spark streaming 的batchDuration slideDuration windowDuration关系

batchDuration：尝试提交Job任务的间隔，请注意这里是尝试。具体代码如下

  /** Checks whether the 'time' is valid wrt slideDuration for generating RDD */
  private[streaming] def isTimeValid(time: Time): Boolean = {
    if (!isInitialized) {
      throw new SparkException (this + " has not been initialized")
    } else if (time <= zeroTime || ! (time - zeroTime).isMultipleOf(slideDuration)) {
      logInfo("Time " + time + " is invalid as zeroTime is " + zeroTime +
        " and slideDuration is " + slideDuration + " and difference is " + (time - zeroTime))
      false
    } else {
      logDebug("Time " + time + " is valid")
      true
    }
  }

假设slideDuration的大小是batchDuration的N倍，那么前N－1次尝试都会无法创建job去执行。

只有第N次尝试的时候，才会提交job。

默认情况下，batchDuration和slideDuration值是相等的，因此每次尝试的时候都会成功。

InputDStream
  override def slideDuration: Duration = {
    if (ssc == null) throw new Exception("ssc is null")
    if (ssc.graph.batchDuration == null) throw new Exception("batchDuration is null")
    ssc.graph.batchDuration
  }

MappedDStream
  override def slideDuration: Duration = parent.slideDuration

但是有一个意外，哪就是如果有window窗口的时候，情况就不一样了。

  def window(windowDuration: Duration, slideDuration: Duration): DStream[T] = ssc.withScope {
    new WindowedDStream(this, windowDuration, slideDuration)
  }

  /**
   * Return a new DStream in which each RDD has a single element generated by reducing all
   * elements in a sliding window over this DStream.
   * @param reduceFunc associative reduce function
   * @param windowDuration width of the window; must be a multiple of this DStream's
   *                       batching interval
   * @param slideDuration  sliding interval of the window (i.e., the interval after which
   *                       the new DStream will generate RDDs); must be a multiple of this
   *                       DStream's batching interval
   */
  def reduceByWindow(
      reduceFunc: (T, T) => T,
      windowDuration: Duration,
      slideDuration: Duration
    ): DStream[T] = ssc.withScope {
    this.reduce(reduceFunc).window(windowDuration, slideDuration).reduce(reduceFunc)
  }

可以看到的是，诸如需要window的方法，都可以自定义slideDuration，可以是slideDuration的值是batchDuration的倍数的任何值。这个值的修改只会影响之后的DStream，之前的DStream的slideDuration还是和batchDuration相等。

那么当任务是如何执行的呢？

每个DStream都会存在一个方法

 override def compute(validTime: Time): Option[RDD[T]]

比如 MappedDStream，他的实现是

  override def compute(validTime: Time): Option[RDD[U]] = {
    parent.getOrCompute(validTime).map(_.map[U](mapFunc))
  }

很简单，就是调用父DStream的getOrCompute，然后在执行map方法。然后逐级调用，直到没有父DStream为止。

我们知道slideDuration的值是在windowDStream才被改变的，那么它会有什么实现呢？

  override def compute(validTime: Time): Option[RDD[T]] = {
    val currentWindow = new Interval(validTime - windowDuration + parent.slideDuration, validTime)
    val rddsInWindow = parent.slice(currentWindow)
    val windowRDD = if (rddsInWindow.flatMap(_.partitioner).distinct.length == 1) {
      logDebug("Using partition aware union for windowing at " + validTime)
      new PartitionerAwareUnionRDD(ssc.sc, rddsInWindow)
    } else {
      logDebug("Using normal union for windowing at " + validTime)
      new UnionRDD(ssc.sc, rddsInWindow)
    }
    Some(windowRDD)
  }

我们看到，WindowedDStream中，会首先获取到此window的范围

val currentWindow = new Interval(validTime - windowDuration + parent.slideDuration, validTime)

然后调用父DStream的slice方法

val rddsInWindow = parent.slice(currentWindow)

再次提醒一下，此时父DStream的slideDuration已经变了，变成和batchDuration一样了。

slice的具体实现是

  /**
   * Return all the RDDs between 'fromTime' to 'toTime' (both included)
   */
  def slice(fromTime: Time, toTime: Time): Seq[RDD[T]] = ssc.withScope {
    if (!isInitialized) {
      throw new SparkException(this + " has not been initialized")
    }

    val alignedToTime = if ((toTime - zeroTime).isMultipleOf(slideDuration)) {
      toTime
    } else {
      logWarning("toTime (" + toTime + ") is not a multiple of slideDuration ("
          + slideDuration + ")")
        toTime.floor(slideDuration, zeroTime)
    }

    val alignedFromTime = if ((fromTime - zeroTime).isMultipleOf(slideDuration)) {
      fromTime
    } else {
      logWarning("fromTime (" + fromTime + ") is not a multiple of slideDuration ("
      + slideDuration + ")")
      fromTime.floor(slideDuration, zeroTime)
    }

    logInfo("Slicing from " + fromTime + " to " + toTime +
      " (aligned to " + alignedFromTime + " and " + alignedToTime + ")")

    alignedFromTime.to(alignedToTime, slideDuration).flatMap(time => {
      if (time >= zeroTime) getOrCompute(time) else None
    })
  }

我们只看最后一段代码就行。

 alignedFromTime.to(alignedToTime, slideDuration).flatMap(time => {
      if (time >= zeroTime) getOrCompute(time) else None
    })

将window范围的time，根据slideDuration，也就是batchDuration的步长，生成batch进行计算。

  /** Checks whether the 'time' is valid wrt slideDuration for generating RDD */
  private[streaming] def isTimeValid(time: Time): Boolean = {
    if (!isInitialized) {
      throw new SparkException (this + " has not been initialized")
    } else if (time <= zeroTime || ! (time - zeroTime).isMultipleOf(slideDuration)) {
      logInfo("Time " + time + " is invalid as zeroTime is " + zeroTime +
        " and slideDuration is " + slideDuration + " and difference is " + (time - zeroTime))
      false
    } else {
      logDebug("Time " + time + " is valid")
      true
    }
  }

这个代码就返回结果就是true。

等把所有执行的结果返回后，windowedDStream会把结果进行整合。

    val windowRDD = if (rddsInWindow.flatMap(_.partitioner).distinct.length == 1) {
      logDebug("Using partition aware union for windowing at " + validTime)
      new PartitionerAwareUnionRDD(ssc.sc, rddsInWindow)
    } else {
      logDebug("Using normal union for windowing at " + validTime)
      new UnionRDD(ssc.sc, rddsInWindow)
    }
    Some(windowRDD)
  }

最终得到我们想要的rdd。