【Spark】DAGScheduler源码浅析2

引入

上一篇文章DAGScheduler源码浅析主要从提交Job的流程角度介绍了DAGScheduler源码中的重要函数和关键点，这篇DAGScheduler源码浅析2主要参考fxjwind的Spark源码分析 – DAGScheduler一文，介绍一下DAGScheduler文件中之前没有介绍的几个重要函数。

事件处理

在Spark 1.0版本之前，在DAGScheduler类中加入eventQueue私有成员，设置eventLoop Thread循环读取事件进行处理。在Spark 1.0源码中，事件处理通过Actor的方式进行，涉及的DAGEventProcessActor类进行主要的事件处理工作。
可能由于scala不再支持原生actor方式，而将akka actor作为官方标准的原因，在我查看Spark 1.4的源码中，DAGScheduler重新采用eventQueue的方式进行事件处理，为了代码逻辑更加清晰，耦合性更小，1.4的源码中编写了DAGSchedulerEventProcessLoop类进行事件处理。

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

这里DAGSchedulerEventProcessLoop继承了EventLoop类，其中：

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)
      }
    }

  }

  ......

我们可以看到，DAGScheduler通过向DAGSchedulerEventProcessLoop对象投递event，即向eventQueue发送event，eventThread不断从eventQueue中获取event并调用onReceive函数进行处理。

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

  ......

JobWaiter

JobWaiter首先实现JobListener的taskSucceeded和jobFailed函数，当DAGScheduler收到tasksuccess或fail的event就会调用相应的函数在tasksuccess会判断当所有task都success时，就表示jobFinished而awaitResult，就是一直等待jobFinished被置位。
可以看到在submitJob函数中创建了JobWaiter实例，作为参数传入的事件实例中，最终在调用handleJobSubmitted函数中，如果发生错误，就会调用JobWaiter的jobFailed函数。

下面是JobWaiter类的代码：

private[spark] class JobWaiter[T](
    dagScheduler: DAGScheduler,
    val jobId: Int,
    totalTasks: Int,
    resultHandler: (Int, T) => Unit)
  extends JobListener {

  private var finishedTasks = 0

  // Is the job as a whole finished (succeeded or failed)?
  @volatile
  private var _jobFinished = totalTasks == 0

  def jobFinished = _jobFinished

  // If the job is finished, this will be its result. In the case of 0 task jobs (e.g. zero
  // partition RDDs), we set the jobResult directly to JobSucceeded.
  private var jobResult: JobResult = if (jobFinished) JobSucceeded else null

  /**
   * Sends a signal to the DAGScheduler to cancel the job. The cancellation itself is handled
   * asynchronously. After the low level scheduler cancels all the tasks belonging to this job, it
   * will fail this job with a SparkException.
   */
  def cancel() {
    dagScheduler.cancelJob(jobId)
  }

  override def taskSucceeded(index: Int, result: Any): Unit = synchronized {
    if (_jobFinished) {
      throw new UnsupportedOperationException("taskSucceeded() called on a finished JobWaiter")
    }
    resultHandler(index, result.asInstanceOf[T])
    finishedTasks += 1
    if (finishedTasks == totalTasks) {
      _jobFinished = true
      jobResult = JobSucceeded
      this.notifyAll()
    }
  }

  override def jobFailed(exception: Exception): Unit = synchronized {
    _jobFinished = true
    jobResult = JobFailed(exception)
    this.notifyAll()
  }

  def awaitResult(): JobResult = synchronized {
    while (!_jobFinished) {
      this.wait()
    }
    return jobResult
  }
}

小结

这一小节内容介绍了DAGScheduler.scala文件中的几个小细节，下一篇文章我会就DAGScheduler.scala文件中stage划分和依赖性进行分析介绍。

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