Flink基礎系列17-Tranform之多流轉換算子 一.多流轉換算子概述 二.代碼實現 參考:

一.多流轉換算子概述

多流轉換算子一般包括：
Split和Select （新版已經移除）
Connect和CoMap
Union

1.1 Split和Select

注：新版Flink已經不存在Split和Select這兩個API了（至少Flink1.12.1沒有！）

Split

DataStream -> SplitStream：根據某些特徵把DataStream拆分成SplitStream;
SplitStream雖然看起來像是兩個Stream，但是其實它是一個特殊的Stream;

Select

SplitStream -> DataStream：從一個SplitStream中獲取一個或者多個DataStream;
我們可以結合split&select將一個DataStream拆分成多個DataStream。

1.2 Connect和CoMap

Connect

DataStream,DataStream -> ConnectedStreams: 連接兩個保持他們類型的數據流，兩個數據流被Connect 之後，只是被放在了一個流中，內部依然保持各自的數據和形式不發生任何變化，兩個流相互獨立。

CoMap

ConnectedStreams -> DataStream: 作用於ConnectedStreams 上，功能與map和flatMap一樣，對ConnectedStreams 中的每一個Stream分別進行map和flatMap操作；

1.3 Union

DataStream -> DataStream：對兩個或者兩個以上的DataStream進行Union操作，產生一個包含多有DataStream元素的新DataStream。

問題：和Connect的區別？

1. Connect 的數據類型可以不同，Connect 只能合併兩個流；
2. Union可以合併多條流，Union的數據結構必須是一樣的；

二.代碼實現

數據準備:
sensor.txt
sensor_1,1547718199,35.8
sensor_6,1547718201,15.4
sensor_7,1547718202,6.7
sensor_10,1547718205,38.1
sensor_1,1547718207,36.3
sensor_1,1547718209,32.8
sensor_1,1547718212,37.1

代碼:

package org.flink.transform;

/**
 * @author 只是甲
 * @date   2021-08-31
 * @remark Flink 基礎Transform  MultipleStreams
 */

import org.flink.beans.SensorReading;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.tuple.Tuple3;
import org.apache.flink.streaming.api.collector.selector.OutputSelector;
import org.apache.flink.streaming.api.datastream.ConnectedStreams;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.datastream.SplitStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.co.CoMapFunction;

import java.util.Collections;

public class TransformTest4_MultipleStreams {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

        // 從文件讀取數據
        DataStream<String> inputStream = env.readTextFile("C:\\Users\\Administrator\\IdeaProjects\\FlinkStudy\\src\\main\\resources\\sensor.txt");

        // 轉換成SensorReading
        DataStream<SensorReading> dataStream = inputStream.map(line -> {
            String[] fields = line.split(",");
            return new SensorReading(fields[0], new Long(fields[1]), new Double(fields[2]));
        } );

        // 1. 分流，按照溫度值30度爲界分爲兩條流
        SplitStream<SensorReading> splitStream = dataStream.split(new OutputSelector<SensorReading>() {
            @Override
            public Iterable<String> select(SensorReading value) {
                return (value.getTemperature() > 30) ? Collections.singletonList("high") : Collections.singletonList("low");
            }
        });

        DataStream<SensorReading> highTempStream = splitStream.select("high");
        DataStream<SensorReading> lowTempStream = splitStream.select("low");
        DataStream<SensorReading> allTempStream = splitStream.select("high", "low");

        highTempStream.print("high");
        lowTempStream.print("low");
        allTempStream.print("all");

        // 2. 合流 connect，將高溫流轉換成二元組類型，與低溫流連接合並之後，輸出狀態信息
        DataStream<Tuple2<String, Double>> warningStream = highTempStream.map(new MapFunction<SensorReading, Tuple2<String, Double>>() {
            @Override
            public Tuple2<String, Double> map(SensorReading value) throws Exception {
                return new Tuple2<>(value.getId(), value.getTemperature());
            }
        });

        ConnectedStreams<Tuple2<String, Double>, SensorReading> connectedStreams = warningStream.connect(lowTempStream);

        DataStream<Object> resultStream = connectedStreams.map(new CoMapFunction<Tuple2<String, Double>, SensorReading, Object>() {
            @Override
            public Object map1(Tuple2<String, Double> value) throws Exception {
                return new Tuple3<>(value.f0, value.f1, "high temp warning");
            }

            @Override
            public Object map2(SensorReading value) throws Exception {
                return new Tuple2<>(value.getId(), "normal");
            }
        });

        resultStream.print();

        // 3. union聯合多條流
//        warningStream.union(lowTempStream);
        highTempStream.union(lowTempStream, allTempStream);

        env.execute();
    }
}

測試記錄:

參考:

1. https://www.bilibili.com/video/BV1qy4y1q728
2. https://ashiamd.github.io/docsify-notes/#/study/BigData/Flink/%E5%B0%9A%E7%A1%85%E8%B0%B7Flink%E5%85%A5%E9%97%A8%E5%88%B0%E5%AE%9E%E6%88%98-%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0?id=_521-%e4%bb%8e%e9%9b%86%e5%90%88%e8%af%bb%e5%8f%96%e6%95%b0%e6%8d%ae