Flink CDC

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CDC（Change Data Capture ）：变更数据获取

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。

Flink-CDC ： 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。 基于”Binlog”

CDC技术应用场景

1. 数据同步，用于备份，容灾
2. 数据分发，一个数据源发送到多个下游
3. 数据采集(E)，面向数据仓库/数据湖的ETL数据集成
  • 常见的开源CDC

  • CDC 分类：基于查询和基于Binlog

  • 传统 CDC ETL 分析
graph LR
    A[Database] -- Update Data --> B[CDC工具]
    B --> C[Search Index]
    B --> D[Cache]

  • 基于Flink CDC 的ETL 分析

  • 基于Flink CDC 的数据打宽

  • 基于Flink CDC 的聚合分析

基础环境

#################################################
# mysql 开启 binlog
vim /etc/my.cnf
 
server-id=1
log-bin=mysql-bin
binlog_format=row
binlog-do-db=cdc_test
 
systemctl restart mysqld # 重启mysql
 
create database cdc_test;
 
CREATE TABLE cdc_test.user_info(
id VARCHAR(200) PRIMARY KEY,
NAME VARCHAR(200),
sex VARCHAR(200));
 
INSERT INTO cdc_test.user_info VALUES ('1001','AA','0');
INSERT INTO cdc_test.user_info VALUES ('1002','BB','0');
INSERT INTO cdc_test.user_info VALUES ('1003','CC','0');
 
#################################################
cd /opt/module/flink-1.13.1/lib
flink-shaded-hadoop-2-uber-2.8.3-10.0.jar
    
#################################################
bin/start-cluster.sh #  开启flink
#  运行
bin/flink run -m hadoop102:8081  -c com.atguigu.FlinkCDC /home/zhj/atguigu-flink-cdc-1.0-SNAPSHOT-jar-with-dependencies.jar

案例 api

package com.atguigu;
 
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.StringDebeziumDeserializationSchema;
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.StreamExecutionEnvironment;
 
public class FlinkCDC {
 
    public static void main(String[] args) throws Exception {
 
        //1.获取Flink 执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
 
        //1.1 开启CK
        env.enableCheckpointing(5000);
        env.getCheckpointConfig().setCheckpointTimeout(10000);
        env.getCheckpointConfig().setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE);
        env.getCheckpointConfig().setMaxConcurrentCheckpoints(1);
 
        env.setStateBackend(new FsStateBackend("hdfs://hadoop102:9820/cdc-test/ck"));
 
        //2.通过FlinkCDC构建SourceFunction
        DebeziumSourceFunction<String> sourceFunction = MySqlSource.<String>builder()
                .hostname("hadoop3")
                .port(3306)
                .username("root")
                .password("123456")
                .databaseList("cdc_test")
                .tableList("cdc_test.user_info")
                .deserializer(new StringDebeziumDeserializationSchema())
                .startupOptions(StartupOptions.initial())
                .build();
        DataStreamSource<String> dataStreamSource = env.addSource(sourceFunction);
 
        //3.数据打印
        dataStreamSource.print();
 
        //4.启动任务
        env.execute("FlinkCDC");
 
    }
}

案例2  

package com.atguigu;
 
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.types.Row;
 
public class FlinkSQLCDC {
 
    public static void main(String[] args) throws Exception {
 
        //1.获取执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        // 并行度
        env.setParallelism(1);
        // 创建表环境
        StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);
 
        //2.使用FLINKSQL DDL模式构建CDC 表
        tableEnv.executeSql("CREATE TABLE user_info ( " +
                " id STRING primary key, " +
                " name STRING, " +
                " sex STRING " +
                ") WITH ( " +
                " 'connector' = 'mysql-cdc', " +
                " 'scan.startup.mode' = 'latest-offset', " +
                " 'hostname' = 'hadoop3', " +
                " 'port' = '3306', " +
                " 'username' = 'root', " +
                " 'password' = '123456', " +
                " 'database-name' = 'cdc_test', " +
                " 'table-name' = 'user_info' " +
                ")");
 
        //3.查询数据并转换为流输出
        Table table = tableEnv.sqlQuery("select * from user_info");
        DataStream<Tuple2<Boolean, Row>> retractStream = tableEnv.toRetractStream(table, Row.class);
        retractStream.print();
        // 另一种输出格式
        tableEnv.sqlQuery("select * from user_info").print();
 
        //4.启动
        env.execute("FlinkSQLCDC");
 
    }
}

pom.xml

<dependencies>
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-java</artifactId>
        <version>1.12.0</version>
    </dependency>
 
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-streaming-java_2.12</artifactId>
        <version>1.12.0</version>
    </dependency>
 
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-clients_2.12</artifactId>
        <version>1.12.0</version>
    </dependency>
 
    <dependency>
        <groupId>org.apache.hadoop</groupId>
        <artifactId>hadoop-client</artifactId>
        <version>3.1.3</version>
    </dependency>
 
    <dependency>
        <groupId>mysql</groupId>
        <artifactId>mysql-connector-java</artifactId>
        <version>5.1.48</version>
    </dependency>
 
    <dependency>
        <groupId>com.alibaba.ververica</groupId>
        <artifactId>flink-connector-mysql-cdc</artifactId>
        <version>1.2.0</version>
    </dependency>
 
    <dependency>
        <groupId>com.alibaba</groupId>
        <artifactId>fastjson</artifactId>
        <version>1.2.75</version>
    </dependency>
</dependencies>
<build>
    <plugins>
        <plugin>
            <groupId>org.apache.maven.plugins</groupId>
            <artifactId>maven-assembly-plugin</artifactId>
            <version>3.0.0</version>
            <configuration>
                <descriptorRefs>
                    <descriptorRef>jar-with-dependencies</descriptorRef>
                </descriptorRefs>
            </configuration>
            <executions>
                <execution>
                    <id>make-assembly</id>
                    <phase>package</phase>
                    <goals>
                        <goal>single</goal>
                    </goals>
                </execution>
            </executions>
        </plugin>
    </plugins>
</build>

CDC设计实现:

1、Chunk切分
2、Chunk读取
3、Chunk分配
4、Chunk汇报
5、Chunk分配

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