一、Spark 运行架构
Spark 运行架构如下图：
各个RDD之间存在着依赖关系，这些依赖关系形成有向无环图DAG，DAGScheduler对这些依赖关系形成的DAG，进行Stage划分，划分的规则很简单，从后往前回溯，遇到窄依赖加入本stage，遇见宽依赖进行Stage切分。完成了Stage的划分,DAGScheduler基于每个Stage生成TaskSet,并将TaskSet提交给TaskScheduler。TaskScheduler 负责具体的task调度,在Worker节点上启动task。
二、源码解析：DAGScheduler中的DAG划分
当RDD触发一个Action操作（如：colllect）后，导致SparkContext.runJob的执行。而在SparkContext的run方法中会调用DAGScheduler的run方法最终调用了DAGScheduler的submit方法：
def submitJob[T, U]( rdd: RDD[T], func: (TaskContext, Iterator[T]) => U, partitions: Seq[Int], callSite: CallSite, resultHandler: (Int, U) => Unit, properties: Properties): JobWaiter[U] = { // Check to make sure we are not launching a task on a partition that does not exist. val maxPartitions = rdd.partitions.length partitions.find(p => p >= maxPartitions || p < 0).foreach { p => throw new IllegalArgumentException( "Attempting to access a non-existent partition: " + p + ". " + "Total number of partitions: " + maxPartitions) } val jobId = nextJobId.getAndIncrement() if (partitions.size == 0) { // Return immediately if the job is running 0 tasks return new JobWaiter[U](this, jobId, 0, resultHandler) } assert(partitions.size > 0) val func2 = func.asInstanceOf[(TaskContext, Iterator[_]) => _] val waiter = new JobWaiter(this, jobId, partitions.size, resultHandler) //给eventProcessLoop发送JobSubmitted消息 eventProcessLoop.post(JobSubmitted( jobId, rdd, func2, partitions.toArray, callSite, waiter, SerializationUtils.clone(properties))) waiter }DAGScheduler的submit方法中，像eventProcessLoop对象发送了JobSubmitted消息。eventProcessLoop是DAGSchedulerEventProcessLoop类的对象
private[scheduler] val eventProcessLoop = new DAGSchedulerEventProcessLoop(this)
DAGSchedulerEventProcessLoop，接收各种消息并进行处理，处理的逻辑在其doOnReceive方法中：
private def doOnReceive(event: DAGSchedulerEvent): Unit = event match { //Job提交 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 => dagScheduler.handleTaskCompletion(completion) case TaskSetFailed(taskSet, reason, exception) => dagScheduler.handleTaskSetFailed(taskSet, reason, exception) case ResubmitFailedStages => dagScheduler.resubmitFailedStages() }可以把DAGSchedulerEventProcessLoop理解成DAGScheduler的对外的功能接口。它对外隐藏了自己内部实现的细节。无论是内部还是外部消息，DAGScheduler可以共用同一消息处理代码，逻辑清晰，处理方式统一。
接下来分析DAGScheduler的Stage划分，handleJobSubmitted方法首先创建ResultStage
try { //创建新stage可能出现异常，比如job运行依赖hdfs文文件被删除 finalStage = newResultStage(finalRDD, func, partitions, jobId, callSite) } catch { case e: Exception => logWarning("Creating new stage failed due to exception - job: " + jobId, e) listener.jobFailed(e) return }然后调用submitStage方法，进行stage的划分。
首先由finalRDD获取它的父RDD依赖，判断依赖类型，如果是窄依赖，则将父RDD压入栈中，如果是宽依赖，则作为父Stage。
看一下源码的具体过程:
private def getMissingParentStages(stage: Stage): List[Stage] = { val missing = new HashSet[Stage] //存储需要返回的父Stage val visited = new HashSet[RDD[_]] //存储访问过的RDD //自己建立栈，以免函数的递归调用导致 val waitingForVisit = new Stack[RDD[_]] def visit(rdd: RDD[_]) { if (!visited(rdd)) { visited += rdd val rddHasUncachedPartitions = getCacheLocs(rdd).contains(Nil) if (rddHasUncachedPartitions) { for (dep <- rdd.dependencies) { dep match { case shufDep: ShuffleDependency[_, _, _] => val mapStage = getShuffleMapStage(shufDep, stage.firstJobId) if (!mapStage.isAvailable) { missing += mapStage //遇到宽依赖，加入父stage } case narrowDep: NarrowDependency[_] => waitingForVisit.push(narrowDep.rdd) //窄依赖入栈, } } } } } //回溯的起始RDD入栈 waitingForVisit.push(stage.rdd) while (waitingForVisit.nonEmpty) { visit(waitingForVisit.pop()) } missing.toList }getMissingParentStages方法是由当前stage，返回他的父stage，父stage的创建由getShuffleMapStage返回，最终会调用newOrUsedShuffleStage方法返回ShuffleMapStage
private def newOrUsedShuffleStage( shuffleDep: ShuffleDependency[_, _, _], firstJobId: Int): ShuffleMapStage = { val rdd = shuffleDep.rdd val numTasks = rdd.partitions.length val stage = newShuffleMapStage(rdd, numTasks, shuffleDep, firstJobId, rdd.creationSite) if (mapOutputTracker.containsShuffle(shuffleDep.shuffleId)) { //Stage已经被计算过，从MapOutputTracker中获取计算结果 val serLocs = mapOutputTracker.getSerializedMapOutputStatuses(shuffleDep.shuffleId) val locs = MapOutputTracker.deserializeMapStatuses(serLocs) (0 until locs.length).foreach { i => if (locs(i) ne null) { // locs(i) will be null if missing stage.addOutputLoc(i, locs(i)) } } } else { // Kind of ugly: need to register RDDs with the cache and map output tracker here // since we can't do it in the RDD constructor because # of partitions is unknown logInfo("Registering RDD " + rdd.id + " (" + rdd.getCreationSite + ")") mapOutputTracker.registerShuffle(shuffleDep.shuffleId, rdd.partitions.length) } stage }现在父Stage已经划分好，下面看看你Stage的提交逻辑
/** Submits stage, but first recursively submits any missing parents. */ private def submitStage(stage: Stage) { val jobId = activeJobForStage(stage) if (jobId.isDefined) { logDebug("submitStage(" + stage + ")") if (!waitingStages(stage) && !runningStages(stage) && !failedStages(stage)) { val missing = getMissingParentStages(stage).sortBy(_.id) logDebug("missing: " + missing) if (missing.isEmpty) { logInfo("Submitting " + stage + " (" + stage.rdd + "), which has no missing parents") //如果没有父stage，则提交当前stage submitMissingTasks(stage, jobId.get) } else { for (parent <- missing) { //如果有父stage，则递归提交父stage submitStage(parent) } waitingStages += stage } } } else { abortStage(stage, "No active job for stage " + stage.id, None) } }提交的过程很简单，首先当前stage获取父stage，如果父stage为空，则当前Stage为起始stage，交给submitMissingTasks处理，如果当前stage不为空，则递归调用submitStage进行提交。
到这里，DAGScheduler中的DAG划分与提交就讲完了，下次解析这些stage是如果封装成TaskSet交给TaskScheduler以及TaskSchedule的调度过程。
来源：https://www.cnblogs.com/zhouyf/p/5687071.html