【极数系列】Flink配置参数如何获取？（06）
文章目录
gitee码云地址
直接下载解压可用 https://gitee.com/shawsongyue/aurora.git
模块：aurora_flink
主类：GetParamsStreamingJob
简介概述
1.几乎所有的批和流的 Flink 应用程序，都依赖于外部配置参数。这些配置参数可以用于指定输入和输出源（如路径或地址）、系统参数（并行度，运行时配置）和特定的应用程序参数（通常使用在用户自定义函数）。
2.为解决以上问题，Flink 提供一个名为 Parametertool 的简单公共类，其中包含了一些基本的工具。请注意，这里说的 Parametertool 并不是必须使用的。Commons CLI 和 argparse4j 等其他框架也可以非常好地兼容 Flink。
3.**ParameterTool**定义了一组静态方法，用于读取配置信息。该工具类内部使用了 Map` 类型，这样使得它可以很容易地与你的配置集成在一起。
01 配置值来自.properties文件
1.通过路径读取
//定义文件路径
String propertiesFilePath = "E:\project\aurora_dev\aurora_flink\src\main\resources\application.properties";
//方式一:直接使用内置工具类
ParameterTool parameter_01 = ParameterTool.fromPropertiesFile(propertiesFilePath);
String jobName_01 = parameter_01.get("jobName");
logger.info("方式一:读取配置文件中指定的key值={}",jobName_01);
2.通过文件流读取
//定义文件路径
String propertiesFilePath = "E:\project\aurora_dev\aurora_flink\src\main\resources\application.properties";
//方式二：使用文件
File propertiesFile = new File(propertiesFilePath);
ParameterTool parameter_02 = ParameterTool.fromPropertiesFile(propertiesFile);
String jobName_02 = parameter_02.get("jobName");
logger.info("方式二:读取配置文件中指定的key值={}",jobName_02);
3.通过IO流读取
//定义文件路径
String propertiesFilePath = "E:\project\aurora_dev\aurora_flink\src\main\resources\application.properties";
//方式三：使用IO流
InputStream propertiesFileInputStream = new FileInputStream(new File(propertiesFilePath));
ParameterTool parameter_03 = ParameterTool.fromPropertiesFile(propertiesFileInputStream);
String jobName_03 = parameter_03.get("jobName");
logger.info("方式三:读取配置文件中指定的key值={}",jobName_03);
02 配置值来自命令行
tips：在idea的命令行传参，格式：–jobName program_job_aurora
ParameterTool parameter_04 = ParameterTool.fromArgs(args);
String jobName_04 = parameter_04.get("jobName");
logger.info("方式四:命令行传参key值={}",jobName_04);
03 配置来自系统属性
tips：在idea的的jvm系统参数设置，格式：-Dinput=hdfs:///mydata
//方式五：获取jvm参数值
ParameterTool parameter_05 = ParameterTool.fromSystemProperties();
String jobName_05 = parameter_05.get("input");
logger.info("方式五:获取jvm参数key值={}",jobName_05);
04 注册以及使用全局变量
注意：Flink全局变量仅支持在富函数中使用，即Rich开头的类使用
//定义文件路径
String propertiesFilePath = "E:\project\aurora_dev\aurora_flink\src\main\resources\application.properties";
//直接使用内置工具类获取参数
ParameterTool parameter_01 = ParameterTool.fromPropertiesFile(propertiesFilePath);
//方式六：注册全局参数
final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.getConfig().setGlobalJobParameters(parameter_01);
//在任意富函数中均可以获取,注意！注意！注意！只有富文本函数才可以使用
//1.创建富函数
RichFlatMapFunction<String, String> richFlatMap = new RichFlatMapFunction<>() {
@Override
public void flatMap(String s, Collector<String> collector) throws Exception {
//获取运行环境
ParameterTool parameters = (ParameterTool) getRuntimeContext().getExecutionConfig().getGlobalJobParameters();
//获取对应的值
String jobName = parameters.getRequired("jobName");
logger.info("方式六:获取全局注册参数key值={}",jobName_05);
}
};
//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.执行富文本处理
dataStreamSource.flatMap(richFlatMap);
//5.启动程序
env.execute();
05 Flink获取参数值Demo
1.项目结构
2.pom.xml文件如下
<?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>
3.配置文件
（1）application.properties
jobName=job_aurora
jobMemory=1024
taskName=task_aurora
（2）log4j2.properties
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:\tmprootLogger.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
4.项目主类
package com.aurora;
import org.apache.flink.api.common.functions.RichFlatMapFunction;
import org.apache.flink.api.java.ExecutionEnvironment;
import org.apache.flink.api.java.utils.ParameterTool;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.io.File;
import java.io.FileInputStream;
import java.io.IOException;
import java.io.InputStream;
import java.util.ArrayList;
/**
* @description flink获取外部参数作业
*
* @author 浅夏的猫
* @datetime 15:54 2024/1/28
*/
public class GetParamsStreamingJob {
private static final Logger logger = LoggerFactory.getLogger(GetParamsStreamingJob.class);
public static void main(String[] args) throws Exception {
//定义文件路径
String propertiesFilePath = "E:\project\aurora_dev\aurora_flink\src\main\resources\application.properties";
//方式一:直接使用内置工具类
ParameterTool parameter_01 = ParameterTool.fromPropertiesFile(propertiesFilePath);
String jobName_01 = parameter_01.get("jobName");
logger.info("方式一:读取配置文件中指定的key值={}",jobName_01);
//方式二：使用文件
File propertiesFile = new File(propertiesFilePath);
ParameterTool parameter_02 = ParameterTool.fromPropertiesFile(propertiesFile);
String jobName_02 = parameter_02.get("jobName");
logger.info("方式二:读取配置文件中指定的key值={}",jobName_02);
//方式三：使用IO流
InputStream propertiesFileInputStream = new FileInputStream(new File(propertiesFilePath));
ParameterTool parameter_03 = ParameterTool.fromPropertiesFile(propertiesFileInputStream);
String jobName_03 = parameter_03.get("jobName");
logger.info("方式三:读取配置文件中指定的key值={}",jobName_03);
//方式四：命令行传参格式：--jobName program_job_aurora
ParameterTool parameter_04 = ParameterTool.fromArgs(args);
String jobName_04 = parameter_04.get("jobName");
logger.info("方式四:命令行传参key值={}",jobName_04);
//方式五：获取jvm参数值
ParameterTool parameter_05 = ParameterTool.fromSystemProperties();
String jobName_05 = parameter_05.get("input");
logger.info("方式五:获取jvm参数key值={}",jobName_05);
//方式六：注册全局参数
final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.getConfig().setGlobalJobParameters(parameter_01);
//在任意富函数中均可以获取,注意！注意！注意！只有富文本函数才可以使用
//1.创建富函数
RichFlatMapFunction<String, String> richFlatMap = new RichFlatMapFunction<>() {
@Override
public void flatMap(String s, Collector<String> collector) throws Exception {
//获取运行环境
ParameterTool parameters = (ParameterTool) getRuntimeContext().getExecutionConfig().getGlobalJobParameters();
//获取对应的值
String jobName = parameters.getRequired("jobName");
logger.info("方式六:获取全局注册参数key值={}",jobName_05);
}
};
//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.执行富文本处理
dataStreamSource.flatMap(richFlatMap);
//5.启动程序
env.execute();
}
}
5.运行查看相关日志