基于 Dinky + FlinkSQL + Flink CDC 同步 MySQL 数据到 Elasticsearch、Kafka

only°夏至besos2025-12-25 8:46

一、概述

Dinky\]是一个开箱即用的一站式实时计算平台以 Apache Flink 为基础，连接 OLAP 和数据湖等众多框架致力于流批一体和湖仓一体的建设与实践。本文以此为FlinkSQL可视化工具。 \[Flink SQL\]使得使用标准 SQL 开发流式应用变得简单，免去代码开发。 \[Flink CDC\] 本文使用\[ MySQL CDC 连接器\] 允许从 MySQL 数据库读取快照数据和增量数据。 #### 环境及主要软件版本说明 * [kafka_2.12-3.4.0.tgz](https://kafka/3.4.0/kafka_2.12-3.4.0.tgz " kafka_2.12-3.4.0.tgz") * [flink-1.14.6-bin-scala_2.12.tgz](https://archive.apache.org/dist/flink/flink-1.14.6/flink-1.14.6-bin-scala_2.12.tgz " flink-1.14.6-bin-scala_2.12.tgz") * [flink-sql-connector-mysql-cdc-2.3.0.jar](https://repo1.maven.org/maven2/com/ververica/flink-sql-connector-mysql-cdc/2.3.0/flink-sql-connector-mysql-cdc-2.3.0.jar " flink-sql-connector-mysql-cdc-2.3.0.jar") * [flink-sql-connector-elasticsearch7_2.12-1.14.6.jar](https://repo.maven.apache.org/maven2/org/apache/flink/flink-sql-connector-elasticsearch7_2.12/1.14.6/flink-sql-connector-elasticsearch7_2.12-1.14.6.jar " flink-sql-connector-elasticsearch7_2.12-1.14.6.jar") * [flink-sql-connector-kafka_2.12-1.14.6.jar](https://repo.maven.apache.org/maven2/org/apache/flink/flink-sql-connector-kafka_2.12/1.14.6/flink-sql-connector-kafka_2.12-1.14.6.jar " flink-sql-connector-kafka_2.12-1.14.6.jar") * [dlink-release-0.7.3.tar.gz](https://datalinkdc/dinky/releases/download/v0.7.3/dlink-release-0.7.3.tar.gz " dlink-release-0.7.3.tar.gz") * Elasticsearch7.x * java8 * MySQL5.7.17 * [kafka-map](https://dushixiang/kafka-map/releases/download/v1.3.1/kafka-map.tar.gz " kafka-map") kafka可视化工具（最新版需要jdk17环境，低版本也需要jdk11） ### 二、软件安装部署 本文全部采用单机最简环境。 #### 1.[Flink](https://nightlies.apache.org/flink/flink-docs-release-1.14/docs/try-flink/local_installation/ " Flink") tar -xzf flink-1.14.6-bin-scala_2.12.tgz cd flink-1.14.6 && ls -l ### 修改配置 flink-1.14.6/conf/flink-conf.yaml,修改成本机的IP、位置 jobmanager.rpc.address: 192.168.xxx.xxx taskmanager.host: 192.168.xxx.xxx jobmanager.memory.process.size: 2048m taskmanager.memory.process.size: 2048m taskmanager.numberOfTaskSlots: 10 rest.port: 8888 rest.address: 0.0.0.0 web.tmpdir: /home/soft/flink_/flink-1.14.6/tmp akka.ask.timeout: 600s ### 将flink-sql-connector-kafka_2.12-1.14.6.jar、flink-sql-connector-elasticsearch7_2.12-1.14.6.jar、 ### flink-sql-connector-mysql-cdc-2.3.0.jar、放到flink-1.14.6/lib/ 下 ### 启动 ./ 启动成功后，即可访问Flink UI 可视化界面 [http://192.168.xxx.xxx:8888/](http://192.168.xxx.xxx:8888/ " http://192.168.xxx.xxx:8888/") #### 2.[Kafka](https://kafka.apache.org/documentation/#quickstart " Kafka") tar -xzf kafka_2.12-3.4.0.tgz cd kafka_2.12-3.4.0 bin/ random-uuid > uuid vi config/kraft/server.properties controller.quorum.voters=1@192.168.XXX.XXX:9093 advertised.listeners=PLAINTEXT://192.168.XXX.XXX:9092 log.dirs=/home/soft/kafka_2.12-3.4.0/kraft-combined-logs ### 使用前面生成的uuid格式化Kafka日志目录 bin/ format -t `cat uuid` -c config/kraft/server.properties Formatting /home/soft/kafka_2.12-3.4.0/kraft-combined-logs with metadata.version 3.4-IV0. ### Start the Kafka Server bin/ -daemon config/kraft/server.properties ### Kafka 服务器成功启动后，您将拥有一个正在运行并可以使用的基本 Kafka 环境。 jps -l 14085 kafka.Kafka #### 启动命令说明############################################################### ### 1、前台启动命令: ### bin/ config/server.properties ### 描述： ### 此种方式是窗口运行。一旦窗口关闭或者执行了CTR+C，那么kafka进程就被kill了，kafka服务端就被关闭了 ### 2、后台启动命令： ### (1) nohup bin/ config/server.properties 2>&1 & ### (2)bin/ -daemon config/server.properties #### 3.Dinky 部署参考：[http://www.dlink.top/docs/next/deploy_guide/build](http://www.dlink.top/docs/next/deploy_guide/build " http://www.dlink.top/docs/next/deploy_guide/build") 1. 数据库初始化 #登录mysql mysql -uroot -p123456 #创建数据库 mysql> create database dinky; #授权 mysql> grant all privileges on dinky.* to 'dinky'@'%' identified by 'dinky@123' with grant option; mysql> flush privileges; #此处用 dinky 用户登录 mysql -h fdw1 -udinky -pdinky@123 mysql> use dinky; # sql文件在下载的压缩包中获取即可 mysql> source /home/soft/dinky/sql/dinky.sql 2. Dinky部署 tar -zxvf dlink-release-0.7.3.tar.gz mv dlink-release-0.7.3.tar.gz dinky #切换目录,修改数据库配置及端口信息 cd /home/soft/dinky/config/ vi application.yml # ------------------------------------------------------------------------------- spring: datasource: url: jdbc:mysql://${MYSQL_ADDR:192.168.XXX.XXX:3306}/${MYSQL_DATABASE:dinky}?useUnicode=true&characterEncoding=UTF-8&autoReconnect=true&useSSL=false&zeroDateTimeBehavior=convertToNull&serverTime zone=Asia/Shanghai&allowPublicKeyRetrieval=true username: ${MYSQL_USERNAME:dinky} password: ${MYSQL_PASSWORD:dinky@123} server: port: 8899 # ------------------------------------------------------------------------------- # 加载依赖Dinky 需要具备自身的 Flink 环境，该 Flink 环境的实现需要用户自己在 Dinky 根目录下 # plugins/flink${FLINK_VERSION} 文件夹并上传相关的 Flink 依赖，如 flink-dist, flink-table 等 cp ${FLINK_HOME}/lib/flink-*.jar /home/soft/dinky/plugins/flink1.14/ # 包含如下的jar flink-csv-1.14.6.jar flink-dist_2.12-1.14.6.jar flink-json-1.14.6.jar flink-shaded-zookeeper-3.4.14.jar flink-sql-connector-elasticsearch7_2.12-1.14.6.jar flink-sql-connector-kafka_2.12-1.14.6.jar flink-sql-connector-mysql-cdc-2.3.0.jar # 完成后启动 sh auto.sh start 1.14 启动完成后访问 [http://192.168.XXX.XXX:8899/](http://192.168.xxx.xxx:8899/ " http://192.168.XXX.XXX:8899/") 通过admin/admin即可登录进系统进行可视化操作。 ### 三、任务创建提交 场景说明：目前 MySQL 中有四张表，t1、t2、t3、t4。需要将 t1、t2 关联查询后结果同步到 Elasticsearch 索引 search01_index，t3、t4 关联查询后结果也同步到Elasticsearch索引 search01_index。Elasticsearch 索引信息是通过Spring Elasticsearch Data 项目启动时自动生成好了。只需要将数据同步即可。另外推送 Kafka 供给其他源处理。 #### 1. 通过 Flink 自带 方式 Flink SQL和数据库字段类型对照说明：[https://nightlies.apache.org/flink/flink-docs-release-1.14/docs/connectors/table/jdbc/#data-type-mapping](https://nightlies.apache.org/flink/flink-docs-release-1.14/docs/connectors/table/jdbc/#data-type-mapping " https://nightlies.apache.org/flink/flink-docs-release-1.14/docs/connectors/table/jdbc/#data-type-mapping") ##### MySQL 同步到 Elasticsearch ### 进入flink/bin 目录下，通过 ./ 命令启动sql客户端工具 Flink SQL> SET execution.checkpointing.interval = 3s; ### source 1 Flink SQL> CREATE TABLE IF NOT EXISTS test1 ( id STRING, name STRING, del_flag TINYINT, PRIMARY KEY (id) NOT ENFORCED ) WITH ( 'connector' = 'mysql-cdc', 'hostname' = '192.168.XXX.XXX', 'port' = '3306', 'username' = 'root', 'password' = '123456', 'database-name' = 'test1的schame', 'table-name' = 'test1' ); ### source 2 Flink SQL> CREATE TABLE IF NOT EXISTS test2 ( id STRING, doc_id STRING, event_detail STRING, country STRING, `time` STRING, entity_type STRING, entity_name STRING, type TINYINT, del_flag TINYINT, PRIMARY KEY (id) NOT ENFORCED ) WITH ( 'connector' = 'mysql-cdc', 'hostname' = '192.168.XXX.XXX', 'port' = '3306', 'username' = 'root', 'password' = '123456', 'database-name' = 'test2的schame', 'table-name' = 'test2' ); ### source 3 Flink SQL>CREATE TABLE IF NOT EXISTS test3 ( id STRING, title STRING, type TINYINT, del_flag TINYINT, PRIMARY KEY (id) NOT ENFORCED ) WITH ( 'connector' = 'mysql-cdc', 'hostname' = '192.168.XXX.XXX', 'port' = '3306', 'username' = 'root', 'password' = '123456', 'database-name' = 'test3的schame', 'table-name' = 'test3' ); ### source 4 Flink SQL>CREATE TABLE IF NOT EXISTS test4 ( id STRING, `time` STRING, country STRING, entity_type STRING, entity_name STRING, detail STRING, fo_id STRING, del_flag TINYINT, PRIMARY KEY (id) NOT ENFORCED ) WITH ( 'connector' = 'mysql-cdc', 'hostname' = '192.168.XXX.XXX', 'port' = '3306', 'username' = 'root', 'password' = '123456', 'database-name' = 'test4的schame', 'table-name' = 'test4' ); ### sink Flink SQL>CREATE TABLE IF NOT EXISTS search01_index ( id STRING, name STRING, eventDetail STRING, country STRING, `time` STRING, entityType STRING, entityName STRING, type TINYINT, PRIMARY KEY (id) NOT ENFORCED ) WITH ( 'connector' = 'elasticsearch-7', 'hosts' = 'http://192.168.XXX.XXX:9200', 'index' = 'search01_index', 'sink.bulk-flush.max-actions' = '1' ); ### transform 1 ，防止进同一个索引时表 t1 和 t3 的 ID 相同，将 ID 进行拼接处理。 Flink SQL>INSERT INTO search01_index SELECT CONCAT('doc_', , '_', ) AS id, , t2.event_detail AS eventDetail, t2.country, t2.`time`, t2.entity_type AS entityType, t2.entity_name AS entityName, t2.type FROM test1 t1 LEFT JOIN test2 t2 ON = t2.doc_id WHERE t1.del_flag = 0 AND t2.del_flag = 0; ### transform 2 ，防止进同一个索引时表 t1 和 t3 的 ID 相同，将 ID 进行拼接处理。 Flink SQL>INSERT INTO search01_index SELECT CONCAT('fo_', , '_', ) AS id, t3.title AS name, t4.detail AS eventDetail, t4.country, t4.`time`, t4.entity_type AS entityType, t4.entity_name AS entityName, t3.type FROM test3 t3 LEFT JOIN test4 t4 ON = t4.fo_id WHERE t3.del_flag = 0 AND t4.del_flag = 0; 执行完成后访问Flink UI [http://192.168.XXX.XXX:8888/#/job/running](http://192.168.xxx.xxx:8888/#/job/running " http://192.168.XXX.XXX:8888/#/job/running") 可以看到正在运行的 Running Jobs，然后对MySQL 表数据进行增删改操作，可以看到 Elasticsearch 中数据同步效果。 ##### MySQL 同步到 Kafka ### 进入flink/bin 目录下，通过 ./ 命令启动sql客户端工具 Flink SQL> SET execution.checkpointing.interval = 3s; ### source 1 Flink SQL> CREATE TABLE IF NOT EXISTS test1 ( id STRING, name STRING, del_flag TINYINT, PRIMARY KEY (id) NOT ENFORCED ) WITH ( 'connector' = 'mysql-cdc', 'hostname' = '192.168.XXX.XXX', 'port' = '3306', 'username' = 'root', 'password' = '123456', 'database-name' = 'test1的schame', 'table-name' = 'test1' ); ### source 2 Flink SQL> CREATE TABLE IF NOT EXISTS test2 ( id STRING, doc_id STRING, event_detail STRING, country STRING, `time` STRING, entity_type STRING, entity_name STRING, type TINYINT, del_flag TINYINT, PRIMARY KEY (id) NOT ENFORCED ) WITH ( 'connector' = 'mysql-cdc', 'hostname' = '192.168.XXX.XXX', 'port' = '3306', 'username' = 'root', 'password' = '123456', 'database-name' = 'test2的schame', 'table-name' = 'test2' ); ### source 3 Flink SQL>CREATE TABLE IF NOT EXISTS test3 ( id STRING, title STRING, type TINYINT, del_flag TINYINT, PRIMARY KEY (id) NOT ENFORCED ) WITH ( 'connector' = 'mysql-cdc', 'hostname' = '192.168.XXX.XXX', 'port' = '3306', 'username' = 'root', 'password' = '123456', 'database-name' = 'test3的schame', 'table-name' = 'test3' ); ### source 4 Flink SQL>CREATE TABLE IF NOT EXISTS test4 ( id STRING, `time` STRING, country STRING, entity_type STRING, entity_name STRING, detail STRING, fo_id STRING, del_flag TINYINT, PRIMARY KEY (id) NOT ENFORCED ) WITH ( 'connector' = 'mysql-cdc', 'hostname' = '192.168.XXX.XXX', 'port' = '3306', 'username' = 'root', 'password' = '123456', 'database-name' = 'test4的schame', 'table-name' = 'test4' ); ### sink Flink SQL>CREATE TABLE IF NOT EXISTS test_kakfa ( id STRING, name STRING, eventDetail STRING, country STRING, `time` STRING, entityType STRING, entityName STRING, type TINYINT, PRIMARY KEY (id) NOT ENFORCED ) WITH ( 'connector' = 'kafka', 'topic' = 'test_kakfa', 'properties.bootstrap.servers' = '192.168.XXX.XXX:9092', '' = 'testGroup', 'scan.startup.mode' = 'earliest-offset', 'format' = 'debezium-json', 'debezium-json.ignore-parse-errors'='true' ); ### transform 1 ，防止进同一个索引时表 t1 和 t3 的 ID 相同，将 ID 进行拼接处理。 Flink SQL>INSERT INTO test_kakfa SELECT CONCAT('doc_', , '_', ) AS id, , t2.event_detail AS eventDetail, t2.country, t2.`time`, t2.entity_type AS entityType, t2.entity_name AS entityName, t2.type FROM test1 t1 LEFT JOIN test2 t2 ON = t2.doc_id WHERE t1.del_flag = 0 AND t2.del_flag = 0; ### transform 2 ，防止进同一个索引时表 t1 和 t3 的 ID 相同，将 ID 进行拼接处理。 Flink SQL>INSERT INTO test_kakfa SELECT CONCAT('fo_', , '_', ) AS id, t3.title AS name, t4.detail AS eventDetail, t4.country, t4.`time`, t4.entity_type AS entityType, t4.entity_name AS entityName, t3.type FROM test3 t3 LEFT JOIN test4 t4 ON = t4.fo_id WHERE t3.del_flag = 0 AND t4.del_flag = 0; 执行完成后访问 Kafka-map UI 可以看到数据已经进入Kafka，数据格式为： {"before":null,"after":{"id":"fo_48_50","name":"标题啊","eventDetail":"内容2","country":"日本","time":"2023-06-01","entityType":"实体2","entityName":"名称2","type":3},"op":"c"} #### 2. 通过 Dinky 方式 登录Dinky可视化界面后选择【注册中心】菜单添加flink的环境 ![基于 Dinky + FlinkSQL + Flink CDC 同步 MySQL 数据到 Elasticsearch、Kafka_sql](https://i-blog.csdnimg.cn/img_convert/131cb32e6294b2322e44bfeb5112bca0.png) 然后选择【数据开发】菜单配置SQL任务 ![基于 Dinky + FlinkSQL + Flink CDC 同步 MySQL 数据到 Elasticsearch、Kafka_sql_02](https://i-blog.csdnimg.cn/img_convert/92e977b8d9d8fa80cbe9e75a68c5c793.png) * mysql2kafka-1 内容 CREATE TABLE IF NOT EXISTS test1 ( id STRING, name STRING, del_flag TINYINT, PRIMARY KEY (id) NOT ENFORCED ) WITH ( 'connector' = 'mysql-cdc', 'hostname' = '192.168.XXX.XXX', 'port' = '3306', 'username' = 'root', 'password' = '123456', 'database-name' = 'test1的schame', 'table-name' = 'test1' ); CREATE TABLE IF NOT EXISTS test2 ( id STRING, doc_id STRING, event_detail STRING, country STRING, `time` STRING, entity_type STRING, entity_name STRING, type TINYINT, del_flag TINYINT, PRIMARY KEY (id) NOT ENFORCED ) WITH ( 'connector' = 'mysql-cdc', 'hostname' = '192.168.XXX.XXX', 'port' = '3306', 'username' = 'root', 'password' = '123456', 'database-name' = 'test2的schame', 'table-name' = 'test2' ); CREATE TABLE IF NOT EXISTS test_kakfa ( id STRING, name STRING, eventDetail STRING, country STRING, `time` STRING, entityType STRING, entityName STRING, type TINYINT, PRIMARY KEY (id) NOT ENFORCED ) WITH ( 'connector' = 'kafka', 'topic' = 'test_kakfa', 'properties.bootstrap.servers' = '192.168.XXX.XXX:9092', '' = 'testGroup', 'scan.startup.mode' = 'earliest-offset', 'format' = 'debezium-json', 'debezium-json.ignore-parse-errors'='true' ); INSERT INTO test_kakfa SELECT CONCAT('doc_', , '_', ) AS id, , t2.event_detail AS eventDetail, t2.country, t2.`time`, t2.entity_type AS entityType, t2.entity_name AS entityName, t2.type FROM test1 t1 LEFT JOIN test2 t2 ON = t2.doc_id WHERE t1.del_flag = 0 AND t2.del_flag = 0; * mysql2kafka-2 内容 CREATE TABLE IF NOT EXISTS test3 ( id STRING, title STRING, type TINYINT, del_flag TINYINT, PRIMARY KEY (id) NOT ENFORCED ) WITH ( 'connector' = 'mysql-cdc', 'hostname' = '192.168.XXX.XXX', 'port' = '3306', 'username' = 'root', 'password' = '123456', 'database-name' = 'test3的schame', 'table-name' = 'test3' ); CREATE TABLE IF NOT EXISTS test4 ( id STRING, `time` STRING, country STRING, entity_type STRING, entity_name STRING, detail STRING, fo_id STRING, del_flag TINYINT, PRIMARY KEY (id) NOT ENFORCED ) WITH ( 'connector' = 'mysql-cdc', 'hostname' = '192.168.XXX.XXX', 'port' = '3306', 'username' = 'root', 'password' = '123456', 'database-name' = 'test4的schame', 'table-name' = 'test4' ); CREATE TABLE IF NOT EXISTS test_kakfa ( id STRING, name STRING, eventDetail STRING, country STRING, `time` STRING, entityType STRING, entityName STRING, type TINYINT, PRIMARY KEY (id) NOT ENFORCED ) WITH ( 'connector' = 'kafka', 'topic' = 'test_kakfa', 'properties.bootstrap.servers' = '192.168.6.36:9092', '' = 'testGroup', 'scan.startup.mode' = 'earliest-offset', 'format' = 'debezium-json', 'debezium-json.ignore-parse-errors'='true' ); INSERT INTO test_kakfa SELECT CONCAT('fo_', , '_', ) AS id, t3.title AS name, t4.detail AS eventDetail, t4.country, t4.`time`, t4.entity_type AS entityType, t4.entity_name AS entityName, t3.type FROM test3 t3 LEFT JOIN test4 t4 ON = t4.fo_id WHERE t3.del_flag = 0 AND t4.del_flag = 0; * mysql2es-1 内容 CREATE TABLE IF NOT EXISTS test1 ( id STRING, name STRING, del_flag TINYINT, PRIMARY KEY (id) NOT ENFORCED ) WITH ( 'connector' = 'mysql-cdc', 'hostname' = '192.168.XXX.XXX', 'port' = '3306', 'username' = 'root', 'password' = '123456', 'database-name' = 'test1的schame', 'table-name' = 'test1' ); CREATE TABLE IF NOT EXISTS test2 ( id STRING, doc_id STRING, event_detail STRING, country STRING, `time` STRING, entity_type STRING, entity_name STRING, type TINYINT, del_flag TINYINT, PRIMARY KEY (id) NOT ENFORCED ) WITH ( 'connector' = 'mysql-cdc', 'hostname' = '192.168.XXX.XXX', 'port' = '3306', 'username' = 'root', 'password' = '123456', 'database-name' = 'test2的schame', 'table-name' = 'test2' ); CREATE TABLE IF NOT EXISTS search01_index ( id STRING, name STRING, eventDetail STRING, country STRING, `time` STRING, entityType STRING, entityName STRING, type TINYINT, PRIMARY KEY (id) NOT ENFORCED ) WITH ( 'connector' = 'elasticsearch-7', 'hosts' = 'http://192.168.XXX.XXX:9200', 'index' = 'search01_index', 'sink.bulk-flush.max-actions' = '1' ); INSERT INTO search01_index SELECT CONCAT('doc_', , '_', ) AS id, , t2.event_detail AS eventDetail, t2.country, t2.`time`, t2.entity_type AS entityType, t2.entity_name AS entityName, t2.type FROM test1 t1 LEFT JOIN test2 t2 ON = t2.doc_id WHERE t1.del_flag = 0 AND t2.del_flag = 0; * mysql2es-2 内容 CREATE TABLE IF NOT EXISTS test3 ( id STRING, title STRING, type TINYINT, del_flag TINYINT, PRIMARY KEY (id) NOT ENFORCED ) WITH ( 'connector' = 'mysql-cdc', 'hostname' = '192.168.XXX.XXX', 'port' = '3306', 'username' = 'root', 'password' = '123456', 'database-name' = 'test3的schame', 'table-name' = 'test3' ); CREATE TABLE IF NOT EXISTS test4 ( id STRING, `time` STRING, country STRING, entity_type STRING, entity_name STRING, detail STRING, fo_id STRING, del_flag TINYINT, PRIMARY KEY (id) NOT ENFORCED ) WITH ( 'connector' = 'mysql-cdc', 'hostname' = '192.168.XXX.XXX', 'port' = '3306', 'username' = 'root', 'password' = '123456', 'database-name' = 'test4的schame', 'table-name' = 'test4' ); CREATE TABLE IF NOT EXISTS search01_index ( id STRING, name STRING, eventDetail STRING, country STRING, `time` STRING, entityType STRING, entityName STRING, type TINYINT, PRIMARY KEY (id) NOT ENFORCED ) WITH ( 'connector' = 'elasticsearch-7', 'hosts' = 'http://192.168.XXX.XXX:9200', 'index' = 'search01_index', 'sink.bulk-flush.max-actions' = '1' ); INSERT INTO search01_index SELECT CONCAT('fo_', , '_', ) AS id, t3.title AS name, t4.detail AS eventDetail, t4.country, t4.`time`, t4.entity_type AS entityType, t4.entity_name AS entityName, t3.type FROM test3 t3 LEFT JOIN test4 t4 ON = t4.fo_id WHERE t3.del_flag = 0 AND t4.del_flag = 0; * 分别点击任务运行后，查看Flink UI ![基于 Dinky + FlinkSQL + Flink CDC 同步 MySQL 数据到 Elasticsearch、Kafka_kafka_03](https://i-blog.csdnimg.cn/img_convert/7ccff8f5858ca027c9e950757ce68a54.png) ![基于 Dinky + FlinkSQL + Flink CDC 同步 MySQL 数据到 Elasticsearch、Kafka_kafka_04](https://i-blog.csdnimg.cn/img_convert/596661de0d383354fa3630227645fba9.png) * 查看Kafka-map ![基于 Dinky + FlinkSQL + Flink CDC 同步 MySQL 数据到 Elasticsearch、Kafka_kafka_05](https://i-blog.csdnimg.cn/img_convert/b04a8345902801a4b5e2180f61aa3842.png) * 查看Elasticsearch Head 插件 ![基于 Dinky + FlinkSQL + Flink CDC 同步 MySQL 数据到 Elasticsearch、Kafka_sql_06](https://i-blog.csdnimg.cn/img_convert/196a45a03e1b6a9a9f68ed261a7aa76d.png) 至此就大功告成了。

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