Flink-join的三种方式

本文主要是介绍Flink-join的三种方式，对大家解决编程问题具有一定的参考价值，需要的程序猿们随着小编来一起学习吧！

Join

/**
 *
 * 将两个数据流,进行join
 *
 * 如果让两个流能够join上,必须满足以下两个条件
 * 1.由于数据是分散在多台机器上,必须将join条件相同的数据通过网络传输到同一台机器的同一个分区中(按照条件进行KeyBy)
 * 2.让每个流中的数据都放慢,等等对方(划分相同类型,长度一样的窗口)
 *
 */
public class EventTumblingWindowJoin {

    public static void main(String[] args) throws Exception {

        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        //1000,o001,c001
        DataStreamSource<String> lines1 = env.socketTextStream("linux01", 7777);
        //1200,c001,图书
        DataStreamSource<String> lines2 = env.socketTextStream("linux01", 8888);

        //按照EventTime进行join，窗口长度为5000秒，使用新的提取EventTime生成WaterMark的API

        //提取两个流的Watermark
        SingleOutputStreamOperator<String> lines1WithWatermark
                = lines1.assignTimestampsAndWatermarks(WatermarkStrategy.<String>forBoundedOutOfOrderness(Duration.ofSeconds(0)).withTimestampAssigner(new SerializableTimestampAssigner<String>() {

            @Override
            public long extractTimestamp(String element, long recordTimestamp) {
                return Long.parseLong(element.split(",")[0]);
            }
        }));

        SingleOutputStreamOperator<String> lines2WithWatermark
                = lines2.assignTimestampsAndWatermarks(WatermarkStrategy.<String>forBoundedOutOfOrderness(Duration.ofSeconds(0)).withTimestampAssigner(new SerializableTimestampAssigner<String>() {

            @Override
            public long extractTimestamp(String element, long recordTimestamp) {
                return Long.parseLong(element.split(",")[0]);
            }
        }));

        //对两个流进行处理

        SingleOutputStreamOperator<Tuple3<Long, String, String>> tpStream1
                = lines1WithWatermark.map(new MapFunction<String, Tuple3<Long, String, String>>() {

            @Override
            public Tuple3<Long, String, String> map(String input) throws Exception {
                String[] fields = input.split(",");
                return Tuple3.of(Long.parseLong(fields[0]), fields[1], fields[2]);
            }
        });

        SingleOutputStreamOperator<Tuple3<Long, String, String>> tpStream2
                = lines2WithWatermark.map(new MapFunction<String, Tuple3<Long, String, String>>() {

            @Override
            public Tuple3<Long, String, String> map(String input) throws Exception {
                String[] fields = input.split(",");
                return Tuple3.of(Long.parseLong(fields[0]), fields[1], fields[2]);
            }
        });

        //将两个流join
        DataStream<Tuple5<Long, String, String, Long, String>> result = tpStream1.join(tpStream2)
                .where(tp1 -> tp1.f2)   //第一个流keyBY的字段
                .equalTo(tp2 -> tp2.f1) //第二个流keyBy的字段
                .window(TumblingEventTimeWindows.of(Time.seconds(5)))   //划分窗口
                //全量聚合的处理逻辑
                .apply(new JoinFunction<Tuple3<Long, String, String>, Tuple3<Long, String, String>, Tuple5<Long, String, String, Long, String>>() {
                    //窗口触发后，条件相同的，并且在同一个窗口内的数据，会传入到join方法中
                    @Override
                    public Tuple5<Long, String, String, Long, String> join(Tuple3<Long, String, String> first, Tuple3<Long, String, String> second) throws Exception {
                        return Tuple5.of(first.f0,first.f1,first.f2,second.f0,second.f2);
                    }
                });

        result.print();

        env.execute();
    }
}

LeftOuterJoin

/**
 * 将两个数据流,实现LeftOuterJoin
 *
 * 如果让两个流能够join上,必须满足以下两个条件
 * 1.由于数据是分散在多台机器上,必须将join条件相同的数据通过网络传输到同一台机器的同一个分区中(按照条件进行KeyBy)
 * 2.让每个流中的数据都放慢,等等对方(划分相同类型,长度一样的窗口)
 *
 */
public class EventTumblingWindowLeftOuterJoin {

    public static void main(String[] args) throws Exception {

        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        //1000,o001,c001
        DataStreamSource<String> lines1 = env.socketTextStream("linux01", 7777);
        //1200,c001,图书
        DataStreamSource<String> lines2 = env.socketTextStream("linux01", 8888);

        //按照EventTime进行join，窗口长度为5000秒，使用新的提取EventTime生成WaterMark的API

        //提取两个流的Watermark
        SingleOutputStreamOperator<String> lines1WithWatermark
                = lines1.assignTimestampsAndWatermarks(WatermarkStrategy.<String>forBoundedOutOfOrderness(Duration.ofSeconds(0)).withTimestampAssigner(new SerializableTimestampAssigner<String>() {

            @Override
            public long extractTimestamp(String element, long recordTimestamp) {
                return Long.parseLong(element.split(",")[0]);
            }
        }));

        SingleOutputStreamOperator<String> lines2WithWatermark
                = lines2.assignTimestampsAndWatermarks(WatermarkStrategy.<String>forBoundedOutOfOrderness(Duration.ofSeconds(0)).withTimestampAssigner(new SerializableTimestampAssigner<String>() {

            @Override
            public long extractTimestamp(String element, long recordTimestamp) {
                return Long.parseLong(element.split(",")[0]);
            }
        }));

        //对两个流进行处理

        SingleOutputStreamOperator<Tuple3<Long, String, String>> tpStream1
                = lines1WithWatermark.map(new MapFunction<String, Tuple3<Long, String, String>>() {

            @Override
            public Tuple3<Long, String, String> map(String input) throws Exception {
                String[] fields = input.split(",");
                return Tuple3.of(Long.parseLong(fields[0]), fields[1], fields[2]);
            }
        });

        SingleOutputStreamOperator<Tuple3<Long, String, String>> tpStream2
                = lines2WithWatermark.map(new MapFunction<String, Tuple3<Long, String, String>>() {

            @Override
            public Tuple3<Long, String, String> map(String input) throws Exception {
                String[] fields = input.split(",");
                return Tuple3.of(Long.parseLong(fields[0]), fields[1], fields[2]);
            }
        });

        //将两个流leftOuterJoin
        DataStream<Tuple5<Long, String, String, Long, String>> result = tpStream1.coGroup(tpStream2)
                .where(tp1 -> tp1.f2) //第一个流keyBy的字段
                .equalTo(tp2 -> tp2.f1)//第二个流keyBy的字段
                .window(TumblingEventTimeWindows.of(Time.seconds(5)))//划分窗口
                .apply(new CoGroupFunction<Tuple3<Long, String, String>, Tuple3<Long, String, String>, Tuple5<Long, String, String, Long, String>>() {
                    /**
                     * coGroup当窗口触发后，每个key会调用一次coGroup
                     * 三种情况会调用coGroup方法
                     * 1.第一个流和第二个流中，都有key相同的数据数据，并且在同一个窗口呢，那么coGroup方法中的两个Iterable都不为empty
                     * 2.第一个流中出现了同一个key的数据，.第二个流中没有出现相同key的数据，那么coGroup方法中的第一个Iterable不为empty，第二个为empty
                     * 3.第二个流中出现了同一个key的数据，.第一个流中没有出现相同key的数据，那么coGroup方法中的第二个Iterable不为empty，第一个为empty
                     * @param first
                     * @param second
                     * @param out
                     * @throws Exception
                     */
                    @Override
                    public void coGroup(Iterable<Tuple3<Long, String, String>> first, Iterable<Tuple3<Long, String, String>> second, Collector<Tuple5<Long, String, String, Long, String>> out) throws Exception {
                        for (Tuple3<Long, String, String> left : first) {
                            //实现左外连接
                            //先循环左流的数据
                            boolean isEmpty = false;
                            for (Tuple3<Long, String, String> right : second) {
                                isEmpty = true;
                                out.collect(Tuple5.of(left.f0, left.f1, left.f2, right.f0, right.f2));
                            }
                            if (!isEmpty) {
                                out.collect(Tuple5.of(left.f0, left.f1, left.f2, null, null));
                            }
                        }
                    }
                });

        result.print();

        env.execute();
    }
}

intervalJoin

/**
 * 将两个数据流不划分窗口，按照时间范围进行join，即intervalJoin
 *
 *   以第一个流中的数据为标准进行比较时间
 *
 *   实现步骤：
 *   1.分别将两个流按照相同的条件进行KeyBy（可以保证key等值的数据一定进入到同一台机器的同一个分区中）
 *   2.将两个数据流的数据缓存到KeyedState，然后将两个流Connected到一起（可以共享状态）
 *
 */
public class EventTumblingWindowIntervalJoin {

    public static void main(String[] args) throws Exception {

        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        //1000,o001,c001
        DataStreamSource<String> lines1 = env.socketTextStream("linux01", 7777);
        //1200,c001,图书
        DataStreamSource<String> lines2 = env.socketTextStream("linux01", 8888);

        //按照EventTime进行join，窗口长度为5000秒，使用新的提取EventTime生成WaterMark的API

        //提取两个流的Watermark
        SingleOutputStreamOperator<String> lines1WithWatermark
                = lines1.assignTimestampsAndWatermarks(WatermarkStrategy.<String>forBoundedOutOfOrderness(Duration.ofSeconds(0)).withTimestampAssigner(new SerializableTimestampAssigner<String>() {

            @Override
            public long extractTimestamp(String element, long recordTimestamp) {
                return Long.parseLong(element.split(",")[0]);
            }
        }));

        SingleOutputStreamOperator<String> lines2WithWatermark
                = lines2.assignTimestampsAndWatermarks(WatermarkStrategy.<String>forBoundedOutOfOrderness(Duration.ofSeconds(0)).withTimestampAssigner(new SerializableTimestampAssigner<String>() {

            @Override
            public long extractTimestamp(String element, long recordTimestamp) {
                return Long.parseLong(element.split(",")[0]);
            }
        }));

        //对两个流进行处理

        SingleOutputStreamOperator<Tuple3<Long, String, String>> tpStream1
                = lines1WithWatermark.map(new MapFunction<String, Tuple3<Long, String, String>>() {

            @Override
            public Tuple3<Long, String, String> map(String input) throws Exception {
                String[] fields = input.split(",");
                return Tuple3.of(Long.parseLong(fields[0]), fields[1], fields[2]);
            }
        });

        SingleOutputStreamOperator<Tuple3<Long, String, String>> tpStream2
                = lines2WithWatermark.map(new MapFunction<String, Tuple3<Long, String, String>>() {

            @Override
            public Tuple3<Long, String, String> map(String input) throws Exception {
                String[] fields = input.split(",");
                return Tuple3.of(Long.parseLong(fields[0]), fields[1], fields[2]);
            }
        });

        //将两个流join
        KeyedStream<Tuple3<Long, String, String>, String> keyedStream1 = tpStream1.keyBy(tp -> tp.f2);
        KeyedStream<Tuple3<Long, String, String>, String> keyedStream2 = tpStream2.keyBy(tp -> tp.f1);

        SingleOutputStreamOperator<Tuple5<Long, String, String, Long, String>> result = keyedStream1.intervalJoin(keyedStream2)
                .between(Time.seconds(-1), Time.seconds(1))  //指定的时间范围
                .upperBoundExclusive() //不包括上界
                .process(new ProcessJoinFunction<Tuple3<Long, String, String>, Tuple3<Long, String, String>, Tuple5<Long, String, String, Long, String>>() {
                    @Override
                    public void processElement(Tuple3<Long, String, String> left, Tuple3<Long, String, String> right, Context ctx, Collector<Tuple5<Long, String, String, Long, String>> out) throws Exception {

                        out.collect(Tuple5.of(left.f0,left.f1,left.f2,right.f0,right.f2));

                    }
                });


        result.print();

        env.execute();
    }
}

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