Spark中saveAsTextFile至stage划分和job提交的源代码分析

之前看了Spark Streaming和Spark SQL, 自己还花了一些时间去玩了些machine learning的算法， 像 线性回归， kmeans， 协同过滤等。

现在回过头来， 打算看一下spark core部分代码， 就先找了下saveAsTextFile这个方法作为入口， 看一下是怎么保存文档到hadoop中，并且怎么切分stage以及提交Task。 中间也会触碰到DAGScheduler， 也能明白为什么大家都说DAGScheduler是作业调度的核心了

看一下saveAsTextFile代码：

def saveAsTextFile(path: String): Unit = withScope {
    // https://issues.apache.org/jira/browse/SPARK-2075
    //
    // NullWritable is a `Comparable` in Hadoop 1.+, so the compiler cannot find an implicit
    // Ordering for it and will use the default `null`. However, it's a `Comparable[NullWritable]`
    // in Hadoop 2.+, so the compiler will call the implicit `Ordering.ordered` method to create an
    // Ordering for `NullWritable`. That's why the compiler will generate different anonymous
    // classes for `saveAsTextFile` in Hadoop 1.+ and Hadoop 2.+.
    //
    // Therefore, here we provide an explicit Ordering `null` to make sure the compiler generate
    // same bytecodes for `saveAsTextFile`.
    val nullWritableClassTag = implicitly[ClassTag[NullWritable]]
    val textClassTag = implicitly[ClassTag[Text]]
    val r = this.mapPartitions { iter =>
      val text = new Text()
      iter.map { x =>
        text.set(x.toString)
        (NullWritable.get(), text)
      }
    }
    RDD.rddToPairRDDFunctions(r)(nullWritableClassTag, textClassTag, null)
      .saveAsHadoopFile[TextOutputFormat[NullWritable, Text]](path)
  }

这里很简单， 就是定义了一些输出类， 给每个partition设置了一下， 然后通过执行saveAsHadoopFile 来创建hadoop的txt文件， 看一下saveAsHadoopFile：

def saveAsHadoopFile[F <: OutputFormat[K, V]](
      path: String)(implicit fm: ClassTag[F]): Unit = self.withScope {
    saveAsHadoopFile(path, keyClass, valueClass, fm.runtimeClass.asInstanceOf[Class[F]])
  }

直接调用了saveAsHadoopFile， 那么我们继续跟进去：

def saveAsHadoopFile(
      path: String,
      keyClass: Class[_],
      valueClass: Class[_],
      outputFormatClass: Class[_ <: OutputFormat[_, _]],
      conf: JobConf = new JobConf(self.context.hadoopConfiguration),
      codec: Option[Class[_ <: CompressionCodec]] = None): Unit = self.withScope {
    // Rename this as hadoopConf internally to avoid shadowing (see SPARK-2038).
    val hadoopConf = conf
    hadoopConf.setOutputKeyClass(keyClass)
    hadoopConf.setOutputValueClass(valueClass)
    conf.setOutputFormat(outputFormatClass)
    for (c <- codec) {
      hadoopConf.setCompressMapOutput(true)
      hadoopConf.set("mapred.output.compress", "true")
      hadoopConf.setMapOutputCompressorClass(c)
      hadoopConf.set("mapred.output.compression.codec", c.getCanonicalName)
      hadoopConf.set("mapred.output.compression.type", CompressionType.BLOCK.toString)
    }

    // Use configured output committer if already set
    if (conf.getOutputCommitter == null) {
      hadoopConf.setOutputCommitter(classOf[FileOutputCommitter])
    }

    // When speculation is on and output committer class name contains "Direct", we should warn
    // users that they may loss data if they are using a direct output committer.
    val speculationEnabled = self.conf.getBoolean("spark.speculation", false)
    val outputCommitterClass = hadoopConf.get("mapred.output.committer.class", "")
    if (speculationEnabled && outputCommitterClass.contains("Direct")) {
      val warningMessage =
        s"$outputCommitterClass may be an output committer that writes data directly to " +
          "the final location. Because speculation is enabled, this output committer may " +
          "cause data loss (see the case in SPARK-10063). If possible, please use a output " +
          "committer that does not have this behavior (e.g. FileOutputCommitter)."
      logWarning(warningMessage)
    }

    FileOutputFormat.setOutputPath(hadoopConf,
      SparkHadoopWriter.createPathFromString(path, hadoopConf))
    saveAsHadoopDataset(hadoopConf)
  }

这个里面其实主要就是设置了hadoopconf的属性，然后设置到了FileOutputFormat里面， 再通过saveAsHadoopDataset继续执行saveAsTextFile：

def saveAsHadoopDataset(conf: JobConf): Unit = self.withScope {
    // Rename this as hadoopConf internally to avoid shadowing (see SPARK-2038).
    val hadoopConf = conf
    val outputFormatInstance = hadoopConf.getOutputFormat
    val keyClass = hadoopConf.getOutputKeyClass
    val valueClass = hadoopConf.getOutputValueClass
    if (outputFormatInstance == null) {
      throw new SparkException("Output format class not set")
    }
    if (keyClass == null) {
      throw new SparkException("Output key class not set")
    }
    if (valueClass == null) {
      throw new SparkException("Output value class not set")
    }
    SparkHadoopUtil.get.addCredentials(hadoopConf)

    logDebug("Saving as hadoop file of type (" + keyClass.getSimpleName + ", " +
      valueClass.getSimpleName + ")")

    if (isOutputSpecValidationEnabled) {
      // FileOutputFormat ignores the filesystem parameter
      val ignoredFs = FileSystem.get(hadoopConf)
      hadoopConf.getOutputFormat.checkOutputSpecs(ignoredFs, hadoopConf)
    }

    val writer = new SparkHadoopWriter(hadoopConf)
    writer.preSetup()

    val writeToFile = (context: TaskContext, iter: Iterator[(K, V)]) => {
      // Hadoop wants a 32-bit task attempt ID, so if ours is bigger than Int.MaxValue, roll it
      // around by taking a mod. We expect that no task will be attempted 2 billion times.
      val taskAttemptId = (context.taskAttemptId % Int.MaxValue).toInt

      val (outputMetrics, bytesWrittenCallback) = initHadoopOutputMetrics(context)

      writer.setup(context.stageId, context.partitionId, taskAttemptId)
      writer.open()
      var recordsWritten = 0L

      Utils.tryWithSafeFinallyAndFailureCallbacks {
        while (iter.hasNext) {
          val record = iter.next()
          writer.write(record._1.asInstanceOf[AnyRef], record._2.asInstanceOf[AnyRef])

          // Update bytes written metric every few records
          maybeUpdateOutputMetrics(bytesWrittenCallback, outputMetrics, recordsWritten)
          recordsWritten += 1
        }
      }(finallyBlock = writer.close())
      writer.commit()
      bytesWrittenCallback.foreach { fn => outputMetrics.setBytesWritten(fn()) }
      outputMetrics.setRecordsWritten(recordsWritten)
    }

    self.context.runJob(self, writeToFile)
    writer.commitJob()
  }

这里主要做了几件事：
1.创建了一个writer： val writer = new SparkHadoopWriter(hadoopConf)
2. 创建了writeToFile这个function， 这个function会被作为一个Job提交： self.context.runJob(self, writeToFile)

writeToFile其实就是从Job中获取数据写到对应的partition里面去

主要还是要看runJob里面做了什么：

  def runJob[T, U: ClassTag](rdd: RDD[T], func: (TaskContext, Iterator[T]) => U): Array[U] = {
    runJob(rdd, func, 0 until rdd.partitions.length)
  }

继续：

def runJob[T, U: ClassTag](
      rdd: RDD[T],
      func: (TaskContext, Iterator[T]) => U,
      partitions: Seq[Int]): Array[U] = {
    val results = new Array[U](partitions.size)
    runJob[T, U](rdd, func, partitions, (index, res) => results(index) = res)
    results
  }

再继续， 这里有一个回调函数(index, res) => results(index) = res， 就是把计算的result存到results里面:

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

注意这里的参数func一直就是在最开始定义的 将数据写到partition里面的writeToFile。

看到这里有调用到dagScheduler， 在初始化SparkContext之前， dagScheduler已经被构造了： （回头会写一下SparkContext的初始化）
private[spark] def dagScheduler: DAGScheduler = _dagScheduler
_dagScheduler = new DAGScheduler(this)

我们看一下dagScheduler里面的runJob干了什么：

  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)
    waiter.awaitResult() match {
      case JobSucceeded =>
        logInfo("Job %d finished: %s, took %f s".format
          (waiter.jobId, callSite.shortForm, (System.nanoTime - start) / 1e9))
      case JobFailed(exception: 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
    }
  }

里面调用了submitJob, 所以我们说Job是通过DAGScheduler去提交的， 可以看到Job提交后会有waiter一直awaitResult()， 将结果打印到日志里面， Job结束的时候writeToFile也执行完成了， txt文件也存到hadoop里面了。

那么接下来看一下DAGScheduler怎么提交Job的， 进入submitJob：

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
  }

里面先确定patition的数量是正常范围内， 然后创建JobId， 如果partions是0 代表最终没有task， 所以直接返回JobWaiter， 如果定partition大于0， 则创建JobWaiter用来返回去执行 awaitResult， 然后通过eventProcessLoop 把JobSubmitted的event加入进去， 那么 eventProcessLoop 是什么呢：

private[scheduler] val eventProcessLoop = new DAGSchedulerEventProcessLoop(this)

看到eventProcessLoop 其实是DAGSchedulerEventProcessLoop，（继承自EventLoop） 那么问题来了， 放进去的event是怎么被调用的呢， 那么我们要回到DAGScheduler的构造过程中， 看到创建DAGScheduler里面执行了eventProcessLoop.start()

这个start直接调用的是EventLoop的start方法：

  def start(): Unit = {
    if (stopped.get) {
      throw new IllegalStateException(name + " has already been stopped")
    }
    // Call onStart before starting the event thread to make sure it happens before onReceive
    onStart()
    eventThread.start()
  }

在DAGSchedulerEventProcessLoop没有定义onStart方法， 所以其实有用的是eventThread.start()方法， 这个方法如下：

private val eventThread = new Thread(name) {
    setDaemon(true)

    override def run(): Unit = {
      try {
        while (!stopped.get) {
          val event = eventQueue.take()
          try {
            onReceive(event)
          } catch {
            case NonFatal(e) => {
              try {
                onError(e)
              } catch {
                case NonFatal(e) => logError("Unexpected error in " + name, e)
              }
            }
          }
        }
      } catch {
        case ie: InterruptedException => // exit even if eventQueue is not empty
        case NonFatal(e) => logError("Unexpected error in " + name, e)
      }
    }

  }

他就是一个thread， 然后start方法回去跑run里面的东西， 所以调用了onReceive(event)方法， 如下： （DAGSchedulerEventProcessLoop的onReceive）

override def onReceive(event: DAGSchedulerEvent): Unit = {
    val timerContext = timer.time()
    try {
      doOnReceive(event)
    } finally {
      timerContext.stop()
    }
  }

继续调用doOnReceive(event) ：

private def doOnReceive(event: DAGSchedulerEvent): Unit = event match {
    case JobSubmitted(jobId, rdd, func, partitions, callSite, listener, properties) =>
      dagScheduler.handleJobSubmitted(jobId, rdd, func, partitions, callSite, listener, properties)

    case MapStageSubmitted(jobId, dependency, callSite, listener, properties) =>
      dagScheduler.handleMapStageSubmitted(jobId, dependency, callSite, listener, properties)

    case StageCancelled(stageId) =>
      dagScheduler.handleStageCancellation(stageId)

    case JobCancelled(jobId) =>
      dagScheduler.handleJobCancellation(jobId)

    case JobGroupCancelled(groupId) =>
      dagScheduler.handleJobGroupCancelled(groupId)

    case AllJobsCancelled =>
      dagScheduler.doCancelAllJobs()

    case ExecutorAdded(execId, host) =>
      dagScheduler.handleExecutorAdded(execId, host)

    case ExecutorLost(execId) =>
      dagScheduler.handleExecutorLost(execId, fetchFailed = false)

    case BeginEvent(task, taskInfo) =>
      dagScheduler.handleBeginEvent(task, taskInfo)

    case GettingResultEvent(taskInfo) =>
      dagScheduler.handleGetTaskResult(taskInfo)

    case completion @ CompletionEvent(task, reason, _, _, taskInfo, taskMetrics) =>
      dagScheduler.handleTaskCompletion(completion)

    case TaskSetFailed(taskSet, reason, exception) =>
      dagScheduler.handleTaskSetFailed(taskSet, reason, exception)

    case ResubmitFailedStages =>
      dagScheduler.resubmitFailedStages()
  }

好啦， 看到了case JobSubmitted。。  这个就是我们event加进去后， 执行的时候就会走到这个case里面， 去执行
dagScheduler.handleJobSubmitted

那么在handleJobSubmitted里面做了什么呢：

 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()
  }

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

在newResultStage里面的代码：

  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
  }

他会通过getParentStagesAndId拿到parents和stage ID然后根据这两个参数创建一个resultStage返回， 那么getParentStagesAndId里面是怎么做的呢：

  private def getParentStagesAndId(rdd: RDD[_], firstJobId: Int): (List[Stage], Int) = {
    val parentStages = getParentStages(rdd, firstJobId)
    val id = nextStageId.getAndIncrement()
    (parentStages, id)
  }

stageID是从一个increment里面创建的， parentStages是从getParentStages方法里面拿的：

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
  }

这里可以看到实际上spark是根据rdd的dependence， 如果是ShuffleDependency那么就分割出来， 如果不是那么放到waitingForVisit的列表中继续查找他的父rdd， 直到循环结束， 或者父rdd的dependence是ShuffleDependency为止， 然后getShuffleMapStage返回到parents里面再返回到前面调用的方法. 所以我们可以看到stage的划分其实是根据rdd的dependence是不是ShuffleDependency来分的。

接下来看一下getShuffleMapStage里面做了什么：

private def getShuffleMapStage(
      shuffleDep: ShuffleDependency[_, _, _],
      firstJobId: Int): ShuffleMapStage = {
    shuffleToMapStage.get(shuffleDep.shuffleId) match {
      case Some(stage) => stage
      case None =>
        // We are going to register ancestor shuffle dependencies
        getAncestorShuffleDependencies(shuffleDep.rdd).foreach { dep =>
          shuffleToMapStage(dep.shuffleId) = newOrUsedShuffleStage(dep, firstJobId)
        }
        // Then register current shuffleDep
        val stage = newOrUsedShuffleStage(shuffleDep, firstJobId)
        shuffleToMapStage(shuffleDep.shuffleId) = stage
        stage
    }
  }

其实就是创建一个shuffleStage及返回。 顺便再通过getAncestorShuffleDependencies 把所有和当前stage相关联的ShuffleDependency全部加到shuffleToMapStage， 以备后用。 好了现在知道了之前创建的那个resultStage其实是根据一堆ShuffleDependency的stage创建出来的， 那么我们回到handleJobSubmitted方法里面， 在拿到了finalStage （一个resultStage）后会根据其创建一个ActiveJob：

val job = new ActiveJob(jobId, finalStage, callSite, listener, properties)

再通过finalStage.setActiveJob(job) 和finalStage关联起来， 最后通过submitStage(finalStage)提交。

submitStage里面：

  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)
    }
  }

这里面其实做的就是先去查祖先stage是不是都active了， 如果不是active的话就放到missing里面， 先提交所有的inactive的stage，并且把当前stage放入waitingStages里面 把所有当前stage的祖先stage都submit后才有可能submit当前的stage。 所以stage都是有关联顺序的， 只有所有祖先stage都提交了， 才会去执行当前stage。 执行当前stage的时候其实是调用submitMissingTasks这个方法是根据stage提交task， 后面有机会说一下。 waitingStages 会通过submitWaitingStages方法去执行：

  private def submitWaitingStages() {
    // TODO: We might want to run this less often, when we are sure that something has become
    // runnable that wasn't before.
    logTrace("Checking for newly runnable parent stages")
    logTrace("running: " + runningStages)
    logTrace("waiting: " + waitingStages)
    logTrace("failed: " + failedStages)
    val waitingStagesCopy = waitingStages.toArray
    waitingStages.clear()
    for (stage <- waitingStagesCopy.sortBy(_.firstJobId)) {
      submitStage(stage)
    }
  }

可以看到里面其实也是调用submitStage去对所有的waitingstage做处理， 最后以task提交。 当task提交后我们的writeToFile就会被执行， 数据就会写到指定的hadoop路径中， 整个过程大概就是这个样子， 哪里不对的麻烦指正一下