基于查询的 CDC：
- 离线调度查询作业，批处理。把一张表同步到其他系统，每次通过查询去获取表中最新的数据；
- 无法保障数据一致性，查的过程中有可能数据已经发生了多次变更；
- 无法保障实时性，基于离线调度存在天然的延迟。
- 影响数据库性能
基于日志的 CDC：
- 实时消费日志，流处理，例如 MySQL 的 binlog 日志完整记录了数据库中的变更，可以把 binlog 文件当作流的数据源；
- 保障数据一致性，因为 binlog 文件包含了所有历史变更明细；
- 保障实时性，因为类似 binlog 的日志文件是可以流式消费的，提供的是实时数据。
因我们的业务场景是要求近实时(分钟级)，所以必须采用基于binlog的技术，canal的demo可以参考我的另外文章。又因为初始化时需要导入全量数据(msyql到kudu)，canal得依赖其他的组件，需要保证数据完整一致性(数据不丢，不重复)，且对数据库影响小(锁表先导入全量数据，在进行增量)。操作起来较为麻烦，此时FlinkCDC闪亮登场( 如何全量，增量和精准一次可参考)。

三- FlinkCDC采集mysql 到 mysql的demo

前置条件：Mysql 必须是 5.7 或 8.0.X

1- mysql必须开启binlog


server-id         = <server-id>  # 可以自定义，但必须唯一
log_bin           = <mysql-bin>  # 可以自定义，binlog文件的前缀名
binlog_format     = ROW          # 必须是row
binlog_row_image  = FULL         # 必须是full

2- 创建一个用户，权限 SELECT, RELOAD, SHOW DATABASES, REPLICATION SLAVE, REPLICATION CLIENT 。必须有reload

GRANT SELECT, RELOAD, SHOW DATABASES, REPLICATION SLAVE, REPLICATION CLIENT ON *.* TO '<flinkuser>'@'<mysqlADD>' identified by '<flinkuserPWD>';

Flink必须是 1.12以上的，如果使用flinkCDC2.0且使用flinkSQL，必须是1.13，java 8

3- 将flink-cdc-connectors的 jar包放入 <FLINK_HOME>/lib 目录下。

下载网页flink-cdc-connector(包括了 mysql postgres和mongdb)

没放入可能会包找不到的错误

org.apache.flink.table.api.ValidationException: Could not find any factory for identifier 'mysql-cdc' that implements 'org.apache.flink.table.factories.DynamicTableFactory' in the classpath.

4- 引入依赖 (注意打包成 jar的时候，不要打包进去。不然会报错)


<dependency>
  <groupId>com.ververica</groupId>
  <artifactId>flink-connector-mysql-cdc</artifactId>
  <version>2.0.0</version>
  <scope>provided</scope> <- 编译打包是不要打包进去，不然运行会报错->
</dependency>


org.apache.flink.client.program.ProgramInvocationException: The main method caused an error: Unable to instantiate java compiler
	at org.apache.flink.client.program.PackagedProgram.callMainMethod(PackagedProgram.java:372)
	at org.apache.flink.client.program.PackagedProgram.invokeInteractiveModeForExecution(PackagedProgram.java:222)
	at org.apache.flink.client.ClientUtils.executeProgram(ClientUtils.java:114)
	at org.apache.flink.client.cli.CliFrontend.executeProgram(CliFrontend.java:812)
	at org.apache.flink.client.cli.CliFrontend.run(CliFrontend.java:246)
	at org.apache.flink.client.cli.CliFrontend.parseAndRun(CliFrontend.java:1054)
	at org.apache.flink.client.cli.CliFrontend.lambda$main$10(CliFrontend.java:1132)
	at org.apache.flink.runtime.security.contexts.NoOpSecurityContext.runSecured(NoOpSecurityContext.java:28)
	at org.apache.flink.client.cli.CliFrontend.main(CliFrontend.java:1132)
Caused by: java.lang.IllegalStateException: Unable to instantiate java compiler
	at org.apache.calcite.rel.metadata.JaninoRelMetadataProvider.compile(JaninoRelMetadataProvider.java:428)
	at org.apache.calcite.rel.metadata.JaninoRelMetadataProvider.load3(JaninoRelMetadataProvider.java:374)
	at org.apache.calcite.rel.metadata.JaninoRelMetadataProvider.lambda$static$0(JaninoRelMetadataProvider.java:109)
	at org.apache.flink.calcite.shaded.com.google.common.cache.CacheLoader$FunctionToCacheLoader.load(CacheLoader.java:165)
	at org.apache.flink.calcite.shaded.com.google.common.cache.LocalCache$LoadingValueReference.loadFuture(LocalCache.java:3529)
	at org.apache.flink.calcite.shaded.com.google.common.cache.LocalCache$Segment.loadSync(LocalCache.java:2278)
	at org.apache.flink.calcite.shaded.com.google.common.cache.LocalCache$Segment.lockedGetOrLoad(LocalCache.java:2155)
	at org.apache.flink.calcite.shaded.com.google.common.cache.LocalCache$Segment.get(LocalCache.java:2045)
	at org.apache.flink.calcite.shaded.com.google.common.cache.LocalCache.get(LocalCache.java:3951)
	at org.apache.flink.calcite.shaded.com.google.common.cache.LocalCache.getOrLoad(LocalCache.java:3974)
	at org.apache.flink.calcite.shaded.com.google.common.cache.LocalCache$LocalLoadingCache.get(LocalCache.java:4958)
	at org.apache.calcite.rel.metadata.JaninoRelMetadataProvider.create(JaninoRelMetadataProvider.java:469)
	at org.apache.calcite.rel.metadata.JaninoRelMetadataProvider.revise(JaninoRelMetadataProvider.java:481)
	at org.apache.calcite.rel.metadata.RelMetadataQueryBase.revise(RelMetadataQueryBase.java:95)
	at org.apache.calcite.rel.metadata.RelMetadataQuery.getPulledUpPredicates(RelMetadataQuery.java:784)
	at org.apache.calcite.rel.rules.ReduceExpressionsRule$ProjectReduceExpressionsRule.onMatch(ReduceExpressionsRule.java:303)
	at org.apache.calcite.plan.AbstractRelOptPlanner.fireRule(AbstractRelOptPlanner.java:333)
	at org.apache.calcite.plan.hep.HepPlanner.applyRule(HepPlanner.java:542)
。。
。。
。。
。。
Caused by: java.lang.ClassCastException: org.codehaus.janino.CompilerFactory cannot be cast to org.codehaus.commons.compiler.ICompilerFactory
	at org.codehaus.commons.compiler.CompilerFactoryFactory.getCompilerFactory(CompilerFactoryFactory.java:129)
	at org.codehaus.commons.compiler.CompilerFactoryFactory.getDefaultCompilerFactory(CompilerFactoryFactory.java:79)
	at org.apache.calcite.rel.metadata.JaninoRelMetadataProvider.compile(JaninoRelMetadataProvider.java:426)

完整的pom文件(包含了 DataStreaming实现和 SQL实现的依赖)


<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="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">
    <modelVersion>4.0.0</modelVersion>
 
    <groupId>org.example</groupId>
    <artifactId>Flink</artifactId>
    <version>1.0-SNAPSHOT</version>
 
    <properties>
        <maven.compiler.source>8</maven.compiler.source>
        <maven.compiler.target>8</maven.compiler.target>
        <flink.version>1.13.0</flink.version>
        <scala.binary.version>2.12</scala.binary.version>
        <mysql.version>5.1.49</mysql.version>
        <flinkcdc.version>2.0.0</flinkcdc.version>
        <fastjson.version>1.2.75</fastjson.version>
    </properties>
 
    <dependencies>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-java</artifactId>
            <version>${flink.version}</version>
            <scope>provided</scope>
        </dependency>
 
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-java_${scala.binary.version}</artifactId>
            <version>${flink.version}</version>
            <scope>provided</scope>
        </dependency>
 
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-clients_${scala.binary.version}</artifactId>
            <version>${flink.version}</version>
            <scope>provided</scope>
        </dependency>
 
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-planner-blink_${scala.binary.version}</artifactId>
            <version>${flink.version}</version>
            <scope>provided</scope>
        </dependency>
 
        <dependency>
            <groupId>com.ververica</groupId>
            <artifactId>flink-connector-mysql-cdc</artifactId>
            <version>${flinkcdc.version}</version>
        </dependency>
 
        <dependency>
            <groupId>com.alibaba</groupId>
            <artifactId>fastjson</artifactId>
            <version>${fastjson.version}</version>
        </dependency>
 
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-connector-jdbc_${scala.binary.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>
 
        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <version>${mysql.version}</version>
        </dependency>
 
    </dependencies>
 
     <build>
        <sourceDirectory>src/main/java/</sourceDirectory>
        <plugins>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-compiler-plugin</artifactId>
                <version>2.3.2</version>
                <configuration>
                    <source>1.8</source>
                    <target>1.8</target>
                    <encoding>UTF-8</encoding>
                    <verbose>true</verbose>
                </configuration>
            </plugin>
          
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-surefire-plugin</artifactId>
                <version>2.8.1</version>
                <configuration>
                    <includes>
                        <include>**/*.java</include>
                    </includes>
                    <skipTests>true</skipTests>
                </configuration>
            </plugin>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-shade-plugin</artifactId>
                <version>2.4.3</version>         
                <executions>
                    <execution>
                        <phase>package</phase>
                        <goals>
                            <goal>shade</goal>
                        </goals>
                    </execution>
                </executions>
            </plugin>
        </plugins>
    </build>
</project>

flink jar包选择provide也是官方的建议

5.1- SQL的实现方式

本地测试从 bigdata.person中读取到，bigdata.person1。

建表语句


CREATE DATABASE bigdata;
use bigdata;
 
CREATE TABLE person(  
id INT UNSIGNED PRIMARY KEY AUTO_INCREMENT,   
name VARCHAR(20) NOT NULL DEFAULT ""
) ENGINE=INNODB DEFAULT CHARSET=UTF8 ;  
 
CREATE TABLE person1(  
id INT UNSIGNED PRIMARY KEY AUTO_INCREMENT,   
name VARCHAR(20) NOT NULL DEFAULT ""
) ENGINE=INNODB DEFAULT CHARSET=UTF8 ;

往数据库写入数据


#随机创建字符串函数
DELIMITER $$
CREATE FUNCTION rand_string(n INT) RETURNS VARCHAR(255)
BEGIN
 DECLARE chars_str VARCHAR(100) DEFAULT 'abcdefghijklmnopqrstuvwxyzABCDEFJHIJKLMNOPQRSTUVWXYZ';
 DECLARE return_str VARCHAR(255) DEFAULT '';
 DECLARE i INT DEFAULT 0;
 WHILE i < n DO
 SET return_str =CONCAT(return_str,SUBSTRING(chars_str,FLOOR(1+RAND()*52),1));
 SET i = i + 1;
 END WHILE;
 RETURN return_str;
END $$
 
 
#往表中插入数据的 存储过程
DELIMITER $$
CREATE PROCEDURE insert_person(IN START INT(10),IN max_num INT(10))  
BEGIN  
DECLARE i INT DEFAULT 0;   
#set autocommit =0 把autocommit设置成0  
 SET autocommit = 0;    
 REPEAT  
 SET i = i + 1;  
 INSERT INTO person (name ) VALUES (rand_string(6));  
 UNTIL i = max_num  
 END REPEAT;  
 COMMIT;  
 END $$
 
 
#调用存储过程，写入数据
DELIMITER ;
CALL insert_person(100001,5000);

FlinkSQL实现方式


import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.TableResult;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
 
 
public class Mysql2MysqlLocal {
    public static void main(String[] args) throws Exception {
 
        EnvironmentSettings envSettings = EnvironmentSettings.newInstance().useBlinkPlanner().inStreamingMode().build();
        TableEnvironment tableEnv = TableEnvironment.create(envSettings);
 
 
        String sourceDDL =
                "CREATE TABLE mysql_binlog (\n" +
                        " id Int,\n" +
                        " name STRING,\n" +
                        " primary key (id) not enforced\n" +
                        ") WITH (\n" +
                        " 'connector' = 'mysql-cdc',\n" +
                        " 'hostname' = '127.0.0.1',\n" +
                        " 'port' = '3306',\n" +
                        " 'username' = 'root',\n" +
                        " 'password' = '123456',\n" +
                        " 'database-name' = 'bigdata',\n" +
                        " 'table-name' = 'person',\n" +
                        " 'scan.startup.mode' = 'earliest-offset'\n" +
                        ")";
  
        String sinkDDL =
                "CREATE TABLE test_cdc (" +
                        " id Int," +
                        " name STRING," +
                        " primary key (id) not enforced" +
                        ") WITH (" +
                        " 'connector' = 'jdbc'," +
                        " 'driver' = 'com.mysql.cj.jdbc.Driver'," +
                        " 'url' = 'jdbc:mysql://127.0.0.1:3306/bigdata?serverTimezone=UTC&useSSL=false'," +
                        " 'username' = 'root'," +
                        " 'password' = '123456'," +
                        " 'table-name' = 'person1'" +
                        ")";
        // 简单的聚合处理
        String transformDmlSQL =  "insert into test_cdc select * from mysql_binlog";
 
        TableResult tableResult = tableEnv.executeSql(sourceDDL);
        TableResult sinkResult = tableEnv.executeSql(sinkDDL);
        tableEnv.executeSql(transformDmlSQL);
 
    }
}

测试步骤：

case1 ：增量测试

1- 本地idea测试的话，直接运行程序

2- 调用存储过程往表person写入数据(也可以手动单条插入)

3- 查看 person1的数据条数是否一致

case2：全量+增量测试

1- 调用存储过程往表person写入 100w条数据

2- 在idea启动运行程序，继续调用存储过程往表person写入100w条数据

3- 查看person1的数据数量

case3：本地运行jar包进行增量测试

1- idea等编译生成 jar包

2- 启动flink集群 <FLINK_HOME>/bin/start-cluster.sh

3- 启动运行 jar程序， bin/flink run -m 127.0.0.1 -c Mysql2MysqlLocal <flink_jar_name>.jar

4- 往person表写入数据

5- 查看person1 表的数据

先写到这里

参考文档：FlinkCDC的git地址

Flink 中文社区 | Flink CDC 2.0 正式发布，详解核心改进

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FlinkCDC同步mysql的demo

一 ： 什么是CDC ？使用场景是什么？

二： 目前有哪些技术

基于查询的 CDC：

基于日志的 CDC：

三- FlinkCDC采集mysql 到 mysql的demo

1- mysql必须开启binlog

2- 创建一个用户，权限 SELECT, RELOAD, SHOW DATABASES, REPLICATION SLAVE, REPLICATION CLIENT 。必须有reload

3- 将flink-cdc-connectors的 jar包放入 <FLINK_HOME>/lib 目录下。

4- 引入依赖 (注意 打包成 jar的时候，不要打包进去。不然会报错)

5.1- SQL的实现方式

一：什么是CDC ？使用场景是什么？

二：目前有哪些技术

4- 引入依赖 (注意打包成 jar的时候，不要打包进去。不然会报错)