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在《0基础学习PyFlink——时间滚动窗口(Tumbling Time Windows)》我们介绍了不会有重复数据的时间滚动窗口。本节我们将介绍存在重复计算数据的时间滑动窗口。
关于滑动窗口，可以先看下《0基础学习PyFlink——个数滑动窗口（Sliding Count Windows）》。下图就是个数滑动窗口示意图。

我们看到个数滑动窗口也会因为窗口内数据不够而不被触发。但是时间滑动窗口则可以解决这个问题，我们只要把窗口改成时间类型即可。
相应的代码我们参考《0基础学习PyFlink——时间滚动窗口(Tumbling Time Windows)》，只要把TumblingProcessingTimeWindows改成SlidingProcessingTimeWindows，并增加一个偏移参数（Time.milliseconds(1)）即可。这意味着我们将运行一个时间长度为2毫秒，每次递进1毫秒的窗口。

完整代码

from typing import Iterable
import time
from pyflink.common import Types, Time
from pyflink.datastream import StreamExecutionEnvironment, RuntimeExecutionMode, WindowFunction
from pyflink.datastream.window import  TimeWindow, SlidingProcessingTimeWindowsclass SumWindowFunction(WindowFunction[tuple, tuple, str, TimeWindow]):def apply(self, key: str, window: TimeWindow, inputs: Iterable[tuple]):print(*inputs, window)return [(key,  len([e for e in inputs]))]word_count_data = [("A",2),("A",1),("A",4),("A",3),("A",6),("A",5),("A",7),("A",8),("A",9),("A",10),("A",11),("A",12),("A",13),("A",14),("A",15),("A",16),("A",17),("A",18),("A",19),("A",20)]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]) # reducingreduced=keyed.window(SlidingProcessingTimeWindows.of(Time.milliseconds(2), Time.milliseconds(1))) \.apply(SumWindowFunction(),Types.TUPLE([Types.STRING(), Types.INT()]))# # define the sinkreduced.print()# submit for executionenv.execute()if __name__ == '__main__':word_count()

运行结果

运行两次上述代码，我们发现每次都不同，而且有重叠计算的元素。

(‘A’, 2) (‘A’, 1) (‘A’, 4) TimeWindow(start=1698773292650, end=1698773292652)
(‘A’, 2) (‘A’, 1) (‘A’, 4) (‘A’, 3) (‘A’, 6) (‘A’, 5) (‘A’, 7) (‘A’, 8) (‘A’, 9) (‘A’, 10) (‘A’, 11) TimeWindow(start=1698773292651, end=1698773292653)
(A,3)
(A,11)
(‘A’, 3) (‘A’, 6) (‘A’, 5) (‘A’, 7) (‘A’, 8) (‘A’, 9) (‘A’, 10) (‘A’, 11) (‘A’, 12) (‘A’, 13) (‘A’, 14) (‘A’, 15) (‘A’, 16) (‘A’, 17) (‘A’, 18) (‘A’, 19) (‘A’, 20) TimeWindow(start=1698773292652, end=1698773292654)
(A,17)

(‘A’, 2) (‘A’, 1) (‘A’, 4) TimeWindow(start=1698773319933, end=1698773319935)
(‘A’, 2) (‘A’, 1) (‘A’, 4) (‘A’, 3) (‘A’, 6) (‘A’, 5) (‘A’, 7) (‘A’, 8) (‘A’, 9) (‘A’, 10) (‘A’, 11) (‘A’, 12) TimeWindow(start=1698773319934, end=1698773319936)
(A,3)
(A,12)
(‘A’, 3) (‘A’, 6) (‘A’, 5) (‘A’, 7) (‘A’, 8) (‘A’, 9) (‘A’, 10) (‘A’, 11) (‘A’, 12) (‘A’, 13) (‘A’, 14) (‘A’, 15) (‘A’, 16) (‘A’, 17) (‘A’, 18) (‘A’, 19) (‘A’, 20) TimeWindow(start=1698773319935, end=1698773319937)
(A,17)

参考资料

• https://nightlies.apache.org/flink/flink-docs-master/api/python/reference/pyflink.datastream/api/pyflink.datastream.window.SlidingProcessingTimeWindows.html#pyflink.datastream.window.SlidingProcessingTimeWindows