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
代码语言：javascript复制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, SlidingProcessingTimeWindows
class 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 file
env.set_parallelism(1)
source_type_info = Types.TUPLE([Types.STRING(), Types.INT()])
# define the source
# mappging
source = env.from_collection(word_count_data, source_type_info)
# source.print()
# keying
keyed=source.key_by(lambda i: i[0])
# reducing
reduced=keyed.window(SlidingProcessingTimeWindows.of(Time.milliseconds(2), Time.milliseconds(1)))
.apply(SumWindowFunction(),
Types.TUPLE([Types.STRING(), Types.INT()]))
# # define the sink
reduced.print()
# submit for execution
env.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
