文章目录

• 前言
• MySQL 启用binlog
• 添加maven依赖
• 使用DataStream API java代码读取CDC数据流
• 使用Flink SQL读取CDC数据流

  ---

  前言

  Flink CDC主要关注于从源数据库（如MySQL、PostgreSQL等）捕获数据变更，并将这些变更实时地提供给Flink作业进行处理。Flink CDC的核心优势在于其实时性和一致性。通过捕获数据库的增量变动记录，Flink CDC能够实时地将这些变更数据同步到Flink流处理作业中，从而实现低延迟的数据处理和分析。同时，Flink CDC还保证了数据的一致性，确保在数据处理过程中数据的准确性和完整性。

  为了实现这一功能，Flink社区开发了flink-cdc-connectors组件。这是一个可以直接从MySQL、PostgreSQL等数据库读取全量数据和增量变更数据的source组件。通过配置相应的连接器和参数，Flink作业可以连接到源数据库，并实时捕获和处理数据变更。

  ---

  MySQL 启用binlog

  在使用CDC之前务必要开启MySQl的binlog。

  修改my.cnf文件，增加：

  server_id=1
  log_bin=mysql-bin
  binlog_format=ROW
  expire_logs_days=15
  binlog_do_db=testdb
  

  添加maven依赖

          
              org.apache.flink
              flink-java
              ${flink.version}
          
          
              org.apache.flink
              flink-streaming-java
              ${flink.version}
          
          
              org.apache.flink
              flink-table-api-java
              ${flink.version}
          
          
              com.ververica
              flink-connector-mysql-cdc
              2.4.0
          
  

  使用DataStream API java代码读取CDC数据流

  创建表

  CREATE TABLE `userdemo` (
  	`user_id` VARCHAR(50) NOT NULL COLLATE 'utf8mb4_general_ci',
  	`user_name` VARCHAR(50) NULL DEFAULT NULL COLLATE 'utf8mb4_general_ci',
  	`age` INT(11) NULL DEFAULT '0',
  	PRIMARY KEY (`user_id`) USING BTREE
  )
  COLLATE='utf8mb4_general_ci'
  ENGINE=InnoDB
  ;
  

  import com.ververica.cdc.connectors.mysql.MySqlSource;
  import com.ververica.cdc.connectors.mysql.table.StartupOptions;
  import com.ververica.cdc.debezium.DebeziumSourceFunction;
  import com.ververica.cdc.debezium.JsonDebeziumDeserializationSchema;
  import org.apache.flink.streaming.api.datastream.DataStreamSource;
  import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
  /**
   * @Date: 2024/3/12 10:03
   * @Description DataStream API CDC
   **/
  public class FlinkMysqlCdc {
      public static void main(String[] args) throws Exception {
          StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
          env.setParallelism(1);
          DebeziumSourceFunction sourceFunction = MySqlSource.builder()
                  .hostname("10.168.192.70")
                  .port(3306)
                  .username("root")
                  .password("XXXXX")
                  .databaseList("testdb")
                  // 这里一定要是db.table的形式
                  .tableList("testdb.userdemo")
                  .serverTimeZone("GMT+8")
  //                .deserializer(new StringDebeziumDeserializationSchema())
                  .deserializer(new JsonDebeziumDeserializationSchema())
                  .startupOptions(StartupOptions.initial())
                  .build();
          DataStreamSource dataStreamSource = env.addSource(sourceFunction);
          dataStreamSource.print();
          env.execute("FlinkDSCDC");
      }
  }
  

  运行程序输出内容入下：

  {"before":null,"after":{"user_id":"001","user_name":"sdaf","age":23},"source":{"version":"1.9.7.Final","connector":"mysql","name":"mysql_binlog_source","ts_ms":1710228835000,"snapshot":"true","db":"testdb","sequence":null,"table":"userdemo","server_id":0,"gtid":null,"file":"xxx.000002","pos":154,"row":0,"thread":null,"query":null},"op":"r","ts_ms":1710228834716,"transaction":null}
  {"before":null,"after":{"user_id":"002","user_name":"DSF","age":35},"source":{"version":"1.9.7.Final","connector":"mysql","name":"mysql_binlog_source","ts_ms":1710228835000,"snapshot":"last","db":"testdb","sequence":null,"table":"userdemo","server_id":0,"gtid":null,"file":"xxx.000002","pos":154,"row":0,"thread":null,"query":null},"op":"r","ts_ms":1710228834720,"transaction":null}
  

  插入数据

  INSERT INTO  userdemo (user_id,user_name,age) VALUES('004','wangwu',26);
  

  {"before":null,"after":{"user_id":"004","user_name":"wangwu","age":26},"source":{"version":"1.9.7.Final","connector":"mysql","name":"mysql_binlog_source","ts_ms":1710235648000,"snapshot":"false","db":"testdb","sequence":null,"table":"userdemo","server_id":1,"gtid":null,"file":"xxx.000002","pos":649,"row":0,"thread":7,"query":null},"op":"c","ts_ms":1710235647380,"transaction":null}
  

  修改userdemo数据

  UPDATE userdemo SET user_name='zhangsan' WHERE user_id='001'
  

  运行结果如下：

  {"before":{"user_id":"001","user_name":"sdaf","age":23},"after":{"user_id":"001","user_name":"zhangsan","age":23},"source":{"version":"1.9.7.Final","connector":"mysql","name":"mysql_binlog_source","ts_ms":1710235526000,"snapshot":"false","db":"testdb","sequence":null,"table":"userdemo","server_id":1,"gtid":null,"file":"xxx.000002","pos":352,"row":0,"thread":7,"query":null},"op":"u","ts_ms":1710235525246,"transaction":null}
  

  使用Flink SQL读取CDC数据流

  import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;  
  import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;  
  import org.apache.flink.table.api.*;  
    
  public class MyFlinkCDCJob {  
      public static void main(String[] args) throws Exception {  
          StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();  
          StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);  
    
          // 配置 MySQL CDC 源  
          String sourceDDL = "CREATE TABLE my_table (" +  
                  " id INT NOT NULL," +  
                  " name STRING," +  
                  " age INT," +  
                  " PRIMARY KEY (id) NOT ENFORCED" +  
                  ") WITH (" +  
                  " 'connector' = 'mysql-cdc'," +  
                  " 'hostname' = 'your_mysql_hostname'," +  
                  " 'port' = '3306'," +  
                  " 'username' = 'your_username'," +  
                  " 'password' = 'your_password'," +  
                  " 'database-name' = 'your_database_name'," +  
                  " 'table-name' = 'your_table_name'" +  
                  ")";  
          tableEnv.executeSql(sourceDDL);  
    
          // 定义 Flink 作业逻辑  
          Table result = tableEnv.sqlQuery("SELECT * FROM my_table");  
          tableEnv.toRetractStream(result, Row.class).print();  
    
          // 执行作业  
          env.execute("My Flink CDC Job");  
      }  
  }
  

  在上面的代码中，我们创建了一个名为 my_table 的表，该表通过 MySQL CDC 连接器连接到 MySQL 数据库。然后，我们执行一个 SQL 查询来选择这个表中的所有数据，并将结果打印到控制台。

  请注意，你需要替换示例代码中的 'your_mysql_hostname', 'your_username', 'your_password', 'your_database_name', 和 'your_table_name' 为你的实际 MySQL 数据库信息。

   
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