debezium、kafka connector 解析 mysql binlog 到 kafak

目的：须要搭建一个能够自动监听MySQL数据库的变化，将变化的数据捕获处理，此处只讲解如何自动捕获mysql 中数据的变化html

使用的技术

debezium ：https://debezium.io/documentation/reference/1.0/connectors/mysql.htmljava

kafka: http://kafka.apache.org/mysql

zookeeper : http://zookeeper.apache.org/linux

mysql 5.7 https://www.mysql.com/sql

1、思路

须要一台 Centos 7.x 的虚拟机，zk、debezium、kafka、confluent 运行在虚拟机上，mysql 运行在 windows 系统上，虚拟机监听 window 环境下的 mysql 数据变化数据库

2、MySQL 环境准备

首先须要找到 mysql 的配置文件：my.ini ,个人路径是：C:\ProgramData\MySQL\MySQL Server 5.7 ，由于监听基础是基于 mysql binlog ，须要开启binlog ，添加以下配置apache

log_bin =D:\mysql-binlog\mysql-bin binlog_format=Row server-id=223344 binlog_row_image = full expire_logs_days = 10 binlog_rows_query_log_events = on

重启 mysql 服务json

net stop mysql57 net start mysql57

此处，MySQL binlog 即开启，能够简单的验证,cmd 窗口 mysql -u root -p 登陆 mysql bootstrap

show binary logs;

能够看到文件内容，即mysql 变化的二进制文件。到此处，MySQL准备就绪。windows

2、zookeeper 、 kafka 准备

下载 zookeeper-3.4.14.tar.gz 、kafka_2.12-2.2.0.tar

mkdir -p  /usr/local/software/zookeeper mkdir -p  /usr/local/software/kafka mkdir -p  /usr/local/software/confluent

准备好路径,并将安装包移入该目录，并解压

mv  zookeeper-3.4.14.tar.gz /usr/local/software/zookeeper
mv kafka_2.12-2.2.0.tar

进入 zookeeper /usr/local/software/zookeeper/zookeeper-3.4.14/conf目录，修改 zoo.cfg （原名 zoo_sample.cfg)内容

dataDir=/opt/data/zookeeper/data dataLogDir=/opt/data/zookeeper/logs

进入 dataDir 目录，建立文件 myid ,并添加内容: 1

此处，zk 的配置修改结束。开启配置 kafka 路径是：/usr/local/software/kafka/kafka_2.12-2.2.0/config，修改 server.properties

broker.id=1 listeners=PLAINTEXT://192.168.91.25:9092
advertised.listeners=PLAINTEXT://192.168.91.25:9092
log.dirs=/opt/data/kafka-logs host.name=192.168.91.25 zookeeper.connect=localhost:2181

3、debezium配置

此处须要 debezium connector 对 mysql 的 jar 包，下载地址：https://debezium.io/releases/1.0/

将下载好的 plugs 上传到虚拟机，解压后名称是： debezium-connector-mysql

移动到： /usr/local/share/kafka/plugins 目录下，若是没有该目录则手动建立

依赖的 jar 包下载好后，配置 kafka 目录中conf connector

目录： /usr/local/software/kafka/kafka_2.12-2.2.0/conf/connect-standalone.properties

bootstrap.servers=本机IP:9092 plugin.path=/usr/local/share/kafka/plugins

此外，在kafka 根目录下建立文件： msyql.properties ,内容

name=mysql connector.class=io.debezium.connector.mysql.MySqlConnector database.hostname=192.168.3.125 database.port=3306 database.user=root database.password=123456 database.server.id=112233 database.server.name=test database.whitelist=orders,users database.history.kafka.bootstrap.servers=192.168.91.25:9092 database.history.kafka.topic=history.test include.schema.changes=true include.query=true # options: adaptive_time_microseconds（default）adaptive(deprecated) connect() time.precision.mode=connect # options: precise(default) double string
decimal.handling.mode=string # options: long(default) precise bigint.unsigned.handling.mode=long

4、启动服务

01.启动zk

cd /usr/local/software/zookeeper/zookeeper-3.4.14

zkServer.sh  start

02.启动kafka

cd  /usr/local/software/kafka/kafka_2.12-2.2.0

./bin/kafka-server-start.sh  -daemon  config/server.properties

03.启动kafka connector

cd  /usr/local/software/kafka/kafka_2.12-2.2.0

./bin/connect-standalone.sh  config/connect-standalone.properties  mysql.properties

04.查看 topic ,在新的端口查看

./bin/kafka-topics.sh --list --zookeeper localhost:2181

5、指定监听的数据库/表

在 postman 中模拟 post 请求，以 json 格式传递参数：表示监听 shiro数据库

{ "name": "shiro", "config": { "connector.class": "io.debezium.connector.mysql.MySqlConnector", "database.hostname": "192.168.3.125", "database.port": "3306", "database.user": "root", "database.password": "123456", "database.server.id": "184054", "database.server.name": "my", "database.whitelist": "shiro", "database.history.kafka.bootstrap.servers": "192.168.91.25:9092", "database.history.kafka.topic": "history.shiro", "include.schema.changes": "true" }}

从新查看topic

在新端口启动 kafka 消费者，消费my.shiro.user

./bin/kafka-console-consumer.sh  --bootstrap-server localhost:9092 --topic my.shiro.user --from-beginning

Java客户端消费者代码

package kafka; import org.apache.kafka.clients.consumer.ConsumerConfig; import org.apache.kafka.clients.consumer.ConsumerRecord; import org.apache.kafka.clients.consumer.ConsumerRecords; import org.apache.kafka.clients.consumer.KafkaConsumer; import java.util.Arrays; import java.util.Properties; /** * Created by baizhuang on 2019/10/25 10:39. */

public class MyConsumer { public static void main(String []args){ //1.建立 kafka 生产者配置信息。
        Properties properties = new Properties(); //2.指定 kafka 集群
        properties.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG,"192.168.91.25:9092"); properties.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG,true); properties.put(ConsumerConfig.AUTO_COMMIT_INTERVAL_MS_CONFIG,"1000"); //key,value 反序列化
        properties.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG,"org.apache.kafka.common.serialization.StringDeserializer"); properties.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG,"org.apache.kafka.common.serialization.StringDeserializer"); properties.put("group.id","test"); KafkaConsumer<String,String> consumer = new  KafkaConsumer<String,String>(properties); consumer.subscribe(Arrays.asList("my.shiro.user")); while (true) { ConsumerRecords<String, String> consumerRecords = consumer.poll(100); for (ConsumerRecord<String, String> consumerRecord : consumerRecords) { System.out.println(consumerRecord.key() + "-----" + consumerRecord.value()); } } } }

View Code

Java 客户端生产者代码

package kafka; import org.apache.kafka.clients.producer.KafkaProducer; import org.apache.kafka.clients.producer.ProducerConfig; import org.apache.kafka.clients.producer.ProducerRecord; import java.util.Properties; /** * Created by baizhuang on 2019/10/24 16:58. */

public class MyProducer { public static void main(String []args){ //1.建立 kafka 生产者配置信息。
        Properties properties = new Properties(); //2.指定 kafka 集群
        properties.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG,"192.168.91.25:9092"); //3.
        properties.put("acks","all"); //4.重试次数
        properties.put("retries",0); //5.批次大小
        properties.put("batch.size",16384); //6.等待时间
        properties.put("linger.ms",1); //7.RecordAccumlate 缓冲区大小
        properties.put("buffer.memory",33554432); //key ,value 序列化
        properties.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer"); properties.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer"); //9.建立生产者
        KafkaProducer<String,String>  producer = new KafkaProducer<String, String>(properties); for(int i=0;i<10;i++){ //10.发送
            String key = String.valueOf(i); String value = "第"+key+"条消息"; producer.send(new ProducerRecord<String, String>("mytopic",key,value)); System.out.println("msg:"+i); } producer.close(); } }

View Code

启动消费者，修改 shiro 数据库的user 表，Java客户端消费者与 linux 消费者都可动态的显示变化的数据