Spark之Task原理分析

在Spark中，一个应用程序要想被执行，肯定要经过以下的步骤：

    

    从这个路线得知，最终一个job是依赖于分布在集群不同节点中的task，通过并行或者并发的运行来完成真正的工作。由此可见，一个个的分布式的task才是Spark的真正执行者。下面先来张task运行框架整体的对Spark的task运行有个大概的了解。

    task运行之前的工作是Driver启动Executor，接着Executor准备好一切运行环境，并向Driver反向注册，最终Driver向Executor发送LunchTask事件消息，从Executor接受到LanchTask那一刻起，task就一发不可收拾了，开始通过java线程来进行以后的工作。当然了，在task正式工作之前，还有一些工作，比如根据stage算法划分好stage，根据task最佳位置计算算法寻找到task的最佳位置(第一期盼都是希望能够在同一个节点的同一个进程中有task所需要的需要，第二才是同一节点的不同进程，第三才是同一机架的不同节点，第四才是不同机架)。这样做的目的是减少网络通信的开销，节省CPU资源，提高系统性能。

 

    其实虽然图片看起来复杂，其实task所做的事情无非以下几点：

1.通过网络拉取运行所需的资源，并反序列化(由于多个task运行在多个Executor中,都是并行运行的,或者并发运行的，一个stage的task,处理的RDD是一样的，这是通过广播变量来完成的)

2.获取shuffleManager，从shuffleManager中获取shuffleWriter(shuffleWriter用于后面的数据处理并把返回的数据结果写入磁盘)

3.调用rdd.iterator()，并传入当前task要处理的partition(针对RDD的某个partition执行自定义的算子或逻辑函数，返回的数据都是通过上面生成的ShuffleWriter，经过HashPartitioner[默认是这个]分区之后写入对应的分区backet，其实就是写入磁盘文件中)

4.封装数据结果为MapStatus ，发送给MapOutputTracker，供ResultTask拉取。(MapStatus里面封装了ShuffleMaptask计算后的数据和存储位置地址等数据信息。其实也就是BlockManager相关信息，BlockManager 是Spark底层的内存，数据，磁盘数据管理的组件)

5.ResultTask拉取ShuffleMapTask的结果数据(经过2/3/4步骤之后的结果)

 

    实现这个过程，task有ShuffleMapTask和ResultTask两个子类task来支撑，前者是用于通过各种map算子和自定义函数转换RDD。后者主要是触发了action操作，把map阶段后的新的RDD拉取过去，再执行我们自定义的函数体，实现各种业务功能。

 

下面通过源码来分析整个流程：

CoarseGrainedExecutorBackend是executor粗粒度真正的后台处理进程。其中比较重要的是以下函数，主要是用于接受其他工作进程所发送的事件消息，并做对应的响应。

override def receive: PartialFunction[Any, Unit]

 

    executor接受到这个事件消息后，task才真正开始工作。其中的executor.launchTask(this, taskDesc)就是主要的实现函数体

case LaunchTask(data) =>
      if (executor == null) {
        exitExecutor(1, "Received LaunchTask command but executor was null")
      } else {
        val taskDesc = TaskDescription.decode(data.value)
        logInfo("Got assigned task " + taskDesc.taskId)
        executor.launchTask(this, taskDesc)
      }

 

    launchTask方法，主要是new出一个TaskRunner线程，并把它放进java的线程池中运行。通过这里也知道其实Spark的底层是依赖Java和Scala共同实现的。

def launchTask(context: ExecutorBackend, taskDescription: TaskDescription): Unit = {
    val tr = new TaskRunner(context, taskDescription)
    runningTasks.put(taskDescription.taskId, tr)
    threadPool.execute(tr)
  }

 

    通过看TaskRunner的实现，知道它是继承Runnable的，因此，就知道线程真正的运行体是run()方法。

class TaskRunner(
      execBackend: ExecutorBackend,
      private val taskDescription: TaskDescription)
    extends Runnable
 
    下面是run( )方法的主要部分源码。
override def run(): Unit = {
      threadId = Thread.currentThread.getId
      Thread.currentThread.setName(threadName)
      val threadMXBean = ManagementFactory.getThreadMXBean
      val taskMemoryManager = new TaskMemoryManager(env.memoryManager, taskId)
      val deserializeStartTime = System.currentTimeMillis()
      val deserializeStartCpuTime = if (threadMXBean.isCurrentThreadCpuTimeSupported) {
        threadMXBean.getCurrentThreadCpuTime
      } else 0L
      Thread.currentThread.setContextClassLoader(replClassLoader)
      val ser = env.closureSerializer.newInstance()
      logInfo(s"Running $taskName (TID $taskId)")
      execBackend.statusUpdate(taskId, TaskState.RUNNING, EMPTY_BYTE_BUFFER)
      var taskStart: Long = 0
      var taskStartCpu: Long = 0
      startGCTime = computeTotalGcTime()
      try {
        // Must be set before updateDependencies() is called, in case fetching dependencies
        // requires access to properties contained within (e.g. for access control).
        //对序列化的数据，进行反序列化
        Executor.taskDeserializationProps.set(taskDescription.properties)
        //通过网络通信的方法，把task运行所需要的文件、资源、jar等拉取过来
        updateDependencies(taskDescription.addedFiles, taskDescription.addedJars)
        //最后,通过正式的反序列化操作,将整个task的数据集拉取过来
        //这里用ClassLoader的原因是通过指定的上下文资源,进行加载和读取。(当然，反射还有另外的功能：通过反射放射动态加载一个类,创建类的对象)
        task = ser.deserialize[Task[Any]](
          taskDescription.serializedTask, Thread.currentThread.getContextClassLoader)
        task.localProperties = taskDescription.properties
        task.setTaskMemoryManager(taskMemoryManager)
        // If this task has been killed before we deserialized it, let's quit now. Otherwise,
        // continue executing the task.
        val killReason = reasonIfKilled
        if (killReason.isDefined) {
          // Throw an exception rather than returning, because returning within a try{} block
          // causes a NonLocalReturnControl exception to be thrown. The NonLocalReturnControl
          // exception will be caught by the catch block, leading to an incorrect ExceptionFailure
          // for the task.
          throw new TaskKilledException(killReason.get)
        }
        logDebug("Task " + taskId + "'s epoch is " + task.epoch)
        env.mapOutputTracker.updateEpoch(task.epoch)
        // Run the actual task and measure its runtime.
        //计算task开始的时间
        taskStart = System.currentTimeMillis()
        taskStartCpu = if (threadMXBean.isCurrentThreadCpuTimeSupported) {
          threadMXBean.getCurrentThreadCpuTime
        } else 0L
        var threwException = true
        /**
          * value 对于ShuffleMapTask来说,就是MapStatus
          * 封装了ShuffleMapTask计算的数据,输出的位置
          * 后面的ShuffleMapTask会去联系MapOutputTracker来获取一个ShuffleMapTask的输出位置,通过网络网络拉取数据
          * ResultTask也是这样的，只不过是查询ShuffleMapTask的结果MapStatus的位置
                    *  总的来说 MapOutputTracker(Map输出任务管理器)，把map和action联系起来了。
          */
        val value = try {
            //真正的task的线程执行方法，下面会详细分析
          val res = task.run( 
            taskAttemptId = taskId,
            attemptNumber = taskDescription.attemptNumber,
            metricsSystem = env.metricsSystem)
          threwException = false
          res
        } finally {
          val releasedLocks = env.blockManager.releaseAllLocksForTask(taskId)
          val freedMemory = taskMemoryManager.cleanUpAllAllocatedMemory()
          if (freedMemory > 0 && !threwException) {
            val errMsg = s"Managed memory leak detected; size = $freedMemory bytes, TID = $taskId"
            if (conf.getBoolean("spark.unsafe.exceptionOnMemoryLeak", false)) {
              throw new SparkException(errMsg)
            } else {
              logWarning(errMsg)
            }
          }
          if (releasedLocks.nonEmpty && !threwException) {
            val errMsg =
              s"${releasedLocks.size} block locks were not released by TID = $taskId:
" +
                releasedLocks.mkString("[", ", ", "]")
            if (conf.getBoolean("spark.storage.exceptionOnPinLeak", false)) {
              throw new SparkException(errMsg)
            } else {
              logInfo(errMsg)
            }
          }
        }
        task.context.fetchFailed.foreach { fetchFailure =>
          // uh-oh.  it appears the user code has caught the fetch-failure without throwing any
          // other exceptions.  Its *possible* this is what the user meant to do (though highly
          // unlikely).  So we will log an error and keep going.
          logError(s"TID ${taskId} completed successfully though internally it encountered " +
            s"unrecoverable fetch failures!  Most likely this means user code is incorrectly " +
            s"swallowing Spark's internal ${classOf[FetchFailedException]}", fetchFailure)
        }
        //task结束的时间
        val taskFinish = System.currentTimeMillis()
        val taskFinishCpu = if (threadMXBean.isCurrentThreadCpuTimeSupported) {
          threadMXBean.getCurrentThreadCpuTime
        } else 0L
        // If the task has been killed, let's fail it.
        task.context.killTaskIfInterrupted()
        //对MapStatus进行各种序列化和封装,后面要发送给MapOutputTracker
        val resultSer = env.serializer.newInstance()
        val beforeSerialization = System.currentTimeMillis()
        val valueBytes = resultSer.serialize(value)
        val afterSerialization = System.currentTimeMillis()
        // Deserialization happens in two parts: first, we deserialize a Task object, which
        // includes the Partition. Second, Task.run() deserializes the RDD and function to be run.
        /**
          * 计算出task的一些统计信息,运行时间/反序列化消耗的时间/JAva虚拟机 GC消耗的时间/反序列化消耗的时间
          */
        task.metrics.setExecutorDeserializeTime(
          (taskStart - deserializeStartTime) + task.executorDeserializeTime)
        task.metrics.setExecutorDeserializeCpuTime(
          (taskStartCpu - deserializeStartCpuTime) + task.executorDeserializeCpuTime)
        // We need to subtract Task.run()'s deserialization time to avoid double-counting
        task.metrics.setExecutorRunTime((taskFinish - taskStart) - task.executorDeserializeTime)
        task.metrics.setExecutorCpuTime(
          (taskFinishCpu - taskStartCpu) - task.executorDeserializeCpuTime)
        task.metrics.setJvmGCTime(computeTotalGcTime() - startGCTime)
        task.metrics.setResultSerializationTime(afterSerialization - beforeSerialization)
        // Note: accumulator updates must be collected after TaskMetrics is updated
        val accumUpdates = task.collectAccumulatorUpdates()
        // TODO: do not serialize value twice
        val directResult = new DirectTaskResult(valueBytes, accumUpdates)
        val serializedDirectResult = ser.serialize(directResult)
        val resultSize = serializedDirectResult.limit
        // directSend = sending directly back to the driver
        //下面是对map结果做序列化和对其做位置等信息的封装，方便网络传输和位置查找。注意，BlockManager 是Spark底层的内存，数据，磁盘数据管理的组件
        val serializedResult: ByteBuffer = {
          if (maxResultSize > 0 && resultSize > maxResultSize) {
            logWarning(s"Finished $taskName (TID $taskId). Result is larger than maxResultSize " +
              s"(${Utils.bytesToString(resultSize)} > ${Utils.bytesToString(maxResultSize)}), " +
              s"dropping it.")
            ser.serialize(new IndirectTaskResult[Any](TaskResultBlockId(taskId), resultSize))
          } else if (resultSize > maxDirectResultSize) {
            val blockId = TaskResultBlockId(taskId)
            env.blockManager.putBytes(
              blockId,
              new ChunkedByteBuffer(serializedDirectResult.duplicate()),
              StorageLevel.MEMORY_AND_DISK_SER)
            logInfo(
              s"Finished $taskName (TID $taskId). $resultSize bytes result sent via BlockManager)")
            ser.serialize(new IndirectTaskResult[Any](blockId, resultSize))
          } else {
            logInfo(s"Finished $taskName (TID $taskId). $resultSize bytes result sent to driver")
            serializedDirectResult
          }
        }
        //调用executor所在的ScoresGrainedExecutorBackend的statusUpdate，更新状态信息
        setTaskFinishedAndClearInterruptStatus()
        execBackend.statusUpdate(taskId, TaskState.FINISHED, serializedResult)
      } catch {
        case t: Throwable if hasFetchFailure && !Utils.isFatalError(t) =>
          val reason = task.context.fetchFailed.get.toTaskFailedReason
          if (!t.isInstanceOf[FetchFailedException]) {
            // there was a fetch failure in the task, but some user code wrapped that exception
            // and threw something else.  Regardless, we treat it as a fetch failure.
            val fetchFailedCls = classOf[FetchFailedException].getName
            logWarning(s"TID ${taskId} encountered a ${fetchFailedCls} and " +
              s"failed, but the ${fetchFailedCls} was hidden by another " +
              s"exception.  Spark is handling this like a fetch failure and ignoring the " +
              s"other exception: $t")
          }
          setTaskFinishedAndClearInterruptStatus()
          execBackend.statusUpdate(taskId, TaskState.FAILED, ser.serialize(reason))

 

    executor的task.run，底层主要是task的run方法，很明显看出来，主要工作是创建一个context，把task运行过程中的上下文记录下来。其中关键的是调用抽象方法，runTask。

final def run(
    taskAttemptId: Long,
    attemptNumber: Int,
    metricsSystem: MetricsSystem): T = {
  SparkEnv.get.blockManager.registerTask(taskAttemptId)
  //创建 context ,task的执行上下文,里面记录task执行的全局性的数据
  //重试次数,task属于哪个stage,task要处理的是哪个rdd,哪个partition等
  context = new TaskContextImpl(
    stageId,
    partitionId,
    taskAttemptId,
    attemptNumber,
    taskMemoryManager,
    localProperties,
    metricsSystem,
    metrics)
  TaskContext.setTaskContext(context)
  taskThread = Thread.currentThread()
 
  if (_reasonIfKilled != null) {
    kill(interruptThread = false, _reasonIfKilled)
  }
 
  new CallerContext(
    "TASK",
    SparkEnv.get.conf.get(APP_CALLER_CONTEXT),
    appId,
    appAttemptId,
    jobId,
    Option(stageId),
    Option(stageAttemptId),
    Option(taskAttemptId),
    Option(attemptNumber)).setCurrentContext()
 
  try {
    //调用抽象方法,runTask
    runTask(context)
  } catch {
    case e: Throwable =>
      // Catch all errors; run task failure callbacks, and rethrow the exception.
      try {
        context.markTaskFailed(e)
      } catch {
        case t: Throwable =>
          e.addSuppressed(t)
      }
      context.markTaskCompleted(Some(e))
      throw e
  } finally {
    try {
      // Call the task completion callbacks. If "markTaskCompleted" is called twice, the second
      // one is no-op.
      context.markTaskCompleted(None)
    } finally {
      try {
        Utils.tryLogNonFatalError {
          // Release memory used by this thread for unrolling blocks
          SparkEnv.get.blockManager.memoryStore.releaseUnrollMemoryForThisTask(MemoryMode.ON_HEAP)
          SparkEnv.get.blockManager.memoryStore.releaseUnrollMemoryForThisTask(
            MemoryMode.OFF_HEAP)
          // Notify any tasks waiting for execution memory to be freed to wake up and try to
          // acquire memory again. This makes impossible the scenario where a task sleeps forever
          // because there are no other tasks left to notify it. Since this is safe to do but may
          // not be strictly necessary, we should revisit whether we can remove this in the
          // future.
          val memoryManager = SparkEnv.get.memoryManager
          memoryManager.synchronized { memoryManager.notifyAll() }
        }
      } finally {
        // Though we unset the ThreadLocal here, the context member variable itself is still
        // queried directly in the TaskRunner to check for FetchFailedExceptions.
        TaskContext.unset()
      }
    }
  }
}

 

    task是抽象方法,意味着这个类只是模板类,仅仅封装了一些子类通用的属性和方法,依赖于子类实现它们,来确定具体的功能。 前面说过task的有两个子类ShuffleMapTask和ResultTask。有了它们,才能运行定义的算子和逻辑

def runTask(context: TaskContext): T
 
def preferredLocations: Seq[TaskLocation] = Nil
 
// Map output tracker epoch. Will be set by TaskSetManager.
var epoch: Long = -1
 
// Task context, to be initialized in run().
@transient var context: TaskContextImpl = _
 
// The actual Thread on which the task is running, if any. Initialized in run().
@volatile @transient private var taskThread: Thread = _
 
// If non-null, this task has been killed and the reason is as specified. This is used in case
// context is not yet initialized when kill() is invoked.
@volatile @transient private var _reasonIfKilled: String = null
 
protected var _executorDeserializeTime: Long = 0
protected var _executorDeserializeCpuTime: Long = 0
 
/**
* If defined, this task has been killed and this option contains the reason.
*/
def reasonIfKilled: Option[String] = Option(_reasonIfKilled)
 
/**
* Returns the amount of time spent deserializing the RDD and function to be run.
*/
def executorDeserializeTime: Long = _executorDeserializeTime
def executorDeserializeCpuTime: Long = _executorDeserializeCpuTime
 
/**
* Collect the latest values of accumulators used in this task. If the task failed,
* filter out the accumulators whose values should not be included on failures.
*/
def collectAccumulatorUpdates(taskFailed: Boolean = false): Seq[AccumulatorV2[_, _]] = {
  if (context != null) {
    // Note: internal accumulators representing task metrics always count failed values
    context.taskMetrics.nonZeroInternalAccums() ++
      // zero value external accumulators may still be useful, e.g. SQLMetrics, we should not
      // filter them out.
      context.taskMetrics.externalAccums.filter(a => !taskFailed || a.countFailedValues)
  } else {
    Seq.empty
  }
}

 

    到此，task整个运行流程已分析一遍，最后，调用下面的函数来更新状态信息  

setTaskFinishedAndClearInterruptStatus()
execBackend.statusUpdate(taskId, TaskState.FINISHED, serializedResult)

 

    最后来总结一下，task的运行一开始不是直接调用底层的task的run方法直接处理job-->stage-->taskSet-->task这条路线的task任务的，它是通过分层和分工的思想来完成。task会派生出两个子类ShuffleMapTask和ResultTask分别完成对应的工作，ShuffleMapTask主要是对task所拥有的的RDD的partition做对应的RDD转换工作，ResultTask主要是根据action动作触发，并拉取ShuffleMapTask阶段的结果做进一步的算子和逻辑函数对数据对真正进一步的处理。这两个阶段是通过MapOutputTracker来连接起来的。