（图片来源：北风网）

进去RDD，随便点击一个action操作,比如foreach操作

  /**
   * Applies a function f to all elements of this RDD.
   */
  def foreach(f: T => Unit): Unit = withScope {
    val cleanF = sc.clean(f)
    sc.runJob(this, (iter: Iterator[T]) => iter.foreach(cleanF))
  }

进入runJob看看，来到了SparkContext的runJob

 /**
   * Run a job on all partitions in an RDD and return the results in an array.
   */
  def runJob[T, U: ClassTag](rdd: RDD[T], func: Iterator[T] => U): Array[U] = {
    runJob(rdd, func, 0 until rdd.partitions.length)
  }

再进

 /**
   * Run a job on a given set of partitions of an RDD, but take a function of type
   * `Iterator[T] => U` instead of `(TaskContext, Iterator[T]) => U`.
   */
  def runJob[T, U: ClassTag](
      rdd: RDD[T],
      func: Iterator[T] => U,
      partitions: Seq[Int]): Array[U] = {
    val cleanedFunc = clean(func)
    runJob(rdd, (ctx: TaskContext, it: Iterator[T]) => cleanedFunc(it), partitions)
  }

再进

 /**
   * Run a function on a given set of partitions in an RDD and return the results as an array.
   */
  def runJob[T, U: ClassTag](
      rdd: RDD[T],
      func: (TaskContext, Iterator[T]) => U,
      partitions: Seq[Int]): Array[U] = {
    val results = new Array[U](partitions.size)
    runJob[T, U](rdd, func, partitions, (index, res) => results(index) = res)
    results
  }

还要进

/**
   * Run a function on a given set of partitions in an RDD and pass the results to the given
   * handler function. This is the main entry point for all actions in Spark.
   */
  def runJob[T, U: ClassTag](
      rdd: RDD[T],
      func: (TaskContext, Iterator[T]) => U,
      partitions: Seq[Int],
      resultHandler: (Int, U) => Unit): Unit = {
    if (stopped.get()) {
      throw new IllegalStateException("SparkContext has been shutdown")
    }
    val callSite = getCallSite
    val cleanedFunc = clean(func)
    logInfo("Starting job: " + callSite.shortForm)
    if (conf.getBoolean("spark.logLineage", false)) {
      logInfo("RDD's recursive dependencies:\n" + rdd.toDebugString)
    }
    dagScheduler.runJob(rdd, cleanedFunc, partitions, callSite, resultHandler, localProperties.get)
    progressBar.foreach(_.finishAll())
    rdd.doCheckpoint()
  }

到这，可以看到出现了dagScheduler.runJob

进去看看

  def runJob[T, U](
      rdd: RDD[T],
      func: (TaskContext, Iterator[T]) => U,
      partitions: Seq[Int],
      callSite: CallSite,
      resultHandler: (Int, U) => Unit,
      properties: Properties): Unit = {
    val start = System.nanoTime
    val waiter = submitJob(rdd, func, partitions, callSite, resultHandler, properties)
    // Note: Do not call Await.ready(future) because that calls `scala.concurrent.blocking`,
    // which causes concurrent SQL executions to fail if a fork-join pool is used. Note that
    // due to idiosyncrasies in Scala, `awaitPermission` is not actually used anywhere so it's
    // safe to pass in null here. For more detail, see SPARK-13747.
    val awaitPermission = null.asInstanceOf[scala.concurrent.CanAwait]
    waiter.completionFuture.ready(Duration.Inf)(awaitPermission)
    waiter.completionFuture.value.get match {
      case scala.util.Success(_) =>
        logInfo("Job %d finished: %s, took %f s".format
          (waiter.jobId, callSite.shortForm, (System.nanoTime - start) / 1e9))
      case scala.util.Failure(exception) =>
        logInfo("Job %d failed: %s, took %f s".format
          (waiter.jobId, callSite.shortForm, (System.nanoTime - start) / 1e9))
        // SPARK-8644: Include user stack trace in exceptions coming from DAGScheduler.
        val callerStackTrace = Thread.currentThread().getStackTrace.tail
        exception.setStackTrace(exception.getStackTrace ++ callerStackTrace)
        throw exception
    }
  }

可以看到，进去了一个submitJob，后面只是判断执行结果

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)
    eventProcessLoop.post(JobSubmitted(
      jobId, rdd, func2, partitions.toArray, callSite, waiter,
      SerializationUtils.clone(properties)))
    waiter
  }

可以看到，new JobWaiter中有一个eventProcessLoop，点进去
是DAGScheduler里重要的一个东西，叫DAGSchedulerEventProcessLoop

 private[scheduler] val eventProcessLoop = new DAGSchedulerEventProcessLoop(this)
  taskScheduler.setDAGScheduler(this)

进去DAGSchedulerEventProcessLoop，发现它有一个方法

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, reason) =>
      val filesLost = reason match {
        case SlaveLost(_, true) => true
        case _ => false
      }
      dagScheduler.handleExecutorLost(execId, filesLost)

    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()
  }

我们刚才是从JobSubmit进来的，所以看到case JobSubmit，可以看到调用了dagScheduler.handleJobSubmitted

再进到.handleJobSubmitted看看

private[scheduler] def handleJobSubmitted(jobId: Int,
      finalRDD: RDD[_],
      func: (TaskContext, Iterator[_]) => _,
      partitions: Array[Int],
      callSite: CallSite,
      listener: JobListener,
      properties: Properties) {
	//第一步：使用触发job 的最后一个RDD，创建finalStage
    var finalStage: ResultStage = null
    try {
      // New stage creation may throw an exception if, for example, jobs are run on a
      // HadoopRDD whose underlying HDFS files have been deleted.
      //可以看到这里传入finalRDD,调用createResultStage
      finalStage = createResultStage(finalRDD, func, partitions, jobId, callSite)
    } catch { 
      case e: Exception =>
        logWarning("Creating new stage failed due to exception - job: " + jobId, e)
        listener.jobFailed(e)
        return
    }
	//第二步：使用finalStage创建一个job
	//job的最后一个stage就是finalStage
    val job = new ActiveJob(jobId, finalStage, callSite, listener, properties)
    clearCacheLocs()
    logInfo("Got job %s (%s) with %d output partitions".format(
      job.jobId, callSite.shortForm, partitions.length))
    logInfo("Final stage: " + finalStage + " (" + finalStage.name + ")")
    logInfo("Parents of final stage: " + finalStage.parents)
    logInfo("Missing parents: " + getMissingParentStages(finalStage))

    val jobSubmissionTime = clock.getTimeMillis()
    //第三步：将job加入内存缓存中
    jobIdToActiveJob(jobId) = job
    activeJobs += job
    finalStage.setActiveJob(job)
    val stageIds = jobIdToStageIds(jobId).toArray
    val stageInfos = stageIds.flatMap(id => stageIdToStage.get(id).map(_.latestInfo))
    listenerBus.post(
      SparkListenerJobStart(job.jobId, jobSubmissionTime, stageInfos, properties))
	//第四步：使用finalStage提交Stage
    submitStage(finalStage)
  }

这个就是DAGScheduler的job调度的核心入口

进入createResultStage看看

  /**
   * Create a ResultStage associated with the provided jobId.
   */
  private def createResultStage(
      rdd: RDD[_],
      func: (TaskContext, Iterator[_]) => _,
      partitions: Array[Int],
      jobId: Int,
      callSite: CallSite): ResultStage = {
    val parents = getOrCreateParentStages(rdd, jobId)
    val id = nextStageId.getAndIncrement()
    //创建ResultStage
    val stage = new ResultStage(id, rdd, func, partitions, parents, jobId, callSite)
    stageIdToStage(id) = stage
    updateJobIdStageIdMaps(jobId, stage)
    stage
  }

创建了stage并返回

进入第四步的submitStage看看

/** 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)) {
		
	//这里，调用了getMissingParentStages去获取当前stage的父stage，很重要
        val missing = getMissingParentStages(stage).sortBy(_.id)
        logDebug("missing: " + missing)
        if (missing.isEmpty) {
          logInfo("Submitting " + stage + " (" + stage.rdd + "), which has no missing parents")
          submitMissingTasks(stage, jobId.get)
        } else {
          for (parent <- missing) {
            submitStage(parent)
          }
          waitingStages += stage
        }
      }
    } else {
      abortStage(stage, "No active job for stage " + stage.id, None)
    }
  }

跟进getMissingParentStages看看，这里涉及了stage的划分算法

private def getMissingParentStages(stage: Stage): List[Stage] = {
    val missing = new HashSet[Stage]
    val visited = new HashSet[RDD[_]]
    // We are manually maintaining a stack here to prevent StackOverflowError
    // caused by recursively visiting
	//初始化了一个栈
    val waitingForVisit = new Stack[RDD[_]]
    //while中进来visit
    def visit(rdd: RDD[_]) {
      if (!visited(rdd)) {
        visited += rdd
        val rddHasUncachedPartitions = getCacheLocs(rdd).contains(Nil)
        if (rddHasUncachedPartitions) {
			//遍历rdd的依赖
          for (dep <- rdd.dependencies) {
            dep match {
            //宽依赖
              case shufDep: ShuffleDependency[_, _, _] =>
              //在这使用宽依赖的rdd，创建stage，并将isShuffleMap设置为true，默认最后一个stage不是shuffleMap stage，之前都是shuffleMap stage
                val mapStage = getOrCreateShuffleMapStage(shufDep, stage.firstJobId)
                if (!mapStage.isAvailable) {
                  missing += mapStage
                }
              //窄依赖，直接放栈中，类似深搜
              case narrowDep: NarrowDependency[_] =>
                waitingForVisit.push(narrowDep.rdd)
            }
          }
        }
      }
    }
    //先往栈里push了一个rdd
    waitingForVisit.push(stage.rdd)
    //如果栈不空
    while (waitingForVisit.nonEmpty) {
    //对栈中的最后一个rdd，调用visit，
      visit(waitingForVisit.pop())
    }
    missing.toList
  }

这个宽窄依赖的逻辑区别就说明了：stage的区分是靠宽依赖窄依赖来区别的，即，stage的划分算法就是这个

进入宽依赖的getOrCreateShuffleMapStage看看

 /**
   * Gets a shuffle map stage if one exists in shuffleIdToMapStage. Otherwise, if the
   * shuffle map stage doesn't already exist, this method will create the shuffle map stage in
   * addition to any missing ancestor shuffle map stages.
   * 如果 shuffleIdToMapStage 中存在一个随机映射阶段，则获取一个随机映射阶段。否则，如果 shuffle map 阶段尚不存在，除了任何缺失的祖先 shuffle map 阶段之外，此方法还将创建 shuffle map 阶段
   */
  private def getOrCreateShuffleMapStage(
      shuffleDep: ShuffleDependency[_, _, _],
      firstJobId: Int): ShuffleMapStage = {
    shuffleIdToMapStage.get(shuffleDep.shuffleId) match {
      case Some(stage) =>
        stage

      case None =>
        // Create stages for all missing ancestor shuffle dependencies.
        getMissingAncestorShuffleDependencies(shuffleDep.rdd).foreach { dep =>
          // Even though getMissingAncestorShuffleDependencies only returns shuffle dependencies
          // that were not already in shuffleIdToMapStage, it's possible that by the time we
          // get to a particular dependency in the foreach loop, it's been added to
          // shuffleIdToMapStage by the stage creation process for an earlier dependency. See
          // SPARK-13902 for more information.
          if (!shuffleIdToMapStage.contains(dep.shuffleId)) {
            createShuffleMapStage(dep, firstJobId)
          }
        }
        // Finally, create a stage for the given shuffle dependency.
        createShuffleMapStage(shuffleDep, firstJobId)
    }
  }

进入最后的createShuffleMapStage

/**
   * Creates a ShuffleMapStage that generates the given shuffle dependency's partitions. If a
   * previously run stage generated the same shuffle data, this function will copy the output
   * locations that are still available from the previous shuffle to avoid unnecessarily
   * regenerating data.
   */
  def createShuffleMapStage(shuffleDep: ShuffleDependency[_, _, _], jobId: Int): ShuffleMapStage = {
    val rdd = shuffleDep.rdd
    val numTasks = rdd.partitions.length
    val parents = getOrCreateParentStages(rdd, jobId)
    val id = nextStageId.getAndIncrement()
    val stage = new ShuffleMapStage(id, rdd, numTasks, parents, jobId, rdd.creationSite, shuffleDep)

    stageIdToStage(id) = stage
    shuffleIdToMapStage(shuffleDep.shuffleId) = stage
    updateJobIdStageIdMaps(jobId, stage)

    if (mapOutputTracker.containsShuffle(shuffleDep.shuffleId)) {
      // A previously run stage generated partitions for this shuffle, so for each output
      // that's still available, copy information about that output location to the new stage
      // (so we don't unnecessarily re-compute that data).
      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)
    }
		//返回一个stage
    stage
  }

OK，回到submitStage，stage划分算法实际上是由getMissingParentStages和submitStage共同组成的，为了方便，再拷下来看看

/** 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)) {
		
	//这里，调用了getMissingParentStages去获取当前stage的父stage，很重要
        val missing = getMissingParentStages(stage).sortBy(_.id)
        logDebug("missing: " + missing)
		
		//如果没有父stage
        if (missing.isEmpty) {
          logInfo("Submitting " + stage + " (" + stage.rdd + "), which has no missing parents")
          submitMissingTasks(stage, jobId.get)
        } else {
		//如果拿到了父stage，则递归调用submitStage，提交父stage，并将当前stage ，放入waitingStages队列中等待执行
		//这里的递归就是stage划分算法的精髓
		//递归直到最初的stage没有父stage
		//然后提交第一个stage，
          for (parent <- missing) {
            submitStage(parent)
          }
          waitingStages += stage
        }
      }
    } else {
      abortStage(stage, "No active job for stage " + stage.id, None)
    }
  }

stage划分算法很重要，必须知道自己的application被划分为几个job，每个job被分为几个stage，每个stage包括哪些代码，如果出现错误，才能从stage定位到具体代码，从而排查问题，或者性能调优。
（job的划分：action操作；stage划分：宽依赖）

总结：

1. 从finalstage倒退
2. 从宽依赖进行新stage划分
3. 使用递归优先提交父stage

OK，然后来看看submitMissingTasks的细节

 /** Called when stage's parents are available and we can now do its task. */
  private def submitMissingTasks(stage: Stage, jobId: Int) {
    logDebug("submitMissingTasks(" + stage + ")")
    // Get our pending tasks and remember them in our pendingTasks entry
    stage.pendingPartitions.clear()

    // First figure out the indexes of partition ids to compute.
    //计算需要创建的task数量
    val partitionsToCompute: Seq[Int] = stage.findMissingPartitions()

    // Use the scheduling pool, job group, description, etc. from an ActiveJob associated
    // with this Stage
    val properties = jobIdToActiveJob(jobId).properties
	//将stage加入runningStages队列
    runningStages += stage
    // SparkListenerStageSubmitted should be posted before testing whether tasks are
    // serializable. If tasks are not serializable, a SparkListenerStageCompleted event
    // will be posted, which should always come after a corresponding SparkListenerStageSubmitted
    // event.
    stage match {
      case s: ShuffleMapStage =>
        outputCommitCoordinator.stageStart(stage = s.id, maxPartitionId = s.numPartitions - 1)
      case s: ResultStage =>
        outputCommitCoordinator.stageStart(
          stage = s.id, maxPartitionId = s.rdd.partitions.length - 1)
    }
    val taskIdToLocations: Map[Int, Seq[TaskLocation]] = try {
      stage match {
        case s: ShuffleMapStage =>
          partitionsToCompute.map { id => (id, getPreferredLocs(stage.rdd, id))}.toMap
        case s: ResultStage =>
          partitionsToCompute.map { id =>
            val p = s.partitions(id)
            (id, getPreferredLocs(stage.rdd, p))
          }.toMap
      }
    } catch {
      case NonFatal(e) =>
        stage.makeNewStageAttempt(partitionsToCompute.size)
        listenerBus.post(SparkListenerStageSubmitted(stage.latestInfo, properties))
        abortStage(stage, s"Task creation failed: $e\n${Utils.exceptionString(e)}", Some(e))
        runningStages -= stage
        return
    }

    stage.makeNewStageAttempt(partitionsToCompute.size, taskIdToLocations.values.toSeq)
    listenerBus.post(SparkListenerStageSubmitted(stage.latestInfo, properties))

    // TODO: Maybe we can keep the taskBinary in Stage to avoid serializing it multiple times.
    // Broadcasted binary for the task, used to dispatch tasks to executors. Note that we broadcast
    // the serialized copy of the RDD and for each task we will deserialize it, which means each
    // task gets a different copy of the RDD. This provides stronger isolation between tasks that
    // might modify state of objects referenced in their closures. This is necessary in Hadoop
    // where the JobConf/Configuration object is not thread-safe.
    var taskBinary: Broadcast[Array[Byte]] = null
    try {
      // For ShuffleMapTask, serialize and broadcast (rdd, shuffleDep).
      // For ResultTask, serialize and broadcast (rdd, func).
      val taskBinaryBytes: Array[Byte] = stage match {
        case stage: ShuffleMapStage =>
          JavaUtils.bufferToArray(
            closureSerializer.serialize((stage.rdd, stage.shuffleDep): AnyRef))
        case stage: ResultStage =>
          JavaUtils.bufferToArray(closureSerializer.serialize((stage.rdd, stage.func): AnyRef))
      }

      taskBinary = sc.broadcast(taskBinaryBytes)
    } catch {
      // In the case of a failure during serialization, abort the stage.
      case e: NotSerializableException =>
        abortStage(stage, "Task not serializable: " + e.toString, Some(e))
        runningStages -= stage

        // Abort execution
        return
      case NonFatal(e) =>
        abortStage(stage, s"Task serialization failed: $e\n${Utils.exceptionString(e)}", Some(e))
        runningStages -= stage
        return
    }
	//为stage创建tasks
    val tasks: Seq[Task[_]] = try {
      stage match {
        case stage: ShuffleMapStage =>
          partitionsToCompute.map { id =>
          //给每个partition创建task
          //给每个task计算最佳位置
            val locs = taskIdToLocations(id)
            val part = stage.rdd.partitions(id)
            new ShuffleMapTask(stage.id, stage.latestInfo.attemptId,
              taskBinary, part, locs, stage.latestInfo.taskMetrics, properties, Option(jobId),
              Option(sc.applicationId), sc.applicationAttemptId)
          }

        case stage: ResultStage =>
          partitionsToCompute.map { id =>
            val p: Int = stage.partitions(id)
            val part = stage.rdd.partitions(p)
            val locs = taskIdToLocations(id)
            new ResultTask(stage.id, stage.latestInfo.attemptId,
              taskBinary, part, locs, id, properties, stage.latestInfo.taskMetrics,
              Option(jobId), Option(sc.applicationId), sc.applicationAttemptId)
          }
      }
    } catch {
      case NonFatal(e) =>
        abortStage(stage, s"Task creation failed: $e\n${Utils.exceptionString(e)}", Some(e))
        runningStages -= stage
        return
    }

    if (tasks.size > 0) {
      logInfo("Submitting " + tasks.size + " missing tasks from " + stage + " (" + stage.rdd + ")")
      stage.pendingPartitions ++= tasks.map(_.partitionId)
      logDebug("New pending partitions: " + stage.pendingPartitions)
      //针对stage的task，创建taskSet对象，调用TaskScheuler的submitTasks
      taskScheduler.submitTasks(new TaskSet(
        tasks.toArray, stage.id, 	stage.latestInfo.attemptId, jobId, properties))
      stage.latestInfo.submissionTime = Some(clock.getTimeMillis())
    } else {
      // Because we posted SparkListenerStageSubmitted earlier, we should mark
      // the stage as completed here in case there are no tasks to run
      markStageAsFinished(stage, None)

      val debugString = stage match {
        case stage: ShuffleMapStage =>
          s"Stage ${stage} is actually done; " +
            s"(available: ${stage.isAvailable}," +
            s"available outputs: ${stage.numAvailableOutputs}," +
            s"partitions: ${stage.numPartitions})"
        case stage : ResultStage =>
          s"Stage ${stage} is actually done; (partitions: ${stage.numPartitions})"
      }
      logDebug(debugString)

      submitWaitingChildStages(stage)
    }
  }

其中涉及的计算task最佳位置算法
taskIdToLocations(id)

val taskIdToLocations: Map[Int, Seq[TaskLocation]] = try {
      stage match {
        case s: ShuffleMapStage =>
          partitionsToCompute.map { id => (id, getPreferredLocs(stage.rdd, id))}.toMap
        case s: ResultStage =>
          partitionsToCompute.map { id =>
            val p = s.partitions(id)
            (id, getPreferredLocs(stage.rdd, p))
          }.toMap
      }

进入getPreferredLocs

  /**
   * Gets the locality information associated with a partition of a particular RDD.
   *
   * This method is thread-safe and is called from both DAGScheduler and SparkContext.
   *获取与特定 RDD 的分区关联的位置信息。此方法是线程安全的，可从 DAGScheduler 和 SparkContext 调用
   * @param rdd whose partitions are to be looked at
   * @param partition to lookup locality information for
   * @return list of machines that are preferred by the partition
   */
  private[spark]
  def getPreferredLocs(rdd: RDD[_], partition: Int): Seq[TaskLocation] = {
    getPreferredLocsInternal(rdd, partition, new HashSet)
  }

进入getPreferredLocsInternal，计算每个task的partition的最佳位置，其实就是从stage的最后一个rdd开始，寻找哪些rdd被cache或者checkpoint了，然后task 的最佳位置就是被缓存的partition的位置，这样task就在那个节点上执行，不需要计算之前的rdd

/**
   * Recursive implementation for getPreferredLocs.
   *
   * This method is thread-safe because it only accesses DAGScheduler state through thread-safe
   * methods (getCacheLocs()); please be careful when modifying this method, because any new
   * DAGScheduler state accessed by it may require additional synchronization.
   */
  private def getPreferredLocsInternal(
      rdd: RDD[_],
      partition: Int,
      visited: HashSet[(RDD[_], Int)]): Seq[TaskLocation] = {
    // If the partition has already been visited, no need to re-visit.
    // This avoids exponential path exploration.  SPARK-695
    if (!visited.add((rdd, partition))) {
      // Nil has already been returned for previously visited partitions.
      return Nil
    }
    // If the partition is cached, return the cache locations
    //寻找是否缓存
    val cached = getCacheLocs(rdd)(partition)
    if (cached.nonEmpty) {
      return cached
    }
    // If the RDD has some placement preferences (as is the case for input RDDs), get those
    //寻找是否checkpoint
    val rddPrefs = rdd.preferredLocations(rdd.partitions(partition)).toList
    if (rddPrefs.nonEmpty) {
      return rddPrefs.map(TaskLocation(_))
    }

    // If the RDD has narrow dependencies, pick the first partition of the first narrow dependency
    // that has any placement preferences. Ideally we would choose based on transfer sizes,
    // but this will do for now.
    //递归调用自己，寻找父RDD，是否缓存或者checkpoint
    rdd.dependencies.foreach {
      case n: NarrowDependency[_] =>
        for (inPart <- n.getParents(partition)) {
          val locs = getPreferredLocsInternal(n.rdd, inPart, visited)
          if (locs != Nil) {
            return locs
          }
        }

      case _ =>
    }
	//如果从最后一个rdd到最开始的rdd，都没有chche或者checkpoint，那就没有最佳位置
    Nil
  }

计算完了task的最佳位置，还要看如何分配task，涉及askScheduler，后面再看