大数据技术 Spark Core(2)Driver上的Task的生成、分配、调度-职坐标

大数据技术 Spark Core(2)Driver上的Task的生成、分配、调度

沉沙 2018-10-08 来源：阅读 1255 评论 0

摘要：本篇教程探讨了大数据技术 Spark Core(2)Driver上的Task的生成、分配、调度，希望阅读本篇文章以后大家有所收获，帮助大家对大数据技术的理解更加深入。

本篇教程探讨了大数据技术 Spark Core(2)Driver上的Task的生成、分配、调度，希望阅读本篇文章以后大家有所收获，帮助大家对大数据技术的理解更加深入。

1. 什么是Task?

在前面的章节里描述过几个角色，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上的几个关系可以这样理解：

    Application: Application是Driver在构建SparkContent的上下文的时候创建的，就像申报员，现在要构建一个能完成任务的集群，需要申报的是这次需要多少个Executor(可以简单理解为虚拟的机器)，每个Executor需要多少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，需要进行最后的Stage分析了，Job并不是提交给Executor运行的，一个Application存在多个Job
    Task: 一个Job可以分成多个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，具体的实现都在DAGScheduler.scala 的submitMissingTasks方法中。
3. 同一个Stage的Task数量

Spark是一个分布式的执行任务的框架，那么同一个Stage的并行任务的拆分就非常的重要，在任务的分解中并不只是stage的步骤的分解，同时也是对同一个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。

比如基于拆解的分析数据的而执行的算子象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是完全可以进行并行计算的，对同一个Stage的task的数量在Spark上是可以控制的。

在这里以ParallelCollectionRDD为简单的例子，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()
        。。。。。。。。。。。
        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
      }

可以看到具体是由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，比如常见的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为例子，比如常见的对应的例子:

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 是可以在parallelize函数中传入，而默认使用defaultParallelism的参数控制

    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))
      }

默认的值是受以下控制：

    配置文件spark.default.parallelism
    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，注意因为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，也就是如果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。
    如果没有设置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参数，作为后序队列调度的参数。

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去添加TaskSetManager到队列中，在Spark中，有两种形态

    FIFOSchedulableBuilder: 单一pool
    FairSchedulableBuilder:   多个pool

4.1.1 FairSchedulableBuilder pool池

通过fairsscheduler.xml的模版来设置参数来控制pool的调度



        FAIR
        3
        4


        FAIR
        5
        2



参数的定义：

    name:   调度池的名称，可根据该参数使用指定pool，EX: sc.setLocalProperty("spark.scheduler.pool", "production1")
    weight:  调度池的权重，调度池根据该参数分配资源。
    minShare: 调度池需要的最小资源数（CPU核数），公平调度器首先会尝试为每个调度池分配最少minShare资源，然后剩余资源才会按照weight大小继续分配
    schedulingMode: 调度池内的调度模式

在TaskSchedulerImpl在submitTasks添加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中的许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

[plain] view plain copy

          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中接收所有的信息，同时也在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
      }

    随机化了有效的executor的列表，为了均匀的分配
    获取池里（前面已经提过油两种池）的排号序的taskSetManager的队列
    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)
        }
      }

在这里就简单介绍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，我们看到了一个参数CPUS_PER_TASK

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

在spark里，我们可以设置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执行的

通过计算每个executor的剩余资源，决定是否需要从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所保存的需要资源的位置进行分配

    尽量分配到task到task所需资源相同的executor里执行，比如ExecutorCacheTaskLocation，HDFSCacheTaskLocation
    尽量分配到task里task所需资源相通的host里执行
    task的数组从最后向前开始分配

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)))
            }
          }
        }

这里的逻辑就相对比较简单，TaskDescription里面包含着executorId，而CoarseGrainedSchedulerBackend里有executor的信息，根据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，如果序列化的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
      }

可以看到最大的消息大小是128M，可以通过spark.rpc.message.maxSize进行配置