sparkStreaming窗口操作 SPARKSTREAMING--REDUCEBYKEYANDWINDOW

SPARKSTREAMING--REDUCEBYKEYANDWINDOW

1、reduceByKeyAndWindow（_+_,Seconds(3), Seconds(2)）

可以看到我们定义的window窗口大小Seconds(3s) ，是指每2s滑动时，需要统计前3s内所有的数据。

2、对于他的重载函数reduceByKeyAndWindow（_+_,_-_,Seconds(3s),seconds(2)）

设计理念是，当滑动窗口的时间Seconds(2) < Seconds(3)（窗口大小）时，两个统计的部分会有重复，那么我们就可以

不用重新获取或者计算，而是通过获取旧信息来更新新的信息，这样即节省了空间又节省了内容，并且效率也大幅提升。

如上图所示，2次统计重复的部分为time3对用的时间片内的数据，这样对于window1，和window2的计算可以如下所示

win1 = time1 + time2 + time3

win2 = time3 + time4 + time5

更新为

win1 = time1 + time2 + time3

win2 = win1+ time4 + time5 - time2 - time3

这样就理解了吧, _+_是对新产生的时间分片（time4,time5内RDD）进行统计，而_-_是对上一个窗口中，过时的时间分片

(time1,time2) 进行统计

3、注意事项

/**
 * Return a new DStream by applying incremental `reduceByKey` over a sliding window.
 * The reduced value of over a new window is calculated using the old window's reduced value :
 *  1. reduce the new values that entered the window (e.g., adding new counts)
 *
 *  2. "inverse reduce" the old values that left the window (e.g., subtracting old counts)
 *
 * This is more efficient than reduceByKeyAndWindow without "inverse reduce" function.
 * However, it is applicable to only "invertible reduce functions".
 * Hash partitioning is used to generate the RDDs with Spark's default number of partitions.
 * @param reduceFunc associative reduce function
 * @param invReduceFunc inverse 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
 * @param filterFunc     Optional function to filter expired key-value pairs;
 *                       only pairs that satisfy the function are retained
 */
def reduceByKeyAndWindow(
    reduceFunc: (V, V) => V,
    invReduceFunc: (V, V) => V,
    windowDuration: Duration,
    slideDuration: Duration = self.slideDuration,
    numPartitions: Int = ssc.sc.defaultParallelism,
    filterFunc: ((K, V)) => Boolean = null
  ): DStream[(K, V)] = ssc.withScope {
  reduceByKeyAndWindow(
    reduceFunc, invReduceFunc, windowDuration,
    slideDuration, defaultPartitioner(numPartitions), filterFunc
  )
}

转载博客：https://www.cnblogs.com/zDanica/p/5471592.html

https://blog.csdn.net/Thomson617/article/details/87780167

1、reduceByKeyAndWindow（_+_,Seconds(3), Seconds(2)）

可以看到我们定义的window窗口大小Seconds(3s) ，是指每2s滑动时，需要统计前3s内所有的数据。

2、对于他的重载函数reduceByKeyAndWindow（_+_,_-_,Seconds(3s),seconds(2)）

设计理念是，当滑动窗口的时间Seconds(2) < Seconds(3)（窗口大小）时，两个统计的部分会有重复，那么我们就可以

不用重新获取或者计算，而是通过获取旧信息来更新新的信息，这样即节省了空间又节省了内容，并且效率也大幅提升。

如上图所示，2次统计重复的部分为time3对用的时间片内的数据，这样对于window1，和window2的计算可以如下所示

win1 = time1 + time2 + time3

win2 = time3 + time4 + time5

更新为

win1 = time1 + time2 + time3

win2 = win1+ time4 + time5 - time2 - time3

这样就理解了吧, _+_是对新产生的时间分片（time4,time5内RDD）进行统计，而_-_是对上一个窗口中，过时的时间分片

(time1,time2) 进行统计

3、注意事项

/**
 * Return a new DStream by applying incremental `reduceByKey` over a sliding window.
 * The reduced value of over a new window is calculated using the old window's reduced value :
 *  1. reduce the new values that entered the window (e.g., adding new counts)
 *
 *  2. "inverse reduce" the old values that left the window (e.g., subtracting old counts)
 *
 * This is more efficient than reduceByKeyAndWindow without "inverse reduce" function.
 * However, it is applicable to only "invertible reduce functions".
 * Hash partitioning is used to generate the RDDs with Spark's default number of partitions.
 * @param reduceFunc associative reduce function
 * @param invReduceFunc inverse 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
 * @param filterFunc     Optional function to filter expired key-value pairs;
 *                       only pairs that satisfy the function are retained
 */
def reduceByKeyAndWindow(
    reduceFunc: (V, V) => V,
    invReduceFunc: (V, V) => V,
    windowDuration: Duration,
    slideDuration: Duration = self.slideDuration,
    numPartitions: Int = ssc.sc.defaultParallelism,
    filterFunc: ((K, V)) => Boolean = null
  ): DStream[(K, V)] = ssc.withScope {
  reduceByKeyAndWindow(
    reduceFunc, invReduceFunc, windowDuration,
    slideDuration, defaultPartitioner(numPartitions), filterFunc
  )
}

sparkStreaming窗口操作 SPARKSTREAMING--REDUCEBYKEYANDWINDOW

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