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ResultTask.scala
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ResultTask.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.
*/
package org.apache.spark.scheduler
import java.io._
import java.nio.ByteBuffer
import org.apache.spark._
import org.apache.spark.broadcast.Broadcast
import org.apache.spark.rdd.RDD
/**
* A task that sends back the output to the driver application.
*
* See [[Task]] for more information.
*
* @param stageId id of the stage this task belongs to
* @param stageAttemptId attempt id of the stage this task belongs to
* @param taskBinary broadcasted version of the serialized RDD and the function to apply on each
* partition of the given RDD. Once deserialized, the type should be
* (RDD[T], (TaskContext, Iterator[T]) => U).
* @param partition partition of the RDD this task is associated with
* @param locs preferred task execution locations for locality scheduling
* @param outputId index of the task in this job (a job can launch tasks on only a subset of the
* input RDD's partitions).
* @param _initialAccums initial set of accumulators to be used in this task for tracking
* internal metrics. Other accumulators will be registered later when
* they are deserialized on the executors.
*/
private[spark] class ResultTask[T, U](
stageId: Int,
stageAttemptId: Int,
taskBinary: Broadcast[Array[Byte]],
partition: Partition,
locs: Seq[TaskLocation],
val outputId: Int,
_initialAccums: Seq[Accumulator[_]] = InternalAccumulator.createAll())
extends Task[U](stageId, stageAttemptId, partition.index, _initialAccums)
with Serializable {
@transient private[this] val preferredLocs: Seq[TaskLocation] = {
if (locs == null) Nil else locs.toSet.toSeq
}
override def runTask(context: TaskContext): U = {
// Deserialize the RDD and the func using the broadcast variables.
val deserializeStartTime = System.currentTimeMillis()
val ser = SparkEnv.get.closureSerializer.newInstance()
val (rdd, func) = ser.deserialize[(RDD[T], (TaskContext, Iterator[T]) => U)](
ByteBuffer.wrap(taskBinary.value), Thread.currentThread.getContextClassLoader)
_executorDeserializeTime = System.currentTimeMillis() - deserializeStartTime
metrics = Some(context.taskMetrics)
func(context, rdd.iterator(partition, context))
}
// This is only callable on the driver side.
override def preferredLocations: Seq[TaskLocation] = preferredLocs
override def toString: String = "ResultTask(" + stageId + ", " + partitionId + ")"
}