Spark源码拜读一

RDD 从一个样子转换成另一个状态，代码执行了，啥也没干，到了最后一步一下干了！懒加载是怎么做到的？
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。返回的还是 f。
map 会创建一个 MapPartitionsRDD，可以看到f 最后还是由 iter 调用它自己的 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 方法是把自己和函数传进 MapPartitionsRDD 了，并没有任何执行。进入 MapPartitionsRDD.scala，它是一个 RDD 的实现类，但里面并没有装数据，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
  }
}

这就是为什么代码都执行了，rdd 该转换了，可是数据并没有动。
这里也可以看到，无论是 map 还是 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 不同，有个sc.runJob，才开始真正执行。

sc.runJob进去是对每一个分区执行函数

  /**
   * 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 初始化的时候创建了，并且还创建了 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
    }