Flink流处理-简单案例-01

一、pom文件

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>

    <groupId>com.robots</groupId>
    <artifactId>robots-flink</artifactId>
    <version>1.0-SNAPSHOT</version>

    <properties>
        <encoding>UTF-8</encoding>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
        <maven.compiler.source>1.8</maven.compiler.source>
        <maven.compiler.target>1.8</maven.compiler.target>
        <java.version>1.8</java.version>
        <scala.version>2.12</scala.version>
        <flink.version>1.13.1</flink.version>
    </properties>

    <dependencies>
        <dependency>
            <groupId>org.projectlombok</groupId>
            <artifactId>lombok</artifactId>
            <version>1.18.16</version>
        </dependency>

        <!--flink客户端-->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-clients_${scala.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>

        <!--scala版本-->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-scala_${scala.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <!--java版本-->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-java</artifactId>
            <version>${flink.version}</version>
        </dependency>

        <!--streaming的scala版本-->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-scala_${scala.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <!--streaming的java版本-->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-java_${scala.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>

        <!--日志输出-->
        <dependency>
            <groupId>org.slf4j</groupId>
            <artifactId>slf4j-log4j12</artifactId>
            <version>1.7.7</version>
            <scope>runtime</scope>
        </dependency>
        <dependency>
            <groupId>log4j</groupId>
            <artifactId>log4j</artifactId>
            <version>1.2.17</version>
            <scope>runtime</scope>
        </dependency>

        <!--json依赖包-->
        <dependency>
            <groupId>com.alibaba</groupId>
            <artifactId>fastjson</artifactId>
            <version>1.2.44</version>
        </dependency>
    </dependencies>

</project>

二、简单流处理代码

import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;

/**
 * @datetime 2022-03-09 上午9:47
 * @desc
 * @menu
 */
public class Flink01App {

    public static void main(String[] args) throws Exception {
        //构建执行任务环境以及任务的启动的入口, 存储全局相关的参数
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        //设置并行度
        env.setParallelism(1);
        //相同类型元素的数据流 source
        DataStreamSource<String> stringDS = env.fromElements("java,SpringBoot", "spring cloud,redis",
                                                                           "kafka,课堂");
        stringDS.print("处理前");
        DataStream<String> flatMapDS = stringDS.flatMap(new FlatMapFunction<String, String>() {
            @Override
            public void flatMap(String value, Collector<String> collector) throws Exception {
                String [] arr =  value.split(",");
                for(String str : arr){
                    collector.collect(str);
                }
            }
        });
        //输出 sink
        flatMapDS.print("处理后");

        //DataStream需要调用execute,可以取个名称
        env.execute("flat map job");
    }
}