/**
 * :: Experimental ::
 * Approximate version of count() that returns a potentially incomplete result
 * within a timeout, even if not all tasks have finished.
 */
@Experimental
def countApprox(
    timeout: Long,
    confidence: Double = 0.95): PartialResult[BoundedDouble] = withScope {

//定义在excutor端计算总数的函数
  val countElements: (TaskContext, Iterator[T]) => Long = { (ctx, iter) =>
    var result = 0L
    while (iter.hasNext) {
      result += 1L
      iter.next()
    }
    result
  }

//定义在driver端的一个监听回调函数，当task完成的时候，会触发里面的merge操作，当超时时间到之后或者任务提前完成的话，会取里面的当前状态，即currentResult
  val evaluator = newCountEvaluator(partitions.length, confidence)

//提交任务
  sc.runApproximateJob(this, countElements, evaluator, timeout)

}

def runApproximateJob[T,U, R](
    rdd: RDD[T],
    func: (TaskContext, Iterator[T]) =>U,
    evaluator: ApproximateEvaluator[U, R],
    timeout: Long): PartialResult[R] = {
  assertNotStopped()
  val callSite = getCallSite
  logInfo(“Starting job: ” + callSite.shortForm)
  val start = System.nanoTime
  val cleanedFunc = clean(func)

// cleanedFunc就是countElements，evaluator就是CountEvaluator，超时时间为timeout
  val result = dagScheduler.runApproximateJob(rdd, cleanedFunc, evaluator, callSite, timeout,
    localProperties.get)
  logInfo(
    “Job finished: ” + callSite.shortForm +“, took ” + (System.nanoTime– start) / 1e9 + ” s”)
  result

}

def runApproximateJob[T,U, R](
    rdd: RDD[T],
    func: (TaskContext, Iterator[T]) =>U,
    evaluator: ApproximateEvaluator[U, R],
    callSite: CallSite,
    timeout: Long,
    properties: Properties): PartialResult[R] = {

//定义一个监听器，当有任务完成的时候触发taskSucceeded，当超时时间到的时候返回CountEvaluator的当前值
  val listener = newApproximateActionListener(rdd, func, evaluator, timeout)
  val func2 = func.asInstanceOf[(TaskContext,Iterator[_]) => _]
  val partitions = (0until rdd.partitions.size).toArray
  val jobId = nextJobId.getAndIncrement()

//提交任务
  eventProcessLoop.post(JobSubmitted(
    jobId, rdd, func2, partitions, allowLocal = false, callSite, listener,
    SerializationUtils.clone(properties)))

//等待计算结果
  listener.awaitResult()    // Will throw an exception if the job fails

}

private[spark] classApproximateActionListener[T, U, R](
    rdd: RDD[T],
    func: (TaskContext, Iterator[T]) =>U,
    evaluator: ApproximateEvaluator[U, R],
    timeout: Long)
  extends JobListener {

  val startTime= System.currentTimeMillis()
  val totalTasks= rdd.partitions.size
  var finishedTasks= 0
  var failure: Option[Exception] = None             // Set if the job has failed (permanently)
  var resultObject: Option[PartialResult[R]] = None// Set if we’ve already returned a PartialResult
 

//当某个分区完成的时候触发taskSucceeded回调函数
  override def taskSucceeded(index: Int, result: Any) {
    synchronized {

//更新CountEvaluator的当前值
      evaluator.merge(index, result.asInstanceOf[U])
      finishedTasks += 1
      if (finishedTasks== totalTasks) {//当全部分区都完成的是退出等待，返回计算结果
        // If we had already returned a PartialResult, set its final value
        resultObject.foreach(r => r.setFinalValue(evaluator.currentResult()))
        // Notify any waiting thread that may have called awaitResult

//退出等待
        this.notifyAll()
      }
    }
  }
  ……
  /**
   * Waits for up to timeout milliseconds since the listener was created and then returns a
   * PartialResult with the result so far. This may be complete if the whole job is done.
   */

//等待计算结果
  def awaitResult(): PartialResult[R] = synchronized {
    val finishTime = startTime+ timeout
    while (true) {
      val time = System.currentTimeMillis()
      if (failure.isDefined) {
        throw failure.get
      } else if (finishedTasks== totalTasks) {//如果在超时时间之内计算完成，则返回全部结果
        return new PartialResult(evaluator.currentResult(),true)
      } else if (time >= finishTime) {//如果已经超时，则返回部分结果
        resultObject = Some(newPartialResult(evaluator.currentResult(), false))
        return resultObject.get
      } else {//如果超时时间没到，则继续休眠
        this.wait(finishTime – time)
      }
    }
    // Should never be reached, but required to keep the compiler happy
    return null
  }

}

private[spark] classCountEvaluator(totalOutputs: Int, confidence: Double)
  extends ApproximateEvaluator[Long, BoundedDouble] {

  var outputsMerged= 0
  var sum: Long =0

  override def merge(outputId: Int, taskResult: Long) {
    outputsMerged += 1
    sum += taskResult
  }

  override def currentResult(): BoundedDouble = {
    if (outputsMerged== totalOutputs) {//全部完成
      new BoundedDouble(sum,1.0, sum,sum)
    } else if (outputsMerged== 0) {//一个任务都没完成
      new BoundedDouble(0,0.0, Double.NegativeInfinity, Double.PositiveInfinity)
    } else {//部分完成，计算其理论总数的正态分布参数
      val p = outputsMerged.toDouble / totalOutputs
      val mean = (sum+ 1 – p) / p
      val variance = (sum+ 1) * (1 – p) / (p * p)
      val stdev = math.sqrt(variance)
      val confFactor = newNormalDistribution().
        inverseCumulativeProbability(1 – (1– confidence) / 2)
      val low = mean – confFactor * stdev
      val high = mean + confFactor * stdev
      new BoundedDouble(mean, confidence, low, high)
    }
  }

}

/**
 * Return an iterator that contains all of the elements in this RDD.
 *
 * The iterator will consume as much memory as the largest partition in this RDD.
 *
 * Note: this results in multiple Spark jobs, and if the input RDD is the result
 * of a wide transformation (e.g. join with different partitioners), to avoid
 * recomputing the input RDD should be cached first.
 */
def toLocalIterator:Iterator[T] = withScope {

//针对每个分区触发一次action
  def collectPartition(p: Int): Array[T] = {
    sc.runJob(this, (iter: Iterator[T]) => iter.toArray, Seq(p), allowLocal =false).head
  }

//调用flatMap将所有记录组装起来返回单个迭代器
  (0 until partitions.length).iterator.flatMap(i => collectPartition(i))

}

scala> val rdd = sc.parallelize(1 to 10,2)

rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[0] at parallelize at <console>:24

scala> val it = rdd.toLocalIterator

it: Iterator[Int] = non-empty iterator

scala> while(it.hasNext){

     | println(it.next)

     | }

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def takeOrdered(num: Int)(implicitord: Ordering[T]): Array[T] = withScope {
  if (num == 0) {
    Array.empty
  } else {
    val mapRDDs = mapPartitions { items =>

//先在excutor端进行排序，按照ord排序规则，转化为前num个优先队列
      // Priority keeps the largest elements, so let’s reverse the ordering.
      val queue = new BoundedPriorityQueue[T](num)(ord.reverse)
      queue ++= util.collection.Utils.takeOrdered(items, num)(ord)
      Iterator.single(queue)
    }
    if (mapRDDs.partitions.length ==0) {
      Array.empty
    } else {

//将分区的计算结果传送给driver，转化为数组，进行排序取前num条记录
      mapRDDs.reduce { (queue1, queue2) =>
        queue1 ++= queue2
        queue1
      }.toArray.sorted(ord)
    }
  }

}

List<Integer> data = Arrays.asList(1,4,3,2,5,6);
JavaRDD<Integer> JavaRDD = jsc.parallelize(data,2);
for(Integer integer:JavaRDD.takeOrdered(2)){
    System.out.println(integer);
}

打印

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