MySql教程
FlinkCDC Mysql到Kafka
本文主要是介绍FlinkCDC Mysql到Kafka，对大家解决编程问题具有一定的参考价值，需要的程序猿们随着小编来一起学习吧！
描述：
利用flink CDC 将读取mysql binlog 将数据从mysql抽离出来发送至kafka
1、pom.xml文件依赖
<?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">
<parent>
<artifactId>FlinkSql</artifactId>
<groupId>org.example</groupId>
<version>1.0-SNAPSHOT</version>
</parent>
<modelVersion>4.0.0</modelVersion>
<artifactId>FlinkSqlTest</artifactId>
<properties>
<flink.version>1.11.3</flink.version>
<scala.binary.version>2.11</scala.binary.version>
<flink-cdc.version>1.1.0</flink-cdc.version>
</properties>
<dependencies>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-streaming-java_2.11</artifactId>
<version>${flink.version}</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-clients_2.11</artifactId>
<version>${flink.version}</version>
</dependency>
<dependency>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-simple</artifactId>
<version>1.7.25</version>
<!-- <scope>test</scope>-->
</dependency>
<!-- <dependency>-->
<!-- <groupId>mysql</groupId>-->
<!-- <artifactId>mysql-connector-java</artifactId>-->
<!-- <version>8.0.16</version>-->
<!-- </dependency>-->
<dependency>
<groupId>com.alibaba.ververica</groupId>
<!-- add the dependency matching your database -->
<artifactId>flink-connector-mysql-cdc</artifactId>
<version>${flink-cdc.version}</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-connector-kafka-0.10_2.11</artifactId>
<version>${flink.version}</version>
<scope> compile</scope>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-connector-jdbc_${scala.binary.version}</artifactId>
<version>${flink.version}</version>
<!-- <scope>provided</scope>-->
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-csv</artifactId>
<version>${flink.version}</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-connector-elasticsearch6_2.11</artifactId>
<version>${flink.version}</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-json</artifactId>
<version>1.11.1</version>
</dependency>
<!-- old planner flink table-->
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-table-planner_2.11</artifactId>
<version>${flink.version}</version>
<!-- <scope>provided</scope>-->
</dependency>
<!--new planner-->
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-table-planner-blink_2.11</artifactId>
<version>${flink.version}</version>
<!-- <scope>provided</scope>-->
</dependency>
<dependency>
<groupId>com.alibaba.ververica</groupId>
<artifactId>flink-format-changelog-json</artifactId>
<version>1.0.0</version>
</dependency>
<!-- 日志相关依赖,flink必须要加，否则报错 -->
<dependency>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-api</artifactId>
<version>1.7.25</version>
</dependency>
<dependency>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-log4j12</artifactId>
<version>1.7.25</version>
</dependency>
</dependencies>
<build>
<plugins>
<!-- 编译插件 -->
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<version>3.6.0</version>
<configuration>
<source>1.8</source>
<target>1.8</target>
<encoding>UTF-8</encoding>
</configuration>
</plugin>
<!-- scala编译插件 -->
<plugin>
<groupId>net.alchim31.maven</groupId>
<artifactId>scala-maven-plugin</artifactId>
<version>3.1.6</version>
<configuration>
<scalaCompatVersion>2.11</scalaCompatVersion>
<scalaVersion>2.11.12</scalaVersion>
<encoding>UTF-8</encoding>
</configuration>
<executions>
<execution>
<id>compile-scala</id>
<phase>compile</phase>
<goals>
<goal>add-source</goal>
<goal>compile</goal>
</goals>
</execution>
<execution>
<id>test-compile-scala</id>
<phase>test-compile</phase>
<goals>
<goal>add-source</goal>
<goal>testCompile</goal>
</goals>
</execution>
</executions>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-assembly-plugin</artifactId>
<version>2.6</version>
<configuration>
<descriptorRefs>
<descriptorRef>jar-with-dependencies</descriptorRef>
</descriptorRefs>
<archive>
<manifest>
<!-- 可以设置jar包的入口类(可选) -->
<mainClass>test.SqlJoinKafka10Test</mainClass>
</manifest>
</archive>
</configuration>
<executions>
<execution>
<id>make-assembly</id>
<phase>package</phase>
<goals>
<goal>single</goal>
</goals>
</execution>
</executions>
</plugin>
</plugins>
</build>
</project>
2、代码
package test
import java.util.concurrent.TimeUnit
import org.apache.flink.api.common.restartstrategy.RestartStrategies
import org.apache.flink.api.common.time.Time
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._
import org.apache.flink.table.api.bridge.scala._
import org.apache.flink.table.api.{EnvironmentSettings, TableResult}
/**
* @program: demo
* @description: 从kafka0.10读取数据，sink到es或者kafka
* @author: yang
* @create: 2021-01-15 11:48
*/
object SqlJoinMysqlKafkaCDCTest {
def main(args: Array[String]): Unit = {
//1、环境准备
val env = StreamExecutionEnvironment.getExecutionEnvironment
// env.setStateBackend(new FsStateBackend("hdfs://locahost:8020/flink/mysql/checkpoints"))
env.setStateBackend(new FsStateBackend("file:///D://tmp//flink/mysql"))
//env.setStateBackend(new FsStateBackend("file:///root/flink1.11/flink-1.11.3/job/checkpoint"))
env.enableCheckpointing(8000)
env.getCheckpointConfig.setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE)
env.getCheckpointConfig.setMaxConcurrentCheckpoints(2)
env.setRestartStrategy(RestartStrategies.fixedDelayRestart(3, Time.of(10, TimeUnit.SECONDS)))
env.getCheckpointConfig.enableExternalizedCheckpoints(CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION)
val settings = EnvironmentSettings.newInstance().useBlinkPlanner().inStreamingMode().build()
val tableEnv = StreamTableEnvironment.create(env, settings)
//读取mysql表数据1
val createMysqlUserTabel =
"""
|CREATE TABLE `user` (
| id INT,
| name STRING,
| create_time TIMESTAMP(3)
|) WITH (
| 'connector' = 'mysql-cdc',
| 'hostname' = 'hostname',
| 'port' = '3306',
| 'username' = 'root',
| 'password' = 'localhost',
| 'database-name' = 'test',
| 'table-name' = 'user'
|)
""".stripMargin
//读取mysql表数据2
val createMysqlWeblogTabel =
"""
|CREATE TABLE user_info (
| id INT,
| username STRING,
| password STRING
|) WITH (
| 'connector' = 'mysql-cdc',
| 'hostname' = 'localhost',
| 'port' = '3306',
| 'username' = 'root',
| 'password' = 'root',
| 'database-name' = 'test',
| 'table-name' = 'user_info'
|)
""".stripMargin
val createMysqlSinkTabel =
"""
|CREATE TABLE user_all (
| id INT,
| name STRING,
| create_time TIMESTAMP(3),
| bid INT,
| username STRING,
| password STRING,
| primary key(id) not ENFORCED
|) WITH (
| 'connector' = 'kafka-0.10',
| 'topic' = 'user_all',
| 'scan.startup.mode' = 'earliest-offset',
| 'properties.bootstrap.servers' = 'localhost:9092',
| 'format' = 'changelog-json'
|)
""".stripMargin
val unoinSql =
"""
|insert into user_all
| select u.id,u.name,u.create_time,ui.bid,ui.username,ui.password
| from (select id,name,create_time from `user`) as u
| left JOIN (select id as bid,username,password from user_info) as ui
| on u.id = ui.bid
""".stripMargin
tableEnv.executeSql(createMysqlUserTabel)
tableEnv.executeSql(createMysqlWeblogTabel)
tableEnv.executeSql(createMysqlSinkTabel)
val result: TableResult = tableEnv.executeSql(unoinSql)
result.print()
}
}
3、创建topic
kafka-topics --create --zookeeper h1:2181,h2:2181,h3:2181 --replication-factor 1 --partitions 1 --topic user_all
4、消费user_all topic,并查询数据
kafka-console-consumer --zookeeper h1:2181,h2:2181,h3:2181 --from-beginning --topic user_all
{"data":{"id":3,"name":"1","create_time":"2021-02-23 10:55:37","bid":3,"username":"1","password":"333"},"op":"-D"}
{"data":{"id":3,"name":"name3","create_time":"2021-02-23 10:55:37","bid":3,"username":"1","password":"333"},"op":"+I"}
{"data":{"id":4,"name":"1","create_time":"2021-02-23 14:56:55","bid":4,"username":"1","password":"4"},"op":"-D"}
{"data":{"id":4,"name":"name4","create_time":"2021-02-23 14:56:55","bid":4,"username":"1","password":"4"},"op":"+I"}
这篇关于FlinkCDC Mysql到Kafka的文章就介绍到这儿，希望我们推荐的文章对大家有所帮助，也希望大家多多支持为之网！
您可能喜欢
-
部署MySQL集群入门：新手必读指南11-20
-
部署MySQL集群教程：初学者指南11-20
-
部署MySQL集群项目实战：新手教程11-20
-
部署MySQL集群资料：新手入门教程11-20
-
MySQL集群部署教程：入门级详解11-20
-
MySQL集群教程：入门与实践指南11-20
-
部署MySQL集群教程：新手入门指南11-20
-
MySQL读写分离教程：轻松入门11-20
-
部署MySQL集群入门：一步一步搭建你的数据库集群11-20
-
部署MySQL集群学习：入门教程11-19
-
如何部署MySQL集群：新手入门教程11-19
-
Mysql安装教程：新手必看的详细安装指南11-19
-
Mysql安装入门：新手必读指南11-18
-
MySQL事务MVCC原理入门详解11-18
-
MySQL资料：新手入门教程11-16
栏目导航
