set = objIn.readObject().asInstanceOf[Array[(String, Long)]].toMap
HashMap(set.toSeq: _*)
}
def removeStage(stageId: Int) {
serializedInfoCache.remove(stageId)
}
def clearCache() {
synchronized {
serializedInfoCache.clear()
}
}
}
/**
* A ShuffleMapTask divides the elements of an RDD into multiple buckets (based on a partitioner
* specified in the ShuffleDependency).
*
* See [[org.apache.spark.scheduler.Task]] for more information.
*
* @param stageId id of the stage this task belongs to
* @param rdd the final RDD in this stage
* @param dep the ShuffleDependency
* @param _partitionId index of the number in the RDD
* @param locs preferred task execution locations for locality scheduling
*/
private[spark] class ShuffleMapTask(
stageId: Int,
var rdd: RDD[_],
var dep: ShuffleDependency[_, _, _],
_partitionId: Int,
@transient private var locs: Seq[TaskLocation])
extends Task[MapStatus](stageId, _partitionId)
with Externalizable
with Logging {
protected def this() = this(0, null, null, 0, null)
@transient private val preferredLocs: Seq[TaskLocation] = {
if (locs == null) Nil else locs.toSet.toSeq
}
var split = if (rdd == null) null else rdd.partitions(partitionId)
override def writeExternal(out: ObjectOutput) {
RDDCheckpointData.synchronized {
split = rdd.partitions(partitionId)
out.writeInt(stageId)
val bytes = ShuffleMapTask.serializeInfo(stageId, rdd, dep)
out.writeInt(bytes.length)
out.write(bytes)
out.writeInt(partitionId)
out.writeLong(epoch)
out.writeObject(split)
}
}
override def readExternal(in: ObjectInput) {
val stageId = in.readInt()
val numBytes = in.readInt()
val bytes = new Array[Byte](numBytes)
in.readFully(bytes)
val (rdd_, dep_) = ShuffleMapTask.deserializeInfo(stageId, bytes)
rdd = rdd_
dep = dep_
partitionId = in.readInt()
epoch = in.readLong()
split = in.readObject().asInstanceOf[Partition]
}
override def runTask(context: TaskContext): MapStatus = {
metrics = Some(context.taskMetrics)
var writer: ShuffleWriter[Any, Any] = null
try {
val manager = SparkEnv.get.shuffleManager
writer = manager.getWriter[Any, Any](dep.shuffleHandle, partitionId, context)
writer.write(rdd.iterator(split, context).asInstanceOf[Iterator[_ <: Product2[Any, Any]]])
return writer.stop(success = true).get
} catch {
case e: Exception =>
if (writer != null) {
writer.stop(success = false)
}
throw e
} finally {
context.executeOnCompleteCallbacks()
}
}
override def preferredLocations: Seq[TaskLocation] = preferredLocs
override def toString = "ShuffleMapTask(%d, %d)".format(stageId, partitionId)
}
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