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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 java.io._
import java.util.zip.{GZIPInputStream, GZIPOutputStream}
import org.apache.spark._
import org.apache.spark.rdd.RDD
import org.apache.spark.rdd.RDDCheckpointData
import org.apache.spark.util.{MetadataCleaner, TimeStampedHashMap}
private[spark] object ResultTask {
// 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.
val serializedInfoCache = new TimeStampedHashMap[Int, Array[Byte]]
val metadataCleaner = new MetadataCleaner("ResultTask", serializedInfoCache.clearOldValues)
def serializeInfo(stageId: Int, rdd: RDD[_], func: (TaskContext, Iterator[_]) => _): 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(func)
objOut.close()
val bytes = out.toByteArray
serializedInfoCache.put(stageId, bytes)
return bytes
}
}
}
def deserializeInfo(stageId: Int, bytes: Array[Byte]): (RDD[_], (TaskContext, Iterator[_]) => _) = {
val loader = Thread.currentThread.getContextClassLoader
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 func = objIn.readObject().asInstanceOf[(TaskContext, Iterator[_]) => _]
return (rdd, func)
}
def clearCache() {
synchronized {
serializedInfoCache.clear()
}
}
}
private[spark] class ResultTask[T, U](
stageId: Int,
var rdd: RDD[T],
var func: (TaskContext, Iterator[T]) => U,
var partition: Int,
@transient locs: Seq[TaskLocation],
val outputId: Int)
extends Task[U](stageId) with Externalizable {
def this() = this(0, null, null, 0, null, 0)
var split = if (rdd == null) {
null
} else {
rdd.partitions(partition)
}
@transient private val preferredLocs: Seq[TaskLocation] = {
if (locs == null) Nil else locs.toSet.toSeq
}
override def run(attemptId: Long): U = {
val context = new TaskContext(stageId, partition, attemptId)
metrics = Some(context.taskMetrics)
try {
func(context, rdd.iterator(split, context))
} finally {
context.executeOnCompleteCallbacks()
}
}
override def preferredLocations: Seq[TaskLocation] = preferredLocs
override def toString = "ResultTask(" + stageId + ", " + partition + ")"
override def writeExternal(out: ObjectOutput) {
RDDCheckpointData.synchronized {
split = rdd.partitions(partition)
out.writeInt(stageId)
val bytes = ResultTask.serializeInfo(
stageId, rdd, func.asInstanceOf[(TaskContext, Iterator[_]) => _])
out.writeInt(bytes.length)
out.write(bytes)
out.writeInt(partition)
out.writeInt(outputId)
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_, func_) = ResultTask.deserializeInfo(stageId, bytes)
rdd = rdd_.asInstanceOf[RDD[T]]
func = func_.asInstanceOf[(TaskContext, Iterator[T]) => U]
partition = in.readInt()
val outputId = in.readInt()
epoch = in.readLong()
split = in.readObject().asInstanceOf[Partition]
}
}
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