在《0基础学习pyflink——个数滚动窗口(tumbling count windows)》一文中，我们了解到如果窗口内元素个数未达到设定窗口大小，计算个数的函数不会被触发。例如，下图中红色部分的元素（b,2）和（d,5）不会被计算：
为了让这些元素也能被计算，我们可以使用时间滚动窗口（Tumbling Time Windows）。这种窗口不依赖于元素的数量，而是基于时间进行触发。只要时间窗口到达，无论窗口内有多少元素，计算都会进行。
我们可以稍作修改《0基础学习PyFlink——个数滚动窗口(Tumbling Count Windows)》的示例，将元素集中在“A”上。以下是修改后的代码：
map代码语言：javascript
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]))]
<p>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)]</p><p>def word_count():
env = StreamExecutionEnvironment.get_execution_environment()
env.set_runtime_mode(RuntimeExecutionMode.STREAMING)</p><h1>write all the data to one file</h1><pre class="brush:php;toolbar:false;"><code>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])</code></pre><p>reduce代码语言：javascript</p><pre><code class="javascript"> # reducing
reduced=keyed.window(TumblingProcessingTimeWindows.of(Time.milliseconds(2))) \
.apply(SumWindowFunction(),
Types.TUPLE([Types.STRING(), Types.INT()]))
# # define the sink
reduced.print()
# submit for execution
env.execute()</code></pre><p>在这个例子中，我们使用了时间滚动窗口，窗口大小设置为2毫秒（<code>Time.milliseconds(2)</code>）。运行这段代码时，由于基于时间触发计算，每个元素都会被计算，输出结果可能会有所不同：</p><p><img src="/uploads/20250428/1745839071680f63df670f9.jpg" alt="0基础学习PyFlink——时间滚动窗口(Tumbling Time Windows)" /></p><p>或</p><p><img src="/uploads/20250428/1745839072680f63e00f547.jpg" alt="0基础学习PyFlink——时间滚动窗口(Tumbling Time Windows)" /></p><p>或</p><p><img src="/uploads/20250428/1745839073680f63e1a7f86.jpg" alt="0基础学习PyFlink——时间滚动窗口(Tumbling Time Windows)" /></p><p>可以看出，结果并不稳定，但每条数据都会被计算，而不是像个数滚动窗口那样某些数据可能不会被触发。</p><p>完整代码如下：</p><pre><code class="javascript">from typing import Iterableimport time from pyflink.common import Types, Time from pyflink.datastream import StreamExecutionEnvironment, RuntimeExecutionMode, WindowFunction from pyflink.datastream.window import TimeWindow, TumblingProcessingTimeWindows
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
<code>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(TumblingProcessingTimeWindows.of(Time.milliseconds(2))) \
.apply(SumWindowFunction(),
Types.TUPLE([Types.STRING(), Types.INT()]))
# # define the sink
reduced.print()
# submit for execution
env.execute()</code>if name == 'main': word_count()
参考资料：https://www.php.cn/link/dc61c1317e2c1637f0f8d2de7fd8da9b
