Spark DAG調度器事件循環處理器

Youtube 視頻

Spark DAG調度器事件循環處理器(Youtube視頻) : https://youtu.be/fT-dpf0KFOA

Bilibili 視頻

Spark DAG調度器事件循環處理器(bilibili視頻) : https://www.bilibili.com/video/av37445034/

DAGSchedulerEventProcessLoop.scala

DAGSchedulerEventProcessLoop(DAG調度器事件循環處理器)繼承抽象類EventLoop(事件循環器)
當調用 post方法增長事件時，其實是往EventLoop中的列表阻塞隊列eventQueue增長元素
EventLoop在DAGScheduler類中結尾處調用 eventProcessLoop.start()
EventLoop中start()方法會調用eventThread中的run()方法，無限循環處理阻塞隊列中的事件,並調用抽像方法onReceive()
子類 DAGSchedulerEventProcessLoop 中進行onReceive()的實現，因此會調用此方法進行實際事件處理操做
處理的事件以下
- JobSubmitted
- MapStageSubmitted
- StageCancelled
- JobCancelled
- JobGroupCancelled
- AllJobsCancelled
- ExecutorAdded
- ExecutorLost
- BeginEvent
- GettingResultEvent
- completion @ CompletionEvent
- TaskSetFailed
- ResubmitFailedStages

private[scheduler] class DAGSchedulerEventProcessLoop(dagScheduler: DAGScheduler)
  extends EventLoop[DAGSchedulerEvent]("dag-scheduler-event-loop") with Logging {

  private[this] val timer = dagScheduler.metricsSource.messageProcessingTimer

  /**
   * The main event loop of the DAG scheduler.
   */
  override def onReceive(event: DAGSchedulerEvent): Unit = {
    val timerContext = timer.time()
    try {
      doOnReceive(event)
    } finally {
      timerContext.stop()
    }
  }

  private def doOnReceive(event: DAGSchedulerEvent): Unit = event match {
    case JobSubmitted(jobId, rdd, func, partitions, callSite, listener, properties) =>
      dagScheduler.handleJobSubmitted(jobId, rdd, func, partitions, callSite, listener, properties)

    case MapStageSubmitted(jobId, dependency, callSite, listener, properties) =>
      dagScheduler.handleMapStageSubmitted(jobId, dependency, callSite, listener, properties)

    case StageCancelled(stageId) =>
      dagScheduler.handleStageCancellation(stageId)

    case JobCancelled(jobId) =>
      dagScheduler.handleJobCancellation(jobId)

    case JobGroupCancelled(groupId) =>
      dagScheduler.handleJobGroupCancelled(groupId)

    case AllJobsCancelled =>
      dagScheduler.doCancelAllJobs()

    case ExecutorAdded(execId, host) =>
      dagScheduler.handleExecutorAdded(execId, host)

    case ExecutorLost(execId) =>
      dagScheduler.handleExecutorLost(execId, fetchFailed = false)

    case BeginEvent(task, taskInfo) =>
      dagScheduler.handleBeginEvent(task, taskInfo)

    case GettingResultEvent(taskInfo) =>
      dagScheduler.handleGetTaskResult(taskInfo)

    case completion @ CompletionEvent(task, reason, _, _, taskInfo, taskMetrics) =>
      dagScheduler.handleTaskCompletion(completion)

    case TaskSetFailed(taskSet, reason, exception) =>
      dagScheduler.handleTaskSetFailed(taskSet, reason, exception)

    case ResubmitFailedStages =>
      dagScheduler.resubmitFailedStages()
  }

  override def onError(e: Throwable): Unit = {
    logError("DAGSchedulerEventProcessLoop failed; shutting down SparkContext", e)
    try {
      dagScheduler.doCancelAllJobs()
    } catch {
      case t: Throwable => logError("DAGScheduler failed to cancel all jobs.", t)
    }
    dagScheduler.sc.stop()
  }

  override def onStop(): Unit = {
    // Cancel any active jobs in postStop hook
    dagScheduler.cleanUpAfterSchedulerStop()
  }
}

EventLoop.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.util

import java.util.concurrent.atomic.AtomicBoolean
import java.util.concurrent.{BlockingQueue, LinkedBlockingDeque}

import scala.util.control.NonFatal

import org.apache.spark.Logging

/**
 * An event loop to receive events from the caller and process all events in the event thread. It
 * will start an exclusive event thread to process all events.
 *
 * Note: The event queue will grow indefinitely. So subclasses should make sure `onReceive` can
 * handle events in time to avoid the potential OOM.
 */
private[spark] abstract class EventLoop[E](name: String) extends Logging {

  private val eventQueue: BlockingQueue[E] = new LinkedBlockingDeque[E]()

  private val stopped = new AtomicBoolean(false)

  private val eventThread = new Thread(name) {
    setDaemon(true)

    override def run(): Unit = {
      try {
        while (!stopped.get) {
          val event = eventQueue.take()
          try {
            onReceive(event)
          } catch {
            case NonFatal(e) => {
              try {
                onError(e)
              } catch {
                case NonFatal(e) => logError("Unexpected error in " + name, e)
              }
            }
          }
        }
      } catch {
        case ie: InterruptedException => // exit even if eventQueue is not empty
        case NonFatal(e) => logError("Unexpected error in " + name, e)
      }
    }

  }

  def start(): Unit = {
    if (stopped.get) {
      throw new IllegalStateException(name + " has already been stopped")
    }
    // Call onStart before starting the event thread to make sure it happens before onReceive
    onStart()
    eventThread.start()
  }

  def stop(): Unit = {
    if (stopped.compareAndSet(false, true)) {
      eventThread.interrupt()
      var onStopCalled = false
      try {
        eventThread.join()
        // Call onStop after the event thread exits to make sure onReceive happens before onStop
        onStopCalled = true
        onStop()
      } catch {
        case ie: InterruptedException =>
          Thread.currentThread().interrupt()
          if (!onStopCalled) {
            // ie is thrown from `eventThread.join()`. Otherwise, we should not call `onStop` since
            // it's already called.
            onStop()
          }
      }
    } else {
      // Keep quiet to allow calling `stop` multiple times.
    }
  }

  /**
   * Put the event into the event queue. The event thread will process it later.
   */
  def post(event: E): Unit = {
    eventQueue.put(event)
  }

  /**
   * Return if the event thread has already been started but not yet stopped.
   */
  def isActive: Boolean = eventThread.isAlive

  /**
   * Invoked when `start()` is called but before the event thread starts.
   */
  protected def onStart(): Unit = {}

  /**
   * Invoked when `stop()` is called and the event thread exits.
   */
  protected def onStop(): Unit = {}

  /**
   * Invoked in the event thread when polling events from the event queue.
   *
   * Note: Should avoid calling blocking actions in `onReceive`, or the event thread will be blocked
   * and cannot process events in time. If you want to call some blocking actions, run them in
   * another thread.
   */
  protected def onReceive(event: E): Unit

  /**
   * Invoked if `onReceive` throws any non fatal error. Any non fatal error thrown from `onError`
   * will be ignored.
   */
  protected def onError(e: Throwable): Unit

}

Spark DAG調度器事件循環處理器

Spark DAG調度器事件循環處理器

更多資源

Youtube 視頻

Bilibili 視頻

DAGSchedulerEventProcessLoop.scala

EventLoop.scala

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