Window Join

package com.atguigu.watermark;

import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.common.functions.JoinFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.tuple.Tuple3;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.windowing.assigners.TumblingEventTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;

public class WindowJoinDemo {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

        SingleOutputStreamOperator<Tuple2<String, Integer>> ds1 = env
                .fromElements(
                        Tuple2.of("a", 1),
                        Tuple2.of("a", 2),
                        Tuple2.of("b", 3),
                        Tuple2.of("c", 4)
                )
                .assignTimestampsAndWatermarks(
                        WatermarkStrategy
                                .<Tuple2<String, Integer>>forMonotonousTimestamps()
                                .withTimestampAssigner((value, ts) -> value.f1 * 1000L)
                );

        SingleOutputStreamOperator<Tuple3<String, Integer,Integer>> ds2 = env
                .fromElements(
                        Tuple3.of("a", 1,1),
                        Tuple3.of("a", 11,1),
                        Tuple3.of("b", 2,1),
                        Tuple3.of("b", 12,1),
                        Tuple3.of("c", 14,1),
                        Tuple3.of("d", 15,1)
                )
                .assignTimestampsAndWatermarks(
                        WatermarkStrategy
                                .<Tuple3<String, Integer,Integer>>forMonotonousTimestamps()
                                .withTimestampAssigner((value, ts) -> value.f1 * 1000L)
                );

        // TODO window join
        // 1. 落在同一个时间窗口范围内才能匹配
        // 2. 根据keyby的key，来进行匹配关联
        // 3. 只能拿到匹配上的数据，类似有固定时间范围的inner join
        DataStream<String> join = ds1.join(ds2)
                .where(r1 -> r1.f0)  // ds1的keyby
                .equalTo(r2 -> r2.f0) // ds2的keyby
                .window(TumblingEventTimeWindows.of(Time.seconds(10)))
                .apply(new JoinFunction<Tuple2<String, Integer>, Tuple3<String, Integer, Integer>, String>() {
                    /**
                     * 关联上的数据，调用join方法
                     * @param first  ds1的数据
                     * @param second ds2的数据
                     * @return
                     * @throws Exception
                     */
                    @Override
                    public String join(Tuple2<String, Integer> first, Tuple3<String, Integer, Integer> second) throws Exception {
                        return first + "<----->" + second;
                    }
                });
        join.print();

        env.execute();
    }
}