java Flink（三十六）Flink多流合并算子UNION、CONNECT、CoGroup、Join

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UNION介绍

  DataStream.union()方法将两条或者多条DataStream合并成一条具有与输入流相同类型的输出DataStream.

  事件合流的方式为FIFO方式。操作符并不会产生一个特定顺序的事件流。union操作符也不会进行去重。每一个输入事件都被发送到了下一个操作符。

说明：

1.union 合并的流的元素必须是相同的

2.union 可以合并多条流

3.union不去重，合流顺序为先进先出

 具体用法：

DataStream<SensorReading> parisStream = ...
DataStream<SensorReading> tokyoStream = ...
DataStream<SensorReading> rioStream = ...
DataStream<SensorReading> allCities = parisStream
  .union(tokyoStream, rioStream)

CONNECT

CONNECT也是用来合并多个数据流的，它和UNION的功能类似，区别在于：

connect只能连接两个数据流，union可以连接多个数据流。
connect所连接的两个数据流的数据类型可以不一致，union所连接的两个数据流的数据类型必须一致。
两个DataStream经过connect之后被转化为ConnectedStreams，ConnectedStreams会对两个流的数据应用不同的处理方法，且双流之间可以共享状态。
connect经常被应用在对一个数据流使用另外一个流进行控制处理的场景上。

具体用法：

合并流：

// first stream
DataStream<Integer> first = ...
// second stream
DataStream<String> second = ...

// connect streams
ConnectedStreams<Integer, String> connected = first.connect(second);

两种keyby后合并

DataStream<Tuple2<Integer, Long>> one = ...
DataStream<Tuple2<Integer, String>> two = ...

// keyBy two connected streams
ConnectedStreams<Tuple2<Int, Long>, Tuple2<Integer, String>> keyedConnect1 = one
  .connect(two)
  .keyBy(0, 0); // key both input streams on first attribute

// alternative: connect two keyed streams
ConnectedStreams<Tuple2<Integer, Long>, Tuple2<Integer, String>> keyedConnect2 = one
  .keyBy(0)
  .connect(two.keyBy(0));

CoGroup：

该操作是将两个数据流/集合按照key进行group，然后将相同key的数据进行处理，但是它和join操作稍有区别，它在一个流/数据集中没有找到与另一个匹配的数据还是会输出。 

import org.apache.flink.api.common.functions.CoGroupFunction;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.source.RichSourceFunction;
import org.apache.flink.streaming.api.windowing.assigners.ProcessingTimeSessionWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.api.windowing.triggers.CountTrigger;
import org.apache.flink.util.Collector;
import java.util.Random;
import java.util.concurrent.TimeUnit;

public class CoGroupMain {

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

        final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

        final Random random = new Random();
        DataStreamSource<Tuple2<String, String>> source1 = env.addSource(new RichSourceFunction<Tuple2<String, String>>() {
            boolean isRunning = true;
            String[] s1 = {"1,a", "2,b", "3,c", "4,d", "5,e"};
            public void run(SourceContext<Tuple2<String, String>> ctx) throws Exception {
                int size = s1.length;
                while (isRunning) {
                    TimeUnit.SECONDS.sleep(1);
                    String[] s = s1[random.nextInt(size)].split(",");
                    Tuple2 t = new Tuple2();
                    t.f0 = s[0];
                    t.f1 = s[1];
                    ctx.collect(t);
                }
            }
            public void cancel() {
                isRunning = false;
            }
        });

        DataStreamSource<Tuple2<String, String>> source2 = env.addSource(new RichSourceFunction<Tuple2<String, String>>() {
            boolean isRunning = true;
            String[] s1 = {"1,a", "2,b", "3,c", "4,d", "5,e", "6,f", "7,g", "8,h"};
            public void run(SourceContext<Tuple2<String, String>> ctx) throws Exception {
                int size = s1.length;
                while (isRunning) {
                    TimeUnit.SECONDS.sleep(3);
                    String[] s = s1[random.nextInt(size)].split(",");
                    Tuple2 t = new Tuple2();
                    t.f0 = s[0];
                    t.f1 = s[1];
                    ctx.collect(t);
                }
            }
            public void cancel() {
                isRunning = false;
            }
        });

        source1.coGroup(source2)
                .where(new KeySelector<Tuple2<String, String>, Object>() {
                    public Object getKey(Tuple2<String, String> value) throws Exception {
                        return value.f0;
                    }
                }).equalTo(new KeySelector<Tuple2<String, String>, Object>() {
            public Object getKey(Tuple2<String, String> value) throws Exception {
                return value.f0;
            }
        }).window(ProcessingTimeSessionWindows.withGap(Time.seconds(3)))
                .trigger(CountTrigger.of(1))
                .apply(new CoGroupFunction<Tuple2<String, String>, Tuple2<String, String>, Object>() {
                    public void coGroup(Iterable<Tuple2<String, String>> first, Iterable<Tuple2<String, String>> second, Collector<Object> out) throws Exception {
                        StringBuffer stringBuffer = new StringBuffer();
                        stringBuffer.append("DataStream first:\n");
                        for (Tuple2<String, String> value : first) {
                            stringBuffer.append(value.f0 + "=>" + value.f1 + "\n");
                        }
                        stringBuffer.append("DataStream second:\n");
                        for (Tuple2<String, String> value : second) {
                            stringBuffer.append(value.f0 + "=>" + value.f1 + "\n");
                        }
                        out.collect(stringBuffer.toString());
                    }
                }).print();
        env.execute();
    }
}

 Join

 flink中常见的join有四个：

1. Tumbling Window Join 
   
   
   
2. Sliding Window Join
   
   
3. Session Window Join
   
   
4. Interval Join

Join的编程模型为：

stream.join(otherStream)
    .where(<KeySelector>)
    .equalTo(<KeySelector>)
    .window(<WindowAssigner>)
    .apply(<JoinFunction>)

Tumbling Window Join的实例：

import org.apache.flink.api.common.functions.JoinFunction;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.TimeCharacteristic;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.windowing.assigners.TumblingProcessingTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.api.windowing.triggers.CountTrigger;

public class TumblingMain {

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

        final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        //设置时间语义
        env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);

        DataStream<Tuple2<String, String>> source1 = env.socketTextStream("192.168.6.23", 9022)
                .map(new MapFunction<String, Tuple2<String, String>>() {
                    public Tuple2<String, String> map(String value) throws Exception {
                        return Tuple2.of(value.split(" ")[0], value.split(" ")[1]);
                    }
                });
        DataStream<Tuple2<String, String>> source2 = env.socketTextStream("192.168.6.23", 9023)
                .map(new MapFunction<String, Tuple2<String, String>>() {
                    public Tuple2<String, String> map(String value) throws Exception {
                        return Tuple2.of(value.split(" ")[0], value.split(" ")[1]);
                    }
                });

        source1.join(source2)
                .where(new KeySelector<Tuple2<String, String>, Object>() {
                    public Object getKey(Tuple2<String, String> value) throws Exception {
                        return value.f0;
                    }
                })
                .equalTo(new KeySelector<Tuple2<String, String>, Object>() {
                    public Object getKey(Tuple2<String, String> value) throws Exception {
                        return value.f0;
                    }
                })
                .window(TumblingProcessingTimeWindows.of(Time.seconds(5)))
                .trigger(CountTrigger.of(1))
                .apply(new JoinFunction<Tuple2<String, String>, Tuple2<String, String>, Object>() {
                    public Object join(Tuple2<String, String> first, Tuple2<String, String> second) throws Exception {
                        if (first.f0.equals(second.f0)) {
                            return first.f1 + " " + second.f1;
                        }
                        return null;
                    }
                }).print();
        env.execute();
    }
}

Interval Join

Interval Join会将两个数据流按照相同的key，并且在其中一个流的时间范围内的数据进行join处理。通常用于把一定时间范围内相关的分组数据拉成一个宽表。我们通常可以用类似下面的表达式来使用interval Join来处理两个数据流

 Interval Join变成模型：

orangeStream
    .keyBy(<KeySelector>)
    .intervalJoin(greenStream.keyBy(<KeySelector>))
    .between(Time.milliseconds(-2), Time.milliseconds(1))
    .process (new ProcessJoinFunction<Integer, Integer, String(){

        @Override
        public void processElement(Integer left, Integer right, Context ctx, Collector<String> out) {
            out.collect(first + "," + second);
        }
    });

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