sprintboot集成flink快速入门demo
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一、flink介绍
Flink是一个批处理和流处理结合的统一计算框架，其核心是一个提供了数据分发以及并行化计算的流数据处理引擎。它的最大亮点是流处理，是业界最顶级的开源流处理引擎。Flink最适合的应用场景是低时延的数据处理（Data Processing）场景：高并发pipeline处理数据，时延毫秒级，且兼具可靠性。
二、环境搭建
安装flink
安装Netcat
Netcat（又称为NC）是一个计算机网络工具，它可以在两台计算机之间建立 TCP/IP 或 UDP 连接。它被广泛用于测试网络中的端口，发送文件等操作。使用 Netcat 可以轻松地进行网络调试和探测，也可以进行加密连接和远程管理等高级网络操作。因为其功能强大而又简单易用，所以在计算机安全领域也有着广泛的应用。
安装nc命令
yum install -y nc
启动socket端口
[root@node01 bin]# nc -lk 8888
三、代码工程
实验目的：无界流之读取socket文本流 pom.xml
<?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">
<parent>
<artifactId>springboot-demo</artifactId>
<groupId>com.et</groupId>
<version>1.0-SNAPSHOT</version>
</parent>
<modelVersion>4.0.0</modelVersion>
<artifactId>flink</artifactId>
<properties>
<maven.compiler.source>8</maven.compiler.source>
<maven.compiler.target>8</maven.compiler.target>
</properties>
<dependencies>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-autoconfigure</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-test</artifactId>
<scope>test</scope>
</dependency>
<!-- 添加 Flink 依赖 -->
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-streaming-java</artifactId>
<version>1.17.0</version>
</dependency>
<!-- https://mvnrepository.com/artifact/org.apache.flink/flink-java -->
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-java</artifactId>
<version>1.17.0</version>
</dependency>
<!-- https://mvnrepository.com/artifact/org.apache.flink/flink-clients -->
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-clients</artifactId>
<version>1.17.0</version>
</dependency>
<!-- https://mvnrepository.com/artifact/org.apache.flink/flink-connector-base -->
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-connector-base</artifactId>
<version>1.17.0</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-connector-files</artifactId>
<version>1.17.0</version>
</dependency>
<!-- https://mvnrepository.com/artifact/org.apache.flink/flink-connector-kafka -->
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-connector-kafka</artifactId>
<version>1.17.0</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-runtime-web</artifactId>
<version>1.17.0</version>
</dependency>
</dependencies>
<build>
<plugins>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-shade-plugin</artifactId>
<executions>
<execution>
<phase>package</phase>
<goals>
<goal>shade</goal>
</goals>
<configuration>
<transformers>
<transformer
implementation="org.apache.maven.plugins.shade.resource.AppendingTransformer">
<resource>META-INF/spring.handlers</resource>
</transformer>
<transformer
implementation="org.springframework.boot.maven.PropertiesMergingResourceTransformer">
<resource>META-INF/spring.factories</resource>
</transformer>
<transformer
implementation="org.apache.maven.plugins.shade.resource.AppendingTransformer">
<resource>META-INF/spring.schemas</resource>
</transformer>
<transformer
implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer" />
<transformer
implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
<mainClass>com.et.flink.job.SocketJob</mainClass>
</transformer>
</transformers>
</configuration>
</execution>
</executions>
</plugin>
</plugins>
</build>
</project>
SoketJob
package com.et.flink.job;
import org.apache.flink.api.common.typeinfo.TypeHint;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;
/**
* @author liuhaihua
* @version 1.0
* @ClassName SocketJob
* @Description todo
* @date 2024年02月29日 17:06
*/
public class SocketJob {
public static void main(String[] args) throws Exception {
// 创建执行环境
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
// 指定并行度,默认电脑线程数
env.setParallelism(3);
// 读取数据socket文本流 指定监听 IP 端口 只有在接收到数据才会执行任务
DataStreamSource<String> socketDS = env.socketTextStream("172.24.4.193", 8888);
// 处理数据: 切换、转换、分组、聚合 得到统计结果
SingleOutputStreamOperator<Tuple2<String, Integer>> sum = socketDS
.flatMap(
(String value, Collector<Tuple2<String, Integer>> out) -> {
String[] words = value.split(" ");
for (String word : words) {
out.collect(Tuple2.of(word, 1));
}
}
)
.setParallelism(2)
// // 显式地提供类型信息:对于flatMap传入Lambda表达式，系统只能推断出返回的是Tuple2类型，而无法得到Tuple2<String, Long>。只有显式设置系统当前返回类型，才能正确解析出完整数据
.returns(new TypeHint<Tuple2<String, Integer>>() {
})
// .returns(Types.TUPLE(Types.STRING,Types.INT))
.keyBy(value -> value.f0)
.sum(1);
// 输出
sum.print();
// 执行
env.execute();
}
}
代码仓库
四、测试
启动socket流
[root@cmn-zentao-002 ~]# nc -l 8888
本地执行
本地直接ideal启动main程序，在socket流中输入
abc bcd cde
bcd cde fgh
cde fgh hij
console日志显示
3> (abc,1)
1> (fgh,1)
3> (bcd,1)
3> (cde,1)
3> (bcd,2)
3> (cde,2)
3> (cde,3)
1> (fgh,2)
2> (hij,1)
集群执行
执行maven打包，将打包的jar上传到集群中
在socker中输入字符，结果和本地一样
五、引用
转载自:https://juejin.cn/post/7340953208872009778