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记一次Flink通过Kafka写入MySQL的过程

一、前言
总体思路：source -->transform -->sink ,即从source获取相应的数据来源，然后进行数据转换，将数据从比较乱的格式，转换成我们需要的格式，转换处理后，然后进行sink功能，也就是将数据写入的相应的数据库DB中或者写入Hive的HDFS文件存储。
思路：
在这里插入图片描述

pom部分放到最后面。

二、方案及代码实现

2.1 Source部分
Source部分构建一个web对象用于保存数据等操作，代码如下：

package com.lzl.flink;import java.util.Date;/**
 * @author lzl
 * @create 2024-01-18 12:19
 * @name pojo
 */publicclassWeb{private String uuid;private String ip;private String area;private String web;private String operate;private Date createDate;public String getArea(){return area;}public String getIp(){return ip;}public String getOperate(){return operate;}public String getUuid(){return uuid;}public String getWeb(){return web;}public Date getCreateDate(){return createDate;}publicvoidsetArea(String area){this.area = area;}publicvoidsetIp(String ip){this.ip = ip;}publicvoidsetOperate(String operate){this.operate = operate;}publicvoidsetUuid(String uuid){this.uuid = uuid;}publicvoidsetWeb(String web){this.web = web;}publicvoidsetCreateDate(Date createDate){this.createDate = createDate;}}

将生成的数据转化为JSON格式，测试如下：

publicstaticvoidwebDataProducer() throws Exception{//构建web对象，在ip为10.117后面加两个随机数
        int randomInt1 = RandomUtils.nextInt(1,255);
        int randomInt2 = RandomUtils.nextInt(1,999);
        int randomInt3 = RandomUtils.nextInt(1,99999);
        List<String> areas = Arrays.asList("深圳","广州","上海","北京","武汉","合肥","杭州","南京");
        List<String> webs = Arrays.asList("www.taobao.com","www.baidu.com","www.jd.com","www.weibo.com","www.qq.com","www.weixin.com","www.360.com","www.lzl.com","www.xiaomi.com");
        List<String> operates = Arrays.asList("register","view","login","buy","click","comment","jump","care","collect");

        Web web =newWeb();//实例化一个web对象，并向对象中放入数据
        web.setUuid("uid_"+ randomInt3);
        web.setIp("10.110."+ randomInt1 +"."+ randomInt2);
        web.setArea(getRandomElement(areas));
        web.setWeb(getRandomElement(webs));
        web.setOperate(getRandomElement(operates));
        web.setCreateDate(newDate());// 转换成JSON格式
        String webJson =JSON.toJSONString(web);
        System.out.println(webJson);//打印出来看看效果}//构建一个从列表里面任意筛选一个元素的函数方法publicstatic<T>TgetRandomElement(List<T> list){
        Collections.shuffle(list);return list.get(0);}publicstaticvoidmain(String[] args){while(true){try{// 每三秒写一条数据
                TimeUnit.SECONDS.sleep(3);webDataProducer();}catch(Exception e){
                e.printStackTrace();}}}

执行测试结果如下：
在这里插入图片描述
至此Source部分结束~~~~！！！！！！

2.2 Transform_1部分

2.2.1 写入kafka方法函数：

package com.lzl.flink;import com.alibaba.fastjson.JSON;import org.apache.commons.lang3.RandomUtils;import org.apache.kafka.clients.producer.KafkaProducer;import org.apache.kafka.clients.producer.ProducerRecord;import java.util.*;import java.util.concurrent.TimeUnit;/**
 * @author lzl
 * @create 2024-01-18 12:18
 * @name KafkaWriter
 */publicclassKafkaWriter{//kafka集群列表publicstatic final String BROKER_LIST="cdh39:9092,cdh40:9092,cdh41:9092";//kafka的topicpublicstatic final String TOPIC_WEB="web";//kafka序列化的方式，采用字符串的形式publicstatic final String KEY_SERIALIZER="org.apache.kafka.common.serialization.StringSerializer";//value的序列化方式publicstatic final String VALUE_SERIALIZER="org.apache.kafka.common.serialization.StringSerializer";publicstaticvoidwriteToKafka() throws Exception {
        Properties props =newProperties();//实例化一个Properties
        props.put("bootstrap.servers",BROKER_LIST);
        props.put("key.serializer",KEY_SERIALIZER);
        props.put("value.serializer",VALUE_SERIALIZER);// 构建Kafka生产者
        KafkaProducer<String, String> producer =newKafkaProducer<>(props);// 将web生成的数据发送给kafka的记录
        String webDataJson =webDataProducer();
        ProducerRecord<String,String> record =newProducerRecord<String,String>(TOPIC_WEB,null,null,webDataJson);// 发送到缓存
        producer.send(record);
        System.out.println("向kafka发送数据："+ webDataJson);
        producer.flush();}publicstaticvoidmain(String[] args){while(true){try{// 每三秒写一条数据
                TimeUnit.SECONDS.sleep(3);writeToKafka();}catch(Exception e){
                e.printStackTrace();}}}

2.2.2 建立 web的topic：
在这里插入图片描述
启动程序测试：
在这里插入图片描述
2.2.3 消费kafka看看是否有数据？

[root@cdh39 kafka]# bin/kafka-console-consumer.sh --bootstrap-server cdh39:9092--from-beginning --topic web
{"area":"合肥","createDate":1705571020461,"ip":"10.110.104.676","operate":"comment","uuid":"uid_29661","web":"www.qq.com"}{"area":"北京","createDate":1705571024048,"ip":"10.110.49.479","operate":"jump","uuid":"uid_77119","web":"www.weibo.com"}{"area":"合肥","createDate":1705571027106,"ip":"10.110.232.960","operate":"click","uuid":"uid_99704","web":"www.taobao.com"}{"area":"上海","createDate":1705571030140,"ip":"10.110.12.252","operate":"buy","uuid":"uid_99850","web":"www.jd.com"}{"area":"合肥","createDate":1705571033228,"ip":"10.110.75.328","operate":"care","uuid":"uid_33135","web":"www.qq.com"}{"area":"上海","createDate":1705571036267,"ip":"10.110.4.862","operate":"collect","uuid":"uid_37279","web":"www.taobao.com"}{"area":"北京","createDate":1705571039361,"ip":"10.110.139.814","operate":"register","uuid":"uid_33016","web":"www.baidu.com"}{"area":"武汉","createDate":1705571042422,"ip":"10.110.159.143","operate":"collect","uuid":"uid_26315","web":"www.lzl.com"}{"area":"南京","createDate":1705571045495,"ip":"10.110.81.685","operate":"login","uuid":"uid_38712","web":"www.baidu.com"}{"area":"南京","createDate":1705571048545,"ip":"10.110.228.267","operate":"comment","uuid":"uid_23297","web":"www.weibo.com"}{"area":"武汉","createDate":1705571051623,"ip":"10.110.102.247","operate":"collect","uuid":"uid_77340","web":"www.lzl.com"}{"area":"武汉","createDate":1705571054687,"ip":"10.110.184.832","operate":"comment","uuid":"uid_35230","web":"www.360.com"}{"area":"武汉","createDate":1705571057760,"ip":"10.110.90.361","operate":"buy","uuid":"uid_52082","web":"www.lzl.com"}{"area":"北京","createDate":1705571060825,"ip":"10.110.37.707","operate":"buy","uuid":"uid_45343","web":"www.weixin.com"}{"area":"上海","createDate":1705571063909,"ip":"10.110.178.901","operate":"care","uuid":"uid_51015","web":"www.baidu.com"}{"area":"杭州","createDate":1705571066945,"ip":"10.110.153.758","operate":"collect","uuid":"uid_46772","web":"www.xiaomi.com"}{"area":"合肥","createDate":1705571069980,"ip":"10.110.177.755","operate":"comment","uuid":"uid_78442","web":"www.taobao.com"}{"area":"广州","createDate":1705571073020,"ip":"10.110.151.427","operate":"register","uuid":"uid_92174","web":"www.weixin.com"}{"area":"上海","createDate":1705571076072,"ip":"10.110.217.622","operate":"jump","uuid":"uid_86059","web":"www.xiaomi.com"}

至此，Transform_1部分结束~~~！！！！

2.3 Sink部分
创建一个MySQLSink，继承RichSinkFunction类。重载里边的open、invoke 、close方法，在执行数据sink之前先执行open方法，然后开始调用invoke方法，调用完之后最后执行close方法关闭资源。即在open里面创建数据库连接，然后调用invoke执行具体的数据库写入程序，完毕之后调用close关闭和释放资源。这里要继承flink的RichSinkFunction接口。代码如下：

package com.lzl.flink;import org.apache.flink.configuration.Configuration;import org.apache.flink.streaming.api.functions.sink.RichSinkFunction;import java.sql.Connection;import java.sql.PreparedStatement;import java.sql.SQLException;import java.sql.Timestamp;import java.util.logging.Logger;/**
 * @author lzl
 * @create 2024-01-22 15:30
 * @name MySqlToPojoSink
 */publicclassMySqlToPojoSinkextendsRichSinkFunction<Web>{privatestatic final Logger log = Logger.getLogger(MySqlToPojoSink.class.getName());privatestatic final long serialVersionUID =1L;private Connection connection =null;private PreparedStatement ps =null;private String tableName ="web";
    
     @Override
    publicvoidopen(Configuration parameters) throws Exception {super.open(parameters);
        log.info("获取数据库连接");// 通过Druid获取数据库连接，准备写入数据库
        connection = DbUtils.getConnection();// 插入数据库的语句   因为我们封装的pojo的类型为PojoType<com.lzl.flink.Web, fields = [area: String, createDate: Date, ip: String, operate: String, uuid: String, web: String]>
        String insertQuery ="INSERT INTO "+   tableName +"(time,ip,uid,area,web,operate) VALUES (?,?,?,?,?,?)";// 执行插入语句
        ps = connection.prepareStatement(insertQuery);}// 重新关闭方法。   关闭并释放资源
    @Override
    publicvoidclose() throws Exception {super.close();if(connection !=null){
            connection.close();}if(ps !=null){
            ps.close();}}// 重写invoke方法
    @Override
    publicvoidinvoke(Web value,Context context) throws Exception {//组装数据，执行插入操作
        ps.setTimestamp(1,newTimestamp(value.getCreateDate().getTime()));
        ps.setString(2,value.getIp());
        ps.setString(3, value.getUuid());
        ps.setString(4, value.getArea());
        ps.setString(5, value.getWeb());
        ps.setString(6, value.getOperate());
        ps.addBatch();// 一次性写入
        int[] count = ps.executeBatch();
        System.out.println("成功写入MySQL数量："+ count.length);}}

特别说明：从kafka读取到的内容是String，里面包含JSON格式。本文是先将它封装成Pojo对象，然后在Sink这里解析它的Value。（开始是尝试通过apply算子将它转换为List，但是失败了（时间有限，后续再弄），最后是通过map算子）

至此，Sink部分结束~！

2.4 Transform_2部分。消费kafka 数据，添加Sink。

package com.lzl.flink;import com.alibaba.fastjson.JSON;import org.apache.flink.api.common.serialization.SimpleStringSchema;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.connectors.kafka.FlinkKafkaConsumer;import java.util.Properties;import java.util.concurrent.TimeUnit;/**
 * @author lzl
 * @create 2024-01-19 8:49
 * @name DataSourceFromKafka
 */publicclassDataSourceFromKafka{publicstaticvoidtransformFromKafka() throws Exception {// 构建流执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);//kafka 配置
        Properties prop =newProperties();
        prop.put("bootstrap.servers", KafkaWriter.BROKER_LIST);
        prop.put("zookeeper.connect","cdh39:2181");
        prop.put("group.id", KafkaWriter.TOPIC_WEB);
        prop.put("key.serializer", KafkaWriter.KEY_SERIALIZER);
        prop.put("value.serializer", KafkaWriter.VALUE_SERIALIZER);
        prop.put("auto.offset.reset","earliest");// 建立流数据源
        DataStreamSource<String> dataStreamSource = env.addSource(newFlinkKafkaConsumer<String>(
                KafkaWriter.TOPIC_WEB,newSimpleStringSchema(),
                prop
        )).setParallelism(1);// 单线程打印，控制台不乱序，不影响结果

        SingleOutputStreamOperator<Web> webStream = env.addSource(newFlinkKafkaConsumer<>("web",newSimpleStringSchema(),
                prop
        )).setParallelism(1).map(string->JSON.parseObject(string,Web.class));

        webStream.addSink(newMySqlToPojoSink());
        env.execute();}publicstaticvoidmain(String[] args) throws Exception {while(true){try{// 每1毫秒写一条数据
                TimeUnit.MILLISECONDS.sleep(1);transformFromKafka();}catch(Exception e){
                e.printStackTrace();}}}}

如果要设置空值报错异常，或者排除空值可以：

package com.lzl.flink;import com.alibaba.fastjson.JSON;import org.apache.flink.api.common.serialization.SimpleStringSchema;import org.apache.flink.streaming.api.CheckpointingMode;import org.apache.flink.streaming.api.datastream.DataStream;import org.apache.flink.streaming.api.datastream.DataStreamSource;import org.apache.flink.streaming.api.environment.CheckpointConfig;import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;import org.apache.flink.streaming.api.functions.ProcessFunction;import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;import org.apache.flink.util.Collector;import java.util.Properties;import java.util.concurrent.TimeUnit;publicclassDataSourceFromKafka{publicstaticvoidtransformFromKafka() throws Exception {// 构建流执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);//checkpoint设置//每隔10s进行启动一个检查点【设置checkpoint的周期】
        env.enableCheckpointing(10000);//设置模式为：exactly_one，仅一次语义
        env.getCheckpointConfig().setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE);//确保检查点之间有1s的时间间隔【checkpoint最小间隔】
        env.getCheckpointConfig().setMinPauseBetweenCheckpoints(1000);//检查点必须在10s之内完成，或者被丢弃【checkpoint超时时间】
        env.getCheckpointConfig().setCheckpointTimeout(10000);//同一时间只允许进行一次检查点
        env.getCheckpointConfig().setMaxConcurrentCheckpoints(1);//表示一旦Flink程序被cancel后，会保留checkpoint数据，以便根据实际需要恢复到指定的checkpoint
        env.getCheckpointConfig().enableExternalizedCheckpoints(CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION);//设置statebackend,将检查点保存在hdfs上面，默认保存在内存中。先保存到resources目录下
        env.setStateBackend(newFsStateBackend("D:java//Flink1.17//src//main//resources"));//         kafka 配置
        Properties prop =newProperties();
        prop.put("bootstrap.servers", KafkaWriter.BROKER_LIST);
        prop.put("zookeeper.connect","cdh39:2181");
        prop.put("group.id", KafkaWriter.TOPIC_WEB);
        prop.put("key.serializer", KafkaWriter.KEY_SERIALIZER);
        prop.put("value.serializer", KafkaWriter.VALUE_SERIALIZER);
        prop.put("auto.offset.reset","earliest")

        DataStreamSource<String> webStream = env.addSource(newFlinkKafkaConsumer<>("web",newSimpleStringSchema(),
                prop
        )).setParallelism(1);//使用process算子 排除空值
        DataStream<Web> processData = webStream.process(newProcessFunction<String, Web>(){
            @Override
            publicvoidprocessElement(String s, Context context, Collector<Web> collector) throws Exception {try{
                    Web webs =JSON.parseObject(s, Web.class);if(webs !=null){
                        collector.collect(webs);}}catch(Exception e){
                    System.out.println("有空值数据");}}});

        processData.addSink(newMySqlToPojoSink());
        env.execute();}publicstaticvoidmain(String[] args) throws Exception {while(true){try{// 每1毫秒写一条数据
                TimeUnit.MILLISECONDS.sleep(1);transformFromKafka();}catch(Exception e){
                e.printStackTrace();}}}}

至此，Transfrom结束~！

2.5 DB部分（这部分可以先做，或者放到前面，因为需要测试）
本次的DB演示采用常规的MySQL数据库。采用Druid工具连接。
思路：创建一个数据库连接的工具，用于连接数据库。使用Druid工具，然后放入具体的Driver，Url，数据库用户名和密码，初始化连接数，最大活动连接数，最小空闲连接数也就是数据库连接池，创建好之后返回需要的连接。

package com.lzl.flink;import com.alibaba.druid.pool.DruidDataSource;import java.sql.Connection;/**
 * @author lzl
 * @create 2024-01-18 17:58
 * @name DbUtils
 */publicclassDbUtils{privatestatic DruidDataSource dataSource;publicstatic Connection getConnection() throws Exception {
        dataSource =newDruidDataSource();
        dataSource.setDriverClassName("com.mysql.cj.jdbc.Driver");
        dataSource.setUrl("jdbc:mysql://cdh129:3306/flink?useSSL=true");
        dataSource.setUsername("root");
        dataSource.setPassword("xxb@5196");// 设置初始化连接数，最大连接数，最小闲置数
        dataSource.setInitialSize(10);
        dataSource.setMaxActive(50);
        dataSource.setMinIdle(5);// 返回连接return dataSource.getConnection();}}

数据库建表语句：

CREATETABLE`web_traffic_analysis`(`time`varchar(64)DEFAULTNULLCOMMENT'时间',`ip`varchar(32)DEFAULTNULLCOMMENT'ip地址',`uid`varchar(32)DEFAULTNULLCOMMENT'uuid',`area`varchar(32)DEFAULTNULLCOMMENT'地区',`web`varchar(64)DEFAULTNULLCOMMENT'网址',`operate`varchar(32)DEFAULTNULLCOMMENT'操作')ENGINE=InnoDBDEFAULTCHARSET=utf8 ROW_FORMAT=DYNAMIC COMMENT='网页流量分析表'

三、启动程序
开始本来是想将上面所有的功能都写成函数方法，然后单独开一个Main（）主函数的入口，然后在主函数下面调用那些方法（生产数据、消费数据方法）。思路是借鉴python的：if name == ‘main’: 下调用很多的方法 。但实际执行过程，是先生成数据，然后将数据写入kafka，然后再消费数据，过程执行非常慢，这个方案被pass了。后来又想到多线程方案，一个线程跑生产数据和写入数据，一个线程跑消费数据和写入下游数据库。这个方法是测试成功了，但是跑了一会儿就出现数据的积压和内存oom了，因为我设定的是1毫秒生产一条数据，写入kafka也需要一定的时间，加上电脑内存不足，有点卡，这个方案也被pass了。最后的方案是将生产数据打包放到集群去跑，本地电脑开启消费kafka数据写入MySQL数据库。结果如下：
生产数据：
在这里插入图片描述
消费和写入数据库数据：
在这里插入图片描述
数据库数据：
在这里插入图片描述
至此结束，后面有其他想法再补充~！

多线程部分代码：

package com.example.study;import com.lzl.flink.DataSourceFromKafka;import com.lzl.flink.KafkaWriter;publicclassWebApplication{publicstaticvoidmain(String[] args) throws Exception {// 创建线程1
     Thread threadOne =newThread(newRunnable(){
        @Override
        publicvoidrun(){while(true){try{
                    KafkaWriter kafkaWriter =newKafkaWriter();
                    kafkaWriter.webDataProducer();
                    kafkaWriter.writeToKafka();
                    System.out.println("线程一在跑~！");}catch(Exception e){
                    e.printStackTrace();}}}});// 创建线程2
 Thread threadTwo =newThread(newRunnable(){
        @Override
        publicvoidrun(){while(true){
                DataSourceFromKafka dataSourceFromKafka =newDataSourceFromKafka();try{
                    dataSourceFromKafka.transformFromKafka();
                    System.out.println("线程二在跑~！");}catch(Exception e){
                    e.printStackTrace();}}}});//启动线程
    threadOne.start();
    threadTwo.start();
    Thread.sleep(5);}}

结果：
在这里插入图片描述

标签： flink kafka mysql
本文转载自: https://blog.csdn.net/m0_48830183/article/details/135631295
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