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
)
}