窗口同组联结(Window CoGroup)

      窗口同组联结（window coGroup）:将两条流合并之后开窗处理匹配的元素，调用时只需要将.join()换为.coGroup()就可以。

stream1.coGroup(stream2)
.where(<KeySelector>)
.equalTo(<KeySelector>)
.window(TumblingEventTimeWindows.of(Time.hours(1)))
.apply(<CoGroupFunction>)

　　CoGroupFunction函数

public interface CoGroupFunction<IN1, IN2, O> extends Function, Serializable { 
    void coGroup(Iterable<IN1> first, Iterable<IN2> second, Collector<O> out) throws Exception;
}

　　有三个参数， 分别代表两条流中的数据以及用于输出的收集器（Collector）。传入了可遍历的数据集合。也就是说，现在不会再去计算窗口中两条流数据集的笛卡尔积，而是直接把收集到的所有数据一次性传入，至于要怎样配对完全是自定义的。

public class CoGroupTest {

    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        DataStream<Tuple2<String, Long>> stream1 = env.fromElements(Tuple2.of("a", 1000L),
                Tuple2.of("b", 1000L),
                Tuple2.of("a", 2000L),
                Tuple2.of("b", 2000L))
                .assignTimestampsAndWatermarks(WatermarkStrategy.<Tuple2<String, Long>>forMonotonousTimestamps()
                        .withTimestampAssigner(new SerializableTimestampAssigner<Tuple2<String, Long>>() {
                            @Override
                            public long extractTimestamp(Tuple2<String, Long> element, long recordTimestamp) {
                                return element.f1;
                            }
                        }));

        DataStream<Tuple2<String, Long>> stream2 = env.fromElements(Tuple2.of("a", 3000L), Tuple2.of("b", 3000L), Tuple2.of("a", 4000L), Tuple2.of("b", 4000L))
                .assignTimestampsAndWatermarks(WatermarkStrategy.<Tuple2<String, Long>>forMonotonousTimestamps()
                        .withTimestampAssigner(new SerializableTimestampAssigner<Tuple2<String, Long>>() {
                            @Override
                            public long extractTimestamp(Tuple2<String, Long> element, long recordTimestamp) {
                                return element.f1;
                            }
                        }));

        stream1.coGroup(stream2)
                .where(stream1Tuple -> stream1Tuple.f0)
                .equalTo(stream2Tuple -> stream2Tuple.f0)
                .window(TumblingEventTimeWindows.of(Time.seconds(5)))
                .apply(new CoGroupFunction<Tuple2<String, Long>, Tuple2<String, Long>, String>() {
                    @Override
                    public void coGroup(Iterable<Tuple2<String, Long>> first, Iterable<Tuple2<String, Long>> second, Collector<String> out) throws Exception {
                        out.collect("stream1:" + first + "==> stream2" + second);
                    }
                }).print();

        env.execute();
    }
}