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。

  1. 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；
  2. 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；
  3. 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：

4.0.0org.exampleflinkCDC1.0-SNAPSHOTflinkStreamUTF-8881.14.62.122.9.17.6.21.82.12${target.java.version}${target.java.version}2.17.1apache.snapshotsApache Development Snapshot Repositoryhttps://repository.apache.org/content/repositories/snapshots/falseorg.apache.flinkflink-java${flink-version}<!--provided-->org.apache.flinkflink-core${flink-version}org.apache.flinkflink-clients_${scala-version}${flink-version}<!--provided-->org.apache.flinkflink-connector-kafka_${scala-version}org.slf4jslf4j-log4j12org.apache.kafkakafka-clients${flink-version}org.apache.kafkakafka-clients2.4.1org.apache.maven.pluginsmaven-compiler-plugin3.1${target.java.version}${target.java.version}<!-- Change the value of ... if your program entry point changes. -->org.apache.maven.pluginsmaven-shade-plugin3.1.1packageshadefalseorg.apache.flink:flink-shaded-force-shadingcom.google.code.findbugs:jsr305org.slf4j:*org.apache.logging.log4j:**:*META-INF/*.SFMETA-INF/*.DSAMETA-INF/*.RSAmyflinkml.DataStreamJoborg.eclipse.m2elifecycle-mapping1.0.0org.apache.maven.pluginsmaven-shade-plugin[3.1.1,)shadeorg.apache.maven.pluginsmaven-compiler-plugin[3.1,)testCompilecompile
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 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(properties);}@Overridepublic void run() {Producer 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(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 producer = createProducer();producer.send(new ProducerRecord(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 {private double threshold;public InfoFilterFunction(double threshold) {this.threshold = threshold;}@Overridepublic 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 {@Overridepublic 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 kafkaConsumer = new FlinkKafkaConsumer("test01",// Kafka 主题名称new SimpleStringSchema(),kafkaProps);// 从 Kafka 中读取数据流DataStream 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：