• Spark 源码阅读——任务提交过程



    当我们在使用spark编写mr作业是,最后都要涉及到调用reduce,foreach或者是count这类action来触发作业的提交,所以,当我们查看这些方法的源码时,发现底层都调用了SparkContext的runJob方法,而SparkContext的runJob方法又调用的DAGScheduler的runJob方法:

    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:
    " + rdd.toDebugString)
      }
      dagScheduler.runJob(rdd, cleanedFunc, partitions, callSite, esultHandler, localProperties.get)
      progressBar.foreach(_.finishAll())
      rdd.doCheckpoint()
    }
    

    这里以rdd和分区信息和对结果集处理的回调函数为参数进入到:

      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
        }
      }
    

    然后调用submitJob方法:

      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
      }
    

    我们注意到里面有一行eventProcessLoop.post(JobSubmitted(...))的代码,这是向消息队列中放入一个作业提交的消息,由另一个线程来循环从队列中取出消息消费,执行相应的逻辑。我们可以看到在DAGScheduler类定义的最后一行调用了eventProcessLoop.start方法来启动这个时间循环线程。 在另一个线程中,通过scala的case class模式匹配并执行了DAGScheduler的handleJobSubmitted方法,这是一个比较核心的方法,所有生成stage,以及stage之间的依赖关系解析,作业的生成,都是在这里完成的。

    private[scheduler] def handleJobSubmitted(jobId: Int,
          finalRDD: RDD[_],
          func: (TaskContext, Iterator[_]) => _,
          partitions: Array[Int],
          callSite: CallSite,
          listener: JobListener,
          properties: Properties) {
        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 = newResultStage(finalRDD, func, partitions, jobId, callSite)
        } catch {
          case e: Exception =>
            logWarning("Creating new stage failed due to exception - job: " + jobId, e)
            listener.jobFailed(e)
            return
        }
    
        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()
        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))
        submitStage(finalStage)
    
        submitWaitingStages()
      }
    

    首先是在

    finalStage = newResultStage(finalRDD, func, partitions, jobId, callSite)
    

    这里将stage之间的依赖关系解析出来,同时根据依赖关系从小到大生成stage id。

      private def newResultStage(
          rdd: RDD[_],
          func: (TaskContext, Iterator[_]) => _,
          partitions: Array[Int],
          jobId: Int,
          callSite: CallSite): ResultStage = {
        val (parentStages: List[Stage], id: Int) = getParentStagesAndId(rdd, jobId)
        val stage = new ResultStage(id, rdd, func, partitions, parentStages, jobId, callSite)
        stageIdToStage(id) = stage
        updateJobIdStageIdMaps(jobId, stage)
        stage
      }
    

    getParentStagesAndId:

      private def getParentStagesAndId(rdd: RDD[_], firstJobId: Int): (List[Stage], Int) = {
        val parentStages = getParentStages(rdd, firstJobId)
        val id = nextStageId.getAndIncrement()
        (parentStages, id)
      }
      
      private def getParentStages(rdd: RDD[_], firstJobId: Int): List[Stage] = {
        val parents = new HashSet[Stage]
        val visited = new HashSet[RDD[_]]
        // We are manually maintaining a stack here to prevent StackOverflowError
        // caused by recursively visiting
        val waitingForVisit = new Stack[RDD[_]]
        def visit(r: RDD[_]) {
          if (!visited(r)) {
            visited += r
            // Kind of ugly: need to register RDDs with the cache here since
            // we can't do it in its constructor because # of partitions is unknown
            for (dep <- r.dependencies) {
              dep match {
                case shufDep: ShuffleDependency[_, _, _] =>
                  parents += getShuffleMapStage(shufDep, firstJobId)
                case _ =>
                  waitingForVisit.push(dep.rdd)
              }
            }
          }
        }
        waitingForVisit.push(rdd)
        while (waitingForVisit.nonEmpty) {
          visit(waitingForVisit.pop())
        }
        parents.toList
      }
    

    可以看到这里使用栈结构深度依次遍历了每一个rdd的所有依赖,如果是shuffle dependency则生成shuffle stage,其他的依赖则先放到栈里,再依次遍历。这里在生成shuffleMapStage的过程中又会递归的调用getParentStagesAndId方法,所以最后生成的finalStage是一个处于依赖树最顶端的包含其所有依赖的子依赖树的结构,stage id的生成从依赖链最底端,从小到大生成。

    之后以finalStage为参数调用submitStage来提交作业,但是在提交的过程中,它会依次递归的解析和提交每个stage所依赖的父stage,最终最先提交的是没有任何依赖的stage。

      private def submitStage(stage: Stage) {
        val jobId = activeJobForStage(stage)
        if (jobId.isDefined) {
          logDebug("submitStage(" + stage + ")")
          if (!waitingStages(stage) && !runningStages(stage) && !failedStages(stage)) {
            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)
        }
      }
    

    通过submitMissingTasks提交stage的所有task。在submitMissingTasks方法中, 首先计算task的分发策略,

    val taskIdToLocations: Map[Int, Seq[TaskLocation]] = try {
          stage match {
            case s: ShuffleMapStage =>
              partitionsToCompute.map { id => (id, getPreferredLocs(stage.rdd, id))}.toMap
            case s: ResultStage =>
              val job = s.activeJob.get
              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
    ${Utils.exceptionString(e)}", Some(e))
            runningStages -= stage
            return
        }
    

    然后序列化task,

        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))
          }
    

    将序列化后的task广播出去,

      taskBinary = sc.broadcast(taskBinaryBytes)
    

    然后将tasks信息封装成task对象数组,

    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)
              }
    
            case stage: ResultStage =>
              val job = stage.activeJob.get
              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)
              }
          }
        } catch {
          case NonFatal(e) =>
            abortStage(stage, s"Task creation failed: $e
    ${Utils.exceptionString(e)}", Some(e))
            runningStages -= stage
            return
        }
    

    调用taskScheduler提交task集合

      taskScheduler.submitTasks(new TaskSet(
            tasks.toArray, stage.id, stage.latestInfo.attemptId, jobId, properties))
    

    这个方法里主要是将taskSet交给TaskSetManager去管理,另外比较关键的是调用了schedulableBuilder中的addTaskSetManager,SchedulableBuilder本身是应用程序级别的调度器,它自己支持两种调度模式,一种是FIFO,另一种是FAIR,调度策略可以通过spark-env.sh中的spark.scheduler.mode进行具体的设置,默认情况下是FIFO。最后在submitTasks中调用了

      backend.reviveOffers()
    

    这里调用了CoarseGrainedSchedulerBackend.reviveOffers给driverEndpoint发送了一个ReviveOffers case object,这个消息其实是发给driverEndpoint自己的(详情见sparkde RpcEnv模块),也就是说最后处理的这个消息的还是driverEndpoint本身。这里会触发driverEndpoint的recieve方法然后路由到makeOffers方法。

        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)
          }.toSeq
          launchTasks(scheduler.resourceOffers(workOffers))
        }
    

    在makeOffers方法中,首先准备好所有可以用于计算的Executor,然后找出可以的workOffers(代表了所有可用ExecutorBackend中可以使用的CPU Cores信息)WorkerOffer会告我们具体Executor可用的资源。而确定task具体运行在哪个ExecutorBackend上的算法是有TaskSetManager的resourceOffers方法决定的,具体算法我们后续讨论。再通过调用launchTask把任务发送给ExecutorBackend去执行。代码如下:

        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
    
              logInfo(s"Launching task ${task.taskId} on executor id: ${task.executorId} hostname: " +
                s"${executorData.executorHost}.")
    
              executorData.executorEndpoint.send(LaunchTask(new SerializableBuffer(serializedTask)))
            }
          }
        }
    

    可以看到这里有将序列化后的task发送给executor的逻辑,所以整体的提交作业到这里就结束了

    出自:https://my.oschina.net/nalenwind/blog/1786172

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  • 原文地址:https://www.cnblogs.com/hd-zg/p/8724597.html
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