spark源码分析, 任务提交及序列化

   // 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))
    }