Flink CDC 基于mysql binlog 实时同步mysql表

环境说明：

flink 1.15.2

mysql 版本5.7    注意：需要开启binlog，因为增量同步是基于binlog捕获数据

windows11 IDEA 本地运行

先上官网使用说明和案例：MySQL CDC Connector — Flink CDC documentation

1. mysql开启binlog (注意，引擎是 InnoDB,如果是ndbcluster，本人测试是捕获不到binlog日志的，增量相当于没用，不知道是不是ndbcluster 下的binlog 配置是否有问题，但是同一集群下，InnoDB的表就可以捕获到binlog日志。听朋友说，ndbcluster 是内存型引擎，有可能不会实时写日志到磁盘，所以捕获不到.....)

# 判断MySQL是否已经开启binlog   on  为打开状态
SHOW VARIABLES LIKE 'log_bin';    

# 查看MySQL的binlog模式
show global variables like "binlog%";

# 查看日志开启状态 
show variables like 'log_%';

# 刷新log日志，立刻产生一个新编号的binlog日志文件，跟重启一个效果 
flush logs;

# 清空所有binlog日志 
reset master;

2. 创建一个用户，赋权

CREATE USER 'flink_cdc_user'@'%' IDENTIFIED BY 'flink@cdc';
GRANT ALL PRIVILEGES ON *.* TO 'flink_cdc_user'@'%';

3. maven依赖:

 
        8
        8
        1.15.2


        
            org.apache.flink
            flink-clients
            ${flink.version}
        
        
            org.apache.flink
            flink-streaming-java
            ${flink.version}
        
        
            org.apache.flink
            flink-runtime-web
            ${flink.version}
        

        
            org.apache.flink
            flink-table-planner_2.12
            ${flink.version}
            
        
        
            org.apache.flink
            flink-connector-jdbc
            ${flink.version}
            
            
        
        
            mysql
            mysql-connector-java
            8.0.29
            
        

        
            org.projectlombok
            lombok
            1.18.22
        
        
        
            com.ververica
            flink-sql-connector-mysql-cdc
            2.3.0
            
        
        
            org.apache.flink
            flink-connector-jdbc
            1.15.2
            
            
        

        
            org.apache.flink
            flink-connector-base
            ${flink.version}
            
        

    

4. 若是打包到集群运行，相关依赖要放开 provided，这样就不会把依赖打入到jar包里面，就不会和flink lib里面的jar包冲突。

lib 里面需要加入的包：从官网下载，放入即可

flink-connector-jdbc-1.15.4.jar

flink-shaded-hadoop-3-uber-3.1.1.7.2.9.0-173-9.0.jar

flink-sql-connector-mysql-cdc-2.3.0.jar

mysql-connector-java-8.0.29.jar

commons-cli-1.5.0.jar

5.mysql建表如下：

#mysql建表：

CREATE TABLE `user` (
  `id` int(11) NOT NULL,
  `username` varchar(255) DEFAULT NULL,
  `password` varchar(255) DEFAULT NULL,
  PRIMARY KEY (`id`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;

CREATE TABLE `user_sink` (
  `id` int(11) NOT NULL,
  `username` varchar(255) DEFAULT NULL,
  `password` varchar(255) DEFAULT NULL,
  PRIMARY KEY (`id`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;

6.测试demo如下：

package com.xgg.flink.stream.sql;

import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;

public class MysqlToMysqlHavePrimaryKey {
    public static void main(String[] args) {
        //1.获取stream的执行环境
        StreamExecutionEnvironment senv = StreamExecutionEnvironment.getExecutionEnvironment();
        senv.setParallelism(1);
        //2.创建表执行环境
        StreamTableEnvironment tEnv = StreamTableEnvironment.create(senv);

        String sourceTable = "CREATE TABLE mysql_cdc_source (" +
                "  id INT,\n" +
                "  username STRING,\n" +
                "  password STRING,\n" +
                "PRIMARY KEY(id) NOT ENFORCED\n" +
                ") WITH (\n" +
                "'connector' = 'mysql-cdc',\n" +
                "'hostname' = 'localhost',\n" +
                "'port' = '3306',\n" +
                "'username' = 'root',\n" +
                "'password' = 'root',\n" +
                "'database-name' = 'test_cdc',\n" +
                "'debezium.snapshot.mode' = 'initial',\n" +
                "'table-name' = 'user'\n" +
                ")";
        tEnv.executeSql(sourceTable);
        String sinkTable = "CREATE TABLE mysql_cdc_sink (" +
                "  id INT,\n" +
                "  username STRING,\n" +
                "  password STRING,\n" +
                "PRIMARY KEY(id) NOT ENFORCED\n" +
                ") WITH (\n" +
                "'connector' = 'jdbc',\n" +
                "'driver' = 'com.mysql.cj.jdbc.Driver',\n" +
                "'url' = 'jdbc:mysql://localhost:3306/test_cdc?rewriteBatchedStatements=true',\n" +
                "'username' = 'root',\n" +
                "'password' = 'root',\n" +
                "'table-name' = 'user_sink'\n" +
                ")";

        tEnv.executeSql(sinkTable);
        tEnv.executeSql("insert into mysql_cdc_sink select id,username,password from mysql_cdc_source");
        tEnv.executeSql("select * from mysql_cdc_source").print();


    }
}

源表进行操作，flink cdc 捕获操作记录进行打印，然后插入到表中。(mysql的cdc可以一边打印，一边写表，无问题。oracle的cdc，如果有多个执行操作，就会只执行一个，比如，先打印再写表，oracle只能打印，写表操作就不会触发。如果不打印，只写表，那就没问题。好像和senv.setParallelism(1);没关系，应该还是底层实现的问题。)

user 源表和目标表 user_sink,数据都如下。

 源表和目标表都是在Mysql有主键的，所以找个参数虽然是初始化操作，后面插入也是 insert into ，但是不管执行多少遍，都不会有重复的数据。

"'debezium.snapshot.mode' = 'initial',\n" +

?rewriteBatchedStatements=true 这个参数是开启批量写，能加大写速度。