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在 《0基础学习PyFlink——个数滚动窗口(Tumbling Count Windows)》一文中，我们介绍了滚动窗口。本节我们要介绍滑动窗口。

滑动（Sliding）和滚动（Tumbling）的区别

正如其名，“滑动”是指这个窗口沿着一定的方向，按着一定的速度“滑行”。

而滚动窗口，则是一个个“衔接着”，而不是像上面那样交错着。

它们的相同之处就是：只有窗口内的事件数量到达窗口要求的数值时，这些窗口才会触发计算。

样例

我们只要对《0基础学习PyFlink——个数滚动窗口(Tumbling Count Windows)》中的代码做轻微的改动即可。为了简化样例，我们只看Key为E的元素的滑动。

word_count_data = [("E",3),("E",1),("E",4),("E",2),("E",6),("E",5)]def word_count():env = StreamExecutionEnvironment.get_execution_environment()env.set_runtime_mode(RuntimeExecutionMode.STREAMING)# write all the data to one fileenv.set_parallelism(1)source_type_info = Types.TUPLE([Types.STRING(), Types.INT()])# define the source# mappgingsource = env.from_collection(word_count_data, source_type_info)# source.print()# keyingkeyed=source.key_by(lambda i: i[0]) 

窗口为2，滑动距离为1

count_window会根据传入的第二参数决定是构建滚动（CountTumblingWindowAssigner）窗口还是滑动（CountSlidingWindowAssigner）窗口。

    def count_window(self, size: int, slide: int = 0):"""Windows this KeyedStream into tumbling or sliding count windows.:param size: The size of the windows in number of elements.:param slide: The slide interval in number of elements... versionadded:: 1.16.0"""if slide == 0:return WindowedStream(self, CountTumblingWindowAssigner(size))else:return WindowedStream(self, CountSlidingWindowAssigner(size, slide))

我们只要给count_window第二个参数传递一个不为0的值，即可达到滑动效果。

    # reducingwindows_size = 2sliding_size = 1reduced=keyed.count_window(windows_size, sliding_size) \.apply(SumWindowFunction(),Types.TUPLE([Types.STRING(), Types.INT()]))# # define the sinkreduced.print()# submit for executionenv.execute()

(E,2)
(E,2)
(E,2)
(E,2)
(E,2)

窗口为3，滑动距离为1

    # reducingwindows_size = 3sliding_size = 1reduced=keyed.count_window(windows_size, sliding_size) \.apply(SumWindowFunction(),Types.TUPLE([Types.STRING(), Types.INT()]))

(E,3)
(E,3)
(E,3)
(E,3)

窗口为3，滑动距离为2

    # reducingwindows_size = 3sliding_size = 2reduced=keyed.count_window(windows_size, sliding_size) \.apply(SumWindowFunction(),Types.TUPLE([Types.STRING(), Types.INT()]))

(E,3)
(E,3)

窗口为3，滑动距离为3

这个就等效于滚动窗口了，因为“滑”过了窗口大小。

    # reducingwindows_size = 3sliding_size = 3reduced=keyed.count_window(windows_size, sliding_size) \.apply(SumWindowFunction(),Types.TUPLE([Types.STRING(), Types.INT()]))

(E,3)
(E,3)

完整代码

from typing import Iterablefrom pyflink.common import Types
from pyflink.datastream import StreamExecutionEnvironment, RuntimeExecutionMode, WindowFunction
from pyflink.datastream.window import CountWindowclass SumWindowFunction(WindowFunction[tuple, tuple, str, CountWindow]):def apply(self, key: str, window: CountWindow, inputs: Iterable[tuple]):return [(key,  len([e for e in inputs]))]word_count_data = [("E",3),("E",1),("E",4),("E",2),("E",6),("E",5)]def word_count():env = StreamExecutionEnvironment.get_execution_environment()env.set_runtime_mode(RuntimeExecutionMode.STREAMING)# write all the data to one fileenv.set_parallelism(1)source_type_info = Types.TUPLE([Types.STRING(), Types.INT()])# define the source# mappgingsource = env.from_collection(word_count_data, source_type_info)# source.print()# keyingkeyed=source.key_by(lambda i: i[0]) # reducingwindows_size = 3sliding_size = 1reduced=keyed.count_window(windows_size, sliding_size) \.apply(SumWindowFunction(),Types.TUPLE([Types.STRING(), Types.INT()]))# # define the sinkreduced.print()# submit for executionenv.execute()if __name__ == '__main__':word_count()

参考资料

• https://nightlies.apache.org/flink/flink-docs-release-1.18/zh/docs/learn-flink/streaming_analytics/