【极数系列】Flink搭建入门项目Demo 秒懂Flink开发运行原理（05）

文章目录

- 引言
- 1.创建mavenx项目
- 2.包结构
- 3.引入pom依赖
- 4.增加log4j2.properties配置
- 5.创建主启动类
- 6.构建打jar包
- 7.flinkUI页面部署

引言

gitee地址：https://gitee.com/shawsongyue/aurora.git
源码直接下载可运行，模块：aurora_flink
Flink 版本：1.18.0
Jdk 版本：11

1.创建mavenx项目

在这里插入图片描述

2.包结构

在这里插入图片描述

3.引入pom依赖

tips：transformer处写主启动类

<?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.xsy</groupId><artifactId>aurora_flink</artifactId><version>1.0-SNAPSHOT</version><!--属性设置--><properties><!--java_JDK版本--><java.version>11</java.version><!--maven打包插件--><maven.plugin.version>3.8.1</maven.plugin.version><!--编译编码UTF-8--><project.build.sourceEncoding>UTF-8</project.build.sourceEncoding><!--输出报告编码UTF-8--><project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding><!--json数据格式处理工具--><fastjson.version>1.2.75</fastjson.version><!--log4j版本--><log4j.version>2.17.1</log4j.version><!--flink版本--><flink.version>1.18.0</flink.version><!--scala版本--><scala.binary.version>2.11</scala.binary.version><!--log4j依赖--><log4j.version>2.17.1</log4j.version></properties><!--通用依赖--><dependencies><!-- json --><dependency><groupId>com.alibaba</groupId><artifactId>fastjson</artifactId><version>${fastjson.version}</version></dependency><!-- https://mvnrepository.com/artifact/org.apache.flink/flink-java --><dependency><groupId>org.apache.flink</groupId><artifactId>flink-java</artifactId><version>${flink.version}</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-streaming-scala_2.12</artifactId><version>${flink.version}</version></dependency><!-- https://mvnrepository.com/artifact/org.apache.flink/flink-clients --><dependency><groupId>org.apache.flink</groupId><artifactId>flink-clients</artifactId><version>${flink.version}</version></dependency><!--================================集成外部依赖==========================================--><!--集成日志框架 start--><dependency><groupId>org.apache.logging.log4j</groupId><artifactId>log4j-slf4j-impl</artifactId><version>${log4j.version}</version></dependency><dependency><groupId>org.apache.logging.log4j</groupId><artifactId>log4j-api</artifactId><version>${log4j.version}</version></dependency><dependency><groupId>org.apache.logging.log4j</groupId><artifactId>log4j-core</artifactId><version>${log4j.version}</version></dependency><!--集成日志框架 end--></dependencies><!--编译打包--><build><finalName>${project.name}</finalName><!--资源文件打包--><resources><resource><directory>src/main/resources</directory></resource><resource><directory>src/main/java</directory><includes><include>**/*.xml</include></includes></resource></resources><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>org.apache.flink:force-shading</exclude><exclude>org.google.code.flindbugs:jar305</exclude><exclude>org.slf4j:*</exclude><excluder>org.apache.logging.log4j:*</excluder></excludes></artifactSet><filters><filter><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"><mainClass>org.xsy.sevenhee.flink.TestStreamJob</mainClass></transformer></transformers></configuration></execution></executions></plugin></plugins><!--插件统一管理--><pluginManagement><plugins><!--maven打包插件--><plugin><groupId>org.springframework.boot</groupId><artifactId>spring-boot-maven-plugin</artifactId><version>${spring.boot.version}</version><configuration><fork>true</fork><finalName>${project.build.finalName}</finalName></configuration><executions><execution><goals><goal>repackage</goal></goals></execution></executions></plugin><!--编译打包插件--><plugin><artifactId>maven-compiler-plugin</artifactId><version>${maven.plugin.version}</version><configuration><source>${java.version}</source><target>${java.version}</target><encoding>UTF-8</encoding><compilerArgs><arg>-parameters</arg></compilerArgs></configuration></plugin></plugins></pluginManagement></build><!--配置Maven项目中需要使用的远程仓库--><repositories><repository><id>aliyun-repos</id><url>https://maven.aliyun.com/nexus/content/groups/public/</url><snapshots><enabled>false</enabled></snapshots></repository></repositories><!--用来配置maven插件的远程仓库--><pluginRepositories><pluginRepository><id>aliyun-plugin</id><url>https://maven.aliyun.com/nexus/content/groups/public/</url><snapshots><enabled>false</enabled></snapshots></pluginRepository></pluginRepositories></project>

4.增加log4j2.properties配置

tips：resource目录下增加该配置，主要用于日志打印

rootLogger.level=INFO
rootLogger.appenderRef.console.ref=ConsoleAppender
appender.console.name=ConsoleAppender
appender.console.type=CONSOLE
appender.console.layout.type=PatternLayout
appender.console.layout.pattern=%d{HH:mm:ss,SSS} %-5p %-60c %x - %m%n
log.file=D:\\tmp

5.创建主启动类

tips：编写了一个简单的有界数据流处理demo程序

step1：创建flink程序运行所需环境
step2：创建数据集
step3：把有限数据集转换为数据源
step4：简单通过flatmap处理数据
step5：输出最终结果
step6：启动任务

package com.aurora;import org.apache.flink.api.common.functions.FlatMapFunction;
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.streaming.api.functions.sink.SinkFunction;
import org.apache.flink.util.Collector;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;import java.util.ArrayList;
import java.util.Random;
import java.util.UUID;/*** @author 浅夏的猫* @description 主启动类* @date 22:46 2024/1/13*/
public class Application {private static final Logger logger = LoggerFactory.getLogger(Application.class);public static void main(String[] args) throws Exception {//1.创建flink程序运行所需环境StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();//2.创建数据集ArrayList<String> list = new ArrayList<>();list.add("001");list.add("002");list.add("003");//3.把有限数据集转换为数据源DataStreamSource<String> dataStreamSource = env.fromCollection(list).setParallelism(1);//4.简单通过flatmap处理数据，SingleOutputStreamOperator<String> flatMap = dataStreamSource.flatMap(new FlatMapFunction<String, String>() {@Overridepublic void flatMap(String record, Collector<String> collector) throws Exception {//数据追加随机数String uuidRecord=record+ UUID.randomUUID().toString();//当前环节处理完需要传递数据给下个环节collector.collect(uuidRecord);}});//5.输出最终结果flatMap.addSink(new SinkFunction<String>() {@Overridepublic void invoke(String value) throws Exception {logger.info("当前正在处理的数据:{}",value);}}).setParallelism(1);//6.启动任务env.execute();}
}