Interval 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.KeyedStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.co.ProcessJoinFunction;
import org.apache.flink.streaming.api.windowing.assigners.TumblingEventTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.util.Collector;

public class IntervalJoinDemo {
    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 interval join
        //1. 分别做keyby，key其实就是关联条件
        KeyedStream<Tuple2<String, Integer>, String> ks1 = ds1.keyBy(r1 -> r1.f0);
        KeyedStream<Tuple3<String, Integer, Integer>, String> ks2 = ds2.keyBy(r2 -> r2.f0);

        //2. 调用 interval join
        ks1.intervalJoin(ks2)
                .between(Time.seconds(-2), Time.seconds(2))
                .process(
                        new ProcessJoinFunction<Tuple2<String, Integer>, Tuple3<String, Integer, Integer>, String>() {
                            /**
                             * 两条流的数据匹配上，才会调用这个方法
                             * @param left  ks1的数据
                             * @param right ks2的数据
                             * @param ctx   上下文
                             * @param out   采集器
                             * @throws Exception
                             */
                            @Override
                            public void processElement(Tuple2<String, Integer> left, Tuple3<String, Integer, Integer> right, Context ctx, Collector<String> out) throws Exception {
                                // 进入这个方法，是关联上的数据
                                out.collect(left + "<------>" + right);
                            }
                        })
                .print();

        env.execute();
    }
}