大数据：Spark Core（二）Driver上的Task的生成、分配、调度

1. 什么是Task?

在前面的章节里描写叙述过几个角色，Driver(Client),Master,Worker(Executor)，Driver会提交Application到Master进行Worker上的Executor上的调度，显然这些都不是Task.

Spark上的几个关系能够这样理解：

• Application: Application是Driver在构建SparkContent的上下文的时候创建的，就像申报员，如今要构建一个能完毕任务的集群，须要申报的是这次须要多少个Executor(能够简单理解为虚拟的机器)。每一个Executor须要多少CPU,多少内存。

• Job: 这是Driver在调用Action的时候生成的Job。让DAGScheduler线程进行最后的调度，代表着这时候RDD的状态分析完了。须要进行最后的Stage分析了，Job并非提交给Executor运行的，一个Application存在多个Job
• Task: 一个Job能够分成多个Task, 相当于这些Task分到了一个Group里，这个Group的ID就是Job ID

2. Task的类型

Task的类型和Stage相关，关于Stage。以及Stage之间的相关依赖构成任务的不同提交，就不在这篇描写叙述了

ShuffleMapStage 转化成 ShuffleMapTask

ResultStage 转化成为 ResultTask

当Spark上的action算子，通过DAG进行提交任务的时候，会通过Stage来决定提交什么类型的任务，详细的实现都在DAGScheduler.scala 的submitMissingTasks方法中。

3. 同一个Stage的Task数量

Spark是一个分布式的运行任务的框架。那么同一个Stage的并行任务的拆分就很的重要。在任务的分解中并不仅仅是stage的步骤的分解，同一时候也是对同一个Stage中的要分析的数据分解，而对数据的分解直接决定对同一个Stage所提交的任务的数量。

对Stage的任务拆解决定着任务的之间的关系，而对同一个Stage的分析数据进行拆解控制着任务的数量。

比方基于拆解的分析数据的而运行的算子象map。这些任务都是独立的，并没有对数据进行最后的归并和整理，这些task是全然能够进行并行计算的，对同一个Stage的task的数量在Spark上是能够控制的。

在这里以ParallelCollectionRDD为简单的样例，先看DAGScheduler.submitMissingTasks的方法

 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.
    val partitionsToCompute: Seq[Int] = stage.findMissingPartitions()
    。
 
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val tasks: Seq[Task[_]] = try {
      stage match {
        case stage: ShuffleMapStage =>
          partitionsToCompute.map { id =>
            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
    }

生产task的数量是由val partitionsToCompute: Seq[Int] = stage.findMissingPartitions()来决定的。在ShuffleMapStage里

override def findMissingPartitions(): Seq[Int] = {
    val missing = (0 until numPartitions).filter(id => outputLocs(id).isEmpty)
    assert(missing.size == numPartitions - _numAvailableOutputs,
      s"${missing.size} missing, expected ${numPartitions - _numAvailableOutputs}")
    missing
  }

能够看到详细是由numPartitions来决定的。在来看numPartitions

val numPartitions = rdd.partitions.length

由rdd.partitions来决定的,对ShuffleMapStage来说rdd就是最后一个value类型的transformation 的RDD。比方常见的MapPartitionsRDD

在MapPartitionsRDD来说的partitions

  override def getPartitions: Array[Partition] = firstParent[T].partitions

是transformation的算子链中的第一个。我们以ParallelCollectionRDD为样例，比方常见的相应的样例:

sparkcontext.parallelize(exampleApacheLogs)

在ParallelCollectionRDD中

override def getPartitions: Array[Partition] = {
    val slices = ParallelCollectionRDD.slice(data, numSlices).toArray
    slices.indices.map(i => new ParallelCollectionPartition(id, i, slices(i))).toArray
  }

在ParallelCollectionRDD中数据的Partitions是由numSlices来决定的

  def parallelize[T: ClassTag](
      seq: Seq[T],
      numSlices: Int = defaultParallelism): RDD[T] = withScope {
    assertNotStopped()
    new ParallelCollectionRDD[T](this, seq, numSlices, Map[Int, Seq[String]]())
  }

numSlices 是能够在parallelize函数中传入，而默认使用defaultParallelism的參数控制

def defaultParallelism: Int = {
    assertNotStopped()
    taskScheduler.defaultParallelism
  }
override def defaultParallelism(): Int = backend.defaultParallelism()

在CoarseGrainedSchedulerBackend.scala 的类中:

  override def defaultParallelism(): Int = {
    conf.getInt("spark.default.parallelism", math.max(totalCoreCount.get(), 2))
  }

默认的值是受下面控制：

1. 配置文件spark.default.parallelism
2. totalCoreCount 的值： CoarseGrainedSchedulerBackend是一个executor管理的backend，里面维护着executor的信息。totalCoreCount就是executor汇报上来的核数，注意由于executor汇报自己是在application分配好后发生的，汇报的信息和获取totalCoreCount的线程是异步的。也就是假设executor没有汇报上来。totalCoreCount.get（）的值并不准确（根绝Master对executor的分配策略。是无法保证分配多少个executor, 在这里spark更依赖executor主动的向driver汇报），这里的策略是无法保证准确的获取executor的核数。

3. 假设没有设置spark.default.parallelism，最小值是2

依赖于rdd.partitions的策略，最后决定task的分配数量。

4. Task的提交和调度分配

在本篇中主要描写叙述集群下的任务调度

4.1 Task的提交

在DAGScheduler将一个Stage中所分配的Task形成一个TaskSet进行提交，在TaskSet里所保存的是Task的集合。还有Stage的Id。以及JobId，注意在这里JobId是作为一个优先级的參数，作为后序队列调度的參数。

在TaskSchedulerImpl.scala中

  override def submitTasks(taskSet: TaskSet) {
    val tasks = taskSet.tasks
    logInfo("Adding task set " + taskSet.id + " with " + tasks.length + " tasks")
    this.synchronized {
      val manager = createTaskSetManager(taskSet, maxTaskFailures)
      val stage = taskSet.stageId
      val stageTaskSets =
        taskSetsByStageIdAndAttempt.getOrElseUpdate(stage, new HashMap[Int, TaskSetManager])
      stageTaskSets(taskSet.stageAttemptId) = manager
      val conflictingTaskSet = stageTaskSets.exists { case (_, ts) =>
        ts.taskSet != taskSet && !ts.isZombie
      }
      if (conflictingTaskSet) {
        throw new IllegalStateException(s"more than one active taskSet for stage $stage:" +
          s" ${stageTaskSets.toSeq.map{_._2.taskSet.id}.mkString(",")}")
      }
      schedulableBuilder.addTaskSetManager(manager, manager.taskSet.properties)
 
      if (!isLocal && !hasReceivedTask) {
        starvationTimer.scheduleAtFixedRate(new TimerTask() {
          override def run() {
            if (!hasLaunchedTask) {
              logWarning("Initial job has not accepted any resources; " +
                "check your cluster UI to ensure that workers are registered " +
                "and have sufficient resources")
            } else {
              this.cancel()
            }
          }
        }, STARVATION_TIMEOUT_MS, STARVATION_TIMEOUT_MS)
      }
      hasReceivedTask = true
    }
    backend.reviveOffers()
  }

将TaskSet 封装成TaskSetManager，通过schedulableBuilder去加入TaskSetManager到队列中，在Spark中，有两种形态

1. FIFOSchedulableBuilder: 单一pool
2. FairSchedulableBuilder:   多个pool

4.1.1 FairSchedulableBuilder pool池

通过fairsscheduler.xml的模版来设置參数来控制pool的调度

<allocations>
  <pool name="production1">
    <schedulingMode>FAIR</schedulingMode>
    <weight>3</weight>
    <minShare>4</minShare>
  </pool>
  <pool name="production2">
    <schedulingMode>FAIR</schedulingMode>
    <weight>5</weight>
    <minShare>2</minShare>
  </pool>
</allocations>

參数的定义：

• name:   调度池的名称，可依据该參数使用指定pool，EX: sc.setLocalProperty("spark.scheduler.pool", "production1")
• weight:  调度池的权重。调度池依据该參数分配资源。
• minShare: 调度池须要的最小资源数（CPU核数），公平调度器首先会尝试为每一个调度池分配最少minShare资源，然后剩余资源才会依照weight大小继续分配
• schedulingMode: 调度池内的调度模式

在TaskSchedulerImpl在submitTasks加入TaskSetManager到pool后，调用了backend.reviveOffers();

  override def reviveOffers() {
    driverEndpoint.send(ReviveOffers)
  }

是向driver的endpoint发送了reviveoffers的消息，Spark中的很多操作都是通过消息来传递的，哪怕DAGScheduler的线程和endpoint的线程都是同一个Driver进程

4.2 Task的分配

Netty 的dispatcher线程接受到revievoffers的消息后，CoarseGrainedSchedulerBackend

      case ReviveOffers =>
        makeOffers()

调用了makeoffers函数

private def makeOffers() {
      // Filter out executors under killing
      val activeExecutors = executorDataMap.filterKeys(executorIsAlive)
      val workOffers = activeExecutors.map { case (id, executorData) =>
        new WorkerOffer(id, executorData.executorHost, executorData.freeCores)
      }.toIndexedSeq
      launchTasks(scheduler.resourceOffers(workOffers))
    }

makeOffers里进行了资源的调度，netty中接收全部的信息，同一时候也在CoarseGrainedSchedulerBackend中维护着executor的状态map:executorDataMap，executor的状态是executor主动汇报的。

通过scheduler.resourceOffers来进行task的资源分配到executor中

 def resourceOffers(offers: IndexedSeq[WorkerOffer]): Seq[Seq[TaskDescription]] = synchronized {
    // Mark each slave as alive and remember its hostname
    // Also track if new executor is added
    var newExecAvail = false
    for (o <- offers) {
      if (!hostToExecutors.contains(o.host)) {
        hostToExecutors(o.host) = new HashSet[String]()
      }
      if (!executorIdToRunningTaskIds.contains(o.executorId)) {
        hostToExecutors(o.host) += o.executorId
        executorAdded(o.executorId, o.host)
        executorIdToHost(o.executorId) = o.host
        executorIdToRunningTaskIds(o.executorId) = HashSet[Long]()
        newExecAvail = true
      }
      for (rack <- getRackForHost(o.host)) {
        hostsByRack.getOrElseUpdate(rack, new HashSet[String]()) += o.host
      }
    }
 
    // Randomly shuffle offers to avoid always placing tasks on the same set of workers.
    val shuffledOffers = Random.shuffle(offers)
    // Build a list of tasks to assign to each worker.
    val tasks = shuffledOffers.map(o => new ArrayBuffer[TaskDescription](o.cores))
    val availableCpus = shuffledOffers.map(o => o.cores).toArray
    val sortedTaskSets = rootPool.getSortedTaskSetQueue
    for (taskSet <- sortedTaskSets) {
      logDebug("parentName: %s, name: %s, runningTasks: %s".format(
        taskSet.parent.name, taskSet.name, taskSet.runningTasks))
      if (newExecAvail) {
        taskSet.executorAdded()
      }
    }
 
    // Take each TaskSet in our scheduling order, and then offer it each node in increasing order
    // of locality levels so that it gets a chance to launch local tasks on all of them.
    // NOTE: the preferredLocality order: PROCESS_LOCAL, NODE_LOCAL, NO_PREF, RACK_LOCAL, ANY
    for (taskSet <- sortedTaskSets) {
      var launchedAnyTask = false
      var launchedTaskAtCurrentMaxLocality = false
      for (currentMaxLocality <- taskSet.myLocalityLevels) {
        do {
          launchedTaskAtCurrentMaxLocality = resourceOfferSingleTaskSet(
            taskSet, currentMaxLocality, shuffledOffers, availableCpus, tasks)
          launchedAnyTask |= launchedTaskAtCurrentMaxLocality
        } while (launchedTaskAtCurrentMaxLocality)
      }
      if (!launchedAnyTask) {
        taskSet.abortIfCompletelyBlacklisted(hostToExecutors)
      }
    }
 
    if (tasks.size > 0) {
      hasLaunchedTask = true
    }
    return tasks
  }

1. 随机化了有效的executor的列表。为了均匀的分配
2. 获取池里（前面已经提过油两种池）的排号序的taskSetManager的队列
3. 对taskSetManager里面的task集合进行调度分配

4.2.1 taskSetManager队列的排序

这里的排序是对单个Pool里的taskSetManager进行排序。Spark有两种排序算法

  var taskSetSchedulingAlgorithm: SchedulingAlgorithm = {
    schedulingMode match {
      case SchedulingMode.FAIR =>
        new FairSchedulingAlgorithm()
      case SchedulingMode.FIFO =>
        new FIFOSchedulingAlgorithm()
      case _ =>
        val msg = "Unsupported scheduling mode: $schedulingMode. Use FAIR or FIFO instead."
        throw new IllegalArgumentException(msg)
    }
  }

在这里就简介FIFOSchedulingAlgorithm的算法

private[spark] class FIFOSchedulingAlgorithm extends SchedulingAlgorithm {
  override def comparator(s1: Schedulable, s2: Schedulable): Boolean = {
    val priority1 = s1.priority
    val priority2 = s2.priority
    var res = math.signum(priority1 - priority2)
    if (res == 0) {
      val stageId1 = s1.stageId
      val stageId2 = s2.stageId
      res = math.signum(stageId1 - stageId2)
    }
    res < 0
  }
}

这里的priority 就是前面说到的JobID, 也就是JobID越小的排序在前面，在相通JobId下的StageId越小的排序在前面

4.2.2 单个TaskSetManager的task调度

TaskSetManager 里保存了TaskSet，也就是DAGScheduler里生成的tasks的集合，在TaskSchedulerImpl.scala中进行了单个的TaskSetManager进行调度

private def resourceOfferSingleTaskSet(
      taskSet: TaskSetManager,
      maxLocality: TaskLocality,
      shuffledOffers: Seq[WorkerOffer],
      availableCpus: Array[Int],
      tasks: IndexedSeq[ArrayBuffer[TaskDescription]]) : Boolean = {
    var launchedTask = false
    for (i <- 0 until shuffledOffers.size) {
      val execId = shuffledOffers(i).executorId
      val host = shuffledOffers(i).host
      if (availableCpus(i) >= CPUS_PER_TASK) {
        try {
          for (task <- taskSet.resourceOffer(execId, host, maxLocality)) {
            tasks(i) += task
            val tid = task.taskId
            taskIdToTaskSetManager(tid) = taskSet
            taskIdToExecutorId(tid) = execId
            executorIdToRunningTaskIds(execId).add(tid)
            availableCpus(i) -= CPUS_PER_TASK
            assert(availableCpus(i) >= 0)
            launchedTask = true
          }
        } catch {
          case e: TaskNotSerializableException =>
            logError(s"Resource offer failed, task set ${taskSet.name} was not serializable")
            // Do not offer resources for this task, but don't throw an error to allow other
            // task sets to be submitted.
            return launchedTask
        }
      }
    }
    return launchedTask
  }

在这里，我们看到了一个參数CPUS_PER_TASK

  val CPUS_PER_TASK = conf.getInt("spark.task.cpus", 1)

在spark里，我们能够设置task所使用的cpu的数量，默认是1个，一个task任务在executor中是启动一个线程来运行的

通过计算每一个executor的剩余资源，决定是否须要从tasksetmanager里分配出task.

  def resourceOffer(
      execId: String,
      host: String,
      maxLocality: TaskLocality.TaskLocality)
    : Option[TaskDescription] =
  {
      .....
 
      dequeueTask(execId, host, allowedLocality).map { case ((index, taskLocality, speculative)) =>
        ......
        new TaskDescription(taskId = taskId, attemptNumber = attemptNum, execId,
          taskName, index, serializedTask)
      }
    } else {
      None
    }
  }

核心函数dequeueTask

  private def dequeueTask(execId: String, host: String, maxLocality: TaskLocality.Value)
    : Option[(Int, TaskLocality.Value, Boolean)] =
  {
    for (index <- dequeueTaskFromList(execId, host, getPendingTasksForExecutor(execId))) {
      return Some((index, TaskLocality.PROCESS_LOCAL, false))
    }
 
    if (TaskLocality.isAllowed(maxLocality, TaskLocality.NODE_LOCAL)) {
      for (index <- dequeueTaskFromList(execId, host, getPendingTasksForHost(host))) {
        return Some((index, TaskLocality.NODE_LOCAL, false))
      }
    }
 
    if (TaskLocality.isAllowed(maxLocality, TaskLocality.NO_PREF)) {
      // Look for noPref tasks after NODE_LOCAL for minimize cross-rack traffic
      for (index <- dequeueTaskFromList(execId, host, pendingTasksWithNoPrefs)) {
        return Some((index, TaskLocality.PROCESS_LOCAL, false))
      }
    }
 
    if (TaskLocality.isAllowed(maxLocality, TaskLocality.RACK_LOCAL)) {
      for {
        rack <- sched.getRackForHost(host)
        index <- dequeueTaskFromList(execId, host, getPendingTasksForRack(rack))
      } {
        return Some((index, TaskLocality.RACK_LOCAL, false))
      }
    }
 
    if (TaskLocality.isAllowed(maxLocality, TaskLocality.ANY)) {
      for (index <- dequeueTaskFromList(execId, host, allPendingTasks)) {
        return Some((index, TaskLocality.ANY, false))
      }
    }
 
    // find a speculative task if all others tasks have been scheduled
    dequeueSpeculativeTask(execId, host, maxLocality).map {
      case (taskIndex, allowedLocality) => (taskIndex, allowedLocality, true)}
  }

在Spark中为了尽量分配任务到task所需的资源的本地,依据task里的preferredLocations所保存的须要资源的位置进行分配

1. 尽量分配到task到task所需资源同样的executor里运行，比方ExecutorCacheTaskLocation，HDFSCacheTaskLocation
2. 尽量分配到task里task所需资源相通的host里运行
3. task的数组从最后向前開始分配

分配完生成TaskDescription。里面记录着taskId, execId, task在数组的位置，和task的整个序列化的内容

4.2.3 Launch Tasks

private def launchTasks(tasks: Seq[Seq[TaskDescription]]) {
      for (task <- tasks.flatten) {
        val serializedTask = ser.serialize(task)
        if (serializedTask.limit >= maxRpcMessageSize) {
          scheduler.taskIdToTaskSetManager.get(task.taskId).foreach { taskSetMgr =>
            try {
              var msg = "Serialized task %s:%d was %d bytes, which exceeds max allowed: " +
                "spark.rpc.message.maxSize (%d bytes). Consider increasing " +
                "spark.rpc.message.maxSize or using broadcast variables for large values."
              msg = msg.format(task.taskId, task.index, serializedTask.limit, maxRpcMessageSize)
              taskSetMgr.abort(msg)
            } catch {
              case e: Exception => logError("Exception in error callback", e)
            }
          }
        }
        else {
          val executorData = executorDataMap(task.executorId)
          executorData.freeCores -= scheduler.CPUS_PER_TASK
 
          logDebug(s"Launching task ${task.taskId} on executor id: ${task.executorId} hostname: " +
            s"${executorData.executorHost}.")
 
          executorData.executorEndpoint.send(LaunchTask(new SerializableBuffer(serializedTask)))
        }
      }
    }

这里的逻辑就相对照较简单，TaskDescription里面包括着executorId。而CoarseGrainedSchedulerBackend里有executor的信息。依据executorId获取到executor的通讯端口，发送LunchTask的信息。

这里有个RPC的消息的大小控制。假设序列化的task的内容超过了最大RPC的消息。这个任务会被丢弃

/** Returns the configured max message size for messages in bytes. */
  def maxMessageSizeBytes(conf: SparkConf): Int = {
    val maxSizeInMB = conf.getInt("spark.rpc.message.maxSize", 128)
    if (maxSizeInMB > MAX_MESSAGE_SIZE_IN_MB) {
      throw new IllegalArgumentException(
        s"spark.rpc.message.maxSize should not be greater than $MAX_MESSAGE_SIZE_IN_MB MB")
    }
    maxSizeInMB * 1024 * 1024
  }

能够看到最大的消息大小是128M，能够通过spark.rpc.message.maxSize进行配置