本节书摘来自华章社区《深入理解Spark:核心思想与源码分析》一书中的第3章，第3.7节创建和启动DAGScheduler，作者耿嘉安，更多章节内容可以访问云栖社区“华章社区”公众号查看

3.7　创建和启动DAGScheduler
DAGScheduler主要用于在任务正式交给TaskSchedulerImpl提交之前做一些准备工作，包括：创建Job，将DAG中的RDD划分到不同的Stage，提交Stage，等等。创建DAG-Scheduler的代码如下。

@volatile private[spark] var dagScheduler: DAGScheduler = _
    dagScheduler = new DAGScheduler(this)

DAGScheduler的数据结构主要维护jobId和stageId的关系、Stage、ActiveJob，以及缓存的RDD的partitions的位置信息，见代码清单3-32。
代码清单3-32　DAGScheduler维护的数据结构

private[scheduler] val nextJobId = new AtomicInteger(0)
private[scheduler] def numTotalJobs: Int = nextJobId.get()
private val nextStageId = new AtomicInteger(0)

private[scheduler] val jobIdToStageIds = new HashMap[Int, HashSet[Int]]
private[scheduler] val stageIdToStage = new HashMap[Int, Stage]
private[scheduler] val shuffleToMapStage = new HashMap[Int, Stage]
private[scheduler] val jobIdToActiveJob = new HashMap[Int, ActiveJob]

    // Stages we need to run whose parents aren't done
    private[scheduler] val waitingStages = new HashSet[Stage]
    // Stages we are running right now
    private[scheduler] val runningStages = new HashSet[Stage]
    // Stages that must be resubmitted due to fetch failures
    private[scheduler] val failedStages = new HashSet[Stage]

    private[scheduler] val activeJobs = new HashSet[ActiveJob]

    // Contains the locations that each RDD's partitions are cached on
    private val cacheLocs = new HashMap[Int, Array[Seq[TaskLocation]]]
    private val failedEpoch = new HashMap[String, Long]

    private val dagSchedulerActorSupervisor =
        env.actorSystem.actorOf(Props(new DAGSchedulerActorSupervisor(this)))

    private val closureSerializer = SparkEnv.get.closureSerializer.newInstance()
在构造DAGScheduler的时候会调用initializeEventProcessActor方法创建DAGScheduler-EventProcessActor，见代码清单3-33。
代码清单3-33　DAGSchedulerEventProcessActor的初始化
    private[scheduler] var eventProcessActor: ActorRef = _
private def initializeEventProcessActor() {
        // blocking the thread until supervisor is started, which ensures eventProcess-Actor is
        // not null before any job is submitted
        implicit val timeout = Timeout(30 seconds)
        val initEventActorReply =
            dagSchedulerActorSupervisor ? Props(new DAGSchedulerEventProcessActor(this))
        eventProcessActor = Await.result(initEventActorReply, timeout.duration).
            asInstanceOf[ActorRef]
}

initializeEventProcessActor()

这里的DAGSchedulerActorSupervisor主要作为DAGSchedulerEventProcessActor的监管者，负责生成DAGSchedulerEventProcessActor。从代码清单3-34可以看出，DAGScheduler-ActorSupervisor对于DAGSchedulerEventProcessActor采用了Akka的一对一监管策略。DAG-SchedulerActorSupervisor一旦生成DAGSchedulerEventProcessActor，并注册到ActorSystem，ActorSystem就会调用DAGSchedulerEventProcessActor的preStart，taskScheduler于是就持有了dagScheduler，见代码清单3-35。从代码清单3-35我们还看到DAG-SchedulerEventProcessActor所能处理的消息类型，比如JobSubmitted、BeginEvent、CompletionEvent等。DAGScheduler-EventProcessActor接受这些消息后会有不同的处理动作。在本章，读者只需要理解到这里即可，后面章节用到时会详细分析。
代码清单3-34　DAGSchedulerActorSupervisor的监管策略

private[scheduler] class DAGSchedulerActorSupervisor(dagScheduler: DAGScheduler)
    extends Actor with Logging {

    override val supervisorStrategy =
        OneForOneStrategy() {
            case x: Exception =>
                logError("eventProcesserActor failed; shutting down SparkContext", x)
                try {
                    dagScheduler.doCancelAllJobs()
                } catch {
                    case t: Throwable => logError("DAGScheduler failed to cancel all jobs.", t)
                }
                dagScheduler.sc.stop()
                Stop
    }

def receive = {
        case p: Props => sender ! context.actorOf(p)
        case _ => logWarning("received unknown message in DAGSchedulerActorSupervisor")
    }
}
代码清单3-35　DAGSchedulerEventProcessActor的实现
private[scheduler] class DAGSchedulerEventProcessActor(dagScheduler: DAGS-cheduler)
    extends Actor with Logging {
    override def preStart() {
        dagScheduler.taskScheduler.setDAGScheduler(dagScheduler)
    }
    /**
    * The main event loop of the DAG scheduler.
    */
    def receive = {
        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()
}
override def postStop() {
    // Cancel any active jobs in postStop hook
    dagScheduler.cleanUpAfterSchedulerStop()
}