spark源码分析之任务提交（一）Rdd#collect方法分析

本文分析调用RDD的collect方法后的任务提交流程。RDD的collect方法调用了SprakContext的runJob方法，之后又调用了重载的runJob方法，然后调用DAGSchulder的runJob方法，然后调用submitJob方法。调用顺序如下。

RDD#collect()

SprakContext#runJob[T, U: ClassTag](rdd: RDD[T], func: Iterator[T] => U)

SparkContext#runJob[T,U:ClassTag](rdd:RDD[T],func:Iterator[T]=>U,partitions: Seq[Int],allowLocal:Boolean)

SparkContext#runJob[T, U: ClassTag](

         rdd: RDD[T],

    func: (TaskContext, Iterator[T]) => U,

    partitions: Seq[Int],

    allowLocal: Boolean

    )

SparkContext#runJob[T, U: ClassTag](

    rdd: RDD[T],

    func: (TaskContext, Iterator[T]) => U,

    partitions: Seq[Int],

    allowLocal: Boolean,

    resultHandler: (Int, U) => Unit)

DAGScheduler#runJob[T, U: ClassTag](

    rdd: RDD[T],

    func: (TaskContext, Iterator[T]) => U,

    partitions: Seq[Int],

    callSite: CallSite,

    allowLocal: Boolean,

    resultHandler: (Int, U) => Unit,

    properties: Properties)

DAGSchduler#submitJob[T, U](

    rdd: RDD[T],

    func: (TaskContext, Iterator[T]) => U,

    partitions: Seq[Int],

    callSite: CallSite,

    allowLocal: Boolean,

    resultHandler: (Int, U) => Unit,

    properties: Properties)

submitJob方法用于将一个Job提交到jobscheduler。submitJob中首先获得Job的最大分区数，确定不会在一个不存在的分区上启动任务（Check to make sure we are not launching a task on a partition thatdoes not exist.）。然后生成当前Job的jobId。然后创建JobWaiter，它用来等待DAGScheduler job的完成，并处理任务完成的结果（An object that waits for a DAGScheduler job to complete. As tasksfinish, it passes their results to the given handler function.），代码如表1，其中resultHandler是runJob中传递过来的函数，闭包了一个数组类型的results。然后向eventProcessLoop发送JobSubmitted事件，eventProcessLoop的类型是DAGSchedulerEventProcessLoop，该类继承了EventLoop，EventLoop用来接收事件event并在专门的event线程处理所有的event事件，内部有一个LinkedBlockingDeque类型的eventQueue用于存放event和一个eventThread用于处理event，即从eventQueue中获取事件，调用onReceive方法处理事件，onReceive()方法由DAGSchedulerEventProcessLoop实现，EventProcessLoop和DAGSchedulerEventProcessLoop#onReceive()的实现如表2和表3。最后返回JobWaiter。

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   def cancel() {     dagScheduler.cancelJob(jobId)   } //Job成功完成，在同步代码块内调用notifyAll方法，通知awaitResult方法内的wait()继续执行。resultHandler负责处理result。在这里用到了闭包，闭包了一个数组类型的results用于存放任务结果。   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()     }   } //Job失败在同步代码块内调用notifyAll方法，通知awaitResult方法内的wait()继续执行。   override def jobFailed(exception: Exception): Unit = synchronized {     _jobFinished = true     jobResult = JobFailed(exception)     this.notifyAll()   } //如果Job还没有完成，在synchronized同步代码块内调用wait方法等待。   def awaitResult(): JobResult = synchronized {     while (!_jobFinished) {       this.wait()     }     return jobResult   } }

表 1JobWaiter实现

private[spark] abstract class EventLoop[E](name: String) extends Logging {   private val eventQueue: BlockingQueue[E] = new LinkedBlockingDeque[E]() //java concurrent包下的阻塞队列，用于存放event事件   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.     }   }   def post(event: E): Unit = {     eventQueue.put(event)   }   def isActive: Boolean = eventThread.isAlive   protected def onStart(): Unit = {}   protected def onStop(): Unit = {}   protected def onReceive(event: E): Unit   protected def onError(e: Throwable): Unit }

表 2EventProcessLoop的实现

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)   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) =>     dagScheduler.handleTaskSetFailed(taskSet, reason)   case ResubmitFailedStages =>     dagScheduler.resubmitFailedStages() }

表 3 DAGSchedulerEventProcessLoop#onReceive()的实现

DAGSchedulerEventProcessLoop中的eventThread处理到JobSubmitted事件，会调用DagScheduler的handleJobSubmitted方法，首先创建finalStage，并对stage进行划分。然后创建ActiveJob并更新Job相关信息。然后向listenerBus发送SparkListenerJobStart事件，提交finalStage。最后提交等待中的Stage。