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1、基本介绍

Flink CDC 是 Apache Flink 提供的一个功能强大的组件，用于实时捕获和处理数据库中的数据变更。可以实时地从各种数据库（如MySQL、PostgreSQL、Oracle、MongoDB等）中捕获数据变更并将其转换为流式数据，FlinkCDC 同步数据有两种方式：

1. FlinkSQL
2. Flink DataStream 和 Table API（本文使用该方式）
   
   对比其他的CDC开源方案，发现FlinkCDC是绝大多数场景最好的选择方式，别在傻傻的只关注Canal了，如下图所示：
   

2、代码实战

2.1、数据源准备

本次我是用MySQL 8.0版本，并且创建好数据库（库名为quick_chat），本次演示表结构如下：

CREATE TABLE `quick_chat_msg` (`id` bigint NOT NULL COMMENT '主键id',`from_id` varchar(20) CHARACTER SET utf8 COLLATE utf8_unicode_ci DEFAULT NULL COMMENT '账户id（发送人）',`to_id` varchar(20) CHARACTER SET utf8 COLLATE utf8_unicode_ci DEFAULT NULL COMMENT '账户id（接收人）',`relation_id` varchar(50) CHARACTER SET utf8 COLLATE utf8_unicode_ci DEFAULT NULL COMMENT '发送关联',`content` varchar(500) DEFAULT NULL COMMENT '消息内容',`msg_type` tinyint(1) DEFAULT NULL COMMENT '消息类型（1：文字，2：语音，3：表情包，4：文件，5：语音通话，6：视频通话）',`extra_info` varchar(500) DEFAULT NULL COMMENT '额外信息',`create_time` datetime DEFAULT NULL COMMENT '创建时间',`deleted` tinyint(1) DEFAULT NULL COMMENT '删除标识',PRIMARY KEY (`id`) USING BTREE
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb3;

需要保证MySQL的Binlog格式是ROW，不过MySQL 8.0版本格式默认就是ROW：

最后，要把数据库时区配置好，否则会出现问题，命令如下：

SET persist time_zone = '+8:00';
SET time_zone = '+8:00';
SHOW VARIABLES LIKE '%time_zone%';

2.2、代码实战

首先，引入Flink CDC相关依赖，内容如下：

<dependencies><!-- Flink connector连接器基础包 --><dependency><groupId>org.apache.flink</groupId><artifactId>flink-connector-base</artifactId><version>1.14.0</version></dependency><!-- Flink CDC MySQL源 --><dependency><groupId>com.ververica</groupId><artifactId>flink-sql-connector-mysql-cdc</artifactId><version>2.3.0</version></dependency><!-- Flink DataStream数据流API --><dependency><groupId>com.ververica</groupId><artifactId>flink-connector-mysql-cdc</artifactId><version>2.2.0</version><scope>provided</scope></dependency><!-- Flink客户端--><dependency><groupId>org.apache.flink</groupId><artifactId>flink-clients_2.12</artifactId><version>1.14.0</version></dependency><!--Flink WebUI，端口8081（默认没有开启）--><dependency><groupId>org.apache.flink</groupId><artifactId>flink-runtime-web_2.12</artifactId><version>1.14.0</version></dependency><!--Flink Table API&SQL程序可以连接到其他外部系统，用于读写批处理表和流式表。--><dependency><groupId>org.apache.flink</groupId><artifactId>flink-table-runtime_2.12</artifactId><version>1.14.0</version></dependency>
</dependencies>

第二步，开发 Sink 监听类，用于监听 MySQL 数据变化：

import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.functions.sink.RichSinkFunction;public class MySinkHandler extends RichSinkFunction<String> {@Overridepublic void invoke(String value, Context context) throws Exception {System.out.println(value);}@Overridepublic void open(Configuration parameters) throws Exception {}@Overridepublic void close() throws Exception {}
}

最后，配置好 Flink CDC 监听进程，随着项目启动运行：

import com.ververica.cdc.connectors.mysql.source.MySqlSource;
import com.ververica.cdc.debezium.JsonDebeziumDeserializationSchema;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.configuration.RestOptions;
import org.apache.flink.streaming.api.datastream.DataStreamSink;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.springframework.stereotype.Component;import javax.annotation.PostConstruct;@Component
public class MySqlSourceExample {@PostConstructpublic void init() throws Exception {// 配置监听数据源MySqlSource<String> source = MySqlSource.<String>builder().hostname("8.141.28.132").port(3306)// 数据库集合，可以配置多个.databaseList("quick_chat")// 表集合，可以配置多个.tableList("quick_chat.quick_chat_msg").username("root").password("root").deserializer(new JsonDebeziumDeserializationSchema()).includeSchemaChanges(true).build();// 配置 Flink WebUIConfiguration configuration = new Configuration();configuration.setInteger(RestOptions.PORT, 8081);StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(configuration);// 检查点间隔时间// checkpoint的侧重点是“容错”，即Flink作业意外失败并重启之后，能够直接从早先打下的checkpoint恢复运行，且不影响作业逻辑的准确性。env.enableCheckpointing(5000);DataStreamSink<String> sink = env.fromSource(source, WatermarkStrategy.noWatermarks(), "MySQL Source").addSink(new MySinkHandler());env.execute();}
}

项目启动完毕后，可以通过8081端口访问Flink UI页面：

2.3、数据格式

上述操作完毕后，我对表数据进行了新增、修改、删除操作，控制台可以看到MySQL变更监听日志输出信息：

# 新增
{"before": null,"after": {"id": 3,"from_id": "dog","to_id": "cat","relation_id": "dog:cat","content": "你好啊","msg_type": 1,"extra_info": null,"create_time": 1729164075000,"deleted": 0},"source": {"version": "1.6.4.Final","connector": "mysql","name": "mysql_binlog_source","ts_ms": 1729135279000,"snapshot": "false","db": "quick_chat","sequence": null,"table": "quick_chat_msg","server_id": 1,"gtid": null,"file": "binlog.000002","pos": 2452,"row": 0,"thread": null,"query": null},"op": "c","ts_ms": 1729135278633,"transaction": null
}

# 修改
{"before": {"id": 3,"from_id": "dog","to_id": "cat","relation_id": "dog:cat","content": "你好啊","msg_type": 1,"extra_info": null,"create_time": 1729164075000,"deleted": 0},"after": {"id": 3,"from_id": "dog","to_id": "cat","relation_id": "dog:cat","content": "你好啊，小猫咪","msg_type": 1,"extra_info": null,"create_time": 1729164075000,"deleted": 0},"source": {"version": "1.6.4.Final","connector": "mysql","name": "mysql_binlog_source","ts_ms": 1729135289000,"snapshot": "false","db": "quick_chat","sequence": null,"table": "quick_chat_msg","server_id": 1,"gtid": null,"file": "binlog.000002","pos": 2825,"row": 0,"thread": null,"query": null},"op": "u","ts_ms": 1729135288473,"transaction": null
}

# 删除
{"before": {"id": 3,"from_id": "dog","to_id": "cat","relation_id": "dog:cat","content": "你好啊，小猫咪","msg_type": 1,"extra_info": null,"create_time": 1729164075000,"deleted": 0},"after": null,"source": {"version": "1.6.4.Final","connector": "mysql","name": "mysql_binlog_source","ts_ms": 1729135301000,"snapshot": "false","db": "quick_chat","sequence": null,"table": "quick_chat_msg","server_id": 1,"gtid": null,"file": "binlog.000002","pos": 3247,"row": 0,"thread": null,"query": null},"op": "d","ts_ms": 1729135300692,"transaction": null
}