在上文《Spark技术内幕：Stage划分及提交源码分析》中，我们分析了Stage的生成和提交。但是Stage的提交，只是DAGScheduler完成了对DAG的划分，生成了一个计算拓扑，即需要按照顺序计算的Stage，Stage中包含了可以以partition为单位并行计算的Task。我们并没有分析Stage中得Task是如何生成并且最终提交到Executor中去的。
这就是本文的主题。
从org.apache.spark.scheduler.DAGScheduler#submitMissingTasks开始，分析Stage是如何生成TaskSet的。
如果一个Stage的所有的parent stage都已经计算完成或者存在于cache中，那么他会调用submitMissingTasks来提交该Stage所包含的Tasks。
org.apache.spark.scheduler.DAGScheduler#submitMissingTasks的计算流程如下：
private[spark] class TaskSet(
val tasks: Array[Task[_]],
val stageId: Int,
val attempt: Int,
val priority: Int,
val properties: Properties) {
val id: String = stageId + "." + attempt
override def toString: String = "TaskSet " + id
} def launchTask(
context: ExecutorBackend, taskId: Long, taskName: String, serializedTask: ByteBuffer) {
val tr = new TaskRunner(context, taskId, taskName, serializedTask)
runningTasks.put(taskId, tr)
threadPool.execute(tr) // 开始在executor中运行
} final def run(attemptId: Long): T = {
context = new TaskContext(stageId, partitionId, attemptId, runningLocally = false)
context.taskMetrics.hostname = Utils.localHostName()
taskThread = Thread.currentThread()
if (_killed) {
kill(interruptThread = false)
}
runTask(context)
} override def runTask(context: TaskContext): U = {
// Deserialize the RDD and the func using the broadcast variables.
val ser = SparkEnv.get.closureSerializer.newInstance()
val (rdd, func) = ser.deserialize[(RDD[T], (TaskContext, Iterator[T]) => U)](
ByteBuffer.wrap(taskBinary.value), Thread.currentThread.getContextClassLoader)
metrics = Some(context.taskMetrics)
try {
func(context, rdd.iterator(partition, context))
} finally {
context.markTaskCompleted()
}
} override def runTask(context: TaskContext): MapStatus = {
// Deserialize the RDD using the broadcast variable.
val ser = SparkEnv.get.closureSerializer.newInstance()
val (rdd, dep) = ser.deserialize[(RDD[_], ShuffleDependency[_, _, _])](
ByteBuffer.wrap(taskBinary.value), Thread.currentThread.getContextClassLoader)
//此处的taskBinary即为在org.apache.spark.scheduler.DAGScheduler#submitMissingTasks序列化的task的广播变量取得的
metrics = Some(context.taskMetrics)
var writer: ShuffleWriter[Any, Any] = null
try {
val manager = SparkEnv.get.shuffleManager
writer = manager.getWriter[Any, Any](dep.shuffleHandle, partitionId, context)
writer.write(rdd.iterator(partition, context).asInstanceOf[Iterator[_ <: Product2[Any, Any]]]) // 将rdd计算的结果写入memory或者disk
return writer.stop(success = true).get
} catch {
case e: Exception =>
if (writer != null) {
writer.stop(success = false)
}
throw e
} finally {
context.markTaskCompleted()
}
}Spark技术内幕: Task向Executor提交的源码解析
原文：http://blog.csdn.net/anzhsoft/article/details/40238111