Interval Join With Late

package com.atguigu.watermark;

import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.api.java.tuple.Tuple;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.tuple.Tuple3;
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.time.Time;
import org.apache.flink.util.Collector;
import org.apache.flink.util.OutputTag;

import java.time.Duration;

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

        SingleOutputStreamOperator<Tuple2<String, Integer>> ds1 = env
                .socketTextStream("192.168.1.7", 9091)
                .map(new MapFunction<String, Tuple2<String, Integer>>() {
                    @Override
                    public Tuple2<String, Integer> map(String value) throws Exception {
                        String[] datas = value.split(",");
                        return Tuple2.of(datas[0], Integer.valueOf(datas[1]));
                    }
                })
                .assignTimestampsAndWatermarks(
                        WatermarkStrategy
                                .<Tuple2<String, Integer>>forBoundedOutOfOrderness(Duration.ofSeconds(3))
                                .withTimestampAssigner((value, ts) -> value.f1 * 1000L)
                );

        SingleOutputStreamOperator<Tuple3<String, Integer, Integer>> ds2 = env
                .socketTextStream("192.168.1.7", 9093)
                .map(new MapFunction<String, Tuple3<String, Integer, Integer>>() {
                    @Override
                    public Tuple3<String, Integer, Integer> map(String value) throws Exception {
                        String[] datas = value.split(",");
                        return Tuple3.of(datas[0], Integer.valueOf(datas[1]), Integer.valueOf(datas[2]));
                    }
                })
                .assignTimestampsAndWatermarks(
                        WatermarkStrategy
                                .<Tuple3<String, Integer, Integer>>forBoundedOutOfOrderness(Duration.ofSeconds(3))
                                .withTimestampAssigner((value, ts) -> value.f1 * 1000L)
                );

        /**
         * TODO Interval join
         * 1、只支持事件时间
         * 2、指定上界、下界的偏移，负号代表时间往前，正号代表时间往后
         * 3、process中，只能处理 join上的数据
         * 4、两条流关联后的watermark，以两条流中最小的为准
         * 5、如果 当前数据的事件时间 < 当前的watermark，就是迟到数据， 主流的process不处理
         *  => between后，可以指定将 左流 或 右流 的迟到数据 放入侧输出流
         */
        //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
        OutputTag<Tuple2<String, Integer>> ks1LateTag = new OutputTag<>("ks1-late", Types.TUPLE(Types.STRING, Types.INT));
        OutputTag<Tuple3<String, Integer, Integer>> ks2LateTag = new OutputTag<>("ks2-late", Types.TUPLE(Types.STRING, Types.INT, Types.INT));
        SingleOutputStreamOperator<String> process = ks1.intervalJoin(ks2)
                .between(Time.seconds(-2), Time.seconds(2))
                .sideOutputLeftLateData(ks1LateTag)  // 将 ks1的迟到数据，放入侧输出流
                .sideOutputRightLateData(ks2LateTag) // 将 ks2的迟到数据，放入侧输出流
                .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);
                            }
                        });

        process.print("主流");
        process.getSideOutput(ks1LateTag).printToErr("ks1迟到数据");
        process.getSideOutput(ks2LateTag).printToErr("ks2迟到数据");

        env.execute();
    }
}