FlinkCDC读取MySQL并写入Kafka案例(com.ververica)

该方法使用的是com.ververica版本的flink-connector-mysql-cdc，另一个版本测试也没问题了，见https://www.cnblogs.com/30go/p/15773357.html。

注意版本，不同版本组合可能会出现一些异常的情况：

flink1.12.7   

flink-connector-mysql-cdc 2.1.1 （com.ververica）

需要注意点，依赖的POM文件如下，标记为粗体的部分是需要注意的地方：

<?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">
    <parent>
        <artifactId>gmall-flink-2021</artifactId>
        <groupId>com.king</groupId>
        <version>1.0-SNAPSHOT</version>
    </parent>
    <modelVersion>4.0.0</modelVersion>

    <artifactId>gmall-flink-cdc-ververica</artifactId>
    <version>1.0.0</version>


    <properties>
        <java.version>1.8</java.version>
        <maven.compiler.source>${java.version}</maven.compiler.source>
        <maven.compiler.target>${java.version}</maven.compiler.target>
        <flink.version>1.12.7</flink.version>
        <scala.version>2.12</scala.version>
        <hadoop.version>3.1.3</hadoop.version>
    </properties>

    <dependencies>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-java</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-api-scala_${scala.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-scala_${scala.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-connector-kafka_${scala.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-clients_${scala.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-cep_${scala.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-json</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>com.alibaba</groupId>
            <artifactId>fastjson</artifactId>
            <version>1.2.68</version>
        </dependency>
        <!--如果保存检查点到 hdfs 上，需要引入此依赖-->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>${hadoop.version}</version>
        </dependency>
        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <version>8.0.16</version>
        </dependency>
        <dependency>
            <groupId>org.apache.kafka</groupId>
            <artifactId>kafka-clients</artifactId>
            <version>2.7.0</version>
        </dependency>
        <dependency>
            <groupId>com.ververica</groupId>
            <artifactId>flink-connector-mysql-cdc</artifactId>
            <version>2.1.1</version>
        </dependency>
        <dependency>
            <groupId>org.projectlombok</groupId>
            <artifactId>lombok</artifactId>
            <version>1.18.20</version>
        </dependency>
        <!--Flink 默认使用的是 slf4j 记录日志，相当于一个日志的接口,我们这里使用 log4j 作为
        具体的日志实现-->
        <dependency>
            <groupId>org.slf4j</groupId>
            <artifactId>slf4j-api</artifactId>
            <version>1.7.32</version>
        </dependency>
        <dependency>
            <groupId>org.slf4j</groupId>
            <artifactId>slf4j-log4j12</artifactId>
            <version>1.7.32</version>
        </dependency>
        <dependency>
            <groupId>org.apache.logging.log4j</groupId>
            <artifactId>log4j-to-slf4j</artifactId>
            <version>2.17.1</version>
        </dependency>
    </dependencies>

    <build>
        <plugins>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-dependency-plugin</artifactId>
                <version>3.2.0</version>
                <executions>
                    <execution>
                        <id>copy-dependencies</id>
                        <!---时机为准备打包->
                        <phase>prepare-package</phase>
                        <goals>
                            <goal>copy-dependencies</goal>
                        </goals>
                        -->
                        <configuration>
                            <!--输出路径-->
                            <outputDirectory>${project.build.directory}/bin/lib</outputDirectory>
                            <overWriteReleases>false</overWriteReleases>
                            <overWriteSnapshots>false</overWriteSnapshots>
                            <!--新文件才会覆盖-->
                            <overWriteIfNewer>true</overWriteIfNewer>
                        </configuration>
                    </execution>
                </executions>
            </plugin>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-jar-plugin</artifactId>
                <configuration>
                    <archive>
                        <manifest>
                            <!--添加外部jar包到classpath-->
                            <addClasspath>true</addClasspath>
                            <!--classpath路径前缀-->
                            <classpathPrefix>lib/</classpathPrefix>
                            <!--主类的全类名-->
                            <mainClass>com.Application</mainClass>
                        </manifest>
                    </archive>
                    <!--jar包输出路径为项目构建路径target下的bin目录-->
                    <outputDirectory>
                        ${project.build.directory}/bin
                    </outputDirectory>
                </configuration>
            </plugin>
        </plugins>
    </build>


</project>

这里直接上主程序：FlinkCdcWithVerverica

package com.king.app

import com.king.config.{DBServerConstant, StateBackendConfig}
import com.king.function.CustomerDeseriallization
import com.king.util.MyKafkaUtil
import com.ververica.cdc.connectors.mysql.MySqlSource
import com.ververica.cdc.connectors.mysql.table.StartupOptions
import com.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.environment.CheckpointConfig
import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
import org.apache.flink.streaming.api.scala._

/**
 * @Author: KingWang
 * @Date: 2022/1/15  
 * @Desc:
 **/
object FlinkCdcWithVerverica {
  def main(args: Array[String]): Unit = {

    //1. 获取执行环境
    val env = StreamExecutionEnvironment.getExecutionEnvironment
    env.setParallelism(1)

    //1.1 开启ck并指定状态后端fs

    env.setStateBackend(new FsStateBackend(StateBackendConfig.getFileCheckPointDir("cdc_ververica")))
      env.enableCheckpointing(30000L) //头尾间隔：每5秒触发一次ck
    env.getCheckpointConfig.setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE)  //
    env.getCheckpointConfig.setCheckpointTimeout(10000L)
    env.getCheckpointConfig.setMaxConcurrentCheckpoints(2)
    env.getCheckpointConfig.enableExternalizedCheckpoints(CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION)
    env.getCheckpointConfig.setMinPauseBetweenCheckpoints(10000l)  //尾和头间隔时间3秒

    env.setRestartStrategy(RestartStrategies.fixedDelayRestart(3, 5000L));



    //2. 通过flinkCDC构建SourceFunction并读取数据
    val dbServer = DBServerConstant.mysql_gmall_flink()
    val sourceFunction = MySqlSource.builder[String]()
      .hostname(dbServer.hostname)
      .port(dbServer.port)
      .username(dbServer.username)
      .password(dbServer.password)
      .databaseList("gmall-210325-flink")


      //如果不添加该参数，则消费指定数据库中所有表的数据
      //如果添加，则需要按照 数据库名.表名 的格式指定，多个表使用逗号隔开
      .tableList("gmall-210325-flink.base_trademark")
//      .deserializer(new StringDebeziumDeserializationSchema())
      .deserializer(new CustomerDeseriallization())

      //监控的方式：
      // 1. initial 初始化全表拷贝，然后再比较
      // 2. earliest 最早的
      // 3. latest  指定最新的
      // 4. specificOffset 指定offset
      // 3. timestamp 比指定的时间大的

      .startupOptions(StartupOptions.latest())
      .build()

    val  dataStream = env.addSource(sourceFunction)

    //3. sink
    dataStream.print()
    dataStream.addSink(MyKafkaUtil.getKafkaProducer("test"))

    //4. 启动任务
    env.execute("flink-cdc")

  }
}

自定义的输出格式：CustomerDeseriallization

package com.king.function

import com.alibaba.fastjson.JSONObject
import com.ververica.cdc.debezium.DebeziumDeserializationSchema
import org.apache.flink.api.common.typeinfo.{BasicTypeInfo, TypeInformation}
import org.apache.flink.util.Collector
import org.apache.kafka.connect.data.{Schema, Struct}
import org.apache.kafka.connect.source.SourceRecord


/**
 * @Author: KingWang
 * @Date: 2021/12/29  
 * @Desc:
 **/
class CustomerDeseriallization extends DebeziumDeserializationSchema[String]{

  /**
   * 封装的数据：
   * {
   *   "database":"",
   *   "tableName":"",
   *   "type":"c r u d",
   *   "before":"",
   *   "after":"",
   *   "ts": ""
   *
   * }
   *
   * @param sourceRecord
   * @param collector
   */
  override def deserialize(sourceRecord: SourceRecord, collector: Collector[String]): Unit = {
    //1. 创建json对象用于保存最终数据
    val result = new JSONObject()


    val value:Struct = sourceRecord.value().asInstanceOf[Struct]
    //2. 获取库名&表名
    val source:Struct = value.getStruct("source")
    val database = source.getString("db")
    val table = source.getString("table")

    //3. 获取before
    val before = value.getStruct("before")
    val beforeObj = if(before != null)  getJSONObjectBySchema(before.schema(),before) else new JSONObject()


    //4. 获取after
    val after = value.getStruct("after")
    val afterObj = if(after != null) getJSONObjectBySchema(after.schema(),after) else new JSONObject()

    //5. 获取操作类型
    val op:String = value.getString("op")

    //6. 获取操作时间
    val ts = source.getInt64("ts_ms")
//    val ts = value.getInt64("ts_ms")


    //7. 拼接结果
    result.put("database", database)
    result.put("table", table)
    result.put("type", op)
    result.put("before", beforeObj)
    result.put("after", afterObj)
    result.put("ts", ts)

    collector.collect(result.toJSONString)

  }

  override def getProducedType: TypeInformation[String] = {
    BasicTypeInfo.STRING_TYPE_INFO
  }


  def getJSONObjectBySchema(schema:Schema,struct:Struct):JSONObject = {
    val fields = schema.fields()
    var jsonBean = new JSONObject()
    val iter = fields.iterator()
    while(iter.hasNext){
      val field = iter.next()
      val key = field.name()
      val value = struct.get(field)
      jsonBean.put(key,value)
    }
    jsonBean
  }

}

这里以设置每次从最新的开始读取，StartupOptions.latest() ，然后运行：

 新增一条数据：

 修改：

删除

到此，圆满结束。

通常情况下，执行步骤依次是：

1. 第一次初始化时，使用StartupOptions.initial()，将所有数据同步，

2. 再使用latest，取最新的记录，同时设置checkpoint检查点，以便于失败时，可以从检查点恢复。