大数据（9e）Flink侧输出流

Posted 2022-12-15 小基基o_O

文章目录

• 概述
• 环境
• OutputTag介绍
• • 实现分流
  • 处理迟到数据
• 处理关窗之后到达的数据

概述

窗口允许迟到的数据，但仍有数据在关窗后到达
Flink提供了侧输出流（sideOutput）来处理关窗之后到达的数据

环境

WIN10+IDEA+JDK1.8+FLINK1.14

<properties>
    <maven.compiler.source>8</maven.compiler.source>
    <maven.compiler.target>8</maven.compiler.target>
    <flink.version>1.14.6</flink.version>
    <scala.binary.version>2.12</scala.binary.version>
</properties>
<dependencies>
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-java</artifactId>
        <version>$flink.version</version>
    </dependency>
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-streaming-java_$scala.binary.version</artifactId>
        <version>$flink.version</version>
    </dependency>
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-clients_$scala.binary.version</artifactId>
        <version>$flink.version</version>
    </dependency>
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-runtime-web_$scala.binary.version</artifactId>
        <version>$flink.version</version>
    </dependency>
</dependencies>

OutputTag介绍

OutputTag是一种命名标记，用于标记算子中的侧输出

实现分流

ctx.output：向由OutputTag标识的侧输出发出记录

import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.ProcessFunction;
import org.apache.flink.util.Collector;
import org.apache.flink.util.OutputTag;

public class Hi 
    public static void main(String[] args) throws Exception 
        //创建执行环境，设置并行度
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment().setParallelism(1);
        //定义输出标签
        OutputTag<Integer> o1 = new OutputTag<Integer>("除以3余1") ;
        OutputTag<Integer> o2 = new OutputTag<Integer>("除以3余2") ;
        //创建流
        SingleOutputStreamOperator<Integer> d = env.fromElements(0, 1, 2, 3, 4, 5, 6, 7, 8, 9);
        //处理
        SingleOutputStreamOperator<Integer> s = d.process(new ProcessFunction<Integer, Integer>() 
            @Override
            public void processElement(Integer value, Context ctx, Collector<Integer> out) 
                //分流
                if (value % 3 == 2) 
                    ctx.output(o2, value); //ctx.output：向由OutputTag标识的侧输出发出记录
                 else if (value % 3 == 1) 
                    ctx.output(o1, value); //ctx.output：向由OutputTag标识的侧输出发出记录
                 else 
                    out.collect(value);
                
            
        );
        //输出
        s.print("被3整除");
        s.getSideOutput(o1).print(o1.getId());
        s.getSideOutput(o2).print(o2.getId());
        //环境执行
        env.execute();
    

测试结果

被3整除> 0
除以3余1> 1
除以3余2> 2
被3整除> 3
除以3余1> 4
除以3余2> 5
被3整除> 6
除以3余1> 7
除以3余2> 8
被3整除> 9

处理迟到数据

import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.ProcessFunction;
import org.apache.flink.streaming.api.functions.source.SourceFunction;
import org.apache.flink.streaming.api.watermark.Watermark;
import org.apache.flink.util.Collector;
import org.apache.flink.util.OutputTag;

public class Hi 
    public static void main(String[] args) throws Exception 
        //创建执行环境，设置并行度
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment().setParallelism(1);
        //定义测输出流的输出标签
        OutputTag<String> outputTag = new OutputTag<String>("迟到标签") ;
        //创建流，添加自定义数据源
        SingleOutputStreamOperator<String> d = env.addSource(new SourceFunction<String>() 
            @Override
            public void run(SourceContext<String> ctx) 
                //发送水位线
                ctx.emitWatermark(new Watermark(1999L));
                //发送2条数据，其中1条迟到
                ctx.collectWithTimestamp("1998", 1998L);
                ctx.collectWithTimestamp("2000", 2000L);
            
            @Override
            public void cancel() 
        );
        //处理
        SingleOutputStreamOperator<String> s = d.process(new ProcessFunction<String, String>() 
            @Override
            public void processElement(String value, Context ctx, Collector<String> out) 
                //获取水位线
                long watermark = ctx.timerService().currentWatermark();
                //判断是否迟到
                if (ctx.timestamp() > watermark) 
                    //冇迟到
                    out.collect(value);
                 else 
                    //迟到：向outputTag发送数据
                    ctx.output(outputTag, value);
                
            
        );
        //输出
        s.print("主流输出");
        s.getSideOutput(outputTag).print("侧输出");
        //环境执行
        env.execute();
    

发送1999水位线，然后发送两条数据，测试结果如下

侧输出> 1998
主流输出> 2000

处理关窗之后到达的数据

开窗后.sideOutputLateData(outputTag)

import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.source.SourceFunction;
import org.apache.flink.streaming.api.watermark.Watermark;
import org.apache.flink.streaming.api.windowing.assigners.TumblingEventTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.util.OutputTag;

public class Hi 
    public static void main(String[] args) throws Exception 
        //创建执行环境，设置并行度
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment().setParallelism(1);
        //定义测输出流的输出标签
        OutputTag<String> outputTag = new OutputTag<String>("迟到标签") ;
        //创建流，添加自定义数据源
        SingleOutputStreamOperator<String> d = env.addSource(new SourceFunction<String>() 
            @Override
            public void run(SourceContext<String> ctx) 
                ctx.collectWithTimestamp("a", 4000L);
                ctx.collectWithTimestamp("b", 5000L);
                ctx.emitWatermark(new Watermark(5999L)); //发送水位线，触发【3000~5999】的窗口关闭
                ctx.collectWithTimestamp("c", 5000L);
                ctx.collectWithTimestamp("d", 5000L);
                ctx.collectWithTimestamp("e", 6000L);
                ctx.collectWithTimestamp("f", 7000L);
            
            @Override
            public void cancel() 
        );
        //处理
        SingleOutputStreamOperator<String> s = d
                //事件时间滚动窗口
                .windowAll(TumblingEventTimeWindows.of(Time.seconds(3L)))
                //侧输出
                .sideOutputLateData(outputTag)
                //拼接字符串
                .reduce((a, b) -> a + "," + b);
        //输出
        s.print("主流输出");
        s.getSideOutput(outputTag).print("侧输出");
        //环境执行
        env.execute();
    

中途发送水位线，触发关窗，测试结果如下

主流输出> a,b
侧输出> c
侧输出> d
主流输出> e,f