Flink2.0学习笔记：Flink服务器搭建与flink作业提交

一，下载flink:Downloads | Apache Flink,解压后放入IDE工作目录：我这里以1.17版本为例

可以看到，flink后期的版本中没有提供window启动脚本:start-cluster.bat

所以这里要通过windows自带的wsl 系统启动它

打开终端依次运行下列命令完成wsl linux 系统的安装以及jdk的安装

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wsl --install
wsl.exe -d Ubuntu
sudo apt update
sudo apt install openjdk-11-jdk -y

之后继续在终端中执行 wsl.exe -d Ubuntu 启动wsl，wsl 默认系统为：Ubuntu，当然也可以切换其他类型的系统，重要的是：wsl会自动挂载windows 目录，这就实现了在wsl上运行windows目录中的项目。

然后一路cd 到flink bin目录，启动flink:

这里启动前要注意修改flink 的配置：把localhost 统统改为 0.0.0.0，，除jobmanager.rpc.address: 这项要设置为wsl 的ip，不然flink集群选举master会失败: $jobmanager.rpc.address: 172.29.145.42$ ,这样启动后，就可以在本机浏览器输入wsl的ip访问flink服务的web ui了

二，提交flink作业

为了方便测试，这里写一个程序每隔1秒向本机(192.168.0.39) 端口：9999发送数据："test flink window hallo word"。

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package org.example.demo01;

import java.io.IOException;
import java.io.OutputStream;
import java.net.Socket;
import java.nio.charset.StandardCharsets;
import java.time.LocalDateTime;
import java.time.format.DateTimeFormatter;

public class PushDataTo9999 {
    private static final String HOST = "192.168.0.39";
    private static final int PORT = 9999;
    private static final String DATA = "test flink window hallo word";

    public static void main(String[] args) {
        try {
            System.out.println("Connecting to " + HOST + ":" + PORT);

            // 创建到WSL的连接
            try (Socket socket = new Socket(HOST, PORT);
                 OutputStream outputStream = socket.getOutputStream()) {

                System.out.println("Connected to " + HOST + ":" + PORT);

                // 持续发送数据
                while (!Thread.currentThread().isInterrupted() && !socket.isClosed()) {
                    // 获取当前系统时间
                    String currentTime = LocalDateTime.now()
                            .format(DateTimeFormatter.ofPattern("HH:mm:ss"));

                    // 每秒发送一次带时间戳的数据
                    String dataToSend = DATA + " " + currentTime + "\n";
                    outputStream.write(dataToSend.getBytes(StandardCharsets.UTF_8));
                    outputStream.flush();
                    System.out.println("Sent: " + dataToSend.trim());

                    // 等待1秒
                    Thread.sleep(1000);
                }
            }
        } catch (IOException | InterruptedException e) {
            System.err.println("Error: " + e.getMessage());
        }
    }
}

然后flink 作业内容为在wsl服务器(172.29.145.42)中监听本机(192.168.0.39)端口9999，并实时统计每个单词出现的次数，这里注意关闭windows 防火墙

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package org.example.demo01;

import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.source.SocketTextStreamFunction;
import org.apache.flink.util.Collector;

/**
 * Hello world!
 */
public class App {
    public static void main(String[] args) throws Exception {

        final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        // 设置为流处理模式
        env.setRuntimeMode(RuntimeExecutionMode.STREAMING);

        // 基本配置
        env.setParallelism(1); // 设置并行度为1
        env.disableOperatorChaining(); // 禁用算子链，使执行更清晰

        // 禁用检查点，因为是简单的演示程序
        env.getCheckpointConfig().disableCheckpointing();

        // 创建周期性的数据源
//        DataStream<String> dataStream = env
//                .socketTextStream("localhost", 9999) // 从socket读取数据
//                .name("source-strings")
//                .setParallelism(1);

        DataStream<String> dataStream = env.addSource(new SocketTextStreamFunction("192.168.0.39", 9999, "\n", 0))
                .name("socket-source");


        // 转换算子 keyBy: 按单词分组并计数
        dataStream.flatMap(new FlatMapFunction<String, Tuple2<String, Integer>>() {
                    @Override
                    public void flatMap(String line, Collector<Tuple2<String, Integer>> out) {
                        for (String word : line.split(" ")) {
                            out.collect(new Tuple2<>(word, 1));
                        }
                    }
                }).name("flatmap-split-words")
                .setParallelism(1)
                .keyBy(tuple -> tuple.f0) // 按单词分组
                .sum(1) // 计算每个单词的出现次数
                .print()
                .name("printer-word-count");

        // 执行任务
        env.execute("Flink Streaming Java API Hello");
    }
}

注意pom 需要加入flink的打包插件：

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<build>
    <plugins>
      <plugin>
        <groupId>org.apache.maven.plugins</groupId>
        <artifactId>maven-shade-plugin</artifactId>
        <version>3.1.1</version>
        <executions>
          <execution>
            <phase>package</phase>
            <goals>
              <goal>shade</goal>
            </goals>
            <configuration>
              <artifactSet>
                <excludes>
                  <exclude>com.google.code.findbugs:jsr305</exclude>
                </excludes>
              </artifactSet>
              <filters>
                <filter>
                  <!-- Do not copy the signatures in the META-INF folder.
                  Otherwise, this might cause SecurityExceptions when using the JAR. -->
                  <artifact>*:*</artifact>
                  <excludes>
                    <exclude>META-INF/*.SF</exclude>
                    <exclude>META-INF/*.DSA</exclude>
                    <exclude>META-INF/*.RSA</exclude>
                  </excludes>
                </filter>
              </filters>
              <transformers>
                <transformer implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
                  <!-- Replace this with the main class of your job -->
                  <mainClass>org.example.demo01.App</mainClass>
                </transformer>
                <transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer"/>
              </transformers>
            </configuration>
          </execution>
        </executions>
      </plugin>
    </plugins>
  </build>

通过maven编译，打包后，我们把jar包通过web ui上传到flink 服务端：

点击我们上传的jar,进入提交项：

提交了后作业会自动启动：

作业的print输出可以在taskmanagers中查看：