搜索

Flink CDC 2.0原理详解和生产实践

程序源代码

共 27495字，需浏览 55分钟

 ·

2022-03-11 01:11

点击上方蓝色字体，选择“设为星标”
回复"面试"获取更多惊喜
八股文交给我，你们专心刷题和面试
Hi，我是王知无，一个大数据领域的原创作者。 
放心关注我，获取更多行业的一手消息。

Flink CDC 概念

CDC 的全称是 Change Data Capture ，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。

应用场景

  1. 数据同步，用于备份，容灾
  2. 数据分发，一个数据源分发给多个下游
  3. 数据采集(E)，面向数据仓库/数据湖的 ETL 数据集成

CDC 技术

目前业界主流的实现机制的可以分为两种：

  1. 基于查询的 CDC
a.离线调度查询作业，批处理。
b.无法保障数据一致性。
c.不保障实时性。
  1. 基于日志的 CDC
a.实时消费日志，流处理。
b.保障数据一致性。
c.提供实时数据。

常见的开源 CDC 方案

Flink CDC 2.0 设计详解

Source 官网 

https://github.com/ververica/flink-cdc-connectors

支持的连接

实战应用

pom 文件

"1.0" encoding="UTF-8"?>
"http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    
        Flink-learning
        com.wudl.flink
        1.0-SNAPSHOT
    
    4.0.0

    Flink-cdc2.0
    
        1.13.0
            1.8
            1.8
    



    
        
            org.projectlombok
            lombok
            1.18.2
            provided
        
        
            org.apache.flink
            flink-java
            ${flink-version}
        

        
            org.apache.flink
            flink-streaming-java_2.12
            ${flink-version}
        

        
            org.apache.flink
            flink-clients_2.12
            ${flink-version}
        

        
            org.apache.hadoop
            hadoop-client
            3.1.3
        

        
            mysql
            mysql-connector-java
            5.1.49
        

        
            org.apache.flink
            flink-table-planner-blink_2.12
            ${flink-version}
        

        
            com.ververica
            flink-connector-mysql-cdc
            2.0.2
        

        
            com.alibaba
            fastjson
            1.2.75
        

        
            org.apache.flink
            flink-connector-jdbc_2.12
            1.13.3
        
    
    
        
            
                org.apache.maven.plugins
                maven-assembly-plugin
                3.0.0
                
                    
                        jar-with-dependencies
                    
                
                
                    
                        make-assembly
                        package
                        
                            single
                        
                    
                
            
        
    

代码

package com.wud.cdc2;

import com.ververica.cdc.connectors.mysql.MySqlSource;
import com.ververica.cdc.connectors.mysql.table.StartupOptions;
import com.ververica.cdc.debezium.DebeziumDeserializationSchema;
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.CheckpointConfig;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.environment.StreamPipelineOptions;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.types.Row;

import static org.apache.flink.table.api.Expressions.$;

public class FlinkCDC {
    public static void main(String[] args) throws Exception {

        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
        StreamTableEnvironment tabEnv = StreamTableEnvironment.create(env);
        env.enableCheckpointing(5000L);
        env.getCheckpointConfig().setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE);
        // 设置任务关闭时候保留最后一次checkpoint 的数据
        env.getCheckpointConfig().enableExternalizedCheckpoints(CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION);
        // 指定ck 的自动重启策略
        env.setStateBackend(new FsStateBackend("hdfs://192.168.1.161:8020/cdc2.0-test/ck"));
        // 设置hdfs 的访问用户名
        System.setProperty("HADOOP_USER_NAME","hdfs");

        DebeziumSourceFunction mySqlSource = MySqlSource.builder()
                .hostname("192.168.1.180")
                .port(3306)
                .username("root")
                .password("123456")
                .databaseList("test")
                .tableList("test.Flink_iceberg")
                .deserializer(new StringDebeziumDeserializationSchema())
                .startupOptions(StartupOptions.initial())
                .build();
        DataStreamSource dataStreamSource = env.addSource(mySqlSource);
        dataStreamSource.print();
        env.execute();


    }
}

执行结果

SourceRecord{sourcePartition={server=mysql_binlog_source}, sourceOffset={ts_sec=1633178585, file=mysql-bin.000036, pos=765, snapshot=true}} ConnectRecord{topic='mysql_binlog_source.test.Flink_iceberg', kafkaPartition=null, key=null, keySchema=null, value=Struct{after=Struct{id=10011,name=flink-mysql,age=19,dt=2021-09-24},source=Struct{version=1.5.2.Final,connector=mysql,name=mysql_binlog_source,ts_ms=1633178585007,snapshot=true,db=test,table=Flink_iceberg,server_id=0,file=mysql-bin.000036,pos=765,row=0},op=r,ts_ms=1633178585013}, valueSchema=Schema{mysql_binlog_source.test.Flink_iceberg.Envelope:STRUCT}, timestamp=null, headers=ConnectHeaders(headers=)}
SourceRecord{sourcePartition={server=mysql_binlog_source}, sourceOffset={ts_sec=1633178585, file=mysql-bin.000036, pos=765, snapshot=true}} ConnectRecord{topic='mysql_binlog_source.test.Flink_iceberg', kafkaPartition=null, key=null, keySchema=null, value=Struct{after=Struct{id=10012,name=flink-mysqA,age=19,dt=2021-09-24},source=Struct{version=1.5.2.Final,connector=mysql,name=mysql_binlog_source,ts_ms=1633178585015,snapshot=true,db=test,table=Flink_iceberg,server_id=0,file=mysql-bin.000036,pos=765,row=0},op=r,ts_ms=1633178585016}, valueSchema=Schema{mysql_binlog_source.test.Flink_iceberg.Envelope:STRUCT}, timestamp=null, headers=ConnectHeaders(headers=)}
SourceRecord{sourcePartition={server=mysql_binlog_source}, sourceOffset={ts_sec=1633178585, file=mysql-bin.000036, pos=765, snapshot=true}} ConnectRecord{topic='mysql_binlog_source.test.Flink_iceberg', kafkaPartition=null, key=null, keySchema=null, value=Struct{after=Struct{id=10012,name=flink-mysqA,age=19,dt=2021-09-24},source=Struct{version=1.5.2.Final,connector=mysql,name=mysql_binlog_source,ts_ms=1633178585017,snapshot=true,db=test,table=Flink_iceberg,server_id=0,file=mysql-bin.000036,pos=765,row=0},op=r,ts_ms=1633178585017}, valueSchema=Schema{mysql_binlog_source.test.Flink_iceberg.Envelope:STRUCT}, timestamp=null, headers=ConnectHeaders(headers=)}
SourceRecord{sourcePartition={server=mysql_binlog_source}, sourceOffset={ts_sec=1633178585, file=mysql-bin.000036, pos=765, snapshot=true}} ConnectRecord{topic='mysql_binlog_source.test.Flink_iceberg', kafkaPartition=null, key=null, keySchema=null, value=Struct{after=Struct{id=10011,name=flink-mysql,age=19,dt=2021-09-24},source=Struct{version=1.5.2.Final,connector=mysql,name=mysql_binlog_source,ts_ms=1633178585017,snapshot=true,db=test,table=Flink_iceberg,server_id=0,file=mysql-bin.000036,pos=765,row=0},op=r,ts_ms=1633178585017}, valueSchema=Schema{mysql_binlog_source.test.Flink_iceberg.Envelope:STRUCT}, timestamp=null, headers=ConnectHeaders(headers=)}
SourceRecord{sourcePartition={server=mysql_binlog_source}, sourceOffset={ts_sec=1633178585, file=mysql-bin.000036, pos=765, snapshot=true}} ConnectRecord{topic='mysql_binlog_source.test.Flink_iceberg', kafkaPartition=null, key=null, keySchema=null, value=Struct{after=Struct{id=10011,name=flink-mysql,age=19,dt=2021-09-24},source=Struct{version=1.5.2.Final,connector=mysql,name=mysql_binlog_source,ts_ms=1633178585017,snapshot=true,db=test,table=Flink_iceberg,server_id=0,file=mysql-bin.000036,pos=765,row=0},op=r,ts_ms=1633178585017}, valueSchema=Schema{mysql_binlog_source.test.Flink_iceberg.Envelope:STRUCT}, timestamp=null, headers=ConnectHeaders(headers=)}
SourceRecord{sourcePartition={server=mysql_binlog_source}, sourceOffset={ts_sec=1633178585, file=mysql-bin.000036, pos=765, snapshot=true}} ConnectRecord{topic='mysql_binlog_source.test.Flink_iceberg', kafkaPartition=null, key=null, keySchema=null, value=Struct{after=Struct{id=10012,name=flink-mysqA,age=19,dt=2021-09-24},source=Struct{version=1.5.2.Final,connector=mysql,name=mysql_binlog_source,ts_ms=1633178585017,snapshot=true,db=test,table=Flink_iceberg,server_id=0,file=mysql-bin.000036,pos=765,row=0},op=r,ts_ms=1633178585017}, valueSchema=Schema{mysql_binlog_source.test.Flink_iceberg.Envelope:STRUCT}, timestamp=null, headers=ConnectHeaders(headers=)}
SourceRecord{sourcePartition={server=mysql_binlog_source}, sourceOffset={ts_sec=1633178585, file=mysql-bin.000036, pos=765, snapshot=true}} ConnectRecord{topic='mysql_binlog_source.test.Flink_iceberg', kafkaPartition=null, key=null, keySchema=null, value=Struct{after=Struct{id=10013,name=flink-mysqA3,age=19,dt=2021-09-24},source=Struct{version=1.5.2.Final,connector=mysql,name=mysql_binlog_source,ts_ms=1633178585017,snapshot=true,db=test,table=Flink_iceberg,server_id=0,file=mysql-bin.000036,pos=765,row=0},op=r,ts_ms=1633178585017}, valueSchema=Schema{mysql_binlog_source.test.Flink_iceberg.Envelope:STRUCT}, timestamp=null, headers=ConnectHeaders(headers=)}
SourceRecord{sourcePartition={server=mysql_binlog_source}, sourceOffset={ts_sec=1633178585, file=mysql-bin.000036, pos=765, snapshot=true}} ConnectRecord{topic='mysql_binlog_source.test.Flink_iceberg', kafkaPartition=null, key=null, keySchema=null, value=Struct{after=Struct{id=10014,name=flink-mysqA4,age=19,dt=2021-09-28},source=Struct{version=1.5.2.Final,connector=mysql,name=mysql_binlog_source,ts_ms=1633178585017,snapshot=true,db=test,table=Flink_iceberg,server_id=0,file=mysql-bin.000036,pos=765,row=0},op=r,ts_ms=1633178585017}, valueSchema=Schema{mysql_binlog_source.test.Flink_iceberg.Envelope:STRUCT}, timestamp=null, headers=ConnectHeaders(headers=)}
SourceRecord{sourcePartition={server=mysql_binlog_source}, sourceOffset={ts_sec=1633178585, file=mysql-bin.000036, pos=765, snapshot=true}} ConnectRecord{topic='mysql_binlog_source.test.Flink_iceberg', kafkaPartition=null, key=null, keySchema=null, value=Struct{after=Struct{id=10011,name=flink-mysql,age=19,dt=2021-09-24},source=Struct{version=1.5.2.Final,connector=mysql,name=mysql_binlog_source,ts_ms=1633178585017,snapshot=true,db=test,table=Flink_iceberg,server_id=0,file=mysql-bin.000036,pos=765,row=0},op=r,ts_ms=1633178585017}, valueSchema=Schema{mysql_binlog_source.test.Flink_iceberg.Envelope:STRUCT}, timestamp=null, headers=ConnectHeaders(headers=)}
SourceRecord{sourcePartition={server=mysql_binlog_source}, sourceOffset={ts_sec=1633178585, file=mysql-bin.000036, pos=765, snapshot=true}} ConnectRecord{topic='mysql_binlog_source.test.Flink_iceberg', kafkaPartition=null, key=null, keySchema=null, value=Struct{after=Struct{id=10012,name=flink-mysqA,age=19,dt=2021-09-24},source=Struct{version=1.5.2.Final,connector=mysql,name=mysql_binlog_source,ts_ms=1633178585017,snapshot=true,db=test,table=Flink_iceberg,server_id=0,file=mysql-bin.000036,pos=765,row=0},op=r,ts_ms=1633178585018}, valueSchema=Schema{mysql_binlog_source.test.Flink_iceberg.Envelope:STRUCT}, timestamp=null, headers=ConnectHeaders(headers=)}
SourceRecord{sourcePartition={server=mysql_binlog_source}, sourceOffset={ts_sec=1633178585, file=mysql-bin.000036, pos=765, snapshot=true}} ConnectRecord{topic='mysql_binlog_source.test.Flink_iceberg', kafkaPartition=null, key=null, keySchema=null, value=Struct{after=Struct{id=10012,name=flink-mysqA,age=19,dt=2021-09-24},source=Struct{version=1.5.2.Final,connector=mysql,name=mysql_binlog_source,ts_ms=1633178585018,snapshot=true,db=test,table=Flink_iceberg,server_id=0,file=mysql-bin.000036,pos=765,row=0},op=r,ts_ms=1633178585018}, valueSchema=Schema{mysql_binlog_source.test.Flink_iceberg.Envelope:STRUCT}, timestamp=null, headers=ConnectHeaders(headers=)}
SourceRecord{sourcePartition={server=mysql_binlog_source}, sourceOffset={ts_sec=1633178585, file=mysql-bin.000036, pos=765, snapshot=true}} ConnectRecord{topic='mysql_binlog_source.test.Flink_iceberg', kafkaPartition=null, key=null, keySchema=null, value=Struct{after=Struct{id=10011,name=flink-mysql,age=19,dt=2021-09-24},source=Struct{version=1.5.2.Final,connector=mysql,name=mysql_binlog_source,ts_ms=1633178585018,snapshot=true,db=test,table=Flink_iceberg,server_id=0,file=mysql-bin.000036,pos=765,row=0},op=r,ts_ms=1633178585018}, valueSchema=Schema{mysql_binlog_source.test.Flink_iceberg.Envelope:STRUCT}, timestamp=null, headers=ConnectHeaders(headers=)}
SourceRecord{sourcePartition={server=mysql_binlog_source}, sourceOffset={ts_sec=1633178585, file=mysql-bin.000036, pos=765, snapshot=true}} ConnectRecord{topic='mysql_binlog_source.test.Flink_iceberg', kafkaPartition=null, key=null, keySchema=null, value=Struct{after=Struct{id=10011,name=flink-mysql,age=19,dt=2021-09-24},source=Struct{version=1.5.2.Final,connector=mysql,name=mysql_binlog_source,ts_ms=1633178585018,snapshot=true,db=test,table=Flink_iceberg,server_id=0,file=mysql-bin.000036,pos=765,row=0},op=r,ts_ms=1633178585018}, valueSchema=Schema{mysql_binlog_source.test.Flink_iceberg.Envelope:STRUCT}, timestamp=null, headers=ConnectHeaders(headers=)}
SourceRecord{sourcePartition={server=mysql_binlog_source}, sourceOffset={ts_sec=1633178585, file=mysql-bin.000036, pos=765, snapshot=true}} ConnectRecord{topic='mysql_binlog_source.test.Flink_iceberg', kafkaPartition=null, key=null, keySchema=null, value=Struct{after=Struct{id=10012,name=flink-mysqA,age=19,dt=2021-09-24},source=Struct{version=1.5.2.Final,connector=mysql,name=mysql_binlog_source,ts_ms=1633178585018,snapshot=true,db=test,table=Flink_iceberg,server_id=0,file=mysql-bin.000036,pos=765,row=0},op=r,ts_ms=1633178585018}, valueSchema=Schema{mysql_binlog_source.test.Flink_iceberg.Envelope:STRUCT}, timestamp=null, headers=ConnectHeaders(headers=)}
SourceRecord{sourcePartition={server=mysql_binlog_source}, sourceOffset={ts_sec=1633178585, file=mysql-bin.000036, pos=765, snapshot=true}} ConnectRecord{topic='mysql_binlog_source.test.Flink_iceberg', kafkaPartition=null, key=null, keySchema=null, value=Struct{after=Struct{id=10013,name=flink-mysqA3,age=19,dt=2021-09-24},source=Struct{version=1.5.2.Final,connector=mysql,name=mysql_binlog_source,ts_ms=1633178585018,snapshot=true,db=test,table=Flink_iceberg,server_id=0,file=mysql-bin.000036,pos=765,row=0},op=r,ts_ms=1633178585018}, valueSchema=Schema{mysql_binlog_source.test.Flink_iceberg.Envelope:STRUCT}, timestamp=null, headers=ConnectHeaders(headers=)}
SourceRecord{sourcePartition={server=mysql_binlog_source}, sourceOffset={ts_sec=1633178585, file=mysql-bin.000036, pos=765, snapshot=true}} ConnectRecord{topic='mysql_binlog_source.test.Flink_iceberg', kafkaPartition=null, key=null, keySchema=null, value=Struct{after=Struct{id=10014,name=flink-mysqA4,age=19,dt=2021-09-28},source=Struct{version=1.5.2.Final,connector=mysql,name=mysql_binlog_source,ts_ms=1633178585018,snapshot=true,db=test,table=Flink_iceberg,server_id=0,file=mysql-bin.000036,pos=765,row=0},op=r,ts_ms=1633178585018}, valueSchema=Schema{mysql_binlog_source.test.Flink_iceberg.Envelope:STRUCT}, timestamp=null, headers=ConnectHeaders(headers=)}
SourceRecord{sourcePartition={server=mysql_binlog_source}, sourceOffset={ts_sec=1633178585, file=mysql-bin.000036, pos=765}} ConnectRecord{topic='mysql_binlog_source.test.Flink_iceberg', kafkaPartition=null, key=null, keySchema=null, value=Struct{after=Struct{id=10050,name=flink-cdc-add,age=21,dt=2021-10-2},source=Struct{version=1.5.2.Final,connector=mysql,name=mysql_binlog_source,ts_ms=1633178585018,snapshot=last,db=test,table=Flink_iceberg,server_id=0,file=mysql-bin.000036,pos=765,row=0},op=r,ts_ms=1633178585018}, valueSchema=Schema{mysql_binlog_source.test.Flink_iceberg.Envelope:STRUCT}, timestamp=null, headers=ConnectHeaders(headers=)}

集群提交

执行命令

[root@basenode flink-1.13.2]# bin/flink run -c com.wud.cdc2.FlinkCDC /opt/datas/Flink-cdc2.0-1.0-SNAPSHOT-jar-with-dependencies.jar

Job has been submitted with JobID 137b680a6bb934e43568f14f6583b62c

手动执行savepoint

给当前程序创建保存点-savepoint

[root@basenode flink-1.13.2]# bin/flink savepoint     e8e918c2517a777e817c630cf1d6b932    hdfs://192.168.1.161:8020/cdc-test/savepoint
Triggering savepoint for job e8e918c2517a777e817c630cf1d6b932.
Waiting for response...
Savepoint completed. Path: hdfs://192.168.1.161:8020/cdc-test/savepoint/savepoint-e8e918-9ef094f349be
You can resume your program from this savepoint with the run command.
[root@basenode flink-1.13.2]#  

界面停止 flink 程序

然后再mysql中添加数据

启动flink 程序

[root@basenode flink-1.13.2]# bin/flink run -s hdfs://192.168.1.161:8020/cdc-test/savepoint/savepoint-e8e918-9ef094f349be -c  com.wud.cdc2.FlinkCDC /opt/datas/Flink-cdc2.0-1.0-SNAPSHOT-jar-with-dependencies.jar
Job has been submitted with JobID 474a0da99820aa6025203f9806b9fcad

查看日志：

接下来 flink cdc 2.0 的自定义序列号函数

从上面可以看出flink cdc 的原始结构

 SourceRecord{sourcePartition={server=mysql_binlog_source}, 
 sourceOffset={file=mysql-bin.000063, pos=154}}
 ConnectRecord{topic='mysql_binlog_source.wudl-gmall.user_info', kafkaPartition=null, key=Struct{id=4000}, keySchema=Schema{mysql_binlog_source.wudl_gmall.user_info.Key:STRUCT}, value=Struct{after=Struct{id=4000,login_name=i0v0k9,nick_name=xxx,name=xxx,phone_num=137xxxxx,email=xxxx@qq.com,user_level=1,birthday=1969-12-04,gender=F,create_time=2020-12-04 23:28:45},source=Struct{version=1.4.1.Final,connector=mysql,name=mysql_binlog_source,ts_ms=0,snapshot=last,db=wudl-gmall,table=user_info,server_id=0,file=mysql-bin.000063,pos=154,row=0},op=c,ts_ms=1636255826014}, valueSchema=Schema{mysql_binlog_source.wudl_gmall.user_info.Envelope:STRUCT}, timestamp=null, headers=ConnectHeaders(headers=)}

我们可以自定义序列化：

package com.wud.cdc2;

import com.alibaba.fastjson.JSONObject;
import com.ververica.cdc.debezium.DebeziumDeserializationSchema;
import io.debezium.data.Envelope;
import org.apache.flink.api.common.typeinfo.BasicTypeInfo;
import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.util.Collector;
import org.apache.kafka.connect.data.Field;
import org.apache.kafka.connect.data.Schema;
import org.apache.kafka.connect.data.Struct;
import org.apache.kafka.connect.source.SourceRecord;
import java.util.List;

public class CustomerDeserialization implements DebeziumDeserializationSchema {
    /**
     *
     * SourceRecord{sourcePartition={server=mysql_binlog_source}, sourceOffset={ts_sec=1636269821, file=mysql-bin.000063, pos=6442}} ConnectRecord{topic='mysql_binlog_source.test.Flink_iceberg', kafkaPartition=null, key=null, keySchema=null, value=Struct{after=Struct{id=102,name=flinksql,age=25,dt=2021-11-08},source=Struct{version=1.5.2.Final,connector=mysql,name=mysql_binlog_source,ts_ms=1636269821531,snapshot=last,db=test,table=Flink_iceberg,server_id=0,file=mysql-bin.000063,pos=6442,row=0},op=r,ts_ms=1636269821531}, valueSchema=Schema{mysql_binlog_source.test.Flink_iceberg.Envelope:STRUCT}, timestamp=null, headers=ConnectHeaders(headers=)}
     *
     *
     *
     *
     *
     * @param sourceRecord   返回一行数据
     * @param collector 数据输出
     * @throws Exception
     */
    @Override
    public void deserialize(SourceRecord sourceRecord, Collector collector) throws Exception {

        JSONObject  result = new JSONObject();
        String topic = sourceRecord.topic();
        String[] fields = topic.split("\\.");
        result.put("db",fields[1]) ;
        result.put("tableName",fields[2]);
        // 获取before 数据
        Struct value = (Struct) sourceRecord.value();
        Struct before = value.getStruct("before");
        JSONObject beforeJson = new JSONObject();
        if (before !=null)
        {
            //获取列信息
            Schema schema = before.schema();
            List fieldList = schema.fields();
            for (Field field:fieldList)
            {
                beforeJson.put(field.name(),before.get(field));
            }
        }
        result.put("before",beforeJson);
        // 获取after 数据
        Struct after = value.getStruct("after");
        JSONObject afterJson = new JSONObject();
        if (after !=null)
        {
            Schema schema = after.schema();
            List afterFields = schema.fields();
            for (Field field:afterFields)
            {
                afterJson.put(field.name(),after.get(field));
            }
        }
        result.put("after", afterJson);
        //获取操作类型
        Envelope.Operation operation = Envelope.operationFor(sourceRecord);
        result.put("op", operation);
        //输出数据
        collector.collect(result.toJSONString());
    }
    @Override
    public TypeInformation getProducedType() {
        return  BasicTypeInfo.STRING_TYPE_INFO;
    }
}

调用flink cdc 的自定义函数

package com.wud.cdc2;

import com.ververica.cdc.connectors.mysql.MySqlSource;
import com.ververica.cdc.connectors.mysql.table.StartupOptions;
import com.ververica.cdc.debezium.DebeziumDeserializationSchema;
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.CheckpointConfig;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.environment.StreamPipelineOptions;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.types.Row;

public class FlinkCDC {
    public static void main(String[] args) throws Exception {

        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
//        env.enableCheckpointing(5000L);
//        env.getCheckpointConfig().setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE);
//        // 设置任务关闭时候保留最后一次checkpoint 的数据
//        env.getCheckpointConfig().enableExternalizedCheckpoints(CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION);
//        // 指定ck 的自动重启策略
//        env.setStateBackend(new FsStateBackend("hdfs://192.168.1.161:8020/cdc2.0-test/ck"));
//        // 设置hdfs 的访问用户名
//        System.setProperty("HADOOP_USER_NAME","hdfs");

        DebeziumSourceFunction mySqlSource = MySqlSource.builder()
                .hostname("192.168.1.180")
                .port(3306)
                .username("root")
                .password("123456")
                .databaseList("test")
                .tableList("test.Flink_iceberg")
//                .deserializer(new StringDebeziumDeserializationSchema())
                .deserializer(new CustomerDeserialization())
                .startupOptions(StartupOptions.initial())
                .build();
        DataStreamSource dataStreamSource = env.addSource(mySqlSource);
        dataStreamSource.print();
        env.execute();
    }
}

新增一条数据可以看出 控制台输出结果：

{"op":"UPDATE","before":{"dt":"2021-11-07","name":"spark","id":104,"age":22},"after":{"dt":"2021-11-07","name":"spark02","id":104,"age":22},"db":"test","tableName":"Flink_iceberg"}

Flink CDC 2.0 SQL 可以做一个 ETL

需要注意的是必须要有主键 否则更新数据是新增一列，加主键后，更新数据不会新增。

数据库表结构:

CREATE TABLE `Flink_iceberg` (
  `id` bigint(64) DEFAULT NULL,
  `name` varchar(64) DEFAULT NULL,
  `age` int(20) DEFAULT NULL,
  `dt` varchar(64) DEFAULT NULL
) ENGINE=InnoDB DEFAULT CHARSET=latin1

实现代码:

package com.wud.cdc2;

import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;

import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;

public class FlinkCdc20MysqlToMysql {
    public static void main(String[] args) throws Exception {

        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
        StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);
        String sourceSql = "CREATE TABLE IF NOT EXISTS mySqlSource (" +
                "id BIGINT primary key, " +
                "name string ," +
                "age int," +
                "dt string" +
                ") with ( " +
                " 'connector' = 'mysql-cdc', " +
                " 'scan.startup.mode' = 'latest-offset', " +
                " 'hostname' = '192.168.1.180', " +
                " 'port' = '3306', " +
                " 'username' = 'root', " +
                " 'password' = '123456', " +
                " 'database-name' = 'test', " +
                " 'table-name' = 'Flink_iceberg' " +
                ")";

        String sinkSql = " CREATE TABLE IF NOT EXISTS mySqlSink (" +
                "id BIGINT primary key , " +
                "name string ," +
                "age int," +
                "dt string" +
                ") with (" +
                " 'connector' = 'jdbc'," +
                " 'url' = 'jdbc:mysql://192.168.1.180:3306/test'," +
                "'table-name' = 'Flink_iceberg-cdc'," +
                " 'username' = 'root'," +
                " 'password' = '123456' " +
                " )";
        tableEnv.executeSql(sourceSql);
        tableEnv.executeSql(sinkSql);
        tableEnv.executeSql("insert  into  mySqlSink select * from mySqlSource ");
//        env.execute("FlinkCdc20MysqlToMysql");
    }
}

新增一条数据和更新数据显示

{"op":"READ","before":{},"after":{"dt":"2021-09-24","name":"flink-mysqA","id":10012,"age":19},"db":"test","tableName":"Flink_iceberg"}
{"op":"READ","before":{},"after":{"dt":"2021-09-24","name":"flink-mysqA","id":10012,"age":19},"db":"test","tableName":"Flink_iceberg"}
{"op":"READ","before":{},"after":{"dt":"2021-09-24","name":"flink-mysql","id":10011,"age":19},"db":"test","tableName":"Flink_iceberg"}
{"op":"READ","before":{},"after":{"dt":"2021-09-24","name":"flink-mysql","id":10011,"age":19},"db":"test","tableName":"Flink_iceberg"}
{"op":"READ","before":{},"after":{"dt":"2021-09-24","name":"flink-mysqA","id":10012,"age":19},"db":"test","tableName":"Flink_iceberg"}
{"op":"READ","before":{},"after":{"dt":"2021-09-24","name":"flink-mysqA3","id":10013,"age":19},"db":"test","tableName":"Flink_iceberg"}
{"op":"READ","before":{},"after":{"dt":"2021-09-28","name":"flink-mysqA4","id":10014,"age":19},"db":"test","tableName":"Flink_iceberg"}
{"op":"READ","before":{},"after":{"dt":"2021-09-24","name":"flink-mysql","id":10011,"age":19},"db":"test","tableName":"Flink_iceberg"}
{"op":"READ","before":{},"after":{"dt":"2021-11-07","name":"flink","id":101,"age":20},"db":"test","tableName":"Flink_iceberg"}
{"op":"READ","before":{},"after":{"dt":"2021-11-08","name":"flinksql","id":102,"age":25},"db":"test","tableName":"Flink_iceberg"}
{"op":"READ","before":{},"after":{"dt":"2021-11-09","name":"flink-table","id":103,"age":21},"db":"test","tableName":"Flink_iceberg"}
{"op":"READ","before":{},"after":{"dt":"2021-11-07","name":"spark","id":104,"age":22},"db":"test","tableName":"Flink_iceberg"}
{"op":"READ","before":{},"after":{"dt":"2021-11-07","name":"hbase","id":105,"age":25},"db":"test","tableName":"Flink_iceberg"}
{"op":"UPDATE","before":{"dt":"2021-11-07","name":"spark","id":104,"age":22},"after":{"dt":"2021-11-07","name":"spark02","id":104,"age":22},"db":"test","tableName":"Flink_iceberg"}
{"op":"CREATE","before":{},"after":{"dt":"2021-11-07","name":"flinkcdc","id":106,"age":22},"db":"test","tableName":"Flink_iceberg"}
如果这个文章对你有帮助，不要忘记 「在看」 「点赞」 「收藏」 三连啊喂！


2022年全网首发|大数据专家级技能模型与学习指南(胜天半子篇)
互联网最坏的时代可能真的来了
我在B站读大学，大数据专业
我们在学习Flink的时候，到底在学习什么？
193篇文章暴揍Flink，这个合集你需要关注一下
Flink生产环境TOP难题与优化，阿里巴巴藏经阁YYDS
Flink CDC我吃定了耶稣也留不住他！| Flink CDC线上问题小盘点
我们在学习Spark的时候，到底在学习什么？
在所有Spark模块中，我愿称SparkSQL为最强！
硬刚Hive | 4万字基础调优面试小总结
数据治理方法论和实践小百科全书
标签体系下的用户画像建设小指南
4万字长文 | ClickHouse基础&实践&调优全视角解析
【面试&个人成长】2021年过半，社招和校招的经验之谈
大数据方向另一个十年开启 |《硬刚系列》第一版完结
我写过的关于成长/面试/职场进阶的文章
当我们在学习Hive的时候在学习什么？「硬刚Hive续集」
浏览 45
点赞
评论
收藏
分享

手机扫一扫分享

分享
举报
评论
图片
表情
推荐
Flink 详解 | Flink CDC 2.0 正式发布，详解核心改进
HBase技术社区
0
Flink CDC 系列 - Flink MongoDB CDC 在 XTransfer 的生产实践
HBase技术社区
0
Flink CDC 2.0 数据处理流程全面解析
浪尖聊大数据
0
Flink Meetup | Flink CDC Meetup · Online，5.21 开讲！
HBase技术社区
0
Flink 原理 | Flink Sort-Shuffle 实现简介
HBase技术社区
0
Flink CDC Meetup · Online，5.21 开讲！
大数据DT
0
Flink 原理 | Flink 新一代流计算和容错——阶段总结和展望
HBase技术社区
0
Flink之Watermark详解
数据分析挖掘与算法
0
Flink 实践 | Flink CDC 系列 - 实时抽取 Oracle 数据，排雷和调优实践
HBase技术社区
0
Flink 原理 | 深入解读 Flink SQL 1.13
HBase技术社区
0
点赞
评论
收藏
分享

手机扫一扫分享

分享
举报