spark源码分析, 任务提交及序列化

 

简易基本流程图如下

 

 

 

 

1. org.apache.spark.scheduler.DAGScheduler#submitMissingTasks

2.　　=> org.apache.spark.scheduler.TaskSchedulerImpl#submitTasks

   // First figure out the indexes of partition ids to compute.
    val partitionsToCompute: Seq[Int] = stage.findMissingPartitions()

    // Use the scheduling pool, job group, description, etc. from an ActiveJob associated
    // with this Stage
    val properties = jobIdToActiveJob(jobId).properties

    runningStages += stage
    // SparkListenerStageSubmitted should be posted before testing whether tasks are
    // serializable. If tasks are not serializable, a SparkListenerStageCompleted event
    // will be posted, which should always come after a corresponding SparkListenerStageSubmitted
    // event.
    stage match {
      case s: ShuffleMapStage =>
        outputCommitCoordinator.stageStart(stage = s.id, maxPartitionId = s.numPartitions - 1)
      case s: ResultStage =>
        outputCommitCoordinator.stageStart(
          stage = s.id, maxPartitionId = s.rdd.partitions.length - 1)
    }
    val taskIdToLocations: Map[Int, Seq[TaskLocation]] = try {
      stage match {
        case s: ShuffleMapStage =>
          partitionsToCompute.map { id => (id, getPreferredLocs(stage.rdd, id))}.toMap
        case s: ResultStage =>
          partitionsToCompute.map { id =>
            val p = s.partitions(id)
            (id, getPreferredLocs(stage.rdd, p))
          }.toMap
      }
    }

//序列化 RDD
    // TODO: Maybe we can keep the taskBinary in Stage to avoid serializing it multiple times.
    // Broadcasted binary for the task, used to dispatch tasks to executors. Note that we broadcast
    // the serialized copy of the RDD and for each task we will deserialize it, which means each
    // task gets a different copy of the RDD. This provides stronger isolation between tasks that
    // might modify state of objects referenced in their closures. This is necessary in Hadoop
    // where the JobConf/Configuration object is not thread-safe.
    var taskBinary: Broadcast[Array[Byte]] = null
    var partitions: Array[Partition] = null
    try {
      // For ShuffleMapTask, serialize and broadcast (rdd, shuffleDep).
      // For ResultTask, serialize and broadcast (rdd, func).
      var taskBinaryBytes: Array[Byte] = null
      // taskBinaryBytes and partitions are both effected by the checkpoint status. We need
      // this synchronization in case another concurrent job is checkpointing this RDD, so we get a
      // consistent view of both variables.
      RDDCheckpointData.synchronized {
        taskBinaryBytes = 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))
        }

        partitions = stage.rdd.partitions
      }

      taskBinary = sc.broadcast(taskBinaryBytes)
    }

//生成 taskset
    val tasks: Seq[Task[_]] = try {
      val serializedTaskMetrics = closureSerializer.serialize(stage.latestInfo.taskMetrics).array()
      stage match {
        case stage: ShuffleMapStage =>
          stage.pendingPartitions.clear()
          partitionsToCompute.map { id =>
            val locs = taskIdToLocations(id)
            val part = partitions(id)
            stage.pendingPartitions += id
            new ShuffleMapTask(stage.id, stage.latestInfo.attemptNumber,
              taskBinary, part, locs, properties, serializedTaskMetrics, Option(jobId),
              Option(sc.applicationId), sc.applicationAttemptId, stage.rdd.isBarrier())
          }

        case stage: ResultStage =>
          partitionsToCompute.map { id =>
            val p: Int = stage.partitions(id)
            val part = partitions(p)
            val locs = taskIdToLocations(id)
            new ResultTask(stage.id, stage.latestInfo.attemptNumber,
              taskBinary, part, locs, id, properties, serializedTaskMetrics,
              Option(jobId), Option(sc.applicationId), sc.applicationAttemptId,
              stage.rdd.isBarrier())
          }
      }
    }

//最终提交 taskset
  if (tasks.size > 0) {
      logInfo(s"Submitting ${tasks.size} missing tasks from $stage (${stage.rdd}) (first 15 " +
        s"tasks are for partitions ${tasks.take(15).map(_.partitionId)})")
      taskScheduler.submitTasks(new TaskSet(
        tasks.toArray, stage.id, stage.latestInfo.attemptNumber, jobId, properties))
    }

　　

 

3. =>  org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend#reviveOffers ,发送消息

def reviveOffers() {
// 类型 CoarseGrainedClusterMessage

driverEndpoint.send(ReviveOffers) }

4. => 自己处理消息org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.DriverEndpoint#receive

    override def receive: PartialFunction[Any, Unit] = {
      case StatusUpdate(executorId, taskId, state, data) =>
       .....
      case ReviveOffers =>
        makeOffers()

      case KillTask(taskId, executorId, interruptThread, reason) =>
        ....
      case KillExecutorsOnHost(host) =>
      .....
      case UpdateDelegationTokens(newDelegationTokens) =>
     .....
      case RemoveExecutor(executorId, reason) =>
     ...
        removeExecutor(executorId, reason)
    }

5.=> org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.DriverEndpoint#makeOffers

   // Make fake resource offers on all executors
    private def makeOffers() {
      // Make sure no executor is killed while some task is launching on it
      val taskDescs = withLock {
        // Filter out executors under killing
        val activeExecutors = executorDataMap.filterKeys(executorIsAlive)
        val workOffers = activeExecutors.map {
          case (id, executorData) =>
            new WorkerOffer(id, executorData.executorHost, executorData.freeCores,
              Some(executorData.executorAddress.hostPort))
        }.toIndexedSeq
        scheduler.resourceOffers(workOffers)
      }
      if (!taskDescs.isEmpty) {
        launchTasks(taskDescs)
      }
    }

 

6.=> org.apache.spark.scheduler.TaskSchedulerImpl#resourceOffers. 按照核心分配算法分配各 task 到 executor 上.

    // 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
        // Record all the executor IDs assigned barrier tasks on.
        val addressesWithDescs = ArrayBuffer[(String, TaskDescription)]()
        for (currentMaxLocality <- taskSet.myLocalityLevels) {
          var launchedTaskAtCurrentMaxLocality = false
          do {
            launchedTaskAtCurrentMaxLocality = resourceOfferSingleTaskSet(taskSet,
              currentMaxLocality, shuffledOffers, availableCpus, tasks, addressesWithDescs)
            launchedAnyTask |= launchedTaskAtCurrentMaxLocality
          } while (launchedTaskAtCurrentMaxLocality)
        }
    }

 

=>org.apache.spark.scheduler.TaskSchedulerImpl#resourceOfferSingleTaskSet

=>org.apache.spark.scheduler.TaskSchedulerImpl#resourceOfferSingleTaskSet

  private def resourceOfferSingleTaskSet(
      taskSet: TaskSetManager,
      maxLocality: TaskLocality,
      shuffledOffers: Seq[WorkerOffer],
      availableCpus: Array[Int],
      tasks: IndexedSeq[ArrayBuffer[TaskDescription]],
      addressesWithDescs: ArrayBuffer[(String, TaskDescription)]) : Boolean = {
    var launchedTask = false
  
  //分配任务
    for (i <- 0 until shuffledOffers.size) {
      val execId = shuffledOffers(i).executorId
      val host = shuffledOffers(i).host
      if (availableCpus(i) >= CPUS_PER_TASK) {
    
          for (task <- taskSet.resourceOffer(execId, host, maxLocality)) {
            tasks(i) += task
            val tid = task.taskId
            taskIdToTaskSetManager.put(tid, taskSet)
            taskIdToExecutorId(tid) = execId
            executorIdToRunningTaskIds(execId).add(tid)
            availableCpus(i) -= CPUS_PER_TASK
            
            launchedTask = true
          }
        
      }
    }
    return launchedTask
  }

==> org.apache.spark.scheduler.TaskSetManager#resourceOffer
 @throws[TaskNotSerializableException]
  def resourceOffer(
      execId: String,
      host: String,
      maxLocality: TaskLocality.TaskLocality)
    : Option[TaskDescription] =
  {
    val offerBlacklisted = taskSetBlacklistHelperOpt.exists { blacklist =>
      blacklist.isNodeBlacklistedForTaskSet(host) ||
        blacklist.isExecutorBlacklistedForTaskSet(execId)
    }
    if (!isZombie && !offerBlacklisted) {
      val curTime = clock.getTimeMillis()

      var allowedLocality = maxLocality

      if (maxLocality != TaskLocality.NO_PREF) {
        allowedLocality = getAllowedLocalityLevel(curTime)
        if (allowedLocality > maxLocality) {
          // We're not allowed to search for farther-away tasks
          allowedLocality = maxLocality
        }
      }

      dequeueTask(execId, host, allowedLocality).map { case ((index, taskLocality, speculative)) =>
        // Found a task; do some bookkeeping and return a task description
        //找到一个任务，然后封装task的信息，包括序列化
        val task = tasks(index)
        //原子自增
        val taskId = sched.newTaskId()
        // Do various bookkeeping
        copiesRunning(index) += 1
        val attemptNum = taskAttempts(index).size
        val info = new TaskInfo(taskId, index, attemptNum, curTime,
          execId, host, taskLocality, speculative)
        taskInfos(taskId) = info
        taskAttempts(index) = info :: taskAttempts(index)
        

        // Serialize and return the task
        val serializedTask: ByteBuffer = try {
          ser.serialize(task)
        } 
        //添加到运行Map中
        addRunningTask(taskId)


        sched.dagScheduler.taskStarted(task, info)
        new TaskDescription(
          taskId,
          attemptNum,
          execId,
          taskName,
          index,
          task.partitionId,
          addedFiles,
          addedJars,
          task.localProperties,
          serializedTask)
      }
    } else {
      None
    }
  }

  

7.=> org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.DriverEndpoint#launchTasks

  // Launch tasks returned by a set of resource offers
    private def launchTasks(tasks: Seq[Seq[TaskDescription]]) {
      for (task <- tasks.flatten) {
        val serializedTask = TaskDescription.encode(task)
        ...
          executorData.executorEndpoint.send(LaunchTask(new SerializableBuffer(serializedTask)))   
    }

8. => org.apache.spark.scheduler.TaskDescription#encode TaskDescription作为 message 发送给 executor

 def encode(taskDescription: TaskDescription): ByteBuffer = {
    val bytesOut = new ByteBufferOutputStream(4096)
    val dataOut = new DataOutputStream(bytesOut)

    dataOut.writeLong(taskDescription.taskId)
    dataOut.writeInt(taskDescription.attemptNumber)
    dataOut.writeUTF(taskDescription.executorId)
    dataOut.writeUTF(taskDescription.name)
    dataOut.writeInt(taskDescription.index)
    dataOut.writeInt(taskDescription.partitionId)

    // Write files.
    serializeStringLongMap(taskDescription.addedFiles, dataOut)

    // Write jars.
    serializeStringLongMap(taskDescription.addedJars, dataOut)

    // Write properties.
    dataOut.writeInt(taskDescription.properties.size())
    taskDescription.properties.asScala.foreach { case (key, value) =>
      dataOut.writeUTF(key)
      // SPARK-19796 -- writeUTF doesn't work for long strings, which can happen for property values
      val bytes = value.getBytes(StandardCharsets.UTF_8)
      dataOut.writeInt(bytes.length)
      dataOut.write(bytes)
    }

    // Write the task. The task is already serialized, so write it directly to the byte buffer.
    Utils.writeByteBuffer(taskDescription.serializedTask, bytesOut)

    dataOut.close()
    bytesOut.close()
    bytesOut.toByteBuffer
  }