Window Operations(窗口函数的使用)

定时的进行一个时间段内的数据处理
Spark Streaming also provides windowed computations, which allow you to apply transformations over a sliding window of data. The following figure illustrates this sliding window.

As shown in the figure, every time the window slides over a source DStream, the source RDDs that fall within the window are combined and operated upon to produce the RDDs of the windowed DStream. In this specific case, the operation is applied over the last 3 time units of data, and slides by 2 time units. This shows that any window operation needs to specify two parameters.

window length[窗口的长度] - The duration of the window (3 in the figure).
sliding interval[窗口的间隔] - The interval at which the window operation is performed (2 in the figure).
These two parameters must be multiples of the batch interval of the source DStream (1 in the figure).
这2个参数和我们的batch size有关系:倍数
每隔多久计算某个范围内的数据:每隔10秒(sliding interval)计算前10分钟(window length)的WC

// Reduce last 30 seconds of data, every 10 seconds
val windowedWordCounts = pairs.reduceByKeyAndWindow((a:Int,b:Int) => (a + b), Seconds(30), Seconds(10))

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