Flink定制化功能开发，demo代码

前言：

   这是一个Flink自定义开发的基础教学。本文将通过flink的DataStream模块API，以kafka为数据源，构建一个基础测试环境；包含一个kafka生产者线程工具，一个自定义FilterFunction算子，一个自定义MapFunction算子，用一个flink任务的代码逻辑，将实时读kafka并多层处理串起来；让读者体会通过Flink构建自定义函数的技巧。

一、Flink的开发模块分析

Flink提供四个基础模块：核心SDK开发API分别是处理实时计算的DataStream和处理离线计算的DataSet；基于这两个SDK，在其上包装了TableAPI开发模块的SDK；在Table API之上，定义了高度抽象可用SQL开发任务的FlinkSQL。在核心开发API之下，还有基础API的接口，可用于对时间，状态，算子等最细粒度的特性对象做操作，如包装自定义算子的ProcessWindowFunction和ProcessFunction等基础函数以及内置的对象状态StateTtlConfig；

FLINK开发API关系结构如下：

二、定制化开发Demo演示

2.1 场景介绍

Flink实时任务的的通用技术架构是消息队列中间件+Flink任务：

将数据采集到Kafka或pulser这类队列中间件的Topic,然后使用Flink内置的kafkaSource，监控Topic的数据情况，做实时处理。

这里提供一个kafka的生产者线程，可以自定义构建需要的数据和上传时间，用于控制写入kafka的数据源；
重写两个DataStream的基础算子：FilterFunction和MapFunction,用于让读者体会，如何对FLINK函数的重新包装，后续更基础的函数原理一样；我这里用String数据对象做处理，减少对象转换的SDK引入，通常要基于业务做数据polo的加工，这个自己处理，将对象换成业务对象；
然后使用Flink将整个业务串起来，从kafka读数据，经过两层处理，最终输出需要的结果；

2.2 本地demo演示

2.2.1 pom文件

这里以flink1.14.6+scala1.12版本为例：

<?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>
    <parent>
        <groupId>org.example</groupId>
        <artifactId>flinkCDC</artifactId>
        <version>1.0-SNAPSHOT</version>
    </parent>

    <artifactId>flinkStream</artifactId>

    <properties>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
        <maven.compiler.source>8</maven.compiler.source>
        <maven.compiler.target>8</maven.compiler.target>
        <flink-version>1.14.6</flink-version>
        <scala-version>2.12</scala-version>
        <hadop-common-version>2.9.1</hadop-common-version>
        <elasticsearch.version>7.6.2</elasticsearch.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>
            </snapshots>
        </repository>
    </repositories>
    <dependencies>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-java</artifactId>
            <version>${flink-version}</version>
            <!--            <scope>provided</scope>-->
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-core</artifactId>
            <version>${flink-version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-clients_${scala-version}</artifactId>
            <version>${flink-version}</version>
            <!--            <scope>provided</scope>-->
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-connector-kafka_${scala-version}</artifactId>
            <exclusions>
                <exclusion>
                    <groupId>org.slf4j</groupId>
                    <artifactId>slf4j-log4j12</artifactId>
                </exclusion>
                <exclusion>
                    <groupId>org.apache.kafka</groupId>
                    <artifactId>kafka-clients</artifactId>
                </exclusion>
            </exclusions>
            <version>${flink-version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.kafka</groupId>
            <artifactId>kafka-clients</artifactId>
            <version>2.4.1</version>
        </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>
                                <transformer
                                        implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer"/>
                                <transformer
                                        implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
                                    <mainClass>myflinkml.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>

2.2.2 kafka生产者线程方法

package org.example.util;

import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.Producer;
import org.apache.kafka.clients.producer.ProducerRecord;

import java.util.*;

/**
 * 向kafka生产数据
 *
 * @author i7杨
 * @date 2024/01/12 13:02:29
 */

public class KafkaProducerUtil extends Thread {

    private String topic;

    public KafkaProducerUtil(String topic) {
        super();
        this.topic = topic;
    }

    private static Producer<String, String> createProducer() {
        // 通过Properties类设置Producer的属性
        Properties properties = new Properties();
//        测试环境 kafka 配置
        properties.put("bootstrap.servers", "ip2:9092,ip:9092,ip3:9092");
        properties.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
        properties.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");
        return new KafkaProducer<String, String>(properties);
    }

    @Override
    public void run() {
        Producer<String, String> producer = createProducer();
        Random random = new Random();
        Random random2 = new Random();

        while (true) {
            int nums = random.nextInt(10);
            int nums2 = random.nextInt(50);
//            double nums2 = random2.nextDouble();

            String time = new Date().getTime() / 1000 + 5 + "";
            String type = "pv";
            try {
                if (nums2 % 2 == 0) {
                    type = "pv";
                } else {
                    type = "uv";

                }
//                String info = "{\"user\":" + nums + ",\"item\":" + nums * 10 + ",\"category\":" + nums2 + ",\"pv\":" + nums2 * 5 + ",\"ts\":\"" + time + "\"}";
                String info = nums + "=" + nums2;

                System.out.println("message : " + info);
                producer.send(new ProducerRecord<String, String>(this.topic, info));
                
            } catch (Exception e) {
                e.printStackTrace();
            }
            System.out.println("=========数据已经写入==========");
            
            try {
                sleep(1000);
            } catch (InterruptedException e) {
                e.printStackTrace();
            }
        }
    }
    
    public static void main(String[] args) {
        new KafkaProducerUtil("test01").run();
    }
    
    public static void sendMessage(String topic, String message) {
        Producer<String, String> producer = createProducer();
        producer.send(new ProducerRecord<String, String>(topic, message));
    }
    
}

2.2.3 自定义基础函数

这里自定义了filter和map两个算子函数，测试逻辑按照数据结构变化：

自定义FilterFunction函数算子：阈值小于40的过滤掉

package org.example.funtion;

import org.apache.flink.api.common.functions.FilterFunction;

/**
 * FilterFunction重构
 *
 * @author i7杨
 * @date 2024/01/12 13:02:29
 */

public class InfoFilterFunction implements FilterFunction<String> {

    private double threshold;

    public InfoFilterFunction(double threshold) {
        this.threshold = threshold;
    }

    @Override
    public boolean filter(String value) throws Exception {

        if (value.split("=").length == 2)
            // 阈值过滤
            return Double.valueOf(value.split("=")[1]) > threshold;
        else return false;
    }
}

自定义MapFunction函数：后缀为2的，添加上特殊信息

package org.example.funtion;

import org.apache.flink.api.common.functions.MapFunction;

public class ActionMapFunction implements MapFunction<String, String> {

    @Override
    public String map(String value) throws Exception {
        System.out.println("value:" + value);
        if (value.endsWith("2"))
            return value.concat(":Special processing information");
        else return value;
    }
}

2.2.4 flink任务代码

任务逻辑：使用kafka工具产生数据，然后监控kafka的topic，讲几个函数串起来，输出结果；

package org.example.service;

import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.kafka.common.serialization.StringDeserializer;
import org.example.funtion.ActionMapFunction;
import org.example.funtion.InfoFilterFunction;

import java.util.*;

public class FlinkTestDemo {
    public static void main(String[] args) throws Exception {

        // 设置执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        // Kafka 配置
        Properties kafkaProps = new Properties();
        kafkaProps.setProperty(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, "ip1:9092,ip2:9092,ip3:9092");
        kafkaProps.setProperty(ConsumerConfig.GROUP_ID_CONFIG, "flink-consumer-group");
        kafkaProps.setProperty(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class.getName());
        kafkaProps.setProperty(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class.getName());
        kafkaProps.setProperty(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "earliest");

        // 创建 Kafka 消费者
        FlinkKafkaConsumer<String> kafkaConsumer = new FlinkKafkaConsumer<>(
                "test01",// Kafka 主题名称
                new SimpleStringSchema(),
                kafkaProps);

        // 从 Kafka 中读取数据流
        DataStream<String> kafkaStream = env.addSource(kafkaConsumer);
        env.disableOperatorChaining();

        kafkaStream
                .filter(new InfoFilterFunction(40))
                .map(new ActionMapFunction())
                .print("阈值大于40以上的message=");

        // 执行任务
        env.execute("This is a testing task");
    }

}

运行结果：

标签： linq c#

本文转载自: https://blog.csdn.net/weixin_42049123/article/details/135561586
版权归原作者 i7杨 所有，如有侵权，请联系我们删除。

Flink定制化功能开发，demo代码

一、Flink的开发模块分析

二、定制化开发Demo演示

2.1 场景介绍

2.2 本地demo演示

2.2.1 pom文件

2.2.2 kafka生产者线程方法

2.2.3 自定义基础函数

2.2.4 flink任务代码

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