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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.nio.ByteBuffer
import java.util.Properties
import org.apache.spark._
import org.apache.spark.executor.TaskMetrics
import org.apache.spark.internal.config.APP_CALLER_CONTEXT
import org.apache.spark.memory.{MemoryMode, TaskMemoryManager}
import org.apache.spark.metrics.MetricsSystem
import org.apache.spark.util._
/**
* A unit of execution. We have two kinds of Task's in Spark:
*
* - [[org.apache.spark.scheduler.ShuffleMapTask]]
* - [[org.apache.spark.scheduler.ResultTask]]
*
* A Spark job consists of one or more stages. The very last stage in a job consists of multiple
* ResultTasks, while earlier stages consist of ShuffleMapTasks. A ResultTask executes the task
* and sends the task output back to the driver application. A ShuffleMapTask executes the task
* and divides the task output to multiple buckets (based on the task's partitioner).
*
* @param stageId id of the stage this task belongs to
* @param stageAttemptId attempt id of the stage this task belongs to
* @param partitionId index of the number in the RDD
* @param localProperties copy of thread-local properties set by the user on the driver side.
* @param serializedTaskMetrics a `TaskMetrics` that is created and serialized on the driver side
* and sent to executor side.
*
* The parameters below are optional:
* @param jobId id of the job this task belongs to
* @param appId id of the app this task belongs to
* @param appAttemptId attempt id of the app this task belongs to
* @param isBarrier whether this task belongs to a barrier stage. Spark must launch all the tasks
* at the same time for a barrier stage.
*/
private[spark] abstract class Task[T](
val stageId: Int,
val stageAttemptId: Int,
val partitionId: Int,
@transient var localProperties: Properties = new Properties,
// The default value is only used in tests.
serializedTaskMetrics: Array[Byte] =
SparkEnv.get.closureSerializer.newInstance().serialize(TaskMetrics.registered).array(),
val jobId: Option[Int] = None,
val appId: Option[String] = None,
val appAttemptId: Option[String] = None,
val isBarrier: Boolean = false) extends Serializable {
@transient lazy val metrics: TaskMetrics =
SparkEnv.get.closureSerializer.newInstance().deserialize(ByteBuffer.wrap(serializedTaskMetrics))
/**
* Called by [[org.apache.spark.executor.Executor]] to run this task.
*
* @param taskAttemptId an identifier for this task attempt that is unique within a SparkContext.
* @param attemptNumber how many times this task has been attempted (0 for the first attempt)
* @return the result of the task along with updates of Accumulators.
*/
final def run(
taskAttemptId: Long,
attemptNumber: Int,
metricsSystem: MetricsSystem): T = {
SparkEnv.get.blockManager.registerTask(taskAttemptId)
// TODO SPARK-24874 Allow create BarrierTaskContext based on partitions, instead of whether
// the stage is barrier.
val taskContext = new TaskContextImpl(
stageId,
stageAttemptId, // stageAttemptId and stageAttemptNumber are semantically equal
partitionId,
taskAttemptId,
attemptNumber,
taskMemoryManager,
localProperties,
metricsSystem,
metrics)
context = if (isBarrier) {
new BarrierTaskContext(taskContext)
} else {
taskContext
}
TaskContext.setTaskContext(context)
taskThread = Thread.currentThread()
if (_reasonIfKilled != null) {
kill(interruptThread = false, _reasonIfKilled)
}
new CallerContext(
"TASK",
SparkEnv.get.conf.get(APP_CALLER_CONTEXT),
appId,
appAttemptId,
jobId,
Option(stageId),
Option(stageAttemptId),
Option(taskAttemptId),
Option(attemptNumber)).setCurrentContext()
try {
runTask(context)
} catch {
case e: Throwable =>
// Catch all errors; run task failure callbacks, and rethrow the exception.
try {
context.markTaskFailed(e)
} catch {
case t: Throwable =>
e.addSuppressed(t)
}
context.markTaskCompleted(Some(e))
throw e
} finally {
try {
// Call the task completion callbacks. If "markTaskCompleted" is called twice, the second
// one is no-op.
context.markTaskCompleted(None)
} finally {
try {
Utils.tryLogNonFatalError {
// Release memory used by this thread for unrolling blocks
SparkEnv.get.blockManager.memoryStore.releaseUnrollMemoryForThisTask(MemoryMode.ON_HEAP)
SparkEnv.get.blockManager.memoryStore.releaseUnrollMemoryForThisTask(
MemoryMode.OFF_HEAP)
// Notify any tasks waiting for execution memory to be freed to wake up and try to
// acquire memory again. This makes impossible the scenario where a task sleeps forever
// because there are no other tasks left to notify it. Since this is safe to do but may
// not be strictly necessary, we should revisit whether we can remove this in the
// future.
val memoryManager = SparkEnv.get.memoryManager
memoryManager.synchronized { memoryManager.notifyAll() }
}
} finally {
// Though we unset the ThreadLocal here, the context member variable itself is still
// queried directly in the TaskRunner to check for FetchFailedExceptions.
TaskContext.unset()
}
}
}
}
private var taskMemoryManager: TaskMemoryManager = _
def setTaskMemoryManager(taskMemoryManager: TaskMemoryManager): Unit = {
this.taskMemoryManager = taskMemoryManager
}
def runTask(context: TaskContext): T
def preferredLocations: Seq[TaskLocation] = Nil
// Map output tracker epoch. Will be set by TaskSetManager.
var epoch: Long = -1
// Task context, to be initialized in run().
@transient var context: TaskContext = _
// The actual Thread on which the task is running, if any. Initialized in run().
@volatile @transient private var taskThread: Thread = _
// If non-null, this task has been killed and the reason is as specified. This is used in case
// context is not yet initialized when kill() is invoked.
@volatile @transient private var _reasonIfKilled: String = null
protected var _executorDeserializeTime: Long = 0
protected var _executorDeserializeCpuTime: Long = 0
/**
* If defined, this task has been killed and this option contains the reason.
*/
def reasonIfKilled: Option[String] = Option(_reasonIfKilled)
/**
* Returns the amount of time spent deserializing the RDD and function to be run.
*/
def executorDeserializeTime: Long = _executorDeserializeTime
def executorDeserializeCpuTime: Long = _executorDeserializeCpuTime
/**
* Collect the latest values of accumulators used in this task. If the task failed,
* filter out the accumulators whose values should not be included on failures.
*/
def collectAccumulatorUpdates(taskFailed: Boolean = false): Seq[AccumulatorV2[_, _]] = {
if (context != null) {
// Note: internal accumulators representing task metrics always count failed values
context.taskMetrics.nonZeroInternalAccums() ++
// zero value external accumulators may still be useful, e.g. SQLMetrics, we should not
// filter them out.
context.taskMetrics.externalAccums.filter(a => !taskFailed || a.countFailedValues)
} else {
Seq.empty
}
}
/**
* Kills a task by setting the interrupted flag to true. This relies on the upper level Spark
* code and user code to properly handle the flag. This function should be idempotent so it can
* be called multiple times.
* If interruptThread is true, we will also call Thread.interrupt() on the Task's executor thread.
*/
def kill(interruptThread: Boolean, reason: String) {
require(reason != null)
_reasonIfKilled = reason
if (context != null) {
context.markInterrupted(reason)
}
if (interruptThread && taskThread != null) {
taskThread.interrupt()
}
}
}
