全量聚合: 窗口需要维护全部原始数据，窗口触发进行全量聚合。
ProcessWindowFunction获得一个包含窗口所有元素的可迭代器，以及一个具有时间和状态信息访问权的上下文对象，这使得它比其他窗口函数提供更大的灵活性。这是以性能和资源消耗为代价的，因为元素不能增量地聚合，而是需要在内部缓冲，直到认为窗口可以处理为止。
WindowFunction的升级版，可以跟ReduceFunction/AggregateFunction/FoldFunction结合使用(推荐用法)
package com.lynch.stream.window;
import org.apache.flink.api.java.tuple.Tuple;
import org.apache.flink.api.java.tuple.Tuple3;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.windowing.ProcessWindowFunction;
import org.apache.flink.streaming.api.windowing.windows.GlobalWindow;
import org.apache.flink.util.Collector;
/**
* 测试ProcessWinFunction
*
* @author dajiangtai
* @create 2019-06-11-18:37
*/
public class TestProcessWinFunctionOnWindow {
public static void main(String[] args) throws Exception{
//获取执行环境
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
//读取数据
DataStream<Tuple3<String,String,Long>> input = env.fromElements(ENGLISH);
//求各班级英语成绩平均分
DataStream<Double> avgScore = input.keyBy(0)
.countWindow(2)
.process(new MyProcessWindowFunction());
avgScore.print();
env.execute("TestProcessWinFunctionOnWindow");
}
public static class MyProcessWindowFunction extends ProcessWindowFunction<Tuple3<String,String,Long>,Double, Tuple, GlobalWindow>{
//iterable 输入流中的元素类型集合
@Override
public void process(Tuple tuple, Context context, Iterable<Tuple3<String, String, Long>> iterable, Collector<Double> out) throws Exception {
long sum = 0;
long count = 0;
for (Tuple3<String,String,Long> in :iterable){
sum+=in.f2;
count++;
}
out.collect((double)(sum/count));
}
}
public static final Tuple3[] ENGLISH = new Tuple3[]{
Tuple3.of("class1","张三",100L),
Tuple3.of("class1","李四",78L),
Tuple3.of("class1","王五",99L),
Tuple3.of("class2","赵六",81L),
Tuple3.of("class2","小七",59L),
Tuple3.of("class2","小八",97L),
};
}
Flink Window那些事——ProcessWindowFunction/ProcessAllWindowFunction
原文：https://www.cnblogs.com/linjiqin/p/12591729.html