Spark源码拜读（一）

RDD从一个样子转换成另外一个状态，代码执行了，啥也没干，到了最后一步一下干了！懒加载是怎么作到的？
打开RDD.scala，看最基础的map方法

/**
   * Return a new RDD by applying a function to all elements of this RDD.
   */
  def map[U: ClassTag](f: T => U): RDD[U] = withScope {
    val cleanF = sc.clean(f)
    new MapPartitionsRDD[U, T](this, (context, pid, iter) => iter.map(cleanF))
  }

其中，val cleanF = sc.clean(f)是把函数f发送到各个task上。返回的仍是f。
map会建立一个MapPartitionsRDD，能够看到f最后仍是由iter调用它本身的map方法来执行的，而这里(context, pid, iter) => iter.map(cleanF)整个是一个函数，也就是说，这个RDD的map方法是把本身和函数传进MapPartitionsRDD了，并无任何执行。进入MapPartitionsRDD.scala，它是一个RDD的实现类，但里面并无装数据，只有个函数传进来。

/**
 * An RDD that applies the provided function to every partition of the parent RDD.
 */
private[spark] class MapPartitionsRDD[U: ClassTag, T: ClassTag](
    var prev: RDD[T],
    f: (TaskContext, Int, Iterator[T]) => Iterator[U],  // (TaskContext, partition index, iterator)
    preservesPartitioning: Boolean = false)
  extends RDD[U](prev) {

  override val partitioner = if (preservesPartitioning) firstParent[T].partitioner else None

  override def getPartitions: Array[Partition] = firstParent[T].partitions

  override def compute(split: Partition, context: TaskContext): Iterator[U] =
    f(context, split.index, firstParent[T].iterator(split, context))

  override def clearDependencies() {
    super.clearDependencies()
    prev = null
  }
}

这就是为何代码都执行了，rdd该转换了，但是数据并无动。
这里也能够看到，不管是map仍是mapPartition，都是把一个分区的数据封装成iterator，执行iterator的同名函数，这个map函数是scala的，不是RDD的。

这是transform算子，直到action算子，foreach

/**
   * Applies a function f to all elements of this RDD.
   */
  def foreach(f: T => Unit): Unit = withScope {
    val cleanF = sc.clean(f)
    sc.runJob(this, (iter: Iterator[T]) => iter.foreach(cleanF))
  }

与map不一样，有个sc.runJob，才开始真正执行。

sc.runJob进去是对每个分区执行函数

/**
   * Run a function on a given set of partitions in an RDD and pass the results to the given
   * handler function. This is the main entry point for all actions in Spark.
   *
   * @param rdd target RDD to run tasks on
   * @param func a function to run on each partition of the RDD
   * @param partitions set of partitions to run on; some jobs may not want to compute on all
   * partitions of the target RDD, e.g. for operations like `first()`
   * @param resultHandler callback to pass each result to
   */
  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()
  }

注意到执行job的是dagScheduler，在sc初始化的时候建立了，而且还建立了taskScheduler。
继续看，

eventProcessLoop.post(JobSubmitted(
      jobId, rdd, func2, partitions.toArray, callSite, waiter,
      SerializationUtils.clone(properties)))

这个job是封装成了DAGSchedulerEvent提交给了一个阻塞队列，由一个线程循环地从队列中取事件进行消费。

private[spark] abstract class EventLoop[E](name: String) extends Logging {

  private val eventQueue: BlockingQueue[E] = new LinkedBlockingDeque[E]()

  private val stopped = new AtomicBoolean(false)

  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)
      }
    }

  }

最后有一个waiter等待job执行结束返回结果

val waiter = submitJob(rdd, func, partitions, callSite, resultHandler, properties)
    ThreadUtils.awaitReady(waiter.completionFuture, Duration.Inf)
    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
    }