1. CDC是什么

CDC 是 Change Data Capture（变更数据获取）的简称。核心思想是，监测并捕获数据库的变动（包括数据或数据表的插入、更新以及删除等），将这些变更按发生的顺序完整记录下来，写入到消息中间件中以供其他服务进行订阅及消费。
在广义的概念上，只要能捕获数据变更的技术，我们都可以称为 CDC 。通常我们说的 CDC 技术主要面向数据库的变更，是一种用于捕获数据库中数据变更的技术。
CDC 技术应用场景非常广泛：
数据同步，用于备份，容灾；
数据分发，一个数据源分发给多个下游；
数据采集(E)，面向数据仓库/数据湖的 ETL 数据集成。

2. CDC 的种类

CDC 主要分为基于查询和基于 Binlog 两种方式，我们主要了解一下这两种之间的区别：

3. 传统CDC与Flink CDC对比

1. 传统 CDC ETL 分析
   

2. 基于 Flink CDC 的 ETL 分析
   

3. 基于 Flink CDC 的聚合分析
   

4. 基于 Flink CDC 的数据打宽
   

4. Flink-CDC 案例

Flink 社区开发了 flink-cdc-connectors 组件，这是一个可以直接从 MySQL、PostgreSQL 等数据库直接读取全量数据和增量变更数据的 source 组件。
开源地址：https://github.com/ververica/flink-cdc-connectors。
示例代码：

import com.alibaba.ververica.cdc.connectors.mysql.MySQLSource;
import com.alibaba.ververica.cdc.debezium.DebeziumSourceFunction;
import com.alibaba.ververica.cdc.debezium.StringDebeziumDeserializationSchema;
import org.apache.flink.api.common.restartstrategy.RestartStrategies;
import org.apache.flink.runtime.state.filesystem.FsStateBackend;
import org.apache.flink.streaming.api.CheckpointingMode;
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 java.util.Properties;

public class FlinkCDC {
 public static void main(String[] args) throws Exception {
 //1.创建执行环境
 StreamExecutionEnvironment env = 
StreamExecutionEnvironment.getExecutionEnvironment();
 env.setParallelism(1);
 //2.Flink-CDC 将读取 binlog 的位置信息以状态的方式保存在 CK,如果想要做到断点
续传,需要从 Checkpoint 或者 Savepoint 启动程序
 //2.1 开启 Checkpoint,每隔 5 秒钟做一次 CK
 env.enableCheckpointing(5000L);
 //2.2 指定 CK 的一致性语义
env.getCheckpointConfig().setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE);
 //2.3 设置任务关闭的时候保留最后一次 CK 数据
env.getCheckpointConfig().enableExternalizedCheckpoints(CheckpointConfig.ExternalizedCheckp
ointCleanup.RETAIN_ON_CANCELLATION);
 //2.4 指定从 CK 自动重启策略
 env.setRestartStrategy(RestartStrategies.fixedDelayRestart(3, 2000L));
 //2.5 设置状态后端
 env.setStateBackend(new FsStateBackend("hdfs://hadoop102:8020/flinkCDC"));
 //2.6 设置访问 HDFS 的用户名
 System.setProperty("HADOOP_USER_NAME", "atguigu");
 //3.创建 Flink-MySQL-CDC 的 Source
 //initial (default): Performs an initial snapshot on the monitored database tables upon 
first startup, and continue to read the latest binlog.
 //latest-offset: Never to perform snapshot on the monitored database tables upon first 
startup, just read from the end of the binlog which means only have the changes since the 
connector was started.
 //timestamp: Never to perform snapshot on the monitored database tables upon first 
startup, and directly read binlog from the specified timestamp. The consumer will traverse the 
binlog from the beginning and ignore change events whose timestamp is smaller than the 
specified timestamp.
 //specific-offset: Never to perform snapshot on the monitored database tables upon 
first startup, and directly read binlog from the specified offset.
 DebeziumSourceFunction<String> mysqlSource = MySQLSource.<String>builder()
 .hostname("hadoop01")
 .port(3306)
 .username("root")
 .password("000000")
 .databaseList("gmall-flink")
 .tableList("gmall-flink.z_user_info") //可选配置项,如果不指定该参数,则会
读取上一个配置下的所有表的数据，注意：指定的时候需要使用"db.table"的方式
 .startupOptions(StartupOptions.initial())
 .deserializer(new StringDebeziumDeserializationSchema())
 .build();
 //4.使用 CDC Source 从 MySQL 读取数据
 DataStreamSource<String> mysqlDS = env.addSource(mysqlSource);
 //5.打印数据
 mysqlDS.print();
 //6.执行任务
 env.execute();
 } 
}

5. Flink SQL 方式的案例

import org.apache.flink.api.common.restartstrategy.RestartStrategies;
import org.apache.flink.runtime.state.filesystem.FsStateBackend;
import org.apache.flink.streaming.api.CheckpointingMode;
import org.apache.flink.streaming.api.environment.CheckpointConfig;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
public class FlinkSQL_CDC {
 public static void main(String[] args) throws Exception {
 //1.创建执行环境
 StreamExecutionEnvironment env = 
StreamExecutionEnvironment.getExecutionEnvironment();
 env.setParallelism(1);
 StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);
 //2.创建 Flink-MySQL-CDC 的 Source
 tableEnv.executeSql("CREATE TABLE user_info (" +
 " id INT," +
 " name STRING," +
 " phone_num STRING" +
 ") WITH (" +
 " 'connector' = 'mysql-cdc'," +
 " 'hostname' = 'hadoop01'," +
 " 'port' = '3306'," +
 " 'username' = 'root'," +
 " 'password' = '000000'," +
 " 'database-name' = 'gmall-flink'," +
 " 'table-name' = 'z_user_info'" +
 ")");
 tableEnv.executeSql("select * from user_info").print();
 env.execute();
 }
}