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Flink 定时加载数据源

news 2025/9/4 16:25:32

一、简介

flink 自定义实时数据源使用流处理比较简单，比如 Kafka、MQ 等，如果使用 MySQL、redis 批处理也比较简单

如果需要定时加载数据作为 flink 数据源使用流处理，比如定时从 mysql 或者 redis 获取一批数据，传入 flink 做处理，如下简单实现

二、pom.xml 文件

注意 flink 好多包从 1.15.0 开始不需要指定 Scala 版本，内部自带
在这里插入图片描述

下面 pom 文件有 flink 两个版本 1.16.0 和 1.12.7（Scala:2.12）

<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.ye</groupId><artifactId>flink-study</artifactId><version>0.1</version><packaging>jar</packaging><name>Flink Quickstart Job</name><properties><project.build.sourceEncoding>UTF-8</project.build.sourceEncoding><flink.version>1.16.0</flink.version><!--<flink.version>1.12.7</flink.version>--><target.java.version>1.8</target.java.version><scala.binary.version>2.12</scala.binary.version><maven.compiler.source>${target.java.version}</maven.compiler.source><maven.compiler.target>${target.java.version}</maven.compiler.target><log4j.version>2.17.1</log4j.version></properties><repositories><repository><id>apache.snapshots</id><name>Apache Development Snapshot Repository</name><url>https://repository.apache.org/content/repositories/snapshots/</url><releases><enabled>false</enabled></releases><snapshots><enabled>true</enabled></snapshots></repository></repositories><dependencies><!-- Apache Flink dependencies --><!-- These dependencies are provided, because they should not be packaged into the JAR file. --><dependency><groupId>org.apache.flink</groupId><artifactId>flink-streaming-java</artifactId><version>${flink.version}</version><scope>provided</scope></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-clients</artifactId><version>${flink.version}</version><scope>provided</scope></dependency>
<!--<dependency><groupId>org.apache.flink</groupId><artifactId>flink-streaming-java_${scala.binary.version}</artifactId><version>${flink.version}</version><scope>provided</scope></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-clients_${scala.binary.version}</artifactId><version>${flink.version}</version><scope>provided</scope></dependency>
--><dependency><groupId>org.apache.flink</groupId><artifactId>flink-connector-files</artifactId><version>${flink.version}</version></dependency><!-- Add connector dependencies here. They must be in the default scope (compile). --><!-- Example:<dependency><groupId>org.apache.flink</groupId><artifactId>flink-connector-kafka</artifactId><version>${flink.version}</version></dependency>--><!-- Add logging framework, to produce console output when running in the IDE. --><!-- These dependencies are excluded from the application JAR by default. --><dependency><groupId>org.apache.logging.log4j</groupId><artifactId>log4j-slf4j-impl</artifactId><version>${log4j.version}</version><scope>runtime</scope></dependency><dependency><groupId>org.apache.logging.log4j</groupId><artifactId>log4j-api</artifactId><version>${log4j.version}</version><scope>runtime</scope></dependency><dependency><groupId>org.apache.logging.log4j</groupId><artifactId>log4j-core</artifactId><version>${log4j.version}</version><scope>runtime</scope></dependency></dependencies><build><plugins><!-- Java Compiler --><plugin><groupId>org.apache.maven.plugins</groupId><artifactId>maven-compiler-plugin</artifactId><version>3.1</version><configuration><source>${target.java.version}</source><target>${target.java.version}</target></configuration></plugin><!-- We use the maven-shade plugin to create a fat jar that contains all necessary dependencies. --><!-- Change the value of <mainClass>...</mainClass> if your program entry point changes. --><plugin><groupId>org.apache.maven.plugins</groupId><artifactId>maven-shade-plugin</artifactId><version>3.1.1</version><executions><!-- Run shade goal on package phase --><execution><phase>package</phase><goals><goal>shade</goal></goals><configuration><createDependencyReducedPom>false</createDependencyReducedPom><artifactSet><excludes><exclude>org.apache.flink:flink-shaded-force-shading</exclude><exclude>com.google.code.findbugs:jsr305</exclude><exclude>org.slf4j:*</exclude><exclude>org.apache.logging.log4j:*</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><transformerimplementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer"/><transformerimplementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer"><mainClass>com.ye.DataStreamJob</mainClass></transformer></transformers></configuration></execution></executions></plugin></plugins><pluginManagement><plugins><!-- This improves the out-of-the-box experience in Eclipse by resolving some warnings. --><plugin><groupId>org.eclipse.m2e</groupId><artifactId>lifecycle-mapping</artifactId><version>1.0.0</version><configuration><lifecycleMappingMetadata><pluginExecutions><pluginExecution><pluginExecutionFilter><groupId>org.apache.maven.plugins</groupId><artifactId>maven-shade-plugin</artifactId><versionRange>[3.1.1,)</versionRange><goals><goal>shade</goal></goals></pluginExecutionFilter><action><ignore/></action></pluginExecution><pluginExecution><pluginExecutionFilter><groupId>org.apache.maven.plugins</groupId><artifactId>maven-compiler-plugin</artifactId><versionRange>[3.1,)</versionRange><goals><goal>testCompile</goal><goal>compile</goal></goals></pluginExecutionFilter><action><ignore/></action></pluginExecution></pluginExecutions></lifecycleMappingMetadata></configuration></plugin></plugins></pluginManagement></build>
</project>

三、自定义数据源

使用 Timer 定时任务（当然也可以使用线程池 Executors）自定义数据源，每过五秒随机生成一串字符串

public class TimerSinkRich extends RichSourceFunction<String> {private  ConcurrentLinkedQueue<String> queue = new ConcurrentLinkedQueue<>();private boolean flag = true;private Timer timer;private TimerTask timerTask;@Overridepublic void open(Configuration parameters) throws Exception {super.open(parameters);timerTask = new TimerTask() {@Overridepublic void run() {// 可以在这块获取 MySQL、redis 等连接并查询数据Random random = new Random();StringBuilder str = new StringBuilder();for (int i = 0; i < 10; i++) {char ranLowLetter = (char) ((random.nextInt(26) + 97));str.append(ranLowLetter);}queue.add(str.toString());}};timer = new Timer();// 延时和执行周期参数可以通过构造方法传递timer.schedule(timerTask,1000,5000);}@Overridepublic void run(SourceContext<String> ctx) throws Exception {while (flag){if(queue.size()>0){ctx.collect(queue.remove());}}}@Overridepublic void cancel() {if(null!=timer) timer.cancel();if(null!=timerTask) timerTask.cancel();// 撤销任务时，flink 默认 30 s（不同 flink 版本可能不同）尝试关闭数据源，关闭失败 TaskManager 不能释放 slot，最终导致失败if(queue.size()<=0) flag = false;}
}

四、flink 加载数据源并启动

public class TimerSinkStreamJob {public static void main(String[] args) throws Exception {StreamExecutionEnvironment executionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment();executionEnvironment.setParallelism(1);DataStreamSource<String> streamSource = executionEnvironment.addSource(new TimerSinkRich());streamSource.print();executionEnvironment.execute("TimerSinkStreamJob 定时任务打印数据");}
}

本地测试成功

在这里插入图片描述

五、上传 flink 集群

1、flink 1.16.0

启动成功
在这里插入图片描述
撤销任务成功
在这里插入图片描述
solt 也成功释放
在这里插入图片描述

2、flink 1.12.7

启动成功
在这里插入图片描述
撤销任务当然也没问题，同样能正常释放 slot
在这里插入图片描述

当然你也可以不要 open() 方法

public class DiySinkRich extends RichSourceFunction<String> {private TimerTask timerTask;private Timer timer;private boolean flag = true;private ConcurrentLinkedQueue<String> queue = new ConcurrentLinkedQueue<>();@Overridepublic void run(SourceFunction.SourceContext<String> ctx) throws Exception {timerTask = new TimerTask() {@Overridepublic void run() {Random random = new Random();StringBuilder str = new StringBuilder();for (int i = 0; i < 10; i++) {char ranLowLetter = (char) ((random.nextInt(26) + 97));str.append(ranLowLetter);}queue.add(str.toString());}};timer = new Timer();timer.schedule(timerTask, 1000, 5000);while (flag) {if (queue.size() > 0) {ctx.collect(queue.remove());}}}@Overridepublic void cancel() {if (timer != null) timer.cancel();if (timerTask != null) timerTask.cancel();if (queue.size() == 0) flag = false;}
}

以上就是 flink 定时加载数据源的简单实例

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