[大数据]【Spark程序执行3】任务划分

【Spark程序执行1】SparkContext对象构建以及RDD依赖解析
【Spark程序执行2】阶段划分（dagScheduler）
【Spark程序执行3】任务划分

承接【Spark程序执行2】阶段划分，在划分好阶段之后，会提交stage：
如下：

 private[scheduler] def handleJobSubmitted(jobId: Int,
      finalRDD: RDD[_],
      func: (TaskContext, Iterator[_]) => _,
      partitions: Array[Int],
      callSite: CallSite,
      listener: JobListener,
      properties: Properties): Unit = {
    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.
      finalStage = createResultStage(finalRDD, func, partitions, jobId, callSite)
    } catch {
      //异常处理逻辑
    }
    // Job submitted, clear internal data.
    barrierJobIdToNumTasksCheckFailures.remove(jobId)

    val job = new ActiveJob(jobId, finalStage, callSite, listener, properties)
  
  	//其他无用处理逻辑
	
    val jobSubmissionTime = clock.getTimeMillis()
    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))
      //提交stage
    submitStage(finalStage)
  }

理解任务划分原理，继续跟踪submitStage代码：

	
  /** Submits stage, but first recursively submits any missing parents. */
  /**提交阶段，但首先递归地提交任何没有父级的stage,所以真正的提交代码在submitMissingTasks中  */
  private def submitStage(stage: Stage): Unit = {
    val jobId = activeJobForStage(stage)
    if (jobId.isDefined) {
      logDebug(s"submitStage($stage (name=${stage.name};" +
        s"jobs=${stage.jobIds.toSeq.sorted.mkString(",")}))")
      if (!waitingStages(stage) && !runningStages(stage) && !failedStages(stage)) {
      //判断是否有父stage，如果有则提交父stage，如果没有，则提交当前stage
        val missing = getMissingParentStages(stage).sortBy(_.id)
        logDebug("missing: " + missing)
        if (missing.isEmpty) {
          logInfo("Submitting " + stage + " (" + stage.rdd + "), which has no missing parents")
         //一直追溯到没有父阶段的阶段，说明这里是第一个阶段
          submitMissingTasks(stage, jobId.get)
        } else {
          for (parent <- missing) {
            //向前追溯时，如果当前stage还有存在父stage的stage，继续向前追溯，
		  //一直到追溯到没有父stage的stage，即missing.isEmpty
            submitStage(parent)
          }
          waitingStages += stage
        }
      }
    } else {
      abortStage(stage, "No active job for stage " + stage.id, None)
    }
  }

该方法中从finalStage（ResultStage对象实例）开始回溯，直到没有Parents Stage 为止，提交整个job的所有stage，在某一个Stage，DAGScheduler会调用submitMissingTasks方法把Task提交给TaskScheduler进行细粒度的Task调度。所以我们继续关注DAGScheduler的submitMissingTasks方法。

private def submitMissingTasks(stage: Stage, jobId: Int): Unit = {
    logDebug("submitMissingTasks(" + stage + ")")
     ......
    // Figure out the indexes of partition ids to compute.
    // 首先找出要计算的分区ID的索引,要特别关注这个参数，这个参数决定了一个stage划分多少个task
    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

    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.
    //不同类型stage的启动器不同
    //模式匹配，根据不同的类型，构建不同的启动器
    stage match {
      case s: ShuffleMapStage =>
        outputCommitCoordinator.stageStart(stage = s.id, maxPartitionId = s.numPartitions - 1)
        //ResultStage:意味着，该stage为最后的stage
      case s: ResultStage =>
        outputCommitCoordinator.stageStart(
          stage = s.id, maxPartitionId = s.rdd.partitions.length - 1)
    }
     //获取task的数据本地性信息
     //task的本地属性，在后续章节中会讲解
     //task的本地属性：确定数据在哪个节点上，就到哪个节点上的Executor上去运行；
    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
    }
    ......
	
	 //重点：tasks 
	 //这里是生成task的关键点，task类型根据stage类型分为以下两种
	 //创建ShuffleMapTask
    val tasks: Seq[Task[_]] = try {
      val serializedTaskMetrics = closureSerializer.serialize(stage.latestInfo.taskMetrics).array()
      stage match {
        case stage: ShuffleMapStage =>
          stage.pendingPartitions.clear()
          //注意这里的partitionsToCompute，这个是finalRDD的分区数索引，
          //这里遍历这个分区数索引来创建task，
          //说明每个stage中task的数量是由stage最后一个rdd的分区数来确定的。
          partitionsToCompute.map { id =>
            val locs = taskIdToLocations(id)
            val part = partitions(id)
            stage.pendingPartitions += id
            //创建task对象，有几个分区，就会有几个task
            new ShuffleMapTask(stage.id, stage.latestInfo.attemptNumber,
              taskBinary, part, locs, properties, serializedTaskMetrics, Option(jobId),
              Option(sc.applicationId), sc.applicationAttemptId, stage.rdd.isBarrier())
          }
          // partitionsToCompute属性，是stage中计算partition个数的返回值
       val partitionsToCompute: Seq[Int] = stage.findMissingPartitions()
     //ShuffleMapStage类中的获取partition个数实现逻辑
       override def findMissingPartitions(): Seq[Int] = {
	        mapOutputTrackerMaster
	      .findMissingPartitions(shuffleDep.shuffleId)
	      .getOrElse(0 until numPartitions)
         }
		//ResultTasks的创建过程与ShuffleStage的task创建过程相似
        case stage: ResultStage =>
          partitionsToCompute.map { id =>
            val p: Int = stage.partitions(id)
            val part = partitions(p)
            val locs = taskIdToLocations(id)
            //创建ResultTask对象
            new ResultTask(stage.id, stage.latestInfo.attemptNumber,
              taskBinary, part, locs, id, properties, serializedTaskMetrics,
              Option(jobId), Option(sc.applicationId), sc.applicationAttemptId,
              stage.rdd.isBarrier())
          }
      }
    } catch {
      case NonFatal(e) =>
        abortStage(stage, s"Task creation failed: $e\n${Utils.exceptionString(e)}", Some(e))
        runningStages -= stage
        return
    }
	//如果tasks不为空，则taskScheduler提交task
    if (tasks.nonEmpty) {
      logInfo(s"Submitting ${tasks.size} missing tasks from $stage (${stage.rdd}) (first 15 " +
        s"tasks are for partitions ${tasks.take(15).map(_.partitionId)})")
      taskScheduler.submitTasks(new TaskSet(
        tasks.toArray, stage.id, stage.latestInfo.attemptNumber, jobId, properties))
    } 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)

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

这个方法主要做了三件事：
第一，获取task数据本地性；具体逻辑见下面Task的本地性源码分析：
第二，每个stage中生成task列表，具体逻辑间注释；
第三，向TaskScheduler提交生成的task列表；

Task的本地性源码分析：

  /**
   * 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
    //先判断，rdd的partition是否已经被访问过
    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
     // 优先获取缓存中的RDD分区地址
    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
    // 然后如果缓存中没有RDD分区地址，则判断RDD本身是否有首选地址，若有，就用来构造TaskLocation
    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.
     //遍历rdd的依赖，如果RDD中有窄依赖，就挑选第一个窄依赖的第一个分区的首选地址
    rdd.dependencies.foreach {
      case n: NarrowDependency[_] =>
        for (inPart <- n.getParents(partition)) {
          val locs = getPreferredLocsInternal(n.rdd, inPart, visited)
          if (locs != Nil) {
            return locs
          }
        }

      case _ =>
    }

    Nil
  }

附上：ShuffleMapTask源码：

/**
 * A ShuffleMapTask divides the elements of an RDD into multiple buckets (based on a partitioner
 * specified in the ShuffleDependency).
 *
 * See [[org.apache.spark.scheduler.Task]] for more information.
 *
 * @param stageId id of the stage this task belongs to
 * @param stageAttemptId attempt id of the stage this task belongs to
 * @param taskBinary broadcast version of the RDD and the ShuffleDependency. Once deserialized,
 *                   the type should be (RDD[_], ShuffleDependency[_, _, _]).
 * @param partition partition of the RDD this task is associated with
 * @param locs preferred task execution locations for locality scheduling
 * @param localProperties copy of thread-local properties set by the user on the driver side.
 * @param serializedTaskMetrics a `TaskMetrics` that is created and serialized on the driver side
 *                              and sent to executor side.
 *
 * The parameters below are optional:
 * @param jobId id of the job this task belongs to
 * @param appId id of the app this task belongs to
 * @param appAttemptId attempt id of the app this task belongs to
 * @param isBarrier whether this task belongs to a barrier stage. Spark must launch all the tasks
 *                  at the same time for a barrier stage.
 */
private[spark] class ShuffleMapTask(
    stageId: Int,
    stageAttemptId: Int,
    taskBinary: Broadcast[Array[Byte]],
    partition: Partition,
    @transient private var locs: Seq[TaskLocation],
    localProperties: Properties,
    serializedTaskMetrics: Array[Byte],
    jobId: Option[Int] = None,
    appId: Option[String] = None,
    appAttemptId: Option[String] = None,
    isBarrier: Boolean = false)
  extends Task[MapStatus](stageId, stageAttemptId, partition.index, localProperties,
    serializedTaskMetrics, jobId, appId, appAttemptId, isBarrier)
  with Logging {

  /** A constructor used only in test suites. This does not require passing in an RDD. */
  def this(partitionId: Int) {
    this(0, 0, null, new Partition { override def index: Int = 0 }, null, new Properties, null)
  }

  @transient private val preferredLocs: Seq[TaskLocation] = {
    if (locs == null) Nil else locs.distinct
  }
	
	//执行Task方法，会根据Task的本地行，在对应的Executor上执行
  override def runTask(context: TaskContext): MapStatus = {
    // Deserialize the RDD using the broadcast variable.
    val threadMXBean = ManagementFactory.getThreadMXBean
    val deserializeStartTimeNs = System.nanoTime()
    val deserializeStartCpuTime = if (threadMXBean.isCurrentThreadCpuTimeSupported) {
      threadMXBean.getCurrentThreadCpuTime
    } else 0L
    val ser = SparkEnv.get.closureSerializer.newInstance()
    val rddAndDep = ser.deserialize[(RDD[_], ShuffleDependency[_, _, _])](
      ByteBuffer.wrap(taskBinary.value), Thread.currentThread.getContextClassLoader)
    _executorDeserializeTimeNs = System.nanoTime() - deserializeStartTimeNs
    _executorDeserializeCpuTime = if (threadMXBean.isCurrentThreadCpuTimeSupported) {
      threadMXBean.getCurrentThreadCpuTime - deserializeStartCpuTime
    } else 0L

    val rdd = rddAndDep._1
    val dep = rddAndDep._2
    // While we use the old shuffle fetch protocol, we use partitionId as mapId in the
    // ShuffleBlockId construction.
    val mapId = if (SparkEnv.get.conf.get(config.SHUFFLE_USE_OLD_FETCH_PROTOCOL)) {
      partitionId
    } else context.taskAttemptId()
    dep.shuffleWriterProcessor.write(rdd, dep, mapId, context, partition)
  }
  
  override def preferredLocations: Seq[TaskLocation] = preferredLocs

  override def toString: String = "ShuffleMapTask(%d, %d)".format(stageId, partitionId)
}

ResultTask源码：

/**
 * A task that sends back the output to the driver application.
 *
 * See [[Task]] for more information.
 *
 * @param stageId id of the stage this task belongs to
 * @param stageAttemptId attempt id of the stage this task belongs to
 * @param taskBinary broadcasted version of the serialized RDD and the function to apply on each
 *                   partition of the given RDD. Once deserialized, the type should be
 *                   (RDD[T], (TaskContext, Iterator[T]) => U).
 * @param partition partition of the RDD this task is associated with
 * @param locs preferred task execution locations for locality scheduling
 * @param outputId index of the task in this job (a job can launch tasks on only a subset of the
 *                 input RDD's partitions).
 * @param localProperties copy of thread-local properties set by the user on the driver side.
 * @param serializedTaskMetrics a `TaskMetrics` that is created and serialized on the driver side
 *                              and sent to executor side.
 *
 * The parameters below are optional:
 * @param jobId id of the job this task belongs to
 * @param appId id of the app this task belongs to
 * @param appAttemptId attempt id of the app this task belongs to
 * @param isBarrier whether this task belongs to a barrier stage. Spark must launch all the tasks
 *                  at the same time for a barrier stage.
 */
private[spark] class ResultTask[T, U](
    stageId: Int,
    stageAttemptId: Int,
    taskBinary: Broadcast[Array[Byte]],
    partition: Partition,
    locs: Seq[TaskLocation],
    val outputId: Int,
    localProperties: Properties,
    serializedTaskMetrics: Array[Byte],
    jobId: Option[Int] = None,
    appId: Option[String] = None,
    appAttemptId: Option[String] = None,
    isBarrier: Boolean = false)
  extends Task[U](stageId, stageAttemptId, partition.index, localProperties, serializedTaskMetrics,
    jobId, appId, appAttemptId, isBarrier)
  with Serializable {

  @transient private[this] val preferredLocs: Seq[TaskLocation] = {
    if (locs == null) Nil else locs.distinct
  }

  override def runTask(context: TaskContext): U = {
    // Deserialize the RDD and the func using the broadcast variables.
    val threadMXBean = ManagementFactory.getThreadMXBean
    val deserializeStartTimeNs = System.nanoTime()
    val deserializeStartCpuTime = if (threadMXBean.isCurrentThreadCpuTimeSupported) {
      threadMXBean.getCurrentThreadCpuTime
    } else 0L
    val ser = SparkEnv.get.closureSerializer.newInstance()
    val (rdd, func) = ser.deserialize[(RDD[T], (TaskContext, Iterator[T]) => U)](
      ByteBuffer.wrap(taskBinary.value), Thread.currentThread.getContextClassLoader)
    _executorDeserializeTimeNs = System.nanoTime() - deserializeStartTimeNs
    _executorDeserializeCpuTime = if (threadMXBean.isCurrentThreadCpuTimeSupported) {
      threadMXBean.getCurrentThreadCpuTime - deserializeStartCpuTime
    } else 0L

    func(context, rdd.iterator(partition, context))
  }
	
	
  // This is only callable on the driver side.
  override def preferredLocations: Seq[TaskLocation] = preferredLocs

  override def toString: String = "ResultTask(" + stageId + ", " + partitionId + ")"
}